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CUDA: add FP32 FlashAttention vector kernel (llama/7188)

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* CUDA: add FP32 FlashAttention vector kernel

* fixup! CUDA: add FP32 FlashAttention vector kernel

* fixup! fixup! CUDA: add FP32 FlashAttention vector kernel

* fixup! fixup! fixup! CUDA: add FP32 FlashAttention vector kernel
This commit is contained in:
Johannes Gäßler 2024-05-12 19:40:45 +02:00 committed by Georgi Gerganov
parent 130f43e4b8
commit e57e95eb0d
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GPG Key ID: 449E073F9DC10735
8 changed files with 898 additions and 454 deletions
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11
ggml-cuda.cu
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@ -2713,6 +2713,7 @@ GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t
}
GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, const ggml_tensor * op) {
ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *) backend->context;
switch (op->op) {
case GGML_OP_UNARY:
switch (ggml_get_unary_op(op)) {
@ -2840,8 +2841,16 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons
case GGML_OP_ARANGE:
case GGML_OP_TIMESTEP_EMBEDDING:
case GGML_OP_LEAKY_RELU:
case GGML_OP_FLASH_ATTN_EXT:
return true;
case GGML_OP_FLASH_ATTN_EXT:
#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
return op->src[0]->ne[0] == 64 || op->src[0]->ne[0] == 128;
#else
if (op->src[0]->ne[0] == 64 || op->src[0]->ne[0] == 128) {
return true;
}
return ggml_cuda_info().devices[cuda_ctx->device].cc >= CC_VOLTA;
#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
default:
return false;
}

4
ggml-cuda/common.cuh
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@ -321,6 +321,10 @@ static __device__ __forceinline__ int __dp4a(const int a, const int b, int c) {
#define FP16_MMA_AVAILABLE !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_VOLTA
static bool fast_fp16_available(const int cc) {
return cc >= CC_PASCAL && cc != 610;
}
static bool fp16_mma_available(const int cc) {
return cc < CC_OFFSET_AMD && cc >= CC_VOLTA;
}

47
ggml-cuda/fattn-common.cuh Normal file
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@ -0,0 +1,47 @@
#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.
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;
}

