lnigam
7b8443ac78
* ggml-cuda: add flash-attn support for DKQ=320/DV=256 with ncols2=32 (GQA=32) Adds MMA-f16 and tile kernel configs, dispatch logic, template instances, and tile .cu file for Mistral Small 4 (head sizes 320/256), restricting to ncols2=32 to support GQA ratio 32 only. * Adding check to return BEST_FATTN_KERNEL_NONE in case GQA!=32 * Apply suggestions from code review Address review comments Co-authored-by: Johannes Gäßler <johannesg@5d6.de> * Address review comments and making kernel config default to DQK=512, DV=512 instead of DQK=256,DV=256 * Fixed bug with sinks=1, with ncols=32, there are two warp-groups created but sinks index is same(0,...,15) for both the groups hence with sinks=1, output is not matching with CPU output. Added sink_base which will be base index for each warp_group (threadIdx.y / np) * Apply suggestions from code review Co-authored-by: Johannes Gäßler <johannesg@5d6.de> * Update ggml/src/ggml-cuda/template-instances/generate_cu_files.py Co-authored-by: Johannes Gäßler <johannesg@5d6.de> --------- Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
1847 lines
88 KiB
Plaintext
1847 lines
88 KiB
Plaintext
#include "common.cuh"
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#include "cp-async.cuh"
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#include "mma.cuh"
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#include "fattn-common.cuh"
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using namespace ggml_cuda_mma;
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// Config options for the MMA kernel.
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// Should not affect results, only speed/register pressure/shared memory use.
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struct fattn_mma_config {
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int nthreads; // Number of threads per CUDA block.
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int occupancy; // Targeted occupancy for the MMA kernel.
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int nbatch_fa; // Number of KV rows per softmax rescaling of KQ rowsums and VKQ accumulators.
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int nbatch_K2; // Number of K half2 values in direction of DKQ to load in parallel.
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int nbatch_V2; // Number of V half2 values in direction of DV to load in parallel.
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int nbatch_combine; // Number of VKQ half2 values in direction of DV to combine in parallel.
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int nstages_target; // Number of pipeline stages to use ideally, 1 == always load data synchronously, 2 == preload data if there is hardware support.
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bool Q_in_reg; // Whether the Q values should be kept permanently in registers.
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constexpr __host__ __device__ fattn_mma_config(
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int nthreads, int occupancy, int nbatch_fa, int nbatch_K2, int nbatch_V2, int nbatch_combine, int nstages_target, bool Q_in_reg) :
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nthreads(nthreads), occupancy(occupancy), nbatch_fa(nbatch_fa), nbatch_K2(nbatch_K2), nbatch_V2(nbatch_V2), nbatch_combine(nbatch_combine),
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nstages_target(nstages_target), Q_in_reg(Q_in_reg) {}
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};
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#define GGML_CUDA_FATTN_MMA_CONFIG_CASE(DKQ_, DV_, ncols_, nthreads_, occupancy_, nbatch_fa_, nbatch_K2_, nbatch_V2_, nbatch_combine_, nstages_target_, Q_in_reg_) \
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if (DKQ == (DKQ_) && DV == (DV_) && ncols == (ncols_)) { \
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static_assert((nthreads_) % 32 == 0 && (nthreads_) <= 512, "bad nthreads"); \
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static_assert( (occupancy_) <= 8, "bad occupancy"); \
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static_assert((nbatch_fa_) % 32 == 0 && (nbatch_fa_) <= 256, "bad nbatch_fa"); \
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static_assert((nbatch_K2_) % 4 == 0 && (nbatch_K2_) <= 512, "bad nbatch_K2"); \
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static_assert((nbatch_V2_) % 4 == 0 && (nbatch_V2_) <= 256, "bad nbatch_V2"); \
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static_assert((nbatch_combine_) % 4 == 0 && (nbatch_combine_) <= 128, "bad nbatch_combine"); \
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static_assert((nstages_target_) >= 1 && (nstages_target_) <= 2, "bad nstages_target"); \
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return fattn_mma_config{(nthreads_), (occupancy_), (nbatch_fa_), (nbatch_K2_), (nbatch_V2_), (nbatch_combine_), (nstages_target_), (Q_in_reg_)}; \
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} \
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static constexpr __host__ __device__ fattn_mma_config ggml_cuda_fattn_mma_get_config_ampere(const int DKQ, const int DV, const int ncols) {
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GGML_CUDA_FATTN_MMA_CONFIG_CASE( 64, 64, 8, 128, 2, 128, 32, 32, 32, 2, true);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE( 64, 64, 16, 128, 2, 64, 32, 32, 32, 2, true);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE( 64, 64, 32, 128, 2, 64, 32, 32, 32, 2, true);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE( 64, 64, 64, 128, 2, 64, 32, 32, 32, 2, true);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE( 80, 80, 8, 128, 2, 128, 40, 40, 40, 2, true);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE( 80, 80, 16, 128, 2, 64, 40, 40, 40, 2, true);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE( 80, 80, 32, 128, 2, 64, 40, 40, 40, 2, true);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE( 80, 80, 64, 128, 2, 64, 40, 40, 40, 2, true);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE( 96, 96, 8, 128, 2, 128, 48, 48, 48, 2, true);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE( 96, 96, 16, 128, 2, 64, 48, 48, 48, 2, true);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE( 96, 96, 32, 128, 2, 64, 48, 48, 48, 2, true);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE( 96, 96, 64, 128, 2, 64, 48, 48, 48, 2, true);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE(112, 112, 8, 128, 2, 128, 56, 56, 56, 2, true);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE(112, 112, 16, 128, 2, 64, 56, 56, 56, 2, true);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE(112, 112, 32, 128, 2, 64, 56, 56, 56, 2, true);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE(112, 112, 64, 128, 2, 64, 56, 56, 56, 2, true);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE(128, 128, 8, 128, 2, 128, 64, 64, 64, 2, true);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE(128, 128, 16, 128, 2, 64, 64, 64, 64, 2, true);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE(128, 128, 32, 128, 2, 64, 64, 64, 64, 2, true);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE(128, 128, 64, 128, 2, 64, 64, 64, 64, 2, true);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE(256, 256, 8, 64, 4, 64, 128, 128, 128, 2, true);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE(256, 256, 16, 64, 4, 32, 128, 128, 128, 2, true);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE(256, 256, 32, 128, 2, 32, 128, 128, 128, 2, true);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE(256, 256, 64, 128, 2, 32, 128, 128, 128, 2, true);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE(320, 256, 32, 128, 2, 32, 128, 128, 128, 1, false);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE(320, 256, 64, 256, 1, 32, 128, 128, 128, 1, false);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE(512, 512, 8, 64, 4, 32, 256, 256, 128, 1, false);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE(512, 512, 16, 64, 4, 32, 256, 256, 128, 1, false);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE(512, 512, 32, 128, 2, 32, 128, 128, 128, 1, false);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE(512, 512, 64, 256, 1, 32, 128, 128, 128, 1, false);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE(576, 512, 8, 64, 4, 32, 288, 256, 128, 1, false);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE(576, 512, 16, 64, 4, 32, 288, 256, 128, 1, false);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE(576, 512, 32, 128, 2, 32, 160, 128, 128, 1, false);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE(576, 512, 64, 256, 1, 32, 160, 128, 128, 1, false);
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return fattn_mma_config(32, 1, 0, 0, 0, 0, 0, false);
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}
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static constexpr __host__ __device__ fattn_mma_config ggml_cuda_fattn_mma_get_config_turing(const int DKQ, const int DV, const int ncols) {
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GGML_CUDA_FATTN_MMA_CONFIG_CASE(256, 256, 8, 128, 2, 64, 128, 128, 128, 2, true);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE(256, 256, 16, 128, 2, 64, 128, 128, 128, 2, true);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE(256, 256, 32, 128, 2, 64, 128, 128, 64, 2, true);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE(256, 256, 64, 128, 2, 64, 128, 128, 64, 2, true);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE(320, 256, 32, 128, 2, 32, 128, 128, 128, 1, false);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE(320, 256, 64, 256, 1, 32, 128, 128, 128, 1, false);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE(512, 512, 8, 64, 4, 32, 96, 64, 128, 1, false);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE(512, 512, 16, 64, 4, 32, 96, 64, 128, 1, false);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE(512, 512, 32, 128, 2, 32, 128, 128, 128, 1, false);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE(512, 512, 64, 256, 1, 32, 128, 128, 128, 1, false);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE(576, 512, 8, 64, 4, 32, 96, 64, 128, 1, false);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE(576, 512, 16, 64, 4, 32, 96, 64, 128, 1, false);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE(576, 512, 32, 128, 2, 32, 160, 128, 128, 1, false);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE(576, 512, 64, 256, 1, 32, 160, 128, 128, 1, false);
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return ggml_cuda_fattn_mma_get_config_ampere(DKQ, DV, ncols);
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}
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static constexpr __host__ __device__ fattn_mma_config ggml_cuda_fattn_mma_get_config_volta(const int DKQ, const int DV, const int ncols) {
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GGML_CUDA_FATTN_MMA_CONFIG_CASE(512, 512, 8, 64, 4, 32, 256, 256, 64, 1, false);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE(512, 512, 16, 64, 4, 32, 256, 256, 64, 1, false);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE(512, 512, 32, 128, 2, 32, 128, 128, 64, 1, false);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE(512, 512, 64, 256, 1, 32, 128, 128, 64, 1, false);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE(576, 512, 8, 64, 4, 32, 288, 256, 64, 1, false);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE(576, 512, 16, 64, 4, 32, 288, 256, 64, 1, false);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE(576, 512, 32, 128, 2, 32, 160, 128, 64, 1, false);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE(576, 512, 64, 256, 1, 32, 160, 128, 64, 1, false);
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// TODO tune specifically for Volta
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return ggml_cuda_fattn_mma_get_config_ampere(DKQ, DV, ncols);
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}
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static constexpr __host__ __device__ fattn_mma_config ggml_cuda_fattn_mma_get_config_rdna(const int DKQ, const int DV, const int ncols) {
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GGML_CUDA_FATTN_MMA_CONFIG_CASE(256, 256, 16, 128, 2, 64, 128, 128, 128, 2, true);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE(256, 256, 32, 128, 2, 64, 128, 128, 64, 2, true);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE(256, 256, 64, 128, 2, 64, 128, 128, 64, 2, true);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE(320, 256, 32, 128, 2, 64, 160, 128, 64, 2, true);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE(320, 256, 64, 128, 2, 64, 160, 128, 64, 2, false);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE(512, 512, 16, 64, 4, 32, 128, 128, 128, 1, false);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE(512, 512, 32, 128, 2, 32, 128, 128, 128, 1, false);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE(512, 512, 64, 256, 1, 32, 128, 128, 128, 1, false);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE(576, 512, 16, 64, 4, 32, 96, 64, 128, 1, false);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE(576, 512, 32, 128, 2, 32, 160, 128, 128, 1, false);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE(576, 512, 64, 256, 1, 32, 160, 128, 128, 1, false);
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// TODO tune specifically for RDNA
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return ggml_cuda_fattn_mma_get_config_ampere(DKQ, DV, ncols);
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}
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static constexpr __host__ __device__ fattn_mma_config ggml_cuda_fattn_mma_get_config_cdna(const int DKQ, const int DV, const int ncols) {
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// Conservative configs for CDNA (MI100+): 64KB LDS, wavefront64, nstages=1 (no cp.async).
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GGML_CUDA_FATTN_MMA_CONFIG_CASE( 64, 64, 8, 128, 2, 128, 32, 32, 32, 1, true);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE( 64, 64, 16, 128, 2, 64, 32, 32, 32, 1, true);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE( 64, 64, 32, 128, 2, 64, 32, 32, 32, 1, true);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE( 64, 64, 64, 256, 2, 64, 32, 32, 32, 1, true);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE( 80, 80, 8, 128, 2, 128, 40, 40, 40, 1, true);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE( 80, 80, 16, 128, 2, 64, 40, 40, 40, 1, true);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE( 80, 80, 32, 128, 2, 64, 40, 40, 40, 1, true);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE( 80, 80, 64, 256, 2, 64, 40, 40, 40, 1, true);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE( 96, 96, 8, 128, 2, 128, 48, 48, 48, 1, true);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE( 96, 96, 16, 128, 2, 64, 48, 48, 48, 1, true);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE( 96, 96, 32, 128, 2, 64, 48, 48, 48, 1, true);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE( 96, 96, 64, 256, 2, 64, 48, 48, 48, 1, true);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE(112, 112, 8, 128, 2, 128, 56, 56, 56, 1, true);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE(112, 112, 16, 128, 2, 64, 56, 56, 56, 1, true);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE(112, 112, 32, 128, 2, 64, 56, 56, 56, 1, true);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE(112, 112, 64, 256, 2, 64, 56, 56, 56, 1, true);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE(128, 128, 8, 128, 2, 128, 64, 64, 64, 1, true);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE(128, 128, 16, 128, 2, 64, 64, 64, 64, 1, true);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE(128, 128, 32, 128, 2, 64, 64, 64, 64, 1, true);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE(128, 128, 64, 256, 2, 64, 64, 64, 64, 1, true);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE(256, 256, 8, 64, 4, 64, 128, 128, 128, 1, true);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE(256, 256, 16, 64, 4, 32, 128, 128, 128, 1, true);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE(256, 256, 32, 128, 2, 32, 128, 128, 128, 1, true);
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GGML_CUDA_FATTN_MMA_CONFIG_CASE(256, 256, 64, 256, 2, 32, 128, 128, 128, 1, true);
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// Fallback for unsupported DKQ values (e.g. 576). Must return non-zero values to satisfy
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// compile-time static_asserts even though the kernel guard prevents runtime execution.
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// nthreads=256 gives nwarps=4 (warp_size=64) or 8 (warp_size=32), nbatch_fa=128 satisfies np*16 divisibility.
