mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-11-11 13:30:35 +00:00
ggml : add Flash Attention (#5021)
* ggml : add ggml_flash_attn_ext API * ggml : fix GQA support in ggml_flash_attn_ext * ggml : online attention (CPU) * metal : initial implementation * metal : f16 precision * metal : reduce branches * metal : specialize for head size * wip : 8 rows per simd group * wip : 4 rows per simd group * wip : template for rows per warp * metal : parallelize across KV size * metal : parallel reduce across heads * metal : efficient flash_attn_f16 implementation * metal : avoid redundant loads of the attention * metal : scale and mask in matrix form * metal : fix comment * llama : avoid ggml_cast, use F32 query * metal : add parallel reduce version (disabled) * metal : move output into local memory + optimize - the result from each simdgroup now stays in the registers - significantly reduced SRAM usage - more efficient skipping of -INF blocks - avoid simdgroup barrier in hot loop - add comments * metal : add tests, fix scaling, support C > 32 * metal : improve precision * ggml : fix f16 mad * metal : minor * metal : support Q > 8 * tests : add ATTN tests * metal : disable buffer allocation logs * tests : more * metal : faster inner loop for C == 32 * metal : fix array initialization * tests : ifdef * ggml : switch to padded F16 mask for ggml_soft_max, ggml_flash_attn_ext * ggml : fix ggml_soft_max mask requirement * cuda : fix soft_max to use correct mask size * cuda : add flash_attn kernel (wip) * metal : optimize softmax for C > 32 * metal : optimize softmax * tests : minor fix * cuda : avoid zeroing fragments * tests : update dims * cuda : fix __hisinf() result check * cuda : avoid warp_reduce for smax * cuda : use int instead of int64_t Noticeably improves performance (thanks to Johannes) * cuda : make loops use the same loop values Thanks Johannes again for the tip * cuda : unroll some of the loops * cuda : avoid __hisinf branches * cuda : use half2 in softmax * cuda : switch to 1 warp for bs > 16 * cuda : speed-up reduce part of the kernel * cuda : unroll Q*K^T loop * cuda : fix -INF block check * cuda : simplify softmax * cuda : fix matrix names * cuda : minor * llama : adapt to F16 KQ_pos * llama : adapt new models to F16 KQ_mask * ggml : fix F16 store (ARM NEON) * llama : fix type of KQ_mask and KQ_pos * ggml : fix CPU soft_max * tests : add hs=256 * cuda : fix build * metal : improve perf via smaller int registers * cuda : adapt soft_max to F16 mask and pos * CUDA: faster FlashAttention, kernel for bs == 1 * 16 cols for Phi-2 * no vec for hs, no hs==256 ncols==32 for Volta * adjust kernel selection logic * 4 warps, 256 stride for all D * no ncols == 64 * Multiple parallel blocks for batch size 1 * fix compile warnings * fix excessive KQ_b loads * fix cmake build * fix KV cache padding, NaN from INFINITY (#6438) * llama : flash_attn cparam + fix defrag * server: support flash_attn param * server: bench: enable flash_attn param * CUDA: refactor host code, dyn. par. blocks * fix flash_attn_vec_f16 race condition * flush softmax exp below threshold to 0 * store temp KQ in registers * Calculate KQ as FP32 if KQV has GGML_PREC_F32 * Add __hgt2_mask implementation for CUDA 11 * fix KQ FP32 precision fpr parallel_blocks > 1 * llama-bench : add -fa,--flash-attn arg * metal : add BS=1 kernel for flash attention (#6508) * metal : add BS=1 kernel for flash attention (wip) * metal : support more than 1 warps * metal : opts * metal : opt * metal : switch to parallel reduce * metal : reduce registers * metal : simplify * metal : initial FA vec kernel * metal : use F32 attention accumulators * batched-bench : add fattn arg * llama : simplify llama_build_kv_store ggml-ci * llama : adapt build_olmo to changes * ggml : fix arm fp16 store on windows * metal : clean-up * metal : clean-up kernel code * metal : minor * tests : remove benchmarks ggml-ci * ggml : fix avx512 const correctness ggml-ci * ggml : fix soft_max with bias on CPU ggml-ci * common : print --flash-attn in help * ggml : fix num dimensions in ggml_flash_attn_ext * llama : force disable flash attention for incompatible models * ggml : ggml_soft_max support F16/F32 mask/pos ggml-ci * cuda : uint -> uint32_t * cuda : "constexpr dim3" -> "const dim3" ggml-ci * cuda : try to fix __hgt2_mask ggml-ci * ggml : add TODO's for F16/F32 mask/pos support in other backends * llama : replace bool need_kq_pos with use_alibi * llama : prep ALiBi support for BERT models ggml-ci * llama : fix n_batch requirements ggml-ci * cont * server : add help for --flash-attn arg * llama : disable FA for AMD * tests : remove TMP_ATTN_BENCH ggml-ci * llama : support save/load state with FA enabled ggml-ci * ci : add CUDA save-load-state tests ggml-ci * llama : llama_kv_cache_clear zeroes data + fix save-load seq ggml-ci * llama : fix copy-paste errors, add TODO * llama : disallow incompatible states * llama : update llama_state_get_size after v_trans field * metal : remove tmp log * llama : add static reminder for llama_state_get_size * metal : fix max nsg ggml-ci * ci : fix arg order ggml-ci --------- Co-authored-by: Johannes Gäßler <johannesg@5d6.de> Co-authored-by: Pierrick HYMBERT <pierrick.hymbert@gmail.com>
This commit is contained in:
Georgi Gerganov
GitHub
parent
952d03dbea
commit
9c67c2773d
@ -336,7 +336,8 @@ function gg_run_open_llama_3b_v2 {
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(time ./bin/imatrix --model ${model_f16} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-imatrix.log
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(time ./bin/save-load-state --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
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(time ./bin/save-load-state --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
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(time ./bin/save-load-state -fa --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
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function check_ppl {
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qnt="$1"
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@ -517,7 +518,10 @@ function gg_run_open_llama_7b_v2 {
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(time ./bin/imatrix --model ${model_f16} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-imatrix.log
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(time ./bin/save-load-state --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
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(time ./bin/save-load-state -ngl 10 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
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(time ./bin/save-load-state -fa -ngl 10 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
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(time ./bin/save-load-state -ngl 99 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
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(time ./bin/save-load-state -fa -ngl 99 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
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function check_ppl {
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qnt="$1"
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@ -947,6 +947,10 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
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params.cont_batching = true;
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return true;
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}
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if (arg == "-fa" || arg == "--flash-attn") {
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params.flash_attn = true;
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return true;
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}
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if (arg == "--color") {
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params.use_color = true;
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return true;
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@ -1513,6 +1517,7 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
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printf(" -ns N, --sequences N number of sequences to decode (default: %d)\n", params.n_sequences);
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printf(" -ps N, --p-split N speculative decoding split probability (default: %.1f)\n", (double)params.p_split);
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printf(" -cb, --cont-batching enable continuous batching (a.k.a dynamic batching) (default: disabled)\n");
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printf(" -fa, --flash-attn enable Flash Attention (default: %s)\n", params.flash_attn ? "enabled" : "disabled");
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printf(" --mmproj MMPROJ_FILE path to a multimodal projector file for LLaVA. see examples/llava/README.md\n");
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printf(" --image IMAGE_FILE path to an image file. use with multimodal models. Specify multiple times for batching\n");
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if (llama_supports_mlock()) {
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@ -1885,6 +1890,7 @@ struct llama_context_params llama_context_params_from_gpt_params(const gpt_param
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cparams.cb_eval = params.cb_eval;
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cparams.cb_eval_user_data = params.cb_eval_user_data;
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cparams.offload_kqv = !params.no_kv_offload;
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cparams.flash_attn = params.flash_attn;
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cparams.type_k = kv_cache_type_from_str(params.cache_type_k);
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cparams.type_v = kv_cache_type_from_str(params.cache_type_v);
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@ -2707,6 +2713,7 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l
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fprintf(stream, "seed: %u # default: -1 (random seed)\n", params.seed);
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fprintf(stream, "simple_io: %s # default: false\n", params.simple_io ? "true" : "false");
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fprintf(stream, "cont_batching: %s # default: false\n", params.cont_batching ? "true" : "false");
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fprintf(stream, "flash_attn: %s # default: false\n", params.flash_attn ? "true" : "false");
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fprintf(stream, "temp: %f # default: 0.8\n", sparams.temp);
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const std::vector<float> tensor_split_vector(params.tensor_split, params.tensor_split + llama_max_devices());
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@ -150,6 +150,7 @@ struct gpt_params {
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bool multiline_input = false; // reverse the usage of `\`
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bool simple_io = false; // improves compatibility with subprocesses and limited consoles
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bool cont_batching = true; // insert new sequences for decoding on-the-fly
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bool flash_attn = false; // flash attention
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bool input_prefix_bos = false; // prefix BOS to user inputs, preceding input_prefix
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bool ignore_eos = false; // ignore generated EOS tokens
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@ -32,7 +32,7 @@ int main(int argc, char ** argv) {
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gpt_params params;
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if (argc == 1 || argv[1][0] == '-') {
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printf("usage: %s MODEL_PATH [N_KV_MAX] [N_BATCH] [N_UBATCH] [IS_PP_SHARED] [NGL] <PP> <TG> <PL>\n" , argv[0]);
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printf("usage: %s MODEL_PATH [N_KV_MAX] [N_BATCH] [N_UBATCH] [FATTN] [IS_PP_SHARED] [NGL] <PP> <TG> <PL>\n" , argv[0]);
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printf(" <PP>, <TG> and PL are comma-separated lists of numbers without spaces\n\n");
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printf(" example: %s ggml-model-f16.gguf 2048 2048 512 0 999 128,256,512 128,256 1,2,4,8,16,32\n\n", argv[0]);
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return 1 ;
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@ -41,6 +41,7 @@ int main(int argc, char ** argv) {
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int n_kv_max = 2048;
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int n_batch = 2048;
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int n_ubatch = 512;
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bool flash_attn = false;
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int is_pp_shared = 0;
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int n_gpu_layers = 0;
