flash_fwd_launch_template.h

/******************************************************************************
 * Copyright (c) 2023, Tri Dao.
 ******************************************************************************/

#pragma once

#include <ATen/cuda/CUDAContext.h>

#include "static_switch.h"
#include "flash.h"
#include "flash_fwd_kernel.h"

template<typename Kernel_traits, bool Is_dropout, bool Is_causal, bool Is_even_MN, bool Is_even_K, bool Return_softmax>
__global__ void flash_fwd_kernel(Flash_fwd_params params) {
    flash::compute_attn<Kernel_traits, Is_dropout, Is_causal, Is_even_MN, Is_even_K, Return_softmax>(params);
}

template<typename Kernel_traits, bool Is_dropout, bool Is_causal>
void run_flash_fwd(Flash_fwd_params &params, cudaStream_t stream) {
    constexpr size_t smem_size = Kernel_traits::kSmemSize;
    // printf("smem_size = %d\n", smem_size);

    // Work-around for gcc 7. It doesn't like nested BOOL_SWITCH.
    // https://github.com/kokkos/kokkos-kernels/issues/349
    // https://github.com/HazyResearch/flash-attention/issues/21

    const int num_m_block = (params.seqlen_q + Kernel_traits::kBlockM - 1) / Kernel_traits::kBlockM;
    dim3 grid(num_m_block, params.b, params.h);
    const bool is_even_MN = params.cu_seqlens_q == nullptr && params.cu_seqlens_k == nullptr && params.seqlen_k % Kernel_traits::kBlockN == 0 && params.seqlen_q % Kernel_traits::kBlockM == 0;
    const bool is_even_K = params.d == Kernel_traits::kHeadDim;
    const bool return_softmax = params.p_ptr != nullptr;
    BOOL_SWITCH(is_even_MN, IsEvenMNConst, [&] {
        BOOL_SWITCH(is_even_K, IsEvenKConst, [&] {
            BOOL_SWITCH(return_softmax, ReturnSoftmaxConst, [&] {
                // Will only return softmax if dropout, to reduce compilation time.
                auto kernel = &flash_fwd_kernel<Kernel_traits, Is_dropout, Is_causal, IsEvenMNConst, IsEvenKConst, ReturnSoftmaxConst && Is_dropout>;
                // auto kernel = &flash_fwd_kernel<Kernel_traits, Is_dropout, Is_causal, IsEvenMNConst, true, ReturnSoftmaxConst && Is_dropout>;
                if (smem_size >= 48 * 1024) {
                    C10_CUDA_CHECK(cudaFuncSetAttribute(
                        kernel, cudaFuncAttributeMaxDynamicSharedMemorySize, smem_size));
                }
                int ctas_per_sm;
                cudaError status_ = cudaOccupancyMaxActiveBlocksPerMultiprocessor(
                    &ctas_per_sm, kernel, Kernel_traits::kNThreads, smem_size);
                // printf("smem_size = %d, CTAs per SM = %d\n", int(smem_size), ctas_per_sm);
                kernel<<<grid, Kernel_traits::kNThreads, smem_size, stream>>>(params);
                C10_CUDA_KERNEL_LAUNCH_CHECK();
            });
        });
    });
}

template<typename T>
void run_mha_fwd_hdim32(Flash_fwd_params &params, cudaStream_t stream) {
    constexpr int Headdim = 32;
    BOOL_SWITCH(params.p_dropout < 1.f, Is_dropout, [&] {
        BOOL_SWITCH(params.is_causal, Is_causal, [&] {
            run_flash_fwd<Flash_fwd_kernel_traits<Headdim, 128, 128, 4, false, false, T>, Is_dropout, Is_causal>(params, stream);
        });
    });
}

