mirror of
https://github.com/huggingface/candle.git
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d9f9c859af
* Add some flash-attn kernel, import the code for flash-attn v2 from Dao-AILab. * More flash attn. * Set up the flash attn parameters. * Get things to compile locally. * Move the flash attention files in a different directory. * Build the static C library with nvcc. * Add more flash attention. * Update the build part. * Better caching. * Exclude flash attention from the default workspace. * Put flash-attn behind a feature gate. * Get the flash attn kernel to run. * Move the flags to a more appropriate place. * Enable flash attention in llama. * Use flash attention in llama.
367 lines
18 KiB
C++
367 lines
18 KiB
C++
/******************************************************************************
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* Copyright (c) 2023, Tri Dao.
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******************************************************************************/
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#pragma once
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#include "cute/algorithm/copy.hpp"
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#include "cutlass/cutlass.h"
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#include "cutlass/layout/layout.h"
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#include <cutlass/numeric_types.h>
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using namespace cute;
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template<int kHeadDim_, int kBlockM_, int kBlockN_, int kNWarps_, typename elem_type=cutlass::half_t>
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struct Flash_kernel_traits {
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#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 800
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using Element = elem_type;
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static constexpr bool Has_cp_async = true;
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#else
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using Element = cutlass::half_t;
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static constexpr bool Has_cp_async = false;
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#endif
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using ElementAccum = float;
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using index_t = uint32_t;
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#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 800
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using MMA_Atom_Arch = std::conditional_t<
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std::is_same_v<elem_type, cutlass::half_t>,
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MMA_Atom<SM80_16x8x16_F32F16F16F32_TN>,
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MMA_Atom<SM80_16x8x16_F32BF16BF16F32_TN>
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>;
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using ValLayoutMNK = Layout<Shape<_1, _2, _1>>;
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#else
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using MMA_Atom_Arch = MMA_Atom<SM75_16x8x8_F32F16F16F32_TN>;
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using ValLayoutMNK = Layout<Shape<_1, _2, _2>>;
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#endif
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#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 750
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using SmemCopyAtom = Copy_Atom<SM75_U32x4_LDSM_N, elem_type>;
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using SmemCopyAtomTransposed = Copy_Atom<SM75_U16x8_LDSM_T, elem_type>;
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#else
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using SmemCopyAtom = Copy_Atom<DefaultCopy, elem_type>;
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using SmemCopyAtomTransposed = Copy_Atom<DefaultCopy, elem_type>;
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#endif
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};
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// If Share_Q_K_smem is true, that forces Is_Q_in_regs to be true
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template<int kHeadDim_, int kBlockM_, int kBlockN_, int kNWarps_, bool Is_Q_in_regs_=false, bool Share_Q_K_smem_=false, typename elem_type=cutlass::half_t,
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typename Base=Flash_kernel_traits<kHeadDim_, kBlockM_, kBlockN_, kNWarps_, elem_type> >
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struct Flash_fwd_kernel_traits : public Base {
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using Element = typename Base::Element;
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using ElementAccum = typename Base::ElementAccum;
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using index_t = typename Base::index_t;
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static constexpr bool Has_cp_async = Base::Has_cp_async;
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using SmemCopyAtom = typename Base::SmemCopyAtom;
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using SmemCopyAtomTransposed = typename Base::SmemCopyAtomTransposed;
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static constexpr bool Share_Q_K_smem = Share_Q_K_smem_;
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static constexpr bool Is_Q_in_regs = Is_Q_in_regs_ || Share_Q_K_smem;
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// The number of threads.