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@ -66,23 +67,27 @@ int main(int argc, char ** argv) {
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}
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if (argc >= 6) {
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is_pp_shared = std::atoi(argv[5]);
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flash_attn = std::atoi(argv[5]);
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}
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if (argc >= 7) {
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n_gpu_layers = std::atoi(argv[6]);
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is_pp_shared = std::atoi(argv[6]);
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}
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if (argc >= 8) {
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n_pp = parse_list(argv[7]);
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n_gpu_layers = std::atoi(argv[7]);
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}
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if (argc >= 9) {
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n_tg = parse_list(argv[8]);
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n_pp = parse_list(argv[8]);
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}
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if (argc >= 10) {
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n_pl = parse_list(argv[9]);
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n_tg = parse_list(argv[9]);
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}
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if (argc >= 11) {
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n_pl = parse_list(argv[10]);
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}
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// init LLM
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@ -108,10 +113,11 @@ int main(int argc, char ** argv) {
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llama_context_params ctx_params = llama_context_default_params();
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ctx_params.seed = 1234;
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ctx_params.n_ctx = n_kv_max;
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ctx_params.n_batch = n_batch;
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ctx_params.n_ubatch = n_ubatch;
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ctx_params.seed = 1234;
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ctx_params.n_ctx = n_kv_max;
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ctx_params.n_batch = n_batch;
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ctx_params.n_ubatch = n_ubatch;
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ctx_params.flash_attn = flash_attn;
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ctx_params.n_threads = params.n_threads;
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ctx_params.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch;
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@ -169,7 +175,7 @@ int main(int argc, char ** argv) {
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}
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LOG_TEE("\n");
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LOG_TEE("%s: n_kv_max = %d, n_batch = %d, n_ubatch = %d, is_pp_shared = %d, n_gpu_layers = %d, n_threads = %u, n_threads_batch = %u\n", __func__, n_kv_max, n_batch, n_ubatch, is_pp_shared, n_gpu_layers, ctx_params.n_threads, ctx_params.n_threads_batch);
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LOG_TEE("%s: n_kv_max = %d, n_batch = %d, n_ubatch = %d, flash_attn = %d, is_pp_shared = %d, n_gpu_layers = %d, n_threads = %u, n_threads_batch = %u\n", __func__, n_kv_max, n_batch, n_ubatch, flash_attn, is_pp_shared, n_gpu_layers, ctx_params.n_threads, ctx_params.n_threads_batch);
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LOG_TEE("\n");
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LOG_TEE("|%6s | %6s | %4s | %6s | %8s | %8s | %8s | %8s | %8s | %8s |\n", "PP", "TG", "B", "N_KV", "T_PP s", "S_PP t/s", "T_TG s", "S_TG t/s", "T s", "S t/s");
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@ -174,6 +174,7 @@ struct cmd_params {
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std::vector<llama_split_mode> split_mode;
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std::vector<int> main_gpu;
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std::vector<bool> no_kv_offload;
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std::vector<bool> flash_attn;
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std::vector<std::vector<float>> tensor_split;
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std::vector<bool> use_mmap;
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std::vector<bool> embeddings;
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@ -195,6 +196,7 @@ static const cmd_params cmd_params_defaults = {
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/* split_mode */ {LLAMA_SPLIT_MODE_LAYER},
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/* main_gpu */ {0},
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/* no_kv_offload */ {false},
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/* flash_attn */ {false},
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/* tensor_split */ {std::vector<float>(llama_max_devices(), 0.0f)},
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/* use_mmap */ {true},
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/* embeddings */ {false},
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@ -220,6 +222,7 @@ static void print_usage(int /* argc */, char ** argv) {
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printf(" -sm, --split-mode <none|layer|row> (default: %s)\n", join(transform_to_str(cmd_params_defaults.split_mode, split_mode_str), ",").c_str());
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printf(" -mg, --main-gpu <i> (default: %s)\n", join(cmd_params_defaults.main_gpu, ",").c_str());
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printf(" -nkvo, --no-kv-offload <0|1> (default: %s)\n", join(cmd_params_defaults.no_kv_offload, ",").c_str());
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printf(" -fa, --flash-attn <0|1> (default: %s)\n", join(cmd_params_defaults.flash_attn, ",").c_str());
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printf(" -mmp, --mmap <0|1> (default: %s)\n", join(cmd_params_defaults.use_mmap, ",").c_str());
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printf(" -embd, --embeddings <0|1> (default: %s)\n", join(cmd_params_defaults.embeddings, ",").c_str());
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printf(" -ts, --tensor-split <ts0/ts1/..> (default: 0)\n");
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@ -393,6 +396,13 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
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}
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auto p = split<bool>(argv[i], split_delim);
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params.no_kv_offload.insert(params.no_kv_offload.end(), p.begin(), p.end());
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} else if (arg == "-fa" || arg == "--flash-attn") {
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if (++i >= argc) {
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invalid_param = true;
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break;
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}
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auto p = split<bool>(argv[i], split_delim);
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params.flash_attn.insert(params.flash_attn.end(), p.begin(), p.end());
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} else if (arg == "-mmp" || arg == "--mmap") {
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if (++i >= argc) {
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invalid_param = true;
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@ -477,6 +487,7 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
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if (params.split_mode.empty()) { params.split_mode = cmd_params_defaults.split_mode; }
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if (params.main_gpu.empty()) { params.main_gpu = cmd_params_defaults.main_gpu; }
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if (params.no_kv_offload.empty()){ params.no_kv_offload = cmd_params_defaults.no_kv_offload; }
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if (params.flash_attn.empty()) { params.flash_attn = cmd_params_defaults.flash_attn; }
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if (params.tensor_split.empty()) { params.tensor_split = cmd_params_defaults.tensor_split; }
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if (params.use_mmap.empty()) { params.use_mmap = cmd_params_defaults.use_mmap; }
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if (params.embeddings.empty()) { params.embeddings = cmd_params_defaults.embeddings; }
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@ -498,6 +509,7 @@ struct cmd_params_instance {
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llama_split_mode split_mode;
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int main_gpu;
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bool no_kv_offload;
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bool flash_attn;
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std::vector<float> tensor_split;
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bool use_mmap;
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bool embeddings;
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@ -532,6 +544,7 @@ struct cmd_params_instance {
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cparams.type_k = type_k;
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cparams.type_v = type_v;
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cparams.offload_kqv = !no_kv_offload;
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cparams.flash_attn = flash_attn;
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cparams.embeddings = embeddings;
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return cparams;
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@ -554,6 +567,7 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
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for (const auto & tk : params.type_k)
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for (const auto & tv : params.type_v)
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for (const auto & nkvo : params.no_kv_offload)
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for (const auto & fa : params.flash_attn)
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for (const auto & nt : params.n_threads) {
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for (const auto & n_prompt : params.n_prompt) {
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if (n_prompt == 0) {
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@ -572,6 +586,7 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
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/* .split_mode = */ sm,
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/* .main_gpu = */ mg,
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/* .no_kv_offload= */ nkvo,
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/* .flash_attn = */ fa,
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/* .tensor_split = */ ts,
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/* .use_mmap = */ mmp,
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/* .embeddings = */ embd,
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@ -596,6 +611,7 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
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/* .split_mode = */ sm,
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/* .main_gpu = */ mg,
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/* .no_kv_offload= */ nkvo,
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/* .flash_attn = */ fa,
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/* .tensor_split = */ ts,
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/* .use_mmap = */ mmp,
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/* .embeddings = */ embd,
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@ -633,6 +649,7 @@ struct test {
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llama_split_mode split_mode;
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int main_gpu;
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bool no_kv_offload;
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bool flash_attn;
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std::vector<float> tensor_split;
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bool use_mmap;
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bool embeddings;
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@ -657,6 +674,7 @@ struct test {
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split_mode = inst.split_mode;
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main_gpu = inst.main_gpu;
