rocm-systems/projects/hip-tests/catch/include/warp_common.hh

/*
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*/

#pragma once

#define HIP_ENABLE_WARP_SYNC_BUILTINS
#define HIP_ENABLE_EXTRA_WARP_SYNC_TYPES

#include <hip_test_common.hh>
#include <resource_guards.hh>
#include <hip/hip_cooperative_groups.h>
#include <hip/hip_fp16.h>
#include <random>
#include <cmath>
#include <iostream>
#include <ios>

#define MASK_SHIFT(x, n) \
  (x & (static_cast<uint64_t>(1) << n)) >> n

const unsigned long long Every5thBit = 0x1084210842108421;
const unsigned long long Every9thBit = 0x8040201008040201;
const unsigned long long Every5thBut9th = Every5thBit & ~Every9thBit;
const unsigned long long AllThreads = ~0;
static constexpr int kNumReduces = 5000;

inline __device__ bool deactivate_thread(const uint64_t* const active_masks) {
  const auto warp =
      cooperative_groups::tiled_partition(cooperative_groups::this_thread_block(), warpSize);
  const auto block = cooperative_groups::this_thread_block();
  const auto warps_per_block = (block.size() + warpSize - 1) / warpSize;
  const auto block_rank = (blockIdx.z * gridDim.y + blockIdx.y) * gridDim.x + blockIdx.x;
  const auto idx = block_rank * warps_per_block + block.thread_rank() / warpSize;
  return !(active_masks[idx] & (static_cast<uint64_t>(1) << warp.thread_rank()));
}

inline std::mt19937& GetRandomGenerator() {
  static std::mt19937 mt(std::random_device{}());
  return mt;
}

template <typename T> inline T GenerateRandomInteger(const T min, const T max) {
  std::uniform_int_distribution<T> dist(min, max);
  return dist(GetRandomGenerator());
}

template <typename T> inline T GenerateRandomReal(const T min, const T max) {
  std::uniform_real_distribution<T> dist(min, max);
  return dist(GetRandomGenerator());
}

inline int generate_width(int warp_size) {
  int exponent = 0;
  while (warp_size >>= 1) {
    ++exponent;
  }

  return GENERATE_COPY(map([](int e) { return 1 << e; }, range(1, exponent + 1)));
}

inline uint64_t get_active_mask(unsigned int warp_id, unsigned int warp_size) {
  uint64_t active_mask = 0;
  switch (warp_id % 5) {
    case 0:  // even threads in the warp
      active_mask = 0xAAAAAAAAAAAAAAAA;
      break;
    case 1:  // odd threads in the warp
      active_mask = 0x5555555555555555;
      break;
    case 2:  // first half of the warp
      for (int i = 0; i < warp_size / 2; i++) {
        active_mask = active_mask | (static_cast<uint64_t>(1) << i);
      }
      break;
    case 3:  // second half of the warp
      for (int i = warp_size / 2; i < warp_size; i++) {
        active_mask = active_mask | (static_cast<uint64_t>(1) << i);
      }
      break;
    case 4:  // all threads
      active_mask = 0xFFFFFFFFFFFFFFFF;
      break;
  }
  return active_mask;
}

template <typename T, std::enable_if_t<std::is_integral<T>::value, bool> = true>
inline T expandPrecision(int X) { return X; }

template <typename T, std::enable_if_t<std::is_floating_point<T>::value, bool> = true>
inline T expandPrecision(int X) {
  return X * 3.141592653589793115997963468544185161590576171875;
}

template <typename T, std::enable_if_t<std::is_same<T, __half>::value, bool> = true>
inline __half expandPrecision(int X) {
  return (__half)expandPrecision<float>(X);
}

template <typename T, std::enable_if_t<std::is_same<T, __half2>::value, bool> = true>
inline __half2 expandPrecision(int X) {
  __half H = expandPrecision<float>(X);
  return {H, H};
}

template <typename T, std::enable_if_t<std::is_integral<T>::value, bool> = true>
inline void expandPrecision(T* Array, int size) {
  (void)Array;
  (void)size;
}

template <typename T, std::enable_if_t<std::is_floating_point<T>::value, bool> = true>
inline void expandPrecision(T *Array, int size) {
  for (int i = 0; i != size; ++i) {
    Array[i] *= 3.141592653589793115997963468544185161590576171875;
  }
}

