mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2025-11-03 09:22:01 +00:00
375 lines
14 KiB
Plaintext
375 lines
14 KiB
Plaintext
#include "common.cuh"
|
|
#include "fattn-common.cuh"
|
|
|
|
template<int D, int ncols, int parallel_blocks, ggml_type type_K, ggml_type type_V> // 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_vec_ext_f32(
|
|
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 nb21,
|
|
const int nb22,
|
|
const int nb23,
|
|
const int ne0,
|
|
const int ne1,
|
|
const int ne2,
|
|
const int ne3) {
|
|
//In this kernel Q, K, V are matrices while i, j, k are matrix indices.
|
|
|
|
constexpr vec_dot_KQ_f32_t vec_dot_KQ = get_vec_dot_KQ_f32<D>(type_K);
|
|
constexpr bool Q_q8_1 = type_K != GGML_TYPE_F16;
|
|
constexpr dequantize_1_f32_t dequantize_1_v = get_dequantize_1_f32(type_V);
|
|
|
|
const int ic0 = (blockIdx.x / parallel_blocks) * ncols; // Index of the Q/QKV column to work on.
|
|
const int ip = blockIdx.x % parallel_blocks; // Index in group of blocks running for the same column in parallel.
|
|
|
|
const int gqa_ratio = ne02 / ne12; // With grouped query attention there are > 1 Q matrices per K, V matrix.
|
|
Q += nb02* blockIdx.y + nb01*ic0;
|
|
K += nb12*(blockIdx.y / gqa_ratio);
|
|
V += nb22*(blockIdx.y / gqa_ratio); // K and V have same shape
|
|
const half * maskh = (const half *) mask + ne11*ic0;
|
|
|
|
const float slope = get_alibi_slope(max_bias, blockIdx.y, n_head_log2, m0, m1);
|
|
|
|
static_assert(D % (2*WARP_SIZE) == 0, "D not divisible by 2*WARP_SIZE == 64.");
|
|
constexpr int nwarps = D / WARP_SIZE;
|
|
const int tid = WARP_SIZE*threadIdx.y + threadIdx.x;
|
|
__builtin_assume(tid < D);
|
|
|
|
__shared__ float KQ[ncols*D];
|
|
#pragma unroll
|
|
for (int j = 0; j < ncols; ++j) {
|
|
KQ[j*D + tid] = -FLT_MAX/2.0f;
|
|
}
|
|
|
|
float kqmax[ncols];
|
|
#pragma unroll
|
|
for (int j = 0; j < ncols; ++j) {
|
|
kqmax[j] = -FLT_MAX/2.0f;
|
|
}
|
|
float kqsum[ncols] = {0.0f};
|
|
|
|
__shared__ float kqmax_shared[ncols][WARP_SIZE];
|
|
__shared__ float kqsum_shared[ncols][WARP_SIZE];
|
|
#pragma unroll
|
|
for (int j = 0; j < ncols; ++j) {
|
|
if (threadIdx.y == 0) {
|
|
kqmax_shared[j][threadIdx.x] = -FLT_MAX/2.0f;
|
|
kqsum_shared[j][threadIdx.x] = 0.0f;
|
|
}
|
|
}
|
|
__syncthreads();
|
|
|
|
// Convert Q to float2 (f16 K) or q8_1 (quantized K) and store in registers:
|
|
float2 Q_f2[ncols][D/(2*WARP_SIZE)];
|
|
int Q_i32[ncols][D/(sizeof(int)*QK8_1) == 0 ? 1 : D >= D/(sizeof(int)*QK8_1)];
|
|
float2 Q_ds[ncols][D/QK8_1 == 0 ? 1 : D/QK8_1];
|
|
if (Q_q8_1) {
|
|
#pragma unroll
|
|
for (int j0 = 0; j0 < ncols; j0 += nwarps) {
|
|
const int j = j0 + threadIdx.y;
|
|
|
|
if (j0 + nwarps > ncols && j >= ncols) {
|
|
break;
|
|
}
|
|
|
|
// Reuse KQ as temporary storage for converting Q to q8_1:
|
|
int * tmp_q_i32 = (int *) &KQ[j*D];
|
|
float2 * tmp_q_ds = (float2 *) (tmp_q_i32 + D/sizeof(int));
|
|
|
|
// Set memory to zero if out of bounds:
|
|
if (ncols > 2 && ic0 + j >= ne01) {
|
|
#pragma unroll
|
|
for (int i0 = 0; i0 < D/sizeof(int); i0 += WARP_SIZE) {
|
|
const int i = i0 + threadIdx.x;
|
|
|
|
tmp_q_i32[i] = 0;
|
|
}
|
|
