[Kernel] Refactor Cutlass c3x (#10049)
Signed-off-by: Varun Sundar Rabindranath <varun@neuralmagic.com> Co-authored-by: Varun Sundar Rabindranath <varun@neuralmagic.com>
This commit is contained in:
Varun Sundar Rabindranath
GitHub
parent
6142ef0ada
commit
8936316d58
@ -1,3 +1,5 @@
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#pragma once
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#include "cutlass_extensions/epilogue/broadcast_load_epilogue_c2x.hpp"
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/*
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@ -1,3 +1,5 @@
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#pragma once
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#include "cutlass_extensions/epilogue/broadcast_load_epilogue_c3x.hpp"
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/*
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@ -28,9 +28,9 @@
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using namespace cute;
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/*
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Epilogue functions can be defined to post-process the output before it is
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written to GPU memory.
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Epilogues must contain a public type named EVTCompute of type Sm80EVT,
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Epilogues defined in,
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csrc/cutlass_extensions/epilogue/scaled_mm_epilogues_c2x.hpp
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must contain a public type named EVTCompute of type Sm80EVT,
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as well as a static prepare_args function that constructs an
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EVTCompute::Arguments struct.
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*/
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@ -1,385 +1,18 @@
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// clang-format will break include orders
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// clang-format off
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#include <cudaTypedefs.h>
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#if defined CUDA_VERSION && CUDA_VERSION >= 12000
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#include <torch/all.h>
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#include "scaled_mm_c3x_sm90_fp8_dispatch.cuh"
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#include "scaled_mm_c3x_sm90_int8_dispatch.cuh"
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#include <ATen/cuda/CUDAContext.h>
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#include <iostream>
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#include <sstream>
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#include <vector>
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#include "cutlass/cutlass.h"
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#include "cute/tensor.hpp"
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#include "cute/atom/mma_atom.hpp"
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#include "cutlass/numeric_types.h"
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#include "cutlass/gemm/device/gemm_universal_adapter.h"
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#include "cutlass/gemm/kernel/gemm_universal.hpp"
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#include "cutlass/epilogue/collective/collective_builder.hpp"
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#include "cutlass/gemm/collective/collective_builder.hpp"
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#include "cutlass_extensions/epilogue/scaled_mm_epilogues_c3x.hpp"
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#include "core/math.hpp"
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#include "cutlass_extensions/common.hpp"
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// clang-format on
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using namespace cute;
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#include "cutlass_extensions/epilogue/scaled_mm_epilogues_c3x.hpp"
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using namespace vllm;
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/*
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This file defines quantized GEMM operations using the CUTLASS 3.x API, for
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NVIDIA GPUs with sm90a (Hopper) or later.
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Epilogue functions can be defined to post-process the output before it is
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written to GPU memory.
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Epilogues must contain a public type named EVTCompute of type Sm90EVT,
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as well as a static prepare_args function that constructs an
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EVTCompute::Arguments struct.
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*/
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namespace {
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// A wrapper for the GEMM kernel that is used to guard against compilation on
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// architectures that will never use the kernel. The purpose of this is to
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// reduce the size of the compiled binary.
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// __CUDA_ARCH__ is not defined in host code, so this lets us smuggle the ifdef
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// into code that will be executed on the device where it is defined.
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template <typename Kernel>
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struct enable_sm90_or_later : Kernel {
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template <typename... Args>
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CUTLASS_DEVICE void operator()(Args&&... args) {
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#if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 900
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Kernel::operator()(std::forward<Args>(args)...);
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#endif
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}
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};
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template <typename ElementAB_, typename ElementD_,
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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_gemm {
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using ElementAB = ElementAB_;
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using ElementD = ElementD_;
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using ElementAcc =
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typename std::conditional<std::is_same_v<ElementAB, int8_t>, int32_t,
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float>::type;
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using EpilogueDescriptor =
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cutlass::epilogue::collective::detail::EpilogueDescriptor<
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TileShape, cutlass::epilogue::collective::EpilogueTileAuto, ElementD,
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ElementD, EpilogueSchedule>;
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using Epilogue = Epilogue_<ElementAcc, ElementD, EpilogueDescriptor>;
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using StrideD = Stride<int64_t, Int<1>, Int<0>>;
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using ElementC = void;
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using StrideC = StrideD;
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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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cutlass::arch::Sm90, cutlass::arch::OpClassTensorOp, TileShape,
