OBJ_GGML_EXT += ggml/src/ggml-rpc.o
endif # GGML_RPC
-OBJ_CUDA_TMPL = $(patsubst %.cu,%.o,$(wildcard ggml/src/ggml-cuda/template-instances/fattn-wmma*.cu))
+OBJ_CUDA_TMPL = $(patsubst %.cu,%.o,$(wildcard ggml/src/ggml-cuda/template-instances/fattn-mma*.cu))
OBJ_CUDA_TMPL += $(patsubst %.cu,%.o,$(wildcard ggml/src/ggml-cuda/template-instances/mmq*.cu))
ifdef GGML_CUDA_FA_ALL_QUANTS
struct ggml_tensor * a,
int k);
-#define GGML_KQ_MASK_PAD 32
+#define GGML_KQ_MASK_PAD 64
// q: [n_embd, n_batch, n_head, 1]
// k: [n_embd, n_kv, n_head_kv, 1]
list(APPEND GGML_HEADERS_CUDA "../../include/ggml-cuda.h")
file(GLOB GGML_SOURCES_CUDA "*.cu")
- file(GLOB SRCS "template-instances/fattn-wmma*.cu")
+ file(GLOB SRCS "template-instances/fattn-mma*.cu")
list(APPEND GGML_SOURCES_CUDA ${SRCS})
file(GLOB SRCS "template-instances/mmq*.cu")
list(APPEND GGML_SOURCES_CUDA ${SRCS})
#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= GGML_CUDA_CC_VOLTA
#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= GGML_CUDA_CC_TURING
-#define INT8_MMA_AVAILABLE
+#define NEW_MMA_AVAILABLE
#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= GGML_CUDA_CC_TURING
#if !(defined(GGML_USE_MUSA) && __MUSA_ARCH__ <= GGML_CUDA_CC_QY1)
return cc >= GGML_CUDA_CC_PASCAL && cc != 610;
}
+// Any FP16 tensor cores are available.
static constexpr bool fp16_mma_available(const int cc) {
return cc < GGML_CUDA_CC_OFFSET_AMD && cc >= GGML_CUDA_CC_VOLTA;
}
-static constexpr bool int8_mma_available(const int cc) {
+// Volta technically had FP16 tensor cores but they work very differently compared to Turing and later.
+static constexpr bool new_mma_available(const int cc) {
return cc < GGML_CUDA_CC_OFFSET_AMD && cc >= GGML_CUDA_CC_TURING;
}
nullptr;
}
+template<int D, int ncols, int KQ_stride> // D == head size
+#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__))
+__launch_bounds__(D, 1)
+#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__))
+static __global__ void flash_attn_stream_k_fixup(
+ float * __restrict__ dst, const float2 * __restrict__ dst_fixup, const int ne01, const int ne02, const int ne11) {
+ const float * dst_fixup_data = ((const float *) dst_fixup) + gridDim.x*(2*2*ncols);
+
+ const int iter_k = ne11 / KQ_stride;
+ const int iter_j = (ne01 + (ncols - 1)) / ncols;
+
+ const int bidx0 = blockIdx.x;
+
+ const int kbc0 = (bidx0 + 0)*iter_k*iter_j*ne02 / gridDim.x;
+ const int kbc0_stop = (bidx0 + 1)*iter_k*iter_j*ne02 / gridDim.x;
+
+ const bool did_not_have_any_data = kbc0 == kbc0_stop;
+ const bool wrote_beginning_of_tile = kbc0 % iter_k == 0;
+ const bool did_not_write_last = kbc0/iter_k == kbc0_stop/iter_k && kbc0_stop % iter_k != 0;
+ if (did_not_have_any_data || wrote_beginning_of_tile || did_not_write_last) {
+ return;
+ }
+
+ const int channel = kbc0 / (iter_k*iter_j);
+ const int jt = (kbc0 - channel*iter_k*iter_j) / iter_k;
+
+ dst += jt*ncols*ne02*D + channel*D;
+
+ // Load the partial result that needs a fixup:
+ float dst_val[ncols] = {0.0f};
+ float max_val[ncols] = {0.0f};
+ float rowsum[ncols] = {0.0f};
+#pragma unroll
+ for (int j = 0; j < ncols; ++j) {
+ if (jt*ncols + j >= ne01) {
+ break;
+ }
+ dst_val[j] = dst[j*ne02*D + threadIdx.x];
+
+ const float2 tmp = dst_fixup[bidx0*ncols + j];
+ max_val[j] = tmp.x;
+ rowsum[j] = tmp.y;
+ }
+
+ // Iterate over previous blocks and compute the combined results.
+ // All CUDA blocks that get here must have a previous block that needs a fixup.
+ int bidx = bidx0 - 1;
+ int kbc_stop = kbc0;
+ while(true) {
+ const int kbc = bidx*iter_k*iter_j*ne02 / gridDim.x;
+ if (kbc == kbc_stop) { // Did not have any data.
+ bidx--;
+ kbc_stop = kbc;
+ continue;
+ }
+
+#pragma unroll
+ for (int j = 0; j < ncols; ++j) {
+ if (jt*ncols + j >= ne01) {
+ break;
+ }
+ const float dst_add = dst_fixup_data[bidx*ncols*D + j*D + threadIdx.x];
+
+ const float2 tmp = dst_fixup[(gridDim.x + bidx)*ncols + j];
+
+ // Scale the current and new value accumulators depending on the max. values.
+ const float max_val_new = fmaxf(max_val[j], tmp.x);
+
+ const float diff_val = max_val[j] - max_val_new;
+ const float diff_add = tmp.x - max_val_new;
+
+ const float scale_val = diff_val >= SOFTMAX_FTZ_THRESHOLD ? expf(diff_val) : 0.0f;
+ const float scale_add = diff_add >= SOFTMAX_FTZ_THRESHOLD ? expf(diff_add) : 0.0f;
+
+ dst_val[j] = scale_val*dst_val[j] + scale_add*dst_add;
+ rowsum[j] = scale_val*rowsum[j] + scale_add*tmp.y;
+
+ max_val[j] = max_val_new;
+ }
+
+ // If this block started in a previous tile we are done and don't need to combine additional partial results.
+ if (kbc % iter_k == 0 || kbc/iter_k < kbc0/iter_k) {
+ break;
+ }
+ bidx--;
+ kbc_stop = kbc;
+ }
+
+ // Write back final result:
+#pragma unroll
+ for (int j = 0; j < ncols; ++j) {
+ if (jt*ncols + j >= ne01) {
+ return;
+ }
+ dst[j*ne02*D + threadIdx.x] = dst_val[j] / rowsum[j];
+ }
+}
+
template<int D, int parallel_blocks> // D == head size
#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__))
__launch_bounds__(D, 1)
}
}
-template <int D, int parallel_blocks>
+// parallel_blocks == 0 is stream-k decomposition
+template <int D, int cols_per_block, int parallel_blocks, int KQ_stride>
void launch_fattn(
ggml_backend_cuda_context & ctx, ggml_tensor * dst, fattn_kernel_t fattn_kernel,
- const int nwarps, const int cols_per_block, const bool need_f16_K, const bool need_f16_V
+ const int nwarps, const size_t nbytes_shared, const bool need_f16_K, const bool need_f16_V
) {
const ggml_tensor * Q = dst->src[0];
const ggml_tensor * K = dst->src[1];
GGML_ASSERT(K->ne[1] % FATTN_KQ_STRIDE == 0 && "Incorrect KV cache padding.");
+ GGML_ASSERT(Q->ne[3] == 1);
+
ggml_cuda_pool & pool = ctx.pool();
cudaStream_t main_stream = ctx.stream();
+ const int nsm = ggml_cuda_info().devices[ggml_cuda_get_device()].nsm;
ggml_cuda_pool_alloc<half> K_f16(pool);
ggml_cuda_pool_alloc<half> V_f16(pool);
ggml_cuda_pool_alloc<float> dst_tmp(pool);
ggml_cuda_pool_alloc<float2> dst_tmp_meta(pool);
- char * K_data = (char *) K->data;
+ const char * K_data = (const char *) K->data;
size_t nb11 = K->nb[1];
size_t nb12 = K->nb[2];
size_t nb13 = K->nb[3];
- char * V_data = (char *) V->data;
+ const char * V_data = (const char *) V->data;
size_t nb21 = V->nb[1];
size_t nb22 = V->nb[2];
size_t nb23 = V->nb[3];
nb23 = nb23*bs*sizeof(half)/ts;
}
- if (parallel_blocks > 1) {
- dst_tmp.alloc(parallel_blocks*ggml_nelements(KQV));
- dst_tmp_meta.alloc(parallel_blocks*ggml_nrows(KQV));
- }
+ const int ntiles_x = ((Q->ne[1] + cols_per_block - 1) / cols_per_block);
+ const int ntiles_total = ntiles_x*Q->ne[2]*Q->ne[3];
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;
+ dim3 blocks_num;
+ if (parallel_blocks == 0) {
+ // For short contexts it can be faster to have the SMs work on whole tiles because this lets us skip the fixup.
+ const int tiles_nwaves = (ntiles_total - nsm - 1) / nsm;
+ const bool tiles_inefficient = 3*nsm < 2*tiles_nwaves*ntiles_total;
+ const bool short_context = K->ne[1] < 4096;
+
+ const int nblocks_stream_k = 2*nsm;
+
+ blocks_num.x = short_context && !tiles_inefficient ? ntiles_total : nblocks_stream_k;
+ blocks_num.y = 1;
+ blocks_num.z = 1;
+
+ dst_tmp_meta.alloc(blocks_num.x*cols_per_block * (2*2 + D) * sizeof(float));
+ } else {
+ blocks_num.x = parallel_blocks*ntiles_x;
+ blocks_num.y = Q->ne[2];
+ blocks_num.z = Q->ne[3];
+
+ if (parallel_blocks > 1) {
+ dst_tmp.alloc(parallel_blocks*ggml_nelements(KQV));
+ dst_tmp_meta.alloc(parallel_blocks*ggml_nrows(KQV));
+ }
+ }
+
float scale = 1.0f;
float max_bias = 0.0f;
float logit_softcap = 0.0f;
- memcpy(&scale, (float *) KQV->op_params + 0, sizeof(float));
- memcpy(&max_bias, (float *) KQV->op_params + 1, sizeof(float));
- memcpy(&logit_softcap, (float *) KQV->op_params + 2, sizeof(float));
+ memcpy(&scale, (const float *) KQV->op_params + 0, sizeof(float));
+ memcpy(&max_bias, (const float *) KQV->op_params + 1, sizeof(float));
+ memcpy(&logit_softcap, (const float *) KQV->op_params + 2, sizeof(float));
if (logit_softcap != 0.0f) {
scale /= logit_softcap;
}
const uint32_t n_head = Q->ne[2];
- const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head));
+ const uint32_t n_head_log2 = 1u << uint32_t(floorf(log2f(float(n_head))));
const float m0 = powf(2.0f, -(max_bias ) / n_head_log2);
const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2);
- fattn_kernel<<<blocks_num, block_dim, shmem, main_stream>>>(
+ fattn_kernel<<<blocks_num, block_dim, nbytes_shared, main_stream>>>(
(const char *) Q->data,
K_data,
V_data,
mask ? ((const char *) mask->data) : nullptr,
- (parallel_blocks) == 1 ? (float *) KQV->data : dst_tmp.ptr, dst_tmp_meta.ptr,
+ (parallel_blocks) > 1 ? dst_tmp.ptr : (float *) KQV->data, dst_tmp_meta.ptr,
scale, max_bias, m0, m1, n_head_log2, logit_softcap,
Q->ne[0], Q->ne[1], Q->ne[2], Q->ne[3],
K->ne[0], K->ne[1], K->ne[2], K->ne[3],
);
CUDA_CHECK(cudaGetLastError());
- if ((parallel_blocks) == 1) {
- return;
- }
+ if constexpr (parallel_blocks == 0) {
+ if (blocks_num.x % ntiles_total != 0) { // Fixup is only needed if the SMs work on fractional tiles.
+ const dim3 block_dim_combine(D, 1, 1);
+ const dim3 blocks_num_combine = blocks_num;
- 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_stream_k_fixup<D, cols_per_block, KQ_stride>
+ <<<blocks_num_combine, block_dim_combine, 0, main_stream>>>
+ ((float *) KQV->data, dst_tmp_meta.ptr, Q->ne[1], Q->ne[2], K->ne[1]);
+ }
+ } else if constexpr (parallel_blocks > 1) {
+ const dim3 block_dim_combine(D, 1, 1);
+ const dim3 blocks_num_combine(Q->ne[1], blocks_num.y, blocks_num.z);
- 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);
+ flash_attn_combine_results<D, parallel_blocks>
+ <<<blocks_num_combine, block_dim_combine, 0, main_stream>>>
+ (dst_tmp.ptr, dst_tmp_meta.ptr, (float *) KQV->data);
+ }
CUDA_CHECK(cudaGetLastError());
}
--- /dev/null
+#include "common.cuh"
+#include "mma.cuh"
+#include "fattn-common.cuh"
+
+template<int D, int ncols, int nwarps, int KQ_stride, bool use_logit_softcap, bool needs_fixup, bool is_fixup>
+static __device__ __forceinline__ void flash_attn_ext_f16_process_tile(
+ const float2 * const __restrict__ Q_f2,
+ const half2 * const __restrict__ K_h2,
+ const half2 * const __restrict__ V_h2,
+ const half * const __restrict__ maskh,
+ float2 * const __restrict__ dstk,
+ float2 * const __restrict__ dstk_fixup,
+ const float scale,
+ const float slope,
+ const float logit_softcap,
+ 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,
+ const int jt,
+ const int kb0_start,
+ const int kb0_stop) {
+#ifdef NEW_MMA_AVAILABLE
+ //In this kernel Q, K, V are matrices while i, j, k are matrix indices.
+
+ typedef mma_A_I16K8<half2> mma_A;
+ typedef mma_B_J8K8<half2> mma_B;
+ typedef mma_C_I16J8<float> mma_C_KQ;
+ typedef mma_C_I16J8<half2> mma_C_VKQ;
+
+ static_assert(nwarps*mma_B::J % ncols == 0, "bad nwarps");
+ constexpr int np = nwarps*mma_B::J / ncols; // Number of parallel CUDA warps per Q column.
+
+ static_assert(D % nwarps == 0, "bad D");
+ static_assert(KQ_stride % nwarps == 0, "bad KQ_stride");
+
+ constexpr int D2_padded = D/2 + 4; // Size of D in half2, padded to avoid shared memory bank conflicts.
+ extern __shared__ half2 tile_KV[]; // Temporary shared buffer for loading K/V data with KQ_stride*D logical elements.
+
+ const int stride_Q = nb01 / sizeof(float2);
+ const int stride_KV = nb11 / sizeof(half2);
+ const int stride_mask = nb31 / sizeof(half);
+
+ mma_B Q_B[D/(2*mma_B::K)];
+ mma_C_VKQ VKQ_C[D/mma_C_VKQ::I];
+
+ float2 KQ_rowsum = {0.0f, 0.0f};
+ float2 KQ_max = {-FLT_MAX/2.0f, -FLT_MAX/2.0f};
+ float2 KQ_max_scale = {0.0f, 0.0f};
+
+ // Temporarily load Q data into tile_KV, will be loaded into registers afterwards.
+ // The loading is done with decreasing granularity for D for better memory bandwidth.
+ const half2 scale_h2 = make_half2(scale, scale);
+#pragma unroll
+ for (int stride_k : {WARP_SIZE, WARP_SIZE/2, WARP_SIZE/4}) {
+ const int k0_start = stride_k == WARP_SIZE ? 0 : D/2 - (D/2) % (2*stride_k);
+ const int k0_stop = D/2 - (D/2) % (1*stride_k);
+ const int stride_j = WARP_SIZE / stride_k;
+
+ if (nwarps*stride_j > ncols && threadIdx.y*stride_j >= ncols) {
+ break;
+ }
+
+#pragma unroll
+ for (int j0 = 0; j0 < ncols; j0 += nwarps*stride_j) {
+ const int j = j0 + threadIdx.y*stride_j + (stride_k == WARP_SIZE ? 0 : threadIdx.x / stride_k);
+
+ if (jt*ncols + j < ne01) {
+#pragma unroll
+ for (int k0 = k0_start; k0 < k0_stop; k0 += stride_k) {
+ const int k = k0 + (stride_k == WARP_SIZE ? threadIdx.x : threadIdx.x % stride_k);
+
+ const float2 tmp = Q_f2[(jt*ncols + j)*stride_Q + k];
+ tile_KV[j*D2_padded + k] = scale_h2 * make_half2(tmp.x, tmp.y);
+ }
+ } else {
+#pragma unroll
+ for (int k0 = k0_start; k0 < k0_stop; k0 += stride_k) {
+ const int k = k0 + (stride_k == WARP_SIZE ? threadIdx.x : threadIdx.x % stride_k);
+
+ tile_KV[j*D2_padded + k] = make_half2(0.0f, 0.0f);
+ }
+ }
+ }
+ }
+
+ __syncthreads();
+
+ {
+ const int j0 = (threadIdx.y / np) * mma_B::J;
+
+#pragma unroll
+ for (int k0 = 0; k0 < D/2; k0 += mma_B::K) {
+ Q_B[k0/mma_B::K].load_ldmatrix(tile_KV + j0*D2_padded + k0, D2_padded);
+ }
+ }
+
+ __syncthreads();
+
+ // Iterate over ne11 == previous tokens:
+ for (int kb0 = kb0_start; kb0 < kb0_stop; ++kb0) {
+ const int k_VKQ_0 = kb0*KQ_stride;
+ mma_C_KQ KQ_C[KQ_stride/(np*mma_C_KQ::I)];
+
+ // Load K data into tile with decreasing granularity for D for better memory bandwidth:
+ static_assert(KQ_stride % (4*nwarps) == 0, "out of bounds");
+#pragma unroll
+ for (int stride_k : {WARP_SIZE, WARP_SIZE/2, WARP_SIZE/4}) {
+ const int k0_start = stride_k == WARP_SIZE ? 0 : D/2 - (D/2) % (2*stride_k);
+ const int k0_stop = D/2 - (D/2) % (1*stride_k);
+ const int stride_i = WARP_SIZE / stride_k;
+
+#pragma unroll
+ for (int i_KQ_0 = 0; i_KQ_0 < KQ_stride; i_KQ_0 += nwarps*stride_i) {
+ const int i_KQ = i_KQ_0 + threadIdx.y*stride_i + (stride_k == WARP_SIZE ? 0 : threadIdx.x / stride_k);
+
+#pragma unroll
+ for (int k_KQ_0 = k0_start; k_KQ_0 < k0_stop; k_KQ_0 += stride_k) {
+ const int k_KQ = k_KQ_0 + (stride_k == WARP_SIZE ? threadIdx.x : threadIdx.x % stride_k);
+
+ tile_KV[i_KQ*D2_padded + k_KQ] = K_h2[(k_VKQ_0 + i_KQ)*stride_KV + k_KQ];
+ }
+ }
+ }
+
+ __syncthreads();
+
+ // Calculate tile of KQ:
+#pragma unroll
+ for (int i_KQ_00 = 0; i_KQ_00 < KQ_stride; i_KQ_00 += np*mma_A::I) {
+ const int i_KQ_0 = i_KQ_00 + (threadIdx.y % np)*mma_A::I;
+#pragma unroll
+ for (int k_KQ_0 = 0; k_KQ_0 < D/2; k_KQ_0 += mma_A::K) {
+ mma_A K_A;
+ K_A.load_ldmatrix(tile_KV + i_KQ_0*D2_padded + k_KQ_0, D2_padded);
+ KQ_C[i_KQ_00/(np*mma_A::I)].mma(K_A, Q_B[k_KQ_0/mma_A::K]);
+ }
+ }
+
+ __syncthreads();
+
+ if (use_logit_softcap) {
+ static_assert(KQ_stride % (np*mma_C_KQ::I) == 0, "bad loop size");
+#pragma unroll
+ for (int i = 0; i < KQ_stride/(np*mma_C_KQ::I); ++i) {
+#pragma unroll
+ for (int l = 0; l < mma_C_KQ::ne; ++l) {
+ KQ_C[i].x[l] = logit_softcap*tanhf(KQ_C[i].x[l]);
+ }
+ }
+ }
+
+ if (maskh) {
+ static_assert(KQ_stride % (np *mma_C_KQ::I) == 0, "bad loop size");
+ static_assert(ncols % (nwarps/np*mma_C_KQ::J) == 0, "bad loop size");
+#pragma unroll
+ for (int i00 = 0; i00 < KQ_stride; i00 += np*mma_C_KQ::I) {
+ const int i0 = i00 + (threadIdx.y % np)*mma_C_KQ::I;
+#pragma unroll
+ for (int l = 0; l < mma_C_KQ::ne; ++l) {
+ const int i = i0 + mma_C_KQ::get_i(l);
+ const int j = (threadIdx.y / np)*mma_C_KQ::J + mma_C_KQ::get_j(l);
+
+ KQ_C[i00/(np*mma_C_KQ::I)].x[l] += slope*__half2float(maskh[j*stride_mask + k_VKQ_0 + i]);
+ }
+ }
+ }
+
+ // Calculate softmax for each KQ column using the current max. value.
+ // The divisor is stored in KQ_rowsum and will be applied at the end.