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return fattn_mma_config(256, 1, 128, 4, 4, 4, 1, false);
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}
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static __host__ fattn_mma_config ggml_cuda_fattn_mma_get_config(const int DKQ, const int DV, const int ncols, const int cc) {
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if (ampere_mma_available(cc)) {
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return ggml_cuda_fattn_mma_get_config_ampere(DKQ, DV, ncols);
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}
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if (turing_mma_available(cc)) {
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return ggml_cuda_fattn_mma_get_config_turing(DKQ, DV, ncols);
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}
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if (amd_mfma_available(cc)) {
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return ggml_cuda_fattn_mma_get_config_cdna(DKQ, DV, ncols);
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}
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if (amd_wmma_available(cc)) {
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return ggml_cuda_fattn_mma_get_config_rdna(DKQ, DV, ncols);
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}
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GGML_ASSERT(volta_mma_available(cc));
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return ggml_cuda_fattn_mma_get_config_volta(DKQ, DV, ncols);
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}
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static constexpr __device__ fattn_mma_config ggml_cuda_fattn_mma_get_config(const int DKQ, const int DV, const int ncols) {
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#if defined(AMPERE_MMA_AVAILABLE)
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return ggml_cuda_fattn_mma_get_config_ampere(DKQ, DV, ncols);
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#elif defined(TURING_MMA_AVAILABLE)
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return ggml_cuda_fattn_mma_get_config_turing(DKQ, DV, ncols);
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#elif defined(AMD_MFMA_AVAILABLE)
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return ggml_cuda_fattn_mma_get_config_cdna(DKQ, DV, ncols);
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#elif defined(VOLTA_MMA_AVAILABLE)
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return ggml_cuda_fattn_mma_get_config_volta(DKQ, DV, ncols);
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#elif defined(AMD_WMMA_AVAILABLE)
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return ggml_cuda_fattn_mma_get_config_rdna(DKQ, DV, ncols);
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#else
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GGML_UNUSED_VARS(DKQ, DV, ncols);
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return fattn_mma_config(32, 1, 0, 0, 0, 0, 0, false);
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#endif // defined(AMPERE_MMA_AVAILABLE)
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}
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static __host__ int ggml_cuda_fattn_mma_get_nthreads(const int DKQ, const int DV, const int ncols, const int cc) {
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return ggml_cuda_fattn_mma_get_config(DKQ, DV, ncols, cc).nthreads;
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}
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static constexpr __device__ int ggml_cuda_fattn_mma_get_nthreads(const int DKQ, const int DV, const int ncols) {
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return ggml_cuda_fattn_mma_get_config(DKQ, DV, ncols).nthreads;
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}
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static __host__ int ggml_cuda_fattn_mma_get_occupancy(const int DKQ, const int DV, const int ncols, const int cc) {
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return ggml_cuda_fattn_mma_get_config(DKQ, DV, ncols, cc).occupancy;
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}
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static constexpr __device__ int ggml_cuda_fattn_mma_get_occupancy(const int DKQ, const int DV, const int ncols) {
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return ggml_cuda_fattn_mma_get_config(DKQ, DV, ncols).occupancy;
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}
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static __host__ int ggml_cuda_fattn_mma_get_nbatch_fa(const int DKQ, const int DV, const int ncols, const int cc) {
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return ggml_cuda_fattn_mma_get_config(DKQ, DV, ncols, cc).nbatch_fa;
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}
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static constexpr __device__ int ggml_cuda_fattn_mma_get_nbatch_fa(const int DKQ, const int DV, const int ncols) {
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return ggml_cuda_fattn_mma_get_config(DKQ, DV, ncols).nbatch_fa;
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}
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static __host__ int ggml_cuda_fattn_mma_get_nbatch_K2(const int DKQ, const int DV, const int ncols, const int cc) {
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return ggml_cuda_fattn_mma_get_config(DKQ, DV, ncols, cc).nbatch_K2;
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}
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|
static constexpr __device__ int ggml_cuda_fattn_mma_get_nbatch_K2(const int DKQ, const int DV, const int ncols) {
|
|
return ggml_cuda_fattn_mma_get_config(DKQ, DV, ncols).nbatch_K2;
|
|
}
|
|
|
|
static __host__ int ggml_cuda_fattn_mma_get_nbatch_V2(const int DKQ, const int DV, const int ncols, const int cc) {
|
|
return ggml_cuda_fattn_mma_get_config(DKQ, DV, ncols, cc).nbatch_V2;
|
|
}
|
|
|
|
static constexpr __device__ int ggml_cuda_fattn_mma_get_nbatch_V2(const int DKQ, const int DV, const int ncols) {
|
|
return ggml_cuda_fattn_mma_get_config(DKQ, DV, ncols).nbatch_V2;
|
|
}
|
|
|
|
static __host__ int ggml_cuda_fattn_mma_get_nbatch_combine(const int DKQ, const int DV, const int ncols, const int cc) {
|
|
return ggml_cuda_fattn_mma_get_config(DKQ, DV, ncols, cc).nbatch_combine;
|
|
}
|
|
|
|
static constexpr __device__ int ggml_cuda_fattn_mma_get_nbatch_combine(const int DKQ, const int DV, const int ncols) {
|
|
return ggml_cuda_fattn_mma_get_config(DKQ, DV, ncols).nbatch_combine;
|
|
}
|
|
|
|
static __host__ int ggml_cuda_fattn_mma_get_nstages_target(const int DKQ, const int DV, const int ncols, const int cc) {
|
|
return ggml_cuda_fattn_mma_get_config(DKQ, DV, ncols, cc).nstages_target;
|
|
}
|
|
|
|
static constexpr __device__ int ggml_cuda_fattn_mma_get_nstages_target(const int DKQ, const int DV, const int ncols) {
|
|
return ggml_cuda_fattn_mma_get_config(DKQ, DV, ncols).nstages_target;
|
|
}
|
|
|
|
static __host__ bool ggml_cuda_fattn_mma_get_Q_in_reg(const int DKQ, const int DV, const int ncols, const int cc) {
|
|
return ggml_cuda_fattn_mma_get_config(DKQ, DV, ncols, cc).Q_in_reg;
|
|
}
|
|
|
|
static constexpr __device__ bool ggml_cuda_fattn_mma_get_Q_in_reg(const int DKQ, const int DV, const int ncols) {
|
|
return ggml_cuda_fattn_mma_get_config(DKQ, DV, ncols).Q_in_reg;
|
|
}
|
|
|
|
static constexpr __device__ int get_cols_per_thread() {
|
|
#if defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE)
|
|
return 1; // AMD has a single column per thread.
|
|
#else
|
|
return 2; // This is specifically KQ columns, Volta only has a single VKQ column.
|
|
#endif // defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE)
|
|
}
|
|
|
|
static __host__ int get_cols_per_warp(const int cc) {
|
|
if (turing_mma_available(cc) || amd_wmma_available(cc) || amd_mfma_available(cc)) {
|
|
return 16;
|
|
} else {
|
|
// Volta
|
|
return 32;
|
|
}
|
|
}
|
|
|
|
// ------------------------------------------------------------------------------------------------------------------
|
|
|
|
static __host__ int ggml_cuda_fattn_mma_get_nstages(const int DKQ, const int DV, const int ncols1, const int ncols2, const int cc) {
|
|
return cp_async_available(cc) && ncols2 >= 2 ? ggml_cuda_fattn_mma_get_nstages_target(DKQ, DV, ncols1*ncols2, cc) : 0;
|
|
}
|
|
|
|
static constexpr __device__ int ggml_cuda_fattn_mma_get_nstages(const int DKQ, const int DV, const int ncols1, const int ncols2) {
|
|
#ifdef CP_ASYNC_AVAILABLE
|
|
return ncols2 >= 2 ? ggml_cuda_fattn_mma_get_nstages_target(DKQ, DV, ncols1*ncols2) : 0;
|
|
#else
|
|
GGML_UNUSED_VARS(DKQ, DV, ncols1, ncols2);
|
|
return 0;
|
|
#endif // CP_ASYNC_AVAILABLE
|
|
}
|
|
|
|
// ------------------------------------------------------------------------------------------------------------------
|
|
|
|
template<int stride_tile, int nwarps, int nbatch_fa, bool use_cp_async, bool oob_check>
|
|
static __device__ __forceinline__ void flash_attn_ext_f16_load_tile(
|
|
const half2 * const __restrict__ KV, half2 * const __restrict__ tile_KV, const int D2, const int stride_KV, const int i_sup) {
|
|
constexpr int warp_size = ggml_cuda_get_physical_warp_size();
|
|
// K/V data is loaded with decreasing granularity for D for better memory bandwidth.
|
|
// The minimum granularity is 16 bytes.
|
|
constexpr int h2_per_chunk = 16/sizeof(half2);
|
|
const int chunks_per_row = D2 / h2_per_chunk;
|
|
if constexpr (use_cp_async) {
|
|
static_assert(warp_size == 32, "bad warp_size");
|
|
static_assert(!oob_check, "OOB check not compatible with cp_async");
|
|
constexpr int preload = 64;
|
|
|
|
const unsigned int tile_KV_32 = ggml_cuda_cvta_generic_to_shared(tile_KV);
|
|
|
|
auto load = [&] __device__ (auto n) {
|
|
const int stride_k = warp_size >> n;
|
|
const int k0_start = stride_k == warp_size ? 0 : chunks_per_row - chunks_per_row % (2*stride_k);
|
|
const int k0_stop = chunks_per_row - chunks_per_row % (1*stride_k);
|
|
const int stride_i = warp_size / stride_k;
|
|
|
|
if (k0_start == k0_stop) {
|
|
return;
|
|
}
|
|
|
|
#pragma unroll
|
|
for (int i0 = 0; i0 < nbatch_fa; i0 += nwarps*stride_i) {
|
|
const int i = i0 + threadIdx.y*stride_i + (stride_k == warp_size ? 0 : threadIdx.x / stride_k);
|
|
|
|
if (i0 + nwarps*stride_i > nbatch_fa && i >= nbatch_fa) {
|
|
break;
|
|
}
|
|
|
|
#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);
|
|
|
|
cp_async_cg_16<preload>(tile_KV_32 + i*(stride_tile*sizeof(half2)) + k*16, KV + i*stride_KV + k*h2_per_chunk);
|
|
}
|
|
}
|
|
};
|
|
// 1: max 32*16=512 bytes, 256 half
|
|
// 2: max 16*16=256 bytes, 128 half
|
|
// 3: max 8*16=128 bytes, 64 half
|
|
// 4: max 4*16= 64 bytes, 32 half
|
|
// 5: max 2*16= 32 bytes, 16 half
|
|
// 6: max 1*16= 16 bytes, 8 half
|
|
ggml_cuda_unroll<6>{}(load);
|
|
} else {
|
|
const half2 zero[4] = {{0.0f, 0.0f}, {0.0f, 0.0f}, {0.0f, 0.0f}, {0.0f, 0.0f}};
|
|
auto load = [&] __device__ (const int n) {
|
|
const int stride_k = 32 >> n;
|
|
const int k0_start = stride_k == 32 ? 0 : chunks_per_row - chunks_per_row % (2*stride_k);
|
|
const int k0_stop = chunks_per_row - chunks_per_row % (1*stride_k);
|
|
