/* Copyright 2017 The TensorFlow Authors. 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

    http://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 <limits>
#include <memory>

#include "tensorflow/compiler/xla/client/local_client.h"
#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h"
#include "tensorflow/compiler/xla/literal_util.h"
#include "tensorflow/compiler/xla/primitive_util.h"
#include "tensorflow/compiler/xla/shape_util.h"
#include "tensorflow/compiler/xla/test.h"
#include "tensorflow/compiler/xla/tests/client_library_test_base.h"
#include "tensorflow/compiler/xla/tests/test_macros.h"
#include "tensorflow/compiler/xla/util.h"
#include "tensorflow/compiler/xla/xla_data.pb.h"
#include "tensorflow/core/lib/gtl/array_slice.h"
#include "tensorflow/core/platform/protobuf.h"
#include "tensorflow/core/platform/test.h"
#include "tensorflow/core/platform/types.h"

namespace xla {
namespace {

class PrngTest : public ClientLibraryTestBase {
 protected:
  template <typename T>
  std::unique_ptr<Literal> UniformTest(T a, T b,
                                       tensorflow::gtl::ArraySlice<int64> dims,
                                       int64 seed = 42);

  // Computes the χ² statistic of a sample of the discrete uniform distribution
  // of the given range size. `expected_count` is the number of times each
  // possible value is expected to be generated. Thus, the sample size is
  // `range_size * expected_count`.
  double UniformChiSquared(int32 range_size, int32 expected_count,
                           int64 seed = 42);
};

template <typename T>
std::unique_ptr<Literal> PrngTest::UniformTest(
    T a, T b, tensorflow::gtl::ArraySlice<int64> dims, int64 seed) {
  XlaBuilder builder(TestName());
  builder.RngUniform(
      builder.ConstantR0<T>(a), builder.ConstantR0<T>(b),
      ShapeUtil::MakeShape(primitive_util::NativeToPrimitiveType<T>(), dims));

  SetSeed(seed);
  auto actual =
      ExecuteAndTransfer(&builder, /*arguments=*/{}).ConsumeValueOrDie();
  EXPECT_THAT(dims, ::testing::ElementsAreArray(actual->shape().dimensions()));
  actual->EachCell<T>([=](tensorflow::gtl::ArraySlice<int64>, T value) {
    EXPECT_LE(a, value);
    EXPECT_LT(value, b);
  });
  return actual;
}

// Uniform random number generation tests
XLA_TEST_F(PrngTest, ScalarU01) { UniformTest<float>(0, 1, {}); }
XLA_TEST_F(PrngTest, ScalarU01limits) {
  UniformTest<float>(std::numeric_limits<float>::min(),
                     std::numeric_limits<float>::max(), {});
}
XLA_TEST_F(PrngTest, ZeroValuesU01) { UniformTest<float>(0, 1, {0}); }
XLA_TEST_F(PrngTest, TenValuesU01) { UniformTest<float>(0, 1, {10}); }
XLA_TEST_F(PrngTest, TenValuesU37) { UniformTest<float>(3, 7, {10}); }
XLA_TEST_F(PrngTest, ZeroValuesR2) { UniformTest<float>(0, 1, {0, 20}); }
XLA_TEST_F(PrngTest, LargeU01) { UniformTest<float>(0, 1, {0x100, 0x100}); }
XLA_TEST_F(PrngTest, TwelveValuesU524) { UniformTest<int32>(5, 24, {12}); }

// TODO(b/71543667): Fix Rng ops on LLVM backends.
XLA_TEST_F(PrngTest, DISABLED_ON_GPU(DISABLED_ON_CPU(ScalarBF16Tests))) {
  for (int64 seed = 0; seed < 100; ++seed) {
    // The largest negative number smaller than zero in bf16 that's not
    // denormalized.
    int32 low_raw = 0x80800000;
    const float low = reinterpret_cast<const float&>(low_raw);
    float high = 0.0f;
    UniformTest<bfloat16>(static_cast<bfloat16>(low),
                          static_cast<bfloat16>(high), {}, /*seed=*/seed);

    // Test odd and even values.
    UniformTest<bfloat16>(static_cast<bfloat16>(32.75),
                          static_cast<bfloat16>(33), {}, /*seed=*/seed);
    UniformTest<bfloat16>(static_cast<bfloat16>(32.50),
                          static_cast<bfloat16>(32.75), {}, /*seed=*/seed);
    UniformTest<bfloat16>(static_cast<bfloat16>(-33.00),
                          static_cast<bfloat16>(-32.75), {}, /*seed=*/seed);
    UniformTest<bfloat16>(static_cast<bfloat16>(-32.75),
                          static_cast<bfloat16>(-32.50), {}, /*seed=*/seed);
  }
}