template<typename T>
void run_mha_fwd_hdim64(Flash_fwd_params &params, cudaStream_t stream) {
    constexpr int Headdim = 64;
    BOOL_SWITCH(params.p_dropout < 1.f, Is_dropout, [&] {
        BOOL_SWITCH(params.is_causal, Is_causal, [&] {
            if constexpr(!Is_dropout) {
                // Using 8 warps is 18% slower for seqlen=2k, 2 warps is 5% slower
                // Using block size (64 x 256) is 27% slower for seqlen=2k
                // Using block size (256 x 64) is 85% slower for seqlen=2k, because of register spilling
                run_flash_fwd<Flash_fwd_kernel_traits<Headdim, 128, 128, 4, false, false, T>, Is_dropout, Is_causal>(params, stream);
                // run_flash_fwd<Flash_fwd_kernel_traits<Headdim, 128, 64, 4, true, false, T>, Is_dropout, Is_causal>(params, stream);
                // run_flash_fwd<Flash_fwd_kernel_traits<Headdim, 128, 64, 4, true, true, T>, Is_dropout, Is_causal>(params, stream);
            } else {
                run_flash_fwd<Flash_fwd_kernel_traits<Headdim, 128, 64, 4, false, false, T>, Is_dropout, Is_causal>(params, stream);
                // run_flash_fwd<Flash_fwd_kernel_traits<Headdim, 128, 64, 4, true, true, T>, Is_dropout, Is_causal>(params, stream);
                // run_flash_fwd<Flash_fwd_kernel_traits<Headdim, 128, 64, 4, true, false, T>, Is_dropout, Is_causal>(params, stream);
                // run_flash_fwd<Flash_fwd_kernel_traits<Headdim, 128, 128, 4, false, false, T>, Is_dropout, Is_causal>(params, stream);
            }
        });
    });
}

template<typename T>
void run_mha_fwd_hdim96(Flash_fwd_params &params, cudaStream_t stream) {
    constexpr int Headdim = 96;
    auto dprops = at::cuda::getCurrentDeviceProperties();
    bool is_sm8x = dprops->major == 8 && dprops->minor > 0;
    BOOL_SWITCH(params.p_dropout < 1.f, Is_dropout, [&] {
        BOOL_SWITCH(params.is_causal, Is_causal, [&] {
            // For sm86 or sm89, 64 x 64 is the fastest for causal (because it's square),
            if (is_sm8x) {
                if constexpr(!Is_causal) {
                    run_flash_fwd<Flash_fwd_kernel_traits<Headdim, 128, 64, 4, false, false, T>, Is_dropout, Is_causal>(params, stream);
                } else {
                    run_flash_fwd<Flash_fwd_kernel_traits<Headdim, 64, 64, 4, false, false, T>, Is_dropout, Is_causal>(params, stream);
                }
            } else {
                run_flash_fwd<Flash_fwd_kernel_traits<Headdim, 128, 64, 4, false, false, T>, Is_dropout, Is_causal>(params, stream);
            }
            // run_flash_fwd<Flash_fwd_kernel_traits<Headdim, 128, 64, 4, true, false, T>, Is_dropout, Is_causal>(params, stream);
            // run_flash_fwd<Flash_fwd_kernel_traits<Headdim, 128, 64, 4, true, true, T>, Is_dropout, Is_causal>(params, stream);
            // These two are always slower
            // run_flash_fwd<Flash_fwd_kernel_traits<96, 128, 128, 4, true, T>>(params, stream);
            // run_flash_fwd<Flash_fwd_kernel_traits<96, 64, 128, 4, true, T>>(params, stream);
        });
    });
}

template<typename T>
void run_mha_fwd_hdim128(Flash_fwd_params &params, cudaStream_t stream) {
    constexpr int Headdim = 128;
    auto dprops = at::cuda::getCurrentDeviceProperties();
    bool is_sm8x = dprops->major == 8 && dprops->minor > 0;
    BOOL_SWITCH(params.p_dropout < 1.f, Is_dropout, [&] {
        BOOL_SWITCH(params.is_causal, Is_causal, [&] {
            if constexpr(!Is_dropout) {
                // For sm86 or sm89, 64 x 64 is the fastest for causal (because it's square),
                // and 128 x 32 (48 KB smem) is the fastest for non-causal since we get 2 CTAs per SM.
                if (is_sm8x) {
                    if constexpr(!Is_causal) {
                        run_flash_fwd<Flash_fwd_kernel_traits<Headdim, 128, 32, 4, false, false, T>, Is_dropout, Is_causal>(params, stream);
                    } else {
                        run_flash_fwd<Flash_fwd_kernel_traits<Headdim, 64, 64, 4, false, false, T>, Is_dropout, Is_causal>(params, stream);
                    }
                } else {
                    run_flash_fwd<Flash_fwd_kernel_traits<Headdim, 128, 64, 4, false, false, T>, Is_dropout, Is_causal>(params, stream);
                }
                // run_flash_fwd<Flash_fwd_kernel_traits<Headdim, 128, 64, 4, true, false, T>, Is_dropout, Is_causal>(params, stream);
                // run_flash_fwd<Flash_fwd_kernel_traits<Headdim, 128, 64, 4, true, true, T>, Is_dropout, Is_causal>(params, stream);
                // run_flash_fwd<Flash_fwd_kernel_traits<Headdim, 64, 128, 4, false, false, T>, Is_dropout, Is_causal>(params, stream);
                // Using 8 warps (128 x 128 and 256 x 64) is 28% slower for seqlen=2k
                // run_flash_fwd<Flash_fwd_kernel_traits<Headdim, 128, 128, 8, false, false, T>, Is_dropout, Is_causal>(params, stream);
                // run_flash_fwd<Flash_fwd_kernel_traits<Headdim, 128, 64, 8, false, false, T>, Is_dropout, Is_causal>(params, stream);
                // 1st ones are good for H100, A100
                // 2nd one is good for A6000 bc we get slightly better occupancy
            } else {
                run_flash_fwd<Flash_fwd_kernel_traits<Headdim, 128, 32, 4, false, false, T>, Is_dropout, Is_causal>(params, stream);
                // run_flash_fwd<Flash_fwd_kernel_traits<Headdim, 64, 64, 4, false, false, T>, Is_dropout, Is_causal>(params, stream);
                // run_flash_fwd<Flash_fwd_kernel_traits<Headdim, 128, 32, 4, true, false, T>, Is_dropout, Is_causal>(params, stream);
                // run_flash_fwd<Flash_fwd_kernel_traits<Headdim, 128, 32, 4, true, true, T>, Is_dropout, Is_causal>(params, stream);
            }
        });
    });
}