const int stride_i = warp_size / stride_k;
|
|
|
|
if (k0_start == k0_stop) {
|
|
return;
|
|
}
|
|
|
|
#pragma unroll
|
|
for (int i0 = 0; i0 < nbatch_fa; i0 += nwarps*stride_i) {
|
|
const int i = i0 + threadIdx.y*stride_i + (stride_k == warp_size ? 0 : threadIdx.x / stride_k);
|
|
|
|
if (i0 + nwarps*stride_i > nbatch_fa && i >= nbatch_fa) {
|
|
break;
|
|
}
|
|
|
|
#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);
|
|
|
|
ggml_cuda_memcpy_1<16>(tile_KV + i*stride_tile + k*4,
|
|
!oob_check || i < i_sup ? KV + i*stride_KV + k*h2_per_chunk : zero);
|
|
}
|
|
}
|
|
};
|
|
// 1: max 32*16=512 bytes, 256 half
|
|
// 2: max 16*16=256 bytes, 128 half
|
|
// 3: max 8*16=128 bytes, 64 half
|
|
// 4: max 4*16= 64 bytes, 32 half
|
|
// 5: max 2*16= 32 bytes, 16 half
|
|
// 6: max 1*16= 16 bytes, 8 half
|
|
ggml_cuda_unroll<6>{}(load);
|
|
}
|
|
}
|
|
|
|
template<int ncols1, int nwarps, int nbatch_fa, bool use_cp_async, bool oob_check>
|
|
static __device__ __forceinline__ void flash_attn_ext_f16_load_mask(
|
|
const half * const __restrict__ mask_h, half * const __restrict__ tile_mask,
|
|
const int stride_mask, const int i_sup, const int j0, const uint3 ne01) {
|
|
constexpr int warp_size = ggml_cuda_get_physical_warp_size();
|
|
if constexpr (use_cp_async) {
|
|
static_assert(nbatch_fa <= 8*warp_size && nbatch_fa % 8 == 0, "bad nbatch_fa");
|
|
static_assert(!oob_check, "OOB check incompatible with cp_async");
|
|
constexpr int preload = nbatch_fa >= 32 ? nbatch_fa * sizeof(half) : 64;
|
|
constexpr int cols_per_warp = 8*warp_size/nbatch_fa;
|
|
constexpr int stride_j = nwarps * cols_per_warp;
|
|
|
|
const unsigned int tile_mask_32 = ggml_cuda_cvta_generic_to_shared(tile_mask);
|
|
|
|
#pragma unroll
|
|
for (int j1 = 0; j1 < ncols1; j1 += stride_j) {
|
|
const int j_sram = j1 + threadIdx.y*cols_per_warp + threadIdx.x / (warp_size/cols_per_warp);
|
|
const int j_vram = fastmodulo(j0 + j_sram, ne01);
|
|
|
|
if (j1 + stride_j > ncols1 && j_sram >= ncols1) {
|
|
break;
|
|
}
|
|
|
|
const int i = 8 * (threadIdx.x % (nbatch_fa/8));
|
|
|
|
cp_async_cg_16<preload>(tile_mask_32 + j_sram*(nbatch_fa*sizeof(half) + 16) + i*sizeof(half), mask_h + j_vram*stride_mask + i);
|
|
}
|
|
} else if constexpr (oob_check) {
|
|
#pragma unroll
|
|
for (int j1 = 0; j1 < ncols1; j1 += nwarps) {
|
|
const int j_sram = j1 + threadIdx.y;
|
|
const int j_vram = fastmodulo(j0 + j_sram, ne01);
|
|
|
|
if (j1 + nwarps > ncols1 && j_sram >= ncols1) {
|
|
break;
|
|
}
|
|
|
|
#pragma unroll
|
|
for (int i0 = 0; i0 < nbatch_fa; i0 += warp_size) {
|
|
const int i = i0 + threadIdx.x;
|
|
|
|
tile_mask[j_sram*(nbatch_fa + 8) + i] = i < i_sup ? mask_h[j_vram*stride_mask + i] : half(0.0f);
|
|
}
|
|
}
|
|
} else if constexpr (nbatch_fa < 2*warp_size) {
|
|
constexpr int cols_per_warp = 2*warp_size/nbatch_fa;
|
|
constexpr int stride_j = nwarps * cols_per_warp;
|
|
#pragma unroll
|
|
for (int j1 = 0; j1 < ncols1; j1 += stride_j) {
|
|
const int j_sram = j1 + threadIdx.y*cols_per_warp + threadIdx.x / (warp_size/cols_per_warp);
|
|
const int j_vram = fastmodulo(j0 + j_sram, ne01);
|
|
|
|
if (j1 + stride_j > ncols1 && j_sram >= ncols1) {
|
|
break;
|
|
}
|
|
|
|
const int i = threadIdx.x % (warp_size/cols_per_warp);
|
|
|
|
ggml_cuda_memcpy_1<sizeof(half2)>(tile_mask + j_sram*(nbatch_fa + 8) + 2*i, mask_h + j_vram*stride_mask + 2*i);
|
|
}
|
|
} else {
|
|
#pragma unroll
|
|
for (int j1 = 0; j1 < ncols1; j1 += nwarps) {
|
|
const int j_sram = j1 + threadIdx.y;
|
|
const int j_vram = fastmodulo(j0 + j_sram, ne01);
|
|
|
|
if (j1 + nwarps > ncols1 && j_sram >= ncols1) {
|
|
break;
|
|
}
|
|
|
|
#pragma unroll
|
|
for (int i0 = 0; i0 < nbatch_fa; i0 += 2*warp_size) {
|
|
const int i = i0 + 2*threadIdx.x;
|
|
|
|
ggml_cuda_memcpy_1<sizeof(half2)>(tile_mask + j_sram*(nbatch_fa + 8) + i, mask_h + j_vram*stride_mask + i);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
template<int DKQ, int DV, int ncols1, int ncols2, int nwarps,
|
|
bool use_logit_softcap, bool V_is_K_view, bool needs_fixup, bool is_fixup, bool last_iter, bool oob_check,
|
|
typename T_A_KQ, typename T_B_KQ, typename T_C_KQ, typename T_A_VKQ, typename T_B_VKQ, typename T_C_VKQ>
|
|
static __device__ __forceinline__ void flash_attn_ext_f16_iter(
|
|
const float2 * const __restrict__ Q_f2,
|
|
const half2 * const __restrict__ K_h2,
|
|
const half2 * const __restrict__ V_h2,
|
|
const half * const __restrict__ mask_h,
|
|
float2 * const __restrict__ dstk,
|
|
float2 * const __restrict__ dstk_fixup,
|
|
const float scale,
|
|
const float slope,
|
|
const float logit_softcap,
|
|
const uint3 ne01,
|
|
const int ne02,
|
|
const int stride_K,
|
|
const int stride_V,
|
|
const int stride_mask,
|
|
half2 * const __restrict__ tile_Q,
|
|
half2 * const __restrict__ tile_K,
|
|
half2 * const __restrict__ tile_V,
|
|
half * const __restrict__ tile_mask,
|
|
T_B_KQ * const __restrict__ Q_B,
|
|
T_C_VKQ * const __restrict__ VKQ_C,
|
|
float * const __restrict__ KQ_max,
|
|
float * const __restrict__ KQ_rowsum,
|
|
const int jt,
|
|
const int kb0,
|
|
const int k_VKQ_sup) {
|
|
#if defined(VOLTA_MMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || (defined(AMD_WMMA_AVAILABLE) && defined(RDNA4)) || defined(AMD_MFMA_AVAILABLE)
|
|
constexpr int warp_size = ggml_cuda_get_physical_warp_size();
|
|
constexpr int ncols = ncols1 * ncols2;
|
|
constexpr int cols_per_warp = T_B_KQ::I;
|
|
constexpr int cols_per_thread = get_cols_per_thread();
|
|
constexpr int np = cols_per_warp > ncols ? nwarps : nwarps * cols_per_warp/ncols; // Number of parallel CUDA warps per Q column.
|
|
constexpr int nbatch_fa = ggml_cuda_fattn_mma_get_nbatch_fa(DKQ, DV, ncols);
|
|
constexpr int nbatch_K2 = ggml_cuda_fattn_mma_get_nbatch_K2(DKQ, DV, ncols);
|
|
constexpr int nbatch_V2 = ggml_cuda_fattn_mma_get_nbatch_V2(DKQ, DV, ncols);
|
|
constexpr bool Q_in_reg = ggml_cuda_fattn_mma_get_Q_in_reg (DKQ, DV, ncols);
|
|
constexpr int nstages = ggml_cuda_fattn_mma_get_nstages (DKQ, DV, ncols1, ncols2);
|
|
|
|
constexpr int stride_tile_Q = DKQ/2 + 4;
|
|
constexpr int stride_tile_K = nbatch_K2 + 4;
|
|
|
|
constexpr int stride_tile_V = V_is_K_view ? stride_tile_K : nbatch_V2 + 4;
|
|
|
|
const int k_VKQ_0 = kb0 * nbatch_fa;
|
|
#if defined(TURING_MMA_AVAILABLE)
|
|
T_C_KQ KQ_C[nbatch_fa/(np*(cols_per_warp == 8 ? T_C_KQ::I : T_C_KQ::J))];
|
|
#elif defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE)
|
|
T_C_KQ KQ_C[nbatch_fa/(np*T_C_KQ::J)];
|
|
#else // Volta
|
|
T_C_KQ KQ_C[nbatch_fa/(np*T_C_KQ::J)];
|
|
#endif // defined(TURING_MMA_AVAILABLE)
|
|
|
|
if constexpr (nstages > 1) {
|
|
static_assert(!oob_check, "OOB check incompatible with multi-stage pipeline");
|
|
static_assert(!V_is_K_view, "K data reuse not implemented multi-stage loading");
|
|
static_assert(nbatch_K2 == DKQ/2, "batching not implemented for multi stage loading");
|
|
constexpr bool use_cp_async = true;
|
|
cp_async_wait_all();
|
|
__syncthreads();
|
|
flash_attn_ext_f16_load_tile<stride_tile_V, nwarps, nbatch_fa, use_cp_async, oob_check>
|
|
(V_h2 + int64_t(k_VKQ_0)*stride_V, tile_V, nbatch_V2, stride_V, k_VKQ_sup);
|
|
} else {
|
|
constexpr bool use_cp_async = nstages == 1;
|
|
if (ncols2 > 1 || mask_h) {
|
|
flash_attn_ext_f16_load_mask<ncols1, nwarps, nbatch_fa, use_cp_async, oob_check>
|
|
(mask_h + k_VKQ_0, tile_mask, stride_mask, k_VKQ_sup, jt*ncols1, ne01);
|
|
}
|
|
}
|
|
|
|
// For MLA K and V have the same data.
|
|
// Therefore, iterate over K in reverse and later re-use the data if possible.
|
|
#pragma unroll
|
|
for (int k0_start = (DKQ/2-1) - (DKQ/2-1) % nbatch_K2; k0_start >= 0; k0_start -= nbatch_K2) {
|
|
const int k0_stop = k0_start + nbatch_K2 < DKQ/2 ? k0_start + nbatch_K2 : DKQ/2;
|
|
const int k0_diff = k0_stop - k0_start;
|
|
|
|
if constexpr (nstages <= 1) {
|
|
constexpr bool use_cp_async = nstages == 1;
|
|
flash_attn_ext_f16_load_tile<stride_tile_K, nwarps, nbatch_fa, use_cp_async, oob_check>
|
|
(K_h2 + int64_t(k_VKQ_0)*stride_K + k0_start, tile_K, k0_diff, stride_K, k_VKQ_sup);
|
|
if (use_cp_async) {
|
|
cp_async_wait_all();
|
|
}
|
|
__syncthreads();
|
|
}
|
|
|
|
// Calculate tile of KQ:
|
|
if constexpr (Q_in_reg) {
|
|
#pragma unroll
|
|
for (int i_KQ_00 = 0; i_KQ_00 < nbatch_fa; i_KQ_00 += np*T_A_KQ::I) {
|
|
const int i_KQ_0 = i_KQ_00 + (threadIdx.y % np)*T_A_KQ::I;
|
|
#pragma unroll
|
|
for (int k_KQ_0 = k0_start; k_KQ_0 < k0_stop; k_KQ_0 += T_A_KQ::J) {
|
|
T_A_KQ K_A;
|
|
load_ldmatrix(K_A, tile_K + i_KQ_0*stride_tile_K + (k_KQ_0 - k0_start), stride_tile_K);
|
|
if constexpr (cols_per_warp == 8) {
|
|
mma(KQ_C[i_KQ_00/(np*T_A_KQ::I)], K_A, Q_B[k_KQ_0/T_A_KQ::J]);
|
|
} else {
|
|
// Wide version of KQ_C is column-major
|
|
#if defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE)
|
|
// AMD matrix C is column-major.
|
|
mma(KQ_C[i_KQ_00/(np*T_A_KQ::I)], K_A, Q_B[k_KQ_0/T_A_KQ::J]);
|
|
#else
|
|
// swap A and B for CUDA.
|
|
mma(KQ_C[i_KQ_00/(np*T_A_KQ::I)], Q_B[k_KQ_0/T_A_KQ::J], K_A);
|
|
#endif // defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE)
|
|
}
|
|
}
|
|
}
|
|
} else {
|
|
#pragma unroll
|
|
for (int k_KQ_0 = k0_start; k_KQ_0 < k0_stop; k_KQ_0 += T_A_KQ::J) {
|
|
load_ldmatrix(Q_B[0], tile_Q + (threadIdx.y / np)*(T_B_KQ::I*stride_tile_Q) + k_KQ_0, stride_tile_Q);
|
|
|
|
#pragma unroll
|
|
for (int i_KQ_00 = 0; i_KQ_00 < nbatch_fa; i_KQ_00 += np*T_A_KQ::I) {
|
|
const int i_KQ_0 = i_KQ_00 + (threadIdx.y % np)*T_A_KQ::I;
|
|
|
|
T_A_KQ K_A;
|
|
load_ldmatrix(K_A, tile_K + i_KQ_0*stride_tile_K + (k_KQ_0 - k0_start), stride_tile_K);
|
|
|
|
if constexpr (cols_per_warp == 8) {
|
|
mma(KQ_C[i_KQ_00/(np*T_A_KQ::I)], K_A, Q_B[0]);
|
|
} else {
|
|
// Wide version of KQ_C is column-major
|
|
#if defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE)
|
|
// AMD matrix C is column-major.
|
|
mma(KQ_C[i_KQ_00/(np*T_A_KQ::I)], K_A, Q_B[0]);
|
|
#else
|
|
// swap A and B for CUDA.
|
|
mma(KQ_C[i_KQ_00/(np*T_A_KQ::I)], Q_B[0], K_A);
|
|
#endif // defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE)
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
if constexpr (nstages <= 1) {
|
|
__syncthreads(); // Only needed if tile_K == tile_V.
|
|
}
|
|
}
|
|
|
|
if (use_logit_softcap) {
|
|
constexpr int stride = cols_per_warp == 8 ? np*T_C_KQ::I : np*T_C_KQ::J;
|
|
static_assert(nbatch_fa % stride == 0, "bad loop size");
|
|
#pragma unroll
|
|
for (int i = 0; i < nbatch_fa/stride; ++i) {
|
|
#pragma unroll
|
|
for (int l = 0; l < T_C_KQ::ne; ++l) {
|
|
KQ_C[i].x[l] = logit_softcap*tanhf(KQ_C[i].x[l]);
|
|
}
|
|
}
|
|
}
|
|
|
|
float KQ_max_new[cols_per_thread];
|
|
#pragma unroll
|
|
for (int col = 0; col < cols_per_thread; ++col) {
|
|
KQ_max_new[col] = KQ_max[col];
|
|
}
|
|
float KQ_rowsum_add[cols_per_thread] = {0.0f};
|
|
|
|
if constexpr (cols_per_warp == 8) {
|
|
if (ncols2 > 1 || mask_h) {
|
|
#pragma unroll
|
|
for (int i00 = 0; i00 < nbatch_fa; i00 += np*T_C_KQ::I) {
|
|
const int i0 = i00 + (threadIdx.y % np)*T_C_KQ::I;
|
|
#pragma unroll
|
|
for (int l = 0; l < T_C_KQ::ne; ++l) {
|
|
const int i = i0 + T_C_KQ::get_i(l);
|
|
const int j = ((threadIdx.y / np)*T_C_KQ::J + T_C_KQ::get_j(l)) / ncols2;
|
|
|
|
KQ_C[i00/(np*T_C_KQ::I)].x[l] += slope * __half2float(tile_mask[j*(nbatch_fa + 8) + 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.
