diff options
| author | Vincent Ambo <mail@tazj.in> | 2022-02-07T23·05+0300 |
|---|---|---|
| committer | clbot <clbot@tvl.fyi> | 2022-02-07T23·09+0000 |
| commit | 5aa5d282eac56a21e74611c1cdbaa97bb5db2dca (patch) | |
| tree | 8cc5dce8157a1470ff76719dd15d65f648a05522 /third_party/abseil_cpp/absl/random | |
| parent | a25675804c4f429fab5ee5201fe25e89865dfd13 (diff) | |
chore(3p/abseil_cpp): unvendor abseil_cpp r/3786
we weren't actually using these sources anymore, okay? Change-Id: If701571d9716de308d3512e1eb22c35db0877a66 Reviewed-on: https://cl.tvl.fyi/c/depot/+/5248 Tested-by: BuildkiteCI Reviewed-by: grfn <grfn@gws.fyi> Autosubmit: tazjin <tazjin@tvl.su>
Diffstat (limited to 'third_party/abseil_cpp/absl/random')
101 files changed, 0 insertions, 25121 deletions
diff --git a/third_party/abseil_cpp/absl/random/BUILD.bazel b/third_party/abseil_cpp/absl/random/BUILD.bazel deleted file mode 100644 index 81e150e62e78..000000000000 --- a/third_party/abseil_cpp/absl/random/BUILD.bazel +++ /dev/null @@ -1,509 +0,0 @@ -# -# Copyright 2019 The Abseil Authors. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -# ABSL random-number generation libraries. - -load("@rules_cc//cc:defs.bzl", "cc_binary", "cc_library", "cc_test") -load( - "//absl:copts/configure_copts.bzl", - "ABSL_DEFAULT_COPTS", - "ABSL_DEFAULT_LINKOPTS", - "ABSL_TEST_COPTS", -) - -package(default_visibility = ["//visibility:public"]) - -licenses(["notice"]) - -cc_library( - name = "random", - hdrs = ["random.h"], - copts = ABSL_DEFAULT_COPTS, - linkopts = ABSL_DEFAULT_LINKOPTS, - deps = [ - ":distributions", - ":seed_sequences", - "//absl/random/internal:nonsecure_base", - "//absl/random/internal:pcg_engine", - "//absl/random/internal:pool_urbg", - "//absl/random/internal:randen_engine", - ], -) - -cc_library( - name = "distributions", - srcs = [ - "discrete_distribution.cc", - "gaussian_distribution.cc", - ], - hdrs = [ - "bernoulli_distribution.h", - "beta_distribution.h", - "discrete_distribution.h", - "distributions.h", - "exponential_distribution.h", - "gaussian_distribution.h", - "log_uniform_int_distribution.h", - "poisson_distribution.h", - "uniform_int_distribution.h", - "uniform_real_distribution.h", - "zipf_distribution.h", - ], - copts = ABSL_DEFAULT_COPTS, - linkopts = ABSL_DEFAULT_LINKOPTS, - deps = [ - "//absl/base:base_internal", - "//absl/base:config", - "//absl/base:core_headers", - "//absl/meta:type_traits", - "//absl/random/internal:distribution_caller", - "//absl/random/internal:fast_uniform_bits", - "//absl/random/internal:fastmath", - "//absl/random/internal:generate_real", - "//absl/random/internal:iostream_state_saver", - "//absl/random/internal:traits", - "//absl/random/internal:uniform_helper", - "//absl/random/internal:wide_multiply", - "//absl/strings", - ], -) - -cc_library( - name = "seed_gen_exception", - srcs = ["seed_gen_exception.cc"], - hdrs = ["seed_gen_exception.h"], - copts = ABSL_DEFAULT_COPTS, - linkopts = ABSL_DEFAULT_LINKOPTS, - deps = ["//absl/base:config"], -) - -cc_library( - name = "seed_sequences", - srcs = ["seed_sequences.cc"], - hdrs = [ - "seed_sequences.h", - ], - copts = ABSL_DEFAULT_COPTS, - linkopts = ABSL_DEFAULT_LINKOPTS, - deps = [ - ":seed_gen_exception", - "//absl/container:inlined_vector", - "//absl/random/internal:nonsecure_base", - "//absl/random/internal:pool_urbg", - "//absl/random/internal:salted_seed_seq", - "//absl/random/internal:seed_material", - "//absl/types:span", - ], -) - -cc_library( - name = "bit_gen_ref", - hdrs = ["bit_gen_ref.h"], - copts = ABSL_DEFAULT_COPTS, - linkopts = ABSL_DEFAULT_LINKOPTS, - deps = [ - ":random", - "//absl/base:core_headers", - "//absl/base:fast_type_id", - "//absl/meta:type_traits", - "//absl/random/internal:distribution_caller", - "//absl/random/internal:fast_uniform_bits", - ], -) - -cc_library( - name = "mock_distributions", - testonly = 1, - hdrs = ["mock_distributions.h"], - deps = [ - ":distributions", - ":mocking_bit_gen", - "//absl/meta:type_traits", - "//absl/random/internal:mock_overload_set", - "@com_google_googletest//:gtest", - ], -) - -cc_library( - name = "mocking_bit_gen", - testonly = 1, - hdrs = [ - "mocking_bit_gen.h", - ], - linkopts = ABSL_DEFAULT_LINKOPTS, - deps = [ - ":distributions", - ":random", - "//absl/base:fast_type_id", - "//absl/container:flat_hash_map", - "//absl/meta:type_traits", - "//absl/random/internal:distribution_caller", - "//absl/strings", - "//absl/types:span", - "//absl/types:variant", - "//absl/utility", - "@com_google_googletest//:gtest", - ], -) - -cc_test( - name = "bernoulli_distribution_test", - size = "small", - timeout = "eternal", # Android can take a very long time - srcs = ["bernoulli_distribution_test.cc"], - copts = ABSL_TEST_COPTS, - linkopts = ABSL_DEFAULT_LINKOPTS, - deps = [ - ":distributions", - ":random", - "//absl/random/internal:pcg_engine", - "//absl/random/internal:sequence_urbg", - "@com_google_googletest//:gtest_main", - ], -) - -cc_test( - name = "beta_distribution_test", - size = "small", - timeout = "eternal", # Android can take a very long time - srcs = ["beta_distribution_test.cc"], - copts = ABSL_TEST_COPTS, - flaky = 1, - linkopts = ABSL_DEFAULT_LINKOPTS, - deps = [ - ":distributions", - ":random", - "//absl/base:raw_logging_internal", - "//absl/random/internal:distribution_test_util", - "//absl/random/internal:pcg_engine", - "//absl/random/internal:sequence_urbg", - "//absl/strings", - "//absl/strings:str_format", - "@com_google_googletest//:gtest_main", - ], -) - -cc_test( - name = "distributions_test", - size = "small", - timeout = "moderate", - srcs = [ - "distributions_test.cc", - ], - copts = ABSL_TEST_COPTS, - linkopts = ABSL_DEFAULT_LINKOPTS, - deps = [ - ":distributions", - ":random", - "//absl/random/internal:distribution_test_util", - "@com_google_googletest//:gtest_main", - ], -) - -cc_test( - name = "generators_test", - size = "small", - srcs = ["generators_test.cc"], - copts = ABSL_TEST_COPTS, - linkopts = ABSL_DEFAULT_LINKOPTS, - deps = [ - ":distributions", - ":random", - "@com_google_googletest//:gtest_main", - ], -) - -cc_test( - name = "log_uniform_int_distribution_test", - size = "medium", - srcs = [ - "log_uniform_int_distribution_test.cc", - ], - copts = ABSL_TEST_COPTS, - linkopts = ABSL_DEFAULT_LINKOPTS, - deps = [ - ":distributions", - ":random", - "//absl/base:raw_logging_internal", - "//absl/random/internal:distribution_test_util", - "//absl/random/internal:pcg_engine", - "//absl/random/internal:sequence_urbg", - "//absl/strings", - "//absl/strings:str_format", - "@com_google_googletest//:gtest_main", - ], -) - -cc_test( - name = "discrete_distribution_test", - size = "medium", - srcs = [ - "discrete_distribution_test.cc", - ], - copts = ABSL_TEST_COPTS, - linkopts = ABSL_DEFAULT_LINKOPTS, - deps = [ - ":distributions", - ":random", - "//absl/base:raw_logging_internal", - "//absl/random/internal:distribution_test_util", - "//absl/random/internal:pcg_engine", - "//absl/random/internal:sequence_urbg", - "//absl/strings", - "@com_google_googletest//:gtest_main", - ], -) - -cc_test( - name = "poisson_distribution_test", - size = "small", - timeout = "eternal", # Android can take a very long time - srcs = [ - "poisson_distribution_test.cc", - ], - copts = ABSL_TEST_COPTS, - linkopts = ABSL_DEFAULT_LINKOPTS, - tags = [ - # Too Slow. - "no_test_android_arm", - "no_test_loonix", - ], - deps = [ - ":distributions", - ":random", - "//absl/base:core_headers", - "//absl/base:raw_logging_internal", - "//absl/container:flat_hash_map", - "//absl/random/internal:distribution_test_util", - "//absl/random/internal:pcg_engine", - "//absl/random/internal:sequence_urbg", - "//absl/strings", - "//absl/strings:str_format", - "@com_google_googletest//:gtest_main", - ], -) - -cc_test( - name = "exponential_distribution_test", - size = "small", - srcs = ["exponential_distribution_test.cc"], - copts = ABSL_TEST_COPTS, - linkopts = ABSL_DEFAULT_LINKOPTS, - deps = [ - ":distributions", - ":random", - "//absl/base:core_headers", - "//absl/base:raw_logging_internal", - "//absl/random/internal:distribution_test_util", - "//absl/random/internal:pcg_engine", - "//absl/random/internal:sequence_urbg", - "//absl/strings", - "//absl/strings:str_format", - "@com_google_googletest//:gtest_main", - ], -) - -cc_test( - name = "gaussian_distribution_test", - size = "small", - timeout = "eternal", # Android can take a very long time - srcs = [ - "gaussian_distribution_test.cc", - ], - copts = ABSL_TEST_COPTS, - linkopts = ABSL_DEFAULT_LINKOPTS, - deps = [ - ":distributions", - ":random", - "//absl/base:core_headers", - "//absl/base:raw_logging_internal", - "//absl/random/internal:distribution_test_util", - "//absl/random/internal:sequence_urbg", - "//absl/strings", - "//absl/strings:str_format", - "@com_google_googletest//:gtest_main", - ], -) - -cc_test( - name = "uniform_int_distribution_test", - size = "medium", - timeout = "long", - srcs = [ - "uniform_int_distribution_test.cc", - ], - copts = ABSL_TEST_COPTS, - linkopts = ABSL_DEFAULT_LINKOPTS, - deps = [ - ":distributions", - ":random", - "//absl/base:raw_logging_internal", - "//absl/random/internal:distribution_test_util", - "//absl/random/internal:pcg_engine", - "//absl/random/internal:sequence_urbg", - "//absl/strings", - "@com_google_googletest//:gtest_main", - ], -) - -cc_test( - name = "uniform_real_distribution_test", - size = "medium", - srcs = [ - "uniform_real_distribution_test.cc", - ], - copts = ABSL_TEST_COPTS, - linkopts = ABSL_DEFAULT_LINKOPTS, - tags = [ - "no_test_android_arm", - "no_test_android_arm64", - "no_test_android_x86", - ], - deps = [ - ":distributions", - ":random", - "//absl/base:raw_logging_internal", - "//absl/random/internal:distribution_test_util", - "//absl/random/internal:pcg_engine", - "//absl/random/internal:sequence_urbg", - "//absl/strings", - "@com_google_googletest//:gtest_main", - ], -) - -cc_test( - name = "zipf_distribution_test", - size = "medium", - srcs = [ - "zipf_distribution_test.cc", - ], - copts = ABSL_TEST_COPTS, - linkopts = ABSL_DEFAULT_LINKOPTS, - deps = [ - ":distributions", - ":random", - "//absl/base:raw_logging_internal", - "//absl/random/internal:distribution_test_util", - "//absl/random/internal:pcg_engine", - "//absl/random/internal:sequence_urbg", - "//absl/strings", - "@com_google_googletest//:gtest_main", - ], -) - -cc_test( - name = "bit_gen_ref_test", - size = "small", - srcs = ["bit_gen_ref_test.cc"], - copts = ABSL_TEST_COPTS, - linkopts = ABSL_DEFAULT_LINKOPTS, - deps = [ - ":bit_gen_ref", - ":random", - "//absl/base:fast_type_id", - "//absl/random/internal:sequence_urbg", - "@com_google_googletest//:gtest_main", - ], -) - -cc_test( - name = "mocking_bit_gen_test", - size = "small", - srcs = ["mocking_bit_gen_test.cc"], - copts = ABSL_TEST_COPTS, - linkopts = ABSL_DEFAULT_LINKOPTS, - deps = [ - ":bit_gen_ref", - ":mock_distributions", - ":mocking_bit_gen", - ":random", - "@com_google_googletest//:gtest_main", - ], -) - -cc_test( - name = "mock_distributions_test", - size = "small", - srcs = ["mock_distributions_test.cc"], - copts = ABSL_TEST_COPTS, - linkopts = ABSL_DEFAULT_LINKOPTS, - deps = [ - ":mock_distributions", - ":mocking_bit_gen", - ":random", - "@com_google_googletest//:gtest_main", - ], -) - -cc_test( - name = "examples_test", - size = "small", - srcs = ["examples_test.cc"], - copts = ABSL_TEST_COPTS, - linkopts = ABSL_DEFAULT_LINKOPTS, - deps = [ - ":random", - "@com_google_googletest//:gtest_main", - ], -) - -cc_test( - name = "seed_sequences_test", - size = "small", - srcs = ["seed_sequences_test.cc"], - copts = ABSL_TEST_COPTS, - linkopts = ABSL_DEFAULT_LINKOPTS, - deps = [ - ":random", - ":seed_sequences", - "//absl/random/internal:nonsecure_base", - "@com_google_googletest//:gtest_main", - ], -) - -BENCHMARK_TAGS = [ - "benchmark", - "no_test_android_arm", - "no_test_android_arm64", - "no_test_android_x86", - "no_test_darwin_x86_64", - "no_test_ios_x86_64", - "no_test_loonix", - "no_test_msvc_x64", - "no_test_wasm", -] - -# Benchmarks for various methods / test utilities -cc_binary( - name = "benchmarks", - testonly = 1, - srcs = [ - "benchmarks.cc", - ], - copts = ABSL_TEST_COPTS, - linkopts = ABSL_DEFAULT_LINKOPTS, - tags = BENCHMARK_TAGS, - deps = [ - ":distributions", - ":random", - ":seed_sequences", - "//absl/base:core_headers", - "//absl/meta:type_traits", - "//absl/random/internal:fast_uniform_bits", - "//absl/random/internal:randen_engine", - "@com_github_google_benchmark//:benchmark_main", - ], -) diff --git a/third_party/abseil_cpp/absl/random/CMakeLists.txt b/third_party/abseil_cpp/absl/random/CMakeLists.txt deleted file mode 100644 index 7d7bec83d994..000000000000 --- a/third_party/abseil_cpp/absl/random/CMakeLists.txt +++ /dev/null @@ -1,1207 +0,0 @@ -# -# Copyright 2019 The Abseil Authors. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -absl_cc_library( - NAME - random_random - HDRS - "random.h" - COPTS - ${ABSL_DEFAULT_COPTS} - LINKOPTS - ${ABSL_DEFAULT_LINKOPTS} - DEPS - absl::random_distributions - absl::random_internal_nonsecure_base - absl::random_internal_pcg_engine - absl::random_internal_pool_urbg - absl::random_internal_randen_engine - absl::random_seed_sequences -) - -absl_cc_library( - NAME - random_bit_gen_ref - HDRS - "bit_gen_ref.h" - COPTS - ${ABSL_DEFAULT_COPTS} - LINKOPTS - ${ABSL_DEFAULT_LINKOPTS} - DEPS - absl::core_headers - absl::random_internal_distribution_caller - absl::random_internal_fast_uniform_bits - absl::type_traits -) - -absl_cc_test( - NAME - random_bit_gen_ref_test - SRCS - "bit_gen_ref_test.cc" - COPTS - ${ABSL_TEST_COPTS} - LINKOPTS - ${ABSL_DEFAULT_LINKOPTS} - DEPS - absl::random_bit_gen_ref - absl::random_random - absl::random_internal_sequence_urbg - absl::fast_type_id - gmock - gtest_main -) - -# Internal-only target, do not depend on directly. -absl_cc_library( - NAME - random_internal_mock_helpers - HDRS - "internal/mock_helpers.h" - COPTS - ${ABSL_DEFAULT_COPTS} - LINKOPTS - ${ABSL_DEFAULT_LINKOPTS} - DEPS - absl::fast_type_id - absl::optional -) - -# Internal-only target, do not depend on directly. -absl_cc_library( - NAME - random_internal_mock_overload_set - HDRS - "internal/mock_overload_set.h" - COPTS - ${ABSL_DEFAULT_COPTS} - LINKOPTS - ${ABSL_DEFAULT_LINKOPTS} - DEPS - absl::random_mocking_bit_gen - absl::random_internal_mock_helpers - TESTONLY -) - -absl_cc_library( - NAME - random_mocking_bit_gen - HDRS - "mock_distributions.h" - "mocking_bit_gen.h" - COPTS - ${ABSL_DEFAULT_COPTS} - LINKOPTS - ${ABSL_DEFAULT_LINKOPTS} - DEPS - absl::flat_hash_map - absl::raw_logging_internal - absl::random_distributions - absl::random_internal_distribution_caller - absl::random_internal_mock_overload_set - absl::random_random - absl::strings - absl::span - absl::type_traits - absl::utility - absl::variant - gmock - gtest - TESTONLY -) - -absl_cc_test( - NAME - random_mock_distributions_test - SRCS - "mock_distributions_test.cc" - COPTS - ${ABSL_TEST_COPTS} - LINKOPTS - ${ABSL_DEFAULT_LINKOPTS} - DEPS - absl::random_mocking_bit_gen - absl::random_random - gmock - gtest_main -) - -absl_cc_test( - NAME - random_mocking_bit_gen_test - SRCS - "mocking_bit_gen_test.cc" - COPTS - ${ABSL_TEST_COPTS} - LINKOPTS - ${ABSL_DEFAULT_LINKOPTS} - DEPS - absl::random_bit_gen_ref - absl::random_mocking_bit_gen - absl::random_random - gmock - gtest_main -) - -absl_cc_library( - NAME - random_distributions - SRCS - "discrete_distribution.cc" - "gaussian_distribution.cc" - HDRS - "bernoulli_distribution.h" - "beta_distribution.h" - "discrete_distribution.h" - "distributions.h" - "exponential_distribution.h" - "gaussian_distribution.h" - "log_uniform_int_distribution.h" - "poisson_distribution.h" - "uniform_int_distribution.h" - "uniform_real_distribution.h" - "zipf_distribution.h" - COPTS - ${ABSL_DEFAULT_COPTS} - LINKOPTS - ${ABSL_DEFAULT_LINKOPTS} - DEPS - absl::base_internal - absl::config - absl::core_headers - absl::random_internal_generate_real - absl::random_internal_distribution_caller - absl::random_internal_fast_uniform_bits - absl::random_internal_fastmath - absl::random_internal_iostream_state_saver - absl::random_internal_traits - absl::random_internal_uniform_helper - absl::random_internal_wide_multiply - absl::strings - absl::type_traits -) - -absl_cc_library( - NAME - random_seed_gen_exception - SRCS - "seed_gen_exception.cc" - HDRS - "seed_gen_exception.h" - COPTS - ${ABSL_DEFAULT_COPTS} - LINKOPTS - ${ABSL_DEFAULT_LINKOPTS} - DEPS - absl::config -) - -absl_cc_library( - NAME - random_seed_sequences - SRCS - "seed_sequences.cc" - HDRS - "seed_sequences.h" - COPTS - ${ABSL_DEFAULT_COPTS} - LINKOPTS - ${ABSL_DEFAULT_LINKOPTS} - DEPS - absl::inlined_vector - absl::random_internal_nonsecure_base - absl::random_internal_pool_urbg - absl::random_internal_salted_seed_seq - absl::random_internal_seed_material - absl::random_seed_gen_exception - absl::span -) - -absl_cc_test( - NAME - random_bernoulli_distribution_test - SRCS - "bernoulli_distribution_test.cc" - COPTS - ${ABSL_TEST_COPTS} - LINKOPTS - ${ABSL_DEFAULT_LINKOPTS} - DEPS - absl::random_distributions - absl::random_random - absl::random_internal_sequence_urbg - absl::random_internal_pcg_engine - gmock - gtest_main -) - -absl_cc_test( - NAME - random_beta_distribution_test - SRCS - "beta_distribution_test.cc" - COPTS - ${ABSL_TEST_COPTS} - LINKOPTS - ${ABSL_DEFAULT_LINKOPTS} - DEPS - absl::random_distributions - absl::random_random - absl::random_internal_distribution_test_util - absl::random_internal_sequence_urbg - absl::random_internal_pcg_engine - absl::raw_logging_internal - absl::strings - absl::str_format - gmock - gtest_main -) - -absl_cc_test( - NAME - random_distributions_test - SRCS - "distributions_test.cc" - COPTS - ${ABSL_TEST_COPTS} - LINKOPTS - ${ABSL_DEFAULT_LINKOPTS} - DEPS - absl::random_distributions - absl::random_random - absl::random_internal_distribution_test_util - gmock - gtest_main -) - -absl_cc_test( - NAME - random_generators_test - SRCS - "generators_test.cc" - COPTS - ${ABSL_TEST_COPTS} - LINKOPTS - ${ABSL_DEFAULT_LINKOPTS} - absl::random_distributions - absl::random_random - absl::raw_logging_internal - gmock - gtest_main -) - -absl_cc_test( - NAME - random_log_uniform_int_distribution_test - SRCS - "log_uniform_int_distribution_test.cc" - COPTS - ${ABSL_TEST_COPTS} - LINKOPTS - ${ABSL_DEFAULT_LINKOPTS} - absl::random_distributions - absl::random_internal_distribution_test_util - absl::random_internal_pcg_engine - absl::random_internal_sequence_urbg - absl::random_random - absl::raw_logging_internal - absl::strings - absl::str_format - gmock - gtest_main -) - -absl_cc_test( - NAME - random_discrete_distribution_test - SRCS - "discrete_distribution_test.cc" - COPTS - ${ABSL_TEST_COPTS} - LINKOPTS - ${ABSL_DEFAULT_LINKOPTS} - DEPS - absl::random_distributions - absl::random_internal_distribution_test_util - absl::random_internal_pcg_engine - absl::random_internal_sequence_urbg - absl::random_random - absl::raw_logging_internal - absl::strings - gmock - gtest_main -) - -absl_cc_test( - NAME - random_poisson_distribution_test - SRCS - "poisson_distribution_test.cc" - COPTS - ${ABSL_TEST_COPTS} - LINKOPTS - ${ABSL_DEFAULT_LINKOPTS} - DEPS - absl::random_distributions - absl::random_random - absl::core_headers - absl::flat_hash_map - absl::random_internal_distribution_test_util - absl::random_internal_pcg_engine - absl::random_internal_sequence_urbg - absl::raw_logging_internal - absl::strings - absl::str_format - gmock - gtest_main -) - -absl_cc_test( - NAME - random_exponential_distribution_test - SRCS - "exponential_distribution_test.cc" - COPTS - ${ABSL_TEST_COPTS} - LINKOPTS - ${ABSL_DEFAULT_LINKOPTS} - DEPS - absl::core_headers - absl::random_distributions - absl::random_internal_distribution_test_util - absl::random_internal_pcg_engine - absl::random_internal_sequence_urbg - absl::random_random - absl::raw_logging_internal - absl::strings - absl::str_format - gmock - gtest_main -) - -absl_cc_test( - NAME - random_gaussian_distribution_test - SRCS - "gaussian_distribution_test.cc" - COPTS - ${ABSL_TEST_COPTS} - LINKOPTS - ${ABSL_DEFAULT_LINKOPTS} - DEPS - absl::core_headers - absl::random_distributions - absl::random_internal_distribution_test_util - absl::random_internal_sequence_urbg - absl::random_random - absl::raw_logging_internal - absl::strings - absl::str_format - gmock - gtest_main -) - -absl_cc_test( - NAME - random_uniform_int_distribution_test - SRCS - "uniform_int_distribution_test.cc" - COPTS - ${ABSL_TEST_COPTS} - LINKOPTS - ${ABSL_DEFAULT_LINKOPTS} - DEPS - absl::random_distributions - absl::random_internal_distribution_test_util - absl::random_internal_pcg_engine - absl::random_internal_sequence_urbg - absl::random_random - absl::raw_logging_internal - absl::strings - gmock - gtest_main -) - -absl_cc_test( - NAME - random_uniform_real_distribution_test - SRCS - "uniform_real_distribution_test.cc" - COPTS - ${ABSL_TEST_COPTS} - LINKOPTS - ${ABSL_DEFAULT_LINKOPTS} - DEPS - absl::random_distributions - absl::random_internal_distribution_test_util - absl::random_internal_pcg_engine - absl::random_internal_sequence_urbg - absl::random_random - absl::strings - gmock - gtest_main -) - -absl_cc_test( - NAME - random_zipf_distribution_test - SRCS - "zipf_distribution_test.cc" - COPTS - ${ABSL_TEST_COPTS} - LINKOPTS - ${ABSL_DEFAULT_LINKOPTS} - DEPS - absl::random_distributions - absl::random_internal_distribution_test_util - absl::random_internal_pcg_engine - absl::random_internal_sequence_urbg - absl::random_random - absl::raw_logging_internal - absl::strings - gmock - gtest_main -) - -absl_cc_test( - NAME - random_examples_test - SRCS - "examples_test.cc" - COPTS - ${ABSL_TEST_COPTS} - LINKOPTS - ${ABSL_DEFAULT_LINKOPTS} - DEPS - absl::random_random - gtest_main -) - -absl_cc_test( - NAME - random_seed_sequences_test - SRCS - "seed_sequences_test.cc" - COPTS - ${ABSL_TEST_COPTS} - LINKOPTS - ${ABSL_DEFAULT_LINKOPTS} - DEPS - absl::random_seed_sequences - absl::random_internal_nonsecure_base - absl::random_random - gmock - gtest_main -) - -# Internal-only target, do not depend on directly. -absl_cc_library( - NAME - random_internal_traits - HDRS - "internal/traits.h" - COPTS - ${ABSL_DEFAULT_COPTS} - LINKOPTS - ${ABSL_DEFAULT_LINKOPTS} - DEPS - absl::config -) - -# Internal-only target, do not depend on directly. -absl_cc_library( - NAME - random_internal_distribution_caller - HDRS - "internal/distribution_caller.h" - COPTS - ${ABSL_DEFAULT_COPTS} - LINKOPTS - ${ABSL_DEFAULT_LINKOPTS} - DEPS - absl::config - absl::utility - absl::fast_type_id -) - -# Internal-only target, do not depend on directly. -absl_cc_library( - NAME - random_internal_fast_uniform_bits - HDRS - "internal/fast_uniform_bits.h" - COPTS - ${ABSL_DEFAULT_COPTS} - LINKOPTS - ${ABSL_DEFAULT_LINKOPTS} - DEPS - absl::config -) - -# Internal-only target, do not depend on directly. -absl_cc_library( - NAME - random_internal_seed_material - SRCS - "internal/seed_material.cc" - HDRS - "internal/seed_material.h" - COPTS - ${ABSL_DEFAULT_COPTS} - LINKOPTS - ${ABSL_DEFAULT_LINKOPTS} - $<$<BOOL:${MINGW}>:"bcrypt"> - DEPS - absl::core_headers - absl::optional - absl::random_internal_fast_uniform_bits - absl::raw_logging_internal - absl::span - absl::strings -) - -# Internal-only target, do not depend on directly. -absl_cc_library( - NAME - random_internal_pool_urbg - SRCS - "internal/pool_urbg.cc" - HDRS - "internal/pool_urbg.h" - COPTS - ${ABSL_DEFAULT_COPTS} - LINKOPTS - ${ABSL_DEFAULT_LINKOPTS} - DEPS - absl::base - absl::config - absl::core_headers - absl::endian - absl::random_internal_randen - absl::random_internal_seed_material - absl::random_internal_traits - absl::random_seed_gen_exception - absl::raw_logging_internal - absl::span -) - -# Internal-only target, do not depend on directly. -absl_cc_library( - NAME - random_internal_explicit_seed_seq - HDRS - "internal/random_internal_explicit_seed_seq.h" - COPTS - ${ABSL_DEFAULT_COPTS} - LINKOPTS - ${ABSL_DEFAULT_LINKOPTS} - DEPS - absl::config - TESTONLY -) - -# Internal-only target, do not depend on directly. -absl_cc_library( - NAME - random_internal_sequence_urbg - HDRS - "internal/sequence_urbg.h" - COPTS - ${ABSL_DEFAULT_COPTS} - LINKOPTS - ${ABSL_DEFAULT_LINKOPTS} - DEPS - absl::config - TESTONLY -) - -# Internal-only target, do not depend on directly. -absl_cc_library( - NAME - random_internal_salted_seed_seq - HDRS - "internal/salted_seed_seq.h" - COPTS - ${ABSL_DEFAULT_COPTS} - LINKOPTS - ${ABSL_DEFAULT_LINKOPTS} - DEPS - absl::inlined_vector - absl::optional - absl::span - absl::random_internal_seed_material - absl::type_traits -) - -# Internal-only target, do not depend on directly. -absl_cc_library( - NAME - random_internal_iostream_state_saver - HDRS - "internal/iostream_state_saver.h" - COPTS - ${ABSL_DEFAULT_COPTS} - LINKOPTS - ${ABSL_DEFAULT_LINKOPTS} - DEPS - absl::int128 - absl::type_traits -) - -# Internal-only target, do not depend on directly. -absl_cc_library( - NAME - random_internal_generate_real - HDRS - "internal/generate_real.h" - COPTS - ${ABSL_DEFAULT_COPTS} - LINKOPTS - ${ABSL_DEFAULT_LINKOPTS} - DEPS - absl::bits - absl::random_internal_fastmath - absl::random_internal_traits - absl::type_traits -) - -# Internal-only target, do not depend on directly. -absl_cc_library( - NAME - random_internal_wide_multiply - HDRS - "internal/wide_multiply.h" - COPTS - ${ABSL_DEFAULT_COPTS} - LINKOPTS - ${ABSL_DEFAULT_LINKOPTS} - DEPS - absl::bits - absl::config - absl::int128 -) - -# Internal-only target, do not depend on directly. -absl_cc_library( - NAME - random_internal_fastmath - HDRS - "internal/fastmath.h" - COPTS - ${ABSL_DEFAULT_COPTS} - LINKOPTS - ${ABSL_DEFAULT_LINKOPTS} - DEPS - absl::bits -) - -# Internal-only target, do not depend on directly. -absl_cc_library( - NAME - random_internal_nonsecure_base - HDRS - "internal/nonsecure_base.h" - COPTS - ${ABSL_DEFAULT_COPTS} - LINKOPTS - ${ABSL_DEFAULT_LINKOPTS} - DEPS - absl::core_headers - absl::optional - absl::random_internal_pool_urbg - absl::random_internal_salted_seed_seq - absl::random_internal_seed_material - absl::span - absl::type_traits -) - -# Internal-only target, do not depend on directly. -absl_cc_library( - NAME - random_internal_pcg_engine - HDRS - "internal/pcg_engine.h" - COPTS - ${ABSL_DEFAULT_COPTS} - LINKOPTS - ${ABSL_DEFAULT_LINKOPTS} - DEPS - absl::config - absl::int128 - absl::random_internal_fastmath - absl::random_internal_iostream_state_saver - absl::type_traits -) - -# Internal-only target, do not depend on directly. -absl_cc_library( - NAME - random_internal_randen_engine - HDRS - "internal/randen_engine.h" - COPTS - ${ABSL_DEFAULT_COPTS} - LINKOPTS - ${ABSL_DEFAULT_LINKOPTS} - DEPS - absl::random_internal_iostream_state_saver - absl::random_internal_randen - absl::raw_logging_internal - absl::type_traits -) - -# Internal-only target, do not depend on directly. -absl_cc_library( - NAME - random_internal_platform - HDRS - "internal/randen_traits.h" - "internal/platform.h" - SRCS - "internal/randen_round_keys.cc" - COPTS - ${ABSL_DEFAULT_COPTS} - LINKOPTS - ${ABSL_DEFAULT_LINKOPTS} - DEPS - absl::config -) - -# Internal-only target, do not depend on directly. -absl_cc_library( - NAME - random_internal_randen - SRCS - "internal/randen.cc" - HDRS - "internal/randen.h" - COPTS - ${ABSL_DEFAULT_COPTS} - LINKOPTS - ${ABSL_DEFAULT_LINKOPTS} - DEPS - absl::random_internal_platform - absl::random_internal_randen_hwaes - absl::random_internal_randen_slow -) - -# Internal-only target, do not depend on directly. -absl_cc_library( - NAME - random_internal_randen_slow - SRCS - "internal/randen_slow.cc" - HDRS - "internal/randen_slow.h" - COPTS - ${ABSL_DEFAULT_COPTS} - LINKOPTS - ${ABSL_DEFAULT_LINKOPTS} - DEPS - absl::random_internal_platform - absl::config -) - -# Internal-only target, do not depend on directly. -absl_cc_library( - NAME - random_internal_randen_hwaes - SRCS - "internal/randen_detect.cc" - HDRS - "internal/randen_detect.h" - "internal/randen_hwaes.h" - COPTS - ${ABSL_DEFAULT_COPTS} - ${ABSL_RANDOM_RANDEN_COPTS} - LINKOPTS - ${ABSL_DEFAULT_LINKOPTS} - DEPS - absl::random_internal_platform - absl::random_internal_randen_hwaes_impl - absl::config -) - -# Internal-only target, do not depend on directly. -absl_cc_library( - NAME - random_internal_randen_hwaes_impl - SRCS - "internal/randen_hwaes.cc" - "internal/randen_hwaes.h" - COPTS - ${ABSL_DEFAULT_COPTS} - ${ABSL_RANDOM_RANDEN_COPTS} - LINKOPTS - ${ABSL_DEFAULT_LINKOPTS} - DEPS - absl::random_internal_platform - absl::config -) - -# Internal-only target, do not depend on directly. -absl_cc_library( - NAME - random_internal_distribution_test_util - SRCS - "internal/chi_square.cc" - "internal/distribution_test_util.cc" - HDRS - "internal/chi_square.h" - "internal/distribution_test_util.h" - COPTS - ${ABSL_DEFAULT_COPTS} - LINKOPTS - ${ABSL_DEFAULT_LINKOPTS} - DEPS - absl::config - absl::core_headers - absl::raw_logging_internal - absl::strings - absl::str_format - absl::span -) - -# Internal-only target, do not depend on directly. -absl_cc_test( - NAME - random_internal_traits_test - SRCS - "internal/traits_test.cc" - COPTS - ${ABSL_TEST_COPTS} - LINKOPTS - ${ABSL_DEFAULT_LINKOPTS} - DEPS - absl::random_internal_traits - gtest_main -) - -# Internal-only target, do not depend on directly. -absl_cc_test( - NAME - random_internal_generate_real_test - SRCS - "internal/generate_real_test.cc" - COPTS - ${ABSL_TEST_COPTS} - LINKOPTS - ${ABSL_DEFAULT_LINKOPTS} - DEPS - absl::bits - absl::flags - absl::random_internal_generate_real - gtest_main -) - -# Internal-only target, do not depend on directly. -absl_cc_test( - NAME - random_internal_distribution_test_util_test - SRCS - "internal/distribution_test_util_test.cc" - COPTS - ${ABSL_TEST_COPTS} - LINKOPTS - ${ABSL_DEFAULT_LINKOPTS} - DEPS - absl::random_internal_distribution_test_util - gtest_main -) - -# Internal-only target, do not depend on directly. -absl_cc_test( - NAME - random_internal_fastmath_test - SRCS - "internal/fastmath_test.cc" - COPTS - ${ABSL_TEST_COPTS} - LINKOPTS - ${ABSL_DEFAULT_LINKOPTS} - DEPS - absl::random_internal_fastmath - gtest_main -) - -# Internal-only target, do not depend on directly. -absl_cc_test( - NAME - random_internal_explicit_seed_seq_test - SRCS - "internal/explicit_seed_seq_test.cc" - COPTS - ${ABSL_TEST_COPTS} - LINKOPTS - ${ABSL_DEFAULT_LINKOPTS} - DEPS - absl::random_internal_explicit_seed_seq - absl::random_seed_sequences - gmock - gtest_main -) - -# Internal-only target, do not depend on directly. -absl_cc_test( - NAME - random_internal_salted_seed_seq_test - SRCS - "internal/salted_seed_seq_test.cc" - COPTS - ${ABSL_TEST_COPTS} - LINKOPTS - ${ABSL_DEFAULT_LINKOPTS} - DEPS - absl::random_internal_salted_seed_seq - gmock - gtest_main -) - -# Internal-only target, do not depend on directly. -absl_cc_test( - NAME - random_internal_chi_square_test - SRCS - "internal/chi_square_test.cc" - COPTS - ${ABSL_TEST_COPTS} - LINKOPTS - ${ABSL_DEFAULT_LINKOPTS} - DEPS - absl::core_headers - absl::random_internal_distribution_test_util - gtest_main -) - -# Internal-only target, do not depend on directly. -absl_cc_test( - NAME - random_internal_fast_uniform_bits_test - SRCS - "internal/fast_uniform_bits_test.cc" - COPTS - ${ABSL_TEST_COPTS} - LINKOPTS - ${ABSL_DEFAULT_LINKOPTS} - DEPS - absl::random_internal_fast_uniform_bits - gtest_main -) - -# Internal-only target, do not depend on directly. -absl_cc_test( - NAME - random_internal_nonsecure_base_test - SRCS - "internal/nonsecure_base_test.cc" - COPTS - ${ABSL_TEST_COPTS} - LINKOPTS - ${ABSL_DEFAULT_LINKOPTS} - DEPS - absl::random_internal_nonsecure_base - absl::random_random - absl::random_distributions - absl::random_seed_sequences - absl::strings - gtest_main -) - -# Internal-only target, do not depend on directly. -absl_cc_test( - NAME - random_internal_seed_material_test - SRCS - "internal/seed_material_test.cc" - COPTS - ${ABSL_TEST_COPTS} - LINKOPTS - ${ABSL_DEFAULT_LINKOPTS} - DEPS - absl::random_internal_seed_material - gmock - gtest_main -) - -# Internal-only target, do not depend on directly. -absl_cc_test( - NAME - random_internal_pool_urbg_test - SRCS - "internal/pool_urbg_test.cc" - COPTS - ${ABSL_TEST_COPTS} - LINKOPTS - ${ABSL_DEFAULT_LINKOPTS} - DEPS - absl::random_internal_pool_urbg - absl::span - absl::type_traits - gtest_main -) - -# Internal-only target, do not depend on directly. -absl_cc_test( - NAME - random_internal_pcg_engine_test - SRCS - "internal/pcg_engine_test.cc" - COPTS - ${ABSL_TEST_COPTS} - LINKOPTS - ${ABSL_DEFAULT_LINKOPTS} - DEPS - absl::random_internal_explicit_seed_seq - absl::random_internal_pcg_engine - absl::time - gmock - gtest_main -) - -# Internal-only target, do not depend on directly. -absl_cc_test( - NAME - random_internal_randen_engine_test - SRCS - "internal/randen_engine_test.cc" - COPTS - ${ABSL_TEST_COPTS} - LINKOPTS - ${ABSL_DEFAULT_LINKOPTS} - DEPS - absl::random_internal_explicit_seed_seq - absl::random_internal_randen_engine - absl::raw_logging_internal - absl::strings - absl::time - gmock - gtest_main -) - -# Internal-only target, do not depend on directly. -absl_cc_test( - NAME - random_internal_randen_test - SRCS - "internal/randen_test.cc" - COPTS - ${ABSL_TEST_COPTS} - LINKOPTS - ${ABSL_DEFAULT_LINKOPTS} - DEPS - absl::random_internal_randen - absl::type_traits - gtest_main -) - -# Internal-only target, do not depend on directly. -absl_cc_test( - NAME - random_internal_randen_slow_test - SRCS - "internal/randen_slow_test.cc" - COPTS - ${ABSL_TEST_COPTS} - LINKOPTS - ${ABSL_DEFAULT_LINKOPTS} - DEPS - absl::random_internal_randen_slow - gtest_main -) - -# Internal-only target, do not depend on directly. -absl_cc_test( - NAME - random_internal_randen_hwaes_test - SRCS - "internal/randen_hwaes_test.cc" - COPTS - ${ABSL_TEST_COPTS} - LINKOPTS - ${ABSL_DEFAULT_LINKOPTS} - DEPS - absl::random_internal_platform - absl::random_internal_randen_hwaes - absl::random_internal_randen_hwaes_impl - absl::raw_logging_internal - absl::str_format - gmock - gtest -) - -# Internal-only target, do not depend on directly. -absl_cc_library( - NAME - random_internal_uniform_helper - HDRS - "internal/uniform_helper.h" - COPTS - ${ABSL_DEFAULT_COPTS} - LINKOPTS - ${ABSL_DEFAULT_LINKOPTS} - DEPS - absl::config - absl::random_internal_traits - absl::type_traits -) - -# Internal-only target, do not depend on directly. -absl_cc_test( - NAME - random_internal_uniform_helper_test - SRCS - "internal/uniform_helper_test.cc" - COPTS - ${ABSL_TEST_COPTS} - LINKOPTS - ${ABSL_DEFAULT_LINKOPTS} - DEPS - absl::random_internal_uniform_helper - gtest_main -) - -# Internal-only target, do not depend on directly. -absl_cc_test( - NAME - random_internal_iostream_state_saver_test - SRCS - "internal/iostream_state_saver_test.cc" - COPTS - ${ABSL_TEST_COPTS} - LINKOPTS - ${ABSL_DEFAULT_LINKOPTS} - DEPS - absl::random_internal_iostream_state_saver - gtest_main -) - -# Internal-only target, do not depend on directly. -absl_cc_test( - NAME - random_internal_wide_multiply_test - SRCS - internal/wide_multiply_test.cc - COPTS - ${ABSL_TEST_COPTS} - LINKOPTS - ${ABSL_DEFAULT_LINKOPTS} - DEPS - absl::random_internal_wide_multiply - absl::bits - absl::int128 - gtest_main -) diff --git a/third_party/abseil_cpp/absl/random/benchmarks.cc b/third_party/abseil_cpp/absl/random/benchmarks.cc deleted file mode 100644 index 87bbb9810a77..000000000000 --- a/third_party/abseil_cpp/absl/random/benchmarks.cc +++ /dev/null @@ -1,383 +0,0 @@ -// Copyright 2017 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -// Benchmarks for absl random distributions as well as a selection of the -// C++ standard library random distributions. - -#include <algorithm> -#include <cstddef> -#include <cstdint> -#include <initializer_list> -#include <iterator> -#include <limits> -#include <random> -#include <type_traits> -#include <vector> - -#include "absl/base/macros.h" -#include "absl/meta/type_traits.h" -#include "absl/random/bernoulli_distribution.h" -#include "absl/random/beta_distribution.h" -#include "absl/random/exponential_distribution.h" -#include "absl/random/gaussian_distribution.h" -#include "absl/random/internal/fast_uniform_bits.h" -#include "absl/random/internal/randen_engine.h" -#include "absl/random/log_uniform_int_distribution.h" -#include "absl/random/poisson_distribution.h" -#include "absl/random/random.h" -#include "absl/random/uniform_int_distribution.h" -#include "absl/random/uniform_real_distribution.h" -#include "absl/random/zipf_distribution.h" -#include "benchmark/benchmark.h" - -namespace { - -// Seed data to avoid reading random_device() for benchmarks. -uint32_t kSeedData[] = { - 0x1B510052, 0x9A532915, 0xD60F573F, 0xBC9BC6E4, 0x2B60A476, 0x81E67400, - 0x08BA6FB5, 0x571BE91F, 0xF296EC6B, 0x2A0DD915, 0xB6636521, 0xE7B9F9B6, - 0xFF34052E, 0xC5855664, 0x53B02D5D, 0xA99F8FA1, 0x08BA4799, 0x6E85076A, - 0x4B7A70E9, 0xB5B32944, 0xDB75092E, 0xC4192623, 0xAD6EA6B0, 0x49A7DF7D, - 0x9CEE60B8, 0x8FEDB266, 0xECAA8C71, 0x699A18FF, 0x5664526C, 0xC2B19EE1, - 0x193602A5, 0x75094C29, 0xA0591340, 0xE4183A3E, 0x3F54989A, 0x5B429D65, - 0x6B8FE4D6, 0x99F73FD6, 0xA1D29C07, 0xEFE830F5, 0x4D2D38E6, 0xF0255DC1, - 0x4CDD2086, 0x8470EB26, 0x6382E9C6, 0x021ECC5E, 0x09686B3F, 0x3EBAEFC9, - 0x3C971814, 0x6B6A70A1, 0x687F3584, 0x52A0E286, 0x13198A2E, 0x03707344, -}; - -// PrecompiledSeedSeq provides kSeedData to a conforming -// random engine to speed initialization in the benchmarks. -class PrecompiledSeedSeq { - public: - using result_type = uint32_t; - - PrecompiledSeedSeq() {} - - template <typename Iterator> - PrecompiledSeedSeq(Iterator begin, Iterator end) {} - - template <typename T> - PrecompiledSeedSeq(std::initializer_list<T> il) {} - - template <typename OutIterator> - void generate(OutIterator begin, OutIterator end) { - static size_t idx = 0; - for (; begin != end; begin++) { - *begin = kSeedData[idx++]; - if (idx >= ABSL_ARRAYSIZE(kSeedData)) { - idx = 0; - } - } - } - - size_t size() const { return ABSL_ARRAYSIZE(kSeedData); } - - template <typename OutIterator> - void param(OutIterator out) const { - std::copy(std::begin(kSeedData), std::end(kSeedData), out); - } -}; - -// use_default_initialization<T> indicates whether the random engine -// T must be default initialized, or whether we may initialize it using -// a seed sequence. This is used because some engines do not accept seed -// sequence-based initialization. -template <typename E> -using use_default_initialization = std::false_type; - -// make_engine<T, SSeq> returns a random_engine which is initialized, -// either via the default constructor, when use_default_initialization<T> -// is true, or via the indicated seed sequence, SSeq. -template <typename Engine, typename SSeq = PrecompiledSeedSeq> -typename absl::enable_if_t<!use_default_initialization<Engine>::value, Engine> -make_engine() { - // Initialize the random engine using the seed sequence SSeq, which - // is constructed from the precompiled seed data. - SSeq seq(std::begin(kSeedData), std::end(kSeedData)); - return Engine(seq); -} - -template <typename Engine, typename SSeq = PrecompiledSeedSeq> -typename absl::enable_if_t<use_default_initialization<Engine>::value, Engine> -make_engine() { - // Initialize the random engine using the default constructor. - return Engine(); -} - -template <typename Engine, typename SSeq> -void BM_Construct(benchmark::State& state) { - for (auto _ : state) { - auto rng = make_engine<Engine, SSeq>(); - benchmark::DoNotOptimize(rng()); - } -} - -template <typename Engine> -void BM_Direct(benchmark::State& state) { - using value_type = typename Engine::result_type; - // Direct use of the URBG. - auto rng = make_engine<Engine>(); - for (auto _ : state) { - benchmark::DoNotOptimize(rng()); - } - state.SetBytesProcessed(sizeof(value_type) * state.iterations()); -} - -template <typename Engine> -void BM_Generate(benchmark::State& state) { - // std::generate makes a copy of the RNG; thus this tests the - // copy-constructor efficiency. - using value_type = typename Engine::result_type; - std::vector<value_type> v(64); - auto rng = make_engine<Engine>(); - while (state.KeepRunningBatch(64)) { - std::generate(std::begin(v), std::end(v), rng); - } -} - -template <typename Engine, size_t elems> -void BM_Shuffle(benchmark::State& state) { - // Direct use of the Engine. - std::vector<uint32_t> v(elems); - while (state.KeepRunningBatch(elems)) { - auto rng = make_engine<Engine>(); - std::shuffle(std::begin(v), std::end(v), rng); - } -} - -template <typename Engine, size_t elems> -void BM_ShuffleReuse(benchmark::State& state) { - // Direct use of the Engine. - std::vector<uint32_t> v(elems); - auto rng = make_engine<Engine>(); - while (state.KeepRunningBatch(elems)) { - std::shuffle(std::begin(v), std::end(v), rng); - } -} - -template <typename Engine, typename Dist, typename... Args> -void BM_Dist(benchmark::State& state, Args&&... args) { - using value_type = typename Dist::result_type; - auto rng = make_engine<Engine>(); - Dist dis{std::forward<Args>(args)...}; - // Compare the following loop performance: - for (auto _ : state) { - benchmark::DoNotOptimize(dis(rng)); - } - state.SetBytesProcessed(sizeof(value_type) * state.iterations()); -} - -template <typename Engine, typename Dist> -void BM_Large(benchmark::State& state) { - using value_type = typename Dist::result_type; - volatile value_type kMin = 0; - volatile value_type kMax = std::numeric_limits<value_type>::max() / 2 + 1; - BM_Dist<Engine, Dist>(state, kMin, kMax); -} - -template <typename Engine, typename Dist> -void BM_Small(benchmark::State& state) { - using value_type = typename Dist::result_type; - volatile value_type kMin = 0; - volatile value_type kMax = std::numeric_limits<value_type>::max() / 64 + 1; - BM_Dist<Engine, Dist>(state, kMin, kMax); -} - -template <typename Engine, typename Dist, int A> -void BM_Bernoulli(benchmark::State& state) { - volatile double a = static_cast<double>(A) / 1000000; - BM_Dist<Engine, Dist>(state, a); -} - -template <typename Engine, typename Dist, int A, int B> -void BM_Beta(benchmark::State& state) { - using value_type = typename Dist::result_type; - volatile value_type a = static_cast<value_type>(A) / 100; - volatile value_type b = static_cast<value_type>(B) / 100; - BM_Dist<Engine, Dist>(state, a, b); -} - -template <typename Engine, typename Dist, int A> -void BM_Gamma(benchmark::State& state) { - using value_type = typename Dist::result_type; - volatile value_type a = static_cast<value_type>(A) / 100; - BM_Dist<Engine, Dist>(state, a); -} - -template <typename Engine, typename Dist, int A = 100> -void BM_Poisson(benchmark::State& state) { - volatile double a = static_cast<double>(A) / 100; - BM_Dist<Engine, Dist>(state, a); -} - -template <typename Engine, typename Dist, int Q = 2, int V = 1> -void BM_Zipf(benchmark::State& state) { - using value_type = typename Dist::result_type; - volatile double q = Q; - volatile double v = V; - BM_Dist<Engine, Dist>(state, std::numeric_limits<value_type>::max(), q, v); -} - -template <typename Engine, typename Dist> -void BM_Thread(benchmark::State& state) { - using value_type = typename Dist::result_type; - auto rng = make_engine<Engine>(); - Dist dis{}; - for (auto _ : state) { - benchmark::DoNotOptimize(dis(rng)); - } - state.SetBytesProcessed(sizeof(value_type) * state.iterations()); -} - -// NOTES: -// -// std::geometric_distribution is similar to the zipf distributions. -// The algorithm for the geometric_distribution is, basically, -// floor(log(1-X) / log(1-p)) - -// Normal benchmark suite -#define BM_BASIC(Engine) \ - BENCHMARK_TEMPLATE(BM_Construct, Engine, PrecompiledSeedSeq); \ - BENCHMARK_TEMPLATE(BM_Construct, Engine, std::seed_seq); \ - BENCHMARK_TEMPLATE(BM_Direct, Engine); \ - BENCHMARK_TEMPLATE(BM_Shuffle, Engine, 10); \ - BENCHMARK_TEMPLATE(BM_Shuffle, Engine, 100); \ - BENCHMARK_TEMPLATE(BM_Shuffle, Engine, 1000); \ - BENCHMARK_TEMPLATE(BM_ShuffleReuse, Engine, 100); \ - BENCHMARK_TEMPLATE(BM_ShuffleReuse, Engine, 1000); \ - BENCHMARK_TEMPLATE(BM_Dist, Engine, \ - absl::random_internal::FastUniformBits<uint32_t>); \ - BENCHMARK_TEMPLATE(BM_Dist, Engine, \ - absl::random_internal::FastUniformBits<uint64_t>); \ - BENCHMARK_TEMPLATE(BM_Dist, Engine, std::uniform_int_distribution<int32_t>); \ - BENCHMARK_TEMPLATE(BM_Dist, Engine, std::uniform_int_distribution<int64_t>); \ - BENCHMARK_TEMPLATE(BM_Dist, Engine, \ - absl::uniform_int_distribution<int32_t>); \ - BENCHMARK_TEMPLATE(BM_Dist, Engine, \ - absl::uniform_int_distribution<int64_t>); \ - BENCHMARK_TEMPLATE(BM_Large, Engine, \ - std::uniform_int_distribution<int32_t>); \ - BENCHMARK_TEMPLATE(BM_Large, Engine, \ - std::uniform_int_distribution<int64_t>); \ - BENCHMARK_TEMPLATE(BM_Large, Engine, \ - absl::uniform_int_distribution<int32_t>); \ - BENCHMARK_TEMPLATE(BM_Large, Engine, \ - absl::uniform_int_distribution<int64_t>); \ - BENCHMARK_TEMPLATE(BM_Dist, Engine, std::uniform_real_distribution<float>); \ - BENCHMARK_TEMPLATE(BM_Dist, Engine, std::uniform_real_distribution<double>); \ - BENCHMARK_TEMPLATE(BM_Dist, Engine, absl::uniform_real_distribution<float>); \ - BENCHMARK_TEMPLATE(BM_Dist, Engine, absl::uniform_real_distribution<double>) - -#define BM_COPY(Engine) BENCHMARK_TEMPLATE(BM_Generate, Engine) - -#define BM_THREAD(Engine) \ - BENCHMARK_TEMPLATE(BM_Thread, Engine, \ - absl::uniform_int_distribution<int64_t>) \ - ->ThreadPerCpu(); \ - BENCHMARK_TEMPLATE(BM_Thread, Engine, \ - absl::uniform_real_distribution<double>) \ - ->ThreadPerCpu(); \ - BENCHMARK_TEMPLATE(BM_Shuffle, Engine, 100)->ThreadPerCpu(); \ - BENCHMARK_TEMPLATE(BM_Shuffle, Engine, 1000)->ThreadPerCpu(); \ - BENCHMARK_TEMPLATE(BM_ShuffleReuse, Engine, 100)->ThreadPerCpu(); \ - BENCHMARK_TEMPLATE(BM_ShuffleReuse, Engine, 1000)->ThreadPerCpu(); - -#define BM_EXTENDED(Engine) \ - /* -------------- Extended Uniform -----------------------*/ \ - BENCHMARK_TEMPLATE(BM_Small, Engine, \ - std::uniform_int_distribution<int32_t>); \ - BENCHMARK_TEMPLATE(BM_Small, Engine, \ - std::uniform_int_distribution<int64_t>); \ - BENCHMARK_TEMPLATE(BM_Small, Engine, \ - absl::uniform_int_distribution<int32_t>); \ - BENCHMARK_TEMPLATE(BM_Small, Engine, \ - absl::uniform_int_distribution<int64_t>); \ - BENCHMARK_TEMPLATE(BM_Small, Engine, std::uniform_real_distribution<float>); \ - BENCHMARK_TEMPLATE(BM_Small, Engine, \ - std::uniform_real_distribution<double>); \ - BENCHMARK_TEMPLATE(BM_Small, Engine, \ - absl::uniform_real_distribution<float>); \ - BENCHMARK_TEMPLATE(BM_Small, Engine, \ - absl::uniform_real_distribution<double>); \ - /* -------------- Other -----------------------*/ \ - BENCHMARK_TEMPLATE(BM_Dist, Engine, std::normal_distribution<double>); \ - BENCHMARK_TEMPLATE(BM_Dist, Engine, absl::gaussian_distribution<double>); \ - BENCHMARK_TEMPLATE(BM_Dist, Engine, std::exponential_distribution<double>); \ - BENCHMARK_TEMPLATE(BM_Dist, Engine, absl::exponential_distribution<double>); \ - BENCHMARK_TEMPLATE(BM_Poisson, Engine, std::poisson_distribution<int64_t>, \ - 100); \ - BENCHMARK_TEMPLATE(BM_Poisson, Engine, absl::poisson_distribution<int64_t>, \ - 100); \ - BENCHMARK_TEMPLATE(BM_Poisson, Engine, std::poisson_distribution<int64_t>, \ - 10 * 100); \ - BENCHMARK_TEMPLATE(BM_Poisson, Engine, absl::poisson_distribution<int64_t>, \ - 10 * 100); \ - BENCHMARK_TEMPLATE(BM_Poisson, Engine, std::poisson_distribution<int64_t>, \ - 13 * 100); \ - BENCHMARK_TEMPLATE(BM_Poisson, Engine, absl::poisson_distribution<int64_t>, \ - 13 * 100); \ - BENCHMARK_TEMPLATE(BM_Dist, Engine, \ - absl::log_uniform_int_distribution<int32_t>); \ - BENCHMARK_TEMPLATE(BM_Dist, Engine, \ - absl::log_uniform_int_distribution<int64_t>); \ - BENCHMARK_TEMPLATE(BM_Dist, Engine, std::geometric_distribution<int64_t>); \ - BENCHMARK_TEMPLATE(BM_Zipf, Engine, absl::zipf_distribution<uint64_t>); \ - BENCHMARK_TEMPLATE(BM_Zipf, Engine, absl::zipf_distribution<uint64_t>, 2, \ - 3); \ - BENCHMARK_TEMPLATE(BM_Bernoulli, Engine, std::bernoulli_distribution, \ - 257305); \ - BENCHMARK_TEMPLATE(BM_Bernoulli, Engine, absl::bernoulli_distribution, \ - 257305); \ - BENCHMARK_TEMPLATE(BM_Beta, Engine, absl::beta_distribution<double>, 65, \ - 41); \ - BENCHMARK_TEMPLATE(BM_Beta, Engine, absl::beta_distribution<double>, 99, \ - 330); \ - BENCHMARK_TEMPLATE(BM_Beta, Engine, absl::beta_distribution<double>, 150, \ - 150); \ - BENCHMARK_TEMPLATE(BM_Beta, Engine, absl::beta_distribution<double>, 410, \ - 580); \ - BENCHMARK_TEMPLATE(BM_Beta, Engine, absl::beta_distribution<float>, 65, 41); \ - BENCHMARK_TEMPLATE(BM_Beta, Engine, absl::beta_distribution<float>, 99, \ - 330); \ - BENCHMARK_TEMPLATE(BM_Beta, Engine, absl::beta_distribution<float>, 150, \ - 150); \ - BENCHMARK_TEMPLATE(BM_Beta, Engine, absl::beta_distribution<float>, 410, \ - 580); \ - BENCHMARK_TEMPLATE(BM_Gamma, Engine, std::gamma_distribution<float>, 199); \ - BENCHMARK_TEMPLATE(BM_Gamma, Engine, std::gamma_distribution<double>, 199); - -// ABSL Recommended interfaces. -BM_BASIC(absl::InsecureBitGen); // === pcg64_2018_engine -BM_BASIC(absl::BitGen); // === randen_engine<uint64_t>. -BM_THREAD(absl::BitGen); -BM_EXTENDED(absl::BitGen); - -// Instantiate benchmarks for multiple engines. -using randen_engine_64 = absl::random_internal::randen_engine<uint64_t>; -using randen_engine_32 = absl::random_internal::randen_engine<uint32_t>; - -// Comparison interfaces. -BM_BASIC(std::mt19937_64); -BM_COPY(std::mt19937_64); -BM_EXTENDED(std::mt19937_64); -BM_BASIC(randen_engine_64); -BM_COPY(randen_engine_64); -BM_EXTENDED(randen_engine_64); - -BM_BASIC(std::mt19937); -BM_COPY(std::mt19937); -BM_BASIC(randen_engine_32); -BM_COPY(randen_engine_32); - -} // namespace diff --git a/third_party/abseil_cpp/absl/random/bernoulli_distribution.h b/third_party/abseil_cpp/absl/random/bernoulli_distribution.h deleted file mode 100644 index 25bd0d5ca420..000000000000 --- a/third_party/abseil_cpp/absl/random/bernoulli_distribution.h +++ /dev/null @@ -1,200 +0,0 @@ -// Copyright 2017 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#ifndef ABSL_RANDOM_BERNOULLI_DISTRIBUTION_H_ -#define ABSL_RANDOM_BERNOULLI_DISTRIBUTION_H_ - -#include <cstdint> -#include <istream> -#include <limits> - -#include "absl/base/optimization.h" -#include "absl/random/internal/fast_uniform_bits.h" -#include "absl/random/internal/iostream_state_saver.h" - -namespace absl { -ABSL_NAMESPACE_BEGIN - -// absl::bernoulli_distribution is a drop in replacement for -// std::bernoulli_distribution. It guarantees that (given a perfect -// UniformRandomBitGenerator) the acceptance probability is *exactly* equal to -// the given double. -// -// The implementation assumes that double is IEEE754 -class bernoulli_distribution { - public: - using result_type = bool; - - class param_type { - public: - using distribution_type = bernoulli_distribution; - - explicit param_type(double p = 0.5) : prob_(p) { - assert(p >= 0.0 && p <= 1.0); - } - - double p() const { return prob_; } - - friend bool operator==(const param_type& p1, const param_type& p2) { - return p1.p() == p2.p(); - } - friend bool operator!=(const param_type& p1, const param_type& p2) { - return p1.p() != p2.p(); - } - - private: - double prob_; - }; - - bernoulli_distribution() : bernoulli_distribution(0.5) {} - - explicit bernoulli_distribution(double p) : param_(p) {} - - explicit bernoulli_distribution(param_type p) : param_(p) {} - - // no-op - void reset() {} - - template <typename URBG> - bool operator()(URBG& g) { // NOLINT(runtime/references) - return Generate(param_.p(), g); - } - - template <typename URBG> - bool operator()(URBG& g, // NOLINT(runtime/references) - const param_type& param) { - return Generate(param.p(), g); - } - - param_type param() const { return param_; } - void param(const param_type& param) { param_ = param; } - - double p() const { return param_.p(); } - - result_type(min)() const { return false; } - result_type(max)() const { return true; } - - friend bool operator==(const bernoulli_distribution& d1, - const bernoulli_distribution& d2) { - return d1.param_ == d2.param_; - } - - friend bool operator!=(const bernoulli_distribution& d1, - const bernoulli_distribution& d2) { - return d1.param_ != d2.param_; - } - - private: - static constexpr uint64_t kP32 = static_cast<uint64_t>(1) << 32; - - template <typename URBG> - static bool Generate(double p, URBG& g); // NOLINT(runtime/references) - - param_type param_; -}; - -template <typename CharT, typename Traits> -std::basic_ostream<CharT, Traits>& operator<<( - std::basic_ostream<CharT, Traits>& os, // NOLINT(runtime/references) - const bernoulli_distribution& x) { - auto saver = random_internal::make_ostream_state_saver(os); - os.precision(random_internal::stream_precision_helper<double>::kPrecision); - os << x.p(); - return os; -} - -template <typename CharT, typename Traits> -std::basic_istream<CharT, Traits>& operator>>( - std::basic_istream<CharT, Traits>& is, // NOLINT(runtime/references) - bernoulli_distribution& x) { // NOLINT(runtime/references) - auto saver = random_internal::make_istream_state_saver(is); - auto p = random_internal::read_floating_point<double>(is); - if (!is.fail()) { - x.param(bernoulli_distribution::param_type(p)); - } - return is; -} - -template <typename URBG> -bool bernoulli_distribution::Generate(double p, - URBG& g) { // NOLINT(runtime/references) - random_internal::FastUniformBits<uint32_t> fast_u32; - - while (true) { - // There are two aspects of the definition of `c` below that are worth - // commenting on. First, because `p` is in the range [0, 1], `c` is in the - // range [0, 2^32] which does not fit in a uint32_t and therefore requires - // 64 bits. - // - // Second, `c` is constructed by first casting explicitly to a signed - // integer and then converting implicitly to an unsigned integer of the same - // size. This is done because the hardware conversion instructions produce - // signed integers from double; if taken as a uint64_t the conversion would - // be wrong for doubles greater than 2^63 (not relevant in this use-case). - // If converted directly to an unsigned integer, the compiler would end up - // emitting code to handle such large values that are not relevant due to - // the known bounds on `c`. To avoid these extra instructions this - // implementation converts first to the signed type and then use the - // implicit conversion to unsigned (which is a no-op). - const uint64_t c = static_cast<int64_t>(p * kP32); - const uint32_t v = fast_u32(g); - // FAST PATH: this path fails with probability 1/2^32. Note that simply - // returning v <= c would approximate P very well (up to an absolute error - // of 1/2^32); the slow path (taken in that range of possible error, in the - // case of equality) eliminates the remaining error. - if (ABSL_PREDICT_TRUE(v != c)) return v < c; - - // It is guaranteed that `q` is strictly less than 1, because if `q` were - // greater than or equal to 1, the same would be true for `p`. Certainly `p` - // cannot be greater than 1, and if `p == 1`, then the fast path would - // necessary have been taken already. - const double q = static_cast<double>(c) / kP32; - - // The probability of acceptance on the fast path is `q` and so the - // probability of acceptance here should be `p - q`. - // - // Note that `q` is obtained from `p` via some shifts and conversions, the - // upshot of which is that `q` is simply `p` with some of the - // least-significant bits of its mantissa set to zero. This means that the - // difference `p - q` will not have any rounding errors. To see why, pretend - // that double has 10 bits of resolution and q is obtained from `p` in such - // a way that the 4 least-significant bits of its mantissa are set to zero. - // For example: - // p = 1.1100111011 * 2^-1 - // q = 1.1100110000 * 2^-1 - // p - q = 1.011 * 2^-8 - // The difference `p - q` has exactly the nonzero mantissa bits that were - // "lost" in `q` producing a number which is certainly representable in a - // double. - const double left = p - q; - - // By construction, the probability of being on this slow path is 1/2^32, so - // P(accept in slow path) = P(accept| in slow path) * P(slow path), - // which means the probability of acceptance here is `1 / (left * kP32)`: - const double here = left * kP32; - - // The simplest way to compute the result of this trial is to repeat the - // whole algorithm with the new probability. This terminates because even - // given arbitrarily unfriendly "random" bits, each iteration either - // multiplies a tiny probability by 2^32 (if c == 0) or strips off some - // number of nonzero mantissa bits. That process is bounded. - if (here == 0) return false; - p = here; - } -} - -ABSL_NAMESPACE_END -} // namespace absl - -#endif // ABSL_RANDOM_BERNOULLI_DISTRIBUTION_H_ diff --git a/third_party/abseil_cpp/absl/random/bernoulli_distribution_test.cc b/third_party/abseil_cpp/absl/random/bernoulli_distribution_test.cc deleted file mode 100644 index b250f8787c6e..000000000000 --- a/third_party/abseil_cpp/absl/random/bernoulli_distribution_test.cc +++ /dev/null @@ -1,217 +0,0 @@ -// Copyright 2017 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#include "absl/random/bernoulli_distribution.h" - -#include <cmath> -#include <cstddef> -#include <random> -#include <sstream> -#include <utility> - -#include "gtest/gtest.h" -#include "absl/random/internal/pcg_engine.h" -#include "absl/random/internal/sequence_urbg.h" -#include "absl/random/random.h" - -namespace { - -class BernoulliTest : public testing::TestWithParam<std::pair<double, size_t>> { -}; - -TEST_P(BernoulliTest, Serialize) { - const double d = GetParam().first; - absl::bernoulli_distribution before(d); - - { - absl::bernoulli_distribution via_param{ - absl::bernoulli_distribution::param_type(d)}; - EXPECT_EQ(via_param, before); - } - - std::stringstream ss; - ss << before; - absl::bernoulli_distribution after(0.6789); - - EXPECT_NE(before.p(), after.p()); - EXPECT_NE(before.param(), after.param()); - EXPECT_NE(before, after); - - ss >> after; - - EXPECT_EQ(before.p(), after.p()); - EXPECT_EQ(before.param(), after.param()); - EXPECT_EQ(before, after); -} - -TEST_P(BernoulliTest, Accuracy) { - // Sadly, the claim to fame for this implementation is precise accuracy, which - // is very, very hard to measure, the improvements come as trials approach the - // limit of double accuracy; thus the outcome differs from the - // std::bernoulli_distribution with a probability of approximately 1 in 2^-53. - const std::pair<double, size_t> para = GetParam(); - size_t trials = para.second; - double p = para.first; - - // We use a fixed bit generator for distribution accuracy tests. This allows - // these tests to be deterministic, while still testing the qualify of the - // implementation. - absl::random_internal::pcg64_2018_engine rng(0x2B7E151628AED2A6); - - size_t yes = 0; - absl::bernoulli_distribution dist(p); - for (size_t i = 0; i < trials; ++i) { - if (dist(rng)) yes++; - } - - // Compute the distribution parameters for a binomial test, using a normal - // approximation for the confidence interval, as there are a sufficiently - // large number of trials that the central limit theorem applies. - const double stddev_p = std::sqrt((p * (1.0 - p)) / trials); - const double expected = trials * p; - const double stddev = trials * stddev_p; - - // 5 sigma, approved by Richard Feynman - EXPECT_NEAR(yes, expected, 5 * stddev) - << "@" << p << ", " - << std::abs(static_cast<double>(yes) - expected) / stddev << " stddev"; -} - -// There must be many more trials to make the mean approximately normal for `p` -// closes to 0 or 1. -INSTANTIATE_TEST_SUITE_P( - All, BernoulliTest, - ::testing::Values( - // Typical values. - std::make_pair(0, 30000), std::make_pair(1e-3, 30000000), - std::make_pair(0.1, 3000000), std::make_pair(0.5, 3000000), - std::make_pair(0.9, 30000000), std::make_pair(0.999, 30000000), - std::make_pair(1, 30000), - // Boundary cases. - std::make_pair(std::nextafter(1.0, 0.0), 1), // ~1 - epsilon - std::make_pair(std::numeric_limits<double>::epsilon(), 1), - std::make_pair(std::nextafter(std::numeric_limits<double>::min(), - 1.0), // min + epsilon - 1), - std::make_pair(std::numeric_limits<double>::min(), // smallest normal - 1), - std::make_pair( - std::numeric_limits<double>::denorm_min(), // smallest denorm - 1), - std::make_pair(std::numeric_limits<double>::min() / 2, 1), // denorm - std::make_pair(std::nextafter(std::numeric_limits<double>::min(), - 0.0), // denorm_max - 1))); - -// NOTE: absl::bernoulli_distribution is not guaranteed to be stable. -TEST(BernoulliTest, StabilityTest) { - // absl::bernoulli_distribution stability relies on FastUniformBits and - // integer arithmetic. - absl::random_internal::sequence_urbg urbg({ - 0x0003eb76f6f7f755ull, 0xFFCEA50FDB2F953Bull, 0xC332DDEFBE6C5AA5ull, - 0x6558218568AB9702ull, 0x2AEF7DAD5B6E2F84ull, 0x1521B62829076170ull, - 0xECDD4775619F1510ull, 0x13CCA830EB61BD96ull, 0x0334FE1EAA0363CFull, - 0xB5735C904C70A239ull, 0xD59E9E0BCBAADE14ull, 0xEECC86BC60622CA7ull, - 0x4864f22c059bf29eull, 0x247856d8b862665cull, 0xe46e86e9a1337e10ull, - 0xd8c8541f3519b133ull, 0xe75b5162c567b9e4ull, 0xf732e5ded7009c5bull, - 0xb170b98353121eacull, 0x1ec2e8986d2362caull, 0x814c8e35fe9a961aull, - 0x0c3cd59c9b638a02ull, 0xcb3bb6478a07715cull, 0x1224e62c978bbc7full, - 0x671ef2cb04e81f6eull, 0x3c1cbd811eaf1808ull, 0x1bbc23cfa8fac721ull, - 0xa4c2cda65e596a51ull, 0xb77216fad37adf91ull, 0x836d794457c08849ull, - 0xe083df03475f49d7ull, 0xbc9feb512e6b0d6cull, 0xb12d74fdd718c8c5ull, - 0x12ff09653bfbe4caull, 0x8dd03a105bc4ee7eull, 0x5738341045ba0d85ull, - 0xe3fd722dc65ad09eull, 0x5a14fd21ea2a5705ull, 0x14e6ea4d6edb0c73ull, - 0x275b0dc7e0a18acfull, 0x36cebe0d2653682eull, 0x0361e9b23861596bull, - }); - - // Generate a string of '0' and '1' for the distribution output. - auto generate = [&urbg](absl::bernoulli_distribution& dist) { - std::string output; - output.reserve(36); - urbg.reset(); - for (int i = 0; i < 35; i++) { - output.append(dist(urbg) ? "1" : "0"); - } - return output; - }; - - const double kP = 0.0331289862362; - { - absl::bernoulli_distribution dist(kP); - auto v = generate(dist); - EXPECT_EQ(35, urbg.invocations()); - EXPECT_EQ(v, "00000000000010000000000010000000000") << dist; - } - { - absl::bernoulli_distribution dist(kP * 10.0); - auto v = generate(dist); - EXPECT_EQ(35, urbg.invocations()); - EXPECT_EQ(v, "00000100010010010010000011000011010") << dist; - } - { - absl::bernoulli_distribution dist(kP * 20.0); - auto v = generate(dist); - EXPECT_EQ(35, urbg.invocations()); - EXPECT_EQ(v, "00011110010110110011011111110111011") << dist; - } - { - absl::bernoulli_distribution dist(1.0 - kP); - auto v = generate(dist); - EXPECT_EQ(35, urbg.invocations()); - EXPECT_EQ(v, "11111111111111111111011111111111111") << dist; - } -} - -TEST(BernoulliTest, StabilityTest2) { - absl::random_internal::sequence_urbg urbg( - {0x0003eb76f6f7f755ull, 0xFFCEA50FDB2F953Bull, 0xC332DDEFBE6C5AA5ull, - 0x6558218568AB9702ull, 0x2AEF7DAD5B6E2F84ull, 0x1521B62829076170ull, - 0xECDD4775619F1510ull, 0x13CCA830EB61BD96ull, 0x0334FE1EAA0363CFull, - 0xB5735C904C70A239ull, 0xD59E9E0BCBAADE14ull, 0xEECC86BC60622CA7ull}); - - // Generate a string of '0' and '1' for the distribution output. - auto generate = [&urbg](absl::bernoulli_distribution& dist) { - std::string output; - output.reserve(13); - urbg.reset(); - for (int i = 0; i < 12; i++) { - output.append(dist(urbg) ? "1" : "0"); - } - return output; - }; - - constexpr double b0 = 1.0 / 13.0 / 0.2; - constexpr double b1 = 2.0 / 13.0 / 0.2; - constexpr double b3 = (5.0 / 13.0 / 0.2) - ((1 - b0) + (1 - b1) + (1 - b1)); - { - absl::bernoulli_distribution dist(b0); - auto v = generate(dist); - EXPECT_EQ(12, urbg.invocations()); - EXPECT_EQ(v, "000011100101") << dist; - } - { - absl::bernoulli_distribution dist(b1); - auto v = generate(dist); - EXPECT_EQ(12, urbg.invocations()); - EXPECT_EQ(v, "001111101101") << dist; - } - { - absl::bernoulli_distribution dist(b3); - auto v = generate(dist); - EXPECT_EQ(12, urbg.invocations()); - EXPECT_EQ(v, "001111101111") << dist; - } -} - -} // namespace diff --git a/third_party/abseil_cpp/absl/random/beta_distribution.h b/third_party/abseil_cpp/absl/random/beta_distribution.h deleted file mode 100644 index c154066fb813..000000000000 --- a/third_party/abseil_cpp/absl/random/beta_distribution.h +++ /dev/null @@ -1,427 +0,0 @@ -// Copyright 2017 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#ifndef ABSL_RANDOM_BETA_DISTRIBUTION_H_ -#define ABSL_RANDOM_BETA_DISTRIBUTION_H_ - -#include <cassert> -#include <cmath> -#include <istream> -#include <limits> -#include <ostream> -#include <type_traits> - -#include "absl/meta/type_traits.h" -#include "absl/random/internal/fast_uniform_bits.h" -#include "absl/random/internal/fastmath.h" -#include "absl/random/internal/generate_real.h" -#include "absl/random/internal/iostream_state_saver.h" - -namespace absl { -ABSL_NAMESPACE_BEGIN - -// absl::beta_distribution: -// Generate a floating-point variate conforming to a Beta distribution: -// pdf(x) \propto x^(alpha-1) * (1-x)^(beta-1), -// where the params alpha and beta are both strictly positive real values. -// -// The support is the open interval (0, 1), but the return value might be equal -// to 0 or 1, due to numerical errors when alpha and beta are very different. -// -// Usage note: One usage is that alpha and beta are counts of number of -// successes and failures. When the total number of trials are large, consider -// approximating a beta distribution with a Gaussian distribution with the same -// mean and variance. One could use the skewness, which depends only on the -// smaller of alpha and beta when the number of trials are sufficiently large, -// to quantify how far a beta distribution is from the normal distribution. -template <typename RealType = double> -class beta_distribution { - public: - using result_type = RealType; - - class param_type { - public: - using distribution_type = beta_distribution; - - explicit param_type(result_type alpha, result_type beta) - : alpha_(alpha), beta_(beta) { - assert(alpha >= 0); - assert(beta >= 0); - assert(alpha <= (std::numeric_limits<result_type>::max)()); - assert(beta <= (std::numeric_limits<result_type>::max)()); - if (alpha == 0 || beta == 0) { - method_ = DEGENERATE_SMALL; - x_ = (alpha >= beta) ? 1 : 0; - return; - } - // a_ = min(beta, alpha), b_ = max(beta, alpha). - if (beta < alpha) { - inverted_ = true; - a_ = beta; - b_ = alpha; - } else { - inverted_ = false; - a_ = alpha; - b_ = beta; - } - if (a_ <= 1 && b_ >= ThresholdForLargeA()) { - method_ = DEGENERATE_SMALL; - x_ = inverted_ ? result_type(1) : result_type(0); - return; - } - // For threshold values, see also: - // Evaluation of Beta Generation Algorithms, Ying-Chao Hung, et. al. - // February, 2009. - if ((b_ < 1.0 && a_ + b_ <= 1.2) || a_ <= ThresholdForSmallA()) { - // Choose Joehnk over Cheng when it's faster or when Cheng encounters - // numerical issues. - method_ = JOEHNK; - a_ = result_type(1) / alpha_; - b_ = result_type(1) / beta_; - if (std::isinf(a_) || std::isinf(b_)) { - method_ = DEGENERATE_SMALL; - x_ = inverted_ ? result_type(1) : result_type(0); - } - return; - } - if (a_ >= ThresholdForLargeA()) { - method_ = DEGENERATE_LARGE; - // Note: on PPC for long double, evaluating - // `std::numeric_limits::max() / ThresholdForLargeA` results in NaN. - result_type r = a_ / b_; - x_ = (inverted_ ? result_type(1) : r) / (1 + r); - return; - } - x_ = a_ + b_; - log_x_ = std::log(x_); - if (a_ <= 1) { - method_ = CHENG_BA; - y_ = result_type(1) / a_; - gamma_ = a_ + a_; - return; - } - method_ = CHENG_BB; - result_type r = (a_ - 1) / (b_ - 1); - y_ = std::sqrt((1 + r) / (b_ * r * 2 - r + 1)); - gamma_ = a_ + result_type(1) / y_; - } - - result_type alpha() const { return alpha_; } - result_type beta() const { return beta_; } - - friend bool operator==(const param_type& a, const param_type& b) { - return a.alpha_ == b.alpha_ && a.beta_ == b.beta_; - } - - friend bool operator!=(const param_type& a, const param_type& b) { - return !(a == b); - } - - private: - friend class beta_distribution; - -#ifdef _MSC_VER - // MSVC does not have constexpr implementations for std::log and std::exp - // so they are computed at runtime. -#define ABSL_RANDOM_INTERNAL_LOG_EXP_CONSTEXPR -#else -#define ABSL_RANDOM_INTERNAL_LOG_EXP_CONSTEXPR constexpr -#endif - - // The threshold for whether std::exp(1/a) is finite. - // Note that this value is quite large, and a smaller a_ is NOT abnormal. - static ABSL_RANDOM_INTERNAL_LOG_EXP_CONSTEXPR result_type - ThresholdForSmallA() { - return result_type(1) / - std::log((std::numeric_limits<result_type>::max)()); - } - - // The threshold for whether a * std::log(a) is finite. - static ABSL_RANDOM_INTERNAL_LOG_EXP_CONSTEXPR result_type - ThresholdForLargeA() { - return std::exp( - std::log((std::numeric_limits<result_type>::max)()) - - std::log(std::log((std::numeric_limits<result_type>::max)())) - - ThresholdPadding()); - } - -#undef ABSL_RANDOM_INTERNAL_LOG_EXP_CONSTEXPR - - // Pad the threshold for large A for long double on PPC. This is done via a - // template specialization below. - static constexpr result_type ThresholdPadding() { return 0; } - - enum Method { - JOEHNK, // Uses algorithm Joehnk - CHENG_BA, // Uses algorithm BA in Cheng - CHENG_BB, // Uses algorithm BB in Cheng - - // Note: See also: - // Hung et al. Evaluation of beta generation algorithms. Communications - // in Statistics-Simulation and Computation 38.4 (2009): 750-770. - // especially: - // Zechner, Heinz, and Ernst Stadlober. Generating beta variates via - // patchwork rejection. Computing 50.1 (1993): 1-18. - - DEGENERATE_SMALL, // a_ is abnormally small. - DEGENERATE_LARGE, // a_ is abnormally large. - }; - - result_type alpha_; - result_type beta_; - - result_type a_; // the smaller of {alpha, beta}, or 1.0/alpha_ in JOEHNK - result_type b_; // the larger of {alpha, beta}, or 1.0/beta_ in JOEHNK - result_type x_; // alpha + beta, or the result in degenerate cases - result_type log_x_; // log(x_) - result_type y_; // "beta" in Cheng - result_type gamma_; // "gamma" in Cheng - - Method method_; - - // Placing this last for optimal alignment. - // Whether alpha_ != a_, i.e. true iff alpha_ > beta_. - bool inverted_; - - static_assert(std::is_floating_point<RealType>::value, - "Class-template absl::beta_distribution<> must be " - "parameterized using a floating-point type."); - }; - - beta_distribution() : beta_distribution(1) {} - - explicit beta_distribution(result_type alpha, result_type beta = 1) - : param_(alpha, beta) {} - - explicit beta_distribution(const param_type& p) : param_(p) {} - - void reset() {} - - // Generating functions - template <typename URBG> - result_type operator()(URBG& g) { // NOLINT(runtime/references) - return (*this)(g, param_); - } - - template <typename URBG> - result_type operator()(URBG& g, // NOLINT(runtime/references) - const param_type& p); - - param_type param() const { return param_; } - void param(const param_type& p) { param_ = p; } - - result_type(min)() const { return 0; } - result_type(max)() const { return 1; } - - result_type alpha() const { return param_.alpha(); } - result_type beta() const { return param_.beta(); } - - friend bool operator==(const beta_distribution& a, - const beta_distribution& b) { - return a.param_ == b.param_; - } - friend bool operator!=(const beta_distribution& a, - const beta_distribution& b) { - return a.param_ != b.param_; - } - - private: - template <typename URBG> - result_type AlgorithmJoehnk(URBG& g, // NOLINT(runtime/references) - const param_type& p); - - template <typename URBG> - result_type AlgorithmCheng(URBG& g, // NOLINT(runtime/references) - const param_type& p); - - template <typename URBG> - result_type DegenerateCase(URBG& g, // NOLINT(runtime/references) - const param_type& p) { - if (p.method_ == param_type::DEGENERATE_SMALL && p.alpha_ == p.beta_) { - // Returns 0 or 1 with equal probability. - random_internal::FastUniformBits<uint8_t> fast_u8; - return static_cast<result_type>((fast_u8(g) & 0x10) != - 0); // pick any single bit. - } - return p.x_; - } - - param_type param_; - random_internal::FastUniformBits<uint64_t> fast_u64_; -}; - -#if defined(__powerpc64__) || defined(__PPC64__) || defined(__powerpc__) || \ - defined(__ppc__) || defined(__PPC__) -// PPC needs a more stringent boundary for long double. -template <> -constexpr long double -beta_distribution<long double>::param_type::ThresholdPadding() { - return 10; -} -#endif - -template <typename RealType> -template <typename URBG> -typename beta_distribution<RealType>::result_type -beta_distribution<RealType>::AlgorithmJoehnk( - URBG& g, // NOLINT(runtime/references) - const param_type& p) { - using random_internal::GeneratePositiveTag; - using random_internal::GenerateRealFromBits; - using real_type = - absl::conditional_t<std::is_same<RealType, float>::value, float, double>; - - // Based on Joehnk, M. D. Erzeugung von betaverteilten und gammaverteilten - // Zufallszahlen. Metrika 8.1 (1964): 5-15. - // This method is described in Knuth, Vol 2 (Third Edition), pp 134. - - result_type u, v, x, y, z; - for (;;) { - u = GenerateRealFromBits<real_type, GeneratePositiveTag, false>( - fast_u64_(g)); - v = GenerateRealFromBits<real_type, GeneratePositiveTag, false>( - fast_u64_(g)); - - // Direct method. std::pow is slow for float, so rely on the optimizer to - // remove the std::pow() path for that case. - if (!std::is_same<float, result_type>::value) { - x = std::pow(u, p.a_); - y = std::pow(v, p.b_); - z = x + y; - if (z > 1) { - // Reject if and only if `x + y > 1.0` - continue; - } - if (z > 0) { - // When both alpha and beta are small, x and y are both close to 0, so - // divide by (x+y) directly may result in nan. - return x / z; - } - } - - // Log transform. - // x = log( pow(u, p.a_) ), y = log( pow(v, p.b_) ) - // since u, v <= 1.0, x, y < 0. - x = std::log(u) * p.a_; - y = std::log(v) * p.b_; - if (!std::isfinite(x) || !std::isfinite(y)) { - continue; - } - // z = log( pow(u, a) + pow(v, b) ) - z = x > y ? (x + std::log(1 + std::exp(y - x))) - : (y + std::log(1 + std::exp(x - y))); - // Reject iff log(x+y) > 0. - if (z > 0) { - continue; - } - return std::exp(x - z); - } -} - -template <typename RealType> -template <typename URBG> -typename beta_distribution<RealType>::result_type -beta_distribution<RealType>::AlgorithmCheng( - URBG& g, // NOLINT(runtime/references) - const param_type& p) { - using random_internal::GeneratePositiveTag; - using random_internal::GenerateRealFromBits; - using real_type = - absl::conditional_t<std::is_same<RealType, float>::value, float, double>; - - // Based on Cheng, Russell CH. Generating beta variates with nonintegral - // shape parameters. Communications of the ACM 21.4 (1978): 317-322. - // (https://dl.acm.org/citation.cfm?id=359482). - static constexpr result_type kLogFour = - result_type(1.3862943611198906188344642429163531361); // log(4) - static constexpr result_type kS = - result_type(2.6094379124341003746007593332261876); // 1+log(5) - - const bool use_algorithm_ba = (p.method_ == param_type::CHENG_BA); - result_type u1, u2, v, w, z, r, s, t, bw_inv, lhs; - for (;;) { - u1 = GenerateRealFromBits<real_type, GeneratePositiveTag, false>( - fast_u64_(g)); - u2 = GenerateRealFromBits<real_type, GeneratePositiveTag, false>( - fast_u64_(g)); - v = p.y_ * std::log(u1 / (1 - u1)); - w = p.a_ * std::exp(v); - bw_inv = result_type(1) / (p.b_ + w); - r = p.gamma_ * v - kLogFour; - s = p.a_ + r - w; - z = u1 * u1 * u2; - if (!use_algorithm_ba && s + kS >= 5 * z) { - break; - } - t = std::log(z); - if (!use_algorithm_ba && s >= t) { - break; - } - lhs = p.x_ * (p.log_x_ + std::log(bw_inv)) + r; - if (lhs >= t) { - break; - } - } - return p.inverted_ ? (1 - w * bw_inv) : w * bw_inv; -} - -template <typename RealType> -template <typename URBG> -typename beta_distribution<RealType>::result_type -beta_distribution<RealType>::operator()(URBG& g, // NOLINT(runtime/references) - const param_type& p) { - switch (p.method_) { - case param_type::JOEHNK: - return AlgorithmJoehnk(g, p); - case param_type::CHENG_BA: - ABSL_FALLTHROUGH_INTENDED; - case param_type::CHENG_BB: - return AlgorithmCheng(g, p); - default: - return DegenerateCase(g, p); - } -} - -template <typename CharT, typename Traits, typename RealType> -std::basic_ostream<CharT, Traits>& operator<<( - std::basic_ostream<CharT, Traits>& os, // NOLINT(runtime/references) - const beta_distribution<RealType>& x) { - auto saver = random_internal::make_ostream_state_saver(os); - os.precision(random_internal::stream_precision_helper<RealType>::kPrecision); - os << x.alpha() << os.fill() << x.beta(); - return os; -} - -template <typename CharT, typename Traits, typename RealType> -std::basic_istream<CharT, Traits>& operator>>( - std::basic_istream<CharT, Traits>& is, // NOLINT(runtime/references) - beta_distribution<RealType>& x) { // NOLINT(runtime/references) - using result_type = typename beta_distribution<RealType>::result_type; - using param_type = typename beta_distribution<RealType>::param_type; - result_type alpha, beta; - - auto saver = random_internal::make_istream_state_saver(is); - alpha = random_internal::read_floating_point<result_type>(is); - if (is.fail()) return is; - beta = random_internal::read_floating_point<result_type>(is); - if (!is.fail()) { - x.param(param_type(alpha, beta)); - } - return is; -} - -ABSL_NAMESPACE_END -} // namespace absl - -#endif // ABSL_RANDOM_BETA_DISTRIBUTION_H_ diff --git a/third_party/abseil_cpp/absl/random/beta_distribution_test.cc b/third_party/abseil_cpp/absl/random/beta_distribution_test.cc deleted file mode 100644 index 277e4dc6eed7..000000000000 --- a/third_party/abseil_cpp/absl/random/beta_distribution_test.cc +++ /dev/null @@ -1,619 +0,0 @@ -// Copyright 2017 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#include "absl/random/beta_distribution.h" - -#include <algorithm> -#include <cstddef> -#include <cstdint> -#include <iterator> -#include <random> -#include <sstream> -#include <string> -#include <unordered_map> -#include <vector> - -#include "gmock/gmock.h" -#include "gtest/gtest.h" -#include "absl/base/internal/raw_logging.h" -#include "absl/random/internal/chi_square.h" -#include "absl/random/internal/distribution_test_util.h" -#include "absl/random/internal/pcg_engine.h" -#include "absl/random/internal/sequence_urbg.h" -#include "absl/random/random.h" -#include "absl/strings/str_cat.h" -#include "absl/strings/str_format.h" -#include "absl/strings/str_replace.h" -#include "absl/strings/strip.h" - -namespace { - -template <typename IntType> -class BetaDistributionInterfaceTest : public ::testing::Test {}; - -using RealTypes = ::testing::Types<float, double, long double>; -TYPED_TEST_CASE(BetaDistributionInterfaceTest, RealTypes); - -TYPED_TEST(BetaDistributionInterfaceTest, SerializeTest) { - // The threshold for whether std::exp(1/a) is finite. - const TypeParam kSmallA = - 1.0f / std::log((std::numeric_limits<TypeParam>::max)()); - // The threshold for whether a * std::log(a) is finite. - const TypeParam kLargeA = - std::exp(std::log((std::numeric_limits<TypeParam>::max)()) - - std::log(std::log((std::numeric_limits<TypeParam>::max)()))); - const TypeParam kLargeAPPC = std::exp( - std::log((std::numeric_limits<TypeParam>::max)()) - - std::log(std::log((std::numeric_limits<TypeParam>::max)())) - 10.0f); - using param_type = typename absl::beta_distribution<TypeParam>::param_type; - - constexpr int kCount = 1000; - absl::InsecureBitGen gen; - const TypeParam kValues[] = { - TypeParam(1e-20), TypeParam(1e-12), TypeParam(1e-8), TypeParam(1e-4), - TypeParam(1e-3), TypeParam(0.1), TypeParam(0.25), - std::nextafter(TypeParam(0.5), TypeParam(0)), // 0.5 - epsilon - std::nextafter(TypeParam(0.5), TypeParam(1)), // 0.5 + epsilon - TypeParam(0.5), TypeParam(1.0), // - std::nextafter(TypeParam(1), TypeParam(0)), // 1 - epsilon - std::nextafter(TypeParam(1), TypeParam(2)), // 1 + epsilon - TypeParam(12.5), TypeParam(1e2), TypeParam(1e8), TypeParam(1e12), - TypeParam(1e20), // - kSmallA, // - std::nextafter(kSmallA, TypeParam(0)), // - std::nextafter(kSmallA, TypeParam(1)), // - kLargeA, // - std::nextafter(kLargeA, TypeParam(0)), // - std::nextafter(kLargeA, std::numeric_limits<TypeParam>::max()), - kLargeAPPC, // - std::nextafter(kLargeAPPC, TypeParam(0)), - std::nextafter(kLargeAPPC, std::numeric_limits<TypeParam>::max()), - // Boundary cases. - std::numeric_limits<TypeParam>::max(), - std::numeric_limits<TypeParam>::epsilon(), - std::nextafter(std::numeric_limits<TypeParam>::min(), - TypeParam(1)), // min + epsilon - std::numeric_limits<TypeParam>::min(), // smallest normal - std::numeric_limits<TypeParam>::denorm_min(), // smallest denorm - std::numeric_limits<TypeParam>::min() / 2, // denorm - std::nextafter(std::numeric_limits<TypeParam>::min(), - TypeParam(0)), // denorm_max - }; - for (TypeParam alpha : kValues) { - for (TypeParam beta : kValues) { - ABSL_INTERNAL_LOG( - INFO, absl::StrFormat("Smoke test for Beta(%a, %a)", alpha, beta)); - - param_type param(alpha, beta); - absl::beta_distribution<TypeParam> before(alpha, beta); - EXPECT_EQ(before.alpha(), param.alpha()); - EXPECT_EQ(before.beta(), param.beta()); - - { - absl::beta_distribution<TypeParam> via_param(param); - EXPECT_EQ(via_param, before); - EXPECT_EQ(via_param.param(), before.param()); - } - - // Smoke test. - for (int i = 0; i < kCount; ++i) { - auto sample = before(gen); - EXPECT_TRUE(std::isfinite(sample)); - EXPECT_GE(sample, before.min()); - EXPECT_LE(sample, before.max()); - } - - // Validate stream serialization. - std::stringstream ss; - ss << before; - absl::beta_distribution<TypeParam> after(3.8f, 1.43f); - EXPECT_NE(before.alpha(), after.alpha()); - EXPECT_NE(before.beta(), after.beta()); - EXPECT_NE(before.param(), after.param()); - EXPECT_NE(before, after); - - ss >> after; - -#if defined(__powerpc64__) || defined(__PPC64__) || defined(__powerpc__) || \ - defined(__ppc__) || defined(__PPC__) - if (std::is_same<TypeParam, long double>::value) { - // Roundtripping floating point values requires sufficient precision - // to reconstruct the exact value. It turns out that long double - // has some errors doing this on ppc. - if (alpha <= std::numeric_limits<double>::max() && - alpha >= std::numeric_limits<double>::lowest()) { - EXPECT_EQ(static_cast<double>(before.alpha()), - static_cast<double>(after.alpha())) - << ss.str(); - } - if (beta <= std::numeric_limits<double>::max() && - beta >= std::numeric_limits<double>::lowest()) { - EXPECT_EQ(static_cast<double>(before.beta()), - static_cast<double>(after.beta())) - << ss.str(); - } - continue; - } -#endif - - EXPECT_EQ(before.alpha(), after.alpha()); - EXPECT_EQ(before.beta(), after.beta()); - EXPECT_EQ(before, after) // - << ss.str() << " " // - << (ss.good() ? "good " : "") // - << (ss.bad() ? "bad " : "") // - << (ss.eof() ? "eof " : "") // - << (ss.fail() ? "fail " : ""); - } - } -} - -TYPED_TEST(BetaDistributionInterfaceTest, DegenerateCases) { - // We use a fixed bit generator for distribution accuracy tests. This allows - // these tests to be deterministic, while still testing the qualify of the - // implementation. - absl::random_internal::pcg64_2018_engine rng(0x2B7E151628AED2A6); - - // Extreme cases when the params are abnormal. - constexpr int kCount = 1000; - const TypeParam kSmallValues[] = { - std::numeric_limits<TypeParam>::min(), - std::numeric_limits<TypeParam>::denorm_min(), - std::nextafter(std::numeric_limits<TypeParam>::min(), - TypeParam(0)), // denorm_max - std::numeric_limits<TypeParam>::epsilon(), - }; - const TypeParam kLargeValues[] = { - std::numeric_limits<TypeParam>::max() * static_cast<TypeParam>(0.9999), - std::numeric_limits<TypeParam>::max() - 1, - std::numeric_limits<TypeParam>::max(), - }; - { - // Small alpha and beta. - // Useful WolframAlpha plots: - // * plot InverseBetaRegularized[x, 0.0001, 0.0001] from 0.495 to 0.505 - // * Beta[1.0, 0.0000001, 0.0000001] - // * Beta[0.9999, 0.0000001, 0.0000001] - for (TypeParam alpha : kSmallValues) { - for (TypeParam beta : kSmallValues) { - int zeros = 0; - int ones = 0; - absl::beta_distribution<TypeParam> d(alpha, beta); - for (int i = 0; i < kCount; ++i) { - TypeParam x = d(rng); - if (x == 0.0) { - zeros++; - } else if (x == 1.0) { - ones++; - } - } - EXPECT_EQ(ones + zeros, kCount); - if (alpha == beta) { - EXPECT_NE(ones, 0); - EXPECT_NE(zeros, 0); - } - } - } - } - { - // Small alpha, large beta. - // Useful WolframAlpha plots: - // * plot InverseBetaRegularized[x, 0.0001, 10000] from 0.995 to 1 - // * Beta[0, 0.0000001, 1000000] - // * Beta[0.001, 0.0000001, 1000000] - // * Beta[1, 0.0000001, 1000000] - for (TypeParam alpha : kSmallValues) { - for (TypeParam beta : kLargeValues) { - absl::beta_distribution<TypeParam> d(alpha, beta); - for (int i = 0; i < kCount; ++i) { - EXPECT_EQ(d(rng), 0.0); - } - } - } - } - { - // Large alpha, small beta. - // Useful WolframAlpha plots: - // * plot InverseBetaRegularized[x, 10000, 0.0001] from 0 to 0.001 - // * Beta[0.99, 1000000, 0.0000001] - // * Beta[1, 1000000, 0.0000001] - for (TypeParam alpha : kLargeValues) { - for (TypeParam beta : kSmallValues) { - absl::beta_distribution<TypeParam> d(alpha, beta); - for (int i = 0; i < kCount; ++i) { - EXPECT_EQ(d(rng), 1.0); - } - } - } - } - { - // Large alpha and beta. - absl::beta_distribution<TypeParam> d(std::numeric_limits<TypeParam>::max(), - std::numeric_limits<TypeParam>::max()); - for (int i = 0; i < kCount; ++i) { - EXPECT_EQ(d(rng), 0.5); - } - } - { - // Large alpha and beta but unequal. - absl::beta_distribution<TypeParam> d( - std::numeric_limits<TypeParam>::max(), - std::numeric_limits<TypeParam>::max() * 0.9999); - for (int i = 0; i < kCount; ++i) { - TypeParam x = d(rng); - EXPECT_NE(x, 0.5f); - EXPECT_FLOAT_EQ(x, 0.500025f); - } - } -} - -class BetaDistributionModel { - public: - explicit BetaDistributionModel(::testing::tuple<double, double> p) - : alpha_(::testing::get<0>(p)), beta_(::testing::get<1>(p)) {} - - double Mean() const { return alpha_ / (alpha_ + beta_); } - - double Variance() const { - return alpha_ * beta_ / (alpha_ + beta_ + 1) / (alpha_ + beta_) / - (alpha_ + beta_); - } - - double Kurtosis() const { - return 3 + 6 * - ((alpha_ - beta_) * (alpha_ - beta_) * (alpha_ + beta_ + 1) - - alpha_ * beta_ * (2 + alpha_ + beta_)) / - alpha_ / beta_ / (alpha_ + beta_ + 2) / (alpha_ + beta_ + 3); - } - - protected: - const double alpha_; - const double beta_; -}; - -class BetaDistributionTest - : public ::testing::TestWithParam<::testing::tuple<double, double>>, - public BetaDistributionModel { - public: - BetaDistributionTest() : BetaDistributionModel(GetParam()) {} - - protected: - template <class D> - bool SingleZTestOnMeanAndVariance(double p, size_t samples); - - template <class D> - bool SingleChiSquaredTest(double p, size_t samples, size_t buckets); - - absl::InsecureBitGen rng_; -}; - -template <class D> -bool BetaDistributionTest::SingleZTestOnMeanAndVariance(double p, - size_t samples) { - D dis(alpha_, beta_); - - std::vector<double> data; - data.reserve(samples); - for (size_t i = 0; i < samples; i++) { - const double variate = dis(rng_); - EXPECT_FALSE(std::isnan(variate)); - // Note that equality is allowed on both sides. - EXPECT_GE(variate, 0.0); - EXPECT_LE(variate, 1.0); - data.push_back(variate); - } - - // We validate that the sample mean and sample variance are indeed from a - // Beta distribution with the given shape parameters. - const auto m = absl::random_internal::ComputeDistributionMoments(data); - - // The variance of the sample mean is variance / n. - const double mean_stddev = std::sqrt(Variance() / static_cast<double>(m.n)); - - // The variance of the sample variance is (approximately): - // (kurtosis - 1) * variance^2 / n - const double variance_stddev = std::sqrt( - (Kurtosis() - 1) * Variance() * Variance() / static_cast<double>(m.n)); - // z score for the sample variance. - const double z_variance = (m.variance - Variance()) / variance_stddev; - - const double max_err = absl::random_internal::MaxErrorTolerance(p); - const double z_mean = absl::random_internal::ZScore(Mean(), m); - const bool pass = - absl::random_internal::Near("z", z_mean, 0.0, max_err) && - absl::random_internal::Near("z_variance", z_variance, 0.0, max_err); - if (!pass) { - ABSL_INTERNAL_LOG( - INFO, - absl::StrFormat( - "Beta(%f, %f), " - "mean: sample %f, expect %f, which is %f stddevs away, " - "variance: sample %f, expect %f, which is %f stddevs away.", - alpha_, beta_, m.mean, Mean(), - std::abs(m.mean - Mean()) / mean_stddev, m.variance, Variance(), - std::abs(m.variance - Variance()) / variance_stddev)); - } - return pass; -} - -template <class D> -bool BetaDistributionTest::SingleChiSquaredTest(double p, size_t samples, - size_t buckets) { - constexpr double kErr = 1e-7; - std::vector<double> cutoffs, expected; - const double bucket_width = 1.0 / static_cast<double>(buckets); - int i = 1; - int unmerged_buckets = 0; - for (; i < buckets; ++i) { - const double p = bucket_width * static_cast<double>(i); - const double boundary = - absl::random_internal::BetaIncompleteInv(alpha_, beta_, p); - // The intention is to add `boundary` to the list of `cutoffs`. It becomes - // problematic, however, when the boundary values are not monotone, due to - // numerical issues when computing the inverse regularized incomplete - // Beta function. In these cases, we merge that bucket with its previous - // neighbor and merge their expected counts. - if ((cutoffs.empty() && boundary < kErr) || - (!cutoffs.empty() && boundary <= cutoffs.back())) { - unmerged_buckets++; - continue; - } - if (boundary >= 1.0 - 1e-10) { - break; - } - cutoffs.push_back(boundary); - expected.push_back(static_cast<double>(1 + unmerged_buckets) * - bucket_width * static_cast<double>(samples)); - unmerged_buckets = 0; - } - cutoffs.push_back(std::numeric_limits<double>::infinity()); - // Merge all remaining buckets. - expected.push_back(static_cast<double>(buckets - i + 1) * bucket_width * - static_cast<double>(samples)); - // Make sure that we don't merge all the buckets, making this test - // meaningless. - EXPECT_GE(cutoffs.size(), 3) << alpha_ << ", " << beta_; - - D dis(alpha_, beta_); - - std::vector<int32_t> counts(cutoffs.size(), 0); - for (int i = 0; i < samples; i++) { - const double x = dis(rng_); - auto it = std::upper_bound(cutoffs.begin(), cutoffs.end(), x); - counts[std::distance(cutoffs.begin(), it)]++; - } - - // Null-hypothesis is that the distribution is beta distributed with the - // provided alpha, beta params (not estimated from the data). - const int dof = cutoffs.size() - 1; - - const double chi_square = absl::random_internal::ChiSquare( - counts.begin(), counts.end(), expected.begin(), expected.end()); - const bool pass = - (absl::random_internal::ChiSquarePValue(chi_square, dof) >= p); - if (!pass) { - for (int i = 0; i < cutoffs.size(); i++) { - ABSL_INTERNAL_LOG( - INFO, absl::StrFormat("cutoff[%d] = %f, actual count %d, expected %d", - i, cutoffs[i], counts[i], - static_cast<int>(expected[i]))); - } - - ABSL_INTERNAL_LOG( - INFO, absl::StrFormat( - "Beta(%f, %f) %s %f, p = %f", alpha_, beta_, - absl::random_internal::kChiSquared, chi_square, - absl::random_internal::ChiSquarePValue(chi_square, dof))); - } - return pass; -} - -TEST_P(BetaDistributionTest, TestSampleStatistics) { - static constexpr int kRuns = 20; - static constexpr double kPFail = 0.02; - const double p = - absl::random_internal::RequiredSuccessProbability(kPFail, kRuns); - static constexpr int kSampleCount = 10000; - static constexpr int kBucketCount = 100; - int failed = 0; - for (int i = 0; i < kRuns; ++i) { - if (!SingleZTestOnMeanAndVariance<absl::beta_distribution<double>>( - p, kSampleCount)) { - failed++; - } - if (!SingleChiSquaredTest<absl::beta_distribution<double>>( - 0.005, kSampleCount, kBucketCount)) { - failed++; - } - } - // Set so that the test is not flaky at --runs_per_test=10000 - EXPECT_LE(failed, 5); -} - -std::string ParamName( - const ::testing::TestParamInfo<::testing::tuple<double, double>>& info) { - std::string name = absl::StrCat("alpha_", ::testing::get<0>(info.param), - "__beta_", ::testing::get<1>(info.param)); - return absl::StrReplaceAll(name, {{"+", "_"}, {"-", "_"}, {".", "_"}}); -} - -INSTANTIATE_TEST_CASE_P( - TestSampleStatisticsCombinations, BetaDistributionTest, - ::testing::Combine(::testing::Values(0.1, 0.2, 0.9, 1.1, 2.5, 10.0, 123.4), - ::testing::Values(0.1, 0.2, 0.9, 1.1, 2.5, 10.0, 123.4)), - ParamName); - -INSTANTIATE_TEST_CASE_P( - TestSampleStatistics_SelectedPairs, BetaDistributionTest, - ::testing::Values(std::make_pair(0.5, 1000), std::make_pair(1000, 0.5), - std::make_pair(900, 1000), std::make_pair(10000, 20000), - std::make_pair(4e5, 2e7), std::make_pair(1e7, 1e5)), - ParamName); - -// NOTE: absl::beta_distribution is not guaranteed to be stable. -TEST(BetaDistributionTest, StabilityTest) { - // absl::beta_distribution stability relies on the stability of - // absl::random_interna::RandU64ToDouble, std::exp, std::log, std::pow, - // and std::sqrt. - // - // This test also depends on the stability of std::frexp. - using testing::ElementsAre; - absl::random_internal::sequence_urbg urbg({ - 0xffff00000000e6c8ull, 0xffff0000000006c8ull, 0x800003766295CFA9ull, - 0x11C819684E734A41ull, 0x832603766295CFA9ull, 0x7fbe76c8b4395800ull, - 0xB3472DCA7B14A94Aull, 0x0003eb76f6f7f755ull, 0xFFCEA50FDB2F953Bull, - 0x13CCA830EB61BD96ull, 0x0334FE1EAA0363CFull, 0x00035C904C70A239ull, - 0x00009E0BCBAADE14ull, 0x0000000000622CA7ull, 0x4864f22c059bf29eull, - 0x247856d8b862665cull, 0xe46e86e9a1337e10ull, 0xd8c8541f3519b133ull, - 0xffe75b52c567b9e4ull, 0xfffff732e5709c5bull, 0xff1f7f0b983532acull, - 0x1ec2e8986d2362caull, 0xC332DDEFBE6C5AA5ull, 0x6558218568AB9702ull, - 0x2AEF7DAD5B6E2F84ull, 0x1521B62829076170ull, 0xECDD4775619F1510ull, - 0x814c8e35fe9a961aull, 0x0c3cd59c9b638a02ull, 0xcb3bb6478a07715cull, - 0x1224e62c978bbc7full, 0x671ef2cb04e81f6eull, 0x3c1cbd811eaf1808ull, - 0x1bbc23cfa8fac721ull, 0xa4c2cda65e596a51ull, 0xb77216fad37adf91ull, - 0x836d794457c08849ull, 0xe083df03475f49d7ull, 0xbc9feb512e6b0d6cull, - 0xb12d74fdd718c8c5ull, 0x12ff09653bfbe4caull, 0x8dd03a105bc4ee7eull, - 0x5738341045ba0d85ull, 0xf3fd722dc65ad09eull, 0xfa14fd21ea2a5705ull, - 0xffe6ea4d6edb0c73ull, 0xD07E9EFE2BF11FB4ull, 0x95DBDA4DAE909198ull, - 0xEAAD8E716B93D5A0ull, 0xD08ED1D0AFC725E0ull, 0x8E3C5B2F8E7594B7ull, - 0x8FF6E2FBF2122B64ull, 0x8888B812900DF01Cull, 0x4FAD5EA0688FC31Cull, - 0xD1CFF191B3A8C1ADull, 0x2F2F2218BE0E1777ull, 0xEA752DFE8B021FA1ull, - }); - - // Convert the real-valued result into a unit64 where we compare - // 5 (float) or 10 (double) decimal digits plus the base-2 exponent. - auto float_to_u64 = [](float d) { - int exp = 0; - auto f = std::frexp(d, &exp); - return (static_cast<uint64_t>(1e5 * f) * 10000) + std::abs(exp); - }; - auto double_to_u64 = [](double d) { - int exp = 0; - auto f = std::frexp(d, &exp); - return (static_cast<uint64_t>(1e10 * f) * 10000) + std::abs(exp); - }; - - std::vector<uint64_t> output(20); - { - // Algorithm Joehnk (float) - absl::beta_distribution<float> dist(0.1f, 0.2f); - std::generate(std::begin(output), std::end(output), - [&] { return float_to_u64(dist(urbg)); }); - EXPECT_EQ(44, urbg.invocations()); - EXPECT_THAT(output, // - testing::ElementsAre( - 998340000, 619030004, 500000001, 999990000, 996280000, - 500000001, 844740004, 847210001, 999970000, 872320000, - 585480007, 933280000, 869080042, 647670031, 528240004, - 969980004, 626050008, 915930002, 833440033, 878040015)); - } - - urbg.reset(); - { - // Algorithm Joehnk (double) - absl::beta_distribution<double> dist(0.1, 0.2); - std::generate(std::begin(output), std::end(output), - [&] { return double_to_u64(dist(urbg)); }); - EXPECT_EQ(44, urbg.invocations()); - EXPECT_THAT( - output, // - testing::ElementsAre( - 99834713000000, 61903356870004, 50000000000001, 99999721170000, - 99628374770000, 99999999990000, 84474397860004, 84721276240001, - 99997407490000, 87232528120000, 58548364780007, 93328932910000, - 86908237770042, 64767917930031, 52824581970004, 96998544140004, - 62605946270008, 91593604380002, 83345031740033, 87804397230015)); - } - - urbg.reset(); - { - // Algorithm Cheng 1 - absl::beta_distribution<double> dist(0.9, 2.0); - std::generate(std::begin(output), std::end(output), - [&] { return double_to_u64(dist(urbg)); }); - EXPECT_EQ(62, urbg.invocations()); - EXPECT_THAT( - output, // - testing::ElementsAre( - 62069004780001, 64433204450001, 53607416560000, 89644295430008, - 61434586310019, 55172615890002, 62187161490000, 56433684810003, - 80454622050005, 86418558710003, 92920514700001, 64645184680001, - 58549183380000, 84881283650005, 71078728590002, 69949694970000, - 73157461710001, 68592191300001, 70747623900000, 78584696930005)); - } - - urbg.reset(); - { - // Algorithm Cheng 2 - absl::beta_distribution<double> dist(1.5, 2.5); - std::generate(std::begin(output), std::end(output), - [&] { return double_to_u64(dist(urbg)); }); - EXPECT_EQ(54, urbg.invocations()); - EXPECT_THAT( - output, // - testing::ElementsAre( - 75000029250001, 76751482860001, 53264575220000, 69193133650005, - 78028324470013, 91573587560002, 59167523770000, 60658618560002, - 80075870540000, 94141320460004, 63196592770003, 78883906300002, - 96797992590001, 76907587800001, 56645167560000, 65408302280003, - 53401156320001, 64731238570000, 83065573750001, 79788333820001)); - } -} - -// This is an implementation-specific test. If any part of the implementation -// changes, then it is likely that this test will change as well. Also, if -// dependencies of the distribution change, such as RandU64ToDouble, then this -// is also likely to change. -TEST(BetaDistributionTest, AlgorithmBounds) { - { - absl::random_internal::sequence_urbg urbg( - {0x7fbe76c8b4395800ull, 0x8000000000000000ull}); - // u=0.499, v=0.5 - absl::beta_distribution<double> dist(1e-4, 1e-4); - double a = dist(urbg); - EXPECT_EQ(a, 2.0202860861567108529e-09); - EXPECT_EQ(2, urbg.invocations()); - } - - // Test that both the float & double algorithms appropriately reject the - // initial draw. - { - // 1/alpha = 1/beta = 2. - absl::beta_distribution<float> dist(0.5, 0.5); - - // first two outputs are close to 1.0 - epsilon, - // thus: (u ^ 2 + v ^ 2) > 1.0 - absl::random_internal::sequence_urbg urbg( - {0xffff00000006e6c8ull, 0xffff00000007c7c8ull, 0x800003766295CFA9ull, - 0x11C819684E734A41ull}); - { - double y = absl::beta_distribution<double>(0.5, 0.5)(urbg); - EXPECT_EQ(4, urbg.invocations()); - EXPECT_EQ(y, 0.9810668952633862) << y; - } - - // ...and: log(u) * a ~= log(v) * b ~= -0.02 - // thus z ~= -0.02 + log(1 + e(~0)) - // ~= -0.02 + 0.69 - // thus z > 0 - urbg.reset(); - { - float x = absl::beta_distribution<float>(0.5, 0.5)(urbg); - EXPECT_EQ(4, urbg.invocations()); - EXPECT_NEAR(0.98106688261032104, x, 0.0000005) << x << "f"; - } - } -} - -} // namespace diff --git a/third_party/abseil_cpp/absl/random/bit_gen_ref.h b/third_party/abseil_cpp/absl/random/bit_gen_ref.h deleted file mode 100644 index 9555460fd491..000000000000 --- a/third_party/abseil_cpp/absl/random/bit_gen_ref.h +++ /dev/null @@ -1,181 +0,0 @@ -// -// Copyright 2018 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. -// -// ----------------------------------------------------------------------------- -// File: bit_gen_ref.h -// ----------------------------------------------------------------------------- -// -// This header defines a bit generator "reference" class, for use in interfaces -// that take both Abseil (e.g. `absl::BitGen`) and standard library (e.g. -// `std::mt19937`) bit generators. - -#ifndef ABSL_RANDOM_BIT_GEN_REF_H_ -#define ABSL_RANDOM_BIT_GEN_REF_H_ - -#include "absl/base/internal/fast_type_id.h" -#include "absl/base/macros.h" -#include "absl/meta/type_traits.h" -#include "absl/random/internal/distribution_caller.h" -#include "absl/random/internal/fast_uniform_bits.h" - -namespace absl { -ABSL_NAMESPACE_BEGIN -namespace random_internal { - -template <typename URBG, typename = void, typename = void, typename = void> -struct is_urbg : std::false_type {}; - -template <typename URBG> -struct is_urbg< - URBG, - absl::enable_if_t<std::is_same< - typename URBG::result_type, - typename std::decay<decltype((URBG::min)())>::type>::value>, - absl::enable_if_t<std::is_same< - typename URBG::result_type, - typename std::decay<decltype((URBG::max)())>::type>::value>, - absl::enable_if_t<std::is_same< - typename URBG::result_type, - typename std::decay<decltype(std::declval<URBG>()())>::type>::value>> - : std::true_type {}; - -template <typename> -struct DistributionCaller; -class MockHelpers; - -} // namespace random_internal - -// ----------------------------------------------------------------------------- -// absl::BitGenRef -// ----------------------------------------------------------------------------- -// -// `absl::BitGenRef` is a type-erasing class that provides a generator-agnostic -// non-owning "reference" interface for use in place of any specific uniform -// random bit generator (URBG). This class may be used for both Abseil -// (e.g. `absl::BitGen`, `absl::InsecureBitGen`) and Standard library (e.g -// `std::mt19937`, `std::minstd_rand`) bit generators. -// -// Like other reference classes, `absl::BitGenRef` does not own the -// underlying bit generator, and the underlying instance must outlive the -// `absl::BitGenRef`. -// -// `absl::BitGenRef` is particularly useful when used with an -// `absl::MockingBitGen` to test specific paths in functions which use random -// values. -// -// Example: -// void TakesBitGenRef(absl::BitGenRef gen) { -// int x = absl::Uniform<int>(gen, 0, 1000); -// } -// -class BitGenRef { - // SFINAE to detect whether the URBG type includes a member matching - // bool InvokeMock(base_internal::FastTypeIdType, void*, void*). - // - // These live inside BitGenRef so that they have friend access - // to MockingBitGen. (see similar methods in DistributionCaller). - template <template <class...