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ClusterShape, cutlass::epilogue::collective::EpilogueTileAuto,
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ElementAcc, float, ElementC, StrideC, 4, ElementD, StrideD, 4,
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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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// clang-format off
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using CollectiveMainloop =
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typename cutlass::gemm::collective::CollectiveBuilder<
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cutlass::arch::Sm90, cutlass::arch::OpClassTensorOp,
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ElementAB, cutlass::layout::RowMajor, 16,
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ElementAB, cutlass::layout::ColumnMajor, 16,
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ElementAcc, TileShape, ClusterShape,
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Stages,
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KernelSchedule>::CollectiveOp;
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// clang-format on
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using KernelType = enable_sm90_or_later<cutlass::gemm::kernel::GemmUniversal<
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cute::Shape<int, int, int, int>, CollectiveMainloop, CollectiveEpilogue,
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cutlass::gemm::PersistentScheduler>>;
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struct GemmKernel : public KernelType {};
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};
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template <typename Gemm, typename... EpilogueArgs>
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void cutlass_gemm_caller(torch::Tensor& out, torch::Tensor const& a,
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torch::Tensor const& b,
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EpilogueArgs&&... epilogue_params) {
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using ElementAB = typename Gemm::ElementAB;
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using ElementD = typename Gemm::ElementD;
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int32_t m = a.size(0);
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int32_t n = b.size(1);
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int32_t k = a.size(1);
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int64_t lda = a.stride(0);
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int64_t ldb = b.stride(1);
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int64_t ldc = out.stride(0);
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using StrideA = Stride<int64_t, Int<1>, int64_t>;
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using StrideB = Stride<int64_t, Int<1>, int64_t>;
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using StrideC = typename Gemm::StrideC;
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StrideA a_stride{lda, Int<1>{}, 0};
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StrideB b_stride{ldb, Int<1>{}, 0};
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StrideC c_stride{ldc, Int<1>{}, Int<0>{}};
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using GemmKernel = typename Gemm::GemmKernel;
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typename GemmKernel::ProblemShape prob_shape{m, n, k, 1};
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auto a_ptr = static_cast<ElementAB*>(a.data_ptr());
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auto b_ptr = static_cast<ElementAB*>(b.data_ptr());
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typename GemmKernel::MainloopArguments mainloop_args{a_ptr, a_stride, b_ptr,
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b_stride};
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auto c_ptr = static_cast<ElementD*>(out.data_ptr());
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typename GemmKernel::EpilogueArguments epilogue_args{
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Gemm::Epilogue::prepare_args(
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std::forward<EpilogueArgs>(epilogue_params)...),
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c_ptr, c_stride, c_ptr, c_stride};
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typename GemmKernel::Arguments args{cutlass::gemm::GemmUniversalMode::kGemm,
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prob_shape, mainloop_args, epilogue_args};
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// Launch the CUTLASS GEMM kernel.
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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.device());
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auto workspace = torch::empty(workspace_size, workspace_options);
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auto stream = at::cuda::getCurrentCUDAStream(a.get_device());
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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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template <typename InType, typename OutType,
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template <typename, typename, typename> typename Epilogue>
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struct sm90_fp8_config_default {
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// M in (128, inf)
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static_assert(std::is_same<InType, cutlass::float_e4m3_t>());
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using KernelSchedule =
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cutlass::gemm::KernelTmaWarpSpecializedPingpongFP8FastAccum;
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using EpilogueSchedule = typename cutlass::epilogue::TmaWarpSpecialized;
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using TileShape = Shape<_128, _128, _128>;
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using ClusterShape = Shape<_2, _1, _1>;
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using Cutlass3xGemm =
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cutlass_3x_gemm<InType, OutType, Epilogue, TileShape, ClusterShape,
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KernelSchedule, EpilogueSchedule>;
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};
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template <typename InType, typename OutType,
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template <typename, typename, typename> typename Epilogue>
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struct sm90_fp8_config_M128 {
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// M in (64, 128]
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static_assert(std::is_same<InType, cutlass::float_e4m3_t>());
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using KernelSchedule =
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cutlass::gemm::KernelTmaWarpSpecializedPingpongFP8FastAccum;
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using EpilogueSchedule = typename cutlass::epilogue::TmaWarpSpecialized;
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using TileShape = Shape<_64, _128, _128>;
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using ClusterShape = Shape<_2, _1, _1>;
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using Cutlass3xGemm =
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cutlass_3x_gemm<InType, OutType, Epilogue, TileShape, ClusterShape,
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KernelSchedule, EpilogueSchedule>;
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};
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template <typename InType, typename OutType,
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template <typename, typename, typename> typename Epilogue>