|
|
static_assert(nbatch_fa % (np*T_C_KQ::I) == 0, "bad loop size");
|
|
#pragma unroll
|
|
for (int k0 = 0; k0 < nbatch_fa; k0 += np*T_C_KQ::I) {
|
|
#pragma unroll
|
|
for (int l = 0; l < T_C_KQ::ne; ++l) {
|
|
if (!oob_check || k0 + (threadIdx.y % np)*T_C_KQ::I + T_C_KQ::get_i(l) < k_VKQ_sup) {
|
|
#if defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE)
|
|
constexpr int KQ_idx = 0;
|
|
#else
|
|
// Turing + Volta:
|
|
const int KQ_idx = l % 2;
|
|
#endif // defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE)
|
|
KQ_max_new[KQ_idx] = fmaxf(KQ_max_new[KQ_idx], KQ_C[k0/(np*T_C_KQ::I)].x[l] + FATTN_KQ_MAX_OFFSET);
|
|
}
|
|
}
|
|
}
|
|
|
|
// Values per KQ column are spread across 8 threads:
|
|
#pragma unroll
|
|
for (int col = 0; col < cols_per_thread; ++col) {
|
|
#pragma unroll
|
|
for (int offset = 16; offset >= 4; offset >>= 1) {
|
|
KQ_max_new[col] = fmaxf(KQ_max_new[col], __shfl_xor_sync(0xFFFFFFFF, KQ_max_new[col], offset, warp_size));
|
|
}
|
|
}
|
|
|
|
static_assert(nbatch_fa % (np*T_C_KQ::I) == 0, "bad loop size");
|
|
#pragma unroll
|
|
for (int k0 = 0; k0 < nbatch_fa; k0 += np*T_C_KQ::I) {
|
|
#pragma unroll
|
|
for (int l = 0; l < T_C_KQ::ne; ++l) {
|
|
if (!oob_check || k0 + (threadIdx.y % np)*T_C_KQ::I + T_C_KQ::get_i(l) < k_VKQ_sup) {
|
|
#if defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE)
|
|
constexpr int KQ_idx = 0;
|
|
#else
|
|
// Turing + Volta:
|
|
const int KQ_idx = l % 2;
|
|
#endif // defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE)
|
|
KQ_C[k0/(np*T_C_KQ::I)].x[l] = expf(KQ_C[k0/(np*T_C_KQ::I)].x[l] - KQ_max_new[KQ_idx]);
|
|
KQ_rowsum_add[KQ_idx] += KQ_C[k0/(np*T_C_KQ::I)].x[l];
|
|
} else {
|
|
KQ_C[k0/(np*T_C_KQ::I)].x[l] = 0.0f;
|
|
}
|
|
}
|
|
}
|
|
} else { // not Turing mma or T_B_KQ::I > 8
|
|
if (ncols2 > 1 || mask_h) {
|
|
#pragma unroll
|
|
for (int i00 = 0; i00 < nbatch_fa; i00 += np*T_C_KQ::J) {
|
|
const int i0 = i00 + (threadIdx.y % np)*T_C_KQ::J;
|
|
#pragma unroll
|
|
for (int l0 = 0; l0 < T_C_KQ::ne; l0 += 2) {
|
|
const int i = (i0 + T_C_KQ::get_j(l0)) / 2;
|
|
const int j = ((threadIdx.y / np)*cols_per_warp + T_C_KQ::get_i(l0)) / ncols2;
|
|
|
|
const float2 tmp = __half22float2(((const half2 *)tile_mask)[j*(nbatch_fa/2 + 4) + i]);
|
|
KQ_C[i00/(np*T_C_KQ::J)].x[l0 + 0] += slope*tmp.x;
|
|
KQ_C[i00/(np*T_C_KQ::J)].x[l0 + 1] += slope*tmp.y;
|
|
}
|
|
}
|
|
}
|
|
|
|
// 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.
|
|
static_assert(nbatch_fa % (np*T_C_KQ::J) == 0, "bad loop size");
|
|
#pragma unroll
|
|
for (int k0 = 0; k0 < nbatch_fa; k0 += np*T_C_KQ::J) {
|
|
#pragma unroll
|
|
for (int l = 0; l < T_C_KQ::ne; ++l) {
|
|
if (!oob_check || k0 + (threadIdx.y % np)*T_C_KQ::J + T_C_KQ::get_j(l) < k_VKQ_sup) {
|
|
#if defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE)
|
|
constexpr int KQ_idx = 0;
|
|
#else
|
|
// Turing + Volta:
|
|
const int KQ_idx = (l/2) % 2;
|
|
#endif // defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE)
|
|
KQ_max_new[KQ_idx] = fmaxf(KQ_max_new[KQ_idx], KQ_C[(k0/(np*T_C_KQ::J))].x[l] + FATTN_KQ_MAX_OFFSET);
|
|
}
|
|
}
|
|
}
|
|
|
|
#pragma unroll
|
|
for (int col = 0; col < cols_per_thread; ++col) {
|
|
#if defined(TURING_MMA_AVAILABLE)
|
|
// Values per KQ column are spread across 4 threads:
|
|
constexpr int offset_first = 2;
|
|
constexpr int offset_last = 1;
|
|
#elif defined(AMD_MFMA_AVAILABLE)
|
|
// MFMA: 4 threads per Q column (threadIdx.x % 16 == col, spaced by 16).
|
|
constexpr int offset_first = 32;
|
|
constexpr int offset_last = 16;
|
|
#elif defined(AMD_WMMA_AVAILABLE)
|
|
// Values per KQ column are spread across 2 threads:
|
|
constexpr int offset_first = 16;
|
|
constexpr int offset_last = 16;
|
|
#else // Volta
|
|
// Values per KQ column are spread across 2 threads:
|
|
constexpr int offset_first = 2;
|
|
constexpr int offset_last = 2;
|
|
#endif // defined(TURING_MMA_AVAILABLE)
|
|
#pragma unroll
|
|
for (int offset = offset_first; offset >= offset_last; offset >>= 1) {
|
|
KQ_max_new[col] = fmaxf(KQ_max_new[col], __shfl_xor_sync(0xFFFFFFFF, KQ_max_new[col], offset, warp_size));
|
|
}
|
|
}
|
|
|
|
static_assert(nbatch_fa % (np*T_C_KQ::J) == 0, "bad loop size");
|
|
#pragma unroll
|
|
for (int k0 = 0; k0 < nbatch_fa; k0 += np*T_C_KQ::J) {
|
|
#pragma unroll
|
|
for (int l = 0; l < T_C_KQ::ne; ++l) {
|
|
if (!oob_check || k0 + (threadIdx.y % np)*T_C_KQ::J + T_C_KQ::get_j(l) < k_VKQ_sup) {
|
|
#if defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE)
|
|
constexpr int KQ_idx = 0;
|
|
#else
|
|
// Turing + Volta:
|
|
const int KQ_idx = (l/2) % 2;
|
|
#endif // defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE)
|
|
KQ_C[(k0/(np*T_C_KQ::J))].x[l] = expf(KQ_C[(k0/(np*T_C_KQ::J))].x[l] - KQ_max_new[KQ_idx]);
|
|
KQ_rowsum_add[KQ_idx] += KQ_C[(k0/(np*T_C_KQ::J))].x[l];
|
|
} else {
|
|
KQ_C[(k0/(np*T_C_KQ::J))].x[l] = 0.0f;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
{
|
|
float KQ_max_scale[cols_per_thread];
|
|
#pragma unroll
|
|
for (int col = 0; col < cols_per_thread; ++col) {
|
|
const float KQ_max_diff = KQ_max[col] - KQ_max_new[col];
|
|
KQ_max_scale[col] = expf(KQ_max_diff);
|
|
KQ_max[col] = KQ_max_new[col];
|
|
|
|
*((uint32_t *) &KQ_max_scale[col]) *= KQ_max_diff >= SOFTMAX_FTZ_THRESHOLD;
|
|
|
|
// Scale previous KQ_rowsum to account for a potential increase in KQ_max:
|
|
KQ_rowsum[col] = KQ_max_scale[col]*KQ_rowsum[col] + KQ_rowsum_add[col];
|
|
}
|
|
|
|
#if defined(TURING_MMA_AVAILABLE)
|
|
if constexpr (cols_per_warp == 8) {
|
|
const half2 KQ_max_scale_h2 = make_half2(KQ_max_scale[0], KQ_max_scale[cols_per_thread - 1]);
|
|
#pragma unroll
|
|
for (int i = 0; i < DV/T_C_VKQ::I; ++i) {
|
|
#pragma unroll
|
|
for (int l = 0; l < T_C_VKQ::ne; ++l) {
|
|
VKQ_C[i].x[l] *= KQ_max_scale_h2;
|
|
}
|
|
}
|
|
} else {
|
|
#pragma unroll
|
|
for (int col = 0; col < cols_per_thread; ++col) {
|
|
const half2 KQ_max_scale_h2 = make_half2(KQ_max_scale[col], KQ_max_scale[col]);
|
|
#pragma unroll
|
|
for (int i = 0; i < (DV/2)/T_C_VKQ::J; ++i) {
|
|
#pragma unroll
|
|
for (int l0 = 0; l0 < T_C_VKQ::ne; l0 += 2) {
|
|
VKQ_C[i].x[l0 + col] *= KQ_max_scale_h2;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
#elif defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE)
|
|
const half2 KQ_max_scale_h2 = make_half2(
|
|
KQ_max_scale[0], KQ_max_scale[0]);
|
|
#pragma unroll
|
|
for (int i = 0; i < (DV/2)/T_C_VKQ::J; ++i) {
|
|
#pragma unroll
|
|
for (int l = 0; l < T_C_VKQ::ne; ++l) {
|
|
VKQ_C[i].x[l] *= KQ_max_scale_h2;
|
|
}
|
|
}
|
|
#else // Volta
|
|
const half2 KQ_max_scale_h2 = make_half2(
|
|
KQ_max_scale[(threadIdx.x / 2) % 2], KQ_max_scale[(threadIdx.x / 2) % 2]);
|
|
#pragma unroll
|
|
for (int i = 0; i < (DV/2)/T_C_VKQ::J; ++i) {
|
|
#pragma unroll
|
|
for (int l = 0; l < T_C_VKQ::ne; ++l) {
|
|
VKQ_C[i].x[l] *= KQ_max_scale_h2;
|
|
}
|
|
}
|
|
#endif // defined(TURING_MMA_AVAILABLE)
|
|
}
|
|
|
|
// Convert KQ C tiles into B tiles for VKQ calculation:
|
|
T_B_VKQ B[nbatch_fa/(np*2*T_B_VKQ::J)];
|
|
static_assert(nbatch_fa % (np*2*T_B_VKQ::J) == 0, "bad loop size");
|
|
if constexpr (cols_per_warp == 8) {
|
|
#pragma unroll
|
|
for (int k = 0; k < nbatch_fa/(np*2*T_B_VKQ::J); ++k) {
|
|
B[k] = get_transposed(get_half2(KQ_C[k]));
|
|
}
|
|
} else {
|
|
for (int k = 0; k < nbatch_fa/(np*2*T_B_VKQ::J); ++k) {
|
|
B[k] = get_half2(KQ_C[k]);
|
|
}
|
|
}
|
|
|
|
if constexpr (nstages > 1) {
|
|
static_assert(!V_is_K_view, "K data reuse not implemented multi-stage loading");
|
|
// Preload K tile for next iteration:
|
|
constexpr bool use_cp_async = true;
|
|
cp_async_wait_all();
|
|
__syncthreads();
|
|
if (!last_iter) {
|
|
if (ncols2 > 1 || mask_h) {
|
|
flash_attn_ext_f16_load_mask<ncols1, nwarps, nbatch_fa, use_cp_async, oob_check>
|
|
(mask_h + k_VKQ_0 + nbatch_fa, tile_mask, stride_mask, k_VKQ_sup, jt*ncols1, ne01);
|
|
}
|
|
flash_attn_ext_f16_load_tile<stride_tile_K, nwarps, nbatch_fa, use_cp_async, oob_check>
|
|
(K_h2 + int64_t(k_VKQ_0 + nbatch_fa)*stride_K, tile_K, nbatch_K2, stride_K, k_VKQ_sup);
|
|
}
|
|
}
|
|
|
|
|
|
// Calculate VKQ tile, need to use logical rather than physical elements for i0 due to transposition of V:
|
|
#pragma unroll
|
|
for (int i0_start = 0; i0_start < DV; i0_start += 2*nbatch_V2) {
|
|
static_assert(DV % (2*nbatch_V2) == 0, "bad loop size");
|
|
const int i0_stop = i0_start + 2*nbatch_V2;
|
|
const int i0_diff = i0_stop - i0_start;
|
|
|
|
if constexpr (nstages <= 1) {
|
|
if (!V_is_K_view || i0_stop > 2*nbatch_K2) {
|
|
constexpr bool use_cp_async = nstages == 1;
|
|
flash_attn_ext_f16_load_tile<stride_tile_V, nwarps, nbatch_fa, use_cp_async, oob_check>
|
|
(V_h2 + int64_t(k_VKQ_0)*stride_V + i0_start/2, tile_V, i0_diff/2, stride_V, k_VKQ_sup);
|
|
if (use_cp_async) {
|
|
cp_async_wait_all();
|
|
}
|
|
__syncthreads();
|
|
}
|
|
}
|
|
const half2 * tile_V_i = !V_is_K_view || i0_stop > 2*nbatch_K2 ? tile_V : tile_V + i0_start/2;
|
|
|
|
#if defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE)
|
|
constexpr int i0_stride = cols_per_warp == 8 ? T_C_VKQ::I : 2*T_C_VKQ::J;
|
|
#pragma unroll
|
|
for (int i_VKQ_0 = i0_start; i_VKQ_0 < i0_stop; i_VKQ_0 += i0_stride) {
|
|
static_assert((nbatch_fa/2) % (np*T_A_VKQ::J) == 0, "bad loop size");
|
|
#pragma unroll
|
|
for (int k00 = 0; k00 < nbatch_fa/2; k00 += np*T_A_VKQ::J) {
|
|
const int k0 = k00 + (threadIdx.y % np)*T_A_VKQ::J;
|
|
|
|
T_A_VKQ A; // Transposed in SRAM but not in registers, gets transposed on load.
|
|
load_ldmatrix_trans(A, tile_V_i + 2*k0*stride_tile_V + (i_VKQ_0 - i0_start)/2, stride_tile_V);
|
|
if constexpr (T_B_KQ::I == 8) {
|
|
mma(VKQ_C[i_VKQ_0/i0_stride], A, B[k00/(np*T_A_VKQ::J)]);
|
|
} else {
|
|
// Wide version of VKQ_C is column-major.
|
|
#if defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE)
|
|
// AMD matrix C is column-major.
|
|
mma(VKQ_C[i_VKQ_0/i0_stride], A, B[k00/(np*T_A_VKQ::J)]);
|
|
#else
|
|
// swap A and B for CUDA.
