[yul-phaser] SimulationRNG: Use a single, shared and seedable generator

test/yulPhaser/SimulationRNG.cpp

@@ -63,6 +63,39 @@ BOOST_AUTO_TEST_CASE(uniformInt_returns_different_values_when_called_multiple_ti
 	BOOST_TEST(counts1 != counts2);
 }
 
+BOOST_AUTO_TEST_CASE(uniformInt_can_be_reset)
+{
+	constexpr size_t numSamples = 10;
+	constexpr uint32_t minValue = 50;
+	constexpr uint32_t maxValue = 80;
+
+	SimulationRNG::reset(1);
+	vector<uint32_t> samples1;
+	for (uint32_t i = 0; i < numSamples; ++i)
+		samples1.push_back(SimulationRNG::uniformInt(minValue, maxValue));
+
+	vector<uint32_t> samples2;
+	for (uint32_t i = 0; i < numSamples; ++i)
+		samples2.push_back(SimulationRNG::uniformInt(minValue, maxValue));
+
+	SimulationRNG::reset(1);
+	vector<uint32_t> samples3;
+	for (uint32_t i = 0; i < numSamples; ++i)
+		samples3.push_back(SimulationRNG::uniformInt(minValue, maxValue));
+
+	SimulationRNG::reset(2);
+	vector<uint32_t> samples4;
+	for (uint32_t i = 0; i < numSamples; ++i)
+		samples4.push_back(SimulationRNG::uniformInt(minValue, maxValue));
+
+	BOOST_TEST(samples1 != samples2);
+	BOOST_TEST(samples1 == samples3);
+	BOOST_TEST(samples1 != samples4);
+	BOOST_TEST(samples2 != samples3);
+	BOOST_TEST(samples2 != samples4);
+	BOOST_TEST(samples3 != samples4);
+}
+
 BOOST_AUTO_TEST_CASE(binomialInt_returns_different_values_when_called_multiple_times)
 {
 	constexpr uint32_t numSamples = 1000;
@@ -89,6 +122,39 @@ BOOST_AUTO_TEST_CASE(binomialInt_returns_different_values_when_called_multiple_t
 	BOOST_TEST(counts1 != counts2);
 }
 
+BOOST_AUTO_TEST_CASE(binomialInt_can_be_reset)
+{
+	constexpr size_t numSamples = 10;
+	constexpr uint32_t numTrials = 10;
+	constexpr double successProbability = 0.6;
+
+	SimulationRNG::reset(1);
+	vector<uint32_t> samples1;
+	for (uint32_t i = 0; i < numSamples; ++i)
+		samples1.push_back(SimulationRNG::binomialInt(numTrials, successProbability));
+
+	vector<uint32_t> samples2;
+	for (uint32_t i = 0; i < numSamples; ++i)
+		samples2.push_back(SimulationRNG::binomialInt(numTrials, successProbability));
+
+	SimulationRNG::reset(1);
+	vector<uint32_t> samples3;
+	for (uint32_t i = 0; i < numSamples; ++i)
+		samples3.push_back(SimulationRNG::binomialInt(numTrials, successProbability));
+
+	SimulationRNG::reset(2);
+	vector<uint32_t> samples4;
+	for (uint32_t i = 0; i < numSamples; ++i)
+		samples4.push_back(SimulationRNG::binomialInt(numTrials, successProbability));
+
+	BOOST_TEST(samples1 != samples2);
+	BOOST_TEST(samples1 == samples3);
+	BOOST_TEST(samples1 != samples4);
+	BOOST_TEST(samples2 != samples3);
+	BOOST_TEST(samples2 != samples4);
+	BOOST_TEST(samples3 != samples4);
+}
+
 BOOST_AUTO_TEST_SUITE_END()
 BOOST_AUTO_TEST_SUITE_END()
 

tools/yulPhaser/SimulationRNG.cpp

@@ -25,20 +25,24 @@
 using namespace solidity;
 using namespace solidity::phaser;
 
+thread_local boost::random::mt19937 SimulationRNG::s_generator(SimulationRNG::generateSeed());
+
 uint32_t SimulationRNG::uniformInt(uint32_t _min, uint32_t _max)
 {
-	// TODO: Seed must be configurable
-	static boost::random::mt19937 generator(time(0));
 	boost::random::uniform_int_distribution<> distribution(_min, _max);
-
+	return distribution(s_generator);
-	return distribution(generator);
 }
 
 uint32_t SimulationRNG::binomialInt(uint32_t _numTrials, double _successProbability)
 {
-	// TODO: Seed must be configurable
-	static boost::random::mt19937 generator(time(0));
 	boost::random::binomial_distribution<> distribution(_numTrials, _successProbability);
-
+	return distribution(s_generator);
-	return distribution(generator);
+}
+
+uint32_t SimulationRNG::generateSeed()
+{
+	// This is not a secure way to seed the generator but it's good enough for simulation purposes.
+	// The only thing that matters for us is that the sequence is different on each run and that
+	// it fits the expected distribution. It does not have to be 100% unpredictable.
+	return time(0);
 }

tools/yulPhaser/SimulationRNG.h

@@ -27,6 +27,10 @@ namespace solidity::phaser
 /**
  * A class that provides functions for generating random numbers good enough for simulation purposes.
  *
+ * The functions share a common instance of the generator which can be reset with a known seed
+ * to deterministically generate a given sequence of numbers. Initially the generator is seeded with
+ * a value from @a generateSeed() which is different on each run.
+ *
  * The numbers are not cryptographically secure so do not use this for anything that requires
  * them to be truly unpredictable.
  */
@@ -35,6 +39,18 @@ class SimulationRNG
 public:
 	static uint32_t uniformInt(uint32_t _min, uint32_t _max);
 	static uint32_t binomialInt(uint32_t _numTrials, double _successProbability);
+
+	/// Resets generator to a known state given by the @a seed. Given the same seed, a fixed
+	/// sequence of calls to the members generating random values is guaranteed to produce the
+	/// same results.
+	static void reset(uint32_t seed) { s_generator = boost::random::mt19937(seed); }
+
+	/// Generates a seed that's different on each run of the program.
+	/// Does **not** use the generator and is not affected by @a reset().
+	static uint32_t generateSeed();
+
+private:
+	thread_local static boost::random::mt19937 s_generator;
 };
 
 }