08_pvc_gemm_f8.cpp

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/*! \file
    \brief CUTLASS Intel PVC Gemm with float8 (float_e4m3_t) input

    This example demonstrates GEMM on PVC with float8 input. cutlass::float_e4m3_t is an 8-bit
    floating point type with 4-bit exponent, 3-bit mantissa and 1 sign bit. The GEMM in this example
    performs the MMA with fp16 input, first upcasting the float_e4m3_t data for both A and B.

    Aside from the input datatypes, this example is identical to 00_pvc_gemm.

    Verification for this example is a standard fp16 GEMM, with input data upcasted on the host.

    To build & run this example (from your build dir):

      $ ninja 08_pvc_gemm_f8
      $ ./examples/sycl/08_pvc_gemm_f8/08_pvc_gemm_f8

    Call with `--help` for information about available options
*/

#include "cutlass/epilogue/collective/default_epilogue.hpp"
#include "cutlass/epilogue/collective/xe_epilogue.hpp"
#include "cutlass/epilogue/fusion/xe_callbacks.hpp"
#include "cutlass/gemm/device/gemm_universal.h"
#include "cutlass/gemm/device/gemm_universal_adapter.h"
#include "cutlass/gemm/collective/collective_mma.hpp"
#include "cutlass/util/GPU_Clock.hpp"

#include <cute/tensor.hpp>
#include <random>

#include "cutlass/util/command_line.h"
#include "cutlass/util/device_memory.h"
#include "cutlass/util/packed_stride.hpp"
#include "cutlass/util/reference/device/gemm_complex.h"
#include "cutlass/util/reference/device/tensor_compare.h"
#include "sycl_common.hpp"
#include "helper.h"

using namespace cute;

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

// Command line options parsing
struct Options {

  bool help;
  bool error;

  int m, n, k, l, iterations;
  float alpha, beta;

  Options():
    help(false),
    error(false),
    m(5120), n(4096), k(4096), l(1), iterations(20),
    alpha(1.f), beta(0.f)
  { }

  // Parses the command line
  void parse(int argc, char const **args) {
    cutlass::CommandLine cmd(argc, args);

    if (cmd.check_cmd_line_flag("help")) {
      help = true;
      return;
    }

    cmd.get_cmd_line_argument("m", m, 5120);
    cmd.get_cmd_line_argument("n", n, 4096);
    cmd.get_cmd_line_argument("k", k, 4096);
    cmd.get_cmd_line_argument("l", l, 1);
    cmd.get_cmd_line_argument("alpha", alpha, 1.f);
    cmd.get_cmd_line_argument("beta", beta, 0.f);
    cmd.get_cmd_line_argument("iterations", iterations, 100);
  }

  /// Prints the usage statement.
  std::ostream & print_usage(std::ostream &out) const {

    out << "PVC GEMM Example\n\n"
      << "Options:\n\n"
      << "  --help                      If specified, displays this usage statement\n\n"
      << "  --m=<int>                   Sets the M extent of the GEMM\n"
      << "  --n=<int>                   Sets the N extent of the GEMM\n"
      << "  --k=<int>                   Sets the K extent of the GEMM\n"
      << "  --l=<int>                   Sets the L extent (batch count) of the GEMM\n"
      << "  --alpha=<s32>               Epilogue scalar alpha\n"
      << "  --beta=<s32>                Epilogue scalar beta\n\n"
      << "  --iterations=<int>          Iterations\n\n";

    return out;
  }
};

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

template <
  class Gemm
>
struct ExampleRunner {

  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 LayoutA = typename Gemm::LayoutA;
  using LayoutB = typename Gemm::LayoutB;
  using LayoutC = typename Gemm::LayoutC;
  using LayoutD = typename Gemm::LayoutD;

  using ElementA = typename Gemm::ElementA;
  using ElementB = typename Gemm::ElementB;
  using ElementAcc = typename Gemm::ElementAccumulator;

  using CollectiveEpilogue = typename Gemm::CollectiveEpilogue;
  using ElementC = typename Gemm::ElementC;
  using ElementOutput = typename CollectiveEpilogue::ElementOutput;
  using ElementCompute = typename CollectiveEpilogue::ElementCompute;
  using ElementAccumulator = typename CollectiveEpilogue::ElementAccumulator;

  using ProblemShapeType = typename Gemm::GemmKernel::ProblemShape;

  //
  // Data members
  //

  /// Initialization
  StrideA stride_A;
  StrideB stride_B;
  StrideC stride_C;
  StrideD stride_D;
  uint64_t seed = 0;

  cutlass::DeviceAllocation<ElementA> block_A;
  cutlass::DeviceAllocation<ElementB> block_B;
  cutlass::DeviceAllocation<ElementC> block_C;
  cutlass::DeviceAllocation<ElementOutput> block_D;
  cutlass::DeviceAllocation<ElementOutput> block_ref_D;

