54_hopper_fp8_warp_specialized_gemm.cu

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/*! \file
    \brief Simple Hopper FP8 GEMM example using CUTLASS 3.0 APIs for NVIDIA Hopper architecture

    This example demonstrate a simple way to instantiate and run a FP8 GEMM using the new CUTLASS 3.0
    APIs on NVIDIA Hopper architecture. New features that will be showcased in this example are as follows:

    1. NVIDIA Hopper architecture introduces a new series of tensor core instructions (GMMA) 
    which are more efficient than the Ampere tensor core instructions.

    2. NVIDIA Hopper architecture includes new Tensor Memory Accelerator (TMA) unit to transfer large 
    blocks of data efficiently between global memory and shared memory. TMA also supports asynchronous
    copies between thread blocks in a cluster.

    3. This example uses the Warp Specialized kernel design (see /media/docs/efficient_gemm.md for details).

    4. This example shows all important fusions used by FP8 gemm kernels, 
    i.e., scale factor for A, B, C, D tensor, the abs_max value of D tensor.

    Examples:

      $ ./examples/54_hopper_fp8_warp_specialized_gemm/54_hopper_fp8_warp_specialized_gemm --m=2048 --n=2048 --k=2048
*/

#include <iostream>

#include "cutlass/cutlass.h"
#include "cutlass/numeric_types.h"

#include "cute/tensor.hpp"
#include "cutlass/tensor_ref.h"
#include "cutlass/gemm/dispatch_policy.hpp"
#include "cutlass/gemm/collective/collective_builder.hpp"
#include "cutlass/gemm/device/gemm_universal_adapter.h"
#include "cutlass/gemm/kernel/gemm_universal.hpp"
#include "cutlass/epilogue/dispatch_policy.hpp"
#include "cutlass/epilogue/collective/collective_builder.hpp"

#include "cutlass/util/command_line.h"
#include "cutlass/util/distribution.h"
#include "cutlass/util/host_tensor.h"
#include "cutlass/util/packed_stride.hpp"
#include "cutlass/util/tensor_view_io.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/gett.hpp"


#include "helper.h"
#include "hopper_fp8_commandline.hpp"

using namespace cute;

#if defined(CUTLASS_ARCH_MMA_SM90_SUPPORTED)

/////////////////////////////////////////////////////////////////////////////////////////////////
/// GEMM kernel configurations
/////////////////////////////////////////////////////////////////////////////////////////////////

// A matrix configuration
using         ElementA    = cutlass::float_e4m3_t;                          // Element type for A matrix operand
using         LayoutA     = cutlass::layout::RowMajor;                      // Layout type for A matrix operand
constexpr int AlignmentA  = 128 / cutlass::sizeof_bits<ElementA>::value;    // Memory access granularity/alignment of A matrix in units of elements (up to 16 bytes)

// B matrix configuration
using         ElementB    = cutlass::float_e5m2_t;                          // Element type for B matrix operand
using         LayoutB     = cutlass::layout::ColumnMajor;                   // Layout type for B matrix operand
constexpr int AlignmentB  = 128 / cutlass::sizeof_bits<ElementB>::value;    // Memory access granularity/alignment of B matrix in units of elements (up to 16 bytes)

// C matrix configuration
using         ElementC    = cutlass::float_e4m3_t;                          // Element type for C and D matrix operands
using         LayoutC     = cutlass::layout::ColumnMajor;                   // Layout type for C and D matrix operands
constexpr int AlignmentC  = 128 / cutlass::sizeof_bits<ElementC>::value;    // Memory access granularity/alignment of C matrix in units of elements (up to 16 bytes)

// D matrix configuration
using         ElementD    = ElementC;
using         LayoutD     = LayoutC;
constexpr int AlignmentD  = AlignmentC;

// Auxiliary matrix configuration and other fusion types
using         ElementAux   = ElementC;
using         LayoutAux    = LayoutC;
using         ElementAmax  = float;
using         ElementBias  = float;