template<typename T>
void run_mha_fwd_hdim160(Flash_fwd_params &params, cudaStream_t stream) {
    constexpr int Headdim = 160;
    auto dprops = at::cuda::getCurrentDeviceProperties();
    bool is_sm8x = dprops->major == 8 && dprops->minor > 0;
    BOOL_SWITCH(params.p_dropout < 1.f, Is_dropout, [&] {
        BOOL_SWITCH(params.is_causal, Is_causal, [&] {
            // For A100, H100, 128 x 32 is the fastest.
            // For sm86 or sm89, 64 x 64 is the fastest for causal (because it's square),
            // and 128 x 64 with 8 warps is the fastest for non-causal.
            if (is_sm8x) {
                if constexpr(!Is_causal) {
                    run_flash_fwd<Flash_fwd_kernel_traits<Headdim, 128, 64, 8, false, false, T>, Is_dropout, Is_causal>(params, stream);
                } else {
                    run_flash_fwd<Flash_fwd_kernel_traits<Headdim, 64, 64, 4, false, false, T>, Is_dropout, Is_causal>(params, stream);
                }
            } else {
                run_flash_fwd<Flash_fwd_kernel_traits<Headdim, 128, 32, 4, false, false, T>, Is_dropout, Is_causal>(params, stream);
            }
            // run_flash_fwd<Flash_fwd_kernel_traits<Headdim, 128, 32, 4, false, true, T>, Is_dropout, Is_causal>(params, stream);
            // run_flash_fwd<Flash_fwd_kernel_traits<Headdim, 128, 64, 4, false, false, T>, Is_dropout, Is_causal>(params, stream);
            // run_flash_fwd<Flash_fwd_kernel_traits<Headdim, 128, 64, 4, false, T>>(params, stream);
            // run_flash_fwd<Flash_fwd_kernel_traits<Headdim, 64, 128, 4, false, T>>(params, stream);
            // run_flash_fwd<Flash_fwd_kernel_traits<Headdim, 64, 64, 4, false, T>>(params, stream);
            // run_flash_fwd<Flash_fwd_kernel_traits<Headdim, 128, 64, 8, false, T>>(params, stream);
            // run_flash_fwd<Flash_fwd_kernel_traits<Headdim, 128, 128, 8, false, T>>(params, stream);
        });
    });
}

template<typename T>
void run_mha_fwd_hdim192(Flash_fwd_params &params, cudaStream_t stream) {
    constexpr int Headdim = 192;
    BOOL_SWITCH(params.p_dropout < 1.f, Is_dropout, [&] {
        BOOL_SWITCH(params.is_causal, Is_causal, [&] {
            if constexpr(!Is_dropout) {
                run_flash_fwd<Flash_fwd_kernel_traits<Headdim, 128, 64, 8, false, false, T>, Is_dropout, Is_causal>(params, stream);
            } else {
                run_flash_fwd<Flash_fwd_kernel_traits<Headdim, 64, 64, 4, false, false, T>, Is_dropout, Is_causal>(params, stream);
            }
            // run_flash_fwd<Flash_fwd_kernel_traits<Headdim, 64, 32, 4, false, false, T>, Is_dropout, Is_causal>(params, stream);
            // run_flash_fwd<Flash_fwd_kernel_traits<Headdim, 128, 32, 8, false, false, T>, Is_dropout, Is_causal>(params, stream);
            // run_flash_fwd<Flash_fwd_kernel_traits<Headdim, 128, 64, 4, false, T>>(params, stream);
            // run_flash_fwd<Flash_fwd_kernel_traits<Headdim, 64, 128, 4, false, T>>(params, stream);
            // run_flash_fwd<Flash_fwd_kernel_traits<Headdim, 128, 128, 8, false, T>>(params, stream);
        });
    });
}