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no_kv_offload = inst.no_kv_offload;
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flash_attn = inst.flash_attn;
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tensor_split = inst.tensor_split;
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use_mmap = inst.use_mmap;
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embeddings = inst.embeddings;
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@ -731,7 +749,7 @@ struct test {
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"n_batch", "n_ubatch",
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"n_threads", "type_k", "type_v",
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"n_gpu_layers", "split_mode",
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"main_gpu", "no_kv_offload",
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"main_gpu", "no_kv_offload", "flash_attn",
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"tensor_split", "use_mmap", "embeddings",
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"n_prompt", "n_gen", "test_time",
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"avg_ns", "stddev_ns",
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@ -753,7 +771,7 @@ struct test {
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}
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if (field == "cuda" || field == "opencl" || field == "vulkan" || field == "kompute" || field == "metal" ||
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field == "gpu_blas" || field == "blas" || field == "sycl" ||field == "f16_kv" || field == "no_kv_offload" ||
|
||||
field == "use_mmap" || field == "embeddings") {
|
||||
field == "flash_attn" || field == "use_mmap" || field == "embeddings") {
|
||||
return BOOL;
|
||||
}
|
||||
if (field == "avg_ts" || field == "stddev_ts") {
|
||||
@ -787,7 +805,7 @@ struct test {
|
||||
std::to_string(n_batch), std::to_string(n_ubatch),
|
||||
std::to_string(n_threads), ggml_type_name(type_k), ggml_type_name(type_v),
|
||||
std::to_string(n_gpu_layers), split_mode_str(split_mode),
|
||||
std::to_string(main_gpu), std::to_string(no_kv_offload),
|
||||
std::to_string(main_gpu), std::to_string(no_kv_offload), std::to_string(flash_attn),
|
||||
tensor_split_str, std::to_string(use_mmap), std::to_string(embeddings),
|
||||
std::to_string(n_prompt), std::to_string(n_gen), test_time,
|
||||
std::to_string(avg_ns()), std::to_string(stdev_ns()),
|
||||
@ -955,6 +973,9 @@ struct markdown_printer : public printer {
|
||||
if (field == "no_kv_offload") {
|
||||
return "nkvo";
|
||||
}
|
||||
if (field == "flash_attn") {
|
||||
return "fa";
|
||||
}
|
||||
if (field == "use_mmap") {
|
||||
return "mmap";
|
||||
}
|
||||
@ -1001,6 +1022,9 @@ struct markdown_printer : public printer {
|
||||
if (params.no_kv_offload.size() > 1 || params.no_kv_offload != cmd_params_defaults.no_kv_offload) {
|
||||
fields.emplace_back("no_kv_offload");
|
||||
}
|
||||
if (params.flash_attn.size() > 1 || params.flash_attn != cmd_params_defaults.flash_attn) {
|
||||
fields.emplace_back("flash_attn");
|
||||
}
|
||||
if (params.tensor_split.size() > 1 || params.tensor_split != cmd_params_defaults.tensor_split) {
|
||||
fields.emplace_back("tensor_split");
|
||||
}
|
||||
|
||||
@ -268,6 +268,7 @@ def start_server_background(args):
|
||||
server_args.extend(['--defrag-thold', "0.1"])
|
||||
server_args.append('--cont-batching')
|
||||
server_args.append('--metrics')
|
||||
server_args.append('--flash-attn')
|
||||
server_args.extend(['--log-format', "text"])
|
||||
args = [str(arg) for arg in [server_path, *server_args]]
|
||||
print(f"bench: starting server with: {' '.join(args)}")
|
||||
|
||||
@ -2377,6 +2377,7 @@ static void server_print_usage(const char * argv0, const gpt_params & params, co
|
||||
printf(" --embeddings enable embedding vector output (default: %s)\n", params.embedding ? "enabled" : "disabled");
|
||||
printf(" -np N, --parallel N number of slots for process requests (default: %d)\n", params.n_parallel);
|
||||
printf(" -cb, --cont-batching enable continuous batching (a.k.a dynamic batching) (default: enabled)\n");
|
||||
printf(" -fa, --flash-attn enable Flash Attention (default: %s)\n", params.flash_attn ? "enabled" : "disabled");
|
||||
printf(" -spf FNAME, --system-prompt-file FNAME\n");
|
||||
printf(" set a file to load a system prompt (initial prompt of all slots), this is useful for chat applications.\n");
|
||||
printf(" -ctk TYPE, --cache-type-k TYPE\n");
|
||||
@ -2742,6 +2743,8 @@ static void server_params_parse(int argc, char ** argv, server_params & sparams,
|
||||
params.embedding = true;
|
||||
} else if (arg == "-cb" || arg == "--cont-batching") {
|
||||
params.cont_batching = true;
|
||||
} else if (arg == "-fa" || arg == "--flash-attn") {
|
||||
params.flash_attn = true;
|
||||
} else if (arg == "-np" || arg == "--parallel") {
|
||||
if (++i >= argc) {
|
||||
invalid_param = true;
|
||||
|
||||
@ -14,6 +14,7 @@
|
||||
#include "ggml-cuda/cpy.cuh"
|
||||
#include "ggml-cuda/diagmask.cuh"
|
||||
#include "ggml-cuda/dmmv.cuh"
|
||||
#include "ggml-cuda/fattn.cuh"
|
||||
#include "ggml-cuda/getrows.cuh"
|
||||
#include "ggml-cuda/im2col.cuh"
|
||||
#include "ggml-cuda/mmq.cuh"
|
||||
@ -140,6 +141,7 @@ static ggml_cuda_device_info ggml_cuda_init() {
|
||||
info.devices[id].cc = 100*prop.major + 10*prop.minor;
|
||||
#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
|
||||
info.devices[id].smpb = prop.sharedMemPerBlock;
|
||||
info.devices[id].nsm = prop.multiProcessorCount;
|
||||
}
|
||||
|
||||
for (int id = 0; id < info.device_count; ++id) {
|
||||
@ -2290,6 +2292,9 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg
|
||||
case GGML_OP_ARGSORT:
|
||||
ggml_cuda_op_argsort(ctx, dst);
|
||||
break;
|
||||
case GGML_OP_FLASH_ATTN_EXT:
|
||||
ggml_cuda_flash_attn_ext(ctx, dst);
|
||||
break;
|
||||
default:
|
||||
return false;
|
||||
}
|
||||
@ -2564,6 +2569,7 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons
|
||||
case GGML_OP_ARANGE:
|
||||
case GGML_OP_TIMESTEP_EMBEDDING:
|
||||
case GGML_OP_LEAKY_RELU:
|
||||
case GGML_OP_FLASH_ATTN_EXT:
|
||||
return true;
|
||||
default:
|
||||
return false;
|
||||
|
||||
@ -142,6 +142,7 @@
|
||||
#define CC_PASCAL 600
|
||||
#define MIN_CC_DP4A 610 // minimum compute capability for __dp4a, an intrinsic for byte-wise dot products
|
||||
#define CC_VOLTA 700
|
||||
#define CC_AMPERE 800
|
||||
#define CC_OFFSET_AMD 1000000
|
||||
#define CC_RDNA1 (CC_OFFSET_AMD + 1010)
|
||||
#define CC_RDNA2 (CC_OFFSET_AMD + 1030)
|
||||
@ -271,7 +272,6 @@ static __device__ __forceinline__ float2 warp_reduce_sum(float2 a) {
|
||||
return a;
|
||||
}
|
||||
|
||||
#ifdef GGML_CUDA_F16
|
||||
static __device__ __forceinline__ half2 warp_reduce_sum(half2 a) {
|
||||
#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL
|
||||
#pragma unroll
|
||||
@ -284,7 +284,6 @@ static __device__ __forceinline__ half2 warp_reduce_sum(half2 a) {
|
||||
NO_DEVICE_CODE;
|
||||
#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL
|
||||
}
|
||||
#endif // GGML_CUDA_F16
|
||||
|
||||
static __device__ __forceinline__ float warp_reduce_max(float x) {
|
||||
#pragma unroll
|
||||
@ -294,19 +293,26 @@ static __device__ __forceinline__ float warp_reduce_max(float x) {
|
||||
return x;
|
||||
}
|
||||
|
||||
//static __device__ __forceinline__ half2 warp_reduce_max(half2 x) {
|
||||
//#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL && CUDART_VERSION >= CUDART_HMAX
|
||||
//#pragma unroll
|
||||
// for (int mask = 16; mask > 0; mask >>= 1) {
|
||||
// x = __hmax2(x, __shfl_xor_sync(0xffffffff, x, mask, 32));
|
||||
// }
|
||||
// return x;
|
||||
//#else
|
||||
// GGML_UNUSED(x);
|
||||
// NO_DEVICE_CODE;
|
||||
//#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL && CUDART_VERSION >= CUDART_HMAX
|
||||
//}
|
||||
static __device__ __forceinline__ half2 warp_reduce_max(half2 x) {
|
||||
#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL && CUDART_VERSION >= CUDART_HMAX
|
||||
#pragma unroll
|
||||
for (int mask = 16; mask > 0; mask >>= 1) {
|
||||
x = __hmax2(x, __shfl_xor_sync(0xffffffff, x, mask, 32));
|
||||
}
|
||||
return x;
|
||||
#else
|
||||
GGML_UNUSED(x);
|
||||
NO_DEVICE_CODE;
|
||||
#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL && CUDART_VERSION >= CUDART_HMAX
|
||||
}
|
||||
|
||||
#if CUDART_VERSION < 12000
|
||||
static __device__ __forceinline__ uint32_t __hgt2_mask(const half2 a, const half2 b) {
|
||||
const uint32_t mask_low = 0x0000FFFF * (float( __low2half(a)) > float( __low2half(b)));
|
||||
const uint32_t mask_high = 0xFFFF0000 * (float(__high2half(a)) > float(__high2half(b)));
|
||||
return mask_low | mask_high;
|
||||
}
|
||||
#endif // CUDART_VERSION < 12000
|
||||
|
||||
#if defined(GGML_USE_HIPBLAS)
|
||||
#define __CUDA_ARCH__ 1300
|
||||
@ -391,6 +397,11 @@ static __device__ __forceinline__ int __dp4a(const int a, const int b, int c) {
|
||||
}
|
||||
#endif // defined(GGML_USE_HIPBLAS)
|
||||
|
||||
#define FP16_AVAILABLE defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) ? \
|
||||
defined(RDNA1) || defined(RDNA2) || defined(RDNA3) : __CUDA_ARCH__ >= CC_PASCAL
|
||||
|
||||
#define FP16_MMA_AVAILABLE !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_VOLTA
|
||||
|
||||
// TODO: move to ggml-common.h
|
||||
static const __device__ int8_t kvalues_iq4nl[16] = {-127, -104, -83, -65, -49, -35, -22, -10, 1, 13, 25, 38, 53, 69, 89, 113};
|
||||
|
||||
@ -404,6 +415,7 @@ struct ggml_cuda_device_info {
|
||||
|
||||
struct cuda_device_info {
|
||||
int cc; // compute capability
|
||||
int nsm; // number of streaming multiprocessors
|
||||
size_t smpb; // max. shared memory per block
|
||||
bool vmm; // virtual memory support
|
||||
size_t vmm_granularity; // granularity of virtual memory
|
||||
|
||||
944
ggml-cuda/fattn.cu
Normal file
944
ggml-cuda/fattn.cu
Normal file
@ -0,0 +1,944 @@
|
||||
#include "common.cuh"
|
||||
#include "fattn.cuh"
|
||||
|
||||
#include <cstdint>
|
||||
|
||||
#if FP16_MMA_AVAILABLE
|
||||
#include <mma.h>
|
||||
#endif
|
||||
|
||||
#define FATTN_KQ_STRIDE 256
|
||||
#define HALF_MAX_HALF __float2half(65504.0f/2) // Use neg. of this instead of -INFINITY to initialize KQ max vals to avoid NaN upon subtraction.
|
||||
#define SOFTMAX_FTZ_THRESHOLD -20.0f // Softmax exp. of values smaller than this are flushed to zero to avoid NaNs.
|
||||
|
||||
template<int D, int parallel_blocks> // D == head size
|
||||
__launch_bounds__(((D + WARP_SIZE - 1) / WARP_SIZE)*WARP_SIZE, 1)
|
||||
static __global__ void flash_attn_vec_ext_f16(
|
||||
const char * __restrict__ Q,
|
||||
const char * __restrict__ K,
|
||||
const char * __restrict__ V,
|
||||
const char * __restrict__ mask,
|
||||
float * __restrict__ dst,
|
||||
float2 * __restrict__ dst_meta,
|
||||
const float scale,
|
||||
const int ne00,
|
||||
const int ne01,
|
||||
const int ne02,
|
||||
const int ne03,
|
||||
const int ne10,
|
||||
const int ne11,
|
||||
const int ne12,
|
||||
const int ne13,
|
||||
const int ne31,
|
||||
const int nb31,
|
||||
const int nb01,
|
||||
const int nb02,
|
||||
const int nb03,
|
||||
const int nb11,
|
||||
const int nb12,
|
||||
const int nb13,
|
||||
const int ne0,
|
||||
const int ne1,
|
||||
const int ne2,
|
||||
const int ne3) {
|
||||
#if FP16_AVAILABLE
|
||||
//In this kernel Q, K, V are matrices while i, j, k are matrix indices.
|
||||
|
||||
const int ic = blockIdx.x / parallel_blocks; // Index of the Q/QKV column to work on.
|
||||
const int ip = blockIdx.x % parallel_blocks; // Index in group of blocks running for the same column in parallel.
|
||||
|
||||
const int gqa_ratio = ne02 / ne12; // With grouped query attention there are > 1 Q matrices per K, V matrix.
|
||||
const float2 * Q_f2 = (const float2 *) (Q + nb02* blockIdx.y + nb01*ic);
|
||||
const half2 * K_h2 = (const half2 *) (K + nb12*(blockIdx.y / gqa_ratio));
|
||||
const half * V_h = (const half *) (V + nb12*(blockIdx.y / gqa_ratio)); // K and V have same shape
|
||||
const half * maskh = (const half *) mask + ne11*ic;
|
||||
|
||||
const int stride_KV = nb11 / sizeof(half);
|
||||
const int stride_KV2 = nb11 / sizeof(half2);
|
||||
|
||||
constexpr int nwarps = (D + WARP_SIZE - 1) / WARP_SIZE;
|
||||
const int tid = WARP_SIZE*threadIdx.y + threadIdx.x;
|
||||
__builtin_assume(tid < nwarps*WARP_SIZE);
|
||||
|
||||
__shared__ half KQ[nwarps*WARP_SIZE];
|
||||
KQ[tid] = -INFINITY;
|
||||
half2 * KQ2 = (half2 *) KQ;
|
||||
|
||||
half kqmax = -HALF_MAX_HALF;
|
||||
half kqsum = 0.0f;
|
||||
|
||||
__shared__ half kqmax_shared[WARP_SIZE];
|
||||
__shared__ half kqsum_shared[WARP_SIZE];
|
||||
if (threadIdx.y == 0) {
|
||||
kqmax_shared[threadIdx.x] = -HALF_MAX_HALF;
|
||||
kqsum_shared[threadIdx.x] = 0.0f;
|
||||
}
|
||||
__syncthreads();
|
||||
|
||||
// Convert Q to half2 and store in registers:
|
||||
half2 Q_h2[(D/2 + WARP_SIZE - 1) / WARP_SIZE];
|
||||
#pragma unroll
|
||||
for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) {
|
||||
const int i = i0 + threadIdx.x;
|
||||
if (i0 + WARP_SIZE > D/2 && i >= D/2) {
|
||||
break;
|
||||
}
|
||||
|
||||
Q_h2[i0/WARP_SIZE] = make_half2(scale, scale) * make_half2(Q_f2[i].x, Q_f2[i].y);
|
||||
}
|
||||
|
||||
half2 VKQ = make_half2(0.0f, 0.0f); // Each thread calculates a single VKQ value.