// Core kernel configurations
using ElementAccumulator  = float;                                          // Element type for internal accumulation
using ElementCompute      = float;                                          // Element type for epilogue computation
using ArchTag             = cutlass::arch::Sm90;                            // Tag indicating the minimum SM that supports the intended feature
using OperatorClass       = cutlass::arch::OpClassTensorOp;                 // Operator class tag
using TileShape           = Shape<_64,_128,_128>;                           // Threadblock-level tile size
using ClusterShape        = Shape<_1,_2,_1>;                                // Shape of the threadblocks in a cluster
using KernelSchedule      = cutlass::gemm::KernelTmaWarpSpecialized;
using EpilogueSchedule    = cutlass::epilogue::TmaWarpSpecialized;
using EpilogueTileType    = cutlass::epilogue::collective::EpilogueTileAuto;
using FusionOperation     = cutlass::epilogue::fusion::ScaledLinCombPerRowBiasEltActAmaxAux<
    LayoutAux, cutlass::epilogue::thread::ReLU, ElementD, ElementCompute, ElementAux, ElementAmax, ElementBias, ElementC>;

using CollectiveEpilogue = typename cutlass::epilogue::collective::CollectiveBuilder<
    ArchTag, OperatorClass,
    TileShape, ClusterShape,
    EpilogueTileType,
    ElementAccumulator, ElementCompute,
    ElementC, LayoutC, AlignmentC,
    ElementD, LayoutD, AlignmentD,
    EpilogueSchedule,
    FusionOperation
  >::CollectiveOp;

using CollectiveMainloop = typename cutlass::gemm::collective::CollectiveBuilder<
    ArchTag, OperatorClass,
    ElementA, LayoutA, AlignmentA,
    ElementB, LayoutB, AlignmentB,
    ElementAccumulator,
    TileShape, ClusterShape,
    cutlass::gemm::collective::StageCountAutoCarveout<
      static_cast<int>(sizeof(typename CollectiveEpilogue::SharedStorage))
    >,
    KernelSchedule
  >::CollectiveOp;

using GemmKernel = cutlass::gemm::kernel::GemmUniversal<
    Shape<int,int,int,int>, // Indicates ProblemShape
    CollectiveMainloop,
    CollectiveEpilogue
>;

using Gemm = cutlass::gemm::device::GemmUniversalAdapter<GemmKernel>;

// Extract information from Gemm kernel.
using EpilogueOutputOp  = typename Gemm::EpilogueOutputOp;
using ElementScalar     = typename EpilogueOutputOp::ElementScalar;
using ElementAmax       = typename EpilogueOutputOp::ElementAmax;
using ActivationFunctor = typename EpilogueOutputOp::ActivationFn;

using StrideA = typename Gemm::GemmKernel::StrideA;
using StrideB = typename Gemm::GemmKernel::StrideB;
using StrideC = typename Gemm::GemmKernel::StrideC;
using StrideD = typename Gemm::GemmKernel::StrideD;
using StrideAux = StrideD;

constexpr bool IsDFp8 = 
    cute::is_same_v<ElementD, cutlass::float_e4m3_t> or
    cute::is_same_v<ElementD, cutlass::float_e5m2_t>;

constexpr bool IsAuxFp8 =
    cute::is_same_v<ElementAux, cutlass::float_e4m3_t> or
    cute::is_same_v<ElementAux, cutlass::float_e5m2_t>;

/// Initialization
StrideA stride_A;
StrideB stride_B;
StrideC stride_C;
StrideD stride_D;
StrideAux stride_aux;
uint64_t seed;

cutlass::HostTensor<ElementA  , LayoutA  > tensor_A;
cutlass::HostTensor<ElementB  , LayoutB  > tensor_B;
cutlass::HostTensor<ElementC  , LayoutC  > tensor_C;
cutlass::HostTensor<ElementD  , LayoutD  > tensor_D;
cutlass::HostTensor<ElementD  , LayoutD  > tensor_ref_D;
cutlass::HostTensor<ElementAux, LayoutAux> tensor_aux;
cutlass::HostTensor<ElementAux, LayoutAux> tensor_ref_aux;

using LayoutScalar = cutlass::layout::PackedVectorLayout;
cutlass::HostTensor<ElementScalar, LayoutScalar> scalar_alpha;
cutlass::HostTensor<ElementScalar, LayoutScalar> scalar_beta;
cutlass::HostTensor<ElementScalar, LayoutScalar> scale_A;
cutlass::HostTensor<ElementScalar, LayoutScalar> scale_B;
cutlass::HostTensor<ElementScalar, LayoutScalar> scale_C;
cutlass::HostTensor<ElementScalar, LayoutScalar> scale_D;
cutlass::HostTensor<ElementScalar, LayoutScalar> scale_aux;
cutlass::HostTensor<ElementAmax  , LayoutScalar> abs_max_D;
cutlass::HostTensor<ElementAmax  , LayoutScalar> reference_abs_max_D;
cutlass::HostTensor<ElementAmax  , LayoutScalar> abs_max_aux;
cutlass::HostTensor<ElementAmax  , LayoutScalar> reference_abs_max_aux;

