DO NOT MERGE: Fix bug in random.

Test: ./run_test.py --bitness 32
Test: ./run_test.py --bitness 64
Test: ./run_test.py --bitness 64 --host
Bug: https://bugs.chromium.org/p/chromium/issues/detail?id=994957
Bug: http://b/139690488
Change-Id: I71708114d7fc8ed90c30b4d32b01d3f3aef7600b
(cherry picked from commit a1d1caa3d831030ce802ed335a9743180911d553)

include/random

@@ -4593,7 +4593,10 @@ public:
 
 template<class _IntType>
 poisson_distribution<_IntType>::param_type::param_type(double __mean)
-    : __mean_(__mean)
+    // According to the standard `inf` is a valid input, but it causes the
+    // distribution to hang, so we replace it with the maximum representable
+    // mean.
+    : __mean_(isinf(__mean) ? numeric_limits<double>::max() : __mean)
 {
     if (__mean_ < 10)
     {
@@ -4611,7 +4614,7 @@ poisson_distribution<_IntType>::param_type::param_type(double __mean)
     {
         __s_ = _VSTD::sqrt(__mean_);
         __d_ = 6 * __mean_ * __mean_;
-        __l_ = static_cast<result_type>(__mean_ - 1.1484);
+        __l_ = std::trunc(__mean_ - 1.1484);
         __omega_ = .3989423 / __s_;
         double __b1_ = .4166667E-1 / __mean_;
         double __b2_ = .3 * __b1_ * __b1_;
@@ -4628,12 +4631,12 @@ template<class _URNG>
 _IntType
 poisson_distribution<_IntType>::operator()(_URNG& __urng, const param_type& __pr)
 {
-    result_type __x;
+    double __tx;
     uniform_real_distribution<double> __urd;
     if (__pr.__mean_ < 10)
     {
-         __x = 0;
+         __tx = 0;
-        for (double __p = __urd(__urng); __p > __pr.__l_; ++__x)
+        for (double __p = __urd(__urng); __p > __pr.__l_; ++__tx)
             __p *= __urd(__urng);
     }
     else
@@ -4643,19 +4646,19 @@ poisson_distribution<_IntType>::operator()(_URNG& __urng, const param_type& __pr
         double __u;
         if (__g > 0)
         {
-            __x = static_cast<result_type>(__g);
+            __tx = std::trunc(__g);
-            if (__x >= __pr.__l_)
+            if (__tx >= __pr.__l_)
-                return __x;
+                return std::__clamp_to_integral<result_type>(__tx);
-            __difmuk = __pr.__mean_ - __x;
+            __difmuk = __pr.__mean_ - __tx;
             __u = __urd(__urng);
             if (__pr.__d_ * __u >= __difmuk * __difmuk * __difmuk)
-                return __x;
+                return std::__clamp_to_integral<result_type>(__tx);
         }
         exponential_distribution<double> __edist;
         for (bool __using_exp_dist = false; true; __using_exp_dist = true)
         {
             double __e;
-            if (__using_exp_dist || __g < 0)
+            if (__using_exp_dist || __g <= 0)
             {
                 double __t;
                 do
@@ -4665,31 +4668,31 @@ poisson_distribution<_IntType>::operator()(_URNG& __urng, const param_type& __pr
                     __u += __u - 1;
                     __t = 1.8 + (__u < 0 ? -__e : __e);
                 } while (__t <= -.6744);
-                __x = __pr.__mean_ + __pr.__s_ * __t;
+                __tx = std::trunc(__pr.__mean_ + __pr.__s_ * __t);
-                __difmuk = __pr.__mean_ - __x;
+                __difmuk = __pr.__mean_ - __tx;
                 __using_exp_dist = true;
             }
             double __px;
             double __py;
-            if (__x < 10)
+            if (__tx < 10 && __tx >= 0)
             {
                 const double __fac[] = {1, 1, 2, 6, 24, 120, 720, 5040,
                                              40320, 362880};
                 __px = -__pr.__mean_;
-                __py = _VSTD::pow(__pr.__mean_, (double)__x) / __fac[__x];
+                __py = _VSTD::pow(__pr.__mean_, (double)__tx) / __fac[static_cast<int>(__tx)];
             }
             else
             {
-                double __del = .8333333E-1 / __x;
+                double __del = .8333333E-1 / __tx;
                 __del -= 4.8 * __del * __del * __del;
-                double __v = __difmuk / __x;
+                double __v = __difmuk / __tx;
                 if (_VSTD::abs(__v) > 0.25)
-                    __px = __x * _VSTD::log(1 + __v) - __difmuk - __del;
+                    __px = __tx * _VSTD::log(1 + __v) - __difmuk - __del;
                 else
-                    __px = __x * __v * __v * (((((((.1250060 * __v + -.1384794) *
+                    __px = __tx * __v * __v * (((((((.1250060 * __v + -.1384794) *
                            __v + .1421878) * __v + -.1661269) * __v + .2000118) *
                            __v + -.2500068) * __v + .3333333) * __v + -.5) - __del;
-                __py = .3989423 / _VSTD::sqrt(__x);
+                __py = .3989423 / _VSTD::sqrt(__tx);
             }
             double __r = (0.5 - __difmuk) / __pr.__s_;
             double __r2 = __r * __r;
@@ -4709,7 +4712,7 @@ poisson_distribution<_IntType>::operator()(_URNG& __urng, const param_type& __pr
             }
         }
     }
-    return __x;
+    return std::__clamp_to_integral<result_type>(__tx);
 }
 