template <typename T>
inline void initializeInput(T *Input, int size) {
  int Values[] = {0, -1, 2, 3, 4, 5, -6, 7,
                  8, -9, 10, 11, 12, 13, -14, 15,
                  16, 17, -18, 19, 20, -21, 22, 23,
                  24, 25, 26, -27, 28, 29, 30, 31,
                  -32, 33, 34, 35, -36, 37, 38, -39,
                  40, 41, 42, 43, -44, -45, 46, 47,
                  48, 49, 50, -51, 52, 53, -54, 55,
                  56, 57, -58, 59, 60, 61, 62, -63};

  for (int i = 0; i != size; ++i) {
    Input[i] = expandPrecision<T>(Values[i]);
  }
}

template <typename T>
inline void initializeExpected(T *Expected, int *Values, int size) {
  for (int i = 0; i != size; ++i) {
    Expected[i] = expandPrecision<T>(Values[i]);
  }
}

template <typename T>
inline bool compareEqual(T X, T Y) { return X == Y; }

template <>
inline bool compareEqual(__half X, __half Y) {
  return __half2float(X) == __half2float(Y);
}

template <>
inline bool compareEqual(__half2 X, __half2 Y) {
  return compareEqual(X.x, Y.x) && compareEqual(X.y, Y.y);
}

inline bool compareMaskEqual(unsigned long long *Actual, unsigned long long *Expected,
                       int i, int warpSize) {
  if (warpSize == 32)
    return (unsigned)Actual[i] == (unsigned)Expected[i];
  return Actual[i] == Expected[i];
}

template <typename T>
inline T alignUp(T num, size_t n) {
  if (num % n == 0) {
    return num;
  }

  return ((num + n - 1) / n) * n;
}

template <class T>
struct DistributionType {
  using type = std::uniform_int_distribution<T>;
};

// there is no std::uniform_real_distribution for 'half' type, so we cast from
// unsigned short, avoiding Nan and Infinity
template <>
struct DistributionType<__half> {
  using type = std::uniform_int_distribution<unsigned short>;
};

template <>
struct DistributionType<float> {
  using type = std::uniform_real_distribution<float>;
};

template <>
struct DistributionType<double> {
  using type = std::uniform_real_distribution<double>;
};


template <class T>
struct MinOp {
  T operator()(const T& lhs, const T& rhs) const
  {
    return std::min(lhs, rhs);
  }
};

template <class T>
struct MaxOp {
  T operator()(const T& lhs, const T& rhs) const
  {
    return std::max(lhs, rhs);
  }
};

template <class T>
struct XorOp {
  __host__ __device__ T operator()(const T& lhs, const T& rhs)
  {
    return (!lhs) != (!rhs) == 1;
  }
};

// typeid(T).name() does seem to return a very descriptive name for primitive types,
// at least on clang, so we roll out an equivalent
template<class T>
const char* typeToString()
{
  if (std::is_same<T, int>::value)
    return "int";
  if (std::is_same<T, unsigned int>::value)
    return "unsigned int";
  if (std::is_same<T, long long>::value)
    return "long long";
  if (std::is_same<T, unsigned long long>::value)
    return "unsigned long long";
  if (std::is_same<T, half>::value)
    return "half";
  if (std::is_same<T, float>::value)
    return "float";
  if (std::is_same<T, double>::value)
    return "double";

  assert(false && "Missing conversion to string for type");
  return "";
}

template<class T, template <typename> class Op>
const char* opToString()
{
  if constexpr (std::is_same<Op<T>, std::plus<T>>::value)
    return "add";
  else if constexpr (std::is_same<Op<T>, MinOp<T>>::value)
    return "min";
  else if constexpr (std::is_same<Op<T>, MaxOp<T>>::value)
    return "max";
  else if constexpr (std::is_same<Op<T>, std::logical_and<T>>::value)
    return "logical_and";
  else if constexpr (std::is_same<Op<T>, std::logical_or<T>>::value)
    return "logical_or";
  else if constexpr (std::is_same<Op<T>, XorOp<T>>::value)
    return "logical_xor";
  else {
    static_assert(std::is_void<T>::value, "Unsupported operator");
    return "";
  }
}

template <class T, class Gen>
void genRandomMasks(LinearAllocGuard<T>& d_buf,
                    LinearAllocGuard<T>& buf,
                    Gen& gen,
                    int numItems)
{
  // masks must be != 0, hence passing 1 as the 'a' distribution parameter
  std::uniform_int_distribution<unsigned long long> dist(1);
  int numBytes = numItems * sizeof(T);
  LinearAllocGuard<T> tmp(LinearAllocs::malloc, numBytes);
  LinearAllocGuard<T> d_tmp(LinearAllocs::hipMalloc, numBytes);