template<typename T>
void run_mha_fwd_hdim224(Flash_fwd_params &params, cudaStream_t stream) {
    constexpr int Headdim = 224;
    int device;
    cudaGetDevice(&device);
    int max_smem_per_block;
    cudaError status_ = cudaDeviceGetAttribute(
        &max_smem_per_block, cudaDevAttrMaxSharedMemoryPerBlockOptin, device);
    // printf("max_smem_per_block = %d\n", max_smem_per_block);
    BOOL_SWITCH(params.p_dropout < 1.f, Is_dropout, [&] {
        BOOL_SWITCH(params.is_causal, Is_causal, [&] {
            if (max_smem_per_block >= 2 * Headdim * (128 + 2 * 64)) {  // 112 KB
                run_flash_fwd<Flash_fwd_kernel_traits<Headdim, 128, 64, 8, false, false, T>, Is_dropout, Is_causal>(params, stream);
            } else {
                run_flash_fwd<Flash_fwd_kernel_traits<Headdim, 64, 64, 4, false, false, T>, Is_dropout, Is_causal>(params, stream);
            }
            // run_flash_fwd<Flash_fwd_kernel_traits<Headdim, 128, 32, 4, false, false, T>, Is_dropout, Is_causal>(params, stream);
            // run_flash_fwd<Flash_fwd_kernel_traits<Headdim, 64, 32, 4, false, false, T>, Is_dropout, Is_causal>(params, stream);
            // We can't do 128 x 32 with 8 warps because with headdim 224, kBlockKSmem = 32.
            // If we have N = 32, there are only 1024 elements to load at once, where each load
            // is 8 elements. This means we can only use 128 threads and not 256 threads.
            // run_flash_fwd<Flash_fwd_kernel_traits<Headdim, 128, 32, 8, false, false, T>, Is_dropout, Is_causal>(params, stream);
        });
    });
}

template<typename T>
void run_mha_fwd_hdim256(Flash_fwd_params &params, cudaStream_t stream) {
    constexpr int Headdim = 256;
    int device;
    cudaGetDevice(&device);
    int max_smem_per_sm, max_smem_per_block;
    cudaError status_ = cudaDeviceGetAttribute(
        &max_smem_per_sm, cudaDevAttrMaxSharedMemoryPerMultiprocessor, device);
    status_ = cudaDeviceGetAttribute(
        &max_smem_per_block, cudaDevAttrMaxSharedMemoryPerBlockOptin, device);
    // printf("max_smem_per_sm = %d, max_smem_per_block = %d\n", max_smem_per_sm, max_smem_per_block);
    BOOL_SWITCH(params.p_dropout < 1.f, Is_dropout, [&] {
        BOOL_SWITCH(params.is_causal, Is_causal, [&] {
            // For A100, we want to run with 128 x 64 (128KB smem).
            // For H100 we want to run with 64 x 64 (96KB smem) since then we can get 2 CTAs per SM.
            if (max_smem_per_block >= 2 * Headdim * (128 + 2 * 64) && max_smem_per_sm < 4 * Headdim * (64 + 2 * 64)) {
                run_flash_fwd<Flash_fwd_kernel_traits<Headdim, 128, 64, 8, false, false, T>, Is_dropout, Is_causal>(params, stream);
            } else {
                run_flash_fwd<Flash_fwd_kernel_traits<Headdim, 64, 64, 4, false, false, T>, Is_dropout, Is_causal>(params, stream);
            }
            // 64 KB
            // run_flash_fwd<Flash_fwd_kernel_traits<Headdim, 64, 32, 4, false, false, T>, Is_dropout, Is_causal>(params, stream);
            // 96 KB
            // run_flash_fwd<Flash_fwd_kernel_traits<Headdim, 128, 32, 8, false, false, T>, Is_dropout, Is_causal>(params, stream);
        });
    });
}