|
|
mma(VKQ_C[i_VKQ_0/i0_stride], B[k00/(np*T_A_VKQ::J)], A);
|
|
#endif // defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE)
|
|
}
|
|
}
|
|
}
|
|
#else // Volta
|
|
constexpr int i0_stride = 2*T_C_VKQ::J;
|
|
#pragma unroll
|
|
for (int i_VKQ_0 = i0_start; i_VKQ_0 < i0_stop; i_VKQ_0 += i0_stride) {
|
|
static_assert(nbatch_fa % (np*T_A_VKQ::I) == 0, "bad loop size");
|
|
static_assert(2*T_B_VKQ::J == T_A_VKQ::I, "bad tile sizes");
|
|
#pragma unroll
|
|
for (int k00 = 0; k00 < nbatch_fa; k00 += np*T_A_VKQ::I) {
|
|
const int k0 = k00 + (threadIdx.y % np)*T_A_VKQ::I;
|
|
|
|
T_A_VKQ A; // Transposed in both SRAM and registers, load normally.
|
|
load_ldmatrix(A, tile_V_i + k0*stride_tile_V + (i_VKQ_0 - i0_start)/2, stride_tile_V);
|
|
mma(VKQ_C[i_VKQ_0/i0_stride], B[k00/(np*T_A_VKQ::I)], A);
|
|
}
|
|
}
|
|
#endif // defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE)
|
|
|
|
if constexpr (nstages <= 1) {
|
|
__syncthreads(); // Only needed if tile_K == tile_V.
|
|
}
|
|
}
|
|
#else
|
|
GGML_UNUSED_VARS(Q_f2, K_h2, V_h2, mask_h, dstk, dstk_fixup,
|
|
scale, slope, logit_softcap, ne01, ne02,
|
|
stride_K, stride_V, stride_mask,
|
|
tile_Q, tile_K, tile_V, tile_mask,
|
|
Q_B, VKQ_C, KQ_max, KQ_rowsum, kb0);
|
|
NO_DEVICE_CODE;
|
|
#endif // defined(VOLTA_MMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || (defined(AMD_WMMA_AVAILABLE) && defined(RDNA4)) || defined(AMD_MFMA_AVAILABLE)
|
|
}
|
|
|
|
#if defined(TURING_MMA_AVAILABLE)
|
|
template<int ncols> struct mma_tile_sizes {
|
|
using T_A_KQ = tile<16, 8, half2>; // row-major
|
|
using T_B_KQ = tile<16, 8, half2>; // column-major
|
|
using T_C_KQ = tile<16, 16, float>; // column-major
|
|
using T_A_VKQ = tile<16, 8, half2>; // row-major
|
|
using T_B_VKQ = tile<16, 8, half2>; // column-major
|
|
using T_C_VKQ = tile<16, 8, half2>; // column-major
|
|
};
|
|
template<> struct mma_tile_sizes<8> {
|
|
using T_A_KQ = tile<16, 8, half2>; // row-major
|
|
using T_B_KQ = tile< 8, 8, half2>; // column-major
|
|
using T_C_KQ = tile<16, 8, float>; // row-major
|
|
using T_A_VKQ = tile<16, 8, half2>; // row-major
|
|
using T_B_VKQ = tile< 8, 8, half2>; // column-major
|
|
using T_C_VKQ = tile<16, 4, half2>; // row-major
|
|
};
|
|
#elif defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE)
|
|
template<int ncols> struct mma_tile_sizes {
|
|
using T_A_KQ = tile<16, 8, half2>; // row-major
|
|
using T_B_KQ = tile<16, 8, half2>; // column-major
|
|
using T_C_KQ = tile<16, 16, float>; // column-major
|
|
using T_A_VKQ = tile<16, 8, half2>; // row-major
|
|
using T_B_VKQ = tile<16, 8, half2>; // column-major
|
|
using T_C_VKQ = tile<16, 8, half2>; // column-major
|
|
};
|
|
#else // Volta
|
|
template<int ncols> struct mma_tile_sizes {
|
|
using T_A_KQ = tile< 8, 4, half2, DATA_LAYOUT_I_MAJOR_MIRRORED>; // row-major
|
|
using T_B_KQ = tile<32, 4, half2, DATA_LAYOUT_I_MAJOR>; // column-major
|
|
using T_C_KQ = tile<32, 8, float, DATA_LAYOUT_I_MAJOR>; // column-major
|
|
using T_A_VKQ = tile< 8, 4, half2, DATA_LAYOUT_J_MAJOR_MIRRORED>; // column-major
|
|
using T_B_VKQ = tile<32, 4, half2, DATA_LAYOUT_I_MAJOR>; // column-major
|
|
using T_C_VKQ = tile<32, 4, half2, DATA_LAYOUT_I_MAJOR>; // column-major
|
|
};
|
|
#endif // defined(TURING_MMA_AVAILABLE)
|
|
|
|
template<int DKQ, int DV, int ncols1, int ncols2, int nwarps, bool use_logit_softcap, bool V_is_K_view, 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__ mask_h,
|
|
const float * const __restrict__ sinks_f,
|
|
float2 * const __restrict__ dstk,
|
|
float2 * const __restrict__ dstk_fixup,
|
|
const float scale,
|
|
const float slope,
|
|
const float logit_softcap,
|
|
const uint3 ne01,
|
|
const int ne02,
|
|
const int gqa_ratio,
|
|
const int ne11,
|
|
const int stride_Q1,
|
|
const int stride_Q2,
|
|
const int stride_K,
|
|
const int stride_V,
|
|
const int stride_mask,
|
|
const int jt,
|
|
const int zt_gqa,
|
|
const int kb0_start,
|
|
const int kb0_stop) {
|
|
#if defined(VOLTA_MMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || (defined(AMD_WMMA_AVAILABLE) && defined(RDNA4)) || defined(AMD_MFMA_AVAILABLE)
|
|
//In this kernel Q, K, V are matrices while i, j, k are matrix indices.
|
|
|
|
constexpr int warp_size = ggml_cuda_get_physical_warp_size();
|
|
constexpr int ncols = ncols1 * ncols2;
|
|
using T_A_KQ = typename mma_tile_sizes<ncols>::T_A_KQ;
|
|
using T_B_KQ = typename mma_tile_sizes<ncols>::T_B_KQ;
|
|
using T_C_KQ = typename mma_tile_sizes<ncols>::T_C_KQ;
|
|
using T_A_VKQ = typename mma_tile_sizes<ncols>::T_A_VKQ;
|
|
using T_B_VKQ = typename mma_tile_sizes<ncols>::T_B_VKQ;
|
|
using T_C_VKQ = typename mma_tile_sizes<ncols>::T_C_VKQ;
|
|
|
|
constexpr int cols_per_warp = T_B_KQ::I;
|
|
constexpr int cols_per_thread = get_cols_per_thread();
|
|
constexpr int np = cols_per_warp > ncols ? nwarps : nwarps * cols_per_warp/ncols; // Number of parallel CUDA warps per Q column.
|
|
constexpr int nbatch_fa = ggml_cuda_fattn_mma_get_nbatch_fa (DKQ, DV, ncols);
|
|
constexpr int nbatch_K2 = ggml_cuda_fattn_mma_get_nbatch_K2 (DKQ, DV, ncols);
|
|
constexpr int nbatch_V2 = ggml_cuda_fattn_mma_get_nbatch_V2 (DKQ, DV, ncols);
|
|
constexpr int nbatch_combine = ggml_cuda_fattn_mma_get_nbatch_combine(DKQ, DV, ncols);
|
|
constexpr bool Q_in_reg = ggml_cuda_fattn_mma_get_Q_in_reg (DKQ, DV, ncols);
|
|
constexpr int nstages = ggml_cuda_fattn_mma_get_nstages (DKQ, DV, ncols1, ncols2);
|
|
|
|
if (cols_per_warp > ncols) {
|
|
NO_DEVICE_CODE;
|
|
return;
|
|
}
|
|
|
|
static_assert(nwarps * (cols_per_warp/ncols2) % ncols1 == 0, "bad nwarps");
|
|
|
|
constexpr int stride_tile_Q = DKQ/2 + 4;
|
|
constexpr int stride_tile_K = nbatch_K2 + 4;
|
|
|
|
constexpr int stride_tile_V = V_is_K_view ? stride_tile_K : nbatch_V2 + 4;
|
|
constexpr int stride_tile_KV_max = stride_tile_K > stride_tile_V ? stride_tile_K : stride_tile_V;
|
|
|
|
extern __shared__ half2 tile_Q[];
|
|
half2 * tile_K = Q_in_reg ? tile_Q : tile_Q + ncols * stride_tile_Q;
|
|
half2 * tile_V = nstages > 1 ? tile_K + nbatch_fa * stride_tile_K : tile_K;
|
|
half * tile_mask = (half *) (nstages > 1 ? tile_V + nbatch_fa * stride_tile_V : tile_V + nbatch_fa * stride_tile_KV_max);
|
|
|
|
T_B_KQ Q_B[(Q_in_reg ? DKQ/(2*T_B_KQ::J) : 1)];
|
|
#if defined(TURING_MMA_AVAILABLE)
|
|
T_C_VKQ VKQ_C[cols_per_warp == 8 ? DV/T_C_VKQ::I : DV/(2*T_C_VKQ::J)];
|
|
#elif defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE)
|
|
T_C_VKQ VKQ_C[ DV/(2*T_C_VKQ::J)];
|
|
#else // Volta
|
|
T_C_VKQ VKQ_C[ DV/(2*T_C_VKQ::J)];
|
|
#endif // defined(TURING_MMA_AVAILABLE)
|
|
|
|
float KQ_rowsum[cols_per_thread] = {0.0f};
|
|
float KQ_max[cols_per_thread];
|
|
#pragma unroll
|
|
for (int col = 0; col < cols_per_thread; ++col) {
|
|
KQ_max[col] = -FLT_MAX/2.0f;
|
|
}
|
|
|
|
// Load Q data into tile_Q, either temporarily or permanently.
|
|
// Q in registers is faster, but register pressure is the biggest bottleneck.
|
|
// 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, warp_size/8}) {
|
|
const int k0_start = stride_k == warp_size ? 0 : DKQ/2 - (DKQ/2) % (2*stride_k);
|
|
const int k0_stop = DKQ/2 - (DKQ/2) % (1*stride_k);
|
|
const int stride_jc = warp_size / stride_k;
|
|
|
|
if (k0_start == k0_stop) {
|
|
continue;
|
|
}
|
|
|
|
#pragma unroll
|
|
for (int jc0 = 0; jc0 < ncols; jc0 += nwarps*stride_jc) {
|
|
const int jc = jc0 + threadIdx.y*stride_jc + (stride_k == warp_size ? 0 : threadIdx.x / stride_k);
|
|
|
|
if (jc0 + nwarps*stride_jc > ncols && jc >= ncols) {
|
|
break;
|
|
}
|
|
|
|
const int j = jc / ncols2;
|
|
const int c = jc % ncols2;
|
|
|
|
if ((ncols1 == 1 || jt*ncols1 + j < int(ne01.z)) && (ncols2 == 1 || zt_gqa*ncols2 + c < gqa_ratio)) {
|
|
#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*ncols1 + j)*stride_Q1 + c*stride_Q2 + k];
|
|
tile_Q[jc*stride_tile_Q + 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_Q[jc*stride_tile_Q + k] = make_half2(0.0f, 0.0f);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
__syncthreads();
|
|
|
|
if (Q_in_reg) {
|
|
const int j0 = (threadIdx.y / np) * cols_per_warp;
|
|
|
|
#pragma unroll
|
|
for (int k0 = 0; k0 < DKQ/2; k0 += T_B_KQ::J) {
|
|
load_ldmatrix(Q_B[k0/T_B_KQ::J], tile_Q + j0*stride_tile_Q + k0, stride_tile_Q);
|
|
}
|
|
}
|
|
|
|
__syncthreads();
|
|
|
|
int kb0 = kb0_start;
|
|
|
|
// Preload mask and K data for first iteration when using cp_async with multiple stages:
|
|
if constexpr (nstages > 1) {
|
|
static_assert(nbatch_K2 == DKQ/2, "batching not implemented for multi-stage pipeline");
|
|
constexpr bool use_cp_async = true;
|
|
constexpr bool oob_check = false;
|
|
constexpr int k_VKQ_sup = nbatch_fa;
|
|
if (ncols2 > 1 || mask_h) {
|
|
flash_attn_ext_f16_load_mask<ncols1, nwarps, nbatch_fa, use_cp_async, oob_check>
|
|
(mask_h + kb0*nbatch_fa, tile_mask, stride_mask, k_VKQ_sup, jt*ncols1, ne01);
|
|
}
|
|
flash_attn_ext_f16_load_tile<stride_tile_K, nwarps, nbatch_fa, use_cp_async, oob_check>
|
|
(K_h2 + int64_t(kb0)*nbatch_fa*stride_K, tile_K, nbatch_K2, stride_K, k_VKQ_sup);
|
|
}
|
|
|
|
// kb0_start is always < kb0_stop so the last iter can be executed unconditionally.
