241 lines
8.9 KiB
Plaintext
241 lines
8.9 KiB
Plaintext
#include <torch/extension.h>
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#include <iostream>
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#include <sstream>
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#include <vector>
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// clang-format will break include orders
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// clang-format off
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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 "common.hpp"
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// clang-format on
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using namespace cute;
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/*
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This defines a quantized GEMM operation with dequantized output, similar to
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torch._scaled_mm. It is defined using the CUTLASS 3.x API, and is used for
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NVIDIA GPUs with sm90a (Hopper) or later.
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A and B may be both either int8 or fp8_e4m3. A can be quantized per-tensor or
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per-row. B can be quantized per-tensor or per-column.
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Any combination of per-tensor and per-row or column is supported.
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A and B must have symmetric quantization (zero point == 0).
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So the GEMM operation is D = (a_scales * A) (b_scales * B), where the
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scales are applied elementwise with numpy-style broadcasting.
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ScaleA and ScaleB define the epilogue functions that apply the scales for
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the A and B operands respectively. These scales may be either per-tensor or
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per row or column.
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*/
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namespace {
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template <typename ElementAB_, typename ElementD_, typename TileShape,
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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 Accum = cutlass::epilogue::fusion::Sm90AccFetch;
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using ScaleA = cutlass::epilogue::fusion::Sm90ColBroadcast<
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0 /*Stages*/, typename EpilogueDescriptor::TileShape, float,
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Stride<Int<1>, Int<0>, Int<0>>>;
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using ScaleBDescriptor =
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cutlass::epilogue::collective::detail::RowBroadcastDescriptor<
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EpilogueDescriptor, float>;
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using ScaleB = cutlass::epilogue::fusion::Sm90RowBroadcast<
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ScaleBDescriptor::Stages, typename EpilogueDescriptor::TileShape,
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typename ScaleBDescriptor::Element, Stride<Int<0>, Int<1>, Int<0>>>;
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using Compute0 = cutlass::epilogue::fusion::Sm90Compute<
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cutlass::multiplies, float, float,
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cutlass::FloatRoundStyle::round_to_nearest>;
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using EVTCompute0 =
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cutlass::epilogue::fusion::Sm90EVT<Compute0, ScaleB, Accum>;
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using Compute1 = cutlass::epilogue::fusion::Sm90Compute<
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cutlass::multiplies, ElementD, float,
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cutlass::FloatRoundStyle::round_to_nearest>;
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using EVTCompute1 =
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cutlass::epilogue::fusion::Sm90EVT<Compute1, ScaleA, EVTCompute0>;
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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 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, EVTCompute1>::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 = 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>
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void cutlass_scaled_mm_dq_dispatcher(torch::Tensor& out, torch::Tensor const& a,
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torch::Tensor const& b,
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torch::Tensor const& a_scales,
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torch::Tensor const& b_scales) {
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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>, Int<0>>;
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using StrideB = Stride<int64_t, Int<1>, Int<0>>;
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using StrideC = typename Gemm::StrideC;
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StrideA a_stride{lda, Int<1>{}, Int<0>{}};
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StrideB b_stride{ldb, Int<1>{}, Int<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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{}, 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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using ScaleA_Args = typename Gemm::ScaleA::Arguments;
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using ScaleB_Args = typename Gemm::ScaleB::Arguments;
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ScaleA_Args a_args = a_scales.numel() == 1
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? ScaleA_Args{nullptr, a_scales.item<float>(), {}}
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: ScaleA_Args{a_scales.data_ptr<float>(), {}, {}};
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ScaleB_Args b_args = b_scales.numel() == 1
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? ScaleB_Args{nullptr, b_scales.item<float>(), {}}
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: ScaleB_Args{b_scales.data_ptr<float>(), {}, {}};
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args.epilogue.thread = {a_args, {b_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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TORCH_CHECK(workspace_size == 0);
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cutlass::Status status = gemm_op.run(args);
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CUTLASS_CHECK(status);
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}
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} // namespace
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void cutlass_scaled_mm_dq_sm90(torch::Tensor& out, torch::Tensor const& a,
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torch::Tensor const& b,
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torch::Tensor const& a_scales,
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torch::Tensor const& b_scales) {
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TORCH_CHECK(a_scales.dtype() == torch::kFloat32);
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TORCH_CHECK(b_scales.dtype() == torch::kFloat32);
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if (a.dtype() == torch::kInt8) {
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TORCH_CHECK(b.dtype() == torch::kInt8);
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using TileShape = Shape<_128, _128, _128>;
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using ClusterShape = Shape<_1, _2, _1>;
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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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if (out.dtype() == torch::kBFloat16) {
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return cutlass_scaled_mm_dq_dispatcher<
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cutlass_3x_gemm<int8_t, cutlass::bfloat16_t, TileShape, ClusterShape,
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KernelSchedule, EpilogueSchedule>>(
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out, a, b, a_scales, b_scales);
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} else {
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TORCH_CHECK(out.dtype() == torch::kFloat16);
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return cutlass_scaled_mm_dq_dispatcher<
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cutlass_3x_gemm<int8_t, cutlass::half_t, TileShape, ClusterShape,
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KernelSchedule, EpilogueSchedule>>(
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out, a, b, a_scales, b_scales);
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}
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} else {
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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 TileShape = Shape<_128, _128, _128>;
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using ClusterShape = Shape<_1, _2, _1>;
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using KernelSchedule =
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typename cutlass::gemm::KernelCpAsyncWarpSpecializedCooperative;
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using EpilogueSchedule =
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typename cutlass::epilogue::TmaWarpSpecializedCooperative;
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if (out.dtype() == torch::kBFloat16) {
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return cutlass_scaled_mm_dq_dispatcher<
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cutlass_3x_gemm<cutlass::float_e4m3_t, cutlass::bfloat16_t, TileShape,
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ClusterShape, KernelSchedule, EpilogueSchedule>>(
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out, a, b, a_scales, b_scales);
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} else {
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TORCH_CHECK(out.dtype() == torch::kFloat16);
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return cutlass_scaled_mm_dq_dispatcher<
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cutlass_3x_gemm<cutlass::float_e4m3_t, cutlass::half_t, TileShape,
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ClusterShape, KernelSchedule, EpilogueSchedule>>(
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out, a, b, a_scales, b_scales);
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}
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}
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}
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