> class Trait, class AlwaysVoid, class... Args> - struct detector : std::false_type {}; - template <template <class...> class Trait, class... Args> - struct detector<Trait, absl::void_t<Trait<Args...>>, Args...> - : std::true_type {}; - - template <class T> - using invoke_mock_t = decltype(std::declval<T*>()->InvokeMock( - std::declval<base_internal::FastTypeIdType>(), std::declval<void*>(), - std::declval<void*>())); - - template <typename T> - using HasInvokeMock = typename detector<invoke_mock_t, void, T>::type; - - public: - BitGenRef(const BitGenRef&) = default; - BitGenRef(BitGenRef&&) = default; - BitGenRef& operator=(const BitGenRef&) = default; - BitGenRef& operator=(BitGenRef&&) = default; - - template <typename URBG, typename absl::enable_if_t< - (!std::is_same<URBG, BitGenRef>::value && - random_internal::is_urbg<URBG>::value && - !HasInvokeMock<URBG>::value)>* = nullptr> - BitGenRef(URBG& gen) // NOLINT - : t_erased_gen_ptr_(reinterpret_cast<uintptr_t>(&gen)), - mock_call_(NotAMock), - generate_impl_fn_(ImplFn<URBG>) {} - - template <typename URBG, - typename absl::enable_if_t<(!std::is_same<URBG, BitGenRef>::value && - random_internal::is_urbg<URBG>::value && - HasInvokeMock<URBG>::value)>* = nullptr> - BitGenRef(URBG& gen) // NOLINT - : t_erased_gen_ptr_(reinterpret_cast<uintptr_t>(&gen)), - mock_call_(&MockCall<URBG>), - generate_impl_fn_(ImplFn<URBG>) {} - - using result_type = uint64_t; - - static constexpr result_type(min)() { - return (std::numeric_limits<result_type>::min)(); - } - - static constexpr result_type(max)() { - return (std::numeric_limits<result_type>::max)(); - } - - result_type operator()() { return generate_impl_fn_(t_erased_gen_ptr_); } - - private: - using impl_fn = result_type (*)(uintptr_t); - using mock_call_fn = bool (*)(uintptr_t, base_internal::FastTypeIdType, void*, - void*); - - template <typename URBG> - static result_type ImplFn(uintptr_t ptr) { - // Ensure that the return values from operator() fill the entire - // range promised by result_type, min() and max(). - absl::random_internal::FastUniformBits<result_type> fast_uniform_bits; - return fast_uniform_bits(*reinterpret_cast<URBG*>(ptr)); - } - - // Get a type-erased InvokeMock pointer. - template <typename URBG> - static bool MockCall(uintptr_t gen_ptr, base_internal::FastTypeIdType type, - void* result, void* arg_tuple) { - return reinterpret_cast<URBG*>(gen_ptr)->InvokeMock(type, result, - arg_tuple); - } - static bool NotAMock(uintptr_t, base_internal::FastTypeIdType, void*, void*) { - return false; - } - - inline bool InvokeMock(base_internal::FastTypeIdType type, void* args_tuple, - void* result) { - if (mock_call_ == NotAMock) return false; // avoids an indirect call. - return mock_call_(t_erased_gen_ptr_, type, args_tuple, result); - } - - uintptr_t t_erased_gen_ptr_; - mock_call_fn mock_call_; - impl_fn generate_impl_fn_; - - template <typename> - friend struct ::absl::random_internal::DistributionCaller; // for InvokeMock - friend class ::absl::random_internal::MockHelpers; // for InvokeMock -}; - -ABSL_NAMESPACE_END -} // namespace absl - -#endif // ABSL_RANDOM_BIT_GEN_REF_H_ diff --git a/third_party/abseil_cpp/absl/random/bit_gen_ref_test.cc b/third_party/abseil_cpp/absl/random/bit_gen_ref_test.cc deleted file mode 100644 index 1135cf2da0bf..000000000000 --- a/third_party/abseil_cpp/absl/random/bit_gen_ref_test.cc +++ /dev/null @@ -1,102 +0,0 @@ -// -// Copyright 2018 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. -// -#include "absl/random/bit_gen_ref.h" - -#include "gmock/gmock.h" -#include "gtest/gtest.h" -#include "absl/base/internal/fast_type_id.h" -#include "absl/random/internal/sequence_urbg.h" -#include "absl/random/random.h" - -namespace absl { -ABSL_NAMESPACE_BEGIN - -class ConstBitGen { - public: - // URBG interface - using result_type = absl::BitGen::result_type; - - static constexpr result_type(min)() { return (absl::BitGen::min)(); } - static constexpr result_type(max)() { return (absl::BitGen::max)(); } - result_type operator()() { return 1; } - - // InvokeMock method - bool InvokeMock(base_internal::FastTypeIdType index, void*, void* result) { - *static_cast<int*>(result) = 42; - return true; - } -}; - -namespace { - -int FnTest(absl::BitGenRef gen_ref) { return absl::Uniform(gen_ref, 1, 7); } - -template <typename T> -class BitGenRefTest : public testing::Test {}; - -using BitGenTypes = - ::testing::Types<absl::BitGen, absl::InsecureBitGen, std::mt19937, - std::mt19937_64, std::minstd_rand>; -TYPED_TEST_SUITE(BitGenRefTest, BitGenTypes); - -TYPED_TEST(BitGenRefTest, BasicTest) { - TypeParam gen; - auto x = FnTest(gen); - EXPECT_NEAR(x, 4, 3); -} - -TYPED_TEST(BitGenRefTest, Copyable) { - TypeParam gen; - absl::BitGenRef gen_ref(gen); - FnTest(gen_ref); // Copy -} - -TEST(BitGenRefTest, PassThroughEquivalence) { - // sequence_urbg returns 64-bit results. - absl::random_internal::sequence_urbg urbg( - {0x0003eb76f6f7f755ull, 0xFFCEA50FDB2F953Bull, 0xC332DDEFBE6C5AA5ull, - 0x6558218568AB9702ull, 0x2AEF7DAD5B6E2F84ull, 0x1521B62829076170ull, - 0xECDD4775619F1510ull, 0x13CCA830EB61BD96ull, 0x0334FE1EAA0363CFull, - 0xB5735C904C70A239ull, 0xD59E9E0BCBAADE14ull, 0xEECC86BC60622CA7ull}); - - std::vector<uint64_t> output(12); - - { - absl::BitGenRef view(urbg); - for (auto& v : output) { - v = view(); - } - } - - std::vector<uint64_t> expected( - {0x0003eb76f6f7f755ull, 0xFFCEA50FDB2F953Bull, 0xC332DDEFBE6C5AA5ull, - 0x6558218568AB9702ull, 0x2AEF7DAD5B6E2F84ull, 0x1521B62829076170ull, - 0xECDD4775619F1510ull, 0x13CCA830EB61BD96ull, 0x0334FE1EAA0363CFull, - 0xB5735C904C70A239ull, 0xD59E9E0BCBAADE14ull, 0xEECC86BC60622CA7ull}); - - EXPECT_THAT(output, testing::Eq(expected)); -} - -TEST(BitGenRefTest, MockingBitGenBaseOverrides) { - ConstBitGen const_gen; - EXPECT_EQ(FnTest(const_gen), 42); - - absl::BitGenRef gen_ref(const_gen); - EXPECT_EQ(FnTest(gen_ref), 42); // Copy -} -} // namespace -ABSL_NAMESPACE_END -} // namespace absl diff --git a/third_party/abseil_cpp/absl/random/discrete_distribution.cc b/third_party/abseil_cpp/absl/random/discrete_distribution.cc deleted file mode 100644 index 081accee52a4..000000000000 --- a/third_party/abseil_cpp/absl/random/discrete_distribution.cc +++ /dev/null @@ -1,98 +0,0 @@ -// Copyright 2017 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#include "absl/random/discrete_distribution.h" - -namespace absl { -ABSL_NAMESPACE_BEGIN -namespace random_internal { - -// Initializes the distribution table for Walker's Aliasing algorithm, described -// in Knuth, Vol 2. as well as in https://en.wikipedia.org/wiki/Alias_method -std::vector<std::pair<double, size_t>> InitDiscreteDistribution( - std::vector<double>* probabilities) { - // The empty-case should already be handled by the constructor. - assert(probabilities); - assert(!probabilities->empty()); - - // Step 1. Normalize the input probabilities to 1.0. - double sum = std::accumulate(std::begin(*probabilities), - std::end(*probabilities), 0.0); - if (std::fabs(sum - 1.0) > 1e-6) { - // Scale `probabilities` only when the sum is too far from 1.0. Scaling - // unconditionally will alter the probabilities slightly. - for (double& item : *probabilities) { - item = item / sum; - } - } - - // Step 2. At this point `probabilities` is set to the conditional - // probabilities of each element which sum to 1.0, to within reasonable error. - // These values are used to construct the proportional probability tables for - // the selection phases of Walker's Aliasing algorithm. - // - // To construct the table, pick an element which is under-full (i.e., an - // element for which `(*probabilities)[i] < 1.0/n`), and pair it with an - // element which is over-full (i.e., an element for which - // `(*probabilities)[i] > 1.0/n`). The smaller value can always be retired. - // The larger may still be greater than 1.0/n, or may now be less than 1.0/n, - // and put back onto the appropriate collection. - const size_t n = probabilities->size(); - std::vector<std::pair<double, size_t>> q; - q.reserve(n); - - std::vector<size_t> over; - std::vector<size_t> under; - size_t idx = 0; - for (const double item : *probabilities) { - assert(item >= 0); - const double v = item * n; - q.emplace_back(v, 0); - if (v < 1.0) { - under.push_back(idx++); - } else { - over.push_back(idx++); - } - } - while (!over.empty() && !under.empty()) { - auto lo = under.back(); - under.pop_back(); - auto hi = over.back(); - over.pop_back(); - - q[lo].second = hi; - const double r = q[hi].first - (1.0 - q[lo].first); - q[hi].first = r; - if (r < 1.0) { - under.push_back(hi); - } else { - over.push_back(hi); - } - } - - // Due to rounding errors, there may be un-paired elements in either - // collection; these should all be values near 1.0. For these values, set `q` - // to 1.0 and set the alternate to the identity. - for (auto i : over) { - q[i] = {1.0, i}; - } - for (auto i : under) { - q[i] = {1.0, i}; - } - return q; -} - -} // namespace random_internal -ABSL_NAMESPACE_END -} // namespace absl diff --git a/third_party/abseil_cpp/absl/random/discrete_distribution.h b/third_party/abseil_cpp/absl/random/discrete_distribution.h deleted file mode 100644 index 171aa11a1eb4..000000000000 --- a/third_party/abseil_cpp/absl/random/discrete_distribution.h +++ /dev/null @@ -1,247 +0,0 @@ -// Copyright 2017 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#ifndef ABSL_RANDOM_DISCRETE_DISTRIBUTION_H_ -#define ABSL_RANDOM_DISCRETE_DISTRIBUTION_H_ - -#include <cassert> -#include <cmath> -#include <istream> -#include <limits> -#include <numeric> -#include <type_traits> -#include <utility> -#include <vector> - -#include "absl/random/bernoulli_distribution.h" -#include "absl/random/internal/iostream_state_saver.h" -#include "absl/random/uniform_int_distribution.h" - -namespace absl { -ABSL_NAMESPACE_BEGIN - -// absl::discrete_distribution -// -// A discrete distribution produces random integers i, where 0 <= i < n -// distributed according to the discrete probability function: -// -// P(i|p0,...,pn−1)=pi -// -// This class is an implementation of discrete_distribution (see -// [rand.dist.samp.discrete]). -// -// The algorithm used is Walker's Aliasing algorithm, described in Knuth, Vol 2. -// absl::discrete_distribution takes O(N) time to precompute the probabilities -// (where N is the number of possible outcomes in the distribution) at -// construction, and then takes O(1) time for each variate generation. Many -// other implementations also take O(N) time to construct an ordered sequence of -// partial sums, plus O(log N) time per variate to binary search. -// -template <typename IntType = int> -class discrete_distribution { - public: - using result_type = IntType; - - class param_type { - public: - using distribution_type = discrete_distribution; - - param_type() { init(); } - - template <typename InputIterator> - explicit param_type(InputIterator begin, InputIterator end) - : p_(begin, end) { - init(); - } - - explicit param_type(std::initializer_list<double> weights) : p_(weights) { - init(); - } - - template <class UnaryOperation> - explicit param_type(size_t nw, double xmin, double xmax, - UnaryOperation fw) { - if (nw > 0) { - p_.reserve(nw); - double delta = (xmax - xmin) / static_cast<double>(nw); - assert(delta > 0); - double t = delta * 0.5; - for (size_t i = 0; i < nw; ++i) { - p_.push_back(fw(xmin + i * delta + t)); - } - } - init(); - } - - const std::vector<double>& probabilities() const { return p_; } - size_t n() const { return p_.size() - 1; } - - friend bool operator==(const param_type& a, const param_type& b) { - return a.probabilities() == b.probabilities(); - } - - friend bool operator!=(const param_type& a, const param_type& b) { - return !(a == b); - } - - private: - friend class discrete_distribution; - - void init(); - - std::vector<double> p_; // normalized probabilities - std::vector<std::pair<double, size_t>> q_; // (acceptance, alternate) pairs - - static_assert(std::is_integral<result_type>::value, - "Class-template absl::discrete_distribution<> must be " - "parameterized using an integral type."); - }; - - discrete_distribution() : param_() {} - - explicit discrete_distribution(const param_type& p) : param_(p) {} - - template <typename InputIterator> - explicit discrete_distribution(InputIterator begin, InputIterator end) - : param_(begin, end) {} - - explicit discrete_distribution(std::initializer_list<double> weights) - : param_(weights) {} - - template <class UnaryOperation> - explicit discrete_distribution(size_t nw, double xmin, double xmax, - UnaryOperation fw) - : param_(nw, xmin, xmax, std::move(fw)) {} - - void reset() {} - - // generating functions - template <typename URBG> - result_type operator()(URBG& g) { // NOLINT(runtime/references) - return (*this)(g, param_); - } - - template <typename URBG> - result_type operator()(URBG& g, // NOLINT(runtime/references) - const param_type& p); - - const param_type& param() const { return param_; } - void param(const param_type& p) { param_ = p; } - - result_type(min)() const { return 0; } - result_type(max)() const { - return static_cast<result_type>(param_.n()); - } // inclusive - - // NOTE [rand.dist.sample.discrete] returns a std::vector<double> not a - // const std::vector<double>&. - const std::vector<double>& probabilities() const { - return param_.probabilities(); - } - - friend bool operator==(const discrete_distribution& a, - const discrete_distribution& b) { - return a.param_ == b.param_; - } - friend bool operator!=(const discrete_distribution& a, - const discrete_distribution& b) { - return a.param_ != b.param_; - } - - private: - param_type param_; -}; - -// -------------------------------------------------------------------------- -// Implementation details only below -// -------------------------------------------------------------------------- - -namespace random_internal { - -// Using the vector `*probabilities`, whose values are the weights or -// probabilities of an element being selected, constructs the proportional -// probabilities used by the discrete distribution. `*probabilities` will be -// scaled, if necessary, so that its entries sum to a value sufficiently close -// to 1.0. -std::vector<std::pair<double, size_t>> InitDiscreteDistribution( - std::vector<double>* probabilities); - -} // namespace random_internal - -template <typename IntType> -void discrete_distribution<IntType>::param_type::init() { - if (p_.empty()) { - p_.push_back(1.0); - q_.emplace_back(1.0, 0); - } else { - assert(n() <= (std::numeric_limits<IntType>::max)()); - q_ = random_internal::InitDiscreteDistribution(&p_); - } -} - -template <typename IntType> -template <typename URBG> -typename discrete_distribution<IntType>::result_type -discrete_distribution<IntType>::operator()( - URBG& g, // NOLINT(runtime/references) - const param_type& p) { - const auto idx = absl::uniform_int_distribution<result_type>(0, p.n())(g); - const auto& q = p.q_[idx]; - const bool selected = absl::bernoulli_distribution(q.first)(g); - return selected ? idx : static_cast<result_type>(q.second); -} - -template <typename CharT, typename Traits, typename IntType> -std::basic_ostream<CharT, Traits>& operator<<( - std::basic_ostream<CharT, Traits>& os, // NOLINT(runtime/references) - const discrete_distribution<IntType>& x) { - auto saver = random_internal::make_ostream_state_saver(os); - const auto& probabilities = x.param().probabilities(); - os << probabilities.size(); - - os.precision(random_internal::stream_precision_helper<double>::kPrecision); - for (const auto& p : probabilities) { - os << os.fill() << p; - } - return os; -} - -template <typename CharT, typename Traits, typename IntType> -std::basic_istream<CharT, Traits>& operator>>( - std::basic_istream<CharT, Traits>& is, // NOLINT(runtime/references) - discrete_distribution<IntType>& x) { // NOLINT(runtime/references) - using param_type = typename discrete_distribution<IntType>::param_type; - auto saver = random_internal::make_istream_state_saver(is); - - size_t n; - std::vector<double> p; - - is >> n; - if (is.fail()) return is; - if (n > 0) { - p.reserve(n); - for (IntType i = 0; i < n && !is.fail(); ++i) { - auto tmp = random_internal::read_floating_point<double>(is); - if (is.fail()) return is; - p.push_back(tmp); - } - } - x.param(param_type(p.begin(), p.end())); - return is; -} - -ABSL_NAMESPACE_END -} // namespace absl - -#endif // ABSL_RANDOM_DISCRETE_DISTRIBUTION_H_ diff --git a/third_party/abseil_cpp/absl/random/discrete_distribution_test.cc b/third_party/abseil_cpp/absl/random/discrete_distribution_test.cc deleted file mode 100644 index 6d007006ef48..000000000000 --- a/third_party/abseil_cpp/absl/random/discrete_distribution_test.cc +++ /dev/null @@ -1,250 +0,0 @@ -// Copyright 2017 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#include "absl/random/discrete_distribution.h" - -#include <cmath> -#include <cstddef> -#include <cstdint> -#include <iterator> -#include <numeric> -#include <random> -#include <sstream> -#include <string> -#include <vector> - -#include "gmock/gmock.h" -#include "gtest/gtest.h" -#include "absl/base/internal/raw_logging.h" -#include "absl/random/internal/chi_square.h" -#include "absl/random/internal/distribution_test_util.h" -#include "absl/random/internal/pcg_engine.h" -#include "absl/random/internal/sequence_urbg.h" -#include "absl/random/random.h" -#include "absl/strings/str_cat.h" -#include "absl/strings/strip.h" - -namespace { - -template <typename IntType> -class DiscreteDistributionTypeTest : public ::testing::Test {}; - -using IntTypes = ::testing::Types<int8_t, uint8_t, int16_t, uint16_t, int32_t, - uint32_t, int64_t, uint64_t>; -TYPED_TEST_SUITE(DiscreteDistributionTypeTest, IntTypes); - -TYPED_TEST(DiscreteDistributionTypeTest, ParamSerializeTest) { - using param_type = - typename absl::discrete_distribution<TypeParam>::param_type; - - absl::discrete_distribution<TypeParam> empty; - EXPECT_THAT(empty.probabilities(), testing::ElementsAre(1.0)); - - absl::discrete_distribution<TypeParam> before({1.0, 2.0, 1.0}); - - // Validate that the probabilities sum to 1.0. We picked values which - // can be represented exactly to avoid floating-point roundoff error. - double s = 0; - for (const auto& x : before.probabilities()) { - s += x; - } - EXPECT_EQ(s, 1.0); - EXPECT_THAT(before.probabilities(), testing::ElementsAre(0.25, 0.5, 0.25)); - - // Validate the same data via an initializer list. - { - std::vector<double> data({1.0, 2.0, 1.0}); - - absl::discrete_distribution<TypeParam> via_param{ - param_type(std::begin(data), std::end(data))}; - - EXPECT_EQ(via_param, before); - } - - std::stringstream ss; - ss << before; - absl::discrete_distribution<TypeParam> after; - - EXPECT_NE(before, after); - - ss >> after; - - EXPECT_EQ(before, after); -} - -TYPED_TEST(DiscreteDistributionTypeTest, Constructor) { - auto fn = [](double x) { return x; }; - { - absl::discrete_distribution<int> unary(0, 1.0, 9.0, fn); - EXPECT_THAT(unary.probabilities(), testing::ElementsAre(1.0)); - } - - { - absl::discrete_distribution<int> unary(2, 1.0, 9.0, fn); - // => fn(1.0 + 0 * 4 + 2) => 3 - // => fn(1.0 + 1 * 4 + 2) => 7 - EXPECT_THAT(unary.probabilities(), testing::ElementsAre(0.3, 0.7)); - } -} - -TEST(DiscreteDistributionTest, InitDiscreteDistribution) { - using testing::Pair; - - { - std::vector<double> p({1.0, 2.0, 3.0}); - std::vector<std::pair<double, size_t>> q = - absl::random_internal::InitDiscreteDistribution(&p); - - EXPECT_THAT(p, testing::ElementsAre(1 / 6.0, 2 / 6.0, 3 / 6.0)); - - // Each bucket is p=1/3, so bucket 0 will send half it's traffic - // to bucket 2, while the rest will retain all of their traffic. - EXPECT_THAT(q, testing::ElementsAre(Pair(0.5, 2), // - Pair(1.0, 1), // - Pair(1.0, 2))); - } - - { - std::vector<double> p({1.0, 2.0, 3.0, 5.0, 2.0}); - - std::vector<std::pair<double, size_t>> q = - absl::random_internal::InitDiscreteDistribution(&p); - - EXPECT_THAT(p, testing::ElementsAre(1 / 13.0, 2 / 13.0, 3 / 13.0, 5 / 13.0, - 2 / 13.0)); - - // A more complex bucketing solution: Each bucket has p=0.2 - // So buckets 0, 1, 4 will send their alternate traffic elsewhere, which - // happens to be bucket 3. - // However, summing up that alternate traffic gives bucket 3 too much - // traffic, so it will send some traffic to bucket 2. - constexpr double b0 = 1.0 / 13.0 / 0.2; - constexpr double b1 = 2.0 / 13.0 / 0.2; - constexpr double b3 = (5.0 / 13.0 / 0.2) - ((1 - b0) + (1 - b1) + (1 - b1)); - - EXPECT_THAT(q, testing::ElementsAre(Pair(b0, 3), // - Pair(b1, 3), // - Pair(1.0, 2), // - Pair(b3, 2), // - Pair(b1, 3))); - } -} - -TEST(DiscreteDistributionTest, ChiSquaredTest50) { - using absl::random_internal::kChiSquared; - - constexpr size_t kTrials = 10000; - constexpr int kBuckets = 50; // inclusive, so actally +1 - - // 1-in-100000 threshold, but remember, there are about 8 tests - // in this file. And the test could fail for other reasons. - // Empirically validated with --runs_per_test=10000. - const int kThreshold = - absl::random_internal::ChiSquareValue(kBuckets, 0.99999); - - std::vector<double> weights(kBuckets, 0); - std::iota(std::begin(weights), std::end(weights), 1); - absl::discrete_distribution<int> dist(std::begin(weights), std::end(weights)); - - // We use a fixed bit generator for distribution accuracy tests. This allows - // these tests to be deterministic, while still testing the qualify of the - // implementation. - absl::random_internal::pcg64_2018_engine rng(0x2B7E151628AED2A6); - - std::vector<int32_t> counts(kBuckets, 0); - for (size_t i = 0; i < kTrials; i++) { - auto x = dist(rng); - counts[x]++; - } - - // Scale weights. - double sum = 0; - for (double x : weights) { - sum += x; - } - for (double& x : weights) { - x = kTrials * (x / sum); - } - - double chi_square = - absl::random_internal::ChiSquare(std::begin(counts), std::end(counts), - std::begin(weights), std::end(weights)); - - if (chi_square > kThreshold) { - double p_value = - absl::random_internal::ChiSquarePValue(chi_square, kBuckets); - - // Chi-squared test failed. Output does not appear to be uniform. - std::string msg; - for (size_t i = 0; i < counts.size(); i++) { - absl::StrAppend(&msg, i, ": ", counts[i], " vs ", weights[i], "\n"); - } - absl::StrAppend(&msg, kChiSquared, " p-value ", p_value, "\n"); - absl::StrAppend(&msg, "High ", kChiSquared, " value: ", chi_square, " > ", - kThreshold); - ABSL_RAW_LOG(INFO, "%s", msg.c_str()); - FAIL() << msg; - } -} - -TEST(DiscreteDistributionTest, StabilityTest) { - // absl::discrete_distribution stabilitiy relies on - // absl::uniform_int_distribution and absl::bernoulli_distribution. - absl::random_internal::sequence_urbg urbg( - {0x0003eb76f6f7f755ull, 0xFFCEA50FDB2F953Bull, 0xC332DDEFBE6C5AA5ull, - 0x6558218568AB9702ull, 0x2AEF7DAD5B6E2F84ull, 0x1521B62829076170ull, - 0xECDD4775619F1510ull, 0x13CCA830EB61BD96ull, 0x0334FE1EAA0363CFull, - 0xB5735C904C70A239ull, 0xD59E9E0BCBAADE14ull, 0xEECC86BC60622CA7ull}); - - std::vector<int> output(6); - - { - absl::discrete_distribution<int32_t> dist({1.0, 2.0, 3.0, 5.0, 2.0}); - EXPECT_EQ(0, dist.min()); - EXPECT_EQ(4, dist.max()); - for (auto& v : output) { - v = dist(urbg); - } - EXPECT_EQ(12, urbg.invocations()); - } - - // With 12 calls to urbg, each call into discrete_distribution consumes - // precisely 2 values: one for the uniform call, and a second for the - // bernoulli. - // - // Given the alt mapping: 0=>3, 1=>3, 2=>2, 3=>2, 4=>3, we can - // - // uniform: 443210143131 - // bernoulli: b0 000011100101 - // bernoulli: b1 001111101101 - // bernoulli: b2 111111111111 - // bernoulli: b3 001111101111 - // bernoulli: b4 001111101101 - // ... - EXPECT_THAT(output, testing::ElementsAre(3, 3, 1, 3, 3, 3)); - - { - urbg.reset(); - absl::discrete_distribution<int64_t> dist({1.0, 2.0, 3.0, 5.0, 2.0}); - EXPECT_EQ(0, dist.min()); - EXPECT_EQ(4, dist.max()); - for (auto& v : output) { - v = dist(urbg); - } - EXPECT_EQ(12, urbg.invocations()); - } - EXPECT_THAT(output, testing::ElementsAre(3, 3, 0, 3, 0, 4)); -} - -} // namespace diff --git a/third_party/abseil_cpp/absl/random/distributions.h b/third_party/abseil_cpp/absl/random/distributions.h deleted file mode 100644 index 31c79694e51b..000000000000 --- a/third_party/abseil_cpp/absl/random/distributions.h +++ /dev/null @@ -1,452 +0,0 @@ -// Copyright 2017 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. -// -// ----------------------------------------------------------------------------- -// File: distributions.h -// ----------------------------------------------------------------------------- -// -// This header defines functions representing distributions, which you use in -// combination with an Abseil random bit generator to produce random values -// according to the rules of that distribution. -// -// The Abseil random library defines the following distributions within this -// file: -// -// * `absl::Uniform` for uniform (constant) distributions having constant -// probability -// * `absl::Bernoulli` for discrete distributions having exactly two outcomes -// * `absl::Beta` for continuous distributions parameterized through two -// free parameters -// * `absl::Exponential` for discrete distributions of events occurring -// continuously and independently at a constant average rate -// * `absl::Gaussian` (also known as "normal distributions") for continuous -// distributions using an associated quadratic function -// * `absl::LogUniform` for continuous uniform distributions where the log -// to the given base of all values is uniform -// * `absl::Poisson` for discrete probability distributions that express the -// probability of a given number of events occurring within a fixed interval -// * `absl::Zipf` for discrete probability distributions commonly used for -// modelling of rare events -// -// Prefer use of these distribution function classes over manual construction of -// your own distribution classes, as it allows library maintainers greater -// flexibility to change the underlying implementation in the future. - -#ifndef ABSL_RANDOM_DISTRIBUTIONS_H_ -#define ABSL_RANDOM_DISTRIBUTIONS_H_ - -#include <algorithm> -#include <cmath> -#include <limits> -#include <random> -#include <type_traits> - -#include "absl/base/internal/inline_variable.h" -#include "absl/random/bernoulli_distribution.h" -#include "absl/random/beta_distribution.h" -#include "absl/random/exponential_distribution.h" -#include "absl/random/gaussian_distribution.h" -#include "absl/random/internal/distribution_caller.h" // IWYU pragma: export -#include "absl/random/internal/uniform_helper.h" // IWYU pragma: export -#include "absl/random/log_uniform_int_distribution.h" -#include "absl/random/poisson_distribution.h" -#include "absl/random/uniform_int_distribution.h" -#include "absl/random/uniform_real_distribution.h" -#include "absl/random/zipf_distribution.h" - -namespace absl { -ABSL_NAMESPACE_BEGIN - -ABSL_INTERNAL_INLINE_CONSTEXPR(IntervalClosedClosedTag, IntervalClosedClosed, - {}); -ABSL_INTERNAL_INLINE_CONSTEXPR(IntervalClosedClosedTag, IntervalClosed, {}); -ABSL_INTERNAL_INLINE_CONSTEXPR(IntervalClosedOpenTag, IntervalClosedOpen, {}); -ABSL_INTERNAL_INLINE_CONSTEXPR(IntervalOpenOpenTag, IntervalOpenOpen, {}); -ABSL_INTERNAL_INLINE_CONSTEXPR(IntervalOpenOpenTag, IntervalOpen, {}); -ABSL_INTERNAL_INLINE_CONSTEXPR(IntervalOpenClosedTag, IntervalOpenClosed, {}); - -// ----------------------------------------------------------------------------- -// absl::Uniform<T>(tag, bitgen, lo, hi) -// ----------------------------------------------------------------------------- -// -// `absl::Uniform()` produces random values of type `T` uniformly distributed in -// a defined interval {lo, hi}. The interval `tag` defines the type of interval -// which should be one of the following possible values: -// -// * `absl::IntervalOpenOpen` -// * `absl::IntervalOpenClosed` -// * `absl::IntervalClosedOpen` -// * `absl::IntervalClosedClosed` -// -// where "open" refers to an exclusive value (excluded) from the output, while -// "closed" refers to an inclusive value (included) from the output. -// -// In the absence of an explicit return type `T`, `absl::Uniform()` will deduce -// the return type based on the provided endpoint arguments {A lo, B hi}. -// Given these endpoints, one of {A, B} will be chosen as the return type, if -// a type can be implicitly converted into the other in a lossless way. The -// lack of any such implicit conversion between {A, B} will produce a -// compile-time error -// -// See https://en.wikipedia.org/wiki/Uniform_distribution_(continuous) -// -// Example: -// -// absl::BitGen bitgen; -// -// // Produce a random float value between 0.0 and 1.0, inclusive -// auto x = absl::Uniform(absl::IntervalClosedClosed, bitgen, 0.0f, 1.0f); -// -// // The most common interval of `absl::IntervalClosedOpen` is available by -// // default: -// -// auto x = absl::Uniform(bitgen, 0.0f, 1.0f); -// -// // Return-types are typically inferred from the arguments, however callers -// // can optionally provide an explicit return-type to the template. -// -// auto x = absl::Uniform<float>(bitgen, 0, 1); -// -template <typename R = void, typename TagType, typename URBG> -typename absl::enable_if_t<!std::is_same<R, void>::value, R> // -Uniform(TagType tag, - URBG&& urbg, // NOLINT(runtime/references) - R lo, R hi) { - using gen_t = absl::decay_t<URBG>; - using distribution_t = random_internal::UniformDistributionWrapper<R>; - - auto a = random_internal::uniform_lower_bound(tag, lo, hi); - auto b = random_internal::uniform_upper_bound(tag, lo, hi); - if (!random_internal::is_uniform_range_valid(a, b)) return lo; - - return random_internal::DistributionCaller<gen_t>::template Call< - distribution_t>(&urbg, tag, lo, hi); -} - -// absl::Uniform<T>(bitgen, lo, hi) -// -// Overload of `Uniform()` using the default closed-open interval of [lo, hi), -// and returning values of type `T` -template <typename R = void, typename URBG> -typename absl::enable_if_t<!std::is_same<R, void>::value, R> // -Uniform(URBG&& urbg, // NOLINT(runtime/references) - R lo, R hi) { - using gen_t = absl::decay_t<URBG>; - using distribution_t = random_internal::UniformDistributionWrapper<R>; - constexpr auto tag = absl::IntervalClosedOpen; - - auto a = random_internal::uniform_lower_bound(tag, lo, hi); - auto b = random_internal::uniform_upper_bound(tag, lo, hi); - if (!random_internal::is_uniform_range_valid(a, b)) return lo; - - return random_internal::DistributionCaller<gen_t>::template Call< - distribution_t>(&urbg, lo, hi); -} - -// absl::Uniform(tag, bitgen, lo, hi) -// -// Overload of `Uniform()` using different (but compatible) lo, hi types. Note -// that a compile-error will result if the return type cannot be deduced -// correctly from the passed types. -template <typename R = void, typename TagType, typename URBG, typename A, - typename B> -typename absl::enable_if_t<std::is_same<R, void>::value, - random_internal::uniform_inferred_return_t<A, B>> -Uniform(TagType tag, - URBG&& urbg, // NOLINT(runtime/references) - A lo, B hi) { - using gen_t = absl::decay_t<URBG>; - using return_t = typename random_internal::uniform_inferred_return_t<A, B>; - using distribution_t = random_internal::UniformDistributionWrapper<return_t>; - - auto a = random_internal::uniform_lower_bound<return_t>(tag, lo, hi); - auto b = random_internal::uniform_upper_bound<return_t>(tag, lo, hi); - if (!random_internal::is_uniform_range_valid(a, b)) return lo; - - return random_internal::DistributionCaller<gen_t>::template Call< - distribution_t>(&urbg, tag, static_cast<return_t>(lo), - static_cast<return_t>(hi)); -} - -// absl::Uniform(bitgen, lo, hi) -// -// Overload of `Uniform()` using different (but compatible) lo, hi types and the -// default closed-open interval of [lo, hi). Note that a compile-error will -// result if the return type cannot be deduced correctly from the passed types. -template <typename R = void, typename URBG, typename A, typename B> -typename absl::enable_if_t<std::is_same<R, void>::value, - random_internal::uniform_inferred_return_t<A, B>> -Uniform(URBG&& urbg, // NOLINT(runtime/references) - A lo, B hi) { - using gen_t = absl::decay_t<URBG>; - using return_t = typename random_internal::uniform_inferred_return_t<A, B>; - using distribution_t = random_internal::UniformDistributionWrapper<return_t>; - - constexpr auto tag = absl::IntervalClosedOpen; - auto a = random_internal::uniform_lower_bound<return_t>(tag, lo, hi); - auto b = random_internal::uniform_upper_bound<return_t>(tag, lo, hi); - if (!random_internal::is_uniform_range_valid(a, b)) return lo; - - return random_internal::DistributionCaller<gen_t>::template Call< - distribution_t>(&urbg, static_cast<return_t>(lo), - static_cast<return_t>(hi)); -} - -// absl::Uniform<unsigned T>(bitgen) -// -// Overload of Uniform() using the minimum and maximum values of a given type -// `T` (which must be unsigned), returning a value of type `unsigned T` -template <typename R, typename URBG> -typename absl::enable_if_t<!std::is_signed<R>::value, R> // -Uniform(URBG&& urbg) { // NOLINT(runtime/references) - using gen_t = absl::decay_t<URBG>; - using distribution_t = random_internal::UniformDistributionWrapper<R>; - - return random_internal::DistributionCaller<gen_t>::template Call< - distribution_t>(&urbg); -} - -// ----------------------------------------------------------------------------- -// absl::Bernoulli(bitgen, p) -// ----------------------------------------------------------------------------- -// -// `absl::Bernoulli` produces a random boolean value, with probability `p` -// (where 0.0 <= p <= 1.0) equaling `true`. -// -// Prefer `absl::Bernoulli` to produce boolean values over other alternatives -// such as comparing an `absl::Uniform()` value to a specific output. -// -// See https://en.wikipedia.org/wiki/Bernoulli_distribution -// -// Example: -// -// absl::BitGen bitgen; -// ... -// if (absl::Bernoulli(bitgen, 1.0/3721.0)) { -// std::cout << "Asteroid field navigation successful."; -// } -// -template <typename URBG> -bool Bernoulli(URBG&& urbg, // NOLINT(runtime/references) - double p) { - using gen_t = absl::decay_t<URBG>; - using distribution_t = absl::bernoulli_distribution; - - return random_internal::DistributionCaller<gen_t>::template Call< - distribution_t>(&urbg, p); -} - -// ----------------------------------------------------------------------------- -// absl::Beta<T>(bitgen, alpha, beta) -// ----------------------------------------------------------------------------- -// -// `absl::Beta` produces a floating point number distributed in the closed -// interval [0,1] and parameterized by two values `alpha` and `beta` as per a -// Beta distribution. `T` must be a floating point type, but may be inferred -// from the types of `alpha` and `beta`. -// -// See https://en.wikipedia.org/wiki/Beta_distribution. -// -// Example: -// -// absl::BitGen bitgen; -// ... -// double sample = absl::Beta(bitgen, 3.0, 2.0); -// -template <typename RealType, typename URBG> -RealType Beta(URBG&& urbg, // NOLINT(runtime/references) - RealType alpha, RealType beta) { - static_assert( - std::is_floating_point<RealType>::value, - "Template-argument 'RealType' must be a floating-point type, in " - "absl::Beta<RealType, URBG>(...)"); - - using gen_t = absl::decay_t<URBG>; - using distribution_t = typename absl::beta_distribution<RealType>; - - return random_internal::DistributionCaller<gen_t>::template Call< - distribution_t>(&urbg, alpha, beta); -} - -// ----------------------------------------------------------------------------- -// absl::Exponential<T>(bitgen, lambda = 1) -// ----------------------------------------------------------------------------- -// -// `absl::Exponential` produces a floating point number representing the -// distance (time) between two consecutive events in a point process of events -// occurring continuously and independently at a constant average rate. `T` must -// be a floating point type, but may be inferred from the type of `lambda`. -// -// See https://en.wikipedia.org/wiki/Exponential_distribution. -// -// Example: -// -// absl::BitGen bitgen; -// ... -// double call_length = absl::Exponential(bitgen, 7.0); -// -template <typename RealType, typename URBG> -RealType Exponential(URBG&& urbg, // NOLINT(runtime/references) - RealType lambda = 1) { - static_assert( - std::is_floating_point<RealType>::value, - "Template-argument 'RealType' must be a floating-point type, in " - "absl::Exponential<RealType, URBG>(...)"); - - using gen_t = absl::decay_t<URBG>; - using distribution_t = typename absl::exponential_distribution<RealType>; - - return random_internal::DistributionCaller<gen_t>::template Call< - distribution_t>(&urbg, lambda); -} - -// ----------------------------------------------------------------------------- -// absl::Gaussian<T>(bitgen, mean = 0, stddev = 1) -// ----------------------------------------------------------------------------- -// -// `absl::Gaussian` produces a floating point number selected from the Gaussian -// (ie. "Normal") distribution. `T` must be a floating point type, but may be -// inferred from the types of `mean` and `stddev`. -// -// See https://en.wikipedia.org/wiki/Normal_distribution -// -// Example: -// -// absl::BitGen bitgen; -// ... -// double giraffe_height = absl::Gaussian(bitgen, 16.3, 3.3); -// -template <typename RealType, typename URBG> -RealType Gaussian(URBG&& urbg, // NOLINT(runtime/references) - RealType mean = 0, RealType stddev = 1) { - static_assert( - std::is_floating_point<RealType>::value, - "Template-argument 'RealType' must be a floating-point type, in " - "absl::Gaussian<RealType, URBG>(...)"); - - using gen_t = absl::decay_t<URBG>; - using distribution_t = typename absl::gaussian_distribution<RealType>; - - return random_internal::DistributionCaller<gen_t>::template Call< - distribution_t>(&urbg, mean, stddev); -} - -// ----------------------------------------------------------------------------- -// absl::LogUniform<T>(bitgen, lo, hi, base = 2) -// ----------------------------------------------------------------------------- -// -// `absl::LogUniform` produces random values distributed where the log to a -// given base of all values is uniform in a closed interval [lo, hi]. `T` must -// be an integral type, but may be inferred from the types of `lo` and `hi`. -// -// I.e., `LogUniform(0, n, b)` is uniformly distributed across buckets -// [0], [1, b-1], [b, b^2-1] .. [b^(k-1), (b^k)-1] .. [b^floor(log(n, b)), n] -// and is uniformly distributed within each bucket. -// -// The resulting probability density is inversely related to bucket size, though -// values in the final bucket may be more likely than previous values. (In the -// extreme case where n = b^i the final value will be tied with zero as the most -// probable result. -// -// If `lo` is nonzero then this distribution is shifted to the desired interval, -// so LogUniform(lo, hi, b) is equivalent to LogUniform(0, hi-lo, b)+lo. -// -// See http://ecolego.facilia.se/ecolego/show/Log-Uniform%20Distribution -// -// Example: -// -// absl::BitGen bitgen; -// ... -// int v = absl::LogUniform(bitgen, 0, 1000); -// -template <typename IntType, typename URBG> -IntType LogUniform(URBG&& urbg, // NOLINT(runtime/references) - IntType lo, IntType hi, IntType base = 2) { - static_assert(std::is_integral<IntType>::value, - "Template-argument 'IntType' must be an integral type, in " - "absl::LogUniform<IntType, URBG>(...)"); - - using gen_t = absl::decay_t<URBG>; - using distribution_t = typename absl::log_uniform_int_distribution<IntType>; - - return random_internal::DistributionCaller<gen_t>::template Call< - distribution_t>(&urbg, lo, hi, base); -} - -// ----------------------------------------------------------------------------- -// absl::Poisson<T>(bitgen, mean = 1) -// ----------------------------------------------------------------------------- -// -// `absl::Poisson` produces discrete probabilities for a given number of events -// occurring within a fixed interval within the closed interval [0, max]. `T` -// must be an integral type. -// -// See https://en.wikipedia.org/wiki/Poisson_distribution -// -// Example: -// -// absl::BitGen bitgen; -// ... -// int requests_per_minute = absl::Poisson<int>(bitgen, 3.2); -// -template <typename IntType, typename URBG> -IntType Poisson(URBG&& urbg, // NOLINT(runtime/references) - double mean = 1.0) { - static_assert(std::is_integral<IntType>::value, - "Template-argument 'IntType' must be an integral type, in " - "absl::Poisson<IntType, URBG>(...)"); - - using gen_t = absl::decay_t<URBG>; - using distribution_t = typename absl::poisson_distribution<IntType>; - - return random_internal::DistributionCaller<gen_t>::template Call< - distribution_t>(&urbg, mean); -} - -// ----------------------------------------------------------------------------- -// absl::Zipf<T>(bitgen, hi = max, q = 2, v = 1) -// ----------------------------------------------------------------------------- -// -// `absl::Zipf` produces discrete probabilities commonly used for modelling of -// rare events over the closed interval [0, hi]. The parameters `v` and `q` -// determine the skew of the distribution. `T` must be an integral type, but -// may be inferred from the type of `hi`. -// -// See http://mathworld.wolfram.com/ZipfDistribution.html -// -// Example: -// -// absl::BitGen bitgen; -// ... -// int term_rank = absl::Zipf<int>(bitgen); -// -template <typename IntType, typename URBG> -IntType Zipf(URBG&& urbg, // NOLINT(runtime/references) - IntType hi = (std::numeric_limits<IntType>::max)(), double q = 2.0, - double v = 1.0) { - static_assert(std::is_integral<IntType>::value, - "Template-argument 'IntType' must be an integral type, in " - "absl::Zipf<IntType, URBG>(...)"); - - using gen_t = absl::decay_t<URBG>; - using distribution_t = typename absl::zipf_distribution<IntType>; - - return random_internal::DistributionCaller<gen_t>::template Call< - distribution_t>(&urbg, hi, q, v); -} - -ABSL_NAMESPACE_END -} // namespace absl - -#endif // ABSL_RANDOM_DISTRIBUTIONS_H_ diff --git a/third_party/abseil_cpp/absl/random/distributions_test.cc b/third_party/abseil_cpp/absl/random/distributions_test.cc deleted file mode 100644 index 5866a07257e5..000000000000 --- a/third_party/abseil_cpp/absl/random/distributions_test.cc +++ /dev/null @@ -1,455 +0,0 @@ -// Copyright 2017 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#include "absl/random/distributions.h" - -#include <cmath> -#include <cstdint> -#include <random> -#include <vector> - -#include "gtest/gtest.h" -#include "absl/random/internal/distribution_test_util.h" -#include "absl/random/random.h" - -namespace { - -constexpr int kSize = 400000; - -class RandomDistributionsTest : public testing::Test {}; - - -struct Invalid {}; - -template <typename A, typename B> -auto InferredUniformReturnT(int) - -> decltype(absl::Uniform(std::declval<absl::InsecureBitGen&>(), - std::declval<A>(), std::declval<B>())); - -template <typename, typename> -Invalid InferredUniformReturnT(...); - -template <typename TagType, typename A, typename B> -auto InferredTaggedUniformReturnT(int) - -> decltype(absl::Uniform(std::declval<TagType>(), - std::declval<absl::InsecureBitGen&>(), - std::declval<A>(), std::declval<B>())); - -template <typename, typename, typename> -Invalid InferredTaggedUniformReturnT(...); - -// Given types <A, B, Expect>, CheckArgsInferType() verifies that -// -// absl::Uniform(gen, A{}, B{}) -// -// returns the type "Expect". -// -// This interface can also be used to assert that a given absl::Uniform() -// overload does not exist / will not compile. Given types <A, B>, the -// expression -// -// decltype(absl::Uniform(..., std::declval<A>(), std::declval<B>())) -// -// will not compile, leaving the definition of InferredUniformReturnT<A, B> to -// resolve (via SFINAE) to the overload which returns type "Invalid". This -// allows tests to assert that an invocation such as -// -// absl::Uniform(gen, 1.23f, std::numeric_limits<int>::max() - 1) -// -// should not compile, since neither type, float nor int, can precisely -// represent both endpoint-values. Writing: -// -// CheckArgsInferType<float, int, Invalid>() -// -// will assert that this overload does not exist. -template <typename A, typename B, typename Expect> -void CheckArgsInferType() { - static_assert( - absl::conjunction< - std::is_same<Expect, decltype(InferredUniformReturnT<A, B>(0))>, - std::is_same<Expect, - decltype(InferredUniformReturnT<B, A>(0))>>::value, - ""); - static_assert( - absl::conjunction< - std::is_same<Expect, decltype(InferredTaggedUniformReturnT< - absl::IntervalOpenOpenTag, A, B>(0))>, - std::is_same<Expect, - decltype(InferredTaggedUniformReturnT< - absl::IntervalOpenOpenTag, B, A>(0))>>::value, - ""); -} - -template <typename A, typename B, typename ExplicitRet> -auto ExplicitUniformReturnT(int) -> decltype( - absl::Uniform<ExplicitRet>(*std::declval<absl::InsecureBitGen*>(), - std::declval<A>(), std::declval<B>())); - -template <typename, typename, typename ExplicitRet> -Invalid ExplicitUniformReturnT(...); - -template <typename TagType, typename A, typename B, typename ExplicitRet> -auto ExplicitTaggedUniformReturnT(int) -> decltype(absl::Uniform<ExplicitRet>( - std::declval<TagType>(), *std::declval<absl::InsecureBitGen*>(), - std::declval<A>(), std::declval<B>())); - -template <typename, typename, typename, typename ExplicitRet> -Invalid ExplicitTaggedUniformReturnT(...); - -// Given types <A, B, Expect>, CheckArgsReturnExpectedType() verifies that -// -// absl::Uniform<Expect>(gen, A{}, B{}) -// -// returns the type "Expect", and that the function-overload has the signature -// -// Expect(URBG&, Expect, Expect) -template <typename A, typename B, typename Expect> -void CheckArgsReturnExpectedType() { - static_assert( - absl::conjunction< - std::is_same<Expect, - decltype(ExplicitUniformReturnT<A, B, Expect>(0))>, - std::is_same<Expect, decltype(ExplicitUniformReturnT<B, A, Expect>( - 0))>>::value, - ""); - static_assert( - absl::conjunction< - std::is_same<Expect, - decltype(ExplicitTaggedUniformReturnT< - absl::IntervalOpenOpenTag, A, B, Expect>(0))>, - std::is_same<Expect, decltype(ExplicitTaggedUniformReturnT< - absl::IntervalOpenOpenTag, B, A, - Expect>(0))>>::value, - ""); -} - -TEST_F(RandomDistributionsTest, UniformTypeInference) { - // Infers common types. - CheckArgsInferType<uint16_t, uint16_t, uint16_t>(); - CheckArgsInferType<uint32_t, uint32_t, uint32_t>(); - CheckArgsInferType<uint64_t, uint64_t, uint64_t>(); - CheckArgsInferType<int16_t, int16_t, int16_t>(); - CheckArgsInferType<int32_t, int32_t, int32_t>(); - CheckArgsInferType<int64_t, int64_t, int64_t>(); - CheckArgsInferType<float, float, float>(); - CheckArgsInferType<double, double, double>(); - - // Explicitly-specified return-values override inferences. - CheckArgsReturnExpectedType<int16_t, int16_t, int32_t>(); - CheckArgsReturnExpectedType<uint16_t, uint16_t, int32_t>(); - CheckArgsReturnExpectedType<int16_t, int16_t, int64_t>(); - CheckArgsReturnExpectedType<int16_t, int32_t, int64_t>(); - CheckArgsReturnExpectedType<int16_t, int32_t, double>(); - CheckArgsReturnExpectedType<float, float, double>(); - CheckArgsReturnExpectedType<int, int, int16_t>(); - - // Properly promotes uint16_t. - CheckArgsInferType<uint16_t, uint32_t, uint32_t>(); - CheckArgsInferType<uint16_t, uint64_t, uint64_t>(); - CheckArgsInferType<uint16_t, int32_t, int32_t>(); - CheckArgsInferType<uint16_t, int64_t, int64_t>(); - CheckArgsInferType<uint16_t, float, float>(); - CheckArgsInferType<uint16_t, double, double>(); - - // Properly promotes int16_t. - CheckArgsInferType<int16_t, int32_t, int32_t>(); - CheckArgsInferType<int16_t, int64_t, int64_t>(); - CheckArgsInferType<int16_t, float, float>(); - CheckArgsInferType<int16_t, double, double>(); - - // Invalid (u)int16_t-pairings do not compile. - // See "CheckArgsInferType" comments above, for how this is achieved. - CheckArgsInferType<uint16_t, int16_t, Invalid>(); - CheckArgsInferType<int16_t, uint32_t, Invalid>(); - CheckArgsInferType<int16_t, uint64_t, Invalid>(); - - // Properly promotes uint32_t. - CheckArgsInferType<uint32_t, uint64_t, uint64_t>(); - CheckArgsInferType<uint32_t, int64_t, int64_t>(); - CheckArgsInferType<uint32_t, double, double>(); - - // Properly promotes int32_t. - CheckArgsInferType<int32_t, int64_t, int64_t>(); - CheckArgsInferType<int32_t, double, double>(); - - // Invalid (u)int32_t-pairings do not compile. - CheckArgsInferType<uint32_t, int32_t, Invalid>(); - CheckArgsInferType<int32_t, uint64_t, Invalid>(); - CheckArgsInferType<int32_t, float, Invalid>(); - CheckArgsInferType<uint32_t, float, Invalid>(); - - // Invalid (u)int64_t-pairings do not compile. - CheckArgsInferType<uint64_t, int64_t, Invalid>(); - CheckArgsInferType<int64_t, float, Invalid>(); - CheckArgsInferType<int64_t, double, Invalid>(); - - // Properly promotes float. - CheckArgsInferType<float, double, double>(); -} - -TEST_F(RandomDistributionsTest, UniformExamples) { - // Examples. - absl::InsecureBitGen gen; - EXPECT_NE(1, absl::Uniform(gen, static_cast<uint16_t>(0), 1.0f)); - EXPECT_NE(1, absl::Uniform(gen, 0, 1.0)); - EXPECT_NE(1, absl::Uniform(absl::IntervalOpenOpen, gen, - static_cast<uint16_t>(0), 1.0f)); - EXPECT_NE(1, absl::Uniform(absl::IntervalOpenOpen, gen, 0, 1.0)); - EXPECT_NE(1, absl::Uniform(absl::IntervalOpenOpen, gen, -1, 1.0)); - EXPECT_NE(1, absl::Uniform<double>(absl::IntervalOpenOpen, gen, -1, 1)); - EXPECT_NE(1, absl::Uniform<float>(absl::IntervalOpenOpen, gen, 0, 1)); - EXPECT_NE(1, absl::Uniform<float>(gen, 0, 1)); -} - -TEST_F(RandomDistributionsTest, UniformNoBounds) { - absl::InsecureBitGen gen; - - absl::Uniform<uint8_t>(gen); - absl::Uniform<uint16_t>(gen); - absl::Uniform<uint32_t>(gen); - absl::Uniform<uint64_t>(gen); -} - -TEST_F(RandomDistributionsTest, UniformNonsenseRanges) { - // The ranges used in this test are undefined behavior. - // The results are arbitrary and subject to future changes. - absl::InsecureBitGen gen; - - // <uint> - EXPECT_EQ(0, absl::Uniform<uint64_t>(gen, 0, 0)); - EXPECT_EQ(1, absl::Uniform<uint64_t>(gen, 1, 0)); - EXPECT_EQ(0, absl::Uniform<uint64_t>(absl::IntervalOpenOpen, gen, 0, 0)); - EXPECT_EQ(1, absl::Uniform<uint64_t>(absl::IntervalOpenOpen, gen, 1, 0)); - - constexpr auto m = (std::numeric_limits<uint64_t>::max)(); - - EXPECT_EQ(m, absl::Uniform(gen, m, m)); - EXPECT_EQ(m, absl::Uniform(gen, m, m - 1)); - EXPECT_EQ(m - 1, absl::Uniform(gen, m - 1, m)); - EXPECT_EQ(m, absl::Uniform(absl::IntervalOpenOpen, gen, m, m)); - EXPECT_EQ(m, absl::Uniform(absl::IntervalOpenOpen, gen, m, m - 1)); - EXPECT_EQ(m - 1, absl::Uniform(absl::IntervalOpenOpen, gen, m - 1, m)); - - // <int> - EXPECT_EQ(0, absl::Uniform<int64_t>(gen, 0, 0)); - EXPECT_EQ(1, absl::Uniform<int64_t>(gen, 1, 0)); - EXPECT_EQ(0, absl::Uniform<int64_t>(absl::IntervalOpenOpen, gen, 0, 0)); - EXPECT_EQ(1, absl::Uniform<int64_t>(absl::IntervalOpenOpen, gen, 1, 0)); - - constexpr auto l = (std::numeric_limits<int64_t>::min)(); - constexpr auto r = (std::numeric_limits<int64_t>::max)(); - - EXPECT_EQ(l, absl::Uniform(gen, l, l)); - EXPECT_EQ(r, absl::Uniform(gen, r, r)); - EXPECT_EQ(r, absl::Uniform(gen, r, r - 1)); - EXPECT_EQ(r - 1, absl::Uniform(gen, r - 1, r)); - EXPECT_EQ(l, absl::Uniform(absl::IntervalOpenOpen, gen, l, l)); - EXPECT_EQ(r, absl::Uniform(absl::IntervalOpenOpen, gen, r, r)); - EXPECT_EQ(r, absl::Uniform(absl::IntervalOpenOpen, gen, r, r - 1)); - EXPECT_EQ(r - 1, absl::Uniform(absl::IntervalOpenOpen, gen, r - 1, r)); - - // <double> - const double e = std::nextafter(1.0, 2.0); // 1 + epsilon - const double f = std::nextafter(1.0, 0.0); // 1 - epsilon - const double g = std::numeric_limits<double>::denorm_min(); - - EXPECT_EQ(1.0, absl::Uniform(gen, 1.0, e)); - EXPECT_EQ(1.0, absl::Uniform(gen, 1.0, f)); - EXPECT_EQ(0.0, absl::Uniform(gen, 0.0, g)); - - EXPECT_EQ(e, absl::Uniform(absl::IntervalOpenOpen, gen, 1.0, e)); - EXPECT_EQ(f, absl::Uniform(absl::IntervalOpenOpen, gen, 1.0, f)); - EXPECT_EQ(g, absl::Uniform(absl::IntervalOpenOpen, gen, 0.0, g)); -} - -// TODO(lar): Validate properties of non-default interval-semantics. -TEST_F(RandomDistributionsTest, UniformReal) { - std::vector<double> values(kSize); - - absl::InsecureBitGen gen; - for (int i = 0; i < kSize; i++) { - values[i] = absl::Uniform(gen, 0, 1.0); - } - - const auto moments = - absl::random_internal::ComputeDistributionMoments(values); - EXPECT_NEAR(0.5, moments.mean, 0.02); - EXPECT_NEAR(1 / 12.0, moments.variance, 0.02); - EXPECT_NEAR(0.0, moments.skewness, 0.02); - EXPECT_NEAR(9 / 5.0, moments.kurtosis, 0.02); -} - -TEST_F(RandomDistributionsTest, UniformInt) { - std::vector<double> values(kSize); - - absl::InsecureBitGen gen; - for (int i = 0; i < kSize; i++) { - const int64_t kMax = 1000000000000ll; - int64_t j = absl::Uniform(absl::IntervalClosedClosed, gen, 0, kMax); - // convert to double. - values[i] = static_cast<double>(j) / static_cast<double>(kMax); - } - - const auto moments = - absl::random_internal::ComputeDistributionMoments(values); - EXPECT_NEAR(0.5, moments.mean, 0.02); - EXPECT_NEAR(1 / 12.0, moments.variance, 0.02); - EXPECT_NEAR(0.0, moments.skewness, 0.02); - EXPECT_NEAR(9 / 5.0, moments.kurtosis, 0.02); - - /* - // NOTE: These are not supported by absl::Uniform, which is specialized - // on integer and real valued types. - - enum E { E0, E1 }; // enum - enum S : int { S0, S1 }; // signed enum - enum U : unsigned int { U0, U1 }; // unsigned enum - - absl::Uniform(gen, E0, E1); - absl::Uniform(gen, S0, S1); - absl::Uniform(gen, U0, U1); - */ -} - -TEST_F(RandomDistributionsTest, Exponential) { - std::vector<double> values(kSize); - - absl::InsecureBitGen gen; - for (int i = 0; i < kSize; i++) { - values[i] = absl::Exponential<double>(gen); - } - - const auto moments = - absl::random_internal::ComputeDistributionMoments(values); - EXPECT_NEAR(1.0, moments.mean, 0.02); - EXPECT_NEAR(1.0, moments.variance, 0.025); - EXPECT_NEAR(2.0, moments.skewness, 0.1); - EXPECT_LT(5.0, moments.kurtosis); -} - -TEST_F(RandomDistributionsTest, PoissonDefault) { - std::vector<double> values(kSize); - - absl::InsecureBitGen gen; - for (int i = 0; i < kSize; i++) { - values[i] = absl::Poisson<int64_t>(gen); - } - - const auto moments = - absl::random_internal::ComputeDistributionMoments(values); - EXPECT_NEAR(1.0, moments.mean, 0.02); - EXPECT_NEAR(1.0, moments.variance, 0.02); - EXPECT_NEAR(1.0, moments.skewness, 0.025); - EXPECT_LT(2.0, moments.kurtosis); -} - -TEST_F(RandomDistributionsTest, PoissonLarge) { - constexpr double kMean = 100000000.0; - std::vector<double> values(kSize); - - absl::InsecureBitGen gen; - for (int i = 0; i < kSize; i++) { - values[i] = absl::Poisson<int64_t>(gen, kMean); - } - - const auto moments = - absl::random_internal::ComputeDistributionMoments(values); - EXPECT_NEAR(kMean, moments.mean, kMean * 0.015); - EXPECT_NEAR(kMean, moments.variance, kMean * 0.015); - EXPECT_NEAR(std::sqrt(kMean), moments.skewness, kMean * 0.02); - EXPECT_LT(2.0, moments.kurtosis); -} - -TEST_F(RandomDistributionsTest, Bernoulli) { - constexpr double kP = 0.5151515151; - std::vector<double> values(kSize); - - absl::InsecureBitGen gen; - for (int i = 0; i < kSize; i++) { - values[i] = absl::Bernoulli(gen, kP); - } - - const auto moments = - absl::random_internal::ComputeDistributionMoments(values); - EXPECT_NEAR(kP, moments.mean, 0.01); -} - -TEST_F(RandomDistributionsTest, Beta) { - constexpr double kAlpha = 2.0; - constexpr double kBeta = 3.0; - std::vector<double> values(kSize); - - absl::InsecureBitGen gen; - for (int i = 0; i < kSize; i++) { - values[i] = absl::Beta(gen, kAlpha, kBeta); - } - - const auto moments = - absl::random_internal::ComputeDistributionMoments(values); - EXPECT_NEAR(0.4, moments.mean, 0.01); -} - -TEST_F(RandomDistributionsTest, Zipf) { - std::vector<double> values(kSize); - - absl::InsecureBitGen gen; - for (int i = 0; i < kSize; i++) { - values[i] = absl::Zipf<int64_t>(gen, 100); - } - - // The mean of a zipf distribution is: H(N, s-1) / H(N,s). - // Given the parameter v = 1, this gives the following function: - // (Hn(100, 1) - Hn(1,1)) / (Hn(100,2) - Hn(1,2)) = 6.5944 - const auto moments = - absl::random_internal::ComputeDistributionMoments(values); - EXPECT_NEAR(6.5944, moments.mean, 2000) << moments; -} - -TEST_F(RandomDistributionsTest, Gaussian) { - std::vector<double> values(kSize); - - absl::InsecureBitGen gen; - for (int i = 0; i < kSize; i++) { - values[i] = absl::Gaussian<double>(gen); - } - - const auto moments = - absl::random_internal::ComputeDistributionMoments(values); - EXPECT_NEAR(0.0, moments.mean, 0.02); - EXPECT_NEAR(1.0, moments.variance, 0.04); - EXPECT_NEAR(0, moments.skewness, 0.2); - EXPECT_NEAR(3.0, moments.kurtosis, 0.5); -} - -TEST_F(RandomDistributionsTest, LogUniform) { - std::vector<double> values(kSize); - - absl::InsecureBitGen gen; - for (int i = 0; i < kSize; i++) { - values[i] = absl::LogUniform<int64_t>(gen, 0, (1 << 10) - 1); - } - - // The mean is the sum of the fractional means of the uniform distributions: - // [0..0][1..1][2..3][4..7][8..15][16..31][32..63] - // [64..127][128..255][256..511][512..1023] - const double mean = (0 + 1 + 1 + 2 + 3 + 4 + 7 + 8 + 15 + 16 + 31 + 32 + 63 + - 64 + 127 + 128 + 255 + 256 + 511 + 512 + 1023) / - (2.0 * 11.0); - - const auto moments = - absl::random_internal::ComputeDistributionMoments(values); - EXPECT_NEAR(mean, moments.mean, 2) << moments; -} - -} // namespace diff --git a/third_party/abseil_cpp/absl/random/examples_test.cc b/third_party/abseil_cpp/absl/random/examples_test.cc deleted file mode 100644 index 1dcb5146f3d0..000000000000 --- a/third_party/abseil_cpp/absl/random/examples_test.cc +++ /dev/null @@ -1,99 +0,0 @@ -// Copyright 2017 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#include <cinttypes> -#include <random> -#include <sstream> -#include <vector> - -#include "gtest/gtest.h" -#include "absl/random/random.h" - -template <typename T> -void Use(T) {} - -TEST(Examples, Basic) { - absl::BitGen gen; - std::vector<int> objs = {10, 20, 30, 40, 50}; - - // Choose an element from a set. - auto elem = objs[absl::Uniform(gen, 0u, objs.size())]; - Use(elem); - - // Generate a uniform value between 1 and 6. - auto dice_roll = absl::Uniform<int>(absl::IntervalClosedClosed, gen, 1, 6); - Use(dice_roll); - - // Generate a random byte. - auto byte = absl::Uniform<uint8_t>(gen); - Use(byte); - - // Generate a fractional value from [0f, 1f). - auto fraction = absl::Uniform<float>(gen, 0, 1); - Use(fraction); - - // Toss a fair coin; 50/50 probability. - bool coin_toss = absl::Bernoulli(gen, 0.5); - Use(coin_toss); - - // Select a file size between 1k and 10MB, biased towards smaller file sizes. - auto file_size = absl::LogUniform<size_t>(gen, 1000, 10 * 1000 * 1000); - Use(file_size); - - // Randomize (shuffle) a collection. - std::shuffle(std::begin(objs), std::end(objs), gen); -} - -TEST(Examples, CreateingCorrelatedVariateSequences) { - // Unexpected PRNG correlation is often a source of bugs, - // so when using absl::BitGen it must be an intentional choice. - // NOTE: All of these only exhibit process-level stability. - - // Create a correlated sequence from system entropy. - { - auto my_seed = absl::MakeSeedSeq(); - - absl::BitGen gen_1(my_seed); - absl::BitGen gen_2(my_seed); // Produces same variates as gen_1. - - EXPECT_EQ(absl::Bernoulli(gen_1, 0.5), absl::Bernoulli(gen_2, 0.5)); - EXPECT_EQ(absl::Uniform<uint32_t>(gen_1), absl::Uniform<uint32_t>(gen_2)); - } - - // Create a correlated sequence from an existing URBG. - { - absl::BitGen gen; - - auto my_seed = absl::CreateSeedSeqFrom(&gen); - absl::BitGen gen_1(my_seed); - absl::BitGen gen_2(my_seed); - - EXPECT_EQ(absl::Bernoulli(gen_1, 0.5), absl::Bernoulli(gen_2, 0.5)); - EXPECT_EQ(absl::Uniform<uint32_t>(gen_1), absl::Uniform<uint32_t>(gen_2)); - } - - // An alternate construction which uses user-supplied data - // instead of a random seed. - { - const char kData[] = "A simple seed string"; - std::seed_seq my_seed(std::begin(kData), std::end(kData)); - - absl::BitGen gen_1(my_seed); - absl::BitGen gen_2(my_seed); - - EXPECT_EQ(absl::Bernoulli(gen_1, 0.5), absl::Bernoulli(gen_2, 0.5)); - EXPECT_EQ(absl::Uniform<uint32_t>(gen_1), absl::Uniform<uint32_t>(gen_2)); - } -} - diff --git a/third_party/abseil_cpp/absl/random/exponential_distribution.h b/third_party/abseil_cpp/absl/random/exponential_distribution.h deleted file mode 100644 index b5caf8a1e1f3..000000000000 --- a/third_party/abseil_cpp/absl/random/exponential_distribution.h +++ /dev/null @@ -1,165 +0,0 @@ -// Copyright 2017 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#ifndef ABSL_RANDOM_EXPONENTIAL_DISTRIBUTION_H_ -#define ABSL_RANDOM_EXPONENTIAL_DISTRIBUTION_H_ - -#include <cassert> -#include <cmath> -#include <istream> -#include <limits> -#include <type_traits> - -#include "absl/meta/type_traits.h" -#include "absl/random/internal/fast_uniform_bits.h" -#include "absl/random/internal/generate_real.h" -#include "absl/random/internal/iostream_state_saver.h" - -namespace absl { -ABSL_NAMESPACE_BEGIN - -// absl::exponential_distribution: -// Generates a number conforming to an exponential distribution and is -// equivalent to the standard [rand.dist.pois.exp] distribution. -template <typename RealType = double> -class exponential_distribution { - public: - using result_type = RealType; - - class param_type { - public: - using distribution_type = exponential_distribution; - - explicit param_type(result_type lambda = 1) : lambda_(lambda) { - assert(lambda > 0); - neg_inv_lambda_ = -result_type(1) / lambda_; - } - - result_type lambda() const { return lambda_; } - - friend bool operator==(const param_type& a, const param_type& b) { - return a.lambda_ == b.lambda_; - } - - friend bool operator!=(const param_type& a, const param_type& b) { - return !(a == b); - } - - private: - friend class exponential_distribution; - - result_type lambda_; - result_type neg_inv_lambda_; - - static_assert( - std::is_floating_point<RealType>::value, - "Class-template absl::exponential_distribution<> must be parameterized " - "using a floating-point type."); - }; - - exponential_distribution() : exponential_distribution(1) {} - - explicit exponential_distribution(result_type lambda) : param_(lambda) {} - - explicit exponential_distribution(const param_type& p) : param_(p) {} - - void reset() {} - - // Generating functions - template <typename URBG> - result_type operator()(URBG& g) { // NOLINT(runtime/references) - return (*this)(g, param_); - } - - template <typename URBG> - result_type operator()(URBG& g, // NOLINT(runtime/references) - const param_type& p); - - param_type param() const { return param_; } - void param(const param_type& p) { param_ = p; } - - result_type(min)() const { return 0; } - result_type(max)() const { - return std::numeric_limits<result_type>::infinity(); - } - - result_type lambda() const { return param_.lambda(); } - - friend bool operator==(const exponential_distribution& a, - const exponential_distribution& b) { - return a.param_ == b.param_; - } - friend bool operator!=(const exponential_distribution& a, - const exponential_distribution& b) { - return a.param_ != b.param_; - } - - private: - param_type param_; - random_internal::FastUniformBits<uint64_t> fast_u64_; -}; - -// -------------------------------------------------------------------------- -// Implementation details follow -// -------------------------------------------------------------------------- - -template <typename RealType> -template <typename URBG> -typename exponential_distribution<RealType>::result_type -exponential_distribution<RealType>::operator()( - URBG& g, // NOLINT(runtime/references) - const param_type& p) { - using random_internal::GenerateNegativeTag; - using random_internal::GenerateRealFromBits; - using real_type = - absl::conditional_t<std::is_same<RealType, float>::value, float, double>; - - const result_type u = GenerateRealFromBits<real_type, GenerateNegativeTag, - false>(fast_u64_(g)); // U(-1, 0) - - // log1p(-x) is mathematically equivalent to log(1 - x) but has more - // accuracy for x near zero. - return p.neg_inv_lambda_ * std::log1p(u); -} - -template <typename CharT, typename Traits, typename RealType> -std::basic_ostream<CharT, Traits>& operator<<( - std::basic_ostream<CharT, Traits>& os, // NOLINT(runtime/references) - const exponential_distribution<RealType>& x) { - auto saver = random_internal::make_ostream_state_saver(os); - os.precision(random_internal::stream_precision_helper<RealType>::kPrecision); - os << x.lambda(); - return os; -} - -template <typename CharT, typename Traits, typename RealType> -std::basic_istream<CharT, Traits>& operator>>( - std::basic_istream<CharT, Traits>& is, // NOLINT(runtime/references) - exponential_distribution<RealType>& x) { // NOLINT(runtime/references) - using result_type = typename exponential_distribution<RealType>::result_type; - using param_type = typename exponential_distribution<RealType>::param_type; - result_type lambda; - - auto saver = random_internal::make_istream_state_saver(is); - lambda = random_internal::read_floating_point<result_type>(is); - if (!is.fail()) { - x.param(param_type(lambda)); - } - return is; -} - -ABSL_NAMESPACE_END -} // namespace absl - -#endif // ABSL_RANDOM_EXPONENTIAL_DISTRIBUTION_H_ diff --git a/third_party/abseil_cpp/absl/random/exponential_distribution_test.cc b/third_party/abseil_cpp/absl/random/exponential_distribution_test.cc deleted file mode 100644 index 8e9e69b64b29..000000000000 --- a/third_party/abseil_cpp/absl/random/exponential_distribution_test.cc +++ /dev/null @@ -1,430 +0,0 @@ -// Copyright 2017 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#include "absl/random/exponential_distribution.h" - -#include <algorithm> -#include <cmath> -#include <cstddef> -#include <cstdint> -#include <iterator> -#include <limits> -#include <random> -#include <sstream> -#include <string> -#include <type_traits> -#include <vector> - -#include "gmock/gmock.h" -#include "gtest/gtest.h" -#include "absl/base/internal/raw_logging.h" -#include "absl/base/macros.h" -#include "absl/random/internal/chi_square.h" -#include "absl/random/internal/distribution_test_util.h" -#include "absl/random/internal/pcg_engine.h" -#include "absl/random/internal/sequence_urbg.h" -#include "absl/random/random.h" -#include "absl/strings/str_cat.h" -#include "absl/strings/str_format.h" -#include "absl/strings/str_replace.h" -#include "absl/strings/strip.h" - -namespace { - -using absl::random_internal::kChiSquared; - -template <typename RealType> -class ExponentialDistributionTypedTest : public ::testing::Test {}; - -#if defined(__EMSCRIPTEN__) -using RealTypes = ::testing::Types<float, double>; -#else -using RealTypes = ::testing::Types<float, double, long double>; -#endif // defined(__EMSCRIPTEN__) -TYPED_TEST_CASE(ExponentialDistributionTypedTest, RealTypes); - -TYPED_TEST(ExponentialDistributionTypedTest, SerializeTest) { - using param_type = - typename absl::exponential_distribution<TypeParam>::param_type; - - const TypeParam kParams[] = { - // Cases around 1. - 1, // - std::nextafter(TypeParam(1), TypeParam(0)), // 1 - epsilon - std::nextafter(TypeParam(1), TypeParam(2)), // 1 + epsilon - // Typical cases. - TypeParam(1e-8), TypeParam(1e-4), TypeParam(1), TypeParam(2), - TypeParam(1e4), TypeParam(1e8), TypeParam(1e20), TypeParam(2.5), - // Boundary cases. - std::numeric_limits<TypeParam>::max(), - std::numeric_limits<TypeParam>::epsilon(), - std::nextafter(std::numeric_limits<TypeParam>::min(), - TypeParam(1)), // min + epsilon - std::numeric_limits<TypeParam>::min(), // smallest normal - // There are some errors dealing with denorms on apple platforms. - std::numeric_limits<TypeParam>::denorm_min(), // smallest denorm - std::numeric_limits<TypeParam>::min() / 2, // denorm - std::nextafter(std::numeric_limits<TypeParam>::min(), - TypeParam(0)), // denorm_max - }; - - constexpr int kCount = 1000; - absl::InsecureBitGen gen; - - for (const TypeParam lambda : kParams) { - // Some values may be invalid; skip those. - if (!std::isfinite(lambda)) continue; - ABSL_ASSERT(lambda > 0); - - const param_type param(lambda); - - absl::exponential_distribution<TypeParam> before(lambda); - EXPECT_EQ(before.lambda(), param.lambda()); - - { - absl::exponential_distribution<TypeParam> via_param(param); - EXPECT_EQ(via_param, before); - EXPECT_EQ(via_param.param(), before.param()); - } - - // Smoke test. - auto sample_min = before.max(); - auto sample_max = before.min(); - for (int i = 0; i < kCount; i++) { - auto sample = before(gen); - EXPECT_GE(sample, before.min()) << before; - EXPECT_LE(sample, before.max()) << before; - if (sample > sample_max) sample_max = sample; - if (sample < sample_min) sample_min = sample; - } - if (!std::is_same<TypeParam, long double>::value) { - ABSL_INTERNAL_LOG(INFO, - absl::StrFormat("Range {%f}: %f, %f, lambda=%f", lambda, - sample_min, sample_max, lambda)); - } - - std::stringstream ss; - ss << before; - - if (!std::isfinite(lambda)) { - // Streams do not deserialize inf/nan correctly. - continue; - } - // Validate stream serialization. - absl::exponential_distribution<TypeParam> after(34.56f); - - EXPECT_NE(before.lambda(), after.lambda()); - EXPECT_NE(before.param(), after.param()); - EXPECT_NE(before, after); - - ss >> after; - -#if defined(__powerpc64__) || defined(__PPC64__) || defined(__powerpc__) || \ - defined(__ppc__) || defined(__PPC__) - if (std::is_same<TypeParam, long double>::value) { - // Roundtripping floating point values requires sufficient precision to - // reconstruct the exact value. It turns out that long double has some - // errors doing this on ppc, particularly for values - // near {1.0 +/- epsilon}. - if (lambda <= std::numeric_limits<double>::max() && - lambda >= std::numeric_limits<double>::lowest()) { - EXPECT_EQ(static_cast<double>(before.lambda()), - static_cast<double>(after.lambda())) - << ss.str(); - } - continue; - } -#endif - - EXPECT_EQ(before.lambda(), after.lambda()) // - << ss.str() << " " // - << (ss.good() ? "good " : "") // - << (ss.bad() ? "bad " : "") // - << (ss.eof() ? "eof " : "") // - << (ss.fail() ? "fail " : ""); - } -} - -// http://www.itl.nist.gov/div898/handbook/eda/section3/eda3667.htm - -class ExponentialModel { - public: - explicit ExponentialModel(double lambda) - : lambda_(lambda), beta_(1.0 / lambda) {} - - double lambda() const { return lambda_; } - - double mean() const { return beta_; } - double variance() const { return beta_ * beta_; } - double stddev() const { return std::sqrt(variance()); } - double skew() const { return 2; } - double kurtosis() const { return 6.0; } - - double CDF(double x) { return 1.0 - std::exp(-lambda_ * x); } - - // The inverse CDF, or PercentPoint function of the distribution - double InverseCDF(double p) { - ABSL_ASSERT(p >= 0.0); - ABSL_ASSERT(p < 1.0); - return -beta_ * std::log(1.0 - p); - } - - private: - const double lambda_; - const double beta_; -}; - -struct Param { - double lambda; - double p_fail; - int trials; -}; - -class ExponentialDistributionTests : public testing::TestWithParam<Param>, - public ExponentialModel { - public: - ExponentialDistributionTests() : ExponentialModel(GetParam().lambda) {} - - // SingleZTest provides a basic z-squared test of the mean vs. expected - // mean for data generated by the poisson distribution. - template <typename D> - bool SingleZTest(const double p, const size_t samples); - - // SingleChiSquaredTest provides a basic chi-squared test of the normal - // distribution. - template <typename D> - double SingleChiSquaredTest(); - - // We use a fixed bit generator for distribution accuracy tests. This allows - // these tests to be deterministic, while still testing the qualify of the - // implementation. - absl::random_internal::pcg64_2018_engine rng_{0x2B7E151628AED2A6}; -}; - -template <typename D> -bool ExponentialDistributionTests::SingleZTest(const double p, - const size_t samples) { - D dis(lambda()); - - std::vector<double> data; - data.reserve(samples); - for (size_t i = 0; i < samples; i++) { - const double x = dis(rng_); - data.push_back(x); - } - - const auto m = absl::random_internal::ComputeDistributionMoments(data); - const double max_err = absl::random_internal::MaxErrorTolerance(p); - const double z = absl::random_internal::ZScore(mean(), m); - const bool pass = absl::random_internal::Near("z", z, 0.0, max_err); - - if (!pass) { - ABSL_INTERNAL_LOG( - INFO, absl::StrFormat("p=%f max_err=%f\n" - " lambda=%f\n" - " mean=%f vs. %f\n" - " stddev=%f vs. %f\n" - " skewness=%f vs. %f\n" - " kurtosis=%f vs. %f\n" - " z=%f vs. 0", - p, max_err, lambda(), m.mean, mean(), - std::sqrt(m.variance), stddev(), m.skewness, - skew(), m.kurtosis, kurtosis(), z)); - } - return pass; -} - -template <typename D> -double ExponentialDistributionTests::SingleChiSquaredTest() { - const size_t kSamples = 10000; - const int kBuckets = 50; - - // The InverseCDF is the percent point function of the distribution, and can - // be used to assign buckets roughly uniformly. - std::vector<double> cutoffs; - const double kInc = 1.0 / static_cast<double>(kBuckets); - for (double p = kInc; p < 1.0; p += kInc) { - cutoffs.push_back(InverseCDF(p)); - } - if (cutoffs.back() != std::numeric_limits<double>::infinity()) { - cutoffs.push_back(std::numeric_limits<double>::infinity()); - } - - D dis(lambda()); - - std::vector<int32_t> counts(cutoffs.size(), 0); - for (int j = 0; j < kSamples; j++) { - const double x = dis(rng_); - auto it = std::upper_bound(cutoffs.begin(), cutoffs.end(), x); - counts[std::distance(cutoffs.begin(), it)]++; - } - - // Null-hypothesis is that the distribution is exponentially distributed - // with the provided lambda (not estimated from the data). - const int dof = static_cast<int>(counts.size()) - 1; - - // Our threshold for logging is 1-in-50. - const double threshold = absl::random_internal::ChiSquareValue(dof, 0.98); - - const double expected = - static_cast<double>(kSamples) / static_cast<double>(counts.size()); - - double chi_square = absl::random_internal::ChiSquareWithExpected( - std::begin(counts), std::end(counts), expected); - double p = absl::random_internal::ChiSquarePValue(chi_square, dof); - - if (chi_square > threshold) { - for (int i = 0; i < cutoffs.size(); i++) { - ABSL_INTERNAL_LOG( - INFO, absl::StrFormat("%d : (%f) = %d", i, cutoffs[i], counts[i])); - } - - ABSL_INTERNAL_LOG(INFO, - absl::StrCat("lambda ", lambda(), "\n", // - " expected ", expected, "\n", // - kChiSquared, " ", chi_square, " (", p, ")\n", - kChiSquared, " @ 0.98 = ", threshold)); - } - return p; -} - -TEST_P(ExponentialDistributionTests, ZTest) { - const size_t kSamples = 10000; - const auto& param = GetParam(); - const int expected_failures = - std::max(1, static_cast<int>(std::ceil(param.trials * param.p_fail))); - const double p = absl::random_internal::RequiredSuccessProbability( - param.p_fail, param.trials); - - int failures = 0; - for (int i = 0; i < param.trials; i++) { - failures += SingleZTest<absl::exponential_distribution<double>>(p, kSamples) - ? 0 - : 1; - } - EXPECT_LE(failures, expected_failures); -} - -TEST_P(ExponentialDistributionTests, ChiSquaredTest) { - const int kTrials = 20; - int failures = 0; - - for (int i = 0; i < kTrials; i++) { - double p_value = - SingleChiSquaredTest<absl::exponential_distribution<double>>(); - if (p_value < 0.005) { // 1/200 - failures++; - } - } - - // There is a 0.10% chance of producing at least one failure, so raise the - // failure threshold high enough to allow for a flake rate < 10,000. - EXPECT_LE(failures, 4); -} - -std::vector<Param> GenParams() { - return { - Param{1.0, 0.02, 100}, - Param{2.5, 0.02, 100}, - Param{10, 0.02, 100}, - // large - Param{1e4, 0.02, 100}, - Param{1e9, 0.02, 100}, - // small - Param{0.1, 0.02, 100}, - Param{1e-3, 0.02, 100}, - Param{1e-5, 0.02, 100}, - }; -} - -std::string ParamName(const ::testing::TestParamInfo<Param>& info) { - const auto& p = info.param; - std::string name = absl::StrCat("lambda_", absl::SixDigits(p.lambda)); - return absl::StrReplaceAll(name, {{"+", "_"}, {"-", "_"}, {".", "_"}}); -} - -INSTANTIATE_TEST_CASE_P(All, ExponentialDistributionTests, - ::testing::ValuesIn(GenParams()), ParamName); - -// NOTE: absl::exponential_distribution is not guaranteed to be stable. -TEST(ExponentialDistributionTest, StabilityTest) { - // absl::exponential_distribution stability relies on std::log1p and - // absl::uniform_real_distribution. - absl::random_internal::sequence_urbg urbg( - {0x0003eb76f6f7f755ull, 0xFFCEA50FDB2F953Bull, 0xC332DDEFBE6C5AA5ull, - 0x6558218568AB9702ull, 0x2AEF7DAD5B6E2F84ull, 0x1521B62829076170ull, - 0xECDD4775619F1510ull, 0x13CCA830EB61BD96ull, 0x0334FE1EAA0363CFull, - 0xB5735C904C70A239ull, 0xD59E9E0BCBAADE14ull, 0xEECC86BC60622CA7ull}); - - std::vector<int> output(14); - - { - absl::exponential_distribution<double> dist; - std::generate(std::begin(output), std::end(output), - [&] { return static_cast<int>(10000.0 * dist(urbg)); }); - - EXPECT_EQ(14, urbg.invocations()); - EXPECT_THAT(output, - testing::ElementsAre(0, 71913, 14375, 5039, 1835, 861, 25936, - 804, 126, 12337, 17984, 27002, 0, 71913)); - } - - urbg.reset(); - { - absl::exponential_distribution<float> dist; - std::generate(std::begin(output), std::end(output), - [&] { return static_cast<int>(10000.0f * dist(urbg)); }); - - EXPECT_EQ(14, urbg.invocations()); - EXPECT_THAT(output, - testing::ElementsAre(0, 71913, 14375, 5039, 1835, 861, 25936, - 804, 126, 12337, 17984, 27002, 0, 71913)); - } -} - -TEST(ExponentialDistributionTest, AlgorithmBounds) { - // Relies on absl::uniform_real_distribution, so some of these comments - // reference that. - absl::exponential_distribution<double> dist; - - { - // This returns the smallest value >0 from absl::uniform_real_distribution. - absl::random_internal::sequence_urbg urbg({0x0000000000000001ull}); - double a = dist(urbg); - EXPECT_EQ(a, 5.42101086242752217004e-20); - } - - { - // This returns a value very near 0.5 from absl::uniform_real_distribution. - absl::random_internal::sequence_urbg urbg({0x7fffffffffffffefull}); - double a = dist(urbg); - EXPECT_EQ(a, 0.693147180559945175204); - } - - { - // This returns the largest value <1 from absl::uniform_real_distribution. - // WolframAlpha: ~39.1439465808987766283058547296341915292187253 - absl::random_internal::sequence_urbg urbg({0xFFFFFFFFFFFFFFeFull}); - double a = dist(urbg); - EXPECT_EQ(a, 36.7368005696771007251); - } - { - // This *ALSO* returns the largest value <1. - absl::random_internal::sequence_urbg urbg({0xFFFFFFFFFFFFFFFFull}); - double a = dist(urbg); - EXPECT_EQ(a, 36.7368005696771007251); - } -} - -} // namespace diff --git a/third_party/abseil_cpp/absl/random/gaussian_distribution.cc b/third_party/abseil_cpp/absl/random/gaussian_distribution.cc deleted file mode 100644 index c7a72cb2f61c..000000000000 --- a/third_party/abseil_cpp/absl/random/gaussian_distribution.cc +++ /dev/null @@ -1,104 +0,0 @@ -// BEGIN GENERATED CODE; DO NOT EDIT -// clang-format off - -#include "absl/random/gaussian_distribution.h" - -namespace absl { -ABSL_NAMESPACE_BEGIN -namespace random_internal { - -const gaussian_distribution_base::Tables - gaussian_distribution_base::zg_ = { - {3.7130862467425505, 3.442619855899000214, 3.223084984581141565, - 3.083228858216868318, 2.978696252647779819, 2.894344007021528942, - 2.82312535054891045, 2.761169372387176857, 2.706113573121819549, - 2.656406411261359679, 2.610972248431847387, 2.56903362592493778, - 2.530009672388827457, 2.493454522095372106, 2.459018177411830486, - 2.426420645533749809, 2.395434278011062457, 2.365871370117638595, - 2.337575241339236776, 2.310413683698762988, 2.284274059677471769, - 2.25905957386919809, 2.234686395590979036, 2.21108140887870297, - 2.188180432076048731, 2.165926793748921497, 2.144270182360394905, - 2.123165708673976138, 2.102573135189237608, 2.082456237992015957, - 2.062782274508307978, 2.043521536655067194, 2.02464697337738464, - 2.006133869963471206, 1.987959574127619033, 1.970103260854325633, - 1.952545729553555764, 1.935269228296621957, 1.918257300864508963, - 1.901494653105150423, 1.884967035707758143, 1.868661140994487768, - 1.852564511728090002, 1.836665460258444904, 1.820952996596124418, - 1.805416764219227366, 1.790046982599857506, 1.77483439558606837, - 1.759770224899592339, 1.744846128113799244, 1.730054160563729182, - 1.71538674071366648, 1.700836618569915748, 1.686396846779167014, - 1.6720607540975998, 1.657821920954023254, 1.643674156862867441, - 1.629611479470633562, 1.615628095043159629, 1.601718380221376581, - 1.587876864890574558, 1.574098216022999264, 1.560377222366167382, - 1.546708779859908844, 1.533087877674041755, 1.519509584765938559, - 1.505969036863201937, 1.492461423781352714, 1.478981976989922842, - 1.465525957342709296, 1.452088642889222792, 1.438665316684561546, - 1.425251254514058319, 1.411841712447055919, 1.398431914131003539, - 1.385017037732650058, 1.371592202427340812, 1.358152454330141534, - 1.34469275175354519, 1.331207949665625279, 1.317692783209412299, - 1.304141850128615054, 1.290549591926194894, 1.27691027356015363, - 1.263217961454619287, 1.249466499573066436, 1.23564948326336066, - 1.221760230539994385, 1.207791750415947662, 1.193736707833126465, - 1.17958738466398616, 1.165335636164750222, 1.150972842148865416, - 1.136489852013158774, 1.121876922582540237, 1.107123647534034028, - 1.092218876907275371, 1.077150624892893482, 1.061905963694822042, - 1.046470900764042922, 1.030830236068192907, 1.014967395251327842, - 0.9988642334929808131, 0.9825008035154263464, 0.9658550794011470098, - 0.9489026255113034436, 0.9316161966151479401, 0.9139652510230292792, - 0.8959153525809346874, 0.8774274291129204872, 0.8584568431938099931, - 0.8389522142975741614, 0.8188539067003538507, 0.7980920606440534693, - 0.7765839878947563557, 0.7542306644540520688, 0.7309119106424850631, - 0.7064796113354325779, 0.6807479186691505202, 0.6534786387399710295, - 0.6243585973360461505, 0.5929629424714434327, 0.5586921784081798625, - 0.5206560387620546848, 0.4774378372966830431, 0.4265479863554152429, - 0.3628714310970211909, 0.2723208648139477384, 0}, - {0.001014352564120377413, 0.002669629083880922793, 0.005548995220771345792, - 0.008624484412859888607, 0.01183947865788486861, 0.01516729801054656976, - 0.01859210273701129151, 0.02210330461592709475, 0.02569329193593428151, - 0.02935631744000685023, 0.03308788614622575758, 0.03688438878665621645, - 0.04074286807444417458, 0.04466086220049143157, 0.04863629585986780496, - 0.05266740190305100461, 0.05675266348104984759, 0.06089077034804041277, - 0.06508058521306804567, 0.06932111739357792179, 0.07361150188411341722, - 0.07795098251397346301, 0.08233889824223575293, 0.08677467189478028919, - 0.09125780082683036809, 0.095787849121731522, 0.1003644410286559929, - 0.1049872554094214289, 0.1096560210148404546, 0.1143705124488661323, - 0.1191305467076509556, 0.1239359802028679736, 0.1287867061959434012, - 0.1336826525834396151, 0.1386237799845948804, 0.1436100800906280339, - 0.1486415742423425057, 0.1537183122081819397, 0.1588403711394795748, - 0.1640078546834206341, 0.1692208922373653057, 0.1744796383307898324, - 0.1797842721232958407, 0.1851349970089926078, 0.1905320403191375633, - 0.1959756531162781534, 0.2014661100743140865, 0.2070037094399269362, - 0.2125887730717307134, 0.2182216465543058426, 0.2239026993850088965, - 0.229632325232116602, 0.2354109422634795556, 0.2412389935454402889, - 0.2471169475123218551, 0.2530452985073261551, 0.2590245673962052742, - 0.2650553022555897087, 0.271138079138385224, 0.2772735029191887857, - 0.2834622082232336471, 0.2897048604429605656, 0.2960021568469337061, - 0.3023548277864842593, 0.3087636380061818397, 0.3152293880650116065, - 0.3217529158759855901, 0.3283350983728509642, 0.3349768533135899506, - 0.3416791412315512977, 0.3484429675463274756, 0.355269384847918035, - 0.3621594953693184626, 0.3691144536644731522, 0.376135469510563536, - 0.3832238110559021416, 0.3903808082373155797, 0.3976078564938743676, - 0.404906420807223999, 0.4122780401026620578, 0.4197243320495753771, - 0.4272469983049970721, 0.4348478302499918513, 0.4425287152754694975, - 0.4502916436820402768, 0.458138716267873114, 0.4660721526894572309, - 0.4740943006930180559, 0.4822076463294863724, 0.4904148252838453348, - 0.4987186354709807201, 0.5071220510755701794, 0.5156282382440030565, - 0.5242405726729852944, 0.5329626593838373561, 0.5417983550254266145, - 0.5507517931146057588, 0.5598274127040882009, 0.5690299910679523787, - 0.5783646811197646898, 0.5878370544347081283, 0.5974531509445183408, - 0.6072195366251219584, 0.6171433708188825973, 0.6272324852499290282, - 0.6374954773350440806, 0.6479418211102242475, 0.6585820000500898219, - 0.6694276673488921414, 0.6804918409973358395, 0.6917891434366769676, - 0.7033360990161600101, 0.7151515074105005976, 0.7272569183441868201, - 0.7396772436726493094, 0.7524415591746134169, 0.7655841738977066102, - 0.7791460859296898134, 0.7931770117713072832, 0.8077382946829627652, - 0.8229072113814113187, 0.8387836052959920519, 0.8555006078694531446, - 0.873243048910072206, 0.8922816507840289901, 0.9130436479717434217, - 0.9362826816850632339, 0.9635996931270905952, 1}}; - -} // namespace random_internal -ABSL_NAMESPACE_END -} // namespace absl - -// clang-format on -// END GENERATED CODE diff --git a/third_party/abseil_cpp/absl/random/gaussian_distribution.h b/third_party/abseil_cpp/absl/random/gaussian_distribution.h deleted file mode 100644 index 4b07a5c0af9d..000000000000 --- a/third_party/abseil_cpp/absl/random/gaussian_distribution.h +++ /dev/null @@ -1,275 +0,0 @@ -// Copyright 2017 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#ifndef ABSL_RANDOM_GAUSSIAN_DISTRIBUTION_H_ -#define ABSL_RANDOM_GAUSSIAN_DISTRIBUTION_H_ - -// absl::gaussian_distribution implements the Ziggurat algorithm -// for generating random gaussian numbers. -// -// Implementation based on "The Ziggurat Method for Generating Random Variables" -// by George Marsaglia and Wai Wan Tsang: http://www.jstatsoft.org/v05/i08/ -// - -#include <cmath> -#include <cstdint> -#include <istream> -#include <limits> -#include <type_traits> - -#include "absl/base/config.h" -#include "absl/random/internal/fast_uniform_bits.h" -#include "absl/random/internal/generate_real.h" -#include "absl/random/internal/iostream_state_saver.h" - -namespace absl { -ABSL_NAMESPACE_BEGIN -namespace random_internal { - -// absl::gaussian_distribution_base implements the underlying ziggurat algorithm -// using the ziggurat tables generated by the gaussian_distribution_gentables -// binary. -// -// The specific algorithm has some of the improvements suggested by the -// 2005 paper, "An Improved Ziggurat Method to Generate Normal Random Samples", -// Jurgen A Doornik. (https://www.doornik.com/research/ziggurat.pdf) -class ABSL_DLL gaussian_distribution_base { - public: - template <typename URBG> - inline double zignor(URBG& g); // NOLINT(runtime/references) - - private: - friend class TableGenerator; - - template <typename URBG> - inline double zignor_fallback(URBG& g, // NOLINT(runtime/references) - bool neg); - - // Constants used for the gaussian distribution. - static constexpr double kR = 3.442619855899; // Start of the tail. - static constexpr double kRInv = 0.29047645161474317; // ~= (1.0 / kR) . - static constexpr double kV = 9.91256303526217e-3; - static constexpr uint64_t kMask = 0x07f; - - // The ziggurat tables store the pdf(f) and inverse-pdf(x) for equal-area - // points on one-half of the normal distribution, where the pdf function, - // pdf = e ^ (-1/2 *x^2), assumes that the mean = 0 & stddev = 1. - // - // These tables are just over 2kb in size; larger tables might improve the - // distributions, but also lead to more cache pollution. - // - // x = {3.71308, 3.44261, 3.22308, ..., 0} - // f = {0.00101, 0.00266, 0.00554, ..., 1} - struct Tables { - double x[kMask + 2]; - double f[kMask + 2]; - }; - static const Tables zg_; - random_internal::FastUniformBits<uint64_t> fast_u64_; -}; - -} // namespace random_internal - -// absl::gaussian_distribution: -// Generates a number conforming to a Gaussian distribution. -template <typename RealType = double> -class gaussian_distribution : random_internal::gaussian_distribution_base { - public: - using result_type = RealType; - - class param_type { - public: - using distribution_type = gaussian_distribution; - - explicit param_type(result_type mean = 0, result_type stddev = 1) - : mean_(mean), stddev_(stddev) {} - - // Returns the mean distribution parameter. The mean specifies the location - // of the peak. The default value is 0.0. - result_type mean() const { return mean_; } - - // Returns the deviation distribution parameter. The default value is 1.0. - result_type stddev() const { return stddev_; } - - friend bool operator==(const param_type& a, const param_type& b) { - return a.mean_ == b.mean_ && a.stddev_ == b.stddev_; - } - - friend bool operator!=(const param_type& a, const param_type& b) { - return !(a == b); - } - - private: - result_type mean_; - result_type stddev_; - - static_assert( - std::is_floating_point<RealType>::value, - "Class-template absl::gaussian_distribution<> must be parameterized " - "using a floating-point type."); - }; - - gaussian_distribution() : gaussian_distribution(0) {} - - explicit gaussian_distribution(result_type mean, result_type stddev = 1) - : param_(mean, stddev) {} - - explicit gaussian_distribution(const param_type& p) : param_(p) {} - - void reset() {} - - // Generating functions - template <typename URBG> - result_type operator()(URBG& g) { // NOLINT(runtime/references) - return (*this)(g, param_); - } - - template <typename URBG> - result_type operator()(URBG& g, // NOLINT(runtime/references) - const param_type& p); - - param_type param() const { return param_; } - void param(const param_type& p) { param_ = p; } - - result_type(min)() const { - return -std::numeric_limits<result_type>::infinity(); - } - result_type(max)() const { - return std::numeric_limits<result_type>::infinity(); - } - - result_type mean() const { return param_.mean(); } - result_type stddev() const { return param_.stddev(); } - - friend bool operator==(const gaussian_distribution& a, - const gaussian_distribution& b) { - return a.param_ == b.param_; - } - friend bool operator!=(const gaussian_distribution& a, - const gaussian_distribution& b) { - return a.param_ != b.param_; - } - - private: - param_type param_; -}; - -// -------------------------------------------------------------------------- -// Implementation details only below -// -------------------------------------------------------------------------- - -template <typename RealType> -template <typename URBG> -typename gaussian_distribution<RealType>::result_type -gaussian_distribution<RealType>::operator()( - URBG& g, // NOLINT(runtime/references) - const param_type& p) { - return p.mean() + p.stddev() * static_cast<result_type>(zignor(g)); -} - -template <typename CharT, typename Traits, typename RealType> -std::basic_ostream<CharT, Traits>& operator<<( - std::basic_ostream<CharT, Traits>& os, // NOLINT(runtime/references) - const gaussian_distribution<RealType>& x) { - auto saver = random_internal::make_ostream_state_saver(os); - os.precision(random_internal::stream_precision_helper<RealType>::kPrecision); - os << x.mean() << os.fill() << x.stddev(); - return os; -} - -template <typename CharT, typename Traits, typename RealType> -std::basic_istream<CharT, Traits>& operator>>( - std::basic_istream<CharT, Traits>& is, // NOLINT(runtime/references) - gaussian_distribution<RealType>& x) { // NOLINT(runtime/references) - using result_type = typename gaussian_distribution<RealType>::result_type; - using param_type = typename gaussian_distribution<RealType>::param_type; - - auto saver = random_internal::make_istream_state_saver(is); - auto mean = random_internal::read_floating_point<result_type>(is); - if (is.fail()) return is; - auto stddev = random_internal::read_floating_point<result_type>(is); - if (!is.fail()) { - x.param(param_type(mean, stddev)); - } - return is; -} - -namespace random_internal { - -template <typename URBG> -inline double gaussian_distribution_base::zignor_fallback(URBG& g, bool neg) { - using random_internal::GeneratePositiveTag; - using random_internal::GenerateRealFromBits; - - // This fallback path happens approximately 0.05% of the time. - double x, y; - do { - // kRInv = 1/r, U(0, 1) - x = kRInv * - std::log(GenerateRealFromBits<double, GeneratePositiveTag, false>( - fast_u64_(g))); - y = -std::log( - GenerateRealFromBits<double, GeneratePositiveTag, false>(fast_u64_(g))); - } while ((y + y) < (x * x)); - return neg ? (x - kR) : (kR - x); -} - -template <typename URBG> -inline double gaussian_distribution_base::zignor( - URBG& g) { // NOLINT(runtime/references) - using random_internal::GeneratePositiveTag; - using random_internal::GenerateRealFromBits; - using random_internal::GenerateSignedTag; - - while (true) { - // We use a single uint64_t to generate both a double and a strip. - // These bits are unused when the generated double is > 1/2^5. - // This may introduce some bias from the duplicated low bits of small - // values (those smaller than 1/2^5, which all end up on the left tail). - uint64_t bits = fast_u64_(g); - int i = static_cast<int>(bits & kMask); // pick a random strip - double j = GenerateRealFromBits<double, GenerateSignedTag, false>( - bits); // U(-1, 1) - const double x = j * zg_.x[i]; - - // Retangular box. Handles >97% of all cases. - // For any given box, this handles between 75% and 99% of values. - // Equivalent to U(01) < (x[i+1] / x[i]), and when i == 0, ~93.5% - if (std::abs(x) < zg_.x[i + 1]) { - return x; - } - - // i == 0: Base box. Sample using a ratio of uniforms. - if (i == 0) { - // This path happens about 0.05% of the time. - return zignor_fallback(g, j < 0); - } - - // i > 0: Wedge samples using precomputed values. - double v = GenerateRealFromBits<double, GeneratePositiveTag, false>( - fast_u64_(g)); // U(0, 1) - if ((zg_.f[i + 1] + v * (zg_.f[i] - zg_.f[i + 1])) < - std::exp(-0.5 * x * x)) { - return x; - } - - // The wedge was missed; reject the value and try again. - } -} - -} // namespace random_internal -ABSL_NAMESPACE_END -} // namespace absl - -#endif // ABSL_RANDOM_GAUSSIAN_DISTRIBUTION_H_ diff --git a/third_party/abseil_cpp/absl/random/gaussian_distribution_test.cc b/third_party/abseil_cpp/absl/random/gaussian_distribution_test.cc deleted file mode 100644 index 02ac578a5c18..000000000000 --- a/third_party/abseil_cpp/absl/random/gaussian_distribution_test.cc +++ /dev/null @@ -1,579 +0,0 @@ -// Copyright 2017 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#include "absl/random/gaussian_distribution.h" - -#include <algorithm> -#include <cmath> -#include <cstddef> -#include <ios> -#include <iterator> -#include <random> -#include <string> -#include <vector> - -#include "gmock/gmock.h" -#include "gtest/gtest.h" -#include "absl/base/internal/raw_logging.h" -#include "absl/base/macros.h" -#include "absl/random/internal/chi_square.h" -#include "absl/random/internal/distribution_test_util.h" -#include "absl/random/internal/sequence_urbg.h" -#include "absl/random/random.h" -#include "absl/strings/str_cat.h" -#include "absl/strings/str_format.h" -#include "absl/strings/str_replace.h" -#include "absl/strings/strip.h" - -namespace { - -using absl::random_internal::kChiSquared; - -template <typename RealType> -class GaussianDistributionInterfaceTest : public ::testing::Test {}; - -using RealTypes = ::testing::Types<float, double, long double>; -TYPED_TEST_CASE(GaussianDistributionInterfaceTest, RealTypes); - -TYPED_TEST(GaussianDistributionInterfaceTest, SerializeTest) { - using param_type = - typename absl::gaussian_distribution<TypeParam>::param_type; - - const TypeParam kParams[] = { - // Cases around 1. - 1, // - std::nextafter(TypeParam(1), TypeParam(0)), // 1 - epsilon - std::nextafter(TypeParam(1), TypeParam(2)), // 1 + epsilon - // Arbitrary values. - TypeParam(1e-8), TypeParam(1e-4), TypeParam(2), TypeParam(1e4), - TypeParam(1e8), TypeParam(1e20), TypeParam(2.5), - // Boundary cases. - std::numeric_limits<TypeParam>::infinity(), - std::numeric_limits<TypeParam>::max(), - std::numeric_limits<TypeParam>::epsilon(), - std::nextafter(std::numeric_limits<TypeParam>::min(), - TypeParam(1)), // min + epsilon - std::numeric_limits<TypeParam>::min(), // smallest normal - // There are some errors dealing with denorms on apple platforms. - std::numeric_limits<TypeParam>::denorm_min(), // smallest denorm - std::numeric_limits<TypeParam>::min() / 2, - std::nextafter(std::numeric_limits<TypeParam>::min(), - TypeParam(0)), // denorm_max - }; - - constexpr int kCount = 1000; - absl::InsecureBitGen gen; - - // Use a loop to generate the combinations of {+/-x, +/-y}, and assign x, y to - // all values in kParams, - for (const auto mod : {0, 1, 2, 3}) { - for (const auto x : kParams) { - if (!std::isfinite(x)) continue; - for (const auto y : kParams) { - const TypeParam mean = (mod & 0x1) ? -x : x; - const TypeParam stddev = (mod & 0x2) ? -y : y; - const param_type param(mean, stddev); - - absl::gaussian_distribution<TypeParam> before(mean, stddev); - EXPECT_EQ(before.mean(), param.mean()); - EXPECT_EQ(before.stddev(), param.stddev()); - - { - absl::gaussian_distribution<TypeParam> via_param(param); - EXPECT_EQ(via_param, before); - EXPECT_EQ(via_param.param(), before.param()); - } - - // Smoke test. - auto sample_min = before.max(); - auto sample_max = before.min(); - for (int i = 0; i < kCount; i++) { - auto sample = before(gen); - if (sample > sample_max) sample_max = sample; - if (sample < sample_min) sample_min = sample; - EXPECT_GE(sample, before.min()) << before; - EXPECT_LE(sample, before.max()) << before; - } - if (!std::is_same<TypeParam, long double>::value) { - ABSL_INTERNAL_LOG( - INFO, absl::StrFormat("Range{%f, %f}: %f, %f", mean, stddev, - sample_min, sample_max)); - } - - std::stringstream ss; - ss << before; - - if (!std::isfinite(mean) || !std::isfinite(stddev)) { - // Streams do not parse inf/nan. - continue; - } - - // Validate stream serialization. - absl::gaussian_distribution<TypeParam> after(-0.53f, 2.3456f); - - EXPECT_NE(before.mean(), after.mean()); - EXPECT_NE(before.stddev(), after.stddev()); - EXPECT_NE(before.param(), after.param()); - EXPECT_NE(before, after); - - ss >> after; - -#if defined(__powerpc64__) || defined(__PPC64__) || defined(__powerpc__) || \ - defined(__ppc__) || defined(__PPC__) || defined(__EMSCRIPTEN__) - if (std::is_same<TypeParam, long double>::value) { - // Roundtripping floating point values requires sufficient precision - // to reconstruct the exact value. It turns out that long double - // has some errors doing this on ppc, particularly for values - // near {1.0 +/- epsilon}. - // - // Emscripten is even worse, implementing long double as a 128-bit - // type, but shipping with a strtold() that doesn't support that. - if (mean <= std::numeric_limits<double>::max() && - mean >= std::numeric_limits<double>::lowest()) { - EXPECT_EQ(static_cast<double>(before.mean()), - static_cast<double>(after.mean())) - << ss.str(); - } - if (stddev <= std::numeric_limits<double>::max() && - stddev >= std::numeric_limits<double>::lowest()) { - EXPECT_EQ(static_cast<double>(before.stddev()), - static_cast<double>(after.stddev())) - << ss.str(); - } - continue; - } -#endif - - EXPECT_EQ(before.mean(), after.mean()); - EXPECT_EQ(before.stddev(), after.stddev()) // - << ss.str() << " " // - << (ss.good() ? "good " : "") // - << (ss.bad() ? "bad " : "") // - << (ss.eof() ? "eof " : "") // - << (ss.fail() ? "fail " : ""); - } - } - } -} - -// http://www.itl.nist.gov/div898/handbook/eda/section3/eda3661.htm - -class GaussianModel { - public: - GaussianModel(double mean, double stddev) : mean_(mean), stddev_(stddev) {} - - double mean() const { return mean_; } - double variance() const { return stddev() * stddev(); } - double stddev() const { return stddev_; } - double skew() const { return 0; } - double kurtosis() const { return 3.0; } - - // The inverse CDF, or PercentPoint function. - double InverseCDF(double p) { - ABSL_ASSERT(p >= 0.0); - ABSL_ASSERT(p < 1.0); - return mean() + stddev() * -absl::random_internal::InverseNormalSurvival(p); - } - - private: - const double mean_; - const double stddev_; -}; - -struct Param { - double mean; - double stddev; - double p_fail; // Z-Test probability of failure. - int trials; // Z-Test trials. -}; - -// GaussianDistributionTests implements a z-test for the gaussian -// distribution. -class GaussianDistributionTests : public testing::TestWithParam<Param>, - public GaussianModel { - public: - GaussianDistributionTests() - : GaussianModel(GetParam().mean, GetParam().stddev) {} - - // SingleZTest provides a basic z-squared test of the mean vs. expected - // mean for data generated by the poisson distribution. - template <typename D> - bool SingleZTest(const double p, const size_t samples); - - // SingleChiSquaredTest provides a basic chi-squared test of the normal - // distribution. - template <typename D> - double SingleChiSquaredTest(); - - // We use a fixed bit generator for distribution accuracy tests. This allows - // these tests to be deterministic, while still testing the qualify of the - // implementation. - absl::random_internal::pcg64_2018_engine rng_{0x2B7E151628AED2A6}; -}; - -template <typename D> -bool GaussianDistributionTests::SingleZTest(const double p, - const size_t samples) { - D dis(mean(), stddev()); - - std::vector<double> data; - data.reserve(samples); - for (size_t i = 0; i < samples; i++) { - const double x = dis(rng_); - data.push_back(x); - } - - const double max_err = absl::random_internal::MaxErrorTolerance(p); - const auto m = absl::random_internal::ComputeDistributionMoments(data); - const double z = absl::random_internal::ZScore(mean(), m); - const bool pass = absl::random_internal::Near("z", z, 0.0, max_err); - - // NOTE: Informational statistical test: - // - // Compute the Jarque-Bera test statistic given the excess skewness - // and kurtosis. The statistic is drawn from a chi-square(2) distribution. - // https://en.wikipedia.org/wiki/Jarque%E2%80%93Bera_test - // - // The null-hypothesis (normal distribution) is rejected when - // (p = 0.05 => jb > 5.99) - // (p = 0.01 => jb > 9.21) - // NOTE: JB has a large type-I error rate, so it will reject the - // null-hypothesis even when it is true more often than the z-test. - // - const double jb = - static_cast<double>(m.n) / 6.0 * - (std::pow(m.skewness, 2.0) + std::pow(m.kurtosis - 3.0, 2.0) / 4.0); - - if (!pass || jb > 9.21) { - ABSL_INTERNAL_LOG( - INFO, absl::StrFormat("p=%f max_err=%f\n" - " mean=%f vs. %f\n" - " stddev=%f vs. %f\n" - " skewness=%f vs. %f\n" - " kurtosis=%f vs. %f\n" - " z=%f vs. 0\n" - " jb=%f vs. 9.21", - p, max_err, m.mean, mean(), std::sqrt(m.variance), - stddev(), m.skewness, skew(), m.kurtosis, - kurtosis(), z, jb)); - } - return pass; -} - -template <typename D> -double GaussianDistributionTests::SingleChiSquaredTest() { - const size_t kSamples = 10000; - const int kBuckets = 50; - - // The InverseCDF is the percent point function of the - // distribution, and can be used to assign buckets - // roughly uniformly. - std::vector<double> cutoffs; - const double kInc = 1.0 / static_cast<double>(kBuckets); - for (double p = kInc; p < 1.0; p += kInc) { - cutoffs.push_back(InverseCDF(p)); - } - if (cutoffs.back() != std::numeric_limits<double>::infinity()) { - cutoffs.push_back(std::numeric_limits<double>::infinity()); - } - - D dis(mean(), stddev()); - - std::vector<int32_t> counts(cutoffs.size(), 0); - for (int j = 0; j < kSamples; j++) { - const double x = dis(rng_); - auto it = std::upper_bound(cutoffs.begin(), cutoffs.end(), x); - counts[std::distance(cutoffs.begin(), it)]++; - } - - // Null-hypothesis is that the distribution is a gaussian distribution - // with the provided mean and stddev (not estimated from the data). - const int dof = static_cast<int>(counts.size()) - 1; - - // Our threshold for logging is 1-in-50. - const double threshold = absl::random_internal::ChiSquareValue(dof, 0.98); - - const double expected = - static_cast<double>(kSamples) / static_cast<double>(counts.size()); - - double chi_square = absl::random_internal::ChiSquareWithExpected( - std::begin(counts), std::end(counts), expected); - double p = absl::random_internal::ChiSquarePValue(chi_square, dof); - - // Log if the chi_square value is above the threshold. - if (chi_square > threshold) { - for (int i = 0; i < cutoffs.size(); i++) { - ABSL_INTERNAL_LOG( - INFO, absl::StrFormat("%d : (%f) = %d", i, cutoffs[i], counts[i])); - } - - ABSL_INTERNAL_LOG( - INFO, absl::StrCat("mean=", mean(), " stddev=", stddev(), "\n", // - " expected ", expected, "\n", // - kChiSquared, " ", chi_square, " (", p, ")\n", // - kChiSquared, " @ 0.98 = ", threshold)); - } - return p; -} - -TEST_P(GaussianDistributionTests, ZTest) { - // TODO(absl-team): Run these tests against std::normal_distribution<double> - // to validate outcomes are similar. - const size_t kSamples = 10000; - const auto& param = GetParam(); - const int expected_failures = - std::max(1, static_cast<int>(std::ceil(param.trials * param.p_fail))); - const double p = absl::random_internal::RequiredSuccessProbability( - param.p_fail, param.trials); - - int failures = 0; - for (int i = 0; i < param.trials; i++) { - failures += - SingleZTest<absl::gaussian_distribution<double>>(p, kSamples) ? 