fp8_blockwise_gemm_kernel.cu

#include <ATen/cuda/CUDAContext.h>
#include <cudaTypedefs.h>
#include <cutlass/arch/arch.h>
#include <cutlass/arch/memory.h>
#include <cutlass/arch/mma.h>
#include <cutlass/array.h>
#include <cutlass/cutlass.h>
#include <cutlass/epilogue/thread/activation.h>
#include <cutlass/epilogue/thread/linear_combination.h>
#include <cutlass/epilogue/threadblock/default_thread_map_tensor_op.h>
#include <cutlass/gemm/device/gemm.h>
#include <cutlass/gemm/device/gemm_universal_adapter.h>
#include <cutlass/gemm/gemm.h>
#include <cutlass/gemm/kernel/default_gemm_universal_with_visitor.h>
#include <cutlass/gemm/thread/mma.h>
#include <cutlass/layout/matrix.h>
#include <cutlass/matrix_coord.h>
#include <cutlass/numeric_types.h>
#include <cutlass/tensor_ref.h>
#include <cutlass/util/host_tensor.h>
#include <cutlass/util/tensor_view_io.h>
#include <torch/all.h>

#include <cute/tensor.hpp>
#include <cutlass/epilogue/collective/collective_builder.hpp>
#include <cutlass/epilogue/collective/default_epilogue.hpp>
#include <cutlass/epilogue/threadblock/fusion/visitors.hpp>
#include <cutlass/gemm/collective/collective_builder.hpp>
#include <cutlass/gemm/dispatch_policy.hpp>
#include <cutlass/gemm/kernel/gemm_universal.hpp>
#include <cutlass/util/packed_stride.hpp>

#include "cutlass_extensions/gemm/cutlass_gemm_caller.cuh"
#include "cutlass_extensions/gemm/fp8_blockwise_gemm_sm90_dispatch.cuh"
#include "utils.h"

using namespace cute;

template <
    typename OutType,
    typename MmaTileShape,
    typename PerSmTileShape,
    typename EpilogueTileShape,
    typename ScalesPerTile,
    int TileSizeM_ = 128,
    class ClusterShape = Shape<_1, _1, _1>>
void launch_sm100_fp8_blockwise_scaled_mm(
    torch::Tensor& out,
    const torch::Tensor& a,
    const torch::Tensor& b,
    const torch::Tensor& scales_a,
    const torch::Tensor& scales_b) {
  static constexpr int ScaleMsPerTile = size<0>(ScalesPerTile{});
  static constexpr int ScaleGranularityM = size<0>(MmaTileShape{}) / ScaleMsPerTile;
  static constexpr int ScaleGranularityN = size<1>(MmaTileShape{}) / size<1>(ScalesPerTile{});
  static constexpr int ScaleGranularityK = size<2>(MmaTileShape{}) / size<2>(ScalesPerTile{});

  using ElementAB = cutlass::float_e4m3_t;
  using ElementA = ElementAB;
  using ElementB = ElementAB;
  using ElementC = void;
  using ElementD = OutType;
  using LayoutA = cutlass::layout::RowMajor;
  using LayoutB = cutlass::layout::ColumnMajor;
  using LayoutD = cutlass::layout::RowMajor;
  using LayoutC = LayoutD;
  // This means both SFA and SFB are column-major.
  using ScaleConfig = cutlass::detail::Sm100BlockwiseScaleConfig<
      ScaleGranularityM,
      ScaleGranularityN,
      ScaleGranularityK,
      cute::UMMA::Major::MN,
      cute::UMMA::Major::K>;
  using LayoutSFA = decltype(ScaleConfig::deduce_layoutSFA());
  using LayoutSFB = decltype(ScaleConfig::deduce_layoutSFB());

  static constexpr int AlignmentA = 128 / cutlass::sizeof_bits<ElementA>::value;
  static constexpr int AlignmentB = 128 / cutlass::sizeof_bits<ElementB>::value;
  static constexpr int AlignmentD = 128 / cutlass::sizeof_bits<ElementD>::value;
  static constexpr int AlignmentC = AlignmentD;

  using ElementAccumulator = float;
  using ElementBlockScale = float;
  using ElementCompute = float;
  using ArchTag = cutlass::arch::Sm100;
  using OperatorClass = cutlass::arch::OpClassTensorOp;

  using CollectiveEpilogue = typename cutlass::epilogue::collective::CollectiveBuilder<
      ArchTag,
      cutlass::arch::OpClassTensorOp,
      PerSmTileShape,
      ClusterShape,
      EpilogueTileShape,
      ElementAccumulator,
      ElementCompute,
      ElementC,
      LayoutC,
      AlignmentC,
      ElementD,
      LayoutD,
      AlignmentD,
      cutlass::epilogue::TmaWarpSpecialized1Sm>::CollectiveOp;

  using CollectiveMainloop = typename cutlass::gemm::collective::CollectiveBuilder<
      ArchTag,
      OperatorClass,
      ElementA,
      cute::tuple<LayoutA, LayoutSFA>,
      AlignmentA,
      ElementB,
      cute::tuple<LayoutB, LayoutSFB>,
      AlignmentB,
      ElementAccumulator,
      MmaTileShape,
      ClusterShape,
      cutlass::gemm::collective::StageCountAutoCarveout<static_cast<int>(
          sizeof(typename CollectiveEpilogue::SharedStorage))>,
      cutlass::gemm::KernelTmaWarpSpecializedBlockwise1SmSm100>::CollectiveOp;