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static constexpr int kNWarps = kNWarps_;
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static constexpr int kNThreads = kNWarps * 32;
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static constexpr int kBlockM = kBlockM_;
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static constexpr int kBlockN = kBlockN_;
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static constexpr int kHeadDim = kHeadDim_;
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static_assert(kHeadDim % 32 == 0);
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static constexpr int kBlockKSmem = kHeadDim % 64 == 0 ? 64 : 32;
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static constexpr int kBlockKGmem = kHeadDim % 128 == 0 ? 128 : (kHeadDim % 64 == 0 ? 64 : 32);
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static constexpr int kSwizzle = kBlockKSmem == 32 ? 2 : 3;
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using TiledMma = TiledMMA<
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typename Base::MMA_Atom_Arch,
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Layout<Shape<Int<kNWarps>,_1,_1>>, // 4x1x1 or 8x1x1 thread group
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typename Base::ValLayoutMNK>; // 1x2x1 or 1x2x2 value group for 16x16x16 MMA and LDSM
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using SmemLayoutAtomQ = decltype(
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composition(Swizzle<kSwizzle, 3, 3>{},
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// This has to be kBlockKSmem, using kHeadDim gives wrong results for d=128
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Layout<Shape<_8, Int<kBlockKSmem>>,
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Stride<Int<kBlockKSmem>, _1>>{}));
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using SmemLayoutQ = decltype(tile_to_shape(
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SmemLayoutAtomQ{},
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Shape<Int<kBlockM>, Int<kHeadDim>>{}));
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using SmemLayoutKV = decltype(tile_to_shape(
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SmemLayoutAtomQ{},
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Shape<Int<kBlockN>, Int<kHeadDim>>{}));
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using SmemLayoutAtomVtransposed = decltype(
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composition(Swizzle<kSwizzle, 3, 3>{},
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// This has to be kBlockN and not 8, otherwise we get wrong results for d=128
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Layout<Shape<Int<kBlockKSmem>, Int<kBlockN>>,
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Stride<_1, Int<kBlockKSmem>>>{}));
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using SmemLayoutVtransposed = decltype(tile_to_shape(
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SmemLayoutAtomVtransposed{},
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Shape<Int<kHeadDim>, Int<kBlockN>>{}));
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// Maybe the VtransposeNoSwizzle just needs to have the right shape
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// And the strides don't matter?
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using SmemLayoutVtransposedNoSwizzle = decltype(SmemLayoutVtransposed{}.layout_fn());
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using SmemLayoutAtomO = decltype(
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composition(Swizzle<kSwizzle, 3, 3>{},
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Layout<Shape<Int<8>, Int<kBlockKSmem>>,
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Stride<Int<kBlockKSmem>, _1>>{}));
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using SmemLayoutO = decltype(tile_to_shape(
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SmemLayoutAtomO{},
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Shape<Int<kBlockM>, Int<kHeadDim>>{}));
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using SmemCopyAtomO = Copy_Atom<DefaultCopy, elem_type>;
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static constexpr int kSmemQCount = size(SmemLayoutQ{});
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static constexpr int kSmemKVCount = size(SmemLayoutKV{}) * 2;
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static constexpr int kSmemQSize = kSmemQCount * sizeof(Element);
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static constexpr int kSmemKVSize = kSmemKVCount * sizeof(Element);
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static constexpr int kSmemSize = Share_Q_K_smem ? std::max(kSmemQSize, kSmemKVSize) : kSmemQSize + kSmemKVSize;
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static constexpr int kGmemElemsPerLoad = sizeof(cute::uint128_t) / sizeof(Element);
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static_assert(kHeadDim % kGmemElemsPerLoad == 0, "kHeadDim must be a multiple of kGmemElemsPerLoad");
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// Using kBlockKSmem here is 6-10% faster than kBlockKGmem for d=128 because of bank conflicts.
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// For example, for d=128, smem is split into 2 "pages", each page takes care of columns
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// 0-63 and 64-127. If we have 16 threads per row for gmem read, when we write to smem,
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// thread 0 - 7 will write to the first page and thread 8 - 15 will write to the second page,
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// to the same banks.