if (threadIdx.x < D/QK8_1) {
|
|
tmp_q_ds[threadIdx.x] = make_float2(0.0f, 0.0f);
|
|
}
|
|
continue;
|
|
}
|
|
|
|
const float * Q_f = (const float *) (Q + j*nb01);
|
|
#pragma unroll
|
|
for (int i0 = 0; i0 < D/sizeof(int); i0 += WARP_SIZE) {
|
|
quantize_q8_1_to_shared<float2>(Q_f + 4*i0, scale, tmp_q_i32, tmp_q_ds);
|
|
}
|
|
}
|
|
|
|
__syncthreads();
|
|
|
|
#pragma unroll
|
|
for (int j = 0; j < ncols; ++j) {
|
|
int * tmp_q_i32 = (int *) &KQ[j*D];
|
|
float2 * tmp_q_ds = (float2 *) (tmp_q_i32 + D/sizeof(int));
|
|
|
|
#pragma unroll
|
|
for (int i0 = 0; i0 < D/sizeof(int); i0 += WARP_SIZE) {
|
|
const int i = i0 + threadIdx.x;
|
|
|
|
Q_i32[j][i0/WARP_SIZE] = tmp_q_i32[i];
|
|
Q_ds[j][i0/WARP_SIZE] = tmp_q_ds[i/QI8_1];
|
|
}
|
|
}
|
|
|
|
__syncthreads();
|
|
} else {
|
|
#pragma unroll
|
|
for (int j = 0; j < ncols; ++j) {
|
|
const float2 * Q_f2_j = (const float2 *) (Q + j*nb01);
|
|
#pragma unroll
|
|
for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) {
|
|
const int i = i0 + threadIdx.x;
|
|
|
|
Q_f2[j][i0/WARP_SIZE] = ncols <= 2 || ic0 + j ? Q_f2_j[i] : make_float2(0.0f, 0.0f);
|
|
Q_f2[j][i0/WARP_SIZE].x *= scale;
|
|
Q_f2[j][i0/WARP_SIZE].y *= scale;
|
|
}
|
|
}
|
|
}
|
|
|
|
float VKQ[ncols] = {0.0f};
|
|
|
|
const int k_start = parallel_blocks == 1 ? 0 : ip*D;
|
|
for (int k_VKQ_0 = k_start; k_VKQ_0 < ne11; k_VKQ_0 += parallel_blocks*D) {
|
|
// Calculate KQ tile and keep track of new maximum KQ values:
|
|
|
|
float kqmax_new_arr[ncols];
|
|
#pragma unroll
|
|
for (int j = 0; j < ncols; ++j) {
|
|
kqmax_new_arr[j] = kqmax[j];
|
|
}
|
|
|
|
#pragma unroll
|
|
for (int i_KQ_0 = 0; i_KQ_0 < D; i_KQ_0 += nwarps) {
|
|
const int i_KQ = i_KQ_0 + threadIdx.y;
|
|
|
|
if ((i_KQ_0 + nwarps > D && i_KQ >= D) || (FATTN_KQ_STRIDE % D != 0 && k_VKQ_0 + i_KQ >= ne11)) {
|
|
break;
|
|
}
|
|
|
|
#pragma unroll
|
|
for (int j = 0; j < ncols; ++j) {
|
|
float sum = vec_dot_KQ(K + (k_VKQ_0 + i_KQ)*nb11, Q_f2[j], Q_i32[j], Q_ds[j]);
|
|
sum = warp_reduce_sum(sum);
|
|
sum += mask ? slope*__half2float(maskh[j*ne11 + k_VKQ_0 + i_KQ]) : 0.0f;
|
|
|
|
kqmax_new_arr[j] = fmaxf(kqmax_new_arr[j], sum);
|
|
|
|
if (threadIdx.x == 0) {
|
|
KQ[j*D + i_KQ] = sum;
|
|
}
|
|
}
|
|
}
|
|
|
|
#pragma unroll
|
|
for (int j = 0; j < ncols; ++j) {
|
|
float kqmax_new_j = kqmax_new_arr[j];
|
|
|
|
kqmax_new_j = warp_reduce_max(kqmax_new_j);
|
|
if (threadIdx.x == 0) {
|
|
kqmax_shared[j][threadIdx.y] = kqmax_new_j;
|
|
}
|
|
}
|
|
|
|
__syncthreads();
|
|
|
|
#pragma unroll
|
|
for (int j = 0; j < ncols; ++j) {
|
|
float kqmax_new_j = kqmax_shared[j][threadIdx.x];
|
|
kqmax_new_j = warp_reduce_max(kqmax_new_j);
|
|
|
|
const float KQ_max_scale = expf(kqmax[j] - kqmax_new_j);
|
|
kqmax[j] = kqmax_new_j;
|
|
|
|
const float val = expf(KQ[j*D + tid] - kqmax[j]);
|
|
kqsum[j] = kqsum[j]*KQ_max_scale + val;
|
|
KQ[j*D + tid] = val;
|
|
|
|
VKQ[j] *= KQ_max_scale;
|
|
}
|
|
|
|
__syncthreads();
|
|
|
|
#pragma unroll
|
|
for (int k = 0; k < D; ++k) {
|
|
if (FATTN_KQ_STRIDE % D != 0 && k_VKQ_0 + k >= ne11) {
|
|
break;
|
|
}
|
|
|
|
const float V_ki = dequantize_1_v(V + (k_VKQ_0 + k)*nb21, tid);
|
|
