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/*! \file
    \brief Tests for device-wide GEMM interface
*/

#pragma once

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

#include "../../common/cutlass_unit_test.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/gemm.h"
#include "cutlass/util/reference/host/gemm_complex.h"

#include "testbed_utils.h"

namespace test {
namespace gemm {
namespace device {

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

template <typename Gemm, typename BinaryOp>
struct GemmWithReductionReference {
  using ElementAccumulator = typename Gemm::ElementAccumulator;
  using ElementCompute = typename Gemm::GemmKernel::Epilogue::ElementCompute;
  using ElementC = typename Gemm::ElementC;
  using ElementT = typename Gemm::GemmKernel::Epilogue::ElementTensor;
  //
  // Data members
  //

  BinaryOp binary_op;

  //
  // Methods
  //

  GemmWithReductionReference() { }

  ElementCompute operator()(
    ElementAccumulator d_y, 
    ElementT t) {
    
    return binary_op(ElementCompute(d_y), ElementCompute(t));
  }
};

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

template <
  typename Gemm,
  typename ReferenceOp
>
struct TestbedGemmWithReduction {

  using ElementAccumulator = typename Gemm::ElementAccumulator;
  using ElementT = typename Gemm::GemmKernel::Epilogue::ElementTensor;

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

  cutlass::HostTensor<typename Gemm::ElementA, typename Gemm::LayoutA> tensor_A;
  cutlass::HostTensor<typename Gemm::ElementB, typename Gemm::LayoutB> tensor_B;
  cutlass::HostTensor<typename Gemm::ElementC, typename Gemm::LayoutC> tensor_C;
  cutlass::HostTensor<typename Gemm::ElementC, typename Gemm::LayoutC> tensor_D;
  cutlass::HostTensor<typename Gemm::ElementAccumulator, typename Gemm::LayoutC> tensor_Reduction;
  cutlass::HostTensor<ElementT, typename Gemm::LayoutC> tensor_Tensor;
  cutlass::HostTensor<ElementAccumulator, typename Gemm::LayoutC> tensor_C_ref;
  cutlass::HostTensor<ElementAccumulator, typename Gemm::LayoutC> reference_d_Y;
  cutlass::HostTensor<typename Gemm::ElementC, typename Gemm::LayoutC> reference_D;
  cutlass::HostTensor<typename Gemm::ElementAccumulator, typename Gemm::LayoutC> reference_Reduction;

  //
  // Methods
  //

  TestbedGemmWithReduction(
    cutlass::Distribution::Kind init_A_ = cutlass::Distribution::Uniform,
    cutlass::Distribution::Kind init_B_ = cutlass::Distribution::Uniform,
    cutlass::Distribution::Kind init_C_ = cutlass::Distribution::Uniform,
    uint64_t seed_ = 2080
  ):
    init_A(init_A_), init_B(init_B_), 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) {

    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 Gemm::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, 0);
    } 
    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);
    }
    else if (dist_kind == cutlass::Distribution::Sequential) {

      for (int m = 0; m < view.extent().row(); ++m) {
        for (int n = 0; n < view.extent().column(); ++n) {
          //view.at({m, n}) = Element(float(((idx ++) % 17) - 8));
          view.at({m, n}) = (n == 0 ? Element(m) : Element());

        }
      }
    } 
    else {
      // TODO: Implement the rest
      EXPECT_TRUE(false) << "Not implemented";
      return false;
    }

    return true;
  }

  /// Initializes data structures
  void initialize(cutlass::gemm::GemmCoord problem_size) {
    //
    // Allocate the GEMM workspace
    //

    tensor_A.resize(problem_size.mk());
    tensor_B.resize(problem_size.kn());
    tensor_C.resize(problem_size.mn());
    tensor_D.resize(problem_size.mn());

    tensor_Reduction.resize({
      problem_size.m(), 
      (problem_size.n() - 1 + Gemm::ThreadblockShape::kN) / Gemm::ThreadblockShape::kN
    });

    tensor_Tensor.resize(problem_size.mn());
    reference_D.resize(problem_size.mn(), false);
    reference_d_Y.resize(problem_size.mn(), false);
    tensor_C_ref.resize(problem_size.mn(), false);
    reference_Reduction.resize({problem_size.m(), 1}, false);

    EXPECT_TRUE(initialize_tensor(tensor_A.host_view(), init_A, seed + 2019));
    EXPECT_TRUE(initialize_tensor(tensor_B.host_view(), init_B, seed + 2018));
    EXPECT_TRUE(initialize_tensor(tensor_C.host_view(), init_C, seed + 2017));
    EXPECT_TRUE(initialize_tensor(tensor_Tensor.host_view(), init_C, seed + 2020));