 template <class _CharT, class _Traits, class _IntType>

test/std/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.geo/eval.pass.cpp

@@ -29,6 +29,20 @@ sqr(T x)
     return x * x;
 }
 
+struct Eng : std::mt19937 {
+  using Base = std::mt19937;
+  using Base::Base;
+};
+
+void test_small_inputs() {
+  Eng engine;
+  std::geometric_distribution<std::int16_t> distribution(5.45361e-311);
+  for (auto i=0; i < 1000; ++i) {
+		volatile auto res = distribution(engine);
+      ((void)res);
+  }
+}
+
 void
 test1()
 {
@@ -295,4 +309,5 @@ int main()
     test4();
     test5();
     test6();
+    test_small_inputs();
 }

test/std/numerics/rand/rand.dis/rand.dist.pois/rand.dist.pois.poisson/eval.pass.cpp

@@ -29,6 +29,68 @@ sqr(T x)
     return x * x;
 }
 
+void test_bad_ranges() {
+  // Test cases where the mean is around the largest representable integer for
+  // `result_type`. These cases don't generate valid poisson distributions, but
+  // at least they don't blow up.
+  std::mt19937 eng;
+  
+  {
+    std::poisson_distribution<std::int16_t> distribution(32710.9);
+    for (int i=0; i < 1000; ++i) {
+      volatile std::int16_t res = distribution(eng);
+      ((void)res);
+    }
+  }
+  {
+    std::poisson_distribution<std::int16_t> distribution(std::numeric_limits<std::int16_t>::max());
+    for (int i=0; i < 1000; ++i) {
+      volatile std::int16_t res = distribution(eng);
+      ((void)res);
+    }
+  }
+  {
+    std::poisson_distribution<std::int16_t> distribution(
+    static_cast<double>(std::numeric_limits<std::int16_t>::max()) + 10);
+    for (int i=0; i < 1000; ++i) {
+      volatile std::int16_t res = distribution(eng);
+      ((void)res);
+    }
+  }
+  {
+    std::poisson_distribution<std::int16_t> distribution(
+      static_cast<double>(std::numeric_limits<std::int16_t>::max()) * 2);
+      for (int i=0; i < 1000; ++i) {
+        volatile std::int16_t res = distribution(eng);
+        ((void)res);
+      }
+  }
+  {
+    // We convert `INF` to `DBL_MAX` otherwise the distribution will hang.
+    std::poisson_distribution<std::int16_t> distribution(std::numeric_limits<double>::infinity());
+    for (int i=0; i < 1000; ++i) {
+      volatile std::int16_t res = distribution(eng);
+      ((void)res);
+    }
+  }
+  {
+    std::poisson_distribution<std::int16_t> distribution(0);
+    for (int i=0; i < 1000; ++i) {
+      volatile std::int16_t res = distribution(eng);
+      ((void)res);
+    }
+  }
+  {
+    // We convert `INF` to `DBL_MAX` otherwise the distribution will hang.
+    std::poisson_distribution<std::int16_t> distribution(-100);
+    for (int i=0; i < 1000; ++i) {
+      volatile std::int16_t res = distribution(eng);
+      ((void)res);
+    }
+  }
+}
+
+
 int main()
 {
     {
@@ -148,4 +210,6 @@ int main()
         assert(std::abs((skew - x_skew) / x_skew) < 0.01);
         assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01);
     }
+
+  test_bad_ranges();
 }