#endif // defined(CUTLASS_ARCH_MMA_SM90_SUPPORTED)

/////////////////////////////////////////////////////////////////////////////////////////////////
/// Testbed utility types
/////////////////////////////////////////////////////////////////////////////////////////////////

/// Result structure
struct Result
{
  double avg_runtime_ms;
  double gflops;
  cutlass::Status status;
  cudaError_t error;
  bool passed;

  Result(
    double avg_runtime_ms = 0,
    double gflops = 0,
    cutlass::Status status = cutlass::Status::kSuccess,
    cudaError_t error = cudaSuccess)
  :
    avg_runtime_ms(avg_runtime_ms), gflops(gflops), status(status), error(error), passed(false)
  {}

};

#if defined(CUTLASS_ARCH_MMA_SM90_SUPPORTED)

/////////////////////////////////////////////////////////////////////////////////////////////////
/// GEMM setup and evaluation
/////////////////////////////////////////////////////////////////////////////////////////////////

/// Helper to initialize a block of device data
template <typename Element, typename Layout>
bool initialize_tensor(
  cutlass::TensorView<Element, Layout> view,
  uint64_t seed) {

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

  return true;
}

/// Initialize operands to be used in the GEMM and reference GEMM
void initialize(const Options &options) {

  stride_A = cutlass::make_cute_packed_stride(StrideA{}, cute::make_shape(options.m, options.k, options.l));
  stride_B = cutlass::make_cute_packed_stride(StrideB{}, cute::make_shape(options.n, options.k, options.l));
  stride_C = cutlass::make_cute_packed_stride(StrideC{}, cute::make_shape(options.m, options.n, options.l));
  stride_D = cutlass::make_cute_packed_stride(StrideD{}, cute::make_shape(options.m, options.n, options.l));
  stride_aux = stride_D;

  auto a_coord = cutlass::make_Coord(options.m * options.l, options.k);
  auto c_coord = cutlass::make_Coord(options.m * options.l, options.n);
  auto b_coord = cutlass::make_Coord(options.k, options.n * options.l);

  tensor_A.resize(a_coord);
  tensor_B.resize(b_coord);
  tensor_C.resize(c_coord);
  tensor_D.resize(c_coord);
  tensor_ref_D.resize(c_coord);

  initialize_tensor(tensor_A.host_view(), seed + 2022);
  initialize_tensor(tensor_B.host_view(), seed + 2023);
  initialize_tensor(tensor_C.host_view(), seed + 2024);

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

  if (options.save_aux) {
    tensor_aux.resize(c_coord);
    tensor_aux.sync_device();
    tensor_ref_aux.resize(c_coord);
  }

  if (options.device_scale) {
    scalar_alpha.resize(cutlass::make_Coord(1));
    scalar_beta.resize(cutlass::make_Coord(1));
    scale_A.resize(cutlass::make_Coord(1));
    scale_B.resize(cutlass::make_Coord(1));
    scale_C.resize(cutlass::make_Coord(1));
    scale_D.resize(cutlass::make_Coord(1));
    scale_aux.resize(cutlass::make_Coord(1));

    cutlass::reference::host::TensorFill(scalar_alpha.host_view(), options.alpha);
    cutlass::reference::host::TensorFill(scalar_beta.host_view(), options.beta);
    cutlass::reference::host::TensorFill(scale_A.host_view(), options.scale_a);
    cutlass::reference::host::TensorFill(scale_B.host_view(), options.scale_b);
    cutlass::reference::host::TensorFill(scale_C.host_view(), options.scale_c);
    cutlass::reference::host::TensorFill(scale_D.host_view(), options.scale_d);
    cutlass::reference::host::TensorFill(scale_aux.host_view(), options.scale_aux);

    scalar_alpha.sync_device();
    scalar_beta.sync_device();
    scale_A.sync_device();
    scale_B.sync_device();
    scale_C.sync_device();
    scale_D.sync_device();
    scale_aux.sync_device();
  }

  if (IsDFp8 && options.save_amax) {
    abs_max_D.resize(cutlass::make_Coord(1));
    abs_max_D.sync_device();
    reference_abs_max_D.resize(cutlass::make_Coord(1));
  }

  if (IsAuxFp8 && options.save_aux && options.save_amax) {
    abs_max_aux.resize(cutlass::make_Coord(1));
    abs_max_aux.sync_device();
    reference_abs_max_aux.resize(cutlass::make_Coord(1));
  }
}

