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/*! \file
    \brief Tests for device-wide Rank 2k update interface
  
*/

#pragma once

#include <iostream>
#include <fstream>
#include <sstream>

#include "../../common/cutlass_unit_test.h"
#include "cutlass/blas3.h"

#include "cutlass/util/host_tensor.h"
#include "cutlass/util/tensor_view_io.h"
#include "cutlass/util/distribution.h"
#include "cutlass/util/reference/host/tensor_fill.h"
#include "cutlass/util/reference/host/tensor_copy.h"
#include "cutlass/util/reference/host/tensor_compare.h"
#include "cutlass/util/reference/host/tensor_norm.h"
#include "cutlass/util/reference/host/error_metrics.h"
#include "cutlass/util/reference/host/rank_k_complex.h"

#include "testbed_utils.h"

namespace test {
namespace gemm {
namespace device {

/////////////////////////////////////////////////////////////////////////////////////////////////

template <typename RankK>
struct TestbedRank2KUniversal {

  using ElementA = typename RankK::ElementA;
  using ElementC = typename RankK::ElementC;
  using ElementAccumulator = typename RankK::ElementAccumulator;
  using ElementCompute = typename RankK::RankKkernel::Epilogue::OutputOp::ElementCompute;

  /// Initialization
  cutlass::Distribution::Kind init_A;
  cutlass::Distribution::Kind init_C;
  uint64_t seed;

  cutlass::HostTensor<typename RankK::ElementA, typename RankK::LayoutA> tensor_A;
  cutlass::HostTensor<typename RankK::ElementC, typename RankK::LayoutC> tensor_C;
  cutlass::HostTensor<typename RankK::ElementC, typename RankK::LayoutC> tensor_D;
  cutlass::HostTensor<typename RankK::ElementC, typename RankK::LayoutC> reference_D;

  //
  // Methods
  //

  TestbedRank2KUniversal(
    cutlass::Distribution::Kind init_A_ = cutlass::Distribution::Uniform,
    cutlass::Distribution::Kind init_C_ = cutlass::Distribution::Uniform,
    uint64_t seed_ = 2080
  ):
    init_A(init_A_), init_C(init_C_), seed(seed_) { }

  /// Helper to initialize a tensor view
  template <typename Element, typename Layout>
  bool initialize_tensor(
    cutlass::TensorView<Element, Layout> view, 
    cutlass::Distribution::Kind dist_kind,
    uint64_t seed,
    int mantissa_in_bits) {

    if (dist_kind == cutlass::Distribution::Uniform) {

      double scope_max, scope_min;
      int bits_input = cutlass::sizeof_bits<Element>::value;
      int bits_output = cutlass::sizeof_bits<typename RankK::ElementC>::value;

      if (bits_input == 1) {
        scope_max = 2;
        scope_min = 0;
      } else if (bits_input <= 8) {
        scope_max = 2;
        scope_min = -2;
      } else if (bits_output == 16) {
        scope_max = 5;
        scope_min = -5;
      } else {
        scope_max = 8;
        scope_min = -8;
      }

      cutlass::reference::host::TensorFillRandomUniform(
        view, seed, scope_max, scope_min, mantissa_in_bits);
    } 
    else if (dist_kind == cutlass::Distribution::Identity) {

      cutlass::reference::host::TensorFillIdentity(view);
    } 
    else if (dist_kind == cutlass::Distribution::Gaussian) {

      cutlass::reference::host::TensorFillRandomGaussian(view, seed, 0, 0.5, mantissa_in_bits);
    }
    else if (dist_kind == cutlass::Distribution::Sequential) {

      cutlass::reference::host::BlockFillSequential(
        view.data(), view.capacity());
    } 
    else {

      EXPECT_TRUE(false) << "Input distribution not implemented";
      return false;
    }

    return true;
  }


  /// Helper to initialize a tensor view
  template <typename Element, typename Layout>
  bool initialize_symmetric_tensor(
    cutlass::TensorView<Element, Layout> view, 
    cutlass::Distribution::Kind dist_kind,
    uint64_t seed,
    int mantissa_in_bits) {

    if (dist_kind == cutlass::Distribution::Uniform) {

      double scope_max, scope_min;
      int bits_input = cutlass::sizeof_bits<Element>::value;
      int bits_output = cutlass::sizeof_bits<typename RankK::ElementC>::value;

      if (bits_input == 1) {
        scope_max = 2;
        scope_min = 0;
      } else if (bits_input <= 8) {
        scope_max = 2;
        scope_min = -2;
      } else if (bits_output == 16) {
        scope_max = 5;
        scope_min = -5;
      } else {
        scope_max = 8;
        scope_min = -8;
      }

      cutlass::reference::host::TensorFillSymmetricRandomUniform(
        view, seed, RankK::kFillModeC, scope_max, scope_min, mantissa_in_bits);
    } 
    else if (dist_kind == cutlass::Distribution::Gaussian) {

      cutlass::reference::host::TensorFillSymmetricRandomGaussian(
        view, seed, RankK::kFillModeC, 0, 0.5, mantissa_in_bits);
    }
    else {