430
ggml-cuda/fattn-vec-f16.cu Normal file
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@ -0,0 +1,430 @@
#include "common.cuh"
#include "fattn-common.cuh"
#include "fattn-vec-f16.cuh"
template<int D, int ncols, 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_vec_ext_f16(
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) {
#if FP16_AVAILABLE
//In this kernel Q, K, V are matrices while i, j, k are matrix indices.
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.
const float2 * Q_f2 = (const float2 *) (Q + nb02* blockIdx.y + nb01*ic0);
const half2 * K_h2 = (const half2 *) (K + nb12*(blockIdx.y / gqa_ratio));
const half * V_h = (const half *) (V + nb12*(blockIdx.y / gqa_ratio)); // K and V have same shape
const half * maskh = (const half *) mask + ne11*ic0;
const int stride_KV = nb11 / sizeof(half);
const int stride_KV2 = nb11 / sizeof(half2);
half slopeh = __float2half(1.0f);
// ALiBi
if (max_bias > 0.0f) {
const int h = blockIdx.y;
const float base = h < n_head_log2 ? m0 : m1;
const int exph = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1;
slopeh = __float2half(powf(base, exph));
}
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__ half KQ[ncols*D];
#pragma unroll
for (int j = 0; j < ncols; ++j) {
KQ[j*D + tid] = -HALF_MAX_HALF;
}
half2 * KQ2 = (half2 *) KQ;
half kqmax[ncols];
#pragma unroll
for (int j = 0; j < ncols; ++j) {
kqmax[j] = -HALF_MAX_HALF;
}
half kqsum[ncols] = {0.0f};
__shared__ half kqmax_shared[ncols][WARP_SIZE];
__shared__ half kqsum_shared[ncols][WARP_SIZE];
#pragma unroll
for (int j = 0; j < ncols; ++j) {
if (threadIdx.y == 0) {
kqmax_shared[j][threadIdx.x] = -HALF_MAX_HALF;
kqsum_shared[j][threadIdx.x] = 0.0f;
}
}
__syncthreads();
// Convert Q to half2 and store in registers:
half2 Q_h2[ncols][D/(2*WARP_SIZE)];
#pragma unroll
for (int j = 0; j < ncols; ++j) {
#pragma unroll
for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) {
const int i = i0 + threadIdx.x;
const float2 tmp = Q_f2[j*(nb01/sizeof(float2)) + i];
Q_h2[j][i0/WARP_SIZE] = make_half2(scale, scale) * make_half2(tmp.x, tmp.y);
}
}
half2 VKQ[ncols] = {{0.0f, 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:
// For unknown reasons using a half array of size 1 for kqmax_new causes a performance regression,
// see https://github.com/ggerganov/llama.cpp/pull/7061 .
// Therefore this variable is defined twice but only used once (so that the compiler can optimize out the unused variable).
half kqmax_new = kqmax[0];
half 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;
}
half2 sum2[ncols] = {{0.0f, 0.0f}};
#pragma unroll
for (int k_KQ_0 = 0; k_KQ_0 < D/2; k_KQ_0 += WARP_SIZE) {
const int k_KQ = k_KQ_0 + threadIdx.x;
const half2 K_ik = K_h2[(k_VKQ_0 + i_KQ)*stride_KV2 + k_KQ];
#pragma unroll
for (int j = 0; j < ncols; ++j) {
sum2[j] += K_ik * Q_h2[j][k_KQ_0/WARP_SIZE];
}
}
#pragma unroll
for (int j = 0; j < ncols; ++j) {
sum2[j] = warp_reduce_sum(sum2[j]);
half sum = __low2half(sum2[j]) + __high2half(sum2[j]);
sum += mask ? slopeh*maskh[j*ne11 + k_VKQ_0 + i_KQ] : __float2half(0.0f);
if (ncols == 1) {
kqmax_new = ggml_cuda_hmax(kqmax_new, sum);
} else {
kqmax_new_arr[j] = ggml_cuda_hmax(kqmax_new_arr[j], sum);
}
if (threadIdx.x == 0) {
KQ[j*D + i_KQ] = sum;
}
}
}
#pragma unroll
for (int j = 0; j < ncols; ++j) {
half kqmax_new_j = ncols == 1 ? kqmax_new : 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) {
half kqmax_new_j = kqmax_shared[j][threadIdx.x];
kqmax_new_j = warp_reduce_max(kqmax_new_j);
const half KQ_max_scale = hexp(kqmax[j] - kqmax_new_j);
kqmax[j] = kqmax_new_j;
const half val = hexp(KQ[j*D + tid] - kqmax[j]);
kqsum[j] = kqsum[j]*KQ_max_scale + val;
KQ[j*D + tid] = val;
VKQ[j] *= __half2half2(KQ_max_scale);
}
__syncthreads();
#pragma unroll
for (int k0 = 0; k0 < D; k0 += 2) {
if (FATTN_KQ_STRIDE % D != 0 && k_VKQ_0 + k0 >= ne11) {
break;
}
half2 V_k;
reinterpret_cast<half&>(V_k.x) = V_h[(k_VKQ_0 + k0 + 0)*stride_KV + tid];