#pragma unroll
|
|
for (int j = 0; j < ncols; ++j) {
|
|
VKQ[j] += V_ki*KQ[j*D + k];
|
|
}
|
|
}
|
|
|
|
__syncthreads();
|
|
}
|
|
|
|
#pragma unroll
|
|
for (int j = 0; j < ncols; ++j) {
|
|
kqsum[j] = warp_reduce_sum(kqsum[j]);
|
|
if (threadIdx.x == 0) {
|
|
kqsum_shared[j][threadIdx.y] = kqsum[j];
|
|
}
|
|
}
|
|
|
|
__syncthreads();
|
|
|
|
#pragma unroll
|
|
for (int j_VKQ = 0; j_VKQ < ncols; ++j_VKQ) {
|
|
if (ncols > 2 && ic0 + j_VKQ >= ne01) {
|
|
break;
|
|
}
|
|
|
|
kqsum[j_VKQ] = kqsum_shared[j_VKQ][threadIdx.x];
|
|
kqsum[j_VKQ] = warp_reduce_sum(kqsum[j_VKQ]);
|
|
|
|
float dst_val = VKQ[j_VKQ];
|
|
if (parallel_blocks == 1) {
|
|
dst_val /= kqsum[j_VKQ];
|
|
}
|
|
const int j_dst = (ic0 + j_VKQ)*parallel_blocks + ip;
|
|
dst[j_dst*D*gridDim.y + D*blockIdx.y + tid] = dst_val;
|
|
}
|
|
|
|
if (parallel_blocks != 1 && tid < ncols && (ncols <= 2 || ic0 + tid < ne01)) {
|
|
dst_meta[(ic0 + tid)*gridDim.y*parallel_blocks + blockIdx.y*parallel_blocks + ip] = make_float2(kqmax[tid], kqsum[tid]);
|
|
}
|
|
}
|
|
|
|
template <int D, int cols_per_block, int parallel_blocks, ggml_type type_K, ggml_type type_V>
|
|
void ggml_cuda_flash_attn_ext_vec_f32_case_impl(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
|
|
constexpr int nwarps = D/WARP_SIZE;
|
|
fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f32<D, cols_per_block, parallel_blocks, type_K, type_V>;
|
|
constexpr bool need_f16_K = D != 128;
|
|
constexpr bool need_f16_V = D != 128 && D != 64;
|
|
launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block, need_f16_K, need_f16_V);
|
|
}
|
|
|
|
template <int D, ggml_type type_K, ggml_type type_V>
|
|
void ggml_cuda_flash_attn_ext_vec_f32_case(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
|
|
ggml_tensor * Q = dst->src[0];
|
|
ggml_tensor * K = dst->src[1];
|
|
ggml_tensor * V = dst->src[2];
|
|
|
|
GGML_ASSERT(K->type == type_K);
|
|
GGML_ASSERT(V->type == type_V);
|
|
|
|
if (Q->ne[1] == 1) {
|
|
constexpr int cols_per_block = 1;
|
|
constexpr int parallel_blocks = 4;
|
|
ggml_cuda_flash_attn_ext_vec_f32_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V>(ctx, dst);
|
|
return;
|
|
}
|
|
|
|
if (Q->ne[1] == 2) {
|
|
constexpr int cols_per_block = 2;
|
|
constexpr int parallel_blocks = 4;
|
|
ggml_cuda_flash_attn_ext_vec_f32_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V>(ctx, dst);
|
|
return;
|
|
}
|
|
|
|
if (Q->ne[1] <= 4) {
|
|
constexpr int cols_per_block = 4;
|
|
constexpr int parallel_blocks = 4;
|
|
ggml_cuda_flash_attn_ext_vec_f32_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V>(ctx, dst);
|
|
return;
|
|
}
|
|
|
|
if (Q->ne[1] <= 8) {
|
|
constexpr int cols_per_block = 8;
|
|
constexpr int parallel_blocks = 4;
|
|
ggml_cuda_flash_attn_ext_vec_f32_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V>(ctx, dst);
|
|
return;
|
|
}
|
|
|
|
constexpr int cols_per_block = 8;
|
|
constexpr int parallel_blocks = 1;
|
|
ggml_cuda_flash_attn_ext_vec_f32_case_impl<D, cols_per_block, parallel_blocks, type_K, type_V>(ctx, dst);
|
|
}
|
|
|
|
#define DECL_FATTN_VEC_F32_CASE(D, type_K, type_V) \
|
|
template void ggml_cuda_flash_attn_ext_vec_f32_case \