0 : 1; - } - EXPECT_LE(failures, expected_failures); -} - -TEST_P(GaussianDistributionTests, ChiSquaredTest) { - const int kTrials = 20; - int failures = 0; - - for (int i = 0; i < kTrials; i++) { - double p_value = - SingleChiSquaredTest<absl::gaussian_distribution<double>>(); - if (p_value < 0.0025) { // 1/400 - failures++; - } - } - // There is a 0.05% chance of producing at least one failure, so raise the - // failure threshold high enough to allow for a flake rate of less than one in - // 10,000. - EXPECT_LE(failures, 4); -} - -std::vector<Param> GenParams() { - return { - // Mean around 0. - Param{0.0, 1.0, 0.01, 100}, - Param{0.0, 1e2, 0.01, 100}, - Param{0.0, 1e4, 0.01, 100}, - Param{0.0, 1e8, 0.01, 100}, - Param{0.0, 1e16, 0.01, 100}, - Param{0.0, 1e-3, 0.01, 100}, - Param{0.0, 1e-5, 0.01, 100}, - Param{0.0, 1e-9, 0.01, 100}, - Param{0.0, 1e-17, 0.01, 100}, - - // Mean around 1. - Param{1.0, 1.0, 0.01, 100}, - Param{1.0, 1e2, 0.01, 100}, - Param{1.0, 1e-2, 0.01, 100}, - - // Mean around 100 / -100 - Param{1e2, 1.0, 0.01, 100}, - Param{-1e2, 1.0, 0.01, 100}, - Param{1e2, 1e6, 0.01, 100}, - Param{-1e2, 1e6, 0.01, 100}, - - // More extreme - Param{1e4, 1e4, 0.01, 100}, - Param{1e8, 1e4, 0.01, 100}, - Param{1e12, 1e4, 0.01, 100}, - }; -} - -std::string ParamName(const ::testing::TestParamInfo<Param>& info) { - const auto& p = info.param; - std::string name = absl::StrCat("mean_", absl::SixDigits(p.mean), "__stddev_", - absl::SixDigits(p.stddev)); - return absl::StrReplaceAll(name, {{"+", "_"}, {"-", "_"}, {".", "_"}}); -} - -INSTANTIATE_TEST_SUITE_P(All, GaussianDistributionTests, - ::testing::ValuesIn(GenParams()), ParamName); - -// NOTE: absl::gaussian_distribution is not guaranteed to be stable. -TEST(GaussianDistributionTest, StabilityTest) { - // absl::gaussian_distribution stability relies on the underlying zignor - // data, absl::random_interna::RandU64ToDouble, std::exp, std::log, and - // std::abs. - absl::random_internal::sequence_urbg urbg( - {0x0003eb76f6f7f755ull, 0xFFCEA50FDB2F953Bull, 0xC332DDEFBE6C5AA5ull, - 0x6558218568AB9702ull, 0x2AEF7DAD5B6E2F84ull, 0x1521B62829076170ull, - 0xECDD4775619F1510ull, 0x13CCA830EB61BD96ull, 0x0334FE1EAA0363CFull, - 0xB5735C904C70A239ull, 0xD59E9E0BCBAADE14ull, 0xEECC86BC60622CA7ull}); - - std::vector<int> output(11); - - { - absl::gaussian_distribution<double> dist; - std::generate(std::begin(output), std::end(output), - [&] { return static_cast<int>(10000000.0 * dist(urbg)); }); - - EXPECT_EQ(13, urbg.invocations()); - EXPECT_THAT(output, // - testing::ElementsAre(1494, 25518841, 9991550, 1351856, - -20373238, 3456682, 333530, -6804981, - -15279580, -16459654, 1494)); - } - - urbg.reset(); - { - absl::gaussian_distribution<float> dist; - std::generate(std::begin(output), std::end(output), - [&] { return static_cast<int>(1000000.0f * dist(urbg)); }); - - EXPECT_EQ(13, urbg.invocations()); - EXPECT_THAT( - output, // - testing::ElementsAre(149, 2551884, 999155, 135185, -2037323, 345668, - 33353, -680498, -1527958, -1645965, 149)); - } -} - -// This is an implementation-specific test. If any part of the implementation -// changes, then it is likely that this test will change as well. -// Also, if dependencies of the distribution change, such as RandU64ToDouble, -// then this is also likely to change. -TEST(GaussianDistributionTest, AlgorithmBounds) { - absl::gaussian_distribution<double> dist; - - // In ~95% of cases, a single value is used to generate the output. - // for all inputs where |x| < 0.750461021389 this should be the case. - // - // The exact constraints are based on the ziggurat tables, and any - // changes to the ziggurat tables may require adjusting these bounds. - // - // for i in range(0, len(X)-1): - // print i, X[i+1]/X[i], (X[i+1]/X[i] > 0.984375) - // - // 0.125 <= |values| <= 0.75 - const uint64_t kValues[] = { - 0x1000000000000100ull, 0x2000000000000100ull, 0x3000000000000100ull, - 0x4000000000000100ull, 0x5000000000000100ull, 0x6000000000000100ull, - // negative values - 0x9000000000000100ull, 0xa000000000000100ull, 0xb000000000000100ull, - 0xc000000000000100ull, 0xd000000000000100ull, 0xe000000000000100ull}; - - // 0.875 <= |values| <= 0.984375 - const uint64_t kExtraValues[] = { - 0x7000000000000100ull, 0x7800000000000100ull, // - 0x7c00000000000100ull, 0x7e00000000000100ull, // - // negative values - 0xf000000000000100ull, 0xf800000000000100ull, // - 0xfc00000000000100ull, 0xfe00000000000100ull}; - - auto make_box = [](uint64_t v, uint64_t box) { - return (v & 0xffffffffffffff80ull) | box; - }; - - // The box is the lower 7 bits of the value. When the box == 0, then - // the algorithm uses an escape hatch to select the result for large - // outputs. - for (uint64_t box = 0; box < 0x7f; box++) { - for (const uint64_t v : kValues) { - // Extra values are added to the sequence to attempt to avoid - // infinite loops from rejection sampling on bugs/errors. - absl::random_internal::sequence_urbg urbg( - {make_box(v, box), 0x0003eb76f6f7f755ull, 0x5FCEA50FDB2F953Bull}); - - auto a = dist(urbg); - EXPECT_EQ(1, urbg.invocations()) << box << " " << std::hex << v; - if (v & 0x8000000000000000ull) { - EXPECT_LT(a, 0.0) << box << " " << std::hex << v; - } else { - EXPECT_GT(a, 0.0) << box << " " << std::hex << v; - } - } - if (box > 10 && box < 100) { - // The center boxes use the fast algorithm for more - // than 98.4375% of values. - for (const uint64_t v : kExtraValues) { - absl::random_internal::sequence_urbg urbg( - {make_box(v, box), 0x0003eb76f6f7f755ull, 0x5FCEA50FDB2F953Bull}); - - auto a = dist(urbg); - EXPECT_EQ(1, urbg.invocations()) << box << " " << std::hex << v; - if (v & 0x8000000000000000ull) { - EXPECT_LT(a, 0.0) << box << " " << std::hex << v; - } else { - EXPECT_GT(a, 0.0) << box << " " << std::hex << v; - } - } - } - } - - // When the box == 0, the fallback algorithm uses a ratio of uniforms, - // which consumes 2 additional values from the urbg. - // Fallback also requires that the initial value be > 0.9271586026096681. - auto make_fallback = [](uint64_t v) { return (v & 0xffffffffffffff80ull); }; - - double tail[2]; - { - // 0.9375 - absl::random_internal::sequence_urbg urbg( - {make_fallback(0x7800000000000000ull), 0x13CCA830EB61BD96ull, - 0x00000076f6f7f755ull}); - tail[0] = dist(urbg); - EXPECT_EQ(3, urbg.invocations()); - EXPECT_GT(tail[0], 0); - } - { - // -0.9375 - absl::random_internal::sequence_urbg urbg( - {make_fallback(0xf800000000000000ull), 0x13CCA830EB61BD96ull, - 0x00000076f6f7f755ull}); - tail[1] = dist(urbg); - EXPECT_EQ(3, urbg.invocations()); - EXPECT_LT(tail[1], 0); - } - EXPECT_EQ(tail[0], -tail[1]); - EXPECT_EQ(418610, static_cast<int64_t>(tail[0] * 100000.0)); - - // When the box != 0, the fallback algorithm computes a wedge function. - // Depending on the box, the threshold for varies as high as - // 0.991522480228. - { - // 0.9921875, 0.875 - absl::random_internal::sequence_urbg urbg( - {make_box(0x7f00000000000000ull, 120), 0xe000000000000001ull, - 0x13CCA830EB61BD96ull}); - tail[0] = dist(urbg); - EXPECT_EQ(2, urbg.invocations()); - EXPECT_GT(tail[0], 0); - } - { - // -0.9921875, 0.875 - absl::random_internal::sequence_urbg urbg( - {make_box(0xff00000000000000ull, 120), 0xe000000000000001ull, - 0x13CCA830EB61BD96ull}); - tail[1] = dist(urbg); - EXPECT_EQ(2, urbg.invocations()); - EXPECT_LT(tail[1], 0); - } - EXPECT_EQ(tail[0], -tail[1]); - EXPECT_EQ(61948, static_cast<int64_t>(tail[0] * 100000.0)); - - // Fallback rejected, try again. - { - // -0.9921875, 0.0625 - absl::random_internal::sequence_urbg urbg( - {make_box(0xff00000000000000ull, 120), 0x1000000000000001, - make_box(0x1000000000000100ull, 50), 0x13CCA830EB61BD96ull}); - dist(urbg); - EXPECT_EQ(3, urbg.invocations()); - } -} - -} // namespace diff --git a/third_party/abseil_cpp/absl/random/generators_test.cc b/third_party/abseil_cpp/absl/random/generators_test.cc deleted file mode 100644 index 41725f139cd3..000000000000 --- a/third_party/abseil_cpp/absl/random/generators_test.cc +++ /dev/null @@ -1,179 +0,0 @@ -// Copyright 2017 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#include <cstddef> -#include <cstdint> -#include <random> -#include <vector> - -#include "gtest/gtest.h" -#include "absl/random/distributions.h" -#include "absl/random/random.h" - -namespace { - -template <typename URBG> -void TestUniform(URBG* gen) { - // [a, b) default-semantics, inferred types. - absl::Uniform(*gen, 0, 100); // int - absl::Uniform(*gen, 0, 1.0); // Promoted to double - absl::Uniform(*gen, 0.0f, 1.0); // Promoted to double - absl::Uniform(*gen, 0.0, 1.0); // double - absl::Uniform(*gen, -1, 1L); // Promoted to long - - // Roll a die. - absl::Uniform(absl::IntervalClosedClosed, *gen, 1, 6); - - // Get a fraction. - absl::Uniform(absl::IntervalOpenOpen, *gen, 0.0, 1.0); - - // Assign a value to a random element. - std::vector<int> elems = {10, 20, 30, 40, 50}; - elems[absl::Uniform(*gen, 0u, elems.size())] = 5; - elems[absl::Uniform<size_t>(*gen, 0, elems.size())] = 3; - - // Choose some epsilon around zero. - absl::Uniform(absl::IntervalOpenOpen, *gen, -1.0, 1.0); - - // (a, b) semantics, inferred types. - absl::Uniform(absl::IntervalOpenOpen, *gen, 0, 1.0); // Promoted to double - - // Explict overriding of types. - absl::Uniform<int>(*gen, 0, 100); - absl::Uniform<int8_t>(*gen, 0, 100); - absl::Uniform<int16_t>(*gen, 0, 100); - absl::Uniform<uint16_t>(*gen, 0, 100); - absl::Uniform<int32_t>(*gen, 0, 1 << 10); - absl::Uniform<uint32_t>(*gen, 0, 1 << 10); - absl::Uniform<int64_t>(*gen, 0, 1 << 10); - absl::Uniform<uint64_t>(*gen, 0, 1 << 10); - - absl::Uniform<float>(*gen, 0.0, 1.0); - absl::Uniform<float>(*gen, 0, 1); - absl::Uniform<float>(*gen, -1, 1); - absl::Uniform<double>(*gen, 0.0, 1.0); - - absl::Uniform<float>(*gen, -1.0, 0); - absl::Uniform<double>(*gen, -1.0, 0); - - // Tagged - absl::Uniform<double>(absl::IntervalClosedClosed, *gen, 0, 1); - absl::Uniform<double>(absl::IntervalClosedOpen, *gen, 0, 1); - absl::Uniform<double>(absl::IntervalOpenOpen, *gen, 0, 1); - absl::Uniform<double>(absl::IntervalOpenClosed, *gen, 0, 1); - absl::Uniform<double>(absl::IntervalClosedClosed, *gen, 0, 1); - absl::Uniform<double>(absl::IntervalOpenOpen, *gen, 0, 1); - - absl::Uniform<int>(absl::IntervalClosedClosed, *gen, 0, 100); - absl::Uniform<int>(absl::IntervalClosedOpen, *gen, 0, 100); - absl::Uniform<int>(absl::IntervalOpenOpen, *gen, 0, 100); - absl::Uniform<int>(absl::IntervalOpenClosed, *gen, 0, 100); - absl::Uniform<int>(absl::IntervalClosedClosed, *gen, 0, 100); - absl::Uniform<int>(absl::IntervalOpenOpen, *gen, 0, 100); - - // With *generator as an R-value reference. - absl::Uniform<int>(URBG(), 0, 100); - absl::Uniform<double>(URBG(), 0.0, 1.0); -} - -template <typename URBG> -void TestExponential(URBG* gen) { - absl::Exponential<float>(*gen); - absl::Exponential<double>(*gen); - absl::Exponential<double>(URBG()); -} - -template <typename URBG> -void TestPoisson(URBG* gen) { - // [rand.dist.pois] Indicates that the std::poisson_distribution - // is parameterized by IntType, however MSVC does not allow 8-bit - // types. - absl::Poisson<int>(*gen); - absl::Poisson<int16_t>(*gen); - absl::Poisson<uint16_t>(*gen); - absl::Poisson<int32_t>(*gen); - absl::Poisson<uint32_t>(*gen); - absl::Poisson<int64_t>(*gen); - absl::Poisson<uint64_t>(*gen); - absl::Poisson<uint64_t>(URBG()); -} - -template <typename URBG> -void TestBernoulli(URBG* gen) { - absl::Bernoulli(*gen, 0.5); - absl::Bernoulli(*gen, 0.5); -} - -template <typename URBG> -void TestZipf(URBG* gen) { - absl::Zipf<int>(*gen, 100); - absl::Zipf<int8_t>(*gen, 100); - absl::Zipf<int16_t>(*gen, 100); - absl::Zipf<uint16_t>(*gen, 100); - absl::Zipf<int32_t>(*gen, 1 << 10); - absl::Zipf<uint32_t>(*gen, 1 << 10); - absl::Zipf<int64_t>(*gen, 1 << 10); - absl::Zipf<uint64_t>(*gen, 1 << 10); - absl::Zipf<uint64_t>(URBG(), 1 << 10); -} - -template <typename URBG> -void TestGaussian(URBG* gen) { - absl::Gaussian<float>(*gen, 1.0, 1.0); - absl::Gaussian<double>(*gen, 1.0, 1.0); - absl::Gaussian<double>(URBG(), 1.0, 1.0); -} - -template <typename URBG> -void TestLogNormal(URBG* gen) { - absl::LogUniform<int>(*gen, 0, 100); - absl::LogUniform<int8_t>(*gen, 0, 100); - absl::LogUniform<int16_t>(*gen, 0, 100); - absl::LogUniform<uint16_t>(*gen, 0, 100); - absl::LogUniform<int32_t>(*gen, 0, 1 << 10); - absl::LogUniform<uint32_t>(*gen, 0, 1 << 10); - absl::LogUniform<int64_t>(*gen, 0, 1 << 10); - absl::LogUniform<uint64_t>(*gen, 0, 1 << 10); - absl::LogUniform<uint64_t>(URBG(), 0, 1 << 10); -} - -template <typename URBG> -void CompatibilityTest() { - URBG gen; - - TestUniform(&gen); - TestExponential(&gen); - TestPoisson(&gen); - TestBernoulli(&gen); - TestZipf(&gen); - TestGaussian(&gen); - TestLogNormal(&gen); -} - -TEST(std_mt19937_64, Compatibility) { - // Validate with std::mt19937_64 - CompatibilityTest<std::mt19937_64>(); -} - -TEST(BitGen, Compatibility) { - // Validate with absl::BitGen - CompatibilityTest<absl::BitGen>(); -} - -TEST(InsecureBitGen, Compatibility) { - // Validate with absl::InsecureBitGen - CompatibilityTest<absl::InsecureBitGen>(); -} - -} // namespace diff --git a/third_party/abseil_cpp/absl/random/internal/BUILD.bazel b/third_party/abseil_cpp/absl/random/internal/BUILD.bazel deleted file mode 100644 index 8485e28b010b..000000000000 --- a/third_party/abseil_cpp/absl/random/internal/BUILD.bazel +++ /dev/null @@ -1,730 +0,0 @@ -# -# Copyright 2019 The Abseil Authors. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# https://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -load("@rules_cc//cc:defs.bzl", "cc_binary", "cc_library", "cc_test") - -# Internal-only implementation classes for Abseil Random -load( - "//absl:copts/configure_copts.bzl", - "ABSL_DEFAULT_COPTS", - "ABSL_DEFAULT_LINKOPTS", - "ABSL_RANDOM_RANDEN_COPTS", - "ABSL_TEST_COPTS", - "absl_random_randen_copts_init", -) - -package(default_visibility = [ - "//absl/random:__pkg__", -]) - -licenses(["notice"]) - -cc_library( - name = "traits", - hdrs = ["traits.h"], - copts = ABSL_DEFAULT_COPTS, - linkopts = ABSL_DEFAULT_LINKOPTS, - deps = ["//absl/base:config"], -) - -cc_library( - name = "distribution_caller", - hdrs = ["distribution_caller.h"], - copts = ABSL_DEFAULT_COPTS, - linkopts = ABSL_DEFAULT_LINKOPTS, - deps = [ - "//absl/base:config", - "//absl/base:fast_type_id", - "//absl/utility", - ], -) - -cc_library( - name = "fast_uniform_bits", - hdrs = [ - "fast_uniform_bits.h", - ], - copts = ABSL_DEFAULT_COPTS, - linkopts = ABSL_DEFAULT_LINKOPTS, - deps = [ - "//absl/base:config", - "//absl/meta:type_traits", - ], -) - -cc_library( - name = "seed_material", - srcs = [ - "seed_material.cc", - ], - hdrs = [ - "seed_material.h", - ], - copts = ABSL_DEFAULT_COPTS, - linkopts = ABSL_DEFAULT_LINKOPTS + select({ - "//absl:windows": ["-DEFAULTLIB:bcrypt.lib"], - "//conditions:default": [], - }), - deps = [ - ":fast_uniform_bits", - "//absl/base:core_headers", - "//absl/base:raw_logging_internal", - "//absl/strings", - "//absl/types:optional", - "//absl/types:span", - ], -) - -cc_library( - name = "pool_urbg", - srcs = [ - "pool_urbg.cc", - ], - hdrs = [ - "pool_urbg.h", - ], - copts = ABSL_DEFAULT_COPTS, - linkopts = select({ - "//absl:windows": [], - "//absl:wasm": [], - "//conditions:default": ["-pthread"], - }) + ABSL_DEFAULT_LINKOPTS, - deps = [ - ":randen", - ":seed_material", - ":traits", - "//absl/base", - "//absl/base:config", - "//absl/base:core_headers", - "//absl/base:endian", - "//absl/base:raw_logging_internal", - "//absl/random:seed_gen_exception", - "//absl/types:span", - ], -) - -cc_library( - name = "explicit_seed_seq", - testonly = 1, - hdrs = [ - "explicit_seed_seq.h", - ], - copts = ABSL_DEFAULT_COPTS, - linkopts = ABSL_DEFAULT_LINKOPTS, - deps = ["//absl/base:config"], -) - -cc_library( - name = "sequence_urbg", - testonly = 1, - hdrs = [ - "sequence_urbg.h", - ], - copts = ABSL_DEFAULT_COPTS, - linkopts = ABSL_DEFAULT_LINKOPTS, - deps = ["//absl/base:config"], -) - -cc_library( - name = "salted_seed_seq", - hdrs = [ - "salted_seed_seq.h", - ], - copts = ABSL_DEFAULT_COPTS, - linkopts = ABSL_DEFAULT_LINKOPTS, - deps = [ - ":seed_material", - "//absl/container:inlined_vector", - "//absl/meta:type_traits", - "//absl/types:optional", - "//absl/types:span", - ], -) - -cc_library( - name = "iostream_state_saver", - hdrs = ["iostream_state_saver.h"], - copts = ABSL_DEFAULT_COPTS, - linkopts = ABSL_DEFAULT_LINKOPTS, - deps = [ - "//absl/meta:type_traits", - "//absl/numeric:int128", - ], -) - -cc_library( - name = "generate_real", - hdrs = [ - "generate_real.h", - ], - copts = ABSL_DEFAULT_COPTS, - linkopts = ABSL_DEFAULT_LINKOPTS, - deps = [ - ":fastmath", - ":traits", - "//absl/base:bits", - "//absl/meta:type_traits", - ], -) - -cc_library( - name = "fastmath", - hdrs = [ - "fastmath.h", - ], - copts = ABSL_DEFAULT_COPTS, - linkopts = ABSL_DEFAULT_LINKOPTS, - deps = ["//absl/base:bits"], -) - -cc_library( - name = "wide_multiply", - hdrs = ["wide_multiply.h"], - copts = ABSL_DEFAULT_COPTS, - linkopts = ABSL_DEFAULT_LINKOPTS, - deps = [ - ":traits", - "//absl/base:bits", - "//absl/base:config", - "//absl/numeric:int128", - ], -) - -cc_library( - name = "nonsecure_base", - hdrs = ["nonsecure_base.h"], - copts = ABSL_DEFAULT_COPTS, - linkopts = ABSL_DEFAULT_LINKOPTS, - deps = [ - ":pool_urbg", - ":salted_seed_seq", - ":seed_material", - "//absl/base:core_headers", - "//absl/meta:type_traits", - "//absl/types:optional", - "//absl/types:span", - ], -) - -cc_library( - name = "pcg_engine", - hdrs = ["pcg_engine.h"], - copts = ABSL_DEFAULT_COPTS, - linkopts = ABSL_DEFAULT_LINKOPTS, - deps = [ - ":fastmath", - ":iostream_state_saver", - "//absl/base:config", - "//absl/meta:type_traits", - "//absl/numeric:int128", - ], -) - -cc_library( - name = "randen_engine", - hdrs = ["randen_engine.h"], - copts = ABSL_DEFAULT_COPTS, - linkopts = ABSL_DEFAULT_LINKOPTS, - deps = [ - ":iostream_state_saver", - ":randen", - "//absl/meta:type_traits", - ], -) - -cc_library( - name = "platform", - srcs = [ - "randen_round_keys.cc", - ], - hdrs = [ - "randen_traits.h", - ], - copts = ABSL_DEFAULT_COPTS, - linkopts = ABSL_DEFAULT_LINKOPTS, - textual_hdrs = [ - "platform.h", - ], - deps = ["//absl/base:config"], -) - -cc_library( - name = "randen", - srcs = [ - "randen.cc", - ], - hdrs = [ - "randen.h", - ], - copts = ABSL_DEFAULT_COPTS, - linkopts = ABSL_DEFAULT_LINKOPTS, - deps = [ - ":platform", - ":randen_hwaes", - ":randen_slow", - "//absl/base:raw_logging_internal", - ], -) - -cc_library( - name = "randen_slow", - srcs = ["randen_slow.cc"], - hdrs = ["randen_slow.h"], - copts = ABSL_DEFAULT_COPTS, - linkopts = ABSL_DEFAULT_LINKOPTS, - deps = [ - ":platform", - "//absl/base:config", - "//absl/base:core_headers", - ], -) - -absl_random_randen_copts_init() - -cc_library( - name = "randen_hwaes", - srcs = [ - "randen_detect.cc", - ], - hdrs = [ - "randen_detect.h", - "randen_hwaes.h", - ], - copts = ABSL_DEFAULT_COPTS, - linkopts = ABSL_DEFAULT_LINKOPTS, - deps = [ - ":platform", - ":randen_hwaes_impl", - "//absl/base:config", - ], -) - -# build with --save_temps to see assembly language output. -cc_library( - name = "randen_hwaes_impl", - srcs = [ - "randen_hwaes.cc", - "randen_hwaes.h", - ], - copts = ABSL_DEFAULT_COPTS + ABSL_RANDOM_RANDEN_COPTS + select({ - "//absl:windows": [], - "//conditions:default": ["-Wno-pass-failed"], - }), - linkopts = ABSL_DEFAULT_LINKOPTS, - deps = [ - ":platform", - "//absl/base:config", - "//absl/base:core_headers", - ], -) - -cc_binary( - name = "gaussian_distribution_gentables", - srcs = [ - "gaussian_distribution_gentables.cc", - ], - linkopts = ABSL_DEFAULT_LINKOPTS, - deps = [ - "//absl/base:core_headers", - "//absl/random:distributions", - ], -) - -cc_library( - name = "distribution_test_util", - testonly = 1, - srcs = [ - "chi_square.cc", - "distribution_test_util.cc", - ], - hdrs = [ - "chi_square.h", - "distribution_test_util.h", - ], - copts = ABSL_DEFAULT_COPTS, - linkopts = ABSL_DEFAULT_LINKOPTS, - deps = [ - "//absl/base:config", - "//absl/base:core_headers", - "//absl/base:raw_logging_internal", - "//absl/strings", - "//absl/strings:str_format", - "//absl/types:span", - ], -) - -# Common tags for tests, etc. -ABSL_RANDOM_NONPORTABLE_TAGS = [ - "no_test_android_arm", - "no_test_android_arm64", - "no_test_android_x86", - "no_test_darwin_x86_64", - "no_test_ios_x86_64", - "no_test_loonix", - "no_test_msvc_x64", - "no_test_wasm", -] - -cc_test( - name = "traits_test", - size = "small", - srcs = ["traits_test.cc"], - copts = ABSL_TEST_COPTS, - linkopts = ABSL_DEFAULT_LINKOPTS, - deps = [ - ":traits", - "@com_google_googletest//:gtest_main", - ], -) - -cc_test( - name = "generate_real_test", - size = "small", - srcs = [ - "generate_real_test.cc", - ], - copts = ABSL_TEST_COPTS, - linkopts = ABSL_DEFAULT_LINKOPTS, - deps = [ - ":generate_real", - "//absl/base:bits", - "//absl/flags:flag", - "@com_google_googletest//:gtest_main", - ], -) - -cc_test( - name = "distribution_test_util_test", - size = "small", - srcs = ["distribution_test_util_test.cc"], - copts = ABSL_TEST_COPTS, - linkopts = ABSL_DEFAULT_LINKOPTS, - deps = [ - ":distribution_test_util", - "@com_google_googletest//:gtest_main", - ], -) - -cc_test( - name = "fastmath_test", - size = "small", - srcs = ["fastmath_test.cc"], - copts = ABSL_TEST_COPTS, - linkopts = ABSL_DEFAULT_LINKOPTS, - deps = [ - ":fastmath", - "@com_google_googletest//:gtest_main", - ], -) - -cc_test( - name = "explicit_seed_seq_test", - size = "small", - srcs = ["explicit_seed_seq_test.cc"], - copts = ABSL_TEST_COPTS, - linkopts = ABSL_DEFAULT_LINKOPTS, - deps = [ - ":explicit_seed_seq", - "//absl/random:seed_sequences", - "@com_google_googletest//:gtest_main", - ], -) - -cc_test( - name = "salted_seed_seq_test", - size = "small", - srcs = ["salted_seed_seq_test.cc"], - copts = ABSL_TEST_COPTS, - linkopts = ABSL_DEFAULT_LINKOPTS, - deps = [ - ":salted_seed_seq", - "@com_google_googletest//:gtest_main", - ], -) - -cc_test( - name = "chi_square_test", - size = "small", - srcs = [ - "chi_square_test.cc", - ], - copts = ABSL_TEST_COPTS, - linkopts = ABSL_DEFAULT_LINKOPTS, - deps = [ - ":distribution_test_util", - "//absl/base:core_headers", - "@com_google_googletest//:gtest_main", - ], -) - -cc_test( - name = "fast_uniform_bits_test", - size = "small", - srcs = [ - "fast_uniform_bits_test.cc", - ], - copts = ABSL_TEST_COPTS, - linkopts = ABSL_DEFAULT_LINKOPTS, - deps = [ - ":fast_uniform_bits", - "@com_google_googletest//:gtest_main", - ], -) - -cc_library( - name = "mock_helpers", - hdrs = ["mock_helpers.h"], - deps = [ - "//absl/base:fast_type_id", - "//absl/types:optional", - ], -) - -cc_library( - name = "mock_overload_set", - testonly = 1, - hdrs = ["mock_overload_set.h"], - deps = [ - ":mock_helpers", - "//absl/random:mocking_bit_gen", - "@com_google_googletest//:gtest", - ], -) - -cc_test( - name = "nonsecure_base_test", - size = "small", - srcs = [ - "nonsecure_base_test.cc", - ], - copts = ABSL_TEST_COPTS, - linkopts = ABSL_DEFAULT_LINKOPTS, - deps = [ - ":nonsecure_base", - "//absl/random", - "//absl/random:distributions", - "//absl/random:seed_sequences", - "//absl/strings", - "@com_google_googletest//:gtest_main", - ], -) - -cc_test( - name = "seed_material_test", - size = "small", - srcs = ["seed_material_test.cc"], - copts = ABSL_TEST_COPTS, - linkopts = ABSL_DEFAULT_LINKOPTS, - deps = [ - ":seed_material", - "@com_google_googletest//:gtest_main", - ], -) - -cc_test( - name = "pool_urbg_test", - size = "small", - srcs = [ - "pool_urbg_test.cc", - ], - copts = ABSL_TEST_COPTS, - linkopts = ABSL_DEFAULT_LINKOPTS, - deps = [ - ":pool_urbg", - "//absl/meta:type_traits", - "//absl/types:span", - "@com_google_googletest//:gtest_main", - ], -) - -cc_test( - name = "pcg_engine_test", - size = "medium", # Trying to measure accuracy. - srcs = ["pcg_engine_test.cc"], - copts = ABSL_TEST_COPTS, - flaky = 1, - linkopts = ABSL_DEFAULT_LINKOPTS, - deps = [ - ":explicit_seed_seq", - ":pcg_engine", - "//absl/time", - "@com_google_googletest//:gtest_main", - ], -) - -cc_test( - name = "randen_engine_test", - size = "medium", - srcs = [ - "randen_engine_test.cc", - ], - copts = ABSL_TEST_COPTS, - linkopts = ABSL_DEFAULT_LINKOPTS, - deps = [ - ":explicit_seed_seq", - ":randen_engine", - "//absl/base:raw_logging_internal", - "//absl/strings", - "//absl/time", - "@com_google_googletest//:gtest_main", - ], -) - -cc_test( - name = "randen_test", - size = "small", - srcs = ["randen_test.cc"], - copts = ABSL_TEST_COPTS, - linkopts = ABSL_DEFAULT_LINKOPTS, - deps = [ - ":randen", - "//absl/meta:type_traits", - "@com_google_googletest//:gtest_main", - ], -) - -cc_test( - name = "randen_slow_test", - size = "small", - srcs = ["randen_slow_test.cc"], - copts = ABSL_TEST_COPTS, - linkopts = ABSL_DEFAULT_LINKOPTS, - deps = [ - ":platform", - ":randen_slow", - "@com_google_googletest//:gtest_main", - ], -) - -cc_test( - name = "randen_hwaes_test", - size = "small", - srcs = ["randen_hwaes_test.cc"], - copts = ABSL_TEST_COPTS, - linkopts = ABSL_DEFAULT_LINKOPTS, - tags = ABSL_RANDOM_NONPORTABLE_TAGS, - deps = [ - ":platform", - ":randen_hwaes", - ":randen_hwaes_impl", # build_cleaner: keep - "//absl/base:raw_logging_internal", - "//absl/strings:str_format", - "@com_google_googletest//:gtest", - ], -) - -cc_test( - name = "wide_multiply_test", - size = "small", - srcs = ["wide_multiply_test.cc"], - copts = ABSL_TEST_COPTS, - linkopts = ABSL_DEFAULT_LINKOPTS, - deps = [ - ":wide_multiply", - "//absl/base:bits", - "//absl/numeric:int128", - "@com_google_googletest//:gtest_main", - ], -) - -cc_library( - name = "nanobenchmark", - srcs = ["nanobenchmark.cc"], - linkopts = ABSL_DEFAULT_LINKOPTS, - textual_hdrs = ["nanobenchmark.h"], - deps = [ - ":platform", - ":randen_engine", - "//absl/base:config", - "//absl/base:core_headers", - "//absl/base:raw_logging_internal", - ], -) - -cc_library( - name = "uniform_helper", - hdrs = ["uniform_helper.h"], - copts = ABSL_DEFAULT_COPTS, - linkopts = ABSL_DEFAULT_LINKOPTS, - deps = [ - ":traits", - "//absl/base:config", - "//absl/meta:type_traits", - ], -) - -cc_test( - name = "nanobenchmark_test", - size = "small", - srcs = ["nanobenchmark_test.cc"], - flaky = 1, - linkopts = ABSL_DEFAULT_LINKOPTS, - tags = [ - "benchmark", - "no_test_ios_x86_64", - "no_test_loonix", # Crashing. - ], - deps = [ - ":nanobenchmark", - "//absl/base:raw_logging_internal", - "//absl/strings", - ], -) - -cc_test( - name = "randen_benchmarks", - size = "medium", - timeout = "long", - srcs = ["randen_benchmarks.cc"], - copts = ABSL_TEST_COPTS + ABSL_RANDOM_RANDEN_COPTS, - flaky = 1, - linkopts = ABSL_DEFAULT_LINKOPTS, - tags = ABSL_RANDOM_NONPORTABLE_TAGS + ["benchmark"], - deps = [ - ":nanobenchmark", - ":platform", - ":randen", - ":randen_engine", - ":randen_hwaes", - ":randen_hwaes_impl", - ":randen_slow", - "//absl/base:raw_logging_internal", - "//absl/strings", - ], -) - -cc_test( - name = "iostream_state_saver_test", - size = "small", - srcs = ["iostream_state_saver_test.cc"], - linkopts = ABSL_DEFAULT_LINKOPTS, - deps = [ - ":iostream_state_saver", - "@com_google_googletest//:gtest_main", - ], -) - -cc_test( - name = "uniform_helper_test", - size = "small", - srcs = ["uniform_helper_test.cc"], - copts = ABSL_TEST_COPTS, - linkopts = ABSL_DEFAULT_LINKOPTS, - deps = [ - ":uniform_helper", - "@com_google_googletest//:gtest_main", - ], -) diff --git a/third_party/abseil_cpp/absl/random/internal/chi_square.cc b/third_party/abseil_cpp/absl/random/internal/chi_square.cc deleted file mode 100644 index 640d48cea6f4..000000000000 --- a/third_party/abseil_cpp/absl/random/internal/chi_square.cc +++ /dev/null @@ -1,232 +0,0 @@ -// Copyright 2017 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#include "absl/random/internal/chi_square.h" - -#include <cmath> - -#include "absl/random/internal/distribution_test_util.h" - -namespace absl { -ABSL_NAMESPACE_BEGIN -namespace random_internal { -namespace { - -#if defined(__EMSCRIPTEN__) -// Workaround __EMSCRIPTEN__ error: llvm_fma_f64 not found. -inline double fma(double x, double y, double z) { - return (x * y) + z; -} -#endif - -// Use Horner's method to evaluate a polynomial. -template <typename T, unsigned N> -inline T EvaluatePolynomial(T x, const T (&poly)[N]) { -#if !defined(__EMSCRIPTEN__) - using std::fma; -#endif - T p = poly[N - 1]; - for (unsigned i = 2; i <= N; i++) { - p = fma(p, x, poly[N - i]); - } - return p; -} - -static constexpr int kLargeDOF = 150; - -// Returns the probability of a normal z-value. -// -// Adapted from the POZ function in: -// Ibbetson D, Algorithm 209 -// Collected Algorithms of the CACM 1963 p. 616 -// -double POZ(double z) { - static constexpr double kP1[] = { - 0.797884560593, -0.531923007300, 0.319152932694, - -0.151968751364, 0.059054035642, -0.019198292004, - 0.005198775019, -0.001075204047, 0.000124818987, - }; - static constexpr double kP2[] = { - 0.999936657524, 0.000535310849, -0.002141268741, 0.005353579108, - -0.009279453341, 0.011630447319, -0.010557625006, 0.006549791214, - -0.002034254874, -0.000794620820, 0.001390604284, -0.000676904986, - -0.000019538132, 0.000152529290, -0.000045255659, - }; - - const double kZMax = 6.0; // Maximum meaningful z-value. - if (z == 0.0) { - return 0.5; - } - double x; - double y = 0.5 * std::fabs(z); - if (y >= (kZMax * 0.5)) { - x = 1.0; - } else if (y < 1.0) { - double w = y * y; - x = EvaluatePolynomial(w, kP1) * y * 2.0; - } else { - y -= 2.0; - x = EvaluatePolynomial(y, kP2); - } - return z > 0.0 ? ((x + 1.0) * 0.5) : ((1.0 - x) * 0.5); -} - -// Approximates the survival function of the normal distribution. -// -// Algorithm 26.2.18, from: -// [Abramowitz and Stegun, Handbook of Mathematical Functions,p.932] -// http://people.math.sfu.ca/~cbm/aands/abramowitz_and_stegun.pdf -// -double normal_survival(double z) { - // Maybe replace with the alternate formulation. - // 0.5 * erfc((x - mean)/(sqrt(2) * sigma)) - static constexpr double kR[] = { - 1.0, 0.196854, 0.115194, 0.000344, 0.019527, - }; - double r = EvaluatePolynomial(z, kR); - r *= r; - return 0.5 / (r * r); -} - -} // namespace - -// Calculates the critical chi-square value given degrees-of-freedom and a -// p-value, usually using bisection. Also known by the name CRITCHI. -double ChiSquareValue(int dof, double p) { - static constexpr double kChiEpsilon = - 0.000001; // Accuracy of the approximation. - static constexpr double kChiMax = - 99999.0; // Maximum chi-squared value. - - const double p_value = 1.0 - p; - if (dof < 1 || p_value > 1.0) { - return 0.0; - } - - if (dof > kLargeDOF) { - // For large degrees of freedom, use the normal approximation by - // Wilson, E. B. and Hilferty, M. M. (1931) - // chi^2 - mean - // Z = -------------- - // stddev - const double z = InverseNormalSurvival(p_value); - const double mean = 1 - 2.0 / (9 * dof); - const double variance = 2.0 / (9 * dof); - // Cannot use this method if the variance is 0. - if (variance != 0) { - return std::pow(z * std::sqrt(variance) + mean, 3.0) * dof; - } - } - - if (p_value <= 0.0) return kChiMax; - - // Otherwise search for the p value by bisection - double min_chisq = 0.0; - double max_chisq = kChiMax; - double current = dof / std::sqrt(p_value); - while ((max_chisq - min_chisq) > kChiEpsilon) { - if (ChiSquarePValue(current, dof) < p_value) { - max_chisq = current; - } else { - min_chisq = current; - } - current = (max_chisq + min_chisq) * 0.5; - } - return current; -} - -// Calculates the p-value (probability) of a given chi-square value -// and degrees of freedom. -// -// Adapted from the POCHISQ function from: -// Hill, I. D. and Pike, M. C. Algorithm 299 -// Collected Algorithms of the CACM 1963 p. 243 -// -double ChiSquarePValue(double chi_square, int dof) { - static constexpr double kLogSqrtPi = - 0.5723649429247000870717135; // Log[Sqrt[Pi]] - static constexpr double kInverseSqrtPi = - 0.5641895835477562869480795; // 1/(Sqrt[Pi]) - - // For large degrees of freedom, use the normal approximation by - // Wilson, E. B. and Hilferty, M. M. (1931) - // Via Wikipedia: - // By the Central Limit Theorem, because the chi-square distribution is the - // sum of k independent random variables with finite mean and variance, it - // converges to a normal distribution for large k. - if (dof > kLargeDOF) { - // Re-scale everything. - const double chi_square_scaled = std::pow(chi_square / dof, 1.0 / 3); - const double mean = 1 - 2.0 / (9 * dof); - const double variance = 2.0 / (9 * dof); - // If variance is 0, this method cannot be used. - if (variance != 0) { - const double z = (chi_square_scaled - mean) / std::sqrt(variance); - if (z > 0) { - return normal_survival(z); - } else if (z < 0) { - return 1.0 - normal_survival(-z); - } else { - return 0.5; - } - } - } - - // The chi square function is >= 0 for any degrees of freedom. - // In other words, probability that the chi square function >= 0 is 1. - if (chi_square <= 0.0) return 1.0; - - // If the degrees of freedom is zero, the chi square function is always 0 by - // definition. In other words, the probability that the chi square function - // is > 0 is zero (chi square values <= 0 have been filtered above). - if (dof < 1) return 0; - - auto capped_exp = [](double x) { return x < -20 ? 0.0 : std::exp(x); }; - static constexpr double kBigX = 20; - - double a = 0.5 * chi_square; - const bool even = !(dof & 1); // True if dof is an even number. - const double y = capped_exp(-a); - double s = even ? y : (2.0 * POZ(-std::sqrt(chi_square))); - - if (dof <= 2) { - return s; - } - - chi_square = 0.5 * (dof - 1.0); - double z = (even ? 1.0 : 0.5); - if (a > kBigX) { - double e = (even ? 0.0 : kLogSqrtPi); - double c = std::log(a); - while (z <= chi_square) { - e = std::log(z) + e; - s += capped_exp(c * z - a - e); - z += 1.0; - } - return s; - } - - double e = (even ? 1.0 : (kInverseSqrtPi / std::sqrt(a))); - double c = 0.0; - while (z <= chi_square) { - e = e * (a / z); - c = c + e; - z += 1.0; - } - return c * y + s; -} - -} // namespace random_internal -ABSL_NAMESPACE_END -} // namespace absl diff --git a/third_party/abseil_cpp/absl/random/internal/chi_square.h b/third_party/abseil_cpp/absl/random/internal/chi_square.h deleted file mode 100644 index 07f4fbe52298..000000000000 --- a/third_party/abseil_cpp/absl/random/internal/chi_square.h +++ /dev/null @@ -1,89 +0,0 @@ -// Copyright 2017 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#ifndef ABSL_RANDOM_INTERNAL_CHI_SQUARE_H_ -#define ABSL_RANDOM_INTERNAL_CHI_SQUARE_H_ - -// The chi-square statistic. -// -// Useful for evaluating if `D` independent random variables are behaving as -// expected, or if two distributions are similar. (`D` is the degrees of -// freedom). -// -// Each bucket should have an expected count of 10 or more for the chi square to -// be meaningful. - -#include <cassert> - -#include "absl/base/config.h" - -namespace absl { -ABSL_NAMESPACE_BEGIN -namespace random_internal { - -constexpr const char kChiSquared[] = "chi-squared"; - -// Returns the measured chi square value, using a single expected value. This -// assumes that the values in [begin, end) are uniformly distributed. -template <typename Iterator> -double ChiSquareWithExpected(Iterator begin, Iterator end, double expected) { - // Compute the sum and the number of buckets. - assert(expected >= 10); // require at least 10 samples per bucket. - double chi_square = 0; - for (auto it = begin; it != end; it++) { - double d = static_cast<double>(*it) - expected; - chi_square += d * d; - } - chi_square = chi_square / expected; - return chi_square; -} - -// Returns the measured chi square value, taking the actual value of each bucket -// from the first set of iterators, and the expected value of each bucket from -// the second set of iterators. -template <typename Iterator, typename Expected> -double ChiSquare(Iterator it, Iterator end, Expected eit, Expected eend) { - double chi_square = 0; - for (; it != end && eit != eend; ++it, ++eit) { - if (*it > 0) { - assert(*eit > 0); - } - double e = static_cast<double>(*eit); - double d = static_cast<double>(*it - *eit); - if (d != 0) { - assert(e > 0); - chi_square += (d * d) / e; - } - } - assert(it == end && eit == eend); - return chi_square; -} - -// ====================================================================== -// The following methods can be used for an arbitrary significance level. -// - -// Calculates critical chi-square values to produce the given p-value using a -// bisection search for a value within epsilon, relying on the monotonicity of -// ChiSquarePValue(). -double ChiSquareValue(int dof, double p); - -// Calculates the p-value (probability) of a given chi-square value. -double ChiSquarePValue(double chi_square, int dof); - -} // namespace random_internal -ABSL_NAMESPACE_END -} // namespace absl - -#endif // ABSL_RANDOM_INTERNAL_CHI_SQUARE_H_ diff --git a/third_party/abseil_cpp/absl/random/internal/chi_square_test.cc b/third_party/abseil_cpp/absl/random/internal/chi_square_test.cc deleted file mode 100644 index 5025defac12c..000000000000 --- a/third_party/abseil_cpp/absl/random/internal/chi_square_test.cc +++ /dev/null @@ -1,365 +0,0 @@ -// Copyright 2017 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#include "absl/random/internal/chi_square.h" - -#include <algorithm> -#include <cstddef> -#include <cstdint> -#include <iterator> -#include <numeric> -#include <vector> - -#include "gtest/gtest.h" -#include "absl/base/macros.h" - -using absl::random_internal::ChiSquare; -using absl::random_internal::ChiSquarePValue; -using absl::random_internal::ChiSquareValue; -using absl::random_internal::ChiSquareWithExpected; - -namespace { - -TEST(ChiSquare, Value) { - struct { - int line; - double chi_square; - int df; - double confidence; - } const specs[] = { - // Testing lookup at 1% confidence - {__LINE__, 0, 0, 0.01}, - {__LINE__, 0.00016, 1, 0.01}, - {__LINE__, 1.64650, 8, 0.01}, - {__LINE__, 5.81221, 16, 0.01}, - {__LINE__, 156.4319, 200, 0.01}, - {__LINE__, 1121.3784, 1234, 0.01}, - {__LINE__, 53557.1629, 54321, 0.01}, - {__LINE__, 651662.6647, 654321, 0.01}, - - // Testing lookup at 99% confidence - {__LINE__, 0, 0, 0.99}, - {__LINE__, 6.635, 1, 0.99}, - {__LINE__, 20.090, 8, 0.99}, - {__LINE__, 32.000, 16, 0.99}, - {__LINE__, 249.4456, 200, 0.99}, - {__LINE__, 1131.1573, 1023, 0.99}, - {__LINE__, 1352.5038, 1234, 0.99}, - {__LINE__, 55090.7356, 54321, 0.99}, - {__LINE__, 656985.1514, 654321, 0.99}, - - // Testing lookup at 99.9% confidence - {__LINE__, 16.2659, 3, 0.999}, - {__LINE__, 22.4580, 6, 0.999}, - {__LINE__, 267.5409, 200, 0.999}, - {__LINE__, 1168.5033, 1023, 0.999}, - {__LINE__, 55345.1741, 54321, 0.999}, - {__LINE__, 657861.7284, 654321, 0.999}, - {__LINE__, 51.1772, 24, 0.999}, - {__LINE__, 59.7003, 30, 0.999}, - {__LINE__, 37.6984, 15, 0.999}, - {__LINE__, 29.5898, 10, 0.999}, - {__LINE__, 27.8776, 9, 0.999}, - - // Testing lookup at random confidences - {__LINE__, 0.000157088, 1, 0.01}, - {__LINE__, 5.31852, 2, 0.93}, - {__LINE__, 1.92256, 4, 0.25}, - {__LINE__, 10.7709, 13, 0.37}, - {__LINE__, 26.2514, 17, 0.93}, - {__LINE__, 36.4799, 29, 0.84}, - {__LINE__, 25.818, 31, 0.27}, - {__LINE__, 63.3346, 64, 0.50}, - {__LINE__, 196.211, 128, 0.9999}, - {__LINE__, 215.21, 243, 0.10}, - {__LINE__, 285.393, 256, 0.90}, - {__LINE__, 984.504, 1024, 0.1923}, - {__LINE__, 2043.85, 2048, 0.4783}, - {__LINE__, 48004.6, 48273, 0.194}, - }; - for (const auto& spec : specs) { - SCOPED_TRACE(spec.line); - // Verify all values are have at most a 1% relative error. - const double val = ChiSquareValue(spec.df, spec.confidence); - const double err = std::max(5e-6, spec.chi_square / 5e3); // 1 part in 5000 - EXPECT_NEAR(spec.chi_square, val, err) << spec.line; - } - - // Relaxed test for extreme values, from - // http://www.ciphersbyritter.com/JAVASCRP/NORMCHIK.HTM#ChiSquare - EXPECT_NEAR(49.2680, ChiSquareValue(100, 1e-6), 5); // 0.000'005 mark - EXPECT_NEAR(123.499, ChiSquareValue(200, 1e-6), 5); // 0.000'005 mark - - EXPECT_NEAR(149.449, ChiSquareValue(100, 0.999), 0.01); - EXPECT_NEAR(161.318, ChiSquareValue(100, 0.9999), 0.01); - EXPECT_NEAR(172.098, ChiSquareValue(100, 0.99999), 0.01); - - EXPECT_NEAR(381.426, ChiSquareValue(300, 0.999), 0.05); - EXPECT_NEAR(399.756, ChiSquareValue(300, 0.9999), 0.1); - EXPECT_NEAR(416.126, ChiSquareValue(300, 0.99999), 0.2); -} - -TEST(ChiSquareTest, PValue) { - struct { - int line; - double pval; - double chi_square; - int df; - } static const specs[] = { - {__LINE__, 1, 0, 0}, - {__LINE__, 0, 0.001, 0}, - {__LINE__, 1.000, 0, 453}, - {__LINE__, 0.134471, 7972.52, 7834}, - {__LINE__, 0.203922, 28.32, 23}, - {__LINE__, 0.737171, 48274, 48472}, - {__LINE__, 0.444146, 583.1234, 579}, - {__LINE__, 0.294814, 138.2, 130}, - {__LINE__, 0.0816532, 12.63, 7}, - {__LINE__, 0, 682.32, 67}, - {__LINE__, 0.49405, 999, 999}, - {__LINE__, 1.000, 0, 9999}, - {__LINE__, 0.997477, 0.00001, 1}, - {__LINE__, 0, 5823.21, 5040}, - }; - for (const auto& spec : specs) { - SCOPED_TRACE(spec.line); - const double pval = ChiSquarePValue(spec.chi_square, spec.df); - EXPECT_NEAR(spec.pval, pval, 1e-3); - } -} - -TEST(ChiSquareTest, CalcChiSquare) { - struct { - int line; - std::vector<int> expected; - std::vector<int> actual; - } const specs[] = { - {__LINE__, - {56, 234, 76, 1, 546, 1, 87, 345, 1, 234}, - {2, 132, 4, 43, 234, 8, 345, 8, 236, 56}}, - {__LINE__, - {123, 36, 234, 367, 345, 2, 456, 567, 234, 567}, - {123, 56, 2345, 8, 345, 8, 2345, 23, 48, 267}}, - {__LINE__, - {123, 234, 345, 456, 567, 678, 789, 890, 98, 76}, - {123, 234, 345, 456, 567, 678, 789, 890, 98, 76}}, - {__LINE__, {3, 675, 23, 86, 2, 8, 2}, {456, 675, 23, 86, 23, 65, 2}}, - {__LINE__, {1}, {23}}, - }; - for (const auto& spec : specs) { - SCOPED_TRACE(spec.line); - double chi_square = 0; - for (int i = 0; i < spec.expected.size(); ++i) { - const double diff = spec.actual[i] - spec.expected[i]; - chi_square += (diff * diff) / spec.expected[i]; - } - EXPECT_NEAR(chi_square, - ChiSquare(std::begin(spec.actual), std::end(spec.actual), - std::begin(spec.expected), std::end(spec.expected)), - 1e-5); - } -} - -TEST(ChiSquareTest, CalcChiSquareInt64) { - const int64_t data[3] = {910293487, 910292491, 910216780}; - // $ python -c "import scipy.stats - // > print scipy.stats.chisquare([910293487, 910292491, 910216780])[0]" - // 4.25410123524 - double sum = std::accumulate(std::begin(data), std::end(data), double{0}); - size_t n = std::distance(std::begin(data), std::end(data)); - double a = ChiSquareWithExpected(std::begin(data), std::end(data), sum / n); - EXPECT_NEAR(4.254101, a, 1e-6); - - // ... Or with known values. - double b = - ChiSquareWithExpected(std::begin(data), std::end(data), 910267586.0); - EXPECT_NEAR(4.254101, b, 1e-6); -} - -TEST(ChiSquareTest, TableData) { - // Test data from - // http://www.itl.nist.gov/div898/handbook/eda/section3/eda3674.htm - // 0.90 0.95 0.975 0.99 0.999 - const double data[100][5] = { - /* 1*/ {2.706, 3.841, 5.024, 6.635, 10.828}, - /* 2*/ {4.605, 5.991, 7.378, 9.210, 13.816}, - /* 3*/ {6.251, 7.815, 9.348, 11.345, 16.266}, - /* 4*/ {7.779, 9.488, 11.143, 13.277, 18.467}, - /* 5*/ {9.236, 11.070, 12.833, 15.086, 20.515}, - /* 6*/ {10.645, 12.592, 14.449, 16.812, 22.458}, - /* 7*/ {12.017, 14.067, 16.013, 18.475, 24.322}, - /* 8*/ {13.362, 15.507, 17.535, 20.090, 26.125}, - /* 9*/ {14.684, 16.919, 19.023, 21.666, 27.877}, - /*10*/ {15.987, 18.307, 20.483, 23.209, 29.588}, - /*11*/ {17.275, 19.675, 21.920, 24.725, 31.264}, - /*12*/ {18.549, 21.026, 23.337, 26.217, 32.910}, - /*13*/ {19.812, 22.362, 24.736, 27.688, 34.528}, - /*14*/ {21.064, 23.685, 26.119, 29.141, 36.123}, - /*15*/ {22.307, 24.996, 27.488, 30.578, 37.697}, - /*16*/ {23.542, 26.296, 28.845, 32.000, 39.252}, - /*17*/ {24.769, 27.587, 30.191, 33.409, 40.790}, - /*18*/ {25.989, 28.869, 31.526, 34.805, 42.312}, - /*19*/ {27.204, 30.144, 32.852, 36.191, 43.820}, - /*20*/ {28.412, 31.410, 34.170, 37.566, 45.315}, - /*21*/ {29.615, 32.671, 35.479, 38.932, 46.797}, - /*22*/ {30.813, 33.924, 36.781, 40.289, 48.268}, - /*23*/ {32.007, 35.172, 38.076, 41.638, 49.728}, - /*24*/ {33.196, 36.415, 39.364, 42.980, 51.179}, - /*25*/ {34.382, 37.652, 40.646, 44.314, 52.620}, - /*26*/ {35.563, 38.885, 41.923, 45.642, 54.052}, - /*27*/ {36.741, 40.113, 43.195, 46.963, 55.476}, - /*28*/ {37.916, 41.337, 44.461, 48.278, 56.892}, - /*29*/ {39.087, 42.557, 45.722, 49.588, 58.301}, - /*30*/ {40.256, 43.773, 46.979, 50.892, 59.703}, - /*31*/ {41.422, 44.985, 48.232, 52.191, 61.098}, - /*32*/ {42.585, 46.194, 49.480, 53.486, 62.487}, - /*33*/ {43.745, 47.400, 50.725, 54.776, 63.870}, - /*34*/ {44.903, 48.602, 51.966, 56.061, 65.247}, - /*35*/ {46.059, 49.802, 53.203, 57.342, 66.619}, - /*36*/ {47.212, 50.998, 54.437, 58.619, 67.985}, - /*37*/ {48.363, 52.192, 55.668, 59.893, 69.347}, - /*38*/ {49.513, 53.384, 56.896, 61.162, 70.703}, - /*39*/ {50.660, 54.572, 58.120, 62.428, 72.055}, - /*40*/ {51.805, 55.758, 59.342, 63.691, 73.402}, - /*41*/ {52.949, 56.942, 60.561, 64.950, 74.745}, - /*42*/ {54.090, 58.124, 61.777, 66.206, 76.084}, - /*43*/ {55.230, 59.304, 62.990, 67.459, 77.419}, - /*44*/ {56.369, 60.481, 64.201, 68.710, 78.750}, - /*45*/ {57.505, 61.656, 65.410, 69.957, 80.077}, - /*46*/ {58.641, 62.830, 66.617, 71.201, 81.400}, - /*47*/ {59.774, 64.001, 67.821, 72.443, 82.720}, - /*48*/ {60.907, 65.171, 69.023, 73.683, 84.037}, - /*49*/ {62.038, 66.339, 70.222, 74.919, 85.351}, - /*50*/ {63.167, 67.505, 71.420, 76.154, 86.661}, - /*51*/ {64.295, 68.669, 72.616, 77.386, 87.968}, - /*52*/ {65.422, 69.832, 73.810, 78.616, 89.272}, - /*53*/ {66.548, 70.993, 75.002, 79.843, 90.573}, - /*54*/ {67.673, 72.153, 76.192, 81.069, 91.872}, - /*55*/ {68.796, 73.311, 77.380, 82.292, 93.168}, - /*56*/ {69.919, 74.468, 78.567, 83.513, 94.461}, - /*57*/ {71.040, 75.624, 79.752, 84.733, 95.751}, - /*58*/ {72.160, 76.778, 80.936, 85.950, 97.039}, - /*59*/ {73.279, 77.931, 82.117, 87.166, 98.324}, - /*60*/ {74.397, 79.082, 83.298, 88.379, 99.607}, - /*61*/ {75.514, 80.232, 84.476, 89.591, 100.888}, - /*62*/ {76.630, 81.381, 85.654, 90.802, 102.166}, - /*63*/ {77.745, 82.529, 86.830, 92.010, 103.442}, - /*64*/ {78.860, 83.675, 88.004, 93.217, 104.716}, - /*65*/ {79.973, 84.821, 89.177, 94.422, 105.988}, - /*66*/ {81.085, 85.965, 90.349, 95.626, 107.258}, - /*67*/ {82.197, 87.108, 91.519, 96.828, 108.526}, - /*68*/ {83.308, 88.250, 92.689, 98.028, 109.791}, - /*69*/ {84.418, 89.391, 93.856, 99.228, 111.055}, - /*70*/ {85.527, 90.531, 95.023, 100.425, 112.317}, - /*71*/ {86.635, 91.670, 96.189, 101.621, 113.577}, - /*72*/ {87.743, 92.808, 97.353, 102.816, 114.835}, - /*73*/ {88.850, 93.945, 98.516, 104.010, 116.092}, - /*74*/ {89.956, 95.081, 99.678, 105.202, 117.346}, - /*75*/ {91.061, 96.217, 100.839, 106.393, 118.599}, - /*76*/ {92.166, 97.351, 101.999, 107.583, 119.850}, - /*77*/ {93.270, 98.484, 103.158, 108.771, 121.100}, - /*78*/ {94.374, 99.617, 104.316, 109.958, 122.348}, - /*79*/ {95.476, 100.749, 105.473, 111.144, 123.594}, - /*80*/ {96.578, 101.879, 106.629, 112.329, 124.839}, - /*81*/ {97.680, 103.010, 107.783, 113.512, 126.083}, - /*82*/ {98.780, 104.139, 108.937, 114.695, 127.324}, - /*83*/ {99.880, 105.267, 110.090, 115.876, 128.565}, - /*84*/ {100.980, 106.395, 111.242, 117.057, 129.804}, - /*85*/ {102.079, 107.522, 112.393, 118.236, 131.041}, - /*86*/ {103.177, 108.648, 113.544, 119.414, 132.277}, - /*87*/ {104.275, 109.773, 114.693, 120.591, 133.512}, - /*88*/ {105.372, 110.898, 115.841, 121.767, 134.746}, - /*89*/ {106.469, 112.022, 116.989, 122.942, 135.978}, - /*90*/ {107.565, 113.145, 118.136, 124.116, 137.208}, - /*91*/ {108.661, 114.268, 119.282, 125.289, 138.438}, - /*92*/ {109.756, 115.390, 120.427, 126.462, 139.666}, - /*93*/ {110.850, 116.511, 121.571, 127.633, 140.893}, - /*94*/ {111.944, 117.632, 122.715, 128.803, 142.119}, - /*95*/ {113.038, 118.752, 123.858, 129.973, 143.344}, - /*96*/ {114.131, 119.871, 125.000, 131.141, 144.567}, - /*97*/ {115.223, 120.990, 126.141, 132.309, 145.789}, - /*98*/ {116.315, 122.108, 127.282, 133.476, 147.010}, - /*99*/ {117.407, 123.225, 128.422, 134.642, 148.230}, - /*100*/ {118.498, 124.342, 129.561, 135.807, 149.449} - /**/}; - - // 0.90 0.95 0.975 0.99 0.999 - for (int i = 0; i < ABSL_ARRAYSIZE(data); i++) { - const double E = 0.0001; - EXPECT_NEAR(ChiSquarePValue(data[i][0], i + 1), 0.10, E) - << i << " " << data[i][0]; - EXPECT_NEAR(ChiSquarePValue(data[i][1], i + 1), 0.05, E) - << i << " " << data[i][1]; - EXPECT_NEAR(ChiSquarePValue(data[i][2], i + 1), 0.025, E) - << i << " " << data[i][2]; - EXPECT_NEAR(ChiSquarePValue(data[i][3], i + 1), 0.01, E) - << i << " " << data[i][3]; - EXPECT_NEAR(ChiSquarePValue(data[i][4], i + 1), 0.001, E) - << i << " " << data[i][4]; - - const double F = 0.1; - EXPECT_NEAR(ChiSquareValue(i + 1, 0.90), data[i][0], F) << i; - EXPECT_NEAR(ChiSquareValue(i + 1, 0.95), data[i][1], F) << i; - EXPECT_NEAR(ChiSquareValue(i + 1, 0.975), data[i][2], F) << i; - EXPECT_NEAR(ChiSquareValue(i + 1, 0.99), data[i][3], F) << i; - EXPECT_NEAR(ChiSquareValue(i + 1, 0.999), data[i][4], F) << i; - } -} - -TEST(ChiSquareTest, ChiSquareTwoIterator) { - // Test data from http://www.stat.yale.edu/Courses/1997-98/101/chigf.htm - // Null-hypothesis: This data is normally distributed. - const int counts[10] = {6, 6, 18, 33, 38, 38, 28, 21, 9, 3}; - const double expected[10] = {4.6, 8.8, 18.4, 30.0, 38.2, - 38.2, 30.0, 18.4, 8.8, 4.6}; - double chi_square = ChiSquare(std::begin(counts), std::end(counts), - std::begin(expected), std::end(expected)); - EXPECT_NEAR(chi_square, 2.69, 0.001); - - // Degrees of freedom: 10 bins. two estimated parameters. = 10 - 2 - 1. - const int dof = 7; - // The critical value of 7, 95% => 14.067 (see above test) - double p_value_05 = ChiSquarePValue(14.067, dof); - EXPECT_NEAR(p_value_05, 0.05, 0.001); // 95%-ile p-value - - double p_actual = ChiSquarePValue(chi_square, dof); - EXPECT_GT(p_actual, 0.05); // Accept the null hypothesis. -} - -TEST(ChiSquareTest, DiceRolls) { - // Assume we are testing 102 fair dice rolls. - // Null-hypothesis: This data is fairly distributed. - // - // The dof value of 4, @95% = 9.488 (see above test) - // The dof value of 5, @95% = 11.070 - const int rolls[6] = {22, 11, 17, 14, 20, 18}; - double sum = std::accumulate(std::begin(rolls), std::end(rolls), double{0}); - size_t n = std::distance(std::begin(rolls), std::end(rolls)); - - double a = ChiSquareWithExpected(std::begin(rolls), std::end(rolls), sum / n); - EXPECT_NEAR(a, 4.70588, 1e-5); - EXPECT_LT(a, ChiSquareValue(4, 0.95)); - - double p_a = ChiSquarePValue(a, 4); - EXPECT_NEAR(p_a, 0.318828, 1e-5); // Accept the null hypothesis. - - double b = ChiSquareWithExpected(std::begin(rolls), std::end(rolls), 17.0); - EXPECT_NEAR(b, 4.70588, 1e-5); - EXPECT_LT(b, ChiSquareValue(5, 0.95)); - - double p_b = ChiSquarePValue(b, 5); - EXPECT_NEAR(p_b, 0.4528180, 1e-5); // Accept the null hypothesis. -} - -} // namespace diff --git a/third_party/abseil_cpp/absl/random/internal/distribution_caller.h b/third_party/abseil_cpp/absl/random/internal/distribution_caller.h deleted file mode 100644 index fc81b787ebe2..000000000000 --- a/third_party/abseil_cpp/absl/random/internal/distribution_caller.h +++ /dev/null @@ -1,92 +0,0 @@ -// -// Copyright 2018 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. -// - -#ifndef ABSL_RANDOM_INTERNAL_DISTRIBUTION_CALLER_H_ -#define ABSL_RANDOM_INTERNAL_DISTRIBUTION_CALLER_H_ - -#include <utility> - -#include "absl/base/config.h" -#include "absl/base/internal/fast_type_id.h" -#include "absl/utility/utility.h" - -namespace absl { -ABSL_NAMESPACE_BEGIN -namespace random_internal { - -// DistributionCaller provides an opportunity to overload the general -// mechanism for calling a distribution, allowing for mock-RNG classes -// to intercept such calls. -template <typename URBG> -struct DistributionCaller { - // SFINAE to detect whether the URBG type includes a member matching - // bool InvokeMock(base_internal::FastTypeIdType, void*, void*). - // - // These live inside BitGenRef so that they have friend access - // to MockingBitGen. (see similar methods in DistributionCaller). - template <template <class...> class Trait, class AlwaysVoid, class... Args> - struct detector : std::false_type {}; - template <template <class...> class Trait, class... Args> - struct detector<Trait, absl::void_t<Trait<Args...>>, Args...> - : std::true_type {}; - - template <class T> - using invoke_mock_t = decltype(std::declval<T*>()->InvokeMock( - std::declval<::absl::base_internal::FastTypeIdType>(), - std::declval<void*>(), std::declval<void*>())); - - using HasInvokeMock = typename detector<invoke_mock_t, void, URBG>::type; - - // Default implementation of distribution caller. - template <typename DistrT, typename... Args> - static typename DistrT::result_type Impl(std::false_type, URBG* urbg, - Args&&... args) { - DistrT dist(std::forward<Args>(args)...); - return dist(*urbg); - } - - // Mock implementation of distribution caller. - // The underlying KeyT must match the KeyT constructed by MockOverloadSet. - template <typename DistrT, typename... Args> - static typename DistrT::result_type Impl(std::true_type, URBG* urbg, - Args&&... args) { - using ResultT = typename DistrT::result_type; - using ArgTupleT = std::tuple<absl::decay_t<Args>...>; - using KeyT = ResultT(DistrT, ArgTupleT); - - ArgTupleT arg_tuple(std::forward<Args>(args)...); - ResultT result; - if (!urbg->InvokeMock(::absl::base_internal::FastTypeId<KeyT>(), &arg_tuple, - &result)) { - auto dist = absl::make_from_tuple<DistrT>(arg_tuple); - result = dist(*urbg); - } - return result; - } - - // Default implementation of distribution caller. - template <typename DistrT, typename... Args> - static typename DistrT::result_type Call(URBG* urbg, Args&&... args) { - return Impl<DistrT, Args...>(HasInvokeMock{}, urbg, - std::forward<Args>(args)...); - } -}; - -} // namespace random_internal -ABSL_NAMESPACE_END -} // namespace absl - -#endif // ABSL_RANDOM_INTERNAL_DISTRIBUTION_CALLER_H_ diff --git a/third_party/abseil_cpp/absl/random/internal/distribution_test_util.cc b/third_party/abseil_cpp/absl/random/internal/distribution_test_util.cc deleted file mode 100644 index e9005658c0a7..000000000000 --- a/third_party/abseil_cpp/absl/random/internal/distribution_test_util.cc +++ /dev/null @@ -1,418 +0,0 @@ -// Copyright 2017 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#include "absl/random/internal/distribution_test_util.h" - -#include <cassert> -#include <cmath> -#include <string> -#include <vector> - -#include "absl/base/internal/raw_logging.h" -#include "absl/base/macros.h" -#include "absl/strings/str_cat.h" -#include "absl/strings/str_format.h" - -namespace absl { -ABSL_NAMESPACE_BEGIN -namespace random_internal { -namespace { - -#if defined(__EMSCRIPTEN__) -// Workaround __EMSCRIPTEN__ error: llvm_fma_f64 not found. -inline double fma(double x, double y, double z) { return (x * y) + z; } -#endif - -} // namespace - -DistributionMoments ComputeDistributionMoments( - absl::Span<const double> data_points) { - DistributionMoments result; - - // Compute m1 - for (double x : data_points) { - result.n++; - result.mean += x; - } - result.mean /= static_cast<double>(result.n); - - // Compute m2, m3, m4 - for (double x : data_points) { - double v = x - result.mean; - result.variance += v * v; - result.skewness += v * v * v; - result.kurtosis += v * v * v * v; - } - result.variance /= static_cast<double>(result.n - 1); - - result.skewness /= static_cast<double>(result.n); - result.skewness /= std::pow(result.variance, 1.5); - - result.kurtosis /= static_cast<double>(result.n); - result.kurtosis /= std::pow(result.variance, 2.0); - return result; - - // When validating the min/max count, the following confidence intervals may - // be of use: - // 3.291 * stddev = 99.9% CI - // 2.576 * stddev = 99% CI - // 1.96 * stddev = 95% CI - // 1.65 * stddev = 90% CI -} - -std::ostream& operator<<(std::ostream& os, const DistributionMoments& moments) { - return os << absl::StrFormat("mean=%f, stddev=%f, skewness=%f, kurtosis=%f", - moments.mean, std::sqrt(moments.variance), - moments.skewness, moments.kurtosis); -} - -double InverseNormalSurvival(double x) { - // inv_sf(u) = -sqrt(2) * erfinv(2u-1) - static constexpr double kSqrt2 = 1.4142135623730950488; - return -kSqrt2 * absl::random_internal::erfinv(2 * x - 1.0); -} - -bool Near(absl::string_view msg, double actual, double expected, double bound) { - assert(bound > 0.0); - double delta = fabs(expected - actual); - if (delta < bound) { - return true; - } - - std::string formatted = absl::StrCat( - msg, " actual=", actual, " expected=", expected, " err=", delta / bound); - ABSL_RAW_LOG(INFO, "%s", formatted.c_str()); - return false; -} - -// TODO(absl-team): Replace with an "ABSL_HAVE_SPECIAL_MATH" and try -// to use std::beta(). As of this writing P0226R1 is not implemented -// in libc++: http://libcxx.llvm.org/cxx1z_status.html -double beta(double p, double q) { - // Beta(x, y) = Gamma(x) * Gamma(y) / Gamma(x+y) - double lbeta = std::lgamma(p) + std::lgamma(q) - std::lgamma(p + q); - return std::exp(lbeta); -} - -// Approximation to inverse of the Error Function in double precision. -// (http://people.maths.ox.ac.uk/gilesm/files/gems_erfinv.pdf) -double erfinv(double x) { -#if !defined(__EMSCRIPTEN__) - using std::fma; -#endif - - double w = 0.0; - double p = 0.0; - w = -std::log((1.0 - x) * (1.0 + x)); - if (w < 6.250000) { - w = w - 3.125000; - p = -3.6444120640178196996e-21; - p = fma(p, w, -1.685059138182016589e-19); - p = fma(p, w, 1.2858480715256400167e-18); - p = fma(p, w, 1.115787767802518096e-17); - p = fma(p, w, -1.333171662854620906e-16); - p = fma(p, w, 2.0972767875968561637e-17); - p = fma(p, w, 6.6376381343583238325e-15); - p = fma(p, w, -4.0545662729752068639e-14); - p = fma(p, w, -8.1519341976054721522e-14); - p = fma(p, w, 2.6335093153082322977e-12); - p = fma(p, w, -1.2975133253453532498e-11); - p = fma(p, w, -5.4154120542946279317e-11); - p = fma(p, w, 1.051212273321532285e-09); - p = fma(p, w, -4.1126339803469836976e-09); - p = fma(p, w, -2.9070369957882005086e-08); - p = fma(p, w, 4.2347877827932403518e-07); - p = fma(p, w, -1.3654692000834678645e-06); - p = fma(p, w, -1.3882523362786468719e-05); - p = fma(p, w, 0.0001867342080340571352); - p = fma(p, w, -0.00074070253416626697512); - p = fma(p, w, -0.0060336708714301490533); - p = fma(p, w, 0.24015818242558961693); - p = fma(p, w, 1.6536545626831027356); - } else if (w < 16.000000) { - w = std::sqrt(w) - 3.250000; - p = 2.2137376921775787049e-09; - p = fma(p, w, 9.0756561938885390979e-08); - p = fma(p, w, -2.7517406297064545428e-07); - p = fma(p, w, 1.8239629214389227755e-08); - p = fma(p, w, 1.5027403968909827627e-06); - p = fma(p, w, -4.013867526981545969e-06); - p = fma(p, w, 2.9234449089955446044e-06); - p = fma(p, w, 1.2475304481671778723e-05); - p = fma(p, w, -4.7318229009055733981e-05); - p = fma(p, w, 6.8284851459573175448e-05); - p = fma(p, w, 2.4031110387097893999e-05); - p = fma(p, w, -0.0003550375203628474796); - p = fma(p, w, 0.00095328937973738049703); - p = fma(p, w, -0.0016882755560235047313); - p = fma(p, w, 0.0024914420961078508066); - p = fma(p, w, -0.0037512085075692412107); - p = fma(p, w, 0.005370914553590063617); - p = fma(p, w, 1.0052589676941592334); - p = fma(p, w, 3.0838856104922207635); - } else { - w = std::sqrt(w) - 5.000000; - p = -2.7109920616438573243e-11; - p = fma(p, w, -2.5556418169965252055e-10); - p = fma(p, w, 1.5076572693500548083e-09); - p = fma(p, w, -3.7894654401267369937e-09); - p = fma(p, w, 7.6157012080783393804e-09); - p = fma(p, w, -1.4960026627149240478e-08); - p = fma(p, w, 2.9147953450901080826e-08); - p = fma(p, w, -6.7711997758452339498e-08); - p = fma(p, w, 2.2900482228026654717e-07); - p = fma(p, w, -9.9298272942317002539e-07); - p = fma(p, w, 4.5260625972231537039e-06); - p = fma(p, w, -1.9681778105531670567e-05); - p = fma(p, w, 7.5995277030017761139e-05); - p = fma(p, w, -0.00021503011930044477347); - p = fma(p, w, -0.00013871931833623122026); - p = fma(p, w, 1.0103004648645343977); - p = fma(p, w, 4.8499064014085844221); - } - return p * x; -} - -namespace { - -// Direct implementation of AS63, BETAIN() -// https://www.jstor.org/stable/2346797?seq=3#page_scan_tab_contents. -// -// BETAIN(x, p, q, beta) -// x: the value of the upper limit x. -// p: the value of the parameter p. -// q: the value of the parameter q. -// beta: the value of ln B(p, q) -// -double BetaIncompleteImpl(const double x, const double p, const double q, - const double beta) { - if (p < (p + q) * x) { - // Incomplete beta function is symmetrical, so return the complement. - return 1. - BetaIncompleteImpl(1.0 - x, q, p, beta); - } - - double psq = p + q; - const double kErr = 1e-14; - const double xc = 1. - x; - const double pre = - std::exp(p * std::log(x) + (q - 1.) * std::log(xc) - beta) / p; - - double term = 1.; - double ai = 1.; - double result = 1.; - int ns = static_cast<int>(q + xc * psq); - - // Use the soper reduction forumla. - double rx = (ns == 0) ? x : x / xc; - double temp = q - ai; - for (;;) { - term = term * temp * rx / (p + ai); - result = result + term; - temp = std::fabs(term); - if (temp < kErr && temp < kErr * result) { - return result * pre; - } - ai = ai + 1.; - --ns; - if (ns >= 0) { - temp = q - ai; - if (ns == 0) { - rx = x; - } - } else { - temp = psq; - psq = psq + 1.; - } - } - - // NOTE: See also TOMS Alogrithm 708. - // http://www.netlib.org/toms/index.html - // - // NOTE: The NWSC library also includes BRATIO / ISUBX (p87) - // https://archive.org/details/DTIC_ADA261511/page/n75 -} - -// Direct implementation of AS109, XINBTA(p, q, beta, alpha) -// https://www.jstor.org/stable/2346798?read-now=1&seq=4#page_scan_tab_contents -// https://www.jstor.org/stable/2346887?seq=1#page_scan_tab_contents -// -// XINBTA(p, q, beta, alhpa) -// p: the value of the parameter p. -// q: the value of the parameter q. -// beta: the value of ln B(p, q) -// alpha: the value of the lower tail area. -// -double BetaIncompleteInvImpl(const double p, const double q, const double beta, - const double alpha) { - if (alpha < 0.5) { - // Inverse Incomplete beta function is symmetrical, return the complement. - return 1. - BetaIncompleteInvImpl(q, p, beta, 1. - alpha); - } - const double kErr = 1e-14; - double value = kErr; - - // Compute the initial estimate. - { - double r = std::sqrt(-std::log(alpha * alpha)); - double y = - r - fma(r, 0.27061, 2.30753) / fma(r, fma(r, 0.04481, 0.99229), 1.0); - if (p > 1. && q > 1.) { - r = (y * y - 3.) / 6.; - double s = 1. / (p + p - 1.); - double t = 1. / (q + q - 1.); - double h = 2. / s + t; - double w = - y * std::sqrt(h + r) / h - (t - s) * (r + 5. / 6. - t / (3. * h)); - value = p / (p + q * std::exp(w + w)); - } else { - r = q + q; - double t = 1.0 / (9. * q); - double u = 1.0 - t + y * std::sqrt(t); - t = r * (u * u * u); - if (t <= 0) { - value = 1.0 - std::exp((std::log((1.0 - alpha) * q) + beta) / q); - } else { - t = (4.0 * p + r - 2.0) / t; - if (t <= 1) { - value = std::exp((std::log(alpha * p) + beta) / p); - } else { - value = 1.0 - 2.0 / (t + 1.0); - } - } - } - } - - // Solve for x using a modified newton-raphson method using the function - // BetaIncomplete. - { - value = std::max(value, kErr); - value = std::min(value, 1.0 - kErr); - - const double r = 1.0 - p; - const double t = 1.0 - q; - double y; - double yprev = 0; - double sq = 1; - double prev = 1; - for (;;) { - if (value < 0 || value > 1.0) { - // Error case; value went infinite. - return std::numeric_limits<double>::infinity(); - } else if (value == 0 || value == 1) { - y = value; - } else { - y = BetaIncompleteImpl(value, p, q, beta); - if (!std::isfinite(y)) { - return y; - } - } - y = (y - alpha) * - std::exp(beta + r * std::log(value) + t * std::log(1.0 - value)); - if (y * yprev <= 0) { - prev = std::max(sq, std::numeric_limits<double>::min()); - } - double g = 1.0; - for (;;) { - const double adj = g * y; - const double adj_sq = adj * adj; - if (adj_sq >= prev) { - g = g / 3.0; - continue; - } - const double tx = value - adj; - if (tx < 0 || tx > 1) { - g = g / 3.0; - continue; - } - if (prev < kErr) { - return value; - } - if (y * y < kErr) { - return value; - } - if (tx == value) { - return value; - } - if (tx == 0 || tx == 1) { - g = g / 3.0; - continue; - } - value = tx; - yprev = y; - break; - } - } - } - - // NOTES: See also: Asymptotic inversion of the incomplete beta function. - // https://core.ac.uk/download/pdf/82140723.pdf - // - // NOTE: See the Boost library documentation as well: - // https://www.boost.org/doc/libs/1_52_0/libs/math/doc/sf_and_dist/html/math_toolkit/special/sf_beta/ibeta_function.html -} - -} // namespace - -double BetaIncomplete(const double x, const double p, const double q) { - // Error cases. - if (p < 0 || q < 0 || x < 0 || x > 1.0) { - return std::numeric_limits<double>::infinity(); - } - if (x == 0 || x == 1) { - return x; - } - // ln(Beta(p, q)) - double beta = std::lgamma(p) + std::lgamma(q) - std::lgamma(p + q); - return BetaIncompleteImpl(x, p, q, beta); -} - -double BetaIncompleteInv(const double p, const double q, const double alpha) { - // Error cases. - if (p < 0 || q < 0 || alpha < 0 || alpha > 1.0) { - return std::numeric_limits<double>::infinity(); - } - if (alpha == 0 || alpha == 1) { - return alpha; - } - // ln(Beta(p, q)) - double beta = std::lgamma(p) + std::lgamma(q) - std::lgamma(p + q); - return BetaIncompleteInvImpl(p, q, beta, alpha); -} - -// Given `num_trials` trials each with probability `p` of success, the -// probability of no failures is `p^k`. To ensure the probability of a failure -// is no more than `p_fail`, it must be that `p^k == 1 - p_fail`. This function -// computes `p` from that equation. -double RequiredSuccessProbability(const double p_fail, const int num_trials) { - double p = std::exp(std::log(1.0 - p_fail) / static_cast<double>(num_trials)); - ABSL_ASSERT(p > 0); - return p; -} - -double ZScore(double expected_mean, const DistributionMoments& moments) { - return (moments.mean - expected_mean) / - (std::sqrt(moments.variance) / - std::sqrt(static_cast<double>(moments.n))); -} - -double MaxErrorTolerance(double acceptance_probability) { - double one_sided_pvalue = 0.5 * (1.0 - acceptance_probability); - const double max_err = InverseNormalSurvival(one_sided_pvalue); - ABSL_ASSERT(max_err > 0); - return max_err; -} - -} // namespace random_internal -ABSL_NAMESPACE_END -} // namespace absl diff --git a/third_party/abseil_cpp/absl/random/internal/distribution_test_util.h b/third_party/abseil_cpp/absl/random/internal/distribution_test_util.h deleted file mode 100644 index 6d94cf6c97bf..000000000000 --- a/third_party/abseil_cpp/absl/random/internal/distribution_test_util.h +++ /dev/null @@ -1,113 +0,0 @@ -// Copyright 2017 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#ifndef ABSL_RANDOM_INTERNAL_DISTRIBUTION_TEST_UTIL_H_ -#define ABSL_RANDOM_INTERNAL_DISTRIBUTION_TEST_UTIL_H_ - -#include <cstddef> -#include <iostream> -#include <vector> - -#include "absl/strings/string_view.h" -#include "absl/types/span.h" - -// NOTE: The functions in this file are test only, and are should not be used in -// non-test code. - -namespace absl { -ABSL_NAMESPACE_BEGIN -namespace random_internal { - -// http://webspace.ship.edu/pgmarr/Geo441/Lectures/Lec%205%20-%20Normality%20Testing.pdf - -// Compute the 1st to 4th standard moments: -// mean, variance, skewness, and kurtosis. -// http://www.itl.nist.gov/div898/handbook/eda/section3/eda35b.htm -struct DistributionMoments { - size_t n = 0; - double mean = 0.0; - double variance = 0.0; - double skewness = 0.0; - double kurtosis = 0.0; -}; -DistributionMoments ComputeDistributionMoments( - absl::Span<const double> data_points); - -std::ostream& operator<<(std::ostream& os, const DistributionMoments& moments); - -// Computes the Z-score for a set of data with the given distribution moments -// compared against `expected_mean`. -double ZScore(double expected_mean, const DistributionMoments& moments); - -// Returns the probability of success required for a single trial to ensure that -// after `num_trials` trials, the probability of at least one failure is no more -// than `p_fail`. -double RequiredSuccessProbability(double p_fail, int num_trials); - -// Computes the maximum distance from the mean tolerable, for Z-Tests that are -// expected to pass with `acceptance_probability`. Will terminate if the -// resulting tolerance is zero (due to passing in 0.0 for -// `acceptance_probability` or rounding errors). -// -// For example, -// MaxErrorTolerance(0.001) = 0.0 -// MaxErrorTolerance(0.5) = ~0.47 -// MaxErrorTolerance(1.0) = inf -double MaxErrorTolerance(double acceptance_probability); - -// Approximation to inverse of the Error Function in double precision. -// (http://people.maths.ox.ac.uk/gilesm/files/gems_erfinv.pdf) -double erfinv(double x); - -// Beta(p, q) = Gamma(p) * Gamma(q) / Gamma(p+q) -double beta(double p, double q); - -// The inverse of the normal survival function. -double InverseNormalSurvival(double x); - -// Returns whether actual is "near" expected, based on the bound. -bool Near(absl::string_view msg, double actual, double expected, double bound); - -// Implements the incomplete regularized beta function, AS63, BETAIN. -// https://www.jstor.org/stable/2346797 -// -// BetaIncomplete(x, p, q), where -// `x` is the value of the upper limit -// `p` is beta parameter p, `q` is beta parameter q. -// -// NOTE: This is a test-only function which is only accurate to within, at most, -// 1e-13 of the actual value. -// -double BetaIncomplete(double x, double p, double q); - -// Implements the inverse of the incomplete regularized beta function, AS109, -// XINBTA. -// https://www.jstor.org/stable/2346798 -// https://www.jstor.org/stable/2346887 -// -// BetaIncompleteInv(p, q, beta, alhpa) -// `p` is beta parameter p, `q` is beta parameter q. -// `alpha` is the value of the lower tail area. -// -// NOTE: This is a test-only function and, when successful, is only accurate to -// within ~1e-6 of the actual value; there are some cases where it diverges from -// the actual value by much more than that. The function uses Newton's method, -// and thus the runtime is highly variable. -double BetaIncompleteInv(double p, double q, double alpha); - -} // namespace random_internal -ABSL_NAMESPACE_END -} // namespace absl - -#endif // ABSL_RANDOM_INTERNAL_DISTRIBUTION_TEST_UTIL_H_ diff --git a/third_party/abseil_cpp/absl/random/internal/distribution_test_util_test.cc b/third_party/abseil_cpp/absl/random/internal/distribution_test_util_test.cc deleted file mode 100644 index c49d44fb4796..000000000000 --- a/third_party/abseil_cpp/absl/random/internal/distribution_test_util_test.cc +++ /dev/null @@ -1,193 +0,0 @@ -// Copyright 2017 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#include "absl/random/internal/distribution_test_util.h" - -#include "gtest/gtest.h" - -namespace { - -TEST(TestUtil, InverseErf) { - const struct { - const double z; - const double value; - } kErfInvTable[] = { - {0.0000001, 8.86227e-8}, - {0.00001, 8.86227e-6}, - {0.5, 0.4769362762044}, - {0.6, 0.5951160814499}, - {0.99999, 3.1234132743}, - {0.9999999, 3.7665625816}, - {0.999999944, 3.8403850690566985}, // = log((1-x) * (1+x)) =~ 16.004 - {0.999999999, 4.3200053849134452}, - }; - - for (const auto& data : kErfInvTable) { - auto value = absl::random_internal::erfinv(data.z); - - // Log using the Wolfram-alpha function name & parameters. - EXPECT_NEAR(value, data.value, 1e-8) - << " InverseErf[" << data.z << "] (expected=" << data.value << ") -> " - << value; - } -} - -const struct { - const double p; - const double q; - const double x; - const double alpha; -} kBetaTable[] = { - {0.5, 0.5, 0.01, 0.06376856085851985}, - {0.5, 0.5, 0.1, 0.2048327646991335}, - {0.5, 0.5, 1, 1}, - {1, 0.5, 0, 0}, - {1, 0.5, 0.01, 0.005012562893380045}, - {1, 0.5, 0.1, 0.0513167019494862}, - {1, 0.5, 0.5, 0.2928932188134525}, - {1, 1, 0.5, 0.5}, - {2, 2, 0.1, 0.028}, - {2, 2, 0.2, 0.104}, - {2, 2, 0.3, 0.216}, - {2, 2, 0.4, 0.352}, - {2, 2, 0.5, 0.5}, - {2, 2, 0.6, 0.648}, - {2, 2, 0.7, 0.784}, - {2, 2, 0.8, 0.896}, - {2, 2, 0.9, 0.972}, - {5.5, 5, 0.5, 0.4361908850559777}, - {10, 0.5, 0.9, 0.1516409096346979}, - {10, 5, 0.5, 0.08978271484375}, - {10, 5, 1, 1}, - {10, 10, 0.5, 0.5}, - {20, 5, 0.8, 0.4598773297575791}, - {20, 10, 0.6, 0.2146816102371739}, - {20, 10, 0.8, 0.9507364826957875}, - {20, 20, 0.5, 0.5}, - {20, 20, 0.6, 0.8979413687105918}, - {30, 10, 0.7, 0.2241297491808366}, - {30, 10, 0.8, 0.7586405487192086}, - {40, 20, 0.7, 0.7001783247477069}, - {1, 0.5, 0.1, 0.0513167019494862}, - {1, 0.5, 0.2, 0.1055728090000841}, - {1, 0.5, 0.3, 0.1633399734659245}, - {1, 0.5, 0.4, 0.2254033307585166}, - {1, 2, 0.2, 0.36}, - {1, 3, 0.2, 0.488}, - {1, 4, 0.2, 0.5904}, - {1, 5, 0.2, 0.67232}, - {2, 2, 0.3, 0.216}, - {3, 2, 0.3, 0.0837}, - {4, 2, 0.3, 0.03078}, - {5, 2, 0.3, 0.010935}, - - // These values test small & large points along the range of the Beta - // function. - // - // When selecting test points, remember that if BetaIncomplete(x, p, q) - // returns the same value to within the limits of precision over a large - // domain of the input, x, then BetaIncompleteInv(alpha, p, q) may return an - // essentially arbitrary value where BetaIncomplete(x, p, q) =~ alpha. - - // BetaRegularized[x, 0.00001, 0.00001], - // For x in {~0.001 ... ~0.999}, => ~0.5 - {1e-5, 1e-5, 1e-5, 0.4999424388184638311}, - {1e-5, 1e-5, (1.0 - 1e-8), 0.5000920948389232964}, - - // BetaRegularized[x, 0.00001, 10000]. - // For x in {~epsilon ... 1.0}, => ~1 - {1e-5, 1e5, 1e-6, 0.9999817708130066936}, - {1e-5, 1e5, (1.0 - 1e-7), 1.0}, - - // BetaRegularized[x, 10000, 0.00001]. - // For x in {0 .. 1-epsilon}, => ~0 - {1e5, 1e-5, 1e-6, 0}, - {1e5, 1e-5, (1.0 - 1e-6), 1.8229186993306369e-5}, -}; - -TEST(BetaTest, BetaIncomplete) { - for (const auto& data : kBetaTable) { - auto value = absl::random_internal::BetaIncomplete(data.x, data.p, data.q); - - // Log using the Wolfram-alpha function name & parameters. - EXPECT_NEAR(value, data.alpha, 1e-12) - << " BetaRegularized[" << data.x << ", " << data.p << ", " << data.q - << "] (expected=" << data.alpha << ") -> " << value; - } -} - -TEST(BetaTest, BetaIncompleteInv) { - for (const auto& data : kBetaTable) { - auto value = - absl::random_internal::BetaIncompleteInv(data.p, data.q, data.alpha); - - // Log using the Wolfram-alpha function name & parameters. - EXPECT_NEAR(value, data.x, 1e-6) - << " InverseBetaRegularized[" << data.alpha << ", " << data.p << ", " - << data.q << "] (expected=" << data.x << ") -> " << value; - } -} - -TEST(MaxErrorTolerance, MaxErrorTolerance) { - std::vector<std::pair<double, double>> cases = { - {0.0000001, 8.86227e-8 * 1.41421356237}, - {0.00001, 8.86227e-6 * 1.41421356237}, - {0.5, 0.4769362762044 * 1.41421356237}, - {0.6, 0.5951160814499 * 1.41421356237}, - {0.99999, 3.1234132743 * 1.41421356237}, - {0.9999999, 3.7665625816 * 1.41421356237}, - {0.999999944, 3.8403850690566985 * 1.41421356237}, - {0.999999999, 4.3200053849134452 * 1.41421356237}}; - for (auto entry : cases) { - EXPECT_NEAR(absl::random_internal::MaxErrorTolerance(entry.first), - entry.second, 1e-8); - } -} - -TEST(ZScore, WithSameMean) { - absl::random_internal::DistributionMoments m; - m.n = 100; - m.mean = 5; - m.variance = 1; - EXPECT_NEAR(absl::random_internal::ZScore(5, m), 0, 1e-12); - - m.n = 1; - m.mean = 0; - m.variance = 1; - EXPECT_NEAR(absl::random_internal::ZScore(0, m), 0, 1e-12); - - m.n = 10000; - m.mean = -5; - m.variance = 100; - EXPECT_NEAR(absl::random_internal::ZScore(-5, m), 0, 1e-12); -} - -TEST(ZScore, DifferentMean) { - absl::random_internal::DistributionMoments m; - m.n = 100; - m.mean = 5; - m.variance = 1; - EXPECT_NEAR(absl::random_internal::ZScore(4, m), 10, 1e-12); - - m.n = 1; - m.mean = 0; - m.variance = 1; - EXPECT_NEAR(absl::random_internal::ZScore(-1, m), 1, 1e-12); - - m.n = 10000; - m.mean = -5; - m.variance = 100; - EXPECT_NEAR(absl::random_internal::ZScore(-4, m), -10, 1e-12); -} -} // namespace diff --git a/third_party/abseil_cpp/absl/random/internal/explicit_seed_seq.h b/third_party/abseil_cpp/absl/random/internal/explicit_seed_seq.h deleted file mode 100644 index 6a743eaf46cc..000000000000 --- a/third_party/abseil_cpp/absl/random/internal/explicit_seed_seq.h +++ /dev/null @@ -1,91 +0,0 @@ -// Copyright 2017 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#ifndef ABSL_RANDOM_INTERNAL_EXPLICIT_SEED_SEQ_H_ -#define ABSL_RANDOM_INTERNAL_EXPLICIT_SEED_SEQ_H_ - -#include <algorithm> -#include <cstddef> -#include <cstdint> -#include <initializer_list> -#include <iterator> -#include <vector> - -#include "absl/base/config.h" - -namespace absl { -ABSL_NAMESPACE_BEGIN -namespace random_internal { - -// This class conforms to the C++ Standard "Seed Sequence" concept -// [rand.req.seedseq]. -// -// An "ExplicitSeedSeq" is meant to provide a conformant interface for -// forwarding pre-computed seed material to the constructor of a class -// conforming to the "Uniform Random Bit Generator" concept. This class makes no -// attempt to mutate the state provided by its constructor, and returns it -// directly via ExplicitSeedSeq::generate(). -// -// If this class is asked to generate more seed material than was provided to -// the constructor, then the remaining bytes will be filled with deterministic, -// nonrandom data. -class ExplicitSeedSeq { - public: - using result_type = uint32_t; - - ExplicitSeedSeq() : state_() {} - - // Copy and move both allowed. - ExplicitSeedSeq(const ExplicitSeedSeq& other) = default; - ExplicitSeedSeq& operator=(const ExplicitSeedSeq& other) = default; - ExplicitSeedSeq(ExplicitSeedSeq&& other) = default; - ExplicitSeedSeq& operator=(ExplicitSeedSeq&& other) = default; - - template <typename Iterator> - ExplicitSeedSeq(Iterator begin, Iterator end) { - for (auto it = begin; it != end; it++) { - state_.push_back(*it & 0xffffffff); - } - } - - template <typename T> - ExplicitSeedSeq(std::initializer_list<T> il) - : ExplicitSeedSeq(il.begin(), il.end()) {} - - size_t size() const { return state_.size(); } - - template <typename OutIterator> - void param(OutIterator out) const { - std::copy(std::begin(state_), std::end(state_), out); - } - - template <typename OutIterator> - void generate(OutIterator begin, OutIterator end) { - for (size_t index = 0; begin != end; begin++) { - *begin = state_.empty() ? 0 : state_[index++]; - if (index >= state_.size()) { - index = 0; - } - } - } - - protected: - std::vector<uint32_t> state_; -}; - -} // namespace random_internal -ABSL_NAMESPACE_END -} // namespace absl - -#endif // ABSL_RANDOM_INTERNAL_EXPLICIT_SEED_SEQ_H_ diff --git a/third_party/abseil_cpp/absl/random/internal/explicit_seed_seq_test.cc b/third_party/abseil_cpp/absl/random/internal/explicit_seed_seq_test.cc deleted file mode 100644 index a55ad73948b9..000000000000 --- a/third_party/abseil_cpp/absl/random/internal/explicit_seed_seq_test.cc +++ /dev/null @@ -1,204 +0,0 @@ -// Copyright 2017 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#include "absl/random/internal/explicit_seed_seq.h" - -#include <iterator> -#include <random> -#include <utility> - -#include "gmock/gmock.h" -#include "gtest/gtest.h" -#include "absl/random/seed_sequences.h" - -namespace { - -template <typename Sseq> -bool ConformsToInterface() { - // Check that the SeedSequence can be default-constructed. - { Sseq default_constructed_seq; } - // Check that the SeedSequence can be constructed with two iterators. - { - uint32_t init_array[] = {1, 3, 5, 7, 9}; - Sseq iterator_constructed_seq(init_array, &init_array[5]); - } - // Check that the SeedSequence can be std::initializer_list-constructed. - { Sseq list_constructed_seq = {1, 3, 5, 7, 9, 11, 13}; } - // Check that param() and size() return state provided to constructor. - { - uint32_t init_array[] = {1, 2, 3, 4, 5}; - Sseq seq(init_array, &init_array[ABSL_ARRAYSIZE(init_array)]); - EXPECT_EQ(seq.size(), ABSL_ARRAYSIZE(init_array)); - - uint32_t state_array[ABSL_ARRAYSIZE(init_array)]; - seq.param(state_array); - - for (int i = 0; i < ABSL_ARRAYSIZE(state_array); i++) { - EXPECT_EQ(state_array[i], i + 1); - } - } - // Check for presence of generate() method. - { - Sseq seq; - uint32_t seeds[5]; - - seq.generate(seeds, &seeds[ABSL_ARRAYSIZE(seeds)]); - } - return true; -} -} // namespace - -TEST(SeedSequences, CheckInterfaces) { - // Control case - EXPECT_TRUE(ConformsToInterface<std::seed_seq>()); - - // Abseil classes - EXPECT_TRUE(ConformsToInterface<absl::random_internal::ExplicitSeedSeq>()); -} - -TEST(ExplicitSeedSeq, DefaultConstructorGeneratesZeros) { - const size_t kNumBlocks = 128; - - uint32_t outputs[kNumBlocks]; - absl::random_internal::ExplicitSeedSeq seq; - seq.generate(outputs, &outputs[kNumBlocks]); - - for (uint32_t& seed : outputs) { - EXPECT_EQ(seed, 0); - } -} - -TEST(ExplicitSeeqSeq, SeedMaterialIsForwardedIdentically) { - const size_t kNumBlocks = 128; - - uint32_t seed_material[kNumBlocks]; - std::random_device urandom{"/dev/urandom"}; - for (uint32_t& seed : seed_material) { - seed = urandom(); - } - absl::random_internal::ExplicitSeedSeq seq(seed_material, - &seed_material[kNumBlocks]); - - // Check that output is same as seed-material provided to constructor. - { - const size_t kNumGenerated = kNumBlocks / 2; - uint32_t outputs[kNumGenerated]; - seq.generate(outputs, &outputs[kNumGenerated]); - for (size_t i = 0; i < kNumGenerated; i++) { - EXPECT_EQ(outputs[i], seed_material[i]); - } - } - // Check that SeedSequence is stateless between invocations: Despite the last - // invocation of generate() only consuming half of the input-entropy, the same - // entropy will be recycled for the next invocation. - { - const size_t kNumGenerated = kNumBlocks; - uint32_t outputs[kNumGenerated]; - seq.generate(outputs, &outputs[kNumGenerated]); - for (size_t i = 0; i < kNumGenerated; i++) { - EXPECT_EQ(outputs[i], seed_material[i]); - } - } - // Check that when more seed-material is asked for than is provided, nonzero - // values are still written. - { - const size_t kNumGenerated = kNumBlocks * 2; - uint32_t outputs[kNumGenerated]; - seq.generate(outputs, &outputs[kNumGenerated]); - for (size_t i = 0; i < kNumGenerated; i++) { - EXPECT_EQ(outputs[i], seed_material[i % kNumBlocks]); - } - } -} - -TEST(ExplicitSeedSeq, CopyAndMoveConstructors) { - using testing::Each; - using testing::Eq; - using testing::Not; - using testing::Pointwise; - - uint32_t entropy[4]; - std::random_device urandom("/dev/urandom"); - for (uint32_t& entry : entropy) { - entry = urandom(); - } - absl::random_internal::ExplicitSeedSeq seq_from_entropy(std::begin(entropy), - std::end(entropy)); - // Copy constructor. - { - absl::random_internal::ExplicitSeedSeq seq_copy(seq_from_entropy); - EXPECT_EQ(seq_copy.size(), seq_from_entropy.size()); - - std::vector<uint32_t> seeds_1; - seeds_1.resize(1000, 0); - std::vector<uint32_t> seeds_2; - seeds_2.resize(1000, 1); - - seq_from_entropy.generate(seeds_1.begin(), seeds_1.end()); - seq_copy.generate(seeds_2.begin(), seeds_2.end()); - - EXPECT_THAT(seeds_1, Pointwise(Eq(), seeds_2)); - } - // Assignment operator. - { - for (uint32_t& entry : entropy) { - entry = urandom(); - } - absl::random_internal::ExplicitSeedSeq another_seq(std::begin(entropy), - std::end(entropy)); - - std::vector<uint32_t> seeds_1; - seeds_1.resize(1000, 0); - std::vector<uint32_t> seeds_2; - seeds_2.resize(1000, 0); - - seq_from_entropy.generate(seeds_1.begin(), seeds_1.end()); - another_seq.generate(seeds_2.begin(), seeds_2.end()); - - // Assert precondition: Sequences generated by seed-sequences are not equal. - EXPECT_THAT(seeds_1, Not(Pointwise(Eq(), seeds_2))); - - // Apply the assignment-operator. - another_seq = seq_from_entropy; - - // Re-generate seeds. - seq_from_entropy.generate(seeds_1.begin(), seeds_1.end()); - another_seq.generate(seeds_2.begin(), seeds_2.end()); - - // Seeds generated by seed-sequences should now be equal. - EXPECT_THAT(seeds_1, Pointwise(Eq(), seeds_2)); - } - // Move constructor. - { - // Get seeds from seed-sequence constructed from entropy. - std::vector<uint32_t> seeds_1; - seeds_1.resize(1000, 0); - seq_from_entropy.generate(seeds_1.begin(), seeds_1.end()); - - // Apply move-constructor move the sequence to another instance. - absl::random_internal::ExplicitSeedSeq moved_seq( - std::move(seq_from_entropy)); - std::vector<uint32_t> seeds_2; - seeds_2.resize(1000, 1); - moved_seq.generate(seeds_2.begin(), seeds_2.end()); - // Verify that seeds produced by moved-instance are the same as original. - EXPECT_THAT(seeds_1, Pointwise(Eq(), seeds_2)); - - // Verify that the moved-from instance now behaves like a - // default-constructed instance. - EXPECT_EQ(seq_from_entropy.size(), 0); - seq_from_entropy.generate(seeds_1.begin(), seeds_1.end()); - EXPECT_THAT(seeds_1, Each(Eq(0))); - } -} diff --git a/third_party/abseil_cpp/absl/random/internal/fast_uniform_bits.h b/third_party/abseil_cpp/absl/random/internal/fast_uniform_bits.h deleted file mode 100644 index 425aaf7d830c..000000000000 --- a/third_party/abseil_cpp/absl/random/internal/fast_uniform_bits.h +++ /dev/null @@ -1,268 +0,0 @@ -// Copyright 2017 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#ifndef ABSL_RANDOM_INTERNAL_FAST_UNIFORM_BITS_H_ -#define ABSL_RANDOM_INTERNAL_FAST_UNIFORM_BITS_H_ - -#include <cstddef> -#include <cstdint> -#include <limits> -#include <type_traits> - -#include "absl/base/config.h" -#include "absl/meta/type_traits.h" - -namespace absl { -ABSL_NAMESPACE_BEGIN -namespace random_internal { -// Returns true if the input value is zero or a power of two. Useful for -// determining if the range of output values in a URBG -template <typename UIntType> -constexpr bool IsPowerOfTwoOrZero(UIntType n) { - return (n == 0) || ((n & (n - 1)) == 0); -} - -// Computes the length of the range of values producible by the URBG, or returns -// zero if that would encompass the entire range of representable values in -// URBG::result_type. -template <typename URBG> -constexpr typename URBG::result_type RangeSize() { - using result_type = typename URBG::result_type; - static_assert((URBG::max)() != (URBG::min)(), "URBG range cannot be 0."); - return ((URBG::max)() == (std::numeric_limits<result_type>::max)() && - (URBG::min)() == std::numeric_limits<result_type>::lowest()) - ? result_type{0} - : ((URBG::max)() - (URBG::min)() + result_type{1}); -} - -// Computes the floor of the log. (i.e., std::floor(std::log2(N)); -template <typename UIntType> -constexpr UIntType IntegerLog2(UIntType n) { - return (n <= 1) ? 0 : 1 + IntegerLog2(n >> 1); -} - -// Returns the number of bits of randomness returned through -// `PowerOfTwoVariate(urbg)`. -template <typename URBG> -constexpr size_t NumBits() { - return RangeSize<URBG>() == 0 - ? std::numeric_limits<typename URBG::result_type>::digits - : IntegerLog2(RangeSize<URBG>()); -} - -// Given a shift value `n`, constructs a mask with exactly the low `n` bits set. -// If `n == 0`, all bits are set. -template <typename UIntType> -constexpr UIntType MaskFromShift(size_t n) { - return ((n % std::numeric_limits<UIntType>::digits) == 0) - ? ~UIntType{0} - : (UIntType{1} << n) - UIntType{1}; -} - -// Tags used to dispatch FastUniformBits::generate to the simple or more complex -// entropy extraction algorithm. -struct SimplifiedLoopTag {}; -struct RejectionLoopTag {}; - -// FastUniformBits implements a fast path to acquire uniform independent bits -// from a type which conforms to the [rand.req.urbg] concept. -// Parameterized by: -// `UIntType`: the result (output) type -// -// The std::independent_bits_engine [rand.adapt.ibits] adaptor can be -// instantiated from an existing generator through a copy or a move. It does -// not, however, facilitate the production of pseudorandom bits from an un-owned -// generator that will outlive the std::independent_bits_engine instance. -template <typename UIntType = uint64_t> -class FastUniformBits { - public: - using result_type = UIntType; - - static constexpr result_type(min)() { return 0; } - static constexpr result_type(max)() { - return (std::numeric_limits<result_type>::max)(); - } - - template <typename URBG> - result_type operator()(URBG& g); // NOLINT(runtime/references) - - private: - static_assert(std::is_unsigned<UIntType>::value, - "Class-template FastUniformBits<> must be parameterized using " - "an unsigned type."); - - // Generate() generates a random value, dispatched on whether - // the underlying URBG must use rejection sampling to generate a value, - // or whether a simplified loop will suffice. - template <typename URBG> - result_type Generate(URBG& g, // NOLINT(runtime/references) - SimplifiedLoopTag); - - template <typename URBG> - result_type Generate(URBG& g, // NOLINT(runtime/references) - RejectionLoopTag); -}; - -template <typename UIntType> -template <typename URBG> -typename FastUniformBits<UIntType>::result_type -FastUniformBits<UIntType>::operator()(URBG& g) { // NOLINT(runtime/references) - // kRangeMask is the mask used when sampling variates from the URBG when the - // width of the URBG range is not a power of 2. - // Y = (2 ^ kRange) - 1 - static_assert((URBG::max)() > (URBG::min)(), - "URBG::max and URBG::min may not be equal."); - - using tag = absl::conditional_t<IsPowerOfTwoOrZero(RangeSize<URBG>()), - SimplifiedLoopTag, RejectionLoopTag>; - return Generate(g, tag{}); -} - -template <typename UIntType> -template <typename URBG> -typename FastUniformBits<UIntType>::result_type -FastUniformBits<UIntType>::Generate(URBG& g, // NOLINT(runtime/references) - SimplifiedLoopTag) { - // The simplified version of FastUniformBits works only on URBGs that have - // a range that is a power of 2. In this case we simply loop and shift without - // attempting to balance the bits across calls. - static_assert(IsPowerOfTwoOrZero(RangeSize<URBG>()), - "incorrect Generate tag for URBG instance"); - - static constexpr size_t kResultBits = - std::numeric_limits<result_type>::digits; - static constexpr size_t kUrbgBits = NumBits<URBG>(); - static constexpr size_t kIters = - (kResultBits / kUrbgBits) + (kResultBits % kUrbgBits != 0); - static constexpr size_t kShift = (kIters == 1) ? 