+ float2 KQ_max_new = KQ_max;
+ static_assert(KQ_stride % (np*mma_C_KQ::I) == 0, "bad loop size");
+#pragma unroll
+ for (int k = 0; k < KQ_stride/(np*mma_C_KQ::I); ++k) {
+#pragma unroll
+ for (int l0 = 0; l0 < mma_C_KQ::ne; l0 += 2) {
+ KQ_max_new.x = fmaxf(KQ_max_new.x, KQ_C[k].x[l0 + 0]);
+ KQ_max_new.y = fmaxf(KQ_max_new.y, KQ_C[k].x[l0 + 1]);
+ }
+ }
+
+ // Values per KQ column are spread across 8 threads, does not need full warp reduce:
+#pragma unroll
+ for (int offset = 16; offset > 2; offset >>= 1) {
+ KQ_max_new.x = fmaxf(KQ_max_new.x, __shfl_xor_sync(0xFFFFFFFF, KQ_max_new.x, offset, WARP_SIZE));
+ KQ_max_new.y = fmaxf(KQ_max_new.y, __shfl_xor_sync(0xFFFFFFFF, KQ_max_new.y, offset, WARP_SIZE));
+ }
+
+ {
+ const float2 diff = make_float2(KQ_max.x - KQ_max_new.x, KQ_max.y - KQ_max_new.y);
+ KQ_max_scale = make_float2(expf(diff.x), expf(diff.y));
+ if (diff.x <= SOFTMAX_FTZ_THRESHOLD) {
+ KQ_max_scale.x = 0.0f;
+ }
+ if (diff.y <= SOFTMAX_FTZ_THRESHOLD) {
+ KQ_max_scale.y = 0.0f;
+ }
+ KQ_max = KQ_max_new;
+ }
+
+ float2 KQ_rowsum_add = make_float2(0.0f, 0.0f);
+ static_assert(KQ_stride % (np*mma_C_KQ::I) == 0, "bad loop size");
+#pragma unroll
+ for (int k = 0; k < KQ_stride/(np*mma_C_KQ::I); ++k) {
+#pragma unroll
+ for (int l = 0; l < mma_C_KQ::ne; ++l) {
+ const float KQ_max_l = l % 2 == 0 ? KQ_max.x : KQ_max.y;
+ const float diff = KQ_C[k].x[l] - KQ_max_l;
+ KQ_C[k].x[l] = expf(diff);
+ if (diff <= SOFTMAX_FTZ_THRESHOLD) {
+ KQ_C[k].x[l] = 0.0f;
+ }
+
+ if (l % 2 == 0) {
+ KQ_rowsum_add.x += KQ_C[k].x[l];
+ } else {
+ KQ_rowsum_add.y += KQ_C[k].x[l];
+ }
+ }
+ }
+
+ // Scale previous KQ_rowsum to account for a potential increase in KQ_max:
+ KQ_rowsum.x = KQ_max_scale.x*KQ_rowsum.x + KQ_rowsum_add.x;
+ KQ_rowsum.y = KQ_max_scale.y*KQ_rowsum.y + KQ_rowsum_add.y;
+
+ const half2 KQ_max_scale_h2 = make_half2(KQ_max_scale.x, KQ_max_scale.y);
+#pragma unroll
+ for (int i = 0; i < D/mma_C_VKQ::I; ++i) {
+#pragma unroll
+ for (int l = 0; l < mma_C_VKQ::ne; ++l) {
+ VKQ_C[i].x[l] *= KQ_max_scale_h2;
+ }
+ }
+
+ // Convert KQ C tiles into B tiles for VKQ calculation:
+ mma_B B[KQ_stride/(np*2*mma_B::K)];
+ static_assert(KQ_stride % (np*2*mma_B::K) == 0, "bad loop size");
+#pragma unroll
+ for (int k = 0; k < KQ_stride/(np*2*mma_B::K); ++k) {
+ B[k] = KQ_C[k].to_mma_B();
+ }
+
+ // Load V data into tile with decreasing granularity for D for better memory bandwidth:
+ static_assert(KQ_stride % (4*nwarps) == 0, "out of bounds");
+#pragma unroll
+ for (int stride_i : {WARP_SIZE, WARP_SIZE/2, WARP_SIZE/4}) {
+ const int i0_start = stride_i == WARP_SIZE ? 0 : D/2 - (D/2) % (2*stride_i);
+ const int i0_stop = D/2 - (D/2) % (1*stride_i);
+ const int stride_k = WARP_SIZE / stride_i;
+
+#pragma unroll
+ for (int k_V_0 = 0; k_V_0 < KQ_stride; k_V_0 += nwarps*stride_k) {
+ const int k_V = k_V_0 + threadIdx.y*stride_k + (stride_i == WARP_SIZE ? 0 : threadIdx.x / stride_i);
+
+#pragma unroll
+ for (int i_V_0 = i0_start; i_V_0 < i0_stop; i_V_0 += stride_i) {
+ const int i_V = i_V_0 + (stride_i == WARP_SIZE ? threadIdx.x : threadIdx.x % stride_i);
+
+ tile_KV[k_V*D2_padded + i_V] = V_h2[(k_VKQ_0 + k_V)*stride_KV + i_V];
+ }
+ }
+ }
+
+ __syncthreads();
+
+ // Calculate VKQ tile:
+#pragma unroll
+ for (int i_VKQ_0 = 0; i_VKQ_0 < D; i_VKQ_0 += mma_C_VKQ::I) {
+ static_assert((KQ_stride/2) % (np*mma_A::K) == 0, "bad loop size");
+#pragma unroll
+ for (int k00 = 0; k00 < KQ_stride/2; k00 += np*mma_A::K) {
+ const int k0 = k00 + (threadIdx.y % np)*mma_A::K;
+
+ mma_A A;
+ A.load_ldmatrix_trans(tile_KV + 2*k0*D2_padded + i_VKQ_0/2, D2_padded);
+ VKQ_C[i_VKQ_0/mma_C_VKQ::I].mma(A, B[k00/(np*mma_A::K)]);
+ }
+ }
+
+ __syncthreads();
+ }
+
+ // Finally, sum up partial KQ rowsums.
+ // The partial sums are spread across 8 threads each, does not need full reduce.
+#pragma unroll
+ for (int offset = 16; offset > 2; offset >>= 1) {
+ KQ_rowsum.x += __shfl_xor_sync(0xFFFFFFFF, KQ_rowsum.x, offset, WARP_SIZE);
+ KQ_rowsum.y += __shfl_xor_sync(0xFFFFFFFF, KQ_rowsum.y, offset, WARP_SIZE);
+ }
+
+ // Write VKQ accumulators to shared memory in column-major format.
+ // It's faster to do small writes to shared memory, then large write to VRAM than to do small writes to VRAM.
+ // Also for np > 1 the combination is done via these values in shared memory.
+ const int j_cwd = threadIdx.y*mma_B::J + mma_B::get_j(-1); // j combine write data
+#pragma unroll
+ for (int k0 = 0; k0 < D/2; k0 += mma_B::K) {
+ const mma_B B = VKQ_C[k0/mma_B::K].to_mma_B(); // Conversion of C to B matrix puts it in column-major format.
+
+#pragma unroll
+ for (int l = 0; l < mma_B::ne; ++l) {
+ const int k = k0 + mma_B::get_k(l);
+
+ tile_KV[j_cwd*D2_padded + k] = B.x[l];
+ }
+ }
+
+ const int j_cwmo = (threadIdx.x % (2*mma_C_VKQ::J)) / mma_C_VKQ::J; // j combine write meta offset
+ const int j_cwm = threadIdx.y*(2*mma_C_VKQ::J) + 2*mma_C_VKQ::get_j(-1) + j_cwmo; // j combine write meta
+ const float2 KQ_cmr = make_float2(((const float *) &KQ_max)[j_cwmo], ((const float *) &KQ_rowsum)[j_cwmo]); // KQ combine max rowsum
+
+ if (((!needs_fixup && !is_fixup) || np > 1) && threadIdx.x < 2*mma_C_VKQ::J) {
+ // Use the 16 bytes of padding in each row to store the meta data: KQ max, KQ rowsum, KQ max scale.
+ ((float2 *) tile_KV)[j_cwm*(D2_padded/2) + D/4] = KQ_cmr;
+ }
+
+ __syncthreads();
+
+ static_assert(np == 1 || np == 2 || np == 4, "bad np");
+ if (np == 1) {
+ // No combination is needed, the meta data can be directly written from registers to VRAM.
+ if (needs_fixup && threadIdx.x < mma_B::J) {
+ float2 * dstk_fixup_meta = dstk_fixup + blockIdx.x*ncols;
+ dstk_fixup_meta[j_cwm] = KQ_cmr;
+ }
+ if (is_fixup && threadIdx.x < mma_B::J) {
+ float2 * dstk_fixup_meta = dstk_fixup + (gridDim.x + blockIdx.x)*ncols;
+ dstk_fixup_meta[j_cwm] = KQ_cmr;
+ }
+ } else if (threadIdx.y % np == 0) {
+ // Combine the meta data for parallel warps via shared memory.
+ // Warps with threadIdx.y % np != 0 must NOT return early.
+ // All threads must return simultaneously to avoid race conditions with work on the next tile.
+
+ float * meta_j = (float *) tile_KV + (threadIdx.y*mma_B::J + threadIdx.x)*D2_padded + D/2;
+
+ float KQ_cm = -FLT_MAX/2; // KQ combine max per parallel warp.
+ if (np*mma_B::J == WARP_SIZE || threadIdx.x < np*mma_B::J) {
+ KQ_cm = meta_j[0];
+ }
+
+ float KQ_cmn = KQ_cm; // KQ combine max new, max between all parallel warps.
+#pragma unroll
+ for (int offset = np*mma_B::J/2; offset >= mma_B::J; offset >>= 1) {
+ KQ_cmn = fmaxf(KQ_cmn, __shfl_xor_sync(0xFFFFFFFF, KQ_cmn, offset, WARP_SIZE));
+ }
+
+ const float KQ_cms = expf(KQ_cm - KQ_cmn); // KQ combine max scale per warp.
+ float KQ_crs = 0.0f; // KQ combine rowsum, scaled sum of all parallel warps.
+ if (np*mma_B::J == WARP_SIZE || threadIdx.x < np*mma_B::J) {
+ KQ_crs = KQ_cms*meta_j[1];
+ }
+#pragma unroll
+ for (int offset = np*mma_B::J/2; offset >= mma_B::J; offset >>= 1) {
+ KQ_crs += __shfl_xor_sync(0xFFFFFFFF, KQ_crs, offset, WARP_SIZE);
+ }
+
+ // Write back combined meta data:
+ if (np*mma_B::J == WARP_SIZE || threadIdx.x < np*mma_B::J) {
+ meta_j[0] = KQ_cmn; // Combined max. KQ values.
+ meta_j[1] = KQ_crs; // Combined KQ rowsums.
+ meta_j[2] = KQ_cms; // KQ max scales per parallel warp.
+ }
+ if (needs_fixup && threadIdx.x < mma_B::J) {
+ float2 * dstk_fixup_meta = dstk_fixup + blockIdx.x*ncols;
+ dstk_fixup_meta[(threadIdx.y/np)*mma_B::J + threadIdx.x] = make_float2(KQ_cmn, KQ_crs);
+ }
+ if (is_fixup && threadIdx.x < mma_B::J) {
+ float2 * dstk_fixup_meta = dstk_fixup + (gridDim.x + blockIdx.x)*ncols;
+ dstk_fixup_meta[(threadIdx.y/np)*mma_B::J + threadIdx.x] = make_float2(KQ_cmn, KQ_crs);
+ }
+ }
+
+ if (np > 1) {
+ __syncthreads();
+ }
+
+ if (np == 1 || threadIdx.y % np == 0) {
+ // The first 2*2*gridDim.x*ncols floats in dstk_fixup are for storing max. values and row sums.
+ // The values after that are for the partial results of the individual blocks.
+ float2 * dstk_fixup_data = dstk_fixup + gridDim.x*(2*ncols) + blockIdx.x*(ncols*(D/2));
+
+#pragma unroll
+ for (int stride_k : {WARP_SIZE, WARP_SIZE/2, WARP_SIZE/4}) {
+ const int k0_start = stride_k == WARP_SIZE ? 0 : D/2 - (D/2) % (2*stride_k);
+ const int k0_stop = D/2 - (D/2) % (1*stride_k);
+ const int stride_j = WARP_SIZE / stride_k;
+
+ if (nwarps*stride_j > ncols && threadIdx.y*stride_j >= ncols) {
+ break;
+ }
+
+#pragma unroll
+ for (int j0_dst = 0; j0_dst < ncols; j0_dst += (nwarps/np)*stride_j) {
+ const int j_dst = j0_dst + (threadIdx.y/np)*stride_j + (stride_k == WARP_SIZE ? 0 : threadIdx.x / stride_k);
+ const int j_tile_KV = (j_dst/mma_B::J)*(np*mma_B::J) + j_dst % mma_B::J;
+
+ if (!is_fixup && jt*ncols + j_dst >= ne01) {
+ continue;
+ }
+ const float * meta_j = (const float *) tile_KV + j_tile_KV*D2_padded + D/2;
+#pragma unroll
+ for (int k0 = k0_start; k0 < k0_stop; k0 += stride_k) {
+ const int k = k0 + (stride_k == WARP_SIZE ? threadIdx.x : threadIdx.x % stride_k);
+
+ float2 dstk_val = make_float2(0.0f, 0.0f);
+#pragma unroll
+ for (int ip = 0; ip < np; ++ip) {
+ const float KQ_crs = np == 1 ? 1.0f : meta_j[ip*mma_B::J*D2_padded + 2];
+ const float2 dstk_val_add = __half22float2(tile_KV[(j_tile_KV + ip*mma_B::J)*D2_padded + k]);
+ dstk_val.x += dstk_val_add.x*KQ_crs;
+ dstk_val.y += dstk_val_add.y*KQ_crs;
+ }
+
+ if (!needs_fixup && !is_fixup) {
+ const float KQ_rowsum_j = meta_j[1];
+ dstk_val.x /= KQ_rowsum_j;
+ dstk_val.y /= KQ_rowsum_j;
+ }
+
+ if (is_fixup) {
+ dstk_fixup_data[j_dst*(D/2) + k] = dstk_val;
+ } else {
+ dstk[(jt*ncols + j_dst)*ne02*(D/2) + k] = dstk_val;
+ }
+ }
+ }
+ }
+ }
+
+ if (np > 1) {
+ __syncthreads();
+ }
+#else
+ NO_DEVICE_CODE;
+#endif // NEW_MMA_AVAILABLE
+}
+
+template<int D, int ncols, int nwarps, int KQ_stride, bool use_logit_softcap>
+#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__))
+__launch_bounds__(nwarps*WARP_SIZE, 2)
+#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__))
+static __global__ void flash_attn_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 float logit_softcap,
+ 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) {
+ // Skip unused kernel variants for faster compilation:
+ if (use_logit_softcap && !(D == 128 || D == 256)) {
+ NO_DEVICE_CODE;
+ return;
+ }
+
+ static_assert(FATTN_KQ_STRIDE % KQ_stride == 0, "bad KQ_stride");
+
+ const int gqa_ratio = ne02 / ne12; // With grouped query attention there are > 1 Q matrices per K, V matrix.
+
+ const int iter_k = ne11 / KQ_stride;
+ const int iter_j = (ne01 + (ncols - 1)) / ncols;
+
+ // kbc == k block continuous, current index in continuous ijk space.
+ int kbc = (blockIdx.x + 0)*iter_k*iter_j*ne02 / gridDim.x;
+ const int kbc_stop = (blockIdx.x + 1)*iter_k*iter_j*ne02 / gridDim.x;
+
+ // If the seams of 2 CUDA blocks fall within an output tile their results need to be combined.
+ // For this we need to track both the block that starts the tile (needs_fixup) and the block that finishes the tile (is_fixup).
+ // In the most general case >2 seams can fall into the same tile.
+
+ // kb0 == k start index when in the output tile.
+ int kb0_start = kbc % iter_k;
+ int kb0_stop = min(iter_k, kb0_start + kbc_stop - kbc);
+ while (kbc < kbc_stop && kb0_stop == iter_k) {
+ const int channel = kbc / (iter_k*iter_j);
+ const int jt = (kbc - channel*iter_k*iter_j) / iter_k; // j index of current tile.
+
+ const float2 * Q_f2 = (const float2 *) (Q + nb02* channel);
+ const half2 * K_h2 = (const half2 *) (K + nb12*(channel / gqa_ratio));
+ const half2 * V_h2 = (const half2 *) (V + nb12*(channel / gqa_ratio)); // K and V have same shape
+ const half * maskh = mask ? (const half *) mask + (nb31/sizeof(half))*jt*ncols : nullptr;
+ float2 * dstk = ((float2 *) dst) + channel*(D/2);
+
+ const float slope = get_alibi_slope(max_bias, channel, n_head_log2, m0, m1);
+
+ constexpr bool is_fixup = false; // All but (potentially) the last iterations write their data to dst rather than the fixup buffer.
+ if (kb0_start == 0) {
+ constexpr bool needs_fixup = false; // CUDA block is working on an entire tile.
+ flash_attn_ext_f16_process_tile<D, ncols, nwarps, KQ_stride, use_logit_softcap, needs_fixup, is_fixup>
+ (Q_f2, K_h2, V_h2, maskh, dstk, dst_meta, scale, slope, logit_softcap,
+ ne00, ne01, ne02, ne03, ne10, ne11, ne12, ne13, ne31, nb31, nb01, nb02, nb03, nb11, nb12, nb13, nb21, nb22, nb23, ne0, ne1, ne2, ne3,
+ jt, kb0_start, kb0_stop);
+ } else {
+ constexpr bool needs_fixup = true; // CUDA block is working on the beginning of a tile.
+ flash_attn_ext_f16_process_tile<D, ncols, nwarps, KQ_stride, use_logit_softcap, needs_fixup, is_fixup>
+ (Q_f2, K_h2, V_h2, maskh, dstk, dst_meta, scale, slope, logit_softcap,
+ ne00, ne01, ne02, ne03, ne10, ne11, ne12, ne13, ne31, nb31, nb01, nb02, nb03, nb11, nb12, nb13, nb21, nb22, nb23, ne0, ne1, ne2, ne3,
+ jt, kb0_start, kb0_stop);
+ }
+
+ kbc += iter_k;
+ kbc -= kbc % iter_k;
+
+ kb0_start = 0;
+ kb0_stop = min(iter_k, kbc_stop - kbc);
+ }
+
+ if (kbc >= kbc_stop) {
+ return;
+ }
+
+ const int channel = kbc / (iter_k*iter_j);
+ const int jt = (kbc - channel*iter_k*iter_j) / iter_k; // j index of current tile.
+
+ const float2 * Q_f2 = (const float2 *) (Q + nb02* channel);
+ const half2 * K_h2 = (const half2 *) (K + nb12*(channel / gqa_ratio));
+ const half2 * V_h2 = (const half2 *) (V + nb12*(channel / gqa_ratio)); // K and V have same shape
+ const half * maskh = mask ? (const half *) mask + (nb31/sizeof(half))*jt*ncols : nullptr;
+ float2 * dstk = ((float2 *) dst) + channel*(D/2);
+
+ const float slope = get_alibi_slope(max_bias, channel, n_head_log2, m0, m1);
+
+ constexpr bool is_fixup = true; // Last index writes its data to fixup buffer to avoid data races with other blocks.
+ constexpr bool needs_fixup = false;
+ flash_attn_ext_f16_process_tile<D, ncols, nwarps, KQ_stride, use_logit_softcap, needs_fixup, is_fixup>
+ (Q_f2, K_h2, V_h2, maskh, dstk, dst_meta, scale, slope, logit_softcap,
+ ne00, ne01, ne02, ne03, ne10, ne11, ne12, ne13, ne31, nb31, nb01, nb02, nb03, nb11, nb12, nb13, nb21, nb22, nb23, ne0, ne1, ne2, ne3,
+ jt, kb0_start, kb0_stop);
+}
+
+template <int D, int cols_per_block>
+void ggml_cuda_flash_attn_ext_mma_f16_case(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+ typedef mma_A_I16K8<half2> mma_A;
+ typedef mma_B_J8K8<half2> mma_B;
+
+ static_assert(D % mma_B::K == 0, "bad D");
+ static_assert(cols_per_block % mma_B::J == 0, "bad cols_per_block");
+
+ const ggml_tensor * KQV = dst;
+
+ constexpr int KQ_stride = D <= 128 ? 64 : 32;
+ constexpr int nwarps = (KQ_stride == 32 && cols_per_block <= 16) ?
+ cols_per_block/mma_B::J * KQ_stride/mma_A::I : (cols_per_block <= 8 ? 4 : 8);
+ constexpr size_t nbytes_shared = std::max(KQ_stride, nwarps*mma_B::J) * (D + 8) * sizeof(half);
+
+ float logit_softcap;
+ memcpy(&logit_softcap, (const float *) KQV->op_params + 2, sizeof(float));
+
+ fattn_kernel_t fattn_kernel;
+ if (logit_softcap == 0.0f) {
+ constexpr bool use_logit_softcap = false;
+ fattn_kernel = flash_attn_ext_f16<D, cols_per_block, nwarps, KQ_stride, use_logit_softcap>;
+ } else {
+ constexpr bool use_logit_softcap = true;
+ fattn_kernel = flash_attn_ext_f16<D, cols_per_block, nwarps, KQ_stride, use_logit_softcap>;
+ }
+ launch_fattn<D, cols_per_block, 0, KQ_stride>(ctx, dst, fattn_kernel, nwarps, nbytes_shared, true, true);
+}
+
+#define DECL_FATTN_MMA_F16_CASE(D, cols_per_block) \
+ template void ggml_cuda_flash_attn_ext_mma_f16_case \
+ <D, cols_per_block>(ggml_backend_cuda_context & ctx, ggml_tensor * dst) \
+
+extern DECL_FATTN_MMA_F16_CASE( 64, 8);
+extern DECL_FATTN_MMA_F16_CASE( 80, 8);
+extern DECL_FATTN_MMA_F16_CASE( 96, 8);
+extern DECL_FATTN_MMA_F16_CASE(112, 8);
+extern DECL_FATTN_MMA_F16_CASE(128, 8);
+extern DECL_FATTN_MMA_F16_CASE(256, 8);
+
+extern DECL_FATTN_MMA_F16_CASE( 64, 16);
+extern DECL_FATTN_MMA_F16_CASE( 80, 16);
+extern DECL_FATTN_MMA_F16_CASE( 96, 16);
+extern DECL_FATTN_MMA_F16_CASE(112, 16);
+extern DECL_FATTN_MMA_F16_CASE(128, 16);
+extern DECL_FATTN_MMA_F16_CASE(256, 16);
+
+extern DECL_FATTN_MMA_F16_CASE( 64, 32);
+extern DECL_FATTN_MMA_F16_CASE( 80, 32);
+extern DECL_FATTN_MMA_F16_CASE( 96, 32);
+extern DECL_FATTN_MMA_F16_CASE(112, 32);
+extern DECL_FATTN_MMA_F16_CASE(128, 32);
+extern DECL_FATTN_MMA_F16_CASE(256, 32);
+
+extern DECL_FATTN_MMA_F16_CASE( 64, 64);
+extern DECL_FATTN_MMA_F16_CASE( 80, 64);
+extern DECL_FATTN_MMA_F16_CASE( 96, 64);
+extern DECL_FATTN_MMA_F16_CASE(112, 64);
+extern DECL_FATTN_MMA_F16_CASE(128, 64);
+extern DECL_FATTN_MMA_F16_CASE(256, 64);
const int ne2,
const int ne3) {
#ifdef FP16_AVAILABLE
+
+#ifndef FLASH_ATTN_AVAILABLE
+ NO_DEVICE_CODE;
+ return;
+#endif // FLASH_ATTN_AVAILABLE
+
// Skip unused kernel variants for faster compilation:
+#ifdef FP16_MMA_AVAILABLE
+ NO_DEVICE_CODE;
+ return;
+#endif // FP16_MMA_AVAILABLE
if (use_logit_softcap && !(D == 128 || D == 256)) {
NO_DEVICE_CODE;
return;
const ggml_tensor * Q = dst->src[0];
switch (Q->ne[0]) {
case 64: {
- constexpr int D = 64;
- constexpr int nwarps = 8;
+ constexpr int D = 64;
+ constexpr int nwarps = 8;
+ constexpr size_t nbytes_shared = 0;
fattn_kernel_t fattn_kernel = flash_attn_tile_ext_f16<D, cols_per_block, nwarps, parallel_blocks, use_logit_softcap>;
- launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block, true, true);
+ launch_fattn<D, cols_per_block, parallel_blocks, -1>(ctx, dst, fattn_kernel, nwarps, nbytes_shared, true, true);
} break;
case 128: {
- constexpr int D = 128;
- constexpr int nwarps = 8;
+ constexpr int D = 128;
+ constexpr int nwarps = 8;
+ constexpr size_t nbytes_shared = 0;
fattn_kernel_t fattn_kernel = flash_attn_tile_ext_f16<D, cols_per_block, nwarps, parallel_blocks, use_logit_softcap>;
- launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block, true, true);
+ launch_fattn<D, cols_per_block, parallel_blocks, -1>(ctx, dst, fattn_kernel, nwarps, nbytes_shared, true, true);
} break;
default: {
GGML_ABORT("FlashAttention without tensor cores only supports head sizes 64 and 128.");
NO_DEVICE_CODE;
return;
#endif // FLASH_ATTN_AVAILABLE
+
// Skip unused kernel variants for faster compilation:
+#ifdef FP16_MMA_AVAILABLE
+ NO_DEVICE_CODE;
+ return;
+#endif // FP16_MMA_AVAILABLE
if (use_logit_softcap && !(D == 128 || D == 256)) {
NO_DEVICE_CODE;
return;
const ggml_tensor * Q = dst->src[0];
switch (Q->ne[0]) {
case 64: {
- constexpr int D = 64;
- constexpr int nwarps = 8;
+ constexpr int D = 64;
+ constexpr int nwarps = 8;
+ constexpr size_t nbytes_shared = 0;
fattn_kernel_t fattn_kernel = flash_attn_tile_ext_f32<D, cols_per_block, nwarps, parallel_blocks, use_logit_softcap>;
- launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block, true, true);
+ launch_fattn<D, cols_per_block, parallel_blocks, -1>(ctx, dst, fattn_kernel, nwarps, nbytes_shared, true, true);
} break;
case 128: {
- constexpr int D = 128;
- constexpr int nwarps = 8;
+ constexpr int D = 128;
+ constexpr int nwarps = 8;
+ constexpr size_t nbytes_shared = 0;
fattn_kernel_t fattn_kernel = flash_attn_tile_ext_f32<D, cols_per_block, nwarps, parallel_blocks, use_logit_softcap>;
- launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block, true, true);
+ launch_fattn<D, cols_per_block, parallel_blocks, -1>(ctx, dst, fattn_kernel, nwarps, nbytes_shared, true, true);
} break;
default: {
GGML_ABORT("FlashAttention without tensor cores only supports head sizes 64 and 128.");
const int ne2,
const int ne3) {
#ifdef FP16_AVAILABLE
+
+#ifndef FLASH_ATTN_AVAILABLE
+ NO_DEVICE_CODE;
+ return;
+#endif // FLASH_ATTN_AVAILABLE
+
// Skip unused kernel variants for faster compilation:
if (use_logit_softcap && !(D == 128 || D == 256)) {
NO_DEVICE_CODE;
fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f16<D, cols_per_block, parallel_blocks, type_K, type_V, use_logit_softcap>;
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);
+ constexpr size_t nbytes_shared = 0;
+ launch_fattn<D, cols_per_block, parallel_blocks, -1>(ctx, dst, fattn_kernel, nwarps, nbytes_shared, need_f16_K, need_f16_V);
}
template <int D, ggml_type type_K, ggml_type type_V>
const int ne1,
const int ne2,
const int ne3) {
+#ifndef FLASH_ATTN_AVAILABLE
+ NO_DEVICE_CODE;
+ return;
+#endif // FLASH_ATTN_AVAILABLE
+
// Skip unused kernel variants for faster compilation:
if (use_logit_softcap && !(D == 128 || D == 256)) {
NO_DEVICE_CODE;
fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f32<D, cols_per_block, parallel_blocks, type_K, type_V, use_logit_softcap>;
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);
+ constexpr size_t nbytes_shared = 0;
+ launch_fattn<D, cols_per_block, parallel_blocks, -1>(ctx, dst, fattn_kernel, nwarps, nbytes_shared, need_f16_K, need_f16_V);
}
template <int D, ggml_type type_K, ggml_type type_V>
--- /dev/null
+// Old and deprecated WMMA FlashAttention implementation.
