389e493055
* CUTLASS 3.8 Release
* update
* Update README.md
* Revert "Update README.md"
This reverts commit b353e36fe8.
* update
* update
---------
Co-authored-by: Haicheng Wu <57973641+hwu36@users.noreply.github.com>
Co-authored-by: Haicheng Wu <haichengw@nvidia.com>
522 lines
20 KiB
C++
522 lines
20 KiB
C++
/***************************************************************************************************
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* Copyright (c) 2023 - 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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* SPDX-License-Identifier: BSD-3-Clause
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*
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* Redistribution and use in source and binary forms, with or without
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* modification, are permitted provided that the following conditions are met:
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*
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* 1. Redistributions of source code must retain the above copyright notice, this
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* list of conditions and the following disclaimer.
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*
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* 2. Redistributions in binary form must reproduce the above copyright notice,
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* this list of conditions and the following disclaimer in the documentation
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* and/or other materials provided with the distribution.
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*
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* 3. Neither the name of the copyright holder nor the names of its
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* contributors may be used to endorse or promote products derived from
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* this software without specific prior written permission.
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*
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* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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* AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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* DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
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* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
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* DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
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* SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
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* OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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*
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**************************************************************************************************/
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#pragma once
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#include "cutlass/arch/arch.h"
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#include "cutlass/gemm/gemm.h"
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#include "cute/layout.hpp"
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#include "cute/numeric/integral_constant.hpp" // cute::false_type
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#include "cute/arch/copy_sm100.hpp"
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//////////////////////////////////////////////////////////////////////////////
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namespace cutlass::detail {
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template <class T, template <int...> class U>
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struct is_kernel_tag_of : cute::false_type {};
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template <template <int...> class U, int... Args>
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struct is_kernel_tag_of<U<Args...>, U> : cute::true_type {};
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template <class T, template <int...> class U>
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constexpr bool is_kernel_tag_of_v = is_kernel_tag_of<T, U>::value;
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template <class T, template <int,bool> class U>
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struct is_asymmetric_dma_kernel_tag_of : cute::false_type {};
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template <template <int, bool> class U, int I0, bool B0>
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struct is_asymmetric_dma_kernel_tag_of<U<I0, B0>, U> : cute::true_type {};
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template <class T, template <int, bool> class U>
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constexpr bool is_asymmetric_dma_kernel_tag_of_v = \
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is_asymmetric_dma_kernel_tag_of<T, U>::value;
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}
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//////////////////////////////////////////////////////////////////////////////
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namespace cutlass::gemm {
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using namespace cute;
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//////////////////////////////////////////////////////////////////////////////
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namespace detail {
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enum class KernelInputTransformType {
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FastF32,
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InterleavedComplexTF32
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};
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} // namespace detail
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//////////////////////////////////////////////////////////////////////////////
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namespace kernel::detail {
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// Has_SwapAB<T>::value will be true only if:
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// class T has member SwapAB and T::SwapAB is true
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template <typename T, typename = void>
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struct Has_SwapAB { static constexpr bool value = false; };
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template <typename T>
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struct Has_SwapAB <T, CUTE_STL_NAMESPACE::void_t<decltype(T::SwapAB)>>
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{ static constexpr bool value = T::SwapAB; };