reinterpret_cast<half&>(V_k.y) = V_h[(k_VKQ_0 + k0 + 1)*stride_KV + tid];
#pragma unroll
for (int j = 0; j < ncols; ++j) {
VKQ[j] += V_k*KQ2[j*(D/2) + k0/2];
}
}
__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) {
kqsum[j_VKQ] = kqsum_shared[j_VKQ][threadIdx.x];
kqsum[j_VKQ] = warp_reduce_sum(kqsum[j_VKQ]);
half dst_val = (__low2half(VKQ[j_VKQ]) + __high2half(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 != 0) {
#pragma unroll
for (int j = 0; j < ncols; ++j) {
dst_meta[(ic0 + j)*gridDim.y*parallel_blocks + blockIdx.y*parallel_blocks + ip] = make_float2(kqmax[j], kqsum[j]);
}
}
#else
NO_DEVICE_CODE;
#endif // FP16_AVAILABLE
}
template <int D, int cols_per_block, int parallel_blocks> void launch_fattn_vec_f16(
const ggml_tensor * Q, const ggml_tensor * K, const ggml_tensor * V, ggml_tensor * KQV, const ggml_tensor * mask,
ggml_cuda_pool & pool, cudaStream_t main_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));
}
constexpr int nwarps = (D + WARP_SIZE - 1) / WARP_SIZE;
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);
flash_attn_vec_ext_f16<D, cols_per_block, parallel_blocks>
<<<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());
}
void ggml_cuda_flash_attn_ext_vec_f16(ggml_backend_cuda_context & ctx, ggml_tensor * 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];
ggml_tensor * KQV = dst;
const int32_t precision = KQV->op_params[2];
GGML_ASSERT(precision == GGML_PREC_DEFAULT);
constexpr int cols_per_block = 1;
constexpr int parallel_blocks = 4;
switch (Q->ne[0]) {
case 64:
launch_fattn_vec_f16< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
break;
case 128:
launch_fattn_vec_f16<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
break;
case 256:
launch_fattn_vec_f16<256, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
break;
default:
GGML_ASSERT(false);
break;
}
}
void ggml_cuda_flash_attn_ext_vec_f16_no_mma(ggml_backend_cuda_context & ctx, ggml_tensor * 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];
ggml_tensor * KQV = dst;
const int32_t precision = KQV->op_params[2];
GGML_ASSERT(precision == GGML_PREC_DEFAULT);
GGML_ASSERT(Q->ne[0] == 64 || Q->ne[0] == 128 && "FlashAttention without tensor cores only supports head sizes 64 and 128.");
if (Q->ne[1] == 1) {
constexpr int cols_per_block = 1;
constexpr int parallel_blocks = 4;
switch (Q->ne[0]) {
case 64:
launch_fattn_vec_f16< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
break;
case 128:
launch_fattn_vec_f16<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
break;
default:
GGML_ASSERT(false);
break;
}
return;
}
if (Q->ne[1] == 2) {
constexpr int cols_per_block = 2;
constexpr int parallel_blocks = 4;
switch (Q->ne[0]) {
case 64:
launch_fattn_vec_f16< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
break;
case 128:
launch_fattn_vec_f16<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
break;
default:
GGML_ASSERT(false);
break;
}
return;
}
if (Q->ne[1] <= 4) {
constexpr int cols_per_block = 4;
constexpr int parallel_blocks = 4;
switch (Q->ne[0]) {
case 64:
launch_fattn_vec_f16< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
break;
case 128:
launch_fattn_vec_f16<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
break;
default:
GGML_ASSERT(false);
break;
}
return;
}
if (Q->ne[1] <= 8) {
constexpr int cols_per_block = 8;
constexpr int parallel_blocks = 4;
switch (Q->ne[0]) {
case 64:
launch_fattn_vec_f16< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
break;
case 128:
launch_fattn_vec_f16<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
break;
default:
GGML_ASSERT(false);
break;
}
return;
}
constexpr int cols_per_block = 8;
constexpr int parallel_blocks = 1;
switch (Q->ne[0]) {
case 64:
launch_fattn_vec_f16< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
break;
case 128:
launch_fattn_vec_f16<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
break;
default:
GGML_ASSERT(false);
break;
}
}