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struct sm90_fp8_config_M64 {
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// M in [1, 64]
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static_assert(std::is_same<InType, cutlass::float_e4m3_t>());
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using KernelSchedule =
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cutlass::gemm::KernelTmaWarpSpecializedPingpongFP8FastAccum;
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using EpilogueSchedule = typename cutlass::epilogue::TmaWarpSpecialized;
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using TileShape = Shape<_64, _64, _128>;
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using ClusterShape = Shape<_1, _8, _1>;
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using Cutlass3xGemm =
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cutlass_3x_gemm<InType, OutType, Epilogue, TileShape, ClusterShape,
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KernelSchedule, EpilogueSchedule>;
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};
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template <typename InType, typename OutType,
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template <typename, typename, typename> typename Epilogue>
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struct sm90_int8_config_default {
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// For M > 128 and any N
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static_assert(std::is_same<InType, int8_t>());
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using KernelSchedule =
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typename cutlass::gemm::KernelTmaWarpSpecializedPingpong;
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using EpilogueSchedule = typename cutlass::epilogue::TmaWarpSpecialized;
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using TileShape = Shape<_128, _128, _128>;
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using ClusterShape = Shape<_2, _1, _1>;
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using Cutlass3xGemm =
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cutlass_3x_gemm<InType, OutType, Epilogue, TileShape, ClusterShape,
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KernelSchedule, EpilogueSchedule>;
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};
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template <typename InType, typename OutType,
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template <typename, typename, typename> typename Epilogue>
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struct sm90_int8_config_M128 {
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// For M in (64, 128] and any N
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static_assert(std::is_same<InType, int8_t>());
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using KernelSchedule =
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typename cutlass::gemm::KernelTmaWarpSpecializedPingpong;
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using EpilogueSchedule = typename cutlass::epilogue::TmaWarpSpecialized;
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using TileShape = Shape<_64, _128, _128>;
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using ClusterShape = Shape<_2, _1, _1>;
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using Cutlass3xGemm =
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cutlass_3x_gemm<InType, OutType, Epilogue, TileShape, ClusterShape,
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KernelSchedule, EpilogueSchedule>;
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};
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template <typename InType, typename OutType,
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template <typename, typename, typename> typename Epilogue>
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struct sm90_int8_config_M64 {
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// For M in (32, 64] and any N
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static_assert(std::is_same<InType, int8_t>());
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using KernelSchedule = typename cutlass::gemm::KernelTmaWarpSpecialized;
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using EpilogueSchedule = typename cutlass::epilogue::TmaWarpSpecialized;
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using TileShape = Shape<_64, _64, _256>;
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using ClusterShape = Shape<_1, _1, _1>;
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using Cutlass3xGemm =
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cutlass_3x_gemm<InType, OutType, Epilogue, TileShape, ClusterShape,
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KernelSchedule, EpilogueSchedule>;
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};
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template <typename InType, typename OutType,
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template <typename, typename, typename> typename Epilogue>
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struct sm90_int8_config_M32_NBig {
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// For M in [1, 32] and N >= 8192
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static_assert(std::is_same<InType, int8_t>());
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using KernelSchedule = typename cutlass::gemm::KernelTmaWarpSpecialized;
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using EpilogueSchedule = typename cutlass::epilogue::TmaWarpSpecialized;
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using TileShape = Shape<_64, _128, _256>;
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using ClusterShape = Shape<_1, _4, _1>;
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using Cutlass3xGemm =
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cutlass_3x_gemm<InType, OutType, Epilogue, TileShape, ClusterShape,
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KernelSchedule, EpilogueSchedule>;
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};
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template <typename InType, typename OutType,
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template <typename, typename, typename> typename Epilogue>
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struct sm90_int8_config_M32_NSmall {
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// For M in [1, 32] and N < 8192
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static_assert(std::is_same<InType, int8_t>());
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using KernelSchedule = typename cutlass::gemm::KernelTmaWarpSpecialized;
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using EpilogueSchedule = typename cutlass::epilogue::TmaWarpSpecialized;
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using TileShape = Shape<_64, _64, _256>;
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using ClusterShape = Shape<_1, _8, _1>;
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using Cutlass3xGemm =
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cutlass_3x_gemm<InType, OutType, Epilogue, TileShape, ClusterShape,
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KernelSchedule, EpilogueSchedule>;
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};
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} // namespace
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template <typename InType, typename OutType,
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template <typename, typename, typename> typename Epilogue,
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typename... EpilogueArgs>
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void cutlass_gemm_sm90_fp8_dispatch(torch::Tensor& out, torch::Tensor const& a,
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torch::Tensor const& b,
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EpilogueArgs&&... args) {