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static constexpr int kGmemThreadsPerRow = kBlockKSmem / kGmemElemsPerLoad;
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static_assert(kNThreads % kGmemThreadsPerRow == 0, "kNThreads must be a multiple of kGmemThreadsPerRow");
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using GmemLayoutAtom = Layout<Shape <Int<kNThreads / kGmemThreadsPerRow>, Int<kGmemThreadsPerRow>>,
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Stride<Int<kGmemThreadsPerRow>, _1>>;
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// We use CACHEGLOBAL instead of CACHEALWAYS for both Q and K/V, since we won't be reading
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// from the same address by the same threadblock. This is slightly faster.
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using Gmem_copy_struct = std::conditional_t<
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Has_cp_async,
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SM80_CP_ASYNC_CACHEGLOBAL<cute::uint128_t>,
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DefaultCopy
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>;
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using GmemTiledCopyQKV = decltype(
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make_tiled_copy(Copy_Atom<Gmem_copy_struct, elem_type>{},
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GmemLayoutAtom{},
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Layout<Shape<_1, _8>>{})); // Val layout, 8 vals per read
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using GmemTiledCopyO = decltype(
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make_tiled_copy(Copy_Atom<DefaultCopy, elem_type>{},
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GmemLayoutAtom{},
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Layout<Shape<_1, _8>>{})); // Val layout, 8 vals per store
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static constexpr int kGmemThreadsPerRowP = kBlockN / kGmemElemsPerLoad;
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static_assert(kNThreads % kGmemThreadsPerRowP == 0, "kNThreads must be a multiple of kGmemThreadsPerRowP");
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using GmemLayoutAtomP = Layout<Shape <Int<kNThreads / kGmemThreadsPerRowP>, Int<kGmemThreadsPerRowP>>,
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Stride<Int<kGmemThreadsPerRowP>, _1>>;
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using GmemTiledCopyP = decltype(
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make_tiled_copy(Copy_Atom<DefaultCopy, elem_type>{},
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GmemLayoutAtomP{},
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Layout<Shape<_1, _8>>{})); // Val layout, 8 vals per store
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};
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// Is_V_in_regs is an option to reduce smem usage, but will increase register pressue.
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// No_double_buffer is another option to reduce smem usage, but will slow things down.
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template<int kHeadDim_, int kBlockM_, int kBlockN_, int kNWarps_,
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int AtomLayoutMSdP_=1, int AtomLayoutNdKV=2, int AtomLayoutMdQ=2,
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bool Is_V_in_regs_=false, bool No_double_buffer_=false, typename elem_type=cutlass::half_t,
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typename Base=Flash_kernel_traits<kHeadDim_, kBlockM_, kBlockN_, kNWarps_, elem_type> >
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struct Flash_bwd_kernel_traits : public Base {
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using Element = typename Base::Element;
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using ElementAccum = typename Base::ElementAccum;
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using index_t = typename Base::index_t;
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static constexpr bool Has_cp_async = Base::Has_cp_async;
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using SmemCopyAtom = typename Base::SmemCopyAtom;
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using SmemCopyAtomTransposed = typename Base::SmemCopyAtomTransposed;
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static constexpr bool Is_V_in_regs = Is_V_in_regs_;
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static constexpr bool No_double_buffer = No_double_buffer_;
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// The number of threads.