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template <typename T>
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static constexpr bool Has_SwapAB_v = Has_SwapAB<T>::value;
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} // namespace kernel::detail
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//////////////////////////////////////////////////////////////////////////////
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//
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// Kernel schedule policies (the base class tags, one for each kernel layer file)
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//
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struct KernelMultistage { };
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struct KernelCpAsyncWarpSpecialized { };
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struct KernelCpAsyncWarpSpecializedPingpong { };
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struct KernelCpAsyncWarpSpecializedCooperative { };
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struct KernelTma { };
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struct KernelTmaWarpSpecialized { };
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struct KernelTmaWarpSpecializedPingpong {
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};
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struct KernelTmaWarpSpecializedCooperative {
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};
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struct KernelPtrArrayTmaWarpSpecializedCooperative { };
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struct KernelPtrArrayTmaWarpSpecializedPingpong { };
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// FP8 related policies (including Blocked Scaled Accumulation)
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struct KernelTmaWarpSpecializedCooperativeFP8BlockScaledAccum: KernelTmaWarpSpecializedCooperative { };
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// Policies to opt into mixed type GEMMs
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struct KernelTmaWarpSpecializedMixedInput : KernelTmaWarpSpecialized { };
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struct KernelTmaWarpSpecializedPingpongMixedInput : KernelTmaWarpSpecializedPingpong { };
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struct KernelTmaWarpSpecializedCooperativeMixedInput: KernelTmaWarpSpecializedCooperative { };
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//////////////////////////////////////////////////////////////////////////////
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//
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// Builder dispatch policies (not a part of the main CUTLASS layers, simply used to opt into
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// specific collective builder dispatches)
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//
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// FP8 related policies (including Fast Accumulation)
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struct KernelTmaWarpSpecializedFP8FastAccum : KernelTmaWarpSpecialized { };
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struct KernelTmaWarpSpecializedPingpongFP8FastAccum : KernelTmaWarpSpecializedPingpong { };
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struct KernelTmaWarpSpecializedCooperativeFP8FastAccum: KernelTmaWarpSpecializedCooperative { };
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struct KernelPtrArrayTmaWarpSpecializedCooperativeFP8FastAccum : KernelPtrArrayTmaWarpSpecializedCooperative { };
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struct KernelPtrArrayTmaWarpSpecializedPingpongFP8FastAccum : KernelPtrArrayTmaWarpSpecializedPingpong { };
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//////////////////////////////////////////////////////////////////////////////
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// Policies for dispatch of epilogue
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struct EpilogueDefault { };
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struct EpilogueTransposed { };
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//////////////////////////////////////////////////////////////////////////////
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//
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// Collective Mainloop Policies
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//
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// 2 stage pipeline through 1 stage in smem, 1 in rmem, WITHOUT predicated gmem loads
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struct MainloopSm70TwoStageUnpredicated {
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constexpr static int Stages = 2;
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using ArchTag = arch::Sm70;
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using Schedule = KernelMultistage;
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using ClusterShape = Shape<_1,_1,_1>;
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};
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// 2 stage pipeline through 1 stage in smem, 1 in rmem, with predicated gmem loads
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struct MainloopSm70TwoStage {
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constexpr static int Stages = 2;
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using ArchTag = arch::Sm70;
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using Schedule = KernelMultistage;
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using ClusterShape = Shape<_1,_1,_1>;
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};
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// n-buffer in smem (cp.async), pipelined with registers, WITHOUT predicated gmem loads
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template<int Stages_>
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struct MainloopSm80CpAsyncUnpredicated {
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constexpr static int Stages = Stages_;
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using ArchTag = arch::Sm80;
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using Schedule = KernelMultistage;
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using ClusterShape = Shape<_1,_1,_1>;
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};
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// n-buffer in smem (cp.async), pipelined with registers, with predicated gmem loads
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template<
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int Stages_,
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class ClusterShape_ = Shape<_1,_1,_1>
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>
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struct MainloopSm80CpAsync {
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constexpr static int Stages = Stages_;