    // 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 Gemm::ElementA(1);
    tensor_B.host_view().at({0, 0}) = typename Gemm::ElementB(1);
    tensor_C.host_view().at({0, 0}) = typename Gemm::ElementC(1);

    for (int m = 0; m < tensor_C_ref.extent().row(); ++m) {
      for (int n = 0; n < tensor_C_ref.extent().column(); ++n) {
        tensor_C_ref.at({m, n}) = ElementAccumulator(tensor_C.at({m, n}));
      }
    }

    tensor_A.sync_device();
    tensor_B.sync_device();
    tensor_C.sync_device();
    tensor_D.sync_device();
    tensor_Reduction.sync_device();
    tensor_Tensor.sync_device();
  }

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

    tensor_Reduction.sync_host();
    tensor_D.sync_host();

    EXPECT_GT(cutlass::reference::host::TensorNorm(tensor_A.host_view()), 0);
    EXPECT_GT(cutlass::reference::host::TensorNorm(tensor_B.host_view()), 0);
    EXPECT_GT(cutlass::reference::host::TensorNorm(tensor_C.host_view()), 0);
    
    EXPECT_GT(cutlass::reference::host::TensorNorm(tensor_D.host_view()), 0);
    EXPECT_GT(cutlass::reference::host::TensorNorm(reference_D.host_view()), 0);
    EXPECT_GT(cutlass::reference::host::TensorNorm(tensor_Reduction.host_view()), 0);

    bool passed = true;
    for (int m = 0; m < tensor_Reduction.extent().row(); ++m) {

      ElementAccumulator reduced_value = ElementAccumulator();
      for (int j = 0; j < tensor_Reduction.extent().column(); ++j) {
        reduced_value += tensor_Reduction.at({m, j});
      }

      if (reduced_value != reference_Reduction.at({m, 0})) {
        std::cout << "Error in bias[" << m << "] - Expected: " << reference_Reduction.at({m, 0}) << ", got: " << reduced_value << std::endl;
        passed = false;
        break;
      }
    }
    EXPECT_TRUE(passed) << "Reduction is incorect.";

    if (!cutlass::reference::host::TensorEquals(reference_D.host_view(), tensor_D.host_view())) {
      EXPECT_TRUE(false) << " mismatched reference";
      passed = false;
    }
    
    if (!passed) {

      /*
      std::stringstream fname;

      fname << "error_Gemm_device_"
        << problem_size.m() << "x"
        << problem_size.n() << "x"
        << problem_size.k() << "_"
        << Gemm::ThreadblockShape::kM << "x"  
        << Gemm::ThreadblockShape::kN << "x"  
        << Gemm::ThreadblockShape::kK << "_"
        << Gemm::WarpShape::kM << "x"  
        << Gemm::WarpShape::kN << "x"  
        << Gemm::WarpShape::kK << ".txt";

      std::ofstream file(fname.str());
      */

      std::ofstream file("testbed_universal_errors_sm70.txt");

      file
        << "problem: " << problem_size 
        << ", alpha: " << alpha << ", beta: " << beta << "\n\n";

      file 
        << "A =\n" << tensor_A.host_view()
        << "\nB =\n" << tensor_B.host_view()
        << "\nC =\n" << tensor_C.host_view()
        << "\nT = \n" << tensor_Tensor.host_view()
        << "\n\nReference =\n" << reference_D.host_view()
        << "\nComputed =\n" << tensor_D.host_view()
        << "\n\nReduction =\n" << tensor_Reduction.host_view() << "\n"
        << "\nReference reduction =\n" << reference_Reduction.host_view() << "\n";
    }

    return passed;
  }

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

    //
    // Verify
    //

    cutlass::reference::host::GemmComplex<
        typename Gemm::ElementA, typename Gemm::LayoutA,
        typename Gemm::ElementB, typename Gemm::LayoutB,
        ElementAccumulator, typename Gemm::LayoutC, 
        ElementAccumulator, ElementAccumulator
    >(
      problem_size,
      alpha, 
      tensor_A.host_ref(),
      Gemm::kTransformA,
      tensor_B.host_ref(),
      Gemm::kTransformB,
      beta, 
      tensor_C_ref.host_ref(), 
      reference_d_Y.host_ref(), 
      ElementAccumulator(0)
    );

    using ElementC = typename Gemm::ElementC;

    ReferenceOp reference_op;

    // compute backwards 
    for (int m = 0; m < problem_size.m(); ++m) {
      ElementAccumulator reduced_value = ElementAccumulator();
      for (int n = 0; n < problem_size.n(); ++n) {
        ElementAccumulator d_full = reference_op(reference_d_Y.at({m, n}), tensor_Tensor.at({m, n}));
        reduced_value += d_full;
        reference_D.at({m, n}) = ElementC(d_full);
      }
      reference_Reduction.at({m, 0}) = reduced_value;
    }

    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
    //

    int smem_size = int(sizeof(typename Gemm::GemmKernel::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.sharedMemPerMultiprocessor < smem_size) {
      return false;
    }

    return true;
  }

  /// Executes one test
  bool run(
    cutlass::gemm::GemmUniversalMode mode,
    cutlass::gemm::GemmCoord problem_size, 
    int batch_count = 1,
    ElementAccumulator alpha = ElementAccumulator(1), 
    ElementAccumulator beta = ElementAccumulator(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;
    }

    this->initialize(problem_size);