+// It is still needed for Volta since the memory layout of NVIDIA tensor cores changed with Turing.
+// Long-term the WMMA code should be replaced with a dedicated Volta implementation.
+
+#include "common.cuh"
+#include "fattn-common.cuh"
+#include "fattn-wmma-f16.cuh"
+
+#ifdef FP16_MMA_AVAILABLE
+#include <mma.h>
+#endif // FP16_MMA_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, bool use_logit_softcap>
+#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__))
+__launch_bounds__(nwarps*WARP_SIZE, 1)
+#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__))
+static __global__ void flash_attn_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 float logit_softcap,
+ 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) {
+#if __CUDA_ARCH__ == GGML_CUDA_CC_VOLTA
+ // Skip unused kernel variants for faster compilation:
+ if (use_logit_softcap && !(D == 128 || D == 256)) {
+ NO_DEVICE_CODE;
+ return;
+ }
+
+ //In this kernel Q, K, V are matrices while i, j, k are matrix indices.
+
+ const int ic0 = ncols*(blockIdx.x / parallel_blocks); // Index of the first 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.
+
+ static_assert(D <= FATTN_KQ_STRIDE, "D must be <= FATTN_KQ_STRIDE.");
+ static_assert(ncols == 8 || ncols % 16 == 0, "ncols must be 8 or a multiple of 16.");
+ constexpr int frag_m = ncols == 8 ? 32 : 16;
+ constexpr int frag_n = ncols == 8 ? 8 : 16;
+ static_assert(D % frag_m == 0, "If ncols == 8 then D % frag_m must be 0.");
+ typedef nvcuda::wmma::fragment<nvcuda::wmma::matrix_a, frag_m, frag_n, 16, half, nvcuda::wmma::row_major> frag_a_K;
+ typedef nvcuda::wmma::fragment<nvcuda::wmma::matrix_a, frag_m, frag_n, 16, half, nvcuda::wmma::col_major> frag_a_V;
+ typedef nvcuda::wmma::fragment<nvcuda::wmma::matrix_b, frag_m, frag_n, 16, half, nvcuda::wmma::col_major> frag_b;
+ typedef nvcuda::wmma::fragment<nvcuda::wmma::accumulator, frag_m, frag_n, 16, KQ_acc_t> frag_c_KQ;
+ typedef nvcuda::wmma::fragment<nvcuda::wmma::accumulator, frag_m, frag_n, 16, half> frag_c_VKQ;
+
+ constexpr int KQ_stride_tc = nwarps*frag_m; // Number of KQ rows calculated in parallel.
+ constexpr int VKQ_ratio = KQ_stride_tc/VKQ_stride; // Number of parallel VKQ accumulators needed to keep all warps busy.
+ static_assert(VKQ_ratio <= nwarps, "VKQ_ratio must be <= nwarps.");
+
+ // Pad internal representation of KQ, KQV to reduce shared memory bank conflicts:
+ constexpr int D_padded = D + 8;
+ constexpr int kqs_padded = FATTN_KQ_STRIDE + 8;
+ constexpr int kqar = sizeof(KQ_acc_t)/sizeof(half);
+
+ const int gqa_ratio = ne02 / ne12; // With grouped query attention there are > 1 Q matrices per K, V matrix.
+ const float * Q_f = (const float *) (Q + nb02* blockIdx.y + nb01*ic0);
+ const half * K_h = (const half *) (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 + (nb31/sizeof(half))* ic0;
+ const half2 * mask2 = (const half2 *) mask + (nb31/sizeof(half))*(ic0/2);
+
+ const int stride_Q = nb01 / sizeof(float);
+ const int stride_KV = nb11 / sizeof(half);
+
+ const float slopef = get_alibi_slope(max_bias, blockIdx.y, n_head_log2, m0, m1);
+ const half slopeh = __float2half(slopef);
+ const half2 slope2 = make_half2(slopef, slopef);
+
+ const half2 logit_softcap_2 = make_half2(logit_softcap, logit_softcap);
+
+ frag_b Q_b[D/16][ncols/frag_n];
+
+ // A single buffer for temporarily holding tiles of KQ and VKQ parts:
+ constexpr int mem_KQ = ncols*kqs_padded*kqar;
+ constexpr int mem_VKQ_parts = VKQ_ratio*ncols*D_padded;
+ __shared__ half KQ[mem_KQ >= mem_VKQ_parts ? mem_KQ : mem_VKQ_parts];
+ float * KQ_f = (float *) KQ;
+ half2 * KQ2 = (half2 *) KQ;
+
+ float KQ_rowsum_f[ncols/nwarps] = {0.0f};
+ float KQ_max_f[ncols/nwarps];
+ float KQ_max_scale_f[ncols/nwarps] = {0.0f};
+
+#pragma unroll
+ for (int j = 0; j < ncols/nwarps; ++j) {
+ KQ_max_f[j] = -FLT_MAX/2.0f;
+ }
+
+ half2 KQ_rowsum_h2[ncols/nwarps] = {{0.0f, 0.0f}};
+ half2 KQ_max_h2[ncols/nwarps];
+ half2 KQ_max_scale_h2[ncols/nwarps] = {{0.0f, 0.0f}};
+
+#pragma unroll
+ for (int j = 0; j < ncols/nwarps; ++j) {
+ KQ_max_h2[j] = make_half2(-HALF_MAX_HALF, -HALF_MAX_HALF);
+ }
+
+ __shared__ half VKQ[ncols*D_padded]; // Accumulator for final VKQ slice.
+ half2 * VKQ2 = (half2 *) VKQ;
+#pragma unroll
+ for (int j0 = 0; j0 < ncols; j0 += nwarps) {
+ const int j = j0 + threadIdx.y;
+#pragma unroll
+ for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) {
+ const int i = i0 + threadIdx.x;
+ if (i0 + WARP_SIZE > D/2 && i >= D/2) {
+ break;
+ }
+ VKQ2[j*(D_padded/2) + i] = make_half2(0.0f, 0.0f);
+ }
+ }
+
+ // Convert Q to half and apply scale, temporarily store in KQ:
+#pragma unroll
+ for (int j0 = 0; j0 < ncols; j0 += nwarps) {
+ const int j = j0 + threadIdx.y;
+#pragma unroll
+ for (int i0 = 0; i0 < D; i0 += WARP_SIZE) {
+ const int i = i0 + threadIdx.x;
+ if (i0 + WARP_SIZE > D && i >= D) {
+ break;
+ }
+ KQ[j*D_padded + i] = ic0 + j < ne01 ? Q_f[j*stride_Q + i] * scale : 0.0f;
+ }
+ }
+
+ __syncthreads();
+
+ // Load Q into tensor core fragments/registers since it will be used frequently:
+#pragma unroll
+ for (int i0 = 0; i0 < D; i0 += 16) {
+#pragma unroll
+ for (int j0 = 0; j0 < ncols; j0 += frag_n) {
+ nvcuda::wmma::load_matrix_sync(Q_b[i0/16][j0/frag_n], KQ + j0*D_padded + i0, D_padded);
+ }
+ }
+
+ __syncthreads();
+
+ // Iterate over ne11 == previous tokens:
+ for (int k_VKQ_0 = ip*FATTN_KQ_STRIDE; k_VKQ_0 < ne11; k_VKQ_0 += parallel_blocks*FATTN_KQ_STRIDE) {
+ // Calculate tile of KQ:
+#pragma unroll
+ for (int i_KQ_0 = 0; i_KQ_0 < FATTN_KQ_STRIDE; i_KQ_0 += KQ_stride_tc) {
+ frag_c_KQ KQ_c[ncols/frag_n];
+#pragma unroll
+ for (int j = 0; j < ncols/frag_n; ++j) {
+ nvcuda::wmma::fill_fragment(KQ_c[j], 0.0f);
+ }
+#pragma unroll
+ for (int k_KQ_0 = 0; k_KQ_0 < D; k_KQ_0 += 16) {
+ frag_a_K K_a;
+ nvcuda::wmma::load_matrix_sync(K_a, K_h + (k_VKQ_0 + i_KQ_0 + frag_m*threadIdx.y)*stride_KV + k_KQ_0, stride_KV);
+#pragma unroll
+ for (int j = 0; j < ncols/frag_n; ++j) {
+ nvcuda::wmma::mma_sync(KQ_c[j], K_a, Q_b[k_KQ_0/16][j], KQ_c[j]);
+ }
+ }
+#pragma unroll
+ for (int j0 = 0; j0 < ncols; j0 += frag_n) {
+ nvcuda::wmma::store_matrix_sync((KQ_acc_t *) KQ + j0*kqs_padded + i_KQ_0 + frag_m*threadIdx.y, KQ_c[j0/frag_n], kqs_padded, nvcuda::wmma::mem_col_major);
+ }
+ }
+
+ __syncthreads();
+
+ // Calculate softmax for each KQ column using the current max. value.
+ // The divisor is stored in KQ_rowsum and will be applied at the end.
+#pragma unroll
+ for (int j0 = 0; j0 < ncols; j0 += nwarps) {
+ const int j = j0 + threadIdx.y;
+
+ if (std::is_same<KQ_acc_t, float>::value) {
+ float KQ_f_tmp[FATTN_KQ_STRIDE / WARP_SIZE];
+#pragma unroll
+ for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += WARP_SIZE) {
+ const int k = k0 + threadIdx.x;
+
+ KQ_f_tmp[k0/WARP_SIZE] = KQ_f[j*kqs_padded + k];
+
+ if (use_logit_softcap) {
+ KQ_f_tmp[k0/WARP_SIZE] = logit_softcap*tanhf(KQ_f_tmp[k0/WARP_SIZE]);
+ }
+ }
+
+ float KQ_max_new = KQ_max_f[j0/nwarps];
+#pragma unroll
+ for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += WARP_SIZE) {
+ const int k = k0 + threadIdx.x;
+
+ KQ_f_tmp[k0/WARP_SIZE] += mask ? __half2float(slopeh*maskh[j*(nb31/sizeof(half)) + k_VKQ_0 + k]) : 0.0f;
+ KQ_max_new = max(KQ_max_new, KQ_f_tmp[k0/WARP_SIZE]);
+ }
+ KQ_max_new = warp_reduce_max(KQ_max_new);
+
+ const float diff = KQ_max_f[j0/nwarps] - KQ_max_new;
+ KQ_max_scale_f[j0/nwarps] = expf(diff);
+ if (diff <= SOFTMAX_FTZ_THRESHOLD) {
+ KQ_max_scale_f[j0/nwarps] = 0.0f;
+ }
+ KQ_max_f[j0/nwarps] = KQ_max_new;
+
+ float KQ_rowsum_add = 0.0f;
+#pragma unroll
+ for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += WARP_SIZE) {
+ const int k = k0 + threadIdx.x;
+
+ const float diff = KQ_f_tmp[k0/WARP_SIZE] - KQ_max_f[j0/nwarps];
+ KQ_f_tmp[k0/WARP_SIZE] = expf(diff);
+ if (diff <= SOFTMAX_FTZ_THRESHOLD) {
+ KQ_f_tmp[k0/WARP_SIZE] = 0.0f;
+ }
+ KQ_rowsum_add += KQ_f_tmp[k0/WARP_SIZE];
+ KQ[j*(kqar*kqs_padded) + k] = KQ_f_tmp[k0/WARP_SIZE];
+ }
+ KQ_rowsum_add = warp_reduce_sum(KQ_rowsum_add);
+
+ // Scale previous KQ_rowsum to account for a potential increase in KQ_max:
+ KQ_rowsum_f[j0/nwarps] = KQ_max_scale_f[j0/nwarps]*KQ_rowsum_f[j0/nwarps] + KQ_rowsum_add;
+ } else {
+ half2 KQ2_tmp[FATTN_KQ_STRIDE/(2*WARP_SIZE)];
+#pragma unroll
+ for (int k0 = 0; k0 < FATTN_KQ_STRIDE/2; k0 += WARP_SIZE) {
+ const int k = k0 + threadIdx.x;
+
+ KQ2_tmp[k0/WARP_SIZE] = KQ2[j*(kqs_padded/2) + k];
+
+ if (use_logit_softcap) {
+ // There is no dedicated tangens hyperbolicus function for half2.
+ KQ2_tmp[k0/WARP_SIZE] = h2exp(KQ2_tmp[k0/WARP_SIZE]*make_half2(2.0f, 2.0f));
+ KQ2_tmp[k0/WARP_SIZE] = (KQ2_tmp[k0/WARP_SIZE] - make_half2(1.0f, 1.0f))
+ /(KQ2_tmp[k0/WARP_SIZE] + make_half2(1.0f, 1.0f));
+
+ KQ2_tmp[k0/WARP_SIZE] *= logit_softcap_2;
+ }
+ }
+
+ half2 KQ_max_new = KQ_max_h2[j0/nwarps];
+#pragma unroll
+ for (int k0 = 0; k0 < FATTN_KQ_STRIDE/2; k0 += WARP_SIZE) {
+ const int k = k0 + threadIdx.x;
+
+ KQ2_tmp[k0/WARP_SIZE] += mask ? slope2*mask2[(j*ne11 + k_VKQ_0)/2 + k] : make_half2(0.0f, 0.0f);
+ KQ_max_new = ggml_cuda_hmax2(KQ_max_new, KQ2_tmp[k0/WARP_SIZE]);
+ }
+ KQ_max_new = __half2half2(warp_reduce_max(ggml_cuda_hmax(__low2half(KQ_max_new), __high2half(KQ_max_new))));
+ const half2 diff = KQ_max_h2[j0/nwarps] - KQ_max_new;
+ KQ_max_scale_h2[j0/nwarps] = h2exp(diff);
+ const uint32_t ftz_mask = __hgt2_mask(diff, make_half2(SOFTMAX_FTZ_THRESHOLD, SOFTMAX_FTZ_THRESHOLD));
+ *((uint32_t *) &KQ_max_scale_h2[j0/nwarps]) &= ftz_mask;
+ KQ_max_h2[j0/nwarps] = KQ_max_new;
+
+ half2 KQ_rowsum_add = make_half2(0.0f, 0.0f);
+#pragma unroll
+ for (int k0 = 0; k0 < FATTN_KQ_STRIDE/2; k0 += WARP_SIZE) {
+ const int k = k0 + threadIdx.x;
+
+ const half2 diff = KQ2_tmp[k0/WARP_SIZE] - KQ_max_h2[j0/nwarps];
+ KQ2_tmp[k0/WARP_SIZE] = h2exp(diff);
+ const uint32_t ftz_mask = __hgt2_mask(diff, make_half2(SOFTMAX_FTZ_THRESHOLD, SOFTMAX_FTZ_THRESHOLD));
+ *((uint32_t *) &KQ2_tmp[k0/WARP_SIZE]) &= ftz_mask;
+ KQ_rowsum_add += KQ2_tmp[k0/WARP_SIZE];
+ KQ2[j*(kqs_padded/2) + k] = KQ2_tmp[k0/WARP_SIZE];
+ }
+ KQ_rowsum_add = warp_reduce_sum(KQ_rowsum_add);
+
+ // Scale previous KQ_rowsum to account for a potential increase in KQ_max:
+ KQ_rowsum_h2[j0/nwarps] = KQ_max_scale_h2[j0/nwarps]*KQ_rowsum_h2[j0/nwarps] + KQ_rowsum_add;
+ }
+ }
+
+ __syncthreads();
+
+ frag_b KQ_b[FATTN_KQ_STRIDE/(VKQ_ratio*16)][ncols/frag_n];
+#pragma unroll
+ for (int j0 = 0; j0 < ncols; j0 += frag_n) {
+#pragma unroll
+ for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += VKQ_ratio*16) {
+ const int k = k0 + (threadIdx.y % VKQ_ratio)*16;
+ nvcuda::wmma::load_matrix_sync(
+ KQ_b[k0/(VKQ_ratio*16)][j0/frag_n],
+ KQ + j0*(kqar*kqs_padded) + k,
+ kqar*kqs_padded);
+ }
+ }
+
+ frag_c_VKQ VKQ_c[D/VKQ_stride][ncols/frag_n];
+#pragma unroll
+ for (int i_VKQ_0 = 0; i_VKQ_0 < D; i_VKQ_0 += VKQ_stride) {
+#pragma unroll
+ for (int j = 0; j < ncols/frag_n; ++j) {
+ nvcuda::wmma::fill_fragment(VKQ_c[i_VKQ_0/VKQ_stride][j], 0.0f);
+ }
+
+#pragma unroll
+ for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += VKQ_ratio*16) {
+ const int k = k0 + (threadIdx.y % VKQ_ratio)*16;
+
+ frag_a_V v_a;
+ nvcuda::wmma::load_matrix_sync(v_a, V_h + (k_VKQ_0 + k)*stride_KV + i_VKQ_0 + frag_m*(threadIdx.y/VKQ_ratio), stride_KV);
+#pragma unroll
+ for (int j = 0; j < ncols/frag_n; ++j) {
+ nvcuda::wmma::mma_sync(VKQ_c[i_VKQ_0/VKQ_stride][j], v_a, KQ_b[k0/(VKQ_ratio*16)][j], VKQ_c[i_VKQ_0/VKQ_stride][j]);
+ }
+ }
+ }
+
+ __syncthreads();
+
+ const int offset_k = (threadIdx.y % VKQ_ratio) * (ncols*D_padded);
+#pragma unroll
+ for (int i_KQ_0 = 0; i_KQ_0 < D; i_KQ_0 += VKQ_stride) {
+#pragma unroll
+ for (int j0 = 0; j0 < ncols; j0 += frag_n) {
+ nvcuda::wmma::store_matrix_sync(
+ KQ + offset_k + j0*D_padded + i_KQ_0 + frag_m*(threadIdx.y/VKQ_ratio),
+ VKQ_c[i_KQ_0/VKQ_stride][j0/frag_n],
+ D_padded, nvcuda::wmma::mem_col_major);
+ }
+ }
+
+ __syncthreads();
+
+#pragma unroll
+ for (int j0 = 0; j0 < ncols; j0 += nwarps) {
+ const int j = j0 + threadIdx.y;
+
+ half2 VKQ_scale;
+ if (std::is_same<KQ_acc_t, float>::value) {
+ VKQ_scale = make_half2(KQ_max_scale_f[j0/nwarps], KQ_max_scale_f[j0/nwarps]);
+ } else {
+ VKQ_scale = KQ_max_scale_h2[j0/nwarps];
+ }
+
+#pragma unroll
+ for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) {
+ const int i = i0 + threadIdx.x;
+ if (i0 + WARP_SIZE > D/2 && i >= D/2) {
+ break;
+ }
+
+ half2 VKQ_add = make_half2(0.0f, 0.0f);
+#pragma unroll
+ for (int l = 0; l < VKQ_ratio; ++l) {
+ VKQ_add += KQ2[l*(ncols*D_padded/2) + j*(D_padded/2) + i];
+ }
+ VKQ2[j*(D_padded/2) + i] = VKQ_scale*VKQ2[j*(D_padded/2) + i] + VKQ_add;
+ }
+ }
+
+ __syncthreads();
+ }
+
+#pragma unroll
+ for (int j0 = 0; j0 < ncols; j0 += nwarps) {
+ const int j_VKQ = j0 + threadIdx.y;
+ if (ic0 + j_VKQ >= ne01) {
+ return;
+ }
+ const int j_dst = (ic0 + j_VKQ)*parallel_blocks + ip;
+
+ float KQ_rowsum_j;
+ if (std::is_same<KQ_acc_t, float>::value) {
+ KQ_rowsum_j = KQ_rowsum_f[j0/nwarps];
+ } else {
+ KQ_rowsum_j = __low2float(KQ_rowsum_h2[j0/nwarps]) + __high2float(KQ_rowsum_h2[j0/nwarps]);
+ }
+
+#pragma unroll
+ for (int i0 = 0; i0 < D; i0 += WARP_SIZE) {
+ const int i = i0 + threadIdx.x;
+ if (i0 + WARP_SIZE > D && i >= D) {
+ break;
+ }
+ float dst_val = VKQ[j_VKQ*D_padded + i];
+ if (parallel_blocks == 1) {
+ dst_val /= KQ_rowsum_j;
+ }
+ dst[j_dst*gridDim.y*D + blockIdx.y*D + i] = dst_val;
+ }
+
+ if (parallel_blocks == 1 || threadIdx.x != 0) {
+ continue;
+ }
+
+ float2 dst_meta_val;
+ if (std::is_same<KQ_acc_t, float>::value) {
+ dst_meta_val.x = KQ_max_f[j0/nwarps];
+ } else {
+ dst_meta_val.x = __low2float(KQ_max_h2[j0/nwarps]);
+ }
+ dst_meta_val.y = KQ_rowsum_j;
+ dst_meta[(ic0 + j_VKQ)*gridDim.y*parallel_blocks + blockIdx.y*parallel_blocks + ip] = dst_meta_val;
+ }
+#else
+ NO_DEVICE_CODE;
+#endif // __CUDA_ARCH__ == GGML_CUDA_CC_VOLTA
+}
+
+constexpr int get_max_power_of_2(int x) {
+ return x % 2 == 0 ? 2*get_max_power_of_2(x/2) : 1;
+}
+
+static_assert(get_max_power_of_2(1) == 1, "Test failed.");
+static_assert(get_max_power_of_2(2) == 2, "Test failed.");
+static_assert(get_max_power_of_2(4) == 4, "Test failed.");
+static_assert(get_max_power_of_2(6) == 2, "Test failed.");
+
+// Number of VKQ rows calculated in parallel:
+constexpr int get_VKQ_stride(int D, int nwarps, int frag_m) {
+ return (get_max_power_of_2(D/frag_m) < nwarps ? get_max_power_of_2(D/frag_m) : nwarps)*frag_m;
+}
+
+static_assert(get_VKQ_stride(128, 1, 32) == 32, "Test failed.");
+static_assert(get_VKQ_stride(128, 2, 32) == 64, "Test failed.");
+static_assert(get_VKQ_stride(128, 4, 32) == 128, "Test failed.");
+static_assert(get_VKQ_stride( 64, 1, 32) == 32, "Test failed.");
+static_assert(get_VKQ_stride( 64, 2, 32) == 64, "Test failed.");