0 : kUrbgBits; - static constexpr auto kMin = (URBG::min)(); - - result_type r = static_cast<result_type>(g() - kMin); - for (size_t n = 1; n < kIters; ++n) { - r = (r << kShift) + static_cast<result_type>(g() - kMin); - } - return r; -} - -template <typename UIntType> -template <typename URBG> -typename FastUniformBits<UIntType>::result_type -FastUniformBits<UIntType>::Generate(URBG& g, // NOLINT(runtime/references) - RejectionLoopTag) { - static_assert(!IsPowerOfTwoOrZero(RangeSize<URBG>()), - "incorrect Generate tag for URBG instance"); - using urbg_result_type = typename URBG::result_type; - - // See [rand.adapt.ibits] for more details on the constants calculated below. - // - // It is preferable to use roughly the same number of bits from each generator - // call, however this is only possible when the number of bits provided by the - // URBG is a divisor of the number of bits in `result_type`. In all other - // cases, the number of bits used cannot always be the same, but it can be - // guaranteed to be off by at most 1. Thus we run two loops, one with a - // smaller bit-width size (`kSmallWidth`) and one with a larger width size - // (satisfying `kLargeWidth == kSmallWidth + 1`). The loops are run - // `kSmallIters` and `kLargeIters` times respectively such - // that - // - // `kResultBits == kSmallIters * kSmallBits - // + kLargeIters * kLargeBits` - // - // where `kResultBits` is the total number of bits in `result_type`. - // - static constexpr size_t kResultBits = - std::numeric_limits<result_type>::digits; // w - static constexpr urbg_result_type kUrbgRange = RangeSize<URBG>(); // R - static constexpr size_t kUrbgBits = NumBits<URBG>(); // m - - // compute the initial estimate of the bits used. - // [rand.adapt.ibits] 2 (c) - static constexpr size_t kA = // ceil(w/m) - (kResultBits / kUrbgBits) + ((kResultBits % kUrbgBits) != 0); // n' - - static constexpr size_t kABits = kResultBits / kA; // w0' - static constexpr urbg_result_type kARejection = - ((kUrbgRange >> kABits) << kABits); // y0' - - // refine the selection to reduce the rejection frequency. - static constexpr size_t kTotalIters = - ((kUrbgRange - kARejection) <= (kARejection / kA)) ? kA : (kA + 1); // n - - // [rand.adapt.ibits] 2 (b) - static constexpr size_t kSmallIters = - kTotalIters - (kResultBits % kTotalIters); // n0 - static constexpr size_t kSmallBits = kResultBits / kTotalIters; // w0 - static constexpr urbg_result_type kSmallRejection = - ((kUrbgRange >> kSmallBits) << kSmallBits); // y0 - - static constexpr size_t kLargeBits = kSmallBits + 1; // w0+1 - static constexpr urbg_result_type kLargeRejection = - ((kUrbgRange >> kLargeBits) << kLargeBits); // y1 - - // - // Because `kLargeBits == kSmallBits + 1`, it follows that - // - // `kResultBits == kSmallIters * kSmallBits + kLargeIters` - // - // and therefore - // - // `kLargeIters == kTotalWidth % kSmallWidth` - // - // Intuitively, each iteration with the large width accounts for one unit - // of the remainder when `kTotalWidth` is divided by `kSmallWidth`. As - // mentioned above, if the URBG width is a divisor of `kTotalWidth`, then - // there would be no need for any large iterations (i.e., one loop would - // suffice), and indeed, in this case, `kLargeIters` would be zero. - static_assert(kResultBits == kSmallIters * kSmallBits + - (kTotalIters - kSmallIters) * kLargeBits, - "Error in looping constant calculations."); - - // The small shift is essentially small bits, but due to the potential - // of generating a smaller result_type from a larger urbg type, the actual - // shift might be 0. - static constexpr size_t kSmallShift = kSmallBits % kResultBits; - static constexpr auto kSmallMask = - MaskFromShift<urbg_result_type>(kSmallShift); - static constexpr size_t kLargeShift = kLargeBits % kResultBits; - static constexpr auto kLargeMask = - MaskFromShift<urbg_result_type>(kLargeShift); - - static constexpr auto kMin = (URBG::min)(); - - result_type s = 0; - for (size_t n = 0; n < kSmallIters; ++n) { - urbg_result_type v; - do { - v = g() - kMin; - } while (v >= kSmallRejection); - - s = (s << kSmallShift) + static_cast<result_type>(v & kSmallMask); - } - - for (size_t n = kSmallIters; n < kTotalIters; ++n) { - urbg_result_type v; - do { - v = g() - kMin; - } while (v >= kLargeRejection); - - s = (s << kLargeShift) + static_cast<result_type>(v & kLargeMask); - } - return s; -} - -} // namespace random_internal -ABSL_NAMESPACE_END -} // namespace absl - -#endif // ABSL_RANDOM_INTERNAL_FAST_UNIFORM_BITS_H_ diff --git a/third_party/abseil_cpp/absl/random/internal/fast_uniform_bits_test.cc b/third_party/abseil_cpp/absl/random/internal/fast_uniform_bits_test.cc deleted file mode 100644 index cee702df852d..000000000000 --- a/third_party/abseil_cpp/absl/random/internal/fast_uniform_bits_test.cc +++ /dev/null @@ -1,336 +0,0 @@ -// Copyright 2017 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#include "absl/random/internal/fast_uniform_bits.h" - -#include <random> - -#include "gtest/gtest.h" - -namespace absl { -ABSL_NAMESPACE_BEGIN -namespace random_internal { -namespace { - -template <typename IntType> -class FastUniformBitsTypedTest : public ::testing::Test {}; - -using IntTypes = ::testing::Types<uint8_t, uint16_t, uint32_t, uint64_t>; - -TYPED_TEST_SUITE(FastUniformBitsTypedTest, IntTypes); - -TYPED_TEST(FastUniformBitsTypedTest, BasicTest) { - using Limits = std::numeric_limits<TypeParam>; - using FastBits = FastUniformBits<TypeParam>; - - EXPECT_EQ(0, (FastBits::min)()); - EXPECT_EQ((Limits::max)(), (FastBits::max)()); - - constexpr int kIters = 10000; - std::random_device rd; - std::mt19937 gen(rd()); - FastBits fast; - for (int i = 0; i < kIters; i++) { - const auto v = fast(gen); - EXPECT_LE(v, (FastBits::max)()); - EXPECT_GE(v, (FastBits::min)()); - } -} - -template <typename UIntType, UIntType Lo, UIntType Hi, UIntType Val = Lo> -struct FakeUrbg { - using result_type = UIntType; - - FakeUrbg() = default; - explicit FakeUrbg(bool r) : reject(r) {} - - static constexpr result_type(max)() { return Hi; } - static constexpr result_type(min)() { return Lo; } - result_type operator()() { - // when reject is set, return Hi half the time. - return ((++calls % 2) == 1 && reject) ? Hi : Val; - } - - bool reject = false; - size_t calls = 0; -}; - -TEST(FastUniformBitsTest, IsPowerOfTwoOrZero) { - EXPECT_TRUE(IsPowerOfTwoOrZero(uint8_t{0})); - EXPECT_TRUE(IsPowerOfTwoOrZero(uint8_t{1})); - EXPECT_TRUE(IsPowerOfTwoOrZero(uint8_t{2})); - EXPECT_FALSE(IsPowerOfTwoOrZero(uint8_t{3})); - EXPECT_TRUE(IsPowerOfTwoOrZero(uint8_t{4})); - EXPECT_TRUE(IsPowerOfTwoOrZero(uint8_t{16})); - EXPECT_FALSE(IsPowerOfTwoOrZero(uint8_t{17})); - EXPECT_FALSE(IsPowerOfTwoOrZero((std::numeric_limits<uint8_t>::max)())); - - EXPECT_TRUE(IsPowerOfTwoOrZero(uint16_t{0})); - EXPECT_TRUE(IsPowerOfTwoOrZero(uint16_t{1})); - EXPECT_TRUE(IsPowerOfTwoOrZero(uint16_t{2})); - EXPECT_FALSE(IsPowerOfTwoOrZero(uint16_t{3})); - EXPECT_TRUE(IsPowerOfTwoOrZero(uint16_t{4})); - EXPECT_TRUE(IsPowerOfTwoOrZero(uint16_t{16})); - EXPECT_FALSE(IsPowerOfTwoOrZero(uint16_t{17})); - EXPECT_FALSE(IsPowerOfTwoOrZero((std::numeric_limits<uint16_t>::max)())); - - EXPECT_TRUE(IsPowerOfTwoOrZero(uint32_t{0})); - EXPECT_TRUE(IsPowerOfTwoOrZero(uint32_t{1})); - EXPECT_TRUE(IsPowerOfTwoOrZero(uint32_t{2})); - EXPECT_FALSE(IsPowerOfTwoOrZero(uint32_t{3})); - EXPECT_TRUE(IsPowerOfTwoOrZero(uint32_t{32})); - EXPECT_FALSE(IsPowerOfTwoOrZero(uint32_t{17})); - EXPECT_FALSE(IsPowerOfTwoOrZero((std::numeric_limits<uint32_t>::max)())); - - EXPECT_TRUE(IsPowerOfTwoOrZero(uint64_t{0})); - EXPECT_TRUE(IsPowerOfTwoOrZero(uint64_t{1})); - EXPECT_TRUE(IsPowerOfTwoOrZero(uint64_t{2})); - EXPECT_FALSE(IsPowerOfTwoOrZero(uint64_t{3})); - EXPECT_TRUE(IsPowerOfTwoOrZero(uint64_t{4})); - EXPECT_TRUE(IsPowerOfTwoOrZero(uint64_t{64})); - EXPECT_FALSE(IsPowerOfTwoOrZero(uint64_t{17})); - EXPECT_FALSE(IsPowerOfTwoOrZero((std::numeric_limits<uint64_t>::max)())); -} - -TEST(FastUniformBitsTest, IntegerLog2) { - EXPECT_EQ(0, IntegerLog2(uint16_t{0})); - EXPECT_EQ(0, IntegerLog2(uint16_t{1})); - EXPECT_EQ(1, IntegerLog2(uint16_t{2})); - EXPECT_EQ(1, IntegerLog2(uint16_t{3})); - EXPECT_EQ(2, IntegerLog2(uint16_t{4})); - EXPECT_EQ(2, IntegerLog2(uint16_t{5})); - EXPECT_EQ(2, IntegerLog2(uint16_t{7})); - EXPECT_EQ(3, IntegerLog2(uint16_t{8})); - EXPECT_EQ(63, IntegerLog2((std::numeric_limits<uint64_t>::max)())); -} - -TEST(FastUniformBitsTest, RangeSize) { - EXPECT_EQ(2, (RangeSize<FakeUrbg<uint8_t, 0, 1>>())); - EXPECT_EQ(3, (RangeSize<FakeUrbg<uint8_t, 0, 2>>())); - EXPECT_EQ(4, (RangeSize<FakeUrbg<uint8_t, 0, 3>>())); - // EXPECT_EQ(0, (RangeSize<FakeUrbg<uint8_t, 2, 2>>())); - EXPECT_EQ(4, (RangeSize<FakeUrbg<uint8_t, 2, 5>>())); - EXPECT_EQ(5, (RangeSize<FakeUrbg<uint8_t, 2, 6>>())); - EXPECT_EQ(9, (RangeSize<FakeUrbg<uint8_t, 2, 10>>())); - EXPECT_EQ( - 0, (RangeSize< - FakeUrbg<uint8_t, 0, (std::numeric_limits<uint8_t>::max)()>>())); - - EXPECT_EQ(4, (RangeSize<FakeUrbg<uint16_t, 0, 3>>())); - EXPECT_EQ(4, (RangeSize<FakeUrbg<uint16_t, 2, 5>>())); - EXPECT_EQ(5, (RangeSize<FakeUrbg<uint16_t, 2, 6>>())); - EXPECT_EQ(18, (RangeSize<FakeUrbg<uint16_t, 1000, 1017>>())); - EXPECT_EQ( - 0, (RangeSize< - FakeUrbg<uint16_t, 0, (std::numeric_limits<uint16_t>::max)()>>())); - - EXPECT_EQ(4, (RangeSize<FakeUrbg<uint32_t, 0, 3>>())); - EXPECT_EQ(4, (RangeSize<FakeUrbg<uint32_t, 2, 5>>())); - EXPECT_EQ(5, (RangeSize<FakeUrbg<uint32_t, 2, 6>>())); - EXPECT_EQ(18, (RangeSize<FakeUrbg<uint32_t, 1000, 1017>>())); - EXPECT_EQ(0, (RangeSize<FakeUrbg<uint32_t, 0, 0xffffffff>>())); - EXPECT_EQ(0xffffffff, (RangeSize<FakeUrbg<uint32_t, 1, 0xffffffff>>())); - EXPECT_EQ(0xfffffffe, (RangeSize<FakeUrbg<uint32_t, 1, 0xfffffffe>>())); - EXPECT_EQ(0xfffffffd, (RangeSize<FakeUrbg<uint32_t, 2, 0xfffffffe>>())); - EXPECT_EQ( - 0, (RangeSize< - FakeUrbg<uint32_t, 0, (std::numeric_limits<uint32_t>::max)()>>())); - - EXPECT_EQ(4, (RangeSize<FakeUrbg<uint64_t, 0, 3>>())); - EXPECT_EQ(4, (RangeSize<FakeUrbg<uint64_t, 2, 5>>())); - EXPECT_EQ(5, (RangeSize<FakeUrbg<uint64_t, 2, 6>>())); - EXPECT_EQ(18, (RangeSize<FakeUrbg<uint64_t, 1000, 1017>>())); - EXPECT_EQ(0x100000000, (RangeSize<FakeUrbg<uint64_t, 0, 0xffffffff>>())); - EXPECT_EQ(0xffffffff, (RangeSize<FakeUrbg<uint64_t, 1, 0xffffffff>>())); - EXPECT_EQ(0xfffffffe, (RangeSize<FakeUrbg<uint64_t, 1, 0xfffffffe>>())); - EXPECT_EQ(0xfffffffd, (RangeSize<FakeUrbg<uint64_t, 2, 0xfffffffe>>())); - EXPECT_EQ(0, (RangeSize<FakeUrbg<uint64_t, 0, 0xffffffffffffffff>>())); - EXPECT_EQ(0xffffffffffffffff, - (RangeSize<FakeUrbg<uint64_t, 1, 0xffffffffffffffff>>())); - EXPECT_EQ(0xfffffffffffffffe, - (RangeSize<FakeUrbg<uint64_t, 1, 0xfffffffffffffffe>>())); - EXPECT_EQ(0xfffffffffffffffd, - (RangeSize<FakeUrbg<uint64_t, 2, 0xfffffffffffffffe>>())); - EXPECT_EQ( - 0, (RangeSize< - FakeUrbg<uint64_t, 0, (std::numeric_limits<uint64_t>::max)()>>())); -} - -// The constants need to be choosen so that an infinite rejection loop doesn't -// happen... -using Urng1_5bit = FakeUrbg<uint8_t, 0, 2, 0>; // ~1.5 bits (range 3) -using Urng4bits = FakeUrbg<uint8_t, 1, 0x10, 2>; -using Urng22bits = FakeUrbg<uint32_t, 0, 0x3fffff, 0x301020>; -using Urng31bits = FakeUrbg<uint32_t, 1, 0xfffffffe, 0x60070f03>; // ~31.9 bits -using Urng32bits = FakeUrbg<uint32_t, 0, 0xffffffff, 0x74010f01>; -using Urng33bits = - FakeUrbg<uint64_t, 1, 0x1ffffffff, 0x013301033>; // ~32.9 bits -using Urng63bits = FakeUrbg<uint64_t, 1, 0xfffffffffffffffe, - 0xfedcba9012345678>; // ~63.9 bits -using Urng64bits = - FakeUrbg<uint64_t, 0, 0xffffffffffffffff, 0x123456780fedcba9>; - -TEST(FastUniformBitsTest, OutputsUpTo32Bits) { - // Tests that how values are composed; the single-bit deltas should be spread - // across each invocation. - Urng1_5bit urng1_5; - Urng4bits urng4; - Urng22bits urng22; - Urng31bits urng31; - Urng32bits urng32; - Urng33bits urng33; - Urng63bits urng63; - Urng64bits urng64; - - // 8-bit types - { - FastUniformBits<uint8_t> fast8; - EXPECT_EQ(0x0, fast8(urng1_5)); - EXPECT_EQ(0x11, fast8(urng4)); - EXPECT_EQ(0x20, fast8(urng22)); - EXPECT_EQ(0x2, fast8(urng31)); - EXPECT_EQ(0x1, fast8(urng32)); - EXPECT_EQ(0x32, fast8(urng33)); - EXPECT_EQ(0x77, fast8(urng63)); - EXPECT_EQ(0xa9, fast8(urng64)); - } - - // 16-bit types - { - FastUniformBits<uint16_t> fast16; - EXPECT_EQ(0x0, fast16(urng1_5)); - EXPECT_EQ(0x1111, fast16(urng4)); - EXPECT_EQ(0x1020, fast16(urng22)); - EXPECT_EQ(0x0f02, fast16(urng31)); - EXPECT_EQ(0x0f01, fast16(urng32)); - EXPECT_EQ(0x1032, fast16(urng33)); - EXPECT_EQ(0x5677, fast16(urng63)); - EXPECT_EQ(0xcba9, fast16(urng64)); - } - - // 32-bit types - { - FastUniformBits<uint32_t> fast32; - EXPECT_EQ(0x0, fast32(urng1_5)); - EXPECT_EQ(0x11111111, fast32(urng4)); - EXPECT_EQ(0x08301020, fast32(urng22)); - EXPECT_EQ(0x0f020f02, fast32(urng31)); - EXPECT_EQ(0x74010f01, fast32(urng32)); - EXPECT_EQ(0x13301032, fast32(urng33)); - EXPECT_EQ(0x12345677, fast32(urng63)); - EXPECT_EQ(0x0fedcba9, fast32(urng64)); - } -} - -TEST(FastUniformBitsTest, Outputs64Bits) { - // Tests that how values are composed; the single-bit deltas should be spread - // across each invocation. - FastUniformBits<uint64_t> fast64; - - { - FakeUrbg<uint8_t, 0, 1, 0> urng0; - FakeUrbg<uint8_t, 0, 1, 1> urng1; - Urng4bits urng4; - Urng22bits urng22; - Urng31bits urng31; - Urng32bits urng32; - Urng33bits urng33; - Urng63bits urng63; - Urng64bits urng64; - - // somewhat degenerate cases only create a single bit. - EXPECT_EQ(0x0, fast64(urng0)); - EXPECT_EQ(64, urng0.calls); - EXPECT_EQ(0xffffffffffffffff, fast64(urng1)); - EXPECT_EQ(64, urng1.calls); - - // less degenerate cases. - EXPECT_EQ(0x1111111111111111, fast64(urng4)); - EXPECT_EQ(16, urng4.calls); - EXPECT_EQ(0x01020c0408301020, fast64(urng22)); - EXPECT_EQ(3, urng22.calls); - EXPECT_EQ(0x387811c3c0870f02, fast64(urng31)); - EXPECT_EQ(3, urng31.calls); - EXPECT_EQ(0x74010f0174010f01, fast64(urng32)); - EXPECT_EQ(2, urng32.calls); - EXPECT_EQ(0x808194040cb01032, fast64(urng33)); - EXPECT_EQ(3, urng33.calls); - EXPECT_EQ(0x1234567712345677, fast64(urng63)); - EXPECT_EQ(2, urng63.calls); - EXPECT_EQ(0x123456780fedcba9, fast64(urng64)); - EXPECT_EQ(1, urng64.calls); - } - - // The 1.5 bit case is somewhat interesting in that the algorithm refinement - // causes one extra small sample. Comments here reference the names used in - // [rand.adapt.ibits] that correspond to this case. - { - Urng1_5bit urng1_5; - - // w = 64 - // R = 3 - // m = 1 - // n' = 64 - // w0' = 1 - // y0' = 2 - // n = (1 <= 0) > 64 : 65 = 65 - // n0 = 65 - (64%65) = 1 - // n1 = 64 - // w0 = 0 - // y0 = 3 - // w1 = 1 - // y1 = 2 - EXPECT_EQ(0x0, fast64(urng1_5)); - EXPECT_EQ(65, urng1_5.calls); - } - - // Validate rejections for non-power-of-2 cases. - { - Urng1_5bit urng1_5(true); - Urng31bits urng31(true); - Urng33bits urng33(true); - Urng63bits urng63(true); - - // For 1.5 bits, there would be 1+2*64, except the first - // value was accepted and shifted off the end. - EXPECT_EQ(0, fast64(urng1_5)); - EXPECT_EQ(128, urng1_5.calls); - EXPECT_EQ(0x387811c3c0870f02, fast64(urng31)); - EXPECT_EQ(6, urng31.calls); - EXPECT_EQ(0x808194040cb01032, fast64(urng33)); - EXPECT_EQ(6, urng33.calls); - EXPECT_EQ(0x1234567712345677, fast64(urng63)); - EXPECT_EQ(4, urng63.calls); - } -} - -TEST(FastUniformBitsTest, URBG32bitRegression) { - // Validate with deterministic 32-bit std::minstd_rand - // to ensure that operator() performs as expected. - - EXPECT_EQ(2147483646, RangeSize<std::minstd_rand>()); - EXPECT_EQ(30, IntegerLog2(RangeSize<std::minstd_rand>())); - - std::minstd_rand gen(1); - FastUniformBits<uint64_t> fast64; - - EXPECT_EQ(0x05e47095f8791f45, fast64(gen)); - EXPECT_EQ(0x028be17e3c07c122, fast64(gen)); - EXPECT_EQ(0x55d2847c1626e8c2, fast64(gen)); -} - -} // namespace -} // namespace random_internal -ABSL_NAMESPACE_END -} // namespace absl diff --git a/third_party/abseil_cpp/absl/random/internal/fastmath.h b/third_party/abseil_cpp/absl/random/internal/fastmath.h deleted file mode 100644 index 6baeb5a7c917..000000000000 --- a/third_party/abseil_cpp/absl/random/internal/fastmath.h +++ /dev/null @@ -1,74 +0,0 @@ -// Copyright 2017 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#ifndef ABSL_RANDOM_INTERNAL_FASTMATH_H_ -#define ABSL_RANDOM_INTERNAL_FASTMATH_H_ - -// This file contains fast math functions (bitwise ops as well as some others) -// which are implementation details of various absl random number distributions. - -#include <cassert> -#include <cmath> -#include <cstdint> - -#include "absl/base/internal/bits.h" - -namespace absl { -ABSL_NAMESPACE_BEGIN -namespace random_internal { - -// Returns the position of the first bit set. -inline int LeadingSetBit(uint64_t n) { - return 64 - base_internal::CountLeadingZeros64(n); -} - -// Compute log2(n) using integer operations. -// While std::log2 is more accurate than std::log(n) / std::log(2), for -// very large numbers--those close to std::numeric_limits<uint64_t>::max() - 2, -// for instance--std::log2 rounds up rather than down, which introduces -// definite skew in the results. -inline int IntLog2Floor(uint64_t n) { - return (n <= 1) ? 0 : (63 - base_internal::CountLeadingZeros64(n)); -} -inline int IntLog2Ceil(uint64_t n) { - return (n <= 1) ? 0 : (64 - base_internal::CountLeadingZeros64(n - 1)); -} - -inline double StirlingLogFactorial(double n) { - assert(n >= 1); - // Using Stirling's approximation. - constexpr double kLog2PI = 1.83787706640934548356; - const double logn = std::log(n); - const double ninv = 1.0 / static_cast<double>(n); - return n * logn - n + 0.5 * (kLog2PI + logn) + (1.0 / 12.0) * ninv - - (1.0 / 360.0) * ninv * ninv * ninv; -} - -// Rotate value right. -// -// We only implement the uint32_t / uint64_t versions because -// 1) those are the only ones we use, and -// 2) those are the only ones where clang detects the rotate idiom correctly. -inline constexpr uint32_t rotr(uint32_t value, uint8_t bits) { - return (value >> (bits & 31)) | (value << ((-bits) & 31)); -} -inline constexpr uint64_t rotr(uint64_t value, uint8_t bits) { - return (value >> (bits & 63)) | (value << ((-bits) & 63)); -} - -} // namespace random_internal -ABSL_NAMESPACE_END -} // namespace absl - -#endif // ABSL_RANDOM_INTERNAL_FASTMATH_H_ diff --git a/third_party/abseil_cpp/absl/random/internal/fastmath_test.cc b/third_party/abseil_cpp/absl/random/internal/fastmath_test.cc deleted file mode 100644 index 65859c25de4c..000000000000 --- a/third_party/abseil_cpp/absl/random/internal/fastmath_test.cc +++ /dev/null @@ -1,110 +0,0 @@ -// Copyright 2017 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#include "absl/random/internal/fastmath.h" - -#include "gtest/gtest.h" - -#if defined(__native_client__) || defined(__EMSCRIPTEN__) -// NACL has a less accurate implementation of std::log2 than most of -// the other platforms. For some values which should have integral results, -// sometimes NACL returns slightly larger values. -// -// The MUSL libc used by emscripten also has a similar bug. -#define ABSL_RANDOM_INACCURATE_LOG2 -#endif - -namespace { - -TEST(DistributionImplTest, LeadingSetBit) { - using absl::random_internal::LeadingSetBit; - constexpr uint64_t kZero = 0; - EXPECT_EQ(0, LeadingSetBit(kZero)); - EXPECT_EQ(64, LeadingSetBit(~kZero)); - - for (int index = 0; index < 64; index++) { - uint64_t x = static_cast<uint64_t>(1) << index; - EXPECT_EQ(index + 1, LeadingSetBit(x)) << index; - EXPECT_EQ(index + 1, LeadingSetBit(x + x - 1)) << index; - } -} - -TEST(FastMathTest, IntLog2FloorTest) { - using absl::random_internal::IntLog2Floor; - constexpr uint64_t kZero = 0; - EXPECT_EQ(0, IntLog2Floor(0)); // boundary. return 0. - EXPECT_EQ(0, IntLog2Floor(1)); - EXPECT_EQ(1, IntLog2Floor(2)); - EXPECT_EQ(63, IntLog2Floor(~kZero)); - - // A boundary case: Converting 0xffffffffffffffff requires > 53 - // bits of precision, so the conversion to double rounds up, - // and the result of std::log2(x) > IntLog2Floor(x). - EXPECT_LT(IntLog2Floor(~kZero), static_cast<int>(std::log2(~kZero))); - - for (int i = 0; i < 64; i++) { - const uint64_t i_pow_2 = static_cast<uint64_t>(1) << i; - EXPECT_EQ(i, IntLog2Floor(i_pow_2)); - EXPECT_EQ(i, static_cast<int>(std::log2(i_pow_2))); - - uint64_t y = i_pow_2; - for (int j = i - 1; j > 0; --j) { - y = y | (i_pow_2 >> j); - EXPECT_EQ(i, IntLog2Floor(y)); - } - } -} - -TEST(FastMathTest, IntLog2CeilTest) { - using absl::random_internal::IntLog2Ceil; - constexpr uint64_t kZero = 0; - EXPECT_EQ(0, IntLog2Ceil(0)); // boundary. return 0. - EXPECT_EQ(0, IntLog2Ceil(1)); - EXPECT_EQ(1, IntLog2Ceil(2)); - EXPECT_EQ(64, IntLog2Ceil(~kZero)); - - // A boundary case: Converting 0xffffffffffffffff requires > 53 - // bits of precision, so the conversion to double rounds up, - // and the result of std::log2(x) > IntLog2Floor(x). - EXPECT_LE(IntLog2Ceil(~kZero), static_cast<int>(std::log2(~kZero))); - - for (int i = 0; i < 64; i++) { - const uint64_t i_pow_2 = static_cast<uint64_t>(1) << i; - EXPECT_EQ(i, IntLog2Ceil(i_pow_2)); -#ifndef ABSL_RANDOM_INACCURATE_LOG2 - EXPECT_EQ(i, static_cast<int>(std::ceil(std::log2(i_pow_2)))); -#endif - - uint64_t y = i_pow_2; - for (int j = i - 1; j > 0; --j) { - y = y | (i_pow_2 >> j); - EXPECT_EQ(i + 1, IntLog2Ceil(y)); - } - } -} - -TEST(FastMathTest, StirlingLogFactorial) { - using absl::random_internal::StirlingLogFactorial; - - EXPECT_NEAR(StirlingLogFactorial(1.0), 0, 1e-3); - EXPECT_NEAR(StirlingLogFactorial(1.50), 0.284683, 1e-3); - EXPECT_NEAR(StirlingLogFactorial(2.0), 0.69314718056, 1e-4); - - for (int i = 2; i < 50; i++) { - double d = static_cast<double>(i); - EXPECT_NEAR(StirlingLogFactorial(d), std::lgamma(d + 1), 3e-5); - } -} - -} // namespace diff --git a/third_party/abseil_cpp/absl/random/internal/gaussian_distribution_gentables.cc b/third_party/abseil_cpp/absl/random/internal/gaussian_distribution_gentables.cc deleted file mode 100644 index a95333d55f55..000000000000 --- a/third_party/abseil_cpp/absl/random/internal/gaussian_distribution_gentables.cc +++ /dev/null @@ -1,143 +0,0 @@ -// Copyright 2017 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -// Generates gaussian_distribution.cc -// -// $ blaze run :gaussian_distribution_gentables > gaussian_distribution.cc -// -#include "absl/random/gaussian_distribution.h" - -#include <cmath> -#include <cstddef> -#include <iostream> -#include <limits> -#include <string> - -#include "absl/base/macros.h" - -namespace absl { -ABSL_NAMESPACE_BEGIN -namespace random_internal { -namespace { - -template <typename T, size_t N> -void FormatArrayContents(std::ostream* os, T (&data)[N]) { - if (!std::numeric_limits<T>::is_exact) { - // Note: T is either an integer or a float. - // float requires higher precision to ensure that values are - // reproduced exactly. - // Trivia: C99 has hexadecimal floating point literals, but C++11 does not. - // Using them would remove all concern of precision loss. - os->precision(std::numeric_limits<T>::max_digits10 + 2); - } - *os << " {"; - std::string separator = ""; - for (size_t i = 0; i < N; ++i) { - *os << separator << data[i]; - if ((i + 1) % 3 != 0) { - separator = ", "; - } else { - separator = ",\n "; - } - } - *os << "}"; -} - -} // namespace - -class TableGenerator : public gaussian_distribution_base { - public: - TableGenerator(); - void Print(std::ostream* os); - - using gaussian_distribution_base::kMask; - using gaussian_distribution_base::kR; - using gaussian_distribution_base::kV; - - private: - Tables tables_; -}; - -// Ziggurat gaussian initialization. For an explanation of the algorithm, see -// the Marsaglia paper, "The Ziggurat Method for Generating Random Variables". -// http://www.jstatsoft.org/v05/i08/ -// -// Further details are available in the Doornik paper -// https://www.doornik.com/research/ziggurat.pdf -// -TableGenerator::TableGenerator() { - // The constants here should match the values in gaussian_distribution.h - static constexpr int kC = kMask + 1; - - static_assert((ABSL_ARRAYSIZE(tables_.x) == kC + 1), - "xArray must be length kMask + 2"); - - static_assert((ABSL_ARRAYSIZE(tables_.x) == ABSL_ARRAYSIZE(tables_.f)), - "fx and x arrays must be identical length"); - - auto f = [](double x) { return std::exp(-0.5 * x * x); }; - auto f_inv = [](double x) { return std::sqrt(-2.0 * std::log(x)); }; - - tables_.x[0] = kV / f(kR); - tables_.f[0] = f(tables_.x[0]); - - tables_.x[1] = kR; - tables_.f[1] = f(tables_.x[1]); - - tables_.x[kC] = 0.0; - tables_.f[kC] = f(tables_.x[kC]); // 1.0 - - for (int i = 2; i < kC; i++) { - double v = (kV / tables_.x[i - 1]) + tables_.f[i - 1]; - tables_.x[i] = f_inv(v); - tables_.f[i] = v; - } -} - -void TableGenerator::Print(std::ostream* os) { - *os << "// BEGIN GENERATED CODE; DO NOT EDIT\n" - "// clang-format off\n" - "\n" - "#include \"absl/random/gaussian_distribution.h\"\n" - "\n" - "namespace absl {\n" - "ABSL_NAMESPACE_BEGIN\n" - "namespace random_internal {\n" - "\n" - "const gaussian_distribution_base::Tables\n" - " gaussian_distribution_base::zg_ = {\n"; - FormatArrayContents(os, tables_.x); - *os << ",\n"; - FormatArrayContents(os, tables_.f); - *os << "};\n" - "\n" - "} // namespace random_internal\n" - "ABSL_NAMESPACE_END\n" - "} // namespace absl\n" - "\n" - "// clang-format on\n" - "// END GENERATED CODE"; - *os << std::endl; -} - -} // namespace random_internal -ABSL_NAMESPACE_END -} // namespace absl - -int main(int, char**) { - std::cerr << "\nCopy the output to gaussian_distribution.cc" << std::endl; - absl::random_internal::TableGenerator generator; - generator.Print(&std::cout); - return 0; -} diff --git a/third_party/abseil_cpp/absl/random/internal/generate_real.h b/third_party/abseil_cpp/absl/random/internal/generate_real.h deleted file mode 100644 index 20f6d20807d5..000000000000 --- a/third_party/abseil_cpp/absl/random/internal/generate_real.h +++ /dev/null @@ -1,146 +0,0 @@ -// Copyright 2017 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#ifndef ABSL_RANDOM_INTERNAL_GENERATE_REAL_H_ -#define ABSL_RANDOM_INTERNAL_GENERATE_REAL_H_ - -// This file contains some implementation details which are used by one or more -// of the absl random number distributions. - -#include <cstdint> -#include <cstring> -#include <limits> -#include <type_traits> - -#include "absl/base/internal/bits.h" -#include "absl/meta/type_traits.h" -#include "absl/random/internal/fastmath.h" -#include "absl/random/internal/traits.h" - -namespace absl { -ABSL_NAMESPACE_BEGIN -namespace random_internal { - -// Tristate tag types controlling the output of GenerateRealFromBits. -struct GeneratePositiveTag {}; -struct GenerateNegativeTag {}; -struct GenerateSignedTag {}; - -// GenerateRealFromBits generates a single real value from a single 64-bit -// `bits` with template fields controlling the output. -// -// The `SignedTag` parameter controls whether positive, negative, -// or either signed/unsigned may be returned. -// When SignedTag == GeneratePositiveTag, range is U(0, 1) -// When SignedTag == GenerateNegativeTag, range is U(-1, 0) -// When SignedTag == GenerateSignedTag, range is U(-1, 1) -// -// When the `IncludeZero` parameter is true, the function may return 0 for some -// inputs, otherwise it never returns 0. -// -// When a value in U(0,1) is required, use: -// Uniform64ToReal<double, PositiveValueT, true>; -// -// When a value in U(-1,1) is required, use: -// Uniform64ToReal<double, SignedValueT, false>; -// -// This generates more distinct values than the mathematical equivalent -// `U(0, 1) * 2.0 - 1.0`. -// -// Scaling the result by powers of 2 (and avoiding a multiply) is also possible: -// GenerateRealFromBits<double>(..., -1); => U(0, 0.5) -// GenerateRealFromBits<double>(..., 1); => U(0, 2) -// -template <typename RealType, // Real type, either float or double. - typename SignedTag = GeneratePositiveTag, // Whether a positive, - // negative, or signed - // value is generated. - bool IncludeZero = true> -inline RealType GenerateRealFromBits(uint64_t bits, int exp_bias = 0) { - using real_type = RealType; - using uint_type = absl::conditional_t<std::is_same<real_type, float>::value, - uint32_t, uint64_t>; - - static_assert( - (std::is_same<double, real_type>::value || - std::is_same<float, real_type>::value), - "GenerateRealFromBits must be parameterized by either float or double."); - - static_assert(sizeof(uint_type) == sizeof(real_type), - "Mismatched unsinged and real types."); - - static_assert((std::numeric_limits<real_type>::is_iec559 && - std::numeric_limits<real_type>::radix == 2), - "RealType representation is not IEEE 754 binary."); - - static_assert((std::is_same<SignedTag, GeneratePositiveTag>::value || - std::is_same<SignedTag, GenerateNegativeTag>::value || - std::is_same<SignedTag, GenerateSignedTag>::value), - ""); - - static constexpr int kExp = std::numeric_limits<real_type>::digits - 1; - static constexpr uint_type kMask = (static_cast<uint_type>(1) << kExp) - 1u; - static constexpr int kUintBits = sizeof(uint_type) * 8; - - int exp = exp_bias + int{std::numeric_limits<real_type>::max_exponent - 2}; - - // Determine the sign bit. - // Depending on the SignedTag, this may use the left-most bit - // or it may be a constant value. - uint_type sign = std::is_same<SignedTag, GenerateNegativeTag>::value - ? (static_cast<uint_type>(1) << (kUintBits - 1)) - : 0; - if (std::is_same<SignedTag, GenerateSignedTag>::value) { - if (std::is_same<uint_type, uint64_t>::value) { - sign = bits & uint64_t{0x8000000000000000}; - } - if (std::is_same<uint_type, uint32_t>::value) { - const uint64_t tmp = bits & uint64_t{0x8000000000000000}; - sign = static_cast<uint32_t>(tmp >> 32); - } - // adjust the bits and the exponent to account for removing - // the leading bit. - bits = bits & uint64_t{0x7FFFFFFFFFFFFFFF}; - exp++; - } - if (IncludeZero) { - if (bits == 0u) return 0; - } - - // Number of leading zeros is mapped to the exponent: 2^-clz - // bits is 0..01xxxxxx. After shifting, we're left with 1xxx...0..0 - int clz = base_internal::CountLeadingZeros64(bits); - bits <<= (IncludeZero ? clz : (clz & 63)); // remove 0-bits. - exp -= clz; // set the exponent. - bits >>= (63 - kExp); - - // Construct the 32-bit or 64-bit IEEE 754 floating-point value from - // the individual fields: sign, exp, mantissa(bits). - uint_type val = - (std::is_same<SignedTag, GeneratePositiveTag>::value ? 0u : sign) | - (static_cast<uint_type>(exp) << kExp) | - (static_cast<uint_type>(bits) & kMask); - - // bit_cast to the output-type - real_type result; - memcpy(static_cast<void*>(&result), static_cast<const void*>(&val), - sizeof(result)); - return result; -} - -} // namespace random_internal -ABSL_NAMESPACE_END -} // namespace absl - -#endif // ABSL_RANDOM_INTERNAL_GENERATE_REAL_H_ diff --git a/third_party/abseil_cpp/absl/random/internal/generate_real_test.cc b/third_party/abseil_cpp/absl/random/internal/generate_real_test.cc deleted file mode 100644 index 4bdc453483e4..000000000000 --- a/third_party/abseil_cpp/absl/random/internal/generate_real_test.cc +++ /dev/null @@ -1,497 +0,0 @@ -// Copyright 2017 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#include "absl/random/internal/generate_real.h" - -#include <cfloat> -#include <cstddef> -#include <cstdint> -#include <string> - -#include "gtest/gtest.h" -#include "absl/base/internal/bits.h" -#include "absl/flags/flag.h" - -ABSL_FLAG(int64_t, absl_random_test_trials, 50000, - "Number of trials for the probability tests."); - -using absl::random_internal::GenerateNegativeTag; -using absl::random_internal::GeneratePositiveTag; -using absl::random_internal::GenerateRealFromBits; -using absl::random_internal::GenerateSignedTag; - -namespace { - -TEST(GenerateRealTest, U64ToFloat_Positive_NoZero_Test) { - auto ToFloat = [](uint64_t a) { - return GenerateRealFromBits<float, GeneratePositiveTag, false>(a); - }; - EXPECT_EQ(ToFloat(0x0000000000000000), 2.710505431e-20f); - EXPECT_EQ(ToFloat(0x0000000000000001), 5.421010862e-20f); - EXPECT_EQ(ToFloat(0x8000000000000000), 0.5); - EXPECT_EQ(ToFloat(0x8000000000000001), 0.5); - EXPECT_EQ(ToFloat(0xFFFFFFFFFFFFFFFF), 0.9999999404f); -} - -TEST(GenerateRealTest, U64ToFloat_Positive_Zero_Test) { - auto ToFloat = [](uint64_t a) { - return GenerateRealFromBits<float, GeneratePositiveTag, true>(a); - }; - EXPECT_EQ(ToFloat(0x0000000000000000), 0.0); - EXPECT_EQ(ToFloat(0x0000000000000001), 5.421010862e-20f); - EXPECT_EQ(ToFloat(0x8000000000000000), 0.5); - EXPECT_EQ(ToFloat(0x8000000000000001), 0.5); - EXPECT_EQ(ToFloat(0xFFFFFFFFFFFFFFFF), 0.9999999404f); -} - -TEST(GenerateRealTest, U64ToFloat_Negative_NoZero_Test) { - auto ToFloat = [](uint64_t a) { - return GenerateRealFromBits<float, GenerateNegativeTag, false>(a); - }; - EXPECT_EQ(ToFloat(0x0000000000000000), -2.710505431e-20f); - EXPECT_EQ(ToFloat(0x0000000000000001), -5.421010862e-20f); - EXPECT_EQ(ToFloat(0x8000000000000000), -0.5); - EXPECT_EQ(ToFloat(0x8000000000000001), -0.5); - EXPECT_EQ(ToFloat(0xFFFFFFFFFFFFFFFF), -0.9999999404f); -} - -TEST(GenerateRealTest, U64ToFloat_Negative_Zero_Test) { - auto ToFloat = [](uint64_t a) { - return GenerateRealFromBits<float, GenerateNegativeTag, true>(a); - }; - EXPECT_EQ(ToFloat(0x0000000000000000), 0.0); - EXPECT_EQ(ToFloat(0x0000000000000001), -5.421010862e-20f); - EXPECT_EQ(ToFloat(0x8000000000000000), -0.5); - EXPECT_EQ(ToFloat(0x8000000000000001), -0.5); - EXPECT_EQ(ToFloat(0xFFFFFFFFFFFFFFFF), -0.9999999404f); -} - -TEST(GenerateRealTest, U64ToFloat_Signed_NoZero_Test) { - auto ToFloat = [](uint64_t a) { - return GenerateRealFromBits<float, GenerateSignedTag, false>(a); - }; - EXPECT_EQ(ToFloat(0x0000000000000000), 5.421010862e-20f); - EXPECT_EQ(ToFloat(0x0000000000000001), 1.084202172e-19f); - EXPECT_EQ(ToFloat(0x7FFFFFFFFFFFFFFF), 0.9999999404f); - EXPECT_EQ(ToFloat(0x8000000000000000), -5.421010862e-20f); - EXPECT_EQ(ToFloat(0x8000000000000001), -1.084202172e-19f); - EXPECT_EQ(ToFloat(0xFFFFFFFFFFFFFFFF), -0.9999999404f); -} - -TEST(GenerateRealTest, U64ToFloat_Signed_Zero_Test) { - auto ToFloat = [](uint64_t a) { - return GenerateRealFromBits<float, GenerateSignedTag, true>(a); - }; - EXPECT_EQ(ToFloat(0x0000000000000000), 0); - EXPECT_EQ(ToFloat(0x0000000000000001), 1.084202172e-19f); - EXPECT_EQ(ToFloat(0x7FFFFFFFFFFFFFFF), 0.9999999404f); - EXPECT_EQ(ToFloat(0x8000000000000000), 0); - EXPECT_EQ(ToFloat(0x8000000000000001), -1.084202172e-19f); - EXPECT_EQ(ToFloat(0xFFFFFFFFFFFFFFFF), -0.9999999404f); -} - -TEST(GenerateRealTest, U64ToFloat_Signed_Bias_Test) { - auto ToFloat = [](uint64_t a) { - return GenerateRealFromBits<float, GenerateSignedTag, true>(a, 1); - }; - EXPECT_EQ(ToFloat(0x0000000000000000), 0); - EXPECT_EQ(ToFloat(0x0000000000000001), 2 * 1.084202172e-19f); - EXPECT_EQ(ToFloat(0x7FFFFFFFFFFFFFFF), 2 * 0.9999999404f); - EXPECT_EQ(ToFloat(0x8000000000000000), 0); - EXPECT_EQ(ToFloat(0x8000000000000001), 2 * -1.084202172e-19f); - EXPECT_EQ(ToFloat(0xFFFFFFFFFFFFFFFF), 2 * -0.9999999404f); -} - -TEST(GenerateRealTest, U64ToFloatTest) { - auto ToFloat = [](uint64_t a) -> float { - return GenerateRealFromBits<float, GeneratePositiveTag, true>(a); - }; - - EXPECT_EQ(ToFloat(0x0000000000000000), 0.0f); - - EXPECT_EQ(ToFloat(0x8000000000000000), 0.5f); - EXPECT_EQ(ToFloat(0x8000000000000001), 0.5f); - EXPECT_EQ(ToFloat(0x800000FFFFFFFFFF), 0.5f); - EXPECT_EQ(ToFloat(0xFFFFFFFFFFFFFFFF), 0.9999999404f); - - EXPECT_GT(ToFloat(0x0000000000000001), 0.0f); - - EXPECT_NE(ToFloat(0x7FFFFF0000000000), ToFloat(0x7FFFFEFFFFFFFFFF)); - - EXPECT_LT(ToFloat(0xFFFFFFFFFFFFFFFF), 1.0f); - int32_t two_to_24 = 1 << 24; - EXPECT_EQ(static_cast<int32_t>(ToFloat(0xFFFFFFFFFFFFFFFF) * two_to_24), - two_to_24 - 1); - EXPECT_NE(static_cast<int32_t>(ToFloat(0xFFFFFFFFFFFFFFFF) * two_to_24 * 2), - two_to_24 * 2 - 1); - EXPECT_EQ(ToFloat(0xFFFFFFFFFFFFFFFF), ToFloat(0xFFFFFF0000000000)); - EXPECT_NE(ToFloat(0xFFFFFFFFFFFFFFFF), ToFloat(0xFFFFFEFFFFFFFFFF)); - EXPECT_EQ(ToFloat(0x7FFFFFFFFFFFFFFF), ToFloat(0x7FFFFF8000000000)); - EXPECT_NE(ToFloat(0x7FFFFFFFFFFFFFFF), ToFloat(0x7FFFFF7FFFFFFFFF)); - EXPECT_EQ(ToFloat(0x3FFFFFFFFFFFFFFF), ToFloat(0x3FFFFFC000000000)); - EXPECT_NE(ToFloat(0x3FFFFFFFFFFFFFFF), ToFloat(0x3FFFFFBFFFFFFFFF)); - - // For values where every bit counts, the values scale as multiples of the - // input. - for (int i = 0; i < 100; ++i) { - EXPECT_EQ(i * ToFloat(0x0000000000000001), ToFloat(i)); - } - - // For each i: value generated from (1 << i). - float exp_values[64]; - exp_values[63] = 0.5f; - for (int i = 62; i >= 0; --i) exp_values[i] = 0.5f * exp_values[i + 1]; - constexpr uint64_t one = 1; - for (int i = 0; i < 64; ++i) { - EXPECT_EQ(ToFloat(one << i), exp_values[i]); - for (int j = 1; j < FLT_MANT_DIG && i - j >= 0; ++j) { - EXPECT_NE(exp_values[i] + exp_values[i - j], exp_values[i]); - EXPECT_EQ(ToFloat((one << i) + (one << (i - j))), - exp_values[i] + exp_values[i - j]); - } - for (int j = FLT_MANT_DIG; i - j >= 0; ++j) { - EXPECT_EQ(exp_values[i] + exp_values[i - j], exp_values[i]); - EXPECT_EQ(ToFloat((one << i) + (one << (i - j))), exp_values[i]); - } - } -} - -TEST(GenerateRealTest, U64ToDouble_Positive_NoZero_Test) { - auto ToDouble = [](uint64_t a) { - return GenerateRealFromBits<double, GeneratePositiveTag, false>(a); - }; - - EXPECT_EQ(ToDouble(0x0000000000000000), 2.710505431213761085e-20); - EXPECT_EQ(ToDouble(0x0000000000000001), 5.42101086242752217004e-20); - EXPECT_EQ(ToDouble(0x0000000000000002), 1.084202172485504434e-19); - EXPECT_EQ(ToDouble(0x8000000000000000), 0.5); - EXPECT_EQ(ToDouble(0x8000000000000001), 0.5); - EXPECT_EQ(ToDouble(0xFFFFFFFFFFFFFFFF), 0.999999999999999888978); -} - -TEST(GenerateRealTest, U64ToDouble_Positive_Zero_Test) { - auto ToDouble = [](uint64_t a) { - return GenerateRealFromBits<double, GeneratePositiveTag, true>(a); - }; - - EXPECT_EQ(ToDouble(0x0000000000000000), 0.0); - EXPECT_EQ(ToDouble(0x0000000000000001), 5.42101086242752217004e-20); - EXPECT_EQ(ToDouble(0x8000000000000000), 0.5); - EXPECT_EQ(ToDouble(0x8000000000000001), 0.5); - EXPECT_EQ(ToDouble(0xFFFFFFFFFFFFFFFF), 0.999999999999999888978); -} - -TEST(GenerateRealTest, U64ToDouble_Negative_NoZero_Test) { - auto ToDouble = [](uint64_t a) { - return GenerateRealFromBits<double, GenerateNegativeTag, false>(a); - }; - - EXPECT_EQ(ToDouble(0x0000000000000000), -2.710505431213761085e-20); - EXPECT_EQ(ToDouble(0x0000000000000001), -5.42101086242752217004e-20); - EXPECT_EQ(ToDouble(0x0000000000000002), -1.084202172485504434e-19); - EXPECT_EQ(ToDouble(0x8000000000000000), -0.5); - EXPECT_EQ(ToDouble(0x8000000000000001), -0.5); - EXPECT_EQ(ToDouble(0xFFFFFFFFFFFFFFFF), -0.999999999999999888978); -} - -TEST(GenerateRealTest, U64ToDouble_Negative_Zero_Test) { - auto ToDouble = [](uint64_t a) { - return GenerateRealFromBits<double, GenerateNegativeTag, true>(a); - }; - - EXPECT_EQ(ToDouble(0x0000000000000000), 0.0); - EXPECT_EQ(ToDouble(0x0000000000000001), -5.42101086242752217004e-20); - EXPECT_EQ(ToDouble(0x0000000000000002), -1.084202172485504434e-19); - EXPECT_EQ(ToDouble(0x8000000000000000), -0.5); - EXPECT_EQ(ToDouble(0x8000000000000001), -0.5); - EXPECT_EQ(ToDouble(0xFFFFFFFFFFFFFFFF), -0.999999999999999888978); -} - -TEST(GenerateRealTest, U64ToDouble_Signed_NoZero_Test) { - auto ToDouble = [](uint64_t a) { - return GenerateRealFromBits<double, GenerateSignedTag, false>(a); - }; - - EXPECT_EQ(ToDouble(0x0000000000000000), 5.42101086242752217004e-20); - EXPECT_EQ(ToDouble(0x0000000000000001), 1.084202172485504434e-19); - EXPECT_EQ(ToDouble(0x7FFFFFFFFFFFFFFF), 0.999999999999999888978); - EXPECT_EQ(ToDouble(0x8000000000000000), -5.42101086242752217004e-20); - EXPECT_EQ(ToDouble(0x8000000000000001), -1.084202172485504434e-19); - EXPECT_EQ(ToDouble(0xFFFFFFFFFFFFFFFF), -0.999999999999999888978); -} - -TEST(GenerateRealTest, U64ToDouble_Signed_Zero_Test) { - auto ToDouble = [](uint64_t a) { - return GenerateRealFromBits<double, GenerateSignedTag, true>(a); - }; - EXPECT_EQ(ToDouble(0x0000000000000000), 0); - EXPECT_EQ(ToDouble(0x0000000000000001), 1.084202172485504434e-19); - EXPECT_EQ(ToDouble(0x7FFFFFFFFFFFFFFF), 0.999999999999999888978); - EXPECT_EQ(ToDouble(0x8000000000000000), 0); - EXPECT_EQ(ToDouble(0x8000000000000001), -1.084202172485504434e-19); - EXPECT_EQ(ToDouble(0xFFFFFFFFFFFFFFFF), -0.999999999999999888978); -} - -TEST(GenerateRealTest, U64ToDouble_GenerateSignedTag_Bias_Test) { - auto ToDouble = [](uint64_t a) { - return GenerateRealFromBits<double, GenerateSignedTag, true>(a, -1); - }; - EXPECT_EQ(ToDouble(0x0000000000000000), 0); - EXPECT_EQ(ToDouble(0x0000000000000001), 1.084202172485504434e-19 / 2); - EXPECT_EQ(ToDouble(0x7FFFFFFFFFFFFFFF), 0.999999999999999888978 / 2); - EXPECT_EQ(ToDouble(0x8000000000000000), 0); - EXPECT_EQ(ToDouble(0x8000000000000001), -1.084202172485504434e-19 / 2); - EXPECT_EQ(ToDouble(0xFFFFFFFFFFFFFFFF), -0.999999999999999888978 / 2); -} - -TEST(GenerateRealTest, U64ToDoubleTest) { - auto ToDouble = [](uint64_t a) { - return GenerateRealFromBits<double, GeneratePositiveTag, true>(a); - }; - - EXPECT_EQ(ToDouble(0x0000000000000000), 0.0); - EXPECT_EQ(ToDouble(0x0000000000000000), 0.0); - - EXPECT_EQ(ToDouble(0x0000000000000001), 5.42101086242752217004e-20); - EXPECT_EQ(ToDouble(0x7fffffffffffffef), 0.499999999999999944489); - EXPECT_EQ(ToDouble(0x8000000000000000), 0.5); - - // For values > 0.5, RandU64ToDouble discards up to 11 bits. (64-53). - EXPECT_EQ(ToDouble(0x8000000000000001), 0.5); - EXPECT_EQ(ToDouble(0x80000000000007FF), 0.5); - EXPECT_EQ(ToDouble(0xFFFFFFFFFFFFFFFF), 0.999999999999999888978); - EXPECT_NE(ToDouble(0x7FFFFFFFFFFFF800), ToDouble(0x7FFFFFFFFFFFF7FF)); - - EXPECT_LT(ToDouble(0xFFFFFFFFFFFFFFFF), 1.0); - EXPECT_EQ(ToDouble(0xFFFFFFFFFFFFFFFF), ToDouble(0xFFFFFFFFFFFFF800)); - EXPECT_NE(ToDouble(0xFFFFFFFFFFFFFFFF), ToDouble(0xFFFFFFFFFFFFF7FF)); - EXPECT_EQ(ToDouble(0x7FFFFFFFFFFFFFFF), ToDouble(0x7FFFFFFFFFFFFC00)); - EXPECT_NE(ToDouble(0x7FFFFFFFFFFFFFFF), ToDouble(0x7FFFFFFFFFFFFBFF)); - EXPECT_EQ(ToDouble(0x3FFFFFFFFFFFFFFF), ToDouble(0x3FFFFFFFFFFFFE00)); - EXPECT_NE(ToDouble(0x3FFFFFFFFFFFFFFF), ToDouble(0x3FFFFFFFFFFFFDFF)); - - EXPECT_EQ(ToDouble(0x1000000000000001), 0.0625); - EXPECT_EQ(ToDouble(0x2000000000000001), 0.125); - EXPECT_EQ(ToDouble(0x3000000000000001), 0.1875); - EXPECT_EQ(ToDouble(0x4000000000000001), 0.25); - EXPECT_EQ(ToDouble(0x5000000000000001), 0.3125); - EXPECT_EQ(ToDouble(0x6000000000000001), 0.375); - EXPECT_EQ(ToDouble(0x7000000000000001), 0.4375); - EXPECT_EQ(ToDouble(0x8000000000000001), 0.5); - EXPECT_EQ(ToDouble(0x9000000000000001), 0.5625); - EXPECT_EQ(ToDouble(0xa000000000000001), 0.625); - EXPECT_EQ(ToDouble(0xb000000000000001), 0.6875); - EXPECT_EQ(ToDouble(0xc000000000000001), 0.75); - EXPECT_EQ(ToDouble(0xd000000000000001), 0.8125); - EXPECT_EQ(ToDouble(0xe000000000000001), 0.875); - EXPECT_EQ(ToDouble(0xf000000000000001), 0.9375); - - // Large powers of 2. - int64_t two_to_53 = int64_t{1} << 53; - EXPECT_EQ(static_cast<int64_t>(ToDouble(0xFFFFFFFFFFFFFFFF) * two_to_53), - two_to_53 - 1); - EXPECT_NE(static_cast<int64_t>(ToDouble(0xFFFFFFFFFFFFFFFF) * two_to_53 * 2), - two_to_53 * 2 - 1); - - // For values where every bit counts, the values scale as multiples of the - // input. - for (int i = 0; i < 100; ++i) { - EXPECT_EQ(i * ToDouble(0x0000000000000001), ToDouble(i)); - } - - // For each i: value generated from (1 << i). - double exp_values[64]; - exp_values[63] = 0.5; - for (int i = 62; i >= 0; --i) exp_values[i] = 0.5 * exp_values[i + 1]; - constexpr uint64_t one = 1; - for (int i = 0; i < 64; ++i) { - EXPECT_EQ(ToDouble(one << i), exp_values[i]); - for (int j = 1; j < DBL_MANT_DIG && i - j >= 0; ++j) { - EXPECT_NE(exp_values[i] + exp_values[i - j], exp_values[i]); - EXPECT_EQ(ToDouble((one << i) + (one << (i - j))), - exp_values[i] + exp_values[i - j]); - } - for (int j = DBL_MANT_DIG; i - j >= 0; ++j) { - EXPECT_EQ(exp_values[i] + exp_values[i - j], exp_values[i]); - EXPECT_EQ(ToDouble((one << i) + (one << (i - j))), exp_values[i]); - } - } -} - -TEST(GenerateRealTest, U64ToDoubleSignedTest) { - auto ToDouble = [](uint64_t a) { - return GenerateRealFromBits<double, GenerateSignedTag, false>(a); - }; - - EXPECT_EQ(ToDouble(0x0000000000000000), 5.42101086242752217004e-20); - EXPECT_EQ(ToDouble(0x0000000000000001), 1.084202172485504434e-19); - - EXPECT_EQ(ToDouble(0x8000000000000000), -5.42101086242752217004e-20); - EXPECT_EQ(ToDouble(0x8000000000000001), -1.084202172485504434e-19); - - const double e_plus = ToDouble(0x0000000000000001); - const double e_minus = ToDouble(0x8000000000000001); - EXPECT_EQ(e_plus, 1.084202172485504434e-19); - EXPECT_EQ(e_minus, -1.084202172485504434e-19); - - EXPECT_EQ(ToDouble(0x3fffffffffffffef), 0.499999999999999944489); - EXPECT_EQ(ToDouble(0xbfffffffffffffef), -0.499999999999999944489); - - // For values > 0.5, RandU64ToDouble discards up to 10 bits. (63-53). - EXPECT_EQ(ToDouble(0x4000000000000000), 0.5); - EXPECT_EQ(ToDouble(0x4000000000000001), 0.5); - EXPECT_EQ(ToDouble(0x40000000000003FF), 0.5); - - EXPECT_EQ(ToDouble(0xC000000000000000), -0.5); - EXPECT_EQ(ToDouble(0xC000000000000001), -0.5); - EXPECT_EQ(ToDouble(0xC0000000000003FF), -0.5); - - EXPECT_EQ(ToDouble(0x7FFFFFFFFFFFFFFe), 0.999999999999999888978); - EXPECT_EQ(ToDouble(0xFFFFFFFFFFFFFFFe), -0.999999999999999888978); - - EXPECT_NE(ToDouble(0x7FFFFFFFFFFFF800), ToDouble(0x7FFFFFFFFFFFF7FF)); - - EXPECT_LT(ToDouble(0x7FFFFFFFFFFFFFFF), 1.0); - EXPECT_GT(ToDouble(0x7FFFFFFFFFFFFFFF), 0.9999999999); - - EXPECT_GT(ToDouble(0xFFFFFFFFFFFFFFFe), -1.0); - EXPECT_LT(ToDouble(0xFFFFFFFFFFFFFFFe), -0.999999999); - - EXPECT_EQ(ToDouble(0xFFFFFFFFFFFFFFFe), ToDouble(0xFFFFFFFFFFFFFC00)); - EXPECT_EQ(ToDouble(0x7FFFFFFFFFFFFFFF), ToDouble(0x7FFFFFFFFFFFFC00)); - EXPECT_NE(ToDouble(0xFFFFFFFFFFFFFFFe), ToDouble(0xFFFFFFFFFFFFF3FF)); - EXPECT_NE(ToDouble(0x7FFFFFFFFFFFFFFF), ToDouble(0x7FFFFFFFFFFFF3FF)); - - EXPECT_EQ(ToDouble(0x1000000000000001), 0.125); - EXPECT_EQ(ToDouble(0x2000000000000001), 0.25); - EXPECT_EQ(ToDouble(0x3000000000000001), 0.375); - EXPECT_EQ(ToDouble(0x4000000000000001), 0.5); - EXPECT_EQ(ToDouble(0x5000000000000001), 0.625); - EXPECT_EQ(ToDouble(0x6000000000000001), 0.75); - EXPECT_EQ(ToDouble(0x7000000000000001), 0.875); - EXPECT_EQ(ToDouble(0x7800000000000001), 0.9375); - EXPECT_EQ(ToDouble(0x7c00000000000001), 0.96875); - EXPECT_EQ(ToDouble(0x7e00000000000001), 0.984375); - EXPECT_EQ(ToDouble(0x7f00000000000001), 0.9921875); - - // 0x8000000000000000 ~= 0 - EXPECT_EQ(ToDouble(0x9000000000000001), -0.125); - EXPECT_EQ(ToDouble(0xa000000000000001), -0.25); - EXPECT_EQ(ToDouble(0xb000000000000001), -0.375); - EXPECT_EQ(ToDouble(0xc000000000000001), -0.5); - EXPECT_EQ(ToDouble(0xd000000000000001), -0.625); - EXPECT_EQ(ToDouble(0xe000000000000001), -0.75); - EXPECT_EQ(ToDouble(0xf000000000000001), -0.875); - - // Large powers of 2. - int64_t two_to_53 = int64_t{1} << 53; - EXPECT_EQ(static_cast<int64_t>(ToDouble(0x7FFFFFFFFFFFFFFF) * two_to_53), - two_to_53 - 1); - EXPECT_EQ(static_cast<int64_t>(ToDouble(0xFFFFFFFFFFFFFFFF) * two_to_53), - -(two_to_53 - 1)); - - EXPECT_NE(static_cast<int64_t>(ToDouble(0x7FFFFFFFFFFFFFFF) * two_to_53 * 2), - two_to_53 * 2 - 1); - - // For values where every bit counts, the values scale as multiples of the - // input. - for (int i = 1; i < 100; ++i) { - EXPECT_EQ(i * e_plus, ToDouble(i)) << i; - EXPECT_EQ(i * e_minus, ToDouble(0x8000000000000000 | i)) << i; - } -} - -TEST(GenerateRealTest, ExhaustiveFloat) { - using absl::base_internal::CountLeadingZeros64; - auto ToFloat = [](uint64_t a) { - return GenerateRealFromBits<float, GeneratePositiveTag, true>(a); - }; - - // Rely on RandU64ToFloat generating values from greatest to least when - // supplied with uint64_t values from greatest (0xfff...) to least (0x0). - // Thus, this algorithm stores the previous value, and if the new value is at - // greater than or equal to the previous value, then there is a collision in - // the generation algorithm. - // - // Use the computation below to convert the random value into a result: - // double res = a() * (1.0f - sample) + b() * sample; - float last_f = 1.0, last_g = 2.0; - uint64_t f_collisions = 0, g_collisions = 0; - uint64_t f_unique = 0, g_unique = 0; - uint64_t total = 0; - auto count = [&](const float r) { - total++; - // `f` is mapped to the range [0, 1) (default) - const float f = 0.0f * (1.0f - r) + 1.0f * r; - if (f >= last_f) { - f_collisions++; - } else { - f_unique++; - last_f = f; - } - // `g` is mapped to the range [1, 2) - const float g = 1.0f * (1.0f - r) + 2.0f * r; - if (g >= last_g) { - g_collisions++; - } else { - g_unique++; - last_g = g; - } - }; - - size_t limit = absl::GetFlag(FLAGS_absl_random_test_trials); - - // Generate all uint64_t which have unique floating point values. - // Counting down from 0xFFFFFFFFFFFFFFFFu ... 0x0u - uint64_t x = ~uint64_t(0); - for (; x != 0 && limit > 0;) { - constexpr int kDig = (64 - FLT_MANT_DIG); - // Set a decrement value & the next point at which to change - // the decrement value. By default these are 1, 0. - uint64_t dec = 1; - uint64_t chk = 0; - - // Adjust decrement and check value based on how many leading 0 - // bits are set in the current value. - const int clz = CountLeadingZeros64(x); - if (clz < kDig) { - dec <<= (kDig - clz); - chk = (~uint64_t(0)) >> (clz + 1); - } - for (; x > chk && limit > 0; x -= dec) { - count(ToFloat(x)); - --limit; - } - } - - static_assert(FLT_MANT_DIG == 24, - "The float type is expected to have a 24 bit mantissa."); - - if (limit != 0) { - // There are between 2^28 and 2^29 unique values in the range [0, 1). For - // the low values of x, there are 2^24 -1 unique values. Once x > 2^24, - // there are 40 * 2^24 unique values. Thus: - // (2 + 4 + 8 ... + 2^23) + 40 * 2^23 - EXPECT_LT(1 << 28, f_unique); - EXPECT_EQ((1 << 24) + 40 * (1 << 23) - 1, f_unique); - EXPECT_EQ(total, f_unique); - EXPECT_EQ(0, f_collisions); - - // Expect at least 2^23 unique values for the range [1, 2) - EXPECT_LE(1 << 23, g_unique); - EXPECT_EQ(total - g_unique, g_collisions); - } -} - -} // namespace diff --git a/third_party/abseil_cpp/absl/random/internal/iostream_state_saver.h b/third_party/abseil_cpp/absl/random/internal/iostream_state_saver.h deleted file mode 100644 index e6e242ee1e66..000000000000 --- a/third_party/abseil_cpp/absl/random/internal/iostream_state_saver.h +++ /dev/null @@ -1,245 +0,0 @@ -// Copyright 2017 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#ifndef ABSL_RANDOM_INTERNAL_IOSTREAM_STATE_SAVER_H_ -#define ABSL_RANDOM_INTERNAL_IOSTREAM_STATE_SAVER_H_ - -#include <cmath> -#include <iostream> -#include <limits> -#include <type_traits> - -#include "absl/meta/type_traits.h" -#include "absl/numeric/int128.h" - -namespace absl { -ABSL_NAMESPACE_BEGIN -namespace random_internal { - -// The null_state_saver does nothing. -template <typename T> -class null_state_saver { - public: - using stream_type = T; - using flags_type = std::ios_base::fmtflags; - - null_state_saver(T&, flags_type) {} - ~null_state_saver() {} -}; - -// ostream_state_saver is a RAII object to save and restore the common -// basic_ostream flags used when implementing `operator <<()` on any of -// the absl random distributions. -template <typename OStream> -class ostream_state_saver { - public: - using ostream_type = OStream; - using flags_type = std::ios_base::fmtflags; - using fill_type = typename ostream_type::char_type; - using precision_type = std::streamsize; - - ostream_state_saver(ostream_type& os, // NOLINT(runtime/references) - flags_type flags, fill_type fill) - : os_(os), - flags_(os.flags(flags)), - fill_(os.fill(fill)), - precision_(os.precision()) { - // Save state in initialized variables. - } - - ~ostream_state_saver() { - // Restore saved state. - os_.precision(precision_); - os_.fill(fill_); - os_.flags(flags_); - } - - private: - ostream_type& os_; - const flags_type flags_; - const fill_type fill_; - const precision_type precision_; -}; - -#if defined(__NDK_MAJOR__) && __NDK_MAJOR__ < 16 -#define ABSL_RANDOM_INTERNAL_IOSTREAM_HEXFLOAT 1 -#else -#define ABSL_RANDOM_INTERNAL_IOSTREAM_HEXFLOAT 0 -#endif - -template <typename CharT, typename Traits> -ostream_state_saver<std::basic_ostream<CharT, Traits>> make_ostream_state_saver( - std::basic_ostream<CharT, Traits>& os, // NOLINT(runtime/references) - std::ios_base::fmtflags flags = std::ios_base::dec | std::ios_base::left | -#if ABSL_RANDOM_INTERNAL_IOSTREAM_HEXFLOAT - std::ios_base::fixed | -#endif - std::ios_base::scientific) { - using result_type = ostream_state_saver<std::basic_ostream<CharT, Traits>>; - return result_type(os, flags, os.widen(' ')); -} - -template <typename T> -typename absl::enable_if_t<!std::is_base_of<std::ios_base, T>::value, - null_state_saver<T>> -make_ostream_state_saver(T& is, // NOLINT(runtime/references) - std::ios_base::fmtflags flags = std::ios_base::dec) { - std::cerr << "null_state_saver"; - using result_type = null_state_saver<T>; - return result_type(is, flags); -} - -// stream_precision_helper<type>::kPrecision returns the base 10 precision -// required to stream and reconstruct a real type exact binary value through -// a binary->decimal->binary transition. -template <typename T> -struct stream_precision_helper { - // max_digits10 may be 0 on MSVC; if so, use digits10 + 3. - static constexpr int kPrecision = - (std::numeric_limits<T>::max_digits10 > std::numeric_limits<T>::digits10) - ? std::numeric_limits<T>::max_digits10 - : (std::numeric_limits<T>::digits10 + 3); -}; - -template <> -struct stream_precision_helper<float> { - static constexpr int kPrecision = 9; -}; -template <> -struct stream_precision_helper<double> { - static constexpr int kPrecision = 17; -}; -template <> -struct stream_precision_helper<long double> { - static constexpr int kPrecision = 36; // assuming fp128 -}; - -// istream_state_saver is a RAII object to save and restore the common -// std::basic_istream<> flags used when implementing `operator >>()` on any of -// the absl random distributions. -template <typename IStream> -class istream_state_saver { - public: - using istream_type = IStream; - using flags_type = std::ios_base::fmtflags; - - istream_state_saver(istream_type& is, // NOLINT(runtime/references) - flags_type flags) - : is_(is), flags_(is.flags(flags)) {} - - ~istream_state_saver() { is_.flags(flags_); } - - private: - istream_type& is_; - flags_type flags_; -}; - -template <typename CharT, typename Traits> -istream_state_saver<std::basic_istream<CharT, Traits>> make_istream_state_saver( - std::basic_istream<CharT, Traits>& is, // NOLINT(runtime/references) - std::ios_base::fmtflags flags = std::ios_base::dec | - std::ios_base::scientific | - std::ios_base::skipws) { - using result_type = istream_state_saver<std::basic_istream<CharT, Traits>>; - return result_type(is, flags); -} - -template <typename T> -typename absl::enable_if_t<!std::is_base_of<std::ios_base, T>::value, - null_state_saver<T>> -make_istream_state_saver(T& is, // NOLINT(runtime/references) - std::ios_base::fmtflags flags = std::ios_base::dec) { - using result_type = null_state_saver<T>; - return result_type(is, flags); -} - -// stream_format_type<T> is a helper struct to convert types which -// basic_iostream cannot output as decimal numbers into types which -// basic_iostream can output as decimal numbers. Specifically: -// * signed/unsigned char-width types are converted to int. -// * TODO(lar): __int128 => uint128, except there is no operator << yet. -// -template <typename T> -struct stream_format_type - : public std::conditional<(sizeof(T) == sizeof(char)), int, T> {}; - -// stream_u128_helper allows us to write out either absl::uint128 or -// __uint128_t types in the same way, which enables their use as internal -// state of PRNG engines. -template <typename T> -struct stream_u128_helper; - -template <> -struct stream_u128_helper<absl::uint128> { - template <typename IStream> - inline absl::uint128 read(IStream& in) { - uint64_t h = 0; - uint64_t l = 0; - in >> h >> l; - return absl::MakeUint128(h, l); - } - - template <typename OStream> - inline void write(absl::uint128 val, OStream& out) { - uint64_t h = absl::Uint128High64(val); - uint64_t l = absl::Uint128Low64(val); - out << h << out.fill() << l; - } -}; - -#ifdef ABSL_HAVE_INTRINSIC_INT128 -template <> -struct stream_u128_helper<__uint128_t> { - template <typename IStream> - inline __uint128_t read(IStream& in) { - uint64_t h = 0; - uint64_t l = 0; - in >> h >> l; - return (static_cast<__uint128_t>(h) << 64) | l; - } - - template <typename OStream> - inline void write(__uint128_t val, OStream& out) { - uint64_t h = static_cast<uint64_t>(val >> 64u); - uint64_t l = static_cast<uint64_t>(val); - out << h << out.fill() << l; - } -}; -#endif - -template <typename FloatType, typename IStream> -inline FloatType read_floating_point(IStream& is) { - static_assert(std::is_floating_point<FloatType>::value, ""); - FloatType dest; - is >> dest; - // Parsing a double value may report a subnormal value as an error - // despite being able to represent it. - // See https://stackoverflow.com/q/52410931/3286653 - // It may also report an underflow when parsing DOUBLE_MIN as an - // ERANGE error, as the parsed value may be smaller than DOUBLE_MIN - // and rounded up. - // See: https://stackoverflow.com/q/42005462 - if (is.fail() && - (std::fabs(dest) == (std::numeric_limits<FloatType>::min)() || - std::fpclassify(dest) == FP_SUBNORMAL)) { - is.clear(is.rdstate() & (~std::ios_base::failbit)); - } - return dest; -} - -} // namespace random_internal -ABSL_NAMESPACE_END -} // namespace absl - -#endif // ABSL_RANDOM_INTERNAL_IOSTREAM_STATE_SAVER_H_ diff --git a/third_party/abseil_cpp/absl/random/internal/iostream_state_saver_test.cc b/third_party/abseil_cpp/absl/random/internal/iostream_state_saver_test.cc deleted file mode 100644 index 7bb8ad959cf6..000000000000 --- a/third_party/abseil_cpp/absl/random/internal/iostream_state_saver_test.cc +++ /dev/null @@ -1,371 +0,0 @@ -// Copyright 2017 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#include "absl/random/internal/iostream_state_saver.h" - -#include <sstream> -#include <string> - -#include "gtest/gtest.h" - -namespace { - -using absl::random_internal::make_istream_state_saver; -using absl::random_internal::make_ostream_state_saver; -using absl::random_internal::stream_precision_helper; - -template <typename T> -typename absl::enable_if_t<std::is_integral<T>::value, T> // -StreamRoundTrip(T t) { - std::stringstream ss; - { - auto saver = make_ostream_state_saver(ss); - ss.precision(stream_precision_helper<T>::kPrecision); - ss << t; - } - T result = 0; - { - auto saver = make_istream_state_saver(ss); - ss >> result; - } - EXPECT_FALSE(ss.fail()) // - << ss.str() << " " // - << (ss.good() ? "good " : "") // - << (ss.bad() ? "bad " : "") // - << (ss.eof() ? "eof " : "") // - << (ss.fail() ? "fail " : ""); - - return result; -} - -template <typename T> -typename absl::enable_if_t<std::is_floating_point<T>::value, T> // -StreamRoundTrip(T t) { - std::stringstream ss; - { - auto saver = make_ostream_state_saver(ss); - ss.precision(stream_precision_helper<T>::kPrecision); - ss << t; - } - T result = 0; - { - auto saver = make_istream_state_saver(ss); - result = absl::random_internal::read_floating_point<T>(ss); - } - EXPECT_FALSE(ss.fail()) // - << ss.str() << " " // - << (ss.good() ? "good " : "") // - << (ss.bad() ? "bad " : "") // - << (ss.eof() ? "eof " : "") // - << (ss.fail() ? "fail " : ""); - - return result; -} - -TEST(IOStreamStateSaver, BasicSaverState) { - std::stringstream ss; - ss.precision(2); - ss.fill('x'); - ss.flags(std::ios_base::dec | std::ios_base::right); - - { - auto saver = make_ostream_state_saver(ss); - ss.precision(10); - EXPECT_NE('x', ss.fill()); - EXPECT_EQ(10, ss.precision()); - EXPECT_NE(std::ios_base::dec | std::ios_base::right, ss.flags()); - - ss << 1.23; - } - - EXPECT_EQ('x', ss.fill()); - EXPECT_EQ(2, ss.precision()); - EXPECT_EQ(std::ios_base::dec | std::ios_base::right, ss.flags()); -} - -TEST(IOStreamStateSaver, RoundTripInts) { - const uint64_t kUintValues[] = { - 0, - 1, - static_cast<uint64_t>(-1), - 2, - static_cast<uint64_t>(-2), - - 1 << 7, - 1 << 8, - 1 << 16, - 1ull << 32, - 1ull << 50, - 1ull << 62, - 1ull << 63, - - (1 << 7) - 1, - (1 << 8) - 1, - (1 << 16) - 1, - (1ull << 32) - 1, - (1ull << 50) - 1, - (1ull << 62) - 1, - (1ull << 63) - 1, - - static_cast<uint64_t>(-(1 << 8)), - static_cast<uint64_t>(-(1 << 16)), - static_cast<uint64_t>(-(1ll << 32)), - static_cast<uint64_t>(-(1ll << 50)), - static_cast<uint64_t>(-(1ll << 62)), - - static_cast<uint64_t>(-(1 << 8) - 1), - static_cast<uint64_t>(-(1 << 16) - 1), - static_cast<uint64_t>(-(1ll << 32) - 1), - static_cast<uint64_t>(-(1ll << 50) - 1), - static_cast<uint64_t>(-(1ll << 62) - 1), - }; - - for (const uint64_t u : kUintValues) { - EXPECT_EQ(u, StreamRoundTrip<uint64_t>(u)); - - int64_t x = static_cast<int64_t>(u); - EXPECT_EQ(x, StreamRoundTrip<int64_t>(x)); - - double d = static_cast<double>(x); - EXPECT_EQ(d, StreamRoundTrip<double>(d)); - - float f = d; - EXPECT_EQ(f, StreamRoundTrip<float>(f)); - } -} - -TEST(IOStreamStateSaver, RoundTripFloats) { - static_assert( - stream_precision_helper<float>::kPrecision >= 9, - "stream_precision_helper<float>::kPrecision should be at least 9"); - - const float kValues[] = { - 1, - std::nextafter(1.0f, 0.0f), // 1 - epsilon - std::nextafter(1.0f, 2.0f), // 1 + epsilon - - 1.0e+1f, - 1.0e-1f, - 1.0e+2f, - 1.0e-2f, - 1.0e+10f, - 1.0e-10f, - - 0.00000051110000111311111111f, - -0.00000051110000111211111111f, - - 1.234678912345678912345e+6f, - 1.234678912345678912345e-6f, - 1.234678912345678912345e+30f, - 1.234678912345678912345e-30f, - 1.234678912345678912345e+38f, - 1.0234678912345678912345e-38f, - - // Boundary cases. - std::numeric_limits<float>::max(), - std::numeric_limits<float>::lowest(), - std::numeric_limits<float>::epsilon(), - std::nextafter(std::numeric_limits<float>::min(), - 1.0f), // min + epsilon - std::numeric_limits<float>::min(), // smallest normal - // There are some errors dealing with denorms on apple platforms. - std::numeric_limits<float>::denorm_min(), // smallest denorm - std::numeric_limits<float>::min() / 2, - std::nextafter(std::numeric_limits<float>::min(), - 0.0f), // denorm_max - std::nextafter(std::numeric_limits<float>::denorm_min(), 1.0f), - }; - - for (const float f : kValues) { - EXPECT_EQ(f, StreamRoundTrip<float>(f)); - EXPECT_EQ(-f, StreamRoundTrip<float>(-f)); - - double d = f; - EXPECT_EQ(d, StreamRoundTrip<double>(d)); - EXPECT_EQ(-d, StreamRoundTrip<double>(-d)); - - // Avoid undefined behavior (overflow/underflow). - if (f <= static_cast<float>(std::numeric_limits<int64_t>::max()) && - f >= static_cast<float>(std::numeric_limits<int64_t>::lowest())) { - int64_t x = static_cast<int64_t>(f); - EXPECT_EQ(x, StreamRoundTrip<int64_t>(x)); - } - } -} - -TEST(IOStreamStateSaver, RoundTripDoubles) { - static_assert( - stream_precision_helper<double>::kPrecision >= 17, - "stream_precision_helper<double>::kPrecision should be at least 17"); - - const double kValues[] = { - 1, - std::nextafter(1.0, 0.0), // 1 - epsilon - std::nextafter(1.0, 2.0), // 1 + epsilon - - 1.0e+1, - 1.0e-1, - 1.0e+2, - 1.0e-2, - 1.0e+10, - 1.0e-10, - - 0.00000051110000111311111111, - -0.00000051110000111211111111, - - 1.234678912345678912345e+6, - 1.234678912345678912345e-6, - 1.234678912345678912345e+30, - 1.234678912345678912345e-30, - 1.234678912345678912345e+38, - 1.0234678912345678912345e-38, - - 1.0e+100, - 1.0e-100, - 1.234678912345678912345e+308, - 1.0234678912345678912345e-308, - 2.22507385850720138e-308, - - // Boundary cases. - std::numeric_limits<double>::max(), - std::numeric_limits<double>::lowest(), - std::numeric_limits<double>::epsilon(), - std::nextafter(std::numeric_limits<double>::min(), - 1.0), // min + epsilon - std::numeric_limits<double>::min(), // smallest normal - // There are some errors dealing with denorms on apple platforms. - std::numeric_limits<double>::denorm_min(), // smallest denorm - std::numeric_limits<double>::min() / 2, - std::nextafter(std::numeric_limits<double>::min(), - 0.0), // denorm_max - std::nextafter(std::numeric_limits<double>::denorm_min(), 1.0f), - }; - - for (const double d : kValues) { - EXPECT_EQ(d, StreamRoundTrip<double>(d)); - EXPECT_EQ(-d, StreamRoundTrip<double>(-d)); - - // Avoid undefined behavior (overflow/underflow). - if (d <= std::numeric_limits<float>::max() && - d >= std::numeric_limits<float>::lowest()) { - float f = static_cast<float>(d); - EXPECT_EQ(f, StreamRoundTrip<float>(f)); - } - - // Avoid undefined behavior (overflow/underflow). - if (d <= static_cast<double>(std::numeric_limits<int64_t>::max()) && - d >= static_cast<double>(std::numeric_limits<int64_t>::lowest())) { - int64_t x = static_cast<int64_t>(d); - EXPECT_EQ(x, StreamRoundTrip<int64_t>(x)); - } - } -} - -#if !defined(__EMSCRIPTEN__) -TEST(IOStreamStateSaver, RoundTripLongDoubles) { - // Technically, C++ only guarantees that long double is at least as large as a - // double. Practically it varies from 64-bits to 128-bits. - // - // So it is best to consider long double a best-effort extended precision - // type. - - static_assert( - stream_precision_helper<long double>::kPrecision >= 36, - "stream_precision_helper<long double>::kPrecision should be at least 36"); - - using real_type = long double; - const real_type kValues[] = { - 1, - std::nextafter(1.0, 0.0), // 1 - epsilon - std::nextafter(1.0, 2.0), // 1 + epsilon - - 1.0e+1, - 1.0e-1, - 1.0e+2, - 1.0e-2, - 1.0e+10, - 1.0e-10, - - 0.00000051110000111311111111, - -0.00000051110000111211111111, - - 1.2346789123456789123456789123456789e+6, - 1.2346789123456789123456789123456789e-6, - 1.2346789123456789123456789123456789e+30, - 1.2346789123456789123456789123456789e-30, - 1.2346789123456789123456789123456789e+38, - 1.2346789123456789123456789123456789e-38, - 1.2346789123456789123456789123456789e+308, - 1.2346789123456789123456789123456789e-308, - - 1.0e+100, - 1.0e-100, - 1.234678912345678912345e+308, - 1.0234678912345678912345e-308, - - // Boundary cases. - std::numeric_limits<real_type>::max(), - std::numeric_limits<real_type>::lowest(), - std::numeric_limits<real_type>::epsilon(), - std::nextafter(std::numeric_limits<real_type>::min(), - real_type(1)), // min + epsilon - std::numeric_limits<real_type>::min(), // smallest normal - // There are some errors dealing with denorms on apple platforms. - std::numeric_limits<real_type>::denorm_min(), // smallest denorm - std::numeric_limits<real_type>::min() / 2, - std::nextafter(std::numeric_limits<real_type>::min(), - 0.0), // denorm_max - std::nextafter(std::numeric_limits<real_type>::denorm_min(), 1.0f), - }; - - int index = -1; - for (const long double dd : kValues) { - index++; - EXPECT_EQ(dd, StreamRoundTrip<real_type>(dd)) << index; - EXPECT_EQ(-dd, StreamRoundTrip<real_type>(-dd)) << index; - - // Avoid undefined behavior (overflow/underflow). - if (dd <= std::numeric_limits<double>::max() && - dd >= std::numeric_limits<double>::lowest()) { - double d = static_cast<double>(dd); - EXPECT_EQ(d, StreamRoundTrip<double>(d)); - } - - // Avoid undefined behavior (overflow/underflow). - if (dd <= std::numeric_limits<int64_t>::max() && - dd >= std::numeric_limits<int64_t>::lowest()) { - int64_t x = static_cast<int64_t>(dd); - EXPECT_EQ(x, StreamRoundTrip<int64_t>(x)); - } - } -} -#endif // !defined(__EMSCRIPTEN__) - -TEST(StrToDTest, DoubleMin) { - const char kV[] = "2.22507385850720138e-308"; - char* end; - double x = std::strtod(kV, &end); - EXPECT_EQ(std::numeric_limits<double>::min(), x); - // errno may equal ERANGE. -} - -TEST(StrToDTest, DoubleDenormMin) { - const char kV[] = "4.94065645841246544e-324"; - char* end; - double x = std::strtod(kV, &end); - EXPECT_EQ(std::numeric_limits<double>::denorm_min(), x); - // errno may equal ERANGE. -} - -} // namespace diff --git a/third_party/abseil_cpp/absl/random/internal/mock_helpers.h b/third_party/abseil_cpp/absl/random/internal/mock_helpers.h deleted file mode 100644 index 9af27ab3a210..000000000000 --- a/third_party/abseil_cpp/absl/random/internal/mock_helpers.h +++ /dev/null @@ -1,127 +0,0 @@ -// -// Copyright 2019 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#ifndef ABSL_RANDOM_INTERNAL_MOCK_HELPERS_H_ -#define ABSL_RANDOM_INTERNAL_MOCK_HELPERS_H_ - -#include <tuple> -#include <type_traits> - -#include "absl/base/internal/fast_type_id.h" -#include "absl/types/optional.h" - -namespace absl { -ABSL_NAMESPACE_BEGIN -namespace random_internal { - -// MockHelpers works in conjunction with MockOverloadSet, MockingBitGen, and -// BitGenRef to enable the mocking capability for absl distribution functions. -// -// MockingBitGen registers mocks based on the typeid of a mock signature, KeyT, -// which is used to generate a unique id. -// -// KeyT is a signature of the form: -// result_type(discriminator_type, std::tuple<args...>) -// The mocked function signature will be composed from KeyT as: -// result_type(args...) -// -class MockHelpers { - using IdType = ::absl::base_internal::FastTypeIdType; - - // Given a key signature type used to index the mock, extract the components. - // KeyT is expected to have the form: - // result_type(discriminator_type, arg_tuple_type) - template <typename KeyT> - struct KeySignature; - - template <typename ResultT, typename DiscriminatorT, typename ArgTupleT> - struct KeySignature<ResultT(DiscriminatorT, ArgTupleT)> { - using result_type = ResultT; - using discriminator_type = DiscriminatorT; - using arg_tuple_type = ArgTupleT; - }; - - // Detector for InvokeMock. - template <class T> - using invoke_mock_t = decltype(std::declval<T*>()->InvokeMock( - std::declval<IdType>(), std::declval<void*>(), std::declval<void*>())); - - // Empty implementation of InvokeMock. - template <typename KeyT, typename ReturnT, typename ArgTupleT, typename URBG, - typename... Args> - static absl::optional<ReturnT> InvokeMockImpl(char, URBG*, Args&&...) { - return absl::nullopt; - } - - // Non-empty implementation of InvokeMock. - template <typename KeyT, typename ReturnT, typename ArgTupleT, typename URBG, - typename = invoke_mock_t<URBG>, typename... Args> - static absl::optional<ReturnT> InvokeMockImpl(int, URBG* urbg, - Args&&... args) { - ArgTupleT arg_tuple(std::forward<Args>(args)...); - ReturnT result; - if (urbg->InvokeMock(::absl::base_internal::FastTypeId<KeyT>(), &arg_tuple, - &result)) { - return result; - } - return absl::nullopt; - } - - public: - // Invoke a mock for the KeyT (may or may not be a signature). - // - // KeyT is used to generate a typeid-based lookup key for the mock. - // KeyT is a signature of the form: - // result_type(discriminator_type, std::tuple<args...>) - // The mocked function signature will be composed from KeyT as: - // result_type(args...) - // - // An instance of arg_tuple_type must be constructable from Args..., since - // the underlying mechanism requires a pointer to an argument tuple. - template <typename KeyT, typename URBG, typename... Args> - static auto MaybeInvokeMock(URBG* urbg, Args&&... args) - -> absl::optional<typename KeySignature<KeyT>::result_type> { - // Use function overloading to dispatch to the implemenation since - // more modern patterns (e.g. require + constexpr) are not supported in all - // compiler configurations. - return InvokeMockImpl<KeyT, typename KeySignature<KeyT>::result_type, - typename KeySignature<KeyT>::arg_tuple_type, URBG>( - 0, urbg, std::forward<Args>(args)...); - } - - // Acquire a mock for the KeyT (may or may not be a signature). - // - // KeyT is used to generate a typeid-based lookup for the mock. - // KeyT is a signature of the form: - // result_type(discriminator_type, std::tuple<args...>) - // The mocked function signature will be composed from KeyT as: - // result_type(args...) - template <typename KeyT, typename MockURBG> - static auto MockFor(MockURBG& m) -> decltype( - std::declval<MockURBG>() - .template RegisterMock<typename KeySignature<KeyT>::result_type, - typename KeySignature<KeyT>::arg_tuple_type>( - std::declval<IdType>())) { - return m.template RegisterMock<typename KeySignature<KeyT>::result_type, - typename KeySignature<KeyT>::arg_tuple_type>( - ::absl::base_internal::FastTypeId<KeyT>()); - } -}; - -} // namespace random_internal -ABSL_NAMESPACE_END -} // namespace absl - -#endif // ABSL_RANDOM_INTERNAL_MOCK_HELPERS_H_ diff --git a/third_party/abseil_cpp/absl/random/internal/mock_overload_set.h b/third_party/abseil_cpp/absl/random/internal/mock_overload_set.h deleted file mode 100644 index dccc6cee679c..000000000000 --- a/third_party/abseil_cpp/absl/random/internal/mock_overload_set.h +++ /dev/null @@ -1,97 +0,0 @@ -// -// Copyright 2019 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#ifndef ABSL_RANDOM_INTERNAL_MOCK_OVERLOAD_SET_H_ -#define ABSL_RANDOM_INTERNAL_MOCK_OVERLOAD_SET_H_ - -#include <type_traits> - -#include "gmock/gmock.h" -#include "gtest/gtest.h" -#include "absl/random/internal/mock_helpers.h" -#include "absl/random/mocking_bit_gen.h" - -namespace absl { -ABSL_NAMESPACE_BEGIN -namespace random_internal { - -template <typename DistrT, typename Fn> -struct MockSingleOverload; - -// MockSingleOverload -// -// MockSingleOverload hooks in to gMock's `ON_CALL` and `EXPECT_CALL` macros. -// EXPECT_CALL(mock_single_overload, Call(...))` will expand to a call to -// `mock_single_overload.gmock_Call(...)`. Because expectations are stored on -// the MockingBitGen (an argument passed inside `Call(...)`), this forwards to -// arguments to MockingBitGen::Register. -// -// The underlying KeyT must match the KeyT constructed by DistributionCaller. -template <typename DistrT, typename Ret, typename... Args> -struct MockSingleOverload<DistrT, Ret(MockingBitGen&, Args...)> { - static_assert(std::is_same<typename DistrT::result_type, Ret>::value, - "Overload signature must have return type matching the " - "distribution result_type."); - using KeyT = Ret(DistrT, std::tuple<Args...>); - auto gmock_Call( - absl::MockingBitGen& gen, // NOLINT(google-runtime-references) - const ::testing::Matcher<Args>&... matchers) - -> decltype(MockHelpers::MockFor<KeyT>(gen).gmock_Call(matchers...)) { - return MockHelpers::MockFor<KeyT>(gen).gmock_Call(matchers...); - } -}; - -template <typename DistrT, typename Ret, typename Arg, typename... Args> -struct MockSingleOverload<DistrT, Ret(Arg, MockingBitGen&, Args...)> { - static_assert(std::is_same<typename DistrT::result_type, Ret>::value, - "Overload signature must have return type matching the " - "distribution result_type."); - using KeyT = Ret(DistrT, std::tuple<Arg, Args...>); - auto gmock_Call( - const ::testing::Matcher<Arg>& matcher, - absl::MockingBitGen& gen, // NOLINT(google-runtime-references) - const ::testing::Matcher<Args>&... matchers) - -> decltype(MockHelpers::MockFor<KeyT>(gen).gmock_Call(matcher, - matchers...)) { - return MockHelpers::MockFor<KeyT>(gen).gmock_Call(matcher, matchers...); - } -}; - -// MockOverloadSet -// -// MockOverloadSet takes a distribution and a collection of signatures and -// performs overload resolution amongst all the overloads. This makes -// `EXPECT_CALL(mock_overload_set, Call(...))` expand and do overload resolution -// correctly. -template <typename DistrT, typename... Signatures> -struct MockOverloadSet; - -template <typename DistrT, typename Sig> -struct MockOverloadSet<DistrT, Sig> : public MockSingleOverload<DistrT, Sig> { - using MockSingleOverload<DistrT, Sig>::gmock_Call; -}; - -template <typename DistrT, typename FirstSig, typename... Rest> -struct MockOverloadSet<DistrT, FirstSig, Rest...> - : public MockSingleOverload<DistrT, FirstSig>, - public MockOverloadSet<DistrT, Rest...> { - using MockSingleOverload<DistrT, FirstSig>::gmock_Call; - using MockOverloadSet<DistrT, Rest...>::gmock_Call; -}; - -} // namespace random_internal -ABSL_NAMESPACE_END -} // namespace absl -#endif // ABSL_RANDOM_INTERNAL_MOCK_OVERLOAD_SET_H_ diff --git a/third_party/abseil_cpp/absl/random/internal/nanobenchmark.cc b/third_party/abseil_cpp/absl/random/internal/nanobenchmark.cc deleted file mode 100644 index c9181813f7f0..000000000000 --- a/third_party/abseil_cpp/absl/random/internal/nanobenchmark.cc +++ /dev/null @@ -1,804 +0,0 @@ -// Copyright 2017 Google Inc. All Rights Reserved. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#include "absl/random/internal/nanobenchmark.h" - -#include <sys/types.h> - -#include <algorithm> // sort -#include <atomic> -#include <cstddef> -#include <cstdint> -#include <cstdlib> -#include <cstring> // memcpy -#include <limits> -#include <string> -#include <utility> -#include <vector> - -#include "absl/base/attributes.h" -#include "absl/base/internal/raw_logging.h" -#include "absl/random/internal/platform.h" -#include "absl/random/internal/randen_engine.h" - -// OS -#if defined(_WIN32) || defined(_WIN64) -#define ABSL_OS_WIN -#include <windows.h> // NOLINT - -#elif defined(__ANDROID__) -#define ABSL_OS_ANDROID - -#elif defined(__linux__) -#define ABSL_OS_LINUX -#include <sched.h> // NOLINT -#include <sys/syscall.h> // NOLINT -#endif - -#if defined(ABSL_ARCH_X86_64) && !defined(ABSL_OS_WIN) -#include <cpuid.h> // NOLINT -#endif - -// __ppc_get_timebase_freq -#if defined(ABSL_ARCH_PPC) -#include <sys/platform/ppc.h> // NOLINT -#endif - -// clock_gettime -#if defined(ABSL_ARCH_ARM) || defined(ABSL_ARCH_AARCH64) -#include <time.h> // NOLINT -#endif - -// ABSL_RANDOM_INTERNAL_ATTRIBUTE_NEVER_INLINE prevents inlining of the method. -#if ABSL_HAVE_ATTRIBUTE(noinline) || (defined(__GNUC__) && !defined(__clang__)) -#define ABSL_RANDOM_INTERNAL_ATTRIBUTE_NEVER_INLINE __attribute__((noinline)) -#elif defined(_MSC_VER) -#define ABSL_RANDOM_INTERNAL_ATTRIBUTE_NEVER_INLINE __declspec(noinline) -#else -#define ABSL_RANDOM_INTERNAL_ATTRIBUTE_NEVER_INLINE -#endif - -namespace absl { -ABSL_NAMESPACE_BEGIN -namespace random_internal_nanobenchmark { -namespace { - -// For code folding. -namespace platform { -#if defined(ABSL_ARCH_X86_64) - -// TODO(janwas): Merge with the one in randen_hwaes.cc? -void Cpuid(const uint32_t level, const uint32_t count, - uint32_t* ABSL_RANDOM_INTERNAL_RESTRICT abcd) { -#if defined(ABSL_OS_WIN) - int regs[4]; - __cpuidex(regs, level, count); - for (int i = 0; i < 4; ++i) { - abcd[i] = regs[i]; - } -#else - uint32_t a, b, c, d; - __cpuid_count(level, count, a, b, c, d); - abcd[0] = a; - abcd[1] = b; - abcd[2] = c; - abcd[3] = d; -#endif -} - -std::string BrandString() { - char brand_string[49]; - uint32_t abcd[4]; - - // Check if brand string is supported (it is on all reasonable Intel/AMD) - Cpuid(0x80000000U, 0, abcd); - if (abcd[0] < 0x80000004U) { - return std::string(); - } - - for (int i = 0; i < 3; ++i) { - Cpuid(0x80000002U + i, 0, abcd); - memcpy(brand_string + i * 16, &abcd, sizeof(abcd)); - } - brand_string[48] = 0; - return brand_string; -} - -// Returns the frequency quoted inside the brand string. This does not -// account for throttling nor Turbo Boost. -double NominalClockRate() { - const std::string& brand_string = BrandString(); - // Brand strings include the maximum configured frequency. These prefixes are - // defined by Intel CPUID documentation. - const char* prefixes[3] = {"MHz", "GHz", "THz"}; - const double multipliers[3] = {1E6, 1E9, 1E12}; - for (size_t i = 0; i < 3; ++i) { - const size_t pos_prefix = brand_string.find(prefixes[i]); - if (pos_prefix != std::string::npos) { - const size_t pos_space = brand_string.rfind(' ', pos_prefix - 1); - if (pos_space != std::string::npos) { - const std::string digits = - brand_string.substr(pos_space + 1, pos_prefix - pos_space - 1); - return std::stod(digits) * multipliers[i]; - } - } - } - - return 0.0; -} - -#endif // ABSL_ARCH_X86_64 -} // namespace platform - -// Prevents the compiler from eliding the computations that led to "output". -template <class T> -inline void PreventElision(T&& output) { -#ifndef ABSL_OS_WIN - // Works by indicating to the compiler that "output" is being read and - // modified. The +r constraint avoids unnecessary writes to memory, but only - // works for built-in types (typically FuncOutput). - asm volatile("" : "+r"(output) : : "memory"); -#else - // MSVC does not support inline assembly anymore (and never supported GCC's - // RTL constraints). Self-assignment with #pragma optimize("off") might be - // expected to prevent elision, but it does not with MSVC 2015. Type-punning - // with volatile pointers generates inefficient code on MSVC 2017. - static std::atomic<T> dummy(T{}); - dummy.store(output, std::memory_order_relaxed); -#endif -} - -namespace timer { - -// Start/Stop return absolute timestamps and must be placed immediately before -// and after the region to measure. We provide separate Start/Stop functions -// because they use different fences. -// -// Background: RDTSC is not 'serializing'; earlier instructions may complete -// after it, and/or later instructions may complete before it. 'Fences' ensure -// regions' elapsed times are independent of such reordering. The only -// documented unprivileged serializing instruction is CPUID, which acts as a -// full fence (no reordering across it in either direction). Unfortunately -// the latency of CPUID varies wildly (perhaps made worse by not initializing -// its EAX input). Because it cannot reliably be deducted from the region's -// elapsed time, it must not be included in the region to measure (i.e. -// between the two RDTSC). -// -// The newer RDTSCP is sometimes described as serializing, but it actually -// only serves as a half-fence with release semantics. Although all -// instructions in the region will complete before the final timestamp is -// captured, subsequent instructions may leak into the region and increase the -// elapsed time. Inserting another fence after the final RDTSCP would prevent -// such reordering without affecting the measured region. -// -// Fortunately, such a fence exists. The LFENCE instruction is only documented -// to delay later loads until earlier loads are visible. However, Intel's -// reference manual says it acts as a full fence (waiting until all earlier -// instructions have completed, and delaying later instructions until it -// completes). AMD assigns the same behavior to MFENCE. -// -// We need a fence before the initial RDTSC to prevent earlier instructions -// from leaking into the region, and arguably another after RDTSC to avoid -// region instructions from completing before the timestamp is recorded. -// When surrounded by fences, the additional RDTSCP half-fence provides no -// benefit, so the initial timestamp can be recorded via RDTSC, which has -// lower overhead than RDTSCP because it does not read TSC_AUX. In summary, -// we define Start = LFENCE/RDTSC/LFENCE; Stop = RDTSCP/LFENCE. -// -// Using Start+Start leads to higher variance and overhead than Stop+Stop. -// However, Stop+Stop includes an LFENCE in the region measurements, which -// adds a delay dependent on earlier loads. The combination of Start+Stop -// is faster than Start+Start and more consistent than Stop+Stop because -// the first LFENCE already delayed subsequent loads before the measured -// region. This combination seems not to have been considered in prior work: -// http://akaros.cs.berkeley.edu/lxr/akaros/kern/arch/x86/rdtsc_test.c -// -// Note: performance counters can measure 'exact' instructions-retired or -// (unhalted) cycle counts. The RDPMC instruction is not serializing and also -// requires fences. Unfortunately, it is not accessible on all OSes and we -// prefer to avoid kernel-mode drivers. Performance counters are also affected -// by several under/over-count errata, so we use the TSC instead. - -// Returns a 64-bit timestamp in unit of 'ticks'; to convert to seconds, -// divide by InvariantTicksPerSecond. -inline uint64_t Start64() { - uint64_t t; -#if defined(ABSL_ARCH_PPC) - asm volatile("mfspr %0, %1" : "=r"(t) : "i"(268)); -#elif defined(ABSL_ARCH_X86_64) -#if defined(ABSL_OS_WIN) - _ReadWriteBarrier(); - _mm_lfence(); - _ReadWriteBarrier(); - t = __rdtsc(); - _ReadWriteBarrier(); - _mm_lfence(); - _ReadWriteBarrier(); -#else - asm volatile( - "lfence\n\t" - "rdtsc\n\t" - "shl $32, %%rdx\n\t" - "or %%rdx, %0\n\t" - "lfence" - : "=a"(t) - : - // "memory" avoids reordering. rdx = TSC >> 32. - // "cc" = flags modified by SHL. - : "rdx", "memory", "cc"); -#endif -#else - // Fall back to OS - unsure how to reliably query cntvct_el0 frequency. - timespec ts; - clock_gettime(CLOCK_REALTIME, &ts); - t = ts.tv_sec * 1000000000LL + ts.tv_nsec; -#endif - return t; -} - -inline uint64_t Stop64() { - uint64_t t; -#if defined(ABSL_ARCH_X86_64) -#if defined(ABSL_OS_WIN) - _ReadWriteBarrier(); - unsigned aux; - t = __rdtscp(&aux); - _ReadWriteBarrier(); - _mm_lfence(); - _ReadWriteBarrier(); -#else - // Use inline asm because __rdtscp generates code to store TSC_AUX (ecx). - asm volatile( - "rdtscp\n\t" - "shl $32, %%rdx\n\t" - "or %%rdx, %0\n\t" - "lfence" - : "=a"(t) - : - // "memory" avoids reordering. rcx = TSC_AUX. rdx = TSC >> 32. - // "cc" = flags modified by SHL. - : "rcx", "rdx", "memory", "cc"); -#endif -#else - t = Start64(); -#endif - return t; -} - -// Returns a 32-bit timestamp with about 4 cycles less overhead than -// Start64. Only suitable for measuring very short regions because the -// timestamp overflows about once a second. -inline uint32_t Start32() { - uint32_t t; -#if defined(ABSL_ARCH_X86_64) -#if defined(ABSL_OS_WIN) - _ReadWriteBarrier(); - _mm_lfence(); - _ReadWriteBarrier(); - t = static_cast<uint32_t>(__rdtsc()); - _ReadWriteBarrier(); - _mm_lfence(); - _ReadWriteBarrier(); -#else - asm volatile( - "lfence\n\t" - "rdtsc\n\t" - "lfence" - : "=a"(t) - : - // "memory" avoids reordering. rdx = TSC >> 32. - : "rdx", "memory"); -#endif -#else - t = static_cast<uint32_t>(Start64()); -#endif - return t; -} - -inline uint32_t Stop32() { - uint32_t t; -#if defined(ABSL_ARCH_X86_64) -#if defined(ABSL_OS_WIN) - _ReadWriteBarrier(); - unsigned aux; - t = static_cast<uint32_t>(__rdtscp(&aux)); - _ReadWriteBarrier(); - _mm_lfence(); - _ReadWriteBarrier(); -#else - // Use inline asm because __rdtscp generates code to store TSC_AUX (ecx). - asm volatile( - "rdtscp\n\t" - "lfence" - : "=a"(t) - : - // "memory" avoids reordering. rcx = TSC_AUX. rdx = TSC >> 32. - : "rcx", "rdx", "memory"); -#endif -#else - t = static_cast<uint32_t>(Stop64()); -#endif - return t; -} - -} // namespace timer - -namespace robust_statistics { - -// Sorts integral values in ascending order (e.g. for Mode). About 3x faster -// than std::sort for input distributions with very few unique values. -template <class T> -void CountingSort(T* values, size_t num_values) { - // Unique values and their frequency (similar to flat_map). - using Unique = std::pair<T, int>; - std::vector<Unique> unique; - for (size_t i = 0; i < num_values; ++i) { - const T value = values[i]; - const auto pos = - std::find_if(unique.begin(), unique.end(), - [value](const Unique u) { return u.first == value; }); - if (pos == unique.end()) { - unique.push_back(std::make_pair(value, 1)); - } else { - ++pos->second; - } - } - - // Sort in ascending order of value (pair.first). - std::sort(unique.begin(), unique.end()); - - // Write that many copies of each unique value to the array. - T* ABSL_RANDOM_INTERNAL_RESTRICT p = values; - for (const auto& value_count : unique) { - std::fill(p, p + value_count.second, value_count.first); - p += value_count.second; - } - ABSL_RAW_CHECK(p == values + num_values, "Did not produce enough output"); -} - -// @return i in [idx_begin, idx_begin + half_count) that minimizes -// sorted[i + half_count] - sorted[i]. -template <typename T> -size_t MinRange(const T* const ABSL_RANDOM_INTERNAL_RESTRICT sorted, - const size_t idx_begin, const size_t half_count) { - T min_range = (std::numeric_limits<T>::max)(); - size_t min_idx = 0; - - for (size_t idx = idx_begin; idx < idx_begin + half_count; ++idx) { - ABSL_RAW_CHECK(sorted[idx] <= sorted[idx + half_count], "Not sorted"); - const T range = sorted[idx + half_count] - sorted[idx]; - if (range < min_range) { - min_range = range; - min_idx = idx; - } - } - - return min_idx; -} - -// Returns an estimate of the mode by calling MinRange on successively -// halved intervals. "sorted" must be in ascending order. This is the -// Half Sample Mode estimator proposed by Bickel in "On a fast, robust -// estimator of the mode", with complexity O(N log N). The mode is less -// affected by outliers in highly-skewed distributions than the median. -// The averaging operation below assumes "T" is an unsigned integer type. -template <typename T> -T ModeOfSorted(const T* const ABSL_RANDOM_INTERNAL_RESTRICT sorted, - const size_t num_values) { - size_t idx_begin = 0; - size_t half_count = num_values / 2; - while (half_count > 1) { - idx_begin = MinRange(sorted, idx_begin, half_count); - half_count >>= 1; - } - - const T x = sorted[idx_begin + 0]; - if (half_count == 0) { - return x; - } - ABSL_RAW_CHECK(half_count == 1, "Should stop at half_count=1"); - const T average = (x + sorted[idx_begin + 1] + 1) / 2; - return average; -} - -// Returns the mode. Side effect: sorts "values". -template <typename T> -T Mode(T* values, const size_t num_values) { - CountingSort(values, num_values); - return ModeOfSorted(values, num_values); -} - -template <typename T, size_t N> -T Mode(T (&values)[N]) { - return Mode(&values[0], N); -} - -// Returns the median value. Side effect: sorts "values". -template <typename T> -T Median(T* values, const size_t num_values) { - ABSL_RAW_CHECK(num_values != 0, "Empty input"); - std::sort(values, values + num_values); - const size_t half = num_values / 2; - // Odd count: return middle - if (num_values % 2) { - return values[half]; - } - // Even count: return average of middle two. - return (values[half] + values[half - 1] + 1) / 2; -} - -// Returns a robust measure of variability. -template <typename T> -T MedianAbsoluteDeviation(const T* values, const size_t num_values, - const T median) { - ABSL_RAW_CHECK(num_values != 0, "Empty input"); - std::vector<T> abs_deviations; - abs_deviations.reserve(num_values); - for (size_t i = 0; i < num_values; ++i) { - const int64_t abs = std::abs(int64_t(values[i]) - int64_t(median)); - abs_deviations.push_back(static_cast<T>(abs)); - } - return Median(abs_deviations.data(), num_values); -} - -} // namespace robust_statistics - -// Ticks := platform-specific timer values (CPU cycles on x86). Must be -// unsigned to guarantee wraparound on overflow. 32 bit timers are faster to -// read than 64 bit. -using Ticks = uint32_t; - -// Returns timer overhead / minimum measurable difference. -Ticks TimerResolution() { - // Nested loop avoids exceeding stack/L1 capacity. - Ticks repetitions[Params::kTimerSamples]; - for (size_t rep = 0; rep < Params::kTimerSamples; ++rep) { - Ticks samples[Params::kTimerSamples]; - for (size_t i = 0; i < Params::kTimerSamples; ++i) { - const Ticks t0 = timer::Start32(); - const Ticks t1 = timer::Stop32(); - samples[i] = t1 - t0; - } - repetitions[rep] = robust_statistics::Mode(samples); - } - return robust_statistics::Mode(repetitions); -} - -static const Ticks timer_resolution = TimerResolution(); - -// Estimates the expected value of "lambda" values with a variable number of -// samples until the variability "rel_mad" is less than "max_rel_mad". -template <class Lambda> -Ticks SampleUntilStable(const double max_rel_mad, double* rel_mad, - const Params& p, const Lambda& lambda) { - auto measure_duration = [&lambda]() -> Ticks { - const Ticks t0 = timer::Start32(); - lambda(); - const Ticks t1 = timer::Stop32(); - return t1 - t0; - }; - - // Choose initial samples_per_eval based on a single estimated duration. - Ticks est = measure_duration(); - static const double ticks_per_second = InvariantTicksPerSecond(); - const size_t ticks_per_eval = ticks_per_second * p.seconds_per_eval; - size_t samples_per_eval = ticks_per_eval / est; - samples_per_eval = (std::max)(samples_per_eval, p.min_samples_per_eval); - - std::vector<Ticks> samples; - samples.reserve(1 + samples_per_eval); - samples.push_back(est); - - // Percentage is too strict for tiny differences, so also allow a small - // absolute "median absolute deviation". - const Ticks max_abs_mad = (timer_resolution + 99) / 100; - *rel_mad = 0.0; // ensure initialized - - for (size_t eval = 0; eval < p.max_evals; ++eval, samples_per_eval *= 2) { - samples.reserve(samples.size() + samples_per_eval); - for (size_t i = 0; i < samples_per_eval; ++i) { - const Ticks r = measure_duration(); - samples.push_back(r); - } - - if (samples.size() >= p.min_mode_samples) { - est = robust_statistics::Mode(samples.data(), samples.size()); - } else { - // For "few" (depends also on the variance) samples, Median is safer. - est = robust_statistics::Median(samples.data(), samples.size()); - } - ABSL_RAW_CHECK(est != 0, "Estimator returned zero duration"); - - // Median absolute deviation (mad) is a robust measure of 'variability'. - const Ticks abs_mad = robust_statistics::MedianAbsoluteDeviation( - samples.data(), samples.size(), est); - *rel_mad = static_cast<double>(static_cast<int>(abs_mad)) / est; - - if (*rel_mad <= max_rel_mad || abs_mad <= max_abs_mad) { - if (p.verbose) { - ABSL_RAW_LOG(INFO, - "%6zu samples => %5u (abs_mad=%4u, rel_mad=%4.2f%%)\n", - samples.size(), est, abs_mad, *rel_mad * 100.0); - } - return est; - } - } - - if (p.verbose) { - ABSL_RAW_LOG(WARNING, - "rel_mad=%4.2f%% still exceeds %4.2f%% after %6zu samples.\n", - *rel_mad * 100.0, max_rel_mad * 100.0, samples.size()); - } - return est; -} - -using InputVec = std::vector<FuncInput>; - -// Returns vector of unique input values. -InputVec UniqueInputs(const FuncInput* inputs, const size_t num_inputs) { - InputVec unique(inputs, inputs + num_inputs); - std::sort(unique.begin(), unique.end()); - unique.erase(std::unique(unique.begin(), unique.end()), unique.end()); - return unique; -} - -// Returns how often we need to call func for sufficient precision, or zero -// on failure (e.g. the elapsed time is too long for a 32-bit tick count). -size_t NumSkip(const Func func, const void* arg, const InputVec& unique, - const Params& p) { - // Min elapsed ticks for any input. - Ticks min_duration = ~0u; - - for (const FuncInput input : unique) { - // Make sure a 32-bit timer is sufficient. - const uint64_t t0 = timer::Start64(); - PreventElision(func(arg, input)); - const uint64_t t1 = timer::Stop64(); - const uint64_t elapsed = t1 - t0; - if (elapsed >= (1ULL << 30)) { - ABSL_RAW_LOG(WARNING, - "Measurement failed: need 64-bit timer for input=%zu\n", - static_cast<size_t>(input)); - return 0; - } - - double rel_mad; - const Ticks total = SampleUntilStable( - p.target_rel_mad, &rel_mad, p, - [func, arg, input]() { PreventElision(func(arg, input)); }); - min_duration = (std::min)(min_duration, total - timer_resolution); - } - - // Number of repetitions required to reach the target resolution. - const size_t max_skip = p.precision_divisor; - // Number of repetitions given the estimated duration. - const size_t num_skip = - min_duration == 0 ? 0 : (max_skip + min_duration - 1) / min_duration; - if (p.verbose) { - ABSL_RAW_LOG(INFO, "res=%u max_skip=%zu min_dur=%u num_skip=%zu\n", - timer_resolution, max_skip, min_duration, num_skip); - } - return num_skip; -} - -// Replicates inputs until we can omit "num_skip" occurrences of an input. -InputVec ReplicateInputs(const FuncInput* inputs, const size_t num_inputs, - const size_t num_unique, const size_t num_skip, - const Params& p) { - InputVec full; - if (num_unique == 1) { - full.assign(p.subset_ratio * num_skip, inputs[0]); - return full; - } - - full.reserve(p.subset_ratio * num_skip * num_inputs); - for (size_t i = 0; i < p.subset_ratio * num_skip; ++i) { - full.insert(full.end(), inputs, inputs + num_inputs); - } - absl::random_internal::randen_engine<uint32_t> rng; - std::shuffle(full.begin(), full.end(), rng); - return full; -} - -// Copies the "full" to "subset" in the same order, but with "num_skip" -// randomly selected occurrences of "input_to_skip" removed. -void FillSubset(const InputVec& full, const FuncInput input_to_skip, - const size_t num_skip, InputVec* subset) { - const size_t count = std::count(full.begin(), full.end(), input_to_skip); - // Generate num_skip random indices: which occurrence to skip. - std::vector<uint32_t> omit; - // Replacement for std::iota, not yet available in MSVC builds. - omit.reserve(count); - for (size_t i = 0; i < count; ++i) { - omit.push_back(i); - } - // omit[] is the same on every call, but that's OK because they identify the - // Nth instance of input_to_skip, so the position within full[] differs. - absl::random_internal::randen_engine<uint32_t> rng; - std::shuffle(omit.begin(), omit.end(), rng); - omit.resize(num_skip); - std::sort(omit.begin(), omit.end()); - - uint32_t occurrence = ~0u; // 0 after preincrement - size_t idx_omit = 0; // cursor within omit[] - size_t idx_subset = 0; // cursor within *subset - for (const FuncInput next : full) { - if (next == input_to_skip) { - ++occurrence; - // Haven't removed enough already - if (idx_omit < num_skip) { - // This one is up for removal - if (occurrence == omit[idx_omit]) { - ++idx_omit; - continue; - } - } - } - if (idx_subset < subset->size()) { - (*subset)[idx_subset++] = next; - } - } - ABSL_RAW_CHECK(idx_subset == subset->size(), "idx_subset not at end"); - ABSL_RAW_CHECK(idx_omit == omit.size(), "idx_omit not at end"); - ABSL_RAW_CHECK(occurrence == count - 1, "occurrence not at end"); -} - -// Returns total ticks elapsed for all inputs. -Ticks TotalDuration(const Func func, const void* arg, const InputVec* inputs, - const Params& p, double* max_rel_mad) { - double rel_mad; - const Ticks duration = - SampleUntilStable(p.target_rel_mad, &rel_mad, p, [func, arg, inputs]() { - for (const FuncInput input : *inputs) { - PreventElision(func(arg, input)); - } - }); - *max_rel_mad = (std::max)(*max_rel_mad, rel_mad); - return duration; -} - -// (Nearly) empty Func for measuring timer overhead/resolution. -ABSL_RANDOM_INTERNAL_ATTRIBUTE_NEVER_INLINE FuncOutput -EmptyFunc(const void* arg, const FuncInput input) { - return input; -} - -// Returns overhead of accessing inputs[] and calling a function; this will -// be deducted from future TotalDuration return values. -Ticks Overhead(const void* arg, const InputVec* inputs, const Params& p) { - double rel_mad; - // Zero tolerance because repeatability is crucial and EmptyFunc is fast. - return SampleUntilStable(0.0, &rel_mad, p, [arg, inputs]() { - for (const FuncInput input : *inputs) { - PreventElision(EmptyFunc(arg, input)); - } - }); -} - -} // namespace - -void PinThreadToCPU(int cpu) { - // We might migrate to another CPU before pinning below, but at least cpu - // will be one of the CPUs on which this thread ran. -#if defined(ABSL_OS_WIN) - if (cpu < 0) { - cpu = static_cast<int>(GetCurrentProcessorNumber()); - ABSL_RAW_CHECK(cpu >= 0, "PinThreadToCPU detect failed"); - if (cpu >= 64) { - // NOTE: On wine, at least, GetCurrentProcessorNumber() sometimes returns - // a value > 64, which is out of range. When this happens, log a message - // and don't set a cpu affinity. - ABSL_RAW_LOG(ERROR, "Invalid CPU number: %d", cpu); - return; - } - } else if (cpu >= 64) { - // User specified an explicit CPU affinity > the valid range. - ABSL_RAW_LOG(FATAL, "Invalid CPU number: %d", cpu); - } - const DWORD_PTR prev = SetThreadAffinityMask(GetCurrentThread(), 1ULL << cpu); - ABSL_RAW_CHECK(prev != 0, "SetAffinity failed"); -#elif defined(ABSL_OS_LINUX) && !defined(ABSL_OS_ANDROID) - if (cpu < 0) { - cpu = sched_getcpu(); - ABSL_RAW_CHECK(cpu >= 0, "PinThreadToCPU detect failed"); - } - const pid_t pid = 0; // current thread - cpu_set_t set; - CPU_ZERO(&set); - CPU_SET(cpu, &set); - const int err = sched_setaffinity(pid, sizeof(set), &set); - ABSL_RAW_CHECK(err == 0, "SetAffinity failed"); -#endif -} - -// Returns tick rate. Invariant means the tick counter frequency is independent -// of CPU throttling or sleep. May be expensive, caller should cache the result. -double InvariantTicksPerSecond() { -#if defined(ABSL_ARCH_PPC) - return __ppc_get_timebase_freq(); -#elif defined(ABSL_ARCH_X86_64) - // We assume the TSC is invariant; it is on all recent Intel/AMD CPUs. - return platform::NominalClockRate(); -#else - // Fall back to clock_gettime nanoseconds. - return 1E9; -#endif -} - -size_t MeasureImpl(const Func func, const void* arg, const size_t num_skip, - const InputVec& unique, const InputVec& full, - const Params& p, Result* results) { - const float mul = 1.0f / static_cast<int>(num_skip); - - InputVec subset(full.size() - num_skip); - const Ticks overhead = Overhead(arg, &full, p); - const Ticks overhead_skip = Overhead(arg, &subset, p); - if (overhead < overhead_skip) { - ABSL_RAW_LOG(WARNING, "Measurement failed: overhead %u < %u\n", overhead, - overhead_skip); - return 0; - } - - if (p.verbose) { - ABSL_RAW_LOG(INFO, "#inputs=%5zu,%5zu overhead=%5u,%5u\n", full.size(), - subset.size(), overhead, overhead_skip); - } - - double max_rel_mad = 0.0; - const Ticks total = TotalDuration(func, arg, &full, p, &max_rel_mad); - - for (size_t i = 0; i < unique.size(); ++i) { - FillSubset(full, unique[i], num_skip, &subset); - const Ticks total_skip = TotalDuration(func, arg, &subset, p, &max_rel_mad); - - if (total < total_skip) { - ABSL_RAW_LOG(WARNING, "Measurement failed: total %u < %u\n", total, - total_skip); - return 0; - } - - const Ticks duration = (total - overhead) - (total_skip - overhead_skip); - results[i].input = unique[i]; - results[i].ticks = duration * mul; - results[i].variability = max_rel_mad; - } - - return unique.size(); -} - -size_t Measure(const Func func, const void* arg, const FuncInput* inputs, - const size_t num_inputs, Result* results, const Params& p) { - ABSL_RAW_CHECK(num_inputs != 0, "No inputs"); - - const InputVec unique = UniqueInputs(inputs, num_inputs); - const size_t num_skip = NumSkip(func, arg, unique, p); // never 0 - if (num_skip == 0) return 0; // NumSkip already printed error message - - const InputVec full = - ReplicateInputs(inputs, num_inputs, unique.size(), num_skip, p); - - // MeasureImpl may fail up to p.max_measure_retries times. - for (size_t i = 0; i < p.max_measure_retries; i++) { - auto result = MeasureImpl(func, arg, num_skip, unique, full, p, results); - if (result != 0) { - return result; - } - } - // All retries failed. (Unusual) - return 0; -} - -} // namespace random_internal_nanobenchmark -ABSL_NAMESPACE_END -} // namespace absl diff --git a/third_party/abseil_cpp/absl/random/internal/nanobenchmark.h b/third_party/abseil_cpp/absl/random/internal/nanobenchmark.h deleted file mode 100644 index a5097ba27b0f..000000000000 --- a/third_party/abseil_cpp/absl/random/internal/nanobenchmark.h +++ /dev/null @@ -1,172 +0,0 @@ -// Copyright 2017 Google Inc. All Rights Reserved. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#ifndef ABSL_RANDOM_INTERNAL_NANOBENCHMARK_H_ -#define ABSL_RANDOM_INTERNAL_NANOBENCHMARK_H_ - -// Benchmarks functions of a single integer argument with realistic branch -// prediction hit rates. Uses a robust estimator to summarize the measurements. -// The precision is about 0.2%. -// -// Examples: see nanobenchmark_test.cc. -// -// Background: Microbenchmarks such as http://github.com/google/benchmark -// can measure elapsed times on the order of a microsecond. Shorter functions -// are typically measured by repeating them thousands of times and dividing -// the total elapsed time by this count. Unfortunately, repetition (especially -// with the same input parameter!) influences the runtime. In time-critical -// code, it is reasonable to expect warm instruction/data caches and TLBs, -// but a perfect record of which branches will be taken is unrealistic. -// Unless the application also repeatedly invokes the measured function with -// the same parameter, the benchmark is measuring something very different - -// a best-case result, almost as if the parameter were made a compile-time -// constant. This may lead to erroneous conclusions about branch-heavy -// algorithms outperforming branch-free alternatives. -// -// Our approach differs in three ways. Adding fences to the timer functions -// reduces variability due to instruction reordering, improving the timer -// resolution to about 40 CPU cycles. However, shorter functions must still -// be invoked repeatedly. For more realistic branch prediction performance, -// we vary the input parameter according to a user-specified distribution. -// Thus, instead of VaryInputs(Measure(Repeat(func))), we change the -// loop nesting to Measure(Repeat(VaryInputs(func))). We also estimate the -// central tendency of the measurement samples with the "half sample mode", -// which is more robust to outliers and skewed data than the mean or median. - -// NOTE: for compatibility with multiple translation units compiled with -// distinct flags, avoid #including headers that define functions. - -#include <stddef.h> -#include <stdint.h> - -#include "absl/base/config.h" - -namespace absl { -ABSL_NAMESPACE_BEGIN -namespace random_internal_nanobenchmark { - -// Input influencing the function being measured (e.g. number of bytes to copy). -using FuncInput = size_t; - -// "Proof of work" returned by Func to ensure the compiler does not elide it. -using FuncOutput = uint64_t; - -// Function to measure: either 1) a captureless lambda or function with two -// arguments or 2) a lambda with capture, in which case the first argument -// is reserved for use by MeasureClosure. -using Func = FuncOutput (*)(const void*, FuncInput); - -// Internal parameters that determine precision/resolution/measuring time. -struct Params { - // For measuring timer overhead/resolution. Used in a nested loop => - // quadratic time, acceptable because we know timer overhead is "low". - // constexpr because this is used to define array bounds. - static constexpr size_t kTimerSamples = 256; - - // Best-case precision, expressed as a divisor of the timer resolution. - // Larger => more calls to Func and higher precision. - size_t precision_divisor = 1024; - - // Ratio between full and subset input distribution sizes. Cannot be less - // than 2; larger values increase measurement time but more faithfully - // model the given input distribution. - size_t subset_ratio = 2; - - // Together with the estimated Func duration, determines how many times to - // call Func before checking the sample variability. Larger values increase - // measurement time, memory/cache use and precision. - double seconds_per_eval = 4E-3; - - // The minimum number of samples before estimating the central tendency. - size_t min_samples_per_eval = 7; - - // The mode is better than median for estimating the central tendency of - // skewed/fat-tailed distributions, but it requires sufficient samples - // relative to the width of half-ranges. - size_t min_mode_samples = 64; - - // Maximum permissible variability (= median absolute deviation / center). - double target_rel_mad = 0.002; - - // Abort after this many evals without reaching target_rel_mad. This - // prevents infinite loops. - size_t max_evals = 9; - - // Retry the measure loop up to this many times. - size_t max_measure_retries = 2; - - // Whether to print additional statistics to stdout. - bool verbose = true; -}; - -// Measurement result for each unique input. -struct Result { - FuncInput input; - - // Robust estimate (mode or median) of duration. - float ticks; - - // Measure of variability (median absolute deviation relative to "ticks"). - float variability; -}; - -// Ensures the thread is running on the specified cpu, and no others. -// Reduces noise due to desynchronized socket RDTSC and context switches. -// If "cpu" is negative, pin to the currently running core. -void PinThreadToCPU(const int cpu = -1); - -// Returns tick rate, useful for converting measurements to seconds. Invariant -// means the tick counter frequency is independent of CPU throttling or sleep. -// This call may be expensive, callers should cache the result. -double InvariantTicksPerSecond(); - -// Precisely measures the number of ticks elapsed when calling "func" with the -// given inputs, shuffled to ensure realistic branch prediction hit rates. -// -// "func" returns a 'proof of work' to ensure its computations are not elided. -// "arg" is passed to Func, or reserved for internal use by MeasureClosure. -// "inputs" is an array of "num_inputs" (not necessarily unique) arguments to -// "func". The values should be chosen to maximize coverage of "func". This -// represents a distribution, so a value's frequency should reflect its -// probability in the real application. Order does not matter; for example, a -// uniform distribution over [0, 4) could be represented as {3,0,2,1}. -// Returns how many Result were written to "results": one per unique input, or -// zero if the measurement failed (an error message goes to stderr). -size_t Measure(const Func func, const void* arg, const FuncInput* inputs, - const size_t num_inputs, Result* results, - const Params& p = Params()); - -// Calls operator() of the given closure (lambda function). -template <class Closure> -static FuncOutput CallClosure(const void* f, const FuncInput input) { - return (*reinterpret_cast<const Closure*>(f))(input); -} - -// Same as Measure, except "closure" is typically a lambda function of -// FuncInput -> FuncOutput with a capture list. -template <class Closure> -static inline size_t MeasureClosure(const Closure& closure, - const FuncInput* inputs, - const size_t num_inputs, Result* results, - const Params& p = Params()) { - return Measure(reinterpret_cast<Func>(&CallClosure<Closure>), - reinterpret_cast<const void*>(&closure), inputs, num_inputs, - results, p); -} - -} // namespace random_internal_nanobenchmark -ABSL_NAMESPACE_END -} // namespace absl - -#endif // ABSL_RANDOM_INTERNAL_NANOBENCHMARK_H_ diff --git a/third_party/abseil_cpp/absl/random/internal/nanobenchmark_test.cc b/third_party/abseil_cpp/absl/random/internal/nanobenchmark_test.cc deleted file mode 100644 index f1571e269ff5..000000000000 --- a/third_party/abseil_cpp/absl/random/internal/nanobenchmark_test.cc +++ /dev/null @@ -1,77 +0,0 @@ -// Copyright 2017 Google Inc. All Rights Reserved. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#include "absl/random/internal/nanobenchmark.h" - -#include "absl/base/internal/raw_logging.h" -#include "absl/strings/numbers.h" - -namespace absl { -ABSL_NAMESPACE_BEGIN -namespace random_internal_nanobenchmark { -namespace { - -uint64_t Div(const void*, FuncInput in) { - // Here we're measuring the throughput because benchmark invocations are - // independent. - const int64_t d1 = 0xFFFFFFFFFFll / int64_t(in); // IDIV - return d1; -} - -template <size_t N> -void MeasureDiv(const FuncInput (&inputs)[N]) { - Result results[N]; - Params params; - params.max_evals = 6; // avoid test timeout - const size_t num_results = Measure(&Div, nullptr, inputs, N, results, params); - if (num_results == 0) { - ABSL_RAW_LOG( - WARNING, - "WARNING: Measurement failed, should not happen when using " - "PinThreadToCPU unless the region to measure takes > 1 second.\n"); - return; - } - for (size_t i = 0; i < num_results; ++i) { - ABSL_RAW_LOG(INFO, "%5zu: %6.2f ticks; MAD=%4.2f%%\n", results[i].input, - results[i].ticks, results[i].variability * 100.0); - ABSL_RAW_CHECK(results[i].ticks != 0.0f, "Zero duration"); - } -} - -void RunAll(const int argc, char* argv[]) { - // Avoid migrating between cores - important on multi-socket systems. - int cpu = -1; - if (argc == 2) { - if (!absl::SimpleAtoi(argv[1], &cpu)) { - ABSL_RAW_LOG(FATAL, "The optional argument must be a CPU number >= 0.\n"); - } - } - PinThreadToCPU(cpu); - - // unpredictable == 1 but the compiler doesn't know that. - const FuncInput unpredictable = argc != 999; - static const FuncInput inputs[] = {unpredictable * 10, unpredictable * 100}; - - MeasureDiv(inputs); -} - -} // namespace -} // namespace random_internal_nanobenchmark -ABSL_NAMESPACE_END -} // namespace absl - -int main(int argc, char* argv[]) { - absl::random_internal_nanobenchmark::RunAll(argc, argv); - return 0; -} diff --git a/third_party/abseil_cpp/absl/random/internal/nonsecure_base.h b/third_party/abseil_cpp/absl/random/internal/nonsecure_base.h deleted file mode 100644 index 730fa2ea12d2..000000000000 --- a/third_party/abseil_cpp/absl/random/internal/nonsecure_base.h +++ /dev/null @@ -1,150 +0,0 @@ -// Copyright 2017 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#ifndef ABSL_RANDOM_INTERNAL_NONSECURE_BASE_H_ -#define ABSL_RANDOM_INTERNAL_NONSECURE_BASE_H_ - -#include <algorithm> -#include <cstdint> -#include <iostream> -#include <iterator> -#include <random> -#include <string> -#include <type_traits> -#include <vector> - -#include "absl/base/macros.h" -#include "absl/meta/type_traits.h" -#include "absl/random/internal/pool_urbg.h" -#include "absl/random/internal/salted_seed_seq.h" -#include "absl/random/internal/seed_material.h" -#include "absl/types/optional.h" -#include "absl/types/span.h" - -namespace absl { -ABSL_NAMESPACE_BEGIN -namespace random_internal { - -// Each instance of NonsecureURBGBase<URBG> will be seeded by variates produced -// by a thread-unique URBG-instance. -template <typename URBG> -class NonsecureURBGBase { - public: - using result_type = typename URBG::result_type; - - // Default constructor - NonsecureURBGBase() : urbg_(ConstructURBG()) {} - - // Copy disallowed, move allowed. - NonsecureURBGBase(const NonsecureURBGBase&) = delete; - NonsecureURBGBase& operator=(const NonsecureURBGBase&) = delete; - NonsecureURBGBase(NonsecureURBGBase&&) = default; - NonsecureURBGBase& operator=(NonsecureURBGBase&&) = default; - - // Constructor using a seed - template <class SSeq, typename = typename absl::enable_if_t< - !std::is_same<SSeq, NonsecureURBGBase>::value>> - explicit NonsecureURBGBase(SSeq&& seq) - : urbg_(ConstructURBG(std::forward<SSeq>(seq))) {} - - // Note: on MSVC, min() or max() can be interpreted as MIN() or MAX(), so we - // enclose min() or max() in parens as (min)() and (max)(). - // Additionally, clang-format requires no space before this construction. - - // NonsecureURBGBase::min() - static constexpr result_type(min)() { return (URBG::min)(); } - - // NonsecureURBGBase::max() - static constexpr result_type(max)() { return (URBG::max)(); } - - // NonsecureURBGBase::operator()() - result_type operator()() { return urbg_(); } - - // NonsecureURBGBase::discard() - void discard(unsigned long long values) { // NOLINT(runtime/int) - urbg_.discard(values); - } - - bool operator==(const NonsecureURBGBase& other) const { - return urbg_ == other.urbg_; - } - - bool operator!=(const NonsecureURBGBase& other) const { - return !(urbg_ == other.urbg_); - } - - private: - // Seeder is a custom seed sequence type where generate() fills the provided - // buffer via the RandenPool entropy source. - struct Seeder { - using result_type = uint32_t; - - size_t size() { return 0; } - - template <typename OutIterator> - void param(OutIterator) const {} - - template <typename RandomAccessIterator> - void generate(RandomAccessIterator begin, RandomAccessIterator end) { - if (begin != end) { - // begin, end must be random access iterators assignable from uint32_t. - generate_impl( - std::integral_constant<bool, sizeof(*begin) == sizeof(uint32_t)>{}, - begin, end); - } - } - - // Commonly, generate is invoked with a pointer to a buffer which - // can be cast to a uint32_t. - template <typename RandomAccessIterator> - void generate_impl(std::integral_constant<bool, true>, - RandomAccessIterator begin, RandomAccessIterator end) { - auto buffer = absl::MakeSpan(begin, end); - auto target = absl::MakeSpan(reinterpret_cast<uint32_t*>(buffer.data()), - buffer.size()); - RandenPool<uint32_t>::Fill(target); - } - - // The non-uint32_t case should be uncommon, and involves an extra copy, - // filling the uint32_t buffer and then mixing into the output. - template <typename RandomAccessIterator> - void generate_impl(std::integral_constant<bool, false>, - RandomAccessIterator begin, RandomAccessIterator end) { - const size_t n = std::distance(begin, end); - absl::InlinedVector<uint32_t, 8> data(n, 0); - RandenPool<uint32_t>::Fill(absl::MakeSpan(data.begin(), data.end())); - std::copy(std::begin(data), std::end(data), begin); - } - }; - - static URBG ConstructURBG() { - Seeder seeder; - return URBG(seeder); - } - - template <typename SSeq> - static URBG ConstructURBG(SSeq&& seq) { // NOLINT(runtime/references) - auto salted_seq = - random_internal::MakeSaltedSeedSeq(std::forward<SSeq>(seq)); - return URBG(salted_seq); - } - - URBG urbg_; -}; - -} // namespace random_internal -ABSL_NAMESPACE_END -} // namespace absl - -#endif // ABSL_RANDOM_INTERNAL_NONSECURE_BASE_H_ diff --git a/third_party/abseil_cpp/absl/random/internal/nonsecure_base_test.cc b/third_party/abseil_cpp/absl/random/internal/nonsecure_base_test.cc deleted file mode 100644 index 698027fc6e0d..000000000000 --- a/third_party/abseil_cpp/absl/random/internal/nonsecure_base_test.cc +++ /dev/null @@ -1,245 +0,0 @@ -// Copyright 2017 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#include "absl/random/internal/nonsecure_base.h" - -#include <algorithm> -#include <iostream> -#include <memory> -#include <random> -#include <sstream> - -#include "gtest/gtest.h" -#include "absl/random/distributions.h" -#include "absl/random/random.h" -#include "absl/strings/str_cat.h" - -namespace { - -using ExampleNonsecureURBG = - absl::random_internal::NonsecureURBGBase<std::mt19937>; - -template <typename T> -void Use(const T&) {} - -} // namespace - -TEST(NonsecureURBGBase, DefaultConstructorIsValid) { - ExampleNonsecureURBG urbg; -} - -// Ensure that the recommended template-instantiations are valid. -TEST(RecommendedTemplates, CanBeConstructed) { - absl::BitGen default_generator; - absl::InsecureBitGen insecure_generator; -} - -TEST(RecommendedTemplates, CanDiscardValues) { - absl::BitGen default_generator; - absl::InsecureBitGen insecure_generator; - - default_generator.discard(5); - insecure_generator.discard(5); -} - -TEST(NonsecureURBGBase, StandardInterface) { - // Names after definition of [rand.req.urbg] in C++ standard. - // e us a value of E - // v is a lvalue of E - // x, y are possibly const values of E - // s is a value of T - // q is a value satisfying requirements of seed_sequence - // z is a value of type unsigned long long - // os is a some specialization of basic_ostream - // is is a some specialization of basic_istream - - using E = absl::random_internal::NonsecureURBGBase<std::minstd_rand>; - - using T = typename E::result_type; - - static_assert(!std::is_copy_constructible<E>::value, - "NonsecureURBGBase should not be copy constructible"); - - static_assert(!absl::is_copy_assignable<E>::value, - "NonsecureURBGBase should not be copy assignable"); - - static_assert(std::is_move_constructible<E>::value, - "NonsecureURBGBase should be move constructible"); - - static_assert(absl::is_move_assignable<E>::value, - "NonsecureURBGBase should be move assignable"); - - static_assert(std::is_same<decltype(std::declval<E>()()), T>::value, - "return type of operator() must be result_type"); - - { - const E x, y; - Use(x); - Use(y); - - static_assert(std::is_same<decltype(x == y), bool>::value, - "return type of operator== must be bool"); - - static_assert(std::is_same<decltype(x != y), bool>::value, - "return type of operator== must be bool"); - } - - E e; - std::seed_seq q{1, 2, 3}; - - E{}; - E{q}; - - // Copy constructor not supported. - // E{x}; - - // result_type seed constructor not supported. - // E{T{1}}; - - // Move constructors are supported. - { - E tmp(q); - E m = std::move(tmp); - E n(std::move(m)); - EXPECT_TRUE(e != n); - } - - // Comparisons work. - { - // MSVC emits error 2718 when using EXPECT_EQ(e, x) - // * actual parameter with __declspec(align('#')) won't be aligned - E a(q); - E b(q); - - EXPECT_TRUE(a != e); - EXPECT_TRUE(a == b); - - a(); - EXPECT_TRUE(a != b); - } - - // e.seed(s) not supported. - - // [rand.req.eng] specifies the parameter as 'unsigned long long' - // e.discard(unsigned long long) is supported. - unsigned long long z = 1; // NOLINT(runtime/int) - e.discard(z); -} - -TEST(NonsecureURBGBase, SeedSeqConstructorIsValid) { - std::seed_seq seq; - ExampleNonsecureURBG rbg(seq); -} - -TEST(NonsecureURBGBase, CompatibleWithDistributionUtils) { - ExampleNonsecureURBG rbg; - - absl::Uniform(rbg, 0, 100); - absl::Uniform(rbg, 0.5, 0.7); - absl::Poisson<uint32_t>(rbg); - absl::Exponential<float>(rbg); -} - -TEST(NonsecureURBGBase, CompatibleWithStdDistributions) { - ExampleNonsecureURBG rbg; - - // Cast to void to suppress [[nodiscard]] warnings - static_cast<void>(std::uniform_int_distribution<uint32_t>(0, 100)(rbg)); - static_cast<void>(std::uniform_real_distribution<float>()(rbg)); - static_cast<void>(std::bernoulli_distribution(0.2)(rbg)); -} - -TEST(NonsecureURBGBase, ConsecutiveDefaultInstancesYieldUniqueVariates) { - const size_t kNumSamples = 128; - - ExampleNonsecureURBG rbg1; - ExampleNonsecureURBG rbg2; - - for (size_t i = 0; i < kNumSamples; i++) { - EXPECT_NE(rbg1(), rbg2()); - } -} - -TEST(NonsecureURBGBase, EqualSeedSequencesYieldEqualVariates) { - std::seed_seq seq; - - ExampleNonsecureURBG rbg1(seq); - ExampleNonsecureURBG rbg2(seq); - - // ExampleNonsecureURBG rbg3({1, 2, 3}); // Should not compile. - - for (uint32_t i = 0; i < 1000; i++) { - EXPECT_EQ(rbg1(), rbg2()); - } - - rbg1.discard(100); - rbg2.discard(100); - - // The sequences should continue after discarding - for (uint32_t i = 0; i < 1000; i++) { - EXPECT_EQ(rbg1(), rbg2()); - } -} - -// This is a PRNG-compatible type specifically designed to test -// that NonsecureURBGBase::Seeder can correctly handle iterators -// to arbitrary non-uint32_t size types. -template <typename T> -struct SeederTestEngine { - using result_type = T; - - static constexpr result_type(min)() { - return (std::numeric_limits<result_type>::min)(); - } - static constexpr result_type(max)() { - return (std::numeric_limits<result_type>::max)(); - } - - template <class SeedSequence, - typename = typename absl::enable_if_t< - !std::is_same<SeedSequence, SeederTestEngine>::value>> - explicit SeederTestEngine(SeedSequence&& seq) { - seed(seq); - } - - SeederTestEngine(const SeederTestEngine&) = default; - SeederTestEngine& operator=(const SeederTestEngine&) = default; - SeederTestEngine(SeederTestEngine&&) = default; - SeederTestEngine& operator=(SeederTestEngine&&) = default; - - result_type operator()() { return state[0]; } - - template <class SeedSequence> - void seed(SeedSequence&& seq) { - std::fill(std::begin(state), std::end(state), T(0)); - seq.generate(std::begin(state), std::end(state)); - } - - T state[2]; -}; - -TEST(NonsecureURBGBase, SeederWorksForU32) { - using U32 = - absl::random_internal::NonsecureURBGBase<SeederTestEngine<uint32_t>>; - U32 x; - EXPECT_NE(0, x()); -} - -TEST(NonsecureURBGBase, SeederWorksForU64) { - using U64 = - absl::random_internal::NonsecureURBGBase<SeederTestEngine<uint64_t>>; - - U64 x; - EXPECT_NE(0, x()); -} diff --git a/third_party/abseil_cpp/absl/random/internal/pcg_engine.h b/third_party/abseil_cpp/absl/random/internal/pcg_engine.h deleted file mode 100644 index 53c23fe1b492..000000000000 --- a/third_party/abseil_cpp/absl/random/internal/pcg_engine.h +++ /dev/null @@ -1,307 +0,0 @@ -// Copyright 2018 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#ifndef ABSL_RANDOM_INTERNAL_PCG_ENGINE_H_ -#define ABSL_RANDOM_INTERNAL_PCG_ENGINE_H_ - -#include <type_traits> - -#include "absl/base/config.h" -#include "absl/meta/type_traits.h" -#include "absl/numeric/int128.h" -#include "absl/random/internal/fastmath.h" -#include "absl/random/internal/iostream_state_saver.h" - -namespace absl { -ABSL_NAMESPACE_BEGIN -namespace random_internal { - -// pcg_engine is a simplified implementation of Melissa O'Neil's PCG engine in -// C++. PCG combines a linear congruential generator (LCG) with output state -// mixing functions to generate each random variate. pcg_engine supports only a -// single sequence (oneseq), and does not support streams. -// -// pcg_engine is parameterized by two types: -// Params, which provides the multiplier and increment values; -// Mix, which mixes the state into the result. -// -template <typename Params, typename Mix> -class pcg_engine { - static_assert(std::is_same<typename Params::state_type, - typename Mix::state_type>::value, - "Class-template absl::pcg_engine must be parameterized by " - "Params and Mix with identical state_type"); - - static_assert(std::is_unsigned<typename Mix::result_type>::value, - "Class-template absl::pcg_engine must be parameterized by " - "an unsigned Mix::result_type"); - - using params_type = Params; - using mix_type = Mix; - using state_type = typename Mix::state_type; - - public: - // C++11 URBG interface: - using result_type = typename Mix::result_type; - - static constexpr result_type(min)() { - return (std::numeric_limits<result_type>::min)(); - } - - static constexpr result_type(max)() { - return (std::numeric_limits<result_type>::max)(); - } - - explicit pcg_engine(uint64_t seed_value = 0) { seed(seed_value); } - - template <class SeedSequence, - typename = typename absl::enable_if_t< - !std::is_same<SeedSequence, pcg_engine>::value>> - explicit pcg_engine(SeedSequence&& seq) { - seed(seq); - } - - pcg_engine(const pcg_engine&) = default; - pcg_engine& operator=(const pcg_engine&) = default; - pcg_engine(pcg_engine&&) = default; - pcg_engine& operator=(pcg_engine&&) = default; - - result_type operator()() { - // Advance the LCG state, always using the new value to generate the output. - state_ = lcg(state_); - return Mix{}(state_); - } - - void seed(uint64_t seed_value = 0) { - state_type tmp = seed_value; - state_ = lcg(tmp + Params::increment()); - } - - template <class SeedSequence> - typename absl::enable_if_t< - !std::is_convertible<SeedSequence, uint64_t>::value, void> - seed(SeedSequence&& seq) { - reseed(seq); - } - - void discard(uint64_t count) { state_ = advance(state_, count); } - - bool operator==(const pcg_engine& other) const { - return state_ == other.state_; - } - - bool operator!=(const pcg_engine& other) const { return !(*this == other); } - - template <class CharT, class Traits> - friend typename absl::enable_if_t<(sizeof(state_type) == 16), - std::basic_ostream<CharT, Traits>&> - operator<<( - std::basic_ostream<CharT, Traits>& os, // NOLINT(runtime/references) - const pcg_engine& engine) { - auto saver = random_internal::make_ostream_state_saver(os); - random_internal::stream_u128_helper<state_type> helper; - helper.write(pcg_engine::params_type::multiplier(), os); - os << os.fill(); - helper.write(pcg_engine::params_type::increment(), os); - os << os.fill(); - helper.write(engine.state_, os); - return os; - } - - template <class CharT, class Traits> - friend typename absl::enable_if_t<(sizeof(state_type) <= 8), - std::basic_ostream<CharT, Traits>&> - operator<<( - std::basic_ostream<CharT, Traits>& os, // NOLINT(runtime/references) - const pcg_engine& engine) { - auto saver = random_internal::make_ostream_state_saver(os); - os << pcg_engine::params_type::multiplier() << os.fill(); - os << pcg_engine::params_type::increment() << os.fill(); - os << engine.state_; - return os; - } - - template <class CharT, class Traits> - friend typename absl::enable_if_t<(sizeof(state_type) == 16), - std::basic_istream<CharT, Traits>&> - operator>>( - std::basic_istream<CharT, Traits>& is, // NOLINT(runtime/references) - pcg_engine& engine) { // NOLINT(runtime/references) - random_internal::stream_u128_helper<state_type> helper; - auto mult = helper.read(is); - auto inc = helper.read(is); - auto tmp = helper.read(is); - if (mult != pcg_engine::params_type::multiplier() || - inc != pcg_engine::params_type::increment()) { - // signal failure by setting the failbit. - is.setstate(is.rdstate() | std::ios_base::failbit); - } - if (!is.fail()) { - engine.state_ = tmp; - } - return is; - } - - template <class CharT, class Traits> - friend typename absl::enable_if_t<(sizeof(state_type) <= 8), - std::basic_istream<CharT, Traits>&> - operator>>( - std::basic_istream<CharT, Traits>& is, // NOLINT(runtime/references) - pcg_engine& engine) { // NOLINT(runtime/references) - state_type mult{}, inc{}, tmp{}; - is >> mult >> inc >> tmp; - if (mult != pcg_engine::params_type::multiplier() || - inc != pcg_engine::params_type::increment()) { - // signal failure by setting the failbit. - is.setstate(is.rdstate() | std::ios_base::failbit); - } - if (!is.fail()) { - engine.state_ = tmp; - } - return is; - } - - private: - state_type state_; - - // Returns the linear-congruential generator next state. - static inline constexpr state_type lcg(state_type s) { - return s * Params::multiplier() + Params::increment(); - } - - // Returns the linear-congruential arbitrary seek state. - inline state_type advance(state_type s, uint64_t n) const { - state_type mult = Params::multiplier(); - state_type inc = Params::increment(); - state_type m = 1; - state_type i = 0; - while (n > 0) { - if (n & 1) { - m *= mult; - i = i * mult + inc; - } - inc = (mult + 1) * inc; - mult *= mult; - n >>= 1; - } - return m * s + i; - } - - template <class SeedSequence> - void reseed(SeedSequence& seq) { - using sequence_result_type = typename SeedSequence::result_type; - constexpr size_t kBufferSize = - sizeof(state_type) / sizeof(sequence_result_type); - sequence_result_type buffer[kBufferSize]; - seq.generate(std::begin(buffer), std::end(buffer)); - // Convert the seed output to a single state value. - state_type tmp = buffer[0]; - for (size_t i = 1; i < kBufferSize; i++) { - tmp <<= (sizeof(sequence_result_type) * 8); - tmp |= buffer[i]; - } - state_ = lcg(tmp + params_type::increment()); - } -}; - -// Parameterized implementation of the PCG 128-bit oneseq state. -// This provides state_type, multiplier, and increment for pcg_engine. -template <uint64_t kMultA, uint64_t kMultB, uint64_t kIncA, uint64_t kIncB> -class pcg128_params { - public: -#if ABSL_HAVE_INTRINSIC_INT128 - using state_type = __uint128_t; - static inline constexpr state_type make_u128(uint64_t a, uint64_t b) { - return (static_cast<__uint128_t>(a) << 64) | b; - } -#else - using state_type = absl::uint128; - static inline constexpr state_type make_u128(uint64_t a, uint64_t b) { - return absl::MakeUint128(a, b); - } -#endif - - static inline constexpr state_type multiplier() { - return make_u128(kMultA, kMultB); - } - static inline constexpr state_type increment() { - return make_u128(kIncA, kIncB); - } -}; - -// Implementation of the PCG xsl_rr_128_64 128-bit mixing function, which -// accepts an input of state_type and mixes it into an output of result_type. -struct pcg_xsl_rr_128_64 { -#if ABSL_HAVE_INTRINSIC_INT128 - using state_type = __uint128_t; -#else - using state_type = absl::uint128; -#endif - using result_type = uint64_t; - - inline uint64_t operator()(state_type state) { - // This is equivalent to the xsl_rr_128_64 mixing function. -#if ABSL_HAVE_INTRINSIC_INT128 - uint64_t rotate = static_cast<uint64_t>(state >> 122u); - state ^= state >> 64; - uint64_t s = static_cast<uint64_t>(state); -#else - uint64_t h = Uint128High64(state); - uint64_t rotate = h >> 58u; - uint64_t s = Uint128Low64(state) ^ h; -#endif - return random_internal::rotr(s, rotate); - } -}; - -// Parameterized implementation of the PCG 64-bit oneseq state. -// This provides state_type, multiplier, and increment for pcg_engine. -template <uint64_t kMult, uint64_t kInc> -class pcg64_params { - public: - using state_type = uint64_t; - static inline constexpr state_type multiplier() { return kMult; } - static inline constexpr state_type increment() { return kInc; } -}; - -// Implementation of the PCG xsh_rr_64_32 64-bit mixing function, which accepts -// an input of state_type and mixes it into an output of result_type. -struct pcg_xsh_rr_64_32 { - using state_type = uint64_t; - using result_type = uint32_t; - inline uint32_t operator()(uint64_t state) { - return random_internal::rotr( - static_cast<uint32_t>(((state >> 18) ^ state) >> 27), state >> 59); - } -}; - -// Stable pcg_engine implementations: -// This is a 64-bit generator using 128-bits of state. -// The output sequence is equivalent to Melissa O'Neil's pcg64_oneseq. -using pcg64_2018_engine = pcg_engine< - random_internal::pcg128_params<0x2360ed051fc65da4ull, 0x4385df649fccf645ull, - 0x5851f42d4c957f2d, 0x14057b7ef767814f>, - random_internal::pcg_xsl_rr_128_64>; - -// This is a 32-bit generator using 64-bits of state. -// This is equivalent to Melissa O'Neil's pcg32_oneseq. -using pcg32_2018_engine = pcg_engine< - random_internal::pcg64_params<0x5851f42d4c957f2dull, 0x14057b7ef767814full>, - random_internal::pcg_xsh_rr_64_32>; - -} // namespace random_internal -ABSL_NAMESPACE_END -} // namespace absl - -#endif // ABSL_RANDOM_INTERNAL_PCG_ENGINE_H_ diff --git a/third_party/abseil_cpp/absl/random/internal/pcg_engine_test.cc b/third_party/abseil_cpp/absl/random/internal/pcg_engine_test.cc deleted file mode 100644 index 4d763e89eb58..000000000000 --- a/third_party/abseil_cpp/absl/random/internal/pcg_engine_test.cc +++ /dev/null @@ -1,638 +0,0 @@ -// Copyright 2018 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#include "absl/random/internal/pcg_engine.h" - -#include <algorithm> -#include <bitset> -#include <random> -#include <sstream> - -#include "gmock/gmock.h" -#include "gtest/gtest.h" -#include "absl/random/internal/explicit_seed_seq.h" -#include "absl/time/clock.h" - -#define UPDATE_GOLDEN 0 - -namespace { - -using absl::random_internal::ExplicitSeedSeq; -using absl::random_internal::pcg32_2018_engine; -using absl::random_internal::pcg64_2018_engine; - -template <typename EngineType> -class PCGEngineTest : public ::testing::Test {}; - -using EngineTypes = ::testing::Types<pcg64_2018_engine, pcg32_2018_engine>; - -TYPED_TEST_SUITE(PCGEngineTest, EngineTypes); - -TYPED_TEST(PCGEngineTest, VerifyReseedChangesAllValues) { - using engine_type = TypeParam; - using result_type = typename engine_type::result_type; - - const size_t kNumOutputs = 16; - engine_type engine; - - // MSVC emits error 2719 without the use of std::ref below. - // * formal parameter with __declspec(align('#')) won't be aligned - - { - std::seed_seq seq1{1, 2, 3, 4, 5, 6, 7}; - engine.seed(seq1); - } - result_type a[kNumOutputs]; - std::generate(std::begin(a), std::end(a), std::ref(engine)); - - { - std::random_device rd; - std::seed_seq seq2{rd(), rd(), rd()}; - engine.seed(seq2); - } - result_type b[kNumOutputs]; - std::generate(std::begin(b), std::end(b), std::ref(engine)); - - // Verify that two uncorrelated values have ~50% of there bits in common. Use - // a 10% margin-of-error to reduce flakiness. - size_t changed_bits = 0; - size_t unchanged_bits = 0; - size_t total_set = 0; - size_t total_bits = 0; - size_t equal_count = 0; - for (size_t i = 0; i < kNumOutputs; ++i) { - equal_count += (a[i] == b[i]) ? 1 : 0; - std::bitset<sizeof(result_type) * 8> bitset(a[i] ^ b[i]); - changed_bits += bitset.count(); - unchanged_bits += bitset.size() - bitset.count(); - - std::bitset<sizeof(result_type) * 8> a_set(a[i]); - std::bitset<sizeof(result_type) * 8> b_set(b[i]); - total_set += a_set.count() + b_set.count(); - total_bits += 2 * 8 * sizeof(result_type); - } - // On average, half the bits are changed between two calls. - EXPECT_LE(changed_bits, 0.60 * (changed_bits + unchanged_bits)); - EXPECT_GE(changed_bits, 0.40 * (changed_bits + unchanged_bits)); - - // verify using a quick normal-approximation to the binomial. - EXPECT_NEAR(total_set, total_bits * 0.5, 4 * std::sqrt(total_bits)) - << "@" << total_set / static_cast<double>(total_bits); - - // Also, A[i] == B[i] with probability (1/range) * N. - // Give this a pretty wide latitude, though. - const double kExpected = kNumOutputs / (1.0 * sizeof(result_type) * 8); - EXPECT_LE(equal_count, 1.0 + kExpected); -} - -// Number of values that needs to be consumed to clean two sizes of buffer -// and trigger third refresh. (slightly overestimates the actual state size). -constexpr size_t kTwoBufferValues = 16; - -TYPED_TEST(PCGEngineTest, VerifyDiscard) { - using engine_type = TypeParam; - - for (size_t num_used = 0; num_used < kTwoBufferValues; ++num_used) { - engine_type engine_used; - for (size_t i = 0; i < num_used; ++i) { - engine_used(); - } - - for (size_t num_discard = 0; num_discard < kTwoBufferValues; - ++num_discard) { - engine_type engine1 = engine_used; - engine_type engine2 = engine_used; - for (size_t i = 0; i < num_discard; ++i) { - engine1(); - } - engine2.discard(num_discard); - for (size_t i = 0; i < kTwoBufferValues; ++i) { - const auto r1 = engine1(); - const auto r2 = engine2(); - ASSERT_EQ(r1, r2) << "used=" << num_used << " discard=" << num_discard; - } - } - } -} - -TYPED_TEST(PCGEngineTest, StreamOperatorsResult) { - using engine_type = TypeParam; - - std::wostringstream os; - std::wistringstream is; - engine_type engine; - - EXPECT_EQ(&(os << engine), &os); - EXPECT_EQ(&(is >> engine), &is); -} - -TYPED_TEST(PCGEngineTest, StreamSerialization) { - using engine_type = TypeParam; - - for (size_t discard = 0; discard < kTwoBufferValues; ++discard) { - ExplicitSeedSeq seed_sequence{12, 34, 56}; - engine_type engine(seed_sequence); - engine.discard(discard); - - std::stringstream stream; - stream << engine; - - engine_type new_engine; - stream >> new_engine; - for (size_t i = 0; i < 64; ++i) { - EXPECT_EQ(engine(), new_engine()) << " " << i; - } - } -} - -constexpr size_t kNumGoldenOutputs = 127; - -// This test is checking if randen_engine is meets interface requirements -// defined in [rand.req.urbg]. -TYPED_TEST(PCGEngineTest, RandomNumberEngineInterface) { - using engine_type = TypeParam; - - using E = engine_type; - using T = typename E::result_type; - - static_assert(std::is_copy_constructible<E>::value, - "engine_type must be copy constructible"); - - static_assert(absl::is_copy_assignable<E>::value, - "engine_type must be copy assignable"); - - static_assert(std::is_move_constructible<E>::value, - "engine_type must be move constructible"); - - static_assert(absl::is_move_assignable<E>::value, - "engine_type must be move assignable"); - - static_assert(std::is_same<decltype(std::declval<E>()()), T>::value, - "return type of operator() must be result_type"); - - // Names after definition of [rand.req.urbg] in C++ standard. - // e us a value of E - // v is a lvalue of E - // x, y are possibly const values of E - // s is a value of T - // q is a value satisfying requirements of seed_sequence - // z is a value of type unsigned long long - // os is a some specialization of basic_ostream - // is is a some specialization of basic_istream - - E e, v; - const E x, y; - T s = 1; - std::seed_seq q{1, 2, 3}; - unsigned long long z = 1; // NOLINT(runtime/int) - std::wostringstream os; - std::wistringstream is; - - E{}; - E{x}; - E{s}; - E{q}; - - e.seed(); - - // MSVC emits error 2718 when using EXPECT_EQ(e, x) - // * actual parameter with __declspec(align('#')) won't be aligned - EXPECT_TRUE(e == x); - - e.seed(q); - { - E tmp(q); - EXPECT_TRUE(e == tmp); - } - - e(); - { - E tmp(q); - EXPECT_TRUE(e != tmp); - } - - e.discard(z); - - static_assert(std::is_same<decltype(x == y), bool>::value, - "return type of operator== must be bool"); - - static_assert(std::is_same<decltype(x != y), bool>::value, - "return type of operator== must be bool"); -} - -TYPED_TEST(PCGEngineTest, RandenEngineSFINAETest) { - using engine_type = TypeParam; - using result_type = typename engine_type::result_type; - - { - engine_type engine(result_type(1)); - engine.seed(result_type(1)); - } - - { - result_type n = 1; - engine_type engine(n); - engine.seed(n); - } - - { - engine_type engine(1); - engine.seed(1); - } - - { - int n = 1; - engine_type engine(n); - engine.seed(n); - } - - { - std::seed_seq seed_seq; - engine_type engine(seed_seq); - engine.seed(seed_seq); - } - - { - engine_type engine{std::seed_seq()}; - engine.seed(std::seed_seq()); - } -} - -// ------------------------------------------------------------------ -// Stability tests for pcg64_2018_engine -// ------------------------------------------------------------------ -TEST(PCG642018EngineTest, VerifyGolden) { - constexpr uint64_t kGolden[kNumGoldenOutputs] = { - 0x01070196e695f8f1, 0x703ec840c59f4493, 0xe54954914b3a44fa, - 0x96130ff204b9285e, 0x7d9fdef535ceb21a, 0x666feed42e1219a0, - 0x981f685721c8326f, 0xad80710d6eab4dda, 0xe202c480b037a029, - 0x5d3390eaedd907e2, 0x0756befb39c6b8aa, 0x1fb44ba6634d62a3, - 0x8d20423662426642, 0x34ea910167a39fb4, 0x93010b43a80d0ab6, - 0x663db08a98fc568a, 0x720b0a1335956fae, 0x2c35483e31e1d3ba, - 0x429f39776337409d, 0xb46d99e638687344, 0x105370b96aedcaee, - 0x3999e92f811cff71, 0xd230f8bcb591cfc9, 0x0dce3db2ba7bdea5, - 0xcf2f52c91eec99af, 0x2bc7c24a8b998a39, 0xbd8af1b0d599a19c, - 0x56bc45abc66059f5, 0x170a46dc170f7f1e, 0xc25daf5277b85fad, - 0xe629c2e0c948eadb, 0x1720a796915542ed, 0x22fb0caa4f909951, - 0x7e0c0f4175acd83d, 0xd9fcab37ff2a860c, 0xab2280fb2054bad1, - 0x58e8a06f37fa9e99, 0xc3a52a30b06528c7, 0x0175f773a13fc1bd, - 0x731cfc584b00e840, 0x404cc7b2648069cb, 0x5bc29153b0b7f783, - 0x771310a38cc999d1, 0x766a572f0a71a916, 0x90f450fb4fc48348, - 0xf080ea3e1c7b1a0d, 0x15471a4507d66a44, 0x7d58e55a78f3df69, - 0x0130a094576ac99c, 0x46669cb2d04b1d87, 0x17ab5bed20191840, - 0x95b177d260adff3e, 0x025fb624b6ee4c07, 0xb35de4330154a95f, - 0xe8510fff67e24c79, 0x132c3cbcd76ed2d3, 0x35e7cc145a093904, - 0x9f5b5b5f81583b79, 0x3ee749a533966233, 0x4af85886cdeda8cd, - 0x0ca5380ecb3ef3aa, 0x4f674eb7661d3192, 0x88a29aad00cd7733, - 0x70b627ca045ffac6, 0x5912b43ea887623d, 0x95dc9fc6f62cf221, - 0x926081a12a5c905b, 0x9c57d4cd7dfce651, 0x85ab2cbf23e3bb5d, - 0xc5cd669f63023152, 0x3067be0fad5d898e, 0x12b56f444cb53d05, - 0xbc2e5a640c3434fc, 0x9280bff0e4613fe1, 0x98819094c528743e, - 0x999d1c98d829df33, 0x9ff82a012dc89242, 0xf99183ed39c8be94, - 0xf0f59161cd421c55, 0x3c705730c2f6c48d, 0x66ad85c6e9278a61, - 0x2a3428e4a428d5d0, 0x79207d68fd04940d, 0xea7f2b402edc8430, - 0xa06b419ac857f63b, 0xcb1dd0e6fbc47e1c, 0x4f55229200ada6a4, - 0x9647b5e6359c927f, 0x30bf8f9197c7efe5, 0xa79519529cc384d0, - 0xbb22c4f339ad6497, 0xd7b9782f59d14175, 0x0dff12fff2ec0118, - 0xa331ad8305343a7c, 0x48dad7e3f17e0862, 0x324c6fb3fd3c9665, - 0xf0e4350e7933dfc4, 0x7ccda2f30b8b03b6, 0xa0afc6179005de40, - 0xee65da6d063b3a30, 0xb9506f42f2bfe87a, 0xc9a2e26b0ef5baa0, - 0x39fa9d4f495011d6, 0xbecc21a45d023948, 0x6bf484c6593f737f, - 0x8065e0070cadc3b7, 0x9ef617ed8d419799, 0xac692cf8c233dd15, - 0xd2ed87583c4ebb98, 0xad95ba1bebfedc62, 0x9b60b160a8264e43, - 0x0bc8c45f71fcf25b, 0x4a78035cdf1c9931, 0x4602dc106667e029, - 0xb335a3c250498ac8, 0x0256ebc4df20cab8, 0x0c61efd153f0c8d9, - 0xe5d0150a4f806f88, 0x99d6521d351e7d87, 0x8d4888c9f80f4325, - 0x106c5735c1ba868d, 0x73414881b880a878, 0x808a9a58a3064751, - 0x339a29f3746de3d5, 0x5410d7fa4f873896, 0xd84623c81d7b8a03, - 0x1f7c7e7a7f47f462, - }; - - pcg64_2018_engine engine(0); -#if UPDATE_GOLDEN - (void)kGolden; // Silence warning. - for (size_t i = 0; i < kNumGoldenOutputs; ++i) { - printf("0x%016lx, ", engine()); - if (i % 3 == 2) { - printf("\n"); - } - } - printf("\n\n\n"); -#else - for (const auto& elem : kGolden) { - EXPECT_EQ(elem, engine()); - } - engine.seed(); - for (const auto& elem : kGolden) { - EXPECT_EQ(elem, engine()); - } -#endif -} - -TEST(PCG642018EngineTest, VerifyGoldenSeeded) { - constexpr uint64_t kGolden[kNumGoldenOutputs] = { - 0xb03988f1e39691ee, 0xbd2a1eb5ac31e97a, 0x8f00d6d433634d02, - 0x1823c28d483d5776, 0x000c3ee3e1aeb74a, 0xfa82ef27a4f3df9c, - 0xc6f382308654e454, 0x414afb1a238996c2, 0x4703a4bc252eb411, - 0x99d64f62c8f7f654, 0xbb07ebe11a34fa44, 0x79eb06a363c06131, - 0xf66ad3756f1c6b21, 0x130c01d5e869f457, 0x5ca2b9963aecbc81, - 0xfef7bebc1de27e6c, 0x1d174faa5ed2cdbf, 0xd75b7a773f2bb889, - 0xc35c872327a170a5, 0x46da6d88646a42fe, 0x4622985e0442dae2, - 0xbe3cbd67297f1f9b, 0xe7c37b4a4798bfd1, 0x173d5dfad15a25c3, - 0x0eb6849ba2961522, 0xb0ff7246e6700d73, 0x88cb9c42d3afa577, - 0xb609731dbd94d917, 0xd3941cda04b40081, 0x28d140f7409bea3a, - 0x3c96699a920a124a, 0xdb28be521958b2fd, 0x0a3f44db3d4c5124, - 0x7ac8e60ba13b70d2, 0x75f03a41ded5195a, 0xaed10ac7c4e4825d, - 0xb92a3b18aadb7adc, 0xda45e0081f2bca46, 0x74d39ab3753143fc, - 0xb686038018fac9ca, 0x4cc309fe99542dbb, 0xf3e1a4fcb311097c, - 0x58763d6fa698d69d, 0xd11c365dbecd8d60, 0x2c15d55725b1dee7, - 0x89805f254d85658c, 0x2374c44dfc62158b, 0x9a8350fa7995328d, - 0x198f838970cf91da, 0x96aff569562c0e53, 0xd76c8c52b7ec6e3f, - 0x23a01cd9ae4baa81, 0x3adb366b6d02a893, 0xb3313e2a4c5b333f, - 0x04c11230b96a5425, 0x1f7f7af04787d571, 0xaddb019365275ec7, - 0x5c960468ccb09f42, 0x8438db698c69a44a, 0x492be1e46111637e, - 0x9c6c01e18100c610, 0xbfe48e75b7d0aceb, 0xb5e0b89ec1ce6a00, - 0x9d280ecbc2fe8997, 0x290d9e991ba5fcab, 0xeec5bec7d9d2a4f0, - 0x726e81488f19150e, 0x1a6df7955a7e462c, 0x37a12d174ba46bb5, - 0x3cdcdffd96b1b5c5, 0x2c5d5ac10661a26e, 0xa742ed18f22e50c4, - 0x00e0ed88ff0d8a35, 0x3d3c1718cb1efc0b, 0x1d70c51ffbccbf11, - 0xfbbb895132a4092f, 0x619d27f2fb095f24, 0x69af68200985e5c4, - 0xbee4885f57373f8d, 0x10b7a6bfe0587e40, 0xa885e6cf2f7e5f0a, - 0x59f879464f767550, 0x24e805d69056990d, 0x860970b911095891, - 0xca3189954f84170d, 0x6652a5edd4590134, 0x5e1008cef76174bf, - 0xcbd417881f2bcfe5, 0xfd49fc9d706ecd17, 0xeebf540221ebd066, - 0x46af7679464504cb, 0xd4028486946956f1, 0xd4f41864b86c2103, - 0x7af090e751583372, 0x98cdaa09278cb642, 0xffd42b921215602f, - 0x1d05bec8466b1740, 0xf036fa78a0132044, 0x787880589d1ecc78, - 0x5644552cfef33230, 0x0a97e275fe06884b, 0x96d1b13333d470b5, - 0xc8b3cdad52d3b034, 0x091357b9db7376fd, 0xa5fe4232555edf8c, - 0x3371bc3b6ada76b5, 0x7deeb2300477c995, 0x6fc6d4244f2849c1, - 0x750e8cc797ca340a, 0x81728613cd79899f, 0x3467f4ee6f9aeb93, - 0x5ef0a905f58c640f, 0x432db85e5101c98a, 0x6488e96f46ac80c2, - 0x22fddb282625048c, 0x15b287a0bc2d4c5d, 0xa7e2343ef1f28bce, - 0xc87ee1aa89bed09e, 0x220610107812c5e9, 0xcbdab6fcd640f586, - 0x8d41047970928784, 0x1aa431509ec1ade0, 0xac3f0be53f518ddc, - 0x16f4428ad81d0cbb, 0x675b13c2736fc4bb, 0x6db073afdd87e32d, - 0x572f3ca2f1a078c6, - }; - - ExplicitSeedSeq seed_sequence{12, 34, 56}; - pcg64_2018_engine engine(seed_sequence); -#if UPDATE_GOLDEN - (void)kGolden; // Silence warning. - for (size_t i = 0; i < kNumGoldenOutputs; ++i) { - printf("0x%016lx, ", engine()); - if (i % 3 == 2) { - printf("\n"); - } - } - printf("\n\n\n"); -#else - for (const auto& elem : kGolden) { - EXPECT_EQ(elem, engine()); - } - engine.seed(seed_sequence); - for (const auto& elem : kGolden) { - EXPECT_EQ(elem, engine()); - } -#endif -} - -TEST(PCG642018EngineTest, VerifyGoldenFromDeserializedEngine) { - constexpr uint64_t kGolden[kNumGoldenOutputs] = { - 0xdd425b47b4113dea, 0x1b07176479d444b0, 0x6b391027586f2e42, - 0xa166f2b15f4a2143, 0xffb6dbd7a179ee97, 0xb2c00035365bf0b1, - 0x8fbb518b45855521, 0xfc789a55ddf87c3b, 0x429531f0f17ff355, - 0xbe708560d603d283, 0x5bff415175c5cb6b, 0xe813491f4ad45394, - 0xa853f4506d55880d, 0x7e538453e568172e, 0xe101f1e098ddd0ec, - 0x6ee31266ee4c766d, 0xa8786d92d66b39d7, 0xfee622a2acf5e5b0, - 0x5fe8e82c102fa7b3, 0x01f10be4cdb53c9d, 0xbe0545366f857022, - 0x12e74f010a339bca, 0xb10d85ca40d5ce34, 0xe80d6feba5054875, - 0x2b7c1ee6d567d4ee, 0x2a9cd043bfd03b66, 0x5cfc531bd239f3f1, - 0x1c4734e4647d70f5, 0x85a8f60f006b5760, 0x6a4239ce76dca387, - 0x8da0f86d7339335c, 0xf055b0468551374d, 0x486e8567e9bea9a0, - 0x4cb531b8405192dd, 0xf813b1ee3157110b, 0x214c2a664a875d8e, - 0x74531237b29b35f7, 0xa6f0267bb77a771e, 0x64b552bff54184a4, - 0xa2d6f7af2d75b6fc, 0x460a10018e03b5ab, 0x76fd1fdcb81d0800, - 0x76f5f81805070d9d, 0x1fb75cb1a70b289a, 0x9dfd25a022c4b27f, - 0x9a31a14a80528e9e, 0x910dc565ddc25820, 0xd6aef8e2b0936c10, - 0xe1773c507fe70225, 0xe027fd7aadd632bc, 0xc1fecb427089c8b8, - 0xb5c74c69fa9dbf26, 0x71bf9b0e4670227d, 0x25f48fad205dcfdd, - 0x905248ec4d689c56, 0x5c2b7631b0de5c9d, 0x9f2ee0f8f485036c, - 0xfd6ce4ebb90bf7ea, 0xd435d20046085574, 0x6b7eadcb0625f986, - 0x679d7d44b48be89e, 0x49683b8e1cdc49de, 0x4366cf76e9a2f4ca, - 0x54026ec1cdad7bed, 0xa9a04385207f28d3, 0xc8e66de4eba074b2, - 0x40b08c42de0f4cc0, 0x1d4c5e0e93c5bbc0, 0x19b80792e470ae2d, - 0x6fcaaeaa4c2a5bd9, 0xa92cb07c4238438e, 0x8bb5c918a007e298, - 0x7cd671e944874cf4, 0x88166470b1ba3cac, 0xd013d476eaeeade6, - 0xcee416947189b3c3, 0x5d7c16ab0dce6088, 0xd3578a5c32b13d27, - 0x3875db5adc9cc973, 0xfbdaba01c5b5dc56, 0xffc4fdd391b231c3, - 0x2334520ecb164fec, 0x361c115e7b6de1fa, 0xeee58106cc3563d7, - 0x8b7f35a8db25ebb8, 0xb29d00211e2cafa6, 0x22a39fe4614b646b, - 0x92ca6de8b998506d, 0x40922fe3d388d1db, 0x9da47f1e540f802a, - 0x811dceebf16a25db, 0xf6524ae22e0e53a9, 0x52d9e780a16eb99d, - 0x4f504286bb830207, 0xf6654d4786bd5cc3, 0x00bd98316003a7e1, - 0xefda054a6ab8f5f3, 0x46cfb0f4c1872827, 0xc22b316965c0f3b2, - 0xd1a28087c7e7562a, 0xaa4f6a094b7f5cff, 0xfe2bc853a041f7da, - 0xe9d531402a83c3ba, 0xe545d8663d3ce4dd, 0xfa2dcd7d91a13fa8, - 0xda1a080e52a127b8, 0x19c98f1f809c3d84, 0x2cef109af4678c88, - 0x53462accab3b9132, 0x176b13a80415394e, 0xea70047ef6bc178b, - 0x57bca80506d6dcdf, 0xd853ba09ff09f5c4, 0x75f4df3a7ddd4775, - 0x209c367ade62f4fe, 0xa9a0bbc74d5f4682, 0x5dfe34bada86c21a, - 0xc2c05bbcd38566d1, 0x6de8088e348c916a, 0x6a7001c6000c2196, - 0xd9fb51865fc4a367, 0x12f320e444ece8ff, 0x6d56f7f793d65035, - 0x138f31b7a865f8aa, 0x58fc68b4026b9adf, 0xcd48954b79fb6436, - 0x27dfce4a0232af87, - }; - -#if UPDATE_GOLDEN - (void)kGolden; // Silence warning. - std::seed_seq seed_sequence{1, 2, 3}; - pcg64_2018_engine engine(seed_sequence); - std::ostringstream stream; - stream << engine; - auto str = stream.str(); - printf("%s\n\n", str.c_str()); - for (size_t i = 0; i < kNumGoldenOutputs; ++i) { - printf("0x%016lx, ", engine()); - if (i % 3 == 2) { - printf("\n"); - } - } - printf("\n\n\n"); -#else - pcg64_2018_engine engine; - std::istringstream stream( - "2549297995355413924 4865540595714422341 6364136223846793005 " - "1442695040888963407 18088519957565336995 4845369368158826708"); - stream >> engine; - for (const auto& elem : kGolden) { - EXPECT_EQ(elem, engine()); - } -#endif -} - -// ------------------------------------------------------------------ -// Stability tests for pcg32_2018_engine -// ------------------------------------------------------------------ -TEST(PCG322018EngineTest, VerifyGolden) { - constexpr uint32_t kGolden[kNumGoldenOutputs] = { - 0x7a7ecbd9, 0x89fd6c06, 0xae646aa8, 0xcd3cf945, 0x6204b303, 0x198c8585, - 0x49fce611, 0xd1e9297a, 0x142d9440, 0xee75f56b, 0x473a9117, 0xe3a45903, - 0xbce807a1, 0xe54e5f4d, 0x497d6c51, 0x61829166, 0xa740474b, 0x031912a8, - 0x9de3defa, 0xd266dbf1, 0x0f38bebb, 0xec3c4f65, 0x07c5057d, 0xbbce03c8, - 0xfd2ac7a8, 0xffcf4773, 0x5b10affb, 0xede1c842, 0xe22b01b7, 0xda133c8c, - 0xaf89b0f4, 0x25d1b8bc, 0x9f625482, 0x7bfd6882, 0x2e2210c0, 0x2c8fb9a6, - 0x42cb3b83, 0x40ce0dab, 0x644a3510, 0x36230ef2, 0xe2cb6d43, 0x1012b343, - 0x746c6c9f, 0x36714cf8, 0xed1f5026, 0x8bbbf83e, 0xe98710f4, 0x8a2afa36, - 0x09035349, 0x6dc1a487, 0x682b634b, 0xc106794f, 0x7dd78beb, 0x628c262b, - 0x852fb232, 0xb153ac4c, 0x4f169d1b, 0xa69ab774, 0x4bd4b6f2, 0xdc351dd3, - 0x93ff3c8c, 0xa30819ab, 0xff07758c, 0x5ab13c62, 0xd16d7fb5, 0xc4950ffa, - 0xd309ae49, 0xb9677a87, 0x4464e317, 0x90dc44f1, 0xc694c1d4, 0x1d5e1168, - 0xadf37a2d, 0xda38990d, 0x1ec4bd33, 0x36ca25ce, 0xfa0dc76a, 0x968a9d43, - 0x6950ac39, 0xdd3276bc, 0x06d5a71e, 0x1f6f282d, 0x5c626c62, 0xdde3fc31, - 0x152194ce, 0xc35ed14c, 0xb1f7224e, 0x47f76bb8, 0xb34fdd08, 0x7011395e, - 0x162d2a49, 0x0d1bf09f, 0x9428a952, 0x03c5c344, 0xd3525616, 0x7816fff3, - 0x6bceb8a8, 0x8345a081, 0x366420fd, 0x182abeda, 0x70f82745, 0xaf15ded8, - 0xc7f52ca2, 0xa98db9c5, 0x919d99ba, 0x9c376c1c, 0xed8d34c2, 0x716ae9f5, - 0xef062fa5, 0xee3b6c56, 0x52325658, 0x61afa9c3, 0xfdaf02f0, 0x961cf3ab, - 0x9f291565, 0x4fbf3045, 0x0590c899, 0xde901385, 0x45005ffb, 0x509db162, - 0x262fa941, 0x4c421653, 0x4b17c21e, 0xea0d1530, 0xde803845, 0x61bfd515, - 0x438523ef, - }; - - pcg32_2018_engine engine(0); -#if UPDATE_GOLDEN - (void)kGolden; // Silence warning. - for (size_t i = 0; i < kNumGoldenOutputs; ++i) { - printf("0x%08x, ", engine()); - if (i % 6 == 5) { - printf("\n"); - } - } - printf("\n\n\n"); -#else - for (const auto& elem : kGolden) { - EXPECT_EQ(elem, engine()); - } - engine.seed(); - for (const auto& elem : kGolden) { - EXPECT_EQ(elem, engine()); - } -#endif -} - -TEST(PCG322018EngineTest, VerifyGoldenSeeded) { - constexpr uint32_t kGolden[kNumGoldenOutputs] = { - 0x60b5a64c, 0x978502f9, 0x80a75f60, 0x241f1158, 0xa4cd1dbb, 0xe7284017, - 0x3b678da5, 0x5223ec99, 0xe4bdd5d9, 0x72190e6d, 0xe6e702c9, 0xff80c768, - 0xcf126ed3, 0x1fbd20ab, 0x60980489, 0xbc72bf89, 0x407ac6c0, 0x00bf3c51, - 0xf9087897, 0x172e4eb6, 0xe9e4f443, 0x1a6098bf, 0xbf44f8c2, 0xdd84a0e5, - 0xd9a52364, 0xc0e2e786, 0x061ae2ba, 0x9facb8e3, 0x6109432d, 0xd4e0a013, - 0xbd8eb9a6, 0x7e86c3b6, 0x629c0e68, 0x05337430, 0xb495b9f4, 0x11ccd65d, - 0xb578db25, 0x66f1246d, 0x6ef20a7f, 0x5e429812, 0x11772130, 0xb944b5c2, - 0x01624128, 0xa2385ab7, 0xd3e10d35, 0xbe570ec3, 0xc951656f, 0xbe8944a0, - 0x7be41062, 0x5709f919, 0xd745feda, 0x9870b9ae, 0xb44b8168, 0x19e7683b, - 0xded8017f, 0xc6e4d544, 0x91ae4225, 0xd6745fba, 0xb992f284, 0x65b12b33, - 0xa9d5fdb4, 0xf105ce1a, 0x35ca1a6e, 0x2ff70dd0, 0xd8335e49, 0xfb71ddf2, - 0xcaeabb89, 0x5c6f5f84, 0x9a811a7d, 0xbcecbbd1, 0x0f661ba0, 0x9ad93b9d, - 0xedd23e0b, 0x42062f48, 0xd38dd7e4, 0x6cd63c9c, 0x640b98ae, 0x4bff5653, - 0x12626371, 0x13266017, 0xe7a698d8, 0x39c74667, 0xe8fdf2e3, 0x52803bf8, - 0x2af6895b, 0x91335b7b, 0x699e4961, 0x00a40fff, 0x253ff2b6, 0x4a6cf672, - 0x9584e85f, 0xf2a5000c, 0x4d58aba8, 0xb8513e6a, 0x767fad65, 0x8e326f9e, - 0x182f15a1, 0x163dab52, 0xdf99c780, 0x047282a1, 0xee4f90dd, 0xd50394ae, - 0x6c9fd5f0, 0xb06a9194, 0x387e3840, 0x04a9487b, 0xf678a4c2, 0xd0a78810, - 0xd502c97e, 0xd6a9b12a, 0x4accc5dc, 0x416ed53e, 0x50411536, 0xeeb89c24, - 0x813a7902, 0x034ebca6, 0xffa52e7c, 0x7ecd3d0e, 0xfa37a0d2, 0xb1fbe2c1, - 0xb7efc6d1, 0xefa4ccee, 0xf6f80424, 0x2283f3d9, 0x68732284, 0x94f3b5c8, - 0xbbdeceb9, - }; - - ExplicitSeedSeq seed_sequence{12, 34, 56}; - pcg32_2018_engine engine(seed_sequence); -#if UPDATE_GOLDEN - (void)kGolden; // Silence warning. - for (size_t i = 0; i < kNumGoldenOutputs; ++i) { - printf("0x%08x, ", engine()); - if (i % 6 == 5) { - printf("\n"); - } - } - printf("\n\n\n"); -#else - for (const auto& elem : kGolden) { - EXPECT_EQ(elem, engine()); - } - engine.seed(seed_sequence); - for (const auto& elem : kGolden) { - EXPECT_EQ(elem, engine()); - } -#endif -} - -TEST(PCG322018EngineTest, VerifyGoldenFromDeserializedEngine) { - constexpr uint64_t kGolden[kNumGoldenOutputs] = { - 0x780f7042, 0xba137215, 0x43ab6f22, 0x0cb55f46, 0x44b2627d, 0x835597af, - 0xea973ea1, 0x0d2abd35, 0x4fdd601c, 0xac4342fe, 0x7db7e93c, 0xe56ebcaf, - 0x3596470a, 0x7770a9ad, 0x9b893320, 0x57db3415, 0xb432de54, 0xa02baf71, - 0xa256aadb, 0x88921fc7, 0xa35fa6b3, 0xde3eca46, 0x605739a7, 0xa890b82b, - 0xe457b7ad, 0x335fb903, 0xeb06790c, 0xb3c54bf6, 0x6141e442, 0xa599a482, - 0xb78987cc, 0xc61dfe9d, 0x0f1d6ace, 0x17460594, 0x8f6a5061, 0x083dc354, - 0xe9c337fb, 0xcfd105f7, 0x926764b6, 0x638d24dc, 0xeaac650a, 0x67d2cb9c, - 0xd807733c, 0x205fc52e, 0xf5399e2e, 0x6c46ddcc, 0xb603e875, 0xce113a25, - 0x3c8d4813, 0xfb584db8, 0xf6d255ff, 0xea80954f, 0x42e8be85, 0xb2feee72, - 0x62bd8d16, 0x1be4a142, 0x97dca1a4, 0xdd6e7333, 0xb2caa20e, 0xa12b1588, - 0xeb3a5a1a, 0x6fa5ba89, 0x077ea931, 0x8ddb1713, 0x0dd03079, 0x2c2ba965, - 0xa77fac17, 0xc8325742, 0x8bb893bf, 0xc2315741, 0xeaceee92, 0x81dd2ee2, - 0xe5214216, 0x1b9b8fb2, 0x01646d03, 0x24facc25, 0xd8c0e0bb, 0xa33fe106, - 0xf34fe976, 0xb3b4b44e, 0x65618fed, 0x032c6192, 0xa9dd72ce, 0xf391887b, - 0xf41c6a6e, 0x05c4bd6d, 0x37fa260e, 0x46b05659, 0xb5f6348a, 0x62d26d89, - 0x39f6452d, 0xb17b30a2, 0xbdd82743, 0x38ecae3b, 0xfe90f0a2, 0xcb2d226d, - 0xcf8a0b1c, 0x0eed3d4d, 0xa1f69cfc, 0xd7ac3ba5, 0xce9d9a6b, 0x121deb4c, - 0x4a0d03f3, 0xc1821ed1, 0x59c249ac, 0xc0abb474, 0x28149985, 0xfd9a82ba, - 0x5960c3b2, 0xeff00cba, 0x6073aa17, 0x25dc0919, 0x9976626e, 0xdd2ccc33, - 0x39ecb6ec, 0xc6e15d13, 0xfac94cfd, 0x28cfd34f, 0xf2d2c32d, 0x51c23d08, - 0x4fdb2f48, 0x97baa807, 0xf2c1004c, 0xc4ae8136, 0x71f31c94, 0x8c92d601, - 0x36caf5cd, - }; - -#if UPDATE_GOLDEN - (void)kGolden; // Silence warning. - std::seed_seq seed_sequence{1, 2, 3}; - pcg32_2018_engine engine(seed_sequence); - std::ostringstream stream; - stream << engine; - auto str = stream.str(); - printf("%s\n\n", str.c_str()); - for (size_t i = 0; i < kNumGoldenOutputs; ++i) { - printf("0x%08x, ", engine()); - if (i % 6 == 5) { - printf("\n"); - } - } - printf("\n\n\n"); - - EXPECT_FALSE(true); -#else - pcg32_2018_engine engine; - std::istringstream stream( - "6364136223846793005 1442695040888963407 6537028157270659894"); - stream >> engine; - for (const auto& elem : kGolden) { - EXPECT_EQ(elem, engine()); - } -#endif -} - -} // namespace diff --git a/third_party/abseil_cpp/absl/random/internal/platform.h b/third_party/abseil_cpp/absl/random/internal/platform.h deleted file mode 100644 index bbdb4e620816..000000000000 --- a/third_party/abseil_cpp/absl/random/internal/platform.h +++ /dev/null @@ -1,171 +0,0 @@ -// Copyright 2017 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#ifndef ABSL_RANDOM_INTERNAL_PLATFORM_H_ -#define ABSL_RANDOM_INTERNAL_PLATFORM_H_ - -// HERMETIC NOTE: The randen_hwaes target must not introduce duplicate -// symbols from arbitrary system and other headers, since it may be built -// with different flags from other targets, using different levels of -// optimization, potentially introducing ODR violations. - -// ----------------------------------------------------------------------------- -// Platform Feature Checks -// ----------------------------------------------------------------------------- - -// Currently supported operating systems and associated preprocessor -// symbols: -// -// Linux and Linux-derived __linux__ -// Android __ANDROID__ (implies __linux__) -// Linux (non-Android) __linux__ && !__ANDROID__ -// Darwin (macOS and iOS) __APPLE__ -// Akaros (http://akaros.org) __ros__ -// Windows _WIN32 -// NaCL __native_client__ -// AsmJS __asmjs__ -// WebAssembly __wasm__ -// Fuchsia __Fuchsia__ -// -// Note that since Android defines both __ANDROID__ and __linux__, one -// may probe for either Linux or Android by simply testing for __linux__. -// -// NOTE: For __APPLE__ platforms, we use #include <TargetConditionals.h> -// to distinguish os variants. -// -// http://nadeausoftware.com/articles/2012/01/c_c_tip_how_use_compiler_predefined_macros_detect_operating_system - -#if defined(__APPLE__) -#include <TargetConditionals.h> -#endif - -// ----------------------------------------------------------------------------- -// Architecture Checks -// ----------------------------------------------------------------------------- - -// These preprocessor directives are trying to determine CPU architecture, -// including necessary headers to support hardware AES. -// -// ABSL_ARCH_{X86/PPC/ARM} macros determine the platform. -#if defined(__x86_64__) || defined(__x86_64) || defined(_M_AMD64) || \ - defined(_M_X64) -#define ABSL_ARCH_X86_64 -#elif defined(__i386) || defined(_M_IX86) -#define ABSL_ARCH_X86_32 -#elif defined(__aarch64__) || defined(__arm64__) || defined(_M_ARM64) -#define ABSL_ARCH_AARCH64 -#elif defined(__arm__) || defined(__ARMEL__) || defined(_M_ARM) -#define ABSL_ARCH_ARM -#elif defined(__powerpc64__) || defined(__PPC64__) || defined(__powerpc__) || \ - defined(__ppc__) || defined(__PPC__) -#define ABSL_ARCH_PPC -#else -// Unsupported architecture. -// * https://sourceforge.net/p/predef/wiki/Architectures/ -// * https://msdn.microsoft.com/en-us/library/b0084kay.aspx -// * for gcc, clang: "echo | gcc -E -dM -" -#endif - -// ----------------------------------------------------------------------------- -// Attribute Checks -// ----------------------------------------------------------------------------- - -// ABSL_RANDOM_INTERNAL_RESTRICT annotates whether pointers may be considered -// to be unaliased. -#if defined(__clang__) || defined(__GNUC__) -#define ABSL_RANDOM_INTERNAL_RESTRICT __restrict__ -#elif defined(_MSC_VER) -#define ABSL_RANDOM_INTERNAL_RESTRICT __restrict -#else -#define ABSL_RANDOM_INTERNAL_RESTRICT -#endif - -// ABSL_HAVE_ACCELERATED_AES indicates whether the currently active compiler -// flags (e.g. -maes) allow using hardware accelerated AES instructions, which -// implies us assuming that the target platform supports them. -#define ABSL_HAVE_ACCELERATED_AES 0 - -#if defined(ABSL_ARCH_X86_64) - -#if defined(__AES__) || defined(__AVX__) -#undef ABSL_HAVE_ACCELERATED_AES -#define ABSL_HAVE_ACCELERATED_AES 1 -#endif - -#elif defined(ABSL_ARCH_PPC) - -// Rely on VSX and CRYPTO extensions for vcipher on PowerPC. -#if (defined(__VEC__) || defined(__ALTIVEC__)) && defined(__VSX__) && \ - defined(__CRYPTO__) -#undef ABSL_HAVE_ACCELERATED_AES -#define ABSL_HAVE_ACCELERATED_AES 1 -#endif - -#elif defined(ABSL_ARCH_ARM) || defined(ABSL_ARCH_AARCH64) - -// http://infocenter.arm.com/help/topic/com.arm.doc.ihi0053c/IHI0053C_acle_2_0.pdf -// Rely on NEON+CRYPTO extensions for ARM. -#if defined(__ARM_NEON) && defined(__ARM_FEATURE_CRYPTO) -#undef ABSL_HAVE_ACCELERATED_AES -#define ABSL_HAVE_ACCELERATED_AES 1 -#endif - -#endif - -// NaCl does not allow AES. -#if defined(__native_client__) -#undef ABSL_HAVE_ACCELERATED_AES -#define ABSL_HAVE_ACCELERATED_AES 0 -#endif - -// ABSL_RANDOM_INTERNAL_AES_DISPATCH indicates whether the currently active -// platform has, or should use run-time dispatch for selecting the -// acclerated Randen implementation. -#define ABSL_RANDOM_INTERNAL_AES_DISPATCH 0 - -#if defined(ABSL_ARCH_X86_64) -// Dispatch is available on x86_64 -#undef ABSL_RANDOM_INTERNAL_AES_DISPATCH -#define ABSL_RANDOM_INTERNAL_AES_DISPATCH 1 -#elif defined(__linux__) && defined(ABSL_ARCH_PPC) -// Or when running linux PPC -#undef ABSL_RANDOM_INTERNAL_AES_DISPATCH -#define ABSL_RANDOM_INTERNAL_AES_DISPATCH 1 -#elif defined(__linux__) && defined(ABSL_ARCH_AARCH64) -// Or when running linux AArch64 -#undef ABSL_RANDOM_INTERNAL_AES_DISPATCH -#define ABSL_RANDOM_INTERNAL_AES_DISPATCH 1 -#elif defined(__linux__) && defined(ABSL_ARCH_ARM) && (__ARM_ARCH >= 8) -// Or when running linux ARM v8 or higher. -// (This captures a lot of Android configurations.) -#undef ABSL_RANDOM_INTERNAL_AES_DISPATCH -#define ABSL_RANDOM_INTERNAL_AES_DISPATCH 1 -#endif - -// NaCl does not allow dispatch. -#if defined(__native_client__) -#undef ABSL_RANDOM_INTERNAL_AES_DISPATCH -#define ABSL_RANDOM_INTERNAL_AES_DISPATCH 0 -#endif - -// iOS does not support dispatch, even on x86, since applications -// should be bundled as fat binaries, with a different build tailored for -// each specific supported platform/architecture. -#if (defined(TARGET_OS_IPHONE) && TARGET_OS_IPHONE) || \ - (defined(TARGET_OS_IPHONE_SIMULATOR) && TARGET_OS_IPHONE_SIMULATOR) -#undef ABSL_RANDOM_INTERNAL_AES_DISPATCH -#define ABSL_RANDOM_INTERNAL_AES_DISPATCH 0 -#endif - -#endif // ABSL_RANDOM_INTERNAL_PLATFORM_H_ diff --git a/third_party/abseil_cpp/absl/random/internal/pool_urbg.cc b/third_party/abseil_cpp/absl/random/internal/pool_urbg.cc deleted file mode 100644 index 5bee530770eb..000000000000 --- a/third_party/abseil_cpp/absl/random/internal/pool_urbg.cc +++ /dev/null @@ -1,254 +0,0 @@ -// Copyright 2017 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#include "absl/random/internal/pool_urbg.h" - -#include <algorithm> -#include <atomic> -#include <cstdint> -#include <cstring> -#include <iterator> - -#include "absl/base/attributes.h" -#include "absl/base/call_once.h" -#include "absl/base/config.h" -#include "absl/base/internal/endian.h" -#include "absl/base/internal/raw_logging.h" -#include "absl/base/internal/spinlock.h" -#include "absl/base/internal/sysinfo.h" -#include "absl/base/internal/unaligned_access.h" -#include "absl/base/optimization.h" -#include "absl/random/internal/randen.h" -#include "absl/random/internal/seed_material.h" -#include "absl/random/seed_gen_exception.h" - -using absl::base_internal::SpinLock; -using absl::base_internal::SpinLockHolder; - -namespace absl { -ABSL_NAMESPACE_BEGIN -namespace random_internal { -namespace { - -// RandenPoolEntry is a thread-safe pseudorandom bit generator, implementing a -// single generator within a RandenPool<T>. It is an internal implementation -// detail, and does not aim to conform to [rand.req.urng]. -// -// NOTE: There are alignment issues when used on ARM, for instance. -// See the allocation code in PoolAlignedAlloc(). -class RandenPoolEntry { - public: - static constexpr size_t kState = RandenTraits::kStateBytes / sizeof(uint32_t); - static constexpr size_t kCapacity = - RandenTraits::kCapacityBytes / sizeof(uint32_t); - - void Init(absl::Span<const uint32_t> data) { - SpinLockHolder l(&mu_); // Always uncontested. - std::copy(data.begin(), data.end(), std::begin(state_)); - next_ = kState; - } - - // Copy bytes into out. - void Fill(uint8_t* out, size_t bytes) ABSL_LOCKS_EXCLUDED(mu_); - - // Returns random bits from the buffer in units of T. - template <typename T> - inline T Generate() ABSL_LOCKS_EXCLUDED(mu_); - - inline void MaybeRefill() ABSL_EXCLUSIVE_LOCKS_REQUIRED(mu_) { - if (next_ >= kState) { - next_ = kCapacity; - impl_.Generate(state_); - } - } - - private: - // Randen URBG state. - uint32_t state_[kState] ABSL_GUARDED_BY(mu_); // First to satisfy alignment. - SpinLock mu_; - const Randen impl_; - size_t next_ ABSL_GUARDED_BY(mu_); -}; - -template <> -inline uint8_t RandenPoolEntry::Generate<uint8_t>() { - SpinLockHolder l(&mu_); - MaybeRefill(); - return static_cast<uint8_t>(state_[next_++]); -} - -template <> -inline uint16_t RandenPoolEntry::Generate<uint16_t>() { - SpinLockHolder l(&mu_); - MaybeRefill(); - return static_cast<uint16_t>(state_[next_++]); -} - -template <> -inline uint32_t RandenPoolEntry::Generate<uint32_t>() { - SpinLockHolder l(&mu_); - MaybeRefill(); - return state_[next_++]; -} - -template <> -inline uint64_t RandenPoolEntry::Generate<uint64_t>() { - SpinLockHolder l(&mu_); - if (next_ >= kState - 1) { - next_ = kCapacity; - impl_.Generate(state_); - } - auto p = state_ + next_; - next_ += 2; - - uint64_t result; - std::memcpy(&result, p, sizeof(result)); - return result; -} - -void RandenPoolEntry::Fill(uint8_t* out, size_t bytes) { - SpinLockHolder l(&mu_); - while (bytes > 0) { - MaybeRefill(); - size_t remaining = (kState - next_) * sizeof(state_[0]); - size_t to_copy = std::min(bytes, remaining); - std::memcpy(out, &state_[next_], to_copy); - out += to_copy; - bytes -= to_copy; - next_ += (to_copy + sizeof(state_[0]) - 1) / sizeof(state_[0]); - } -} - -// Number of pooled urbg entries. -static constexpr int kPoolSize = 8; - -// Shared pool entries. -static absl::once_flag pool_once; -ABSL_CACHELINE_ALIGNED static RandenPoolEntry* shared_pools[kPoolSize]; - -// Returns an id in the range [0 ... kPoolSize), which indexes into the -// pool of random engines. -// -// Each thread to access the pool is assigned a sequential ID (without reuse) -// from the pool-id space; the id is cached in a thread_local variable. -// This id is assigned based on the arrival-order of the thread to the -// GetPoolID call; this has no binary, CL, or runtime stability because -// on subsequent runs the order within the same program may be significantly -// different. However, as other thread IDs are not assigned sequentially, -// this is not expected to matter. -int GetPoolID() { - static_assert(kPoolSize >= 1, - "At least one urbg instance is required for PoolURBG"); - - ABSL_CONST_INIT static std::atomic<int64_t> sequence{0}; - -#ifdef ABSL_HAVE_THREAD_LOCAL - static thread_local int my_pool_id = -1; - if (ABSL_PREDICT_FALSE(my_pool_id < 0)) { - my_pool_id = (sequence++ % kPoolSize); - } - return my_pool_id; -#else - static pthread_key_t tid_key = [] { - pthread_key_t tmp_key; - int err = pthread_key_create(&tmp_key, nullptr); - if (err) { - ABSL_RAW_LOG(FATAL, "pthread_key_create failed with %d", err); - } - return tmp_key; - }(); - - // Store the value in the pthread_{get/set}specific. However an uninitialized - // value is 0, so add +1 to distinguish from the null value. - intptr_t my_pool_id = - reinterpret_cast<intptr_t>(pthread_getspecific(tid_key)); - if (ABSL_PREDICT_FALSE(my_pool_id == 0)) { - // No allocated ID, allocate the next value, cache it, and return. - my_pool_id = (sequence++ % kPoolSize) + 1; - int err = pthread_setspecific(tid_key, reinterpret_cast<void*>(my_pool_id)); - if (err) { - ABSL_RAW_LOG(FATAL, "pthread_setspecific failed with %d", err); - } - } - return my_pool_id - 1; -#endif -} - -// Allocate a RandenPoolEntry with at least 32-byte alignment, which is required -// by ARM platform code. -RandenPoolEntry* PoolAlignedAlloc() { - constexpr size_t kAlignment = - ABSL_CACHELINE_SIZE > 32 ? ABSL_CACHELINE_SIZE : 32; - - // Not all the platforms that we build for have std::aligned_alloc, however - // since we never free these objects, we can over allocate and munge the - // pointers to the correct alignment. - void* memory = std::malloc(sizeof(RandenPoolEntry) + kAlignment); - auto x = reinterpret_cast<intptr_t>(memory); - auto y = x % kAlignment; - void* aligned = - (y == 0) ? memory : reinterpret_cast<void*>(x + kAlignment - y); - return new (aligned) RandenPoolEntry(); -} - -// Allocate and initialize kPoolSize objects of type RandenPoolEntry. -// -// The initialization strategy is to initialize one object directly from -// OS entropy, then to use that object to seed all of the individual -// pool instances. -void InitPoolURBG() { - static constexpr size_t kSeedSize = - RandenTraits::kStateBytes / sizeof(uint32_t); - // Read the seed data from OS entropy once. - uint32_t seed_material[kPoolSize * kSeedSize]; - if (!random_internal::ReadSeedMaterialFromOSEntropy( - absl::MakeSpan(seed_material))) { - random_internal::ThrowSeedGenException(); - } - for (int i = 0; i < kPoolSize; i++) { - shared_pools[i] = PoolAlignedAlloc(); - shared_pools[i]->Init( - absl::MakeSpan(&seed_material[i * kSeedSize], kSeedSize)); - } -} - -// Returns the pool entry for the current thread. -RandenPoolEntry* GetPoolForCurrentThread() { - absl::call_once(pool_once, InitPoolURBG); - return shared_pools[GetPoolID()]; -} - -} // namespace - -template <typename T> -typename RandenPool<T>::result_type RandenPool<T>::Generate() { - auto* pool = GetPoolForCurrentThread(); - return pool->Generate<T>(); -} - -template <typename T> -void RandenPool<T>::Fill(absl::Span<result_type> data) { - auto* pool = GetPoolForCurrentThread(); - pool->Fill(reinterpret_cast<uint8_t*>(data.data()), - data.size() * sizeof(result_type)); -} - -template class RandenPool<uint8_t>; -template class RandenPool<uint16_t>; -template class RandenPool<uint32_t>; -template class RandenPool<uint64_t>; - -} // namespace random_internal -ABSL_NAMESPACE_END -} // namespace absl diff --git a/third_party/abseil_cpp/absl/random/internal/pool_urbg.h b/third_party/abseil_cpp/absl/random/internal/pool_urbg.h deleted file mode 100644 index 05721929f54e..000000000000 --- a/third_party/abseil_cpp/absl/random/internal/pool_urbg.h +++ /dev/null @@ -1,131 +0,0 @@ -// Copyright 2017 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#ifndef ABSL_RANDOM_INTERNAL_POOL_URBG_H_ -#define ABSL_RANDOM_INTERNAL_POOL_URBG_H_ - -#include <cinttypes> -#include <limits> - -#include "absl/random/internal/traits.h" -#include "absl/types/span.h" - -namespace absl { -ABSL_NAMESPACE_BEGIN -namespace random_internal { - -// RandenPool is a thread-safe random number generator [random.req.urbg] that -// uses an underlying pool of Randen generators to generate values. Each thread -// has affinity to one instance of the underlying pool generators. Concurrent -// access is guarded by a spin-lock. -template <typename T> -class RandenPool { - public: - using result_type = T; - static_assert(std::is_unsigned<result_type>::value, - "RandenPool template argument must be a built-in unsigned " - "integer type"); - - static constexpr result_type(min)() { - return (std::numeric_limits<result_type>::min)(); - } - - static constexpr result_type(max)() { - return (std::numeric_limits<result_type>::max)(); - } - - RandenPool() {} - - // Returns a single value. - inline result_type operator()() { return Generate(); } - - // Fill data with random values. - static void Fill(absl::Span<result_type> data); - - protected: - // Generate returns a single value. - static result_type Generate(); -}; - -extern template class RandenPool<uint8_t>; -extern template class RandenPool<uint16_t>; -extern template class RandenPool<uint32_t>; -extern template class RandenPool<uint64_t>; - -// PoolURBG uses an underlying pool of random generators to implement a -// thread-compatible [random.req.urbg] interface with an internal cache of -// values. -template <typename T, size_t kBufferSize> -class PoolURBG { - // Inheritance to access the protected static members of RandenPool. - using unsigned_type = typename make_unsigned_bits<T>::type; - using PoolType = RandenPool<unsigned_type>; - using SpanType = absl::Span<unsigned_type>; - - static constexpr size_t kInitialBuffer = kBufferSize + 1; - static constexpr size_t kHalfBuffer = kBufferSize / 2; - - public: - using result_type = T; - - static_assert(std::is_unsigned<result_type>::value, - "PoolURBG must be parameterized by an unsigned integer type"); - - static_assert(kBufferSize > 1, - "PoolURBG must be parameterized by a buffer-size > 1"); - - static_assert(kBufferSize <= 256, - "PoolURBG must be parameterized by a buffer-size <= 256"); - - static constexpr result_type(min)() { - return (std::numeric_limits<result_type>::min)(); - } - - static constexpr result_type(max)() { - return (std::numeric_limits<result_type>::max)(); - } - - PoolURBG() : next_(kInitialBuffer) {} - - // copy-constructor does not copy cache. - PoolURBG(const PoolURBG&) : next_(kInitialBuffer) {} - const PoolURBG& operator=(const PoolURBG&) { - next_ = kInitialBuffer; - return *this; - } - - // move-constructor does move cache. - PoolURBG(PoolURBG&&) = default; - PoolURBG& operator=(PoolURBG&&) = default; - - inline result_type operator()() { - if (next_ >= kBufferSize) { - next_ = (kBufferSize > 2 && next_ > kBufferSize) ? kHalfBuffer : 0; - PoolType::Fill(SpanType(reinterpret_cast<unsigned_type*>(state_ + next_), - kBufferSize - next_)); - } - return state_[next_++]; - } - - private: - // Buffer size. - size_t next_; // index within state_ - result_type state_[kBufferSize]; -}; - -} // namespace random_internal -ABSL_NAMESPACE_END -} // namespace absl - -#endif // ABSL_RANDOM_INTERNAL_POOL_URBG_H_ diff --git a/third_party/abseil_cpp/absl/random/internal/pool_urbg_test.cc b/third_party/abseil_cpp/absl/random/internal/pool_urbg_test.cc deleted file mode 100644 index 53f4eacf16fe..000000000000 --- a/third_party/abseil_cpp/absl/random/internal/pool_urbg_test.cc +++ /dev/null @@ -1,182 +0,0 @@ -// Copyright 2017 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#include "absl/random/internal/pool_urbg.h" - -#include <algorithm> -#include <bitset> -#include <cmath> -#include <cstdint> -#include <iterator> - -#include "gtest/gtest.h" -#include "absl/meta/type_traits.h" -#include "absl/types/span.h" - -using absl::random_internal::PoolURBG; -using absl::random_internal::RandenPool; - -namespace { - -// is_randen_pool trait is true when parameterized by an RandenPool -template <typename T> -using is_randen_pool = typename absl::disjunction< // - std::is_same<T, RandenPool<uint8_t>>, // - std::is_same<T, RandenPool<uint16_t>>, // - std::is_same<T, RandenPool<uint32_t>>, // - std::is_same<T, RandenPool<uint64_t>>>; // - -// MyFill either calls RandenPool::Fill() or std::generate(..., rng) -template <typename T, typename V> -typename absl::enable_if_t<absl::negation<is_randen_pool<T>>::value, void> // -MyFill(T& rng, absl::Span<V> data) { // NOLINT(runtime/references) - std::generate(std::begin(data), std::end(data), rng); -} - -template <typename T, typename V> -typename absl::enable_if_t<is_randen_pool<T>::value, void> // -MyFill(T& rng, absl::Span<V> data) { // NOLINT(runtime/references) - rng.Fill(data); -} - -template <typename EngineType> -class PoolURBGTypedTest : public ::testing::Test {}; - -using EngineTypes = ::testing::Types< // - RandenPool<uint8_t>, // - RandenPool<uint16_t>, // - RandenPool<uint32_t>, // - RandenPool<uint64_t>, // - PoolURBG<uint8_t, 2>, // - PoolURBG<uint16_t, 2>, // - PoolURBG<uint32_t, 2>, // - PoolURBG<uint64_t, 2>, // - PoolURBG<unsigned int, 8>, // NOLINT(runtime/int) - PoolURBG<unsigned long, 8>, // NOLINT(runtime/int) - PoolURBG<unsigned long int, 4>, // NOLINT(runtime/int) - PoolURBG<unsigned long long, 4>>; // NOLINT(runtime/int) - -TYPED_TEST_SUITE(PoolURBGTypedTest, EngineTypes); - -// This test is checks that the engines meet the URBG interface requirements -// defined in [rand.req.urbg]. -TYPED_TEST(PoolURBGTypedTest, URBGInterface) { - using E = TypeParam; - using T = typename E::result_type; - - static_assert(std::is_copy_constructible<E>::value, - "engine must be copy constructible"); - - static_assert(absl::is_copy_assignable<E>::value, - "engine must be copy assignable"); - - E e; - const E x; - - e(); - - static_assert(std::is_same<decltype(e()), T>::value, - "return type of operator() must be result_type"); - - E u0(x); - u0(); - - E u1 = e; - u1(); -} - -// This validates that sequences are independent. -TYPED_TEST(PoolURBGTypedTest, VerifySequences) { - using E = TypeParam; - using result_type = typename E::result_type; - - E rng; - (void)rng(); // Discard one value. - - constexpr int kNumOutputs = 64; - result_type a[kNumOutputs]; - result_type b[kNumOutputs]; - std::fill(std::begin(b), std::end(b), 0); - - // Fill a using Fill or generate, depending on the engine type. - { - E x = rng; - MyFill(x, absl::MakeSpan(a)); - } - - // Fill b using std::generate(). - { - E x = rng; - std::generate(std::begin(b), std::end(b), x); - } - - // Test that generated sequence changed as sequence of bits, i.e. if about - // half of the bites were flipped between two non-correlated values. - size_t changed_bits = 0; - size_t unchanged_bits = 0; - size_t total_set = 0; - size_t total_bits = 0; - size_t equal_count = 0; - for (size_t i = 0; i < kNumOutputs; ++i) { - equal_count += (a[i] == b[i]) ? 1 : 0; - std::bitset<sizeof(result_type) * 8> bitset(a[i] ^ b[i]); - changed_bits += bitset.count(); - unchanged_bits += bitset.size() - bitset.count(); - - std::bitset<sizeof(result_type) * 8> a_set(a[i]); - std::bitset<sizeof(result_type) * 8> b_set(b[i]); - total_set += a_set.count() + b_set.count(); - total_bits += 2 * 8 * sizeof(result_type); - } - // On average, half the bits are changed between two calls. - EXPECT_LE(changed_bits, 0.60 * (changed_bits + unchanged_bits)); - EXPECT_GE(changed_bits, 0.40 * (changed_bits + unchanged_bits)); - - // verify using a quick normal-approximation to the binomial. - EXPECT_NEAR(total_set, total_bits * 0.5, 4 * std::sqrt(total_bits)) - << "@" << total_set / static_cast<double>(total_bits); - - // Also, A[i] == B[i] with probability (1/range) * N. - // Give this a pretty wide latitude, though. - const double kExpected = kNumOutputs / (1.0 * sizeof(result_type) * 8); - EXPECT_LE(equal_count, 1.0 + kExpected); -} - -} // namespace - -/* -$ nanobenchmarks 1 RandenPool construct -$ nanobenchmarks 1 PoolURBG construct - -RandenPool<uint32_t> | 1 | 1000 | 48482.00 ticks | 48.48 ticks | 13.9 ns -RandenPool<uint32_t> | 10 | 2000 | 1028795.00 ticks | 51.44 ticks | 14.7 ns -RandenPool<uint32_t> | 100 | 1000 | 5119968.00 ticks | 51.20 ticks | 14.6 ns -RandenPool<uint32_t> | 1000 | 500 | 25867936.00 ticks | 51.74 ticks | 14.8 ns - -RandenPool<uint64_t> | 1 | 1000 | 49921.00 ticks | 49.92 ticks | 14.3 ns -RandenPool<uint64_t> | 10 | 2000 | 1208269.00 ticks | 60.41 ticks | 17.3 ns -RandenPool<uint64_t> | 100 | 1000 | 5844955.00 ticks | 58.45 ticks | 16.7 ns -RandenPool<uint64_t> | 1000 | 500 | 28767404.00 ticks | 57.53 ticks | 16.4 ns - -PoolURBG<uint32_t,8> | 1 | 1000 | 86431.00 ticks | 86.43 ticks | 24.7 ns -PoolURBG<uint32_t,8> | 10 | 1000 | 206191.00 ticks | 20.62 ticks | 5.9 ns -PoolURBG<uint32_t,8> | 100 | 1000 | 1516049.00 ticks | 15.16 ticks | 4.3 ns -PoolURBG<uint32_t,8> | 1000 | 500 | 7613936.00 ticks | 15.23 ticks | 4.4 ns - -PoolURBG<uint64_t,4> | 1 | 1000 | 96668.00 ticks | 96.67 ticks | 27.6 ns -PoolURBG<uint64_t,4> | 10 | 1000 | 282423.00 ticks | 28.24 ticks | 8.1 ns -PoolURBG<uint64_t,4> | 100 | 1000 | 2609587.00 ticks | 26.10 ticks | 7.5 ns -PoolURBG<uint64_t,4> | 1000 | 500 | 12408757.00 ticks | 24.82 ticks | 7.1 ns - -*/ diff --git a/third_party/abseil_cpp/absl/random/internal/randen.cc b/third_party/abseil_cpp/absl/random/internal/randen.cc deleted file mode 100644 index 78a1e00c0832..000000000000 --- a/third_party/abseil_cpp/absl/random/internal/randen.cc +++ /dev/null @@ -1,91 +0,0 @@ -// Copyright 2017 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#include "absl/random/internal/randen.h" - -#include "absl/base/internal/raw_logging.h" -#include "absl/random/internal/randen_detect.h" - -// RANDen = RANDom generator or beetroots in Swiss German. -// 'Strong' (well-distributed, unpredictable, backtracking-resistant) random -// generator, faster in some benchmarks than std::mt19937_64 and pcg64_c32. -// -// High-level summary: -// 1) Reverie (see "A Robust and Sponge-Like PRNG with Improved Efficiency") is -// a sponge-like random generator that requires a cryptographic permutation. -// It improves upon "Provably Robust Sponge-Based PRNGs and KDFs" by -// achieving backtracking resistance with only one Permute() per buffer. -// -// 2) "Simpira v2: A Family of Efficient Permutations Using the AES Round -// Function" constructs up to 1024-bit permutations using an improved -// Generalized Feistel network with 2-round AES-128 functions. This Feistel -// block shuffle achieves diffusion faster and is less vulnerable to -// sliced-biclique attacks than the Type-2 cyclic shuffle. -// -// 3) "Improving the Generalized Feistel" and "New criterion for diffusion -// property" extends the same kind of improved Feistel block shuffle to 16 -// branches, which enables a 2048-bit permutation. -// -// We combine these three ideas and also change Simpira's subround keys from -// structured/low-entropy counters to digits of Pi. - -namespace absl { -ABSL_NAMESPACE_BEGIN -namespace random_internal { -namespace { - -struct RandenState { - const void* keys; - bool has_crypto; -}; - -RandenState GetRandenState() { - static const RandenState state = []() { - RandenState tmp; -#if ABSL_RANDOM_INTERNAL_AES_DISPATCH - // HW AES Dispatch. - if (HasRandenHwAesImplementation() && CPUSupportsRandenHwAes()) { - tmp.has_crypto = true; - tmp.keys = RandenHwAes::GetKeys(); - } else { - tmp.has_crypto = false; - tmp.keys = RandenSlow::GetKeys(); - } -#elif ABSL_HAVE_ACCELERATED_AES - // HW AES is enabled. - tmp.has_crypto = true; - tmp.keys = RandenHwAes::GetKeys(); -#else - // HW AES is disabled. - tmp.has_crypto = false; - tmp.keys = RandenSlow::GetKeys(); -#endif - return tmp; - }(); - return state; -} - -} // namespace - -Randen::Randen() { - auto tmp = GetRandenState(); - keys_ = tmp.keys; -#if ABSL_RANDOM_INTERNAL_AES_DISPATCH - has_crypto_ = tmp.has_crypto; -#endif -} - -} // namespace random_internal -ABSL_NAMESPACE_END -} // namespace absl diff --git a/third_party/abseil_cpp/absl/random/internal/randen.h b/third_party/abseil_cpp/absl/random/internal/randen.h deleted file mode 100644 index c2834aaf3d2c..000000000000 --- a/third_party/abseil_cpp/absl/random/internal/randen.h +++ /dev/null @@ -1,102 +0,0 @@ -// Copyright 2017 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#ifndef ABSL_RANDOM_INTERNAL_RANDEN_H_ -#define ABSL_RANDOM_INTERNAL_RANDEN_H_ - -#include <cstddef> - -#include "absl/random/internal/platform.h" -#include "absl/random/internal/randen_hwaes.h" -#include "absl/random/internal/randen_slow.h" -#include "absl/random/internal/randen_traits.h" - -namespace absl { -ABSL_NAMESPACE_BEGIN -namespace random_internal { - -// RANDen = RANDom generator or beetroots in Swiss German. -// 'Strong' (well-distributed, unpredictable, backtracking-resistant) random -// generator, faster in some benchmarks than std::mt19937_64 and pcg64_c32. -// -// Randen implements the basic state manipulation methods. -class Randen { - public: - static constexpr size_t kStateBytes = RandenTraits::kStateBytes; - static constexpr size_t kCapacityBytes = RandenTraits::kCapacityBytes; - static constexpr size_t kSeedBytes = RandenTraits::kSeedBytes; - - ~Randen() = default; - - Randen(); - - // Generate updates the randen sponge. The outer portion of the sponge - // (kCapacityBytes .. kStateBytes) may be consumed as PRNG state. - template <typename T, size_t N> - void Generate(T (&state)[N]) const { - static_assert(N * sizeof(T) == kStateBytes, - "Randen::Generate() requires kStateBytes of state"); -#if ABSL_RANDOM_INTERNAL_AES_DISPATCH - // HW AES Dispatch. - if (has_crypto_) { - RandenHwAes::Generate(keys_, state); - } else { - RandenSlow::Generate(keys_, state); - } -#elif ABSL_HAVE_ACCELERATED_AES - // HW AES is enabled. - RandenHwAes::Generate(keys_, state); -#else - // HW AES is disabled. - RandenSlow::Generate(keys_, state); -#endif - } - - // Absorb incorporates additional seed material into the randen sponge. After - // absorb returns, Generate must be called before the state may be consumed. - template <typename S, size_t M, typename T, size_t N> - void Absorb(const S (&seed)[M], T (&state)[N]) const { - static_assert(M * sizeof(S) == RandenTraits::kSeedBytes, - "Randen::Absorb() requires kSeedBytes of seed"); - - static_assert(N * sizeof(T) == RandenTraits::kStateBytes, - "Randen::Absorb() requires kStateBytes of state"); -#if ABSL_RANDOM_INTERNAL_AES_DISPATCH - // HW AES Dispatch. - if (has_crypto_) { - RandenHwAes::Absorb(seed, state); - } else { - RandenSlow::Absorb(seed, state); - } -#elif ABSL_HAVE_ACCELERATED_AES - // HW AES is enabled. - RandenHwAes::Absorb(seed, state); -#else - // HW AES is disabled. - RandenSlow::Absorb(seed, state); -#endif - } - - private: - const void* keys_; -#if ABSL_RANDOM_INTERNAL_AES_DISPATCH - bool has_crypto_; -#endif -}; - -} // namespace random_internal -ABSL_NAMESPACE_END -} // namespace absl - -#endif // ABSL_RANDOM_INTERNAL_RANDEN_H_ diff --git a/third_party/abseil_cpp/absl/random/internal/randen_benchmarks.cc b/third_party/abseil_cpp/absl/random/internal/randen_benchmarks.cc deleted file mode 100644 index f589172c0466..000000000000 --- a/third_party/abseil_cpp/absl/random/internal/randen_benchmarks.cc +++ /dev/null @@ -1,174 +0,0 @@ -// Copyright 2017 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. -// -#include "absl/random/internal/randen.h" - -#include <cstdint> -#include <cstdio> -#include <cstring> - -#include "absl/base/internal/raw_logging.h" -#include "absl/random/internal/nanobenchmark.h" -#include "absl/random/internal/platform.h" -#include "absl/random/internal/randen_engine.h" -#include "absl/random/internal/randen_hwaes.h" -#include "absl/random/internal/randen_slow.h" -#include "absl/strings/numbers.h" - -namespace { - -using absl::random_internal::Randen; -using absl::random_internal::RandenHwAes; -using absl::random_internal::RandenSlow; - -using absl::random_internal_nanobenchmark::FuncInput; -using absl::random_internal_nanobenchmark::FuncOutput; -using absl::random_internal_nanobenchmark::InvariantTicksPerSecond; -using absl::random_internal_nanobenchmark::MeasureClosure; -using absl::random_internal_nanobenchmark::Params; -using absl::random_internal_nanobenchmark::PinThreadToCPU; -using absl::random_internal_nanobenchmark::Result; - -// Local state parameters. -static constexpr size_t kStateSizeT = Randen::kStateBytes / sizeof(uint64_t); -static constexpr size_t kSeedSizeT = Randen::kSeedBytes / sizeof(uint32_t); - -// Randen implementation benchmarks. -template <typename T> -struct AbsorbFn : public T { - mutable uint64_t state[kStateSizeT] = {}; - mutable uint32_t seed[kSeedSizeT] = {}; - - static constexpr size_t bytes() { return sizeof(seed); } - - FuncOutput operator()(const FuncInput num_iters) const { - for (size_t i = 0; i < num_iters; ++i) { - this->Absorb(seed, state); - } - return state[0]; - } -}; - -template <typename T> -struct GenerateFn : public T { - mutable uint64_t state[kStateSizeT]; - GenerateFn() { std::memset(state, 0, sizeof(state)); } - - static constexpr size_t bytes() { return sizeof(state); } - - FuncOutput operator()(const FuncInput num_iters) const { - const auto* keys = this->GetKeys(); - for (size_t i = 0; i < num_iters; ++i) { - this->Generate(keys, state); - } - return state[0]; - } -}; - -template <typename UInt> -struct Engine { - mutable absl::random_internal::randen_engine<UInt> rng; - - static constexpr size_t bytes() { return sizeof(UInt); } - - FuncOutput operator()(const FuncInput num_iters) const { - for (size_t i = 0; i < num_iters - 1; ++i) { - rng(); - } - return rng(); - } -}; - -template <size_t N> -void Print(const char* name, const size_t n, const Result (&results)[N], - const size_t bytes) { - if (n == 0) { - ABSL_RAW_LOG( - WARNING, - "WARNING: Measurement failed, should not happen when using " - "PinThreadToCPU unless the region to measure takes > 1 second.\n"); - return; - } - - static const double ns_per_tick = 1e9 / InvariantTicksPerSecond(); - static constexpr const double kNsPerS = 1e9; // ns/s - static constexpr const double kMBPerByte = 1.0 / 1048576.0; // Mb / b - static auto header = [] { - return printf("%20s %8s: %12s ticks; %9s (%9s) %8s\n", "Name", "Count", - "Total", "Variance", "Time", "bytes/s"); - }(); - (void)header; - - for (size_t i = 0; i < n; ++i) { - const double ticks_per_call = results[i].ticks / results[i].input; - const double ns_per_call = ns_per_tick * ticks_per_call; - const double bytes_per_ns = bytes / ns_per_call; - const double mb_per_s = bytes_per_ns * kNsPerS * kMBPerByte; - // Output - printf("%20s %8zu: %12.2f ticks; MAD=%4.2f%% (%6.1f ns) %8.1f Mb/s\n", - name, results[i].input, results[i].ticks, - results[i].variability * 100.0, ns_per_call, mb_per_s); - } -} - -// Fails here -template <typename Op, size_t N> -void Measure(const char* name, const FuncInput (&inputs)[N]) { - Op op; - - Result results[N]; - Params params; - params.verbose = false; - params.max_evals = 6; // avoid test timeout - const size_t num_results = MeasureClosure(op, inputs, N, results, params); - Print(name, num_results, results, op.bytes()); -} - -// unpredictable == 1 but the compiler does not know that. -void RunAll(const int argc, char* argv[]) { - if (argc == 2) { - int cpu = -1; - if (!absl::SimpleAtoi(argv[1], &cpu)) { - ABSL_RAW_LOG(FATAL, "The optional argument must be a CPU number >= 0.\n"); - } - PinThreadToCPU(cpu); - } - - // The compiler cannot reduce this to a constant. - const FuncInput unpredictable = (argc != 999); - static const FuncInput inputs[] = {unpredictable * 100, unpredictable * 1000}; - -#if !defined(ABSL_INTERNAL_DISABLE_AES) && ABSL_HAVE_ACCELERATED_AES - Measure<AbsorbFn<RandenHwAes>>("Absorb (HwAes)", inputs); -#endif - Measure<AbsorbFn<RandenSlow>>("Absorb (Slow)", inputs); - -#if !defined(ABSL_INTERNAL_DISABLE_AES) && ABSL_HAVE_ACCELERATED_AES - Measure<GenerateFn<RandenHwAes>>("Generate (HwAes)", inputs); -#endif - Measure<GenerateFn<RandenSlow>>("Generate (Slow)", inputs); - - // Measure the production engine. - static const FuncInput inputs1[] = {unpredictable * 1000, - unpredictable * 10000}; - Measure<Engine<uint64_t>>("randen_engine<uint64_t>", inputs1); - Measure<Engine<uint32_t>>("randen_engine<uint32_t>", inputs1); -} - -} // namespace - -int main(int argc, char* argv[]) { - RunAll(argc, argv); - return 0; -} diff --git a/third_party/abseil_cpp/absl/random/internal/randen_detect.cc b/third_party/abseil_cpp/absl/random/internal/randen_detect.cc deleted file mode 100644 index bbe7b965329c..000000000000 --- a/third_party/abseil_cpp/absl/random/internal/randen_detect.cc +++ /dev/null @@ -1,221 +0,0 @@ -// Copyright 2017 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -// HERMETIC NOTE: The randen_hwaes target must not introduce duplicate -// symbols from arbitrary system and other headers, since it may be built -// with different flags from other targets, using different levels of -// optimization, potentially introducing ODR violations. - -#include "absl/random/internal/randen_detect.h" - -#include <cstdint> -#include <cstring> - -#include "absl/random/internal/platform.h" - -#if defined(ABSL_ARCH_X86_64) -#define ABSL_INTERNAL_USE_X86_CPUID -#elif defined(ABSL_ARCH_PPC) || defined(ABSL_ARCH_ARM) || \ - defined(ABSL_ARCH_AARCH64) -#if defined(__ANDROID__) -#define ABSL_INTERNAL_USE_ANDROID_GETAUXVAL -#define ABSL_INTERNAL_USE_GETAUXVAL -#elif defined(__linux__) -#define ABSL_INTERNAL_USE_LINUX_GETAUXVAL -#define ABSL_INTERNAL_USE_GETAUXVAL -#endif -#endif - -#if defined(ABSL_INTERNAL_USE_X86_CPUID) -#if defined(_WIN32) || defined(_WIN64) -#include <intrin.h> // NOLINT(build/include_order) -#pragma intrinsic(__cpuid) -#else -// MSVC-equivalent __cpuid intrinsic function. -static void __cpuid(int cpu_info[4], int info_type) { - __asm__ volatile("cpuid \n\t" - : "=a"(cpu_info[0]), "=b"(cpu_info[1]), "=c"(cpu_info[2]), - "=d"(cpu_info[3]) - : "a"(info_type), "c"(0)); -} -#endif -#endif // ABSL_INTERNAL_USE_X86_CPUID - -// On linux, just use the c-library getauxval call. -#if defined(ABSL_INTERNAL_USE_LINUX_GETAUXVAL) - -extern "C" unsigned long getauxval(unsigned long type); // NOLINT(runtime/int) - -static uint32_t GetAuxval(uint32_t hwcap_type) { - return static_cast<uint32_t>(getauxval(hwcap_type)); -} - -#endif - -// On android, probe the system's C library for getauxval(). -// This is the same technique used by the android NDK cpu features library -// as well as the google open-source cpu_features library. -// -// TODO(absl-team): Consider implementing a fallback of directly reading -// /proc/self/auxval. -#if defined(ABSL_INTERNAL_USE_ANDROID_GETAUXVAL) -#include <dlfcn.h> - -static uint32_t GetAuxval(uint32_t hwcap_type) { - // NOLINTNEXTLINE(runtime/int) - typedef unsigned long (*getauxval_func_t)(unsigned long); - - dlerror(); // Cleaning error state before calling dlopen. - void* libc_handle = dlopen("libc.so", RTLD_NOW); - if (!libc_handle) { - return 0; - } - uint32_t result = 0; - void* sym = dlsym(libc_handle, "getauxval"); - if (sym) { - getauxval_func_t func; - memcpy(&func, &sym, sizeof(func)); - result = static_cast<uint32_t>((*func)(hwcap_type)); - } - dlclose(libc_handle); - return result; -} - -#endif - -namespace absl { -ABSL_NAMESPACE_BEGIN -namespace random_internal { - -// The default return at the end of the function might be unreachable depending -// on the configuration. Ignore that warning. -#if defined(__clang__) -#pragma clang diagnostic push -#pragma clang diagnostic ignored "-Wunreachable-code-return" -#endif - -// CPUSupportsRandenHwAes returns whether the CPU is a microarchitecture -// which supports the crpyto/aes instructions or extensions necessary to use the -// accelerated RandenHwAes implementation. -// -// 1. For x86 it is sufficient to use the CPUID instruction to detect whether -// the cpu supports AES instructions. Done. -// -// Fon non-x86 it is much more complicated. -// -// 2. When ABSL_INTERNAL_USE_GETAUXVAL is defined, use getauxval() (either -// the direct c-library version, or the android probing version which loads -// libc), and read the hardware capability bits. -// This is based on the technique used by boringssl uses to detect -// cpu capabilities, and should allow us to enable crypto in the android -// builds where it is supported. -// -// 3. Use the default for the compiler architecture. -// - -bool CPUSupportsRandenHwAes() { -#if defined(ABSL_INTERNAL_USE_X86_CPUID) - // 1. For x86: Use CPUID to detect the required AES instruction set. - int regs[4]; - __cpuid(reinterpret_cast<int*>(regs), 1); - return regs[2] & (1 << 25); // AES - -#elif defined(ABSL_INTERNAL_USE_GETAUXVAL) - // 2. Use getauxval() to read the hardware bits and determine - // cpu capabilities. - -#define AT_HWCAP 16 -#define AT_HWCAP2 26 -#if defined(ABSL_ARCH_PPC) - // For Power / PPC: Expect that the cpu supports VCRYPTO - // See https://members.openpowerfoundation.org/document/dl/576 - // VCRYPTO should be present in POWER8 >= 2.07. - // Uses Linux kernel constants from arch/powerpc/include/uapi/asm/cputable.h - static const uint32_t kVCRYPTO = 0x02000000; - const uint32_t hwcap = GetAuxval(AT_HWCAP2); - return (hwcap & kVCRYPTO) != 0; - -#elif defined(ABSL_ARCH_ARM) - // For ARM: Require crypto+neon - // http://infocenter.arm.com/help/index.jsp?topic=/com.arm.doc.ddi0500f/CIHBIBBA.html - // Uses Linux kernel constants from arch/arm64/include/asm/hwcap.h - static const uint32_t kNEON = 1 << 12; - uint32_t hwcap = GetAuxval(AT_HWCAP); - if ((hwcap & kNEON) == 0) { - return false; - } - - // And use it again to detect AES. - static const uint32_t kAES = 1 << 0; - const uint32_t hwcap2 = GetAuxval(AT_HWCAP2); - return (hwcap2 & kAES) != 0; - -#elif defined(ABSL_ARCH_AARCH64) - // For AARCH64: Require crypto+neon - // http://infocenter.arm.com/help/index.jsp?topic=/com.arm.doc.ddi0500f/CIHBIBBA.html - static const uint32_t kNEON = 1 << 1; - static const uint32_t kAES = 1 << 3; - const uint32_t hwcap = GetAuxval(AT_HWCAP); - return ((hwcap & kNEON) != 0) && ((hwcap & kAES) != 0); -#endif - -#else // ABSL_INTERNAL_USE_GETAUXVAL - // 3. By default, assume that the compiler default. - return ABSL_HAVE_ACCELERATED_AES ? true : false; - -#endif - // NOTE: There are some other techniques that may be worth trying: - // - // * Use an environment variable: ABSL_RANDOM_USE_HWAES - // - // * Rely on compiler-generated target-based dispatch. - // Using x86/gcc it might look something like this: - // - // int __attribute__((target("aes"))) HasAes() { return 1; } - // int __attribute__((target("default"))) HasAes() { return 0; } - // - // This does not work on all architecture/compiler combinations. - // - // * On Linux consider reading /proc/cpuinfo and/or /proc/self/auxv. - // These files have lines which are easy to parse; for ARM/AARCH64 it is quite - // easy to find the Features: line and extract aes / neon. Likewise for - // PPC. - // - // * Fork a process and test for SIGILL: - // - // * Many architectures have instructions to read the ISA. Unfortunately - // most of those require that the code is running in ring 0 / - // protected-mode. - // - // There are several examples. e.g. Valgrind detects PPC ISA 2.07: - // https://github.com/lu-zero/valgrind/blob/master/none/tests/ppc64/test_isa_2_07_part1.c - // - // MRS <Xt>, ID_AA64ISAR0_EL1 ; Read ID_AA64ISAR0_EL1 into Xt - // - // uint64_t val; - // __asm __volatile("mrs %0, id_aa64isar0_el1" :"=&r" (val)); - // - // * Use a CPUID-style heuristic database. - // - // * On Apple (__APPLE__), AES is available on Arm v8. - // https://stackoverflow.com/questions/45637888/how-to-determine-armv8-features-at-runtime-on-ios -} - -#if defined(__clang__) -#pragma clang diagnostic pop -#endif - -} // namespace random_internal -ABSL_NAMESPACE_END -} // namespace absl diff --git a/third_party/abseil_cpp/absl/random/internal/randen_detect.h b/third_party/abseil_cpp/absl/random/internal/randen_detect.h deleted file mode 100644 index f283f4322647..000000000000 --- a/third_party/abseil_cpp/absl/random/internal/randen_detect.h +++ /dev/null @@ -1,33 +0,0 @@ -// Copyright 2017 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#ifndef ABSL_RANDOM_INTERNAL_RANDEN_DETECT_H_ -#define ABSL_RANDOM_INTERNAL_RANDEN_DETECT_H_ - -#include "absl/base/config.h" - -namespace absl { -ABSL_NAMESPACE_BEGIN -namespace random_internal { - -// Returns whether the current CPU supports RandenHwAes implementation. -// This typically involves supporting cryptographic extensions on whichever -// platform is currently running. -bool CPUSupportsRandenHwAes(); - -} // namespace random_internal -ABSL_NAMESPACE_END -} // namespace absl - -#endif // ABSL_RANDOM_INTERNAL_RANDEN_DETECT_H_ diff --git a/third_party/abseil_cpp/absl/random/internal/randen_engine.h b/third_party/abseil_cpp/absl/random/internal/randen_engine.h deleted file mode 100644 index 6b33731336e7..000000000000 --- a/third_party/abseil_cpp/absl/random/internal/randen_engine.h +++ /dev/null @@ -1,230 +0,0 @@ -// Copyright 2017 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#ifndef ABSL_RANDOM_INTERNAL_RANDEN_ENGINE_H_ -#define ABSL_RANDOM_INTERNAL_RANDEN_ENGINE_H_ - -#include <algorithm> -#include <cinttypes> -#include <cstdlib> -#include <iostream> -#include <iterator> -#include <limits> -#include <type_traits> - -#include "absl/meta/type_traits.h" -#include "absl/random/internal/iostream_state_saver.h" -#include "absl/random/internal/randen.h" - -namespace absl { -ABSL_NAMESPACE_BEGIN -namespace random_internal { - -// Deterministic pseudorandom byte generator with backtracking resistance -// (leaking the state does not compromise prior outputs). Based on Reverie -// (see "A Robust and Sponge-Like PRNG with Improved Efficiency") instantiated -// with an improved Simpira-like permutation. -// Returns values of type "T" (must be a built-in unsigned integer type). -// -// RANDen = RANDom generator or beetroots in Swiss High German. -// 'Strong' (well-distributed, unpredictable, backtracking-resistant) random -// generator, faster in some benchmarks than std::mt19937_64 and pcg64_c32. -template <typename T> -class alignas(16) randen_engine { - public: - // C++11 URBG interface: - using result_type = T; - static_assert(std::is_unsigned<result_type>::value, - "randen_engine template argument must be a built-in unsigned " - "integer type"); - - static constexpr result_type(min)() { - return (std::numeric_limits<result_type>::min)(); - } - - static constexpr result_type(max)() { - return (std::numeric_limits<result_type>::max)(); - } - - explicit randen_engine(result_type seed_value = 0) { seed(seed_value); } - - template <class SeedSequence, - typename = typename absl::enable_if_t< - !std::is_same<SeedSequence, randen_engine>::value>> - explicit randen_engine(SeedSequence&& seq) { - seed(seq); - } - - randen_engine(const randen_engine&) = default; - - // Returns random bits from the buffer in units of result_type. - result_type operator()() { - // Refill the buffer if needed (unlikely). - if (next_ >= kStateSizeT) { - next_ = kCapacityT; - impl_.Generate(state_); - } - - return state_[next_++]; - } - - template <class SeedSequence> - typename absl::enable_if_t< - !std::is_convertible<SeedSequence, result_type>::value> - seed(SeedSequence&& seq) { - // Zeroes the state. - seed(); - reseed(seq); - } - - void seed(result_type seed_value = 0) { - next_ = kStateSizeT; - // Zeroes the inner state and fills the outer state with seed_value to - // mimics behaviour of reseed - std::fill(std::begin(state_), std::begin(state_) + kCapacityT, 0); - std::fill(std::begin(state_) + kCapacityT, std::end(state_), seed_value); - } - - // Inserts entropy into (part of) the state. Calling this periodically with - // sufficient entropy ensures prediction resistance (attackers cannot predict - // future outputs even if state is compromised). - template <class SeedSequence> - void reseed(SeedSequence& seq) { - using sequence_result_type = typename SeedSequence::result_type; - static_assert(sizeof(sequence_result_type) == 4, - "SeedSequence::result_type must be 32-bit"); - - constexpr size_t kBufferSize = - Randen::kSeedBytes / sizeof(sequence_result_type); - alignas(16) sequence_result_type buffer[kBufferSize]; - - // Randen::Absorb XORs the seed into state, which is then mixed by a call - // to Randen::Generate. Seeding with only the provided entropy is preferred - // to using an arbitrary generate() call, so use [rand.req.seed_seq] - // size as a proxy for the number of entropy units that can be generated - // without relying on seed sequence mixing... - const size_t entropy_size = seq.size(); - if (entropy_size < kBufferSize) { - // ... and only request that many values, or 256-bits, when unspecified. - const size_t requested_entropy = (entropy_size == 0) ? 8u : entropy_size; - std::fill(std::begin(buffer) + requested_entropy, std::end(buffer), 0); - seq.generate(std::begin(buffer), std::begin(buffer) + requested_entropy); - // The Randen paper suggests preferentially initializing even-numbered - // 128-bit vectors of the randen state (there are 16 such vectors). - // The seed data is merged into the state offset by 128-bits, which - // implies prefering seed bytes [16..31, ..., 208..223]. Since the - // buffer is 32-bit values, we swap the corresponding buffer positions in - // 128-bit chunks. - size_t dst = kBufferSize; - while (dst > 7) { - // leave the odd bucket as-is. - dst -= 4; - size_t src = dst >> 1; - // swap 128-bits into the even bucket - std::swap(buffer[--dst], buffer[--src]); - std::swap(buffer[--dst], buffer[--src]); - std::swap(buffer[--dst], buffer[--src]); - std::swap(buffer[--dst], buffer[--src]); - } - } else { - seq.generate(std::begin(buffer), std::end(buffer)); - } - impl_.Absorb(buffer, state_); - - // Generate will be called when operator() is called - next_ = kStateSizeT; - } - - void discard(uint64_t count) { - uint64_t step = std::min<uint64_t>(kStateSizeT - next_, count); - count -= step; - - constexpr uint64_t kRateT = kStateSizeT - kCapacityT; - while (count > 0) { - next_ = kCapacityT; - impl_.Generate(state_); - step = std::min<uint64_t>(kRateT, count); - count -= step; - } - next_ += step; - } - - bool operator==(const randen_engine& other) const { - return next_ == other.next_ && - std::equal(std::begin(state_), std::end(state_), - std::begin(other.state_)); - } - - bool operator!=(const randen_engine& other) const { - return !(*this == other); - } - - template <class CharT, class Traits> - friend std::basic_ostream<CharT, Traits>& operator<<( - std::basic_ostream<CharT, Traits>& os, // NOLINT(runtime/references) - const randen_engine<T>& engine) { // NOLINT(runtime/references) - using numeric_type = - typename random_internal::stream_format_type<result_type>::type; - auto saver = random_internal::make_ostream_state_saver(os); - for (const auto& elem : engine.state_) { - // In the case that `elem` is `uint8_t`, it must be cast to something - // larger so that it prints as an integer rather than a character. For - // simplicity, apply the cast all circumstances. - os << static_cast<numeric_type>(elem) << os.fill(); - } - os << engine.next_; - return os; - } - - template <class CharT, class Traits> - friend std::basic_istream<CharT, Traits>& operator>>( - std::basic_istream<CharT, Traits>& is, // NOLINT(runtime/references) - randen_engine<T>& engine) { // NOLINT(runtime/references) - using numeric_type = - typename random_internal::stream_format_type<result_type>::type; - result_type state[kStateSizeT]; - size_t next; - for (auto& elem : state) { - // It is not possible to read uint8_t from wide streams, so it is - // necessary to read a wider type and then cast it to uint8_t. - numeric_type value; - is >> value; - elem = static_cast<result_type>(value); - } - is >> next; - if (is.fail()) { - return is; - } - std::memcpy(engine.state_, state, sizeof(engine.state_)); - engine.next_ = next; - return is; - } - - private: - static constexpr size_t kStateSizeT = - Randen::kStateBytes / sizeof(result_type); - static constexpr size_t kCapacityT = - Randen::kCapacityBytes / sizeof(result_type); - - // First kCapacityT are `inner', the others are accessible random bits. - alignas(16) result_type state_[kStateSizeT]; - size_t next_; // index within state_ - Randen impl_; -}; - -} // namespace random_internal -ABSL_NAMESPACE_END -} // namespace absl - -#endif // ABSL_RANDOM_INTERNAL_RANDEN_ENGINE_H_ diff --git a/third_party/abseil_cpp/absl/random/internal/randen_engine_test.cc b/third_party/abseil_cpp/absl/random/internal/randen_engine_test.cc deleted file mode 100644 index c8e7685bddad..000000000000 --- a/third_party/abseil_cpp/absl/random/internal/randen_engine_test.cc +++ /dev/null @@ -1,656 +0,0 @@ -// Copyright 2017 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#include "absl/random/internal/randen_engine.h" - -#include <algorithm> -#include <bitset> -#include <random> -#include <sstream> - -#include "gmock/gmock.h" -#include "gtest/gtest.h" -#include "absl/base/internal/raw_logging.h" -#include "absl/random/internal/explicit_seed_seq.h" -#include "absl/strings/str_cat.h" -#include "absl/time/clock.h" - -#define UPDATE_GOLDEN 0 - -using randen_u64 = absl::random_internal::randen_engine<uint64_t>; -using randen_u32 = absl::random_internal::randen_engine<uint32_t>; -using absl::random_internal::ExplicitSeedSeq; - -namespace { - -template <typename UIntType> -class RandenEngineTypedTest : public ::testing::Test {}; - -using UIntTypes = ::testing::Types<uint8_t, uint16_t, uint32_t, uint64_t>; - -TYPED_TEST_SUITE(RandenEngineTypedTest, UIntTypes); - -TYPED_TEST(RandenEngineTypedTest, VerifyReseedChangesAllValues) { - using randen = typename absl::random_internal::randen_engine<TypeParam>; - using result_type = typename randen::result_type; - - const size_t kNumOutputs = (sizeof(randen) * 2 / sizeof(TypeParam)) + 1; - randen engine; - - // MSVC emits error 2719 without the use of std::ref below. - // * formal parameter with __declspec(align('#')) won't be aligned - - { - std::seed_seq seq1{1, 2, 3, 4, 5, 6, 7}; - engine.seed(seq1); - } - result_type a[kNumOutputs]; - std::generate(std::begin(a), std::end(a), std::ref(engine)); - - { - std::random_device rd; - std::seed_seq seq2{rd(), rd(), rd()}; - engine.seed(seq2); - } - result_type b[kNumOutputs]; - std::generate(std::begin(b), std::end(b), std::ref(engine)); - - // Test that generated sequence changed as sequence of bits, i.e. if about - // half of the bites were flipped between two non-correlated values. - size_t changed_bits = 0; - size_t unchanged_bits = 0; - size_t total_set = 0; - size_t total_bits = 0; - size_t equal_count = 0; - for (size_t i = 0; i < kNumOutputs; ++i) { - equal_count += (a[i] == b[i]) ? 