    //
    // Initialize the GEMM operator
    //

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

    Gemm gemm_op;

    size_t workspace_size = Gemm::get_workspace_size(arguments);

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

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


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

    //
    // Run the GEMM
    //

    status = gemm_op();

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

    //
    // Verify
    //

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

    if (!passed) {
      std::cout << "Failed with batch_count/split_k_slices = " << batch_count << std::endl;
    }

    //
    // Profile
    //

    #if 0 // profiling disabled for now.

    int const kWorkspaces = 100;

    cutlass::DeviceAllocation<typename Gemm::ElementA> profiling_tensor_A(tensor_A.capacity() * kWorkspaces);
    cutlass::DeviceAllocation<typename Gemm::ElementB> profiling_tensor_B(tensor_B.capacity() * kWorkspaces);
    cutlass::DeviceAllocation<typename Gemm::ElementC> profiling_tensor_C(tensor_C.capacity() * kWorkspaces);
    cutlass::DeviceAllocation<typename Gemm::ElementC> profiling_tensor_D(tensor_D.capacity() * kWorkspaces);
    cutlass::DeviceAllocation<typename Gemm::ElementC> profiling_tensor_Reduction(tensor_Reduction.capacity() * kWorkspaces);
    cutlass::DeviceAllocation<ElementT> profiling_tensor_Tensor(tensor_Tensor.capacity() * kWorkspaces);

    cudaEvent_t events[2];
    for (auto & event : events) {
      cudaError_t result = cudaEventCreate(&event);
      if (result != cudaSuccess) {
        EXPECT_EQ(result, cudaSuccess) << " cudaEventCreate() failed with error " << cudaGetErrorString(result);
        return false;
        break;
      }
    }

    int const kWarmupIterations = 5;
    int const kProfilingIterations = 100;

    for (int i = 0; i < kWarmupIterations; ++i) {
      status = gemm_op();
      EXPECT_TRUE(status == cutlass::Status::kSuccess) << to_string(status);
    }
    

    cudaError_t result = cudaEventRecord(events[0]);
    EXPECT_EQ(result, cudaSuccess);

    for (int i = 0; i < kProfilingIterations; ++i) {

      typename Gemm::Arguments arguments{
        mode,
        problem_size,
        batch_count,
        {alpha, beta},
        profiling_tensor_A.get() + tensor_A.capacity() * (i % kWorkspaces),
        profiling_tensor_B.get() + tensor_B.capacity() * (i % kWorkspaces),
        profiling_tensor_C.get() + tensor_C.capacity() * (i % kWorkspaces),
        profiling_tensor_D.get() + tensor_D.capacity() * (i % kWorkspaces),
        profiling_tensor_Reduction.get() + tensor_Reduction.capacity() * (i % kWorkspaces),
        profiling_tensor_Tensor.get() + tensor_Tensor.capacity() * (i % kWorkspaces),
        problem_size.m() * problem_size.k(),
        problem_size.n() * problem_size.k(),
        problem_size.m() * problem_size.n(),
        problem_size.m() * problem_size.n(),
        problem_size.m(),
        problem_size.m() * problem_size.n(),
        tensor_A.layout().stride(0),
        tensor_B.layout().stride(0),
        tensor_C.layout().stride(0),
        tensor_D.layout().stride(0),
        tensor_Reduction.layout().stride(0),
        tensor_Tensor.layout().stride(0),
      };

      gemm_op.initialize(arguments, workspace.get());
      status = gemm_op();
      EXPECT_TRUE(status == cutlass::Status::kSuccess) << to_string(status);
    }

    result = cudaEventRecord(events[1]);
    EXPECT_EQ(result, cudaSuccess);

    result = cudaDeviceSynchronize();
    EXPECT_EQ(result, cudaSuccess);

    float elapsed_time = 0;
    result = cudaEventElapsedTime(&elapsed_time, events[0], events[1]);
    EXPECT_EQ(result, cudaSuccess);

    double average_time = double(elapsed_time) / double(kProfilingIterations);

    std::cout << problem_size << ": " << average_time << " ms" << std::endl;

    for (auto & event : events) {
      cudaEventDestroy(event);
    }
    #endif

    return passed;
  }
};

/////////////////////////////////////////////////////////////////////////////////////////////////
template <typename Gemm, typename ReferenceOp>
bool TestGemmWithReduction(
  cutlass::gemm::GemmCoord const & problem_size,
  cutlass::gemm::GemmUniversalMode mode,
  int batch_count = 1,
  double alpha = 1.0, 
  double beta = 2.0) {

  bool passed = true;

  TestbedGemmWithReduction<Gemm, ReferenceOp> testbed;
  
  using ElementAccumulator = typename Gemm::ElementAccumulator;

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

  return passed;
}

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

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

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