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using ArchTag = cute::conditional_t<(size(ClusterShape_{}) > 1), arch::Sm90, arch::Sm80>;
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using Schedule = KernelMultistage;
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using ClusterShape = ClusterShape_;
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};
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// n-buffer in smem (cp.async), pipelined with Hopper GMMA, with predicated gmem loads, warp specialized dynamic schedule
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template<
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int Stages_,
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class ClusterShape_ = Shape<_1,_1,_1>,
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class KernelSchedule = KernelCpAsyncWarpSpecialized
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>
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struct MainloopSm90CpAsyncGmmaWarpSpecialized {
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constexpr static int Stages = Stages_;
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using ClusterShape = ClusterShape_;
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using ArchTag = arch::Sm90;
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using Schedule = KernelSchedule;
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};
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// n-buffer in smem (cp.async), pipelined with Hopper GMMA, with predicated gmem loads, warp specialized dynamic schedule
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template<
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int Stages_,
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class ClusterShape_ = Shape<_1,_1,_1>,
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class KernelSchedule = KernelCpAsyncWarpSpecialized
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>
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struct MainloopSm90CpAsyncGmmaRmemAWarpSpecialized {
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constexpr static int Stages = Stages_;
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using ClusterShape = ClusterShape_;
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using ArchTag = arch::Sm90;
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using Schedule = KernelSchedule;
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};
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// n-buffer in smem (Hopper TMA), pipelined with Hopper GMMA and TMA, static schedule between TMA and GMMA
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template<
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int Stages_,
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class ClusterShape_ = Shape<_1,_1,_1>,
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int PipelineAsyncMmaStages_ = 1
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>
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struct MainloopSm90TmaGmma {
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constexpr static int Stages = Stages_;
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using ClusterShape = ClusterShape_;
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constexpr static int PipelineAsyncMmaStages = PipelineAsyncMmaStages_;
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using ArchTag = arch::Sm90;
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using Schedule = KernelTma;
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};
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// n-buffer in smem (Hopper TMA), pipelined with Hopper GMMA and TMA, Warp specialized dynamic schedule
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template<
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int Stages_,
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class ClusterShape_ = Shape<_1,_1,_1>,
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class KernelSchedule = KernelTmaWarpSpecializedCooperative
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>
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struct MainloopSm90TmaGmmaWarpSpecialized {
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constexpr static int Stages = Stages_;
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using ClusterShape = ClusterShape_;
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using ArchTag = arch::Sm90;
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using Schedule = KernelSchedule;
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};
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// n-buffer in smem (Hopper TMA), pipelined with Hopper GMMA and TMA, Warp specialized dynamic schedule
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// With GMMA's A data from registers.
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template<
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int Stages_,
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class ClusterShape_ = Shape<_1,_1,_1>,
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class KernelSchedule = KernelTmaWarpSpecialized
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>
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struct MainloopSm90TmaGmmaRmemAWarpSpecialized {
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constexpr static int Stages = Stages_;
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using ClusterShape = ClusterShape_;
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using ArchTag = arch::Sm90;
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using Schedule = KernelSchedule;
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static_assert(
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cute::is_same_v<Schedule, KernelTmaWarpSpecialized> ||
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cute::is_same_v<Schedule, KernelTmaWarpSpecializedPingpong> ||
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cute::is_same_v<Schedule, KernelTmaWarpSpecializedCooperative>,
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"KernelSchedule must be one of the warp specialized policies");
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};
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template<
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int Stages_,
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class ClusterShape_ = Shape<_1,_1,_1>,
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class KernelSchedule = KernelTmaWarpSpecialized
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>
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struct MainloopSm90TmaGmmaRmemAWarpSpecializedMixedInput {
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constexpr static int Stages = Stages_;
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using ClusterShape = ClusterShape_;
