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llama.cpp-mtp-turboquant/ggml/src/ggml-cuda/fattn.cu
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#include "common.cuh"
#include "fattn-common.cuh"
#include "fattn-mma-f16.cuh"
#include "fattn-tile.cuh"
#include "fattn-vec.cuh"
#include "fattn-wmma-f16.cuh"
#include "fattn.cuh"
template <int DKQ, int DV, int ncols2>
static void ggml_cuda_flash_attn_ext_mma_f16_switch_ncols1(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
const int cc = ggml_cuda_info().devices[ggml_cuda_get_device()].cc;
const ggml_tensor * Q = dst->src[0];
if constexpr (ncols2 <= 8) {
if (turing_mma_available(cc) && Q->ne[1] <= 8/ncols2) {
ggml_cuda_flash_attn_ext_mma_f16_case<DKQ, DV, 8/ncols2, ncols2>(ctx, dst);
return;
}
}
if constexpr (ncols2 <= 16) {
if (Q->ne[1] <= 16/ncols2) {
ggml_cuda_flash_attn_ext_mma_f16_case<DKQ, DV, 16/ncols2, ncols2>(ctx, dst);
return;
}
}
if (Q->ne[1] <= 32/ncols2 || (GGML_CUDA_CC_IS_NVIDIA(cc) && ggml_cuda_highest_compiled_arch(cc) == GGML_CUDA_CC_TURING) ||
(GGML_CUDA_CC_IS_AMD(cc) && DKQ > 256)) {
ggml_cuda_flash_attn_ext_mma_f16_case<DKQ, DV, 32/ncols2, ncols2>(ctx, dst);
return;
}
ggml_cuda_flash_attn_ext_mma_f16_case<DKQ, DV, 64/ncols2, ncols2>(ctx, dst);
}
template <int DKQ, int DV>
static void ggml_cuda_flash_attn_ext_mma_f16_switch_ncols2(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
const int cc = ggml_cuda_info().devices[ggml_cuda_get_device()].cc;
const ggml_tensor * KQV = dst;
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];
float max_bias = 0.0f;
memcpy(&max_bias, (const float *) KQV->op_params + 1, sizeof(float));
// Edge cases like no mask, ALiBi, unpadded K/V, or misaligned addresses for large data transfers
// are put into the template specialization without GQA optimizations.
bool use_gqa_opt = mask && max_bias == 0.0f && K->ne[1] % FATTN_KQ_STRIDE == 0;
for (const ggml_tensor * t : {Q, K, V, mask}) {
if (t == nullptr || ggml_is_quantized(t->type)) {
continue;
}
for (size_t i = 1; i < GGML_MAX_DIMS; ++i) {
if (t->nb[i] % 16 != 0) {
use_gqa_opt = false;
break;
}
}
}
GGML_ASSERT(Q->ne[2] % K->ne[2] == 0);
const int gqa_ratio = Q->ne[2] / K->ne[2];
// On Volta the GQA optimizations aren't as impactful vs. minimizing wasted compute:
if (cc == GGML_CUDA_CC_VOLTA) {
if (use_gqa_opt && gqa_ratio % 8 == 0) {
ggml_cuda_flash_attn_ext_mma_f16_switch_ncols1<DKQ, DV, 8>(ctx, dst);
return;
}
if (use_gqa_opt && gqa_ratio % 4 == 0) {
ggml_cuda_flash_attn_ext_mma_f16_switch_ncols1<DKQ, DV, 4>(ctx, dst);
return;
}
if constexpr (DKQ <= 256) {
if (use_gqa_opt && gqa_ratio % 2 == 0) {
ggml_cuda_flash_attn_ext_mma_f16_switch_ncols1<DKQ, DV, 2>(ctx, dst);
return;
}
ggml_cuda_flash_attn_ext_mma_f16_switch_ncols1<DKQ, DV, 1>(ctx, dst);
return;
} else {