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static constexpr int kNWarps = kNWarps_;
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static constexpr int kNThreads = kNWarps * 32;
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static constexpr int kBlockM = kBlockM_;
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static constexpr int kBlockN = kBlockN_;
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static constexpr int kHeadDim = kHeadDim_;
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static_assert(kHeadDim % 32 == 0);
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static constexpr int kBlockKSmem = kHeadDim % 64 == 0 ? 64 : 32;
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static constexpr int kBlockKGmem = kHeadDim % 128 == 0 ? 128 : (kHeadDim % 64 == 0 ? 64 : 32);
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static constexpr int kSwizzle = kBlockKSmem == 32 ? 2 : 3;
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static constexpr int AtomLayoutMSdP = AtomLayoutMSdP_;
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static_assert(kNWarps % AtomLayoutMSdP == 0);
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static_assert(kNWarps % AtomLayoutNdKV == 0);
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static_assert(kNWarps % AtomLayoutMdQ == 0);
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using TiledMmaSdP = TiledMMA<
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typename Base::MMA_Atom_Arch,
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Layout<Shape<Int<AtomLayoutMSdP>, Int<kNWarps / AtomLayoutMSdP>, _1>>,
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typename Base::ValLayoutMNK>; // 1x2x1 or 1x2x2 value group for 16x16x16 MMA and LDSM
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using TiledMmadKV = TiledMMA<
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typename Base::MMA_Atom_Arch,
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Layout<Shape<Int<AtomLayoutNdKV>, Int<kNWarps / AtomLayoutNdKV>, _1>>,
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typename Base::ValLayoutMNK>; // 1x2x1 or 1x2x2 value group for 16x16x16 MMA and LDSM
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using TiledMmadQ = TiledMMA<
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typename Base::MMA_Atom_Arch,
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Layout<Shape<Int<AtomLayoutMdQ>, Int<kNWarps / AtomLayoutMdQ>, _1>>, // 2x4x1 or 4x2x1 thread group
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typename Base::ValLayoutMNK>; // 1x2x1 or 1x2x2 value group for 16x16x16 MMA and LDSM
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using SmemLayoutAtomQdO = decltype(
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composition(Swizzle<kSwizzle, 3, 3>{},
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Layout<Shape<_8, Int<kBlockKSmem>>,
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Stride<Int<kBlockKSmem>, _1>>{}));
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using SmemLayoutQdO = decltype(tile_to_shape(
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SmemLayoutAtomQdO{},
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make_shape(Int<kBlockM>{}, Int<kHeadDim>{})));
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using SmemLayoutAtomKV = decltype(
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composition(Swizzle<kSwizzle, 3, 3>{},
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Layout<Shape<Int<kBlockM / kNWarps>, Int<kBlockKSmem>>,
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Stride<Int<kBlockKSmem>, _1>>{}));
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using SmemLayoutKV = decltype(tile_to_shape(
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// SmemLayoutAtomQdO{},
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SmemLayoutAtomKV{},
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make_shape(Int<kBlockN>{}, Int<kHeadDim>{})));
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using SmemLayoutAtomKtransposed = decltype(
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composition(Swizzle<kSwizzle, 3, 3>{},
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Layout<Shape<Int<kBlockKSmem>, Int<kBlockN>>,
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Stride<_1, Int<kBlockKSmem>>>{}));
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using SmemLayoutKtransposed = decltype(tile_to_shape(
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SmemLayoutAtomKtransposed{},
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make_shape(Int<kHeadDim>{}, Int<kBlockN>{})));
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// Maybe the KtransposeNoSwizzle just needs to have the right shape
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// And the strides don't matter?
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using SmemLayoutKtransposedNoSwizzle = decltype(SmemLayoutKtransposed{}.layout_fn());
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// TODO: generalize to other values of kBlockN
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// TODO: what should be the Swizzle here? 3 is faster than 1, and 1 is faster than 2
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// static constexpr int kPBlockN = kBlockN;
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static_assert(kBlockN >= 64);
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// TD [2023-03-19]: Idk why kPBlockN = 16 and kSwizzlePdS=3 is the fastest.