  //
  // Methods
  //
  template <typename SrcT, typename DstT>
  void convert_e4m3_to_fp16(const SrcT* d_src, DstT* d_dst, size_t size) {
      SrcT* h_src = new SrcT[size];
      syclcompat::memcpy(h_src, d_src, size * sizeof(SrcT));
      syclcompat::wait();
      
      DstT* h_dst = new DstT[size];
      for (size_t i = 0; i < size; ++i) {
          h_dst[i] = static_cast<DstT>(h_src[i]);
      }

      syclcompat::memcpy(d_dst, h_dst, size * sizeof(DstT));
      syclcompat::wait();
  }

  bool verify(const ProblemShapeType& problem_size, ElementCompute alpha, ElementCompute beta) {
      auto [M, N, K, L] = problem_size;

      cutlass::DeviceAllocation<half_t> block_A_fp16(block_A.size());
      cutlass::DeviceAllocation<half_t> block_B_fp16(block_B.size());

      // fp8 -> fp16
      convert_e4m3_to_fp16<float_e4m3_t, half_t>(
          block_A.get(),
          block_A_fp16.get(),
          block_A.size()
      );
      convert_e4m3_to_fp16<float_e4m3_t, half_t>(
          block_B.get(),
          block_B_fp16.get(),
          block_B.size()
      );

      cutlass::TensorRef ref_A(block_A_fp16.get(), LayoutA::packed({M, K}));
      cutlass::TensorRef ref_B(block_B_fp16.get(), LayoutB::packed({K, N}));
      cutlass::TensorRef ref_C(block_C.get(), LayoutC::packed({ M, N }));
      cutlass::TensorRef ref_D(block_ref_D.get(), LayoutD::packed({ M, N }));

      cutlass::reference::device::GemmComplex(
          { M, N, K },
          alpha,
          ref_A,  // fp16
          cutlass::ComplexTransform::kNone,
          ref_B,  // fp16
          cutlass::ComplexTransform::kNone,
          beta,
          ref_C,  //fp32
          ref_D,  //fp32
          ElementAccumulator(0),
          L,
          M * K,
          K * N,
          M * N,
          M * N 
      );
      syclcompat::wait();

      bool passed = cutlass::reference::device::BlockCompareEqual(
          block_ref_D.get(), block_D.get(), block_D.size());

      return passed;
  }

  /// Initialize operands to be used in the GEMM and reference GEMM
  void initialize(const ProblemShapeType& problem_size) {
    auto problem_shape_MNKL = cute::append<4>(problem_size, 1);
    auto [M, N, K, L] = problem_shape_MNKL;

    stride_A = cutlass::make_cute_packed_stride(StrideA{}, cute::make_shape(M, K, L));
    stride_B = cutlass::make_cute_packed_stride(StrideB{}, cute::make_shape(N, K, L));
    stride_C = cutlass::make_cute_packed_stride(StrideC{}, cute::make_shape(M, N, L));
    stride_D = cutlass::make_cute_packed_stride(StrideD{}, cute::make_shape(M, N, L));

    block_A.reset(static_cast<std::size_t>(M) * K * L);
    block_B.reset(static_cast<std::size_t>(K) * N * L);
    block_C.reset(static_cast<std::size_t>(M) * N * L);
    block_D.reset(static_cast<std::size_t>(M) * N * L);
    block_ref_D.reset(static_cast<std::size_t>(M) * N * L);

    initialize_block(block_A, seed + 2023);
    initialize_block(block_B, seed + 2022);
    initialize_block(block_C, seed + 2021);
  }
  
  cutlass::Status run(const Options& options, const cutlass::KernelHardwareInfo& hw_info) {
    ProblemShapeType problem_size = ProblemShapeType{options.m, options.n, options.k, options.l};

    initialize(problem_size);

    typename Gemm::GemmKernel::Arguments arguments{
      cutlass::gemm::GemmUniversalMode::kGemm,
      problem_size,
      {block_A.get(), stride_A, block_B.get(), stride_B},
      {{options.alpha, options.beta}, block_C.get(), stride_C, block_D.get(), stride_D},
      hw_info
    };

    Gemm gemm_op;

    size_t workspace_size = Gemm::get_workspace_size(arguments);
    cutlass::device_memory::allocation<uint8_t> workspace(workspace_size);

    if (options.n < 16) {
        std::cout << "Invalid Problem Size: N must be >= 16 for FP8 input with F16 MMA (XE_8x16x16_F32F16F16F32_TT). Got N=" << options.n << std::endl;
        std::exit(1);
    }

    if (gemm_op.can_implement(arguments) != cutlass::Status::kSuccess){
      std::cout << "Invalid Problem Size: " << options.m << 'x' << options.n << 'x' << options.k << 'x' << options.l << std::endl;
      std::exit(1);
    }

