llama.cpp/ggml-cuda/fattn-common.cuh

#include "common.cuh"

#include <cstdint>

#define FATTN_KQ_STRIDE       256
#define HALF_MAX_HALF         __float2half(65504.0f/2) // Use neg. of this instead of -INFINITY to initialize KQ max vals to avoid NaN upon subtraction.
#define SOFTMAX_FTZ_THRESHOLD -20.0f                   // Softmax exp. of values smaller than this are flushed to zero to avoid NaNs.

typedef void (* fattn_kernel_t)(
        const char * __restrict__ Q,
        const char * __restrict__ K,
        const char * __restrict__ V,
        const char * __restrict__ mask,
        float      * __restrict__ dst,
        float2     * __restrict__ dst_meta,
        const float scale,
        const float max_bias,
        const float m0,
        const float m1,
        const uint32_t n_head_log2,
        const int ne00,
        const int ne01,
        const int ne02,
        const int ne03,
        const int ne10,
        const int ne11,
        const int ne12,
        const int ne13,
        const int ne31,
        const int nb31,
        const int nb01,
        const int nb02,
        const int nb03,
        const int nb11,
        const int nb12,
        const int nb13,
        const int ne0,
        const int ne1,
        const int ne2,
        const int ne3);

template<int D, int parallel_blocks> // D == head size
#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
__launch_bounds__(D, 1)
#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
static __global__ void flash_attn_combine_results(
        const float  * __restrict__ VKQ_parts,
        const float2 * __restrict__ VKQ_meta,
        float * __restrict__ dst) {
    VKQ_parts += parallel_blocks*D * gridDim.y*blockIdx.x;
    VKQ_meta  += parallel_blocks   * gridDim.y*blockIdx.x;
    dst       +=                 D * gridDim.y*blockIdx.x;

    const int tid = threadIdx.x;
    __builtin_assume(tid < D);

    __shared__ float2 meta[parallel_blocks];
    if (tid < 2*parallel_blocks) {
        ((float *) meta)[threadIdx.x] = ((const float *)VKQ_meta) [blockIdx.y*(2*parallel_blocks) + tid];
    }

    __syncthreads();

    float kqmax = meta[0].x;
#pragma unroll
    for (int l = 1; l < parallel_blocks; ++l) {
        kqmax = max(kqmax, meta[l].x);
    }

    float VKQ_numerator   = 0.0f;
    float VKQ_denominator = 0.0f;
#pragma unroll
    for (int l = 0; l < parallel_blocks; ++l) {
        const float diff = meta[l].x - kqmax;
        const float KQ_max_scale = expf(diff);
        const uint32_t ftz_mask = 0xFFFFFFFF * (diff > SOFTMAX_FTZ_THRESHOLD);
        *((uint32_t *) &KQ_max_scale) &= ftz_mask;

        VKQ_numerator   += KQ_max_scale * VKQ_parts[l*gridDim.y*D + blockIdx.y*D + tid];
        VKQ_denominator += KQ_max_scale * meta[l].y;
    }

    dst[blockIdx.y*D + tid] = VKQ_numerator / VKQ_denominator;
}

template <int D, int parallel_blocks>
void launch_fattn(ggml_backend_cuda_context & ctx, ggml_tensor * dst, fattn_kernel_t fattn_kernel, int nwarps, int cols_per_block) {
    const ggml_tensor * Q = dst->src[0];
    const ggml_tensor * K = dst->src[1];
    const ggml_tensor * V = dst->src[2];

    const ggml_tensor * mask = dst->src[3];

    ggml_tensor * KQV = dst;

    GGML_ASSERT(Q->type == GGML_TYPE_F32);
    GGML_ASSERT(K->type == GGML_TYPE_F16);
    GGML_ASSERT(V->type == GGML_TYPE_F16);
    GGML_ASSERT(KQV->type == GGML_TYPE_F32);

    GGML_ASSERT(!mask || mask->type == GGML_TYPE_F16);
    GGML_ASSERT(!mask || mask->ne[1] >= GGML_PAD(Q->ne[1], 16) &&
                                "the Flash-Attention CUDA kernel requires the mask to be padded to 16 and at least n_queries big");

    GGML_ASSERT(K->ne[1] % FATTN_KQ_STRIDE == 0 && "Incorrect KV cache padding.");

    ggml_cuda_pool & pool = ctx.pool();
    cudaStream_t main_stream = ctx.stream();

    ggml_cuda_pool_alloc<float>  dst_tmp(pool);
    ggml_cuda_pool_alloc<float2> dst_tmp_meta(pool);

    if (parallel_blocks > 1) {
        dst_tmp.alloc(parallel_blocks*ggml_nelements(KQV));
        dst_tmp_meta.alloc(parallel_blocks*ggml_nrows(KQV));
    }

    const dim3 block_dim(WARP_SIZE, nwarps, 1);
    const dim3 blocks_num(parallel_blocks*((Q->ne[1] + cols_per_block - 1) / cols_per_block), Q->ne[2], Q->ne[3]);
    const int  shmem = 0;

    float scale    = 1.0f;
    float max_bias = 0.0f;

    memcpy(&scale,    (float *) KQV->op_params + 0, sizeof(float));
    memcpy(&max_bias, (float *) KQV->op_params + 1, sizeof(float));

    const uint32_t n_head      = Q->ne[2];
    const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head));

    const float m0 = powf(2.0f, -(max_bias       ) / n_head_log2);
    const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2);

    fattn_kernel<<<blocks_num, block_dim, shmem, main_stream>>>(
        (const char *) Q->data,
        (const char *) K->data,
        (const char *) V->data,
        mask ? ((const char *) mask->data) : nullptr,
        (parallel_blocks) == 1 ? (float *) KQV->data : dst_tmp.ptr, dst_tmp_meta.ptr,
        scale, max_bias, m0, m1, n_head_log2,
        Q->ne[0], Q->ne[1], Q->ne[2], Q->ne[3],
        K->ne[0], K->ne[1], K->ne[2], K->ne[3],
        mask ? mask->ne[1] : 0, mask ?  mask->nb[1] : 0,
        Q->nb[1], Q->nb[2], Q->nb[3],
        K->nb[1], K->nb[2], K->nb[3],
        KQV->ne[0], KQV->ne[1], KQV->ne[2], KQV->ne[3]
    );
    CUDA_CHECK(cudaGetLastError());

    if ((parallel_blocks) == 1) {
        return;
    }

    const dim3 block_dim_combine(D, 1, 1);
    const dim3 blocks_num_combine(Q->ne[1], blocks_num.y, blocks_num.z);
    const int  shmem_combine = 0;

    flash_attn_combine_results<D, parallel_blocks>
        <<<blocks_num_combine, block_dim_combine, shmem_combine, main_stream>>>
        (dst_tmp.ptr, dst_tmp_meta.ptr, (float *) KQV->data);
    CUDA_CHECK(cudaGetLastError());
}