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static_assert(std::is_same<InType, cutlass::float_e4m3_t>());
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TORCH_CHECK(a.dtype() == torch::kFloat8_e4m3fn);
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TORCH_CHECK(b.dtype() == torch::kFloat8_e4m3fn);
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using Cutlass3xGemmDefault =
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typename sm90_fp8_config_default<InType, OutType,
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Epilogue>::Cutlass3xGemm;
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using Cutlass3xGemmM64 =
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typename sm90_fp8_config_M64<InType, OutType, Epilogue>::Cutlass3xGemm;
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using Cutlass3xGemmM128 =
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typename sm90_fp8_config_M128<InType, OutType, Epilogue>::Cutlass3xGemm;
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uint32_t const m = a.size(0);
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uint32_t const mp2 =
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std::max(static_cast<uint32_t>(64), next_pow_2(m)); // next power of 2
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if (mp2 <= 64) {
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// m in [1, 64]
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return cutlass_gemm_caller<Cutlass3xGemmM64>(
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out, a, b, std::forward<EpilogueArgs>(args)...);
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} else if (mp2 <= 128) {
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// m in (64, 128]
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return cutlass_gemm_caller<Cutlass3xGemmM128>(
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out, a, b, std::forward<EpilogueArgs>(args)...);
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} else {
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// m in (128, inf)
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return cutlass_gemm_caller<Cutlass3xGemmDefault>(
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out, a, b, std::forward<EpilogueArgs>(args)...);
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}
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}
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template <typename InType, typename OutType,
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template <typename, typename, typename> typename Epilogue,
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typename... EpilogueArgs>
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void cutlass_gemm_sm90_int8_dispatch(torch::Tensor& out, torch::Tensor const& a,
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torch::Tensor const& b,
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EpilogueArgs&&... args) {
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static_assert(std::is_same<InType, int8_t>());
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TORCH_CHECK(a.dtype() == torch::kInt8);
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TORCH_CHECK(b.dtype() == torch::kInt8);
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using Cutlass3xGemmDefault =
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typename sm90_int8_config_default<InType, OutType,
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Epilogue>::Cutlass3xGemm;
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using Cutlass3xGemmM128 =
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typename sm90_int8_config_M128<InType, OutType, Epilogue>::Cutlass3xGemm;
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using Cutlass3xGemmM64 =
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typename sm90_int8_config_M64<InType, OutType, Epilogue>::Cutlass3xGemm;
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using Cutlass3xGemmM32NBig =
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typename sm90_int8_config_M32_NBig<InType, OutType,
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Epilogue>::Cutlass3xGemm;
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using Cutlass3xGemmM32NSmall =
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typename sm90_int8_config_M32_NSmall<InType, OutType,
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Epilogue>::Cutlass3xGemm;
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uint32_t const n = out.size(1);
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bool const is_small_n = n < 8192;
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uint32_t const m = a.size(0);
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uint32_t const mp2 =
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std::max(static_cast<uint32_t>(32), next_pow_2(m)); // next power of 2
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if (mp2 <= 32) {
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// m in [1, 32]
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if (is_small_n) {
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return cutlass_gemm_caller<Cutlass3xGemmM32NSmall>(
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out, a, b, std::forward<EpilogueArgs>(args)...);
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} else {
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return cutlass_gemm_caller<Cutlass3xGemmM32NBig>(
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out, a, b, std::forward<EpilogueArgs>(args)...);
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}
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} else if (mp2 <= 64) {
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// m in (32, 64]
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return cutlass_gemm_caller<Cutlass3xGemmM64>(
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||||
out, a, b, std::forward<EpilogueArgs>(args)...);
|
||||
} else if (mp2 <= 128) {
|
||||
// m in (64, 128]
|
||||
return cutlass_gemm_caller<Cutlass3xGemmM128>(
|
||||
out, a, b, std::forward<EpilogueArgs>(args)...);
|
||||
} else {
|
||||
// m in (128, inf)
|
||||
return cutlass_gemm_caller<Cutlass3xGemmDefault>(
|
||||
out, a, b, std::forward<EpilogueArgs>(args)...);
|
||||
}
|
||||
}
|
||||
|
||||
template <template <typename, typename, typename> typename Epilogue,
|
||||
typename... EpilogueArgs>
|
||||
void cutlass_scaled_mm_sm90_epilogue(torch::Tensor& out, torch::Tensor const& a,
|
||||
|
||||
160
csrc/quantization/cutlass_w8a8/scaled_mm_c3x.cuh
Normal file
160
csrc/quantization/cutlass_w8a8/scaled_mm_c3x.cuh
Normal file
@ -0,0 +1,160 @@
|
||||
#pragma once
|
||||
|
||||
// clang-format will break include orders
|
||||
// clang-format off
|
||||
#include <torch/all.h>
|
||||
|
||||
#include <ATen/cuda/CUDAContext.h>
|
||||
|
||||
#include "cutlass/cutlass.h"
|
||||
|
||||
#include "cute/tensor.hpp"
|
||||
#include "cute/atom/mma_atom.hpp"
|
||||
#include "cutlass/numeric_types.h"
|
||||
|
||||
#include "cutlass/gemm/device/gemm_universal_adapter.h"
|
||||
#include "cutlass/gemm/kernel/gemm_universal.hpp"
|
||||
#include "cutlass/epilogue/collective/collective_builder.hpp"
|
||||
#include "cutlass/gemm/collective/collective_builder.hpp"
|
||||
|
||||
#include "core/math.hpp"
|
||||
#include "cutlass_extensions/common.hpp"
|
||||
// clang-format on
|
||||
|
||||
/*
|
||||
Epilogues defined in,
|
||||
csrc/cutlass_extensions/epilogue/scaled_mm_epilogues_c3x.hpp,
|
||||
must contain a public type named EVTCompute of type Sm90EVT, as well as a
|
||||
static prepare_args function that constructs an EVTCompute::Arguments struct.
|
||||
*/
|
||||
|
||||
using namespace cute;
|
||||
|
||||
namespace vllm {
|
||||
|
||||
// A wrapper for the GEMM kernel that is used to guard against compilation on
|
||||
// architectures that will never use the kernel. The purpose of this is to
|
||||
// reduce the size of the compiled binary.
|
||||
// __CUDA_ARCH__ is not defined in host code, so this lets us smuggle the ifdef
|
||||
// into code that will be executed on the device where it is defined.