+static_assert(get_VKQ_stride( 64, 4, 32) == 64, "Test failed.");
+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, typename KQ_acc_t>
+void ggml_cuda_flash_attn_ext_wmma_f16_case(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+ const ggml_tensor * KQV = dst;
+ const ggml_tensor * Q = dst->src[0];
+
+ constexpr int nwarps = 4;
+
+ constexpr int frag_m = cols_per_block == 8 && D % 32 == 0 ? 32 : 16;
+ const int blocks_num_pb1 = ((Q->ne[1] + cols_per_block - 1) / cols_per_block)*Q->ne[2]*Q->ne[3];
+ const int nsm = ggml_cuda_info().devices[ggml_cuda_get_device()].nsm;
+
+ float logit_softcap;
+ memcpy(&logit_softcap, (const float *) KQV->op_params + 2, sizeof(float));
+
+ if (4*blocks_num_pb1 < 2*nsm) {
+ constexpr int parallel_blocks = 4;
+ fattn_kernel_t fattn_kernel;
+ if (logit_softcap == 0.0f) {
+ constexpr bool use_logit_softcap = false;
+ fattn_kernel = flash_attn_ext_f16<
+ D, cols_per_block, nwarps, get_VKQ_stride(D, nwarps, frag_m), parallel_blocks, KQ_acc_t, use_logit_softcap>;
+ } else {
+ constexpr bool use_logit_softcap = true;
+ fattn_kernel = flash_attn_ext_f16<
+ D, cols_per_block, nwarps, get_VKQ_stride(D, nwarps, frag_m), parallel_blocks, KQ_acc_t, use_logit_softcap>;
+ }
+ launch_fattn<D, cols_per_block, parallel_blocks, -1>(ctx, dst, fattn_kernel, nwarps, 0, true, true);
+ return;
+ }
+ if (2*blocks_num_pb1 < 2*nsm) {
+ constexpr int parallel_blocks = 2;
+ fattn_kernel_t fattn_kernel;
+ if (logit_softcap == 0.0f) {
+ constexpr bool use_logit_softcap = false;
+ fattn_kernel = flash_attn_ext_f16<
+ D, cols_per_block, nwarps, get_VKQ_stride(D, nwarps, frag_m), parallel_blocks, KQ_acc_t, use_logit_softcap>;
+ } else {
+ constexpr bool use_logit_softcap = true;
+ fattn_kernel = flash_attn_ext_f16<
+ D, cols_per_block, nwarps, get_VKQ_stride(D, nwarps, frag_m), parallel_blocks, KQ_acc_t, use_logit_softcap>;
+ }
+ launch_fattn<D, cols_per_block, parallel_blocks, -1>(ctx, dst, fattn_kernel, nwarps, 0, true, true);
+ return;
+ }
+ constexpr int parallel_blocks = 1;
+ fattn_kernel_t fattn_kernel;
+ if (logit_softcap == 0.0f) {
+ constexpr bool use_logit_softcap = false;
+ fattn_kernel = flash_attn_ext_f16<
+ D, cols_per_block, nwarps, get_VKQ_stride(D, nwarps, frag_m), parallel_blocks, KQ_acc_t, use_logit_softcap>;
+ } else {
+ constexpr bool use_logit_softcap = true;
+ fattn_kernel = flash_attn_ext_f16<
+ D, cols_per_block, nwarps, get_VKQ_stride(D, nwarps, frag_m), parallel_blocks, KQ_acc_t, use_logit_softcap>;
+ }
+ launch_fattn<D, cols_per_block, parallel_blocks, -1>(ctx, dst, fattn_kernel, nwarps, 0, true, true);
+}
+
+void ggml_cuda_flash_attn_ext_wmma_f16(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+ const ggml_tensor * KQV = dst;
+ const ggml_tensor * Q = dst->src[0];
+
+ const enum ggml_prec prec = ggml_flash_attn_ext_get_prec(KQV);
+
+ if (prec != GGML_PREC_DEFAULT) {
+ if (Q->ne[1] <= 32 || Q->ne[0] > 128) {
+ constexpr int cols_per_block = 16;
+ switch (Q->ne[0]) {
+ case 64:
+ ggml_cuda_flash_attn_ext_wmma_f16_case< 64, cols_per_block, float>(ctx, dst);
+ break;
+ case 80:
+ ggml_cuda_flash_attn_ext_wmma_f16_case< 80, cols_per_block, float>(ctx, dst);
+ break;
+ case 96:
+ ggml_cuda_flash_attn_ext_wmma_f16_case< 96, cols_per_block, float>(ctx, dst);
+ break;
+ case 112:
+ ggml_cuda_flash_attn_ext_wmma_f16_case<112, cols_per_block, float>(ctx, dst);
+ break;
+ case 128:
+ ggml_cuda_flash_attn_ext_wmma_f16_case<128, cols_per_block, float>(ctx, dst);
+ break;
+ case 256:
+ ggml_cuda_flash_attn_ext_wmma_f16_case<256, cols_per_block, float>(ctx, dst);
+ break;
+ default:
+ GGML_ABORT("fatal error");
+ break;
+ }
+ } else {
+ constexpr int cols_per_block = 32;
+ switch (Q->ne[0]) {
+ case 64:
+ ggml_cuda_flash_attn_ext_wmma_f16_case< 64, cols_per_block, float>(ctx, dst);
+ break;
+ case 80:
+ ggml_cuda_flash_attn_ext_wmma_f16_case< 80, cols_per_block, float>(ctx, dst);
+ break;
+ case 96:
+ ggml_cuda_flash_attn_ext_wmma_f16_case< 96, cols_per_block, float>(ctx, dst);
+ break;
+ case 112:
+ ggml_cuda_flash_attn_ext_wmma_f16_case<112, cols_per_block, float>(ctx, dst);
+ break;
+ case 128:
+ ggml_cuda_flash_attn_ext_wmma_f16_case<128, cols_per_block, float>(ctx, dst);
+ break;
+ // case 256:
+ // ggml_cuda_flash_attn_ext_wmma_f16_case<128, cols_per_block, float>(ctx, dst);
+ // break;
+ default:
+ GGML_ABORT("fatal error");
+ break;
+ }
+ }
+ return;
+ }
+
+ if (Q->ne[1] <= 8 && Q->ne[0] % WARP_SIZE == 0) {
+ constexpr int cols_per_block = 8;
+ switch (Q->ne[0]) {
+ case 64:
+ ggml_cuda_flash_attn_ext_wmma_f16_case< 64, cols_per_block, half>(ctx, dst);
+ break;
+ case 96:
+ ggml_cuda_flash_attn_ext_wmma_f16_case< 96, cols_per_block, half>(ctx, dst);
+ break;
+ case 128:
+ ggml_cuda_flash_attn_ext_wmma_f16_case<128, cols_per_block, half>(ctx, dst);
+ break;
+ case 256:
+ ggml_cuda_flash_attn_ext_wmma_f16_case<256, cols_per_block, half>(ctx, dst);
+ break;
+ default:
+ GGML_ABORT("fatal error");
+ break;
+ }
+ return;
+ }
+
+ if (Q->ne[1] <= 32) {
+ constexpr int cols_per_block = 16;
+ switch (Q->ne[0]) {
+ case 64:
+ ggml_cuda_flash_attn_ext_wmma_f16_case< 64, cols_per_block, half>(ctx, dst);
+ break;
+ case 80:
+ ggml_cuda_flash_attn_ext_wmma_f16_case< 80, cols_per_block, half>(ctx, dst);
+ break;
+ case 96:
+ ggml_cuda_flash_attn_ext_wmma_f16_case< 96, cols_per_block, half>(ctx, dst);
+ break;
+ case 112:
+ ggml_cuda_flash_attn_ext_wmma_f16_case<112, cols_per_block, half>(ctx, dst);
+ break;
+ case 128:
+ ggml_cuda_flash_attn_ext_wmma_f16_case<128, cols_per_block, half>(ctx, dst);
+ break;
+ case 256:
+ ggml_cuda_flash_attn_ext_wmma_f16_case<256, cols_per_block, half>(ctx, dst);
+ break;
+ default:
+ GGML_ABORT("fatal error");
+ break;
+ }
+ return;
+ }
+
+ constexpr int cols_per_block = 32;
+ switch (Q->ne[0]) {
+ case 64:
+ ggml_cuda_flash_attn_ext_wmma_f16_case< 64, cols_per_block, half>(ctx, dst);
+ break;
+ case 80:
+ ggml_cuda_flash_attn_ext_wmma_f16_case< 80, cols_per_block, half>(ctx, dst);
+ break;
+ case 96:
+ ggml_cuda_flash_attn_ext_wmma_f16_case< 96, cols_per_block, half>(ctx, dst);
+ break;
+ case 112:
+ ggml_cuda_flash_attn_ext_wmma_f16_case<112, cols_per_block, half>(ctx, dst);
+ break;
+ case 128:
+ ggml_cuda_flash_attn_ext_wmma_f16_case<128, cols_per_block, half>(ctx, dst);
+ break;
+ case 256:
+ ggml_cuda_flash_attn_ext_wmma_f16_case<256, cols_per_block, half>(ctx, dst);
+ break;
+ default:
+ GGML_ABORT("fatal error");
+ break;
+ }
+}
#include "common.cuh"
-#include "fattn-common.cuh"
-#ifdef FP16_MMA_AVAILABLE
-#include <mma.h>
-#endif // FP16_MMA_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, bool use_logit_softcap>
-#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__))
-__launch_bounds__(nwarps*WARP_SIZE, 1)
-#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__))
-static __global__ void flash_attn_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 float logit_softcap,
- 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) {
-#ifdef FP16_MMA_AVAILABLE
- // Skip unused kernel variants for faster compilation:
- if (use_logit_softcap && !(D == 128 || D == 256)) {
- NO_DEVICE_CODE;
- return;
- }
-
- //In this kernel Q, K, V are matrices while i, j, k are matrix indices.
-
- const int ic0 = ncols*(blockIdx.x / parallel_blocks); // Index of the first 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.
-
- static_assert(D <= FATTN_KQ_STRIDE, "D must be <= FATTN_KQ_STRIDE.");
- static_assert(ncols == 8 || ncols % 16 == 0, "ncols must be 8 or a multiple of 16.");
- constexpr int frag_m = ncols == 8 ? 32 : 16;
- constexpr int frag_n = ncols == 8 ? 8 : 16;
- static_assert(D % frag_m == 0, "If ncols == 8 then D % frag_m must be 0.");
- typedef nvcuda::wmma::fragment<nvcuda::wmma::matrix_a, frag_m, frag_n, 16, half, nvcuda::wmma::row_major> frag_a_K;
- typedef nvcuda::wmma::fragment<nvcuda::wmma::matrix_a, frag_m, frag_n, 16, half, nvcuda::wmma::col_major> frag_a_V;
- typedef nvcuda::wmma::fragment<nvcuda::wmma::matrix_b, frag_m, frag_n, 16, half, nvcuda::wmma::col_major> frag_b;
- typedef nvcuda::wmma::fragment<nvcuda::wmma::accumulator, frag_m, frag_n, 16, KQ_acc_t> frag_c_KQ;
- typedef nvcuda::wmma::fragment<nvcuda::wmma::accumulator, frag_m, frag_n, 16, half> frag_c_VKQ;
-
- constexpr int KQ_stride_tc = nwarps*frag_m; // Number of KQ rows calculated in parallel.
- constexpr int VKQ_ratio = KQ_stride_tc/VKQ_stride; // Number of parallel VKQ accumulators needed to keep all warps busy.
- static_assert(VKQ_ratio <= nwarps, "VKQ_ratio must be <= nwarps.");
-
- // Pad internal representation of KQ, KQV to reduce shared memory bank conflicts:
- constexpr int D_padded = D + 8;
- constexpr int kqs_padded = FATTN_KQ_STRIDE + 8;
- constexpr int kqar = sizeof(KQ_acc_t)/sizeof(half);
-
- const int gqa_ratio = ne02 / ne12; // With grouped query attention there are > 1 Q matrices per K, V matrix.
- const float * Q_f = (const float *) (Q + nb02* blockIdx.y + nb01*ic0);
- const half * K_h = (const half *) (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 + (nb31/sizeof(half))* ic0;
- const half2 * mask2 = (const half2 *) mask + (nb31/sizeof(half))*(ic0/2);
-
- const int stride_Q = nb01 / sizeof(float);
- const int stride_KV = nb11 / sizeof(half);
-
- const float slopef = get_alibi_slope(max_bias, blockIdx.y, n_head_log2, m0, m1);
- const half slopeh = __float2half(slopef);
- const half2 slope2 = make_half2(slopef, slopef);
-
- const half2 logit_softcap_2 = make_half2(logit_softcap, logit_softcap);
-
- frag_b Q_b[D/16][ncols/frag_n];
-
- // A single buffer for temporarily holding tiles of KQ and VKQ parts:
- constexpr int mem_KQ = ncols*kqs_padded*kqar;
- constexpr int mem_VKQ_parts = VKQ_ratio*ncols*D_padded;
- __shared__ half KQ[mem_KQ >= mem_VKQ_parts ? mem_KQ : mem_VKQ_parts];
- float * KQ_f = (float *) KQ;
- half2 * KQ2 = (half2 *) KQ;
-
- float KQ_rowsum_f[ncols/nwarps] = {0.0f};
- float KQ_max_f[ncols/nwarps];
- float KQ_max_scale_f[ncols/nwarps] = {0.0f};
-
-#pragma unroll
- for (int j = 0; j < ncols/nwarps; ++j) {
- KQ_max_f[j] = -FLT_MAX/2.0f;
- }
-
- half2 KQ_rowsum_h2[ncols/nwarps] = {{0.0f, 0.0f}};
- half2 KQ_max_h2[ncols/nwarps];
- half2 KQ_max_scale_h2[ncols/nwarps] = {{0.0f, 0.0f}};
-
-#pragma unroll
- for (int j = 0; j < ncols/nwarps; ++j) {
- KQ_max_h2[j] = make_half2(-HALF_MAX_HALF, -HALF_MAX_HALF);
- }
-
- __shared__ half VKQ[ncols*D_padded]; // Accumulator for final VKQ slice.
- half2 * VKQ2 = (half2 *) VKQ;
-#pragma unroll
- for (int j0 = 0; j0 < ncols; j0 += nwarps) {
- const int j = j0 + threadIdx.y;
-#pragma unroll
- for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) {
- const int i = i0 + threadIdx.x;
- if (i0 + WARP_SIZE > D/2 && i >= D/2) {
- break;
- }
- VKQ2[j*(D_padded/2) + i] = make_half2(0.0f, 0.0f);
- }
- }
-
- // Convert Q to half and apply scale, temporarily store in KQ:
-#pragma unroll
- for (int j0 = 0; j0 < ncols; j0 += nwarps) {
- const int j = j0 + threadIdx.y;
-#pragma unroll
- for (int i0 = 0; i0 < D; i0 += WARP_SIZE) {
- const int i = i0 + threadIdx.x;
- if (i0 + WARP_SIZE > D && i >= D) {
- break;
- }
- KQ[j*D_padded + i] = ic0 + j < ne01 ? Q_f[j*stride_Q + i] * scale : 0.0f;
- }
- }
-
- __syncthreads();
-
- // Load Q into tensor core fragments/registers since it will be used frequently:
-#pragma unroll
- for (int i0 = 0; i0 < D; i0 += 16) {
-#pragma unroll
- for (int j0 = 0; j0 < ncols; j0 += frag_n) {
- nvcuda::wmma::load_matrix_sync(Q_b[i0/16][j0/frag_n], KQ + j0*D_padded + i0, D_padded);
- }
- }
-
- __syncthreads();
-
- // Iterate over ne11 == previous tokens:
- for (int k_VKQ_0 = ip*FATTN_KQ_STRIDE; k_VKQ_0 < ne11; k_VKQ_0 += parallel_blocks*FATTN_KQ_STRIDE) {
- // Calculate tile of KQ:
-#pragma unroll
- for (int i_KQ_0 = 0; i_KQ_0 < FATTN_KQ_STRIDE; i_KQ_0 += KQ_stride_tc) {
- frag_c_KQ KQ_c[ncols/frag_n];
-#pragma unroll
- for (int j = 0; j < ncols/frag_n; ++j) {
- nvcuda::wmma::fill_fragment(KQ_c[j], 0.0f);
- }
-#pragma unroll
- for (int k_KQ_0 = 0; k_KQ_0 < D; k_KQ_0 += 16) {
- frag_a_K K_a;
- nvcuda::wmma::load_matrix_sync(K_a, K_h + (k_VKQ_0 + i_KQ_0 + frag_m*threadIdx.y)*stride_KV + k_KQ_0, stride_KV);
-#pragma unroll
- for (int j = 0; j < ncols/frag_n; ++j) {
- nvcuda::wmma::mma_sync(KQ_c[j], K_a, Q_b[k_KQ_0/16][j], KQ_c[j]);
- }
- }
-#pragma unroll
- for (int j0 = 0; j0 < ncols; j0 += frag_n) {
- nvcuda::wmma::store_matrix_sync((KQ_acc_t *) KQ + j0*kqs_padded + i_KQ_0 + frag_m*threadIdx.y, KQ_c[j0/frag_n], kqs_padded, nvcuda::wmma::mem_col_major);
- }
- }
-
- __syncthreads();
-
- // Calculate softmax for each KQ column using the current max. value.
- // The divisor is stored in KQ_rowsum and will be applied at the end.
-#pragma unroll
- for (int j0 = 0; j0 < ncols; j0 += nwarps) {
- const int j = j0 + threadIdx.y;
-
- if (std::is_same<KQ_acc_t, float>::value) {
- float KQ_f_tmp[FATTN_KQ_STRIDE / WARP_SIZE];
-#pragma unroll
- for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += WARP_SIZE) {
- const int k = k0 + threadIdx.x;
-
- KQ_f_tmp[k0/WARP_SIZE] = KQ_f[j*kqs_padded + k];
-
- if (use_logit_softcap) {
- KQ_f_tmp[k0/WARP_SIZE] = logit_softcap*tanhf(KQ_f_tmp[k0/WARP_SIZE]);
- }
- }
-
- float KQ_max_new = KQ_max_f[j0/nwarps];
-#pragma unroll
- for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += WARP_SIZE) {
- const int k = k0 + threadIdx.x;
-
- KQ_f_tmp[k0/WARP_SIZE] += mask ? __half2float(slopeh*maskh[j*(nb31/sizeof(half)) + k_VKQ_0 + k]) : 0.0f;
- KQ_max_new = max(KQ_max_new, KQ_f_tmp[k0/WARP_SIZE]);
- }
- KQ_max_new = warp_reduce_max(KQ_max_new);
-
- const float diff = KQ_max_f[j0/nwarps] - KQ_max_new;
- KQ_max_scale_f[j0/nwarps] = expf(diff);
- if (diff <= SOFTMAX_FTZ_THRESHOLD) {
- KQ_max_scale_f[j0/nwarps] = 0.0f;
- }
- KQ_max_f[j0/nwarps] = KQ_max_new;
-
- float KQ_rowsum_add = 0.0f;
-#pragma unroll
- for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += WARP_SIZE) {
- const int k = k0 + threadIdx.x;
-
- const float diff = KQ_f_tmp[k0/WARP_SIZE] - KQ_max_f[j0/nwarps];
- KQ_f_tmp[k0/WARP_SIZE] = expf(diff);
- if (diff <= SOFTMAX_FTZ_THRESHOLD) {
- KQ_f_tmp[k0/WARP_SIZE] = 0.0f;
- }
- KQ_rowsum_add += KQ_f_tmp[k0/WARP_SIZE];
- KQ[j*(kqar*kqs_padded) + k] = KQ_f_tmp[k0/WARP_SIZE];
- }
- KQ_rowsum_add = warp_reduce_sum(KQ_rowsum_add);
-
- // Scale previous KQ_rowsum to account for a potential increase in KQ_max:
- KQ_rowsum_f[j0/nwarps] = KQ_max_scale_f[j0/nwarps]*KQ_rowsum_f[j0/nwarps] + KQ_rowsum_add;
- } else {
- half2 KQ2_tmp[FATTN_KQ_STRIDE/(2*WARP_SIZE)];
-#pragma unroll
- for (int k0 = 0; k0 < FATTN_KQ_STRIDE/2; k0 += WARP_SIZE) {
- const int k = k0 + threadIdx.x;
-
- KQ2_tmp[k0/WARP_SIZE] = KQ2[j*(kqs_padded/2) + k];
-
- if (use_logit_softcap) {
- // There is no dedicated tangens hyperbolicus function for half2.