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using ArchTag = arch::Sm90;
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using Schedule = KernelSchedule;
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static_assert(
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cute::is_same_v<Schedule, KernelTmaWarpSpecialized> ||
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cute::is_same_v<Schedule, KernelTmaWarpSpecializedPingpong> ||
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cute::is_same_v<Schedule, KernelTmaWarpSpecializedCooperative>,
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"KernelSchedule must be one of the warp specialized policies");
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};
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// n-buffer in smem (Hopper TMA), pipelined with Hopper GMMA and TMA, Warp specialized dynamic schedule
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// For FP8 kernels
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template<
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int Stages_,
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class ClusterShape_ = Shape<_1,_1,_1>,
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class KernelSchedule = KernelTmaWarpSpecialized
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>
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struct MainloopSm90TmaGmmaWarpSpecializedFP8
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: MainloopSm90TmaGmmaWarpSpecialized<Stages_, ClusterShape_, KernelSchedule> {
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static_assert(
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cute::is_same_v<KernelSchedule, KernelTmaWarpSpecialized> ||
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cute::is_same_v<KernelSchedule, KernelTmaWarpSpecializedPingpong> ||
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cute::is_same_v<KernelSchedule, KernelTmaWarpSpecializedCooperative>,
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"KernelSchedule must be one of the warp specialized policies");
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};
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// n-buffer in smem (Hopper TMA), pipelined with Hopper GMMA and TMA, Warp specialized dynamic schedule
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// For FP8 kernels with Block Scaling
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template<
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int Stages_,
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class ClusterShape_ = Shape<_1,_1,_1>,
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class KernelSchedule = KernelTmaWarpSpecialized
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>
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struct MainloopSm90TmaGmmaWarpSpecializedBlockScalingFP8
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: MainloopSm90TmaGmmaWarpSpecialized<Stages_, ClusterShape_, KernelSchedule> {
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static_assert(
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cute::is_same_v<KernelSchedule, KernelTmaWarpSpecializedCooperativeFP8BlockScaledAccum>,
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"KernelSchedule must be one of the warp specialized policies");
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};
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// n-buffer in smem (Hopper TMA), pipelined with Hopper GMMA and TMA, Warp specialized dynamic schedule for Ptr-Array and Grouped Gemm
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template<
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int Stages_,
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class ClusterShape_ = Shape<_1,_1,_1>,
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class KernelSchedule = KernelPtrArrayTmaWarpSpecializedCooperative
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>
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struct MainloopSm90ArrayTmaGmmaWarpSpecialized {
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constexpr static int Stages = Stages_;
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using ClusterShape = ClusterShape_;
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using ArchTag = arch::Sm90;
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using Schedule = KernelSchedule;
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static_assert(
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cute::is_base_of_v<KernelPtrArrayTmaWarpSpecializedCooperative, KernelSchedule> ||
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cute::is_base_of_v<KernelPtrArrayTmaWarpSpecializedPingpong, KernelSchedule>,
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"KernelSchedule must be one of the Ptr-Array or Grouped Gemm TMA Warp Specialized Cooperative or Pingpong policies");
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};
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// n-buffer in smem (Hopper TMA), pipelined with Hopper sparse GMMA and TMA, Warp specialized dynamic schedule
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template<
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int Stages_,
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class ClusterShape_ = Shape<_1,_1,_1>,
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class KernelSchedule = KernelTmaWarpSpecializedCooperative
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>
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struct MainloopSm90TmaGmmaWarpSpecializedSparse {
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constexpr static int Stages = Stages_;
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using ClusterShape = ClusterShape_;
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using ArchTag = arch::Sm90;
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using Schedule = KernelSchedule;
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};
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// For slow-accumulation sparse FP8 kernels
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template<
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int Stages,
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class ClusterShape = Shape<_1,_1,_1>,
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class KernelSchedule = KernelTmaWarpSpecializedCooperative
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>
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struct MainloopSm90TmaGmmaWarpSpecializedSparseFP8
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: MainloopSm90TmaGmmaWarpSpecializedSparse<Stages, ClusterShape, KernelSchedule> {
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};
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template<
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int SchedulerPipelineStageCount_,
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int AccumulatorPipelineStageCount_