|
||||
template <typename Kernel>
|
||||
struct enable_sm90_or_later : Kernel {
|
||||
template <typename... Args>
|
||||
CUTLASS_DEVICE void operator()(Args&&... args) {
|
||||
#if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 900
|
||||
Kernel::operator()(std::forward<Args>(args)...);
|
||||
#endif
|
||||
}
|
||||
};
|
||||
|
||||
template <typename ElementAB_, typename ElementD_,
|
||||
template <typename, typename, typename> typename Epilogue_,
|
||||
typename TileShape, typename ClusterShape, typename KernelSchedule,
|
||||
typename EpilogueSchedule>
|
||||
struct cutlass_3x_gemm {
|
||||
using ElementAB = ElementAB_;
|
||||
using ElementD = ElementD_;
|
||||
using ElementAcc =
|
||||
typename std::conditional<std::is_same_v<ElementAB, int8_t>, int32_t,
|
||||
float>::type;
|
||||
|
||||
using EpilogueDescriptor =
|
||||
cutlass::epilogue::collective::detail::EpilogueDescriptor<
|
||||
TileShape, cutlass::epilogue::collective::EpilogueTileAuto, ElementD,
|
||||
ElementD, EpilogueSchedule>;
|
||||
|
||||
using Epilogue = Epilogue_<ElementAcc, ElementD, EpilogueDescriptor>;
|
||||
|
||||
using StrideD = Stride<int64_t, Int<1>, Int<0>>;
|
||||
using ElementC = void;
|
||||
using StrideC = StrideD;
|
||||
|
||||
using EVTCompute = typename Epilogue::EVTCompute;
|
||||
|
||||
using CollectiveEpilogue =
|
||||
typename cutlass::epilogue::collective::CollectiveBuilder<
|
||||
cutlass::arch::Sm90, cutlass::arch::OpClassTensorOp, TileShape,
|
||||
ClusterShape, cutlass::epilogue::collective::EpilogueTileAuto,
|
||||
ElementAcc, float, ElementC, StrideC, 4, ElementD, StrideD, 4,
|
||||
EpilogueSchedule, EVTCompute>::CollectiveOp;
|
||||
|
||||
static constexpr size_t CEStorageSize =
|
||||
sizeof(typename CollectiveEpilogue::SharedStorage);
|
||||
using Stages = typename cutlass::gemm::collective::StageCountAutoCarveout<
|
||||
static_cast<int>(CEStorageSize)>;
|
||||
|
||||
// clang-format off
|
||||
using CollectiveMainloop =
|
||||
typename cutlass::gemm::collective::CollectiveBuilder<
|
||||
cutlass::arch::Sm90, cutlass::arch::OpClassTensorOp,
|
||||
ElementAB, cutlass::layout::RowMajor, 16,
|
||||
ElementAB, cutlass::layout::ColumnMajor, 16,
|
||||
ElementAcc, TileShape, ClusterShape,
|
||||
Stages,
|
||||
KernelSchedule>::CollectiveOp;
|
||||
// clang-format on
|
||||
|
||||
using KernelType = enable_sm90_or_later<cutlass::gemm::kernel::GemmUniversal<
|
||||
cute::Shape<int, int, int, int>, CollectiveMainloop, CollectiveEpilogue,
|
||||
cutlass::gemm::PersistentScheduler>>;
|
||||
|
||||
struct GemmKernel : public KernelType {};
|
||||
};
|
||||
|
||||
template <typename Gemm, typename... EpilogueArgs>
|
||||
void cutlass_gemm_caller(torch::Tensor& out, torch::Tensor const& a,
|
||||
torch::Tensor const& b,
|
||||
EpilogueArgs&&... epilogue_params) {
|
||||
using ElementAB = typename Gemm::ElementAB;
|
||||
using ElementD = typename Gemm::ElementD;
|
||||
|
||||
int32_t m = a.size(0);
|
||||
int32_t n = b.size(1);
|
||||
int32_t k = a.size(1);
|
||||
|
||||
int64_t lda = a.stride(0);
|
||||
int64_t ldb = b.stride(1);
|
||||
int64_t ldc = out.stride(0);
|
||||
|
||||
using StrideA = Stride<int64_t, Int<1>, int64_t>;
|
||||
using StrideB = Stride<int64_t, Int<1>, int64_t>;
|
||||
using StrideC = typename Gemm::StrideC;
|
||||
|
||||
StrideA a_stride{lda, Int<1>{}, 0};
|
||||
StrideB b_stride{ldb, Int<1>{}, 0};
|
||||
StrideC c_stride{ldc, Int<1>{}, Int<0>{}};
|
||||
|
||||
using GemmKernel = typename Gemm::GemmKernel;
|
||||
typename GemmKernel::ProblemShape prob_shape{m, n, k, 1};
|
||||
|
||||
auto a_ptr = static_cast<ElementAB*>(a.data_ptr());
|
||||
auto b_ptr = static_cast<ElementAB*>(b.data_ptr());
|
||||
typename GemmKernel::MainloopArguments mainloop_args{a_ptr, a_stride, b_ptr,
|
||||
b_stride};
|
||||
|
||||
auto c_ptr = static_cast<ElementD*>(out.data_ptr());
|
||||
typename GemmKernel::EpilogueArguments epilogue_args{
|
||||
Gemm::Epilogue::prepare_args(
|
||||
std::forward<EpilogueArgs>(epilogue_params)...),
|
||||
c_ptr, c_stride, c_ptr, c_stride};
|
||||
|
||||
typename GemmKernel::Arguments args{cutlass::gemm::GemmUniversalMode::kGemm,
|
||||
prob_shape, mainloop_args, epilogue_args};
|
||||
|
||||
// Launch the CUTLASS GEMM kernel.