- KQ2_tmp[k0/WARP_SIZE] = h2exp(KQ2_tmp[k0/WARP_SIZE]*make_half2(2.0f, 2.0f));
- KQ2_tmp[k0/WARP_SIZE] = (KQ2_tmp[k0/WARP_SIZE] - make_half2(1.0f, 1.0f))
- /(KQ2_tmp[k0/WARP_SIZE] + make_half2(1.0f, 1.0f));
-
- KQ2_tmp[k0/WARP_SIZE] *= logit_softcap_2;
- }
- }
-
- half2 KQ_max_new = KQ_max_h2[j0/nwarps];
-#pragma unroll
- for (int k0 = 0; k0 < FATTN_KQ_STRIDE/2; k0 += WARP_SIZE) {
- const int k = k0 + threadIdx.x;
-
- KQ2_tmp[k0/WARP_SIZE] += mask ? slope2*mask2[(j*ne11 + k_VKQ_0)/2 + k] : make_half2(0.0f, 0.0f);
- KQ_max_new = ggml_cuda_hmax2(KQ_max_new, KQ2_tmp[k0/WARP_SIZE]);
- }
- KQ_max_new = __half2half2(warp_reduce_max(ggml_cuda_hmax(__low2half(KQ_max_new), __high2half(KQ_max_new))));
- const half2 diff = KQ_max_h2[j0/nwarps] - KQ_max_new;
- KQ_max_scale_h2[j0/nwarps] = h2exp(diff);
- const uint32_t ftz_mask = __hgt2_mask(diff, make_half2(SOFTMAX_FTZ_THRESHOLD, SOFTMAX_FTZ_THRESHOLD));
- *((uint32_t *) &KQ_max_scale_h2[j0/nwarps]) &= ftz_mask;
- KQ_max_h2[j0/nwarps] = KQ_max_new;
-
- half2 KQ_rowsum_add = make_half2(0.0f, 0.0f);
-#pragma unroll
- for (int k0 = 0; k0 < FATTN_KQ_STRIDE/2; k0 += WARP_SIZE) {
- const int k = k0 + threadIdx.x;
-
- const half2 diff = KQ2_tmp[k0/WARP_SIZE] - KQ_max_h2[j0/nwarps];
- KQ2_tmp[k0/WARP_SIZE] = h2exp(diff);
- const uint32_t ftz_mask = __hgt2_mask(diff, make_half2(SOFTMAX_FTZ_THRESHOLD, SOFTMAX_FTZ_THRESHOLD));
- *((uint32_t *) &KQ2_tmp[k0/WARP_SIZE]) &= ftz_mask;
- KQ_rowsum_add += KQ2_tmp[k0/WARP_SIZE];
- KQ2[j*(kqs_padded/2) + k] = KQ2_tmp[k0/WARP_SIZE];
- }
- KQ_rowsum_add = warp_reduce_sum(KQ_rowsum_add);
-
- // Scale previous KQ_rowsum to account for a potential increase in KQ_max:
- KQ_rowsum_h2[j0/nwarps] = KQ_max_scale_h2[j0/nwarps]*KQ_rowsum_h2[j0/nwarps] + KQ_rowsum_add;
- }
- }
-
- __syncthreads();
-
- frag_b KQ_b[FATTN_KQ_STRIDE/(VKQ_ratio*16)][ncols/frag_n];
-#pragma unroll
- for (int j0 = 0; j0 < ncols; j0 += frag_n) {
-#pragma unroll
- for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += VKQ_ratio*16) {
- const int k = k0 + (threadIdx.y % VKQ_ratio)*16;
- nvcuda::wmma::load_matrix_sync(
- KQ_b[k0/(VKQ_ratio*16)][j0/frag_n],
- KQ + j0*(kqar*kqs_padded) + k,
- kqar*kqs_padded);
- }
- }
-
- frag_c_VKQ VKQ_c[D/VKQ_stride][ncols/frag_n];
-#pragma unroll
- for (int i_VKQ_0 = 0; i_VKQ_0 < D; i_VKQ_0 += VKQ_stride) {
-#pragma unroll
- for (int j = 0; j < ncols/frag_n; ++j) {
- nvcuda::wmma::fill_fragment(VKQ_c[i_VKQ_0/VKQ_stride][j], 0.0f);
- }
-
-#pragma unroll
- for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += VKQ_ratio*16) {
- const int k = k0 + (threadIdx.y % VKQ_ratio)*16;
-
- frag_a_V v_a;
- nvcuda::wmma::load_matrix_sync(v_a, V_h + (k_VKQ_0 + k)*stride_KV + i_VKQ_0 + frag_m*(threadIdx.y/VKQ_ratio), stride_KV);
-#pragma unroll
- for (int j = 0; j < ncols/frag_n; ++j) {
- nvcuda::wmma::mma_sync(VKQ_c[i_VKQ_0/VKQ_stride][j], v_a, KQ_b[k0/(VKQ_ratio*16)][j], VKQ_c[i_VKQ_0/VKQ_stride][j]);
- }
- }
- }
-
- __syncthreads();
-
- const int offset_k = (threadIdx.y % VKQ_ratio) * (ncols*D_padded);
-#pragma unroll
- for (int i_KQ_0 = 0; i_KQ_0 < D; i_KQ_0 += VKQ_stride) {
-#pragma unroll
- for (int j0 = 0; j0 < ncols; j0 += frag_n) {
- nvcuda::wmma::store_matrix_sync(
- KQ + offset_k + j0*D_padded + i_KQ_0 + frag_m*(threadIdx.y/VKQ_ratio),
- VKQ_c[i_KQ_0/VKQ_stride][j0/frag_n],
- D_padded, nvcuda::wmma::mem_col_major);
- }
- }
-
- __syncthreads();
-
-#pragma unroll
- for (int j0 = 0; j0 < ncols; j0 += nwarps) {
- const int j = j0 + threadIdx.y;
-
- half2 VKQ_scale;
- if (std::is_same<KQ_acc_t, float>::value) {
- VKQ_scale = make_half2(KQ_max_scale_f[j0/nwarps], KQ_max_scale_f[j0/nwarps]);
- } else {
- VKQ_scale = KQ_max_scale_h2[j0/nwarps];
- }
-
-#pragma unroll
- for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) {
- const int i = i0 + threadIdx.x;
- if (i0 + WARP_SIZE > D/2 && i >= D/2) {
- break;
- }
-
- half2 VKQ_add = make_half2(0.0f, 0.0f);
-#pragma unroll
- for (int l = 0; l < VKQ_ratio; ++l) {
- VKQ_add += KQ2[l*(ncols*D_padded/2) + j*(D_padded/2) + i];
- }
- VKQ2[j*(D_padded/2) + i] = VKQ_scale*VKQ2[j*(D_padded/2) + i] + VKQ_add;
- }
- }
-
- __syncthreads();
- }
-
-#pragma unroll
- for (int j0 = 0; j0 < ncols; j0 += nwarps) {
- const int j_VKQ = j0 + threadIdx.y;
- if (ic0 + j_VKQ >= ne01) {
- return;
- }
- const int j_dst = (ic0 + j_VKQ)*parallel_blocks + ip;
-
- float KQ_rowsum_j;
- if (std::is_same<KQ_acc_t, float>::value) {
- KQ_rowsum_j = KQ_rowsum_f[j0/nwarps];
- } else {
- KQ_rowsum_j = __low2float(KQ_rowsum_h2[j0/nwarps]) + __high2float(KQ_rowsum_h2[j0/nwarps]);
- }
-
-#pragma unroll
- for (int i0 = 0; i0 < D; i0 += WARP_SIZE) {
- const int i = i0 + threadIdx.x;
- if (i0 + WARP_SIZE > D && i >= D) {
- break;
- }
- float dst_val = VKQ[j_VKQ*D_padded + i];
- if (parallel_blocks == 1) {
- dst_val /= KQ_rowsum_j;
- }
- dst[j_dst*gridDim.y*D + blockIdx.y*D + i] = dst_val;
- }
-
- if (parallel_blocks == 1 || threadIdx.x != 0) {
- continue;
- }
-
- float2 dst_meta_val;
- if (std::is_same<KQ_acc_t, float>::value) {
- dst_meta_val.x = KQ_max_f[j0/nwarps];
- } else {
- dst_meta_val.x = __low2float(KQ_max_h2[j0/nwarps]);
- }
- dst_meta_val.y = KQ_rowsum_j;
- dst_meta[(ic0 + j_VKQ)*gridDim.y*parallel_blocks + blockIdx.y*parallel_blocks + ip] = dst_meta_val;
- }
-#else
- NO_DEVICE_CODE;
-#endif // FP16_MMA_AVAILABLE
-}
-
-constexpr int get_max_power_of_2(int x) {
- return x % 2 == 0 ? 2*get_max_power_of_2(x/2) : 1;
-}
-
-static_assert(get_max_power_of_2(1) == 1, "Test failed.");
-static_assert(get_max_power_of_2(2) == 2, "Test failed.");
-static_assert(get_max_power_of_2(4) == 4, "Test failed.");
-static_assert(get_max_power_of_2(6) == 2, "Test failed.");
-
-// Number of VKQ rows calculated in parallel:
-constexpr int get_VKQ_stride(int D, int nwarps, int frag_m) {
- return (get_max_power_of_2(D/frag_m) < nwarps ? get_max_power_of_2(D/frag_m) : nwarps)*frag_m;
-}
-
-static_assert(get_VKQ_stride(128, 1, 32) == 32, "Test failed.");
-static_assert(get_VKQ_stride(128, 2, 32) == 64, "Test failed.");
-static_assert(get_VKQ_stride(128, 4, 32) == 128, "Test failed.");
-static_assert(get_VKQ_stride( 64, 1, 32) == 32, "Test failed.");
-static_assert(get_VKQ_stride( 64, 2, 32) == 64, "Test failed.");
-static_assert(get_VKQ_stride( 64, 4, 32) == 64, "Test failed.");
-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, typename KQ_acc_t>
-void ggml_cuda_flash_attn_ext_wmma_f16_case(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
- const ggml_tensor * KQV = dst;
- const ggml_tensor * Q = dst->src[0];
-
- constexpr int nwarps = 4;
-
- constexpr int frag_m = cols_per_block == 8 && D % 32 == 0 ? 32 : 16;
- const int blocks_num_pb1 = ((Q->ne[1] + cols_per_block - 1) / cols_per_block)*Q->ne[2]*Q->ne[3];
- const int nsm = ggml_cuda_info().devices[ggml_cuda_get_device()].nsm;
-
- float logit_softcap;
- memcpy(&logit_softcap, (const float *) KQV->op_params + 2, sizeof(float));
-
- if (4*blocks_num_pb1 < 2*nsm) {
- constexpr int parallel_blocks = 4;
- fattn_kernel_t fattn_kernel;
- if (logit_softcap == 0.0f) {
- constexpr bool use_logit_softcap = false;
- fattn_kernel = flash_attn_ext_f16<
- D, cols_per_block, nwarps, get_VKQ_stride(D, nwarps, frag_m), parallel_blocks, KQ_acc_t, use_logit_softcap>;
- } else {
- constexpr bool use_logit_softcap = true;
- fattn_kernel = flash_attn_ext_f16<
- D, cols_per_block, nwarps, get_VKQ_stride(D, nwarps, frag_m), parallel_blocks, KQ_acc_t, use_logit_softcap>;
- }
- launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block, true, true);
- return;
- }
- if (2*blocks_num_pb1 < 2*nsm) {
- constexpr int parallel_blocks = 2;
- fattn_kernel_t fattn_kernel;
- if (logit_softcap == 0.0f) {
- constexpr bool use_logit_softcap = false;
- fattn_kernel = flash_attn_ext_f16<
- D, cols_per_block, nwarps, get_VKQ_stride(D, nwarps, frag_m), parallel_blocks, KQ_acc_t, use_logit_softcap>;
- } else {
- constexpr bool use_logit_softcap = true;
- fattn_kernel = flash_attn_ext_f16<
- D, cols_per_block, nwarps, get_VKQ_stride(D, nwarps, frag_m), parallel_blocks, KQ_acc_t, use_logit_softcap>;
- }
- launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block, true, true);
- return;
- }
- constexpr int parallel_blocks = 1;
- fattn_kernel_t fattn_kernel;
- if (logit_softcap == 0.0f) {
- constexpr bool use_logit_softcap = false;
- fattn_kernel = flash_attn_ext_f16<
- D, cols_per_block, nwarps, get_VKQ_stride(D, nwarps, frag_m), parallel_blocks, KQ_acc_t, use_logit_softcap>;
- } else {
- constexpr bool use_logit_softcap = true;
- fattn_kernel = flash_attn_ext_f16<
- D, cols_per_block, nwarps, get_VKQ_stride(D, nwarps, frag_m), parallel_blocks, KQ_acc_t, use_logit_softcap>;
- }
- launch_fattn<D, parallel_blocks>(ctx, dst, fattn_kernel, nwarps, cols_per_block, true, true);
-}
-
-#define DECL_FATTN_WMMA_F16_CASE(D, cols_per_block, KQ_acc_t) \
- template void ggml_cuda_flash_attn_ext_wmma_f16_case \
- <D, cols_per_block, KQ_acc_t>(ggml_backend_cuda_context & ctx, ggml_tensor * dst) \
-
-extern DECL_FATTN_WMMA_F16_CASE( 64, 16, float);
-extern DECL_FATTN_WMMA_F16_CASE( 80, 16, float);
-extern DECL_FATTN_WMMA_F16_CASE( 96, 16, float);
-extern DECL_FATTN_WMMA_F16_CASE(112, 16, float);
-extern DECL_FATTN_WMMA_F16_CASE(128, 16, float);
-extern DECL_FATTN_WMMA_F16_CASE(256, 16, float);
-
-extern DECL_FATTN_WMMA_F16_CASE( 64, 32, float);
-extern DECL_FATTN_WMMA_F16_CASE( 80, 32, float);
-extern DECL_FATTN_WMMA_F16_CASE( 96, 32, float);
-extern DECL_FATTN_WMMA_F16_CASE(112, 32, float);
-extern DECL_FATTN_WMMA_F16_CASE(128, 32, float);
-// extern DECL_FATTN_WMMA_F16_CASE(256, 16, float);
-
-extern DECL_FATTN_WMMA_F16_CASE( 64, 8, half);
-extern DECL_FATTN_WMMA_F16_CASE( 96, 8, half);
-extern DECL_FATTN_WMMA_F16_CASE(128, 8, half);
-extern DECL_FATTN_WMMA_F16_CASE(256, 8, half);
-
-extern DECL_FATTN_WMMA_F16_CASE( 64, 16, half);
-extern DECL_FATTN_WMMA_F16_CASE( 80, 16, half);
-extern DECL_FATTN_WMMA_F16_CASE( 96, 16, half);
-extern DECL_FATTN_WMMA_F16_CASE(112, 16, half);
-extern DECL_FATTN_WMMA_F16_CASE(128, 16, half);
-extern DECL_FATTN_WMMA_F16_CASE(256, 16, half);
-
-extern DECL_FATTN_WMMA_F16_CASE( 64, 32, half);
-extern DECL_FATTN_WMMA_F16_CASE( 80, 32, half);
-extern DECL_FATTN_WMMA_F16_CASE( 96, 32, half);
-extern DECL_FATTN_WMMA_F16_CASE(112, 32, half);
-extern DECL_FATTN_WMMA_F16_CASE(128, 32, half);
-extern DECL_FATTN_WMMA_F16_CASE(256, 16, half);
+void ggml_cuda_flash_attn_ext_wmma_f16(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
#include "common.cuh"
#include "fattn-common.cuh"
+#include "fattn-mma-f16.cuh"
#include "fattn-tile-f16.cuh"
#include "fattn-tile-f32.cuh"
#include "fattn-vec-f16.cuh"
#include "fattn-wmma-f16.cuh"
#include "fattn.cuh"
-#include <cstdint>
+template <int cols_per_block>
+static void ggml_cuda_flash_attn_ext_mma_f16_switch_hs(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+ const ggml_tensor * Q = dst->src[0];
-static void ggml_cuda_flash_attn_ext_wmma_f16(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
- const ggml_tensor * KQV = dst;
- const ggml_tensor * Q = dst->src[0];
-
- const enum ggml_prec prec = ggml_flash_attn_ext_get_prec(KQV);
-
- if (prec != GGML_PREC_DEFAULT) {
- if (Q->ne[1] <= 32 || Q->ne[0] > 128) {
- constexpr int cols_per_block = 16;
- switch (Q->ne[0]) {
- case 64:
- ggml_cuda_flash_attn_ext_wmma_f16_case< 64, cols_per_block, float>(ctx, dst);
- break;
- case 80:
- ggml_cuda_flash_attn_ext_wmma_f16_case< 80, cols_per_block, float>(ctx, dst);
- break;
- case 96:
- ggml_cuda_flash_attn_ext_wmma_f16_case< 96, cols_per_block, float>(ctx, dst);
- break;
- case 112:
- ggml_cuda_flash_attn_ext_wmma_f16_case<112, cols_per_block, float>(ctx, dst);
- break;
- case 128:
- ggml_cuda_flash_attn_ext_wmma_f16_case<128, cols_per_block, float>(ctx, dst);
- break;
- case 256:
- ggml_cuda_flash_attn_ext_wmma_f16_case<256, cols_per_block, float>(ctx, dst);
- break;
- default:
- GGML_ABORT("fatal error");
- break;
- }
- } else {
- constexpr int cols_per_block = 32;
- switch (Q->ne[0]) {
- case 64:
- ggml_cuda_flash_attn_ext_wmma_f16_case< 64, cols_per_block, float>(ctx, dst);
- break;
- case 80:
- ggml_cuda_flash_attn_ext_wmma_f16_case< 80, cols_per_block, float>(ctx, dst);
- break;
- case 96:
- ggml_cuda_flash_attn_ext_wmma_f16_case< 96, cols_per_block, float>(ctx, dst);
- break;
- case 112:
- ggml_cuda_flash_attn_ext_wmma_f16_case<112, cols_per_block, float>(ctx, dst);
- break;
- case 128:
- ggml_cuda_flash_attn_ext_wmma_f16_case<128, cols_per_block, float>(ctx, dst);
- break;
- // case 256:
- // ggml_cuda_flash_attn_ext_wmma_f16_case<128, cols_per_block, float>(ctx, dst);
- // break;
- default:
- GGML_ABORT("fatal error");
- break;
- }
- }
- return;
- }
-
- if (Q->ne[1] <= 8 && Q->ne[0] % WARP_SIZE == 0) {
- constexpr int cols_per_block = 8;
- switch (Q->ne[0]) {
- case 64:
- ggml_cuda_flash_attn_ext_wmma_f16_case< 64, cols_per_block, half>(ctx, dst);
- break;
- case 96:
- ggml_cuda_flash_attn_ext_wmma_f16_case< 96, cols_per_block, half>(ctx, dst);
- break;
- case 128:
- ggml_cuda_flash_attn_ext_wmma_f16_case<128, cols_per_block, half>(ctx, dst);
- break;
- case 256:
- ggml_cuda_flash_attn_ext_wmma_f16_case<256, cols_per_block, half>(ctx, dst);
- break;
- default:
- GGML_ABORT("fatal error");
- break;
- }
- return;
- }
-
- if (Q->ne[1] <= 32) {
- constexpr int cols_per_block = 16;
- switch (Q->ne[0]) {
- case 64:
- ggml_cuda_flash_attn_ext_wmma_f16_case< 64, cols_per_block, half>(ctx, dst);
- break;
- case 80:
- ggml_cuda_flash_attn_ext_wmma_f16_case< 80, cols_per_block, half>(ctx, dst);
- break;
- case 96:
- ggml_cuda_flash_attn_ext_wmma_f16_case< 96, cols_per_block, half>(ctx, dst);
- break;
- case 112:
- ggml_cuda_flash_attn_ext_wmma_f16_case<112, cols_per_block, half>(ctx, dst);
- break;
- case 128:
- ggml_cuda_flash_attn_ext_wmma_f16_case<128, cols_per_block, half>(ctx, dst);
- break;
- case 256:
- ggml_cuda_flash_attn_ext_wmma_f16_case<256, cols_per_block, half>(ctx, dst);
- break;
- default:
- GGML_ABORT("fatal error");
- break;
- }
- return;
- }
-
- constexpr int cols_per_block = 32;
switch (Q->ne[0]) {
case 64:
- ggml_cuda_flash_attn_ext_wmma_f16_case< 64, cols_per_block, half>(ctx, dst);
+ ggml_cuda_flash_attn_ext_mma_f16_case< 64, cols_per_block>(ctx, dst);
break;
case 80:
- ggml_cuda_flash_attn_ext_wmma_f16_case< 80, cols_per_block, half>(ctx, dst);
+ ggml_cuda_flash_attn_ext_mma_f16_case< 80, cols_per_block>(ctx, dst);
break;
case 96:
- ggml_cuda_flash_attn_ext_wmma_f16_case< 96, cols_per_block, half>(ctx, dst);
+ ggml_cuda_flash_attn_ext_mma_f16_case< 96, cols_per_block>(ctx, dst);
break;
case 112:
- ggml_cuda_flash_attn_ext_wmma_f16_case<112, cols_per_block, half>(ctx, dst);
+ ggml_cuda_flash_attn_ext_mma_f16_case<112, cols_per_block>(ctx, dst);
break;
case 128:
- ggml_cuda_flash_attn_ext_wmma_f16_case<128, cols_per_block, half>(ctx, dst);
+ ggml_cuda_flash_attn_ext_mma_f16_case<128, cols_per_block>(ctx, dst);
break;
case 256:
- ggml_cuda_flash_attn_ext_wmma_f16_case<256, cols_per_block, half>(ctx, dst);
+ ggml_cuda_flash_attn_ext_mma_f16_case<256, cols_per_block>(ctx, dst);
break;
default:
GGML_ABORT("fatal error");
break;
}
}
+
+static void ggml_cuda_flash_attn_ext_mma_f16(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+ const ggml_tensor * Q = dst->src[0];
+
+ if (Q->ne[1] <= 8) {
+ ggml_cuda_flash_attn_ext_mma_f16_switch_hs<8>(ctx, dst);
+ return;
+ }
+
+ if (Q->ne[1] <= 16) {
+ ggml_cuda_flash_attn_ext_mma_f16_switch_hs<16>(ctx, dst);
+ return;
+ }
+
+ if (Q->ne[1] <= 32) {
+ ggml_cuda_flash_attn_ext_mma_f16_switch_hs<32>(ctx, dst);
+ return;
+ }
+
+ ggml_cuda_flash_attn_ext_mma_f16_switch_hs<64>(ctx, dst);
+}
+
#define FATTN_VEC_F16_CASE(D, type_K, type_V) \
if (Q->ne[0] == (D) && K->type == (type_K) && V->type == (type_V)) { \
ggml_cuda_flash_attn_ext_vec_f16_case<D, type_K, type_V>(ctx, dst); \
return;
}
- if (!fp16_mma_available(cc)) {
- if (Q->ne[1] <= 8) {
- ggml_cuda_flash_attn_ext_vec_f16(ctx, dst);
+ if (!new_mma_available(cc)) {
+ if (prec == GGML_PREC_DEFAULT) {
+ if (Q->ne[1] <= 8) {
+ ggml_cuda_flash_attn_ext_vec_f16(ctx, dst);
+ } else {
+ ggml_cuda_flash_attn_ext_tile_f16(ctx, dst);
+ }
} else {
- ggml_cuda_flash_attn_ext_tile_f16(ctx, dst);
+ if (Q->ne[1] <= 8) {
+ ggml_cuda_flash_attn_ext_vec_f32(ctx, dst);
+ } else {
+ ggml_cuda_flash_attn_ext_tile_f32(ctx, dst);
+ }
}
return;
}
}
}
- ggml_cuda_flash_attn_ext_wmma_f16(ctx, dst);
+ // The MMA implementation needs Turing or newer, use the old WMMA code for Volta:
+ if (cc == GGML_CUDA_CC_VOLTA) {
+ ggml_cuda_flash_attn_ext_wmma_f16(ctx, dst);
+ }
+
+ ggml_cuda_flash_attn_ext_mma_f16(ctx, dst);
}
+// This file contains primitives that expose the tensor core PTX instructions for CUDA code.
+// The primitives can be used in a similar way as the nvcuda::wmma interface but with a well-defined memory layout.
+// The documentation for the PTX instructions can be found under:
+// https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#matrix-multiply-accumulate-operation-using-mma-instruction
+//
+// Like with nvcuda::wmma there are three types of matrix tiles: A, B, and C with A @ B = C.
+// A is a row-major matrix with shape I x K.
+// B is a column-major matrix with shape K x J.
+// C is a column-major matrix with shape I x J.
+// Note that along their lowest dimension I, J, and K are measured in physical 32 bit elements instead of logical elements.
+// The functions get_i, get_j, and get_k can be used to get the physical 32 bit index of the lth element of a thread within a tile.
+// All matrix tiles have ne physical 32 bit elements per warp.
+//
+// As described in the documentation, all pointers for load_ldmatrix must be to shared memory and aligned to 16 bytes.
+
#include "common.cuh"
-struct mma_int_A_I16K4 {
+
+#if CUDART_VERSION >= 11800
+
+static __device__ __forceinline__ int ggml_cuda_movmatrix(const int x) {
+ int ret = 0;
+
+#ifdef NEW_MMA_AVAILABLE
+ asm("movmatrix.sync.aligned.m8n8.trans.b16 %0, %1;"
+ : "+r"(ret) : "r"(x));
+#else
+ NO_DEVICE_CODE;
+#endif // defined(NEW_MMA_AVAILABLE)
+ return ret;
+}
+
+#else
+
+static __device__ __forceinline__ int ggml_cuda_movmatrix(const int x) {
+ // Imagine transposing row-major matrix to column-major matrix.