    CUTLASS_CHECK(gemm_op.initialize(arguments, workspace.get()));

    // Run the GEMM
    CUTLASS_CHECK(gemm_op.run());

    syclcompat::wait();

    // Verify that the result is correct
    bool passed = verify(problem_size, options.alpha, options.beta);
    std::cout << "Disposition: " << (passed ? "Passed" : "Failed") << std::endl;

    if(!passed) return cutlass::Status::kErrorInternal;

    if (options.iterations > 0) {
      GPU_Clock timer;
      timer.start();
      for (int i = 0; i < options.iterations; ++i) {
        gemm_op.run();
      }
      syclcompat::wait();

      float cute_time = timer.seconds() / options.iterations;
      double tflops = (2.0 * options.m * options.n * options.k * options.l) * 1e-12;
      std::cout << "Problem Size: " << options.m << 'x' << options.n << 'x' << options.k << 'x' << options.l << std::endl;
      printf("Cutlass GEMM Performance:     [%4.3f]TFlop/s  (%6.4f)ms\n", tflops / cute_time, cute_time*1000);
    }

    return cutlass::Status::kSuccess;
  }

};

int main(int argc, const char** argv)
{
  //
  // Parse options
  //

  Options options;

  options.parse(argc, argv);

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

  if (options.error) {
    std::cerr << "Aborting execution." << std::endl;
    return -1;
  }

  //
  // Run examples
  //

  cutlass::KernelHardwareInfo hw_info;

  hw_info.sm_count = cutlass::KernelHardwareInfo::query_device_multiprocessor_count(hw_info.device_id);

  bool passed;

  using ElementAccumulator = float;
  using ElementComputeEpilogue = float; 
  // TODO: support E5M2
  using ElementInputA = cutlass::float_e4m3_t; 
  using ElementInputB = cutlass::float_e4m3_t; 
  using ElementOutput = float;

  using LayoutA = cutlass::layout::RowMajor;
  using LayoutB = cutlass::layout::RowMajor;
  using LayoutC = cutlass::layout::RowMajor;
  using LayoutD = cutlass::layout::RowMajor;

  using GmemTiledCopyA = XE_2D_U8x32x32_LD_V;
  using GmemTiledCopyB = XE_2D_U8x32x32_LD_V;

  using TileShape = Shape<_256, _256, _32>;

  // TODO: Consider smaller tile size to reduce register pressure
  using TiledMma =
      typename TiledMMAHelper<MMA_Atom<XE_8x16x16_F32F16F16F32_TT>, Layout<TileShape>,
      Layout<Shape<_8, _4, _1>, Stride<_4, _1, _0>>>::TiledMMA;

  constexpr int PipelineStages = 2;
  using GEMMDispatchPolicy = cutlass::gemm::MainloopIntelW8A8<PipelineStages>;
  using EpilogueDispatchPolicy = cutlass::epilogue::IntelPVCEpilogue;

  using EpilogueOp = cutlass::epilogue::fusion::LinearCombination<ElementOutput, ElementComputeEpilogue,
          ElementAccumulator, ElementAccumulator, cutlass::FloatRoundStyle::round_to_nearest>;

  using FusionCallBacks = cutlass::epilogue::fusion::FusionCallbacks<EpilogueDispatchPolicy, EpilogueOp, TileShape,
          decltype(tile_shape(TiledMma()))>;
  using CollectiveEpilogue = cutlass::epilogue::collective::CollectiveEpilogue<
          EpilogueDispatchPolicy,
          TileShape,
          ElementAccumulator,
          cutlass::gemm::TagToStrideC_t<LayoutC>,
          ElementOutput,
          cutlass::gemm::TagToStrideC_t<LayoutD>,
          FusionCallBacks,
          XE_2D_U32x8x16_LD_N,
          void, void,
          XE_2D_U32x8x16_ST_N,
          void, void>;

  // Mainloop
  using CollectiveMainloop = cutlass::gemm::collective::CollectiveMma<
          GEMMDispatchPolicy,
          TileShape,
          ElementInputA,
          cutlass::gemm::TagToStrideA_t<LayoutA>,
          ElementInputB,
          cutlass::gemm::TagToStrideB_t<LayoutB>,
          TiledMma,
          GmemTiledCopyA, void, void, cute::identity,
          GmemTiledCopyB, void, void, cute::identity
  >;

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

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

  ExampleRunner<Gemm> runner;

  CUTLASS_CHECK(runner.run(options, hw_info));

  return 0;
}