  buf = std::move(tmp);
  d_buf = std::move(d_tmp);

  for (int i = 0; i < numItems; i++) {
    T mask = dist(gen);

    if (getWarpSize() == 32)
      mask &= 0xFFFFFFFF;

    buf.ptr()[i] = mask;
  }

  HIP_CHECK(hipMemcpy(d_buf.ptr(), buf.ptr(), numBytes, hipMemcpyHostToDevice));
}

// generates a random __half (instead of using uniform_real_distribution<float> casting to __half
// which is problematic)
// @expDist needs to be between [0-2^5-2]
template <class Gen>
__half genRandomHalf(std::uniform_int_distribution<unsigned short>& dist,
                     Gen& gen)
{
  __half_raw tmp;

  tmp.x = dist(gen);
  // rewrite the exponent to force the number to be (-8<x<8) and at the same time avoid NaN or
  // infinity
  tmp.x &= 0xBBFF;
  return tmp;
}

// generates a random buffer in buf, copies it to device memory in d_buf
template <class T, class Dist, class Gen>
void genRandomBuffers(LinearAllocGuard<T>& d_buf,
                      LinearAllocGuard<T>& buf,
                      Dist& dist,
                      Gen& gen,
                      int numItems)
{
  int numBytes = numItems * sizeof(T);
  LinearAllocGuard<T> tmp(LinearAllocs::malloc, numBytes);
  LinearAllocGuard<T> d_tmp(LinearAllocs::hipMalloc, numBytes);

  buf = std::move(tmp);
  d_buf = std::move(d_tmp);

  for (int i = 0; i < numItems; i++)
    if constexpr (std::is_same<T, __half>::value)
      buf.ptr()[i] = genRandomHalf(dist, gen);
    else
      buf.ptr()[i] = dist(gen);

  HIP_CHECK(hipMemcpy(d_buf.ptr(), buf.ptr(), numBytes, hipMemcpyHostToDevice));
}

// given an operation produces the expected result of the reduction
// @mask indicates the lanes that will participate in the computation
template <class T, class Op>
T calculateExpected(const T* input, Op op, unsigned long long mask)
{
  T result;
  int wavefrontSize = getWarpSize();

  if (std::is_same<Op, std::plus<T>>::value) {
    T tmp[64] = { 0 };

    for (int i = 0; i < wavefrontSize; i++) {
       if (mask & (1ul << i)) {
         tmp[i] = input[i];
       }
    }

    for (int modulo = 2; modulo <= wavefrontSize; modulo *= 2) {
      for (int i = 0; i < wavefrontSize; i += modulo) {
        int j = i + modulo / 2;

        if (j < wavefrontSize)
          tmp[i] += tmp[j];
      }
    }
    result = tmp[0];
  } else {
    bool initialized = false;

    for (int i = 0; i < wavefrontSize; i++) {
      if (mask & (1ul << i)) {
        if (initialized)
          result = op(input[i], result);
        else {
          result = input[i];
          initialized = true;
        }
      }
    }
  }
  return result;
}

template <class T>
void printMismatch(const T& result, const T& expected, const T* input, unsigned long long mask)
{
  std::ios init(NULL);

  init.copyfmt(std::cout);
  std::cout << "\nMismatch\n";
  std::cout << "Mask: 0x" << std::hex << std::setfill('0') << std::setw(16) << mask << "\n";
  std::cout.copyfmt(init);

  for (int i = 0; i < getWarpSize(); i++) {
    if ((1ul << i) & mask) {
      if constexpr (std::is_same<T, __half>::value)
                     std::cout << "Lane " << i << ": " << __half2float(input[i]) << "\n";
      else
        std::cout << "Lane " << i << ": " << input[i] << "\n";
    }
  }

  if constexpr (std::is_same<T, __half>::value) {
    std::cout << "Result: " << __half2float(result) << "\n";
    std::cout << "Expected: " << __half2float(expected) << "\n";
  } else {
    std::cout << "Result: " << result << "\n";
    std::cout << "Expected: " << expected << "\n";
  }
}

template <class T>
void compareFloatingPoint(const T& result, const T& expected, unsigned long long mask, const T* input)
{
  using namespace Catch::Matchers;
  if constexpr (std::is_same<T, __half>::value) {
    float resultFloat = __half2float(result);
    float expectedFloat = __half2float(expected);
    float absDifference = fabs(resultFloat - expectedFloat);
    float relativeEpsilon = 0.1 * fmax(resultFloat, expectedFloat);
    float eps = 0.01f;