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>
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struct KernelTmaWarpSpecializedSm100 final {
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static constexpr int SchedulerPipelineStageCount = SchedulerPipelineStageCount_;
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static constexpr int AccumulatorPipelineStageCount = AccumulatorPipelineStageCount_;
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};
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// Gemm with block scaling factors
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template<
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int SchedulerPipelineStageCount_,
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int AccumulatorPipelineStageCount_
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>
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struct KernelTmaWarpSpecializedBlockScaledSm100 final {
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static constexpr int SchedulerPipelineStageCount = SchedulerPipelineStageCount_;
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static constexpr int AccumulatorPipelineStageCount = AccumulatorPipelineStageCount_;
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};
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// Ptr-Array Dense GEMM: SM100 tensor op policy that applies to both 1SM and 2SM MMA atoms
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template<
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int SchedulerPipelineStageCount_,
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int AccumulatorPipelineStageCount_
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>
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struct KernelPtrArrayTmaWarpSpecializedSm100 final {
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static constexpr int SchedulerPipelineStageCount = SchedulerPipelineStageCount_;
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static constexpr int AccumulatorPipelineStageCount = AccumulatorPipelineStageCount_;
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};
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// Ptr-Array Block Scaled GEMM
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template<
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int SchedulerPipelineStageCount_,
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int AccumulatorPipelineStageCount_
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>
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struct KernelPtrArrayTmaWarpSpecializedBlockScaledSm100 final {
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static constexpr int SchedulerPipelineStageCount = SchedulerPipelineStageCount_;
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static constexpr int AccumulatorPipelineStageCount = AccumulatorPipelineStageCount_;
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};
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//////////////////////////////////////////////////////////////////////////////
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//
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// Collective Builder Tag Property
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//
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struct KernelSchedule1Sm {};
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struct KernelSchedule2Sm {};
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struct KernelScheduleSm100 {};
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struct KernelScheduleSm100DenseGemm : KernelScheduleSm100 {};
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struct KernelScheduleBlockScaledGemmSm100 : KernelScheduleSm100 {};
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struct KernelScheduleMxNvf4Sm100 : KernelScheduleBlockScaledGemmSm100 {};
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struct KernelScheduleMxf8f6f4Sm100 : KernelScheduleBlockScaledGemmSm100 {};
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struct KernelScheduleSm100PtrArrayDenseGemm : KernelScheduleSm100DenseGemm {};
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struct KernelSchedulePtrArrayBlockScaledGemmSm100 : KernelScheduleBlockScaledGemmSm100 {};
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struct KernelSchedulePtrArrayMxNvf4Sm100 : KernelSchedulePtrArrayBlockScaledGemmSm100 {};
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struct KernelSchedulePtrArrayMxf8f6f4Sm100 : KernelSchedulePtrArrayBlockScaledGemmSm100 {};
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//
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// Collective Builder Tag
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// Only used in CollectiveBuilder
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//
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// Dense GEMM: Specialize for 1SM vs 2SM
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struct KernelTmaWarpSpecialized1SmSm100 final : KernelSchedule1Sm, KernelScheduleSm100DenseGemm {};
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struct KernelTmaWarpSpecialized2SmSm100 final : KernelSchedule2Sm, KernelScheduleSm100DenseGemm {};
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// Block Scaled Dense GEMM: Specialize for instruction type, scale factor vector size, and 1SM vs. 2SM
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struct KernelTmaWarpSpecialized1SmBlockScaledSm100 final : KernelSchedule1Sm, KernelScheduleBlockScaledGemmSm100 { };
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struct KernelTmaWarpSpecialized2SmBlockScaledSm100 final : KernelSchedule2Sm, KernelScheduleBlockScaledGemmSm100 { };
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struct KernelTmaWarpSpecialized1SmNvf4Sm100 final : KernelSchedule1Sm, KernelScheduleMxNvf4Sm100 { };
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struct KernelTmaWarpSpecialized2SmNvf4Sm100 final : KernelSchedule2Sm, KernelScheduleMxNvf4Sm100 { };
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struct KernelTmaWarpSpecialized1SmMxf4Sm100 final : KernelSchedule1Sm, KernelScheduleMxNvf4Sm100 { };
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struct KernelTmaWarpSpecialized2SmMxf4Sm100 final : KernelSchedule2Sm, KernelScheduleMxNvf4Sm100 { };
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struct KernelTmaWarpSpecialized1SmMxf8f6f4Sm100 final : KernelSchedule1Sm, KernelScheduleMxf8f6f4Sm100 { };
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struct KernelTmaWarpSpecialized2SmMxf8f6f4Sm100 final : KernelSchedule2Sm, KernelScheduleMxf8f6f4Sm100 { };
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// Ptr-Array Dense GEMM: Specialize for 1SM vs 2SM
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struct KernelPtrArrayTmaWarpSpecialized1SmSm100 final : KernelSchedule1Sm, KernelScheduleSm100PtrArrayDenseGemm {};