GGML_ABORT("fatal error");
}
}
if (use_gqa_opt && gqa_ratio > 4) {
ggml_cuda_flash_attn_ext_mma_f16_switch_ncols1<DKQ, DV, 8>(ctx, dst);
return;
}
if (use_gqa_opt && gqa_ratio > 2) {
ggml_cuda_flash_attn_ext_mma_f16_switch_ncols1<DKQ, DV, 4>(ctx, dst);
return;
}
if constexpr (DKQ <= 256) {
if (use_gqa_opt && gqa_ratio > 1) {
ggml_cuda_flash_attn_ext_mma_f16_switch_ncols1<DKQ, DV, 2>(ctx, dst);
return;
}
ggml_cuda_flash_attn_ext_mma_f16_switch_ncols1<DKQ, DV, 1>(ctx, dst);
} else {
GGML_ABORT("fatal error");
}
}
static void ggml_cuda_flash_attn_ext_mma_f16(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
const int cc = ggml_cuda_info().devices[ggml_cuda_get_device()].cc;
const ggml_tensor * KQV = dst;
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];
switch (Q->ne[0]) {
case 64:
GGML_ASSERT(V->ne[0] == 64);
ggml_cuda_flash_attn_ext_mma_f16_switch_ncols2< 64, 64>(ctx, dst);
break;
case 80:
GGML_ASSERT(V->ne[0] == 80);
ggml_cuda_flash_attn_ext_mma_f16_switch_ncols2< 80, 80>(ctx, dst);
break;
case 96:
GGML_ASSERT(V->ne[0] == 96);
ggml_cuda_flash_attn_ext_mma_f16_switch_ncols2< 96, 96>(ctx, dst);
break;
case 112:
GGML_ASSERT(V->ne[0] == 112);
ggml_cuda_flash_attn_ext_mma_f16_switch_ncols2<112, 112>(ctx, dst);
break;
case 128:
GGML_ASSERT(V->ne[0] == 128);
ggml_cuda_flash_attn_ext_mma_f16_switch_ncols2<128, 128>(ctx, dst);
break;
case 192: {
// MiMo-V2.5 / V2.5-Pro / V2-Flash: gqa_ratio is 8 (SWA) or 16 (full attn)
GGML_ASSERT(V->ne[0] == 128);
float max_bias = 0.0f;
memcpy(&max_bias, (const float *) KQV->op_params + 1, sizeof(float));
const bool use_gqa_opt = mask && max_bias == 0.0f;
GGML_ASSERT(use_gqa_opt);
GGML_ASSERT(Q->ne[2] % K->ne[2] == 0);
const int gqa_ratio = Q->ne[2] / K->ne[2];
if (gqa_ratio % 16 == 0) {
ggml_cuda_flash_attn_ext_mma_f16_switch_ncols1<192, 128, 16>(ctx, dst);
} else {
GGML_ASSERT(gqa_ratio % 8 == 0);
ggml_cuda_flash_attn_ext_mma_f16_switch_ncols1<192, 128, 8>(ctx, dst);
}
} break;
case 256:
GGML_ASSERT(V->ne[0] == 256);
ggml_cuda_flash_attn_ext_mma_f16_switch_ncols2<256, 256>(ctx, dst);
break;
case 320:
// For Mistral Small 4, go straight to the ncols1 switch (ncols2=32-only build).
GGML_ASSERT(V->ne[0] == 256);
{
float max_bias = 0.0f;
memcpy(&max_bias, (const float *) KQV->op_params + 1, sizeof(float));
const bool use_gqa_opt = mask && max_bias == 0.0f;
GGML_ASSERT(use_gqa_opt);
GGML_ASSERT(Q->ne[2] % K->ne[2] == 0);
const int gqa_ratio = Q->ne[2] / K->ne[2];
GGML_ASSERT(gqa_ratio % 32 == 0);
ggml_cuda_flash_attn_ext_mma_f16_switch_ncols1<320, 256, 32>(ctx, dst);
}
break;
case 512:
GGML_ASSERT(V->ne[0] == 512);
ggml_cuda_flash_attn_ext_mma_f16_switch_ncols2<512, 512>(ctx, dst);
break;
case 576: {
// For Deepseek, go straight to the ncols1 switch to avoid compiling unnecessary kernels.
