mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-27 06:39:25 +01:00
331 lines
11 KiB
Plaintext
331 lines
11 KiB
Plaintext
#include "common.cuh"
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#include "fattn-common.cuh"
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#include "fattn-vec-f16.cuh"
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template<int D, int ncols, int parallel_blocks> // D == head size
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#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
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__launch_bounds__(D, 1)
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#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
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static __global__ void flash_attn_vec_ext_f16(
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const char * __restrict__ Q,
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const char * __restrict__ K,
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const char * __restrict__ V,
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const char * __restrict__ mask,
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float * __restrict__ dst,
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float2 * __restrict__ dst_meta,
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const float scale,
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const float max_bias,
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const float m0,
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const float m1,
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const uint32_t n_head_log2,
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const int ne00,
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const int ne01,
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const int ne02,
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const int ne03,
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const int ne10,
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const int ne11,
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const int ne12,
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const int ne13,
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const int ne31,
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const int nb31,
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const int nb01,
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const int nb02,
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const int nb03,
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const int nb11,
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const int nb12,
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const int nb13,
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const int ne0,
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const int ne1,
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const int ne2,
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const int ne3) {
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#if FP16_AVAILABLE
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//In this kernel Q, K, V are matrices while i, j, k are matrix indices.
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const int ic0 = (blockIdx.x / parallel_blocks) * ncols; // Index of the Q/QKV column to work on.
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const int ip = blockIdx.x % parallel_blocks; // Index in group of blocks running for the same column in parallel.
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const int gqa_ratio = ne02 / ne12; // With grouped query attention there are > 1 Q matrices per K, V matrix.
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const float2 * Q_f2 = (const float2 *) (Q + nb02* blockIdx.y + nb01*ic0);
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const half2 * K_h2 = (const half2 *) (K + nb12*(blockIdx.y / gqa_ratio));
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const half * V_h = (const half *) (V + nb12*(blockIdx.y / gqa_ratio)); // K and V have same shape
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const half * maskh = (const half *) mask + ne11*ic0;
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const int stride_KV = nb11 / sizeof(half);
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const int stride_KV2 = nb11 / sizeof(half2);
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const float slopef = get_alibi_slope(max_bias, blockIdx.y, n_head_log2, m0, m1);
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const half slopeh = __float2half(slopef);
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static_assert(D % (2*WARP_SIZE) == 0, "D not divisible by 2*WARP_SIZE == 64.");
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constexpr int nwarps = D / WARP_SIZE;
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const int tid = WARP_SIZE*threadIdx.y + threadIdx.x;
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__builtin_assume(tid < D);
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__shared__ half KQ[ncols*D];
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#pragma unroll
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for (int j = 0; j < ncols; ++j) {
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KQ[j*D + tid] = -HALF_MAX_HALF;
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}
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half2 * KQ2 = (half2 *) KQ;
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half kqmax[ncols];
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#pragma unroll
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for (int j = 0; j < ncols; ++j) {
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kqmax[j] = -HALF_MAX_HALF;
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}
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half kqsum[ncols] = {0.0f};
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__shared__ half kqmax_shared[ncols][WARP_SIZE];
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__shared__ half kqsum_shared[ncols][WARP_SIZE];
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#pragma unroll
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for (int j = 0; j < ncols; ++j) {
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if (threadIdx.y == 0) {
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kqmax_shared[j][threadIdx.x] = -HALF_MAX_HALF;
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kqsum_shared[j][threadIdx.x] = 0.0f;
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}
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}
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__syncthreads();
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// Convert Q to half2 and store in registers:
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half2 Q_h2[ncols][D/(2*WARP_SIZE)];
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#pragma unroll
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for (int j = 0; j < ncols; ++j) {
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#pragma unroll
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for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) {
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const int i = i0 + threadIdx.x;
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const float2 tmp = ncols <= 2 || ic0 + j < ne01 ? Q_f2[j*(nb01/sizeof(float2)) + i] : make_float2(0.0f, 0.0f);
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Q_h2[j][i0/WARP_SIZE] = make_half2(scale, scale) * make_half2(tmp.x, tmp.y);
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}
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}
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half2 VKQ[ncols] = {{0.0f, 0.0f}};
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const int k_start = parallel_blocks == 1 ? 0 : ip*D;
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for (int k_VKQ_0 = k_start; k_VKQ_0 < ne11; k_VKQ_0 += parallel_blocks*D) {
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// Calculate KQ tile and keep track of new maximum KQ values:
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// For unknown reasons using a half array of size 1 for kqmax_new causes a performance regression,
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// see https://github.com/ggerganov/llama.cpp/pull/7061 .
