[Kernel] CUTLASS grouped gemm fp8 MoE kernel (#13972)
Signed-off-by: ElizaWszola <eliza@neuralmagic.com> Signed-off-by: ElizaWszola <ewszola@redhat.com> Co-authored-by: Lucas Wilkinson <wilkinson.lucas@gmail.com>
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ElizaWszola
149
csrc/quantization/cutlass_w8a8/moe/grouped_mm_c3x.cuh
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149
csrc/quantization/cutlass_w8a8/moe/grouped_mm_c3x.cuh
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#pragma once
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#include "cutlass/cutlass.h"
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#include "cutlass/gemm/collective/collective_builder.hpp"
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#include "cutlass/epilogue/collective/collective_builder.hpp"
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#include "cutlass/gemm/device/gemm_universal_adapter.h"
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#include "cutlass_extensions/epilogue/scaled_mm_epilogues_c3x.hpp"
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#include "cutlass_extensions/common.hpp"
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#include "get_group_starts.cuh"
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using namespace cute;
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namespace {
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using ProblemShape =
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cutlass::gemm::GroupProblemShape<cute::Shape<int, int, int>>;
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using ElementAccumulator = float;
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using ArchTag = cutlass::arch::Sm90;
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using OperatorClass = cutlass::arch::OpClassTensorOp;
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using LayoutA = cutlass::layout::RowMajor;
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using LayoutB = cutlass::layout::ColumnMajor;
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using LayoutC = cutlass::layout::RowMajor;
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template <typename ElementAB_, typename ElementC_,
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template <typename, typename, typename> typename Epilogue_,
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typename TileShape, typename ClusterShape, typename KernelSchedule,
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typename EpilogueSchedule>
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struct cutlass_3x_group_gemm {
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using ElementAB = ElementAB_;
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using ElementC = void;
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using ElementD = ElementC_;
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using ElementAccumulator = float;
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using Epilogue = Epilogue_<ElementAccumulator, ElementD, TileShape>;
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using StrideC =
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cute::remove_pointer_t<cute::Stride<int64_t, cute::Int<1>, cute::Int<0>>>;
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static constexpr int AlignmentAB =
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128 / cutlass::sizeof_bits<ElementAB>::value;
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static constexpr int AlignmentC = 128 / cutlass::sizeof_bits<ElementD>::value;
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using EVTCompute = typename Epilogue::EVTCompute;
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using CollectiveEpilogue =
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typename cutlass::epilogue::collective::CollectiveBuilder<
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ArchTag, OperatorClass, TileShape, ClusterShape,
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cutlass::epilogue::collective::EpilogueTileAuto, ElementAccumulator,
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ElementAccumulator, ElementC, LayoutC*, AlignmentC, ElementD,
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LayoutC*, AlignmentC, EpilogueSchedule, EVTCompute>::CollectiveOp;
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static constexpr size_t CEStorageSize =
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sizeof(typename CollectiveEpilogue::SharedStorage);
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using Stages = typename cutlass::gemm::collective::StageCountAutoCarveout<
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static_cast<int>(CEStorageSize)>;
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using CollectiveMainloop =
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typename cutlass::gemm::collective::CollectiveBuilder<
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ArchTag, OperatorClass, ElementAB, LayoutA*, AlignmentAB, ElementAB,
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LayoutB*, AlignmentAB, ElementAccumulator, TileShape, ClusterShape,
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Stages, KernelSchedule>::CollectiveOp;
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using KernelType = enable_sm90_only<cutlass::gemm::kernel::GemmUniversal<
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ProblemShape, CollectiveMainloop, CollectiveEpilogue>>;
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struct GemmKernel : public KernelType {};
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};
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template <typename Gemm>
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void cutlass_group_gemm_caller(
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torch::Tensor& out_tensors, torch::Tensor const& a_tensors,
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torch::Tensor const& b_tensors, torch::Tensor const& a_scales,
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torch::Tensor const& b_scales, torch::Tensor const& expert_offsets,
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torch::Tensor const& problem_sizes, torch::Tensor const& a_strides,
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torch::Tensor const& b_strides, torch::Tensor const& c_strides) {
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using ElementAB = typename Gemm::ElementAB;
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using ElementD = typename Gemm::ElementD;
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int num_experts = static_cast<int>(expert_offsets.size(0));
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int k_size = a_tensors.size(1);
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int n_size = out_tensors.size(1);
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bool per_act_token = a_scales.numel() != 1;
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bool per_out_ch = b_scales.numel() != num_experts;
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auto stream = at::cuda::getCurrentCUDAStream(a_tensors.device().index());
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auto options_int =
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torch::TensorOptions().dtype(torch::kInt64).device(a_tensors.device());
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torch::Tensor a_ptrs = torch::empty(num_experts, options_int);
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torch::Tensor b_ptrs = torch::empty(num_experts, options_int);
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torch::Tensor out_ptrs = torch::empty(num_experts, options_int);
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torch::Tensor a_scales_ptrs = torch::empty(num_experts, options_int);
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torch::Tensor b_scales_ptrs = torch::empty(num_experts, options_int);
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run_get_group_gemm_starts(expert_offsets, a_ptrs, b_ptrs, out_ptrs,
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a_scales_ptrs, b_scales_ptrs, a_tensors, b_tensors,
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out_tensors, a_scales, b_scales);
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using GemmKernel = typename Gemm::GemmKernel;
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using StrideA = Stride<int64_t, Int<1>, Int<0>>;
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using StrideB = Stride<int64_t, Int<1>, Int<0>>;
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using StrideC = typename GemmKernel::InternalStrideC;
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ProblemShape::UnderlyingProblemShape* problem_sizes_as_shapes =
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static_cast<ProblemShape::UnderlyingProblemShape*>(
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problem_sizes.data_ptr());
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ProblemShape prob_shape{num_experts, problem_sizes_as_shapes, nullptr};
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typename GemmKernel::MainloopArguments mainloop_args{
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static_cast<const ElementAB**>(a_ptrs.data_ptr()),
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static_cast<StrideA*>(a_strides.data_ptr()),
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static_cast<const ElementAB**>(b_ptrs.data_ptr()),
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static_cast<StrideB*>(b_strides.data_ptr())};
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// Currently, we are only able to do broadcast on either all or none a_scales
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// and on either all or none b_scales
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typename GemmKernel::EpilogueArguments epilogue_args{
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Gemm::Epilogue::prepare_args(
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static_cast<const ElementAccumulator**>(a_scales_ptrs.data_ptr()),
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static_cast<const ElementAccumulator**>(b_scales_ptrs.data_ptr()),
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per_act_token, per_out_ch),
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nullptr, static_cast<StrideC*>(c_strides.data_ptr()),
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static_cast<ElementD**>(out_ptrs.data_ptr()),
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static_cast<StrideC*>(c_strides.data_ptr())};
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typename GemmKernel::Arguments args{
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cutlass::gemm::GemmUniversalMode::kGrouped, prob_shape, mainloop_args,
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epilogue_args};
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using GemmOp = cutlass::gemm::device::GemmUniversalAdapter<GemmKernel>;
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GemmOp gemm_op;
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CUTLASS_CHECK(gemm_op.can_implement(args));
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size_t workspace_size = gemm_op.get_workspace_size(args);
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auto const workspace_options =
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torch::TensorOptions().dtype(torch::kUInt8).device(a_tensors.device());
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auto workspace = torch::empty(workspace_size, workspace_options);
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cutlass::Status status = gemm_op.run(args, workspace.data_ptr(), stream);
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CUTLASS_CHECK(status);
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}
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} // namespace
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