|
|
if constexpr (ncols2 == 1) {
|
|
constexpr bool oob_check = true;
|
|
for (; kb0 < kb0_stop-1; ++kb0) {
|
|
constexpr bool last_iter = false;
|
|
constexpr int k_VKQ_sup = nbatch_fa;
|
|
flash_attn_ext_f16_iter
|
|
<DKQ, DV, ncols1, ncols2, nwarps, use_logit_softcap, V_is_K_view, needs_fixup, is_fixup, last_iter, oob_check,
|
|
T_A_KQ, T_B_KQ, T_C_KQ, T_A_VKQ, T_B_VKQ, T_C_VKQ>
|
|
(Q_f2, K_h2, V_h2, mask_h, dstk, dstk_fixup, scale, slope, logit_softcap,
|
|
ne01, ne02, stride_K, stride_V, stride_mask, tile_Q, tile_K, tile_V, tile_mask, Q_B, VKQ_C,
|
|
KQ_max, KQ_rowsum, jt, kb0, k_VKQ_sup);
|
|
}
|
|
constexpr bool last_iter = true;
|
|
const int k_VKQ_sup = ne11 - kb0*nbatch_fa;
|
|
flash_attn_ext_f16_iter
|
|
<DKQ, DV, ncols1, ncols2, nwarps, use_logit_softcap, V_is_K_view, needs_fixup, is_fixup, last_iter, oob_check,
|
|
T_A_KQ, T_B_KQ, T_C_KQ, T_A_VKQ, T_B_VKQ, T_C_VKQ>
|
|
(Q_f2, K_h2, V_h2, mask_h, dstk, dstk_fixup, scale, slope, logit_softcap,
|
|
ne01, ne02, stride_K, stride_V, stride_mask, tile_Q, tile_K, tile_V, tile_mask, Q_B, VKQ_C,
|
|
KQ_max, KQ_rowsum, jt, kb0, k_VKQ_sup);
|
|
} else {
|
|
constexpr bool oob_check = false;
|
|
for (; kb0 < kb0_stop-1; ++kb0) {
|
|
constexpr bool last_iter = false;
|
|
constexpr int k_VKQ_sup = nbatch_fa;
|
|
flash_attn_ext_f16_iter
|
|
<DKQ, DV, ncols1, ncols2, nwarps, use_logit_softcap, V_is_K_view, needs_fixup, is_fixup, last_iter, oob_check,
|
|
T_A_KQ, T_B_KQ, T_C_KQ, T_A_VKQ, T_B_VKQ, T_C_VKQ>
|
|
(Q_f2, K_h2, V_h2, mask_h, dstk, dstk_fixup, scale, slope, logit_softcap,
|
|
ne01, ne02, stride_K, stride_V, stride_mask, tile_Q, tile_K, tile_V, tile_mask, Q_B, VKQ_C,
|
|
KQ_max, KQ_rowsum, jt, kb0, k_VKQ_sup);
|
|
}
|
|
constexpr bool last_iter = true;
|
|
constexpr int k_VKQ_sup = nbatch_fa;
|
|
flash_attn_ext_f16_iter
|
|
<DKQ, DV, ncols1, ncols2, nwarps, use_logit_softcap, V_is_K_view, needs_fixup, is_fixup, last_iter, oob_check,
|
|
T_A_KQ, T_B_KQ, T_C_KQ, T_A_VKQ, T_B_VKQ, T_C_VKQ>
|
|
(Q_f2, K_h2, V_h2, mask_h, dstk, dstk_fixup, scale, slope, logit_softcap,
|
|
ne01, ne02, stride_K, stride_V, stride_mask, tile_Q, tile_K, tile_V, tile_mask, Q_B, VKQ_C,
|
|
KQ_max, KQ_rowsum, jt, kb0, k_VKQ_sup);
|
|
}
|
|
|
|
// With multi-stage loading there is no __syncthreads at the end of the iter,
|
|
// there can be a race condition on shared memory access for combining/writing back results.
|
|
if constexpr (nstages > 1 && nwarps*cols_per_warp > nbatch_fa) {
|
|
__syncthreads();
|
|
}
|
|
|
|
// Finally, sum up partial KQ rowsums.
|
|
{
|
|
#if defined(TURING_MMA_AVAILABLE)
|
|
// The partial sums are spread across 8/4 threads.
|
|
constexpr int offset_first = cols_per_warp == 8 ? 16 : 2;
|
|
constexpr int offset_last = cols_per_warp == 8 ? 4 : 1;
|
|
#elif defined(AMD_MFMA_AVAILABLE)
|
|
// The partial sums are spread across 4 threads (wavefront64, 16 cols).
|
|
constexpr int offset_first = 32;
|
|
constexpr int offset_last = 16;
|
|
#elif defined(AMD_WMMA_AVAILABLE)
|
|
// The partial sums are spread across 2 threads.
|
|
constexpr int offset_first = 16;
|
|
constexpr int offset_last = 16;
|
|
#else // Volta
|
|
// The partial sums are spread across 2 threads.
|
|
constexpr int offset_first = 2;
|
|
constexpr int offset_last = 2;
|
|
#endif // defined(TURING_MMA_AVAILABLE)
|
|
#pragma unroll
|
|
for (int col = 0; col < cols_per_thread; ++col) {
|
|
#pragma unroll
|
|
for (int offset = offset_first; offset >= offset_last; offset >>= 1) {
|
|
KQ_rowsum[col] += __shfl_xor_sync(0xFFFFFFFF, KQ_rowsum[col], offset, warp_size);
|
|
}
|
|
}
|
|
}
|
|
|
|
// If attention sinks are used, potentially re-scale if KQ_max is small.
|
|
// Also add the sink as a value to KQ_rowsum, this is done after synchronization of KQ_rowsum
|
|
// so it's being done unconditionally for every thread.
|
|
if (!is_fixup && (np == 1 || threadIdx.y % np == 0) && sinks_f) {
|
|
float KQ_max_scale[cols_per_thread];
|
|
#pragma unroll
|
|
for (int col = 0; col < cols_per_thread; ++col) {
|
|
const int jc = (threadIdx.y/np)*cols_per_warp + (cols_per_warp == 8 ? T_C_KQ::get_j(col) : T_C_KQ::get_i(2*col));
|
|
const float sink = sinks_f[jc % ncols2];
|
|
|
|
const float KQ_max_new = fmaxf(KQ_max[col], sink);
|
|
const float KQ_max_diff = KQ_max[col] - KQ_max_new;
|
|
KQ_max_scale[col] = expf(KQ_max_diff);
|
|
KQ_max[col] = KQ_max_new;
|
|
|
|
*((uint32_t *) &KQ_max_scale[col]) *= KQ_max_diff >= SOFTMAX_FTZ_THRESHOLD;
|
|
|
|
const float KQ_max_add = expf(sink - KQ_max_new);
|
|
KQ_rowsum[col] = KQ_max_scale[col]*KQ_rowsum[col] + KQ_max_add;
|
|
}
|
|
|
|
#if defined(TURING_MMA_AVAILABLE)
|
|
if constexpr (cols_per_warp == 8) {
|
|
const half2 KQ_max_scale_h2 = make_half2(KQ_max_scale[0], KQ_max_scale[cols_per_thread - 1]);
|
|
#pragma unroll
|
|
for (int i = 0; i < DV/T_C_VKQ::I; ++i) {
|
|
#pragma unroll
|
|
for (int l = 0; l < T_C_VKQ::ne; ++l) {
|
|
VKQ_C[i].x[l] *= KQ_max_scale_h2;
|
|
}
|
|
}
|
|
} else {
|
|
#pragma unroll
|
|
for (int col = 0; col < cols_per_thread; ++col) {
|
|
const half2 KQ_max_scale_h2 = make_half2(KQ_max_scale[col], KQ_max_scale[col]);
|
|
#pragma unroll
|
|
for (int i = 0; i < (DV/2)/T_C_VKQ::J; ++i) {
|
|
#pragma unroll
|
|
for (int l0 = 0; l0 < T_C_VKQ::ne; l0 += 2) {
|
|
VKQ_C[i].x[l0 + col] *= KQ_max_scale_h2;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
#elif defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE)
|
|
const half2 KQ_max_scale_h2 = make_half2(KQ_max_scale[0], KQ_max_scale[0]);
|
|
#pragma unroll
|
|
for (int i = 0; i < (DV/2)/T_C_VKQ::J; ++i) {
|
|
#pragma unroll
|
|
for (int l = 0; l < T_C_VKQ::ne; ++l) {
|
|
VKQ_C[i].x[l] *= KQ_max_scale_h2;
|
|
}
|
|
}
|
|
#else // Volta
|
|
const int col = (threadIdx.x / 2) % 2;
|
|
const half2 KQ_max_scale_h2 = make_half2(KQ_max_scale[col], KQ_max_scale[col]);
|
|
#pragma unroll
|
|
for (int i = 0; i < (DV/2)/T_C_VKQ::J; ++i) {
|
|
#pragma unroll
|
|
for (int l = 0; l < T_C_VKQ::ne; ++l) {
|
|
VKQ_C[i].x[l] *= KQ_max_scale_h2;
|
|
}
|
|
}
|
|
#endif // defined(TURING_MMA_AVAILABLE)
|
|
}
|
|
|
|
// Combine VKQ accumulator values if np > 1.
|
|
// It's also faster to do small writes to shared memory, then large write to VRAM than to do small writes to VRAM.
|
|
// So also write VKQ accumulators to shared memory in column-major format if np == 1.
|
|
|
|
constexpr int tile_stride = nbatch_combine + 4;
|
|
static_assert((DV/2) % nbatch_combine == 0, "bad nbatch_combine");
|
|
|
|
if constexpr (cols_per_warp == 8) {
|
|
const int jc_cwmo = (threadIdx.x % (2*T_C_VKQ::J)) / T_C_VKQ::J; // jc combine write meta offset
|
|
const int jc_cwm = threadIdx.y*(2*T_C_VKQ::J) + 2*T_C_VKQ::get_j(-1) + jc_cwmo; // jc combine write meta
|
|
const float2 KQ_cmr = make_float2(KQ_max[jc_cwmo], KQ_rowsum[jc_cwmo]); // KQ combine max rowsum
|
|
|
|
if (((!needs_fixup && !is_fixup) || np > 1) && threadIdx.x < 2*T_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_Q)[jc_cwm*(tile_stride/2) + nbatch_combine/2] = KQ_cmr;
|
|
}
|
|
|
|
__syncthreads();
|
|
|
|
if (np == 1) {
|
|
// No combination is needed, the meta data can be directly written from registers to VRAM.
|
|
if (needs_fixup && threadIdx.x < T_B_KQ::I) {
|
|
float2 * dstk_fixup_meta = dstk_fixup + blockIdx.x*ncols;
|
|
dstk_fixup_meta[jc_cwm] = KQ_cmr;
|
|
}
|
|
if (is_fixup && threadIdx.x < T_B_KQ::I) {
|
|
float2 * dstk_fixup_meta = dstk_fixup + (gridDim.x + blockIdx.x)*ncols;
|
|
dstk_fixup_meta[jc_cwm] = KQ_cmr;
|
|
}
|
|
}
|
|
} else {
|
|
// jc_cwm = jc combine write meta
|
|
// KQ_cmr = KQ combine max rowsum
|
|
// Use the 16 bytes of padding in each Q column to store the meta data: KQ max, KQ rowsum, KQ max scale.
|
|
#if defined(TURING_MMA_AVAILABLE)
|
|
const int jc_cwm = threadIdx.y*cols_per_warp + T_C_VKQ::get_i(threadIdx.x % 4);
|
|
const float2 KQ_cmr = make_float2(KQ_max[threadIdx.x % cols_per_thread], KQ_rowsum[threadIdx.x % cols_per_thread]);
|
|
const bool thread_should_write = threadIdx.x % 4 < cols_per_thread;
|
|
#elif defined(AMD_WMMA_AVAILABLE) || defined(AMD_MFMA_AVAILABLE)
|
|
const int jc_cwm = threadIdx.y*cols_per_warp + T_C_VKQ::get_i(0);
|
|
const float2 KQ_cmr = make_float2(KQ_max[0], KQ_rowsum[0]);
|
|
const bool thread_should_write = threadIdx.x / 16 < cols_per_thread;
|
|
#else // Volta
|
|
const int jc_cwm = threadIdx.y*cols_per_warp + T_C_KQ::get_i(threadIdx.x & 2);
|
|
const float2 KQ_cmr = make_float2(KQ_max[(threadIdx.x & 2) / 2], KQ_rowsum[(threadIdx.x & 2) / 2]);
|
|
const bool thread_should_write = T_C_KQ::J == 8 || T_C_KQ::get_j(threadIdx.x & 2) < 8;
|
|
#endif // defined(TURING_MMA_AVAILABLE)
|
|
|
|
if (((!needs_fixup && !is_fixup) || np > 1) && thread_should_write) {
|
|
((float2 *) tile_Q)[jc_cwm*(tile_stride/2) + nbatch_combine/2] = KQ_cmr;
|
|
}
|
|
|
|
__syncthreads();
|
|
|
|
if (np == 1) {
|
|
// No combination is needed, the meta data can be directly written from registers to VRAM.
|
|
if (needs_fixup && thread_should_write) {
|
|
float2 * dstk_fixup_meta = dstk_fixup + blockIdx.x*ncols;
|
|
dstk_fixup_meta[jc_cwm] = KQ_cmr;
|
|
}
|
|
if (is_fixup && thread_should_write) {
|
|
float2 * dstk_fixup_meta = dstk_fixup + (gridDim.x + blockIdx.x)*ncols;
|
|
dstk_fixup_meta[jc_cwm] = KQ_cmr;
|
|
}
|
|
}
|
|
}
|
|
|
|
if (np > 1 && 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.
|
|
|
|
constexpr int nmeta = np*cols_per_warp >= warp_size ? np*cols_per_warp/warp_size : 1;
|
|
|
|
const int jc_meta = threadIdx.y*cols_per_warp + (np*cols_per_warp < warp_size ? threadIdx.x % (np*cols_per_warp) : threadIdx.x);
|
|
float2 * const meta_ptr = ((float2 *) tile_Q) + jc_meta*(tile_stride/2) + nbatch_combine/2;
|
|
float2 meta[nmeta];
|
|
#pragma unroll
|
|
for (int imeta = 0; imeta < nmeta; ++imeta) {
|
|
meta[imeta] = meta_ptr[imeta * warp_size * tile_stride/2];
|
|
}
|
|
|
|
float KQ_cmn = meta[0].x; // KQ combine max new, max between all parallel warps.
|
|
#pragma unroll
|
|
for (int imeta = 1; imeta < nmeta; ++imeta) {
|
|
KQ_cmn = fmaxf(KQ_cmn, meta[imeta].x);
|
|
}
|
|
#pragma unroll
|
|
for (int offset = np*cols_per_warp/2; offset >= cols_per_warp; offset >>= 1) {
|
|
if (offset < warp_size) {
|
|
KQ_cmn = fmaxf(KQ_cmn, __shfl_xor_sync(0xFFFFFFFF, KQ_cmn, offset, warp_size));
|
|
}
|
|
}
|
|
|
|
float KQ_cms[nmeta]; // KQ combine max scale per warp.
|
|
#pragma unroll
|
|
for (int imeta = 0; imeta < nmeta; ++imeta) {
|
|
KQ_cms[imeta] = expf(meta[imeta].x - KQ_cmn);
|
|
}
|
|
|
|
float KQ_crs = KQ_cms[0]*meta[0].y; // KQ combine rowsum, scaled sum of all parallel warps.