|
||||
|
||||
const int k_start = parallel_blocks == 1 ? 0 : ip*D;
|
||||
for (int k_VKQ_0 = k_start; k_VKQ_0 < ne11; k_VKQ_0 += parallel_blocks*D) {
|
||||
// Calculate KQ tile and keep track of new maximum KQ values:
|
||||
half kqmax_new = kqmax;
|
||||
#pragma unroll
|
||||
for (int i_KQ_0 = 0; i_KQ_0 < D; i_KQ_0 += nwarps) {
|
||||
const int i_KQ = i_KQ_0 + threadIdx.y;
|
||||
|
||||
if ((i_KQ_0 + nwarps > D && i_KQ >= D) || (FATTN_KQ_STRIDE % D != 0 && k_VKQ_0 + i_KQ >= ne11)) {
|
||||
break;
|
||||
}
|
||||
|
||||
half2 sum2 = make_half2(0.0f, 0.0f);
|
||||
#pragma unroll
|
||||
for (int k_KQ_0 = 0; k_KQ_0 < D/2; k_KQ_0 += WARP_SIZE) {
|
||||
const int k_KQ = k_KQ_0 + threadIdx.x;
|
||||
if (k_KQ_0 + WARP_SIZE > D/2 && k_KQ >= D/2) {
|
||||
break;
|
||||
}
|
||||
|
||||
const half2 K_ik = K_h2[(k_VKQ_0 + i_KQ)*stride_KV2 + k_KQ];
|
||||
sum2 += K_ik * Q_h2[k_KQ_0/WARP_SIZE];
|
||||
}
|
||||
|
||||
sum2 = warp_reduce_sum(sum2);
|
||||
half sum = __low2half(sum2) + __high2half(sum2);
|
||||
sum += mask ? maskh[k_VKQ_0 + i_KQ] : __float2half(0.0f);
|
||||
kqmax_new = __hmax(kqmax_new, sum);
|
||||
if (threadIdx.x == 0) {
|
||||
KQ[i_KQ] = sum;
|
||||
}
|
||||
}
|
||||
|
||||
kqmax_new = warp_reduce_max(kqmax_new);
|
||||
if (threadIdx.x == 0) {
|
||||
kqmax_shared[threadIdx.y] = kqmax_new;
|
||||
}
|
||||
__syncthreads();
|
||||
kqmax_new = kqmax_shared[threadIdx.x];
|
||||
kqmax_new = warp_reduce_max(kqmax_new);
|
||||
|
||||
const half KQ_max_scale = hexp(kqmax - kqmax_new);
|
||||
kqmax = kqmax_new;
|
||||
|
||||
const half val = hexp(KQ[tid] - kqmax);
|
||||
kqsum = kqsum*KQ_max_scale + val;
|
||||
KQ[tid] = val;
|
||||
|
||||
VKQ *= __half2half2(KQ_max_scale);
|
||||
|
||||
__syncthreads();
|
||||
|
||||
if (tid < D) {
|
||||
#pragma unroll
|
||||
for (int k0 = 0; k0 < D; k0 += 2) {
|
||||
if (FATTN_KQ_STRIDE % D != 0 && k_VKQ_0 + k0 >= ne11) {
|
||||
break;
|
||||
}
|
||||
|
||||
half2 V_k;
|
||||
reinterpret_cast<half&>(V_k.x) = V_h[(k_VKQ_0 + k0 + 0)*stride_KV + tid];
|
||||
reinterpret_cast<half&>(V_k.y) = V_h[(k_VKQ_0 + k0 + 1)*stride_KV + tid];
|
||||
VKQ += V_k*KQ2[k0/2];
|
||||
}
|
||||
}
|
||||
|
||||
__syncthreads();
|
||||
}
|
||||
|
||||
if (tid >= D) {
|
||||
kqsum = 0.0f;
|
||||
}
|
||||
|
||||
kqsum = warp_reduce_sum(kqsum);
|
||||
if (threadIdx.x == 0) {
|
||||
kqsum_shared[threadIdx.y] = kqsum;
|
||||
}
|
||||
__syncthreads();
|
||||
kqsum = kqsum_shared[threadIdx.x];
|
||||
kqsum = warp_reduce_sum(kqsum);
|
||||
|
||||
if (tid >= D) {
|
||||
return;
|
||||
}
|
||||
|
||||
half dst_val = (__low2half(VKQ) + __high2half(VKQ));
|
||||
if (parallel_blocks == 1) {
|
||||
dst_val /= kqsum;
|
||||
}
|
||||
dst[D*gridDim.y*blockIdx.x + D*blockIdx.y + tid] = dst_val;
|
||||
|
||||
if (parallel_blocks == 1 || tid != 0) {
|
||||
return;
|
||||
}
|
||||
dst_meta[ic*gridDim.y*parallel_blocks + blockIdx.y*parallel_blocks + ip] = make_float2(kqmax, kqsum);
|
||||
#else
|
||||
NO_DEVICE_CODE;
|
||||
#endif // FP16_AVAILABLE
|
||||
}
|
||||
|
||||
// D == head size, VKQ_stride == num VKQ rows calculated in parallel:
|
||||
template<int D, int ncols, int nwarps, int VKQ_stride, int parallel_blocks, typename KQ_acc_t>
|
||||
__launch_bounds__(nwarps*WARP_SIZE, 1)
|
||||
static __global__ void flash_attn_ext_f16(
|
||||
const char * __restrict__ Q,
|
||||
const char * __restrict__ K,
|
||||
const char * __restrict__ V,
|
||||
const char * __restrict__ mask,
|
||||
float * __restrict__ dst,
|
||||
float2 * __restrict__ dst_meta,
|
||||
const float scale,
|
||||
const int ne00,
|
||||
const int ne01,
|
||||
const int ne02,
|
||||
const int ne03,
|
||||
const int ne10,
|
||||
const int ne11,
|
||||
const int ne12,
|
||||
const int ne13,
|
||||
const int ne31,
|
||||
const int nb31,
|
||||
const int nb01,
|
||||
const int nb02,
|
||||
const int nb03,
|
||||
const int nb11,
|
||||
const int nb12,
|
||||
const int nb13,
|
||||
const int ne0,
|
||||
const int ne1,
|
||||
const int ne2,
|
||||
const int ne3) {
|
||||
#if FP16_MMA_AVAILABLE
|
||||
//In this kernel Q, K, V are matrices while i, j, k are matrix indices.
|
||||
|
||||
const int ic0 = ncols*(blockIdx.x / parallel_blocks); // Index of the first Q/QKV column to work on.
|
||||
const int ip = blockIdx.x % parallel_blocks; // Index in group of blocks running for the same column in parallel.
|
||||
|
||||
static_assert(D <= FATTN_KQ_STRIDE, "D must be <= FATTN_KQ_STRIDE.");
|
||||
static_assert(ncols == 8 || ncols % 16 == 0, "ncols must be 8 or a multiple of 16.");
|
||||
constexpr int frag_m = ncols == 8 ? 32 : 16;
|
||||
constexpr int frag_n = ncols == 8 ? 8 : 16;
|
||||
static_assert(D % frag_m == 0, "If ncols == 8 then D % frag_m must be 0.");
|
||||
typedef nvcuda::wmma::fragment<nvcuda::wmma::matrix_a, frag_m, frag_n, 16, half, nvcuda::wmma::row_major> frag_a_K;
|
||||
typedef nvcuda::wmma::fragment<nvcuda::wmma::matrix_a, frag_m, frag_n, 16, half, nvcuda::wmma::col_major> frag_a_V;
|
||||
typedef nvcuda::wmma::fragment<nvcuda::wmma::matrix_b, frag_m, frag_n, 16, half, nvcuda::wmma::col_major> frag_b;
|
||||
typedef nvcuda::wmma::fragment<nvcuda::wmma::accumulator, frag_m, frag_n, 16, KQ_acc_t> frag_c_KQ;
|
||||
typedef nvcuda::wmma::fragment<nvcuda::wmma::accumulator, frag_m, frag_n, 16, half> frag_c_VKQ;
|
||||
|
||||
constexpr int KQ_stride_tc = nwarps*frag_m; // Number of KQ rows calculated in parallel.
|
||||
constexpr int VKQ_ratio = KQ_stride_tc/VKQ_stride; // Number of parallel VKQ accumulators needed to keep all warps busy.
|
||||
static_assert(VKQ_ratio <= nwarps, "VKQ_ratio must be <= nwarps.");
|
||||
|
||||
// Pad internal representation of KQ, KQV to reduce shared memory bank conflicts:
|
||||
constexpr int D_padded = D + 8;
|
||||
constexpr int kqs_padded = FATTN_KQ_STRIDE + 8;
|
||||
constexpr int kqar = sizeof(KQ_acc_t)/sizeof(half);
|
||||
|
||||
const int gqa_ratio = ne02 / ne12; // With grouped query attention there are > 1 Q matrices per K, V matrix.
|
||||
const float * Q_f = (const float *) (Q + nb02* blockIdx.y + nb01*ic0);
|
||||
const half * K_h = (const half *) (K + nb12*(blockIdx.y / gqa_ratio));
|
||||
const half * V_h = (const half *) (V + nb12*(blockIdx.y / gqa_ratio)); // K and V have same shape
|
||||
const half * maskh = (const half *) mask + (nb31/sizeof(half))* ic0;
|
||||
const half2 * mask2 = (const half2 *) mask + (nb31/sizeof(half))*(ic0/2);
|
||||
|
||||
const int stride_Q = nb01 / sizeof(float);
|
||||
const int stride_KV = nb11 / sizeof(half);
|
||||
|
||||
frag_b Q_b[D/16][ncols/frag_n];
|
||||
|
||||
// A single buffer for temporarily holding tiles of KQ and VKQ parts:
|
||||
constexpr int mem_KQ = ncols*kqs_padded*kqar;
|
||||
constexpr int mem_VKQ_parts = VKQ_ratio*ncols*D_padded;
|
||||
__shared__ half KQ[mem_KQ >= mem_VKQ_parts ? mem_KQ : mem_VKQ_parts];
|
||||
float * KQ_f = (float *) KQ;
|
||||
half2 * KQ2 = (half2 *) KQ;
|
||||
|
||||
float KQ_rowsum_f[ncols/nwarps] = {0.0f};
|
||||
float KQ_max_f[ncols/nwarps];
|
||||
float KQ_max_scale_f[ncols/nwarps] = {0.0f};
|
||||
|
||||
#pragma unroll
|
||||
for (int j = 0; j < ncols/nwarps; ++j) {
|
||||
KQ_max_f[j] = -FLT_MAX/2.0f;
|
||||
}
|
||||
|
||||
half2 KQ_rowsum_h2[ncols/nwarps] = {{0.0f, 0.0f}};
|
||||
half2 KQ_max_h2[ncols/nwarps];
|
||||
half2 KQ_max_scale_h2[ncols/nwarps] = {{0.0f, 0.0f}};
|
||||
|
||||
#pragma unroll
|
||||
for (int j = 0; j < ncols/nwarps; ++j) {
|
||||
KQ_max_h2[j] = make_half2(-HALF_MAX_HALF, -HALF_MAX_HALF);