// TODO(b/71543667): Fix Rng ops on LLVM backends.
XLA_TEST_F(PrngTest, DISABLED_ON_GPU(DISABLED_ON_CPU(ScalarBF16CountTests))) {
  // There are 3 BF16 values in the range of [32.25, 33): 32.25, 32.5, 32.75,
  // they should get similar counts.
  bfloat16 low = static_cast<bfloat16>(32.25);
  bfloat16 high = static_cast<bfloat16>(33);
  bfloat16 interval = static_cast<bfloat16>(0.25);
  std::vector<int32> counts(static_cast<int64>((high - low) / interval), 0);

  constexpr int64 count = 100;
  for (int64 seed = 0; seed < count; ++seed) {
    auto result = UniformTest<bfloat16>(low, high, {}, /*seed=*/seed);
    result->Literal::EachCell<bfloat16>(
        [&](tensorflow::gtl::ArraySlice<int64>, bfloat16 value) {
          int64 index = static_cast<int64>((value - low) / interval);
          counts[index]++;
        });
  }
  // Each bucket should have similar amount of counts. That is, not more than
  // 10% of total counts. This mostly tests that we don't fall into a 1:2:2
  // distribution, which yields 20% expected difference.
  EXPECT_LT(std::abs(counts[0] - counts[1]), count * 0.1);
  EXPECT_LT(std::abs(counts[1] - counts[2]), count * 0.1);
}

namespace {
template <typename T>
T Square(T x) {
  return x * x;
}
}  // namespace

double PrngTest::UniformChiSquared(int32 range_size, int32 expected_count,
                                   int64 seed) {
  int32 sample_size = range_size * expected_count;

  XlaBuilder builder(TestName());
  builder.RngUniform(builder.ConstantR0<int32>(0),
                     builder.ConstantR0<int32>(range_size),
                     ShapeUtil::MakeShape(S32, {sample_size}));

  SetSeed(seed);
  auto actual =
      ExecuteAndTransfer(&builder, /*arguments=*/{}).ConsumeValueOrDie();
  std::vector<int32> counts(range_size, 0);
  actual->EachCell<int32>([&counts](tensorflow::gtl::ArraySlice<int64>,
                                    int32 value) { ++counts[value]; });
  int64 sum = 0;
  for (int32 i = 0; i < range_size; ++i) {
    sum += Square(static_cast<int64>(counts[i] - expected_count));
  }
  return static_cast<double>(sum) / expected_count;
}

// We only test distribution of uniform discrete PRNG as other types are based
// on it.
// These range sizes are arbitrary but include prime numbers, powers of 2, and
// other composite numbers.
// The level of significance in all these cases is 1/20.
// TODO(b/35723038): Use parametrized tests where possible.
XLA_TEST_F(PrngTest, Uniformity7) {
  EXPECT_LT(UniformChiSquared(7, 256), 12.5916);
}
XLA_TEST_F(PrngTest, Uniformity61) {
  EXPECT_LT(UniformChiSquared(61, 256), 79.0819);
}
XLA_TEST_F(PrngTest, Uniformity64) {
  EXPECT_LT(UniformChiSquared(64, 256), 82.5287);
}
XLA_TEST_F(PrngTest, Uniformity108) {
  EXPECT_LT(UniformChiSquared(108, 256), 132.144);
}
XLA_TEST_F(PrngTest, Uniformity256) {
  EXPECT_LT(UniformChiSquared(256, 256), 293.248);
}

XLA_TEST_F(PrngTest, MapUsingRng) {
  // Build a x -> (x + U[0,1)) computation.
  auto build_sum_rng = [this](XlaBuilder& builder) {
    auto b = builder.CreateSubBuilder("sum_with_rng");
    auto x = b->Parameter(0, ShapeUtil::MakeShape(F32, {}), "input");
    b->Add(x, b->RngUniform(b->ConstantR0<float>(0), b->ConstantR0<float>(1),
                            ShapeUtil::MakeShape(F32, {})));
    return b->BuildAndNoteError();
  };

  XlaBuilder builder(TestName());
  std::unique_ptr<Literal> param0_literal =
      Literal::CreateR1<float>({2.2f, 5.3f, 4.4f, 5.5f});
  TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr<GlobalData> param0_data,
                          client_->TransferToServer(*param0_literal));

  auto param0 = builder.Parameter(0, param0_literal->shape(), "param0");
  auto fn = build_sum_rng(builder);
  builder.Map({param0}, fn, {0});

  TF_ASSERT_OK_AND_ASSIGN(auto computation, builder.Build());