|
||||
using GemmOp = cutlass::gemm::device::GemmUniversalAdapter<GemmKernel>;
|
||||
GemmOp gemm_op;
|
||||
CUTLASS_CHECK(gemm_op.can_implement(args));
|
||||
|
||||
size_t workspace_size = gemm_op.get_workspace_size(args);
|
||||
auto const workspace_options =
|
||||
torch::TensorOptions().dtype(torch::kUInt8).device(a.device());
|
||||
auto workspace = torch::empty(workspace_size, workspace_options);
|
||||
|
||||
auto stream = at::cuda::getCurrentCUDAStream(a.get_device());
|
||||
|
||||
cutlass::Status status = gemm_op.run(args, workspace.data_ptr(), stream);
|
||||
CUTLASS_CHECK(status);
|
||||
}
|
||||
|
||||
} // namespace vllm
|
||||
@ -0,0 +1,96 @@
|
||||
#pragma once
|
||||
|
||||
#include "scaled_mm_c3x.cuh"
|
||||
|
||||
/**
|
||||
* This file defines Gemm kernel configurations for SM90 (fp8) based on the Gemm
|
||||
* shape.
|
||||
*/
|
||||
|
||||
namespace vllm {
|
||||
|
||||
template <typename InType, typename OutType,
|
||||
template <typename, typename, typename> typename Epilogue>
|
||||
struct sm90_fp8_config_default {
|
||||
// M in (128, inf)
|
||||
static_assert(std::is_same<InType, cutlass::float_e4m3_t>());
|
||||
using KernelSchedule =
|
||||
cutlass::gemm::KernelTmaWarpSpecializedPingpongFP8FastAccum;
|
||||
using EpilogueSchedule = typename cutlass::epilogue::TmaWarpSpecialized;
|
||||
using TileShape = Shape<_128, _128, _128>;
|
||||
using ClusterShape = Shape<_2, _1, _1>;
|
||||
using Cutlass3xGemm =
|
||||
cutlass_3x_gemm<InType, OutType, Epilogue, TileShape, ClusterShape,
|
||||
KernelSchedule, EpilogueSchedule>;
|
||||
};
|
||||
|
||||
template <typename InType, typename OutType,
|
||||
template <typename, typename, typename> typename Epilogue>
|
||||
struct sm90_fp8_config_M128 {
|
||||
// M in (64, 128]
|
||||
static_assert(std::is_same<InType, cutlass::float_e4m3_t>());
|
||||
using KernelSchedule =
|
||||
cutlass::gemm::KernelTmaWarpSpecializedPingpongFP8FastAccum;
|
||||
using EpilogueSchedule = typename cutlass::epilogue::TmaWarpSpecialized;
|
||||
using TileShape = Shape<_64, _128, _128>;
|
||||
using ClusterShape = Shape<_2, _1, _1>;
|
||||
using Cutlass3xGemm =
|
||||
cutlass_3x_gemm<InType, OutType, Epilogue, TileShape, ClusterShape,
|
||||
KernelSchedule, EpilogueSchedule>;
|
||||
};
|
||||
|
||||
template <typename InType, typename OutType,
|
||||
template <typename, typename, typename> typename Epilogue>
|
||||
struct sm90_fp8_config_M64 {
|
||||
// M in [1, 64]
|
||||
static_assert(std::is_same<InType, cutlass::float_e4m3_t>());
|
||||
using KernelSchedule =
|
||||
cutlass::gemm::KernelTmaWarpSpecializedPingpongFP8FastAccum;
|
||||
using EpilogueSchedule = typename cutlass::epilogue::TmaWarpSpecialized;
|
||||
using TileShape = Shape<_64, _64, _128>;
|
||||
using ClusterShape = Shape<_1, _8, _1>;
|
||||
|
||||
using Cutlass3xGemm =
|
||||
cutlass_3x_gemm<InType, OutType, Epilogue, TileShape, ClusterShape,
|
||||
KernelSchedule, EpilogueSchedule>;
|
||||
};
|
||||
|
||||
template <typename InType, typename OutType,
|
||||
template <typename, typename, typename> typename Epilogue,
|
||||
typename... EpilogueArgs>
|
||||
inline void cutlass_gemm_sm90_fp8_dispatch(torch::Tensor& out,
|
||||
torch::Tensor const& a,
|
||||
torch::Tensor const& b,
|
||||
EpilogueArgs&&... args) {
|
||||
static_assert(std::is_same<InType, cutlass::float_e4m3_t>());
|
||||
TORCH_CHECK(a.dtype() == torch::kFloat8_e4m3fn);
|
||||
TORCH_CHECK(b.dtype() == torch::kFloat8_e4m3fn);
|
||||
|
||||