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static constexpr int kPBlockN = 64;
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static_assert(kPBlockN == 16 || kPBlockN == 32 || kPBlockN == 64);
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// static constexpr int kSwizzlePdS = kPBlockN == 16 ? 1 : (kPBlockN == 32 ? 2 : 3);
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static constexpr int kSwizzlePdS = 3;
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using SmemLayoutAtomPdS = decltype(
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composition(Swizzle<kSwizzlePdS, 3, 3>{},
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Layout<Shape<Int<kBlockM>, Int<kPBlockN>>,
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Stride<Int<kPBlockN>, _1>>{}));
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using SmemLayoutPdS = decltype(tile_to_shape(
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SmemLayoutAtomPdS{},
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make_shape(Int<kBlockM>{}, Int<kBlockN>{})));
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using SmemLayoutAtomPdStransposed = decltype(
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composition(Swizzle<kSwizzlePdS, 3, 3>{},
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Layout<Shape<Int<kPBlockN>, Int<kBlockM>>,
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Stride<_1, Int<kPBlockN>>>{}));
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using SmemLayoutPdStransposed = decltype(tile_to_shape(
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SmemLayoutAtomPdStransposed{},
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make_shape(Int<kBlockN>{}, Int<kBlockM>{})));
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using SmemLayoutPdStransposedNoSwizzle = decltype(SmemLayoutPdStransposed{}.layout_fn());
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using SmemCopyAtomPdS = Copy_Atom<DefaultCopy, elem_type>;
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using SmemLayoutAtomQdOtransposed = decltype(
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composition(Swizzle<kSwizzle, 3, 3>{},
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Layout<Shape<Int<kBlockKSmem>, Int<kBlockM>>,
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Stride<_1, Int<kBlockKSmem>>>{}));
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using SmemLayoutQdOtransposed = decltype(tile_to_shape(
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SmemLayoutAtomQdOtransposed{},
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make_shape(Int<kHeadDim>{}, Int<kBlockM>{})));
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using SmemLayoutQdOtransposedNoSwizzle = decltype(SmemLayoutQdOtransposed{}.layout_fn());
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using SmemLayoutAtomdKV = decltype(
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composition(Swizzle<kSwizzle, 3, 3>{},
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Layout<Shape<_8, Int<kBlockKSmem>>,
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Stride<Int<kBlockKSmem>, _1>>{}));
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using SmemLayoutdKV = decltype(tile_to_shape(
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SmemLayoutAtomdKV{},
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make_shape(Int<kBlockN>{}, Int<kHeadDim>{})));
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using SmemCopyAtomdKV = Copy_Atom<DefaultCopy, elem_type>;
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using SmemLayoutAtomdQ = decltype(
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composition(Swizzle<kSwizzle, 3, 3>{},
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Layout<Shape<_8, Int<kBlockKSmem>>,
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Stride<Int<kBlockKSmem>, _1>>{}));
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using SmemLayoutdQ = decltype(tile_to_shape(
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SmemLayoutAtomdQ{},
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make_shape(Int<kBlockM>{}, Int<kHeadDim>{})));
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using SmemCopyAtomdQ = Copy_Atom<DefaultCopy, elem_type>;
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static constexpr int kSmemQdOCount = size(SmemLayoutQdO{}) * (No_double_buffer ? 2 : 3); // Double buffer for sQ
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static constexpr int kSmemKVCount = size(SmemLayoutKV{}) * 2;
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static constexpr int kSmemdSCount = size(SmemLayoutPdS{});
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static constexpr int kSmemPCount = size(SmemLayoutPdS{});
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static constexpr int kSmemdQCount = size(SmemLayoutdQ{});
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static constexpr int kSmemdPsumCount = kBlockM;
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static constexpr int kSmemQdOSize = kSmemQdOCount * sizeof(Element);
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static constexpr int kSmemKVSize = kSmemKVCount * sizeof(Element);
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static constexpr int kSmemdSSize = kSmemdSCount * sizeof(Element);
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static constexpr int kSmemPSize = kSmemPCount * sizeof(Element);
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static constexpr int kSmemdQSize = kSmemdQCount * sizeof(Element);
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static constexpr int kSmemdPsumSize = kSmemdPsumCount * sizeof(ElementAccum);
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static constexpr int kSmemSize = kSmemQdOSize
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+ (!Is_V_in_regs
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? kSmemKVSize + kSmemdSSize + std::max(kSmemPSize, kSmemdQSize)
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: std::max(kSmemKVSize, kSmemKVSize / 2 + kSmemdSSize + std::max(kSmemPSize, kSmemdQSize)));
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static constexpr int kSmemSize1colblock = kSmemQdOSize
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+ (!Is_V_in_regs
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? kSmemKVSize + kSmemdSSize + kSmemPSize
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: std::max(kSmemKVSize, kSmemKVSize / 2 + kSmemdSSize + kSmemPSize));
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static constexpr int kSmemSize1rowblock = kSmemQdOSize / 3 * 2 + kSmemKVSize / 2 * 3
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+ kSmemdSSize + kSmemPSize;
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static constexpr int kGmemElemsPerLoad = sizeof(cute::uint128_t) / sizeof(Element);
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static_assert(kHeadDim % kGmemElemsPerLoad == 0, "kHeadDim must be a multiple of kGmemElemsPerLoad");
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// Using kBlockKSmem instead of kHeadDim here to avoid bank conflicts, but doesn't seem
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// to affect speed in practice.