1 : 0; - std::bitset<sizeof(result_type) * 8> bitset(a[i] ^ b[i]); - changed_bits += bitset.count(); - unchanged_bits += bitset.size() - bitset.count(); - - std::bitset<sizeof(result_type) * 8> a_set(a[i]); - std::bitset<sizeof(result_type) * 8> b_set(b[i]); - total_set += a_set.count() + b_set.count(); - total_bits += 2 * 8 * sizeof(result_type); - } - // On average, half the bits are changed between two calls. - EXPECT_LE(changed_bits, 0.60 * (changed_bits + unchanged_bits)); - EXPECT_GE(changed_bits, 0.40 * (changed_bits + unchanged_bits)); - - // Verify using a quick normal-approximation to the binomial. - EXPECT_NEAR(total_set, total_bits * 0.5, 4 * std::sqrt(total_bits)) - << "@" << total_set / static_cast<double>(total_bits); - - // Also, A[i] == B[i] with probability (1/range) * N. - // Give this a pretty wide latitude, though. - const double kExpected = kNumOutputs / (1.0 * sizeof(result_type) * 8); - EXPECT_LE(equal_count, 1.0 + kExpected); -} - -// Number of values that needs to be consumed to clean two sizes of buffer -// and trigger third refresh. (slightly overestimates the actual state size). -constexpr size_t kTwoBufferValues = sizeof(randen_u64) / sizeof(uint16_t) + 1; - -TYPED_TEST(RandenEngineTypedTest, VerifyDiscard) { - using randen = typename absl::random_internal::randen_engine<TypeParam>; - - for (size_t num_used = 0; num_used < kTwoBufferValues; ++num_used) { - randen engine_used; - for (size_t i = 0; i < num_used; ++i) { - engine_used(); - } - - for (size_t num_discard = 0; num_discard < kTwoBufferValues; - ++num_discard) { - randen engine1 = engine_used; - randen engine2 = engine_used; - for (size_t i = 0; i < num_discard; ++i) { - engine1(); - } - engine2.discard(num_discard); - for (size_t i = 0; i < kTwoBufferValues; ++i) { - const auto r1 = engine1(); - const auto r2 = engine2(); - ASSERT_EQ(r1, r2) << "used=" << num_used << " discard=" << num_discard; - } - } - } -} - -TYPED_TEST(RandenEngineTypedTest, StreamOperatorsResult) { - using randen = typename absl::random_internal::randen_engine<TypeParam>; - std::wostringstream os; - std::wistringstream is; - randen engine; - - EXPECT_EQ(&(os << engine), &os); - EXPECT_EQ(&(is >> engine), &is); -} - -TYPED_TEST(RandenEngineTypedTest, StreamSerialization) { - using randen = typename absl::random_internal::randen_engine<TypeParam>; - - for (size_t discard = 0; discard < kTwoBufferValues; ++discard) { - ExplicitSeedSeq seed_sequence{12, 34, 56}; - randen engine(seed_sequence); - engine.discard(discard); - - std::stringstream stream; - stream << engine; - - randen new_engine; - stream >> new_engine; - for (size_t i = 0; i < 64; ++i) { - EXPECT_EQ(engine(), new_engine()) << " " << i; - } - } -} - -constexpr size_t kNumGoldenOutputs = 127; - -// This test is checking if randen_engine is meets interface requirements -// defined in [rand.req.urbg]. -TYPED_TEST(RandenEngineTypedTest, RandomNumberEngineInterface) { - using randen = typename absl::random_internal::randen_engine<TypeParam>; - - using E = randen; - using T = typename E::result_type; - - static_assert(std::is_copy_constructible<E>::value, - "randen_engine must be copy constructible"); - - static_assert(absl::is_copy_assignable<E>::value, - "randen_engine must be copy assignable"); - - static_assert(std::is_move_constructible<E>::value, - "randen_engine must be move constructible"); - - static_assert(absl::is_move_assignable<E>::value, - "randen_engine must be move assignable"); - - static_assert(std::is_same<decltype(std::declval<E>()()), T>::value, - "return type of operator() must be result_type"); - - // Names after definition of [rand.req.urbg] in C++ standard. - // e us a value of E - // v is a lvalue of E - // x, y are possibly const values of E - // s is a value of T - // q is a value satisfying requirements of seed_sequence - // z is a value of type unsigned long long - // os is a some specialization of basic_ostream - // is is a some specialization of basic_istream - - E e, v; - const E x, y; - T s = 1; - std::seed_seq q{1, 2, 3}; - unsigned long long z = 1; // NOLINT(runtime/int) - std::wostringstream os; - std::wistringstream is; - - E{}; - E{x}; - E{s}; - E{q}; - - e.seed(); - - // MSVC emits error 2718 when using EXPECT_EQ(e, x) - // * actual parameter with __declspec(align('#')) won't be aligned - EXPECT_TRUE(e == x); - - e.seed(q); - { - E tmp(q); - EXPECT_TRUE(e == tmp); - } - - e(); - { - E tmp(q); - EXPECT_TRUE(e != tmp); - } - - e.discard(z); - - static_assert(std::is_same<decltype(x == y), bool>::value, - "return type of operator== must be bool"); - - static_assert(std::is_same<decltype(x != y), bool>::value, - "return type of operator== must be bool"); -} - -TYPED_TEST(RandenEngineTypedTest, RandenEngineSFINAETest) { - using randen = typename absl::random_internal::randen_engine<TypeParam>; - using result_type = typename randen::result_type; - - { - randen engine(result_type(1)); - engine.seed(result_type(1)); - } - - { - result_type n = 1; - randen engine(n); - engine.seed(n); - } - - { - randen engine(1); - engine.seed(1); - } - - { - int n = 1; - randen engine(n); - engine.seed(n); - } - - { - std::seed_seq seed_seq; - randen engine(seed_seq); - engine.seed(seed_seq); - } - - { - randen engine{std::seed_seq()}; - engine.seed(std::seed_seq()); - } -} - -TEST(RandenTest, VerifyGoldenRanden64Default) { - constexpr uint64_t kGolden[kNumGoldenOutputs] = { - 0xc3c14f134e433977, 0xdda9f47cd90410ee, 0x887bf3087fd8ca10, - 0xf0b780f545c72912, 0x15dbb1d37696599f, 0x30ec63baff3c6d59, - 0xb29f73606f7f20a6, 0x02808a316f49a54c, 0x3b8feaf9d5c8e50e, - 0x9cbf605e3fd9de8a, 0xc970ae1a78183bbb, 0xd8b2ffd356301ed5, - 0xf4b327fe0fc73c37, 0xcdfd8d76eb8f9a19, 0xc3a506eb91420c9d, - 0xd5af05dd3eff9556, 0x48db1bb78f83c4a1, 0x7023920e0d6bfe8c, - 0x58d3575834956d42, 0xed1ef4c26b87b840, 0x8eef32a23e0b2df3, - 0x497cabf3431154fc, 0x4e24370570029a8b, 0xd88b5749f090e5ea, - 0xc651a582a970692f, 0x78fcec2cbb6342f5, 0x463cb745612f55db, - 0x352ee4ad1816afe3, 0x026ff374c101da7e, 0x811ef0821c3de851, - 0x6f7e616704c4fa59, 0xa0660379992d58fc, 0x04b0a374a3b795c7, - 0x915f3445685da798, 0x26802a8ac76571ce, 0x4663352533ce1882, - 0xb9fdefb4a24dc738, 0x5588ba3a4d6e6c51, 0xa2101a42d35f1956, - 0x607195a5e200f5fd, 0x7e100308f3290764, 0xe1e5e03c759c0709, - 0x082572cc5da6606f, 0xcbcf585399e432f1, 0xe8a2be4f8335d8f1, - 0x0904469acbfee8f2, 0xf08bd31b6daecd51, 0x08e8a1f1a69da69a, - 0x6542a20aad57bff5, 0x2e9705bb053d6b46, 0xda2fc9db0713c391, - 0x78e3a810213b6ffb, 0xdc16a59cdd85f8a6, 0xc0932718cd55781f, - 0xb9bfb29c2b20bfe5, 0xb97289c1be0f2f9c, 0xc0a2a0e403a892d4, - 0x5524bb834771435b, 0x8265da3d39d1a750, 0xff4af3ab8d1b78c5, - 0xf0ec5f424bcad77f, 0x66e455f627495189, 0xc82d3120b57e3270, - 0x3424e47dc22596e3, 0xbc0c95129ccedcdd, 0xc191c595afc4dcbf, - 0x120392bd2bb70939, 0x7f90650ea6cd6ab4, 0x7287491832695ad3, - 0xa7c8fac5a7917eb0, 0xd088cb9418be0361, 0x7c1bf9839c7c1ce5, - 0xe2e991fa58e1e79e, 0x78565cdefd28c4ad, 0x7351b9fef98bafad, - 0x2a9eac28b08c96bf, 0x6c4f179696cb2225, 0x13a685861bab87e0, - 0x64c6de5aa0501971, 0x30537425cac70991, 0x01590d9dc6c532b7, - 0x7e05e3aa8ec720dc, 0x74a07d9c54e3e63f, 0x738184388f3bc1d2, - 0x26ffdc5067be3acb, 0x6bcdf185561f255f, 0xa0eaf2e1cf99b1c6, - 0x171df81934f68604, 0x7ea5a21665683e5a, 0x5d1cb02075ba1cea, - 0x957f38cbd2123fdf, 0xba6364eff80de02f, 0x606e0a0e41d452ee, - 0x892d8317de82f7a2, 0xe707b1db50f7b43e, 0x4eb28826766fcf5b, - 0x5a362d56e80a0951, 0x6ee217df16527d78, 0xf6737962ba6b23dd, - 0x443e63857d4076ca, 0x790d9a5f048adfeb, 0xd796b052151ee94d, - 0x033ed95c12b04a03, 0x8b833ff84893da5d, 0x3d6724b1bb15eab9, - 0x9877c4225061ca76, 0xd68d6810adf74fb3, 0x42e5352fe30ce989, - 0x265b565a7431fde7, 0x3cdbf7e358df4b8b, 0x2922a47f6d3e8779, - 0x52d2242f65b37f88, 0x5d836d6e2958d6b5, 0x29d40f00566d5e26, - 0x288db0e1124b14a0, 0x6c056608b7d9c1b6, 0x0b9471bdb8f19d32, - 0x8fb946504faa6c9d, 0x8943a9464540251c, 0xfd1fe27d144a09e0, - 0xea6ac458da141bda, 0x8048f217633fce36, 0xfeda1384ade74d31, - 0x4334b8b02ff7612f, 0xdbc8441f5227e216, 0x096d119a3605c85b, - 0x2b72b31c21b7d7d0}; - - randen_u64 engine; -#if UPDATE_GOLDEN - (void)kGolden; // Silence warning. - for (size_t i = 0; i < kNumGoldenOutputs; ++i) { - printf("0x%016lx, ", engine()); - if (i % 3 == 2) { - printf("\n"); - } - } - printf("\n\n\n"); -#else - for (const auto& elem : kGolden) { - EXPECT_EQ(elem, engine()); - } - engine.seed(); - for (const auto& elem : kGolden) { - EXPECT_EQ(elem, engine()); - } -#endif -} - -TEST(RandenTest, VerifyGoldenRanden64Seeded) { - constexpr uint64_t kGolden[kNumGoldenOutputs] = { - 0x83a9e58f94d3dcd5, 0x70bbdff3d97949fb, 0x0438481f7471c1b4, - 0x34fdc58ee5fb5930, 0xceee4f2d2a937d17, 0xb5a26a68e432aea9, - 0x8b64774a3fb51740, 0xd89ac1fc74249c74, 0x03910d1d23fc3fdf, - 0xd38f630878aa897f, 0x0ee8f0f5615f7e44, 0x98f5a53df8279d52, - 0xb403f52c25938d0e, 0x240072996ea6e838, 0xd3a791246190fa61, - 0xaaedd3df7a7b4f80, 0xc6eacabe05deaf6e, 0xb7967dd8790edf4d, - 0x9a0a8e67e049d279, 0x0494f606aebc23e7, 0x598dcd687bc3e0ee, - 0x010ac81802d452a1, 0x6407c87160aa2842, 0x5a56e276486f93a0, - 0xc887a399d46a8f02, 0x9e1e6100fe93b740, 0x12d02e330f8901f6, - 0xc39ca52b47e790b7, 0xb0b0a2fa11e82e61, 0x1542d841a303806a, - 0x1fe659fd7d6e9d86, 0xb8c90d80746541ac, 0x239d56a5669ddc94, - 0xd40db57c8123d13c, 0x3abc2414153a0db0, 0x9bad665630cb8d61, - 0x0bd1fb90ee3f4bbc, 0x8f0b4d7e079b4e42, 0xfa0fb0e0ee59e793, - 0x51080b283e071100, 0x2c4b9e715081cc15, 0xbe10ed49de4941df, - 0xf8eaac9d4b1b0d37, 0x4bcce4b54605e139, 0xa64722b76765dda6, - 0xb9377d738ca28ab5, 0x779fad81a8ccc1af, 0x65cb3ee61ffd3ba7, - 0xd74e79087862836f, 0xd05b9c584c3f25bf, 0x2ba93a4693579827, - 0xd81530aff05420ce, 0xec06cea215478621, 0x4b1798a6796d65ad, - 0xf142f3fb3a6f6fa6, 0x002b7bf7e237b560, 0xf47f2605ef65b4f8, - 0x9804ec5517effc18, 0xaed3d7f8b7d481cd, 0x5651c24c1ce338d1, - 0x3e7a38208bf0a3c6, 0x6796a7b614534aed, 0x0d0f3b848358460f, - 0x0fa5fe7600b19524, 0x2b0cf38253faaedc, 0x10df9188233a9fd6, - 0x3a10033880138b59, 0x5fb0b0d23948e80f, 0x9e76f7b02fbf5350, - 0x0816052304b1a985, 0x30c9880db41fd218, 0x14aa399b65e20f28, - 0xe1454a8cace787b4, 0x325ac971b6c6f0f5, 0x716b1aa2784f3d36, - 0x3d5ce14accfd144f, 0x6c0c97710f651792, 0xbc5b0f59fb333532, - 0x2a90a7d2140470bc, 0x8da269f55c1e1c8d, 0xcfc37143895792ca, - 0xbe21eab1f30b238f, 0x8c47229dee4d65fd, 0x5743614ed1ed7d54, - 0x351372a99e9c476e, 0x2bd5ea15e5db085f, 0x6925fde46e0af4ca, - 0xed3eda2bdc1f45bd, 0xdef68c68d460fa6e, 0xe42a0de76253e2b5, - 0x4e5176dcbc29c305, 0xbfd85fba9f810f6e, 0x76a5a2a9beb815c6, - 0x01edc4ddceaf414c, 0xa4e98904b4bb3b4b, 0x00bd63ac7d2f1ddd, - 0xb8491fe6e998ddbb, 0xb386a3463dda6800, 0x0081887688871619, - 0x33d394b3344e9a38, 0x815dba65a3a8baf9, 0x4232f6ec02c2fd1a, - 0xb5cff603edd20834, 0x580189243f687663, 0xa8d5a2cbdc27fe99, - 0x725d881693fa0131, 0xa2be2c13db2c7ac5, 0x7b6a9614b509fd78, - 0xb6b136d71e717636, 0x660f1a71aff046ea, 0x0ba10ae346c8ec9e, - 0xe66dde53e3145b41, 0x3b18288c88c26be6, 0x4d9d9d2ff02db933, - 0x4167da8c70f46e8a, 0xf183beef8c6318b4, 0x4d889e1e71eeeef1, - 0x7175c71ad6689b6b, 0xfb9e42beacd1b7dd, 0xc33d0e91b29b5e0d, - 0xd39b83291ce47922, 0xc4d570fb8493d12e, 0x23d5a5724f424ae6, - 0x5245f161876b6616, 0x38d77dbd21ab578d, 0x9c3423311f4ecbfe, - 0x76fe31389bacd9d5, - }; - - ExplicitSeedSeq seed_sequence{12, 34, 56}; - randen_u64 engine(seed_sequence); -#if UPDATE_GOLDEN - (void)kGolden; // Silence warning. - for (size_t i = 0; i < kNumGoldenOutputs; ++i) { - printf("0x%016lx, ", engine()); - if (i % 3 == 2) { - printf("\n"); - } - } - printf("\n\n\n"); -#else - for (const auto& elem : kGolden) { - EXPECT_EQ(elem, engine()); - } - engine.seed(seed_sequence); - for (const auto& elem : kGolden) { - EXPECT_EQ(elem, engine()); - } -#endif -} - -TEST(RandenTest, VerifyGoldenRanden32Default) { - constexpr uint64_t kGolden[2 * kNumGoldenOutputs] = { - 0x4e433977, 0xc3c14f13, 0xd90410ee, 0xdda9f47c, 0x7fd8ca10, 0x887bf308, - 0x45c72912, 0xf0b780f5, 0x7696599f, 0x15dbb1d3, 0xff3c6d59, 0x30ec63ba, - 0x6f7f20a6, 0xb29f7360, 0x6f49a54c, 0x02808a31, 0xd5c8e50e, 0x3b8feaf9, - 0x3fd9de8a, 0x9cbf605e, 0x78183bbb, 0xc970ae1a, 0x56301ed5, 0xd8b2ffd3, - 0x0fc73c37, 0xf4b327fe, 0xeb8f9a19, 0xcdfd8d76, 0x91420c9d, 0xc3a506eb, - 0x3eff9556, 0xd5af05dd, 0x8f83c4a1, 0x48db1bb7, 0x0d6bfe8c, 0x7023920e, - 0x34956d42, 0x58d35758, 0x6b87b840, 0xed1ef4c2, 0x3e0b2df3, 0x8eef32a2, - 0x431154fc, 0x497cabf3, 0x70029a8b, 0x4e243705, 0xf090e5ea, 0xd88b5749, - 0xa970692f, 0xc651a582, 0xbb6342f5, 0x78fcec2c, 0x612f55db, 0x463cb745, - 0x1816afe3, 0x352ee4ad, 0xc101da7e, 0x026ff374, 0x1c3de851, 0x811ef082, - 0x04c4fa59, 0x6f7e6167, 0x992d58fc, 0xa0660379, 0xa3b795c7, 0x04b0a374, - 0x685da798, 0x915f3445, 0xc76571ce, 0x26802a8a, 0x33ce1882, 0x46633525, - 0xa24dc738, 0xb9fdefb4, 0x4d6e6c51, 0x5588ba3a, 0xd35f1956, 0xa2101a42, - 0xe200f5fd, 0x607195a5, 0xf3290764, 0x7e100308, 0x759c0709, 0xe1e5e03c, - 0x5da6606f, 0x082572cc, 0x99e432f1, 0xcbcf5853, 0x8335d8f1, 0xe8a2be4f, - 0xcbfee8f2, 0x0904469a, 0x6daecd51, 0xf08bd31b, 0xa69da69a, 0x08e8a1f1, - 0xad57bff5, 0x6542a20a, 0x053d6b46, 0x2e9705bb, 0x0713c391, 0xda2fc9db, - 0x213b6ffb, 0x78e3a810, 0xdd85f8a6, 0xdc16a59c, 0xcd55781f, 0xc0932718, - 0x2b20bfe5, 0xb9bfb29c, 0xbe0f2f9c, 0xb97289c1, 0x03a892d4, 0xc0a2a0e4, - 0x4771435b, 0x5524bb83, 0x39d1a750, 0x8265da3d, 0x8d1b78c5, 0xff4af3ab, - 0x4bcad77f, 0xf0ec5f42, 0x27495189, 0x66e455f6, 0xb57e3270, 0xc82d3120, - 0xc22596e3, 0x3424e47d, 0x9ccedcdd, 0xbc0c9512, 0xafc4dcbf, 0xc191c595, - 0x2bb70939, 0x120392bd, 0xa6cd6ab4, 0x7f90650e, 0x32695ad3, 0x72874918, - 0xa7917eb0, 0xa7c8fac5, 0x18be0361, 0xd088cb94, 0x9c7c1ce5, 0x7c1bf983, - 0x58e1e79e, 0xe2e991fa, 0xfd28c4ad, 0x78565cde, 0xf98bafad, 0x7351b9fe, - 0xb08c96bf, 0x2a9eac28, 0x96cb2225, 0x6c4f1796, 0x1bab87e0, 0x13a68586, - 0xa0501971, 0x64c6de5a, 0xcac70991, 0x30537425, 0xc6c532b7, 0x01590d9d, - 0x8ec720dc, 0x7e05e3aa, 0x54e3e63f, 0x74a07d9c, 0x8f3bc1d2, 0x73818438, - 0x67be3acb, 0x26ffdc50, 0x561f255f, 0x6bcdf185, 0xcf99b1c6, 0xa0eaf2e1, - 0x34f68604, 0x171df819, 0x65683e5a, 0x7ea5a216, 0x75ba1cea, 0x5d1cb020, - 0xd2123fdf, 0x957f38cb, 0xf80de02f, 0xba6364ef, 0x41d452ee, 0x606e0a0e, - 0xde82f7a2, 0x892d8317, 0x50f7b43e, 0xe707b1db, 0x766fcf5b, 0x4eb28826, - 0xe80a0951, 0x5a362d56, 0x16527d78, 0x6ee217df, 0xba6b23dd, 0xf6737962, - 0x7d4076ca, 0x443e6385, 0x048adfeb, 0x790d9a5f, 0x151ee94d, 0xd796b052, - 0x12b04a03, 0x033ed95c, 0x4893da5d, 0x8b833ff8, 0xbb15eab9, 0x3d6724b1, - 0x5061ca76, 0x9877c422, 0xadf74fb3, 0xd68d6810, 0xe30ce989, 0x42e5352f, - 0x7431fde7, 0x265b565a, 0x58df4b8b, 0x3cdbf7e3, 0x6d3e8779, 0x2922a47f, - 0x65b37f88, 0x52d2242f, 0x2958d6b5, 0x5d836d6e, 0x566d5e26, 0x29d40f00, - 0x124b14a0, 0x288db0e1, 0xb7d9c1b6, 0x6c056608, 0xb8f19d32, 0x0b9471bd, - 0x4faa6c9d, 0x8fb94650, 0x4540251c, 0x8943a946, 0x144a09e0, 0xfd1fe27d, - 0xda141bda, 0xea6ac458, 0x633fce36, 0x8048f217, 0xade74d31, 0xfeda1384, - 0x2ff7612f, 0x4334b8b0, 0x5227e216, 0xdbc8441f, 0x3605c85b, 0x096d119a, - 0x21b7d7d0, 0x2b72b31c}; - - randen_u32 engine; -#if UPDATE_GOLDEN - (void)kGolden; // Silence warning. - for (size_t i = 0; i < 2 * kNumGoldenOutputs; ++i) { - printf("0x%08x, ", engine()); - if (i % 6 == 5) { - printf("\n"); - } - } - printf("\n\n\n"); -#else - for (const auto& elem : kGolden) { - EXPECT_EQ(elem, engine()); - } - engine.seed(); - for (const auto& elem : kGolden) { - EXPECT_EQ(elem, engine()); - } -#endif -} - -TEST(RandenTest, VerifyGoldenRanden32Seeded) { - constexpr uint64_t kGolden[2 * kNumGoldenOutputs] = { - 0x94d3dcd5, 0x83a9e58f, 0xd97949fb, 0x70bbdff3, 0x7471c1b4, 0x0438481f, - 0xe5fb5930, 0x34fdc58e, 0x2a937d17, 0xceee4f2d, 0xe432aea9, 0xb5a26a68, - 0x3fb51740, 0x8b64774a, 0x74249c74, 0xd89ac1fc, 0x23fc3fdf, 0x03910d1d, - 0x78aa897f, 0xd38f6308, 0x615f7e44, 0x0ee8f0f5, 0xf8279d52, 0x98f5a53d, - 0x25938d0e, 0xb403f52c, 0x6ea6e838, 0x24007299, 0x6190fa61, 0xd3a79124, - 0x7a7b4f80, 0xaaedd3df, 0x05deaf6e, 0xc6eacabe, 0x790edf4d, 0xb7967dd8, - 0xe049d279, 0x9a0a8e67, 0xaebc23e7, 0x0494f606, 0x7bc3e0ee, 0x598dcd68, - 0x02d452a1, 0x010ac818, 0x60aa2842, 0x6407c871, 0x486f93a0, 0x5a56e276, - 0xd46a8f02, 0xc887a399, 0xfe93b740, 0x9e1e6100, 0x0f8901f6, 0x12d02e33, - 0x47e790b7, 0xc39ca52b, 0x11e82e61, 0xb0b0a2fa, 0xa303806a, 0x1542d841, - 0x7d6e9d86, 0x1fe659fd, 0x746541ac, 0xb8c90d80, 0x669ddc94, 0x239d56a5, - 0x8123d13c, 0xd40db57c, 0x153a0db0, 0x3abc2414, 0x30cb8d61, 0x9bad6656, - 0xee3f4bbc, 0x0bd1fb90, 0x079b4e42, 0x8f0b4d7e, 0xee59e793, 0xfa0fb0e0, - 0x3e071100, 0x51080b28, 0x5081cc15, 0x2c4b9e71, 0xde4941df, 0xbe10ed49, - 0x4b1b0d37, 0xf8eaac9d, 0x4605e139, 0x4bcce4b5, 0x6765dda6, 0xa64722b7, - 0x8ca28ab5, 0xb9377d73, 0xa8ccc1af, 0x779fad81, 0x1ffd3ba7, 0x65cb3ee6, - 0x7862836f, 0xd74e7908, 0x4c3f25bf, 0xd05b9c58, 0x93579827, 0x2ba93a46, - 0xf05420ce, 0xd81530af, 0x15478621, 0xec06cea2, 0x796d65ad, 0x4b1798a6, - 0x3a6f6fa6, 0xf142f3fb, 0xe237b560, 0x002b7bf7, 0xef65b4f8, 0xf47f2605, - 0x17effc18, 0x9804ec55, 0xb7d481cd, 0xaed3d7f8, 0x1ce338d1, 0x5651c24c, - 0x8bf0a3c6, 0x3e7a3820, 0x14534aed, 0x6796a7b6, 0x8358460f, 0x0d0f3b84, - 0x00b19524, 0x0fa5fe76, 0x53faaedc, 0x2b0cf382, 0x233a9fd6, 0x10df9188, - 0x80138b59, 0x3a100338, 0x3948e80f, 0x5fb0b0d2, 0x2fbf5350, 0x9e76f7b0, - 0x04b1a985, 0x08160523, 0xb41fd218, 0x30c9880d, 0x65e20f28, 0x14aa399b, - 0xace787b4, 0xe1454a8c, 0xb6c6f0f5, 0x325ac971, 0x784f3d36, 0x716b1aa2, - 0xccfd144f, 0x3d5ce14a, 0x0f651792, 0x6c0c9771, 0xfb333532, 0xbc5b0f59, - 0x140470bc, 0x2a90a7d2, 0x5c1e1c8d, 0x8da269f5, 0x895792ca, 0xcfc37143, - 0xf30b238f, 0xbe21eab1, 0xee4d65fd, 0x8c47229d, 0xd1ed7d54, 0x5743614e, - 0x9e9c476e, 0x351372a9, 0xe5db085f, 0x2bd5ea15, 0x6e0af4ca, 0x6925fde4, - 0xdc1f45bd, 0xed3eda2b, 0xd460fa6e, 0xdef68c68, 0x6253e2b5, 0xe42a0de7, - 0xbc29c305, 0x4e5176dc, 0x9f810f6e, 0xbfd85fba, 0xbeb815c6, 0x76a5a2a9, - 0xceaf414c, 0x01edc4dd, 0xb4bb3b4b, 0xa4e98904, 0x7d2f1ddd, 0x00bd63ac, - 0xe998ddbb, 0xb8491fe6, 0x3dda6800, 0xb386a346, 0x88871619, 0x00818876, - 0x344e9a38, 0x33d394b3, 0xa3a8baf9, 0x815dba65, 0x02c2fd1a, 0x4232f6ec, - 0xedd20834, 0xb5cff603, 0x3f687663, 0x58018924, 0xdc27fe99, 0xa8d5a2cb, - 0x93fa0131, 0x725d8816, 0xdb2c7ac5, 0xa2be2c13, 0xb509fd78, 0x7b6a9614, - 0x1e717636, 0xb6b136d7, 0xaff046ea, 0x660f1a71, 0x46c8ec9e, 0x0ba10ae3, - 0xe3145b41, 0xe66dde53, 0x88c26be6, 0x3b18288c, 0xf02db933, 0x4d9d9d2f, - 0x70f46e8a, 0x4167da8c, 0x8c6318b4, 0xf183beef, 0x71eeeef1, 0x4d889e1e, - 0xd6689b6b, 0x7175c71a, 0xacd1b7dd, 0xfb9e42be, 0xb29b5e0d, 0xc33d0e91, - 0x1ce47922, 0xd39b8329, 0x8493d12e, 0xc4d570fb, 0x4f424ae6, 0x23d5a572, - 0x876b6616, 0x5245f161, 0x21ab578d, 0x38d77dbd, 0x1f4ecbfe, 0x9c342331, - 0x9bacd9d5, 0x76fe3138, - }; - - ExplicitSeedSeq seed_sequence{12, 34, 56}; - randen_u32 engine(seed_sequence); -#if UPDATE_GOLDEN - (void)kGolden; // Silence warning. - for (size_t i = 0; i < 2 * kNumGoldenOutputs; ++i) { - printf("0x%08x, ", engine()); - if (i % 6 == 5) { - printf("\n"); - } - } - printf("\n\n\n"); -#else - for (const auto& elem : kGolden) { - EXPECT_EQ(elem, engine()); - } - engine.seed(seed_sequence); - for (const auto& elem : kGolden) { - EXPECT_EQ(elem, engine()); - } -#endif -} - -TEST(RandenTest, VerifyGoldenFromDeserializedEngine) { - constexpr uint64_t kGolden[kNumGoldenOutputs] = { - 0x067f9f9ab919657a, 0x0534605912988583, 0x8a303f72feaa673f, - 0x77b7fd747909185c, 0xd9af90403c56d891, 0xd939c6cb204d14b5, - 0x7fbe6b954a47b483, 0x8b31a47cc34c768d, 0x3a9e546da2701a9c, - 0x5246539046253e71, 0x417191ffb2a848a1, 0x7b1c7bf5a5001d09, - 0x9489b15d194f2361, 0xfcebdeea3bcd2461, 0xd643027c854cec97, - 0x5885397f91e0d21c, 0x53173b0efae30d58, 0x1c9c71168449fac1, - 0xe358202b711ed8aa, 0x94e3918ed1d8227c, 0x5bb4e251450144cf, - 0xb5c7a519b489af3b, 0x6f8b560b1f7b3469, 0xfde11dd4a1c74eef, - 0x33383d2f76457dcf, 0x3060c0ec6db9fce1, 0x18f451fcddeec766, - 0xe73c5d6b9f26da2a, 0x8d4cc566671b32a4, 0xb8189b73776bc9ff, - 0x497a70f9caf0bc23, 0x23afcc509791dcea, 0x18af70dc4b27d306, - 0xd3853f955a0ce5b9, 0x441db6c01a0afb17, 0xd0136c3fb8e1f13f, - 0x5e4fd6fc2f33783c, 0xe0d24548adb5da51, 0x0f4d8362a7d3485a, - 0x9f572d68270fa563, 0x6351fbc823024393, 0xa66dbfc61810e9ab, - 0x0ff17fc14b651af8, 0xd74c55dafb99e623, 0x36303bc1ad85c6c2, - 0x4920cd6a2af7e897, 0x0b8848addc30fecd, 0x9e1562eda6488e93, - 0x197553807d607828, 0xbef5eaeda5e21235, 0x18d91d2616aca527, - 0xb7821937f5c873cd, 0x2cd4ae5650dbeefc, 0xb35a64376f75ffdf, - 0x9226d414d647fe07, 0x663f3db455bbb35e, 0xa829eead6ae93247, - 0x7fd69c204dd0d25f, 0xbe1411f891c9acb1, 0xd476f34a506d5f11, - 0xf423d2831649c5ca, 0x1e503962951abd75, 0xeccc9e8b1e34b537, - 0xb11a147294044854, 0xc4cf27f0abf4929d, 0xe9193abf6fa24c8c, - 0xa94a259e3aba8808, 0x21dc414197deffa3, 0xa2ae211d1ff622ae, - 0xfe3995c46be5a4f4, 0xe9984c284bf11128, 0xcb1ce9d2f0851a80, - 0x42fee17971d87cd8, 0xac76a98d177adc88, 0xa0973b3dedc4af6f, - 0xdf56d6bbcb1b8e86, 0xf1e6485f407b11c9, 0x2c63de4deccb15c0, - 0x6fe69db32ed4fad7, 0xaa51a65f84bca1f1, 0x242f2ee81d608afc, - 0x8eb88b2b69fc153b, 0x22c20098baf73fd1, 0x57759466f576488c, - 0x075ca562cea1be9d, 0x9a74814d73d28891, 0x73d1555fc02f4d3d, - 0xc17f8f210ee89337, 0x46cca7999eaeafd4, 0x5db8d6a327a0d8ac, - 0xb79b4f93c738d7a1, 0x9994512f0036ded1, 0xd3883026f38747f4, - 0xf31f7458078d097c, 0x736ce4d480680669, 0x7a496f4c7e1033e3, - 0xecf85bf297fbc68c, 0x9e37e1d0f24f3c4e, 0x15b6e067ca0746fc, - 0xdd4a39905c5db81c, 0xb5dfafa7bcfdf7da, 0xca6646fb6f92a276, - 0x1c6b35f363ef0efd, 0x6a33d06037ad9f76, 0x45544241afd8f80f, - 0x83f8d83f859c90c5, 0x22aea9c5365e8c19, 0xfac35b11f20b6a6a, - 0xd1acf49d1a27dd2f, 0xf281cd09c4fed405, 0x076000a42cd38e4f, - 0x6ace300565070445, 0x463a62781bddc4db, 0x1477126b46b569ac, - 0x127f2bb15035fbb8, 0xdfa30946049c04a8, 0x89072a586ba8dd3e, - 0x62c809582bb7e74d, 0x22c0c3641406c28b, 0x9b66e36c47ff004d, - 0xb9cd2c7519653330, 0x18608d79cd7a598d, 0x92c0bd1323e53e32, - 0x887ff00de8524aa5, 0xa074410b787abd10, 0x18ab41b8057a2063, - 0x1560abf26bc5f987}; - -#if UPDATE_GOLDEN - (void)kGolden; // Silence warning. - std::seed_seq seed_sequence{1, 2, 3, 4, 5}; - randen_u64 engine(seed_sequence); - std::ostringstream stream; - stream << engine; - auto str = stream.str(); - printf("%s\n\n", str.c_str()); - for (size_t i = 0; i < kNumGoldenOutputs; ++i) { - printf("0x%016lx, ", engine()); - if (i % 3 == 2) { - printf("\n"); - } - } - printf("\n\n\n"); -#else - randen_u64 engine; - std::istringstream stream( - "0 0 9824501439887287479 3242284395352394785 243836530774933777 " - "4047941804708365596 17165468127298385802 949276103645889255 " - "10659970394998657921 1657570836810929787 11697746266668051452 " - "9967209969299905230 14140390331161524430 7383014124183271684 " - "13146719127702337852 13983155220295807171 11121125587542359264 " - "195757810993252695 17138580243103178492 11326030747260920501 " - "8585097322474965590 18342582839328350995 15052982824209724634 " - "7321861343874683609 1806786911778767826 10100850842665572955 " - "9249328950653985078 13600624835326909759 11137960060943860251 " - "10208781341792329629 9282723971471525577 16373271619486811032 32"); - stream >> engine; - for (const auto& elem : kGolden) { - EXPECT_EQ(elem, engine()); - } -#endif -} - -TEST(RandenTest, IsFastOrSlow) { - // randen_engine typically costs ~5ns per value for the optimized code paths, - // and the ~1000ns per value for slow code paths. However when running under - // msan, asan, etc. it can take much longer. - // - // The estimated operation time is something like: - // - // linux, optimized ~5ns - // ppc, optimized ~7ns - // nacl (slow), ~1100ns - // - // `kCount` is chosen below so that, in debug builds and without hardware - // acceleration, the test (assuming ~1us per call) should finish in ~0.1s - static constexpr size_t kCount = 100000; - randen_u64 engine; - randen_u64::result_type sum = 0; - auto start = absl::GetCurrentTimeNanos(); - for (int i = 0; i < kCount; i++) { - sum += engine(); - } - auto duration = absl::GetCurrentTimeNanos() - start; - - ABSL_INTERNAL_LOG(INFO, absl::StrCat(static_cast<double>(duration) / - static_cast<double>(kCount), - "ns")); - - EXPECT_GT(sum, 0); - EXPECT_GE(duration, kCount); // Should be slower than 1ns per call. -} - -} // namespace diff --git a/third_party/abseil_cpp/absl/random/internal/randen_hwaes.cc b/third_party/abseil_cpp/absl/random/internal/randen_hwaes.cc deleted file mode 100644 index b5a3f90aee63..000000000000 --- a/third_party/abseil_cpp/absl/random/internal/randen_hwaes.cc +++ /dev/null @@ -1,573 +0,0 @@ -// Copyright 2017 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -// HERMETIC NOTE: The randen_hwaes target must not introduce duplicate -// symbols from arbitrary system and other headers, since it may be built -// with different flags from other targets, using different levels of -// optimization, potentially introducing ODR violations. - -#include "absl/random/internal/randen_hwaes.h" - -#include <cstdint> -#include <cstring> - -#include "absl/base/attributes.h" -#include "absl/random/internal/platform.h" -#include "absl/random/internal/randen_traits.h" - -// ABSL_RANDEN_HWAES_IMPL indicates whether this file will contain -// a hardware accelerated implementation of randen, or whether it -// will contain stubs that exit the process. -#if defined(ABSL_ARCH_X86_64) || defined(ABSL_ARCH_X86_32) -// The platform.h directives are sufficient to indicate whether -// we should build accelerated implementations for x86. -#if (ABSL_HAVE_ACCELERATED_AES || ABSL_RANDOM_INTERNAL_AES_DISPATCH) -#define ABSL_RANDEN_HWAES_IMPL 1 -#endif -#elif defined(ABSL_ARCH_PPC) -// The platform.h directives are sufficient to indicate whether -// we should build accelerated implementations for PPC. -// -// NOTE: This has mostly been tested on 64-bit Power variants, -// and not embedded cpus such as powerpc32-8540 -#if ABSL_HAVE_ACCELERATED_AES -#define ABSL_RANDEN_HWAES_IMPL 1 -#endif -#elif defined(ABSL_ARCH_ARM) || defined(ABSL_ARCH_AARCH64) -// ARM is somewhat more complicated. We might support crypto natively... -#if ABSL_HAVE_ACCELERATED_AES || \ - (defined(__ARM_NEON) && defined(__ARM_FEATURE_CRYPTO)) -#define ABSL_RANDEN_HWAES_IMPL 1 - -#elif ABSL_RANDOM_INTERNAL_AES_DISPATCH && !defined(__APPLE__) && \ - (defined(__GNUC__) && __GNUC__ > 4 || __GNUC__ == 4 && __GNUC_MINOR__ > 9) -// ...or, on GCC, we can use an ASM directive to -// instruct the assember to allow crypto instructions. -#define ABSL_RANDEN_HWAES_IMPL 1 -#define ABSL_RANDEN_HWAES_IMPL_CRYPTO_DIRECTIVE 1 -#endif -#else -// HWAES is unsupported by these architectures / platforms: -// __myriad2__ -// __mips__ -// -// Other architectures / platforms are unknown. -// -// See the Abseil documentation on supported macros at: -// https://abseil.io/docs/cpp/platforms/macros -#endif - -#if !defined(ABSL_RANDEN_HWAES_IMPL) -// No accelerated implementation is supported. -// The RandenHwAes functions are stubs that print an error and exit. - -#include <cstdio> -#include <cstdlib> - -namespace absl { -ABSL_NAMESPACE_BEGIN -namespace random_internal { - -// No accelerated implementation. -bool HasRandenHwAesImplementation() { return false; } - -// NOLINTNEXTLINE -const void* RandenHwAes::GetKeys() { - // Attempted to dispatch to an unsupported dispatch target. - const int d = ABSL_RANDOM_INTERNAL_AES_DISPATCH; - fprintf(stderr, "AES Hardware detection failed (%d).\n", d); - exit(1); - return nullptr; -} - -// NOLINTNEXTLINE -void RandenHwAes::Absorb(const void*, void*) { - // Attempted to dispatch to an unsupported dispatch target. - const int d = ABSL_RANDOM_INTERNAL_AES_DISPATCH; - fprintf(stderr, "AES Hardware detection failed (%d).\n", d); - exit(1); -} - -// NOLINTNEXTLINE -void RandenHwAes::Generate(const void*, void*) { - // Attempted to dispatch to an unsupported dispatch target. - const int d = ABSL_RANDOM_INTERNAL_AES_DISPATCH; - fprintf(stderr, "AES Hardware detection failed (%d).\n", d); - exit(1); -} - -} // namespace random_internal -ABSL_NAMESPACE_END -} // namespace absl - -#else // defined(ABSL_RANDEN_HWAES_IMPL) -// -// Accelerated implementations are supported. -// We need the per-architecture includes and defines. -// -namespace { - -using absl::random_internal::RandenTraits; - -// Randen operates on 128-bit vectors. -struct alignas(16) u64x2 { - uint64_t data[2]; -}; - -} // namespace - -// TARGET_CRYPTO defines a crypto attribute for each architecture. -// -// NOTE: Evaluate whether we should eliminate ABSL_TARGET_CRYPTO. -#if (defined(__clang__) || defined(__GNUC__)) -#if defined(ABSL_ARCH_X86_64) || defined(ABSL_ARCH_X86_32) -#define ABSL_TARGET_CRYPTO __attribute__((target("aes"))) -#elif defined(ABSL_ARCH_PPC) -#define ABSL_TARGET_CRYPTO __attribute__((target("crypto"))) -#else -#define ABSL_TARGET_CRYPTO -#endif -#else -#define ABSL_TARGET_CRYPTO -#endif - -#if defined(ABSL_ARCH_PPC) -// NOTE: Keep in mind that PPC can operate in little-endian or big-endian mode, -// however the PPC altivec vector registers (and thus the AES instructions) -// always operate in big-endian mode. - -#include <altivec.h> -// <altivec.h> #defines vector __vector; in C++, this is bad form. -#undef vector -#undef bool - -// Rely on the PowerPC AltiVec vector operations for accelerated AES -// instructions. GCC support of the PPC vector types is described in: -// https://gcc.gnu.org/onlinedocs/gcc-4.9.0/gcc/PowerPC-AltiVec_002fVSX-Built-in-Functions.html -// -// Already provides operator^=. -using Vector128 = __vector unsigned long long; // NOLINT(runtime/int) - -namespace { -inline ABSL_TARGET_CRYPTO Vector128 ReverseBytes(const Vector128& v) { - // Reverses the bytes of the vector. - const __vector unsigned char perm = {15, 14, 13, 12, 11, 10, 9, 8, - 7, 6, 5, 4, 3, 2, 1, 0}; - return vec_perm(v, v, perm); -} - -// WARNING: these load/store in native byte order. It is OK to load and then -// store an unchanged vector, but interpreting the bits as a number or input -// to AES will have undefined results. -inline ABSL_TARGET_CRYPTO Vector128 Vector128Load(const void* from) { - return vec_vsx_ld(0, reinterpret_cast<const Vector128*>(from)); -} - -inline ABSL_TARGET_CRYPTO void Vector128Store(const Vector128& v, void* to) { - vec_vsx_st(v, 0, reinterpret_cast<Vector128*>(to)); -} - -// One round of AES. "round_key" is a public constant for breaking the -// symmetry of AES (ensures previously equal columns differ afterwards). -inline ABSL_TARGET_CRYPTO Vector128 AesRound(const Vector128& state, - const Vector128& round_key) { - return Vector128(__builtin_crypto_vcipher(state, round_key)); -} - -// Enables native loads in the round loop by pre-swapping. -inline ABSL_TARGET_CRYPTO void SwapEndian(u64x2* state) { - for (uint32_t block = 0; block < RandenTraits::kFeistelBlocks; ++block) { - Vector128Store(ReverseBytes(Vector128Load(state + block)), state + block); - } -} - -} // namespace - -#elif defined(ABSL_ARCH_ARM) || defined(ABSL_ARCH_AARCH64) - -// This asm directive will cause the file to be compiled with crypto extensions -// whether or not the cpu-architecture supports it. -#if ABSL_RANDEN_HWAES_IMPL_CRYPTO_DIRECTIVE -asm(".arch_extension crypto\n"); - -// Override missing defines. -#if !defined(__ARM_NEON) -#define __ARM_NEON 1 -#endif - -#if !defined(__ARM_FEATURE_CRYPTO) -#define __ARM_FEATURE_CRYPTO 1 -#endif - -#endif - -// Rely on the ARM NEON+Crypto advanced simd types, defined in <arm_neon.h>. -// uint8x16_t is the user alias for underlying __simd128_uint8_t type. -// http://infocenter.arm.com/help/topic/com.arm.doc.ihi0073a/IHI0073A_arm_neon_intrinsics_ref.pdf -// -// <arm_neon> defines the following -// -// typedef __attribute__((neon_vector_type(16))) uint8_t uint8x16_t; -// typedef __attribute__((neon_vector_type(16))) int8_t int8x16_t; -// typedef __attribute__((neon_polyvector_type(16))) int8_t poly8x16_t; -// -// vld1q_v -// vst1q_v -// vaeseq_v -// vaesmcq_v -#include <arm_neon.h> - -// Already provides operator^=. -using Vector128 = uint8x16_t; - -namespace { - -inline ABSL_TARGET_CRYPTO Vector128 Vector128Load(const void* from) { - return vld1q_u8(reinterpret_cast<const uint8_t*>(from)); -} - -inline ABSL_TARGET_CRYPTO void Vector128Store(const Vector128& v, void* to) { - vst1q_u8(reinterpret_cast<uint8_t*>(to), v); -} - -// One round of AES. "round_key" is a public constant for breaking the -// symmetry of AES (ensures previously equal columns differ afterwards). -inline ABSL_TARGET_CRYPTO Vector128 AesRound(const Vector128& state, - const Vector128& round_key) { - // It is important to always use the full round function - omitting the - // final MixColumns reduces security [https://eprint.iacr.org/2010/041.pdf] - // and does not help because we never decrypt. - // - // Note that ARM divides AES instructions differently than x86 / PPC, - // And we need to skip the first AddRoundKey step and add an extra - // AddRoundKey step to the end. Lucky for us this is just XOR. - return vaesmcq_u8(vaeseq_u8(state, uint8x16_t{})) ^ round_key; -} - -inline ABSL_TARGET_CRYPTO void SwapEndian(void*) {} - -} // namespace - -#elif defined(ABSL_ARCH_X86_64) || defined(ABSL_ARCH_X86_32) -// On x86 we rely on the aesni instructions -#include <wmmintrin.h> - -namespace { - -// Vector128 class is only wrapper for __m128i, benchmark indicates that it's -// faster than using __m128i directly. -class Vector128 { - public: - // Convert from/to intrinsics. - inline explicit Vector128(const __m128i& Vector128) : data_(Vector128) {} - - inline __m128i data() const { return data_; } - - inline Vector128& operator^=(const Vector128& other) { - data_ = _mm_xor_si128(data_, other.data()); - return *this; - } - - private: - __m128i data_; -}; - -inline ABSL_TARGET_CRYPTO Vector128 Vector128Load(const void* from) { - return Vector128(_mm_load_si128(reinterpret_cast<const __m128i*>(from))); -} - -inline ABSL_TARGET_CRYPTO void Vector128Store(const Vector128& v, void* to) { - _mm_store_si128(reinterpret_cast<__m128i*>(to), v.data()); -} - -// One round of AES. "round_key" is a public constant for breaking the -// symmetry of AES (ensures previously equal columns differ afterwards). -inline ABSL_TARGET_CRYPTO Vector128 AesRound(const Vector128& state, - const Vector128& round_key) { - // It is important to always use the full round function - omitting the - // final MixColumns reduces security [https://eprint.iacr.org/2010/041.pdf] - // and does not help because we never decrypt. - return Vector128(_mm_aesenc_si128(state.data(), round_key.data())); -} - -inline ABSL_TARGET_CRYPTO void SwapEndian(void*) {} - -} // namespace - -#endif - -#ifdef __clang__ -#pragma clang diagnostic push -#pragma clang diagnostic ignored "-Wunknown-pragmas" -#endif - -// At this point, all of the platform-specific features have been defined / -// implemented. -// -// REQUIRES: using Vector128 = ... -// REQUIRES: Vector128 Vector128Load(void*) {...} -// REQUIRES: void Vector128Store(Vector128, void*) {...} -// REQUIRES: Vector128 AesRound(Vector128, Vector128) {...} -// REQUIRES: void SwapEndian(uint64_t*) {...} -// -// PROVIDES: absl::random_internal::RandenHwAes::Absorb -// PROVIDES: absl::random_internal::RandenHwAes::Generate -namespace { - -// Block shuffles applies a shuffle to the entire state between AES rounds. -// Improved odd-even shuffle from "New criterion for diffusion property". -inline ABSL_TARGET_CRYPTO void BlockShuffle(u64x2* state) { - static_assert(RandenTraits::kFeistelBlocks == 16, - "Expecting 16 FeistelBlocks."); - - constexpr size_t shuffle[RandenTraits::kFeistelBlocks] = { - 7, 2, 13, 4, 11, 8, 3, 6, 15, 0, 9, 10, 1, 14, 5, 12}; - - const Vector128 v0 = Vector128Load(state + shuffle[0]); - const Vector128 v1 = Vector128Load(state + shuffle[1]); - const Vector128 v2 = Vector128Load(state + shuffle[2]); - const Vector128 v3 = Vector128Load(state + shuffle[3]); - const Vector128 v4 = Vector128Load(state + shuffle[4]); - const Vector128 v5 = Vector128Load(state + shuffle[5]); - const Vector128 v6 = Vector128Load(state + shuffle[6]); - const Vector128 v7 = Vector128Load(state + shuffle[7]); - const Vector128 w0 = Vector128Load(state + shuffle[8]); - const Vector128 w1 = Vector128Load(state + shuffle[9]); - const Vector128 w2 = Vector128Load(state + shuffle[10]); - const Vector128 w3 = Vector128Load(state + shuffle[11]); - const Vector128 w4 = Vector128Load(state + shuffle[12]); - const Vector128 w5 = Vector128Load(state + shuffle[13]); - const Vector128 w6 = Vector128Load(state + shuffle[14]); - const Vector128 w7 = Vector128Load(state + shuffle[15]); - - Vector128Store(v0, state + 0); - Vector128Store(v1, state + 1); - Vector128Store(v2, state + 2); - Vector128Store(v3, state + 3); - Vector128Store(v4, state + 4); - Vector128Store(v5, state + 5); - Vector128Store(v6, state + 6); - Vector128Store(v7, state + 7); - Vector128Store(w0, state + 8); - Vector128Store(w1, state + 9); - Vector128Store(w2, state + 10); - Vector128Store(w3, state + 11); - Vector128Store(w4, state + 12); - Vector128Store(w5, state + 13); - Vector128Store(w6, state + 14); - Vector128Store(w7, state + 15); -} - -// Feistel round function using two AES subrounds. Very similar to F() -// from Simpira v2, but with independent subround keys. Uses 17 AES rounds -// per 16 bytes (vs. 10 for AES-CTR). Computing eight round functions in -// parallel hides the 7-cycle AESNI latency on HSW. Note that the Feistel -// XORs are 'free' (included in the second AES instruction). -inline ABSL_TARGET_CRYPTO const u64x2* FeistelRound( - u64x2* state, const u64x2* ABSL_RANDOM_INTERNAL_RESTRICT keys) { - static_assert(RandenTraits::kFeistelBlocks == 16, - "Expecting 16 FeistelBlocks."); - - // MSVC does a horrible job at unrolling loops. - // So we unroll the loop by hand to improve the performance. - const Vector128 s0 = Vector128Load(state + 0); - const Vector128 s1 = Vector128Load(state + 1); - const Vector128 s2 = Vector128Load(state + 2); - const Vector128 s3 = Vector128Load(state + 3); - const Vector128 s4 = Vector128Load(state + 4); - const Vector128 s5 = Vector128Load(state + 5); - const Vector128 s6 = Vector128Load(state + 6); - const Vector128 s7 = Vector128Load(state + 7); - const Vector128 s8 = Vector128Load(state + 8); - const Vector128 s9 = Vector128Load(state + 9); - const Vector128 s10 = Vector128Load(state + 10); - const Vector128 s11 = Vector128Load(state + 11); - const Vector128 s12 = Vector128Load(state + 12); - const Vector128 s13 = Vector128Load(state + 13); - const Vector128 s14 = Vector128Load(state + 14); - const Vector128 s15 = Vector128Load(state + 15); - - // Encode even blocks with keys. - const Vector128 e0 = AesRound(s0, Vector128Load(keys + 0)); - const Vector128 e2 = AesRound(s2, Vector128Load(keys + 1)); - const Vector128 e4 = AesRound(s4, Vector128Load(keys + 2)); - const Vector128 e6 = AesRound(s6, Vector128Load(keys + 3)); - const Vector128 e8 = AesRound(s8, Vector128Load(keys + 4)); - const Vector128 e10 = AesRound(s10, Vector128Load(keys + 5)); - const Vector128 e12 = AesRound(s12, Vector128Load(keys + 6)); - const Vector128 e14 = AesRound(s14, Vector128Load(keys + 7)); - - // Encode odd blocks with even output from above. - const Vector128 o1 = AesRound(e0, s1); - const Vector128 o3 = AesRound(e2, s3); - const Vector128 o5 = AesRound(e4, s5); - const Vector128 o7 = AesRound(e6, s7); - const Vector128 o9 = AesRound(e8, s9); - const Vector128 o11 = AesRound(e10, s11); - const Vector128 o13 = AesRound(e12, s13); - const Vector128 o15 = AesRound(e14, s15); - - // Store odd blocks. (These will be shuffled later). - Vector128Store(o1, state + 1); - Vector128Store(o3, state + 3); - Vector128Store(o5, state + 5); - Vector128Store(o7, state + 7); - Vector128Store(o9, state + 9); - Vector128Store(o11, state + 11); - Vector128Store(o13, state + 13); - Vector128Store(o15, state + 15); - - return keys + 8; -} - -// Cryptographic permutation based via type-2 Generalized Feistel Network. -// Indistinguishable from ideal by chosen-ciphertext adversaries using less than -// 2^64 queries if the round function is a PRF. This is similar to the b=8 case -// of Simpira v2, but more efficient than its generic construction for b=16. -inline ABSL_TARGET_CRYPTO void Permute( - u64x2* state, const u64x2* ABSL_RANDOM_INTERNAL_RESTRICT keys) { - // (Successfully unrolled; the first iteration jumps into the second half) -#ifdef __clang__ -#pragma clang loop unroll_count(2) -#endif - for (size_t round = 0; round < RandenTraits::kFeistelRounds; ++round) { - keys = FeistelRound(state, keys); - BlockShuffle(state); - } -} - -} // namespace - -namespace absl { -ABSL_NAMESPACE_BEGIN -namespace random_internal { - -bool HasRandenHwAesImplementation() { return true; } - -const void* ABSL_TARGET_CRYPTO RandenHwAes::GetKeys() { - // Round keys for one AES per Feistel round and branch. - // The canonical implementation uses first digits of Pi. -#if defined(ABSL_ARCH_PPC) - return kRandenRoundKeysBE; -#else - return kRandenRoundKeys; -#endif -} - -// NOLINTNEXTLINE -void ABSL_TARGET_CRYPTO RandenHwAes::Absorb(const void* seed_void, - void* state_void) { - static_assert(RandenTraits::kCapacityBytes / sizeof(Vector128) == 1, - "Unexpected Randen kCapacityBlocks"); - static_assert(RandenTraits::kStateBytes / sizeof(Vector128) == 16, - "Unexpected Randen kStateBlocks"); - - auto* state = - reinterpret_cast<u64x2 * ABSL_RANDOM_INTERNAL_RESTRICT>(state_void); - const auto* seed = - reinterpret_cast<const u64x2 * ABSL_RANDOM_INTERNAL_RESTRICT>(seed_void); - - Vector128 b1 = Vector128Load(state + 1); - b1 ^= Vector128Load(seed + 0); - Vector128Store(b1, state + 1); - - Vector128 b2 = Vector128Load(state + 2); - b2 ^= Vector128Load(seed + 1); - Vector128Store(b2, state + 2); - - Vector128 b3 = Vector128Load(state + 3); - b3 ^= Vector128Load(seed + 2); - Vector128Store(b3, state + 3); - - Vector128 b4 = Vector128Load(state + 4); - b4 ^= Vector128Load(seed + 3); - Vector128Store(b4, state + 4); - - Vector128 b5 = Vector128Load(state + 5); - b5 ^= Vector128Load(seed + 4); - Vector128Store(b5, state + 5); - - Vector128 b6 = Vector128Load(state + 6); - b6 ^= Vector128Load(seed + 5); - Vector128Store(b6, state + 6); - - Vector128 b7 = Vector128Load(state + 7); - b7 ^= Vector128Load(seed + 6); - Vector128Store(b7, state + 7); - - Vector128 b8 = Vector128Load(state + 8); - b8 ^= Vector128Load(seed + 7); - Vector128Store(b8, state + 8); - - Vector128 b9 = Vector128Load(state + 9); - b9 ^= Vector128Load(seed + 8); - Vector128Store(b9, state + 9); - - Vector128 b10 = Vector128Load(state + 10); - b10 ^= Vector128Load(seed + 9); - Vector128Store(b10, state + 10); - - Vector128 b11 = Vector128Load(state + 11); - b11 ^= Vector128Load(seed + 10); - Vector128Store(b11, state + 11); - - Vector128 b12 = Vector128Load(state + 12); - b12 ^= Vector128Load(seed + 11); - Vector128Store(b12, state + 12); - - Vector128 b13 = Vector128Load(state + 13); - b13 ^= Vector128Load(seed + 12); - Vector128Store(b13, state + 13); - - Vector128 b14 = Vector128Load(state + 14); - b14 ^= Vector128Load(seed + 13); - Vector128Store(b14, state + 14); - - Vector128 b15 = Vector128Load(state + 15); - b15 ^= Vector128Load(seed + 14); - Vector128Store(b15, state + 15); -} - -// NOLINTNEXTLINE -void ABSL_TARGET_CRYPTO RandenHwAes::Generate(const void* keys_void, - void* state_void) { - static_assert(RandenTraits::kCapacityBytes == sizeof(Vector128), - "Capacity mismatch"); - - auto* state = reinterpret_cast<u64x2*>(state_void); - const auto* keys = reinterpret_cast<const u64x2*>(keys_void); - - const Vector128 prev_inner = Vector128Load(state); - - SwapEndian(state); - - Permute(state, keys); - - SwapEndian(state); - - // Ensure backtracking resistance. - Vector128 inner = Vector128Load(state); - inner ^= prev_inner; - Vector128Store(inner, state); -} - -#ifdef __clang__ -#pragma clang diagnostic pop -#endif - -} // namespace random_internal -ABSL_NAMESPACE_END -} // namespace absl - -#endif // (ABSL_RANDEN_HWAES_IMPL) diff --git a/third_party/abseil_cpp/absl/random/internal/randen_hwaes.h b/third_party/abseil_cpp/absl/random/internal/randen_hwaes.h deleted file mode 100644 index bce36b522609..000000000000 --- a/third_party/abseil_cpp/absl/random/internal/randen_hwaes.h +++ /dev/null @@ -1,50 +0,0 @@ -// Copyright 2017 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#ifndef ABSL_RANDOM_INTERNAL_RANDEN_HWAES_H_ -#define ABSL_RANDOM_INTERNAL_RANDEN_HWAES_H_ - -#include "absl/base/config.h" - -// HERMETIC NOTE: The randen_hwaes target must not introduce duplicate -// symbols from arbitrary system and other headers, since it may be built -// with different flags from other targets, using different levels of -// optimization, potentially introducing ODR violations. - -namespace absl { -ABSL_NAMESPACE_BEGIN -namespace random_internal { - -// RANDen = RANDom generator or beetroots in Swiss German. -// 'Strong' (well-distributed, unpredictable, backtracking-resistant) random -// generator, faster in some benchmarks than std::mt19937_64 and pcg64_c32. -// -// RandenHwAes implements the basic state manipulation methods. -class RandenHwAes { - public: - static void Generate(const void* keys, void* state_void); - static void Absorb(const void* seed_void, void* state_void); - static const void* GetKeys(); -}; - -// HasRandenHwAesImplementation returns true when there is an accelerated -// implementation, and false otherwise. If there is no implementation, -// then attempting to use it will abort the program. -bool HasRandenHwAesImplementation(); - -} // namespace random_internal -ABSL_NAMESPACE_END -} // namespace absl - -#endif // ABSL_RANDOM_INTERNAL_RANDEN_HWAES_H_ diff --git a/third_party/abseil_cpp/absl/random/internal/randen_hwaes_test.cc b/third_party/abseil_cpp/absl/random/internal/randen_hwaes_test.cc deleted file mode 100644 index 66ddb43fd6a7..000000000000 --- a/third_party/abseil_cpp/absl/random/internal/randen_hwaes_test.cc +++ /dev/null @@ -1,104 +0,0 @@ -// Copyright 2017 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#include "absl/random/internal/randen_hwaes.h" - -#include "gmock/gmock.h" -#include "gtest/gtest.h" -#include "absl/base/internal/raw_logging.h" -#include "absl/random/internal/platform.h" -#include "absl/random/internal/randen_detect.h" -#include "absl/random/internal/randen_traits.h" -#include "absl/strings/str_format.h" - -namespace { - -using absl::random_internal::RandenHwAes; -using absl::random_internal::RandenTraits; - -// Local state parameters. -constexpr size_t kSeedBytes = - RandenTraits::kStateBytes - RandenTraits::kCapacityBytes; -constexpr size_t kStateSizeT = RandenTraits::kStateBytes / sizeof(uint64_t); -constexpr size_t kSeedSizeT = kSeedBytes / sizeof(uint32_t); - -struct alignas(16) randen { - uint64_t state[kStateSizeT]; - uint32_t seed[kSeedSizeT]; -}; - -TEST(RandenHwAesTest, Default) { - EXPECT_TRUE(absl::random_internal::CPUSupportsRandenHwAes()); - - constexpr uint64_t kGolden[] = { - 0x6c6534090ee6d3ee, 0x044e2b9b9d5333c6, 0xc3c14f134e433977, - 0xdda9f47cd90410ee, 0x887bf3087fd8ca10, 0xf0b780f545c72912, - 0x15dbb1d37696599f, 0x30ec63baff3c6d59, 0xb29f73606f7f20a6, - 0x02808a316f49a54c, 0x3b8feaf9d5c8e50e, 0x9cbf605e3fd9de8a, - 0xc970ae1a78183bbb, 0xd8b2ffd356301ed5, 0xf4b327fe0fc73c37, - 0xcdfd8d76eb8f9a19, 0xc3a506eb91420c9d, 0xd5af05dd3eff9556, - 0x48db1bb78f83c4a1, 0x7023920e0d6bfe8c, 0x58d3575834956d42, - 0xed1ef4c26b87b840, 0x8eef32a23e0b2df3, 0x497cabf3431154fc, - 0x4e24370570029a8b, 0xd88b5749f090e5ea, 0xc651a582a970692f, - 0x78fcec2cbb6342f5, 0x463cb745612f55db, 0x352ee4ad1816afe3, - 0x026ff374c101da7e, 0x811ef0821c3de851, - }; - - alignas(16) randen d; - memset(d.state, 0, sizeof(d.state)); - RandenHwAes::Generate(RandenHwAes::GetKeys(), d.state); - - uint64_t* id = d.state; - for (const auto& elem : kGolden) { - auto a = absl::StrFormat("%#x", elem); - auto b = absl::StrFormat("%#x", *id++); - EXPECT_EQ(a, b); - } -} - -} // namespace - -int main(int argc, char* argv[]) { - testing::InitGoogleTest(&argc, argv); - - ABSL_RAW_LOG(INFO, "ABSL_HAVE_ACCELERATED_AES=%d", ABSL_HAVE_ACCELERATED_AES); - ABSL_RAW_LOG(INFO, "ABSL_RANDOM_INTERNAL_AES_DISPATCH=%d", - ABSL_RANDOM_INTERNAL_AES_DISPATCH); - -#if defined(ABSL_ARCH_X86_64) - ABSL_RAW_LOG(INFO, "ABSL_ARCH_X86_64"); -#elif defined(ABSL_ARCH_X86_32) - ABSL_RAW_LOG(INFO, "ABSL_ARCH_X86_32"); -#elif defined(ABSL_ARCH_AARCH64) - ABSL_RAW_LOG(INFO, "ABSL_ARCH_AARCH64"); -#elif defined(ABSL_ARCH_ARM) - ABSL_RAW_LOG(INFO, "ABSL_ARCH_ARM"); -#elif defined(ABSL_ARCH_PPC) - ABSL_RAW_LOG(INFO, "ABSL_ARCH_PPC"); -#else - ABSL_RAW_LOG(INFO, "ARCH Unknown"); -#endif - - int x = absl::random_internal::HasRandenHwAesImplementation(); - ABSL_RAW_LOG(INFO, "HasRandenHwAesImplementation = %d", x); - - int y = absl::random_internal::CPUSupportsRandenHwAes(); - ABSL_RAW_LOG(INFO, "CPUSupportsRandenHwAes = %d", x); - - if (!x || !y) { - ABSL_RAW_LOG(INFO, "Skipping Randen HWAES tests."); - return 0; - } - return RUN_ALL_TESTS(); -} diff --git a/third_party/abseil_cpp/absl/random/internal/randen_round_keys.cc b/third_party/abseil_cpp/absl/random/internal/randen_round_keys.cc deleted file mode 100644 index 5fb3ca556db6..000000000000 --- a/third_party/abseil_cpp/absl/random/internal/randen_round_keys.cc +++ /dev/null @@ -1,462 +0,0 @@ -// Copyright 2017 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#include "absl/random/internal/randen_traits.h" - -// This file contains only the round keys for randen. -// -// "Nothing up my sleeve" numbers from the first hex digits of Pi, obtained -// from http://hexpi.sourceforge.net/. The array was generated by following -// Python script: - -/* -python >tmp.cc << EOF -"""Generates Randen round keys array from pi-hex.62500.txt file.""" -import binascii - -KEYS = 17 * 8 - -def chunks(l, n): - """Yield successive n-sized chunks from l.""" - for i in range(0, len(l), n): - yield l[i:i + n] - -def pairwise(t): - """Transforms sequence into sequence of pairs.""" - it = iter(t) - return zip(it,it) - -def digits_from_pi(): - """Reads digits from hexpi.sourceforge.net file.""" - with open("pi-hex.62500.txt") as file: - return file.read() - -def digits_from_urandom(): - """Reads digits from /dev/urandom.""" - with open("/dev/urandom") as file: - return binascii.hexlify(file.read(KEYS * 16)) - -def print_row(b) - print(" 0x{0}, 0x{1}, 0x{2}, 0x{3}, 0x{4}, 0x{5}, 0x{6}, 0x{7}, 0x{8}, 0x{9}, -0x{10}, 0x{11}, 0x{12}, 0x{13}, 0x{14}, 0x{15},".format(*b)) - - -digits = digits_from_pi() -#digits = digits_from_urandom() - -print("namespace {") -print("static constexpr size_t kKeyBytes = {0};\n".format(KEYS * 16)) -print("}") - -print("alignas(16) const unsigned char kRandenRoundKeysBE[kKeyBytes] = {") - -for i, u16 in zip(range(KEYS), chunks(digits, 32)): - b = list(chunks(u16, 2)) - print_row(b) - -print("};") - -print("alignas(16) const unsigned char kRandenRoundKeys[kKeyBytes] = {") - -for i, u16 in zip(range(KEYS), chunks(digits, 32)): - b = list(chunks(u16, 2)) - b.reverse() - print_row(b) - -print("};") - -EOF - -*/ - -namespace absl { -ABSL_NAMESPACE_BEGIN -namespace random_internal { -namespace { -static constexpr size_t kKeyBytes = 2176; -} - -alignas(16) const unsigned char kRandenRoundKeysBE[kKeyBytes] = { - 0x24, 0x3F, 0x6A, 0x88, 0x85, 0xA3, 0x08, 0xD3, 0x13, 0x19, 0x8A, 0x2E, - 0x03, 0x70, 0x73, 0x44, 0xA4, 0x09, 0x38, 0x22, 0x29, 0x9F, 0x31, 0xD0, - 0x08, 0x2E, 0xFA, 0x98, 0xEC, 0x4E, 0x6C, 0x89, 0x45, 0x28, 0x21, 0xE6, - 0x38, 0xD0, 0x13, 0x77, 0xBE, 0x54, 0x66, 0xCF, 0x34, 0xE9, 0x0C, 0x6C, - 0xC0, 0xAC, 0x29, 0xB7, 0xC9, 0x7C, 0x50, 0xDD, 0x3F, 0x84, 0xD5, 0xB5, - 0xB5, 0x47, 0x09, 0x17, 0x92, 0x16, 0xD5, 0xD9, 0x89, 0x79, 0xFB, 0x1B, - 0xD1, 0x31, 0x0B, 0xA6, 0x98, 0xDF, 0xB5, 0xAC, 0x2F, 0xFD, 0x72, 0xDB, - 0xD0, 0x1A, 0xDF, 0xB7, 0xB8, 0xE1, 0xAF, 0xED, 0x6A, 0x26, 0x7E, 0x96, - 0xBA, 0x7C, 0x90, 0x45, 0xF1, 0x2C, 0x7F, 0x99, 0x24, 0xA1, 0x99, 0x47, - 0xB3, 0x91, 0x6C, 0xF7, 0x08, 0x01, 0xF2, 0xE2, 0x85, 0x8E, 0xFC, 0x16, - 0x63, 0x69, 0x20, 0xD8, 0x71, 0x57, 0x4E, 0x69, 0xA4, 0x58, 0xFE, 0xA3, - 0xF4, 0x93, 0x3D, 0x7E, 0x0D, 0x95, 0x74, 0x8F, 0x72, 0x8E, 0xB6, 0x58, - 0x71, 0x8B, 0xCD, 0x58, 0x82, 0x15, 0x4A, 0xEE, 0x7B, 0x54, 0xA4, 0x1D, - 0xC2, 0x5A, 0x59, 0xB5, 0x9C, 0x30, 0xD5, 0x39, 0x2A, 0xF2, 0x60, 0x13, - 0xC5, 0xD1, 0xB0, 0x23, 0x28, 0x60, 0x85, 0xF0, 0xCA, 0x41, 0x79, 0x18, - 0xB8, 0xDB, 0x38, 0xEF, 0x8E, 0x79, 0xDC, 0xB0, 0x60, 0x3A, 0x18, 0x0E, - 0x6C, 0x9E, 0x0E, 0x8B, 0xB0, 0x1E, 0x8A, 0x3E, 0xD7, 0x15, 0x77, 0xC1, - 0xBD, 0x31, 0x4B, 0x27, 0x78, 0xAF, 0x2F, 0xDA, 0x55, 0x60, 0x5C, 0x60, - 0xE6, 0x55, 0x25, 0xF3, 0xAA, 0x55, 0xAB, 0x94, 0x57, 0x48, 0x98, 0x62, - 0x63, 0xE8, 0x14, 0x40, 0x55, 0xCA, 0x39, 0x6A, 0x2A, 0xAB, 0x10, 0xB6, - 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0x9A, 0x53, 0xE4, 0x79, 0xB6, 0xF8, 0x45, 0x65, 0xD2, 0x8E, 0x49, 0xBC, - 0x4B, 0xFB, 0x97, 0x90, 0xE1, 0xDD, 0xF2, 0xDA, 0xA4, 0xCB, 0x7E, 0x33, - 0x62, 0xFB, 0x13, 0x41, 0xCE, 0xE4, 0xC6, 0xE8, 0xEF, 0x20, 0xCA, 0xDA, - 0x36, 0x77, 0x4C, 0x01, 0xD0, 0x7E, 0x9E, 0xFE, 0x2B, 0xF1, 0x1F, 0xB4, - 0x95, 0xDB, 0xDA, 0x4D, 0xAE, 0x90, 0x91, 0x98, 0xEA, 0xAD, 0x8E, 0x71, - 0x6B, 0x93, 0xD5, 0xA0, 0xD0, 0x8E, 0xD1, 0xD0, 0xAF, 0xC7, 0x25, 0xE0, - 0x8E, 0x3C, 0x5B, 0x2F, 0x8E, 0x75, 0x94, 0xB7, 0x8F, 0xF6, 0xE2, 0xFB, - 0xF2, 0x12, 0x2B, 0x64, 0x88, 0x88, 0xB8, 0x12, 0x90, 0x0D, 0xF0, 0x1C, - 0x4F, 0xAD, 0x5E, 0xA0, 0x68, 0x8F, 0xC3, 0x1C, 0xD1, 0xCF, 0xF1, 0x91, - 0xB3, 0xA8, 0xC1, 0xAD, 0x2F, 0x2F, 0x22, 0x18, 0xBE, 0x0E, 0x17, 0x77, - 0xEA, 0x75, 0x2D, 0xFE, 0x8B, 0x02, 0x1F, 0xA1, 0xE5, 0xA0, 0xCC, 0x0F, - 0xB5, 0x6F, 0x74, 0xE8, 0x18, 0xAC, 0xF3, 0xD6, 0xCE, 0x89, 0xE2, 0x99, - 0xB4, 0xA8, 0x4F, 0xE0, 0xFD, 0x13, 0xE0, 0xB7, 0x7C, 0xC4, 0x3B, 0x81, - 0xD2, 0xAD, 0xA8, 0xD9, 0x16, 0x5F, 0xA2, 0x66, 0x80, 0x95, 0x77, 0x05, - 0x93, 0xCC, 0x73, 0x14, 0x21, 0x1A, 0x14, 0x77, 0xE6, 0xAD, 0x20, 0x65, - 0x77, 0xB5, 0xFA, 0x86, 0xC7, 0x54, 0x42, 0xF5, 0xFB, 0x9D, 0x35, 0xCF, - 0xEB, 0xCD, 0xAF, 0x0C, 0x7B, 0x3E, 0x89, 0xA0, 0xD6, 0x41, 0x1B, 0xD3, - 0xAE, 0x1E, 0x7E, 0x49, 0x00, 0x25, 0x0E, 0x2D, 0x20, 0x71, 0xB3, 0x5E, - 0x22, 0x68, 0x00, 0xBB, 0x57, 0xB8, 0xE0, 0xAF, 0x24, 0x64, 0x36, 0x9B, - 0xF0, 0x09, 0xB9, 0x1E, 0x55, 0x63, 0x91, 0x1D, 0x59, 0xDF, 0xA6, 0xAA, - 0x78, 0xC1, 0x43, 0x89, 0xD9, 0x5A, 0x53, 0x7F, 0x20, 0x7D, 0x5B, 0xA2, - 0x02, 0xE5, 0xB9, 0xC5, 0x83, 0x26, 0x03, 0x76, 0x62, 0x95, 0xCF, 0xA9, - 0x11, 0xC8, 0x19, 0x68, 0x4E, 0x73, 0x4A, 0x41, 0xB3, 0x47, 0x2D, 0xCA, - 0x7B, 0x14, 0xA9, 0x4A, 0x1B, 0x51, 0x00, 0x52, 0x9A, 0x53, 0x29, 0x15, - 0xD6, 0x0F, 0x57, 0x3F, 0xBC, 0x9B, 0xC6, 0xE4, 0x2B, 0x60, 0xA4, 0x76, - 0x81, 0xE6, 0x74, 0x00, 0x08, 0xBA, 0x6F, 0xB5, 0x57, 0x1B, 0xE9, 0x1F, - 0xF2, 0x96, 0xEC, 0x6B, 0x2A, 0x0D, 0xD9, 0x15, 0xB6, 0x63, 0x65, 0x21, - 0xE7, 0xB9, 0xF9, 0xB6, 0xFF, 0x34, 0x05, 0x2E, 0xC5, 0x85, 0x56, 0x64, - 0x53, 0xB0, 0x2D, 0x5D, 0xA9, 0x9F, 0x8F, 0xA1, 0x08, 0xBA, 0x47, 0x99, - 0x6E, 0x85, 0x07, 0x6A, 0x4B, 0x7A, 0x70, 0xE9, 0xB5, 0xB3, 0x29, 0x44, - 0xDB, 0x75, 0x09, 0x2E, 0xC4, 0x19, 0x26, 0x23, 0xAD, 0x6E, 0xA6, 0xB0, - 0x49, 0xA7, 0xDF, 0x7D, 0x9C, 0xEE, 0x60, 0xB8, 0x8F, 0xED, 0xB2, 0x66, - 0xEC, 0xAA, 0x8C, 0x71, 0x69, 0x9A, 0x18, 0xFF, 0x56, 0x64, 0x52, 0x6C, - 0xC2, 0xB1, 0x9E, 0xE1, 0x19, 0x36, 0x02, 0xA5, 0x75, 0x09, 0x4C, 0x29, - 0xA0, 0x59, 0x13, 0x40, 0xE4, 0x18, 0x3A, 0x3E, 0x3F, 0x54, 0x98, 0x9A, - 0x5B, 0x42, 0x9D, 0x65, 0x6B, 0x8F, 0xE4, 0xD6, 0x99, 0xF7, 0x3F, 0xD6, - 0xA1, 0xD2, 0x9C, 0x07, 0xEF, 0xE8, 0x30, 0xF5, 0x4D, 0x2D, 0x38, 0xE6, - 0xF0, 0x25, 0x5D, 0xC1, 0x4C, 0xDD, 0x20, 0x86, 0x84, 0x70, 0xEB, 0x26, - 0x63, 0x82, 0xE9, 0xC6, 0x02, 0x1E, 0xCC, 0x5E, 0x09, 0x68, 0x6B, 0x3F, - 0x3E, 0xBA, 0xEF, 0xC9, 0x3C, 0x97, 0x18, 0x14, 0x6B, 0x6A, 0x70, 0xA1, - 0x68, 0x7F, 0x35, 0x84, 0x52, 0xA0, 0xE2, 0x86, 0xB7, 0x9C, 0x53, 0x05, - 0xAA, 0x50, 0x07, 0x37, 0x3E, 0x07, 0x84, 0x1C, 0x7F, 0xDE, 0xAE, 0x5C, - 0x8E, 0x7D, 0x44, 0xEC, 0x57, 0x16, 0xF2, 0xB8, 0xB0, 0x3A, 0xDA, 0x37, - 0xF0, 0x50, 0x0C, 0x0D, 0xF0, 0x1C, 0x1F, 0x04, 0x02, 0x00, 0xB3, 0xFF, - 0xAE, 0x0C, 0xF5, 0x1A, 0x3C, 0xB5, 0x74, 0xB2, 0x25, 0x83, 0x7A, 0x58, - 0xDC, 0x09, 0x21, 0xBD, 0xD1, 0x91, 0x13, 0xF9, 0x7C, 0xA9, 0x2F, 0xF6, - 0x94, 0x32, 0x47, 0x73, 0x22, 0xF5, 0x47, 0x01, 0x3A, 0xE5, 0xE5, 0x81, - 0x37, 0xC2, 0xDA, 0xDC, 0xC8, 0xB5, 0x76, 0x34, 0x9A, 0xF3, 0xDD, 0xA7, - 0xA9, 0x44, 0x61, 0x46, 0x0F, 0xD0, 0x03, 0x0E, 0xEC, 0xC8, 0xC7, 0x3E, - 0xA4, 0x75, 0x1E, 0x41, 0xE2, 0x38, 0xCD, 0x99, 0x3B, 0xEA, 0x0E, 0x2F, - 0x32, 0x80, 0xBB, 0xA1, 0x18, 0x3E, 0xB3, 0x31, 0x4E, 0x54, 0x8B, 0x38, - 0x4F, 0x6D, 0xB9, 0x08, 0x6F, 0x42, 0x0D, 0x03, 0xF6, 0x0A, 0x04, 0xBF, - 0x2C, 0xB8, 0x12, 0x90, 0x24, 0x97, 0x7C, 0x79, 0x56, 0x79, 0xB0, 0x72, - 0xBC, 0xAF, 0x89, 0xAF, 0xDE, 0x9A, 0x77, 0x1F, 0xD9, 0x93, 0x08, 0x10, - 0xB3, 0x8B, 0xAE, 0x12, 0xDC, 0xCF, 0x3F, 0x2E, 0x55, 0x12, 0x72, 0x1F, - 0x2E, 0x6B, 0x71, 0x24, 0x50, 0x1A, 0xDD, 0xE6, 0x9F, 0x84, 0xCD, 0x87, - 0x7A, 0x58, 0x47, 0x18, 0x74, 0x08, 0xDA, 0x17, 0xBC, 0x9F, 0x9A, 0xBC, - 0xE9, 0x4B, 0x7D, 0x8C, 0xEC, 0x7A, 0xEC, 0x3A, 0xDB, 0x85, 0x1D, 0xFA, - 0x63, 0x09, 0x43, 0x66, 0xC4, 0x64, 0xC3, 0xD2, 0xEF, 0x1C, 0x18, 0x47, - 0x32, 0x15, 0xD8, 0x08, 0xDD, 0x43, 0x3B, 0x37, 0x24, 0xC2, 0xBA, 0x16, - 0x12, 0xA1, 0x4D, 0x43, 0x2A, 0x65, 0xC4, 0x51, 0x50, 0x94, 0x00, 0x02, - 0x13, 0x3A, 0xE4, 0xDD, 0x71, 0xDF, 0xF8, 0x9E, 0x10, 0x31, 0x4E, 0x55, - 0x81, 0xAC, 0x77, 0xD6, 0x5F, 0x11, 0x19, 0x9B, 0x04, 0x35, 0x56, 0xF1, - 0xD7, 0xA3, 0xC7, 0x6B, 0x3C, 0x11, 0x18, 0x3B, 0x59, 0x24, 0xA5, 0x09, - 0xF2, 0x8F, 0xE6, 0xED, 0x97, 0xF1, 0xFB, 0xFA, 0x9E, 0xBA, 0xBF, 0x2C, - 0x1E, 0x15, 0x3C, 0x6E, 0x86, 0xE3, 0x45, 0x70, 0xEA, 0xE9, 0x6F, 0xB1, - 0x86, 0x0E, 0x5E, 0x0A, 0x5A, 0x3E, 0x2A, 0xB3, 0x77, 0x1F, 0xE7, 0x1C, - 0x4E, 0x3D, 0x06, 0xFA, 0x29, 0x65, 0xDC, 0xB9, 0x99, 0xE7, 0x1D, 0x0F, - 0x80, 0x3E, 0x89, 0xD6, 0x52, 0x66, 0xC8, 0x25, 0x2E, 0x4C, 0xC9, 0x78, - 0x9C, 0x10, 0xB3, 0x6A, 0xC6, 0x15, 0x0E, 0xBA, 0x94, 0xE2, 0xEA, 0x78, - 0xA6, 0xFC, 0x3C, 0x53, 0x1E, 0x0A, 0x2D, 0xF4, 0xF2, 0xF7, 0x4E, 0xA7, - 0x36, 0x1D, 0x2B, 0x3D, 0x19, 0x39, 0x26, 0x0F, 0x19, 0xC2, 0x79, 0x60, - 0x52, 0x23, 0xA7, 0x08, 0xF7, 0x13, 0x12, 0xB6, 0xEB, 0xAD, 0xFE, 0x6E, - 0xEA, 0xC3, 0x1F, 0x66, 0xE3, 0xBC, 0x45, 0x95, 0xA6, 0x7B, 0xC8, 0x83, - 0xB1, 0x7F, 0x37, 0xD1, 0x01, 0x8C, 0xFF, 0x28, 0xC3, 0x32, 0xDD, 0xEF, - 0xBE, 0x6C, 0x5A, 0xA5, 0x65, 0x58, 0x21, 0x85, 0x68, 0xAB, 0x97, 0x02, - 0xEE, 0xCE, 0xA5, 0x0F, 0xDB, 0x2F, 0x95, 0x3B, 0x2A, 0xEF, 0x7D, 0xAD, - 0x5B, 0x6E, 0x2F, 0x84, 0x15, 0x21, 0xB6, 0x28, 0x29, 0x07, 0x61, 0x70, - 0xEC, 0xDD, 0x47, 0x75, 0x61, 0x9F, 0x15, 0x10, 0x13, 0xCC, 0xA8, 0x30, - 0xEB, 0x61, 0xBD, 0x96, 0x03, 0x34, 0xFE, 0x1E, 0xAA, 0x03, 0x63, 0xCF, - 0xB5, 0x73, 0x5C, 0x90, 0x4C, 0x70, 0xA2, 0x39, 0xD5, 0x9E, 0x9E, 0x0B, - 0xCB, 0xAA, 0xDE, 0x14, 0xEE, 0xCC, 0x86, 0xBC, 0x60, 0x62, 0x2C, 0xA7, - 0x9C, 0xAB, 0x5C, 0xAB, 0xB2, 0xF3, 0x84, 0x6E, 0x64, 0x8B, 0x1E, 0xAF, - 0x19, 0xBD, 0xF0, 0xCA, 0xA0, 0x23, 0x69, 0xB9, 0x65, 0x5A, 0xBB, 0x50, - 0x40, 0x68, 0x5A, 0x32, 0x3C, 0x2A, 0xB4, 0xB3, 0x31, 0x9E, 0xE9, 0xD5, - 0xC0, 0x21, 0xB8, 0xF7, 0x9B, 0x54, 0x0B, 0x19, 0x87, 0x5F, 0xA0, 0x99, - 0x95, 0xF7, 0x99, 0x7E, 0x62, 0x3D, 0x7D, 0xA8, 0xF8, 0x37, 0x88, 0x9A, - 0x97, 0xE3, 0x2D, 0x77, 0x11, 0xED, 0x93, 0x5F, 0x16, 0x68, 0x12, 0x81, - 0x0E, 0x35, 0x88, 0x29, 0xC7, 0xE6, 0x1F, 0xD6, 0x96, 0xDE, 0xDF, 0xA1, - 0x78, 0x58, 0xBA, 0x99, 0x57, 0xF5, 0x84, 0xA5, 0x1B, 0x22, 0x72, 0x63, - 0x9B, 0x83, 0xC3, 0xFF, 0x1A, 0xC2, 0x46, 0x96, 0xCD, 0xB3, 0x0A, 0xEB, - 0x53, 0x2E, 0x30, 0x54, 0x8F, 0xD9, 0x48, 0xE4, 0x6D, 0xBC, 0x31, 0x28, - 0x58, 0xEB, 0xF2, 0xEF, 0x34, 0xC6, 0xFF, 0xEA, 0xFE, 0x28, 0xED, 0x61, - 0xEE, 0x7C, 0x3C, 0x73, 0x5D, 0x4A, 0x14, 0xD9, 0xE8, 0x64, 0xB7, 0xE3, - 0x42, 0x10, 0x5D, 0x14, 0x20, 0x3E, 0x13, 0xE0, 0x45, 0xEE, 0xE2, 0xB6, - 0xA3, 0xAA, 0xAB, 0xEA, 0xDB, 0x6C, 0x4F, 0x15, 0xFA, 0xCB, 0x4F, 0xD0, - 0xC7, 0x42, 0xF4, 0x42, 0xEF, 0x6A, 0xBB, 0xB5, 0x65, 0x4F, 0x3B, 0x1D, - 0x41, 0xCD, 0x21, 0x05, 0xD8, 0x1E, 0x79, 0x9E, 0x86, 0x85, 0x4D, 0xC7, - 0xE4, 0x4B, 0x47, 0x6A, 0x3D, 0x81, 0x62, 0x50, 0xCF, 0x62, 0xA1, 0xF2, - 0x5B, 0x8D, 0x26, 0x46, 0xFC, 0x88, 0x83, 0xA0, 0xC1, 0xC7, 0xB6, 0xA3, - 0x7F, 0x15, 0x24, 0xC3, 0x69, 0xCB, 0x74, 0x92, 0x47, 0x84, 0x8A, 0x0B, - 0x56, 0x92, 0xB2, 0x85, 0x09, 0x5B, 0xBF, 0x00, 0xAD, 0x19, 0x48, 0x9D, - 0x14, 0x62, 0xB1, 0x74, 0x23, 0x82, 0x0D, 0x00, 0x58, 0x42, 0x8D, 0x2A, - 0x0C, 0x55, 0xF5, 0xEA, 0x1D, 0xAD, 0xF4, 0x3E, 0x23, 0x3F, 0x70, 0x61, - 0x33, 0x72, 0xF0, 0x92, 0x8D, 0x93, 0x7E, 0x41, 0xD6, 0x5F, 0xEC, 0xF1, - 0x6C, 0x22, 0x3B, 0xDB, 0x7C, 0xDE, 0x37, 0x59, 0xCB, 0xEE, 0x74, 0x60, - 0x40, 0x85, 0xF2, 0xA7, 0xCE, 0x77, 0x32, 0x6E, 0xA6, 0x07, 0x80, 0x84, - 0x19, 0xF8, 0x50, 0x9E, 0xE8, 0xEF, 0xD8, 0x55, 0x61, 0xD9, 0x97, 0x35, - 0xA9, 0x69, 0xA7, 0xAA, 0xC5, 0x0C, 0x06, 0xC2, 0x5A, 0x04, 0xAB, 0xFC, - 0x80, 0x0B, 0xCA, 0xDC, 0x9E, 0x44, 0x7A, 0x2E, 0xC3, 0x45, 0x34, 0x84, - 0xFD, 0xD5, 0x67, 0x05, 0x0E, 0x1E, 0x9E, 0xC9, 0xDB, 0x73, 0xDB, 0xD3, - 0x10, 0x55, 0x88, 0xCD, 0x67, 0x5F, 0xDA, 0x79, 0xE3, 0x67, 0x43, 0x40, - 0xC5, 0xC4, 0x34, 0x65, 0x71, 0x3E, 0x38, 0xD8, 0x3D, 0x28, 0xF8, 0x9E, - 0xF1, 0x6D, 0xFF, 0x20, 0x15, 0x3E, 0x21, 0xE7, 0x8F, 0xB0, 0x3D, 0x4A, - 0xE6, 0xE3, 0x9F, 0x2B, 0xDB, 0x83, 0xAD, 0xF7, 0xE9, 0x3D, 0x5A, 0x68, - 0x94, 0x81, 0x40, 0xF7, 0xF6, 0x4C, 0x26, 0x1C, 0x94, 0x69, 0x29, 0x34, - 0x41, 0x15, 0x20, 0xF7, 0x76, 0x02, 0xD4, 0xF7, 0xBC, 0xF4, 0x6B, 0x2E, - 0xD4, 0xA1, 0x00, 0x68, 0xD4, 0x08, 0x24, 0x71, 0x33, 0x20, 0xF4, 0x6A, - 0x43, 0xB7, 0xD4, 0xB7, 0x50, 0x00, 0x61, 0xAF, 0x1E, 0x39, 0xF6, 0x2E, - 0x97, 0x24, 0x45, 0x46, -}; - -alignas(16) const unsigned char kRandenRoundKeys[kKeyBytes] = { - 0x44, 0x73, 0x70, 0x03, 0x2E, 0x8A, 0x19, 0x13, 0xD3, 0x08, 0xA3, 0x85, - 0x88, 0x6A, 0x3F, 0x24, 0x89, 0x6C, 0x4E, 0xEC, 0x98, 0xFA, 0x2E, 0x08, - 0xD0, 0x31, 0x9F, 0x29, 0x22, 0x38, 0x09, 0xA4, 0x6C, 0x0C, 0xE9, 0x34, - 0xCF, 0x66, 0x54, 0xBE, 0x77, 0x13, 0xD0, 0x38, 0xE6, 0x21, 0x28, 0x45, - 0x17, 0x09, 0x47, 0xB5, 0xB5, 0xD5, 0x84, 0x3F, 0xDD, 0x50, 0x7C, 0xC9, - 0xB7, 0x29, 0xAC, 0xC0, 0xAC, 0xB5, 0xDF, 0x98, 0xA6, 0x0B, 0x31, 0xD1, - 0x1B, 0xFB, 0x79, 0x89, 0xD9, 0xD5, 0x16, 0x92, 0x96, 0x7E, 0x26, 0x6A, - 0xED, 0xAF, 0xE1, 0xB8, 0xB7, 0xDF, 0x1A, 0xD0, 0xDB, 0x72, 0xFD, 0x2F, - 0xF7, 0x6C, 0x91, 0xB3, 0x47, 0x99, 0xA1, 0x24, 0x99, 0x7F, 0x2C, 0xF1, - 0x45, 0x90, 0x7C, 0xBA, 0x69, 0x4E, 0x57, 0x71, 0xD8, 0x20, 0x69, 0x63, - 0x16, 0xFC, 0x8E, 0x85, 0xE2, 0xF2, 0x01, 0x08, 0x58, 0xB6, 0x8E, 0x72, - 0x8F, 0x74, 0x95, 0x0D, 0x7E, 0x3D, 0x93, 0xF4, 0xA3, 0xFE, 0x58, 0xA4, - 0xB5, 0x59, 0x5A, 0xC2, 0x1D, 0xA4, 0x54, 0x7B, 0xEE, 0x4A, 0x15, 0x82, - 0x58, 0xCD, 0x8B, 0x71, 0xF0, 0x85, 0x60, 0x28, 0x23, 0xB0, 0xD1, 0xC5, - 0x13, 0x60, 0xF2, 0x2A, 0x39, 0xD5, 0x30, 0x9C, 0x0E, 0x18, 0x3A, 0x60, - 0xB0, 0xDC, 0x79, 0x8E, 0xEF, 0x38, 0xDB, 0xB8, 0x18, 0x79, 0x41, 0xCA, - 0x27, 0x4B, 0x31, 0xBD, 0xC1, 0x77, 0x15, 0xD7, 0x3E, 0x8A, 0x1E, 0xB0, - 0x8B, 0x0E, 0x9E, 0x6C, 0x94, 0xAB, 0x55, 0xAA, 0xF3, 0x25, 0x55, 0xE6, - 0x60, 0x5C, 0x60, 0x55, 0xDA, 0x2F, 0xAF, 0x78, 0xB6, 0x10, 0xAB, 0x2A, - 0x6A, 0x39, 0xCA, 0x55, 0x40, 0x14, 0xE8, 0x63, 0x62, 0x98, 0x48, 0x57, - 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0x40, 0x43, 0x67, 0xE3, 0x79, 0xDA, 0x5F, 0x67, 0x4A, 0x3D, 0xB0, 0x8F, - 0xE7, 0x21, 0x3E, 0x15, 0x20, 0xFF, 0x6D, 0xF1, 0x9E, 0xF8, 0x28, 0x3D, - 0xF7, 0x40, 0x81, 0x94, 0x68, 0x5A, 0x3D, 0xE9, 0xF7, 0xAD, 0x83, 0xDB, - 0x2B, 0x9F, 0xE3, 0xE6, 0xF7, 0xD4, 0x02, 0x76, 0xF7, 0x20, 0x15, 0x41, - 0x34, 0x29, 0x69, 0x94, 0x1C, 0x26, 0x4C, 0xF6, 0x6A, 0xF4, 0x20, 0x33, - 0x71, 0x24, 0x08, 0xD4, 0x68, 0x00, 0xA1, 0xD4, 0x2E, 0x6B, 0xF4, 0xBC, - 0x46, 0x45, 0x24, 0x97, 0x2E, 0xF6, 0x39, 0x1E, 0xAF, 0x61, 0x00, 0x50, - 0xB7, 0xD4, 0xB7, 0x43, -}; - -} // namespace random_internal -ABSL_NAMESPACE_END -} // namespace absl diff --git a/third_party/abseil_cpp/absl/random/internal/randen_slow.cc b/third_party/abseil_cpp/absl/random/internal/randen_slow.cc deleted file mode 100644 index 4e5f3dc1c7b7..000000000000 --- a/third_party/abseil_cpp/absl/random/internal/randen_slow.cc +++ /dev/null @@ -1,457 +0,0 @@ -// Copyright 2017 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#include "absl/random/internal/randen_slow.h" - -#include <cstddef> -#include <cstdint> -#include <cstring> - -#include "absl/base/attributes.h" -#include "absl/random/internal/platform.h" -#include "absl/random/internal/randen_traits.h" - -#if ABSL_HAVE_ATTRIBUTE(always_inline) || \ - (defined(__GNUC__) && !defined(__clang__)) -#define ABSL_RANDOM_INTERNAL_ATTRIBUTE_ALWAYS_INLINE \ - __attribute__((always_inline)) -#elif defined(_MSC_VER) -// We can achieve something similar to attribute((always_inline)) with MSVC by -// using the __forceinline keyword, however this is not perfect. MSVC is -// much less aggressive about inlining, and even with the __forceinline keyword. -#define ABSL_RANDOM_INTERNAL_ATTRIBUTE_ALWAYS_INLINE __forceinline -#else -#define ABSL_RANDOM_INTERNAL_ATTRIBUTE_ALWAYS_INLINE -#endif - -namespace { - -// AES portions based on rijndael-alg-fst.c, -// https://fastcrypto.org/front/misc/rijndael-alg-fst.c -// -// Implementation of -// http://www.csrc.nist.gov/publications/fips/fips197/fips-197.pdf -constexpr uint32_t te0[256] = { - 0xc66363a5, 0xf87c7c84, 0xee777799, 0xf67b7b8d, 0xfff2f20d, 0xd66b6bbd, - 0xde6f6fb1, 0x91c5c554, 0x60303050, 0x02010103, 0xce6767a9, 0x562b2b7d, - 0xe7fefe19, 0xb5d7d762, 0x4dababe6, 0xec76769a, 0x8fcaca45, 0x1f82829d, - 0x89c9c940, 0xfa7d7d87, 0xeffafa15, 0xb25959eb, 0x8e4747c9, 0xfbf0f00b, - 0x41adadec, 0xb3d4d467, 0x5fa2a2fd, 0x45afafea, 0x239c9cbf, 0x53a4a4f7, - 0xe4727296, 0x9bc0c05b, 0x75b7b7c2, 0xe1fdfd1c, 0x3d9393ae, 0x4c26266a, - 0x6c36365a, 0x7e3f3f41, 0xf5f7f702, 0x83cccc4f, 0x6834345c, 0x51a5a5f4, - 0xd1e5e534, 0xf9f1f108, 0xe2717193, 0xabd8d873, 0x62313153, 0x2a15153f, - 0x0804040c, 0x95c7c752, 0x46232365, 0x9dc3c35e, 0x30181828, 0x379696a1, - 0x0a05050f, 0x2f9a9ab5, 0x0e070709, 0x24121236, 0x1b80809b, 0xdfe2e23d, - 0xcdebeb26, 0x4e272769, 0x7fb2b2cd, 0xea75759f, 0x1209091b, 0x1d83839e, - 0x582c2c74, 0x341a1a2e, 0x361b1b2d, 0xdc6e6eb2, 0xb45a5aee, 0x5ba0a0fb, - 0xa45252f6, 0x763b3b4d, 0xb7d6d661, 0x7db3b3ce, 0x5229297b, 0xdde3e33e, - 0x5e2f2f71, 0x13848497, 0xa65353f5, 0xb9d1d168, 0x00000000, 0xc1eded2c, - 0x40202060, 0xe3fcfc1f, 0x79b1b1c8, 0xb65b5bed, 0xd46a6abe, 0x8dcbcb46, - 0x67bebed9, 0x7239394b, 0x944a4ade, 0x984c4cd4, 0xb05858e8, 0x85cfcf4a, - 0xbbd0d06b, 0xc5efef2a, 0x4faaaae5, 0xedfbfb16, 0x864343c5, 0x9a4d4dd7, - 0x66333355, 0x11858594, 0x8a4545cf, 0xe9f9f910, 0x04020206, 0xfe7f7f81, - 0xa05050f0, 0x783c3c44, 0x259f9fba, 0x4ba8a8e3, 0xa25151f3, 0x5da3a3fe, - 0x804040c0, 0x058f8f8a, 0x3f9292ad, 0x219d9dbc, 0x70383848, 0xf1f5f504, - 0x63bcbcdf, 0x77b6b6c1, 0xafdada75, 0x42212163, 0x20101030, 0xe5ffff1a, - 0xfdf3f30e, 0xbfd2d26d, 0x81cdcd4c, 0x180c0c14, 0x26131335, 0xc3ecec2f, - 0xbe5f5fe1, 0x359797a2, 0x884444cc, 0x2e171739, 0x93c4c457, 0x55a7a7f2, - 0xfc7e7e82, 0x7a3d3d47, 0xc86464ac, 0xba5d5de7, 0x3219192b, 0xe6737395, - 0xc06060a0, 0x19818198, 0x9e4f4fd1, 0xa3dcdc7f, 0x44222266, 0x542a2a7e, - 0x3b9090ab, 0x0b888883, 0x8c4646ca, 0xc7eeee29, 0x6bb8b8d3, 0x2814143c, - 0xa7dede79, 0xbc5e5ee2, 0x160b0b1d, 0xaddbdb76, 0xdbe0e03b, 0x64323256, - 0x743a3a4e, 0x140a0a1e, 0x924949db, 0x0c06060a, 0x4824246c, 0xb85c5ce4, - 0x9fc2c25d, 0xbdd3d36e, 0x43acacef, 0xc46262a6, 0x399191a8, 0x319595a4, - 0xd3e4e437, 0xf279798b, 0xd5e7e732, 0x8bc8c843, 0x6e373759, 0xda6d6db7, - 0x018d8d8c, 0xb1d5d564, 0x9c4e4ed2, 0x49a9a9e0, 0xd86c6cb4, 0xac5656fa, - 0xf3f4f407, 0xcfeaea25, 0xca6565af, 0xf47a7a8e, 0x47aeaee9, 0x10080818, - 0x6fbabad5, 0xf0787888, 0x4a25256f, 0x5c2e2e72, 0x381c1c24, 0x57a6a6f1, - 0x73b4b4c7, 0x97c6c651, 0xcbe8e823, 0xa1dddd7c, 0xe874749c, 0x3e1f1f21, - 0x964b4bdd, 0x61bdbddc, 0x0d8b8b86, 0x0f8a8a85, 0xe0707090, 0x7c3e3e42, - 0x71b5b5c4, 0xcc6666aa, 0x904848d8, 0x06030305, 0xf7f6f601, 0x1c0e0e12, - 0xc26161a3, 0x6a35355f, 0xae5757f9, 0x69b9b9d0, 0x17868691, 0x99c1c158, - 0x3a1d1d27, 0x279e9eb9, 0xd9e1e138, 0xebf8f813, 0x2b9898b3, 0x22111133, - 0xd26969bb, 0xa9d9d970, 0x078e8e89, 0x339494a7, 0x2d9b9bb6, 0x3c1e1e22, - 0x15878792, 0xc9e9e920, 0x87cece49, 0xaa5555ff, 0x50282878, 0xa5dfdf7a, - 0x038c8c8f, 0x59a1a1f8, 0x09898980, 0x1a0d0d17, 0x65bfbfda, 0xd7e6e631, - 0x844242c6, 0xd06868b8, 0x824141c3, 0x299999b0, 0x5a2d2d77, 0x1e0f0f11, - 0x7bb0b0cb, 0xa85454fc, 0x6dbbbbd6, 0x2c16163a, -}; - -constexpr uint32_t te1[256] = { - 0xa5c66363, 0x84f87c7c, 0x99ee7777, 0x8df67b7b, 0x0dfff2f2, 0xbdd66b6b, - 0xb1de6f6f, 0x5491c5c5, 0x50603030, 0x03020101, 0xa9ce6767, 0x7d562b2b, - 0x19e7fefe, 0x62b5d7d7, 0xe64dabab, 0x9aec7676, 0x458fcaca, 0x9d1f8282, - 0x4089c9c9, 0x87fa7d7d, 0x15effafa, 0xebb25959, 0xc98e4747, 0x0bfbf0f0, - 0xec41adad, 0x67b3d4d4, 0xfd5fa2a2, 0xea45afaf, 0xbf239c9c, 0xf753a4a4, - 0x96e47272, 0x5b9bc0c0, 0xc275b7b7, 0x1ce1fdfd, 0xae3d9393, 0x6a4c2626, - 0x5a6c3636, 0x417e3f3f, 0x02f5f7f7, 0x4f83cccc, 0x5c683434, 0xf451a5a5, - 0x34d1e5e5, 0x08f9f1f1, 0x93e27171, 0x73abd8d8, 0x53623131, 0x3f2a1515, - 0x0c080404, 0x5295c7c7, 0x65462323, 0x5e9dc3c3, 0x28301818, 0xa1379696, - 0x0f0a0505, 0xb52f9a9a, 0x090e0707, 0x36241212, 0x9b1b8080, 0x3ddfe2e2, - 0x26cdebeb, 0x694e2727, 0xcd7fb2b2, 0x9fea7575, 0x1b120909, 0x9e1d8383, - 0x74582c2c, 0x2e341a1a, 0x2d361b1b, 0xb2dc6e6e, 0xeeb45a5a, 0xfb5ba0a0, - 0xf6a45252, 0x4d763b3b, 0x61b7d6d6, 0xce7db3b3, 0x7b522929, 0x3edde3e3, - 0x715e2f2f, 0x97138484, 0xf5a65353, 0x68b9d1d1, 0x00000000, 0x2cc1eded, - 0x60402020, 0x1fe3fcfc, 0xc879b1b1, 0xedb65b5b, 0xbed46a6a, 0x468dcbcb, - 0xd967bebe, 0x4b723939, 0xde944a4a, 0xd4984c4c, 0xe8b05858, 0x4a85cfcf, - 0x6bbbd0d0, 0x2ac5efef, 0xe54faaaa, 0x16edfbfb, 0xc5864343, 0xd79a4d4d, - 0x55663333, 0x94118585, 0xcf8a4545, 0x10e9f9f9, 0x06040202, 0x81fe7f7f, - 0xf0a05050, 0x44783c3c, 0xba259f9f, 0xe34ba8a8, 0xf3a25151, 0xfe5da3a3, - 0xc0804040, 0x8a058f8f, 0xad3f9292, 0xbc219d9d, 0x48703838, 0x04f1f5f5, - 0xdf63bcbc, 0xc177b6b6, 0x75afdada, 0x63422121, 0x30201010, 0x1ae5ffff, - 0x0efdf3f3, 0x6dbfd2d2, 0x4c81cdcd, 0x14180c0c, 0x35261313, 0x2fc3ecec, - 0xe1be5f5f, 0xa2359797, 0xcc884444, 0x392e1717, 0x5793c4c4, 0xf255a7a7, - 0x82fc7e7e, 0x477a3d3d, 0xacc86464, 0xe7ba5d5d, 0x2b321919, 0x95e67373, - 0xa0c06060, 0x98198181, 0xd19e4f4f, 0x7fa3dcdc, 0x66442222, 0x7e542a2a, - 0xab3b9090, 0x830b8888, 0xca8c4646, 0x29c7eeee, 0xd36bb8b8, 0x3c281414, - 0x79a7dede, 0xe2bc5e5e, 0x1d160b0b, 0x76addbdb, 0x3bdbe0e0, 0x56643232, - 0x4e743a3a, 0x1e140a0a, 0xdb924949, 0x0a0c0606, 0x6c482424, 0xe4b85c5c, - 0x5d9fc2c2, 0x6ebdd3d3, 0xef43acac, 0xa6c46262, 0xa8399191, 0xa4319595, - 0x37d3e4e4, 0x8bf27979, 0x32d5e7e7, 0x438bc8c8, 0x596e3737, 0xb7da6d6d, - 0x8c018d8d, 0x64b1d5d5, 0xd29c4e4e, 0xe049a9a9, 0xb4d86c6c, 0xfaac5656, - 0x07f3f4f4, 0x25cfeaea, 0xafca6565, 0x8ef47a7a, 0xe947aeae, 0x18100808, - 0xd56fbaba, 0x88f07878, 0x6f4a2525, 0x725c2e2e, 0x24381c1c, 0xf157a6a6, - 0xc773b4b4, 0x5197c6c6, 0x23cbe8e8, 0x7ca1dddd, 0x9ce87474, 0x213e1f1f, - 0xdd964b4b, 0xdc61bdbd, 0x860d8b8b, 0x850f8a8a, 0x90e07070, 0x427c3e3e, - 0xc471b5b5, 0xaacc6666, 0xd8904848, 0x05060303, 0x01f7f6f6, 0x121c0e0e, - 0xa3c26161, 0x5f6a3535, 0xf9ae5757, 0xd069b9b9, 0x91178686, 0x5899c1c1, - 0x273a1d1d, 0xb9279e9e, 0x38d9e1e1, 0x13ebf8f8, 0xb32b9898, 0x33221111, - 0xbbd26969, 0x70a9d9d9, 0x89078e8e, 0xa7339494, 0xb62d9b9b, 0x223c1e1e, - 0x92158787, 0x20c9e9e9, 0x4987cece, 0xffaa5555, 0x78502828, 0x7aa5dfdf, - 0x8f038c8c, 0xf859a1a1, 0x80098989, 0x171a0d0d, 0xda65bfbf, 0x31d7e6e6, - 0xc6844242, 0xb8d06868, 0xc3824141, 0xb0299999, 0x775a2d2d, 0x111e0f0f, - 0xcb7bb0b0, 0xfca85454, 0xd66dbbbb, 0x3a2c1616, -}; - -constexpr uint32_t te2[256] = { - 0x63a5c663, 0x7c84f87c, 0x7799ee77, 0x7b8df67b, 0xf20dfff2, 0x6bbdd66b, - 0x6fb1de6f, 0xc55491c5, 0x30506030, 0x01030201, 0x67a9ce67, 0x2b7d562b, - 0xfe19e7fe, 0xd762b5d7, 0xabe64dab, 0x769aec76, 0xca458fca, 0x829d1f82, - 0xc94089c9, 0x7d87fa7d, 0xfa15effa, 0x59ebb259, 0x47c98e47, 0xf00bfbf0, - 0xadec41ad, 0xd467b3d4, 0xa2fd5fa2, 0xafea45af, 0x9cbf239c, 0xa4f753a4, - 0x7296e472, 0xc05b9bc0, 0xb7c275b7, 0xfd1ce1fd, 0x93ae3d93, 0x266a4c26, - 0x365a6c36, 0x3f417e3f, 0xf702f5f7, 0xcc4f83cc, 0x345c6834, 0xa5f451a5, - 0xe534d1e5, 0xf108f9f1, 0x7193e271, 0xd873abd8, 0x31536231, 0x153f2a15, - 0x040c0804, 0xc75295c7, 0x23654623, 0xc35e9dc3, 0x18283018, 0x96a13796, - 0x050f0a05, 0x9ab52f9a, 0x07090e07, 0x12362412, 0x809b1b80, 0xe23ddfe2, - 0xeb26cdeb, 0x27694e27, 0xb2cd7fb2, 0x759fea75, 0x091b1209, 0x839e1d83, - 0x2c74582c, 0x1a2e341a, 0x1b2d361b, 0x6eb2dc6e, 0x5aeeb45a, 0xa0fb5ba0, - 0x52f6a452, 0x3b4d763b, 0xd661b7d6, 0xb3ce7db3, 0x297b5229, 0xe33edde3, - 0x2f715e2f, 0x84971384, 0x53f5a653, 0xd168b9d1, 0x00000000, 0xed2cc1ed, - 0x20604020, 0xfc1fe3fc, 0xb1c879b1, 0x5bedb65b, 0x6abed46a, 0xcb468dcb, - 0xbed967be, 0x394b7239, 0x4ade944a, 0x4cd4984c, 0x58e8b058, 0xcf4a85cf, - 0xd06bbbd0, 0xef2ac5ef, 0xaae54faa, 0xfb16edfb, 0x43c58643, 0x4dd79a4d, - 0x33556633, 0x85941185, 0x45cf8a45, 0xf910e9f9, 0x02060402, 0x7f81fe7f, - 0x50f0a050, 0x3c44783c, 0x9fba259f, 0xa8e34ba8, 0x51f3a251, 0xa3fe5da3, - 0x40c08040, 0x8f8a058f, 0x92ad3f92, 0x9dbc219d, 0x38487038, 0xf504f1f5, - 0xbcdf63bc, 0xb6c177b6, 0xda75afda, 0x21634221, 0x10302010, 0xff1ae5ff, - 0xf30efdf3, 0xd26dbfd2, 0xcd4c81cd, 0x0c14180c, 0x13352613, 0xec2fc3ec, - 0x5fe1be5f, 0x97a23597, 0x44cc8844, 0x17392e17, 0xc45793c4, 0xa7f255a7, - 0x7e82fc7e, 0x3d477a3d, 0x64acc864, 0x5de7ba5d, 0x192b3219, 0x7395e673, - 0x60a0c060, 0x81981981, 0x4fd19e4f, 0xdc7fa3dc, 0x22664422, 0x2a7e542a, - 0x90ab3b90, 0x88830b88, 0x46ca8c46, 0xee29c7ee, 0xb8d36bb8, 0x143c2814, - 0xde79a7de, 0x5ee2bc5e, 0x0b1d160b, 0xdb76addb, 0xe03bdbe0, 0x32566432, - 0x3a4e743a, 0x0a1e140a, 0x49db9249, 0x060a0c06, 0x246c4824, 0x5ce4b85c, - 0xc25d9fc2, 0xd36ebdd3, 0xacef43ac, 0x62a6c462, 0x91a83991, 0x95a43195, - 0xe437d3e4, 0x798bf279, 0xe732d5e7, 0xc8438bc8, 0x37596e37, 0x6db7da6d, - 0x8d8c018d, 0xd564b1d5, 0x4ed29c4e, 0xa9e049a9, 0x6cb4d86c, 0x56faac56, - 0xf407f3f4, 0xea25cfea, 0x65afca65, 0x7a8ef47a, 0xaee947ae, 0x08181008, - 0xbad56fba, 0x7888f078, 0x256f4a25, 0x2e725c2e, 0x1c24381c, 0xa6f157a6, - 0xb4c773b4, 0xc65197c6, 0xe823cbe8, 0xdd7ca1dd, 0x749ce874, 0x1f213e1f, - 0x4bdd964b, 0xbddc61bd, 0x8b860d8b, 0x8a850f8a, 0x7090e070, 0x3e427c3e, - 0xb5c471b5, 0x66aacc66, 0x48d89048, 0x03050603, 0xf601f7f6, 0x0e121c0e, - 0x61a3c261, 0x355f6a35, 0x57f9ae57, 0xb9d069b9, 0x86911786, 0xc15899c1, - 0x1d273a1d, 0x9eb9279e, 0xe138d9e1, 0xf813ebf8, 0x98b32b98, 0x11332211, - 0x69bbd269, 0xd970a9d9, 0x8e89078e, 0x94a73394, 0x9bb62d9b, 0x1e223c1e, - 0x87921587, 0xe920c9e9, 0xce4987ce, 0x55ffaa55, 0x28785028, 0xdf7aa5df, - 0x8c8f038c, 0xa1f859a1, 0x89800989, 0x0d171a0d, 0xbfda65bf, 0xe631d7e6, - 0x42c68442, 0x68b8d068, 0x41c38241, 0x99b02999, 0x2d775a2d, 0x0f111e0f, - 0xb0cb7bb0, 0x54fca854, 0xbbd66dbb, 0x163a2c16, -}; - -constexpr uint32_t te3[256] = { - 0x6363a5c6, 0x7c7c84f8, 0x777799ee, 0x7b7b8df6, 0xf2f20dff, 0x6b6bbdd6, - 0x6f6fb1de, 0xc5c55491, 0x30305060, 0x01010302, 0x6767a9ce, 0x2b2b7d56, - 0xfefe19e7, 0xd7d762b5, 0xababe64d, 0x76769aec, 0xcaca458f, 0x82829d1f, - 0xc9c94089, 0x7d7d87fa, 0xfafa15ef, 0x5959ebb2, 0x4747c98e, 0xf0f00bfb, - 0xadadec41, 0xd4d467b3, 0xa2a2fd5f, 0xafafea45, 0x9c9cbf23, 0xa4a4f753, - 0x727296e4, 0xc0c05b9b, 0xb7b7c275, 0xfdfd1ce1, 0x9393ae3d, 0x26266a4c, - 0x36365a6c, 0x3f3f417e, 0xf7f702f5, 0xcccc4f83, 0x34345c68, 0xa5a5f451, - 0xe5e534d1, 0xf1f108f9, 0x717193e2, 0xd8d873ab, 0x31315362, 0x15153f2a, - 0x04040c08, 0xc7c75295, 0x23236546, 0xc3c35e9d, 0x18182830, 0x9696a137, - 0x05050f0a, 0x9a9ab52f, 0x0707090e, 0x12123624, 0x80809b1b, 0xe2e23ddf, - 0xebeb26cd, 0x2727694e, 0xb2b2cd7f, 0x75759fea, 0x09091b12, 0x83839e1d, - 0x2c2c7458, 0x1a1a2e34, 0x1b1b2d36, 0x6e6eb2dc, 0x5a5aeeb4, 0xa0a0fb5b, - 0x5252f6a4, 0x3b3b4d76, 0xd6d661b7, 0xb3b3ce7d, 0x29297b52, 0xe3e33edd, - 0x2f2f715e, 0x84849713, 0x5353f5a6, 0xd1d168b9, 0x00000000, 0xeded2cc1, - 0x20206040, 0xfcfc1fe3, 0xb1b1c879, 0x5b5bedb6, 0x6a6abed4, 0xcbcb468d, - 0xbebed967, 0x39394b72, 0x4a4ade94, 0x4c4cd498, 0x5858e8b0, 0xcfcf4a85, - 0xd0d06bbb, 0xefef2ac5, 0xaaaae54f, 0xfbfb16ed, 0x4343c586, 0x4d4dd79a, - 0x33335566, 0x85859411, 0x4545cf8a, 0xf9f910e9, 0x02020604, 0x7f7f81fe, - 0x5050f0a0, 0x3c3c4478, 0x9f9fba25, 0xa8a8e34b, 0x5151f3a2, 0xa3a3fe5d, - 0x4040c080, 0x8f8f8a05, 0x9292ad3f, 0x9d9dbc21, 0x38384870, 0xf5f504f1, - 0xbcbcdf63, 0xb6b6c177, 0xdada75af, 0x21216342, 0x10103020, 0xffff1ae5, - 0xf3f30efd, 0xd2d26dbf, 0xcdcd4c81, 0x0c0c1418, 0x13133526, 0xecec2fc3, - 0x5f5fe1be, 0x9797a235, 0x4444cc88, 0x1717392e, 0xc4c45793, 0xa7a7f255, - 0x7e7e82fc, 0x3d3d477a, 0x6464acc8, 0x5d5de7ba, 0x19192b32, 0x737395e6, - 0x6060a0c0, 0x81819819, 0x4f4fd19e, 0xdcdc7fa3, 0x22226644, 0x2a2a7e54, - 0x9090ab3b, 0x8888830b, 0x4646ca8c, 0xeeee29c7, 0xb8b8d36b, 0x14143c28, - 0xdede79a7, 0x5e5ee2bc, 0x0b0b1d16, 0xdbdb76ad, 0xe0e03bdb, 0x32325664, - 0x3a3a4e74, 0x0a0a1e14, 0x4949db92, 0x06060a0c, 0x24246c48, 0x5c5ce4b8, - 0xc2c25d9f, 0xd3d36ebd, 0xacacef43, 0x6262a6c4, 0x9191a839, 0x9595a431, - 0xe4e437d3, 0x79798bf2, 0xe7e732d5, 0xc8c8438b, 0x3737596e, 0x6d6db7da, - 0x8d8d8c01, 0xd5d564b1, 0x4e4ed29c, 0xa9a9e049, 0x6c6cb4d8, 0x5656faac, - 0xf4f407f3, 0xeaea25cf, 0x6565afca, 0x7a7a8ef4, 0xaeaee947, 0x08081810, - 0xbabad56f, 0x787888f0, 0x25256f4a, 0x2e2e725c, 0x1c1c2438, 0xa6a6f157, - 0xb4b4c773, 0xc6c65197, 0xe8e823cb, 0xdddd7ca1, 0x74749ce8, 0x1f1f213e, - 0x4b4bdd96, 0xbdbddc61, 0x8b8b860d, 0x8a8a850f, 0x707090e0, 0x3e3e427c, - 0xb5b5c471, 0x6666aacc, 0x4848d890, 0x03030506, 0xf6f601f7, 0x0e0e121c, - 0x6161a3c2, 0x35355f6a, 0x5757f9ae, 0xb9b9d069, 0x86869117, 0xc1c15899, - 0x1d1d273a, 0x9e9eb927, 0xe1e138d9, 0xf8f813eb, 0x9898b32b, 0x11113322, - 0x6969bbd2, 0xd9d970a9, 0x8e8e8907, 0x9494a733, 0x9b9bb62d, 0x1e1e223c, - 0x87879215, 0xe9e920c9, 0xcece4987, 0x5555ffaa, 0x28287850, 0xdfdf7aa5, - 0x8c8c8f03, 0xa1a1f859, 0x89898009, 0x0d0d171a, 0xbfbfda65, 0xe6e631d7, - 0x4242c684, 0x6868b8d0, 0x4141c382, 0x9999b029, 0x2d2d775a, 0x0f0f111e, - 0xb0b0cb7b, 0x5454fca8, 0xbbbbd66d, 0x16163a2c, -}; - -// Software implementation of the Vector128 class, using uint32_t -// as an underlying vector register. -struct alignas(16) Vector128 { - uint32_t s[4]; -}; - -inline ABSL_RANDOM_INTERNAL_ATTRIBUTE_ALWAYS_INLINE Vector128 -Vector128Load(const void* from) { - Vector128 result; - const uint8_t* src = reinterpret_cast<const uint8_t*>(from); - result.s[0] = static_cast<uint32_t>(src[0]) << 24 | - static_cast<uint32_t>(src[1]) << 16 | - static_cast<uint32_t>(src[2]) << 8 | - static_cast<uint32_t>(src[3]); - result.s[1] = static_cast<uint32_t>(src[4]) << 24 | - static_cast<uint32_t>(src[5]) << 16 | - static_cast<uint32_t>(src[6]) << 8 | - static_cast<uint32_t>(src[7]); - result.s[2] = static_cast<uint32_t>(src[8]) << 24 | - static_cast<uint32_t>(src[9]) << 16 | - static_cast<uint32_t>(src[10]) << 8 | - static_cast<uint32_t>(src[11]); - result.s[3] = static_cast<uint32_t>(src[12]) << 24 | - static_cast<uint32_t>(src[13]) << 16 | - static_cast<uint32_t>(src[14]) << 8 | - static_cast<uint32_t>(src[15]); - return result; -} - -inline ABSL_RANDOM_INTERNAL_ATTRIBUTE_ALWAYS_INLINE void Vector128Store( - const Vector128& v, void* to) { - uint8_t* dst = reinterpret_cast<uint8_t*>(to); - dst[0] = static_cast<uint8_t>(v.s[0] >> 24); - dst[1] = static_cast<uint8_t>(v.s[0] >> 16); - dst[2] = static_cast<uint8_t>(v.s[0] >> 8); - dst[3] = static_cast<uint8_t>(v.s[0]); - dst[4] = static_cast<uint8_t>(v.s[1] >> 24); - dst[5] = static_cast<uint8_t>(v.s[1] >> 16); - dst[6] = static_cast<uint8_t>(v.s[1] >> 8); - dst[7] = static_cast<uint8_t>(v.s[1]); - dst[8] = static_cast<uint8_t>(v.s[2] >> 24); - dst[9] = static_cast<uint8_t>(v.s[2] >> 16); - dst[10] = static_cast<uint8_t>(v.s[2] >> 8); - dst[11] = static_cast<uint8_t>(v.s[2]); - dst[12] = static_cast<uint8_t>(v.s[3] >> 24); - dst[13] = static_cast<uint8_t>(v.s[3] >> 16); - dst[14] = static_cast<uint8_t>(v.s[3] >> 8); - dst[15] = static_cast<uint8_t>(v.s[3]); -} - -// One round of AES. "round_key" is a public constant for breaking the -// symmetry of AES (ensures previously equal columns differ afterwards). -inline ABSL_RANDOM_INTERNAL_ATTRIBUTE_ALWAYS_INLINE Vector128 -AesRound(const Vector128& state, const Vector128& round_key) { - Vector128 result; - result.s[0] = round_key.s[0] ^ // - te0[uint8_t(state.s[0] >> 24)] ^ // - te1[uint8_t(state.s[1] >> 16)] ^ // - te2[uint8_t(state.s[2] >> 8)] ^ // - te3[uint8_t(state.s[3])]; - result.s[1] = round_key.s[1] ^ // - te0[uint8_t(state.s[1] >> 24)] ^ // - te1[uint8_t(state.s[2] >> 16)] ^ // - te2[uint8_t(state.s[3] >> 8)] ^ // - te3[uint8_t(state.s[0])]; - result.s[2] = round_key.s[2] ^ // - te0[uint8_t(state.s[2] >> 24)] ^ // - te1[uint8_t(state.s[3] >> 16)] ^ // - te2[uint8_t(state.s[0] >> 8)] ^ // - te3[uint8_t(state.s[1])]; - result.s[3] = round_key.s[3] ^ // - te0[uint8_t(state.s[3] >> 24)] ^ // - te1[uint8_t(state.s[0] >> 16)] ^ // - te2[uint8_t(state.s[1] >> 8)] ^ // - te3[uint8_t(state.s[2])]; - return result; -} - -using ::absl::random_internal::RandenTraits; - -// Randen operates on 128-bit vectors. -struct alignas(16) u64x2 { - uint64_t data[2]; -}; - -// The improved Feistel block shuffle function for 16 blocks. -inline ABSL_RANDOM_INTERNAL_ATTRIBUTE_ALWAYS_INLINE void BlockShuffle( - u64x2* state) { - static_assert(RandenTraits::kFeistelBlocks == 16, - "Feistel block shuffle only works for 16 blocks."); - - constexpr size_t shuffle[RandenTraits::kFeistelBlocks] = { - 7, 2, 13, 4, 11, 8, 3, 6, 15, 0, 9, 10, 1, 14, 5, 12}; - - // The fully unrolled loop without the memcpy improves the speed by about - // 30% over the equivalent: -#if 0 - u64x2 source[RandenTraits::kFeistelBlocks]; - std::memcpy(source, state, sizeof(source)); - for (size_t i = 0; i < RandenTraits::kFeistelBlocks; i++) { - const u64x2 v0 = source[shuffle[i]]; - state[i] = v0; - } - return; -#endif - - const u64x2 v0 = state[shuffle[0]]; - const u64x2 v1 = state[shuffle[1]]; - const u64x2 v2 = state[shuffle[2]]; - const u64x2 v3 = state[shuffle[3]]; - const u64x2 v4 = state[shuffle[4]]; - const u64x2 v5 = state[shuffle[5]]; - const u64x2 v6 = state[shuffle[6]]; - const u64x2 v7 = state[shuffle[7]]; - const u64x2 w0 = state[shuffle[8]]; - const u64x2 w1 = state[shuffle[9]]; - const u64x2 w2 = state[shuffle[10]]; - const u64x2 w3 = state[shuffle[11]]; - const u64x2 w4 = state[shuffle[12]]; - const u64x2 w5 = state[shuffle[13]]; - const u64x2 w6 = state[shuffle[14]]; - const u64x2 w7 = state[shuffle[15]]; - state[0] = v0; - state[1] = v1; - state[2] = v2; - state[3] = v3; - state[4] = v4; - state[5] = v5; - state[6] = v6; - state[7] = v7; - state[8] = w0; - state[9] = w1; - state[10] = w2; - state[11] = w3; - state[12] = w4; - state[13] = w5; - state[14] = w6; - state[15] = w7; -} - -// Feistel round function using two AES subrounds. Very similar to F() -// from Simpira v2, but with independent subround keys. Uses 17 AES rounds -// per 16 bytes (vs. 10 for AES-CTR). Computing eight round functions in -// parallel hides the 7-cycle AESNI latency on HSW. Note that the Feistel -// XORs are 'free' (included in the second AES instruction). -inline ABSL_RANDOM_INTERNAL_ATTRIBUTE_ALWAYS_INLINE const u64x2* FeistelRound( - u64x2* ABSL_RANDOM_INTERNAL_RESTRICT state, - const u64x2* ABSL_RANDOM_INTERNAL_RESTRICT keys) { - for (size_t branch = 0; branch < RandenTraits::kFeistelBlocks; branch += 4) { - const Vector128 s0 = Vector128Load(state + branch); - const Vector128 s1 = Vector128Load(state + branch + 1); - const Vector128 f0 = AesRound(s0, Vector128Load(keys)); - keys++; - const Vector128 o1 = AesRound(f0, s1); - Vector128Store(o1, state + branch + 1); - - // Manually unroll this loop once. about 10% better than not unrolled. - const Vector128 s2 = Vector128Load(state + branch + 2); - const Vector128 s3 = Vector128Load(state + branch + 3); - const Vector128 f2 = AesRound(s2, Vector128Load(keys)); - keys++; - const Vector128 o3 = AesRound(f2, s3); - Vector128Store(o3, state + branch + 3); - } - return keys; -} - -// Cryptographic permutation based via type-2 Generalized Feistel Network. -// Indistinguishable from ideal by chosen-ciphertext adversaries using less than -// 2^64 queries if the round function is a PRF. This is similar to the b=8 case -// of Simpira v2, but more efficient than its generic construction for b=16. -inline ABSL_RANDOM_INTERNAL_ATTRIBUTE_ALWAYS_INLINE void Permute( - u64x2* state, const u64x2* ABSL_RANDOM_INTERNAL_RESTRICT keys) { - for (size_t round = 0; round < RandenTraits::kFeistelRounds; ++round) { - keys = FeistelRound(state, keys); - BlockShuffle(state); - } -} - -} // namespace - -namespace absl { -ABSL_NAMESPACE_BEGIN -namespace random_internal { - -const void* RandenSlow::GetKeys() { - // Round keys for one AES per Feistel round and branch. - // The canonical implementation uses first digits of Pi. - return kRandenRoundKeys; -} - -void RandenSlow::Absorb(const void* seed_void, void* state_void) { - auto* state = - reinterpret_cast<uint64_t * ABSL_RANDOM_INTERNAL_RESTRICT>(state_void); - const auto* seed = - reinterpret_cast<const uint64_t * ABSL_RANDOM_INTERNAL_RESTRICT>( - seed_void); - - constexpr size_t kCapacityBlocks = - RandenTraits::kCapacityBytes / sizeof(uint64_t); - static_assert( - kCapacityBlocks * sizeof(uint64_t) == RandenTraits::kCapacityBytes, - "Not i*V"); - - for (size_t i = kCapacityBlocks; - i < RandenTraits::kStateBytes / sizeof(uint64_t); ++i) { - state[i] ^= seed[i - kCapacityBlocks]; - } -} - -void RandenSlow::Generate(const void* keys_void, void* state_void) { - static_assert(RandenTraits::kCapacityBytes == sizeof(u64x2), - "Capacity mismatch"); - - auto* state = reinterpret_cast<u64x2*>(state_void); - const auto* keys = reinterpret_cast<const u64x2*>(keys_void); - - const u64x2 prev_inner = state[0]; - - Permute(state, keys); - - // Ensure backtracking resistance. - state[0].data[0] ^= prev_inner.data[0]; - state[0].data[1] ^= prev_inner.data[1]; -} - -} // namespace random_internal -ABSL_NAMESPACE_END -} // namespace absl diff --git a/third_party/abseil_cpp/absl/random/internal/randen_slow.h b/third_party/abseil_cpp/absl/random/internal/randen_slow.h deleted file mode 100644 index b6f137eb94b1..000000000000 --- a/third_party/abseil_cpp/absl/random/internal/randen_slow.h +++ /dev/null @@ -1,40 +0,0 @@ -// Copyright 2017 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#ifndef ABSL_RANDOM_INTERNAL_RANDEN_SLOW_H_ -#define ABSL_RANDOM_INTERNAL_RANDEN_SLOW_H_ - -#include <cstddef> - -#include "absl/base/config.h" - -namespace absl { -ABSL_NAMESPACE_BEGIN -namespace random_internal { - -// RANDen = RANDom generator or beetroots in Swiss German. -// RandenSlow implements the basic state manipulation methods for -// architectures lacking AES hardware acceleration intrinsics. -class RandenSlow { - public: - static void Generate(const void* keys, void* state_void); - static void Absorb(const void* seed_void, void* state_void); - static const void* GetKeys(); -}; - -} // namespace random_internal -ABSL_NAMESPACE_END -} // namespace absl - -#endif // ABSL_RANDOM_INTERNAL_RANDEN_SLOW_H_ diff --git a/third_party/abseil_cpp/absl/random/internal/randen_slow_test.cc b/third_party/abseil_cpp/absl/random/internal/randen_slow_test.cc deleted file mode 100644 index 4a5358370586..000000000000 --- a/third_party/abseil_cpp/absl/random/internal/randen_slow_test.cc +++ /dev/null @@ -1,63 +0,0 @@ -// Copyright 2017 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#include "absl/random/internal/randen_slow.h" - -#include <cstring> - -#include "gtest/gtest.h" -#include "absl/random/internal/randen_traits.h" - -namespace { - -using absl::random_internal::RandenSlow; -using absl::random_internal::RandenTraits; - -// Local state parameters. -constexpr size_t kSeedBytes = - RandenTraits::kStateBytes - RandenTraits::kCapacityBytes; -constexpr size_t kStateSizeT = RandenTraits::kStateBytes / sizeof(uint64_t); -constexpr size_t kSeedSizeT = kSeedBytes / sizeof(uint32_t); - -struct alignas(16) randen { - uint64_t state[kStateSizeT]; - uint32_t seed[kSeedSizeT]; -}; - -TEST(RandenSlowTest, Default) { - constexpr uint64_t kGolden[] = { - 0x6c6534090ee6d3ee, 0x044e2b9b9d5333c6, 0xc3c14f134e433977, - 0xdda9f47cd90410ee, 0x887bf3087fd8ca10, 0xf0b780f545c72912, - 0x15dbb1d37696599f, 0x30ec63baff3c6d59, 0xb29f73606f7f20a6, - 0x02808a316f49a54c, 0x3b8feaf9d5c8e50e, 0x9cbf605e3fd9de8a, - 0xc970ae1a78183bbb, 0xd8b2ffd356301ed5, 0xf4b327fe0fc73c37, - 0xcdfd8d76eb8f9a19, 0xc3a506eb91420c9d, 0xd5af05dd3eff9556, - 0x48db1bb78f83c4a1, 0x7023920e0d6bfe8c, 0x58d3575834956d42, - 0xed1ef4c26b87b840, 0x8eef32a23e0b2df3, 0x497cabf3431154fc, - 0x4e24370570029a8b, 0xd88b5749f090e5ea, 0xc651a582a970692f, - 0x78fcec2cbb6342f5, 0x463cb745612f55db, 0x352ee4ad1816afe3, - 0x026ff374c101da7e, 0x811ef0821c3de851, - }; - - alignas(16) randen d; - std::memset(d.state, 0, sizeof(d.state)); - RandenSlow::Generate(RandenSlow::GetKeys(), d.state); - - uint64_t* id = d.state; - for (const auto& elem : kGolden) { - EXPECT_EQ(elem, *id++); - } -} - -} // namespace diff --git a/third_party/abseil_cpp/absl/random/internal/randen_test.cc b/third_party/abseil_cpp/absl/random/internal/randen_test.cc deleted file mode 100644 index c186fe0d686b..000000000000 --- a/third_party/abseil_cpp/absl/random/internal/randen_test.cc +++ /dev/null @@ -1,70 +0,0 @@ -// Copyright 2017 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#include "absl/random/internal/randen.h" - -#include <cstring> - -#include "gtest/gtest.h" -#include "absl/meta/type_traits.h" - -namespace { - -using absl::random_internal::Randen; - -// Local state parameters. -constexpr size_t kStateSizeT = Randen::kStateBytes / sizeof(uint64_t); - -TEST(RandenTest, CopyAndMove) { - static_assert(std::is_copy_constructible<Randen>::value, - "Randen must be copy constructible"); - - static_assert(absl::is_copy_assignable<Randen>::value, - "Randen must be copy assignable"); - - static_assert(std::is_move_constructible<Randen>::value, - "Randen must be move constructible"); - - static_assert(absl::is_move_assignable<Randen>::value, - "Randen must be move assignable"); -} - -TEST(RandenTest, Default) { - constexpr uint64_t kGolden[] = { - 0x6c6534090ee6d3ee, 0x044e2b9b9d5333c6, 0xc3c14f134e433977, - 0xdda9f47cd90410ee, 0x887bf3087fd8ca10, 0xf0b780f545c72912, - 0x15dbb1d37696599f, 0x30ec63baff3c6d59, 0xb29f73606f7f20a6, - 0x02808a316f49a54c, 0x3b8feaf9d5c8e50e, 0x9cbf605e3fd9de8a, - 0xc970ae1a78183bbb, 0xd8b2ffd356301ed5, 0xf4b327fe0fc73c37, - 0xcdfd8d76eb8f9a19, 0xc3a506eb91420c9d, 0xd5af05dd3eff9556, - 0x48db1bb78f83c4a1, 0x7023920e0d6bfe8c, 0x58d3575834956d42, - 0xed1ef4c26b87b840, 0x8eef32a23e0b2df3, 0x497cabf3431154fc, - 0x4e24370570029a8b, 0xd88b5749f090e5ea, 0xc651a582a970692f, - 0x78fcec2cbb6342f5, 0x463cb745612f55db, 0x352ee4ad1816afe3, - 0x026ff374c101da7e, 0x811ef0821c3de851, - }; - - alignas(16) uint64_t state[kStateSizeT]; - std::memset(state, 0, sizeof(state)); - - Randen r; - r.Generate(state); - - auto id = std::begin(state); - for (const auto& elem : kGolden) { - EXPECT_EQ(elem, *id++); - } -} - -} // namespace diff --git a/third_party/abseil_cpp/absl/random/internal/randen_traits.h b/third_party/abseil_cpp/absl/random/internal/randen_traits.h deleted file mode 100644 index 53caa936140a..000000000000 --- a/third_party/abseil_cpp/absl/random/internal/randen_traits.h +++ /dev/null @@ -1,88 +0,0 @@ -// Copyright 2017 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#ifndef ABSL_RANDOM_INTERNAL_RANDEN_TRAITS_H_ -#define ABSL_RANDOM_INTERNAL_RANDEN_TRAITS_H_ - -// HERMETIC NOTE: The randen_hwaes target must not introduce duplicate -// symbols from arbitrary system and other headers, since it may be built -// with different flags from other targets, using different levels of -// optimization, potentially introducing ODR violations. - -#include <cstddef> - -#include "absl/base/config.h" - -namespace absl { -ABSL_NAMESPACE_BEGIN -namespace random_internal { - -// RANDen = RANDom generator or beetroots in Swiss German. -// 'Strong' (well-distributed, unpredictable, backtracking-resistant) random -// generator, faster in some benchmarks than std::mt19937_64 and pcg64_c32. -// -// High-level summary: -// 1) Reverie (see "A Robust and Sponge-Like PRNG with Improved Efficiency") is -// a sponge-like random generator that requires a cryptographic permutation. -// It improves upon "Provably Robust Sponge-Based PRNGs and KDFs" by -// achieving backtracking resistance with only one Permute() per buffer. -// -// 2) "Simpira v2: A Family of Efficient Permutations Using the AES Round -// Function" constructs up to 1024-bit permutations using an improved -// Generalized Feistel network with 2-round AES-128 functions. This Feistel -// block shuffle achieves diffusion faster and is less vulnerable to -// sliced-biclique attacks than the Type-2 cyclic shuffle. -// -// 3) "Improving the Generalized Feistel" and "New criterion for diffusion -// property" extends the same kind of improved Feistel block shuffle to 16 -// branches, which enables a 2048-bit permutation. -// -// Combine these three ideas and also change Simpira's subround keys from -// structured/low-entropy counters to digits of Pi (or other random source). - -// RandenTraits contains the basic algorithm traits, such as the size of the -// state, seed, sponge, etc. -struct RandenTraits { - // Size of the entire sponge / state for the randen PRNG. - static constexpr size_t kStateBytes = 256; // 2048-bit - - // Size of the 'inner' (inaccessible) part of the sponge. Larger values would - // require more frequent calls to RandenGenerate. - static constexpr size_t kCapacityBytes = 16; // 128-bit - - // Size of the default seed consumed by the sponge. - static constexpr size_t kSeedBytes = kStateBytes - kCapacityBytes; - - // Assuming 128-bit blocks, the number of blocks in the state. - // Largest size for which security proofs are known. - static constexpr size_t kFeistelBlocks = 16; - - // Ensures SPRP security and two full subblock diffusions. - // Must be > 4 * log2(kFeistelBlocks). - static constexpr size_t kFeistelRounds = 16 + 1; - - // Size of the key. A 128-bit key block is used for every-other - // feistel block (Type-2 generalized Feistel network) in each round. - static constexpr size_t kKeyBytes = 16 * kFeistelRounds * kFeistelBlocks / 2; -}; - -// Randen key arrays. In randen_round_keys.cc -extern const unsigned char kRandenRoundKeys[RandenTraits::kKeyBytes]; -extern const unsigned char kRandenRoundKeysBE[RandenTraits::kKeyBytes]; - -} // namespace random_internal -ABSL_NAMESPACE_END -} // namespace absl - -#endif // ABSL_RANDOM_INTERNAL_RANDEN_TRAITS_H_ diff --git a/third_party/abseil_cpp/absl/random/internal/salted_seed_seq.h b/third_party/abseil_cpp/absl/random/internal/salted_seed_seq.h deleted file mode 100644 index 5953a090f80c..000000000000 --- a/third_party/abseil_cpp/absl/random/internal/salted_seed_seq.h +++ /dev/null @@ -1,167 +0,0 @@ -// Copyright 2017 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#ifndef ABSL_RANDOM_INTERNAL_SALTED_SEED_SEQ_H_ -#define ABSL_RANDOM_INTERNAL_SALTED_SEED_SEQ_H_ - -#include <cstdint> -#include <cstdlib> -#include <initializer_list> -#include <iterator> -#include <memory> -#include <type_traits> -#include <utility> - -#include "absl/container/inlined_vector.h" -#include "absl/meta/type_traits.h" -#include "absl/random/internal/seed_material.h" -#include "absl/types/optional.h" -#include "absl/types/span.h" - -namespace absl { -ABSL_NAMESPACE_BEGIN -namespace random_internal { - -// This class conforms to the C++ Standard "Seed Sequence" concept -// [rand.req.seedseq]. -// -// A `SaltedSeedSeq` is meant to wrap an existing seed sequence and modify -// generated sequence by mixing with extra entropy. This entropy may be -// build-dependent or process-dependent. The implementation may change to be -// have either or both kinds of entropy. If salt is not available sequence is -// not modified. -template <typename SSeq> -class SaltedSeedSeq { - public: - using inner_sequence_type = SSeq; - using result_type = typename SSeq::result_type; - - SaltedSeedSeq() : seq_(absl::make_unique<SSeq>()) {} - - template <typename Iterator> - SaltedSeedSeq(Iterator begin, Iterator end) - : seq_(absl::make_unique<SSeq>(begin, end)) {} - - template <typename T> - SaltedSeedSeq(std::initializer_list<T> il) - : SaltedSeedSeq(il.begin(), il.end()) {} - - SaltedSeedSeq(const SaltedSeedSeq&) = delete; - SaltedSeedSeq& operator=(const SaltedSeedSeq&) = delete; - - SaltedSeedSeq(SaltedSeedSeq&&) = default; - SaltedSeedSeq& operator=(SaltedSeedSeq&&) = default; - - template <typename RandomAccessIterator> - void generate(RandomAccessIterator begin, RandomAccessIterator end) { - // The common case is that generate is called with ContiguousIterators - // to uint arrays. Such contiguous memory regions may be optimized, - // which we detect here. - using tag = absl::conditional_t< - (std::is_pointer<RandomAccessIterator>::value && - std::is_same<absl::decay_t<decltype(*begin)>, uint32_t>::value), - ContiguousAndUint32Tag, DefaultTag>; - if (begin != end) { - generate_impl(begin, end, tag{}); - } - } - - template <typename OutIterator> - void param(OutIterator out) const { - seq_->param(out); - } - - size_t size() const { return seq_->size(); } - - private: - struct ContiguousAndUint32Tag {}; - struct DefaultTag {}; - - // Generate which requires the iterators are contiguous pointers to uint32_t. - void generate_impl(uint32_t* begin, uint32_t* end, ContiguousAndUint32Tag) { - generate_contiguous(absl::MakeSpan(begin, end)); - } - - // The uncommon case for generate is that it is called with iterators over - // some other buffer type which is assignable from a 32-bit value. In this - // case we allocate a temporary 32-bit buffer and then copy-assign back - // to the initial inputs. - template <typename RandomAccessIterator> - void generate_impl(RandomAccessIterator begin, RandomAccessIterator end, - DefaultTag) { - return generate_and_copy(std::distance(begin, end), begin); - } - - // Fills the initial seed buffer the underlying SSeq::generate() call, - // mixing in the salt material. - void generate_contiguous(absl::Span<uint32_t> buffer) { - seq_->generate(buffer.begin(), buffer.end()); - const uint32_t salt = absl::random_internal::GetSaltMaterial().value_or(0); - MixIntoSeedMaterial(absl::MakeConstSpan(&salt, 1), buffer); - } - - // Allocates a seed buffer of `n` elements, generates the seed, then - // copies the result into the `out` iterator. - template <typename Iterator> - void generate_and_copy(size_t n, Iterator out) { - // Allocate a temporary buffer, generate, and then copy. - absl::InlinedVector<uint32_t, 8> data(n, 0); - generate_contiguous(absl::MakeSpan(data.data(), data.size())); - std::copy(data.begin(), data.end(), out); - } - - // Because [rand.req.seedseq] is not required to be copy-constructible, - // copy-assignable nor movable, we wrap it with unique pointer to be able - // to move SaltedSeedSeq. - std::unique_ptr<SSeq> seq_; -}; - -// is_salted_seed_seq indicates whether the type is a SaltedSeedSeq. -template <typename T, typename = void> -struct is_salted_seed_seq : public std::false_type {}; - -template <typename T> -struct is_salted_seed_seq< - T, typename std::enable_if<std::is_same< - T, SaltedSeedSeq<typename T::inner_sequence_type>>::value>::type> - : public std::true_type {}; - -// MakeSaltedSeedSeq returns a salted variant of the seed sequence. -// When provided with an existing SaltedSeedSeq, returns the input parameter, -// otherwise constructs a new SaltedSeedSeq which embodies the original -// non-salted seed parameters. -template < - typename SSeq, // - typename EnableIf = absl::enable_if_t<is_salted_seed_seq<SSeq>::value>> -SSeq MakeSaltedSeedSeq(SSeq&& seq) { - return SSeq(std::forward<SSeq>(seq)); -} - -template < - typename SSeq, // - typename EnableIf = absl::enable_if_t<!is_salted_seed_seq<SSeq>::value>> -SaltedSeedSeq<typename std::decay<SSeq>::type> MakeSaltedSeedSeq(SSeq&& seq) { - using sseq_type = typename std::decay<SSeq>::type; - using result_type = typename sseq_type::result_type; - - absl::InlinedVector<result_type, 8> data; - seq.param(std::back_inserter(data)); - return SaltedSeedSeq<sseq_type>(data.begin(), data.end()); -} - -} // namespace random_internal -ABSL_NAMESPACE_END -} // namespace absl - -#endif // ABSL_RANDOM_INTERNAL_SALTED_SEED_SEQ_H_ diff --git a/third_party/abseil_cpp/absl/random/internal/salted_seed_seq_test.cc b/third_party/abseil_cpp/absl/random/internal/salted_seed_seq_test.cc deleted file mode 100644 index 0bf19a63ef8c..000000000000 --- a/third_party/abseil_cpp/absl/random/internal/salted_seed_seq_test.cc +++ /dev/null @@ -1,168 +0,0 @@ -// Copyright 2017 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#include "absl/random/internal/salted_seed_seq.h" - -#include <iterator> -#include <random> -#include <utility> -#include <vector> - -#include "gmock/gmock.h" -#include "gtest/gtest.h" - -using absl::random_internal::GetSaltMaterial; -using absl::random_internal::MakeSaltedSeedSeq; -using absl::random_internal::SaltedSeedSeq; -using testing::Eq; -using testing::Pointwise; - -namespace { - -template <typename Sseq> -void ConformsToInterface() { - // Check that the SeedSequence can be default-constructed. - { Sseq default_constructed_seq; } - // Check that the SeedSequence can be constructed with two iterators. - { - uint32_t init_array[] = {1, 3, 5, 7, 9}; - Sseq iterator_constructed_seq(std::begin(init_array), std::end(init_array)); - } - // Check that the SeedSequence can be std::initializer_list-constructed. - { Sseq list_constructed_seq = {1, 3, 5, 7, 9, 11, 13}; } - // Check that param() and size() return state provided to constructor. - { - uint32_t init_array[] = {1, 2, 3, 4, 5}; - Sseq seq(std::begin(init_array), std::end(init_array)); - EXPECT_EQ(seq.size(), ABSL_ARRAYSIZE(init_array)); - - std::vector<uint32_t> state_vector; - seq.param(std::back_inserter(state_vector)); - - EXPECT_EQ(state_vector.size(), ABSL_ARRAYSIZE(init_array)); - for (int i = 0; i < state_vector.size(); i++) { - EXPECT_EQ(state_vector[i], i + 1); - } - } - // Check for presence of generate() method. - { - Sseq seq; - uint32_t seeds[5]; - - seq.generate(std::begin(seeds), std::end(seeds)); - } -} - -TEST(SaltedSeedSeq, CheckInterfaces) { - // Control case - ConformsToInterface<std::seed_seq>(); - - // Abseil classes - ConformsToInterface<SaltedSeedSeq<std::seed_seq>>(); -} - -TEST(SaltedSeedSeq, CheckConstructingFromOtherSequence) { - std::vector<uint32_t> seed_values(10, 1); - std::seed_seq seq(seed_values.begin(), seed_values.end()); - auto salted_seq = MakeSaltedSeedSeq(std::move(seq)); - - EXPECT_EQ(seq.size(), salted_seq.size()); - - std::vector<uint32_t> param_result; - seq.param(std::back_inserter(param_result)); - - EXPECT_EQ(seed_values, param_result); -} - -TEST(SaltedSeedSeq, SaltedSaltedSeedSeqIsNotDoubleSalted) { - uint32_t init[] = {1, 3, 5, 7, 9}; - - std::seed_seq seq(std::begin(init), std::end(init)); - - // The first salting. - SaltedSeedSeq<std::seed_seq> salted_seq = MakeSaltedSeedSeq(std::move(seq)); - uint32_t a[16]; - salted_seq.generate(std::begin(a), std::end(a)); - - // The second salting. - SaltedSeedSeq<std::seed_seq> salted_salted_seq = - MakeSaltedSeedSeq(std::move(salted_seq)); - uint32_t b[16]; - salted_salted_seq.generate(std::begin(b), std::end(b)); - - // ... both should be equal. - EXPECT_THAT(b, Pointwise(Eq(), a)) << "a[0] " << a[0]; -} - -TEST(SaltedSeedSeq, SeedMaterialIsSalted) { - const size_t kNumBlocks = 16; - - uint32_t seed_material[kNumBlocks]; - std::random_device urandom{"/dev/urandom"}; - for (uint32_t& seed : seed_material) { - seed = urandom(); - } - - std::seed_seq seq(std::begin(seed_material), std::end(seed_material)); - SaltedSeedSeq<std::seed_seq> salted_seq(std::begin(seed_material), - std::end(seed_material)); - - bool salt_is_available = GetSaltMaterial().has_value(); - - // If salt is available generated sequence should be different. - if (salt_is_available) { - uint32_t outputs[kNumBlocks]; - uint32_t salted_outputs[kNumBlocks]; - - seq.generate(std::begin(outputs), std::end(outputs)); - salted_seq.generate(std::begin(salted_outputs), std::end(salted_outputs)); - - EXPECT_THAT(outputs, Pointwise(testing::Ne(), salted_outputs)); - } -} - -TEST(SaltedSeedSeq, GenerateAcceptsDifferentTypes) { - const size_t kNumBlocks = 4; - - SaltedSeedSeq<std::seed_seq> seq({1, 2, 3}); - - uint32_t expected[kNumBlocks]; - seq.generate(std::begin(expected), std::end(expected)); - - // 32-bit outputs - { - unsigned long seed_material[kNumBlocks]; // NOLINT(runtime/int) - seq.generate(std::begin(seed_material), std::end(seed_material)); - EXPECT_THAT(seed_material, Pointwise(Eq(), expected)); - } - { - unsigned int seed_material[kNumBlocks]; // NOLINT(runtime/int) - seq.generate(std::begin(seed_material), std::end(seed_material)); - EXPECT_THAT(seed_material, Pointwise(Eq(), expected)); - } - - // 64-bit outputs. - { - uint64_t seed_material[kNumBlocks]; - seq.generate(std::begin(seed_material), std::end(seed_material)); - EXPECT_THAT(seed_material, Pointwise(Eq(), expected)); - } - { - int64_t seed_material[kNumBlocks]; - seq.generate(std::begin(seed_material), std::end(seed_material)); - EXPECT_THAT(seed_material, Pointwise(Eq(), expected)); - } -} - -} // namespace diff --git a/third_party/abseil_cpp/absl/random/internal/seed_material.cc b/third_party/abseil_cpp/absl/random/internal/seed_material.cc deleted file mode 100644 index 4d38a57419ec..000000000000 --- a/third_party/abseil_cpp/absl/random/internal/seed_material.cc +++ /dev/null @@ -1,219 +0,0 @@ -// Copyright 2017 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#include "absl/random/internal/seed_material.h" - -#include <fcntl.h> - -#ifndef _WIN32 -#include <unistd.h> -#else -#include <io.h> -#endif - -#include <algorithm> -#include <cerrno> -#include <cstdint> -#include <cstdlib> -#include <cstring> - -#include "absl/base/internal/raw_logging.h" -#include "absl/strings/ascii.h" -#include "absl/strings/escaping.h" -#include "absl/strings/string_view.h" -#include "absl/strings/strip.h" - -#if defined(__native_client__) - -#include <nacl/nacl_random.h> -#define ABSL_RANDOM_USE_NACL_SECURE_RANDOM 1 - -#elif defined(_WIN32) - -#include <windows.h> -#define ABSL_RANDOM_USE_BCRYPT 1 -#pragma comment(lib, "bcrypt.lib") - -#elif defined(__Fuchsia__) -#include <zircon/syscalls.h> - -#endif - -#if defined(ABSL_RANDOM_USE_BCRYPT) -#include <bcrypt.h> - -#ifndef BCRYPT_SUCCESS -#define BCRYPT_SUCCESS(Status) (((NTSTATUS)(Status)) >= 0) -#endif -// Also link bcrypt; this can be done via linker options or: -// #pragma comment(lib, "bcrypt.lib") -#endif - -namespace absl { -ABSL_NAMESPACE_BEGIN -namespace random_internal { -namespace { - -// Read OS Entropy for random number seeds. -// TODO(absl-team): Possibly place a cap on how much entropy may be read at a -// time. - -#if defined(ABSL_RANDOM_USE_BCRYPT) - -// On Windows potentially use the BCRYPT CNG API to read available entropy. -bool ReadSeedMaterialFromOSEntropyImpl(absl::Span<uint32_t> values) { - BCRYPT_ALG_HANDLE hProvider; - NTSTATUS ret; - ret = BCryptOpenAlgorithmProvider(&hProvider, BCRYPT_RNG_ALGORITHM, - MS_PRIMITIVE_PROVIDER, 0); - if (!