|
|
#pragma unroll
|
|
for (int imeta = 1; imeta < nmeta; ++imeta) {
|
|
KQ_crs += KQ_cms[imeta]*meta[imeta].y;
|
|
}
|
|
#pragma unroll
|
|
for (int offset = np*cols_per_warp/2; offset >= cols_per_warp; offset >>= 1) {
|
|
if (offset < warp_size) {
|
|
KQ_crs += __shfl_xor_sync(0xFFFFFFFF, KQ_crs, offset, warp_size);
|
|
}
|
|
}
|
|
|
|
__syncthreads();
|
|
|
|
// Write back combined meta data:
|
|
#pragma unroll
|
|
for (int imeta = 0; imeta < nmeta; ++imeta) {
|
|
if (np*cols_per_warp >= warp_size || threadIdx.x < np*cols_per_warp) {
|
|
// Combined KQ max scale + rowsum.
|
|
meta_ptr[imeta * warp_size * tile_stride/2] = make_float2(KQ_cms[imeta], KQ_crs);
|
|
}
|
|
}
|
|
|
|
// Combined KQ max + rowsum.
|
|
static_assert(cols_per_warp <= warp_size);
|
|
if (needs_fixup && (cols_per_warp == warp_size || threadIdx.x < cols_per_warp)) {
|
|
float2 * dstk_fixup_meta = dstk_fixup + blockIdx.x*ncols;
|
|
dstk_fixup_meta[(threadIdx.y/np)*cols_per_warp + threadIdx.x] = make_float2(KQ_cmn, KQ_crs);
|
|
}
|
|
if (is_fixup && (cols_per_warp == warp_size || threadIdx.x < cols_per_warp)) {
|
|
float2 * dstk_fixup_meta = dstk_fixup + (gridDim.x + blockIdx.x)*ncols;
|
|
dstk_fixup_meta[(threadIdx.y/np)*cols_per_warp + threadIdx.x] = make_float2(KQ_cmn, KQ_crs);
|
|
}
|
|
} else if (np > 1) {
|
|
// Warps with threadIdx.y % np == 0 execute a __syncthreads() in the if branch.
|
|
// Therefore, all other warps also need to execute a __syncthreads().
|
|
// Otherwise the points at which warps synchronize with each other would become misaligned.
|
|
__syncthreads();
|
|
}
|
|
|
|
#pragma unroll
|
|
for (int k00 = 0; k00 < DV/2; k00 += nbatch_combine) {
|
|
if constexpr (cols_per_warp == 8) {
|
|
const int jc_cwd = threadIdx.y*T_B_KQ::I + T_B_KQ::get_i(-1); // jc combine write data
|
|
#pragma unroll
|
|
for (int k1 = 0; k1 < nbatch_combine; k1 += T_B_KQ::J) {
|
|
const T_B_KQ B = get_transposed(VKQ_C[(k00 + k1)/T_B_KQ::J]); // Conversion of C to B matrix puts it in column-major format.
|
|
|
|
#pragma unroll
|
|
for (int l = 0; l < T_B_KQ::ne; ++l) {
|
|
const int k = k1 + T_B_KQ::get_j(l);
|
|
|
|
tile_Q[jc_cwd*tile_stride + k] = B.x[l];
|
|
}
|
|
}
|
|
} else {
|
|
const int j0 = threadIdx.y*cols_per_warp;
|
|
#pragma unroll
|
|
for (int k1 = 0; k1 < nbatch_combine; k1 += T_C_VKQ::J) {
|
|
#pragma unroll
|
|
for (int l = 0; l < T_C_VKQ::ne; ++l) {
|
|
const int j = j0 + T_C_VKQ::get_i(l);
|
|
const int k = k1 + T_C_VKQ::get_j(l);
|
|
|
|
tile_Q[j*tile_stride + k] = VKQ_C[(k00 + k1)/T_C_VKQ::J].x[l];
|
|
}
|
|
}
|
|
}
|
|
|
|
__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*(DV/2));
|
|
|
|
#pragma unroll
|
|
for (int stride_k : {warp_size, warp_size/2, warp_size/4, warp_size/8}) {
|
|
const int k0_start = stride_k == warp_size ? 0 : nbatch_combine - nbatch_combine % (2*stride_k);
|
|
const int k0_stop = nbatch_combine - nbatch_combine % (1*stride_k);
|
|
const int stride_jc = warp_size / stride_k;
|
|
|
|
if (k0_start == k0_stop) {
|
|
continue;
|
|
}
|
|
|
|
#pragma unroll
|
|
for (int jc0_dst = 0; jc0_dst < ncols; jc0_dst += (nwarps/np)*stride_jc) {
|
|
const int jc_dst = jc0_dst + (threadIdx.y/np)*stride_jc + (stride_k == warp_size ? 0 : threadIdx.x / stride_k);
|
|
|
|
if (jc0_dst + (nwarps/np)*stride_jc > ncols && jc_dst >= ncols) {
|
|
break;
|
|
}
|
|
|
|
const int jc_tile_K = (jc_dst/cols_per_warp)*(np*cols_per_warp) + jc_dst % cols_per_warp;
|
|
|
|
const int j_dst = jc_dst / ncols2;
|
|
const int c_dst = jc_dst % ncols2;
|
|
|
|
if (!is_fixup && ((ncols1 > 1 && jt*ncols1 + j_dst >= int(ne01.z)) || (ncols2 > 1 && zt_gqa*ncols2 + c_dst >= gqa_ratio))) {
|
|
continue;
|
|
}
|
|
|
|
const float * meta_j = (const float *) tile_Q + jc_tile_K*tile_stride + nbatch_combine;
|
|
#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*cols_per_warp * tile_stride + 0];
|
|
const float2 dstk_val_add = __half22float2(tile_Q[(jc_tile_K + ip*cols_per_warp) * tile_stride + 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[jc_dst*(DV/2) + k00 + k] = dstk_val;
|
|
} else {
|
|
dstk[((jt*ncols1 + j_dst)*ne02 + c_dst)*(DV/2) + k00 + k] = dstk_val;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
if (np > 1) {
|
|
__syncthreads();
|
|
}
|
|
}
|
|
#else
|
|
GGML_UNUSED_VARS(Q_f2, K_h2, V_h2, mask_h, sinks_f, dstk, dstk_fixup,
|
|
scale, slope, logit_softcap, ne01, ne02, gqa_ratio,
|
|
stride_Q1, stride_Q2, stride_K, stride_V, stride_mask,
|
|
jt, kb0_start, kb0_stop);
|
|
NO_DEVICE_CODE;
|
|
#endif // defined(VOLTA_MMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || (defined(AMD_WMMA_AVAILABLE) && defined(RDNA4)) || defined(AMD_MFMA_AVAILABLE)
|
|
}
|
|
|
|
template<int DKQ, int DV, int ncols1, int ncols2, bool use_logit_softcap, bool V_is_K_view>
|
|
__launch_bounds__(ggml_cuda_fattn_mma_get_nthreads(DKQ, DV, ncols1*ncols2), ggml_cuda_fattn_mma_get_occupancy(DKQ, DV, ncols1*ncols2))
|
|
static __global__ void flash_attn_ext_f16(
|
|
const char * __restrict__ Q,
|
|
const char * __restrict__ K,
|
|
const char * __restrict__ V,
|
|
const char * __restrict__ mask,
|
|
const char * __restrict__ sinks,
|
|
const int * __restrict__ KV_max,
|
|
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 int32_t ne00, const uint3 ne01, const int32_t ne02, const int32_t ne03,
|
|
const int32_t nb01, const int32_t nb02, const int32_t nb03,
|
|
const int32_t ne10, const int32_t ne11, const int32_t ne12, const int32_t ne13,
|
|
const int32_t nb11, const int32_t nb12, const int64_t nb13,
|
|
const int32_t nb21, const int32_t nb22, const int64_t nb23,
|
|
const int32_t ne31, const int32_t ne32, const int32_t ne33,
|
|
const int32_t nb31, const int32_t nb32, const int64_t nb33) {
|
|
#if defined(FLASH_ATTN_AVAILABLE) && (defined(VOLTA_MMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || (defined(AMD_WMMA_AVAILABLE) && defined(RDNA4)) || defined(AMD_MFMA_AVAILABLE))
|
|
|
|
// Skip unused kernel variants for faster compilation:
|
|
if (use_logit_softcap && !(DKQ == 128 || DKQ == 256 || DKQ == 512)) {
|
|
NO_DEVICE_CODE;
|
|
return;
|
|
}
|
|
#ifdef VOLTA_MMA_AVAILABLE
|
|
if (ncols1*ncols2 < 32) {
|
|
NO_DEVICE_CODE;
|
|
return;
|
|
}
|
|
#endif // VOLTA_MMA_AVAILABLE
|
|
|
|
#if __CUDA_ARCH__ == GGML_CUDA_CC_TURING
|
|
if (ncols1*ncols2 > 32) {
|
|
NO_DEVICE_CODE;
|
|
return;
|
|
}
|
|
#endif // __CUDA_ARCH__ == GGML_CUDA_CC_TURING
|
|
|
|
#if defined(AMD_WMMA_AVAILABLE)
|
|
if (ncols1*ncols2 > 32 || ncols1*ncols2 < 16 || DKQ > 128 || ncols2 == 1) {
|
|
NO_DEVICE_CODE;
|
|
return;
|
|
}
|
|
#endif // defined(AMD_WMMA_AVAILABLE)
|
|
|
|
#if defined(AMD_MFMA_AVAILABLE)
|
|
if (DKQ != 64 && DKQ != 80 && DKQ != 96 && DKQ != 112 && DKQ != 128) {
|
|
NO_DEVICE_CODE;
|
|
return;
|
|
}
|
|
#endif // defined(AMD_MFMA_AVAILABLE)
|
|
|
|
constexpr int warp_size = ggml_cuda_get_physical_warp_size();
|
|
constexpr int ncols = ncols1 * ncols2;
|
|
constexpr int nbatch_fa = ggml_cuda_fattn_mma_get_nbatch_fa(DKQ, DV, ncols);
|
|
constexpr int nthreads = ggml_cuda_fattn_mma_get_nthreads(DKQ, DV, ncols);
|
|
constexpr int nwarps = nthreads / warp_size;
|
|
|
|
const int gqa_ratio = ne02 / ne12; // With grouped query attention there are > 1 Q matrices per K, V matrix.
|
|
|
|
const int stride_Q1 = nb01 / sizeof(float2);
|
|
const int stride_Q2 = nb02 / sizeof(float2);
|
|
const int stride_K = nb11 / sizeof(half2);
|
|
const int stride_mask = nb31 / sizeof(half);
|
|
|
|
const int stride_V = V_is_K_view ? stride_K : nb21 / sizeof(half2);
|
|
|
|
const int iter_k = (ne11 + (nbatch_fa - 1)) / nbatch_fa;
|
|
const int iter_j = (ne01.z + (ncols1 - 1)) / ncols1;
|
|
const int iter_z_gqa = (gqa_ratio + (ncols2 - 1)) / ncols2;
|
|
|
|
// kbc == k block continuous, current index in continuous ijk space.
|
|
int kbc = int64_t(blockIdx.x + 0)*(iter_k*iter_j*iter_z_gqa*ne12*ne03) / gridDim.x;
|
|
const int kbc_stop = int64_t(blockIdx.x + 1)*(iter_k*iter_j*iter_z_gqa*ne12*ne03) / 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) {
|
|
// z_KV == K/V head index, zt_gqa = Q head start index per K/V head, jt = token position start index
|
|
const int sequence = kbc /(iter_k*iter_j*iter_z_gqa*ne12);
|
|
const int z_KV = (kbc - iter_k*iter_j*iter_z_gqa*ne12 * sequence)/(iter_k*iter_j*iter_z_gqa);
|
|
const int zt_gqa = (kbc - iter_k*iter_j*iter_z_gqa*ne12 * sequence - iter_k*iter_j*iter_z_gqa * z_KV)/(iter_k*iter_j);
|
|
const int jt = (kbc - iter_k*iter_j*iter_z_gqa*ne12 * sequence - iter_k*iter_j*iter_z_gqa * z_KV - iter_k*iter_j * zt_gqa) / iter_k;
|
|
|
|
const int zt_Q = z_KV*gqa_ratio + zt_gqa*ncols2; // Global Q head start index.
|
|
|
|
const float2 * Q_f2 = (const float2 *) (Q + nb03*sequence + nb02*zt_Q);
|
|
const half2 * K_h2 = (const half2 *) (K + nb13*sequence + nb12*z_KV);
|
|
const half * mask_h = ncols2 == 1 && !mask ? nullptr :
|
|
(const half *) (mask + nb33*(sequence % ne33));
|
|
float2 * dstk = ((float2 *) dst) + (sequence*ne01.z*ne02 + zt_Q) * (DV/2);
|
|
|
|
const half2 * V_h2 = V_is_K_view ? K_h2 : (const half2 *) (V + nb23*sequence + nb22*z_KV);
|
|
const float * sinks_f = sinks ? (const float *) sinks + zt_Q : nullptr;
|
|
|
|
const float slope = ncols2 == 1 ? get_alibi_slope(max_bias, zt_Q, n_head_log2, m0, m1) : 1.0f;
|
|
|
|
if (KV_max) {
|
|
kb0_stop = min(kb0_stop, KV_max[sequence*iter_j + jt] / nbatch_fa);
|
|
}
|
|
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<DKQ, DV, ncols1, ncols2, nwarps, use_logit_softcap, V_is_K_view, needs_fixup, is_fixup>
|
|
(Q_f2, K_h2, V_h2, mask_h, sinks_f, dstk, dst_meta, scale, slope, logit_softcap,
|
|
ne01, ne02, gqa_ratio, ne11, stride_Q1, stride_Q2, stride_K, stride_V, stride_mask, jt, zt_gqa, kb0_start, kb0_stop);
|
|
} else {
|
|
constexpr bool needs_fixup = true; // CUDA block is missing the beginning of a tile.
|
|
flash_attn_ext_f16_process_tile<DKQ, DV, ncols1, ncols2, nwarps, use_logit_softcap, V_is_K_view, needs_fixup, is_fixup>
|
|
(Q_f2, K_h2, V_h2, mask_h, sinks_f, dstk, dst_meta, scale, slope, logit_softcap,
|
|
ne01, ne02, gqa_ratio, ne11, stride_Q1, stride_Q2, stride_K, stride_V, stride_mask, jt, zt_gqa, 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;
|
|
}
|
|
|
|
// z_KV == K/V head index, zt_gqa = Q head start index per K/V head, jt = token position start index.