5
ggml-cuda/fattn-vec-f16.cuh Normal file
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@ -0,0 +1,5 @@
#include "common.cuh"
void ggml_cuda_flash_attn_ext_vec_f16(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
void ggml_cuda_flash_attn_ext_vec_f16_no_mma(ggml_backend_cuda_context & ctx, ggml_tensor * dst);

384
ggml-cuda/fattn-vec-f32.cu Normal file
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@ -0,0 +1,384 @@
#include "common.cuh"
#include "fattn-common.cuh"
#include "fattn-vec-f32.cuh"
template<int D, int ncols, 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_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 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.
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.
const float2 * Q_f2 = (const float2 *) (Q + nb02* blockIdx.y + nb01*ic0);
const half2 * K_h2 = (const half2 *) (K + nb12*(blockIdx.y / gqa_ratio));
const half * V_h = (const half *) (V + nb12*(blockIdx.y / gqa_ratio)); // K and V have same shape
const half * maskh = (const half *) mask + ne11*ic0;
const int stride_KV = nb11 / sizeof(half);
const int stride_KV2 = nb11 / sizeof(half2);
float slope = 1.0f;
// ALiBi
if (max_bias > 0.0f) {
const int h = blockIdx.y;
const float base = h < n_head_log2 ? m0 : m1;
const int exph = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1;
slope = powf(base, exph);
}
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 half2 and store in registers:
float2 Q_h2[ncols][D/(2*WARP_SIZE)];
#pragma unroll
for (int j = 0; j < ncols; ++j) {
#pragma unroll
for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) {
const int i = i0 + threadIdx.x;
Q_h2[j][i0/WARP_SIZE] = Q_f2[j*(nb01/sizeof(float2)) + i];
Q_h2[j][i0/WARP_SIZE].x *= scale;
Q_h2[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;
}
float sum[ncols] = {0.0f};
#pragma unroll
for (int k_KQ_0 = 0; k_KQ_0 < D/2; k_KQ_0 += WARP_SIZE) {
const int k_KQ = k_KQ_0 + threadIdx.x;
const half2 K_ik = K_h2[(k_VKQ_0 + i_KQ)*stride_KV2 + k_KQ];
#pragma unroll
for (int j = 0; j < ncols; ++j) {
sum[j] += __low2float(K_ik) * Q_h2[j][k_KQ_0/WARP_SIZE].x;
sum[j] += __high2float(K_ik) * Q_h2[j][k_KQ_0/WARP_SIZE].y;
}
}
#pragma unroll
for (int j = 0; j < ncols; ++j) {
sum[j] = warp_reduce_sum(sum[j]);
sum[j] += mask ? slope*__half2float(maskh[j*ne11 + k_VKQ_0 + i_KQ]) : 0.0f;
kqmax_new_arr[j] = fmaxf(kqmax_new_arr[j], sum[j]);
if (threadIdx.x == 0) {
KQ[j*D + i_KQ] = sum[j];
}
}
}
#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 = __half2float(V_h[(k_VKQ_0 + k)*stride_KV + 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) {
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 != 0) {
#pragma unroll
for (int j = 0; j < ncols; ++j) {
dst_meta[(ic0 + j)*gridDim.y*parallel_blocks + blockIdx.y*parallel_blocks + ip] = make_float2(kqmax[j], kqsum[j]);
}
}
}
template <int D, int cols_per_block, int parallel_blocks> void launch_fattn_vec_f32(
const ggml_tensor * Q, const ggml_tensor * K, const ggml_tensor * V, ggml_tensor * KQV, const ggml_tensor * mask,
ggml_cuda_pool & pool, cudaStream_t main_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));
}
constexpr int nwarps = (D + WARP_SIZE - 1) / WARP_SIZE;
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);
flash_attn_vec_ext_f32<D, cols_per_block, parallel_blocks>
<<<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());
}
void ggml_cuda_flash_attn_ext_vec_f32(ggml_backend_cuda_context & ctx, ggml_tensor * 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];
ggml_tensor * KQV = dst;
GGML_ASSERT(Q->ne[0] == 64 || Q->ne[0] == 128 && "FlashAttention without tensor cores only supports head sizes 64 and 128.");
if (Q->ne[1] == 1) {
constexpr int cols_per_block = 1;
constexpr int parallel_blocks = 4;
switch (Q->ne[0]) {
case 64:
launch_fattn_vec_f32< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
break;
case 128:
launch_fattn_vec_f32<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
break;
default:
GGML_ASSERT(false);
break;
}
return;
}
if (Q->ne[1] == 2) {
constexpr int cols_per_block = 2;
constexpr int parallel_blocks = 4;
switch (Q->ne[0]) {
case 64:
launch_fattn_vec_f32< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
break;
case 128:
launch_fattn_vec_f32<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
break;
default:
GGML_ASSERT(false);
break;
}
return;
}
if (Q->ne[1] <= 4) {
constexpr int cols_per_block = 4;
constexpr int parallel_blocks = 4;
switch (Q->ne[0]) {
case 64:
launch_fattn_vec_f32< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
break;
case 128:
launch_fattn_vec_f32<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
break;
default:
GGML_ASSERT(false);
break;
}
return;
}
if (Q->ne[1] <= 8) {
constexpr int cols_per_block = 8;
constexpr int parallel_blocks = 4;
switch (Q->ne[0]) {
case 64:
launch_fattn_vec_f32< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
break;
case 128:
launch_fattn_vec_f32<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
break;
default:
GGML_ASSERT(false);
break;
}
return;
}
constexpr int cols_per_block = 8;
constexpr int parallel_blocks = 1;
switch (Q->ne[0]) {
case 64:
launch_fattn_vec_f32< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
break;
case 128:
launch_fattn_vec_f32<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
break;
default:
GGML_ASSERT(false);
break;
}
}