|
||||
}
|
||||
|
||||
__shared__ half VKQ[ncols*D_padded]; // Accumulator for final VKQ slice.
|
||||
half2 * VKQ2 = (half2 *) VKQ;
|
||||
#pragma unroll
|
||||
for (int j0 = 0; j0 < ncols; j0 += nwarps) {
|
||||
const int j = j0 + threadIdx.y;
|
||||
#pragma unroll
|
||||
for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) {
|
||||
const int i = i0 + threadIdx.x;
|
||||
if (i0 + WARP_SIZE > D/2 && i >= D/2) {
|
||||
break;
|
||||
}
|
||||
VKQ2[j*(D_padded/2) + i] = make_half2(0.0f, 0.0f);
|
||||
}
|
||||
}
|
||||
|
||||
// Convert Q to half and apply scale, temporarily store in KQ:
|
||||
#pragma unroll
|
||||
for (int j0 = 0; j0 < ncols; j0 += nwarps) {
|
||||
const int j = j0 + threadIdx.y;
|
||||
#pragma unroll
|
||||
for (int i0 = 0; i0 < D; i0 += WARP_SIZE) {
|
||||
const int i = i0 + threadIdx.x;
|
||||
if (i0 + WARP_SIZE > D && i >= D) {
|
||||
break;
|
||||
}
|
||||
KQ[j*D_padded + i] = ic0 + j < ne01 ? Q_f[j*stride_Q + i] * scale : 0.0f;
|
||||
}
|
||||
}
|
||||
|
||||
__syncthreads();
|
||||
|
||||
// Load Q into tensor core fragments/registers since it will be used frequently:
|
||||
#pragma unroll
|
||||
for (int i0 = 0; i0 < D; i0 += 16) {
|
||||
#pragma unroll
|
||||
for (int j0 = 0; j0 < ncols; j0 += frag_n) {
|
||||
nvcuda::wmma::load_matrix_sync(Q_b[i0/16][j0/frag_n], KQ + j0*D_padded + i0, D_padded);
|
||||
}
|
||||
}
|
||||
|
||||
__syncthreads();
|
||||
|
||||
// Iterate over ne11 == previous tokens:
|
||||
for (int k_VKQ_0 = ip*FATTN_KQ_STRIDE; k_VKQ_0 < ne11; k_VKQ_0 += parallel_blocks*FATTN_KQ_STRIDE) {
|
||||
// Calculate tile of KQ:
|
||||
#pragma unroll
|
||||
for (int i_KQ_0 = 0; i_KQ_0 < FATTN_KQ_STRIDE; i_KQ_0 += KQ_stride_tc) {
|
||||
frag_c_KQ KQ_c[ncols/frag_n];
|
||||
#pragma unroll
|
||||
for (int j = 0; j < ncols/frag_n; ++j) {
|
||||
nvcuda::wmma::fill_fragment(KQ_c[j], 0.0f);
|
||||
}
|
||||
#pragma unroll
|
||||
for (int k_KQ_0 = 0; k_KQ_0 < D; k_KQ_0 += 16) {
|
||||
frag_a_K K_a;
|
||||
nvcuda::wmma::load_matrix_sync(K_a, K_h + (k_VKQ_0 + i_KQ_0 + frag_m*threadIdx.y)*stride_KV + k_KQ_0, stride_KV);
|
||||
#pragma unroll
|
||||
for (int j = 0; j < ncols/frag_n; ++j) {
|
||||
nvcuda::wmma::mma_sync(KQ_c[j], K_a, Q_b[k_KQ_0/16][j], KQ_c[j]);
|
||||
}
|
||||
}
|
||||
#pragma unroll
|
||||
for (int j0 = 0; j0 < ncols; j0 += frag_n) {
|
||||
nvcuda::wmma::store_matrix_sync((KQ_acc_t *) KQ + j0*kqs_padded + i_KQ_0 + frag_m*threadIdx.y, KQ_c[j0/frag_n], kqs_padded, nvcuda::wmma::mem_col_major);
|
||||
}
|
||||
}
|
||||
|
||||
__syncthreads();
|
||||
|
||||
// Calculate softmax for each KQ column using the current max. value.
|
||||
// The divisor is stored in KQ_rowsum and will be applied at the end.
|
||||
#pragma unroll
|
||||
for (int j0 = 0; j0 < ncols; j0 += nwarps) {
|
||||
const int j = j0 + threadIdx.y;
|
||||
|
||||
if (std::is_same<KQ_acc_t, float>::value) {
|
||||
float KQ_f_tmp[FATTN_KQ_STRIDE / WARP_SIZE];
|
||||
#pragma unroll
|
||||
for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += WARP_SIZE) {
|
||||
const int k = k0 + threadIdx.x;
|
||||
|
||||
KQ_f_tmp[k0/WARP_SIZE] = KQ_f[j*kqs_padded + k];
|
||||
}
|
||||
|
||||
float KQ_max_new = KQ_max_f[j0/nwarps];
|
||||
#pragma unroll
|
||||
for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += WARP_SIZE) {
|
||||
const int k = k0 + threadIdx.x;
|
||||
|
||||
KQ_f_tmp[k0/WARP_SIZE] += mask ? __half2float(maskh[j*(nb31/sizeof(half)) + k_VKQ_0 + k]) : 0.0f;
|
||||
KQ_max_new = max(KQ_max_new, KQ_f_tmp[k0/WARP_SIZE]);
|
||||
}
|
||||
KQ_max_new = warp_reduce_max(KQ_max_new);
|
||||
|
||||
const float diff = KQ_max_f[j0/nwarps] - KQ_max_new;
|
||||
KQ_max_scale_f[j0/nwarps] = expf(diff);
|
||||
if (diff <= SOFTMAX_FTZ_THRESHOLD) {
|
||||
KQ_max_scale_f[j0/nwarps] = 0.0f;
|
||||
}
|
||||
KQ_max_f[j0/nwarps] = KQ_max_new;
|
||||
|
||||
float KQ_rowsum_add = 0.0f;
|
||||
#pragma unroll
|
||||
for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += WARP_SIZE) {
|
||||
const int k = k0 + threadIdx.x;
|
||||
|
||||
const float diff = KQ_f_tmp[k0/WARP_SIZE] - KQ_max_f[j0/nwarps];
|
||||
KQ_f_tmp[k0/WARP_SIZE] = expf(diff);
|
||||
if (diff <= SOFTMAX_FTZ_THRESHOLD) {
|
||||
KQ_f_tmp[k0/WARP_SIZE] = 0.0f;
|
||||
}
|
||||
KQ_rowsum_add += KQ_f_tmp[k0/WARP_SIZE];
|
||||
KQ[j*(kqar*kqs_padded) + k] = KQ_f_tmp[k0/WARP_SIZE];
|
||||
}
|
||||
KQ_rowsum_add = warp_reduce_sum(KQ_rowsum_add);
|
||||
|
||||
// Scale previous KQ_rowsum to account for a potential increase in KQ_max:
|
||||
KQ_rowsum_f[j0/nwarps] = KQ_max_scale_f[j0/nwarps]*KQ_rowsum_f[j0/nwarps] + KQ_rowsum_add;
|
||||
} else {
|
||||
half2 KQ2_tmp[FATTN_KQ_STRIDE/(2*WARP_SIZE)];
|
||||
#pragma unroll
|
||||
for (int k0 = 0; k0 < FATTN_KQ_STRIDE/2; k0 += WARP_SIZE) {
|
||||
const int k = k0 + threadIdx.x;
|
||||
|
||||
KQ2_tmp[k0/WARP_SIZE] = KQ2[j*(kqs_padded/2) + k];
|
||||
}
|
||||
|
||||
half2 KQ_max_new = KQ_max_h2[j0/nwarps];
|
||||
#pragma unroll
|
||||
for (int k0 = 0; k0 < FATTN_KQ_STRIDE/2; k0 += WARP_SIZE) {
|
||||
const int k = k0 + threadIdx.x;
|
||||
|
||||
KQ2_tmp[k0/WARP_SIZE] += mask ? mask2[(j*ne11 + k_VKQ_0)/2 + k] : make_half2(0.0f, 0.0f);
|
||||
KQ_max_new = __hmax2(KQ_max_new, KQ2_tmp[k0/WARP_SIZE]);
|
||||
}
|
||||
KQ_max_new = __half2half2(warp_reduce_max(__hmax(__low2half(KQ_max_new), __high2half(KQ_max_new))));
|
||||
const half2 diff = KQ_max_h2[j0/nwarps] - KQ_max_new;
|
||||
KQ_max_scale_h2[j0/nwarps] = h2exp(diff);
|
||||
const uint32_t ftz_mask = __hgt2_mask(diff, make_half2(SOFTMAX_FTZ_THRESHOLD, SOFTMAX_FTZ_THRESHOLD));
|
||||
*((uint32_t *) &KQ_max_scale_h2[j0/nwarps]) &= ftz_mask;
|
||||
KQ_max_h2[j0/nwarps] = KQ_max_new;
|
||||
|
||||
half2 KQ_rowsum_add = make_half2(0.0f, 0.0f);
|
||||
#pragma unroll
|
||||
for (int k0 = 0; k0 < FATTN_KQ_STRIDE/2; k0 += WARP_SIZE) {
|
||||
const int k = k0 + threadIdx.x;
|
||||
|
||||
const half2 diff = KQ2_tmp[k0/WARP_SIZE] - KQ_max_h2[j0/nwarps];
|
||||
KQ2_tmp[k0/WARP_SIZE] = h2exp(diff);
|
||||
const uint32_t ftz_mask = __hgt2_mask(diff, make_half2(SOFTMAX_FTZ_THRESHOLD, SOFTMAX_FTZ_THRESHOLD));
|
||||
*((uint32_t *) &KQ2_tmp[k0/WARP_SIZE]) &= ftz_mask;
|
||||
KQ_rowsum_add += KQ2_tmp[k0/WARP_SIZE];
|
||||
KQ2[j*(kqs_padded/2) + k] = KQ2_tmp[k0/WARP_SIZE];
|
||||
}
|
||||
KQ_rowsum_add = warp_reduce_sum(KQ_rowsum_add);
|
||||
|
||||
// Scale previous KQ_rowsum to account for a potential increase in KQ_max:
|
||||
KQ_rowsum_h2[j0/nwarps] = KQ_max_scale_h2[j0/nwarps]*KQ_rowsum_h2[j0/nwarps] + KQ_rowsum_add;
|
||||
}
|
||||
}
|
||||
|
||||
__syncthreads();
|
||||
|
||||
frag_b KQ_b[FATTN_KQ_STRIDE/(VKQ_ratio*16)][ncols/frag_n];
|
||||
#pragma unroll
|
||||
for (int j0 = 0; j0 < ncols; j0 += frag_n) {
|
||||
#pragma unroll
|
||||
for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += VKQ_ratio*16) {
|
||||
const int k = k0 + (threadIdx.y % VKQ_ratio)*16;
|
||||
nvcuda::wmma::load_matrix_sync(
|
||||
KQ_b[k0/(VKQ_ratio*16)][j0/frag_n],
|
||||
KQ + j0*(kqar*kqs_padded) + k,
|
||||
kqar*kqs_padded);
|
||||
}
|
||||
}
|
||||
|
||||
frag_c_VKQ VKQ_c[D/VKQ_stride][ncols/frag_n];
|
||||
#pragma unroll
|
||||
for (int i_VKQ_0 = 0; i_VKQ_0 < D; i_VKQ_0 += VKQ_stride) {
|
||||
#pragma unroll
|
||||
for (int j = 0; j < ncols/frag_n; ++j) {
|
||||
nvcuda::wmma::fill_fragment(VKQ_c[i_VKQ_0/VKQ_stride][j], 0.0f);
|
||||
}
|
||||
|
||||
#pragma unroll
|
||||
for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += VKQ_ratio*16) {
|
||||
const int k = k0 + (threadIdx.y % VKQ_ratio)*16;
|
||||
|
||||
frag_a_V v_a;
|
||||
nvcuda::wmma::load_matrix_sync(v_a, V_h + (k_VKQ_0 + k)*stride_KV + i_VKQ_0 + frag_m*(threadIdx.y/VKQ_ratio), stride_KV);
|
||||
#pragma unroll
|
||||
for (int j = 0; j < ncols/frag_n; ++j) {
|
||||
nvcuda::wmma::mma_sync(VKQ_c[i_VKQ_0/VKQ_stride][j], v_a, KQ_b[k0/(VKQ_ratio*16)][j], VKQ_c[i_VKQ_0/VKQ_stride][j]);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
__syncthreads();
|
||||
|
||||
const int offset_k = (threadIdx.y % VKQ_ratio) * (ncols*D_padded);
|
||||
#pragma unroll
|
||||
for (int i_KQ_0 = 0; i_KQ_0 < D; i_KQ_0 += VKQ_stride) {
|
||||
#pragma unroll
|
||||
for (int j0 = 0; j0 < ncols; j0 += frag_n) {
|
||||
nvcuda::wmma::store_matrix_sync(
|
||||
KQ + offset_k + j0*D_padded + i_KQ_0 + frag_m*(threadIdx.y/VKQ_ratio),
|
||||
VKQ_c[i_KQ_0/VKQ_stride][j0/frag_n],
|
||||
D_padded, nvcuda::wmma::mem_col_major);
|
||||
}
|
||||
}
|
||||
|
||||
__syncthreads();
|
||||
|
||||
#pragma unroll
|
||||
for (int j0 = 0; j0 < ncols; j0 += nwarps) {
|
||||
const int j = j0 + threadIdx.y;
|
||||
|
||||
half2 VKQ_scale;
|
||||
if (std::is_same<KQ_acc_t, float>::value) {
|
||||
VKQ_scale = make_half2(KQ_max_scale_f[j0/nwarps], KQ_max_scale_f[j0/nwarps]);
|
||||
} else {
|
||||
VKQ_scale = KQ_max_scale_h2[j0/nwarps];
|
||||
}
|
||||
|
||||
#pragma unroll
|
||||
for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) {
|
||||
const int i = i0 + threadIdx.x;
|
||||
if (i0 + WARP_SIZE > D/2 && i >= D/2) {
|
||||
break;
|
||||
}
|
||||
|
||||