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

  Gemm gemm_op;

  int m = a.size(0);
  int k = a.size(1);
  int n = b.size(1);

  auto a_ptr = static_cast<ElementAB*>(a.data_ptr());
  auto b_ptr = static_cast<ElementAB*>(b.data_ptr());
  auto scales_a_ptr = static_cast<float*>(scales_a.data_ptr());
  auto scales_b_ptr = static_cast<float*>(scales_b.data_ptr());
  auto c_ptr = static_cast<ElementD*>(out.data_ptr());

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

  StrideA a_stride = cutlass::make_cute_packed_stride(StrideA{}, cute::make_shape(m, k, 1));
  StrideB b_stride = cutlass::make_cute_packed_stride(StrideB{}, cute::make_shape(n, k, 1));
  StrideC c_stride = cutlass::make_cute_packed_stride(StrideC{}, cute::make_shape(m, n, 1));
  LayoutSFA layout_SFA = ScaleConfig::tile_atom_to_shape_SFA(make_shape(m, n, k, 1));
  LayoutSFB layout_SFB = ScaleConfig::tile_atom_to_shape_SFB(make_shape(m, n, k, 1));

  typename GemmKernel::MainloopArguments mainloop_args{
      a_ptr, a_stride, b_ptr, b_stride, scales_a_ptr, layout_SFA, scales_b_ptr, layout_SFB};

  typename GemmKernel::EpilogueArguments epilogue_args{{}, c_ptr, c_stride, c_ptr, c_stride};
  epilogue_args.thread.alpha = 1.0f;

  typename GemmKernel::Arguments args = {
      cutlass::gemm::GemmUniversalMode::kGemm, {m, n, k, 1}, mainloop_args, epilogue_args};

  auto can_implement = gemm_op.can_implement(args);
  TORCH_CHECK(can_implement == cutlass::Status::kSuccess, cutlassGetStatusString(can_implement))

  size_t workspace_size = gemm_op.get_workspace_size(args);
  cutlass::device_memory::allocation<uint8_t> workspace(workspace_size);

  auto init_status = gemm_op.initialize(args, workspace.get());
  TORCH_CHECK(init_status == cutlass::Status::kSuccess, cutlassGetStatusString(init_status));

  auto stream = at::cuda::getCurrentCUDAStream(a.get_device());

  auto status = gemm_op.run(stream);
  TORCH_CHECK(status == cutlass::Status::kSuccess, cutlassGetStatusString(status))
}

template <typename OutType>
void sm100_fp8_blockwise_dispatch_shape(
    torch::Tensor& out,
    const torch::Tensor& a,
    const torch::Tensor& b,
    const torch::Tensor& scales_a,
    const torch::Tensor& scales_b) {
  if (a.size(0) <= 128) {
    using MmaTileShape = Shape<_64, _128, _128>;
    using PerSmTileShape = Shape<_64, _128, _128>;
    using EpilogueTileShape = Shape<_64, _64>;
    using ScalesPerTile = Shape<_64, _1, _1>;
    launch_sm100_fp8_blockwise_scaled_mm<OutType, MmaTileShape, PerSmTileShape, EpilogueTileShape, ScalesPerTile>(
        out, a, b, scales_a, scales_b);
  } else {
    using MmaTileShape = Shape<_128, _128, _128>;
    using PerSmTileShape = Shape<_128, _128, _128>;
    using EpilogueTileShape = Shape<_128, _64>;
    using ScalesPerTile = Shape<_128, _1, _1>;
    launch_sm100_fp8_blockwise_scaled_mm<OutType, MmaTileShape, PerSmTileShape, EpilogueTileShape, ScalesPerTile>(
        out, a, b, scales_a, scales_b);
  }
}

torch::Tensor fp8_blockwise_scaled_mm(
    const torch::Tensor& mat_a,
    const torch::Tensor& mat_b,
    const torch::Tensor& scales_a,
    const torch::Tensor& scales_b,
    const torch::Dtype& out_dtype) {
  TORCH_CHECK(mat_a.is_cuda(), "mat_a must be a CUDA tensor");
  TORCH_CHECK(mat_b.is_cuda(), "mat_b must be a CUDA tensor");
  TORCH_CHECK(mat_a.dim() == 2, "mat_a must be a 2D tensor");
  TORCH_CHECK(mat_b.dim() == 2, "mat_b must be a 2D tensor");
  TORCH_CHECK(mat_a.stride(1) == 1, "mat_a must be a row major tensor");
  TORCH_CHECK(mat_b.stride(0) == 1, "mat_b must be a column major tensor");
  TORCH_CHECK(mat_a.size(1) == mat_b.size(0), "mat_a and mat_b shapes cannot be multiplied");