3
ggml-cuda/fattn-vec-f32.cuh Normal file
View File

@ -0,0 +1,3 @@
#include "common.cuh"
void ggml_cuda_flash_attn_ext_vec_f32(ggml_backend_cuda_context & ctx, ggml_tensor * dst);

468
ggml-cuda/fattn.cu
View File

@ -1,4 +1,7 @@
#include "common.cuh"
#include "fattn-common.cuh"
#include "fattn-vec-f16.cuh"
#include "fattn-vec-f32.cuh"
#include "fattn.cuh"
#include <cstdint>
@ -7,251 +10,6 @@
#include <mma.h>
#endif
#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.
template<int D, int ncols, 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_vec_ext_f16(
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) {
#if FP16_AVAILABLE
//In this kernel Q, K, V are matrices while i, j, k are matrix indices.
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.
const float2 * Q_f2 = (const float2 *) (Q + nb02* blockIdx.y + nb01*ic0);
const half2 * K_h2 = (const half2 *) (K + nb12*(blockIdx.y / gqa_ratio));
const half * V_h = (const half *) (V + nb12*(blockIdx.y / gqa_ratio)); // K and V have same shape
const half * maskh = (const half *) mask + ne11*ic0;
const int stride_KV = nb11 / sizeof(half);
const int stride_KV2 = nb11 / sizeof(half2);
half slopeh = __float2half(1.0f);
// ALiBi
if (max_bias > 0.0f) {
const int h = blockIdx.y;
const float base = h < n_head_log2 ? m0 : m1;
const int exph = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1;
slopeh = __float2half(powf(base, exph));
}
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__ half KQ[ncols*D];
#pragma unroll
for (int j = 0; j < ncols; ++j) {
KQ[j*D + tid] = -HALF_MAX_HALF;
}
half2 * KQ2 = (half2 *) KQ;
half kqmax[ncols];
#pragma unroll
for (int j = 0; j < ncols; ++j) {
kqmax[j] = -HALF_MAX_HALF;
}
half kqsum[ncols] = {0.0f};
__shared__ half kqmax_shared[ncols][WARP_SIZE];
__shared__ half kqsum_shared[ncols][WARP_SIZE];
#pragma unroll
for (int j = 0; j < ncols; ++j) {
if (threadIdx.y == 0) {
kqmax_shared[j][threadIdx.x] = -HALF_MAX_HALF;
kqsum_shared[j][threadIdx.x] = 0.0f;
}
}
__syncthreads();
// Convert Q to half2 and store in registers:
half2 Q_h2[ncols][D/(2*WARP_SIZE)];
#pragma unroll
for (int j = 0; j < ncols; ++j) {
#pragma unroll
for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) {
const int i = i0 + threadIdx.x;
const float2 tmp = Q_f2[j*(nb01/sizeof(float2)) + i];
Q_h2[j][i0/WARP_SIZE] = make_half2(scale, scale) * make_half2(tmp.x, tmp.y);
}
}
half2 VKQ[ncols] = {{0.0f, 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:
// For unknown reasons using a half array of size 1 for kqmax_new causes a performance regression,
// see https://github.com/ggerganov/llama.cpp/pull/7061 .
// Therefore this variable is defined twice but only used once (so that the compiler can optimize out the unused variable).
half kqmax_new = kqmax[0];
half 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;
}
half2 sum2[ncols] = {{0.0f, 0.0f}};
#pragma unroll
for (int k_KQ_0 = 0; k_KQ_0 < D/2; k_KQ_0 += WARP_SIZE) {
const int k_KQ = k_KQ_0 + threadIdx.x;
const half2 K_ik = K_h2[(k_VKQ_0 + i_KQ)*stride_KV2 + k_KQ];
#pragma unroll
for (int j = 0; j < ncols; ++j) {
sum2[j] += K_ik * Q_h2[j][k_KQ_0/WARP_SIZE];
}
}
#pragma unroll