+ const int src_i_low = 2 * (threadIdx.x % 4);
+ const int src_i_high = src_i_low + 1;
+ const int src_j = threadIdx.x / 4;
+
+ const int src_laneid_low = src_i_low * 4 + src_j / 2;
+ const int src_laneid_high = src_i_high * 4 + src_j / 2;
+
+ const int shift_low = ((src_j + 0) % 2) * 16;
+ const int shift_high = ((src_j + 1) % 2) * 16;
+
+ const int ret_low = (__shfl_sync(0xFFFFFFFF, x, src_laneid_low, WARP_SIZE) >> shift_low) & 0x0000FFFF;
+ const int ret_high = (__shfl_sync(0xFFFFFFFF, x, src_laneid_high, WARP_SIZE) << shift_high) & 0xFFFF0000;
+
+ return ret_low | ret_high;
+}
+
+#endif // CUDART_VERSION >= 11800
+
+
+template <typename T>
+struct mma_A_I16K4 {
+ static_assert(sizeof(T) == 4, "bad type size");
+
static constexpr int I = 16;
static constexpr int K = 4;
static constexpr int ne = 2;
- int x[ne] = {0};
+ T x[ne];
static __device__ __forceinline__ int get_i(const int l) {
const int ret = (l%2) * (I/2) + threadIdx.x / K;
return ret;
}
- __device__ __forceinline__ void load(const int * __restrict__ xs0, const int & stride) {
-#if defined(INT8_MMA_AVAILABLE)
- const int * xs = xs0 + (threadIdx.x%I)*stride;
- asm("ldmatrix.sync.aligned.m8n8.x2.b16 {%0, %1}, [%2];"
- : "+r"(x[0]), "+r"(x[1])
- : "l"(xs));
-#else
+ __device__ __forceinline__ void load_generic(const T * __restrict__ xs0, const int & stride) {
#pragma unroll
for (int l = 0; l < ne; ++l) {
x[l] = xs0[get_i(l)*stride + get_k(l)];
}
-#endif // defined(INT8_MMA_AVAILABLE)
+ }
+
+ __device__ __forceinline__ void load_ldmatrix(const T * __restrict__ xs0, const int & stride) {
+#ifdef NEW_MMA_AVAILABLE
+ int * xi = (int *) x;
+ const int * xs = (const int *) xs0 + (threadIdx.x%I)*stride;
+ asm("ldmatrix.sync.aligned.m8n8.x2.b16 {%0, %1}, [%2];"
+ : "+r"(xi[0]), "+r"(xi[1])
+ : "l"(xs));
+#else
+ load_generic(xs0, stride);
+#endif // NEW_MMA_AVAILABLE
}
};
-struct mma_int_A_I16K8 {
+template <typename T>
+struct mma_A_I16K8 {
+ static_assert(sizeof(T) == 4, "bad type size");
+
static constexpr int I = 16;
static constexpr int K = 8;
static constexpr int ne = 4;
- int x[ne] = {0};
+ T x[ne];
static __device__ __forceinline__ int get_i(const int l) {
const int ret = (l%2) * (I/2) + threadIdx.x / (K/2);
return ret;
}
- __device__ __forceinline__ void load(const int * __restrict__ xs0, const int & stride) {
-#if defined(INT8_MMA_AVAILABLE)
- const int * xs = xs0 + (threadIdx.x%I)*stride + (threadIdx.x/I)*(K/2);
- asm("ldmatrix.sync.aligned.m8n8.x4.b16 {%0, %1, %2, %3}, [%4];"
- : "+r"(x[0]), "+r"(x[1]), "+r"(x[2]), "+r"(x[3])
- : "l"(xs));
-#else
+ __device__ __forceinline__ void load_generic(const T * __restrict__ xs0, const int & stride) {
#pragma unroll
for (int l = 0; l < ne; ++l) {
x[l] = xs0[get_i(l)*stride + get_k(l)];
}
-#endif // defined(INT8_MMA_AVAILABLE)
}
- __device__ __forceinline__ void load_low(const int * __restrict__ xs0, const int & stride) {
- ((mma_int_A_I16K4 *) x)[0].load(xs0, stride);
+ __device__ __forceinline__ void load_ldmatrix(const T * __restrict__ xs0, const int & stride) {
+#ifdef NEW_MMA_AVAILABLE
+ int * xi = (int * ) x;
+ const int * xs = (const int *) xs0 + (threadIdx.x%I)*stride + (threadIdx.x/I)*(K/2);
+ asm("ldmatrix.sync.aligned.m8n8.x4.b16 {%0, %1, %2, %3}, [%4];"
+ : "+r"(xi[0]), "+r"(xi[1]), "+r"(xi[2]), "+r"(xi[3])
+ : "l"(xs));
+#else
+ GGML_UNUSED(xs0);
+ GGML_UNUSED(stride);
+ NO_DEVICE_CODE;
+#endif // NEW_MMA_AVAILABLE
+ }
+
+ __device__ __forceinline__ void load_ldmatrix_trans(const T * __restrict__ xs0, const int & stride) {
+#ifdef NEW_MMA_AVAILABLE
+ int * xi = (int * ) x;
+ const int * xs = (const int *) xs0 + (threadIdx.x%I)*stride + (threadIdx.x/I)*(K/2);
+ asm("ldmatrix.sync.aligned.m8n8.x4.trans.b16 {%0, %1, %2, %3}, [%4];"
+ : "+r"(xi[0]), "+r"(xi[2]), "+r"(xi[1]), "+r"(xi[3])
+ : "l"(xs));
+#else
+ GGML_UNUSED(xs0);
+ GGML_UNUSED(stride);
+ NO_DEVICE_CODE;
+#endif // NEW_MMA_AVAILABLE
+ }
+
+ __device__ __forceinline__ void transpose() {
+ int * xi = (int *) x;
+ xi[0] = ggml_cuda_movmatrix(xi[0]);
+
+ const int tmp = ggml_cuda_movmatrix(xi[1]);
+ xi[1] = ggml_cuda_movmatrix(xi[2]);
+ xi[2] = tmp;
+
+ xi[3] = ggml_cuda_movmatrix(xi[3]);
}
};
-struct mma_int_B_J8K4 {
+template <typename T>
+struct mma_B_J8K4 {
+ static_assert(sizeof(T) == 4, "bad type size");
+
static constexpr int J = 8;
static constexpr int K = 4;
static constexpr int ne = 1;
- int x[ne] = {0};
+ T x[ne];
static __device__ __forceinline__ int get_j(const int /* l */) {
const int ret = threadIdx.x / K;
return ret;
}
- __device__ __forceinline__ void load(const int * __restrict__ xs0, const int & stride) {
-#if defined(INT8_MMA_AVAILABLE) && false // Loading as 4 byte values is faster
- const int * xs = xs0 + (threadIdx.x%J)*stride;
- asm("ldmatrix.sync.aligned.m8n8.x1.b16 {%0}, [%1];"
- : "+r"(x[0])
- : "l"(xs));
-#else
+ __device__ __forceinline__ void load_generic(const T * __restrict__ xs0, const int & stride) {
#pragma unroll
for (int l = 0; l < ne; ++l) {
x[l] = xs0[get_j(l)*stride + get_k(l)];
}
-#endif // defined(INT8_MMA_AVAILABLE)
+ }
+
+ __device__ __forceinline__ void load_ldmatrix(const T * __restrict__ xs0, const int & stride) {
+#ifdef NEW_MMA_AVAILABLE
+ int * xi = (int *) x;
+ const int * xs = (const int *) xs0 + (threadIdx.x%J)*stride;
+ asm("ldmatrix.sync.aligned.m8n8.x1.b16 {%0}, [%1];"
+ : "+r"(xi[0]) : "l"(xs));
+#else
+ load_generic(xs0, stride);
+#endif // NEW_MMA_AVAILABLE
}
};
-struct mma_int_B_J8K8 {
+template <typename T>
+struct mma_B_J8K8 {
+ static_assert(sizeof(T) == 4, "bad type size");
+
static constexpr int J = 8;
static constexpr int K = 8;
static constexpr int ne = 2;
- int x[ne] = {0};
+ T x[ne];
static __device__ __forceinline__ int get_j(const int /* l */) {
const int ret = threadIdx.x / (K/2);
return ret;
}
- __device__ __forceinline__ void load(const int * __restrict__ xs0, const int & stride) {
-#if defined(INT8_MMA_AVAILABLE) && false // Loading as 4 byte values is faster
- const int * xs = xs0 + (threadIdx.x%J)*stride + ((threadIdx.x/J)*(K/2)) % K;
- asm("ldmatrix.sync.aligned.m8n8.x2.b16 {%0, %1}, [%2];"
- : "+r"(x[0]), "+r"(x[1])
- : "l"(xs));
-#else
+ __device__ __forceinline__ void load_generic(const T * __restrict__ xs0, const int & stride) {
#pragma unroll
for (int l = 0; l < ne; ++l) {
x[l] = xs0[get_j(l)*stride + get_k(l)];
}
-#endif // defined(INT8_MMA_AVAILABLE)
+ }
+
+ __device__ __forceinline__ void load_ldmatrix(const T * __restrict__ xs0, const int & stride) {
+#ifdef NEW_MMA_AVAILABLE
+ int * xi = (int *) x;
+ const int * xs = (const int *) xs0 + (threadIdx.x%J)*stride + ((threadIdx.x/J)*(K/2)) % K;
+ asm("ldmatrix.sync.aligned.m8n8.x2.b16 {%0, %1}, [%2];"
+ : "+r"(xi[0]), "+r"(xi[1])
+ : "l"(xs));
+#else
+ load_generic(xs0, stride);
+#endif // NEW_MMA_AVAILABLE
}
};
-struct mma_int_C_I16J8 {
+template <typename T>
+struct mma_C_I16J8 {};
+
+template <>
+struct mma_C_I16J8<int> {
static constexpr int I = 16;
static constexpr int J = 8;
static constexpr int ne = 4;
return ret;
}
- __device__ __forceinline__ void mma_K4(const mma_int_A_I16K4 & mma_A, const mma_int_B_J8K4 & mma_B) {
-#ifdef INT8_MMA_AVAILABLE
+ __device__ __forceinline__ void mma(const mma_A_I16K4<int> & mma_A, const mma_B_J8K4<int> & mma_B) {
+#ifdef NEW_MMA_AVAILABLE
#if __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE
asm("mma.sync.aligned.m16n8k16.row.col.s32.s8.s8.s32 {%0, %1, %2, %3}, {%4, %5}, {%6}, {%0, %1, %2, %3};"
: "+r"(x[0]), "+r"(x[1]), "+r"(x[2]), "+r"(x[3])
GGML_UNUSED(mma_A);
GGML_UNUSED(mma_B);
NO_DEVICE_CODE;
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
}
- __device__ __forceinline__ void mma_K8(const mma_int_A_I16K8 & mma_A, const mma_int_B_J8K8 & mma_B) {
-#ifdef INT8_MMA_AVAILABLE
+ __device__ __forceinline__ void mma(const mma_A_I16K8<int> & mma_A, const mma_B_J8K8<int> & mma_B) {
+#ifdef NEW_MMA_AVAILABLE
#if __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE
asm("mma.sync.aligned.m16n8k32.row.col.s32.s8.s8.s32 {%0, %1, %2, %3}, {%4, %5, %6, %7}, {%8, %9}, {%0, %1, %2, %3};"
: "+r"(x[0]), "+r"(x[1]), "+r"(x[2]), "+r"(x[3])
GGML_UNUSED(mma_A);
GGML_UNUSED(mma_B);
NO_DEVICE_CODE;
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
+ }
+};
+
+template <>
+struct mma_C_I16J8<half2> {
+ static constexpr int I = 16;
+ static constexpr int J = 4;
+ static constexpr int ne = 2;
+
+ half2 x[ne] = {{0.0f, 0.0f}, {0.0f, 0.0f}};
+
+ static __device__ __forceinline__ int get_i(const int l) {
+ const int ret = l * (I/2) + threadIdx.x / J;
+ GGML_CUDA_ASSUME(ret >= 0);
+ GGML_CUDA_ASSUME(ret < I);
+ return ret;
+ }
+
+ static __device__ __forceinline__ int get_j(const int /* l */) {
+ const int ret = threadIdx.x % J;
+ GGML_CUDA_ASSUME(ret >= 0);
+ GGML_CUDA_ASSUME(ret < J);
+ return ret;
+ }
+
+ __device__ __forceinline__ void mma(const mma_A_I16K8<half2> & mma_A, const mma_B_J8K8<half2> & mma_B) {
+#ifdef NEW_MMA_AVAILABLE
+ int * Axi = (int *) mma_A.x;
+ int * Bxi = (int *) mma_B.x;
+ int * xi = (int *) x;
+#if __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE
+ asm("mma.sync.aligned.m16n8k16.row.col.f16.f16.f16.f16 {%0, %1}, {%2, %3, %4, %5}, {%6, %7}, {%0, %1};"
+ : "+r"(xi[0]), "+r"(xi[1])
+ : "r"(Axi[0]), "r"(Axi[1]), "r"(Axi[2]), "r"(Axi[3]), "r"(Bxi[0]), "r"(Bxi[1]));
+#else
+ // On Turing m16n8k16 mma is not available, use 2x m8n8k8 mma instead:
+ asm("mma.sync.aligned.m16n8k8.row.col.f16.f16.f16.f16 {%0, %1}, {%2, %3}, {%4}, {%0, %1};"
+ : "+r"(xi[0]), "+r"(xi[1])
+ : "r"(Axi[0]), "r"(Axi[1]), "r"(Bxi[0]));
+ asm("mma.sync.aligned.m16n8k8.row.col.f16.f16.f16.f16 {%0, %1}, {%2, %3}, {%4}, {%0, %1};"
+ : "+r"(xi[0]), "+r"(xi[1])
+ : "r"(Axi[2]), "r"(Axi[3]), "r"(Bxi[1]));
+#endif // __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE
+#else
+ GGML_UNUSED(mma_A);
+ GGML_UNUSED(mma_B);
+ NO_DEVICE_CODE;
+#endif // NEW_MMA_AVAILABLE
+ }
+
+ __device__ __forceinline__ mma_B_J8K8<half2> to_mma_B() {
+ mma_B_J8K8<half2> mma_B;
+
+ int * xi = (int *) x;
+ int * Bxi = (int *) mma_B.x;
+ Bxi[0] = ggml_cuda_movmatrix(xi[0]);
+ Bxi[1] = ggml_cuda_movmatrix(xi[1]);
+
+ return mma_B;
+ }
+};
+
+template <>
+struct mma_C_I16J8<float> {
+ static constexpr int I = 16;
+ static constexpr int J = 8;
+ static constexpr int ne = 4;
+
+ float x[ne] = {0.0f, 0.0f, 0.0f, 0.0f};
+
+ static __device__ __forceinline__ int get_i(const int l) {
+ const int ret = (l/2) * (I/2) + threadIdx.x / (J/2);
+ GGML_CUDA_ASSUME(ret >= 0);
+ GGML_CUDA_ASSUME(ret < I);
+ return ret;
+ }
+
+ static __device__ __forceinline__ int get_j(const int l) {
+ const int ret = 2 * (threadIdx.x % (J/2)) + l%2;
+ GGML_CUDA_ASSUME(ret >= 0);
+ GGML_CUDA_ASSUME(ret < J);
+ return ret;
+ }
+
+ __device__ __forceinline__ void mma(const mma_A_I16K8<half2> & mma_A, const mma_B_J8K8<half2> & mma_B) {
+#ifdef NEW_MMA_AVAILABLE
+ int * Axi = (int *) mma_A.x;
+ int * Bxi = (int *) mma_B.x;
+ int * xi = (int *) x;
+#if __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE
+ asm("mma.sync.aligned.m16n8k16.row.col.f32.f16.f16.f32 {%0, %1, %2, %3}, {%4, %5, %6, %7}, {%8, %9}, {%0, %1, %2, %3};"
+ : "+r"(xi[0]), "+r"(xi[1]), "+r"(xi[2]), "+r"(xi[3])
+ : "r"(Axi[0]), "r"(Axi[1]), "r"(Axi[2]), "r"(Axi[3]), "r"(Bxi[0]), "r"(Bxi[1]));
+#else
+ // On Turing m16n8k16 mma is not available, use 2x m8n8k8 mma instead:
+ asm("mma.sync.aligned.m16n8k8.row.col.f32.f16.f16.f32 {%0, %1, %2, %3}, {%4, %5}, {%6}, {%0, %1, %2, %3};"
+ : "+r"(xi[0]), "+r"(xi[1]), "+r"(xi[2]), "+r"(xi[3])
+ : "r"(Axi[0]), "r"(Axi[1]), "r"(Bxi[0]));
+ asm("mma.sync.aligned.m16n8k8.row.col.f32.f16.f16.f32 {%0, %1, %2, %3}, {%4, %5}, {%6}, {%0, %1, %2, %3};"
+ : "+r"(xi[0]), "+r"(xi[1]), "+r"(xi[2]), "+r"(xi[3])
+ : "r"(Axi[2]), "r"(Axi[3]), "r"(Bxi[1]));
+#endif // __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE
+#else
+ GGML_UNUSED(mma_A);
+ GGML_UNUSED(mma_B);
+ NO_DEVICE_CODE;
+#endif // NEW_MMA_AVAILABLE
+ }
+
+ __device__ __forceinline__ mma_B_J8K8<half2> to_mma_B() {
+ mma_B_J8K8<half2> mma_B;
+ mma_B.x[0] = make_half2(x[0], x[1]);
+ mma_B.x[1] = make_half2(x[2], x[3]);
+
+ int * Bxi = (int *) mma_B.x;
+ Bxi[0] = ggml_cuda_movmatrix(Bxi[0]);
+ Bxi[1] = ggml_cuda_movmatrix(Bxi[1]);
+
+ return mma_B;
+ }
+
+ __device__ __forceinline__ void load_generic(const float * __restrict__ xs0, const int & stride) {
+#pragma unroll
+ for (int l = 0; l < ne; ++l) {
+ x[l] = xs0[get_j(l)*stride + get_i(l)];
+ }
}
};
return false;
}
- if (int8_mma_available(cc)) {
+ if (new_mma_available(cc)) {
return true;
}
};
static constexpr int get_mmq_x_max_host(const int cc) {
- return int8_mma_available(cc) ? 128 :
+ return new_mma_available(cc) ? 128 :
#ifdef GGML_CUDA_FORCE_MMQ
cc >= GGML_CUDA_CC_VOLTA && cc < GGML_CUDA_CC_OFFSET_AMD ? 128 : 64;
#else
}
static constexpr __device__ int get_mmq_x_max_device() {
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
return 128;
-#else // INT8_MMA_AVAILABLE
+#else // NEW_MMA_AVAILABLE
#if defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)
return 128;
#endif // __CUDA_ARCH__ >= GGML_CUDA_CC_VOLTA
#endif // defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
}
static constexpr int get_mmq_y_host(const int cc) {
#define MMQ_TILE_Y_K (WARP_SIZE + WARP_SIZE/QI8_1)
static int mmq_get_granularity_host(const int mmq_x, const int cc) {
- return int8_mma_available(cc) && mmq_x >= 48 ? 16 : 8;
+ return new_mma_available(cc) && mmq_x >= 48 ? 16 : 8;
}
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
static constexpr __device__ int mmq_get_granularity_device(const int mmq_x) {
return mmq_x >= 48 ? 16 : 8;
}
static constexpr __device__ int mmq_get_granularity_device(const int /* mmq_x */) {
return 8;
}
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
// ------------------------------------------------------------
template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q4_0(
const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) {
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
int * x_qs = (int *) x_tile;
float * x_df = (float *) (x_qs + 2*WARP_SIZE);
#else
constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_Q4_0, mmq_y);
int * x_qs = (int *) x_tile;
float * x_df = (float *) (x_qs + txs.qs);
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
const int kbx = threadIdx.x / QI4_0;
const int kqsx = threadIdx.x % QI4_0;
const block_q4_0 * bxi = (const block_q4_0 *) x + kbx0 + i*stride + kbx;
const int qs0 = get_int_b2(bxi->qs, kqsx);
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + kbx*(2*QI4_0) + kqsx + 0] = __vsubss4((qs0 >> 0) & 0x0F0F0F0F, 0x08080808);
x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + kbx*(2*QI4_0) + kqsx + QI4_0] = __vsubss4((qs0 >> 4) & 0x0F0F0F0F, 0x08080808);
#else
x_qs[i*(WARP_SIZE + 1) + threadIdx.x] = qs0;
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
}
const int blocks_per_tile_x_row = WARP_SIZE / QI4_0;
const block_q4_0 * bxi = (const block_q4_0 *) x + kbx0 + i*stride + kbxd;
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
x_df[i*MMQ_MMA_TILE_X_K_Q8_0 + kbxd] = bxi->d;
#else
x_df[i*(WARP_SIZE/QI4_0) + i/QI4_0 + kbxd] = bxi->d;
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
}
}
template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q4_1(
const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) {
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
int * x_qs = (int *) x_tile;
half2 * x_dm = (half2 *) (x_qs + 2*WARP_SIZE);
#else
constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_Q4_1, mmq_y);
int * x_qs = (int *) x_tile;
half2 * x_dm = (half2 *) (x_qs + txs.qs);
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
const int kbx = threadIdx.x / QI4_1;
const int kqsx = threadIdx.x % QI4_1;
const block_q4_1 * bxi = (const block_q4_1 *) x + kbx0 + i*stride + kbx;
const int qs0 = get_int_b4(bxi->qs, kqsx);
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
x_qs[i*MMQ_MMA_TILE_X_K_Q8_1 + kbx*(2*QI4_1) + kqsx + 0] = (qs0 >> 0) & 0x0F0F0F0F;
x_qs[i*MMQ_MMA_TILE_X_K_Q8_1 + kbx*(2*QI4_1) + kqsx + QI4_1] = (qs0 >> 4) & 0x0F0F0F0F;
#else
x_qs[i*(WARP_SIZE + 1) + threadIdx.x] = qs0;
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
}
const int blocks_per_tile_x_row = WARP_SIZE / QI4_1;
const block_q4_1 * bxi = (const block_q4_1 *) x + kbx0 + i*stride + kbxd;
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
x_dm[i*MMQ_MMA_TILE_X_K_Q8_1 + kbxd] = bxi->dm;
#else
x_dm[i*(WARP_SIZE/QI4_1) + i/QI4_1 + kbxd] = bxi->dm;
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
}
}
template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q5_0(
const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) {
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
int * x_qs = (int *) x_tile;
float * x_df = (float *) (x_qs + WARP_SIZE*2);
#else
constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_Q5_0, mmq_y);
int * x_qs = (int *) x_tile;
float * x_df = (float *) (x_qs + txs.qs);
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
const int kbx = threadIdx.x / QI5_0;
const int kqsx = threadIdx.x % QI5_0;
qs1 |= (qh << 9) & 0x10000000; // 19 -> 28
qs1 = __vsubss4(qs1, 0x10101010); // subtract 16
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + kbx*(2*QI5_0) + kqsx + 0] = qs0;
x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + kbx*(2*QI5_0) + kqsx + QI5_0] = qs1;
#else
x_qs[i*(2*WARP_SIZE + 1) + kbx*(2*QI5_0) + kqsx + 0] = qs0;