GGML_ASSERT(V->ne[0] == 512);
float max_bias = 0.0f;
memcpy(&max_bias, (const float *) KQV->op_params + 1, sizeof(float));
const bool use_gqa_opt = mask && max_bias == 0.0f;
GGML_ASSERT(use_gqa_opt);
GGML_ASSERT(Q->ne[2] % K->ne[2] == 0);
const int gqa_ratio = Q->ne[2] / K->ne[2];
if (gqa_ratio == 20) { // GLM 4.7 Flash
if (cc >= GGML_CUDA_CC_DGX_SPARK) {
if (Q->ne[1] <= 8) {
ggml_cuda_flash_attn_ext_mma_f16_switch_ncols1<576, 512, 16>(ctx, dst);
break;
}
ggml_cuda_flash_attn_ext_mma_f16_switch_ncols1<576, 512, 4>(ctx, dst);
break;
}
if (cc >= GGML_CUDA_CC_BLACKWELL) {
if (Q->ne[1] <= 4 && K->ne[1] >= 65536) {
ggml_cuda_flash_attn_ext_mma_f16_switch_ncols1<576, 512, 16>(ctx, dst);
break;
}
ggml_cuda_flash_attn_ext_mma_f16_switch_ncols1<576, 512, 4>(ctx, dst);
break;
}
if (cc >= GGML_CUDA_CC_ADA_LOVELACE) {
if (Q->ne[1] <= 4) {
ggml_cuda_flash_attn_ext_mma_f16_switch_ncols1<576, 512, 16>(ctx, dst);
break;
}
ggml_cuda_flash_attn_ext_mma_f16_switch_ncols1<576, 512, 4>(ctx, dst);
break;
}
if (cc >= GGML_CUDA_CC_TURING) {
if (Q->ne[1] <= 4) {
if (K->ne[1] <= 16384) {
ggml_cuda_flash_attn_ext_mma_f16_switch_ncols1<576, 512, 16>(ctx, dst);
break;
}
ggml_cuda_flash_attn_ext_mma_f16_switch_ncols1<576, 512, 32>(ctx, dst);
break;
}
ggml_cuda_flash_attn_ext_mma_f16_switch_ncols1<576, 512, 4>(ctx, dst);
break;
}
// Volta:
ggml_cuda_flash_attn_ext_mma_f16_switch_ncols1<576, 512, 4>(ctx, dst);
} else if (gqa_ratio % 16 == 0) {
ggml_cuda_flash_attn_ext_mma_f16_switch_ncols1<576, 512, 16>(ctx, dst);
} else {
ggml_cuda_flash_attn_ext_mma_f16_switch_ncols1<576, 512, 4>(ctx, dst);
}
} break;
case 640: {
// Padded turbo KV cache for GLM-4.7 Flash (K head_dim=576 zero-padded to 640).
// D=640 shared memory (Q storage = ncols*(DKQ/2+4)*4) exceeds hardware limit at ncols1>=4.
// Cap at ncols1=2 (ncols=32): Q=32*324*4=41KB + KV≈37KB = ~78KB total.
GGML_ASSERT(V->ne[0] == 512);
if (Q->ne[1] <= 1) {
ggml_cuda_flash_attn_ext_mma_f16_case<640, 512, 1, 16>(ctx, dst);
} else {
ggml_cuda_flash_attn_ext_mma_f16_case<640, 512, 2, 16>(ctx, dst);
}
} break;
default:
GGML_ABORT("fatal error");
break;
}
}
#define FATTN_VEC_CASE(D, type_K, type_V) \
{ \
const bool type_K_okay = K->type == (type_K) || (K->type == GGML_TYPE_F32 && (type_K) == GGML_TYPE_F16); \
const bool type_V_okay = V->type == (type_V) || (V->type == GGML_TYPE_F32 && (type_V) == GGML_TYPE_F16); \
if (Q->ne[0] == (D) && type_K_okay && type_V_okay) { \
ggml_cuda_flash_attn_ext_vec_case<D, type_K, type_V>(ctx, dst); \
return; \
} \
} \
#define FATTN_VEC_CASES_ALL_D(type_K, type_V) \
FATTN_VEC_CASE( 64, type_K, type_V) \
FATTN_VEC_CASE(128, type_K, type_V) \
FATTN_VEC_CASE(256, type_K, type_V) \
static void ggml_cuda_flash_attn_ext_vec(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];
#ifdef GGML_CUDA_FA_ALL_QUANTS
FATTN_VEC_CASES_ALL_D(GGML_TYPE_F16, GGML_TYPE_F16)
FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q4_0, GGML_TYPE_F16)
FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q4_1, GGML_TYPE_F16)
FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q5_0, GGML_TYPE_F16)
FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q5_1, GGML_TYPE_F16)
FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q8_0, GGML_TYPE_F16)
FATTN_VEC_CASES_ALL_D(GGML_TYPE_BF16, GGML_TYPE_F16)
FATTN_VEC_CASES_ALL_D(GGML_TYPE_F16, GGML_TYPE_Q4_0)
FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q4_0, GGML_TYPE_Q4_0)
FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q4_1, GGML_TYPE_Q4_0)
FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q5_0, GGML_TYPE_Q4_0)
FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q5_1, GGML_TYPE_Q4_0)
FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q8_0, GGML_TYPE_Q4_0)
FATTN_VEC_CASES_ALL_D(GGML_TYPE_BF16, GGML_TYPE_Q4_0)
FATTN_VEC_CASES_ALL_D(GGML_TYPE_F16, GGML_TYPE_Q4_1)
FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q4_0, GGML_TYPE_Q4_1)
FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q4_1, GGML_TYPE_Q4_1)
FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q5_0, GGML_TYPE_Q4_1)
FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q5_1, GGML_TYPE_Q4_1)
FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q8_0, GGML_TYPE_Q4_1)
FATTN_VEC_CASES_ALL_D(GGML_TYPE_BF16, GGML_TYPE_Q4_1)
FATTN_VEC_CASES_ALL_D(GGML_TYPE_F16, GGML_TYPE_Q5_0)
FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q4_0, GGML_TYPE_Q5_0)
FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q4_1, GGML_TYPE_Q5_0)
FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q5_0, GGML_TYPE_Q5_0)
FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q5_1, GGML_TYPE_Q5_0)
FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q8_0, GGML_TYPE_Q5_0)
FATTN_VEC_CASES_ALL_D(GGML_TYPE_BF16, GGML_TYPE_Q5_0)
FATTN_VEC_CASES_ALL_D(GGML_TYPE_F16, GGML_TYPE_Q5_1)
FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q4_0, GGML_TYPE_Q5_1)
FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q4_1, GGML_TYPE_Q5_1)
FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q5_0, GGML_TYPE_Q5_1)
FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q5_1, GGML_TYPE_Q5_1)
FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q8_0, GGML_TYPE_Q5_1)
FATTN_VEC_CASES_ALL_D(GGML_TYPE_BF16, GGML_TYPE_Q5_1)
FATTN_VEC_CASES_ALL_D(GGML_TYPE_F16, GGML_TYPE_Q8_0)
FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q4_0, GGML_TYPE_Q8_0)
FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q4_1, GGML_TYPE_Q8_0)
FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q5_0, GGML_TYPE_Q8_0)
FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q5_1, GGML_TYPE_Q8_0)
FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q8_0, GGML_TYPE_Q8_0)
FATTN_VEC_CASES_ALL_D(GGML_TYPE_BF16, GGML_TYPE_Q8_0)
FATTN_VEC_CASES_ALL_D(GGML_TYPE_F16, GGML_TYPE_BF16)
FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q4_0, GGML_TYPE_BF16)
FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q4_1, GGML_TYPE_BF16)
FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q5_0, GGML_TYPE_BF16)
FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q5_1, GGML_TYPE_BF16)
FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q8_0, GGML_TYPE_BF16)
FATTN_VEC_CASES_ALL_D(GGML_TYPE_BF16, GGML_TYPE_BF16)
#else
FATTN_VEC_CASES_ALL_D(GGML_TYPE_F16, GGML_TYPE_F16)
FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q4_0, GGML_TYPE_Q4_0)
FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q8_0, GGML_TYPE_Q8_0)
FATTN_VEC_CASES_ALL_D(GGML_TYPE_BF16, GGML_TYPE_BF16)
#endif // GGML_CUDA_FA_ALL_QUANTS
// TurboQuant3 KV cache types (always enabled)
FATTN_VEC_CASES_ALL_D(GGML_TYPE_TURBO3_0, GGML_TYPE_TURBO3_0)
// Mixed turbo3/q8_0 KV cache types
FATTN_VEC_CASES_ALL_D(GGML_TYPE_TURBO3_0, GGML_TYPE_Q8_0)
FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q8_0, GGML_TYPE_TURBO3_0)
// TurboQuant2 KV cache types (always enabled)
FATTN_VEC_CASES_ALL_D(GGML_TYPE_TURBO2_0, GGML_TYPE_TURBO2_0)
// Mixed turbo2/q8_0 KV cache types
FATTN_VEC_CASES_ALL_D(GGML_TYPE_TURBO2_0, GGML_TYPE_Q8_0)
FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q8_0, GGML_TYPE_TURBO2_0)
// Mixed turbo3/turbo2 KV cache types
FATTN_VEC_CASES_ALL_D(GGML_TYPE_TURBO3_0, GGML_TYPE_TURBO2_0)
FATTN_VEC_CASES_ALL_D(GGML_TYPE_TURBO2_0, GGML_TYPE_TURBO3_0)
// TurboQuant4 KV cache types (always enabled)
FATTN_VEC_CASES_ALL_D(GGML_TYPE_TURBO4_0, GGML_TYPE_TURBO4_0)
// Mixed turbo4/q8_0 KV cache types
FATTN_VEC_CASES_ALL_D(GGML_TYPE_TURBO4_0, GGML_TYPE_Q8_0)
FATTN_VEC_CASES_ALL_D(GGML_TYPE_Q8_0, GGML_TYPE_TURBO4_0)
// Mixed turbo4/turbo3 KV cache types
FATTN_VEC_CASES_ALL_D(GGML_TYPE_TURBO4_0, GGML_TYPE_TURBO3_0)
FATTN_VEC_CASES_ALL_D(GGML_TYPE_TURBO3_0, GGML_TYPE_TURBO4_0)
// Mixed turbo4/turbo2 KV cache types
FATTN_VEC_CASES_ALL_D(GGML_TYPE_TURBO4_0, GGML_TYPE_TURBO2_0)
FATTN_VEC_CASES_ALL_D(GGML_TYPE_TURBO2_0, GGML_TYPE_TURBO4_0)
GGML_ABORT("fatal error");
}
// Best FlashAttention kernel for a specific GPU:
enum best_fattn_kernel {
BEST_FATTN_KERNEL_NONE = 0,
BEST_FATTN_KERNEL_TILE = 200,
BEST_FATTN_KERNEL_VEC = 100,
BEST_FATTN_KERNEL_WMMA_F16 = 300,
BEST_FATTN_KERNEL_MMA_F16 = 400,
};
static best_fattn_kernel ggml_cuda_get_best_fattn_kernel(const int device, const ggml_tensor * dst) {
#ifndef FLASH_ATTN_AVAILABLE
GGML_UNUSED(device); GGML_UNUSED(dst);
return BEST_FATTN_KERNEL_NONE;
#endif// FLASH_ATTN_AVAILABLE
const ggml_tensor * KQV = dst;
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];
const int gqa_ratio = Q->ne[2] / K->ne[2];
GGML_ASSERT(Q->ne[2] % K->ne[2] == 0);
float max_bias = 0.0f;
memcpy(&max_bias, (const float *) KQV->op_params + 1, sizeof(float));
// The effective batch size for the kernel can be increased by gqa_ratio.