for (int j = 0; j < ncols; ++j) {
sum2[j] = warp_reduce_sum(sum2[j]);
half sum = __low2half(sum2[j]) + __high2half(sum2[j]);
sum += mask ? slopeh*maskh[j*ne11 + k_VKQ_0 + i_KQ] : __float2half(0.0f);
if (ncols == 1) {
kqmax_new = ggml_cuda_hmax(kqmax_new, sum);
} else {
kqmax_new_arr[j] = ggml_cuda_hmax(kqmax_new_arr[j], sum);
}
if (threadIdx.x == 0) {
KQ[j*D + i_KQ] = sum;
}
}
}
#pragma unroll
for (int j = 0; j < ncols; ++j) {
half kqmax_new_j = ncols == 1 ? kqmax_new : 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) {
half kqmax_new_j = kqmax_shared[j][threadIdx.x];
kqmax_new_j = warp_reduce_max(kqmax_new_j);
const half KQ_max_scale = hexp(kqmax[j] - kqmax_new_j);
kqmax[j] = kqmax_new_j;
const half val = hexp(KQ[j*D + tid] - kqmax[j]);
kqsum[j] = kqsum[j]*KQ_max_scale + val;
KQ[j*D + tid] = val;
VKQ[j] *= __half2half2(KQ_max_scale);
}
__syncthreads();
#pragma unroll
for (int k0 = 0; k0 < D; k0 += 2) {
if (FATTN_KQ_STRIDE % D != 0 && k_VKQ_0 + k0 >= ne11) {
break;
}
half2 V_k;
reinterpret_cast<half&>(V_k.x) = V_h[(k_VKQ_0 + k0 + 0)*stride_KV + tid];
reinterpret_cast<half&>(V_k.y) = V_h[(k_VKQ_0 + k0 + 1)*stride_KV + tid];
#pragma unroll
for (int j = 0; j < ncols; ++j) {
VKQ[j] += V_k*KQ2[j*(D/2) + k0/2];
}
}
__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) {
kqsum[j_VKQ] = kqsum_shared[j_VKQ][threadIdx.x];
kqsum[j_VKQ] = warp_reduce_sum(kqsum[j_VKQ]);
half dst_val = (__low2half(VKQ[j_VKQ]) + __high2half(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 != 0) {
#pragma unroll
for (int j = 0; j < ncols; ++j) {
dst_meta[(ic0 + j)*gridDim.y*parallel_blocks + blockIdx.y*parallel_blocks + ip] = make_float2(kqmax[j], kqsum[j]);
}
}
#else
NO_DEVICE_CODE;
#endif // FP16_AVAILABLE
}
// D == head size, VKQ_stride == num VKQ rows calculated in parallel:
template<int D, int ncols, int nwarps, int VKQ_stride, int parallel_blocks, typename KQ_acc_t>
#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__))
@ -655,54 +413,6 @@ static __global__ void flash_attn_ext_f16(
#endif // FP16_MMA_AVAILABLE
}
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) {
#if FP16_AVAILABLE
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;
#else
NO_DEVICE_CODE;
#endif // FP16_AVAILABLE
}
constexpr int get_max_power_of_2(int x) {
return x % 2 == 0 ? 2*get_max_power_of_2(x/2) : 1;
}
@ -727,66 +437,6 @@ static_assert(get_VKQ_stride( 80, 1, 16) == 16, "Test failed.");
static_assert(get_VKQ_stride( 80, 2, 16) == 16, "Test failed.");
static_assert(get_VKQ_stride( 80, 4, 16) == 16, "Test failed.");
template <int D, int cols_per_block, int parallel_blocks> void launch_fattn_vec_f16(
const ggml_tensor * Q, const ggml_tensor * K, const ggml_tensor * V, ggml_tensor * KQV, const ggml_tensor * mask,
ggml_cuda_pool & pool, cudaStream_t main_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));
}
constexpr int nwarps = (D + WARP_SIZE - 1) / WARP_SIZE;
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);
flash_attn_vec_ext_f16<D, cols_per_block, parallel_blocks>
<<<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());
}
template <int D, int cols_per_block, int nwarps, int parallel_blocks, typename KQ_acc_t> void launch_fattn_f16_impl(