using Cutlass3xGemmDefault =
|
||||
typename sm90_fp8_config_default<InType, OutType,
|
||||
Epilogue>::Cutlass3xGemm;
|
||||
using Cutlass3xGemmM64 =
|
||||
typename sm90_fp8_config_M64<InType, OutType, Epilogue>::Cutlass3xGemm;
|
||||
using Cutlass3xGemmM128 =
|
||||
typename sm90_fp8_config_M128<InType, OutType, Epilogue>::Cutlass3xGemm;
|
||||
|
||||
uint32_t const m = a.size(0);
|
||||
uint32_t const mp2 =
|
||||
std::max(static_cast<uint32_t>(64), next_pow_2(m)); // next power of 2
|
||||
|
||||
if (mp2 <= 64) {
|
||||
// m in [1, 64]
|
||||
return cutlass_gemm_caller<Cutlass3xGemmM64>(
|
||||
out, a, b, std::forward<EpilogueArgs>(args)...);
|
||||
} else if (mp2 <= 128) {
|
||||
// m in (64, 128]
|
||||
return cutlass_gemm_caller<Cutlass3xGemmM128>(
|
||||
out, a, b, std::forward<EpilogueArgs>(args)...);
|
||||
} else {
|
||||
// m in (128, inf)
|
||||
return cutlass_gemm_caller<Cutlass3xGemmDefault>(
|
||||
out, a, b, std::forward<EpilogueArgs>(args)...);
|
||||
}
|
||||
}
|
||||
|
||||
} // namespace vllm
|
||||
@ -0,0 +1,140 @@
|
||||
#pragma once
|
||||
|
||||
#include "scaled_mm_c3x.cuh"
|
||||
|
||||
/**
|
||||
* This file defines Gemm kernel configurations for SM90 (int8) based on the
|
||||
* Gemm shape.
|
||||
*/
|
||||
|
||||
namespace vllm {
|
||||
|
||||
template <typename InType, typename OutType,
|
||||
template <typename, typename, typename> typename Epilogue>
|
||||
struct sm90_int8_config_default {
|
||||
// For M > 128 and any N
|
||||
static_assert(std::is_same<InType, int8_t>());
|
||||
using KernelSchedule =
|
||||
typename cutlass::gemm::KernelTmaWarpSpecializedPingpong;
|
||||
using EpilogueSchedule = typename cutlass::epilogue::TmaWarpSpecialized;
|
||||
using TileShape = Shape<_128, _128, _128>;
|
||||
using ClusterShape = Shape<_2, _1, _1>;
|
||||
using Cutlass3xGemm =
|
||||
cutlass_3x_gemm<InType, OutType, Epilogue, TileShape, ClusterShape,
|
||||
KernelSchedule, EpilogueSchedule>;
|
||||
};
|
||||
|
||||
template <typename InType, typename OutType,
|
||||
template <typename, typename, typename> typename Epilogue>
|
||||
struct sm90_int8_config_M128 {
|
||||
// For M in (64, 128] and any N
|
||||
static_assert(std::is_same<InType, int8_t>());
|
||||
using KernelSchedule =
|
||||
typename cutlass::gemm::KernelTmaWarpSpecializedPingpong;
|
||||
using EpilogueSchedule = typename cutlass::epilogue::TmaWarpSpecialized;
|
||||
using TileShape = Shape<_64, _128, _128>;
|
||||
using ClusterShape = Shape<_2, _1, _1>;
|
||||
using Cutlass3xGemm =
|
||||
cutlass_3x_gemm<InType, OutType, Epilogue, TileShape, ClusterShape,
|
||||
KernelSchedule, EpilogueSchedule>;
|
||||
};
|
||||
|
||||
template <typename InType, typename OutType,
|
||||
template <typename, typename, typename> typename Epilogue>
|
||||
struct sm90_int8_config_M64 {
|
||||
// For M in (32, 64] and any N
|
||||
static_assert(std::is_same<InType, int8_t>());
|
||||
using KernelSchedule = typename cutlass::gemm::KernelTmaWarpSpecialized;
|
||||
using EpilogueSchedule = typename cutlass::epilogue::TmaWarpSpecialized;
|
||||
using TileShape = Shape<_64, _64, _256>;
|
||||
using ClusterShape = Shape<_1, _1, _1>;
|
||||
using Cutlass3xGemm =
|
||||
cutlass_3x_gemm<InType, OutType, Epilogue, TileShape, ClusterShape,
|
||||
KernelSchedule, EpilogueSchedule>;
|
||||
};
|
||||
|
||||
template <typename InType, typename OutType,
|
||||
template <typename, typename, typename> typename Epilogue>
|
||||
struct sm90_int8_config_M32_NBig {
|
||||