// The kernel versions without this optimization are also used for ALiBi, if there is no mask, or if the KV cache is not padded,
bool gqa_opt_applies = gqa_ratio >= 2 && mask && max_bias == 0.0f && K->ne[1] % FATTN_KQ_STRIDE == 0;
for (const ggml_tensor * t : {Q, K, V, mask}) {
if (t == nullptr || ggml_is_quantized(t->type)) {
continue;
}
for (size_t i = 1; i < GGML_MAX_DIMS; ++i) {
if (t->nb[i] % 16 != 0) {
gqa_opt_applies = false;
break;
}
}
}
const int cc = ggml_cuda_info().devices[device].cc;
switch (K->ne[0]) {
case 40:
case 64:
case 72:
case 80:
case 96:
case 128:
case 112:
case 256:
if (V->ne[0] != K->ne[0]) {
return BEST_FATTN_KERNEL_NONE;
}
break;
case 192:
if (V->ne[0] != 128 || !gqa_opt_applies) {
return BEST_FATTN_KERNEL_NONE;
}
if (gqa_ratio % 8 != 0) {
return BEST_FATTN_KERNEL_NONE;
}
break;
case 320:
if (V->ne[0] != 256 || !gqa_opt_applies) {
return BEST_FATTN_KERNEL_NONE;
}
if (gqa_ratio % 32 != 0) {
return BEST_FATTN_KERNEL_NONE;
}
break;
case 512:
if (V->ne[0] != K->ne[0]) {
return BEST_FATTN_KERNEL_NONE;
}
if (!gqa_opt_applies) {
return BEST_FATTN_KERNEL_NONE;
}
break;
case 576:
case 640:
if (V->ne[0] != 512) {
return BEST_FATTN_KERNEL_NONE;
}
if (!gqa_opt_applies) {
return BEST_FATTN_KERNEL_NONE;
}
break;
default:
return BEST_FATTN_KERNEL_NONE;
}
#ifndef GGML_CUDA_FA_ALL_QUANTS
if (K->type != V->type) {
// Allow mixed turbo KV types (any combination of turbo2, turbo3, q8_0)
auto is_turbo = [](ggml_type t) {
return t == GGML_TYPE_TURBO2_0 || t == GGML_TYPE_TURBO3_0 || t == GGML_TYPE_TURBO4_0 || t == GGML_TYPE_Q8_0;
};
if (!is_turbo(K->type) || !is_turbo(V->type)) {
return BEST_FATTN_KERNEL_NONE;
}
}
#endif // GGML_CUDA_FA_ALL_QUANTS
switch (K->type) {
case GGML_TYPE_F32:
case GGML_TYPE_F16:
break;
case GGML_TYPE_Q4_1:
case GGML_TYPE_Q5_0:
case GGML_TYPE_Q5_1:
#ifndef GGML_CUDA_FA_ALL_QUANTS
return BEST_FATTN_KERNEL_NONE;
#endif // GGML_CUDA_FA_ALL_QUANTS
case GGML_TYPE_Q4_0:
case GGML_TYPE_Q8_0:
case GGML_TYPE_BF16:
break;
case GGML_TYPE_TURBO3_0:
// turbo3 VEC kernel instantiated for D in {64, 128, 256}.
if (K->ne[0] % 64 != 0) {
return BEST_FATTN_KERNEL_NONE;
}
break;
case GGML_TYPE_TURBO2_0:
// turbo2 VEC kernel instantiated for D in {64, 128, 256}.
if (K->ne[0] % 64 != 0) {
return BEST_FATTN_KERNEL_NONE;
}
break;
case GGML_TYPE_TURBO4_0:
// turbo4 VEC kernel instantiated for D in {64, 128, 256}.
if (K->ne[0] % 64 != 0) {
return BEST_FATTN_KERNEL_NONE;
}
break;
default:
return BEST_FATTN_KERNEL_NONE;
}
if (mask && mask->ne[2] != 1) {
return BEST_FATTN_KERNEL_NONE;
}
// For small batch sizes the vector kernel may be preferable over the kernels optimized for large batch sizes:
// 192 satisfies % 64 == 0 but has no vec instance (DKQ != DV); force it onto the MMA path.
const bool can_use_vector_kernel = Q->ne[0] <= 256 && Q->ne[0] % 64 == 0 && Q->ne[0] != 192 && K->ne[1] % FATTN_KQ_STRIDE == 0;
// If Turing tensor cores are available, use them:
if (turing_mma_available(cc) && Q->ne[0] != 40 && Q->ne[0] != 72) {
if (can_use_vector_kernel) {
if (!ggml_is_quantized(K->type) && !ggml_is_quantized(V->type)) {
if (cc >= GGML_CUDA_CC_ADA_LOVELACE && Q->ne[1] == 1 && Q->ne[3] == 1 && !(gqa_ratio > 4 && K->ne[1] >= 8192)) {
return BEST_FATTN_KERNEL_VEC;
}
} else {
if (cc >= GGML_CUDA_CC_ADA_LOVELACE) {
if (Q->ne[1] <= 2) {
return BEST_FATTN_KERNEL_VEC;
}
} else {
if (Q->ne[1] == 1) {
return BEST_FATTN_KERNEL_VEC;
}
}
}
if (!gqa_opt_applies && Q->ne[1] == 1) {
return BEST_FATTN_KERNEL_VEC;
}
}
return BEST_FATTN_KERNEL_MMA_F16;
}
const int ncols2_max = Q->ne[0] == 320 ? 32 : ((Q->ne[0] == 576 || Q->ne[0] == 192 || Q->ne[0] == 640) ? 16 : 8);
int gqa_ratio_eff = 1;
while (gqa_ratio % (2*gqa_ratio_eff) == 0 && gqa_ratio_eff < ncols2_max) {
gqa_ratio_eff *= 2;
}
if (volta_mma_available(cc) && Q->ne[0] != 40 && Q->ne[0] != 72) {
if (can_use_vector_kernel && Q->ne[1] * gqa_ratio_eff <= 2) {
return BEST_FATTN_KERNEL_VEC;
}
if (Q->ne[1] * gqa_ratio_eff <= 16) {
return BEST_FATTN_KERNEL_TILE; // On Volta tensor cores are only faster for sufficiently large matrices.