      EXPECT_TRUE(false) << "Input distribution (symmetric tensor) not implemented";
      return false;
    }

    return true;
  }
  /// Initializes data structures
  void initialize(cutlass::gemm::GemmCoord problem_size) {
    //
    // Allocate the RankK workspace
    //

    tensor_A.resize(problem_size.mk());
    tensor_C.resize(problem_size.mn());
    tensor_D.resize(problem_size.mn());
    reference_D.resize(problem_size.mn(), false);

    EXPECT_TRUE(initialize_tensor(tensor_A.host_view(), init_A, seed + 2019, cutlass::MantissaInBits<typename RankK::ElementA>::bits));
    EXPECT_TRUE(initialize_symmetric_tensor(tensor_C.host_view(), init_C, seed + 2017, cutlass::MantissaInBits<typename RankK::ElementC>::bits));

    // It is possible to randomly initialize to all zeros, so override this with non-zeros
    // in the upper left corner of each operand.
    tensor_A.host_view().at({0, 0}) = typename RankK::ElementA(1);
    tensor_C.host_view().at({0, 0}) = typename RankK::ElementC(1);

    cutlass::reference::host::TensorCopy(reference_D.host_view(), tensor_C.host_view());

    tensor_A.sync_device();
    tensor_C.sync_device();
    tensor_D.sync_device();
  }

  /// Compares computed reference with device reference and outputs to a file if incorrect
  bool compare_reference(
    cutlass::gemm::GemmCoord problem_size,
    ElementCompute alpha, 
    ElementCompute beta) {

    tensor_D.sync_host();

    EXPECT_GT(cutlass::reference::host::TensorNorm(tensor_A.host_view()), 0);
    EXPECT_GT(cutlass::reference::host::TensorNorm(tensor_C.host_view()), 0);

    if (tensor_D.size() > 1)
      EXPECT_GT(cutlass::reference::host::TensorNorm(tensor_D.host_view()), 0);

    if (reference_D.size() > 1)
      EXPECT_GT(cutlass::reference::host::TensorNorm(reference_D.host_view()), 0);

    double l2_norm = cutlass::reference::host::TensorRelativeErrorMetric(reference_D.host_view(), tensor_D.host_view());

    bool passed = l2_norm < cutlass::MantissaInBits<typename RankK::ElementA>::error;

    return passed;
  }

  /// Verifies the result is a RankK
  bool verify(
    cutlass::gemm::GemmCoord problem_size, 
    ElementCompute alpha, 
    ElementCompute beta) {

    //
    // Verify
    //
    cutlass::reference::host::Rank2KComplex<
        typename RankK::ElementA, typename RankK::LayoutA,
        typename RankK::ElementC, typename RankK::LayoutC, 
        ElementCompute, ElementAccumulator
    >(
      problem_size,
      alpha, 
      tensor_A.host_ref(),
      RankK::kTransformA,
      beta, 
      tensor_C.host_ref(), 
      reference_D.host_ref(),
      ElementAccumulator(0),
      RankK::kFillModeC,
      RankK::kBlasMode
    );

    return compare_reference(problem_size, alpha, beta);
  }

  /// Returns true if the CUDA device is sufficient to execute the kernel.
  bool sufficient() const {
    //
    // Determine SMEM requirements and waive if not satisfied
    //

    size_t smem_size = sizeof(typename RankK::RankKkernel::SharedStorage);

    cudaDeviceProp properties;
    int device_idx;
    cudaError_t result = cudaGetDevice(&device_idx);

    if (result != cudaSuccess) {
      throw std::runtime_error("cudaGetDevice() API call failed.");
    }

    result = cudaGetDeviceProperties(&properties, device_idx);

    if (result != cudaSuccess) {
      throw std::runtime_error("cudaGetDeviceProperties() failed");
    }

    if (properties.sharedMemPerBlockOptin < smem_size) {
      return false;
    }

    return true;
  }

  /// Executes one test
  bool run(
    cutlass::gemm::GemmUniversalMode mode,
    cutlass::gemm::GemmCoord problem_size,
    int batch_count = 1,
    ElementCompute alpha = ElementCompute(1), 
    ElementCompute beta = ElementCompute(0)) {

    // Waive test if insufficient CUDA device
    if (!sufficient()) {
      if (CUTLASS_TEST_UNIT_ENABLE_WARNINGS) {
        std::cerr << "Test waived due to insufficient CUDA device." << std::endl;
      }
      return true;
    }

#if 0
    std::cout << "[TestbedRankKUniversal::run()] problem(m, n, k): " << problem_size
              << " alpha: " << ElementCompute(alpha)
              << " beta: " << ElementCompute(beta) << std::endl;
#endif

    this->initialize(problem_size);

    //
    // Initialize the RankK operator
    //

    typename RankK::Arguments arguments{
      mode,
      problem_size,
      batch_count,
      {alpha, beta},
      tensor_A.device_data(),
      tensor_C.device_data(),
      tensor_D.device_data(),
      problem_size.n() * problem_size.k(),
      problem_size.m() * problem_size.n(),
      problem_size.m() * problem_size.n(),
      tensor_A.layout().stride(0),
      tensor_C.layout().stride(0),
      tensor_D.layout().stride(0)
    };