(BCRYPT_SUCCESS(ret))) { - ABSL_RAW_LOG(ERROR, "Failed to open crypto provider."); - return false; - } - ret = BCryptGenRandom( - hProvider, // provider - reinterpret_cast<UCHAR*>(values.data()), // buffer - static_cast<ULONG>(sizeof(uint32_t) * values.size()), // bytes - 0); // flags - BCryptCloseAlgorithmProvider(hProvider, 0); - return BCRYPT_SUCCESS(ret); -} - -#elif defined(ABSL_RANDOM_USE_NACL_SECURE_RANDOM) - -// On NaCL use nacl_secure_random to acquire bytes. -bool ReadSeedMaterialFromOSEntropyImpl(absl::Span<uint32_t> values) { - auto buffer = reinterpret_cast<uint8_t*>(values.data()); - size_t buffer_size = sizeof(uint32_t) * values.size(); - - uint8_t* output_ptr = buffer; - while (buffer_size > 0) { - size_t nread = 0; - const int error = nacl_secure_random(output_ptr, buffer_size, &nread); - if (error != 0 || nread > buffer_size) { - ABSL_RAW_LOG(ERROR, "Failed to read secure_random seed data: %d", error); - return false; - } - output_ptr += nread; - buffer_size -= nread; - } - return true; -} - -#elif defined(__Fuchsia__) - -bool ReadSeedMaterialFromOSEntropyImpl(absl::Span<uint32_t> values) { - auto buffer = reinterpret_cast<uint8_t*>(values.data()); - size_t buffer_size = sizeof(uint32_t) * values.size(); - zx_cprng_draw(buffer, buffer_size); - return true; -} - -#else - -// On *nix, read entropy from /dev/urandom. -bool ReadSeedMaterialFromOSEntropyImpl(absl::Span<uint32_t> values) { - const char kEntropyFile[] = "/dev/urandom"; - - auto buffer = reinterpret_cast<uint8_t*>(values.data()); - size_t buffer_size = sizeof(uint32_t) * values.size(); - - int dev_urandom = open(kEntropyFile, O_RDONLY); - bool success = (-1 != dev_urandom); - if (!success) { - return false; - } - - while (success && buffer_size > 0) { - int bytes_read = read(dev_urandom, buffer, buffer_size); - int read_error = errno; - success = (bytes_read > 0); - if (success) { - buffer += bytes_read; - buffer_size -= bytes_read; - } else if (bytes_read == -1 && read_error == EINTR) { - success = true; // Need to try again. - } - } - close(dev_urandom); - return success; -} - -#endif - -} // namespace - -bool ReadSeedMaterialFromOSEntropy(absl::Span<uint32_t> values) { - assert(values.data() != nullptr); - if (values.data() == nullptr) { - return false; - } - if (values.empty()) { - return true; - } - return ReadSeedMaterialFromOSEntropyImpl(values); -} - -void MixIntoSeedMaterial(absl::Span<const uint32_t> sequence, - absl::Span<uint32_t> seed_material) { - // Algorithm is based on code available at - // https://gist.github.com/imneme/540829265469e673d045 - constexpr uint32_t kInitVal = 0x43b0d7e5; - constexpr uint32_t kHashMul = 0x931e8875; - constexpr uint32_t kMixMulL = 0xca01f9dd; - constexpr uint32_t kMixMulR = 0x4973f715; - constexpr uint32_t kShiftSize = sizeof(uint32_t) * 8 / 2; - - uint32_t hash_const = kInitVal; - auto hash = [&](uint32_t value) { - value ^= hash_const; - hash_const *= kHashMul; - value *= hash_const; - value ^= value >> kShiftSize; - return value; - }; - - auto mix = [&](uint32_t x, uint32_t y) { - uint32_t result = kMixMulL * x - kMixMulR * y; - result ^= result >> kShiftSize; - return result; - }; - - for (const auto& seq_val : sequence) { - for (auto& elem : seed_material) { - elem = mix(elem, hash(seq_val)); - } - } -} - -absl::optional<uint32_t> GetSaltMaterial() { - // Salt must be common for all generators within the same process so read it - // only once and store in static variable. - static const auto salt_material = []() -> absl::optional<uint32_t> { - uint32_t salt_value = 0; - - if (random_internal::ReadSeedMaterialFromOSEntropy( - MakeSpan(&salt_value, 1))) { - return salt_value; - } - - return absl::nullopt; - }(); - - return salt_material; -} - -} // namespace random_internal -ABSL_NAMESPACE_END -} // namespace absl diff --git a/third_party/abseil_cpp/absl/random/internal/seed_material.h b/third_party/abseil_cpp/absl/random/internal/seed_material.h deleted file mode 100644 index 4be10e925685..000000000000 --- a/third_party/abseil_cpp/absl/random/internal/seed_material.h +++ /dev/null @@ -1,104 +0,0 @@ -// Copyright 2017 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#ifndef ABSL_RANDOM_INTERNAL_SEED_MATERIAL_H_ -#define ABSL_RANDOM_INTERNAL_SEED_MATERIAL_H_ - -#include <cassert> -#include <cstdint> -#include <cstdlib> -#include <string> -#include <vector> - -#include "absl/base/attributes.h" -#include "absl/random/internal/fast_uniform_bits.h" -#include "absl/types/optional.h" -#include "absl/types/span.h" - -namespace absl { -ABSL_NAMESPACE_BEGIN -namespace random_internal { - -// Returns the number of 32-bit blocks needed to contain the given number of -// bits. -constexpr size_t SeedBitsToBlocks(size_t seed_size) { - return (seed_size + 31) / 32; -} - -// Amount of entropy (measured in bits) used to instantiate a Seed Sequence, -// with which to create a URBG. -constexpr size_t kEntropyBitsNeeded = 256; - -// Amount of entropy (measured in 32-bit blocks) used to instantiate a Seed -// Sequence, with which to create a URBG. -constexpr size_t kEntropyBlocksNeeded = - random_internal::SeedBitsToBlocks(kEntropyBitsNeeded); - -static_assert(kEntropyBlocksNeeded > 0, - "Entropy used to seed URBGs must be nonzero."); - -// Attempts to fill a span of uint32_t-values using an OS-provided source of -// true entropy (eg. /dev/urandom) into an array of uint32_t blocks of data. The -// resulting array may be used to initialize an instance of a class conforming -// to the C++ Standard "Seed Sequence" concept [rand.req.seedseq]. -// -// If values.data() == nullptr, the behavior is undefined. -ABSL_MUST_USE_RESULT -bool ReadSeedMaterialFromOSEntropy(absl::Span<uint32_t> values); - -// Attempts to fill a span of uint32_t-values using variates generated by an -// existing instance of a class conforming to the C++ Standard "Uniform Random -// Bit Generator" concept [rand.req.urng]. The resulting data may be used to -// initialize an instance of a class conforming to the C++ Standard -// "Seed Sequence" concept [rand.req.seedseq]. -// -// If urbg == nullptr or values.data() == nullptr, the behavior is undefined. -template <typename URBG> -ABSL_MUST_USE_RESULT bool ReadSeedMaterialFromURBG( - URBG* urbg, absl::Span<uint32_t> values) { - random_internal::FastUniformBits<uint32_t> distr; - - assert(urbg != nullptr && values.data() != nullptr); - if (urbg == nullptr || values.data() == nullptr) { - return false; - } - - for (uint32_t& seed_value : values) { - seed_value = distr(*urbg); - } - return true; -} - -// Mixes given sequence of values with into given sequence of seed material. -// Time complexity of this function is O(sequence.size() * -// seed_material.size()). -// -// Algorithm is based on code available at -// https://gist.github.com/imneme/540829265469e673d045 -// by Melissa O'Neill. -void MixIntoSeedMaterial(absl::Span<const uint32_t> sequence, - absl::Span<uint32_t> seed_material); - -// Returns salt value. -// -// Salt is obtained only once and stored in static variable. -// -// May return empty value if optaining the salt was not possible. -absl::optional<uint32_t> GetSaltMaterial(); - -} // namespace random_internal -ABSL_NAMESPACE_END -} // namespace absl - -#endif // ABSL_RANDOM_INTERNAL_SEED_MATERIAL_H_ diff --git a/third_party/abseil_cpp/absl/random/internal/seed_material_test.cc b/third_party/abseil_cpp/absl/random/internal/seed_material_test.cc deleted file mode 100644 index 6db2820ec707..000000000000 --- a/third_party/abseil_cpp/absl/random/internal/seed_material_test.cc +++ /dev/null @@ -1,202 +0,0 @@ -// Copyright 2017 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#include "absl/random/internal/seed_material.h" - -#include <bitset> -#include <cstdlib> -#include <cstring> -#include <random> - -#include "gmock/gmock.h" -#include "gtest/gtest.h" - -#ifdef __ANDROID__ -// Android assert messages only go to system log, so death tests cannot inspect -// the message for matching. -#define ABSL_EXPECT_DEATH_IF_SUPPORTED(statement, regex) \ - EXPECT_DEATH_IF_SUPPORTED(statement, ".*") -#else -#define ABSL_EXPECT_DEATH_IF_SUPPORTED(statement, regex) \ - EXPECT_DEATH_IF_SUPPORTED(statement, regex) -#endif - -namespace { - -using testing::Each; -using testing::ElementsAre; -using testing::Eq; -using testing::Ne; -using testing::Pointwise; - -TEST(SeedBitsToBlocks, VerifyCases) { - EXPECT_EQ(0, absl::random_internal::SeedBitsToBlocks(0)); - EXPECT_EQ(1, absl::random_internal::SeedBitsToBlocks(1)); - EXPECT_EQ(1, absl::random_internal::SeedBitsToBlocks(31)); - EXPECT_EQ(1, absl::random_internal::SeedBitsToBlocks(32)); - EXPECT_EQ(2, absl::random_internal::SeedBitsToBlocks(33)); - EXPECT_EQ(4, absl::random_internal::SeedBitsToBlocks(127)); - EXPECT_EQ(4, absl::random_internal::SeedBitsToBlocks(128)); - EXPECT_EQ(5, absl::random_internal::SeedBitsToBlocks(129)); -} - -TEST(ReadSeedMaterialFromOSEntropy, SuccessiveReadsAreDistinct) { - constexpr size_t kSeedMaterialSize = 64; - uint32_t seed_material_1[kSeedMaterialSize] = {}; - uint32_t seed_material_2[kSeedMaterialSize] = {}; - - EXPECT_TRUE(absl::random_internal::ReadSeedMaterialFromOSEntropy( - absl::Span<uint32_t>(seed_material_1, kSeedMaterialSize))); - EXPECT_TRUE(absl::random_internal::ReadSeedMaterialFromOSEntropy( - absl::Span<uint32_t>(seed_material_2, kSeedMaterialSize))); - - EXPECT_THAT(seed_material_1, Pointwise(Ne(), seed_material_2)); -} - -TEST(ReadSeedMaterialFromOSEntropy, ReadZeroBytesIsNoOp) { - uint32_t seed_material[32] = {}; - std::memset(seed_material, 0xAA, sizeof(seed_material)); - EXPECT_TRUE(absl::random_internal::ReadSeedMaterialFromOSEntropy( - absl::Span<uint32_t>(seed_material, 0))); - - EXPECT_THAT(seed_material, Each(Eq(0xAAAAAAAA))); -} - -TEST(ReadSeedMaterialFromOSEntropy, NullPtrVectorArgument) { -#ifdef NDEBUG - EXPECT_FALSE(absl::random_internal::ReadSeedMaterialFromOSEntropy( - absl::Span<uint32_t>(nullptr, 32))); -#else - bool result; - ABSL_EXPECT_DEATH_IF_SUPPORTED( - result = absl::random_internal::ReadSeedMaterialFromOSEntropy( - absl::Span<uint32_t>(nullptr, 32)), - "!= nullptr"); - (void)result; // suppress unused-variable warning -#endif -} - -TEST(ReadSeedMaterialFromURBG, SeedMaterialEqualsVariateSequence) { - // Two default-constructed instances of std::mt19937_64 are guaranteed to - // produce equal variate-sequences. - std::mt19937 urbg_1; - std::mt19937 urbg_2; - constexpr size_t kSeedMaterialSize = 1024; - uint32_t seed_material[kSeedMaterialSize] = {}; - - EXPECT_TRUE(absl::random_internal::ReadSeedMaterialFromURBG( - &urbg_1, absl::Span<uint32_t>(seed_material, kSeedMaterialSize))); - for (uint32_t seed : seed_material) { - EXPECT_EQ(seed, urbg_2()); - } -} - -TEST(ReadSeedMaterialFromURBG, ReadZeroBytesIsNoOp) { - std::mt19937_64 urbg; - uint32_t seed_material[32]; - std::memset(seed_material, 0xAA, sizeof(seed_material)); - EXPECT_TRUE(absl::random_internal::ReadSeedMaterialFromURBG( - &urbg, absl::Span<uint32_t>(seed_material, 0))); - - EXPECT_THAT(seed_material, Each(Eq(0xAAAAAAAA))); -} - -TEST(ReadSeedMaterialFromURBG, NullUrbgArgument) { - constexpr size_t kSeedMaterialSize = 32; - uint32_t seed_material[kSeedMaterialSize]; -#ifdef NDEBUG - EXPECT_FALSE(absl::random_internal::ReadSeedMaterialFromURBG<std::mt19937_64>( - nullptr, absl::Span<uint32_t>(seed_material, kSeedMaterialSize))); -#else - bool result; - ABSL_EXPECT_DEATH_IF_SUPPORTED( - result = absl::random_internal::ReadSeedMaterialFromURBG<std::mt19937_64>( - nullptr, absl::Span<uint32_t>(seed_material, kSeedMaterialSize)), - "!= nullptr"); - (void)result; // suppress unused-variable warning -#endif -} - -TEST(ReadSeedMaterialFromURBG, NullPtrVectorArgument) { - std::mt19937_64 urbg; -#ifdef NDEBUG - EXPECT_FALSE(absl::random_internal::ReadSeedMaterialFromURBG( - &urbg, absl::Span<uint32_t>(nullptr, 32))); -#else - bool result; - ABSL_EXPECT_DEATH_IF_SUPPORTED( - result = absl::random_internal::ReadSeedMaterialFromURBG( - &urbg, absl::Span<uint32_t>(nullptr, 32)), - "!= nullptr"); - (void)result; // suppress unused-variable warning -#endif -} - -// The avalanche effect is a desirable cryptographic property of hashes in which -// changing a single bit in the input causes each bit of the output to be -// changed with probability near 50%. -// -// https://en.wikipedia.org/wiki/Avalanche_effect - -TEST(MixSequenceIntoSeedMaterial, AvalancheEffectTestOneBitLong) { - std::vector<uint32_t> seed_material = {1, 2, 3, 4, 5, 6, 7, 8}; - - // For every 32-bit number with exactly one bit set, verify the avalanche - // effect holds. In order to reduce flakiness of tests, accept values - // anywhere in the range of 30%-70%. - for (uint32_t v = 1; v != 0; v <<= 1) { - std::vector<uint32_t> seed_material_copy = seed_material; - absl::random_internal::MixIntoSeedMaterial( - absl::Span<uint32_t>(&v, 1), - absl::Span<uint32_t>(seed_material_copy.data(), - seed_material_copy.size())); - - uint32_t changed_bits = 0; - for (size_t i = 0; i < seed_material.size(); i++) { - std::bitset<sizeof(uint32_t) * 8> bitset(seed_material[i] ^ - seed_material_copy[i]); - changed_bits += bitset.count(); - } - - EXPECT_LE(changed_bits, 0.7 * sizeof(uint32_t) * 8 * seed_material.size()); - EXPECT_GE(changed_bits, 0.3 * sizeof(uint32_t) * 8 * seed_material.size()); - } -} - -TEST(MixSequenceIntoSeedMaterial, AvalancheEffectTestOneBitShort) { - std::vector<uint32_t> seed_material = {1}; - - // For every 32-bit number with exactly one bit set, verify the avalanche - // effect holds. In order to reduce flakiness of tests, accept values - // anywhere in the range of 30%-70%. - for (uint32_t v = 1; v != 0; v <<= 1) { - std::vector<uint32_t> seed_material_copy = seed_material; - absl::random_internal::MixIntoSeedMaterial( - absl::Span<uint32_t>(&v, 1), - absl::Span<uint32_t>(seed_material_copy.data(), - seed_material_copy.size())); - - uint32_t changed_bits = 0; - for (size_t i = 0; i < seed_material.size(); i++) { - std::bitset<sizeof(uint32_t) * 8> bitset(seed_material[i] ^ - seed_material_copy[i]); - changed_bits += bitset.count(); - } - - EXPECT_LE(changed_bits, 0.7 * sizeof(uint32_t) * 8 * seed_material.size()); - EXPECT_GE(changed_bits, 0.3 * sizeof(uint32_t) * 8 * seed_material.size()); - } -} - -} // namespace diff --git a/third_party/abseil_cpp/absl/random/internal/sequence_urbg.h b/third_party/abseil_cpp/absl/random/internal/sequence_urbg.h deleted file mode 100644 index bc96a12cd29c..000000000000 --- a/third_party/abseil_cpp/absl/random/internal/sequence_urbg.h +++ /dev/null @@ -1,60 +0,0 @@ -// Copyright 2017 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#ifndef ABSL_RANDOM_INTERNAL_SEQUENCE_URBG_H_ -#define ABSL_RANDOM_INTERNAL_SEQUENCE_URBG_H_ - -#include <cstdint> -#include <cstring> -#include <limits> -#include <type_traits> -#include <vector> - -#include "absl/base/config.h" - -namespace absl { -ABSL_NAMESPACE_BEGIN -namespace random_internal { - -// `sequence_urbg` is a simple random number generator which meets the -// requirements of [rand.req.urbg], and is solely for testing absl -// distributions. -class sequence_urbg { - public: - using result_type = uint64_t; - - static constexpr result_type(min)() { - return (std::numeric_limits<result_type>::min)(); - } - static constexpr result_type(max)() { - return (std::numeric_limits<result_type>::max)(); - } - - sequence_urbg(std::initializer_list<result_type> data) : i_(0), data_(data) {} - void reset() { i_ = 0; } - - result_type operator()() { return data_[i_++ % data_.size()]; } - - size_t invocations() const { return i_; } - - private: - size_t i_; - std::vector<result_type> data_; -}; - -} // namespace random_internal -ABSL_NAMESPACE_END -} // namespace absl - -#endif // ABSL_RANDOM_INTERNAL_SEQUENCE_URBG_H_ diff --git a/third_party/abseil_cpp/absl/random/internal/traits.h b/third_party/abseil_cpp/absl/random/internal/traits.h deleted file mode 100644 index 75772bd9abb4..000000000000 --- a/third_party/abseil_cpp/absl/random/internal/traits.h +++ /dev/null @@ -1,101 +0,0 @@ -// Copyright 2017 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#ifndef ABSL_RANDOM_INTERNAL_TRAITS_H_ -#define ABSL_RANDOM_INTERNAL_TRAITS_H_ - -#include <cstdint> -#include <limits> -#include <type_traits> - -#include "absl/base/config.h" - -namespace absl { -ABSL_NAMESPACE_BEGIN -namespace random_internal { - -// random_internal::is_widening_convertible<A, B> -// -// Returns whether a type A is widening-convertible to a type B. -// -// A is widening-convertible to B means: -// A a = <any number>; -// B b = a; -// A c = b; -// EXPECT_EQ(a, c); -template <typename A, typename B> -class is_widening_convertible { - // As long as there are enough bits in the exact part of a number: - // - unsigned can fit in float, signed, unsigned - // - signed can fit in float, signed - // - float can fit in float - // So we define rank to be: - // - rank(float) -> 2 - // - rank(signed) -> 1 - // - rank(unsigned) -> 0 - template <class T> - static constexpr int rank() { - return !std::numeric_limits<T>::is_integer + - std::numeric_limits<T>::is_signed; - } - - public: - // If an arithmetic-type B can represent at least as many digits as a type A, - // and B belongs to a rank no lower than A, then A can be safely represented - // by B through a widening-conversion. - static constexpr bool value = - std::numeric_limits<A>::digits <= std::numeric_limits<B>::digits && - rank<A>() <= rank<B>(); -}; - -// unsigned_bits<N>::type returns the unsigned int type with the indicated -// number of bits. -template <size_t N> -struct unsigned_bits; - -template <> -struct unsigned_bits<8> { - using type = uint8_t; -}; -template <> -struct unsigned_bits<16> { - using type = uint16_t; -}; -template <> -struct unsigned_bits<32> { - using type = uint32_t; -}; -template <> -struct unsigned_bits<64> { - using type = uint64_t; -}; - -#ifdef ABSL_HAVE_INTRINSIC_INT128 -template <> -struct unsigned_bits<128> { - using type = __uint128_t; -}; -#endif - -template <typename IntType> -struct make_unsigned_bits { - using type = typename unsigned_bits<std::numeric_limits< - typename std::make_unsigned<IntType>::type>::digits>::type; -}; - -} // namespace random_internal -ABSL_NAMESPACE_END -} // namespace absl - -#endif // ABSL_RANDOM_INTERNAL_TRAITS_H_ diff --git a/third_party/abseil_cpp/absl/random/internal/traits_test.cc b/third_party/abseil_cpp/absl/random/internal/traits_test.cc deleted file mode 100644 index a844887d3e44..000000000000 --- a/third_party/abseil_cpp/absl/random/internal/traits_test.cc +++ /dev/null @@ -1,126 +0,0 @@ -// Copyright 2017 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#include "absl/random/internal/traits.h" - -#include <cstdint> -#include <type_traits> - -#include "gtest/gtest.h" - -namespace { - -using absl::random_internal::is_widening_convertible; - -// CheckWideningConvertsToSelf<T1, T2, ...>() -// -// For each type T, checks: -// - T IS widening-convertible to itself. -// -template <typename T> -void CheckWideningConvertsToSelf() { - static_assert(is_widening_convertible<T, T>::value, - "Type is not convertible to self!"); -} - -template <typename T, typename Next, typename... Args> -void CheckWideningConvertsToSelf() { - CheckWideningConvertsToSelf<T>(); - CheckWideningConvertsToSelf<Next, Args...>(); -} - -// CheckNotWideningConvertibleWithSigned<T1, T2, ...>() -// -// For each unsigned-type T, checks that: -// - T is NOT widening-convertible to Signed(T) -// - Signed(T) is NOT widening-convertible to T -// -template <typename T> -void CheckNotWideningConvertibleWithSigned() { - using signed_t = typename std::make_signed<T>::type; - - static_assert(!is_widening_convertible<T, signed_t>::value, - "Unsigned type is convertible to same-sized signed-type!"); - static_assert(!is_widening_convertible<signed_t, T>::value, - "Signed type is convertible to same-sized unsigned-type!"); -} - -template <typename T, typename Next, typename... Args> -void CheckNotWideningConvertibleWithSigned() { - CheckNotWideningConvertibleWithSigned<T>(); - CheckWideningConvertsToSelf<Next, Args...>(); -} - -// CheckWideningConvertsToLargerType<T1, T2, ...>() -// -// For each successive unsigned-types {Ti, Ti+1}, checks that: -// - Ti IS widening-convertible to Ti+1 -// - Ti IS widening-convertible to Signed(Ti+1) -// - Signed(Ti) is NOT widening-convertible to Ti -// - Signed(Ti) IS widening-convertible to Ti+1 -template <typename T, typename Higher> -void CheckWideningConvertsToLargerTypes() { - using signed_t = typename std::make_signed<T>::type; - using higher_t = Higher; - using signed_higher_t = typename std::make_signed<Higher>::type; - - static_assert(is_widening_convertible<T, higher_t>::value, - "Type not embeddable into larger type!"); - static_assert(is_widening_convertible<T, signed_higher_t>::value, - "Type not embeddable into larger signed type!"); - static_assert(!is_widening_convertible<signed_t, higher_t>::value, - "Signed type is embeddable into larger unsigned type!"); - static_assert(is_widening_convertible<signed_t, signed_higher_t>::value, - "Signed type not embeddable into larger signed type!"); -} - -template <typename T, typename Higher, typename Next, typename... Args> -void CheckWideningConvertsToLargerTypes() { - CheckWideningConvertsToLargerTypes<T, Higher>(); - CheckWideningConvertsToLargerTypes<Higher, Next, Args...>(); -} - -// CheckWideningConvertsTo<T, U, [expect]> -// -// Checks that T DOES widening-convert to U. -// If "expect" is false, then asserts that T does NOT widening-convert to U. -template <typename T, typename U, bool expect = true> -void CheckWideningConvertsTo() { - static_assert(is_widening_convertible<T, U>::value == expect, - "Unexpected result for is_widening_convertible<T, U>!"); -} - -TEST(TraitsTest, IsWideningConvertibleTest) { - constexpr bool kInvalid = false; - - CheckWideningConvertsToSelf< - uint8_t, uint16_t, uint32_t, uint64_t, - int8_t, int16_t, int32_t, int64_t, - float, double>(); - CheckNotWideningConvertibleWithSigned< - uint8_t, uint16_t, uint32_t, uint64_t>(); - CheckWideningConvertsToLargerTypes< - uint8_t, uint16_t, uint32_t, uint64_t>(); - - CheckWideningConvertsTo<float, double>(); - CheckWideningConvertsTo<uint16_t, float>(); - CheckWideningConvertsTo<uint32_t, double>(); - CheckWideningConvertsTo<uint64_t, double, kInvalid>(); - CheckWideningConvertsTo<double, float, kInvalid>(); - - CheckWideningConvertsTo<bool, int>(); - CheckWideningConvertsTo<bool, float>(); -} - -} // namespace diff --git a/third_party/abseil_cpp/absl/random/internal/uniform_helper.h b/third_party/abseil_cpp/absl/random/internal/uniform_helper.h deleted file mode 100644 index 1243bc1c62ab..000000000000 --- a/third_party/abseil_cpp/absl/random/internal/uniform_helper.h +++ /dev/null @@ -1,244 +0,0 @@ -// Copyright 2019 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. -// -#ifndef ABSL_RANDOM_INTERNAL_UNIFORM_HELPER_H_ -#define ABSL_RANDOM_INTERNAL_UNIFORM_HELPER_H_ - -#include <cmath> -#include <limits> -#include <type_traits> - -#include "absl/base/config.h" -#include "absl/meta/type_traits.h" -#include "absl/random/internal/traits.h" - -namespace absl { -ABSL_NAMESPACE_BEGIN - -template <typename IntType> -class uniform_int_distribution; - -template <typename RealType> -class uniform_real_distribution; - -// Interval tag types which specify whether the interval is open or closed -// on either boundary. - -namespace random_internal { -template <typename T> -struct TagTypeCompare {}; - -template <typename T> -constexpr bool operator==(TagTypeCompare<T>, TagTypeCompare<T>) { - // Tags are mono-states. They always compare equal. - return true; -} -template <typename T> -constexpr bool operator!=(TagTypeCompare<T>, TagTypeCompare<T>) { - return false; -} - -} // namespace random_internal - -struct IntervalClosedClosedTag - : public random_internal::TagTypeCompare<IntervalClosedClosedTag> {}; -struct IntervalClosedOpenTag - : public random_internal::TagTypeCompare<IntervalClosedOpenTag> {}; -struct IntervalOpenClosedTag - : public random_internal::TagTypeCompare<IntervalOpenClosedTag> {}; -struct IntervalOpenOpenTag - : public random_internal::TagTypeCompare<IntervalOpenOpenTag> {}; - -namespace random_internal { - -// In the absence of an explicitly provided return-type, the template -// "uniform_inferred_return_t<A, B>" is used to derive a suitable type, based on -// the data-types of the endpoint-arguments {A lo, B hi}. -// -// Given endpoints {A lo, B hi}, one of {A, B} will be chosen as the -// return-type, if one type can be implicitly converted into the other, in a -// lossless way. The template "is_widening_convertible" implements the -// compile-time logic for deciding if such a conversion is possible. -// -// If no such conversion between {A, B} exists, then the overload for -// absl::Uniform() will be discarded, and the call will be ill-formed. -// Return-type for absl::Uniform() when the return-type is inferred. -template <typename A, typename B> -using uniform_inferred_return_t = - absl::enable_if_t<absl::disjunction<is_widening_convertible<A, B>, - is_widening_convertible<B, A>>::value, - typename std::conditional< - is_widening_convertible<A, B>::value, B, A>::type>; - -// The functions -// uniform_lower_bound(tag, a, b) -// and -// uniform_upper_bound(tag, a, b) -// are used as implementation-details for absl::Uniform(). -// -// Conceptually, -// [a, b] == [uniform_lower_bound(IntervalClosedClosed, a, b), -// uniform_upper_bound(IntervalClosedClosed, a, b)] -// (a, b) == [uniform_lower_bound(IntervalOpenOpen, a, b), -// uniform_upper_bound(IntervalOpenOpen, a, b)] -// [a, b) == [uniform_lower_bound(IntervalClosedOpen, a, b), -// uniform_upper_bound(IntervalClosedOpen, a, b)] -// (a, b] == [uniform_lower_bound(IntervalOpenClosed, a, b), -// uniform_upper_bound(IntervalOpenClosed, a, b)] -// -template <typename IntType, typename Tag> -typename absl::enable_if_t< - absl::conjunction< - std::is_integral<IntType>, - absl::disjunction<std::is_same<Tag, IntervalOpenClosedTag>, - std::is_same<Tag, IntervalOpenOpenTag>>>::value, - IntType> -uniform_lower_bound(Tag, IntType a, IntType) { - return a < (std::numeric_limits<IntType>::max)() ? (a + 1) : a; -} - -template <typename FloatType, typename Tag> -typename absl::enable_if_t< - absl::conjunction< - std::is_floating_point<FloatType>, - absl::disjunction<std::is_same<Tag, IntervalOpenClosedTag>, - std::is_same<Tag, IntervalOpenOpenTag>>>::value, - FloatType> -uniform_lower_bound(Tag, FloatType a, FloatType b) { - return std::nextafter(a, b); -} - -template <typename NumType, typename Tag> -typename absl::enable_if_t< - absl::disjunction<std::is_same<Tag, IntervalClosedClosedTag>, - std::is_same<Tag, IntervalClosedOpenTag>>::value, - NumType> -uniform_lower_bound(Tag, NumType a, NumType) { - return a; -} - -template <typename IntType, typename Tag> -typename absl::enable_if_t< - absl::conjunction< - std::is_integral<IntType>, - absl::disjunction<std::is_same<Tag, IntervalClosedOpenTag>, - std::is_same<Tag, IntervalOpenOpenTag>>>::value, - IntType> -uniform_upper_bound(Tag, IntType, IntType b) { - return b > (std::numeric_limits<IntType>::min)() ? (b - 1) : b; -} - -template <typename FloatType, typename Tag> -typename absl::enable_if_t< - absl::conjunction< - std::is_floating_point<FloatType>, - absl::disjunction<std::is_same<Tag, IntervalClosedOpenTag>, - std::is_same<Tag, IntervalOpenOpenTag>>>::value, - FloatType> -uniform_upper_bound(Tag, FloatType, FloatType b) { - return b; -} - -template <typename IntType, typename Tag> -typename absl::enable_if_t< - absl::conjunction< - std::is_integral<IntType>, - absl::disjunction<std::is_same<Tag, IntervalClosedClosedTag>, - std::is_same<Tag, IntervalOpenClosedTag>>>::value, - IntType> -uniform_upper_bound(Tag, IntType, IntType b) { - return b; -} - -template <typename FloatType, typename Tag> -typename absl::enable_if_t< - absl::conjunction< - std::is_floating_point<FloatType>, - absl::disjunction<std::is_same<Tag, IntervalClosedClosedTag>, - std::is_same<Tag, IntervalOpenClosedTag>>>::value, - FloatType> -uniform_upper_bound(Tag, FloatType, FloatType b) { - return std::nextafter(b, (std::numeric_limits<FloatType>::max)()); -} - -// Returns whether the bounds are valid for the underlying distribution. -// Inputs must have already been resolved via uniform_*_bound calls. -// -// The c++ standard constraints in [rand.dist.uni.int] are listed as: -// requires: lo <= hi. -// -// In the uniform_int_distrubtion, {lo, hi} are closed, closed. Thus: -// [0, 0] is legal. -// [0, 0) is not legal, but [0, 1) is, which translates to [0, 0]. -// (0, 1) is not legal, but (0, 2) is, which translates to [1, 1]. -// (0, 0] is not legal, but (0, 1] is, which translates to [1, 1]. -// -// The c++ standard constraints in [rand.dist.uni.real] are listed as: -// requires: lo <= hi. -// requires: (hi - lo) <= numeric_limits<T>::max() -// -// In the uniform_real_distribution, {lo, hi} are closed, open, Thus: -// [0, 0] is legal, which is [0, 0+epsilon). -// [0, 0) is legal. -// (0, 0) is not legal, but (0-epsilon, 0+epsilon) is. -// (0, 0] is not legal, but (0, 0+epsilon] is. -// -template <typename FloatType> -absl::enable_if_t<std::is_floating_point<FloatType>::value, bool> -is_uniform_range_valid(FloatType a, FloatType b) { - return a <= b && std::isfinite(b - a); -} - -template <typename IntType> -absl::enable_if_t<std::is_integral<IntType>::value, bool> -is_uniform_range_valid(IntType a, IntType b) { - return a <= b; -} - -// UniformDistribution selects either absl::uniform_int_distribution -// or absl::uniform_real_distribution depending on the NumType parameter. -template <typename NumType> -using UniformDistribution = - typename std::conditional<std::is_integral<NumType>::value, - absl::uniform_int_distribution<NumType>, - absl::uniform_real_distribution<NumType>>::type; - -// UniformDistributionWrapper is used as the underlying distribution type -// by the absl::Uniform template function. It selects the proper Abseil -// uniform distribution and provides constructor overloads that match the -// expected parameter order as well as adjusting distribtuion bounds based -// on the tag. -template <typename NumType> -struct UniformDistributionWrapper : public UniformDistribution<NumType> { - template <typename TagType> - explicit UniformDistributionWrapper(TagType, NumType lo, NumType hi) - : UniformDistribution<NumType>( - uniform_lower_bound<NumType>(TagType{}, lo, hi), - uniform_upper_bound<NumType>(TagType{}, lo, hi)) {} - - explicit UniformDistributionWrapper(NumType lo, NumType hi) - : UniformDistribution<NumType>( - uniform_lower_bound<NumType>(IntervalClosedOpenTag(), lo, hi), - uniform_upper_bound<NumType>(IntervalClosedOpenTag(), lo, hi)) {} - - explicit UniformDistributionWrapper() - : UniformDistribution<NumType>(std::numeric_limits<NumType>::lowest(), - (std::numeric_limits<NumType>::max)()) {} -}; - -} // namespace random_internal -ABSL_NAMESPACE_END -} // namespace absl - -#endif // ABSL_RANDOM_INTERNAL_UNIFORM_HELPER_H_ diff --git a/third_party/abseil_cpp/absl/random/internal/uniform_helper_test.cc b/third_party/abseil_cpp/absl/random/internal/uniform_helper_test.cc deleted file mode 100644 index 173c49b0b7f8..000000000000 --- a/third_party/abseil_cpp/absl/random/internal/uniform_helper_test.cc +++ /dev/null @@ -1,279 +0,0 @@ -// Copyright 2017 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#include "absl/random/internal/uniform_helper.h" - -#include <cmath> -#include <cstdint> -#include <random> - -#include "gtest/gtest.h" - -namespace { - -using absl::IntervalClosedClosedTag; -using absl::IntervalClosedOpenTag; -using absl::IntervalOpenClosedTag; -using absl::IntervalOpenOpenTag; -using absl::random_internal::uniform_inferred_return_t; -using absl::random_internal::uniform_lower_bound; -using absl::random_internal::uniform_upper_bound; - -class UniformHelperTest : public testing::Test {}; - -TEST_F(UniformHelperTest, UniformBoundFunctionsGeneral) { - constexpr IntervalClosedClosedTag IntervalClosedClosed; - constexpr IntervalClosedOpenTag IntervalClosedOpen; - constexpr IntervalOpenClosedTag IntervalOpenClosed; - constexpr IntervalOpenOpenTag IntervalOpenOpen; - - // absl::uniform_int_distribution natively assumes IntervalClosedClosed - // absl::uniform_real_distribution natively assumes IntervalClosedOpen - - EXPECT_EQ(uniform_lower_bound(IntervalOpenClosed, 0, 100), 1); - EXPECT_EQ(uniform_lower_bound(IntervalOpenOpen, 0, 100), 1); - EXPECT_GT(uniform_lower_bound<float>(IntervalOpenClosed, 0, 1.0), 0); - EXPECT_GT(uniform_lower_bound<float>(IntervalOpenOpen, 0, 1.0), 0); - EXPECT_GT(uniform_lower_bound<double>(IntervalOpenClosed, 0, 1.0), 0); - EXPECT_GT(uniform_lower_bound<double>(IntervalOpenOpen, 0, 1.0), 0); - - EXPECT_EQ(uniform_lower_bound(IntervalClosedClosed, 0, 100), 0); - EXPECT_EQ(uniform_lower_bound(IntervalClosedOpen, 0, 100), 0); - EXPECT_EQ(uniform_lower_bound<float>(IntervalClosedClosed, 0, 1.0), 0); - EXPECT_EQ(uniform_lower_bound<float>(IntervalClosedOpen, 0, 1.0), 0); - EXPECT_EQ(uniform_lower_bound<double>(IntervalClosedClosed, 0, 1.0), 0); - EXPECT_EQ(uniform_lower_bound<double>(IntervalClosedOpen, 0, 1.0), 0); - - EXPECT_EQ(uniform_upper_bound(IntervalOpenOpen, 0, 100), 99); - EXPECT_EQ(uniform_upper_bound(IntervalClosedOpen, 0, 100), 99); - EXPECT_EQ(uniform_upper_bound<float>(IntervalOpenOpen, 0, 1.0), 1.0); - EXPECT_EQ(uniform_upper_bound<float>(IntervalClosedOpen, 0, 1.0), 1.0); - EXPECT_EQ(uniform_upper_bound<double>(IntervalOpenOpen, 0, 1.0), 1.0); - EXPECT_EQ(uniform_upper_bound<double>(IntervalClosedOpen, 0, 1.0), 1.0); - - EXPECT_EQ(uniform_upper_bound(IntervalOpenClosed, 0, 100), 100); - EXPECT_EQ(uniform_upper_bound(IntervalClosedClosed, 0, 100), 100); - EXPECT_GT(uniform_upper_bound<float>(IntervalOpenClosed, 0, 1.0), 1.0); - EXPECT_GT(uniform_upper_bound<float>(IntervalClosedClosed, 0, 1.0), 1.0); - EXPECT_GT(uniform_upper_bound<double>(IntervalOpenClosed, 0, 1.0), 1.0); - EXPECT_GT(uniform_upper_bound<double>(IntervalClosedClosed, 0, 1.0), 1.0); - - // Negative value tests - EXPECT_EQ(uniform_lower_bound(IntervalOpenClosed, -100, -1), -99); - EXPECT_EQ(uniform_lower_bound(IntervalOpenOpen, -100, -1), -99); - EXPECT_GT(uniform_lower_bound<float>(IntervalOpenClosed, -2.0, -1.0), -2.0); - EXPECT_GT(uniform_lower_bound<float>(IntervalOpenOpen, -2.0, -1.0), -2.0); - EXPECT_GT(uniform_lower_bound<double>(IntervalOpenClosed, -2.0, -1.0), -2.0); - EXPECT_GT(uniform_lower_bound<double>(IntervalOpenOpen, -2.0, -1.0), -2.0); - - EXPECT_EQ(uniform_lower_bound(IntervalClosedClosed, -100, -1), -100); - EXPECT_EQ(uniform_lower_bound(IntervalClosedOpen, -100, -1), -100); - EXPECT_EQ(uniform_lower_bound<float>(IntervalClosedClosed, -2.0, -1.0), -2.0); - EXPECT_EQ(uniform_lower_bound<float>(IntervalClosedOpen, -2.0, -1.0), -2.0); - EXPECT_EQ(uniform_lower_bound<double>(IntervalClosedClosed, -2.0, -1.0), - -2.0); - EXPECT_EQ(uniform_lower_bound<double>(IntervalClosedOpen, -2.0, -1.0), -2.0); - - EXPECT_EQ(uniform_upper_bound(IntervalOpenOpen, -100, -1), -2); - EXPECT_EQ(uniform_upper_bound(IntervalClosedOpen, -100, -1), -2); - EXPECT_EQ(uniform_upper_bound<float>(IntervalOpenOpen, -2.0, -1.0), -1.0); - EXPECT_EQ(uniform_upper_bound<float>(IntervalClosedOpen, -2.0, -1.0), -1.0); - EXPECT_EQ(uniform_upper_bound<double>(IntervalOpenOpen, -2.0, -1.0), -1.0); - EXPECT_EQ(uniform_upper_bound<double>(IntervalClosedOpen, -2.0, -1.0), -1.0); - - EXPECT_EQ(uniform_upper_bound(IntervalOpenClosed, -100, -1), -1); - EXPECT_EQ(uniform_upper_bound(IntervalClosedClosed, -100, -1), -1); - EXPECT_GT(uniform_upper_bound<float>(IntervalOpenClosed, -2.0, -1.0), -1.0); - EXPECT_GT(uniform_upper_bound<float>(IntervalClosedClosed, -2.0, -1.0), -1.0); - EXPECT_GT(uniform_upper_bound<double>(IntervalOpenClosed, -2.0, -1.0), -1.0); - EXPECT_GT(uniform_upper_bound<double>(IntervalClosedClosed, -2.0, -1.0), - -1.0); - - EXPECT_GT(uniform_lower_bound(IntervalOpenClosed, 1.0, 2.0), 1.0); - EXPECT_LT(uniform_lower_bound(IntervalOpenClosed, 1.0, +0.0), 1.0); - EXPECT_LT(uniform_lower_bound(IntervalOpenClosed, 1.0, -0.0), 1.0); - EXPECT_LT(uniform_lower_bound(IntervalOpenClosed, 1.0, -1.0), 1.0); -} - -TEST_F(UniformHelperTest, UniformBoundFunctionsIntBounds) { - // Verifies the saturating nature of uniform_lower_bound and - // uniform_upper_bound - constexpr IntervalOpenOpenTag IntervalOpenOpen; - - // uint max. - constexpr auto m = (std::numeric_limits<uint64_t>::max)(); - - EXPECT_EQ(1, uniform_lower_bound(IntervalOpenOpen, 0u, 0u)); - EXPECT_EQ(m, uniform_lower_bound(IntervalOpenOpen, m, m)); - EXPECT_EQ(m, uniform_lower_bound(IntervalOpenOpen, m - 1, m - 1)); - EXPECT_EQ(0, uniform_upper_bound(IntervalOpenOpen, 0u, 0u)); - EXPECT_EQ(m - 1, uniform_upper_bound(IntervalOpenOpen, m, m)); - - // int min/max - constexpr auto l = (std::numeric_limits<int64_t>::min)(); - constexpr auto r = (std::numeric_limits<int64_t>::max)(); - EXPECT_EQ(1, uniform_lower_bound(IntervalOpenOpen, 0, 0)); - EXPECT_EQ(l + 1, uniform_lower_bound(IntervalOpenOpen, l, l)); - EXPECT_EQ(r, uniform_lower_bound(IntervalOpenOpen, r - 1, r - 1)); - EXPECT_EQ(r, uniform_lower_bound(IntervalOpenOpen, r, r)); - EXPECT_EQ(-1, uniform_upper_bound(IntervalOpenOpen, 0, 0)); - EXPECT_EQ(l, uniform_upper_bound(IntervalOpenOpen, l, l)); - EXPECT_EQ(r - 1, uniform_upper_bound(IntervalOpenOpen, r, r)); -} - -TEST_F(UniformHelperTest, UniformBoundFunctionsRealBounds) { - // absl::uniform_real_distribution natively assumes IntervalClosedOpen; - // use the inverse here so each bound has to change. - constexpr IntervalOpenClosedTag IntervalOpenClosed; - - // Edge cases: the next value toward itself is itself. - EXPECT_EQ(1.0, uniform_lower_bound(IntervalOpenClosed, 1.0, 1.0)); - EXPECT_EQ(1.0f, uniform_lower_bound(IntervalOpenClosed, 1.0f, 1.0f)); - - // rightmost and leftmost finite values. - constexpr auto r = (std::numeric_limits<double>::max)(); - const auto re = std::nexttoward(r, 0.0); - constexpr auto l = -r; - const auto le = std::nexttoward(l, 0.0); - - EXPECT_EQ(l, uniform_lower_bound(IntervalOpenClosed, l, l)); // (l,l) - EXPECT_EQ(r, uniform_lower_bound(IntervalOpenClosed, r, r)); // (r,r) - EXPECT_EQ(le, uniform_lower_bound(IntervalOpenClosed, l, r)); // (l,r) - EXPECT_EQ(le, uniform_lower_bound(IntervalOpenClosed, l, 0.0)); // (l, 0) - EXPECT_EQ(le, uniform_lower_bound(IntervalOpenClosed, l, le)); // (l, le) - EXPECT_EQ(r, uniform_lower_bound(IntervalOpenClosed, re, r)); // (re, r) - - EXPECT_EQ(le, uniform_upper_bound(IntervalOpenClosed, l, l)); // (l,l) - EXPECT_EQ(r, uniform_upper_bound(IntervalOpenClosed, r, r)); // (r,r) - EXPECT_EQ(r, uniform_upper_bound(IntervalOpenClosed, l, r)); // (l,r) - EXPECT_EQ(r, uniform_upper_bound(IntervalOpenClosed, l, re)); // (l,re) - EXPECT_EQ(r, uniform_upper_bound(IntervalOpenClosed, 0.0, r)); // (0, r) - EXPECT_EQ(r, uniform_upper_bound(IntervalOpenClosed, re, r)); // (re, r) - EXPECT_EQ(r, uniform_upper_bound(IntervalOpenClosed, le, re)); // (le, re) - - const double e = std::nextafter(1.0, 2.0); // 1 + epsilon - const double f = std::nextafter(1.0, 0.0); // 1 - epsilon - - // (1.0, 1.0 + epsilon) - EXPECT_EQ(e, uniform_lower_bound(IntervalOpenClosed, 1.0, e)); - EXPECT_EQ(std::nextafter(e, 2.0), - uniform_upper_bound(IntervalOpenClosed, 1.0, e)); - - // (1.0-epsilon, 1.0) - EXPECT_EQ(1.0, uniform_lower_bound(IntervalOpenClosed, f, 1.0)); - EXPECT_EQ(e, uniform_upper_bound(IntervalOpenClosed, f, 1.0)); - - // denorm cases. - const double g = std::numeric_limits<double>::denorm_min(); - const double h = std::nextafter(g, 1.0); - - // (0, denorm_min) - EXPECT_EQ(g, uniform_lower_bound(IntervalOpenClosed, 0.0, g)); - EXPECT_EQ(h, uniform_upper_bound(IntervalOpenClosed, 0.0, g)); - - // (denorm_min, 1.0) - EXPECT_EQ(h, uniform_lower_bound(IntervalOpenClosed, g, 1.0)); - EXPECT_EQ(e, uniform_upper_bound(IntervalOpenClosed, g, 1.0)); - - // Edge cases: invalid bounds. - EXPECT_EQ(f, uniform_lower_bound(IntervalOpenClosed, 1.0, -1.0)); -} - -struct Invalid {}; - -template <typename A, typename B> -auto InferredUniformReturnT(int) -> uniform_inferred_return_t<A, B>; - -template <typename, typename> -Invalid InferredUniformReturnT(...); - -// Given types <A, B, Expect>, CheckArgsInferType() verifies that -// -// uniform_inferred_return_t<A, B> and -// uniform_inferred_return_t<B, A> -// -// returns the type "Expect". -// -// This interface can also be used to assert that a given inferred return types -// are invalid. Writing: -// -// CheckArgsInferType<float, int, Invalid>() -// -// will assert that this overload does not exist. -template <typename A, typename B, typename Expect> -void CheckArgsInferType() { - static_assert( - absl::conjunction< - std::is_same<Expect, decltype(InferredUniformReturnT<A, B>(0))>, - std::is_same<Expect, - decltype(InferredUniformReturnT<B, A>(0))>>::value, - ""); -} - -TEST_F(UniformHelperTest, UniformTypeInference) { - // Infers common types. - CheckArgsInferType<uint16_t, uint16_t, uint16_t>(); - CheckArgsInferType<uint32_t, uint32_t, uint32_t>(); - CheckArgsInferType<uint64_t, uint64_t, uint64_t>(); - CheckArgsInferType<int16_t, int16_t, int16_t>(); - CheckArgsInferType<int32_t, int32_t, int32_t>(); - CheckArgsInferType<int64_t, int64_t, int64_t>(); - CheckArgsInferType<float, float, float>(); - CheckArgsInferType<double, double, double>(); - - // Properly promotes uint16_t. - CheckArgsInferType<uint16_t, uint32_t, uint32_t>(); - CheckArgsInferType<uint16_t, uint64_t, uint64_t>(); - CheckArgsInferType<uint16_t, int32_t, int32_t>(); - CheckArgsInferType<uint16_t, int64_t, int64_t>(); - CheckArgsInferType<uint16_t, float, float>(); - CheckArgsInferType<uint16_t, double, double>(); - - // Properly promotes int16_t. - CheckArgsInferType<int16_t, int32_t, int32_t>(); - CheckArgsInferType<int16_t, int64_t, int64_t>(); - CheckArgsInferType<int16_t, float, float>(); - CheckArgsInferType<int16_t, double, double>(); - - // Invalid (u)int16_t-pairings do not compile. - // See "CheckArgsInferType" comments above, for how this is achieved. - CheckArgsInferType<uint16_t, int16_t, Invalid>(); - CheckArgsInferType<int16_t, uint32_t, Invalid>(); - CheckArgsInferType<int16_t, uint64_t, Invalid>(); - - // Properly promotes uint32_t. - CheckArgsInferType<uint32_t, uint64_t, uint64_t>(); - CheckArgsInferType<uint32_t, int64_t, int64_t>(); - CheckArgsInferType<uint32_t, double, double>(); - - // Properly promotes int32_t. - CheckArgsInferType<int32_t, int64_t, int64_t>(); - CheckArgsInferType<int32_t, double, double>(); - - // Invalid (u)int32_t-pairings do not compile. - CheckArgsInferType<uint32_t, int32_t, Invalid>(); - CheckArgsInferType<int32_t, uint64_t, Invalid>(); - CheckArgsInferType<int32_t, float, Invalid>(); - CheckArgsInferType<uint32_t, float, Invalid>(); - - // Invalid (u)int64_t-pairings do not compile. - CheckArgsInferType<uint64_t, int64_t, Invalid>(); - CheckArgsInferType<int64_t, float, Invalid>(); - CheckArgsInferType<int64_t, double, Invalid>(); - - // Properly promotes float. - CheckArgsInferType<float, double, double>(); -} - -} // namespace diff --git a/third_party/abseil_cpp/absl/random/internal/wide_multiply.h b/third_party/abseil_cpp/absl/random/internal/wide_multiply.h deleted file mode 100644 index 0afcbe08e229..000000000000 --- a/third_party/abseil_cpp/absl/random/internal/wide_multiply.h +++ /dev/null @@ -1,111 +0,0 @@ -// Copyright 2017 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#ifndef ABSL_RANDOM_INTERNAL_WIDE_MULTIPLY_H_ -#define ABSL_RANDOM_INTERNAL_WIDE_MULTIPLY_H_ - -#include <cstdint> -#include <limits> -#include <type_traits> - -#if (defined(_WIN32) || defined(_WIN64)) && defined(_M_IA64) -#include <intrin.h> // NOLINT(build/include_order) -#pragma intrinsic(_umul128) -#define ABSL_INTERNAL_USE_UMUL128 1 -#endif - -#include "absl/base/config.h" -#include "absl/base/internal/bits.h" -#include "absl/numeric/int128.h" -#include "absl/random/internal/traits.h" - -namespace absl { -ABSL_NAMESPACE_BEGIN -namespace random_internal { - -// Helper object to multiply two 64-bit values to a 128-bit value. -// MultiplyU64ToU128 multiplies two 64-bit values to a 128-bit value. -// If an intrinsic is available, it is used, otherwise use native 32-bit -// multiplies to construct the result. -inline absl::uint128 MultiplyU64ToU128(uint64_t a, uint64_t b) { -#if defined(ABSL_HAVE_INTRINSIC_INT128) - return absl::uint128(static_cast<__uint128_t>(a) * b); -#elif defined(ABSL_INTERNAL_USE_UMUL128) - // uint64_t * uint64_t => uint128 multiply using imul intrinsic on MSVC. - uint64_t high = 0; - const uint64_t low = _umul128(a, b, &high); - return absl::MakeUint128(high, low); -#else - // uint128(a) * uint128(b) in emulated mode computes a full 128-bit x 128-bit - // multiply. However there are many cases where that is not necessary, and it - // is only necessary to support a 64-bit x 64-bit = 128-bit multiply. This is - // for those cases. - const uint64_t a00 = static_cast<uint32_t>(a); - const uint64_t a32 = a >> 32; - const uint64_t b00 = static_cast<uint32_t>(b); - const uint64_t b32 = b >> 32; - - const uint64_t c00 = a00 * b00; - const uint64_t c32a = a00 * b32; - const uint64_t c32b = a32 * b00; - const uint64_t c64 = a32 * b32; - - const uint32_t carry = - static_cast<uint32_t>(((c00 >> 32) + static_cast<uint32_t>(c32a) + - static_cast<uint32_t>(c32b)) >> - 32); - - return absl::MakeUint128(c64 + (c32a >> 32) + (c32b >> 32) + carry, - c00 + (c32a << 32) + (c32b << 32)); -#endif -} - -// wide_multiply<T> multiplies two N-bit values to a 2N-bit result. -template <typename UIntType> -struct wide_multiply { - static constexpr size_t kN = std::numeric_limits<UIntType>::digits; - using input_type = UIntType; - using result_type = typename random_internal::unsigned_bits<kN * 2>::type; - - static result_type multiply(input_type a, input_type b) { - return static_cast<result_type>(a) * b; - } - - static input_type hi(result_type r) { return r >> kN; } - static input_type lo(result_type r) { return r; } - - static_assert(std::is_unsigned<UIntType>::value, - "Class-template wide_multiply<> argument must be unsigned."); -}; - -#ifndef ABSL_HAVE_INTRINSIC_INT128 -template <> -struct wide_multiply<uint64_t> { - using input_type = uint64_t; - using result_type = absl::uint128; - - static result_type multiply(uint64_t a, uint64_t b) { - return MultiplyU64ToU128(a, b); - } - - static uint64_t hi(result_type r) { return absl::Uint128High64(r); } - static uint64_t lo(result_type r) { return absl::Uint128Low64(r); } -}; -#endif - -} // namespace random_internal -ABSL_NAMESPACE_END -} // namespace absl - -#endif // ABSL_RANDOM_INTERNAL_WIDE_MULTIPLY_H_ diff --git a/third_party/abseil_cpp/absl/random/internal/wide_multiply_test.cc b/third_party/abseil_cpp/absl/random/internal/wide_multiply_test.cc deleted file mode 100644 index ca8ce923b710..000000000000 --- a/third_party/abseil_cpp/absl/random/internal/wide_multiply_test.cc +++ /dev/null @@ -1,66 +0,0 @@ -// Copyright 2017 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#include "absl/random/internal/wide_multiply.h" - -#include "gtest/gtest.h" -#include "absl/base/internal/bits.h" -#include "absl/numeric/int128.h" - -using absl::random_internal::MultiplyU64ToU128; - -namespace { - -TEST(WideMultiplyTest, MultiplyU64ToU128Test) { - constexpr uint64_t k1 = 1; - constexpr uint64_t kMax = ~static_cast<uint64_t>(0); - - EXPECT_EQ(absl::uint128(0), MultiplyU64ToU128(0, 0)); - - // Max uint64_t - EXPECT_EQ(MultiplyU64ToU128(kMax, kMax), - absl::MakeUint128(0xfffffffffffffffe, 0x0000000000000001)); - EXPECT_EQ(absl::MakeUint128(0, kMax), MultiplyU64ToU128(kMax, 1)); - EXPECT_EQ(absl::MakeUint128(0, kMax), MultiplyU64ToU128(1, kMax)); - for (int i = 0; i < 64; ++i) { - EXPECT_EQ(absl::MakeUint128(0, kMax) << i, - MultiplyU64ToU128(kMax, k1 << i)); - EXPECT_EQ(absl::MakeUint128(0, kMax) << i, - MultiplyU64ToU128(k1 << i, kMax)); - } - - // 1-bit x 1-bit. - for (int i = 0; i < 64; ++i) { - for (int j = 0; j < 64; ++j) { - EXPECT_EQ(absl::MakeUint128(0, 1) << (i + j), - MultiplyU64ToU128(k1 << i, k1 << j)); - EXPECT_EQ(absl::MakeUint128(0, 1) << (i + j), - MultiplyU64ToU128(k1 << i, k1 << j)); - } - } - - // Verified multiplies - EXPECT_EQ(MultiplyU64ToU128(0xffffeeeeddddcccc, 0xbbbbaaaa99998888), - absl::MakeUint128(0xbbbb9e2692c5dddc, 0xc28f7531048d2c60)); - EXPECT_EQ(MultiplyU64ToU128(0x0123456789abcdef, 0xfedcba9876543210), - absl::MakeUint128(0x0121fa00ad77d742, 0x2236d88fe5618cf0)); - EXPECT_EQ(MultiplyU64ToU128(0x0123456789abcdef, 0xfdb97531eca86420), - absl::MakeUint128(0x0120ae99d26725fc, 0xce197f0ecac319e0)); - EXPECT_EQ(MultiplyU64ToU128(0x97a87f4f261ba3f2, 0xfedcba9876543210), - absl::MakeUint128(0x96fbf1a8ae78d0ba, 0x5a6dd4b71f278320)); - EXPECT_EQ(MultiplyU64ToU128(0xfedcba9876543210, 0xfdb97531eca86420), - absl::MakeUint128(0xfc98c6981a413e22, 0x342d0bbf48948200)); -} - -} // namespace diff --git a/third_party/abseil_cpp/absl/random/log_uniform_int_distribution.h b/third_party/abseil_cpp/absl/random/log_uniform_int_distribution.h deleted file mode 100644 index 960816e2f8ff..000000000000 --- a/third_party/abseil_cpp/absl/random/log_uniform_int_distribution.h +++ /dev/null @@ -1,254 +0,0 @@ -// Copyright 2017 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#ifndef ABSL_RANDOM_LOG_UNIFORM_INT_DISTRIBUTION_H_ -#define ABSL_RANDOM_LOG_UNIFORM_INT_DISTRIBUTION_H_ - -#include <algorithm> -#include <cassert> -#include <cmath> -#include <istream> -#include <limits> -#include <ostream> -#include <type_traits> - -#include "absl/random/internal/fastmath.h" -#include "absl/random/internal/generate_real.h" -#include "absl/random/internal/iostream_state_saver.h" -#include "absl/random/internal/traits.h" -#include "absl/random/uniform_int_distribution.h" - -namespace absl { -ABSL_NAMESPACE_BEGIN - -// log_uniform_int_distribution: -// -// Returns a random variate R in range [min, max] such that -// floor(log(R-min, base)) is uniformly distributed. -// We ensure uniformity by discretization using the -// boundary sets [0, 1, base, base * base, ... min(base*n, max)] -// -template <typename IntType = int> -class log_uniform_int_distribution { - private: - using unsigned_type = - typename random_internal::make_unsigned_bits<IntType>::type; - - public: - using result_type = IntType; - - class param_type { - public: - using distribution_type = log_uniform_int_distribution; - - explicit param_type( - result_type min = 0, - result_type max = (std::numeric_limits<result_type>::max)(), - result_type base = 2) - : min_(min), - max_(max), - base_(base), - range_(static_cast<unsigned_type>(max_) - - static_cast<unsigned_type>(min_)), - log_range_(0) { - assert(max_ >= min_); - assert(base_ > 1); - - if (base_ == 2) { - // Determine where the first set bit is on range(), giving a log2(range) - // value which can be used to construct bounds. - log_range_ = (std::min)(random_internal::LeadingSetBit(range()), - std::numeric_limits<unsigned_type>::digits); - } else { - // NOTE: Computing the logN(x) introduces error from 2 sources: - // 1. Conversion of int to double loses precision for values >= - // 2^53, which may cause some log() computations to operate on - // different values. - // 2. The error introduced by the division will cause the result - // to differ from the expected value. - // - // Thus a result which should equal K may equal K +/- epsilon, - // which can eliminate some values depending on where the bounds fall. - const double inv_log_base = 1.0 / std::log(base_); - const double log_range = std::log(static_cast<double>(range()) + 0.5); - log_range_ = static_cast<int>(std::ceil(inv_log_base * log_range)); - } - } - - result_type(min)() const { return min_; } - result_type(max)() const { return max_; } - result_type base() const { return base_; } - - friend bool operator==(const param_type& a, const param_type& b) { - return a.min_ == b.min_ && a.max_ == b.max_ && a.base_ == b.base_; - } - - friend bool operator!=(const param_type& a, const param_type& b) { - return !(a == b); - } - - private: - friend class log_uniform_int_distribution; - - int log_range() const { return log_range_; } - unsigned_type range() const { return range_; } - - result_type min_; - result_type max_; - result_type base_; - unsigned_type range_; // max - min - int log_range_; // ceil(logN(range_)) - - static_assert(std::is_integral<IntType>::value, - "Class-template absl::log_uniform_int_distribution<> must be " - "parameterized using an integral type."); - }; - - log_uniform_int_distribution() : log_uniform_int_distribution(0) {} - - explicit log_uniform_int_distribution( - result_type min, - result_type max = (std::numeric_limits<result_type>::max)(), - result_type base = 2) - : param_(min, max, base) {} - - explicit log_uniform_int_distribution(const param_type& p) : param_(p) {} - - void reset() {} - - // generating functions - template <typename URBG> - result_type operator()(URBG& g) { // NOLINT(runtime/references) - return (*this)(g, param_); - } - - template <typename URBG> - result_type operator()(URBG& g, // NOLINT(runtime/references) - const param_type& p) { - return (p.min)() + Generate(g, p); - } - - result_type(min)() const { return (param_.min)(); } - result_type(max)() const { return (param_.max)(); } - result_type base() const { return param_.base(); } - - param_type param() const { return param_; } - void param(const param_type& p) { param_ = p; } - - friend bool operator==(const log_uniform_int_distribution& a, - const log_uniform_int_distribution& b) { - return a.param_ == b.param_; - } - friend bool operator!=(const log_uniform_int_distribution& a, - const log_uniform_int_distribution& b) { - return a.param_ != b.param_; - } - - private: - // Returns a log-uniform variate in the range [0, p.range()]. The caller - // should add min() to shift the result to the correct range. - template <typename URNG> - unsigned_type Generate(URNG& g, // NOLINT(runtime/references) - const param_type& p); - - param_type param_; -}; - -template <typename IntType> -template <typename URBG> -typename log_uniform_int_distribution<IntType>::unsigned_type -log_uniform_int_distribution<IntType>::Generate( - URBG& g, // NOLINT(runtime/references) - const param_type& p) { - // sample e over [0, log_range]. Map the results of e to this: - // 0 => 0 - // 1 => [1, b-1] - // 2 => [b, (b^2)-1] - // n => [b^(n-1)..(b^n)-1] - const int e = absl::uniform_int_distribution<int>(0, p.log_range())(g); - if (e == 0) { - return 0; - } - const int d = e - 1; - - unsigned_type base_e, top_e; - if (p.base() == 2) { - base_e = static_cast<unsigned_type>(1) << d; - - top_e = (e >= std::numeric_limits<unsigned_type>::digits) - ? (std::numeric_limits<unsigned_type>::max)() - : (static_cast<unsigned_type>(1) << e) - 1; - } else { - const double r = std::pow(p.base(), d); - const double s = (r * p.base()) - 1.0; - - base_e = - (r > static_cast<double>((std::numeric_limits<unsigned_type>::max)())) - ? (std::numeric_limits<unsigned_type>::max)() - : static_cast<unsigned_type>(r); - - top_e = - (s > static_cast<double>((std::numeric_limits<unsigned_type>::max)())) - ? (std::numeric_limits<unsigned_type>::max)() - : static_cast<unsigned_type>(s); - } - - const unsigned_type lo = (base_e >= p.range()) ? p.range() : base_e; - const unsigned_type hi = (top_e >= p.range()) ? p.range() : top_e; - - // choose uniformly over [lo, hi] - return absl::uniform_int_distribution<result_type>(lo, hi)(g); -} - -template <typename CharT, typename Traits, typename IntType> -std::basic_ostream<CharT, Traits>& operator<<( - std::basic_ostream<CharT, Traits>& os, // NOLINT(runtime/references) - const log_uniform_int_distribution<IntType>& x) { - using stream_type = - typename random_internal::stream_format_type<IntType>::type; - auto saver = random_internal::make_ostream_state_saver(os); - os << static_cast<stream_type>((x.min)()) << os.fill() - << static_cast<stream_type>((x.max)()) << os.fill() - << static_cast<stream_type>(x.base()); - return os; -} - -template <typename CharT, typename Traits, typename IntType> -std::basic_istream<CharT, Traits>& operator>>( - std::basic_istream<CharT, Traits>& is, // NOLINT(runtime/references) - log_uniform_int_distribution<IntType>& x) { // NOLINT(runtime/references) - using param_type = typename log_uniform_int_distribution<IntType>::param_type; - using result_type = - typename log_uniform_int_distribution<IntType>::result_type; - using stream_type = - typename random_internal::stream_format_type<IntType>::type; - - stream_type min; - stream_type max; - stream_type base; - - auto saver = random_internal::make_istream_state_saver(is); - is >> min >> max >> base; - if (!is.fail()) { - x.param(param_type(static_cast<result_type>(min), - static_cast<result_type>(max), - static_cast<result_type>(base))); - } - return is; -} - -ABSL_NAMESPACE_END -} // namespace absl - -#endif // ABSL_RANDOM_LOG_UNIFORM_INT_DISTRIBUTION_H_ diff --git a/third_party/abseil_cpp/absl/random/log_uniform_int_distribution_test.cc b/third_party/abseil_cpp/absl/random/log_uniform_int_distribution_test.cc deleted file mode 100644 index 5e780d96d306..000000000000 --- a/third_party/abseil_cpp/absl/random/log_uniform_int_distribution_test.cc +++ /dev/null @@ -1,280 +0,0 @@ -// Copyright 2017 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#include "absl/random/log_uniform_int_distribution.h" - -#include <cstddef> -#include <cstdint> -#include <iterator> -#include <random> -#include <sstream> -#include <string> -#include <vector> - -#include "gmock/gmock.h" -#include "gtest/gtest.h" -#include "absl/base/internal/raw_logging.h" -#include "absl/random/internal/chi_square.h" -#include "absl/random/internal/distribution_test_util.h" -#include "absl/random/internal/pcg_engine.h" -#include "absl/random/internal/sequence_urbg.h" -#include "absl/random/random.h" -#include "absl/strings/str_cat.h" -#include "absl/strings/str_format.h" -#include "absl/strings/str_replace.h" -#include "absl/strings/strip.h" - -namespace { - -template <typename IntType> -class LogUniformIntDistributionTypeTest : public ::testing::Test {}; - -using IntTypes = ::testing::Types<int8_t, int16_t, int32_t, int64_t, // - uint8_t, uint16_t, uint32_t, uint64_t>; -TYPED_TEST_CASE(LogUniformIntDistributionTypeTest, IntTypes); - -TYPED_TEST(LogUniformIntDistributionTypeTest, SerializeTest) { - using param_type = - typename absl::log_uniform_int_distribution<TypeParam>::param_type; - using Limits = std::numeric_limits<TypeParam>; - - constexpr int kCount = 1000; - absl::InsecureBitGen gen; - for (const auto& param : { - param_type(0, 1), // - param_type(0, 2), // - param_type(0, 2, 10), // - param_type(9, 32, 4), // - param_type(1, 101, 10), // - param_type(1, Limits::max() / 2), // - param_type(0, Limits::max() - 1), // - param_type(0, Limits::max(), 2), // - param_type(0, Limits::max(), 10), // - param_type(Limits::min(), 0), // - param_type(Limits::lowest(), Limits::max()), // - param_type(Limits::min(), Limits::max()), // - }) { - // Validate parameters. - const auto min = param.min(); - const auto max = param.max(); - const auto base = param.base(); - absl::log_uniform_int_distribution<TypeParam> before(min, max, base); - EXPECT_EQ(before.min(), param.min()); - EXPECT_EQ(before.max(), param.max()); - EXPECT_EQ(before.base(), param.base()); - - { - absl::log_uniform_int_distribution<TypeParam> via_param(param); - EXPECT_EQ(via_param, before); - } - - // Validate stream serialization. - std::stringstream ss; - ss << before; - - absl::log_uniform_int_distribution<TypeParam> after(3, 6, 17); - - EXPECT_NE(before.max(), after.max()); - EXPECT_NE(before.base(), after.base()); - EXPECT_NE(before.param(), after.param()); - EXPECT_NE(before, after); - - ss >> after; - - EXPECT_EQ(before.min(), after.min()); - EXPECT_EQ(before.max(), after.max()); - EXPECT_EQ(before.base(), after.base()); - EXPECT_EQ(before.param(), after.param()); - EXPECT_EQ(before, after); - - // Smoke test. - auto sample_min = after.max(); - auto sample_max = after.min(); - for (int i = 0; i < kCount; i++) { - auto sample = after(gen); - EXPECT_GE(sample, after.min()); - EXPECT_LE(sample, after.max()); - if (sample > sample_max) sample_max = sample; - if (sample < sample_min) sample_min = sample; - } - ABSL_INTERNAL_LOG(INFO, - absl::StrCat("Range: ", +sample_min, ", ", +sample_max)); - } -} - -using log_uniform_i32 = absl::log_uniform_int_distribution<int32_t>; - -class LogUniformIntChiSquaredTest - : public testing::TestWithParam<log_uniform_i32::param_type> { - public: - // The ChiSquaredTestImpl provides a chi-squared goodness of fit test for - // data generated by the log-uniform-int distribution. - double ChiSquaredTestImpl(); - - // We use a fixed bit generator for distribution accuracy tests. This allows - // these tests to be deterministic, while still testing the qualify of the - // implementation. - absl::random_internal::pcg64_2018_engine rng_{0x2B7E151628AED2A6}; -}; - -double LogUniformIntChiSquaredTest::ChiSquaredTestImpl() { - using absl::random_internal::kChiSquared; - - const auto& param = GetParam(); - - // Check the distribution of L=log(log_uniform_int_distribution, base), - // expecting that L is roughly uniformly distributed, that is: - // - // P[L=0] ~= P[L=1] ~= ... ~= P[L=log(max)] - // - // For a total of X entries, each bucket should contain some number of samples - // in the interval [X/k - a, X/k + a]. - // - // Where `a` is approximately sqrt(X/k). This is validated by bucketing - // according to the log function and using a chi-squared test for uniformity. - - const bool is_2 = (param.base() == 2); - const double base_log = 1.0 / std::log(param.base()); - const auto bucket_index = [base_log, is_2, ¶m](int32_t x) { - uint64_t y = static_cast<uint64_t>(x) - param.min(); - return (y == 0) ? 0 - : is_2 ? static_cast<int>(1 + std::log2(y)) - : static_cast<int>(1 + std::log(y) * base_log); - }; - const int max_bucket = bucket_index(param.max()); // inclusive - const size_t trials = 15 + (max_bucket + 1) * 10; - - log_uniform_i32 dist(param); - - std::vector<int64_t> buckets(max_bucket + 1); - for (size_t i = 0; i < trials; ++i) { - const auto sample = dist(rng_); - // Check the bounds. - ABSL_ASSERT(sample <= dist.max()); - ABSL_ASSERT(sample >= dist.min()); - // Convert the output of the generator to one of num_bucket buckets. - int bucket = bucket_index(sample); - ABSL_ASSERT(bucket <= max_bucket); - ++buckets[bucket]; - } - - // The null-hypothesis is that the distribution is uniform with respect to - // log-uniform-int bucketization. - const int dof = buckets.size() - 1; - const double expected = trials / static_cast<double>(buckets.size()); - - const double threshold = absl::random_internal::ChiSquareValue(dof, 0.98); - - double chi_square = absl::random_internal::ChiSquareWithExpected( - std::begin(buckets), std::end(buckets), expected); - - const double p = absl::random_internal::ChiSquarePValue(chi_square, dof); - - if (chi_square > threshold) { - ABSL_INTERNAL_LOG(INFO, "values"); - for (size_t i = 0; i < buckets.size(); i++) { - ABSL_INTERNAL_LOG(INFO, absl::StrCat(i, ": ", buckets[i])); - } - ABSL_INTERNAL_LOG(INFO, - absl::StrFormat("trials=%d\n" - "%s(data, %d) = %f (%f)\n" - "%s @ 0.98 = %f", - trials, kChiSquared, dof, chi_square, p, - kChiSquared, threshold)); - } - return p; -} - -TEST_P(LogUniformIntChiSquaredTest, MultiTest) { - const int kTrials = 5; - int failures = 0; - for (int i = 0; i < kTrials; i++) { - double p_value = ChiSquaredTestImpl(); - if (p_value < 0.005) { - failures++; - } - } - - // There is a 0.10% chance of producing at least one failure, so raise the - // failure threshold high enough to allow for a flake rate < 10,000. - EXPECT_LE(failures, 4); -} - -// Generate the parameters for the test. -std::vector<log_uniform_i32::param_type> GenParams() { - using Param = log_uniform_i32::param_type; - using Limits = std::numeric_limits<int32_t>; - - return std::vector<Param>{ - Param{0, 1, 2}, - Param{1, 1, 2}, - Param{0, 2, 2}, - Param{0, 3, 2}, - Param{0, 4, 2}, - Param{0, 9, 10}, - Param{0, 10, 10}, - Param{0, 11, 10}, - Param{1, 10, 10}, - Param{0, (1 << 8) - 1, 2}, - Param{0, (1 << 8), 2}, - Param{0, (1 << 30) - 1, 2}, - Param{-1000, 1000, 10}, - Param{0, Limits::max(), 2}, - Param{0, Limits::max(), 3}, - Param{0, Limits::max(), 10}, - Param{Limits::min(), 0}, - Param{Limits::min(), Limits::max(), 2}, - }; -} - -std::string ParamName( - const ::testing::TestParamInfo<log_uniform_i32::param_type>& info) { - const auto& p = info.param; - std::string name = - absl::StrCat("min_", p.min(), "__max_", p.max(), "__base_", p.base()); - return absl::StrReplaceAll(name, {{"+", "_"}, {"-", "_"}, {".", "_"}}); -} - -INSTANTIATE_TEST_SUITE_P(All, LogUniformIntChiSquaredTest, - ::testing::ValuesIn(GenParams()), ParamName); - -// NOTE: absl::log_uniform_int_distribution is not guaranteed to be stable. -TEST(LogUniformIntDistributionTest, StabilityTest) { - using testing::ElementsAre; - // absl::uniform_int_distribution stability relies on - // absl::random_internal::LeadingSetBit, std::log, std::pow. - absl::random_internal::sequence_urbg urbg( - {0x0003eb76f6f7f755ull, 0xFFCEA50FDB2F953Bull, 0xC332DDEFBE6C5AA5ull, - 0x6558218568AB9702ull, 0x2AEF7DAD5B6E2F84ull, 0x1521B62829076170ull, - 0xECDD4775619F1510ull, 0x13CCA830EB61BD96ull, 0x0334FE1EAA0363CFull, - 0xB5735C904C70A239ull, 0xD59E9E0BCBAADE14ull, 0xEECC86BC60622CA7ull}); - - std::vector<int> output(6); - - { - absl::log_uniform_int_distribution<int32_t> dist(0, 256); - std::generate(std::begin(output), std::end(output), - [&] { return dist(urbg); }); - EXPECT_THAT(output, ElementsAre(256, 66, 4, 6, 57, 103)); - } - urbg.reset(); - { - absl::log_uniform_int_distribution<int32_t> dist(0, 256, 10); - std::generate(std::begin(output), std::end(output), - [&] { return dist(urbg); }); - EXPECT_THAT(output, ElementsAre(8, 4, 0, 0, 0, 69)); - } -} - -} // namespace diff --git a/third_party/abseil_cpp/absl/random/mock_distributions.h b/third_party/abseil_cpp/absl/random/mock_distributions.h deleted file mode 100644 index 764ab370abeb..000000000000 --- a/third_party/abseil_cpp/absl/random/mock_distributions.h +++ /dev/null @@ -1,266 +0,0 @@ -// Copyright 2018 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. -// -// ----------------------------------------------------------------------------- -// File: mock_distributions.h -// ----------------------------------------------------------------------------- -// -// This file contains mock distribution functions for use alongside an -// `absl::MockingBitGen` object within the Googletest testing framework. Such -// mocks are useful to provide deterministic values as return values within -// (otherwise random) Abseil distribution functions. -// -// The return type of each function is a mock expectation object which -// is used to set the match result. -// -// More information about the Googletest testing framework is available at -// https://github.com/google/googletest -// -// EXPECT_CALL and ON_CALL need to be made within the same DLL component as -// the call to absl::Uniform and related methods, otherwise mocking will fail -// since the underlying implementation creates a type-specific pointer which -// will be distinct across different DLL boundaries. -// -// Example: -// -// absl::MockingBitGen mock; -// EXPECT_CALL(absl::MockUniform<int>(), Call(mock, 1, 1000)) -// .WillRepeatedly(testing::ReturnRoundRobin({20, 40})); -// -// EXPECT_EQ(absl::Uniform<int>(gen, 1, 1000), 20); -// EXPECT_EQ(absl::Uniform<int>(gen, 1, 1000), 40); -// EXPECT_EQ(absl::Uniform<int>(gen, 1, 1000), 20); -// EXPECT_EQ(absl::Uniform<int>(gen, 1, 1000), 40); - -#ifndef ABSL_RANDOM_MOCK_DISTRIBUTIONS_H_ -#define ABSL_RANDOM_MOCK_DISTRIBUTIONS_H_ - -#include <limits> -#include <type_traits> -#include <utility> - -#include "gmock/gmock.h" -#include "gtest/gtest.h" -#include "absl/meta/type_traits.h" -#include "absl/random/distributions.h" -#include "absl/random/internal/mock_overload_set.h" -#include "absl/random/mocking_bit_gen.h" - -namespace absl { -ABSL_NAMESPACE_BEGIN - -// ----------------------------------------------------------------------------- -// absl::MockUniform -// ----------------------------------------------------------------------------- -// -// Matches calls to absl::Uniform. -// -// `absl::MockUniform` is a class template used in conjunction with Googletest's -// `ON_CALL()` and `EXPECT_CALL()` macros. To use it, default-construct an -// instance of it inside `ON_CALL()` or `EXPECT_CALL()`, and use `Call(...)` the -// same way one would define mocks on a Googletest `MockFunction()`. -// -// Example: -// -// absl::MockingBitGen mock; -// EXPECT_CALL(absl::MockUniform<uint32_t>(), Call(mock)) -// .WillOnce(Return(123456)); -// auto x = absl::Uniform<uint32_t>(mock); -// assert(x == 123456) -// -template <typename R> -using MockUniform = random_internal::MockOverloadSet< - random_internal::UniformDistributionWrapper<R>, - R(IntervalClosedOpenTag, MockingBitGen&, R, R), - R(IntervalClosedClosedTag, MockingBitGen&, R, R), - R(IntervalOpenOpenTag, MockingBitGen&, R, R), - R(IntervalOpenClosedTag, MockingBitGen&, R, R), R(MockingBitGen&, R, R), - R(MockingBitGen&)>; - -// ----------------------------------------------------------------------------- -// absl::MockBernoulli -// ----------------------------------------------------------------------------- -// -// Matches calls to absl::Bernoulli. -// -// `absl::MockBernoulli` is a class used in conjunction with Googletest's -// `ON_CALL()` and `EXPECT_CALL()` macros. To use it, default-construct an -// instance of it inside `ON_CALL()` or `EXPECT_CALL()`, and use `Call(...)` the -// same way one would define mocks on a Googletest `MockFunction()`. -// -// Example: -// -// absl::MockingBitGen mock; -// EXPECT_CALL(absl::MockBernoulli(), Call(mock, testing::_)) -// .WillOnce(Return(false)); -// assert(absl::Bernoulli(mock, 0.5) == false); -// -using MockBernoulli = - random_internal::MockOverloadSet<absl::bernoulli_distribution, - bool(MockingBitGen&, double)>; - -// ----------------------------------------------------------------------------- -// absl::MockBeta -// ----------------------------------------------------------------------------- -// -// Matches calls to absl::Beta. -// -// `absl::MockBeta` is a class used in conjunction with Googletest's `ON_CALL()` -// and `EXPECT_CALL()` macros. To use it, default-construct an instance of it -// inside `ON_CALL()` or `EXPECT_CALL()`, and use `Call(...)` the same way one -// would define mocks on a Googletest `MockFunction()`. -// -// Example: -// -// absl::MockingBitGen mock; -// EXPECT_CALL(absl::MockBeta(), Call(mock, 3.0, 2.0)) -// .WillOnce(Return(0.567)); -// auto x = absl::Beta<double>(mock, 3.0, 2.0); -// assert(x == 0.567); -// -template <typename RealType> -using MockBeta = - random_internal::MockOverloadSet<absl::beta_distribution<RealType>, - RealType(MockingBitGen&, RealType, - RealType)>; - -// ----------------------------------------------------------------------------- -// absl::MockExponential -// ----------------------------------------------------------------------------- -// -// Matches calls to absl::Exponential. -// -// `absl::MockExponential` is a class template used in conjunction with -// Googletest's `ON_CALL()` and `EXPECT_CALL()` macros. To use it, -// default-construct an instance of it inside `ON_CALL()` or `EXPECT_CALL()`, -// and use `Call(...)` the same way one would define mocks on a -// Googletest `MockFunction()`. -// -// Example: -// -// absl::MockingBitGen mock; -// EXPECT_CALL(absl::MockExponential<double>(), Call(mock, 0.5)) -// .WillOnce(Return(12.3456789)); -// auto x = absl::Exponential<double>(mock, 0.5); -// assert(x == 12.3456789) -// -template <typename RealType> -using MockExponential = - random_internal::MockOverloadSet<absl::exponential_distribution<RealType>, - RealType(MockingBitGen&, RealType)>; - -// ----------------------------------------------------------------------------- -// absl::MockGaussian -// ----------------------------------------------------------------------------- -// -// Matches calls to absl::Gaussian. -// -// `absl::MockGaussian` is a class template used in conjunction with -// Googletest's `ON_CALL()` and `EXPECT_CALL()` macros. To use it, -// default-construct an instance of it inside `ON_CALL()` or `EXPECT_CALL()`, -// and use `Call(...)` the same way one would define mocks on a -// Googletest `MockFunction()`. -// -// Example: -// -// absl::MockingBitGen mock; -// EXPECT_CALL(absl::MockGaussian<double>(), Call(mock, 16.3, 3.3)) -// .WillOnce(Return(12.3456789)); -// auto x = absl::Gaussian<double>(mock, 16.3, 3.3); -// assert(x == 12.3456789) -// -template <typename RealType> -using MockGaussian = - random_internal::MockOverloadSet<absl::gaussian_distribution<RealType>, - RealType(MockingBitGen&, RealType, - RealType)>; - -// ----------------------------------------------------------------------------- -// absl::MockLogUniform -// ----------------------------------------------------------------------------- -// -// Matches calls to absl::LogUniform. -// -// `absl::MockLogUniform` is a class template used in conjunction with -// Googletest's `ON_CALL()` and `EXPECT_CALL()` macros. To use it, -// default-construct an instance of it inside `ON_CALL()` or `EXPECT_CALL()`, -// and use `Call(...)` the same way one would define mocks on a -// Googletest `MockFunction()`. -// -// Example: -// -// absl::MockingBitGen mock; -// EXPECT_CALL(absl::MockLogUniform<int>(), Call(mock, 10, 10000, 10)) -// .WillOnce(Return(1221)); -// auto x = absl::LogUniform<int>(mock, 10, 10000, 10); -// assert(x == 1221) -// -template <typename IntType> -using MockLogUniform = random_internal::MockOverloadSet< - absl::log_uniform_int_distribution<IntType>, - IntType(MockingBitGen&, IntType, IntType, IntType)>; - -// ----------------------------------------------------------------------------- -// absl::MockPoisson -// ----------------------------------------------------------------------------- -// -// Matches calls to absl::Poisson. -// -// `absl::MockPoisson` is a class template used in conjunction with Googletest's -// `ON_CALL()` and `EXPECT_CALL()` macros. To use it, default-construct an -// instance of it inside `ON_CALL()` or `EXPECT_CALL()`, and use `Call(...)` the -// same way one would define mocks on a Googletest `MockFunction()`. -// -// Example: -// -// absl::MockingBitGen mock; -// EXPECT_CALL(absl::MockPoisson<int>(), Call(mock, 2.0)) -// .WillOnce(Return(1221)); -// auto x = absl::Poisson<int>(mock, 2.0); -// assert(x == 1221) -// -template <typename IntType> -using MockPoisson = - random_internal::MockOverloadSet<absl::poisson_distribution<IntType>, - IntType(MockingBitGen&, double)>; - -// ----------------------------------------------------------------------------- -// absl::MockZipf -// ----------------------------------------------------------------------------- -// -// Matches calls to absl::Zipf. -// -// `absl::MockZipf` is a class template used in conjunction with Googletest's -// `ON_CALL()` and `EXPECT_CALL()` macros. To use it, default-construct an -// instance of it inside `ON_CALL()` or `EXPECT_CALL()`, and use `Call(...)` the -// same way one would define mocks on a Googletest `MockFunction()`. -// -// Example: -// -// absl::MockingBitGen mock; -// EXPECT_CALL(absl::MockZipf<int>(), Call(mock, 1000000, 2.0, 1.0)) -// .WillOnce(Return(1221)); -// auto x = absl::Zipf<int>(mock, 1000000, 2.0, 1.0); -// assert(x == 1221) -// -template <typename IntType> -using MockZipf = - random_internal::MockOverloadSet<absl::zipf_distribution<IntType>, - IntType(MockingBitGen&, IntType, double, - double)>; - -ABSL_NAMESPACE_END -} // namespace absl - -#endif // ABSL_RANDOM_MOCK_DISTRIBUTIONS_H_ diff --git a/third_party/abseil_cpp/absl/random/mock_distributions_test.cc b/third_party/abseil_cpp/absl/random/mock_distributions_test.cc deleted file mode 100644 index de23bafe1e55..000000000000 --- a/third_party/abseil_cpp/absl/random/mock_distributions_test.cc +++ /dev/null @@ -1,72 +0,0 @@ -// Copyright 2018 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#include "absl/random/mock_distributions.h" - -#include "gtest/gtest.h" -#include "absl/random/mocking_bit_gen.h" -#include "absl/random/random.h" - -namespace { -using ::testing::Return; - -TEST(MockDistributions, Examples) { - absl::MockingBitGen gen; - - EXPECT_NE(absl::Uniform<int>(gen, 1, 1000000), 20); - EXPECT_CALL(absl::MockUniform<int>(), Call(gen, 1, 1000000)) - .WillOnce(Return(20)); - EXPECT_EQ(absl::Uniform<int>(gen, 1, 1000000), 20); - - EXPECT_NE(absl::Uniform<double>(gen, 0.0, 100.0), 5.0); - EXPECT_CALL(absl::MockUniform<double>(), Call(gen, 0.0, 100.0)) - .WillOnce(Return(5.0)); - EXPECT_EQ(absl::Uniform<double>(gen, 0.0, 100.0), 5.0); - - EXPECT_NE(absl::Exponential<double>(gen, 1.0), 42); - EXPECT_CALL(absl::MockExponential<double>(), Call(gen, 1.0)) - .WillOnce(Return(42)); - EXPECT_EQ(absl::Exponential<double>(gen, 1.0), 42); - - EXPECT_NE(absl::Poisson<int>(gen, 1.0), 500); - EXPECT_CALL(absl::MockPoisson<int>(), Call(gen, 1.0)).WillOnce(Return(500)); - EXPECT_EQ(absl::Poisson<int>(gen, 1.0), 500); - - EXPECT_NE(absl::Bernoulli(gen, 0.000001), true); - EXPECT_CALL(absl::MockBernoulli(), Call(gen, 0.000001)) - .WillOnce(Return(true)); - EXPECT_EQ(absl::Bernoulli(gen, 0.000001), true); - - EXPECT_NE(absl::Beta<double>(gen, 3.0, 2.0), 0.567); - EXPECT_CALL(absl::MockBeta<double>(), Call(gen, 3.0, 2.0)) - .WillOnce(Return(0.567)); - EXPECT_EQ(absl::Beta<double>(gen, 3.0, 2.0), 0.567); - - EXPECT_NE(absl::Zipf<int>(gen, 1000000, 2.0, 1.0), 1221); - EXPECT_CALL(absl::MockZipf<int>(), Call(gen, 1000000, 2.0, 1.0)) - .WillOnce(Return(1221)); - EXPECT_EQ(absl::Zipf<int>(gen, 1000000, 2.0, 1.0), 1221); - - EXPECT_NE(absl::Gaussian<double>(gen, 0.0, 1.0), 0.001); - EXPECT_CALL(absl::MockGaussian<double>(), Call(gen, 0.0, 1.0)) - .WillOnce(Return(0.001)); - EXPECT_EQ(absl::Gaussian<double>(gen, 0.0, 1.0), 0.001); - - EXPECT_NE(absl::LogUniform<int>(gen, 0, 1000000, 2), 2040); - EXPECT_CALL(absl::MockLogUniform<int>(), Call(gen, 0, 1000000, 2)) - .WillOnce(Return(2040)); - EXPECT_EQ(absl::LogUniform<int>(gen, 0, 1000000, 2), 2040); -} - -} // namespace diff --git a/third_party/abseil_cpp/absl/random/mocking_bit_gen.h b/third_party/abseil_cpp/absl/random/mocking_bit_gen.h deleted file mode 100644 index 6d2f2c836245..000000000000 --- a/third_party/abseil_cpp/absl/random/mocking_bit_gen.h +++ /dev/null @@ -1,228 +0,0 @@ -// Copyright 2018 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. -// -// ----------------------------------------------------------------------------- -// mocking_bit_gen.h -// ----------------------------------------------------------------------------- -// -// This file includes an `absl::MockingBitGen` class to use as a mock within the -// Googletest testing framework. Such a mock is useful to provide deterministic -// values as return values within (otherwise random) Abseil distribution -// functions. Such determinism within a mock is useful within testing frameworks -// to test otherwise indeterminate APIs. -// -// More information about the Googletest testing framework is available at -// https://github.com/google/googletest - -#ifndef ABSL_RANDOM_MOCKING_BIT_GEN_H_ -#define ABSL_RANDOM_MOCKING_BIT_GEN_H_ - -#include <iterator> -#include <limits> -#include <memory> -#include <tuple> -#include <type_traits> -#include <utility> - -#include "gmock/gmock.h" -#include "gtest/gtest.h" -#include "absl/base/internal/fast_type_id.h" -#include "absl/container/flat_hash_map.h" -#include "absl/meta/type_traits.h" -#include "absl/random/distributions.h" -#include "absl/random/internal/distribution_caller.h" -#include "absl/random/random.h" -#include "absl/strings/str_cat.h" -#include "absl/strings/str_join.h" -#include "absl/types/span.h" -#include "absl/types/variant.h" -#include "absl/utility/utility.h" - -namespace absl { -ABSL_NAMESPACE_BEGIN - -namespace random_internal { -template <typename> -struct DistributionCaller; -class MockHelpers; - -} // namespace random_internal -class BitGenRef; - -// MockingBitGen -// -// `absl::MockingBitGen` is a mock Uniform Random Bit Generator (URBG) class -// which can act in place of an `absl::BitGen` URBG within tests using the -// Googletest testing framework. -// -// Usage: -// -// Use an `absl::MockingBitGen` along with a mock distribution object (within -// mock_distributions.h) inside Googletest constructs such as ON_CALL(), -// EXPECT_TRUE(), etc. to produce deterministic results conforming to the -// distribution's API contract. -// -// Example: -// -// // Mock a call to an `absl::Bernoulli` distribution using Googletest -// absl::MockingBitGen bitgen; -// -// ON_CALL(absl::MockBernoulli(), Call(bitgen, 0.5)) -// .WillByDefault(testing::Return(true)); -// EXPECT_TRUE(absl::Bernoulli(bitgen, 0.5)); -// -// // Mock a call to an `absl::Uniform` distribution within Googletest -// absl::MockingBitGen bitgen; -// -// ON_CALL(absl::MockUniform<int>(), Call(bitgen, testing::_, testing::_)) -// .WillByDefault([] (int low, int high) { -// return (low + high) / 2; -// }); -// -// EXPECT_EQ(absl::Uniform<int>(gen, 0, 10), 5); -// EXPECT_EQ(absl::Uniform<int>(gen, 30, 40), 35); -// -// At this time, only mock distributions supplied within the Abseil random -// library are officially supported. -// -// EXPECT_CALL and ON_CALL need to be made within the same DLL component as -// the call to absl::Uniform and related methods, otherwise mocking will fail -// since the underlying implementation creates a type-specific pointer which -// will be distinct across different DLL boundaries. -// -class MockingBitGen { - public: - MockingBitGen() = default; - - ~MockingBitGen() { - for (const auto& del : deleters_) del(); - } - - // URBG interface - using result_type = absl::BitGen::result_type; - - static constexpr result_type(min)() { return (absl::BitGen::min)(); } - static constexpr result_type(max)() { return (absl::BitGen::max)(); } - result_type operator()() { return gen_(); } - - private: - using match_impl_fn = void (*)(void* mock_fn, void* t_erased_arg_tuple, - void* t_erased_result); - - struct MockData { - void* mock_fn = nullptr; - match_impl_fn match_impl = nullptr; - }; - - // GetMockFnType returns the testing::MockFunction for a result and tuple. - // This method only exists for type deduction and is otherwise unimplemented. - template <typename ResultT, typename... Args> - static auto GetMockFnType(ResultT, std::tuple<Args...>) - -> ::testing::MockFunction<ResultT(Args...)>; - - // MockFnCaller is a helper method for use with absl::apply to - // apply an ArgTupleT to a compatible MockFunction. - // NOTE: MockFnCaller is essentially equivalent to the lambda: - // [fn](auto... args) { return fn->Call(std::move(args)...)} - // however that fails to build on some supported platforms. - template <typename ResultT, typename MockFnType, typename Tuple> - struct MockFnCaller; - // specialization for std::tuple. - template <typename ResultT, typename MockFnType, typename... Args> - struct MockFnCaller<ResultT, MockFnType, std::tuple<Args...>> { - MockFnType* fn; - inline ResultT operator()(Args... args) { - return fn->Call(std::move(args)...); - } - }; - - // MockingBitGen::RegisterMock - // - // RegisterMock<ResultT, ArgTupleT>(FastTypeIdType) is the main extension - // point for extending the MockingBitGen framework. It provides a mechanism to - // install a mock expectation for a function like ResultT(Args...) keyed by - // type_idex onto the MockingBitGen context. The key is that the type_index - // used to register must match the type index used to call the mock. - // - // The returned MockFunction<...> type can be used to setup additional - // distribution parameters of the expectation. - template <typename ResultT, typename ArgTupleT> - auto RegisterMock(base_internal::FastTypeIdType type) - -> decltype(GetMockFnType(std::declval<ResultT>(), - std::declval<ArgTupleT>()))& { - using MockFnType = decltype( - GetMockFnType(std::declval<ResultT>(), std::declval<ArgTupleT>())); - auto& mock = mocks_[type]; - if (!mock.mock_fn) { - auto* mock_fn = new MockFnType; - mock.mock_fn = mock_fn; - mock.match_impl = &MatchImpl<ResultT, ArgTupleT>; - deleters_.emplace_back([mock_fn] { delete mock_fn; }); - } - return *static_cast<MockFnType*>(mock.mock_fn); - } - - // MockingBitGen::MatchImpl<> is a dispatch function which converts the - // generic type-erased parameters into a specific mock invocation call. - // Requires tuple_args to point to a ArgTupleT, which is a std::tuple<Args...> - // used to invoke the mock function. - // Requires result to point to a ResultT, which is the result of the call. - template <typename ResultT, typename ArgTupleT> - static void MatchImpl(/*MockFnType<ResultT, Args...>*/ void* mock_fn, - /*ArgTupleT*/ void* args_tuple, - /*ResultT*/ void* result) { - using MockFnType = decltype( - GetMockFnType(std::declval<ResultT>(), std::declval<ArgTupleT>())); - *static_cast<ResultT*>(result) = absl::apply( - MockFnCaller<ResultT, MockFnType, ArgTupleT>{ - static_cast<MockFnType*>(mock_fn)}, - *static_cast<ArgTupleT*>(args_tuple)); - } - - // MockingBitGen::InvokeMock - // - // InvokeMock(FastTypeIdType, args, result) is the entrypoint for invoking - // mocks registered on MockingBitGen. - // - // When no mocks are registered on the provided FastTypeIdType, returns false. - // Otherwise attempts to invoke the mock function ResultT(Args...) that - // was previously registered via the type_index. - // Requires tuple_args to point to a ArgTupleT, which is a std::tuple<Args...> - // used to invoke the mock function. - // Requires result to point to a ResultT, which is the result of the call. - inline bool InvokeMock(base_internal::FastTypeIdType type, void* args_tuple, - void* result) { - // Trigger a mock, if there exists one that matches `param`. - auto it = mocks_.find(type); - if (it == mocks_.end()) return false; - auto* mock_data = static_cast<MockData*>(&it->second); - mock_data->match_impl(mock_data->mock_fn, args_tuple, result); - return true; - } - - absl::flat_hash_map<base_internal::FastTypeIdType, MockData> mocks_; - std::vector<std::function<void()>> deleters_; - absl::BitGen gen_; - - template <typename> - friend struct ::absl::random_internal::DistributionCaller; // for InvokeMock - friend class ::absl::BitGenRef; // for InvokeMock - friend class ::absl::random_internal::MockHelpers; // for RegisterMock, - // InvokeMock -}; - -ABSL_NAMESPACE_END -} // namespace absl - -#endif // ABSL_RANDOM_MOCKING_BIT_GEN_H_ diff --git a/third_party/abseil_cpp/absl/random/mocking_bit_gen_test.cc b/third_party/abseil_cpp/absl/random/mocking_bit_gen_test.cc deleted file mode 100644 index f0ffc9ac9283..000000000000 --- a/third_party/abseil_cpp/absl/random/mocking_bit_gen_test.cc +++ /dev/null @@ -1,347 +0,0 @@ -// -// Copyright 2018 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. -// -#include "absl/random/mocking_bit_gen.h" - -#include <numeric> -#include <random> - -#include "gmock/gmock.h" -#include "gtest/gtest-spi.h" -#include "gtest/gtest.h" -#include "absl/random/bit_gen_ref.h" -#include "absl/random/mock_distributions.h" -#include "absl/random/random.h" - -namespace { -using ::testing::Ne; -using ::testing::Return; - -TEST(BasicMocking, AllDistributionsAreOverridable) { - absl::MockingBitGen gen; - - EXPECT_NE(absl::Uniform<int>(gen, 1, 1000000), 20); - EXPECT_CALL(absl::MockUniform<int>(), Call(gen, 1, 1000000)) - .WillOnce(Return(20)); - EXPECT_EQ(absl::Uniform<int>(gen, 1, 1000000), 20); - - EXPECT_NE(absl::Uniform<double>(gen, 0.0, 100.0), 5.0); - EXPECT_CALL(absl::MockUniform<double>(), Call(gen, 0.0, 100.0)) - .WillOnce(Return(5.0)); - EXPECT_EQ(absl::Uniform<double>(gen, 0.0, 100.0), 5.0); - - EXPECT_NE(absl::Exponential<double>(gen, 1.0), 42); - EXPECT_CALL(absl::MockExponential<double>(), Call(gen, 1.0)) - .WillOnce(Return(42)); - EXPECT_EQ(absl::Exponential<double>(gen, 1.0), 42); - - EXPECT_NE(absl::Poisson<int>(gen, 1.0), 500); - EXPECT_CALL(absl::MockPoisson<int>(), Call(gen, 1.0)).WillOnce(Return(500)); - EXPECT_EQ(absl::Poisson<int>(gen, 1.0), 500); - - EXPECT_NE(absl::Bernoulli(gen, 0.000001), true); - EXPECT_CALL(absl::MockBernoulli(), Call(gen, 0.000001)) - .WillOnce(Return(true)); - EXPECT_EQ(absl::Bernoulli(gen, 0.000001), true); - - EXPECT_NE(absl::Zipf<int>(gen, 1000000, 2.0, 1.0), 1221); - EXPECT_CALL(absl::MockZipf<int>(), Call(gen, 1000000, 2.0, 1.0)) - .WillOnce(Return(1221)); - EXPECT_EQ(absl::Zipf<int>(gen, 1000000, 2.0, 1.0), 1221); - - EXPECT_NE(absl::Gaussian<double>(gen, 0.0, 1.0), 0.001); - EXPECT_CALL(absl::MockGaussian<double>(), Call(gen, 0.0, 1.0)) - .WillOnce(Return(0.001)); - EXPECT_EQ(absl::Gaussian<double>(gen, 0.0, 1.0), 0.001); - - EXPECT_NE(absl::LogUniform<int>(gen, 0, 1000000, 2), 500000); - EXPECT_CALL(absl::MockLogUniform<int>(), Call(gen, 0, 1000000, 2)) - .WillOnce(Return(500000)); - EXPECT_EQ(absl::LogUniform<int>(gen, 0, 1000000, 2), 500000); -} - -TEST(BasicMocking, OnDistribution) { - absl::MockingBitGen gen; - - EXPECT_NE(absl::Uniform<int>(gen, 1, 1000000), 20); - ON_CALL(absl::MockUniform<int>(), Call(gen, 1, 1000000)) - .WillByDefault(Return(20)); - EXPECT_EQ(absl::Uniform<int>(gen, 1, 1000000), 20); - - EXPECT_NE(absl::Uniform<double>(gen, 0.0, 100.0), 5.0); - ON_CALL(absl::MockUniform<double>(), Call(gen, 0.0, 100.0)) - .WillByDefault(Return(5.0)); - EXPECT_EQ(absl::Uniform<double>(gen, 0.0, 100.0), 5.0); - - EXPECT_NE(absl::Exponential<double>(gen, 1.0), 42); - ON_CALL(absl::MockExponential<double>(), Call(gen, 1.0)) - .WillByDefault(Return(42)); - EXPECT_EQ(absl::Exponential<double>(gen, 1.0), 42); - - EXPECT_NE(absl::Poisson<int>(gen, 1.0), 500); - ON_CALL(absl::MockPoisson<int>(), Call(gen, 1.0)).WillByDefault(Return(500)); - EXPECT_EQ(absl::Poisson<int>(gen, 1.0), 500); - - EXPECT_NE(absl::Bernoulli(gen, 0.000001), true); - ON_CALL(absl::MockBernoulli(), Call(gen, 0.000001)) - .WillByDefault(Return(true)); - EXPECT_EQ(absl::Bernoulli(gen, 0.000001), true); - - EXPECT_NE(absl::Zipf<int>(gen, 1000000, 2.0, 1.0), 1221); - ON_CALL(absl::MockZipf<int>(), Call(gen, 1000000, 2.0, 1.0)) - .WillByDefault(Return(1221)); - EXPECT_EQ(absl::Zipf<int>(gen, 1000000, 2.0, 1.0), 1221); - - EXPECT_NE(absl::Gaussian<double>(gen, 0.0, 1.0), 0.001); - ON_CALL(absl::MockGaussian<double>(), Call(gen, 0.0, 1.0)) - .WillByDefault(Return(0.001)); - EXPECT_EQ(absl::Gaussian<double>(gen, 0.0, 1.0), 0.001); - - EXPECT_NE(absl::LogUniform<int>(gen, 0, 1000000, 2), 2040); - ON_CALL(absl::MockLogUniform<int>(), Call(gen, 0, 1000000, 2)) - .WillByDefault(Return(2040)); - EXPECT_EQ(absl::LogUniform<int>(gen, 0, 1000000, 2), 2040); -} - -TEST(BasicMocking, GMockMatchers) { - absl::MockingBitGen gen; - - EXPECT_NE(absl::Zipf<int>(gen, 1000000, 2.0, 1.0), 1221); - ON_CALL(absl::MockZipf<int>(), Call(gen, 1000000, 2.0, 1.0)) - .WillByDefault(Return(1221)); - EXPECT_EQ(absl::Zipf<int>(gen, 1000000, 2.0, 1.0), 1221); -} - -TEST(BasicMocking, OverridesWithMultipleGMockExpectations) { - absl::MockingBitGen gen; - - EXPECT_CALL(absl::MockUniform<int>(), Call(gen, 1, 10000)) - .WillOnce(Return(20)) - .WillOnce(Return(40)) - .WillOnce(Return(60)); - EXPECT_EQ(absl::Uniform(gen, 1, 10000), 20); - EXPECT_EQ(absl::Uniform(gen, 1, 10000), 40); - EXPECT_EQ(absl::Uniform(gen, 1, 10000), 60); -} - -TEST(BasicMocking, DefaultArgument) { - absl::MockingBitGen gen; - - ON_CALL(absl::MockExponential<double>(), Call(gen, 1.0)) - .WillByDefault(Return(200)); - - EXPECT_EQ(absl::Exponential<double>(gen), 200); - EXPECT_EQ(absl::Exponential<double>(gen, 1.0), 200); -} - -TEST(BasicMocking, MultipleGenerators) { - auto get_value = [](absl::BitGenRef gen_ref) { - return absl::Uniform(gen_ref, 1, 1000000); - }; - absl::MockingBitGen unmocked_generator; - absl::MockingBitGen mocked_with_3; - absl::MockingBitGen mocked_with_11; - - EXPECT_CALL(absl::MockUniform<int>(), Call(mocked_with_3, 1, 1000000)) - .WillOnce(Return(3)) - .WillRepeatedly(Return(17)); - EXPECT_CALL(absl::MockUniform<int>(), Call(mocked_with_11, 1, 1000000)) - .WillOnce(Return(11)) - .WillRepeatedly(Return(17)); - - // Ensure that unmocked generator generates neither value. - int unmocked_value = get_value(unmocked_generator); - EXPECT_NE(unmocked_value, 3); - EXPECT_NE(unmocked_value, 11); - // Mocked generators should generate their mocked values. - EXPECT_EQ(get_value(mocked_with_3), 3); - EXPECT_EQ(get_value(mocked_with_11), 11); - // Ensure that the mocks have expired. - EXPECT_NE(get_value(mocked_with_3), 3); - EXPECT_NE(get_value(mocked_with_11), 11); -} - -TEST(BasicMocking, MocksNotTrigeredForIncorrectTypes) { - absl::MockingBitGen gen; - EXPECT_CALL(absl::MockUniform<uint32_t>(), Call(gen)).WillOnce(Return(42)); - - EXPECT_NE(absl::Uniform<uint16_t>(gen), 42); // Not mocked - EXPECT_EQ(absl::Uniform<uint32_t>(gen), 42); // Mock triggered -} - -TEST(BasicMocking, FailsOnUnsatisfiedMocks) { - EXPECT_NONFATAL_FAILURE( - []() { - absl::MockingBitGen gen; - EXPECT_CALL(absl::MockExponential<double>(), Call(gen, 1.0)) - .WillOnce(Return(3.0)); - // Does not call absl::Exponential(). - }(), - "unsatisfied and active"); -} - -TEST(OnUniform, RespectsUniformIntervalSemantics) { - absl::MockingBitGen gen; - - EXPECT_CALL(absl::MockUniform<int>(), - Call(absl::IntervalClosed, gen, 1, 1000000)) - .WillOnce(Return(301)); - EXPECT_NE(absl::Uniform(gen, 1, 1000000), 301); // Not mocked - EXPECT_EQ(absl::Uniform(absl::IntervalClosed, gen, 1, 1000000), 301); -} - -TEST(OnUniform, RespectsNoArgUnsignedShorthand) { - absl::MockingBitGen gen; - EXPECT_CALL(absl::MockUniform<uint32_t>(), Call(gen)).WillOnce(Return(42)); - EXPECT_EQ(absl::Uniform<uint32_t>(gen), 42); -} - -TEST(RepeatedlyModifier, ForceSnakeEyesForManyDice) { - auto roll_some_dice = [](absl::BitGenRef gen_ref) { - std::vector<int> results(16); - for (auto& r : results) { - r = absl::Uniform(absl::IntervalClosed, gen_ref, 1, 6); - } - return results; - }; - std::vector<int> results; - absl::MockingBitGen gen; - - // Without any mocked calls, not all dice roll a "6". - results = roll_some_dice(gen); - EXPECT_LT(std::accumulate(std::begin(results), std::end(results), 0), - results.size() * 6); - - // Verify that we can force all "6"-rolls, with mocking. - ON_CALL(absl::MockUniform<int>(), Call(absl::IntervalClosed, gen, 1, 6)) - .WillByDefault(Return(6)); - results = roll_some_dice(gen); - EXPECT_EQ(std::accumulate(std::begin(results), std::end(results), 0), - results.size() * 6); -} - -TEST(WillOnce, DistinctCounters) { - absl::MockingBitGen gen; - EXPECT_CALL(absl::MockUniform<int>(), Call(gen, 1, 1000000)) - .Times(3) - .WillRepeatedly(Return(0)); - EXPECT_CALL(absl::MockUniform<int>(), Call(gen, 1000001, 2000000)) - .Times(3) - .WillRepeatedly(Return(1)); - EXPECT_EQ(absl::Uniform(gen, 1000001, 2000000), 1); - EXPECT_EQ(absl::Uniform(gen, 1, 1000000), 0); - EXPECT_EQ(absl::Uniform(gen, 1000001, 2000000), 1); - EXPECT_EQ(absl::Uniform(gen, 1, 1000000), 0); - EXPECT_EQ(absl::Uniform(gen, 1000001, 2000000), 1); - EXPECT_EQ(absl::Uniform(gen, 1, 1000000), 0); -} - -TEST(TimesModifier, ModifierSaturatesAndExpires) { - EXPECT_NONFATAL_FAILURE( - []() { - absl::MockingBitGen gen; - EXPECT_CALL(absl::MockUniform<int>(), Call(gen, 1, 1000000)) - .Times(3) - .WillRepeatedly(Return(15)) - .RetiresOnSaturation(); - - EXPECT_EQ(absl::Uniform(gen, 1, 1000000), 15); - EXPECT_EQ(absl::Uniform(gen, 1, 1000000), 15); - EXPECT_EQ(absl::Uniform(gen, 1, 1000000), 15); - // Times(3) has expired - Should get a different value now. - - EXPECT_NE(absl::Uniform(gen, 1, 1000000), 15); - }(), - ""); -} - -TEST(TimesModifier, Times0) { - absl::MockingBitGen gen; - EXPECT_CALL(absl::MockBernoulli(), Call(gen, 0.0)).Times(0); - EXPECT_CALL(absl::MockPoisson<int>(), Call(gen, 1.0)).Times(0); -} - -TEST(AnythingMatcher, MatchesAnyArgument) { - using testing::_; - - { - absl::MockingBitGen gen; - ON_CALL(absl::MockUniform<int>(), Call(absl::IntervalClosed, gen, _, 1000)) - .WillByDefault(Return(11)); - ON_CALL(absl::MockUniform<int>(), - Call(absl::IntervalClosed, gen, _, Ne(1000))) - .WillByDefault(Return(99)); - - EXPECT_EQ(absl::Uniform(absl::IntervalClosed, gen, 10, 1000000), 99); - EXPECT_EQ(absl::Uniform(absl::IntervalClosed, gen, 10, 1000), 11); - } - - { - absl::MockingBitGen gen; - ON_CALL(absl::MockUniform<int>(), Call(gen, 1, _)) - .WillByDefault(Return(25)); - ON_CALL(absl::MockUniform<int>(), Call(gen, Ne(1), _)) - .WillByDefault(Return(99)); - EXPECT_EQ(absl::Uniform(gen, 3, 1000000), 99); - EXPECT_EQ(absl::Uniform(gen, 1, 1000000), 25); - } - - { - absl::MockingBitGen gen; - ON_CALL(absl::MockUniform<int>(), Call(gen, _, _)) - .WillByDefault(Return(145)); - EXPECT_EQ(absl::Uniform(gen, 1, 1000), 145); - EXPECT_EQ(absl::Uniform(gen, 10, 1000), 145); - EXPECT_EQ(absl::Uniform(gen, 100, 1000), 145); - } -} - -TEST(AnythingMatcher, WithWillByDefault) { - using testing::_; - absl::MockingBitGen gen; - std::vector<int> values = {11, 22, 33, 44, 55, 66, 77, 88, 99, 1010}; - - ON_CALL(absl::MockUniform<size_t>(), Call(gen, 0, _)) - .WillByDefault(Return(0)); - for (int i = 0; i < 100; i++) { - auto& elem = values[absl::Uniform(gen, 0u, values.size())]; - EXPECT_EQ(elem, 11); - } -} - -TEST(BasicMocking, WillByDefaultWithArgs) { - using testing::_; - - absl::MockingBitGen gen; - ON_CALL(absl::MockPoisson<int>(), Call(gen, _)) - .WillByDefault( - [](double lambda) { return static_cast<int>(lambda * 10); }); - EXPECT_EQ(absl::Poisson<int>(gen, 1.7), 17); - EXPECT_EQ(absl::Poisson<int>(gen, 0.03), 0); -} - -TEST(MockingBitGen, InSequenceSucceedsInOrder) { - absl::MockingBitGen gen; - - testing::InSequence seq; - - EXPECT_CALL(absl::MockPoisson<int>(), Call(gen, 1.0)).WillOnce(Return(3)); - EXPECT_CALL(absl::MockPoisson<int>(), Call(gen, 2.0)).WillOnce(Return(4)); - - EXPECT_EQ(absl::Poisson<int>(gen, 1.0), 3); - EXPECT_EQ(absl::Poisson<int>(gen, 2.0), 4); -} - -} // namespace diff --git a/third_party/abseil_cpp/absl/random/poisson_distribution.h b/third_party/abseil_cpp/absl/random/poisson_distribution.h deleted file mode 100644 index cb5f5d5d0ff7..000000000000 --- a/third_party/abseil_cpp/absl/random/poisson_distribution.h +++ /dev/null @@ -1,258 +0,0 @@ -// Copyright 2017 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#ifndef ABSL_RANDOM_POISSON_DISTRIBUTION_H_ -#define ABSL_RANDOM_POISSON_DISTRIBUTION_H_ - -#include <cassert> -#include <cmath> -#include <istream> -#include <limits> -#include <ostream> -#include <type_traits> - -#include "absl/random/internal/fast_uniform_bits.h" -#include "absl/random/internal/fastmath.h" -#include "absl/random/internal/generate_real.h" -#include "absl/random/internal/iostream_state_saver.h" - -namespace absl { -ABSL_NAMESPACE_BEGIN - -// absl::poisson_distribution: -// Generates discrete variates conforming to a Poisson distribution. -// p(n) = (mean^n / n!) exp(-mean) -// -// Depending on the parameter, the distribution selects one of the following -// algorithms: -// * The standard algorithm, attributed to Knuth, extended using a split method -// for larger values -// * The "Ratio of Uniforms as a convenient method for sampling from classical -// discrete distributions", Stadlober, 1989. -// http://www.sciencedirect.com/science/article/pii/0377042790903495 -// -// NOTE: param_type.mean() is a double, which permits values larger than -// poisson_distribution<IntType>::max(), however this should be avoided and -// the distribution results are limited to the max() value. -// -// The goals of this implementation are to provide good performance while still -// beig thread-safe: This limits the implementation to not using lgamma provided -// by <math.h>. -// -template <typename IntType = int> -class poisson_distribution { - public: - using result_type = IntType; - - class param_type { - public: - using distribution_type = poisson_distribution; - explicit param_type(double mean = 1.0); - - double mean() const { return mean_; } - - friend bool operator==(const param_type& a, const param_type& b) { - return a.mean_ == b.mean_; - } - - friend bool operator!