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struct KernelPtrArrayTmaWarpSpecialized2SmSm100 final : KernelSchedule2Sm, KernelScheduleSm100PtrArrayDenseGemm {};
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// Ptr-Array Block Scaled Dense GEMM: Specialize for instruction type, scale factor vector size, and 1SM vs. 2SM
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struct KernelPtrArrayTmaWarpSpecialized1SmBlockScaledSm100 final : KernelSchedule1Sm, KernelSchedulePtrArrayBlockScaledGemmSm100 { };
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struct KernelPtrArrayTmaWarpSpecialized2SmBlockScaledSm100 final : KernelSchedule2Sm, KernelSchedulePtrArrayBlockScaledGemmSm100 { };
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struct KernelPtrArrayTmaWarpSpecialized1SmNvf4Sm100 final : KernelSchedule1Sm, KernelSchedulePtrArrayMxNvf4Sm100 { };
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struct KernelPtrArrayTmaWarpSpecialized2SmNvf4Sm100 final : KernelSchedule2Sm, KernelSchedulePtrArrayMxNvf4Sm100 { };
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struct KernelPtrArrayTmaWarpSpecialized1SmMxf4Sm100 final : KernelSchedule1Sm, KernelSchedulePtrArrayMxNvf4Sm100 { };
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struct KernelPtrArrayTmaWarpSpecialized2SmMxf4Sm100 final : KernelSchedule2Sm, KernelSchedulePtrArrayMxNvf4Sm100 { };
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struct KernelPtrArrayTmaWarpSpecialized1SmMxf8f6f4Sm100 final : KernelSchedule1Sm, KernelSchedulePtrArrayMxf8f6f4Sm100 { };
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struct KernelPtrArrayTmaWarpSpecialized2SmMxf8f6f4Sm100 final : KernelSchedule2Sm, KernelSchedulePtrArrayMxf8f6f4Sm100 { };
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// n-buffer in smem, pipelined with Blackwell UMMA and TMA, Warp specialized dynamic schedule
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template<
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int Stages_,
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int SchedulerPipelineStageCount_,
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int AccumulatorPipelineStageCount_,
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class ClusterShape_ = Shape<_1,_1,_1>
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>
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struct MainloopSm100TmaUmmaWarpSpecialized {
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constexpr static int Stages = Stages_;
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using ClusterShape = ClusterShape_;
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using ArchTag = arch::Sm100;
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using Schedule = KernelTmaWarpSpecializedSm100<SchedulerPipelineStageCount_, AccumulatorPipelineStageCount_>;
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constexpr static bool IsOverlappingAccum = false;
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};
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// n-buffer in smem, pipelined with Blackwell UMMA and TMA, Warp specialized dynamic schedule
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template<
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int Stages_,
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int SchedulerPipelineStageCount_,
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int AccumulatorPipelineStageCount_,
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class ClusterShape_ = Shape<_1,_1,_1>
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>
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struct MainloopSm100TmaUmmaWarpSpecializedBlockScaled {
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constexpr static int Stages = Stages_;
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using ClusterShape = ClusterShape_;
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using ArchTag = arch::Sm100;
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constexpr static bool IsOverlappingAccum = AccumulatorPipelineStageCount_ == 1;
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using Schedule = KernelTmaWarpSpecializedBlockScaledSm100<SchedulerPipelineStageCount_, AccumulatorPipelineStageCount_>;
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};
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// n-buffer in smem, pipelined with Blackwell UMMA and TMA, Warp specialized dynamic schedule
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template<
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int Stages_,
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int SchedulerPipelineStageCount_,
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int AccumulatorPipelineStageCount_,
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class ClusterShape_ = Shape<_1,_1,_1>
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>
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struct MainloopSm100ArrayTmaUmmaWarpSpecialized {
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constexpr static int Stages = Stages_;
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using ClusterShape = ClusterShape_;
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using ArchTag = arch::Sm100;
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constexpr static bool IsOverlappingAccum = false;
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using Schedule = KernelPtrArrayTmaWarpSpecializedSm100<SchedulerPipelineStageCount_, AccumulatorPipelineStageCount_>;
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};
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// n-buffer in smem, pipelined with Blackwell UMMA and TMA, Warp specialized dynamic schedule
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|
template<
|
|
int Stages_,
|
|
int SchedulerPipelineStageCount_,
|
|
int AccumulatorPipelineStageCount_,
|
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class ClusterShape_ = Shape<_1,_1,_1>
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|
>
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struct MainloopSm100ArrayTmaUmmaWarpSpecializedBlockScaled {
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constexpr static int Stages = Stages_;
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using ClusterShape = ClusterShape_;
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using ArchTag = arch::Sm100;
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constexpr static bool IsOverlappingAccum = AccumulatorPipelineStageCount_ == 1;
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using Schedule = KernelPtrArrayTmaWarpSpecializedBlockScaledSm100<SchedulerPipelineStageCount_, AccumulatorPipelineStageCount_>;
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};
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//////////////////////////////////////////////////////////////////////////////
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} // namespace cutlass::gemm
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