half2 VKQ_add = make_half2(0.0f, 0.0f);
|
||||
#pragma unroll
|
||||
for (int l = 0; l < VKQ_ratio; ++l) {
|
||||
VKQ_add += KQ2[l*(ncols*D_padded/2) + j*(D_padded/2) + i];
|
||||
}
|
||||
VKQ2[j*(D_padded/2) + i] = VKQ_scale*VKQ2[j*(D_padded/2) + i] + VKQ_add;
|
||||
}
|
||||
}
|
||||
|
||||
__syncthreads();
|
||||
}
|
||||
|
||||
#pragma unroll
|
||||
for (int j0 = 0; j0 < ncols; j0 += nwarps) {
|
||||
const int j_VKQ = j0 + threadIdx.y;
|
||||
if (ic0 + j_VKQ >= ne01) {
|
||||
return;
|
||||
}
|
||||
const int j_dst = (ic0 + j_VKQ)*parallel_blocks + ip;
|
||||
|
||||
float KQ_rowsum_j;
|
||||
if (std::is_same<KQ_acc_t, float>::value) {
|
||||
KQ_rowsum_j = KQ_rowsum_f[j0/nwarps];
|
||||
} else {
|
||||
KQ_rowsum_j = __low2float(KQ_rowsum_h2[j0/nwarps]) + __high2float(KQ_rowsum_h2[j0/nwarps]);
|
||||
}
|
||||
|
||||
#pragma unroll
|
||||
for (int i0 = 0; i0 < D; i0 += WARP_SIZE) {
|
||||
const int i = i0 + threadIdx.x;
|
||||
if (i0 + WARP_SIZE > D && i >= D) {
|
||||
break;
|
||||
}
|
||||
float dst_val = VKQ[j_VKQ*D_padded + i];
|
||||
if (parallel_blocks == 1) {
|
||||
dst_val /= KQ_rowsum_j;
|
||||
}
|
||||
dst[j_dst*gridDim.y*D + blockIdx.y*D + i] = dst_val;
|
||||
}
|
||||
|
||||
if (parallel_blocks == 1 || threadIdx.x != 0) {
|
||||
continue;
|
||||
}
|
||||
|
||||
float2 dst_meta_val;
|
||||
if (std::is_same<KQ_acc_t, float>::value) {
|
||||
dst_meta_val.x = KQ_max_f[j0/nwarps];
|
||||
} else {
|
||||
dst_meta_val.x = __low2float(KQ_max_h2[j0/nwarps]);
|
||||
}
|
||||
dst_meta_val.y = KQ_rowsum_j;
|
||||
dst_meta[(ic0 + j_VKQ)*gridDim.y*parallel_blocks + blockIdx.y*parallel_blocks + ip] = dst_meta_val;
|
||||
}
|
||||
#else
|
||||
NO_DEVICE_CODE;
|
||||
#endif // FP16_MMA_AVAILABLE
|
||||
}
|
||||
|
||||
template<int D, int parallel_blocks> // D == head size
|
||||
__launch_bounds__(D, 1)
|
||||
static __global__ void flash_attn_combine_results(
|
||||
const float * __restrict__ VKQ_parts,
|
||||
const float2 * __restrict__ VKQ_meta,
|
||||
float * __restrict__ dst) {
|
||||
#if FP16_AVAILABLE
|
||||
VKQ_parts += parallel_blocks*D * gridDim.y*blockIdx.x;
|
||||
VKQ_meta += parallel_blocks * gridDim.y*blockIdx.x;
|
||||
dst += D * gridDim.y*blockIdx.x;
|
||||
|
||||
const int tid = threadIdx.x;
|
||||
__builtin_assume(tid < D);
|
||||
|
||||
__shared__ float2 meta[parallel_blocks];
|
||||
if (tid < 2*parallel_blocks) {
|
||||
((float *) meta)[threadIdx.x] = ((const float *)VKQ_meta) [blockIdx.y*(2*parallel_blocks) + tid];
|
||||
}
|
||||
|
||||
__syncthreads();
|
||||
|
||||
float kqmax = meta[0].x;
|
||||
#pragma unroll
|
||||
for (int l = 1; l < parallel_blocks; ++l) {
|
||||
kqmax = max(kqmax, meta[l].x);
|
||||
}
|
||||
|
||||
float VKQ_numerator = 0.0f;
|
||||
float VKQ_denominator = 0.0f;
|
||||
#pragma unroll
|
||||
for (int l = 0; l < parallel_blocks; ++l) {
|
||||
const float diff = meta[l].x - kqmax;
|
||||
const float KQ_max_scale = expf(diff);
|
||||
const uint32_t ftz_mask = 0xFFFFFFFF * (diff > SOFTMAX_FTZ_THRESHOLD);
|
||||
*((uint32_t *) &KQ_max_scale) &= ftz_mask;
|
||||
|
||||
VKQ_numerator += KQ_max_scale * VKQ_parts[l*gridDim.y*D + blockIdx.y*D + tid];
|
||||
VKQ_denominator += KQ_max_scale * meta[l].y;
|
||||
}
|
||||
|
||||
dst[blockIdx.y*D + tid] = VKQ_numerator / VKQ_denominator;
|
||||
#else
|
||||
NO_DEVICE_CODE;
|
||||
#endif // FP16_AVAILABLE
|
||||
}
|
||||
|
||||
constexpr int get_max_power_of_2(int x) {
|
||||
return x % 2 == 0 ? 2*get_max_power_of_2(x/2) : 1;
|
||||
}
|
||||
|
||||
static_assert(get_max_power_of_2(1) == 1, "Test failed.");
|
||||
static_assert(get_max_power_of_2(2) == 2, "Test failed.");
|
||||
static_assert(get_max_power_of_2(4) == 4, "Test failed.");
|
||||
static_assert(get_max_power_of_2(6) == 2, "Test failed.");
|
||||
|
||||
// Number of VKQ rows calculated in parallel:
|
||||
constexpr int get_VKQ_stride(int D, int nwarps, int frag_m) {
|
||||
return (get_max_power_of_2(D/frag_m) < nwarps ? get_max_power_of_2(D/frag_m) : nwarps)*frag_m;
|
||||
}
|
||||
|
||||
static_assert(get_VKQ_stride(128, 1, 32) == 32, "Test failed.");
|
||||
static_assert(get_VKQ_stride(128, 2, 32) == 64, "Test failed.");
|
||||
static_assert(get_VKQ_stride(128, 4, 32) == 128, "Test failed.");
|
||||
static_assert(get_VKQ_stride( 64, 1, 32) == 32, "Test failed.");
|
||||
static_assert(get_VKQ_stride( 64, 2, 32) == 64, "Test failed.");
|
||||
static_assert(get_VKQ_stride( 64, 4, 32) == 64, "Test failed.");
|
||||
static_assert(get_VKQ_stride( 80, 1, 16) == 16, "Test failed.");
|
||||
static_assert(get_VKQ_stride( 80, 2, 16) == 16, "Test failed.");
|
||||
static_assert(get_VKQ_stride( 80, 4, 16) == 16, "Test failed.");
|
||||
|
||||
template <int D, int parallel_blocks> void launch_fattn_vec_f16(
|
||||
const ggml_tensor * Q, const ggml_tensor * K, const ggml_tensor * V, ggml_tensor * KQV, const ggml_tensor * mask,
|
||||
ggml_cuda_pool & pool, cudaStream_t main_stream
|
||||
) {
|
||||
ggml_cuda_pool_alloc<float> dst_tmp(pool);
|
||||
ggml_cuda_pool_alloc<float2> dst_tmp_meta(pool);
|
||||
|
||||
if (parallel_blocks > 1) {
|
||||
dst_tmp.alloc(parallel_blocks*ggml_nelements(KQV));
|
||||
dst_tmp_meta.alloc(parallel_blocks*ggml_nrows(KQV));
|
||||
}
|
||||
|
||||
constexpr int nwarps = (D + WARP_SIZE - 1) / WARP_SIZE;
|
||||
const dim3 block_dim(WARP_SIZE, nwarps, 1);
|
||||
const dim3 blocks_num(parallel_blocks*Q->ne[1], Q->ne[2], Q->ne[3]);
|
||||
const int shmem = 0;
|
||||
|
||||
float scale;
|
||||
memcpy(&scale, KQV->op_params, sizeof(float));
|
||||
|
||||
flash_attn_vec_ext_f16<D, parallel_blocks>
|
||||
<<<blocks_num, block_dim, shmem, main_stream>>> (
|
||||
(const char *) Q->data,
|
||||
(const char *) K->data,
|
||||
(const char *) V->data,
|
||||
mask ? ((const char *) mask->data) : nullptr,
|
||||
parallel_blocks == 1 ? (float *) KQV->data : dst_tmp.ptr, dst_tmp_meta.ptr,
|
||||
scale,
|
||||
Q->ne[0], Q->ne[1], Q->ne[2], Q->ne[3],
|
||||
K->ne[0], K->ne[1], K->ne[2], K->ne[3],
|
||||
mask ? mask->ne[1] : 0, mask ? mask->nb[1] : 0,
|
||||
Q->nb[1], Q->nb[2], Q->nb[3],
|
||||
K->nb[1], K->nb[2], K->nb[3],
|
||||
KQV->ne[0], KQV->ne[1], KQV->ne[2], KQV->ne[3]
|
||||
);
|
||||
CUDA_CHECK(cudaGetLastError());
|
||||
|
||||
if (parallel_blocks == 1) {
|
||||
return;
|
||||
}
|
||||
|
||||
const dim3 block_dim_combine(D, 1, 1);
|
||||
const dim3 blocks_num_combine(Q->ne[1], blocks_num.y, blocks_num.z);
|
||||
const int shmem_combine = 0;
|
||||
|
||||
flash_attn_combine_results<D, parallel_blocks>
|
||||
<<<blocks_num_combine, block_dim_combine, shmem_combine, main_stream>>>
|
||||
(dst_tmp.ptr, dst_tmp_meta.ptr, (float *) KQV->data);
|
||||
CUDA_CHECK(cudaGetLastError());
|
||||
}
|
||||
|
||||
template <int D, int cols_per_block, int nwarps, int parallel_blocks, typename KQ_acc_t> void launch_fattn_f16_impl(
|
||||
const ggml_tensor * Q, const ggml_tensor * K, const ggml_tensor * V, ggml_tensor * KQV, const ggml_tensor * mask,
|
||||
ggml_cuda_pool & pool, cudaStream_t main_stream
|
||||
) {
|
||||
ggml_cuda_pool_alloc<float> dst_tmp(pool);
|
||||
ggml_cuda_pool_alloc<float2> dst_tmp_meta(pool);
|
||||
|
||||
if (parallel_blocks > 1) {
|
||||
dst_tmp.alloc(parallel_blocks*ggml_nelements(KQV));
|
||||
dst_tmp_meta.alloc(parallel_blocks*ggml_nrows(KQV));
|
||||
}
|
||||
|
||||
constexpr int frag_m = (cols_per_block) == 8 && (D) % 32 == 0 ? 32 : 16;
|
||||
const dim3 block_dim(WARP_SIZE, nwarps, 1);
|
||||
const dim3 blocks_num(parallel_blocks*(Q->ne[1] + cols_per_block - 1) / cols_per_block, Q->ne[2], Q->ne[3]);
|
||||
const int shmem = 0;
|
||||
|
||||
float scale;
|
||||
memcpy(&scale, KQV->op_params, sizeof(float));
|
||||
|
||||
flash_attn_ext_f16<D, cols_per_block, nwarps, get_VKQ_stride(D, nwarps, frag_m), parallel_blocks, KQ_acc_t>
|
||||
<<<blocks_num, block_dim, shmem, main_stream>>> (
|
||||
(const char *) Q->data,
|
||||
(const char *) K->data,
|
||||
(const char *) V->data,
|
||||
mask ? ((const char *) mask->data) : nullptr,
|
||||
(parallel_blocks) == 1 ? (float *) KQV->data : dst_tmp.ptr, dst_tmp_meta.ptr,
|
||||
scale,
|
||||
Q->ne[0], Q->ne[1], Q->ne[2], Q->ne[3],
|
||||
K->ne[0], K->ne[1], K->ne[2], K->ne[3],
|
||||
mask ? mask->ne[1] : 0, mask ? mask->nb[1] : 0,
|
||||
Q->nb[1], Q->nb[2], Q->nb[3],
|
||||
K->nb[1], K->nb[2], K->nb[3],
|
||||
KQV->ne[0], KQV->ne[1], KQV->ne[2], KQV->ne[3]
|
||||
);
|
||||
CUDA_CHECK(cudaGetLastError());
|
||||
|
||||
if ((parallel_blocks) == 1) {
|
||||
return;
|
||||
}
|
||||
|
||||
const dim3 block_dim_combine(D, 1, 1);
|
||||
const dim3 blocks_num_combine(Q->ne[1], blocks_num.y, blocks_num.z);
|
||||
const int shmem_combine = 0;
|
||||
|
||||
flash_attn_combine_results<D, parallel_blocks>
|
||||
<<<blocks_num_combine, block_dim_combine, shmem_combine, main_stream>>>
|
||||
(dst_tmp.ptr, dst_tmp_meta.ptr, (float *) KQV->data);
|
||||
CUDA_CHECK(cudaGetLastError());
|
||||
}
|
||||
|
||||
template <int D, int cols_per_block, int nwarps, typename KQ_acc_t> void launch_fattn_f16(
|
||||