|
|
const int sequence = kbc /(iter_k*iter_j*iter_z_gqa*ne12);
|
|
const int z_KV = (kbc - iter_k*iter_j*iter_z_gqa*ne12 * sequence)/(iter_k*iter_j*iter_z_gqa);
|
|
const int zt_gqa = (kbc - iter_k*iter_j*iter_z_gqa*ne12 * sequence - iter_k*iter_j*iter_z_gqa * z_KV)/(iter_k*iter_j);
|
|
const int jt = (kbc - iter_k*iter_j*iter_z_gqa*ne12 * sequence - iter_k*iter_j*iter_z_gqa * z_KV - iter_k*iter_j * zt_gqa) / iter_k;
|
|
|
|
const int zt_Q = z_KV*gqa_ratio + zt_gqa*ncols2; // Global Q head start index.
|
|
|
|
const float2 * Q_f2 = (const float2 *) (Q + nb03*sequence + nb02*zt_Q);
|
|
const half2 * K_h2 = (const half2 *) (K + nb13*sequence + nb12*z_KV);
|
|
const half * mask_h = ncols2 == 1 && !mask ? nullptr :
|
|
(const half *) (mask + nb33*(sequence % ne33));
|
|
float2 * dstk = ((float2 *) dst) + (sequence*ne01.z*ne02 + zt_Q) * (DV/2);
|
|
|
|
const half2 * V_h2 = V_is_K_view ? K_h2 : (const half2 *) (V + nb23*sequence + nb22*z_KV);
|
|
const float * sinks_f = sinks ? (const float *) sinks + zt_Q : nullptr;
|
|
|
|
const float slope = ncols2 == 1 ? get_alibi_slope(max_bias, zt_Q, n_head_log2, m0, m1) : 1.0f;
|
|
|
|
if (KV_max) {
|
|
kb0_stop = min(kb0_stop, KV_max[sequence*iter_j + jt] / nbatch_fa);
|
|
}
|
|
|
|
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<DKQ, DV, ncols1, ncols2, nwarps, use_logit_softcap, V_is_K_view, needs_fixup, is_fixup>
|
|
(Q_f2, K_h2, V_h2, mask_h, sinks_f, dstk, dst_meta, scale, slope, logit_softcap,
|
|
ne01, ne02, gqa_ratio, ne11, stride_Q1, stride_Q2, stride_K, stride_V, stride_mask, jt, zt_gqa, kb0_start, kb0_stop);
|
|
#else
|
|
GGML_UNUSED_VARS(Q, K, V, mask, sinks, KV_max, dst, dst_meta, scale,
|
|
max_bias, m0, m1, n_head_log2, logit_softcap,
|
|
ne00, ne01, ne02, ne03,
|
|
nb01, nb02, nb03,
|
|
ne10, ne11, ne12, ne13,
|
|
nb11, nb12, nb13,
|
|
nb21, nb22, nb23,
|
|
ne31, ne32, ne33,
|
|
nb31, nb32, nb33);
|
|
NO_DEVICE_CODE;
|
|
#endif // defined(FLASH_ATTN_AVAILABLE) && (defined(VOLTA_MMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || (defined(AMD_WMMA_AVAILABLE) && defined(RDNA4)) || defined(AMD_MFMA_AVAILABLE))
|
|
}
|
|
|
|
template <int DKQ, int DV, int ncols1, int ncols2>
|
|
void ggml_cuda_flash_attn_ext_mma_f16_case(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
|
|
const ggml_tensor * KQV = dst;
|
|
const int id = ggml_cuda_get_device();
|
|
const int cc = ggml_cuda_info().devices[id].cc;
|
|
|
|
constexpr int ncols = ncols1 * ncols2;
|
|
|
|
const int nthreads = ggml_cuda_fattn_mma_get_nthreads (DKQ, DV, ncols, cc);
|
|
const int nbatch_fa = ggml_cuda_fattn_mma_get_nbatch_fa (DKQ, DV, ncols, cc);
|
|
const int nbatch_K2 = ggml_cuda_fattn_mma_get_nbatch_K2 (DKQ, DV, ncols, cc);
|
|
const int nbatch_V2 = ggml_cuda_fattn_mma_get_nbatch_V2 (DKQ, DV, ncols, cc);
|
|
const int nbatch_combine = ggml_cuda_fattn_mma_get_nbatch_combine(DKQ, DV, ncols, cc);
|
|
const bool Q_in_reg = ggml_cuda_fattn_mma_get_Q_in_reg (DKQ, DV, ncols, cc);
|
|
const int nstages = ggml_cuda_fattn_mma_get_nstages (DKQ, DV, ncols1, ncols2, cc);
|
|
|
|
const int cols_per_warp = std::min(ncols, get_cols_per_warp(cc));
|
|
const int warp_size_host = ggml_cuda_info().devices[ctx.device].warp_size;
|
|
const int nwarps = nthreads / warp_size_host;
|
|
|
|
constexpr bool V_is_K_view = DKQ == 576; // Guaranteed by the kernel selection logic in fattn.cu
|
|
|
|
const size_t nbytes_shared_KV_1stage = nbatch_fa * std::max(nbatch_K2 + 4, nbatch_V2 + 4) * sizeof(half2);
|
|
const size_t nbytes_shared_KV_2stage = nbatch_fa * (nbatch_K2 + 4 + nbatch_V2 + 4) * sizeof(half2);
|
|
const size_t nbytes_shared_Q = ncols * (DKQ/2 + 4) * sizeof(half2);
|
|
const size_t nbytes_shared_mask = ncols1 * (nbatch_fa/2 + 4) * sizeof(half2);
|
|
const size_t nbytes_shared_combine = nwarps*cols_per_warp * (nbatch_combine + 4) * sizeof(half2);
|
|
|
|
const size_t nbytes_shared_KV = nstages <= 1 ? nbytes_shared_KV_1stage : nbytes_shared_KV_2stage;
|
|
|
|
const size_t nbytes_shared_total = std::max(nbytes_shared_combine, Q_in_reg ?
|
|
std::max(nbytes_shared_Q, nbytes_shared_KV + nbytes_shared_mask) :
|
|
nbytes_shared_Q + nbytes_shared_KV + nbytes_shared_mask);
|
|
|
|
float logit_softcap;
|
|
memcpy(&logit_softcap, (const float *) KQV->op_params + 2, sizeof(float));
|
|
|
|
#if defined(GGML_USE_HIP)
|
|
using fattn_kernel_ptr_t = const void*;
|
|
#else
|
|
using fattn_kernel_ptr_t = fattn_kernel_t;
|
|
#endif // defined(GGML_USE_HIP)
|
|
fattn_kernel_t fattn_kernel;
|
|
if (logit_softcap == 0.0f) {
|
|
constexpr bool use_logit_softcap = false;
|
|
fattn_kernel = flash_attn_ext_f16<DKQ, DV, ncols1, ncols2, use_logit_softcap, V_is_K_view>;
|
|
|
|
#if !defined(GGML_USE_MUSA)
|
|
static bool shared_memory_limit_raised[GGML_CUDA_MAX_DEVICES] = {false};
|
|
if (!shared_memory_limit_raised[id]) {
|
|
CUDA_CHECK(cudaFuncSetAttribute(reinterpret_cast<fattn_kernel_ptr_t>(fattn_kernel), cudaFuncAttributeMaxDynamicSharedMemorySize, nbytes_shared_total));
|
|
shared_memory_limit_raised[id] = true;
|
|
}
|
|
#endif // !defined(GGML_USE_MUSA)
|
|
} else {
|
|
constexpr bool use_logit_softcap = true;
|
|
fattn_kernel = flash_attn_ext_f16<DKQ, DV, ncols1, ncols2, use_logit_softcap, V_is_K_view>;
|
|
|
|
#if !defined(GGML_USE_MUSA)
|
|
static bool shared_memory_limit_raised[GGML_CUDA_MAX_DEVICES] = {false};
|
|
if (!shared_memory_limit_raised[id]) {
|
|
CUDA_CHECK(cudaFuncSetAttribute(reinterpret_cast<fattn_kernel_ptr_t>(fattn_kernel), cudaFuncAttributeMaxDynamicSharedMemorySize, nbytes_shared_total));
|
|
shared_memory_limit_raised[id] = true;
|
|
}
|
|
#endif // !defined(GGML_USE_MUSA)
|
|
}
|
|
|
|
launch_fattn<DV, ncols1, ncols2>
|
|
(ctx, dst, fattn_kernel, nwarps, nbytes_shared_total, nbatch_fa, true, true, true, warp_size_host);
|
|
}
|
|
|
|
|
|
#define DECL_FATTN_MMA_F16_CASE(DKQ, DV, ncols1, ncols2) \
|
|
template void ggml_cuda_flash_attn_ext_mma_f16_case \
|
|
<DKQ, DV, ncols1, ncols2>(ggml_backend_cuda_context & ctx, ggml_tensor * dst) \
|
|
|
|
#define DECL_FATTN_MMA_F16_CASE_ALL_NCOLS2(DKQ, DV, ncols) \
|
|
extern DECL_FATTN_MMA_F16_CASE(DKQ, DV, (ncols)/ 1, 1); \
|
|
extern DECL_FATTN_MMA_F16_CASE(DKQ, DV, (ncols)/ 2, 2); \
|
|
extern DECL_FATTN_MMA_F16_CASE(DKQ, DV, (ncols)/ 4, 4); \
|
|
extern DECL_FATTN_MMA_F16_CASE(DKQ, DV, (ncols)/ 8, 8); \
|
|
extern DECL_FATTN_MMA_F16_CASE(DKQ, DV, (ncols)/16, 16); \
|
|
|
|
DECL_FATTN_MMA_F16_CASE_ALL_NCOLS2( 64, 64, 8)
|
|
DECL_FATTN_MMA_F16_CASE_ALL_NCOLS2( 80, 80, 8)
|
|
DECL_FATTN_MMA_F16_CASE_ALL_NCOLS2( 96, 96, 8)
|
|
DECL_FATTN_MMA_F16_CASE_ALL_NCOLS2(112, 112, 8)
|
|
DECL_FATTN_MMA_F16_CASE_ALL_NCOLS2(128, 128, 8)
|
|
DECL_FATTN_MMA_F16_CASE_ALL_NCOLS2(256, 256, 8)
|
|
|
|
DECL_FATTN_MMA_F16_CASE_ALL_NCOLS2( 64, 64, 16)
|
|
DECL_FATTN_MMA_F16_CASE_ALL_NCOLS2( 80, 80, 16)
|
|
DECL_FATTN_MMA_F16_CASE_ALL_NCOLS2( 96, 96, 16)
|
|
DECL_FATTN_MMA_F16_CASE_ALL_NCOLS2(112, 112, 16)
|
|
DECL_FATTN_MMA_F16_CASE_ALL_NCOLS2(128, 128, 16)
|
|
DECL_FATTN_MMA_F16_CASE_ALL_NCOLS2(256, 256, 16)
|
|
|
|
DECL_FATTN_MMA_F16_CASE_ALL_NCOLS2( 64, 64, 32)
|
|
DECL_FATTN_MMA_F16_CASE_ALL_NCOLS2( 80, 80, 32)
|
|
DECL_FATTN_MMA_F16_CASE_ALL_NCOLS2( 96, 96, 32)
|
|
DECL_FATTN_MMA_F16_CASE_ALL_NCOLS2(112, 112, 32)
|
|
DECL_FATTN_MMA_F16_CASE_ALL_NCOLS2(128, 128, 32)
|
|
DECL_FATTN_MMA_F16_CASE_ALL_NCOLS2(256, 256, 32)
|
|
|
|
DECL_FATTN_MMA_F16_CASE_ALL_NCOLS2( 64, 64, 64)
|
|
DECL_FATTN_MMA_F16_CASE_ALL_NCOLS2( 80, 80, 64)
|
|
DECL_FATTN_MMA_F16_CASE_ALL_NCOLS2( 96, 96, 64)
|
|
DECL_FATTN_MMA_F16_CASE_ALL_NCOLS2(112, 112, 64)
|
|
DECL_FATTN_MMA_F16_CASE_ALL_NCOLS2(128, 128, 64)
|
|
DECL_FATTN_MMA_F16_CASE_ALL_NCOLS2(256, 256, 64)
|
|
|
|
extern DECL_FATTN_MMA_F16_CASE(512, 512, 2, 4);
|
|
extern DECL_FATTN_MMA_F16_CASE(512, 512, 4, 4);
|
|
extern DECL_FATTN_MMA_F16_CASE(512, 512, 8, 4);
|
|
extern DECL_FATTN_MMA_F16_CASE(512, 512, 16, 4);
|
|
extern DECL_FATTN_MMA_F16_CASE(512, 512, 1, 8);
|
|
extern DECL_FATTN_MMA_F16_CASE(512, 512, 2, 8);
|
|
extern DECL_FATTN_MMA_F16_CASE(512, 512, 4, 8);
|
|
extern DECL_FATTN_MMA_F16_CASE(512, 512, 8, 8);
|
|
|
|
// The number of viable configurations for Deepseek is very limited:
|
|
extern DECL_FATTN_MMA_F16_CASE(576, 512, 1, 16);
|
|
extern DECL_FATTN_MMA_F16_CASE(576, 512, 2, 16);
|
|
extern DECL_FATTN_MMA_F16_CASE(576, 512, 4, 16);
|
|
|
|
// Mistral Small 4 (DKQ=320, DV=256), GQA=32-only build:
|
|
extern DECL_FATTN_MMA_F16_CASE(320, 256, 1, 32);
|
|
extern DECL_FATTN_MMA_F16_CASE(320, 256, 2, 32);
|
|
|
|
// For GLM 4.7 Flash
|
|
extern DECL_FATTN_MMA_F16_CASE(576, 512, 4, 4);
|
|
extern DECL_FATTN_MMA_F16_CASE(576, 512, 8, 4);
|
|
extern DECL_FATTN_MMA_F16_CASE(576, 512, 16, 4);
|
|
extern DECL_FATTN_MMA_F16_CASE(576, 512, 1, 32);
|
|
extern DECL_FATTN_MMA_F16_CASE(576, 512, 2, 32);
|