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static constexpr int kGmemThreadsPerRow = kBlockKSmem / kGmemElemsPerLoad;
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static_assert(kNThreads % kGmemThreadsPerRow == 0, "kNThreads must be a multiple of kGmemThreadsPerRow");
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using GmemLayoutAtom = Layout<Shape <Int<kNThreads / kGmemThreadsPerRow>, Int<kGmemThreadsPerRow>>,
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Stride<Int<kGmemThreadsPerRow>, _1>>;
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// We use CACHEGLOBAL instead of CACHEALWAYS for both Q and K/V, since we won't be reading
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// from the same address by the same threadblock. This is slightly faster.
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using Gmem_copy_struct = std::conditional_t<
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Has_cp_async,
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SM80_CP_ASYNC_CACHEGLOBAL<cute::uint128_t>,
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DefaultCopy
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>;
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using GmemTiledCopyQKV = decltype(
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make_tiled_copy(Copy_Atom<Gmem_copy_struct, elem_type>{},
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GmemLayoutAtom{},
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Layout<Shape<_1, _8>>{})); // Val layout, 8 vals per read
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using GmemTiledCopydO = decltype(
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make_tiled_copy(Copy_Atom<DefaultCopy, elem_type>{},
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GmemLayoutAtom{},
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Layout<Shape < _1, _8>>{})); // Val layout, 8 vals per store
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using GmemTiledCopydKV = decltype(
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make_tiled_copy(Copy_Atom<DefaultCopy, elem_type>{},
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GmemLayoutAtom{},
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Layout<Shape < _1, _8>>{})); // Val layout, 8 vals per store
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using GmemTiledCopydQ = decltype(
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make_tiled_copy(Copy_Atom<DefaultCopy, elem_type>{},
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GmemLayoutAtom{},
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Layout<Shape < _1, _8>>{})); // Val layout, 8 vals per store
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using GmemLayoutAtomdQaccum = std::conditional_t<
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kBlockKSmem == 32,
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Layout<Shape <_32, _8>, // Thread layout, 8 threads per row
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Stride< _8, _1>>,
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Layout<Shape <_16, _16>, // Thread layout, 16 threads per row
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Stride< _16, _1>>
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>;
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using GmemTiledCopydQaccum = decltype(
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make_tiled_copy(Copy_Atom<DefaultCopy, ElementAccum>{},
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GmemLayoutAtomdQaccum{},
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Layout<Shape < _1, _4>>{})); // Val layout, 4 vals per store
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using GmemTiledCopydQaccumAtomicAdd = decltype(
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make_tiled_copy(Copy_Atom<DefaultCopy, ElementAccum>{},
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Layout<Shape <_8, _32>, // Thread layout, 8 threads per row
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Stride<_32, _1>>{},
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Layout<Shape < _1, _1>>{})); // Val layout, 1 val per store
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};
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////////////////////////////////////////////////////////////////////////////////////////////////////
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