x_qs[i*(2*WARP_SIZE + 1) + kbx*(2*QI5_0) + kqsx + QI5_0] = qs1;
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
}
const int blocks_per_tile_x_row = WARP_SIZE / QI5_0;
const block_q5_0 * bxi = (const block_q5_0 *) x + kbx0 + i*stride + kbxd;
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
x_df[i*MMQ_MMA_TILE_X_K_Q8_0 + kbxd] = bxi->d;
#else
x_df[i*(WARP_SIZE/QI5_0) + i/QI5_0 + kbxd] = bxi->d;
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
}
}
template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q5_1(
const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) {
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
int * x_qs = (int *) x_tile;
half2 * x_dm = (half2 *) (x_qs + 2*WARP_SIZE);
#else
constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_Q5_1, mmq_y);
int * x_qs = (int *) x_tile;
half2 * x_dm = (half2 *) (x_qs + txs.qs);
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
const int kbx = threadIdx.x / QI5_1;
const int kqsx = threadIdx.x % QI5_1;
qs1 |= (qh << 2) & 0x00100000; // 18 -> 20
qs1 |= (qh << 9) & 0x10000000; // 19 -> 28
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
x_qs[i*MMQ_MMA_TILE_X_K_Q8_1 + kbx*(2*QI5_1) + kqsx + 0] = qs0;
x_qs[i*MMQ_MMA_TILE_X_K_Q8_1 + kbx*(2*QI5_1) + kqsx + QI5_1] = qs1;
#else
x_qs[i*(2*WARP_SIZE + 1) + kbx*(2*QI5_1) + kqsx + 0] = qs0;
x_qs[i*(2*WARP_SIZE + 1) + kbx*(2*QI5_1) + kqsx + QI5_1] = qs1;
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
}
const int blocks_per_tile_x_row = WARP_SIZE / QI5_1;
const block_q5_1 * bxi = (const block_q5_1 *) x + kbx0 + i*stride + kbxd;
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
x_dm[i*MMQ_MMA_TILE_X_K_Q8_1 + kbxd] = bxi->dm;
#else
x_dm[i*(WARP_SIZE/QI5_1) + i/QI5_1 + kbxd] = bxi->dm;
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
}
}
template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q8_0(
const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) {
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
int * x_qs = (int *) x_tile;
float * x_df = (float *) (x_tile + 2*WARP_SIZE);
#else
constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_Q8_0, mmq_y);
int * x_qs = (int *) x_tile;
float * x_df = (float *) (x_qs + txs.qs);
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
const int kbx = threadIdx.x / QI8_0;
const int kqsx = threadIdx.x % QI8_0;
const block_q8_0 * bxi = (const block_q8_0 *) x + kbx0 + i*stride + kbx;
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + 0 + threadIdx.x] = get_int_b2(bxi[0].qs, kqsx);
x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + WARP_SIZE + threadIdx.x] = get_int_b2(bxi[WARP_SIZE/QI8_0].qs, kqsx);
#else
x_qs[i*(2*WARP_SIZE + 1) + 0 + threadIdx.x] = get_int_b2(bxi[0].qs, kqsx);
x_qs[i*(2*WARP_SIZE + 1) + WARP_SIZE + threadIdx.x] = get_int_b2(bxi[WARP_SIZE/QI8_0].qs, kqsx);
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
}
const int blocks_per_tile_x_row = 2*WARP_SIZE / QI8_0;
const block_q8_0 * bxi = (const block_q8_0 *) x + kbx0 + i*stride + kbxd;
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
x_df[i*MMQ_MMA_TILE_X_K_Q8_0 + kbxd] = bxi->d;
#else
x_df[i*(2*WARP_SIZE/QI8_0) + i/(QI8_0/2) + kbxd] = bxi->d;
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
}
}
static __device__ __forceinline__ void vec_dot_q8_0_q8_1_mma(
const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k00) {
- typedef mma_int_A_I16K8 mma_A;
- typedef mma_int_B_J8K8 mma_B;
- typedef mma_int_C_I16J8 mma_C;
+ typedef mma_A_I16K8<int> mma_A;
+ typedef mma_B_J8K8<int> mma_B;
+ typedef mma_C_I16J8<int> mma_C;
constexpr int granularity = mmq_get_granularity_device(mmq_x);
constexpr int rows_per_warp = 2 * granularity;
for (int k01 = 0; k01 < WARP_SIZE; k01 += QI8_0) {
const int k0 = k00 + k01;
- A[n][k01/QI8_0].load(x_qs + (i0 + n*mma_A::I)*MMQ_MMA_TILE_X_K_Q8_0 + k0, MMQ_MMA_TILE_X_K_Q8_0);
+ A[n][k01/QI8_0].load_ldmatrix(x_qs + (i0 + n*mma_A::I)*MMQ_MMA_TILE_X_K_Q8_0 + k0, MMQ_MMA_TILE_X_K_Q8_0);
}
#pragma unroll
mma_B B;
float dB[mma_C::ne/2];
- B.load(y_qs + j0*MMQ_TILE_Y_K + k01, MMQ_TILE_Y_K);
+ B.load_generic(y_qs + j0*MMQ_TILE_Y_K + k01, MMQ_TILE_Y_K); // faster than load_ldmatrix
#pragma unroll
for (int l = 0; l < mma_C::ne/2; ++l) {
#pragma unroll
for (int n = 0; n < ntx; ++n) {
mma_C C;
- C.mma_K8(A[n][k01/QI8_0], B);
+ C.mma(A[n][k01/QI8_0], B);
#pragma unroll
for (int l = 0; l < mma_C::ne; ++l) {
static __device__ __forceinline__ void vec_dot_q8_1_q8_1_mma(
const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k00) {
- typedef mma_int_A_I16K8 mma_A;
- typedef mma_int_B_J8K8 mma_B;
- typedef mma_int_C_I16J8 mma_C;
+ typedef mma_A_I16K8<int> mma_A;
+ typedef mma_B_J8K8<int> mma_B;
+ typedef mma_C_I16J8<int> mma_C;
constexpr int granularity = mmq_get_granularity_device(mmq_x);
constexpr int rows_per_warp = 2 * granularity;
for (int k01 = 0; k01 < WARP_SIZE; k01 += QI8_1) {
const int k0 = k00 + k01;
- A[n][k01/QI8_1].load(x_qs + (i0 + n*mma_A::I)*MMQ_MMA_TILE_X_K_Q8_1 + k0, MMQ_MMA_TILE_X_K_Q8_1);
+ A[n][k01/QI8_1].load_ldmatrix(x_qs + (i0 + n*mma_A::I)*MMQ_MMA_TILE_X_K_Q8_1 + k0, MMQ_MMA_TILE_X_K_Q8_1);
}
#pragma unroll
mma_B B;
float2 dsB[mma_C::ne/2];
- B.load(y_qs + j0*MMQ_TILE_Y_K + k01, MMQ_TILE_Y_K);
+ B.load_generic(y_qs + j0*MMQ_TILE_Y_K + k01, MMQ_TILE_Y_K); // faster than load_ldmatrix
#pragma unroll
for (int l = 0; l < mma_C::ne/2; ++l) {
#pragma unroll
for (int n = 0; n < ntx; ++n) {
mma_C C;
- C.mma_K8(A[n][k01/QI8_1], B);
+ C.mma(A[n][k01/QI8_1], B);
#pragma unroll
for (int l = 0; l < mma_C::ne; ++l) {
template <int mmq_x, int mmq_y, int nwarps>
static __device__ __forceinline__ void vec_dot_q8_0_16_q8_1_mma(
const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k00) {
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
- typedef mma_int_A_I16K4 mma_A;
- typedef mma_int_A_I16K8 mma_A_K8;
- typedef mma_int_B_J8K4 mma_B;
- typedef mma_int_C_I16J8 mma_C;
+ typedef mma_A_I16K4<int> mma_A;
+ typedef mma_A_I16K8<int> mma_A_K8;
+ typedef mma_B_J8K4<int> mma_B;
+ typedef mma_C_I16J8<int> mma_C;
constexpr int granularity = mmq_get_granularity_device(mmq_x);
constexpr int rows_per_warp = 2 * granularity;
for (int k01 = 0; k01 < WARP_SIZE; k01 += 8) {
const int k0 = k00 + k01;
- ((mma_A_K8 *) A[n])[k01/8].load(x_qs + (i0 + n*mma_A::I)*MMQ_MMA_TILE_X_K_Q3_K + k0, MMQ_MMA_TILE_X_K_Q3_K);
+ ((mma_A_K8 *) A[n])[k01/8].load_ldmatrix(x_qs + (i0 + n*mma_A::I)*MMQ_MMA_TILE_X_K_Q3_K + k0, MMQ_MMA_TILE_X_K_Q3_K);
}
#pragma unroll
mma_B B[2];
float dB[mma_C::ne/2];
- B[0].load(y_qs + j0*MMQ_TILE_Y_K + (k01 + 0), MMQ_TILE_Y_K);
- B[1].load(y_qs + j0*MMQ_TILE_Y_K + (k01 + mma_B::K), MMQ_TILE_Y_K);
+ // Here load_generic is faster than load_ldmatrix.
+ B[0].load_generic(y_qs + j0*MMQ_TILE_Y_K + (k01 + 0), MMQ_TILE_Y_K);
+ B[1].load_generic(y_qs + j0*MMQ_TILE_Y_K + (k01 + mma_B::K), MMQ_TILE_Y_K);
#pragma unroll
for (int l = 0; l < mma_C::ne/2; ++l) {
#pragma unroll
for (int n = 0; n < ntx; ++n) {
mma_C C[2];
- C[0].mma_K4(A[n][k01/4 + 0], B[0]);
- C[1].mma_K4(A[n][k01/4 + 1], B[1]);
+ C[0].mma(A[n][k01/4 + 0], B[0]);
+ C[1].mma(A[n][k01/4 + 1], B[1]);
#pragma unroll
for (int l = 0; l < mma_C::ne; ++l) {
#else
GGML_UNUSED(x); GGML_UNUSED(y); GGML_UNUSED(sum);
NO_DEVICE_CODE;
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
}
template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q2_K(
const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) {
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
int * x_qs = (int *) x_tile;
half2 * x_dm = (half2 *) (x_qs + 2*WARP_SIZE);
#else
constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_Q2_K, mmq_y);
int * x_qs = (int *) x_tile;
half2 * x_dm = (half2 *) (x_qs + txs.qs);
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
const int kqsx = threadIdx.x % QI2_K;
const int x_qs_k = (x_ql_0 >> (2*l)) & 0x03030303;
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
x_qs[i*MMQ_MMA_TILE_X_K_Q2_K + k] = x_qs_k;
#else
x_qs[i*(2*WARP_SIZE + 1) + k] = x_qs_k;
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
}
const int sc_m = bxi->scales[kqsx];
const half2 x_dm_ik = make_half2(bxi_dmf.x*(sc_m & 0x0F), bxi_dmf.y*(sc_m >> 4));
#endif // FAST_FP16_AVAILABLE
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
x_dm[i*MMQ_MMA_TILE_X_K_Q2_K + kqsx] = x_dm_ik;
#else
x_dm[i*(WARP_SIZE + 1) + kqsx] = x_dm_ik;
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
}
}
template <int mmq_x, int mmq_y, int nwarps>
static __device__ __forceinline__ void vec_dot_q2_K_q8_1_mma(
const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k00) {
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
- typedef mma_int_A_I16K4 mma_A;
- typedef mma_int_A_I16K8 mma_A_K8;
- typedef mma_int_B_J8K4 mma_B;
- typedef mma_int_C_I16J8 mma_C;
+ typedef mma_A_I16K4<int> mma_A;
+ typedef mma_A_I16K8<int> mma_A_K8;
+ typedef mma_B_J8K4<int> mma_B;
+ typedef mma_C_I16J8<int> mma_C;
constexpr int granularity = mmq_get_granularity_device(mmq_x);
constexpr int rows_per_warp = 2 * granularity;
for (int k01 = 0; k01 < WARP_SIZE; k01 += QI8_1) {
const int k0 = k00 + k01;
- ((mma_A_K8 *) A[n])[k01/QI8_1].load(x_qs + (i0 + n*mma_A::I)*MMQ_MMA_TILE_X_K_Q2_K + k0, MMQ_MMA_TILE_X_K_Q2_K);
+ ((mma_A_K8 *) A[n])[k01/QI8_1].load_ldmatrix(x_qs + (i0 + n*mma_A::I)*MMQ_MMA_TILE_X_K_Q2_K + k0, MMQ_MMA_TILE_X_K_Q2_K);
}
}
for (int k01 = 0; k01 < WARP_SIZE; k01 += QI8_1) {
mma_B B[2];
- B[0].load(y_qs + j0*MMQ_TILE_Y_K + (k01 + 0), MMQ_TILE_Y_K);
- B[1].load(y_qs + j0*MMQ_TILE_Y_K + (k01 + mma_B::K), MMQ_TILE_Y_K);
+ // Here load_generic is faster than load_ldmatrix.
+ B[0].load_generic(y_qs + j0*MMQ_TILE_Y_K + (k01 + 0), MMQ_TILE_Y_K);
+ B[1].load_generic(y_qs + j0*MMQ_TILE_Y_K + (k01 + mma_B::K), MMQ_TILE_Y_K);
mma_C Cm[2];
if (k01 >= WARP_SIZE * 3/4) {
mma_A A1;
A1.x[0] = 0x01010101;
A1.x[1] = 0x01010101;
- Cm[0].mma_K4(A1, B[0]);
- Cm[1].mma_K4(A1, B[1]);
+ Cm[0].mma(A1, B[0]);
+ Cm[1].mma(A1, B[1]);
}
#pragma unroll
for (int n = 0; n < ntx; ++n) {
mma_C Cd[2];
- Cd[0].mma_K4(A[n][k01/4 + 0], B[0]);
- Cd[1].mma_K4(A[n][k01/4 + 1], B[1]);
+ Cd[0].mma(A[n][k01/4 + 0], B[0]);
+ Cd[1].mma(A[n][k01/4 + 1], B[1]);
#pragma unroll
for (int l = 0; l < mma_C::ne; ++l) {
#else
GGML_UNUSED(x); GGML_UNUSED(y); GGML_UNUSED(sum);
NO_DEVICE_CODE;
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
}
template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q3_K(
const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) {
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
int * x_qs = (int *) x_tile;
float * x_df = (float *) (x_qs + WARP_SIZE*2);
#else
int * x_qs = (int *) x_tile;
float * x_df = (float *) (x_qs + txs.qs);
int * x_sc = (int *) (x_df + txs.dm);
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
const int kqsx = threadIdx.x % QI3_K;
const int x_qs_k = __vsubss4(x_ql_k | x_qh_k, 0x04040404);
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
x_qs[i*MMQ_MMA_TILE_X_K_Q3_K + k] = x_qs_k;
#else
x_qs[i*(2*WARP_SIZE + 1) + k] = x_qs_k;
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
}
}
const int sc = __vsubss4(sc_low | sc_high, 0x20202020);
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
const int8_t * sc8 = (const int8_t *) ≻
const float d = bxi->d;
}
#else
x_sc[i*(WARP_SIZE/8) + i/8 + threadIdx.x % (WARP_SIZE/8)] = sc;
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
}
-#ifndef INT8_MMA_AVAILABLE
+#ifndef NEW_MMA_AVAILABLE
#pragma unroll
for (int i0 = 0; i0 < mmq_y; i0 += nwarps*WARP_SIZE) {
int i = (i0 + threadIdx.y*WARP_SIZE + threadIdx.x) % mmq_y;
x_df[i] = bxi->d;
}
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
}
template <int mmq_x, int mmq_y, int nwarps>
template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q4_K(
const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) {
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
int * x_qs = (int *) x_tile;
half2 * x_dm = (half2 *) (x_qs + 2*WARP_SIZE);
#else
int * x_qs = (int *) x_tile;
half2 * x_dm = (half2 *) (x_qs + txs.qs);
int * x_sc = (int *) (x_dm + txs.dm);
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
#pragma unroll
for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
const block_q4_K * bxi = (const block_q4_K *) x + kbx0 + i*stride;
const int qs0 = get_int_b4(bxi->qs, threadIdx.x);
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
x_qs[i*MMQ_MMA_TILE_X_K_Q8_1 + 16*(threadIdx.x/8) + threadIdx.x % 8 + 0] = (qs0 >> 0) & 0x0F0F0F0F;
x_qs[i*MMQ_MMA_TILE_X_K_Q8_1 + 16*(threadIdx.x/8) + threadIdx.x % 8 + 8] = (qs0 >> 4) & 0x0F0F0F0F;
#else
x_qs[i*(WARP_SIZE + 1) + threadIdx.x] = qs0;
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
}
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
#pragma unroll
for (int i0 = 0; i0 < mmq_y; i0 += nwarps*16) {
x_sc[i*(WARP_SIZE/8) + i/8 + ksc] = scales8;
}
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
}
template <int mmq_x, int mmq_y, int nwarps>
template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q5_K(
const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) {
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
int * x_qs = (int *) x_tile;
half2 * x_dm = (half2 *) (x_qs + WARP_SIZE*2);
#else
int * x_qs = (int *) x_tile;
half2 * x_dm = (half2 *) (x_qs + txs.qs);
int * x_sc = (int *) (x_dm + txs.dm);
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
#pragma unroll
for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
const int kq0 = ky - ky % (QI5_K/2) + threadIdx.x % (QI5_K/4) + 0;
const int kq1 = ky - ky % (QI5_K/2) + threadIdx.x % (QI5_K/4) + QI5_K/4;
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
x_qs[i*MMQ_MMA_TILE_X_K_Q8_1 + kq0] = ql0 | qh0;
x_qs[i*MMQ_MMA_TILE_X_K_Q8_1 + kq1] = ql1 | qh1;
#else
x_qs[i*(2*WARP_SIZE + 1) + kq0] = ql0 | qh0;
x_qs[i*(2*WARP_SIZE + 1) + kq1] = ql1 | qh1;
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
}
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
#pragma unroll
for (int i0 = 0; i0 < mmq_y; i0 += nwarps*16) {
x_sc[i*(WARP_SIZE/8) + i/8 + ksc] = scales8;
}
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
}
template <int mmq_x, int mmq_y, int nwarps>
template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_q6_K(
const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) {
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
int * x_qs = (int *) x_tile;
float * x_df = (float *) (x_qs + WARP_SIZE*2);
int * x_sc = (int *) (x_df + WARP_SIZE/QI6_K);
int * x_qs = (int *) x_tile;
float * x_df = (float *) (x_qs + txs.qs);
int * x_sc = (int *) (x_df + txs.dm);
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
#pragma unroll
for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
const int kq0 = 2*threadIdx.x - threadIdx.x % (QI6_K/2) + 0;
const int kq1 = 2*threadIdx.x - threadIdx.x % (QI6_K/2) + QI6_K/2;
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
x_qs[i*MMQ_MMA_TILE_X_K_Q6_K + kq0] = __vsubss4(ql0 | qh0, 0x20202020);
x_qs[i*MMQ_MMA_TILE_X_K_Q6_K + kq1] = __vsubss4(ql1 | qh1, 0x20202020);
#else
x_qs[i*(2*WARP_SIZE + 1) + kq0] = __vsubss4(ql0 | qh0, 0x20202020);
x_qs[i*(2*WARP_SIZE + 1) + kq1] = __vsubss4(ql1 | qh1, 0x20202020);
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
}
const int blocks_per_tile_x_row = WARP_SIZE / QI6_K; // == 1 if QK_K == 256
const block_q6_K * bxi = (const block_q6_K *) x + kbx0 + i*stride + kbxd;
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
x_df[i*MMQ_MMA_TILE_X_K_Q6_K + kbxd] = bxi->d;
#else
x_df[i*(WARP_SIZE/QI6_K) + i/QI6_K + kbxd] = bxi->d;
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
}
#pragma unroll
const block_q6_K * bxi = (const block_q6_K *) x + kbx0 + i*stride + (threadIdx.x % (WARP_SIZE/8)) / 4;
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
x_sc[i*MMQ_MMA_TILE_X_K_Q6_K + threadIdx.x % (WARP_SIZE/8)] = get_int_b2(bxi->scales, threadIdx.x % (QI6_K/8));
#else
x_sc[i*(WARP_SIZE/8) + i/8 + threadIdx.x % (WARP_SIZE/8)] = get_int_b2(bxi->scales, threadIdx.x % (QI6_K/8));
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
}
}
template <int mmq_x, int mmq_y, int nwarps>
static __device__ __forceinline__ void vec_dot_q6_K_q8_1_mma(
const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int & k00) {
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
- typedef mma_int_A_I16K4 mma_A;
- typedef mma_int_B_J8K4 mma_B;
- typedef mma_int_C_I16J8 mma_C;
+ typedef mma_A_I16K4<int> mma_A;
+ typedef mma_B_J8K4<int> mma_B;
+ typedef mma_C_I16J8<int> mma_C;
constexpr int granularity = mmq_get_granularity_device(mmq_x);
constexpr int rows_per_warp = 2 * granularity;
for (int k01 = 0; k01 < WARP_SIZE; k01 += 8) {
const int k0 = k00 + k01;
- A[n][k01/4 + 0].load(x_qs + (i0 + n*mma_A::I)*MMQ_MMA_TILE_X_K_Q6_K + (k0 + 0), MMQ_MMA_TILE_X_K_Q6_K);
- A[n][k01/4 + 1].load(x_qs + (i0 + n*mma_A::I)*MMQ_MMA_TILE_X_K_Q6_K + (k0 + mma_A::K), MMQ_MMA_TILE_X_K_Q6_K);
+ A[n][k01/4 + 0].load_ldmatrix(x_qs + (i0 + n*mma_A::I)*MMQ_MMA_TILE_X_K_Q6_K + (k0 + 0), MMQ_MMA_TILE_X_K_Q6_K);
+ A[n][k01/4 + 1].load_ldmatrix(x_qs + (i0 + n*mma_A::I)*MMQ_MMA_TILE_X_K_Q6_K + (k0 + mma_A::K), MMQ_MMA_TILE_X_K_Q6_K);
}
#pragma unroll
mma_B B[2];
float dB[mma_C::ne/2];
- B[0].load(y_qs + j0*MMQ_TILE_Y_K + 0 + k01, MMQ_TILE_Y_K);
- B[1].load(y_qs + j0*MMQ_TILE_Y_K + mma_B::K + k01, MMQ_TILE_Y_K);
+ // Here load_generic is faster than load_ldmatrix.