/// Populates a Gemm::Arguments structure from the given commandline options
typename Gemm::Arguments args_from_options(const Options &options)
{
  typename Gemm::Arguments arguments{
    cutlass::gemm::GemmUniversalMode::kGemm,
    {options.m, options.n, options.k, options.l},
    {tensor_A.device_data(), stride_A, tensor_B.device_data(), stride_B},
    {
      {}, // epilogue.thread
      tensor_C.device_data(), stride_C,
      tensor_D.device_data(), stride_D
    }
  };

  auto &fusion_args = arguments.epilogue.thread;
  fusion_args.alpha = options.alpha;
  fusion_args.beta = options.beta;
  fusion_args.alpha_ptr = scalar_alpha.device_data();
  fusion_args.beta_ptr = scalar_beta.device_data();
  fusion_args.scale_a = options.scale_a;
  fusion_args.scale_b = options.scale_b;
  fusion_args.scale_c = options.scale_c;
  fusion_args.scale_a_ptr = scale_A.device_data();
  fusion_args.scale_b_ptr = scale_B.device_data();
  fusion_args.scale_c_ptr = scale_C.device_data();

  // ignored if tensor types are not fp8
  fusion_args.scale_d = options.scale_d;
  fusion_args.scale_aux = options.scale_aux;
  fusion_args.scale_d_ptr = scale_D.device_data();
  fusion_args.scale_aux_ptr = scale_aux.device_data();

  // leaving/setting these as nullptr disables the fusion at runtime
  fusion_args.bias_ptr = nullptr;

  if (options.save_aux) {
    fusion_args.aux_ptr = tensor_aux.device_data();
    fusion_args.dAux = stride_aux;
    if (options.save_amax) {
      fusion_args.amax_aux_ptr = abs_max_aux.device_data();
    }
  }

  if (options.save_amax) {
    fusion_args.amax_D_ptr = abs_max_D.device_data();
  }

  return arguments;
}

bool verify(const Options &options) {
  //
  // Compute reference output
  //

  // Create instantiation for device reference gemm kernel
  auto A = cute::make_tensor(tensor_A.host_data(),
      cute::make_layout(cute::make_shape(options.m, options.k, options.l), stride_A));
  auto B = cute::make_tensor(tensor_B.host_data(),
      cute::make_layout(cute::make_shape(options.n, options.k, options.l), stride_B));
  auto C = cute::make_tensor(tensor_C.host_data(),
      cute::make_layout(cute::make_shape(options.m, options.n, options.l), stride_C));
  auto D = cute::make_tensor(tensor_ref_D.host_data(),
      cute::make_layout(cute::make_shape(options.m, options.n, options.l), stride_D));
  auto Aux = cute::make_tensor(tensor_ref_aux.host_data(),
      cute::make_layout(cute::make_shape(options.m, options.n, options.l), stride_aux));
  using unused_t = decltype(D);

  cutlass::reference::host::GettMainloopParams<ElementAccumulator, decltype(A), decltype(B)> mainloop_params{A, B};

  cutlass::reference::host::GettEpilogueParams<
      ElementScalar,
      ElementScalar,
      ElementAccumulator,
      ElementCompute,
      decltype(C),
      decltype(D),
      unused_t, // bias
      decltype(Aux),
      unused_t, // valpha
      unused_t, // vbeta
      ActivationFunctor
  > epilogue_params;

  epilogue_params.C = C;
  epilogue_params.D = D;
  epilogue_params.Aux = Aux;
  epilogue_params.alpha = options.alpha;
  epilogue_params.beta = options.beta;
  epilogue_params.scale_a = options.scale_a;
  epilogue_params.scale_b = options.scale_b;
  epilogue_params.scale_c = options.scale_c;
  epilogue_params.scale_d = options.scale_d;
  epilogue_params.scale_aux = options.scale_aux;
  epilogue_params.abs_max_D = reference_abs_max_D.host_data();
  epilogue_params.abs_max_Aux = reference_abs_max_aux.host_data();