|
|
<D, type_K, type_V>(ggml_backend_cuda_context & ctx, ggml_tensor * dst) \
|
|
|
|
extern DECL_FATTN_VEC_F32_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q4_0);
|
|
extern DECL_FATTN_VEC_F32_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q4_1);
|
|
extern DECL_FATTN_VEC_F32_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q5_0);
|
|
extern DECL_FATTN_VEC_F32_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q5_1);
|
|
extern DECL_FATTN_VEC_F32_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q8_0);
|
|
extern DECL_FATTN_VEC_F32_CASE( 64, GGML_TYPE_F16, GGML_TYPE_F16);
|
|
|
|
extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q4_0);
|
|
extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q4_0);
|
|
extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q4_0);
|
|
extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q4_0);
|
|
extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q4_0);
|
|
extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q4_0);
|
|
|
|
extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q4_1);
|
|
extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q4_1);
|
|
extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q4_1);
|
|
extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q4_1);
|
|
extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q4_1);
|
|
extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q4_1);
|
|
|
|
extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q5_0);
|
|
extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q5_0);
|
|
extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q5_0);
|
|
extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q5_0);
|
|
extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q5_0);
|
|
extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q5_0);
|
|
|
|
extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q5_1);
|
|
extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q5_1);
|
|
extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q5_1);
|
|
extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q5_1);
|
|
extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q5_1);
|
|
extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q5_1);
|
|
|
|
extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q8_0);
|
|
extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q8_0);
|
|
extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q8_0);
|
|
extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q8_0);
|
|
extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q8_0);
|
|
extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q8_0);
|
|
|
|
extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_F16);
|
|
extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_F16);
|
|
extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_F16);
|
|
extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_F16);
|
|
extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_F16);
|
|
extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_F16);
|
|
|
|
extern DECL_FATTN_VEC_F32_CASE(256, GGML_TYPE_F16, GGML_TYPE_F16);
|