  ExecutionOptions execution_options = execution_options_;
  execution_options.set_seed(125);
  TF_ASSERT_OK_AND_ASSIGN(
      auto actual, client_->ExecuteAndTransfer(
                       computation,
                       /*arguments=*/{param0_data.get()}, &execution_options));

  EXPECT_EQ(ShapeUtil::ElementsIn(actual->shape()),
            ShapeUtil::ElementsIn(param0_literal->shape()));
  for (int i = 0; i < ShapeUtil::ElementsIn(actual->shape()); ++i) {
    EXPECT_GE(actual->data<float>()[i], param0_literal->data<float>()[i]);
    EXPECT_LT(actual->data<float>()[i],
              param0_literal->data<float>()[i] + 1.0f);
  }
}

// This tests demonstrates the global seeding behavior.
// * If a seed is passed in via Execute (ExecuteAndTransfer) then the output is
//   fixed (i.e., there is a single output for a given seed);
// * If no seed is passed in then the output of every call can be different;
XLA_TEST_F(PrngTest, PassInGlobalRngSeed) {
  // Build a U[0,1) computation.
  auto build_computation = [this]() {
    XlaBuilder builder(TestName());
    builder.RngUniform(builder.ConstantR0<float>(0),
                       builder.ConstantR0<float>(1),
                       ShapeUtil::MakeShape(F32, {10}));
    return builder.Build();
  };

  ExecutionOptions execution_options1 = execution_options_;
  execution_options1.set_seed(42);

  ExecutionOptions execution_options2 = execution_options_;
  execution_options2.set_seed(65);

  std::unique_ptr<Literal> result1;
  {
    TF_ASSERT_OK_AND_ASSIGN(auto computation, build_computation());
    TF_ASSERT_OK_AND_ASSIGN(
        result1, client_->ExecuteAndTransfer(computation, /*arguments=*/{},
                                             &execution_options1));
  }
  std::unique_ptr<Literal> result2;
  std::unique_ptr<Literal> result3;
  {
    TF_ASSERT_OK_AND_ASSIGN(auto computation, build_computation());
    TF_ASSERT_OK_AND_ASSIGN(
        result2, client_->ExecuteAndTransfer(computation, /*arguments=*/{},
                                             &execution_options1));
    TF_ASSERT_OK_AND_ASSIGN(
        result3, client_->ExecuteAndTransfer(computation, /*arguments=*/{},
                                             &execution_options1));
  }

  std::unique_ptr<Literal> result4;
  std::unique_ptr<Literal> result5;
  std::unique_ptr<Literal> result6;
  {
    TF_ASSERT_OK_AND_ASSIGN(auto computation, build_computation());
    TF_ASSERT_OK_AND_ASSIGN(
        result4, client_->ExecuteAndTransfer(computation, /*arguments=*/{},
                                             &execution_options2));
    TF_ASSERT_OK_AND_ASSIGN(
        result5, client_->ExecuteAndTransfer(computation, /*arguments=*/{},
                                             &execution_options_));
    TF_ASSERT_OK_AND_ASSIGN(
        result6, client_->ExecuteAndTransfer(computation, /*arguments=*/{},
                                             &execution_options_));
  }

  EXPECT_TRUE(LiteralTestUtil::Equal(*result1, *result2));
  EXPECT_TRUE(LiteralTestUtil::Equal(*result1, *result3));
  EXPECT_FALSE(LiteralTestUtil::Equal(*result1, *result4));
  EXPECT_FALSE(LiteralTestUtil::Equal(*result4, *result5));
  EXPECT_FALSE(LiteralTestUtil::Equal(*result5, *result6));
}

XLA_TEST_F(PrngTest, TenValuesN01) {
  XlaBuilder builder(TestName());
  builder.RngNormal(builder.ConstantR0<float>(0), builder.ConstantR0<float>(1),
                    ShapeUtil::MakeShape(F32, {10}));

  SetSeed(42);
  ExecuteAndTransfer(&builder, /*arguments=*/{}).ConsumeValueOrDie();
  // TODO(b/25995601): Test that resultant values are reasonable
}

XLA_TEST_F(PrngTest, RngUniformCrash) {
  XlaBuilder builder(TestName());

  // This used to crash XLA during LLVM IR generation for CPUs.
  auto rng_uniform = builder.RngUniform(builder.ConstantR0<int32>(0),
                                        builder.ConstantR0<int32>(1000 * 1000),
                                        ShapeUtil::MakeShape(S32, {}));
  SetSeed(0);
  ExecuteAndTransfer(&builder, /*arguments=*/{}).ConsumeValueOrDie();
}

}  // namespace
}  // namespace xla