+ B[0].load_generic(y_qs + j0*MMQ_TILE_Y_K + 0 + k01, MMQ_TILE_Y_K);
+ B[1].load_generic(y_qs + j0*MMQ_TILE_Y_K + mma_B::K + k01, MMQ_TILE_Y_K);
#pragma unroll
for (int l = 0; l < mma_C::ne/2; ++l) {
#pragma unroll
for (int n = 0; n < ntx; ++n) {
mma_C C[2];
- C[0].mma_K4(A[n][k01/4 + 0], B[0]);
- C[1].mma_K4(A[n][k01/4 + 1], B[1]);
+ C[0].mma(A[n][k01/4 + 0], B[0]);
+ C[1].mma(A[n][k01/4 + 1], B[1]);
#pragma unroll
for (int l = 0; l < mma_C::ne; ++l) {
#else
GGML_UNUSED(x); GGML_UNUSED(y); GGML_UNUSED(sum);
NO_DEVICE_CODE;
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
}
template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_iq4_nl(
const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) {
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
int * x_qs = (int *) x_tile;
float * x_df = (float *) (x_qs + WARP_SIZE*2);
#else
constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_IQ4_NL, mmq_y);
int * x_qs = (int *) x_tile;
float * x_df = (float *) (x_qs + txs.qs);
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
const int kbx = threadIdx.x / QI4_NL;
const int kqsx = threadIdx.x % QI4_NL;
const int aux_q4 = get_int_b2(bxi->qs, kqsx);
const int2 v = get_int_from_table_16(aux_q4);
const int k0 = 8 * (threadIdx.x / 4) + threadIdx.x % 4;
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + k0 + 0] = v.x;
x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + k0 + 4] = v.y;
#else
x_qs[i*(2*WARP_SIZE + 1) + k0 + 0] = v.x;
x_qs[i*(2*WARP_SIZE + 1) + k0 + 4] = v.y;
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
}
const int blocks_per_tile_x_row = WARP_SIZE / QI4_NL;
const block_iq4_nl * bxi = (const block_iq4_nl *) x + kbx0 + i*stride + kbxd;
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
x_df[i*MMQ_MMA_TILE_X_K_Q8_0 + kbxd] = __half2float(bxi->d);
#else
x_df[i*(WARP_SIZE/4) + i/4 + kbxd] = __half2float(bxi->d);
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
}
}
template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_iq2_xxs(
const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) {
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
int * x_qs = (int *) x_tile;
float * x_df = (float *) (x_qs + WARP_SIZE*2);
#else
constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_IQ2_XXS, mmq_y);
int * x_qs = (int *) x_tile;
float * x_df = (float *) (x_qs + txs.qs);
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
const int kqsx = threadIdx.x % (QI2_XXS/2);
const int signs1 = __vcmpne4(((signs_packed & 0x30) << 3) | ((signs_packed & 0xC0) << 17), 0x00000000);
const int grid1 = __vsub4(grid_pos[1] ^ signs1, signs1);
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + 8*kqsx + (2*l + 0)] = grid0;
x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + 8*kqsx + (2*l + 1)] = grid1;
#else
x_qs[i*(2*WARP_SIZE + 1) + 8*kqsx + (2*l + 0)] = grid0;
x_qs[i*(2*WARP_SIZE + 1) + 8*kqsx + (2*l + 1)] = grid1;
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
}
const int ls = aux32 >> 28;
const float d = bxi->d;
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
x_df[i*MMQ_MMA_TILE_X_K_Q8_0 + kqsx] = (ls*d + d/2)/4;
#else
x_df[i*(WARP_SIZE/4) + i/4 + kqsx] = (ls*d + d/2)/4;
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
}
}
template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_iq2_xs(
const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) {
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
int * x_qs = (int *) x_tile;
float * x_df = (float *) (x_qs + WARP_SIZE*2);
#else
constexpr tile_x_sizes txs = MMQ_DP4A_TXS_Q8_0_16;
int * x_qs = (int *) x_tile;
float * x_df = (float *) (x_qs + txs.qs);
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
const int kqsx = threadIdx.x % (QI2_XS/2);
const int grid_l = __vsub4(grid_pos[0] ^ signs[0], signs[0]);
const int grid_h = __vsub4(grid_pos[1] ^ signs[1], signs[1]);
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
x_qs[i*MMQ_MMA_TILE_X_K_Q3_K + 8*kqsx + (2*l + 0)] = grid_l;
x_qs[i*MMQ_MMA_TILE_X_K_Q3_K + 8*kqsx + (2*l + 1)] = grid_h;
#else
x_qs[i*(2*WARP_SIZE + 1) + 8*kqsx + (2*l + 0)] = grid_l;
x_qs[i*(2*WARP_SIZE + 1) + 8*kqsx + (2*l + 1)] = grid_h;
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
}
const int ls = bxi->scales[kqsx];
const float d = bxi->d;
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
x_df[i*MMQ_MMA_TILE_X_K_Q3_K + 2*kqsx+0] = ((ls & 0x0F)*d + d/2)/4;
x_df[i*MMQ_MMA_TILE_X_K_Q3_K + 2*kqsx+1] = ((ls >> 4)*d + d/2)/4;
#else
x_df[i*(2*WARP_SIZE*2/QI8_0) + i/(QI8_0/4) + 2*kqsx+0] = ((ls & 0x0F)*d + d/2)/4;
x_df[i*(2*WARP_SIZE*2/QI8_0) + i/(QI8_0/4) + 2*kqsx+1] = ((ls >> 4)*d + d/2)/4;
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
}
}
template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_iq2_s(
const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) {
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
int * x_qs = (int *) x_tile;
float * x_df = (float *) (x_qs + WARP_SIZE*2);
#else
constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_IQ2_S, mmq_y);
int * x_qs = (int *) x_tile;
float * x_df = (float *) (x_qs + txs.qs);
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
const int kqsx = threadIdx.x % (QI2_S/2);
const int grid_l = __vsub4(grid_pos[0] ^ signs0, signs0);
const int grid_h = __vsub4(grid_pos[1] ^ signs1, signs1);
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
x_qs[i*MMQ_MMA_TILE_X_K_Q3_K + 8*kqsx + (2*l + 0)] = grid_l;
x_qs[i*MMQ_MMA_TILE_X_K_Q3_K + 8*kqsx + (2*l + 1)] = grid_h;
#else
x_qs[i*(2*WARP_SIZE + 1) + 8*kqsx + (2*l + 0)] = grid_l;
x_qs[i*(2*WARP_SIZE + 1) + 8*kqsx + (2*l + 1)] = grid_h;
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
}
const int ls = bxi->scales[kqsx];
const float d = bxi->d;
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
x_df[i*MMQ_MMA_TILE_X_K_Q3_K + 2*kqsx+0] = ((ls & 0x0F)*d + d/2)/4;
x_df[i*MMQ_MMA_TILE_X_K_Q3_K + 2*kqsx+1] = ((ls >> 4)*d + d/2)/4;
#else
x_df[i*(2*WARP_SIZE*2/QI8_0) + i/(QI8_0/4) + 2*kqsx+0] = ((ls & 0x0F)*d + d/2)/4;
x_df[i*(2*WARP_SIZE*2/QI8_0) + i/(QI8_0/4) + 2*kqsx+1] = ((ls >> 4)*d + d/2)/4;
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
}
}
template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_iq3_xxs(
const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) {
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
int * x_qs = (int *) x_tile;
float * x_df = (float *) (x_qs + WARP_SIZE*2);
#else
constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_IQ3_XXS, mmq_y);
int * x_qs = (int *) x_tile;
float * x_df = (float *) (x_qs + txs.qs);
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
const int kqsx = threadIdx.x % (QI3_XXS/2);
const int grid_l = __vsub4(grid_pos.x ^ signs[0], signs[0]);
const int grid_h = __vsub4(grid_pos.y ^ signs[1], signs[1]);
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + 8*kqsx + (2*l + 0)] = grid_l;
x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + 8*kqsx + (2*l + 1)] = grid_h;
#else
x_qs[i*(2*WARP_SIZE + 1) + 8*kqsx + (2*l + 0)] = grid_l;
x_qs[i*(2*WARP_SIZE + 1) + 8*kqsx + (2*l + 1)] = grid_h;
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
}
const int ls = aux32 >> 28;
const float d = bxi->d;
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
x_df[i*MMQ_MMA_TILE_X_K_Q8_0 + kqsx] = (ls*d + d/2)/2;
#else
x_df[i*(WARP_SIZE/4) + i/4 + kqsx] = (ls*d + d/2)/2;
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
}
}
template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_iq3_s(
const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) {
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
int * x_qs = (int *) x_tile;
float * x_df = (float *) (x_qs + WARP_SIZE*2);
#else
constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_IQ3_S, mmq_y);
int * x_qs = (int *) x_tile;
float * x_df = (float *) (x_qs + txs.qs);
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
const int kqsx = threadIdx.x % (QI3_S/2);
const int grid_l = __vsub4(grid_pos.x ^ signs0, signs0);
const int grid_h = __vsub4(grid_pos.y ^ signs1, signs1);
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + 8*kqsx + (2*l+0)] = grid_l;
x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + 8*kqsx + (2*l+1)] = grid_h;
#else
x_qs[i*(2*WARP_SIZE + 1) + 8*kqsx + (2*l+0)] = grid_l;
x_qs[i*(2*WARP_SIZE + 1) + 8*kqsx + (2*l+1)] = grid_h;
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
}
const int ls = 1 + 2*((bxi->scales[kqsx/2] >> (((2*kqsx) << 1) & 0x04)) & 0x0F);
const float d = bxi->d;
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
x_df[i*MMQ_MMA_TILE_X_K_Q8_0 + kqsx] = ls*d;
#else
x_df[i*(WARP_SIZE/4) + i/4 + kqsx] = ls*d;
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
}
}
template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_iq1_s(
const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) {
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
int * x_qs = (int *) x_tile;
half2 * x_ds = (half2 *) (x_qs + WARP_SIZE*2);
#else
constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_IQ3_S, mmq_y);
int * x_qs = (int *) x_tile;
half2 * x_ds = (half2 *) (x_qs + txs.qs);
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
const int kqsx = threadIdx.x % QI1_S;
const int grid0 = (grid >> 0) & 0x0F0F0F0F;
const int grid1 = (grid >> 4) & 0x0F0F0F0F;
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
x_qs[i*MMQ_MMA_TILE_X_K_Q8_1 + 8*kqsx + (2*l+0)] = grid0;
x_qs[i*MMQ_MMA_TILE_X_K_Q8_1 + 8*kqsx + (2*l+1)] = grid1;
#else
x_qs[i*(2*WARP_SIZE + 1) + 8*kqsx + (2*l+0)] = grid0;
x_qs[i*(2*WARP_SIZE + 1) + 8*kqsx + (2*l+1)] = grid1;
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
}
const float d1q = __half2float(bxi->d) * (((qh >> 11) & 0x0E) + 1);
const float delta = -1.0f + IQ1S_DELTA - (qh & 0x8000) * (2.0f*IQ1S_DELTA/0x8000);
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
x_ds[i*MMQ_MMA_TILE_X_K_Q8_1 + kqsx] = make_half2(d1q, d1q*delta);
#else
x_ds[i*(WARP_SIZE/4) + i/4 + kqsx] = make_half2(d1q, d1q*delta);
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
}
}
template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinline__ void load_tiles_iq4_xs(
const char * __restrict__ x, int * __restrict__ x_tile, const int & kbx0, const int & i_max, const int & stride) {
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
int * x_qs = (int *) x_tile;
float * x_df = (float *) (x_qs + WARP_SIZE*2);
#else
constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_IQ4_XS, mmq_y);
int * x_qs = (int *) x_tile;
float * x_df = (float *) (x_qs + txs.qs);
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
const int kbx = 0; // threadIdx.x / QI4_XS
const int kqsx = threadIdx.x; // threadIdx.x % QI4_XS
const int aux_q4 = get_int_b4(bxi->qs, kqsx);
const int2 v = get_int_from_table_16(aux_q4);
const int k0 = 8 * (threadIdx.x / 4) + threadIdx.x % 4;
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + k0 + 0] = v.x;
x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + k0 + 4] = v.y;
#else
x_qs[i*(2*WARP_SIZE + 1) + k0 + 0] = v.x;
x_qs[i*(2*WARP_SIZE + 1) + k0 + 4] = v.y;
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
}
#pragma unroll
const int ls = ((bxi->scales_l[(threadIdx.x % 8)/2] >> (4*(threadIdx.x % 2))) & 0x0F)
| (((bxi->scales_h >> (2*(threadIdx.x % 8))) & 0x03) << 4);
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
x_df[i*MMQ_MMA_TILE_X_K_Q8_0 + threadIdx.x % 8] = d * (ls - 32);
#else
x_df[i*(WARP_SIZE/4) + i/4 + threadIdx.x % 8] = d * (ls - 32);
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
}
}
static __device__ __forceinline__ void mmq_write_back_mma(
const float * __restrict__ sum, float * __restrict__ dst, const int & stride, const int & i_max, const int & j_max) {
- typedef mma_int_C_I16J8 mma_C;
+ typedef mma_C_I16J8<int> mma_C;
constexpr int granularity = mmq_get_granularity_device(mmq_x);
constexpr int rows_per_warp = 2 * granularity;
constexpr int ntx = rows_per_warp/mma_C::I; // Number of x minitiles per warp.
const int i0 = (threadIdx.y / ntx) * (ntx*mma_C::I);
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
static_assert(nwarps*mma_C::I == mmq_y, "nwarps*mma_C::I != mmq_y");
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
#pragma unroll
for (int j0 = 0; j0 < mmq_x; j0 += ntx*mma_C::J) {
int * tile_y = (int *) data_mul_mat_q;
int * tile_x = tile_y + GGML_PAD(mmq_x*(WARP_SIZE + WARP_SIZE/QI8_1), nwarps*WARP_SIZE);
-#ifdef INT8_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
constexpr vec_dot_mmq_t vec_dot = mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, type>::vec_dot_mma;
constexpr mmq_write_back_t write_back = mmq_write_back_mma<mmq_x, mmq_y, nwarps, need_check>;
#else
constexpr vec_dot_mmq_t vec_dot = mmq_type_traits<mmq_x, mmq_y, nwarps, need_check, type>::vec_dot_dp4a;
constexpr mmq_write_back_t write_back = mmq_write_back_dp4a<mmq_x, mmq_y, nwarps, need_check>;
-#endif // INT8_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
constexpr int blocks_per_iter = MMQ_ITER_K / qk;
const int jt = kbc / (blocks_per_ne00*nty);
const int it = (kbc - jt*(blocks_per_ne00*nty)) / blocks_per_ne00;
- constexpr bool fixup = true; // Last index writes it data to fixup buffer to avoid data races with other blocks.
+ constexpr bool fixup = true; // Last index writes its data to fixup buffer to avoid data races with other blocks.
mul_mat_q_process_tile<type, mmq_x, nwarps, need_check, fixup>
(x, yc, dst, tmp_fixup, ne00, ne01, stride01, ne10, ne11, stride11, ne0,
it, jt, kb0_start, kb0_stop);
static int mmq_get_shmem(const int mmq_x, const int mmq_y, const int cc) {
const tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(type, mmq_y);
const int mmq_tile_x_k = mmq_get_mma_tile_x_k(type);
- const int shmem_x = int8_mma_available(cc) ? mmq_y*mmq_tile_x_k*sizeof(int) : txs.qs*sizeof(int) + txs.dm*sizeof(half2) + txs.sc*sizeof(int);
+ const int shmem_x = new_mma_available(cc) ? mmq_y*mmq_tile_x_k*sizeof(int) : txs.qs*sizeof(int) + txs.dm*sizeof(half2) + txs.sc*sizeof(int);
const int shmem_y = mmq_x*sizeof(block_q8_1_mmq);
return shmem_x + GGML_PAD(shmem_y, MMQ_NWARPS*WARP_SIZE*sizeof(int));
}
--- /dev/null
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-mma-f16.cuh"
+
+DECL_FATTN_MMA_F16_CASE(64, 16);
+DECL_FATTN_MMA_F16_CASE(80, 16);
+DECL_FATTN_MMA_F16_CASE(96, 16);
+DECL_FATTN_MMA_F16_CASE(112, 16);
+DECL_FATTN_MMA_F16_CASE(128, 16);
+DECL_FATTN_MMA_F16_CASE(256, 16);
--- /dev/null
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-mma-f16.cuh"
+
+DECL_FATTN_MMA_F16_CASE(64, 32);
+DECL_FATTN_MMA_F16_CASE(80, 32);
+DECL_FATTN_MMA_F16_CASE(96, 32);
+DECL_FATTN_MMA_F16_CASE(112, 32);
+DECL_FATTN_MMA_F16_CASE(128, 32);
+DECL_FATTN_MMA_F16_CASE(256, 32);
--- /dev/null
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-mma-f16.cuh"
+
+DECL_FATTN_MMA_F16_CASE(64, 64);
+DECL_FATTN_MMA_F16_CASE(80, 64);
+DECL_FATTN_MMA_F16_CASE(96, 64);
+DECL_FATTN_MMA_F16_CASE(112, 64);
+DECL_FATTN_MMA_F16_CASE(128, 64);
+DECL_FATTN_MMA_F16_CASE(256, 64);
--- /dev/null
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../fattn-mma-f16.cuh"
+
+DECL_FATTN_MMA_F16_CASE(64, 8);
+DECL_FATTN_MMA_F16_CASE(80, 8);
+DECL_FATTN_MMA_F16_CASE(96, 8);
+DECL_FATTN_MMA_F16_CASE(112, 8);
+DECL_FATTN_MMA_F16_CASE(128, 8);
+DECL_FATTN_MMA_F16_CASE(256, 8);
+++ /dev/null
-// This file has been autogenerated by generate_cu_files.py, do not edit manually.
-
-#include "../fattn-wmma-f16.cuh"
-
-DECL_FATTN_WMMA_F16_CASE(64, 16, float);
-DECL_FATTN_WMMA_F16_CASE(80, 16, float);
-DECL_FATTN_WMMA_F16_CASE(96, 16, float);
-DECL_FATTN_WMMA_F16_CASE(112, 16, float);
-DECL_FATTN_WMMA_F16_CASE(128, 16, float);
-DECL_FATTN_WMMA_F16_CASE(256, 16, float);
+++ /dev/null
-// This file has been autogenerated by generate_cu_files.py, do not edit manually.
-
-#include "../fattn-wmma-f16.cuh"
-
-DECL_FATTN_WMMA_F16_CASE(64, 32, float);
-DECL_FATTN_WMMA_F16_CASE(80, 32, float);
-DECL_FATTN_WMMA_F16_CASE(96, 32, float);
-DECL_FATTN_WMMA_F16_CASE(112, 32, float);
-DECL_FATTN_WMMA_F16_CASE(128, 32, float);
+++ /dev/null
-// This file has been autogenerated by generate_cu_files.py, do not edit manually.
-
-#include "../fattn-wmma-f16.cuh"
-
-DECL_FATTN_WMMA_F16_CASE(64, 16, half);
-DECL_FATTN_WMMA_F16_CASE(80, 16, half);
-DECL_FATTN_WMMA_F16_CASE(96, 16, half);
-DECL_FATTN_WMMA_F16_CASE(112, 16, half);
-DECL_FATTN_WMMA_F16_CASE(128, 16, half);
-DECL_FATTN_WMMA_F16_CASE(256, 16, half);
+++ /dev/null
-// This file has been autogenerated by generate_cu_files.py, do not edit manually.
-
-#include "../fattn-wmma-f16.cuh"
-
-DECL_FATTN_WMMA_F16_CASE(64, 32, half);
-DECL_FATTN_WMMA_F16_CASE(80, 32, half);
-DECL_FATTN_WMMA_F16_CASE(96, 32, half);
-DECL_FATTN_WMMA_F16_CASE(112, 32, half);
-DECL_FATTN_WMMA_F16_CASE(128, 32, half);
-DECL_FATTN_WMMA_F16_CASE(256, 32, half);
+++ /dev/null
-// This file has been autogenerated by generate_cu_files.py, do not edit manually.
-
-#include "../fattn-wmma-f16.cuh"
-
-DECL_FATTN_WMMA_F16_CASE(64, 8, half);
-DECL_FATTN_WMMA_F16_CASE(96, 8, half);
-DECL_FATTN_WMMA_F16_CASE(128, 8, half);
-DECL_FATTN_WMMA_F16_CASE(256, 8, half);
DECL_FATTN_VEC_F{vkq_size}_CASE({head_size}, {type_k}, {type_v});
"""
-SOURCE_FATTN_WMMA_START = """// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+SOURCE_FATTN_MMA_START = """// This file has been autogenerated by generate_cu_files.py, do not edit manually.
-#include "../fattn-wmma-f16.cuh"
+#include "../fattn-mma-f16.cuh"
"""
-SOURCE_FATTN_WMMA_CASE = "DECL_FATTN_WMMA_F16_CASE({head_size}, {cols_per_block}, {kq_acc_t});\n"
+SOURCE_FATTN_MMA_CASE = "DECL_FATTN_MMA_F16_CASE({head_size}, {cols_per_block});\n"
TYPES_MMQ = [
"GGML_TYPE_Q4_0", "GGML_TYPE_Q4_1", "GGML_TYPE_Q5_0", "GGML_TYPE_Q5_1", "GGML_TYPE_Q8_0",
with open(f"fattn-vec-f{vkq_size}-instance-hs{head_size}-{get_short_name(type_k)}-{get_short_name(type_v)}.cu", "w") as f:
f.write(SOURCE_FATTN_VEC.format(vkq_size=vkq_size, head_size=head_size, type_k=type_k, type_v=type_v))
-for kq_acc_t in ["half", "float"]:
- for cols_per_block in [8, 16, 32]:
- if kq_acc_t == "float" and cols_per_block == 8:
- continue
+for cols_per_block in [8, 16, 32, 64]:
+ with open(f"fattn-mma-f16-instance-cpb{cols_per_block}.cu", "w") as f:
+ f.write(SOURCE_FATTN_MMA_START)
- with open(f"fattn-wmma-f16-instance-kq{kq_acc_t}-cpb{cols_per_block}.cu", "w") as f:
- f.write(SOURCE_FATTN_WMMA_START)
-
- for head_size in [64, 80, 96, 112, 128, 256]:
- if cols_per_block == 8 and head_size % 32 != 0: # wmma fragment is 8x32
- continue
- if kq_acc_t == "float" and cols_per_block == 32 and head_size == 256: # register spilling, bad performance
- continue
- f.write(SOURCE_FATTN_WMMA_CASE.format(kq_acc_t=kq_acc_t, cols_per_block=cols_per_block, head_size=head_size))
+ for head_size in [64, 80, 96, 112, 128, 256]:
+ f.write(SOURCE_FATTN_MMA_CASE.format(cols_per_block=cols_per_block, head_size=head_size))
for type in TYPES_MMQ:
with open(f"mmq-instance-{get_short_name(type)}.cu", "w") as f:
#define CU_MEM_LOCATION_TYPE_DEVICE hipMemLocationTypeDevice
#define CU_MEM_ACCESS_FLAGS_PROT_READWRITE hipMemAccessFlagsProtReadWrite
#define CU_CHECK(fn) {hipError_t err = fn; if(err != hipSuccess) { GGML_ABORT("HipVMM Failure: %s\n", hipGetErrorString(err)); }}
+#define __shfl_sync(mask, var, laneMask, width) __shfl(var, laneMask, width)
#define __shfl_xor_sync(mask, var, laneMask, width) __shfl_xor(var, laneMask, width)
#define cublasComputeType_t hipblasDatatype_t //deprecated, new hipblasComputeType_t not in 5.6
#define cublasCreate hipblasCreate
list(APPEND GGML_HEADERS_ROCM "../../include/ggml-cuda.h")
file(GLOB GGML_SOURCES_ROCM "../ggml-cuda/*.cu")
-file(GLOB SRCS "../ggml-cuda/template-instances/fattn-wmma*.cu")
+file(GLOB SRCS "../ggml-cuda/template-instances/fattn-mma*.cu")
list(APPEND GGML_SOURCES_ROCM ${SRCS})
file(GLOB SRCS "../ggml-cuda/template-instances/mmq*.cu")
list(APPEND GGML_SOURCES_ROCM ${SRCS})
list(APPEND GGML_HEADERS_MUSA "../../include/ggml-cuda.h")
file(GLOB GGML_SOURCES_MUSA "../ggml-cuda/*.cu")
- file(GLOB SRCS "../ggml-cuda/template-instances/fattn-wmma*.cu")
+ file(GLOB SRCS "../ggml-cuda/template-instances/fattn-mma*.cu")
list(APPEND GGML_SOURCES_MUSA ${SRCS})
file(GLOB SRCS "../ggml-cuda/template-instances/mmq*.cu")
list(APPEND GGML_SOURCES_MUSA ${SRCS})
overview / pkg / ggml / sources / llama.cpp / commitdiff