}
return BEST_FATTN_KERNEL_MMA_F16;
}
// Use the WMMA kernel if possible:
if (ggml_cuda_should_use_wmma_fattn(cc) && K->ne[1] % FATTN_KQ_STRIDE == 0 && Q->ne[0] != 40 && Q->ne[0] != 72 && Q->ne[0] != 192 && Q->ne[0] != 512 && Q->ne[0] != 576 && Q->ne[0] != 640) {
if (can_use_vector_kernel && Q->ne[1] <= 2) {
return BEST_FATTN_KERNEL_VEC;
}
return BEST_FATTN_KERNEL_WMMA_F16;
}
// AMD MFMA needs a certain minimum batch size to outscale the tile kernel for large head sizes.
if ((amd_mfma_available(cc) && Q->ne[0] <= 256) && Q->ne[0] != 40 && Q->ne[0] != 72) {
if ((Q->ne[0] <= 64 && Q->ne[1] * gqa_ratio_eff > 8)) {
return BEST_FATTN_KERNEL_MMA_F16;
}
if ((Q->ne[0] <= 128 && Q->ne[1] * gqa_ratio_eff > 16)) {
return BEST_FATTN_KERNEL_MMA_F16;
}
if ((Q->ne[0] <= 256 && Q->ne[1] * gqa_ratio_eff > 64)) {
return BEST_FATTN_KERNEL_MMA_F16;
}
}
// AMD WMMA is always faster than the tile kernel if the full tile width of 16 can be utilized.
if ((amd_wmma_available(cc) && gqa_opt_applies && Q->ne[0] <= 128) && Q->ne[0] != 40 && Q->ne[0] != 72 && Q->ne[1] * gqa_ratio_eff > 8) {
return BEST_FATTN_KERNEL_MMA_F16;
}
// If there are no tensor cores available, use the generic tile kernel:
if (can_use_vector_kernel) {
if (!ggml_is_quantized(K->type) && !ggml_is_quantized(V->type)) {
if (Q->ne[1] == 1) {
if (!gqa_opt_applies) {
return BEST_FATTN_KERNEL_VEC;
}
}
} else {
if (Q->ne[1] <= 2) {
return BEST_FATTN_KERNEL_VEC;
}
}
}
return BEST_FATTN_KERNEL_TILE;
}
void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
ggml_cuda_set_device(ctx.device);
switch (ggml_cuda_get_best_fattn_kernel(ggml_cuda_get_device(), dst)) {
case BEST_FATTN_KERNEL_NONE:
GGML_ABORT("fatal error");
case BEST_FATTN_KERNEL_TILE:
ggml_cuda_flash_attn_ext_tile(ctx, dst);
break;
case BEST_FATTN_KERNEL_VEC:
ggml_cuda_flash_attn_ext_vec(ctx, dst);
break;
case BEST_FATTN_KERNEL_WMMA_F16:
ggml_cuda_flash_attn_ext_wmma_f16(ctx, dst);
break;
case BEST_FATTN_KERNEL_MMA_F16:
ggml_cuda_flash_attn_ext_mma_f16(ctx, dst);
break;
}
}
bool ggml_cuda_flash_attn_ext_supported(int device, const ggml_tensor * dst) {
return ggml_cuda_get_best_fattn_kernel(device, dst) != BEST_FATTN_KERNEL_NONE;
}
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