  // get reference result
  cutlass::reference::host::Gemm3x(mainloop_params, epilogue_params);

  // compare_reference
  tensor_D.sync_host();
  bool passed = cutlass::reference::host::TensorEquals(tensor_ref_D.host_view(), tensor_D.host_view());

  if (IsDFp8 && options.save_amax) {
    abs_max_D.sync_host();
    passed &= abs_max_D.at(cutlass::make_Coord(0)) == reference_abs_max_D.at(cutlass::make_Coord(0));
  }

  if (options.save_aux) {
    tensor_aux.sync_host();
    passed &= cutlass::reference::host::TensorEquals(tensor_ref_aux.host_view(), tensor_aux.host_view());
    if (IsAuxFp8 && options.save_amax) {
      abs_max_aux.sync_host();
      passed &= abs_max_aux.at(cutlass::make_Coord(0)) == reference_abs_max_aux.at(cutlass::make_Coord(0));
    }
  }

  return passed;
}

/// Execute a given example GEMM computation
template <typename Gemm>
int run(Options &options)
{
  initialize(options);

  // Instantiate CUTLASS kernel depending on templates
  Gemm gemm;

  // Create a structure of gemm kernel arguments suitable for invoking an instance of Gemm
  auto arguments = args_from_options(options);

  // Using the arguments, query for extra workspace required for matrix multiplication computation
  size_t workspace_size = Gemm::get_workspace_size(arguments);

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

  // Check if the problem size is supported or not
  CUTLASS_CHECK(gemm.can_implement(arguments));

  // Initialize CUTLASS kernel with arguments and workspace pointer
  CUTLASS_CHECK(gemm.initialize(arguments, workspace.get()));

  // Correctness / Warmup iteration
  CUTLASS_CHECK(gemm.run());

  // Check if output from CUTLASS kernel and reference kernel are equal or not
  Result result;
  result.passed = verify(options);

  std::cout << "  Disposition: " << (result.passed ? "Passed" : "Failed") << std::endl;

  if (!result.passed) {
    exit(-1);
  }

  // Run profiling loop
  if (options.iterations > 0)
  {
    GpuTimer timer;
    timer.start();
    for (int iter = 0; iter < options.iterations; ++iter) {
      CUTLASS_CHECK(gemm.run());
    }
    timer.stop();

    // Compute average runtime and GFLOPs.
    float elapsed_ms = timer.elapsed_millis();
    result.avg_runtime_ms = double(elapsed_ms) / double(options.iterations);
    result.gflops = options.gflops(result.avg_runtime_ms / 1000.0);

    std::cout << "  Problem Size: " << options.m << 'x' << options.n << 'x' << options.k << 'x' << options.l << std::endl;
    std::cout << "  Avg runtime: " << result.avg_runtime_ms << " ms" << std::endl;
    std::cout << "  GFLOPS: " << result.gflops << std::endl;
  }

  return 0;
}

#endif // defined(CUTLASS_ARCH_MMA_SM90_SUPPORTED)

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

int main(int argc, char const **args) {

  // CUTLASS must be compiled with CUDA 12.0 Toolkit to run this example
  // and must have compute capability at least 90.
  if (__CUDACC_VER_MAJOR__ < 12) {
    std::cerr << "This example requires CUDA 12 or newer.\n";
    // Returning zero so this test passes on older Toolkits. Its actions are no-op.
    return 0;
  }

  cudaDeviceProp props;
  int current_device_id;
  CUDA_CHECK(cudaGetDevice(&current_device_id));
  CUDA_CHECK(cudaGetDeviceProperties(&props, current_device_id));
  cudaError_t error = cudaGetDeviceProperties(&props, 0);
  if (props.major < 9) {
    std::cerr
      << "This example requires a GPU of NVIDIA's Hopper Architecture or "
      << "later (compute capability 90 or greater).\n";
    return 0;
  }

  //
  // Parse options
  //

  Options options;

  options.parse(argc, args);

  if (options.help) {
    options.print_usage(std::cout) << std::endl;
    return 0;
  }

  //
  // Evaluate CUTLASS kernels
  //

#if defined(CUTLASS_ARCH_MMA_SM90_SUPPORTED)
  run<Gemm>(options);
#endif

  return 0;
}

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