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// Therefore this variable is defined twice but only used once (so that the compiler can optimize out the unused variable).
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half kqmax_new = kqmax[0];
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half kqmax_new_arr[ncols];
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#pragma unroll
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for (int j = 0; j < ncols; ++j) {
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kqmax_new_arr[j] = kqmax[j];
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}
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#pragma unroll
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for (int i_KQ_0 = 0; i_KQ_0 < D; i_KQ_0 += nwarps) {
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const int i_KQ = i_KQ_0 + threadIdx.y;
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if ((i_KQ_0 + nwarps > D && i_KQ >= D) || (FATTN_KQ_STRIDE % D != 0 && k_VKQ_0 + i_KQ >= ne11)) {
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break;
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}
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half2 sum2[ncols] = {{0.0f, 0.0f}};
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#pragma unroll
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for (int k_KQ_0 = 0; k_KQ_0 < D/2; k_KQ_0 += WARP_SIZE) {
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const int k_KQ = k_KQ_0 + threadIdx.x;
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const half2 K_ik = K_h2[(k_VKQ_0 + i_KQ)*stride_KV2 + k_KQ];
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#pragma unroll
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for (int j = 0; j < ncols; ++j) {
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sum2[j] += K_ik * Q_h2[j][k_KQ_0/WARP_SIZE];
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}
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}
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#pragma unroll
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for (int j = 0; j < ncols; ++j) {
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sum2[j] = warp_reduce_sum(sum2[j]);
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half sum = __low2half(sum2[j]) + __high2half(sum2[j]);
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sum += mask ? slopeh*maskh[j*ne11 + k_VKQ_0 + i_KQ] : __float2half(0.0f);
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if (ncols == 1) {
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kqmax_new = ggml_cuda_hmax(kqmax_new, sum);
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} else {
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kqmax_new_arr[j] = ggml_cuda_hmax(kqmax_new_arr[j], sum);
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}
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if (threadIdx.x == 0) {
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KQ[j*D + i_KQ] = sum;
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}
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}
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}
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#pragma unroll
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for (int j = 0; j < ncols; ++j) {
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half kqmax_new_j = ncols == 1 ? kqmax_new : kqmax_new_arr[j];
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kqmax_new_j = warp_reduce_max(kqmax_new_j);
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if (threadIdx.x == 0) {
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kqmax_shared[j][threadIdx.y] = kqmax_new_j;
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}
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}
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__syncthreads();
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#pragma unroll
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for (int j = 0; j < ncols; ++j) {
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half kqmax_new_j = kqmax_shared[j][threadIdx.x];
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kqmax_new_j = warp_reduce_max(kqmax_new_j);
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const half KQ_max_scale = hexp(kqmax[j] - kqmax_new_j);
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kqmax[j] = kqmax_new_j;
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const half val = hexp(KQ[j*D + tid] - kqmax[j]);
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kqsum[j] = kqsum[j]*KQ_max_scale + val;
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KQ[j*D + tid] = val;
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VKQ[j] *= __half2half2(KQ_max_scale);
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}
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__syncthreads();
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#pragma unroll
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for (int k0 = 0; k0 < D; k0 += 2) {
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if (FATTN_KQ_STRIDE % D != 0 && k_VKQ_0 + k0 >= ne11) {
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break;
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}
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half2 V_k;
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reinterpret_cast<half&>(V_k.x) = V_h[(k_VKQ_0 + k0 + 0)*stride_KV + tid];
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reinterpret_cast<half&>(V_k.y) = V_h[(k_VKQ_0 + k0 + 1)*stride_KV + tid];
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#pragma unroll
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for (int j = 0; j < ncols; ++j) {
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VKQ[j] += V_k*KQ2[j*(D/2) + k0/2];
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}
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}
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__syncthreads();
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}
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#pragma unroll
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for (int j = 0; j < ncols; ++j) {
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kqsum[j] = warp_reduce_sum(kqsum[j]);
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if (threadIdx.x == 0) {
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kqsum_shared[j][threadIdx.y] = kqsum[j];
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}
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}
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__syncthreads();
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#pragma unroll
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for (int j_VKQ = 0; j_VKQ < ncols; ++j_VKQ) {
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if (ncols > 2 && ic0 + j_VKQ >= ne01) {
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break;
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}
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kqsum[j_VKQ] = kqsum_shared[j_VKQ][threadIdx.x];
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kqsum[j_VKQ] = warp_reduce_sum(kqsum[j_VKQ]);
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half dst_val = (__low2half(VKQ[j_VKQ]) + __high2half(VKQ[j_VKQ]));
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if (parallel_blocks == 1) {
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dst_val /= kqsum[j_VKQ];
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}
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const int j_dst = (ic0 + j_VKQ)*parallel_blocks + ip;
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dst[j_dst*D*gridDim.y + D*blockIdx.y + tid] = dst_val;
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}