=(const param_type& a, const param_type& b) { - return !(a == b); - } - - private: - friend class poisson_distribution; - - double mean_; - double emu_; // e ^ -mean_ - double lmu_; // ln(mean_) - double s_; - double log_k_; - int split_; - - static_assert(std::is_integral<IntType>::value, - "Class-template absl::poisson_distribution<> must be " - "parameterized using an integral type."); - }; - - poisson_distribution() : poisson_distribution(1.0) {} - - explicit poisson_distribution(double mean) : param_(mean) {} - - explicit poisson_distribution(const param_type& p) : param_(p) {} - - void reset() {} - - // generating functions - template <typename URBG> - result_type operator()(URBG& g) { // NOLINT(runtime/references) - return (*this)(g, param_); - } - - template <typename URBG> - result_type operator()(URBG& g, // NOLINT(runtime/references) - const param_type& p); - - param_type param() const { return param_; } - void param(const param_type& p) { param_ = p; } - - result_type(min)() const { return 0; } - result_type(max)() const { return (std::numeric_limits<result_type>::max)(); } - - double mean() const { return param_.mean(); } - - friend bool operator==(const poisson_distribution& a, - const poisson_distribution& b) { - return a.param_ == b.param_; - } - friend bool operator!=(const poisson_distribution& a, - const poisson_distribution& b) { - return a.param_ != b.param_; - } - - private: - param_type param_; - random_internal::FastUniformBits<uint64_t> fast_u64_; -}; - -// ----------------------------------------------------------------------------- -// Implementation details follow -// ----------------------------------------------------------------------------- - -template <typename IntType> -poisson_distribution<IntType>::param_type::param_type(double mean) - : mean_(mean), split_(0) { - assert(mean >= 0); - assert(mean <= (std::numeric_limits<result_type>::max)()); - // As a defensive measure, avoid large values of the mean. The rejection - // algorithm used does not support very large values well. It my be worth - // changing algorithms to better deal with these cases. - assert(mean <= 1e10); - if (mean_ < 10) { - // For small lambda, use the knuth method. - split_ = 1; - emu_ = std::exp(-mean_); - } else if (mean_ <= 50) { - // Use split-knuth method. - split_ = 1 + static_cast<int>(mean_ / 10.0); - emu_ = std::exp(-mean_ / static_cast<double>(split_)); - } else { - // Use ratio of uniforms method. - constexpr double k2E = 0.7357588823428846; - constexpr double kSA = 0.4494580810294493; - - lmu_ = std::log(mean_); - double a = mean_ + 0.5; - s_ = kSA + std::sqrt(k2E * a); - const double mode = std::ceil(mean_) - 1; - log_k_ = lmu_ * mode - absl::random_internal::StirlingLogFactorial(mode); - } -} - -template <typename IntType> -template <typename URBG> -typename poisson_distribution<IntType>::result_type -poisson_distribution<IntType>::operator()( - URBG& g, // NOLINT(runtime/references) - const param_type& p) { - using random_internal::GeneratePositiveTag; - using random_internal::GenerateRealFromBits; - using random_internal::GenerateSignedTag; - - if (p.split_ != 0) { - // Use Knuth's algorithm with range splitting to avoid floating-point - // errors. Knuth's algorithm is: Ui is a sequence of uniform variates on - // (0,1); return the number of variates required for product(Ui) < - // exp(-lambda). - // - // The expected number of variates required for Knuth's method can be - // computed as follows: - // The expected value of U is 0.5, so solving for 0.5^n < exp(-lambda) gives - // the expected number of uniform variates - // required for a given lambda, which is: - // lambda = [2, 5, 9, 10, 11, 12, 13, 14, 15, 16, 17] - // n = [3, 8, 13, 15, 16, 18, 19, 21, 22, 24, 25] - // - result_type n = 0; - for (int split = p.split_; split > 0; --split) { - double r = 1.0; - do { - r *= GenerateRealFromBits<double, GeneratePositiveTag, true>( - fast_u64_(g)); // U(-1, 0) - ++n; - } while (r > p.emu_); - --n; - } - return n; - } - - // Use ratio of uniforms method. - // - // Let u ~ Uniform(0, 1), v ~ Uniform(-1, 1), - // a = lambda + 1/2, - // s = 1.5 - sqrt(3/e) + sqrt(2(lambda + 1/2)/e), - // x = s * v/u + a. - // P(floor(x) = k | u^2 < f(floor(x))/k), where - // f(m) = lambda^m exp(-lambda)/ m!, for 0 <= m, and f(m) = 0 otherwise, - // and k = max(f). - const double a = p.mean_ + 0.5; - for (;;) { - const double u = GenerateRealFromBits<double, GeneratePositiveTag, false>( - fast_u64_(g)); // U(0, 1) - const double v = GenerateRealFromBits<double, GenerateSignedTag, false>( - fast_u64_(g)); // U(-1, 1) - - const double x = std::floor(p.s_ * v / u + a); - if (x < 0) continue; // f(negative) = 0 - const double rhs = x * p.lmu_; - // clang-format off - double s = (x <= 1.0) ? 0.0 - : (x == 2.0) ? 0.693147180559945 - : absl::random_internal::StirlingLogFactorial(x); - // clang-format on - const double lhs = 2.0 * std::log(u) + p.log_k_ + s; - if (lhs < rhs) { - return x > (max)() ? (max)() - : static_cast<result_type>(x); // f(x)/k >= u^2 - } - } -} - -template <typename CharT, typename Traits, typename IntType> -std::basic_ostream<CharT, Traits>& operator<<( - std::basic_ostream<CharT, Traits>& os, // NOLINT(runtime/references) - const poisson_distribution<IntType>& x) { - auto saver = random_internal::make_ostream_state_saver(os); - os.precision(random_internal::stream_precision_helper<double>::kPrecision); - os << x.mean(); - return os; -} - -template <typename CharT, typename Traits, typename IntType> -std::basic_istream<CharT, Traits>& operator>>( - std::basic_istream<CharT, Traits>& is, // NOLINT(runtime/references) - poisson_distribution<IntType>& x) { // NOLINT(runtime/references) - using param_type = typename poisson_distribution<IntType>::param_type; - - auto saver = random_internal::make_istream_state_saver(is); - double mean = random_internal::read_floating_point<double>(is); - if (!is.fail()) { - x.param(param_type(mean)); - } - return is; -} - -ABSL_NAMESPACE_END -} // namespace absl - -#endif // ABSL_RANDOM_POISSON_DISTRIBUTION_H_ diff --git a/third_party/abseil_cpp/absl/random/poisson_distribution_test.cc b/third_party/abseil_cpp/absl/random/poisson_distribution_test.cc deleted file mode 100644 index 8baabd111892..000000000000 --- a/third_party/abseil_cpp/absl/random/poisson_distribution_test.cc +++ /dev/null @@ -1,573 +0,0 @@ -// Copyright 2017 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#include "absl/random/poisson_distribution.h" - -#include <algorithm> -#include <cstddef> -#include <cstdint> -#include <iterator> -#include <random> -#include <sstream> -#include <string> -#include <vector> - -#include "gmock/gmock.h" -#include "gtest/gtest.h" -#include "absl/base/internal/raw_logging.h" -#include "absl/base/macros.h" -#include "absl/container/flat_hash_map.h" -#include "absl/random/internal/chi_square.h" -#include "absl/random/internal/distribution_test_util.h" -#include "absl/random/internal/pcg_engine.h" -#include "absl/random/internal/sequence_urbg.h" -#include "absl/random/random.h" -#include "absl/strings/str_cat.h" -#include "absl/strings/str_format.h" -#include "absl/strings/str_replace.h" -#include "absl/strings/strip.h" - -// Notes about generating poisson variates: -// -// It is unlikely that any implementation of std::poisson_distribution -// will be stable over time and across library implementations. For instance -// the three different poisson variate generators listed below all differ: -// -// https://github.com/ampl/gsl/tree/master/randist/poisson.c -// * GSL uses a gamma + binomial + knuth method to compute poisson variates. -// -// https://github.com/gcc-mirror/gcc/blob/master/libstdc%2B%2B-v3/include/bits/random.tcc -// * GCC uses the Devroye rejection algorithm, based on -// Devroye, L. Non-Uniform Random Variates Generation. Springer-Verlag, -// New York, 1986, Ch. X, Sects. 3.3 & 3.4 (+ Errata!), ~p.511 -// http://www.nrbook.com/devroye/ -// -// https://github.com/llvm-mirror/libcxx/blob/master/include/random -// * CLANG uses a different rejection method, which appears to include a -// normal-distribution approximation and an exponential distribution to -// compute the threshold, including a similar factorial approximation to this -// one, but it is unclear where the algorithm comes from, exactly. -// - -namespace { - -using absl::random_internal::kChiSquared; - -// The PoissonDistributionInterfaceTest provides a basic test that -// absl::poisson_distribution conforms to the interface and serialization -// requirements imposed by [rand.req.dist] for the common integer types. - -template <typename IntType> -class PoissonDistributionInterfaceTest : public ::testing::Test {}; - -using IntTypes = ::testing::Types<int, int8_t, int16_t, int32_t, int64_t, - uint8_t, uint16_t, uint32_t, uint64_t>; -TYPED_TEST_CASE(PoissonDistributionInterfaceTest, IntTypes); - -TYPED_TEST(PoissonDistributionInterfaceTest, SerializeTest) { - using param_type = typename absl::poisson_distribution<TypeParam>::param_type; - const double kMax = - std::min(1e10 /* assertion limit */, - static_cast<double>(std::numeric_limits<TypeParam>::max())); - - const double kParams[] = { - // Cases around 1. - 1, // - std::nextafter(1.0, 0.0), // 1 - epsilon - std::nextafter(1.0, 2.0), // 1 + epsilon - // Arbitrary values. - 1e-8, 1e-4, - 0.0000005, // ~7.2e-7 - 0.2, // ~0.2x - 0.5, // 0.72 - 2, // ~2.8 - 20, // 3x ~9.6 - 100, 1e4, 1e8, 1.5e9, 1e20, - // Boundary cases. - std::numeric_limits<double>::max(), - std::numeric_limits<double>::epsilon(), - std::nextafter(std::numeric_limits<double>::min(), - 1.0), // min + epsilon - std::numeric_limits<double>::min(), // smallest normal - std::numeric_limits<double>::denorm_min(), // smallest denorm - std::numeric_limits<double>::min() / 2, // denorm - std::nextafter(std::numeric_limits<double>::min(), - 0.0), // denorm_max - }; - - - constexpr int kCount = 1000; - absl::InsecureBitGen gen; - for (const double m : kParams) { - const double mean = std::min(kMax, m); - const param_type param(mean); - - // Validate parameters. - absl::poisson_distribution<TypeParam> before(mean); - EXPECT_EQ(before.mean(), param.mean()); - - { - absl::poisson_distribution<TypeParam> via_param(param); - EXPECT_EQ(via_param, before); - EXPECT_EQ(via_param.param(), before.param()); - } - - // Smoke test. - auto sample_min = before.max(); - auto sample_max = before.min(); - for (int i = 0; i < kCount; i++) { - auto sample = before(gen); - EXPECT_GE(sample, before.min()); - EXPECT_LE(sample, before.max()); - if (sample > sample_max) sample_max = sample; - if (sample < sample_min) sample_min = sample; - } - - ABSL_INTERNAL_LOG(INFO, absl::StrCat("Range {", param.mean(), "}: ", - +sample_min, ", ", +sample_max)); - - // Validate stream serialization. - std::stringstream ss; - ss << before; - - absl::poisson_distribution<TypeParam> after(3.8); - - EXPECT_NE(before.mean(), after.mean()); - EXPECT_NE(before.param(), after.param()); - EXPECT_NE(before, after); - - ss >> after; - - EXPECT_EQ(before.mean(), after.mean()) // - << ss.str() << " " // - << (ss.good() ? "good " : "") // - << (ss.bad() ? "bad " : "") // - << (ss.eof() ? "eof " : "") // - << (ss.fail() ? "fail " : ""); - } -} - -// See http://www.itl.nist.gov/div898/handbook/eda/section3/eda366j.htm - -class PoissonModel { - public: - explicit PoissonModel(double mean) : mean_(mean) {} - - double mean() const { return mean_; } - double variance() const { return mean_; } - double stddev() const { return std::sqrt(variance()); } - double skew() const { return 1.0 / mean_; } - double kurtosis() const { return 3.0 + 1.0 / mean_; } - - // InitCDF() initializes the CDF for the distribution parameters. - void InitCDF(); - - // The InverseCDF, or the Percent-point function returns x, P(x) < v. - struct CDF { - size_t index; - double pmf; - double cdf; - }; - CDF InverseCDF(double p) { - CDF target{0, 0, p}; - auto it = std::upper_bound( - std::begin(cdf_), std::end(cdf_), target, - [](const CDF& a, const CDF& b) { return a.cdf < b.cdf; }); - return *it; - } - - void LogCDF() { - ABSL_INTERNAL_LOG(INFO, absl::StrCat("CDF (mean = ", mean_, ")")); - for (const auto c : cdf_) { - ABSL_INTERNAL_LOG(INFO, - absl::StrCat(c.index, ": pmf=", c.pmf, " cdf=", c.cdf)); - } - } - - private: - const double mean_; - - std::vector<CDF> cdf_; -}; - -// The goal is to compute an InverseCDF function, or percent point function for -// the poisson distribution, and use that to partition our output into equal -// range buckets. However there is no closed form solution for the inverse cdf -// for poisson distributions (the closest is the incomplete gamma function). -// Instead, `InitCDF` iteratively computes the PMF and the CDF. This enables -// searching for the bucket points. -void PoissonModel::InitCDF() { - if (!cdf_.empty()) { - // State already initialized. - return; - } - ABSL_ASSERT(mean_ < 201.0); - - const size_t max_i = 50 * stddev() + mean(); - const double e_neg_mean = std::exp(-mean()); - ABSL_ASSERT(e_neg_mean > 0); - - double d = 1; - double last_result = e_neg_mean; - double cumulative = e_neg_mean; - if (e_neg_mean > 1e-10) { - cdf_.push_back({0, e_neg_mean, cumulative}); - } - for (size_t i = 1; i < max_i; i++) { - d *= (mean() / i); - double result = e_neg_mean * d; - cumulative += result; - if (result < 1e-10 && result < last_result && cumulative > 0.999999) { - break; - } - if (result > 1e-7) { - cdf_.push_back({i, result, cumulative}); - } - last_result = result; - } - ABSL_ASSERT(!cdf_.empty()); -} - -// PoissonDistributionZTest implements a z-test for the poisson distribution. - -struct ZParam { - double mean; - double p_fail; // Z-Test probability of failure. - int trials; // Z-Test trials. - size_t samples; // Z-Test samples. -}; - -class PoissonDistributionZTest : public testing::TestWithParam<ZParam>, - public PoissonModel { - public: - PoissonDistributionZTest() : PoissonModel(GetParam().mean) {} - - // ZTestImpl provides a basic z-squared test of the mean vs. expected - // mean for data generated by the poisson distribution. - template <typename D> - bool SingleZTest(const double p, const size_t samples); - - // We use a fixed bit generator for distribution accuracy tests. This allows - // these tests to be deterministic, while still testing the qualify of the - // implementation. - absl::random_internal::pcg64_2018_engine rng_{0x2B7E151628AED2A6}; -}; - -template <typename D> -bool PoissonDistributionZTest::SingleZTest(const double p, - const size_t samples) { - D dis(mean()); - - absl::flat_hash_map<int32_t, int> buckets; - std::vector<double> data; - data.reserve(samples); - for (int j = 0; j < samples; j++) { - const auto x = dis(rng_); - buckets[x]++; - data.push_back(x); - } - - // The null-hypothesis is that the distribution is a poisson distribution with - // the provided mean (not estimated from the data). - const auto m = absl::random_internal::ComputeDistributionMoments(data); - const double max_err = absl::random_internal::MaxErrorTolerance(p); - const double z = absl::random_internal::ZScore(mean(), m); - const bool pass = absl::random_internal::Near("z", z, 0.0, max_err); - - if (!pass) { - ABSL_INTERNAL_LOG( - INFO, absl::StrFormat("p=%f max_err=%f\n" - " mean=%f vs. %f\n" - " stddev=%f vs. %f\n" - " skewness=%f vs. %f\n" - " kurtosis=%f vs. %f\n" - " z=%f", - p, max_err, m.mean, mean(), std::sqrt(m.variance), - stddev(), m.skewness, skew(), m.kurtosis, - kurtosis(), z)); - } - return pass; -} - -TEST_P(PoissonDistributionZTest, AbslPoissonDistribution) { - const auto& param = GetParam(); - const int expected_failures = - std::max(1, static_cast<int>(std::ceil(param.trials * param.p_fail))); - const double p = absl::random_internal::RequiredSuccessProbability( - param.p_fail, param.trials); - - int failures = 0; - for (int i = 0; i < param.trials; i++) { - failures += - SingleZTest<absl::poisson_distribution<int32_t>>(p, param.samples) ? 0 - : 1; - } - EXPECT_LE(failures, expected_failures); -} - -std::vector<ZParam> GetZParams() { - // These values have been adjusted from the "exact" computed values to reduce - // failure rates. - // - // It turns out that the actual values are not as close to the expected values - // as would be ideal. - return std::vector<ZParam>({ - // Knuth method. - ZParam{0.5, 0.01, 100, 1000}, - ZParam{1.0, 0.01, 100, 1000}, - ZParam{10.0, 0.01, 100, 5000}, - // Split-knuth method. - ZParam{20.0, 0.01, 100, 10000}, - ZParam{50.0, 0.01, 100, 10000}, - // Ratio of gaussians method. - ZParam{51.0, 0.01, 100, 10000}, - ZParam{200.0, 0.05, 10, 100000}, - ZParam{100000.0, 0.05, 10, 1000000}, - }); -} - -std::string ZParamName(const ::testing::TestParamInfo<ZParam>& info) { - const auto& p = info.param; - std::string name = absl::StrCat("mean_", absl::SixDigits(p.mean)); - return absl::StrReplaceAll(name, {{"+", "_"}, {"-", "_"}, {".", "_"}}); -} - -INSTANTIATE_TEST_SUITE_P(All, PoissonDistributionZTest, - ::testing::ValuesIn(GetZParams()), ZParamName); - -// The PoissonDistributionChiSquaredTest class provides a basic test framework -// for variates generated by a conforming poisson_distribution. -class PoissonDistributionChiSquaredTest : public testing::TestWithParam<double>, - public PoissonModel { - public: - PoissonDistributionChiSquaredTest() : PoissonModel(GetParam()) {} - - // The ChiSquaredTestImpl provides a chi-squared goodness of fit test for data - // generated by the poisson distribution. - template <typename D> - double ChiSquaredTestImpl(); - - private: - void InitChiSquaredTest(const double buckets); - - std::vector<size_t> cutoffs_; - std::vector<double> expected_; - - // We use a fixed bit generator for distribution accuracy tests. This allows - // these tests to be deterministic, while still testing the qualify of the - // implementation. - absl::random_internal::pcg64_2018_engine rng_{0x2B7E151628AED2A6}; -}; - -void PoissonDistributionChiSquaredTest::InitChiSquaredTest( - const double buckets) { - if (!cutoffs_.empty() && !expected_.empty()) { - return; - } - InitCDF(); - - // The code below finds cuttoffs that yield approximately equally-sized - // buckets to the extent that it is possible. However for poisson - // distributions this is particularly challenging for small mean parameters. - // Track the expected proportion of items in each bucket. - double last_cdf = 0; - const double inc = 1.0 / buckets; - for (double p = inc; p <= 1.0; p += inc) { - auto result = InverseCDF(p); - if (!cutoffs_.empty() && cutoffs_.back() == result.index) { - continue; - } - double d = result.cdf - last_cdf; - cutoffs_.push_back(result.index); - expected_.push_back(d); - last_cdf = result.cdf; - } - cutoffs_.push_back(std::numeric_limits<size_t>::max()); - expected_.push_back(std::max(0.0, 1.0 - last_cdf)); -} - -template <typename D> -double PoissonDistributionChiSquaredTest::ChiSquaredTestImpl() { - const int kSamples = 2000; - const int kBuckets = 50; - - // The poisson CDF fails for large mean values, since e^-mean exceeds the - // machine precision. For these cases, using a normal approximation would be - // appropriate. - ABSL_ASSERT(mean() <= 200); - InitChiSquaredTest(kBuckets); - - D dis(mean()); - - std::vector<int32_t> counts(cutoffs_.size(), 0); - for (int j = 0; j < kSamples; j++) { - const size_t x = dis(rng_); - auto it = std::lower_bound(std::begin(cutoffs_), std::end(cutoffs_), x); - counts[std::distance(cutoffs_.begin(), it)]++; - } - - // Normalize the counts. - std::vector<int32_t> e(expected_.size(), 0); - for (int i = 0; i < e.size(); i++) { - e[i] = kSamples * expected_[i]; - } - - // The null-hypothesis is that the distribution is a poisson distribution with - // the provided mean (not estimated from the data). - const int dof = static_cast<int>(counts.size()) - 1; - - // The threshold for logging is 1-in-50. - const double threshold = absl::random_internal::ChiSquareValue(dof, 0.98); - - const double chi_square = absl::random_internal::ChiSquare( - std::begin(counts), std::end(counts), std::begin(e), std::end(e)); - - const double p = absl::random_internal::ChiSquarePValue(chi_square, dof); - - // Log if the chi_squared value is above the threshold. - if (chi_square > threshold) { - LogCDF(); - - ABSL_INTERNAL_LOG(INFO, absl::StrCat("VALUES buckets=", counts.size(), - " samples=", kSamples)); - for (size_t i = 0; i < counts.size(); i++) { - ABSL_INTERNAL_LOG( - INFO, absl::StrCat(cutoffs_[i], ": ", counts[i], " vs. E=", e[i])); - } - - ABSL_INTERNAL_LOG( - INFO, - absl::StrCat(kChiSquared, "(data, dof=", dof, ") = ", chi_square, " (", - p, ")\n", " vs.\n", kChiSquared, " @ 0.98 = ", threshold)); - } - return p; -} - -TEST_P(PoissonDistributionChiSquaredTest, AbslPoissonDistribution) { - const int kTrials = 20; - - // Large values are not yet supported -- this requires estimating the cdf - // using the normal distribution instead of the poisson in this case. - ASSERT_LE(mean(), 200.0); - if (mean() > 200.0) { - return; - } - - int failures = 0; - for (int i = 0; i < kTrials; i++) { - double p_value = ChiSquaredTestImpl<absl::poisson_distribution<int32_t>>(); - if (p_value < 0.005) { - failures++; - } - } - // There is a 0.10% chance of producing at least one failure, so raise the - // failure threshold high enough to allow for a flake rate < 10,000. - EXPECT_LE(failures, 4); -} - -INSTANTIATE_TEST_SUITE_P(All, PoissonDistributionChiSquaredTest, - ::testing::Values(0.5, 1.0, 2.0, 10.0, 50.0, 51.0, - 200.0)); - -// NOTE: absl::poisson_distribution is not guaranteed to be stable. -TEST(PoissonDistributionTest, StabilityTest) { - using testing::ElementsAre; - // absl::poisson_distribution stability relies on stability of - // std::exp, std::log, std::sqrt, std::ceil, std::floor, and - // absl::FastUniformBits, absl::StirlingLogFactorial, absl::RandU64ToDouble. - absl::random_internal::sequence_urbg urbg({ - 0x035b0dc7e0a18acfull, 0x06cebe0d2653682eull, 0x0061e9b23861596bull, - 0x0003eb76f6f7f755ull, 0xFFCEA50FDB2F953Bull, 0xC332DDEFBE6C5AA5ull, - 0x6558218568AB9702ull, 0x2AEF7DAD5B6E2F84ull, 0x1521B62829076170ull, - 0xECDD4775619F1510ull, 0x13CCA830EB61BD96ull, 0x0334FE1EAA0363CFull, - 0xB5735C904C70A239ull, 0xD59E9E0BCBAADE14ull, 0xEECC86BC60622CA7ull, - 0x4864f22c059bf29eull, 0x247856d8b862665cull, 0xe46e86e9a1337e10ull, - 0xd8c8541f3519b133ull, 0xe75b5162c567b9e4ull, 0xf732e5ded7009c5bull, - 0xb170b98353121eacull, 0x1ec2e8986d2362caull, 0x814c8e35fe9a961aull, - 0x0c3cd59c9b638a02ull, 0xcb3bb6478a07715cull, 0x1224e62c978bbc7full, - 0x671ef2cb04e81f6eull, 0x3c1cbd811eaf1808ull, 0x1bbc23cfa8fac721ull, - 0xa4c2cda65e596a51ull, 0xb77216fad37adf91ull, 0x836d794457c08849ull, - 0xe083df03475f49d7ull, 0xbc9feb512e6b0d6cull, 0xb12d74fdd718c8c5ull, - 0x12ff09653bfbe4caull, 0x8dd03a105bc4ee7eull, 0x5738341045ba0d85ull, - 0xf3fd722dc65ad09eull, 0xfa14fd21ea2a5705ull, 0xffe6ea4d6edb0c73ull, - 0xD07E9EFE2BF11FB4ull, 0x95DBDA4DAE909198ull, 0xEAAD8E716B93D5A0ull, - 0xD08ED1D0AFC725E0ull, 0x8E3C5B2F8E7594B7ull, 0x8FF6E2FBF2122B64ull, - 0x8888B812900DF01Cull, 0x4FAD5EA0688FC31Cull, 0xD1CFF191B3A8C1ADull, - 0x2F2F2218BE0E1777ull, 0xEA752DFE8B021FA1ull, 0xE5A0CC0FB56F74E8ull, - 0x18ACF3D6CE89E299ull, 0xB4A84FE0FD13E0B7ull, 0x7CC43B81D2ADA8D9ull, - 0x165FA26680957705ull, 0x93CC7314211A1477ull, 0xE6AD206577B5FA86ull, - 0xC75442F5FB9D35CFull, 0xEBCDAF0C7B3E89A0ull, 0xD6411BD3AE1E7E49ull, - 0x00250E2D2071B35Eull, 0x226800BB57B8E0AFull, 0x2464369BF009B91Eull, - 0x5563911D59DFA6AAull, 0x78C14389D95A537Full, 0x207D5BA202E5B9C5ull, - 0x832603766295CFA9ull, 0x11C819684E734A41ull, 0xB3472DCA7B14A94Aull, - }); - - std::vector<int> output(10); - - // Method 1. - { - absl::poisson_distribution<int> dist(5); - std::generate(std::begin(output), std::end(output), - [&] { return dist(urbg); }); - } - EXPECT_THAT(output, // mean = 4.2 - ElementsAre(1, 0, 0, 4, 2, 10, 3, 3, 7, 12)); - - // Method 2. - { - urbg.reset(); - absl::poisson_distribution<int> dist(25); - std::generate(std::begin(output), std::end(output), - [&] { return dist(urbg); }); - } - EXPECT_THAT(output, // mean = 19.8 - ElementsAre(9, 35, 18, 10, 35, 18, 10, 35, 18, 10)); - - // Method 3. - { - urbg.reset(); - absl::poisson_distribution<int> dist(121); - std::generate(std::begin(output), std::end(output), - [&] { return dist(urbg); }); - } - EXPECT_THAT(output, // mean = 124.1 - ElementsAre(161, 122, 129, 124, 112, 112, 117, 120, 130, 114)); -} - -TEST(PoissonDistributionTest, AlgorithmExpectedValue_1) { - // This tests small values of the Knuth method. - // The underlying uniform distribution will generate exactly 0.5. - absl::random_internal::sequence_urbg urbg({0x8000000000000001ull}); - absl::poisson_distribution<int> dist(5); - EXPECT_EQ(7, dist(urbg)); -} - -TEST(PoissonDistributionTest, AlgorithmExpectedValue_2) { - // This tests larger values of the Knuth method. - // The underlying uniform distribution will generate exactly 0.5. - absl::random_internal::sequence_urbg urbg({0x8000000000000001ull}); - absl::poisson_distribution<int> dist(25); - EXPECT_EQ(36, dist(urbg)); -} - -TEST(PoissonDistributionTest, AlgorithmExpectedValue_3) { - // This variant uses the ratio of uniforms method. - absl::random_internal::sequence_urbg urbg( - {0x7fffffffffffffffull, 0x8000000000000000ull}); - - absl::poisson_distribution<int> dist(121); - EXPECT_EQ(121, dist(urbg)); -} - -} // namespace diff --git a/third_party/abseil_cpp/absl/random/random.h b/third_party/abseil_cpp/absl/random/random.h deleted file mode 100644 index 71b6309288e6..000000000000 --- a/third_party/abseil_cpp/absl/random/random.h +++ /dev/null @@ -1,189 +0,0 @@ -// Copyright 2017 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. -// -// ----------------------------------------------------------------------------- -// File: random.h -// ----------------------------------------------------------------------------- -// -// This header defines the recommended Uniform Random Bit Generator (URBG) -// types for use within the Abseil Random library. These types are not -// suitable for security-related use-cases, but should suffice for most other -// uses of generating random values. -// -// The Abseil random library provides the following URBG types: -// -// * BitGen, a good general-purpose bit generator, optimized for generating -// random (but not cryptographically secure) values -// * InsecureBitGen, a slightly faster, though less random, bit generator, for -// cases where the existing BitGen is a drag on performance. - -#ifndef ABSL_RANDOM_RANDOM_H_ -#define ABSL_RANDOM_RANDOM_H_ - -#include <random> - -#include "absl/random/distributions.h" // IWYU pragma: export -#include "absl/random/internal/nonsecure_base.h" // IWYU pragma: export -#include "absl/random/internal/pcg_engine.h" // IWYU pragma: export -#include "absl/random/internal/pool_urbg.h" -#include "absl/random/internal/randen_engine.h" -#include "absl/random/seed_sequences.h" // IWYU pragma: export - -namespace absl { -ABSL_NAMESPACE_BEGIN - -// ----------------------------------------------------------------------------- -// absl::BitGen -// ----------------------------------------------------------------------------- -// -// `absl::BitGen` is a general-purpose random bit generator for generating -// random values for use within the Abseil random library. Typically, you use a -// bit generator in combination with a distribution to provide random values. -// -// Example: -// -// // Create an absl::BitGen. There is no need to seed this bit generator. -// absl::BitGen gen; -// -// // Generate an integer value in the closed interval [1,6] -// int die_roll = absl::uniform_int_distribution<int>(1, 6)(gen); -// -// `absl::BitGen` is seeded by default with non-deterministic data to produce -// different sequences of random values across different instances, including -// different binary invocations. This behavior is different than the standard -// library bit generators, which use golden values as their seeds. Default -// construction intentionally provides no stability guarantees, to avoid -// accidental dependence on such a property. -// -// `absl::BitGen` may be constructed with an optional seed sequence type, -// conforming to [rand.req.seed_seq], which will be mixed with additional -// non-deterministic data. -// -// Example: -// -// // Create an absl::BitGen using an std::seed_seq seed sequence -// std::seed_seq seq{1,2,3}; -// absl::BitGen gen_with_seed(seq); -// -// // Generate an integer value in the closed interval [1,6] -// int die_roll2 = absl::uniform_int_distribution<int>(1, 6)(gen_with_seed); -// -// `absl::BitGen` meets the requirements of the Uniform Random Bit Generator -// (URBG) concept as per the C++17 standard [rand.req.urng] though differs -// slightly with [rand.req.eng]. Like its standard library equivalents (e.g. -// `std::mersenne_twister_engine`) `absl::BitGen` is not cryptographically -// secure. -// -// Constructing two `absl::BitGen`s with the same seed sequence in the same -// binary will produce the same sequence of variates within the same binary, but -// need not do so across multiple binary invocations. -// -// This type has been optimized to perform better than Mersenne Twister -// (https://en.wikipedia.org/wiki/Mersenne_Twister) and many other complex URBG -// types on modern x86, ARM, and PPC architectures. -// -// This type is thread-compatible, but not thread-safe. - -// --------------------------------------------------------------------------- -// absl::BitGen member functions -// --------------------------------------------------------------------------- - -// absl::BitGen::operator()() -// -// Calls the BitGen, returning a generated value. - -// absl::BitGen::min() -// -// Returns the smallest possible value from this bit generator. - -// absl::BitGen::max() -// -// Returns the largest possible value from this bit generator. - -// absl::BitGen::discard(num) -// -// Advances the internal state of this bit generator by `num` times, and -// discards the intermediate results. -// --------------------------------------------------------------------------- - -using BitGen = random_internal::NonsecureURBGBase< - random_internal::randen_engine<uint64_t>>; - -// ----------------------------------------------------------------------------- -// absl::InsecureBitGen -// ----------------------------------------------------------------------------- -// -// `absl::InsecureBitGen` is an efficient random bit generator for generating -// random values, recommended only for performance-sensitive use cases where -// `absl::BitGen` is not satisfactory when compute-bounded by bit generation -// costs. -// -// Example: -// -// // Create an absl::InsecureBitGen -// absl::InsecureBitGen gen; -// for (size_t i = 0; i < 1000000; i++) { -// -// // Generate a bunch of random values from some complex distribution -// auto my_rnd = some_distribution(gen, 1, 1000); -// } -// -// Like `absl::BitGen`, `absl::InsecureBitGen` is seeded by default with -// non-deterministic data to produce different sequences of random values across -// different instances, including different binary invocations. (This behavior -// is different than the standard library bit generators, which use golden -// values as their seeds.) -// -// `absl::InsecureBitGen` may be constructed with an optional seed sequence -// type, conforming to [rand.req.seed_seq], which will be mixed with additional -// non-deterministic data. (See std_seed_seq.h for more information.) -// -// `absl::InsecureBitGen` meets the requirements of the Uniform Random Bit -// Generator (URBG) concept as per the C++17 standard [rand.req.urng] though -// its implementation differs slightly with [rand.req.eng]. Like its standard -// library equivalents (e.g. `std::mersenne_twister_engine`) -// `absl::InsecureBitGen` is not cryptographically secure. -// -// Prefer `absl::BitGen` over `absl::InsecureBitGen` as the general type is -// often fast enough for the vast majority of applications. - -using InsecureBitGen = - random_internal::NonsecureURBGBase<random_internal::pcg64_2018_engine>; - -// --------------------------------------------------------------------------- -// absl::InsecureBitGen member functions -// --------------------------------------------------------------------------- - -// absl::InsecureBitGen::operator()() -// -// Calls the InsecureBitGen, returning a generated value. - -// absl::InsecureBitGen::min() -// -// Returns the smallest possible value from this bit generator. - -// absl::InsecureBitGen::max() -// -// Returns the largest possible value from this bit generator. - -// absl::InsecureBitGen::discard(num) -// -// Advances the internal state of this bit generator by `num` times, and -// discards the intermediate results. -// --------------------------------------------------------------------------- - -ABSL_NAMESPACE_END -} // namespace absl - -#endif // ABSL_RANDOM_RANDOM_H_ diff --git a/third_party/abseil_cpp/absl/random/seed_gen_exception.cc b/third_party/abseil_cpp/absl/random/seed_gen_exception.cc deleted file mode 100644 index fdcb54a86cdd..000000000000 --- a/third_party/abseil_cpp/absl/random/seed_gen_exception.cc +++ /dev/null @@ -1,46 +0,0 @@ -// Copyright 2017 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#include "absl/random/seed_gen_exception.h" - -#include <iostream> - -#include "absl/base/config.h" - -namespace absl { -ABSL_NAMESPACE_BEGIN - -static constexpr const char kExceptionMessage[] = - "Failed generating seed-material for URBG."; - -SeedGenException::~SeedGenException() = default; - -const char* SeedGenException::what() const noexcept { - return kExceptionMessage; -} - -namespace random_internal { - -void ThrowSeedGenException() { -#ifdef ABSL_HAVE_EXCEPTIONS - throw absl::SeedGenException(); -#else - std::cerr << kExceptionMessage << std::endl; - std::terminate(); -#endif -} - -} // namespace random_internal -ABSL_NAMESPACE_END -} // namespace absl diff --git a/third_party/abseil_cpp/absl/random/seed_gen_exception.h b/third_party/abseil_cpp/absl/random/seed_gen_exception.h deleted file mode 100644 index 535390056437..000000000000 --- a/third_party/abseil_cpp/absl/random/seed_gen_exception.h +++ /dev/null @@ -1,55 +0,0 @@ -// Copyright 2017 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. -// -// ----------------------------------------------------------------------------- -// File: seed_gen_exception.h -// ----------------------------------------------------------------------------- -// -// This header defines an exception class which may be thrown if unpredictable -// events prevent the derivation of suitable seed-material for constructing a -// bit generator conforming to [rand.req.urng] (eg. entropy cannot be read from -// /dev/urandom on a Unix-based system). -// -// Note: if exceptions are disabled, `std::terminate()` is called instead. - -#ifndef ABSL_RANDOM_SEED_GEN_EXCEPTION_H_ -#define ABSL_RANDOM_SEED_GEN_EXCEPTION_H_ - -#include <exception> - -#include "absl/base/config.h" - -namespace absl { -ABSL_NAMESPACE_BEGIN - -//------------------------------------------------------------------------------ -// SeedGenException -//------------------------------------------------------------------------------ -class SeedGenException : public std::exception { - public: - SeedGenException() = default; - ~SeedGenException() override; - const char* what() const noexcept override; -}; - -namespace random_internal { - -// throw delegator -[[noreturn]] void ThrowSeedGenException(); - -} // namespace random_internal -ABSL_NAMESPACE_END -} // namespace absl - -#endif // ABSL_RANDOM_SEED_GEN_EXCEPTION_H_ diff --git a/third_party/abseil_cpp/absl/random/seed_sequences.cc b/third_party/abseil_cpp/absl/random/seed_sequences.cc deleted file mode 100644 index 426eafd3c8de..000000000000 --- a/third_party/abseil_cpp/absl/random/seed_sequences.cc +++ /dev/null @@ -1,29 +0,0 @@ -// Copyright 2017 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#include "absl/random/seed_sequences.h" - -#include "absl/random/internal/pool_urbg.h" - -namespace absl { -ABSL_NAMESPACE_BEGIN - -SeedSeq MakeSeedSeq() { - SeedSeq::result_type seed_material[8]; - random_internal::RandenPool<uint32_t>::Fill(absl::MakeSpan(seed_material)); - return SeedSeq(std::begin(seed_material), std::end(seed_material)); -} - -ABSL_NAMESPACE_END -} // namespace absl diff --git a/third_party/abseil_cpp/absl/random/seed_sequences.h b/third_party/abseil_cpp/absl/random/seed_sequences.h deleted file mode 100644 index ff1340cc8eed..000000000000 --- a/third_party/abseil_cpp/absl/random/seed_sequences.h +++ /dev/null @@ -1,110 +0,0 @@ -// Copyright 2017 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. -// -// ----------------------------------------------------------------------------- -// File: seed_sequences.h -// ----------------------------------------------------------------------------- -// -// This header contains utilities for creating and working with seed sequences -// conforming to [rand.req.seedseq]. In general, direct construction of seed -// sequences is discouraged, but use-cases for construction of identical bit -// generators (using the same seed sequence) may be helpful (e.g. replaying a -// simulation whose state is derived from variates of a bit generator). - -#ifndef ABSL_RANDOM_SEED_SEQUENCES_H_ -#define ABSL_RANDOM_SEED_SEQUENCES_H_ - -#include <iterator> -#include <random> - -#include "absl/random/internal/salted_seed_seq.h" -#include "absl/random/internal/seed_material.h" -#include "absl/random/seed_gen_exception.h" -#include "absl/types/span.h" - -namespace absl { -ABSL_NAMESPACE_BEGIN - -// ----------------------------------------------------------------------------- -// absl::SeedSeq -// ----------------------------------------------------------------------------- -// -// `absl::SeedSeq` constructs a seed sequence according to [rand.req.seedseq] -// for use within bit generators. `absl::SeedSeq`, unlike `std::seed_seq` -// additionally salts the generated seeds with extra implementation-defined -// entropy. For that reason, you can use `absl::SeedSeq` in combination with -// standard library bit generators (e.g. `std::mt19937`) to introduce -// non-determinism in your seeds. -// -// Example: -// -// absl::SeedSeq my_seed_seq({a, b, c}); -// std::mt19937 my_bitgen(my_seed_seq); -// -using SeedSeq = random_internal::SaltedSeedSeq<std::seed_seq>; - -// ----------------------------------------------------------------------------- -// absl::CreateSeedSeqFrom(bitgen*) -// ----------------------------------------------------------------------------- -// -// Constructs a seed sequence conforming to [rand.req.seedseq] using variates -// produced by a provided bit generator. -// -// You should generally avoid direct construction of seed sequences, but -// use-cases for reuse of a seed sequence to construct identical bit generators -// may be helpful (eg. replaying a simulation whose state is derived from bit -// generator values). -// -// If bitgen == nullptr, then behavior is undefined. -// -// Example: -// -// absl::BitGen my_bitgen; -// auto seed_seq = absl::CreateSeedSeqFrom(&my_bitgen); -// absl::BitGen new_engine(seed_seq); // derived from my_bitgen, but not -// // correlated. -// -template <typename URBG> -SeedSeq CreateSeedSeqFrom(URBG* urbg) { - SeedSeq::result_type - seed_material[random_internal::kEntropyBlocksNeeded]; - - if (!random_internal::ReadSeedMaterialFromURBG( - urbg, absl::MakeSpan(seed_material))) { - random_internal::ThrowSeedGenException(); - } - return SeedSeq(std::begin(seed_material), std::end(seed_material)); -} - -// ----------------------------------------------------------------------------- -// absl::MakeSeedSeq() -// ----------------------------------------------------------------------------- -// -// Constructs an `absl::SeedSeq` salting the generated values using -// implementation-defined entropy. The returned sequence can be used to create -// equivalent bit generators correlated using this sequence. -// -// Example: -// -// auto my_seed_seq = absl::MakeSeedSeq(); -// std::mt19937 rng1(my_seed_seq); -// std::mt19937 rng2(my_seed_seq); -// EXPECT_EQ(rng1(), rng2()); -// -SeedSeq MakeSeedSeq(); - -ABSL_NAMESPACE_END -} // namespace absl - -#endif // ABSL_RANDOM_SEED_SEQUENCES_H_ diff --git a/third_party/abseil_cpp/absl/random/seed_sequences_test.cc b/third_party/abseil_cpp/absl/random/seed_sequences_test.cc deleted file mode 100644 index fe1100bda0d5..000000000000 --- a/third_party/abseil_cpp/absl/random/seed_sequences_test.cc +++ /dev/null @@ -1,126 +0,0 @@ -// Copyright 2017 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#include "absl/random/seed_sequences.h" - -#include <iterator> -#include <random> - -#include "gmock/gmock.h" -#include "gtest/gtest.h" -#include "absl/random/internal/nonsecure_base.h" -#include "absl/random/random.h" -namespace { - -TEST(SeedSequences, Examples) { - { - absl::SeedSeq seed_seq({1, 2, 3}); - absl::BitGen bitgen(seed_seq); - - EXPECT_NE(0, bitgen()); - } - { - absl::BitGen engine; - auto seed_seq = absl::CreateSeedSeqFrom(&engine); - absl::BitGen bitgen(seed_seq); - - EXPECT_NE(engine(), bitgen()); - } - { - auto seed_seq = absl::MakeSeedSeq(); - std::mt19937 random(seed_seq); - - EXPECT_NE(0, random()); - } -} - -TEST(CreateSeedSeqFrom, CompatibleWithStdTypes) { - using ExampleNonsecureURBG = - absl::random_internal::NonsecureURBGBase<std::minstd_rand0>; - - // Construct a URBG instance. - ExampleNonsecureURBG rng; - - // Construct a Seed Sequence from its variates. - auto seq_from_rng = absl::CreateSeedSeqFrom(&rng); - - // Ensure that another URBG can be validly constructed from the Seed Sequence. - std::mt19937_64{seq_from_rng}; -} - -TEST(CreateSeedSeqFrom, CompatibleWithBitGenerator) { - // Construct a URBG instance. - absl::BitGen rng; - - // Construct a Seed Sequence from its variates. - auto seq_from_rng = absl::CreateSeedSeqFrom(&rng); - - // Ensure that another URBG can be validly constructed from the Seed Sequence. - std::mt19937_64{seq_from_rng}; -} - -TEST(CreateSeedSeqFrom, CompatibleWithInsecureBitGen) { - // Construct a URBG instance. - absl::InsecureBitGen rng; - - // Construct a Seed Sequence from its variates. - auto seq_from_rng = absl::CreateSeedSeqFrom(&rng); - - // Ensure that another URBG can be validly constructed from the Seed Sequence. - std::mt19937_64{seq_from_rng}; -} - -TEST(CreateSeedSeqFrom, CompatibleWithRawURBG) { - // Construct a URBG instance. - std::random_device urandom; - - // Construct a Seed Sequence from its variates, using 64b of seed-material. - auto seq_from_rng = absl::CreateSeedSeqFrom(&urandom); - - // Ensure that another URBG can be validly constructed from the Seed Sequence. - std::mt19937_64{seq_from_rng}; -} - -template <typename URBG> -void TestReproducibleVariateSequencesForNonsecureURBG() { - const size_t kNumVariates = 1000; - - URBG rng; - // Reused for both RNG instances. - auto reusable_seed = absl::CreateSeedSeqFrom(&rng); - - typename URBG::result_type variates[kNumVariates]; - { - URBG child(reusable_seed); - for (auto& variate : variates) { - variate = child(); - } - } - // Ensure that variate-sequence can be "replayed" by identical RNG. - { - URBG child(reusable_seed); - for (auto& variate : variates) { - ASSERT_EQ(variate, child()); - } - } -} - -TEST(CreateSeedSeqFrom, ReproducesVariateSequencesForInsecureBitGen) { - TestReproducibleVariateSequencesForNonsecureURBG<absl::InsecureBitGen>(); -} - -TEST(CreateSeedSeqFrom, ReproducesVariateSequencesForBitGenerator) { - TestReproducibleVariateSequencesForNonsecureURBG<absl::BitGen>(); -} -} // namespace diff --git a/third_party/abseil_cpp/absl/random/uniform_int_distribution.h b/third_party/abseil_cpp/absl/random/uniform_int_distribution.h deleted file mode 100644 index c1f54ccebce5..000000000000 --- a/third_party/abseil_cpp/absl/random/uniform_int_distribution.h +++ /dev/null @@ -1,275 +0,0 @@ -// Copyright 2017 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. -// -// ----------------------------------------------------------------------------- -// File: uniform_int_distribution.h -// ----------------------------------------------------------------------------- -// -// This header defines a class for representing a uniform integer distribution -// over the closed (inclusive) interval [a,b]. You use this distribution in -// combination with an Abseil random bit generator to produce random values -// according to the rules of the distribution. -// -// `absl::uniform_int_distribution` is a drop-in replacement for the C++11 -// `std::uniform_int_distribution` [rand.dist.uni.int] but is considerably -// faster than the libstdc++ implementation. - -#ifndef ABSL_RANDOM_UNIFORM_INT_DISTRIBUTION_H_ -#define ABSL_RANDOM_UNIFORM_INT_DISTRIBUTION_H_ - -#include <cassert> -#include <istream> -#include <limits> -#include <type_traits> - -#include "absl/base/optimization.h" -#include "absl/random/internal/fast_uniform_bits.h" -#include "absl/random/internal/iostream_state_saver.h" -#include "absl/random/internal/traits.h" -#include "absl/random/internal/wide_multiply.h" - -namespace absl { -ABSL_NAMESPACE_BEGIN - -// absl::uniform_int_distribution<T> -// -// This distribution produces random integer values uniformly distributed in the -// closed (inclusive) interval [a, b]. -// -// Example: -// -// absl::BitGen gen; -// -// // Use the distribution to produce a value between 1 and 6, inclusive. -// int die_roll = absl::uniform_int_distribution<int>(1, 6)(gen); -// -template <typename IntType = int> -class uniform_int_distribution { - private: - using unsigned_type = - typename random_internal::make_unsigned_bits<IntType>::type; - - public: - using result_type = IntType; - - class param_type { - public: - using distribution_type = uniform_int_distribution; - - explicit param_type( - result_type lo = 0, - result_type hi = (std::numeric_limits<result_type>::max)()) - : lo_(lo), - range_(static_cast<unsigned_type>(hi) - - static_cast<unsigned_type>(lo)) { - // [rand.dist.uni.int] precondition 2 - assert(lo <= hi); - } - - result_type a() const { return lo_; } - result_type b() const { - return static_cast<result_type>(static_cast<unsigned_type>(lo_) + range_); - } - - friend bool operator==(const param_type& a, const param_type& b) { - return a.lo_ == b.lo_ && a.range_ == b.range_; - } - - friend bool operator!=(const param_type& a, const param_type& b) { - return !(a == b); - } - - private: - friend class uniform_int_distribution; - unsigned_type range() const { return range_; } - - result_type lo_; - unsigned_type range_; - - static_assert(std::is_integral<result_type>::value, - "Class-template absl::uniform_int_distribution<> must be " - "parameterized using an integral type."); - }; // param_type - - uniform_int_distribution() : uniform_int_distribution(0) {} - - explicit uniform_int_distribution( - result_type lo, - result_type hi = (std::numeric_limits<result_type>::max)()) - : param_(lo, hi) {} - - explicit uniform_int_distribution(const param_type& param) : param_(param) {} - - // uniform_int_distribution<T>::reset() - // - // Resets the uniform int distribution. Note that this function has no effect - // because the distribution already produces independent values. - void reset() {} - - template <typename URBG> - result_type operator()(URBG& gen) { // NOLINT(runtime/references) - return (*this)(gen, param()); - } - - template <typename URBG> - result_type operator()( - URBG& gen, const param_type& param) { // NOLINT(runtime/references) - return param.a() + Generate(gen, param.range()); - } - - result_type a() const { return param_.a(); } - result_type b() const { return param_.b(); } - - param_type param() const { return param_; } - void param(const param_type& params) { param_ = params; } - - result_type(min)() const { return a(); } - result_type(max)() const { return b(); } - - friend bool operator==(const uniform_int_distribution& a, - const uniform_int_distribution& b) { - return a.param_ == b.param_; - } - friend bool operator!=(const uniform_int_distribution& a, - const uniform_int_distribution& b) { - return !(a == b); - } - - private: - // Generates a value in the *closed* interval [0, R] - template <typename URBG> - unsigned_type Generate(URBG& g, // NOLINT(runtime/references) - unsigned_type R); - param_type param_; -}; - -// ----------------------------------------------------------------------------- -// Implementation details follow -// ----------------------------------------------------------------------------- -template <typename CharT, typename Traits, typename IntType> -std::basic_ostream<CharT, Traits>& operator<<( - std::basic_ostream<CharT, Traits>& os, - const uniform_int_distribution<IntType>& x) { - using stream_type = - typename random_internal::stream_format_type<IntType>::type; - auto saver = random_internal::make_ostream_state_saver(os); - os << static_cast<stream_type>(x.a()) << os.fill() - << static_cast<stream_type>(x.b()); - return os; -} - -template <typename CharT, typename Traits, typename IntType> -std::basic_istream<CharT, Traits>& operator>>( - std::basic_istream<CharT, Traits>& is, - uniform_int_distribution<IntType>& x) { - using param_type = typename uniform_int_distribution<IntType>::param_type; - using result_type = typename uniform_int_distribution<IntType>::result_type; - using stream_type = - typename random_internal::stream_format_type<IntType>::type; - - stream_type a; - stream_type b; - - auto saver = random_internal::make_istream_state_saver(is); - is >> a >> b; - if (!is.fail()) { - x.param( - param_type(static_cast<result_type>(a), static_cast<result_type>(b))); - } - return is; -} - -template <typename IntType> -template <typename URBG> -typename random_internal::make_unsigned_bits<IntType>::type -uniform_int_distribution<IntType>::Generate( - URBG& g, // NOLINT(runtime/references) - typename random_internal::make_unsigned_bits<IntType>::type R) { - random_internal::FastUniformBits<unsigned_type> fast_bits; - unsigned_type bits = fast_bits(g); - const unsigned_type Lim = R + 1; - if ((R & Lim) == 0) { - // If the interval's length is a power of two range, just take the low bits. - return bits & R; - } - - // Generates a uniform variate on [0, Lim) using fixed-point multiplication. - // The above fast-path guarantees that Lim is representable in unsigned_type. - // - // Algorithm adapted from - // http://lemire.me/blog/2016/06/30/fast-random-shuffling/, with added - // explanation. - // - // The algorithm creates a uniform variate `bits` in the interval [0, 2^N), - // and treats it as the fractional part of a fixed-point real value in [0, 1), - // multiplied by 2^N. For example, 0.25 would be represented as 2^(N - 2), - // because 2^N * 0.25 == 2^(N - 2). - // - // Next, `bits` and `Lim` are multiplied with a wide-multiply to bring the - // value into the range [0, Lim). The integral part (the high word of the - // multiplication result) is then very nearly the desired result. However, - // this is not quite accurate; viewing the multiplication result as one - // double-width integer, the resulting values for the sample are mapped as - // follows: - // - // If the result lies in this interval: Return this value: - // [0, 2^N) 0 - // [2^N, 2 * 2^N) 1 - // ... ... - // [K * 2^N, (K + 1) * 2^N) K - // ... ... - // [(Lim - 1) * 2^N, Lim * 2^N) Lim - 1 - // - // While all of these intervals have the same size, the result of `bits * Lim` - // must be a multiple of `Lim`, and not all of these intervals contain the - // same number of multiples of `Lim`. In particular, some contain - // `F = floor(2^N / Lim)` and some contain `F + 1 = ceil(2^N / Lim)`. This - // difference produces a small nonuniformity, which is corrected by applying - // rejection sampling to one of the values in the "larger intervals" (i.e., - // the intervals containing `F + 1` multiples of `Lim`. - // - // An interval contains `F + 1` multiples of `Lim` if and only if its smallest - // value modulo 2^N is less than `2^N % Lim`. The unique value satisfying - // this property is used as the one for rejection. That is, a value of - // `bits * Lim` is rejected if `(bit * Lim) % 2^N < (2^N % Lim)`. - - using helper = random_internal::wide_multiply<unsigned_type>; - auto product = helper::multiply(bits, Lim); - - // Two optimizations here: - // * Rejection occurs with some probability less than 1/2, and for reasonable - // ranges considerably less (in particular, less than 1/(F+1)), so - // ABSL_PREDICT_FALSE is apt. - // * `Lim` is an overestimate of `threshold`, and doesn't require a divide. - if (ABSL_PREDICT_FALSE(helper::lo(product) < Lim)) { - // This quantity is exactly equal to `2^N % Lim`, but does not require high - // precision calculations: `2^N % Lim` is congruent to `(2^N - Lim) % Lim`. - // Ideally this could be expressed simply as `-X` rather than `2^N - X`, but - // for types smaller than int, this calculation is incorrect due to integer - // promotion rules. - const unsigned_type threshold = - ((std::numeric_limits<unsigned_type>::max)() - Lim + 1) % Lim; - while (helper::lo(product) < threshold) { - bits = fast_bits(g); - product = helper::multiply(bits, Lim); - } - } - - return helper::hi(product); -} - -ABSL_NAMESPACE_END -} // namespace absl - -#endif // ABSL_RANDOM_UNIFORM_INT_DISTRIBUTION_H_ diff --git a/third_party/abseil_cpp/absl/random/uniform_int_distribution_test.cc b/third_party/abseil_cpp/absl/random/uniform_int_distribution_test.cc deleted file mode 100644 index 276d72ad2045..000000000000 --- a/third_party/abseil_cpp/absl/random/uniform_int_distribution_test.cc +++ /dev/null @@ -1,259 +0,0 @@ -// Copyright 2017 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#include "absl/random/uniform_int_distribution.h" - -#include <cmath> -#include <cstdint> -#include <iterator> -#include <random> -#include <sstream> -#include <vector> - -#include "gmock/gmock.h" -#include "gtest/gtest.h" -#include "absl/base/internal/raw_logging.h" -#include "absl/random/internal/chi_square.h" -#include "absl/random/internal/distribution_test_util.h" -#include "absl/random/internal/pcg_engine.h" -#include "absl/random/internal/sequence_urbg.h" -#include "absl/random/random.h" -#include "absl/strings/str_cat.h" - -namespace { - -template <typename IntType> -class UniformIntDistributionTest : public ::testing::Test {}; - -using IntTypes = ::testing::Types<int8_t, uint8_t, int16_t, uint16_t, int32_t, - uint32_t, int64_t, uint64_t>; -TYPED_TEST_SUITE(UniformIntDistributionTest, IntTypes); - -TYPED_TEST(UniformIntDistributionTest, ParamSerializeTest) { - // This test essentially ensures that the parameters serialize, - // not that the values generated cover the full range. - using Limits = std::numeric_limits<TypeParam>; - using param_type = - typename absl::uniform_int_distribution<TypeParam>::param_type; - const TypeParam kMin = std::is_unsigned<TypeParam>::value ? 37 : -105; - const TypeParam kNegOneOrZero = std::is_unsigned<TypeParam>::value ? 0 : -1; - - constexpr int kCount = 1000; - absl::InsecureBitGen gen; - for (const auto& param : { - param_type(), - param_type(2, 2), // Same - param_type(9, 32), - param_type(kMin, 115), - param_type(kNegOneOrZero, Limits::max()), - param_type(Limits::min(), Limits::max()), - param_type(Limits::lowest(), Limits::max()), - param_type(Limits::min() + 1, Limits::max() - 1), - }) { - const auto a = param.a(); - const auto b = param.b(); - absl::uniform_int_distribution<TypeParam> before(a, b); - EXPECT_EQ(before.a(), param.a()); - EXPECT_EQ(before.b(), param.b()); - - { - // Initialize via param_type - absl::uniform_int_distribution<TypeParam> via_param(param); - EXPECT_EQ(via_param, before); - } - - // Initialize via iostreams - std::stringstream ss; - ss << before; - - absl::uniform_int_distribution<TypeParam> after(Limits::min() + 3, - Limits::max() - 5); - - EXPECT_NE(before.a(), after.a()); - EXPECT_NE(before.b(), after.b()); - EXPECT_NE(before.param(), after.param()); - EXPECT_NE(before, after); - - ss >> after; - - EXPECT_EQ(before.a(), after.a()); - EXPECT_EQ(before.b(), after.b()); - EXPECT_EQ(before.param(), after.param()); - EXPECT_EQ(before, after); - - // Smoke test. - auto sample_min = after.max(); - auto sample_max = after.min(); - for (int i = 0; i < kCount; i++) { - auto sample = after(gen); - EXPECT_GE(sample, after.min()); - EXPECT_LE(sample, after.max()); - if (sample > sample_max) { - sample_max = sample; - } - if (sample < sample_min) { - sample_min = sample; - } - } - std::string msg = absl::StrCat("Range: ", +sample_min, ", ", +sample_max); - ABSL_RAW_LOG(INFO, "%s", msg.c_str()); - } -} - -TYPED_TEST(UniformIntDistributionTest, ViolatesPreconditionsDeathTest) { -#if GTEST_HAS_DEATH_TEST - // Hi < Lo - EXPECT_DEBUG_DEATH({ absl::uniform_int_distribution<TypeParam> dist(10, 1); }, - ""); -#endif // GTEST_HAS_DEATH_TEST -#if defined(NDEBUG) - // opt-mode, for invalid parameters, will generate a garbage value, - // but should not enter an infinite loop. - absl::InsecureBitGen gen; - absl::uniform_int_distribution<TypeParam> dist(10, 1); - auto x = dist(gen); - - // Any value will generate a non-empty string. - EXPECT_FALSE(absl::StrCat(+x).empty()) << x; -#endif // NDEBUG -} - -TYPED_TEST(UniformIntDistributionTest, TestMoments) { - constexpr int kSize = 100000; - using Limits = std::numeric_limits<TypeParam>; - using param_type = - typename absl::uniform_int_distribution<TypeParam>::param_type; - - // We use a fixed bit generator for distribution accuracy tests. This allows - // these tests to be deterministic, while still testing the qualify of the - // implementation. - absl::random_internal::pcg64_2018_engine rng{0x2B7E151628AED2A6}; - - std::vector<double> values(kSize); - for (const auto& param : - {param_type(0, Limits::max()), param_type(13, 127)}) { - absl::uniform_int_distribution<TypeParam> dist(param); - for (int i = 0; i < kSize; i++) { - const auto sample = dist(rng); - ASSERT_LE(dist.param().a(), sample); - ASSERT_GE(dist.param().b(), sample); - values[i] = sample; - } - - auto moments = absl::random_internal::ComputeDistributionMoments(values); - const double a = dist.param().a(); - const double b = dist.param().b(); - const double n = (b - a + 1); - const double mean = (a + b) / 2; - const double var = ((b - a + 1) * (b - a + 1) - 1) / 12; - const double kurtosis = 3 - 6 * (n * n + 1) / (5 * (n * n - 1)); - - // TODO(ahh): this is not the right bound - // empirically validated with --runs_per_test=10000. - EXPECT_NEAR(mean, moments.mean, 0.01 * var); - EXPECT_NEAR(var, moments.variance, 0.015 * var); - EXPECT_NEAR(0.0, moments.skewness, 0.025); - EXPECT_NEAR(kurtosis, moments.kurtosis, 0.02 * kurtosis); - } -} - -TYPED_TEST(UniformIntDistributionTest, ChiSquaredTest50) { - using absl::random_internal::kChiSquared; - - constexpr size_t kTrials = 1000; - constexpr int kBuckets = 50; // inclusive, so actally +1 - constexpr double kExpected = - static_cast<double>(kTrials) / static_cast<double>(kBuckets); - - // Empirically validated with --runs_per_test=10000. - const int kThreshold = - absl::random_internal::ChiSquareValue(kBuckets, 0.999999); - - const TypeParam min = std::is_unsigned<TypeParam>::value ? 37 : -37; - const TypeParam max = min + kBuckets; - - // We use a fixed bit generator for distribution accuracy tests. This allows - // these tests to be deterministic, while still testing the qualify of the - // implementation. - absl::random_internal::pcg64_2018_engine rng{0x2B7E151628AED2A6}; - - absl::uniform_int_distribution<TypeParam> dist(min, max); - - std::vector<int32_t> counts(kBuckets + 1, 0); - for (size_t i = 0; i < kTrials; i++) { - auto x = dist(rng); - counts[x - min]++; - } - double chi_square = absl::random_internal::ChiSquareWithExpected( - std::begin(counts), std::end(counts), kExpected); - if (chi_square > kThreshold) { - double p_value = - absl::random_internal::ChiSquarePValue(chi_square, kBuckets); - - // Chi-squared test failed. Output does not appear to be uniform. - std::string msg; - for (const auto& a : counts) { - absl::StrAppend(&msg, a, "\n"); - } - absl::StrAppend(&msg, kChiSquared, " p-value ", p_value, "\n"); - absl::StrAppend(&msg, "High ", kChiSquared, " value: ", chi_square, " > ", - kThreshold); - ABSL_RAW_LOG(INFO, "%s", msg.c_str()); - FAIL() << msg; - } -} - -TEST(UniformIntDistributionTest, StabilityTest) { - // absl::uniform_int_distribution stability relies only on integer operations. - absl::random_internal::sequence_urbg urbg( - {0x0003eb76f6f7f755ull, 0xFFCEA50FDB2F953Bull, 0xC332DDEFBE6C5AA5ull, - 0x6558218568AB9702ull, 0x2AEF7DAD5B6E2F84ull, 0x1521B62829076170ull, - 0xECDD4775619F1510ull, 0x13CCA830EB61BD96ull, 0x0334FE1EAA0363CFull, - 0xB5735C904C70A239ull, 0xD59E9E0BCBAADE14ull, 0xEECC86BC60622CA7ull}); - - std::vector<int> output(12); - - { - absl::uniform_int_distribution<int32_t> dist(0, 4); - for (auto& v : output) { - v = dist(urbg); - } - } - EXPECT_EQ(12, urbg.invocations()); - EXPECT_THAT(output, testing::ElementsAre(4, 4, 3, 2, 1, 0, 1, 4, 3, 1, 3, 1)); - - { - urbg.reset(); - absl::uniform_int_distribution<int32_t> dist(0, 100); - for (auto& v : output) { - v = dist(urbg); - } - } - EXPECT_EQ(12, urbg.invocations()); - EXPECT_THAT(output, testing::ElementsAre(97, 86, 75, 41, 36, 16, 38, 92, 67, - 30, 80, 38)); - - { - urbg.reset(); - absl::uniform_int_distribution<int32_t> dist(0, 10000); - for (auto& v : output) { - v = dist(urbg); - } - } - EXPECT_EQ(12, urbg.invocations()); - EXPECT_THAT(output, testing::ElementsAre(9648, 8562, 7439, 4089, 3571, 1602, - 3813, 9195, 6641, 2986, 7956, 3765)); -} - -} // namespace diff --git a/third_party/abseil_cpp/absl/random/uniform_real_distribution.h b/third_party/abseil_cpp/absl/random/uniform_real_distribution.h deleted file mode 100644 index 5ba17b2341cf..000000000000 --- a/third_party/abseil_cpp/absl/random/uniform_real_distribution.h +++ /dev/null @@ -1,202 +0,0 @@ -// Copyright 2017 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. -// -// ----------------------------------------------------------------------------- -// File: uniform_real_distribution.h -// ----------------------------------------------------------------------------- -// -// This header defines a class for representing a uniform floating-point -// distribution over a half-open interval [a,b). You use this distribution in -// combination with an Abseil random bit generator to produce random values -// according to the rules of the distribution. -// -// `absl::uniform_real_distribution` is a drop-in replacement for the C++11 -// `std::uniform_real_distribution` [rand.dist.uni.real] but is considerably -// faster than the libstdc++ implementation. -// -// Note: the standard-library version may occasionally return `1.0` when -// default-initialized. See https://bugs.llvm.org//show_bug.cgi?id=18767 -// `absl::uniform_real_distribution` does not exhibit this behavior. - -#ifndef ABSL_RANDOM_UNIFORM_REAL_DISTRIBUTION_H_ -#define ABSL_RANDOM_UNIFORM_REAL_DISTRIBUTION_H_ - -#include <cassert> -#include <cmath> -#include <cstdint> -#include <istream> -#include <limits> -#include <type_traits> - -#include "absl/meta/type_traits.h" -#include "absl/random/internal/fast_uniform_bits.h" -#include "absl/random/internal/generate_real.h" -#include "absl/random/internal/iostream_state_saver.h" - -namespace absl { -ABSL_NAMESPACE_BEGIN - -// absl::uniform_real_distribution<T> -// -// This distribution produces random floating-point values uniformly distributed -// over the half-open interval [a, b). -// -// Example: -// -// absl::BitGen gen; -// -// // Use the distribution to produce a value between 0.0 (inclusive) -// // and 1.0 (exclusive). -// double value = absl::uniform_real_distribution<double>(0, 1)(gen); -// -template <typename RealType = double> -class uniform_real_distribution { - public: - using result_type = RealType; - - class param_type { - public: - using distribution_type = uniform_real_distribution; - - explicit param_type(result_type lo = 0, result_type hi = 1) - : lo_(lo), hi_(hi), range_(hi - lo) { - // [rand.dist.uni.real] preconditions 2 & 3 - assert(lo <= hi); - // NOTE: For integral types, we can promote the range to an unsigned type, - // which gives full width of the range. However for real (fp) types, this - // is not possible, so value generation cannot use the full range of the - // real type. - assert(range_ <= (std::numeric_limits<result_type>::max)()); - assert(std::isfinite(range_)); - } - - result_type a() const { return lo_; } - result_type b() const { return hi_; } - - friend bool operator==(const param_type& a, const param_type& b) { - return a.lo_ == b.lo_ && a.hi_ == b.hi_; - } - - friend bool operator!=(const param_type& a, const param_type& b) { - return !(a == b); - } - - private: - friend class uniform_real_distribution; - result_type lo_, hi_, range_; - - static_assert(std::is_floating_point<RealType>::value, - "Class-template absl::uniform_real_distribution<> must be " - "parameterized using a floating-point type."); - }; - - uniform_real_distribution() : uniform_real_distribution(0) {} - - explicit uniform_real_distribution(result_type lo, result_type hi = 1) - : param_(lo, hi) {} - - explicit uniform_real_distribution(const param_type& param) : param_(param) {} - - // uniform_real_distribution<T>::reset() - // - // Resets the uniform real distribution. Note that this function has no effect - // because the distribution already produces independent values. - void reset() {} - - template <typename URBG> - result_type operator()(URBG& gen) { // NOLINT(runtime/references) - return operator()(gen, param_); - } - - template <typename URBG> - result_type operator()(URBG& gen, // NOLINT(runtime/references) - const param_type& p); - - result_type a() const { return param_.a(); } - result_type b() const { return param_.b(); } - - param_type param() const { return param_; } - void param(const param_type& params) { param_ = params; } - - result_type(min)() const { return a(); } - result_type(max)() const { return b(); } - - friend bool operator==(const uniform_real_distribution& a, - const uniform_real_distribution& b) { - return a.param_ == b.param_; - } - friend bool operator!=(const uniform_real_distribution& a, - const uniform_real_distribution& b) { - return a.param_ != b.param_; - } - - private: - param_type param_; - random_internal::FastUniformBits<uint64_t> fast_u64_; -}; - -// ----------------------------------------------------------------------------- -// Implementation details follow -// ----------------------------------------------------------------------------- -template <typename RealType> -template <typename URBG> -typename uniform_real_distribution<RealType>::result_type -uniform_real_distribution<RealType>::operator()( - URBG& gen, const param_type& p) { // NOLINT(runtime/references) - using random_internal::GeneratePositiveTag; - using random_internal::GenerateRealFromBits; - using real_type = - absl::conditional_t<std::is_same<RealType, float>::value, float, double>; - - while (true) { - const result_type sample = - GenerateRealFromBits<real_type, GeneratePositiveTag, true>( - fast_u64_(gen)); - const result_type res = p.a() + (sample * p.range_); - if (res < p.b() || p.range_ <= 0 || !std::isfinite(p.range_)) { - return res; - } - // else sample rejected, try again. - } -} - -template <typename CharT, typename Traits, typename RealType> -std::basic_ostream<CharT, Traits>& operator<<( - std::basic_ostream<CharT, Traits>& os, // NOLINT(runtime/references) - const uniform_real_distribution<RealType>& x) { - auto saver = random_internal::make_ostream_state_saver(os); - os.precision(random_internal::stream_precision_helper<RealType>::kPrecision); - os << x.a() << os.fill() << x.b(); - return os; -} - -template <typename CharT, typename Traits, typename RealType> -std::basic_istream<CharT, Traits>& operator>>( - std::basic_istream<CharT, Traits>& is, // NOLINT(runtime/references) - uniform_real_distribution<RealType>& x) { // NOLINT(runtime/references) - using param_type = typename uniform_real_distribution<RealType>::param_type; - using result_type = typename uniform_real_distribution<RealType>::result_type; - auto saver = random_internal::make_istream_state_saver(is); - auto a = random_internal::read_floating_point<result_type>(is); - if (is.fail()) return is; - auto b = random_internal::read_floating_point<result_type>(is); - if (!is.fail()) { - x.param(param_type(a, b)); - } - return is; -} -ABSL_NAMESPACE_END -} // namespace absl - -#endif // ABSL_RANDOM_UNIFORM_REAL_DISTRIBUTION_H_ diff --git a/third_party/abseil_cpp/absl/random/uniform_real_distribution_test.cc b/third_party/abseil_cpp/absl/random/uniform_real_distribution_test.cc deleted file mode 100644 index be107cdde467..000000000000 --- a/third_party/abseil_cpp/absl/random/uniform_real_distribution_test.cc +++ /dev/null @@ -1,343 +0,0 @@ -// Copyright 2017 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#include "absl/random/uniform_real_distribution.h" - -#include <cmath> -#include <cstdint> -#include <iterator> -#include <random> -#include <sstream> -#include <string> -#include <vector> - -#include "gmock/gmock.h" -#include "gtest/gtest.h" -#include "absl/base/internal/raw_logging.h" -#include "absl/random/internal/chi_square.h" -#include "absl/random/internal/distribution_test_util.h" -#include "absl/random/internal/pcg_engine.h" -#include "absl/random/internal/sequence_urbg.h" -#include "absl/random/random.h" -#include "absl/strings/str_cat.h" - -// NOTES: -// * Some documentation on generating random real values suggests that -// it is possible to use std::nextafter(b, DBL_MAX) to generate a value on -// the closed range [a, b]. Unfortunately, that technique is not universally -// reliable due to floating point quantization. -// -// * absl::uniform_real_distribution<float> generates between 2^28 and 2^29 -// distinct floating point values in the range [0, 1). -// -// * absl::uniform_real_distribution<float> generates at least 2^23 distinct -// floating point values in the range [1, 2). This should be the same as -// any other range covered by a single exponent in IEEE 754. -// -// * absl::uniform_real_distribution<double> generates more than 2^52 distinct -// values in the range [0, 1), and should generate at least 2^52 distinct -// values in the range of [1, 2). -// - -namespace { - -template <typename RealType> -class UniformRealDistributionTest : public ::testing::Test {}; - -#if defined(__EMSCRIPTEN__) -using RealTypes = ::testing::Types<float, double>; -#else -using RealTypes = ::testing::Types<float, double, long double>; -#endif // defined(__EMSCRIPTEN__) - -TYPED_TEST_SUITE(UniformRealDistributionTest, RealTypes); - -TYPED_TEST(UniformRealDistributionTest, ParamSerializeTest) { - using param_type = - typename absl::uniform_real_distribution<TypeParam>::param_type; - - constexpr const TypeParam a{1152921504606846976}; - - constexpr int kCount = 1000; - absl::InsecureBitGen gen; - for (const auto& param : { - param_type(), - param_type(TypeParam(2.0), TypeParam(2.0)), // Same - param_type(TypeParam(-0.1), TypeParam(0.1)), - param_type(TypeParam(0.05), TypeParam(0.12)), - param_type(TypeParam(-0.05), TypeParam(0.13)), - param_type(TypeParam(-0.05), TypeParam(-0.02)), - // double range = 0 - // 2^60 , 2^60 + 2^6 - param_type(a, TypeParam(1152921504606847040)), - // 2^60 , 2^60 + 2^7 - param_type(a, TypeParam(1152921504606847104)), - // double range = 2^8 - // 2^60 , 2^60 + 2^8 - param_type(a, TypeParam(1152921504606847232)), - // float range = 0 - // 2^60 , 2^60 + 2^36 - param_type(a, TypeParam(1152921573326323712)), - // 2^60 , 2^60 + 2^37 - param_type(a, TypeParam(1152921642045800448)), - // float range = 2^38 - // 2^60 , 2^60 + 2^38 - param_type(a, TypeParam(1152921779484753920)), - // Limits - param_type(0, std::numeric_limits<TypeParam>::max()), - param_type(std::numeric_limits<TypeParam>::lowest(), 0), - param_type(0, std::numeric_limits<TypeParam>::epsilon()), - param_type(-std::numeric_limits<TypeParam>::epsilon(), - std::numeric_limits<TypeParam>::epsilon()), - param_type(std::numeric_limits<TypeParam>::epsilon(), - 2 * std::numeric_limits<TypeParam>::epsilon()), - }) { - // Validate parameters. - const auto a = param.a(); - const auto b = param.b(); - absl::uniform_real_distribution<TypeParam> before(a, b); - EXPECT_EQ(before.a(), param.a()); - EXPECT_EQ(before.b(), param.b()); - - { - absl::uniform_real_distribution<TypeParam> via_param(param); - EXPECT_EQ(via_param, before); - } - - std::stringstream ss; - ss << before; - absl::uniform_real_distribution<TypeParam> after(TypeParam(1.0), - TypeParam(3.1)); - - EXPECT_NE(before.a(), after.a()); - EXPECT_NE(before.b(), after.b()); - EXPECT_NE(before.param(), after.param()); - EXPECT_NE(before, after); - - ss >> after; - - EXPECT_EQ(before.a(), after.a()); - EXPECT_EQ(before.b(), after.b()); - EXPECT_EQ(before.param(), after.param()); - EXPECT_EQ(before, after); - - // Smoke test. - auto sample_min = after.max(); - auto sample_max = after.min(); - for (int i = 0; i < kCount; i++) { - auto sample = after(gen); - // Failure here indicates a bug in uniform_real_distribution::operator(), - // or bad parameters--range too large, etc. - if (after.min() == after.max()) { - EXPECT_EQ(sample, after.min()); - } else { - EXPECT_GE(sample, after.min()); - EXPECT_LT(sample, after.max()); - } - if (sample > sample_max) { - sample_max = sample; - } - if (sample < sample_min) { - sample_min = sample; - } - } - - if (!std::is_same<TypeParam, long double>::value) { - // static_cast<double>(long double) can overflow. - std::string msg = absl::StrCat("Range: ", static_cast<double>(sample_min), - ", ", static_cast<double>(sample_max)); - ABSL_RAW_LOG(INFO, "%s", msg.c_str()); - } - } -} - -#ifdef _MSC_VER -#pragma warning(push) -#pragma warning(disable:4756) // Constant arithmetic overflow. -#endif -TYPED_TEST(UniformRealDistributionTest, ViolatesPreconditionsDeathTest) { -#if GTEST_HAS_DEATH_TEST - // Hi < Lo - EXPECT_DEBUG_DEATH( - { absl::uniform_real_distribution<TypeParam> dist(10.0, 1.0); }, ""); - - // Hi - Lo > numeric_limits<>::max() - EXPECT_DEBUG_DEATH( - { - absl::uniform_real_distribution<TypeParam> dist( - std::numeric_limits<TypeParam>::lowest(), - std::numeric_limits<TypeParam>::max()); - }, - ""); -#endif // GTEST_HAS_DEATH_TEST -#if defined(NDEBUG) - // opt-mode, for invalid parameters, will generate a garbage value, - // but should not enter an infinite loop. - absl::InsecureBitGen gen; - { - absl::uniform_real_distribution<TypeParam> dist(10.0, 1.0); - auto x = dist(gen); - EXPECT_FALSE(std::isnan(x)) << x; - } - { - absl::uniform_real_distribution<TypeParam> dist( - std::numeric_limits<TypeParam>::lowest(), - std::numeric_limits<TypeParam>::max()); - auto x = dist(gen); - // Infinite result. - EXPECT_FALSE(std::isfinite(x)) << x; - } -#endif // NDEBUG -} -#ifdef _MSC_VER -#pragma warning(pop) // warning(disable:4756) -#endif - -TYPED_TEST(UniformRealDistributionTest, TestMoments) { - constexpr int kSize = 1000000; - std::vector<double> values(kSize); - - // We use a fixed bit generator for distribution accuracy tests. This allows - // these tests to be deterministic, while still testing the qualify of the - // implementation. - absl::random_internal::pcg64_2018_engine rng{0x2B7E151628AED2A6}; - - absl::uniform_real_distribution<TypeParam> dist; - for (int i = 0; i < kSize; i++) { - values[i] = dist(rng); - } - - const auto moments = - absl::random_internal::ComputeDistributionMoments(values); - EXPECT_NEAR(0.5, moments.mean, 0.01); - EXPECT_NEAR(1 / 12.0, moments.variance, 0.015); - EXPECT_NEAR(0.0, moments.skewness, 0.02); - EXPECT_NEAR(9 / 5.0, moments.kurtosis, 0.015); -} - -TYPED_TEST(UniformRealDistributionTest, ChiSquaredTest50) { - using absl::random_internal::kChiSquared; - using param_type = - typename absl::uniform_real_distribution<TypeParam>::param_type; - - constexpr size_t kTrials = 100000; - constexpr int kBuckets = 50; - constexpr double kExpected = - static_cast<double>(kTrials) / static_cast<double>(kBuckets); - - // 1-in-100000 threshold, but remember, there are about 8 tests - // in this file. And the test could fail for other reasons. - // Empirically validated with --runs_per_test=10000. - const int kThreshold = - absl::random_internal::ChiSquareValue(kBuckets - 1, 0.999999); - - // We use a fixed bit generator for distribution accuracy tests. This allows - // these tests to be deterministic, while still testing the qualify of the - // implementation. - absl::random_internal::pcg64_2018_engine rng{0x2B7E151628AED2A6}; - - for (const auto& param : {param_type(0, 1), param_type(5, 12), - param_type(-5, 13), param_type(-5, -2)}) { - const double min_val = param.a(); - const double max_val = param.b(); - const double factor = kBuckets / (max_val - min_val); - - std::vector<int32_t> counts(kBuckets, 0); - absl::uniform_real_distribution<TypeParam> dist(param); - for (size_t i = 0; i < kTrials; i++) { - auto x = dist(rng); - auto bucket = static_cast<size_t>((x - min_val) * factor); - counts[bucket]++; - } - - double chi_square = absl::random_internal::ChiSquareWithExpected( - std::begin(counts), std::end(counts), kExpected); - if (chi_square > kThreshold) { - double p_value = - absl::random_internal::ChiSquarePValue(chi_square, kBuckets); - - // Chi-squared test failed. Output does not appear to be uniform. - std::string msg; - for (const auto& a : counts) { - absl::StrAppend(&msg, a, "\n"); - } - absl::StrAppend(&msg, kChiSquared, " p-value ", p_value, "\n"); - absl::StrAppend(&msg, "High ", kChiSquared, " value: ", chi_square, " > ", - kThreshold); - ABSL_RAW_LOG(INFO, "%s", msg.c_str()); - FAIL() << msg; - } - } -} - -TYPED_TEST(UniformRealDistributionTest, StabilityTest) { - // absl::uniform_real_distribution stability relies only on - // random_internal::RandU64ToDouble and random_internal::RandU64ToFloat. - absl::random_internal::sequence_urbg urbg( - {0x0003eb76f6f7f755ull, 0xFFCEA50FDB2F953Bull, 0xC332DDEFBE6C5AA5ull, - 0x6558218568AB9702ull, 0x2AEF7DAD5B6E2F84ull, 0x1521B62829076170ull, - 0xECDD4775619F1510ull, 0x13CCA830EB61BD96ull, 0x0334FE1EAA0363CFull, - 0xB5735C904C70A239ull, 0xD59E9E0BCBAADE14ull, 0xEECC86BC60622CA7ull}); - - std::vector<int> output(12); - - absl::uniform_real_distribution<TypeParam> dist; - std::generate(std::begin(output), std::end(output), [&] { - return static_cast<int>(TypeParam(1000000) * dist(urbg)); - }); - - EXPECT_THAT( - output, // - testing::ElementsAre(59, 999246, 762494, 395876, 167716, 82545, 925251, - 77341, 12527, 708791, 834451, 932808)); -} - -TEST(UniformRealDistributionTest, AlgorithmBounds) { - absl::uniform_real_distribution<double> dist; - - { - // This returns the smallest value >0 from absl::uniform_real_distribution. - absl::random_internal::sequence_urbg urbg({0x0000000000000001ull}); - double a = dist(urbg); - EXPECT_EQ(a, 5.42101086242752217004e-20); - } - - { - // This returns a value very near 0.5 from absl::uniform_real_distribution. - absl::random_internal::sequence_urbg urbg({0x7fffffffffffffefull}); - double a = dist(urbg); - EXPECT_EQ(a, 0.499999999999999944489); - } - { - // This returns a value very near 0.5 from absl::uniform_real_distribution. - absl::random_internal::sequence_urbg urbg({0x8000000000000000ull}); - double a = dist(urbg); - EXPECT_EQ(a, 0.5); - } - - { - // This returns the largest value <1 from absl::uniform_real_distribution. - absl::random_internal::sequence_urbg urbg({0xFFFFFFFFFFFFFFEFull}); - double a = dist(urbg); - EXPECT_EQ(a, 0.999999999999999888978); - } - { - // This *ALSO* returns the largest value <1. - absl::random_internal::sequence_urbg urbg({0xFFFFFFFFFFFFFFFFull}); - double a = dist(urbg); - EXPECT_EQ(a, 0.999999999999999888978); - } -} - -} // namespace diff --git a/third_party/abseil_cpp/absl/random/zipf_distribution.h b/third_party/abseil_cpp/absl/random/zipf_distribution.h deleted file mode 100644 index 22ebc756cfef..000000000000 --- a/third_party/abseil_cpp/absl/random/zipf_distribution.h +++ /dev/null @@ -1,271 +0,0 @@ -// Copyright 2017 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#ifndef ABSL_RANDOM_ZIPF_DISTRIBUTION_H_ -#define ABSL_RANDOM_ZIPF_DISTRIBUTION_H_ - -#include <cassert> -#include <cmath> -#include <istream> -#include <limits> -#include <ostream> -#include <type_traits> - -#include "absl/random/internal/iostream_state_saver.h" -#include "absl/random/uniform_real_distribution.h" - -namespace absl { -ABSL_NAMESPACE_BEGIN - -// absl::zipf_distribution produces random integer-values in the range [0, k], -// distributed according to the discrete probability function: -// -// P(x) = (v + x) ^ -q -// -// The parameter `v` must be greater than 0 and the parameter `q` must be -// greater than 1. If either of these parameters take invalid values then the -// behavior is undefined. -// -// IntType is the result_type generated by the generator. It must be of integral -// type; a static_assert ensures this is the case. -// -// The implementation is based on W.Hormann, G.Derflinger: -// -// "Rejection-Inversion to Generate Variates from Monotone Discrete -// Distributions" -// -// http://eeyore.wu-wien.ac.at/papers/96-04-04.wh-der.ps.gz -// -template <typename IntType = int> -class zipf_distribution { - public: - using result_type = IntType; - - class param_type { - public: - using distribution_type = zipf_distribution; - - // Preconditions: k > 0, v > 0, q > 1 - // The precondidtions are validated when NDEBUG is not defined via - // a pair of assert() directives. - // If NDEBUG is defined and either or both of these parameters take invalid - // values, the behavior of the class is undefined. - explicit param_type(result_type k = (std::numeric_limits<IntType>::max)(), - double q = 2.0, double v = 1.0); - - result_type k() const { return k_; } - double q() const { return q_; } - double v() const { return v_; } - - friend bool operator==(const param_type& a, const param_type& b) { - return a.k_ == b.k_ && a.q_ == b.q_ && a.v_ == b.v_; - } - friend bool operator!=(const param_type& a, const param_type& b) { - return !(a == b); - } - - private: - friend class zipf_distribution; - inline double h(double x) const; - inline double hinv(double x) const; - inline double compute_s() const; - inline double pow_negative_q(double x) const; - - // Parameters here are exactly the same as the parameters of Algorithm ZRI - // in the paper. - IntType k_; - double q_; - double v_; - - double one_minus_q_; // 1-q - double s_; - double one_minus_q_inv_; // 1 / 1-q - double hxm_; // h(k + 0.5) - double hx0_minus_hxm_; // h(x0) - h(k + 0.5) - - static_assert(std::is_integral<IntType>::value, - "Class-template absl::zipf_distribution<> must be " - "parameterized using an integral type."); - }; - - zipf_distribution() - : zipf_distribution((std::numeric_limits<IntType>::max)()) {} - - explicit zipf_distribution(result_type k, double q = 2.0, double v = 1.0) - : param_(k, q, v) {} - - explicit zipf_distribution(const param_type& p) : param_(p) {} - - void reset() {} - - template <typename URBG> - result_type operator()(URBG& g) { // NOLINT(runtime/references) - return (*this)(g, param_); - } - - template <typename URBG> - result_type operator()(URBG& g, // NOLINT(runtime/references) - const param_type& p); - - result_type k() const { return param_.k(); } - double q() const { return param_.q(); } - double v() const { return param_.v(); } - - param_type param() const { return param_; } - void param(const param_type& p) { param_ = p; } - - result_type(min)() const { return 0; } - result_type(max)() const { return k(); } - - friend bool operator==(const zipf_distribution& a, - const zipf_distribution& b) { - return a.param_ == b.param_; - } - friend bool operator!=(const zipf_distribution& a, - const zipf_distribution& b) { - return a.param_ != b.param_; - } - - private: - param_type param_; -}; - -// -------------------------------------------------------------------------- -// Implementation details follow -// -------------------------------------------------------------------------- - -template <typename IntType> -zipf_distribution<IntType>::param_type::param_type( - typename zipf_distribution<IntType>::result_type k, double q, double v) - : k_(k), q_(q), v_(v), one_minus_q_(1 - q) { - assert(q > 1); - assert(v > 0); - assert(k > 0); - one_minus_q_inv_ = 1 / one_minus_q_; - - // Setup for the ZRI algorithm (pg 17 of the paper). - // Compute: h(i max) => h(k + 0.5) - constexpr double kMax = 18446744073709549568.0; - double kd = static_cast<double>(k); - // TODO(absl-team): Determine if this check is needed, and if so, add a test - // that fails for k > kMax - if (kd > kMax) { - // Ensure that our maximum value is capped to a value which will - // round-trip back through double. - kd = kMax; - } - hxm_ = h(kd + 0.5); - - // Compute: h(0) - const bool use_precomputed = (v == 1.0 && q == 2.0); - const double h0x5 = use_precomputed ? (-1.0 / 1.5) // exp(-log(1.5)) - : h(0.5); - const double elogv_q = (v_ == 1.0) ? 1 : pow_negative_q(v_); - - // h(0) = h(0.5) - exp(log(v) * -q) - hx0_minus_hxm_ = (h0x5 - elogv_q) - hxm_; - - // And s - s_ = use_precomputed ? 0.46153846153846123 : compute_s(); -} - -template <typename IntType> -double zipf_distribution<IntType>::param_type::h(double x) const { - // std::exp(one_minus_q_ * std::log(v_ + x)) * one_minus_q_inv_; - x += v_; - return (one_minus_q_ == -1.0) - ? (-1.0 / x) // -exp(-log(x)) - : (std::exp(std::log(x) * one_minus_q_) * one_minus_q_inv_); -} - -template <typename IntType> -double zipf_distribution<IntType>::param_type::hinv(double x) const { - // std::exp(one_minus_q_inv_ * std::log(one_minus_q_ * x)) - v_; - return -v_ + ((one_minus_q_ == -1.0) - ? (-1.0 / x) // exp(-log(-x)) - : std::exp(one_minus_q_inv_ * std::log(one_minus_q_ * x))); -} - -template <typename IntType> -double zipf_distribution<IntType>::param_type::compute_s() const { - // 1 - hinv(h(1.5) - std::exp(std::log(v_ + 1) * -q_)); - return 1.0 - hinv(h(1.5) - pow_negative_q(v_ + 1.0)); -} - -template <typename IntType> -double zipf_distribution<IntType>::param_type::pow_negative_q(double x) const { - // std::exp(std::log(x) * -q_); - return q_ == 2.0 ? (1.0 / (x * x)) : std::exp(std::log(x) * -q_); -} - -template <typename IntType> -template <typename URBG> -typename zipf_distribution<IntType>::result_type -zipf_distribution<IntType>::operator()( - URBG& g, const param_type& p) { // NOLINT(runtime/references) - absl::uniform_real_distribution<double> uniform_double; - double k; - for (;;) { - const double v = uniform_double(g); - const double u = p.hxm_ + v * p.hx0_minus_hxm_; - const double x = p.hinv(u); - k = rint(x); // std::floor(x + 0.5); - if (k > p.k()) continue; // reject k > max_k - if (k - x <= p.s_) break; - const double h = p.h(k + 0.5); - const double r = p.pow_negative_q(p.v_ + k); - if (u >= h - r) break; - } - IntType ki = static_cast<IntType>(k); - assert(ki <= p.k_); - return ki; -} - -template <typename CharT, typename Traits, typename IntType> -std::basic_ostream<CharT, Traits>& operator<<( - std::basic_ostream<CharT, Traits>& os, // NOLINT(runtime/references) - const zipf_distribution<IntType>& x) { - using stream_type = - typename random_internal::stream_format_type<IntType>::type; - auto saver = random_internal::make_ostream_state_saver(os); - os.precision(random_internal::stream_precision_helper<double>::kPrecision); - os << static_cast<stream_type>(x.k()) << os.fill() << x.q() << os.fill() - << x.v(); - return os; -} - -template <typename CharT, typename Traits, typename IntType> -std::basic_istream<CharT, Traits>& operator>>( - std::basic_istream<CharT, Traits>& is, // NOLINT(runtime/references) - zipf_distribution<IntType>& x) { // NOLINT(runtime/references) - using result_type = typename zipf_distribution<IntType>::result_type; - using param_type = typename zipf_distribution<IntType>::param_type; - using stream_type = - typename random_internal::stream_format_type<IntType>::type; - stream_type k; - double q; - double v; - - auto saver = random_internal::make_istream_state_saver(is); - is >> k >> q >> v; - if (!is.fail()) { - x.param(param_type(static_cast<result_type>(k), q, v)); - } - return is; -} - -ABSL_NAMESPACE_END -} // namespace absl - -#endif // ABSL_RANDOM_ZIPF_DISTRIBUTION_H_ diff --git a/third_party/abseil_cpp/absl/random/zipf_distribution_test.cc b/third_party/abseil_cpp/absl/random/zipf_distribution_test.cc deleted file mode 100644 index f8cf70e0dddf..000000000000 --- a/third_party/abseil_cpp/absl/random/zipf_distribution_test.cc +++ /dev/null @@ -1,427 +0,0 @@ -// Copyright 2017 The Abseil Authors. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// https://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#include "absl/random/zipf_distribution.h" - -#include <algorithm> -#include <cstddef> -#include <cstdint> -#include <iterator> -#include <random> -#include <string> -#include <utility> -#include <vector> - -#include "gmock/gmock.h" -#include "gtest/gtest.h" -#include "absl/base/internal/raw_logging.h" -#include "absl/random/internal/chi_square.h" -#include "absl/random/internal/pcg_engine.h" -#include "absl/random/internal/sequence_urbg.h" -#include "absl/random/random.h" -#include "absl/strings/str_cat.h" -#include "absl/strings/str_replace.h" -#include "absl/strings/strip.h" - -namespace { - -using ::absl::random_internal::kChiSquared; -using ::testing::ElementsAre; - -template <typename IntType> -class ZipfDistributionTypedTest : public ::testing::Test {}; - -using IntTypes = ::testing::Types<int, int8_t, int16_t, int32_t, int64_t, - uint8_t, uint16_t, uint32_t, uint64_t>; -TYPED_TEST_CASE(ZipfDistributionTypedTest, IntTypes); - -TYPED_TEST(ZipfDistributionTypedTest, SerializeTest) { - using param_type = typename absl::zipf_distribution<TypeParam>::param_type; - - constexpr int kCount = 1000; - absl::InsecureBitGen gen; - for (const auto& param : { - param_type(), - param_type(32), - param_type(100, 3, 2), - param_type(std::numeric_limits<TypeParam>::max(), 4, 3), - param_type(std::numeric_limits<TypeParam>::max() / 2), - }) { - // Validate parameters. - const auto k = param.k(); - const auto q = param.q(); - const auto v = param.v(); - - absl::zipf_distribution<TypeParam> before(k, q, v); - EXPECT_EQ(before.k(), param.k()); - EXPECT_EQ(before.q(), param.q()); - EXPECT_EQ(before.v(), param.v()); - - { - absl::zipf_distribution<TypeParam> via_param(param); - EXPECT_EQ(via_param, before); - } - - // Validate stream serialization. - std::stringstream ss; - ss << before; - absl::zipf_distribution<TypeParam> after(4, 5.5, 4.4); - - EXPECT_NE(before.k(), after.k()); - EXPECT_NE(before.q(), after.q()); - EXPECT_NE(before.v(), after.v()); - EXPECT_NE(before.param(), after.param()); - EXPECT_NE(before, after); - - ss >> after; - - EXPECT_EQ(before.k(), after.k()); - EXPECT_EQ(before.q(), after.q()); - EXPECT_EQ(before.v(), after.v()); - EXPECT_EQ(before.param(), after.param()); - EXPECT_EQ(before, after); - - // Smoke test. - auto sample_min = after.max(); - auto sample_max = after.min(); - for (int i = 0; i < kCount; i++) { - auto sample = after(gen); - EXPECT_GE(sample, after.min()); - EXPECT_LE(sample, after.max()); - if (sample > sample_max) sample_max = sample; - if (sample < sample_min) sample_min = sample; - } - ABSL_INTERNAL_LOG(INFO, - absl::StrCat("Range: ", +sample_min, ", ", +sample_max)); - } -} - -class ZipfModel { - public: - ZipfModel(size_t k, double q, double v) : k_(k), q_(q), v_(v) {} - - double mean() const { return mean_; } - - // For the other moments of the Zipf distribution, see, for example, - // http://mathworld.wolfram.com/ZipfDistribution.html - - // PMF(k) = (1 / k^s) / H(N,s) - // Returns the probability that any single invocation returns k. - double PMF(size_t i) { return i >= hnq_.size() ? 0.0 : hnq_[i] / sum_hnq_; } - - // CDF = H(k, s) / H(N,s) - double CDF(size_t i) { - if (i >= hnq_.size()) { - return 1.0; - } - auto it = std::begin(hnq_); - double h = 0.0; - for (const auto end = it; it != end; it++) { - h += *it; - } - return h / sum_hnq_; - } - - // The InverseCDF returns the k values which bound p on the upper and lower - // bound. Since there is no closed-form solution, this is implemented as a - // bisction of the cdf. - std::pair<size_t, size_t> InverseCDF(double p) { - size_t min = 0; - size_t max = hnq_.size(); - while (max > min + 1) { - size_t target = (max + min) >> 1; - double x = CDF(target); - if (x > p) { - max = target; - } else { - min = target; - } - } - return {min, max}; - } - - // Compute the probability totals, which are based on the generalized harmonic - // number, H(N,s). - // H(N,s) == SUM(k=1..N, 1 / k^s) - // - // In the limit, H(N,s) == zetac(s) + 1. - // - // NOTE: The mean of a zipf distribution could be computed here as well. - // Mean := H(N, s-1) / H(N,s). - // Given the parameter v = 1, this gives the following function: - // (Hn(100, 1) - Hn(1,1)) / (Hn(100,2) - Hn(1,2)) = 6.5944 - // - void Init() { - if (!hnq_.empty()) { - return; - } - hnq_.clear(); - hnq_.reserve(std::min(k_, size_t{1000})); - - sum_hnq_ = 0; - double qm1 = q_ - 1.0; - double sum_hnq_m1 = 0; - for (size_t i = 0; i < k_; i++) { - // Partial n-th generalized harmonic number - const double x = v_ + i; - - // H(n, q-1) - const double hnqm1 = - (q_ == 2.0) ? (1.0 / x) - : (q_ == 3.0) ? (1.0 / (x * x)) : std::pow(x, -qm1); - sum_hnq_m1 += hnqm1; - - // H(n, q) - const double hnq = - (q_ == 2.0) ? (1.0 / (x * x)) - : (q_ == 3.0) ? (1.0 / (x * x * x)) : std::pow(x, -q_); - sum_hnq_ += hnq; - hnq_.push_back(hnq); - if (i > 1000 && hnq <= 1e-10) { - // The harmonic number is too small. - break; - } - } - assert(sum_hnq_ > 0); - mean_ = sum_hnq_m1 / sum_hnq_; - } - - private: - const size_t k_; - const double q_; - const double v_; - - double mean_; - std::vector<double> hnq_; - double sum_hnq_; -}; - -using zipf_u64 = absl::zipf_distribution<uint64_t>; - -class ZipfTest : public testing::TestWithParam<zipf_u64::param_type>, - public ZipfModel { - public: - ZipfTest() : ZipfModel(GetParam().k(), GetParam().q(), GetParam().v()) {} - - // We use a fixed bit generator for distribution accuracy tests. This allows - // these tests to be deterministic, while still testing the qualify of the - // implementation. - absl::random_internal::pcg64_2018_engine rng_{0x2B7E151628AED2A6}; -}; - -TEST_P(ZipfTest, ChiSquaredTest) { - const auto& param = GetParam(); - Init(); - - size_t trials = 10000; - - // Find the split-points for the buckets. - std::vector<size_t> points; - std::vector<double> expected; - { - double last_cdf = 0.0; - double min_p = 1.0; - for (double p = 0.01; p < 1.0; p += 0.01) { - auto x = InverseCDF(p); - if (points.empty() || points.back() < x.second) { - const double p = CDF(x.second); - points.push_back(x.second); - double q = p - last_cdf; - expected.push_back(q); - last_cdf = p; - if (q < min_p) { - min_p = q; - } - } - } - if (last_cdf < 0.999) { - points.push_back(std::numeric_limits<size_t>::max()); - double q = 1.0 - last_cdf; - expected.push_back(q); - if (q < min_p) { - min_p = q; - } - } else { - points.back() = std::numeric_limits<size_t>::max(); - expected.back() += (1.0 - last_cdf); - } - // The Chi-Squared score is not completely scale-invariant; it works best - // when the small values are in the small digits. - trials = static_cast<size_t>(8.0 / min_p); - } - ASSERT_GT(points.size(), 0); - - // Generate n variates and fill the counts vector with the count of their - // occurrences. - std::vector<int64_t> buckets(points.size(), 0); - double avg = 0; - { - zipf_u64 dis(param); - for (size_t i = 0; i < trials; i++) { - uint64_t x = dis(rng_); - ASSERT_LE(x, dis.max()); - ASSERT_GE(x, dis.min()); - avg += static_cast<double>(x); - auto it = std::upper_bound(std::begin(points), std::end(points), - static_cast<size_t>(x)); - buckets[std::distance(std::begin(points), it)]++; - } - avg = avg / static_cast<double>(trials); - } - - // Validate the output using the Chi-Squared test. - for (auto& e : expected) { - e *= trials; - } - - // The null-hypothesis is that the distribution is a poisson distribution with - // the provided mean (not estimated from the data). - const int dof = static_cast<int>(expected.size()) - 1; - - // NOTE: This test runs about 15x per invocation, so a value of 0.9995 is - // approximately correct for a test suite failure rate of 1 in 100. In - // practice we see failures slightly higher than that. - const double threshold = absl::random_internal::ChiSquareValue(dof, 0.9999); - - const double chi_square = absl::random_internal::ChiSquare( - std::begin(buckets), std::end(buckets), std::begin(expected), - std::end(expected)); - - const double p_actual = - absl::random_internal::ChiSquarePValue(chi_square, dof); - - // Log if the chi_squared value is above the threshold. - if (chi_square > threshold) { - ABSL_INTERNAL_LOG(INFO, "values"); - for (size_t i = 0; i < expected.size(); i++) { - ABSL_INTERNAL_LOG(INFO, absl::StrCat(points[i], ": ", buckets[i], - " vs. E=", expected[i])); - } - ABSL_INTERNAL_LOG(INFO, absl::StrCat("trials ", trials)); - ABSL_INTERNAL_LOG(INFO, - absl::StrCat("mean ", avg, " vs. expected ", mean())); - ABSL_INTERNAL_LOG(INFO, absl::StrCat(kChiSquared, "(data, ", dof, ") = ", - chi_square, " (", p_actual, ")")); - ABSL_INTERNAL_LOG(INFO, - absl::StrCat(kChiSquared, " @ 0.9995 = ", threshold)); - FAIL() << kChiSquared << " value of " << chi_square - << " is above the threshold."; - } -} - -std::vector<zipf_u64::param_type> GenParams() { - using param = zipf_u64::param_type; - const auto k = param().k(); - const auto q = param().q(); - const auto v = param().v(); - const uint64_t k2 = 1 << 10; - return std::vector<zipf_u64::param_type>{ - // Default - param(k, q, v), - // vary K - param(4, q, v), param(1 << 4, q, v), param(k2, q, v), - // vary V - param(k2, q, 0.5), param(k2, q, 1.5), param(k2, q, 2.5), param(k2, q, 10), - // vary Q - param(k2, 1.5, v), param(k2, 3, v), param(k2, 5, v), param(k2, 10, v), - // Vary V & Q - param(k2, 1.5, 0.5), param(k2, 3, 1.5), param(k, 10, 10)}; -} - -std::string ParamName( - const ::testing::TestParamInfo<zipf_u64::param_type>& info) { - const auto& p = info.param; - std::string name = absl::StrCat("k_", p.k(), "__q_", absl::SixDigits(p.q()), - "__v_", absl::SixDigits(p.v())); - return absl::StrReplaceAll(name, {{"+", "_"}, {"-", "_"}, {".", "_"}}); -} - -INSTANTIATE_TEST_SUITE_P(All, ZipfTest, ::testing::ValuesIn(GenParams()), - ParamName); - -// NOTE: absl::zipf_distribution is not guaranteed to be stable. -TEST(ZipfDistributionTest, StabilityTest) { - // absl::zipf_distribution stability relies on - // absl::uniform_real_distribution, std::log, std::exp, std::log1p - absl::random_internal::sequence_urbg urbg( - {0x0003eb76f6f7f755ull, 0xFFCEA50FDB2F953Bull, 0xC332DDEFBE6C5AA5ull, - 0x6558218568AB9702ull, 0x2AEF7DAD5B6E2F84ull, 0x1521B62829076170ull, - 0xECDD4775619F1510ull, 0x13CCA830EB61BD96ull, 0x0334FE1EAA0363CFull, - 0xB5735C904C70A239ull, 0xD59E9E0BCBAADE14ull, 0xEECC86BC60622CA7ull}); - - std::vector<int> output(10); - - { - absl::zipf_distribution<int32_t> dist; - std::generate(std::begin(output), std::end(output), - [&] { return dist(urbg); }); - EXPECT_THAT(output, ElementsAre(10031, 0, 0, 3, 6, 0, 7, 47, 0, 0)); - } - urbg.reset(); - { - absl::zipf_distribution<int32_t> dist(std::numeric_limits<int32_t>::max(), - 3.3); - std::generate(std::begin(output), std::end(output), - [&] { return dist(urbg); }); - EXPECT_THAT(output, ElementsAre(44, 0, 0, 0, 0, 1, 0, 1, 3, 0)); - } -} - -TEST(ZipfDistributionTest, AlgorithmBounds) { - absl::zipf_distribution<int32_t> dist; - - // Small values from absl::uniform_real_distribution map to larger Zipf - // distribution values. - const std::pair<uint64_t, int32_t> kInputs[] = { - {0xffffffffffffffff, 0x0}, {0x7fffffffffffffff, 0x0}, - {0x3ffffffffffffffb, 0x1}, {0x1ffffffffffffffd, 0x4}, - {0xffffffffffffffe, 0x9}, {0x7ffffffffffffff, 0x12}, - {0x3ffffffffffffff, 0x25}, {0x1ffffffffffffff, 0x4c}, - {0xffffffffffffff, 0x99}, {0x7fffffffffffff, 0x132}, - {0x3fffffffffffff, 0x265}, {0x1fffffffffffff, 0x4cc}, - {0xfffffffffffff, 0x999}, {0x7ffffffffffff, 0x1332}, - {0x3ffffffffffff, 0x2665}, {0x1ffffffffffff, 0x4ccc}, - {0xffffffffffff, 0x9998}, {0x7fffffffffff, 0x1332f}, - {0x3fffffffffff, 0x2665a}, {0x1fffffffffff, 0x4cc9e}, - {0xfffffffffff, 0x998e0}, {0x7ffffffffff, 0x133051}, - {0x3ffffffffff, 0x265ae4}, {0x1ffffffffff, 0x4c9ed3}, - {0xffffffffff, 0x98e223}, {0x7fffffffff, 0x13058c4}, - {0x3fffffffff, 0x25b178e}, {0x1fffffffff, 0x4a062b2}, - {0xfffffffff, 0x8ee23b8}, {0x7ffffffff, 0x10b21642}, - {0x3ffffffff, 0x1d89d89d}, {0x1ffffffff, 0x2fffffff}, - {0xffffffff, 0x45d1745d}, {0x7fffffff, 0x5a5a5a5a}, - {0x3fffffff, 0x69ee5846}, {0x1fffffff, 0x73ecade3}, - {0xfffffff, 0x79a9d260}, {0x7ffffff, 0x7cc0532b}, - {0x3ffffff, 0x7e5ad146}, {0x1ffffff, 0x7f2c0bec}, - {0xffffff, 0x7f95adef}, {0x7fffff, 0x7fcac0da}, - {0x3fffff, 0x7fe55ae2}, {0x1fffff, 0x7ff2ac0e}, - {0xfffff, 0x7ff955ae}, {0x7ffff, 0x7ffcaac1}, - {0x3ffff, 0x7ffe555b}, {0x1ffff, 0x7fff2aac}, - {0xffff, 0x7fff9556}, {0x7fff, 0x7fffcaab}, - {0x3fff, 0x7fffe555}, {0x1fff, 0x7ffff2ab}, - {0xfff, 0x7ffff955}, {0x7ff, 0x7ffffcab}, - {0x3ff, 0x7ffffe55}, {0x1ff, 0x7fffff2b}, - {0xff, 0x7fffff95}, {0x7f, 0x7fffffcb}, - {0x3f, 0x7fffffe5}, {0x1f, 0x7ffffff3}, - {0xf, 0x7ffffff9}, {0x7, 0x7ffffffd}, - {0x3, 0x7ffffffe}, {0x1, 0x7fffffff}, - }; - - for (const auto& instance : kInputs) { - absl::random_internal::sequence_urbg urbg({instance.first}); - EXPECT_EQ(instance.second, dist(urbg)); - } -} - -} // namespace |