    REQUIRE(!__hisnan(result));
    REQUIRE(!__hisinf(result));

    if (relativeEpsilon > eps) {
      if (absDifference > 0.0001) {
        if (absDifference >= eps * fabs(fmax(resultFloat, expectedFloat))) {
          printMismatch(result, expected, input, mask);
          std::cout << "Relative epsilon: " << relativeEpsilon << "\n";
          std::cout << "Difference: " << absDifference << "\n";
        }
       }

      REQUIRE_THAT(__half2float(resultFloat), WithinRel(expectedFloat, eps));
    }
  } else {
    // for float or double, also lossy in terms of precision
    T absDifference = fabs(result - expected);
    T relativeEpsilon = 0.1 * fmax(result, expected);
    T eps = 0.01;

    if (relativeEpsilon > eps) {
      if (absDifference > 0.0001) {
        if (absDifference >= eps * fabs(fmax(result, expected))) {
          printMismatch(result, expected, input, mask);
          std::cout << "Relative epsilon: " << relativeEpsilon << "\n";
          std::cout << "Difference: " << absDifference << "\n";
        }

        REQUIRE_THAT(result, WithinRel(expected, eps));
      }
    }
  }
}

// @tparam Reduce a functor; abstracts away kernel dispatching
//         (via hiprtc or normal execution)
template <class T, class Reduce, template <typename> class Op>
void runTestReduce(int iteration, Reduce reduce)
{
  using namespace Catch::Matchers;
  using distribution = typename DistributionType<T>::type;
  unsigned int wavefrontSize = getWarpSize();
  // one result per reduce per thread to be checked
  LinearAllocGuard<T> d_output(LinearAllocs::hipMalloc, kNumReduces * wavefrontSize * sizeof(T));
  LinearAllocGuard<T> output(LinearAllocs::malloc, kNumReduces * wavefrontSize * sizeof(T));
  std::mt19937_64 gen(iteration);
  // for float16, we generate any random unsigned short, but cap the exponent later on
  // to keep it in the range (-8.0..8.0) (just to avoid overflows)
  // On the rest of the types, just use a bigger reduced range of numbers to avoid overflows too
  T a = std::is_same<T, half>::value? std::numeric_limits<unsigned short>::lowest() : -1023;
  T b = std::is_same<T, half>::value? std::numeric_limits<unsigned short>::max() : 1023;
  distribution dist(a, b);
  LinearAllocGuard<T> input, d_input;
  LinearAllocGuard<unsigned long long> masks, d_masks;
  Op<T> op;
  int numReduce = 0;

  genRandomBuffers(d_input, input, dist, gen, kNumReduces * wavefrontSize);
  genRandomMasks(d_masks, masks, gen, kNumReduces);
  reduce(d_output.ptr(), d_input.ptr(), d_masks.ptr(), kNumReduces, op);
  HIP_CHECK(hipDeviceSynchronize());
  HIP_CHECK(hipMemcpy(output.ptr(), d_output.ptr(), d_output.size_bytes(), hipMemcpyDeviceToHost));

  while (numReduce < kNumReduces) {
    T expected = calculateExpected<T>(input.ptr(), op, masks.ptr()[numReduce]);
    int lane = 0;

    while (lane < wavefrontSize) {
      auto result = output.ptr()[numReduce * wavefrontSize + lane];
      unsigned long long mask = masks.ptr()[numReduce];

      if ((1ul << lane) & mask) {
        if constexpr (std::is_integral<T>::value || std::is_same<Op<T>, MinOp<T>>::value ||
                      std::is_same<Op<T>, MaxOp<T>>::value) {
          // for integral types or min/max the result should match exactly
          if constexpr (std::is_same<T, __half>::value)
            REQUIRE(__half2float(result) == __half2float(expected));
          else {
            if (result != expected) {
              printMismatch(result, expected, input.ptr(), mask);
              REQUIRE(result == expected);
            }
          }
        } else
          compareFloatingPoint(result, expected, mask, input.ptr());

      }
      lane++;
    }
    numReduce++;
  }
}