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if (parallel_blocks != 1 && tid < ncols && (ncols <= 2 || ic0 + tid < ne01)) {
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dst_meta[(ic0 + tid)*gridDim.y*parallel_blocks + blockIdx.y*parallel_blocks + ip] = make_float2(kqmax[tid], kqsum[tid]);
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}
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#else
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NO_DEVICE_CODE;
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#endif // FP16_AVAILABLE
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}
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void ggml_cuda_flash_attn_ext_vec_f16(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
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ggml_tensor * KQV = dst;
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ggml_tensor * Q = dst->src[0];
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const int32_t precision = KQV->op_params[2];
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GGML_ASSERT(precision == GGML_PREC_DEFAULT);
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constexpr int cols_per_block = 1;
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constexpr int parallel_blocks = 4;
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switch (Q->ne[0]) {
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case 64: {
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constexpr int D = 64;
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constexpr int nwarps = D/WARP_SIZE;
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fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f16<D, cols_per_block, parallel_blocks>;
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launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block);
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} break;
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case 128: {
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constexpr int D = 128;
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constexpr int nwarps = D/WARP_SIZE;
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fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f16<D, cols_per_block, parallel_blocks>;
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launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block);
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} break;
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case 256: {
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constexpr int D = 256;
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constexpr int nwarps = D/WARP_SIZE;
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fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f16<D, cols_per_block, parallel_blocks>;
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launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block);
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} break;
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default:
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GGML_ASSERT(false);
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break;
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}
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}
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template <int cols_per_block, int parallel_blocks>
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void launch_fattn_vec_f16_64_128(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
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const ggml_tensor * Q = dst->src[0];
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switch (Q->ne[0]) {
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case 64: {
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constexpr int D = 64;
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constexpr int nwarps = D/WARP_SIZE;
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fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f16<D, cols_per_block, parallel_blocks>;
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launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block);
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} break;
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case 128: {
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constexpr int D = 128;
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constexpr int nwarps = D/WARP_SIZE;
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fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f16<D, cols_per_block, parallel_blocks>;
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launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block);
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} break;
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default: {
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GGML_ASSERT(false && "FlashAttention without tensor cores only supports head sizes 64 and 128.");
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} break;
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}
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}
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void ggml_cuda_flash_attn_ext_vec_f16_no_mma(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
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const ggml_tensor * KQV = dst;
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const ggml_tensor * Q = dst->src[0];
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const int32_t precision = KQV->op_params[2];
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GGML_ASSERT(precision == GGML_PREC_DEFAULT);
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if (Q->ne[1] == 1) {
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ggml_cuda_flash_attn_ext_vec_f16(ctx, dst);
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return;
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}
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if (Q->ne[1] == 2) {
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constexpr int cols_per_block = 2;
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constexpr int parallel_blocks = 4;
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launch_fattn_vec_f16_64_128<cols_per_block, parallel_blocks>(ctx, dst);
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return;
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}
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if (Q->ne[1] <= 4) {
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constexpr int cols_per_block = 4;
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constexpr int parallel_blocks = 4;
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launch_fattn_vec_f16_64_128<cols_per_block, parallel_blocks>(ctx, dst);
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return;
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}
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if (Q->ne[1] <= 8) {
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constexpr int cols_per_block = 8;
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constexpr int parallel_blocks = 4;
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launch_fattn_vec_f16_64_128<cols_per_block, parallel_blocks>(ctx, dst);
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return;
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}
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constexpr int cols_per_block = 8;
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constexpr int parallel_blocks = 1;
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launch_fattn_vec_f16_64_128<cols_per_block, parallel_blocks>(ctx, dst);
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}
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