// For M in [1, 32] and N >= 8192
|
||||
static_assert(std::is_same<InType, int8_t>());
|
||||
using KernelSchedule = typename cutlass::gemm::KernelTmaWarpSpecialized;
|
||||
using EpilogueSchedule = typename cutlass::epilogue::TmaWarpSpecialized;
|
||||
using TileShape = Shape<_64, _128, _256>;
|
||||
using ClusterShape = Shape<_1, _4, _1>;
|
||||
using Cutlass3xGemm =
|
||||
cutlass_3x_gemm<InType, OutType, Epilogue, TileShape, ClusterShape,
|
||||
KernelSchedule, EpilogueSchedule>;
|
||||
};
|
||||
|
||||
template <typename InType, typename OutType,
|
||||
template <typename, typename, typename> typename Epilogue>
|
||||
struct sm90_int8_config_M32_NSmall {
|
||||
// For M in [1, 32] and N < 8192
|
||||
static_assert(std::is_same<InType, int8_t>());
|
||||
using KernelSchedule = typename cutlass::gemm::KernelTmaWarpSpecialized;
|
||||
using EpilogueSchedule = typename cutlass::epilogue::TmaWarpSpecialized;
|
||||
using TileShape = Shape<_64, _64, _256>;
|
||||
using ClusterShape = Shape<_1, _8, _1>;
|
||||
using Cutlass3xGemm =
|
||||
cutlass_3x_gemm<InType, OutType, Epilogue, TileShape, ClusterShape,
|
||||
KernelSchedule, EpilogueSchedule>;
|
||||
};
|
||||
|
||||
template <typename InType, typename OutType,
|
||||
template <typename, typename, typename> typename Epilogue,
|
||||
typename... EpilogueArgs>
|
||||
inline void cutlass_gemm_sm90_int8_dispatch(torch::Tensor& out,
|
||||
torch::Tensor const& a,
|
||||
torch::Tensor const& b,
|
||||
EpilogueArgs&&... args) {
|
||||
static_assert(std::is_same<InType, int8_t>());
|
||||
TORCH_CHECK(a.dtype() == torch::kInt8);
|
||||
TORCH_CHECK(b.dtype() == torch::kInt8);
|
||||
|
||||
using Cutlass3xGemmDefault =
|
||||
typename sm90_int8_config_default<InType, OutType,
|
||||
Epilogue>::Cutlass3xGemm;
|
||||
using Cutlass3xGemmM128 =
|
||||
typename sm90_int8_config_M128<InType, OutType, Epilogue>::Cutlass3xGemm;
|
||||
using Cutlass3xGemmM64 =
|
||||
typename sm90_int8_config_M64<InType, OutType, Epilogue>::Cutlass3xGemm;
|
||||
using Cutlass3xGemmM32NBig =
|
||||
typename sm90_int8_config_M32_NBig<InType, OutType,
|
||||
Epilogue>::Cutlass3xGemm;
|
||||
using Cutlass3xGemmM32NSmall =
|
||||
typename sm90_int8_config_M32_NSmall<InType, OutType,
|
||||
Epilogue>::Cutlass3xGemm;
|
||||
|
||||
uint32_t const n = out.size(1);
|
||||
bool const is_small_n = n < 8192;
|
||||
|
||||
uint32_t const m = a.size(0);
|
||||
uint32_t const mp2 =
|
||||
std::max(static_cast<uint32_t>(32), next_pow_2(m)); // next power of 2
|
||||
|
||||
if (mp2 <= 32) {
|
||||
// m in [1, 32]
|
||||
if (is_small_n) {
|
||||
return cutlass_gemm_caller<Cutlass3xGemmM32NSmall>(
|
||||
out, a, b, std::forward<EpilogueArgs>(args)...);
|
||||
} else {
|
||||
return cutlass_gemm_caller<Cutlass3xGemmM32NBig>(
|
||||
out, a, b, std::forward<EpilogueArgs>(args)...);
|
||||
}
|
||||
} else if (mp2 <= 64) {
|
||||
// m in (32, 64]
|
||||
return cutlass_gemm_caller<Cutlass3xGemmM64>(
|
||||
out, a, b, std::forward<EpilogueArgs>(args)...);
|
||||
} else if (mp2 <= 128) {
|
||||
// m in (64, 128]
|
||||
return cutlass_gemm_caller<Cutlass3xGemmM128>(
|
||||
out, a, b, std::forward<EpilogueArgs>(args)...);
|
||||
} else {
|
||||
// m in (128, inf)
|
||||
return cutlass_gemm_caller<Cutlass3xGemmDefault>(
|
||||
out, a, b, std::forward<EpilogueArgs>(args)...);
|
||||
}
|
||||
}
|
||||
|
||||
} // namespace vllm
|
||||
Loading…
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Reference in New Issue
Block a user