const ggml_tensor * Q, const ggml_tensor * K, const ggml_tensor * V, ggml_tensor * KQV, const ggml_tensor * mask,
ggml_cuda_pool & pool, cudaStream_t main_stream
@ -891,95 +541,22 @@ void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst
const int32_t precision = KQV->op_params[2];
if (!fast_fp16_available(cc)) {
ggml_cuda_flash_attn_ext_vec_f32(ctx, dst);
return;
}
if (!fp16_mma_available(cc)) {
GGML_ASSERT(precision == GGML_PREC_DEFAULT);
GGML_ASSERT(Q->ne[0] == 64 || Q->ne[0] == 128 && "FlashAttention without tensor cores only supports head sizes 64 and 128.");
if (Q->ne[1] == 1) {
constexpr int cols_per_block = 1;
constexpr int parallel_blocks = 4;
switch (Q->ne[0]) {
case 64:
launch_fattn_vec_f16< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
break;
case 128:
launch_fattn_vec_f16<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
break;
default:
GGML_ASSERT(false);
break;
}
return;
}
if (Q->ne[1] == 2) {
constexpr int cols_per_block = 2;
constexpr int parallel_blocks = 4;
switch (Q->ne[0]) {
case 64:
launch_fattn_vec_f16< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
break;
case 128:
launch_fattn_vec_f16<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
break;
default:
GGML_ASSERT(false);
break;
}
return;
}
if (Q->ne[1] <= 4) {
constexpr int cols_per_block = 4;
constexpr int parallel_blocks = 4;
switch (Q->ne[0]) {
case 64:
launch_fattn_vec_f16< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
break;
case 128:
launch_fattn_vec_f16<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
break;
default:
GGML_ASSERT(false);
break;
}
return;
}
if (Q->ne[1] <= 8) {
constexpr int cols_per_block = 8;
constexpr int parallel_blocks = 4;
switch (Q->ne[0]) {
case 64:
launch_fattn_vec_f16< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
break;
case 128:
launch_fattn_vec_f16<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
break;
default:
GGML_ASSERT(false);
break;
}
return;
}
constexpr int cols_per_block = 8;
constexpr int parallel_blocks = 1;
switch (Q->ne[0]) {
case 64:
launch_fattn_vec_f16< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
break;
case 128:
launch_fattn_vec_f16<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
break;
default:
GGML_ASSERT(false);
break;
}
ggml_cuda_flash_attn_ext_vec_f16_no_mma(ctx, dst);
return;
}
if (precision != GGML_PREC_DEFAULT) {
if (Q->ne[1] == 1 && (Q->ne[0] == 64 || Q->ne[0] == 128)) {
ggml_cuda_flash_attn_ext_vec_f32(ctx, dst);
return;
}
if (Q->ne[1] <= 32 || Q->ne[0] > 128) {
constexpr int cols_per_block = 16;
constexpr int nwarps = 4;
@ -1037,22 +614,7 @@ void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst
}
if (Q->ne[1] == 1 && Q->ne[0] % (2*WARP_SIZE) == 0) {
constexpr int cols_per_block = 1;
constexpr int parallel_blocks = 4;
switch (Q->ne[0]) {
case 64:
launch_fattn_vec_f16< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
break;
case 128:
launch_fattn_vec_f16<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
break;
case 256:
launch_fattn_vec_f16<256, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
break;
default:
GGML_ASSERT(false);
break;
}
ggml_cuda_flash_attn_ext_vec_f16(ctx, dst);
return;
}