  TORCH_CHECK(
      (mat_a.size(1) * mat_a.element_size()) % 16 == 0, "mat_a must be multiple of 16 bytes for memory alignment");
  TORCH_CHECK(
      (mat_b.size(0) * mat_b.element_size()) % 16 == 0, "mat_b must be multiple of 16 bytes for memory alignment");
  TORCH_CHECK(mat_a.scalar_type() == torch::kFloat8_e4m3fn, "mat_a must be Float8_e4m3fn");
  TORCH_CHECK(mat_b.scalar_type() == torch::kFloat8_e4m3fn, "mat_b must be Float8_e4m3fn");
  TORCH_CHECK(out_dtype == torch::kHalf || out_dtype == torch::kBFloat16, "out_dtype must be Half or BFloat16");

  auto is_contiguous_vector = [](const torch::Tensor& t) {
    auto t_sizes = t.sizes();
    return t.is_contiguous() &&
           (t.dim() == 1 || (t.dim() == 2 && *std::min_element(t_sizes.begin(), t_sizes.end()) == 1));
  };

  TORCH_CHECK(mat_a.size(0) == scales_a.size(0), "size of scales_a is not matched");
  TORCH_CHECK(mat_a.size(1) / 128 == scales_a.size(1), "size of scales_a is not matched");
  TORCH_CHECK(scales_a.stride(0) == 1 || is_contiguous_vector(scales_a), "scales_a must be M major");
  TORCH_CHECK(mat_b.size(0) / 128 == scales_b.size(0), "size of scales_b is not matched");
  TORCH_CHECK(mat_b.size(1) / 128 == scales_b.size(1), "size of scales_b is not matched");
  TORCH_CHECK(scales_b.stride(0) == 1 || is_contiguous_vector(scales_b), "scales_b must be K major");
  TORCH_CHECK(scales_a.scalar_type() == torch::kFloat32, "scales_a must be Float32");
  TORCH_CHECK(scales_b.scalar_type() == torch::kFloat32, "scales_b must be Float32");

  torch::Tensor out = torch::empty({mat_a.size(0), mat_b.size(1)}, mat_a.options().dtype(out_dtype));
  TORCH_CHECK((out.size(1) * out.element_size()) % 16 == 0, "out must be multiple of 16 bytes for memory alignment");

  auto sm_version = getSMVersion();

  int64_t original_rows = mat_a.size(0);
  torch::Tensor mat_a_padded = pad_tensor(mat_a, /*alignment=*/4);
  torch::Tensor scales_a_padded = pad_tensor(scales_a, /*alignment=*/4, /*col_major=*/true);
  torch::Tensor out_padded = torch::empty({mat_a_padded.size(0), mat_b.size(1)}, out.options());

#if defined(CUTLASS_ARCH_MMA_SM90_SUPPORTED)
#if defined CUDA_VERSION && CUDA_VERSION >= 12000
  if (sm_version == 90) {
    torch::Tensor scales_b_contiguous = scales_b.contiguous();
    if (out_dtype == torch::kBFloat16) {
      cutlass_gemm_blockwise_sm90_fp8_dispatch<cutlass::bfloat16_t>(
          out_padded, mat_a_padded, mat_b, scales_a_padded, scales_b_contiguous);
    } else {
      cutlass_gemm_blockwise_sm90_fp8_dispatch<cutlass::half_t>(
          out_padded, mat_a_padded, mat_b, scales_a_padded, scales_b_contiguous);
    }
    return out_padded.slice(0, 0, original_rows);
  }
#endif
#endif

#if defined(CUTLASS_ARCH_MMA_SM100A_SUPPORTED) || defined(CUTLASS_ARCH_MMA_SM100_SUPPORTED)
#if defined CUDA_VERSION && CUDA_VERSION >= 12080
  if (sm_version == 100) {
    if (out_dtype == torch::kBFloat16) {
      sm100_fp8_blockwise_dispatch_shape<cutlass::bfloat16_t>(
          out_padded, mat_a_padded, mat_b, scales_a_padded, scales_b);
    } else {
      sm100_fp8_blockwise_dispatch_shape<cutlass::half_t>(out_padded, mat_a_padded, mat_b, scales_a_padded, scales_b);
    }
    return out_padded.slice(0, 0, original_rows);
  }
#endif
#endif
  TORCH_CHECK_NOT_IMPLEMENTED(
      false, "No implemented fp8_blockwise_scaled_mm for current compute capability: ", sm_version);
}