const ggml_tensor * Q, const ggml_tensor * K, const ggml_tensor * V, ggml_tensor * KQV, const ggml_tensor * mask,
|
||||
const int nsm, ggml_cuda_pool & pool, cudaStream_t main_stream
|
||||
) {
|
||||
const int blocks_num_pb1 = ((Q->ne[1] + cols_per_block - 1) / cols_per_block)*Q->ne[2]*Q->ne[3];
|
||||
|
||||
if (4*blocks_num_pb1 < 2*nsm) {
|
||||
launch_fattn_f16_impl<D, cols_per_block, nwarps, 4, KQ_acc_t>(Q, K, V, KQV, mask, pool, main_stream);
|
||||
return;
|
||||
}
|
||||
if (2*blocks_num_pb1 < 2*nsm) {
|
||||
launch_fattn_f16_impl<D, cols_per_block, nwarps, 2, KQ_acc_t>(Q, K, V, KQV, mask, pool, main_stream);
|
||||
return;
|
||||
}
|
||||
launch_fattn_f16_impl<D, cols_per_block, nwarps, 1, KQ_acc_t>(Q, K, V, KQV, mask, pool, main_stream);
|
||||
}
|
||||
|
||||
void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
|
||||
const ggml_tensor * Q = dst->src[0];
|
||||
const ggml_tensor * K = dst->src[1];
|
||||
const ggml_tensor * V = dst->src[2];
|
||||
|
||||
const ggml_tensor * mask = dst->src[3];
|
||||
|
||||
ggml_tensor * KQV = dst;
|
||||
|
||||
GGML_ASSERT(Q->type == GGML_TYPE_F32);
|
||||
GGML_ASSERT(K->type == GGML_TYPE_F16);
|
||||
GGML_ASSERT(V->type == GGML_TYPE_F16);
|
||||
GGML_ASSERT(KQV->type == GGML_TYPE_F32);
|
||||
|
||||
GGML_ASSERT(!mask || mask->type == GGML_TYPE_F16);
|
||||
GGML_ASSERT(!mask || mask->ne[1] >= GGML_PAD(Q->ne[1], 16) &&
|
||||
"the Flash-Attention CUDA kernel requires the mask to be padded to 16 and at least n_queries big");
|
||||
|
||||
GGML_ASSERT(K->ne[1] % FATTN_KQ_STRIDE == 0 && "Incorrect KV cache padding.");
|
||||
|
||||
ggml_cuda_set_device(ctx.device);
|
||||
|
||||
const int nsm = ggml_cuda_info().devices[ggml_cuda_get_device()].nsm;
|
||||
|
||||
const int32_t precision = KQV->op_params[1];
|
||||
|
||||
if (precision != GGML_PREC_DEFAULT) {
|
||||
if (Q->ne[1] <= 32 || Q->ne[0] > 128) {
|
||||
constexpr int cols_per_block = 16;
|
||||
constexpr int nwarps = 4;
|
||||
switch (Q->ne[0]) {
|
||||
case 64:
|
||||
launch_fattn_f16< 64, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
|
||||
break;
|
||||
case 80:
|
||||
launch_fattn_f16< 80, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
|
||||
break;
|
||||
case 96:
|
||||
launch_fattn_f16< 96, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
|
||||
break;
|
||||
case 112:
|
||||
launch_fattn_f16<112, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
|
||||
break;
|
||||
case 128:
|
||||
launch_fattn_f16<128, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
|
||||
break;
|
||||
case 256:
|
||||
launch_fattn_f16<256, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
|
||||
break;
|
||||
default:
|
||||
GGML_ASSERT(false);
|
||||
break;
|
||||
}
|
||||
} else {
|
||||
constexpr int cols_per_block = 32;
|
||||
constexpr int nwarps = 4;
|
||||
switch (Q->ne[0]) {
|
||||
case 64:
|
||||
launch_fattn_f16< 64, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
|
||||
break;
|
||||
case 80:
|
||||
launch_fattn_f16< 80, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
|
||||
break;
|
||||
case 96:
|
||||
launch_fattn_f16< 96, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
|
||||
break;
|
||||
case 112:
|
||||
launch_fattn_f16<112, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
|
||||
break;
|
||||
case 128:
|
||||
launch_fattn_f16<128, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
|
||||
break;
|
||||
// case 256:
|
||||
// launch_fattn_f16<256, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
|
||||
// break;
|
||||
default:
|
||||
GGML_ASSERT(false);
|
||||
break;
|
||||
}
|
||||
}
|
||||
return;
|
||||
}
|
||||
|
||||
if (Q->ne[1] == 1 && Q->ne[0] % (2*WARP_SIZE) == 0) {
|
||||
constexpr int parallel_blocks = 4;
|
||||
switch (Q->ne[0]) {
|
||||
case 64:
|
||||
launch_fattn_vec_f16< 64, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
|
||||
break;
|
||||
case 128:
|
||||
launch_fattn_vec_f16<128, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
|
||||
break;
|
||||
case 256:
|
||||
launch_fattn_vec_f16<256, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
|
||||
break;
|
||||
default:
|
||||
GGML_ASSERT(false);
|
||||
break;
|
||||
}
|
||||
return;
|
||||
}
|
||||
|
||||
if (Q->ne[1] <= 8 && Q->ne[0] % WARP_SIZE == 0) {
|
||||
constexpr int cols_per_block = 8;
|
||||
constexpr int nwarps = 4;
|
||||
switch (Q->ne[0]) {
|
||||
case 64:
|
||||
launch_fattn_f16< 64, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
|
||||
break;
|
||||
case 96:
|
||||
launch_fattn_f16< 96, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
|
||||
break;
|
||||
case 128:
|
||||
launch_fattn_f16<128, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
|
||||
break;
|
||||
case 256:
|
||||
launch_fattn_f16<256, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
|
||||
break;
|
||||
default:
|
||||
GGML_ASSERT(false);
|
||||
break;
|
||||
}
|
||||
return;
|
||||
}
|
||||
|
||||
if (Q->ne[1] <= 32) {
|
||||
constexpr int cols_per_block = 16;
|
||||
constexpr int nwarps = 4;
|
||||
switch (Q->ne[0]) {
|
||||
case 64:
|
||||
launch_fattn_f16< 64, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
|
||||
break;
|
||||
case 80:
|
||||
launch_fattn_f16< 80, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
|
||||
break;
|
||||
case 96:
|
||||
launch_fattn_f16< 96, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
|
||||
break;
|
||||
case 112:
|
||||
launch_fattn_f16<112, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
|
||||
break;
|
||||
case 128:
|
||||
launch_fattn_f16<128, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
|
||||
break;
|
||||
case 256:
|
||||
launch_fattn_f16<256, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
|
||||
break;
|
||||
default:
|
||||
GGML_ASSERT(false);
|
||||
break;
|
||||
}
|
||||
return;
|
||||
}
|
||||
|
||||
constexpr int cols_per_block = 32;
|
||||
constexpr int nwarps = 4;
|
||||
switch (Q->ne[0]) {
|
||||
case 64:
|
||||
launch_fattn_f16< 64, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
|
||||
break;
|
||||
case 80:
|
||||
launch_fattn_f16< 80, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
|
||||
break;
|
||||
case 96:
|
||||
launch_fattn_f16< 96, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
|
||||
break;
|
||||
case 112:
|
||||
launch_fattn_f16<112, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
|
||||
break;
|
||||
case 128:
|
||||
launch_fattn_f16<128, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
|
||||
break;
|
||||
case 256:
|
||||
launch_fattn_f16<256, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
|
||||
break;
|
||||
default:
|
||||
GGML_ASSERT(false);
|
||||
break;
|
||||
}
|
||||
return;
|
||||
}
|
||||
3
ggml-cuda/fattn.cuh
Normal file
3
ggml-cuda/fattn.cuh
Normal file
@ -0,0 +1,3 @@
|
||||
#include "common.cuh"
|
||||
|
||||
void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
|
||||
@ -1,7 +1,17 @@
|
||||
#include "softmax.cuh"
|
||||
|
||||
template <bool vals_smem, int ncols_template, int block_size_template>
|
||||
static __global__ void soft_max_f32(const float * x, const float * mask, const float * pos, float * dst, const int ncols_par, const int nrows_y, const float scale, const float max_bias, const float m0, const float m1, uint32_t n_head_log2) {
|
||||
template <typename T>
|
||||
static __device__ __forceinline__ float t2f32(T val) {
|
||||
return (float) val;
|
||||
}
|
||||
|
||||
template <>
|
||||
__device__ float __forceinline__ t2f32<half>(half val) {
|
||||
return __half2float(val);
|
||||
}
|
||||
|
||||
template <bool vals_smem, int ncols_template, int block_size_template, typename T>
|
||||
static __global__ void soft_max_f32(const float * x, const T * mask, const T * pos, float * dst, const int ncols_par, const int nrows_y, const float scale, const float max_bias, const float m0, const float m1, uint32_t n_head_log2) {
|
||||
const int ncols = ncols_template == 0 ? ncols_par : ncols_template;
|
||||
|
||||
const int tid = threadIdx.x;
|
||||
@ -43,7 +53,7 @@ static __global__ void soft_max_f32(const float * x, const float * mask, const f
|
||||
const int64_t ix = (int64_t)rowx*ncols + col;
|
||||
const int64_t iy = (int64_t)rowy*ncols + col;
|
||||
|
||||
const float val = x[ix]*scale + (mask ? mask[iy] : 0.0f) + (pos ? slope*pos[col] : 0.0f);
|
||||
const float val = x[ix]*scale + (mask ? t2f32(mask[iy]) : 0.0f) + (pos ? slope*t2f32(pos[col]) : 0.0f);
|
||||
|
||||
vals[col] = val;
|
||||
max_val = max(max_val, val);
|
||||
@ -114,7 +124,8 @@ static __global__ void soft_max_f32(const float * x, const float * mask, const f
|
||||
}
|
||||
}
|
||||
|
||||
static void soft_max_f32_cuda(const float * x, const float * mask, const float * pos, float * dst, const int ncols_x, const int nrows_x, const int nrows_y, const float scale, const float max_bias, cudaStream_t stream) {
|
||||
template<typename T>
|
||||
static void soft_max_f32_cuda(const float * x, const T * mask, const T * pos, float * dst, const int ncols_x, const int nrows_x, const int nrows_y, const float scale, const float max_bias, cudaStream_t stream) {