    RankK rank2k_op;

    size_t workspace_size = RankK::get_workspace_size(arguments);

    cutlass::device_memory::allocation<uint8_t> workspace(workspace_size);

    cutlass::Status status = rank2k_op.initialize(arguments, workspace.get());

    EXPECT_TRUE(status == cutlass::Status::kSuccess) << to_string(status);

    //
    // Run the RankK
    //

    status = rank2k_op();

    EXPECT_TRUE(status == cutlass::Status::kSuccess) << to_string(status);

    //
    // Verify
    //

    bool passed = this->verify(problem_size, alpha, beta);

    //if (true) {
    if (!passed) {
      std::stringstream fname;

      fname << "error_RankK_device_"
            << "fill_mode_c_"
            << (RankK::kFillModeC == cutlass::FillMode::kLower ? "lower_" :
                (RankK::kFillModeC == cutlass::FillMode::kUpper ? "upper_" : "invalid_"))
            << "mnk_"
            << problem_size.m() << "x"
            << problem_size.n() << "x"
            << problem_size.k() << "_"
            << RankK::ThreadblockShape::kM << "x"  
            << RankK::ThreadblockShape::kN << "x"  
            << RankK::ThreadblockShape::kK << "_"
            << RankK::WarpShape::kM << "x"  
            << RankK::WarpShape::kN << "x"  
            << RankK::WarpShape::kK << ".txt";

      std::cout << fname.str() << std::endl;

      std::ofstream results(fname.str());

      results << problem_size << std::endl;

      results
        << "\nA:\n" << tensor_A.host_view() << "\n"
        << "\nC:\n" << tensor_C.host_view() << "\n"
        << "\nD reference:\n" << reference_D.host_view() << "\n"
        << "\nD computed:\n" << tensor_D.host_view() << "\n";

    }

    return passed;
  }
};

/////////////////////////////////////////////////////////////////////////////////////////////////
template <typename RankK>
bool TestRank2kUniversal(
  cutlass::gemm::GemmCoord const & problem_size,
  cutlass::gemm::GemmUniversalMode mode,
  int batch_count,
  double alpha = 1.0, 
  double beta = 2.0) {

  bool passed = true;

  TestbedRank2KUniversal<RankK> testbed;
  
  using ElementCompute = typename RankK::EpilogueOutputOp::ElementCompute;

  passed = testbed.run(
    mode,
    problem_size,
    batch_count,
    cutlass::from_real<ElementCompute>(alpha), 
    cutlass::from_real<ElementCompute>(beta)
  );

  return passed;
}

template <typename RankK>
bool TestAllRankKUniversal() {
  bool passed = true;


  int const kMinimumOperandElementSize = int(cutlass::sizeof_bits<typename RankK::ElementA>::value);
  int const kAlignmentN = 128 / kMinimumOperandElementSize;
  int const kAlignmentK = 128 / kMinimumOperandElementSize;

  cutlass::gemm::GemmUniversalMode modes[] = {
    cutlass::gemm::GemmUniversalMode::kGemm,
  };

  int problem_size_n[] = {
    kAlignmentN, 512 - 2*kAlignmentN
  };

  int problem_size_k[] = {
    kAlignmentK, 
    RankK::ThreadblockShape::kK * RankK::kStages - kAlignmentK, 
    RankK::ThreadblockShape::kK * RankK::kStages * 3 - kAlignmentK
  };

  int batch_counts[] = {      // may be interpretted as batch count or split-K slices
    1                         // Just running one batch for now (removing 2, 3, 5, 7)
  };

  double problem_alpha[] = {
    1.0
  };

  double problem_beta[] = {
    2.0
  };


  using ElementCompute = typename RankK::EpilogueOutputOp::ElementCompute;

  for (cutlass::gemm::GemmUniversalMode mode : modes) {
    for (int n : problem_size_n) {
      for (int k : problem_size_k) {
        for (int batch_count : batch_counts) {

          for (auto alpha : problem_alpha) {
            for (auto beta : problem_beta) {

              if (mode == cutlass::gemm::GemmUniversalMode::kGemm ||
                mode == cutlass::gemm::GemmUniversalMode::kGemmSplitKParallel) {
              }

              cutlass::gemm::GemmCoord problem_size(n, n, k);

              TestbedRank2KUniversal<RankK> testbed;

              passed = testbed.run(
                mode,
                problem_size,
                batch_count,
                cutlass::from_real<ElementCompute>(alpha), 
                cutlass::from_real<ElementCompute>(beta)
              );

              if (!passed) {
                return false;
              }
            }
          }
        }
      }
    }
  }

  return passed;
}

/////////////////////////////////////////////////////////////////////////////////////////////////

} // namespace device
} // namespace gemm
} // namespace test

/////////////////////////////////////////////////////////////////////////////////////////////////