|
||||
int nth = WARP_SIZE;
|
||||
while (nth < ncols_x && nth < CUDA_SOFT_MAX_BLOCK_SIZE) nth *= 2;
|
||||
const dim3 block_dims(nth, 1, 1);
|
||||
@ -167,15 +178,19 @@ static void soft_max_f32_cuda(const float * x, const float * mask, const float *
|
||||
void ggml_cuda_op_soft_max(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
|
||||
const ggml_tensor * src0 = dst->src[0];
|
||||
const ggml_tensor * src1 = dst->src[1];
|
||||
const ggml_tensor * src2 = dst->src[2];
|
||||
|
||||
const float * src0_d = (const float *)src0->data;
|
||||
const float * src1_d = src1 ? (const float *)src1->data : nullptr;
|
||||
const void * src1_d = src1 ? (const void *)src1->data : nullptr;
|
||||
|
||||
float * dst_d = (float *)dst->data;
|
||||
cudaStream_t stream = ctx.stream();
|
||||
|
||||
GGML_ASSERT(src0->type == GGML_TYPE_F32);
|
||||
GGML_ASSERT( dst->type == GGML_TYPE_F32);
|
||||
|
||||
GGML_ASSERT(!src1 || src1->type == GGML_TYPE_F32); // src1 contains mask and it is optional
|
||||
GGML_ASSERT(!src1 || src1->type == GGML_TYPE_F16 || src1->type == GGML_TYPE_F32); // src1 contains mask and it is optional
|
||||
GGML_ASSERT(!src2 || src2->type == GGML_TYPE_F16 || src2->type == GGML_TYPE_F32); // src2 contains positions and it is optional
|
||||
|
||||
const int64_t ne00 = src0->ne[0];
|
||||
const int64_t nrows_x = ggml_nrows(src0);
|
||||
@ -188,14 +203,25 @@ void ggml_cuda_op_soft_max(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
|
||||
memcpy(&max_bias, (float *) dst->op_params + 1, sizeof(float));
|
||||
|
||||
// positions tensor
|
||||
float * src2_dd = nullptr;
|
||||
void * src2_d = nullptr;
|
||||
|
||||
ggml_tensor * src2 = dst->src[2];
|
||||
const bool use_src2 = src2 != nullptr;
|
||||
|
||||
if (use_src2) {
|
||||
src2_dd = (float *)src2->data;
|
||||
src2_d = (void *)src2->data;
|
||||
}
|
||||
|
||||
soft_max_f32_cuda(src0_d, src1_d, src2_dd, dst_d, ne00, nrows_x, nrows_y, scale, max_bias, stream);
|
||||
const bool use_f16 = (src1 && src1->type == GGML_TYPE_F16) || (src2 && src2->type == GGML_TYPE_F16);
|
||||
|
||||
if (use_f16) {
|
||||
const half * src1_dd = (const half *)src1_d;
|
||||
const half * src2_dd = (const half *)src2_d;
|
||||
|
||||
soft_max_f32_cuda(src0_d, src1_dd, src2_dd, dst_d, ne00, nrows_x, nrows_y, scale, max_bias, stream);
|
||||
} else {
|
||||
const float * src1_dd = (const float *)src1_d;
|
||||
const float * src2_dd = (const float *)src2_d;
|
||||
|
||||
soft_max_f32_cuda(src0_d, src1_dd, src2_dd, dst_d, ne00, nrows_x, nrows_y, scale, max_bias, stream);
|
||||
}
|
||||
}
|
||||
|
||||
@ -1427,6 +1427,7 @@ static void ggml_vk_graph_compute(struct ggml_kompute_context * ctx, struct ggml
|
||||
for (int i = node_start; i < node_end; ++i) {
|
||||
struct ggml_tensor * src0 = gf->nodes[i]->src[0];
|
||||
struct ggml_tensor * src1 = gf->nodes[i]->src[1];
|
||||
struct ggml_tensor * src2 = gf->nodes[i]->src[2]; GGML_UNUSED(src2);
|
||||
struct ggml_tensor * dst = gf->nodes[i];
|
||||
GGML_ASSERT(dst->data != nullptr);
|
||||
|
||||
@ -1559,6 +1560,12 @@ static void ggml_vk_graph_compute(struct ggml_kompute_context * ctx, struct ggml
|
||||
{
|
||||
float scale;
|
||||
memcpy(&scale, dst->op_params, sizeof(float));
|
||||
|
||||
#pragma message("TODO: add ggml_vk_soft_max() F16/F32 src1 and src2 support")
|
||||
#pragma message("ref: https://github.com/ggerganov/llama.cpp/pull/5021")
|
||||
GGML_ASSERT(!src1 || src1t == GGML_TYPE_F32);
|
||||
GGML_ASSERT(src2 == nullptr);
|
||||
|
||||
ggml_vk_soft_max(seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, ne00, ne01, ne02, ne03, scale);
|
||||
} break;
|
||||
case GGML_OP_DIAG_MASK_INF:
|
||||
|
||||
547
ggml-metal.m
547
ggml-metal.m
@ -46,8 +46,10 @@ enum ggml_metal_kernel_type {
|
||||
GGML_METAL_KERNEL_TYPE_GELU_QUICK_4,
|
||||
GGML_METAL_KERNEL_TYPE_SILU,
|
||||
GGML_METAL_KERNEL_TYPE_SILU_4,
|
||||
GGML_METAL_KERNEL_TYPE_SOFT_MAX,
|
||||
GGML_METAL_KERNEL_TYPE_SOFT_MAX_4,
|
||||
GGML_METAL_KERNEL_TYPE_SOFT_MAX_F16,
|
||||
GGML_METAL_KERNEL_TYPE_SOFT_MAX_F16_4,
|
||||
GGML_METAL_KERNEL_TYPE_SOFT_MAX_F32,
|
||||
GGML_METAL_KERNEL_TYPE_SOFT_MAX_F32_4,
|
||||
GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF,
|
||||
GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF_8,
|
||||
GGML_METAL_KERNEL_TYPE_GET_ROWS_F32,
|
||||
@ -177,6 +179,14 @@ enum ggml_metal_kernel_type {
|
||||
GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_ASC,
|
||||
GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_DESC,
|
||||
GGML_METAL_KERNEL_TYPE_LEAKY_RELU_F32,
|
||||
GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H64,
|
||||
GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H80,
|
||||
GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H96,
|
||||
GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H112,
|
||||
GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H128,
|
||||
GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H256,
|
||||
GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H128,
|
||||
GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H256,
|
||||
GGML_METAL_KERNEL_TYPE_CPY_F32_F16,
|
||||
GGML_METAL_KERNEL_TYPE_CPY_F32_F32,
|
||||
GGML_METAL_KERNEL_TYPE_CPY_F32_Q8_0,
|
||||
@ -443,7 +453,7 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) {
|
||||
}
|
||||
|
||||
/*
|
||||
GGML_METAL_LOG_INFO("%s: loaded %-32s %16p | th_max = %4d | th_width = %4d\n", __func__, "kernel_"#name, (void *) kernel->pipeline, \
|
||||
GGML_METAL_LOG_INFO("%s: loaded %-40s %16p | th_max = %4d | th_width = %4d\n", __func__, "kernel_"#name, (void *) kernel->pipeline, \
|
||||
(int) kernel->pipeline.maxTotalThreadsPerThreadgroup, \
|
||||
(int) kernel->pipeline.threadExecutionWidth); \
|
||||
*/
|
||||
@ -459,172 +469,182 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) {
|
||||
return NULL; \
|
||||
} \
|
||||
} else { \
|
||||
GGML_METAL_LOG_WARN("%s: skipping %-32s (not supported)\n", __func__, "kernel_"#name); \
|
||||
GGML_METAL_LOG_WARN("%s: skipping %-40s (not supported)\n", __func__, "kernel_"#name); \
|
||||
}
|
||||
|
||||
// simd_sum and simd_max requires MTLGPUFamilyApple7
|
||||
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ADD, add, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ADD_ROW, add_row, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL, mul, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_ROW, mul_row, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIV, div, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIV_ROW, div_row, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SCALE, scale, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SCALE_4, scale_4, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CLAMP, clamp, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_TANH, tanh, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_RELU, relu, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU, gelu, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU_4, gelu_4, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU_QUICK, gelu_quick, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU_QUICK_4, gelu_quick_4, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SILU, silu, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SILU_4, silu_4, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SOFT_MAX, soft_max, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SOFT_MAX_4, soft_max_4, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF, diag_mask_inf, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF_8, diag_mask_inf_8, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_F32, get_rows_f32, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_F16, get_rows_f16, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_0, get_rows_q4_0, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_1, get_rows_q4_1, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_0, get_rows_q5_0, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_1, get_rows_q5_1, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q8_0, get_rows_q8_0, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q2_K, get_rows_q2_K, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q3_K, get_rows_q3_K, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_K, get_rows_q4_K, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_K, get_rows_q5_K, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q6_K, get_rows_q6_K, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XXS, get_rows_iq2_xxs, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XS, get_rows_iq2_xs, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_XXS, get_rows_iq3_xxs, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_S, get_rows_iq3_s, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_S, get_rows_iq2_s, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_S, get_rows_iq1_s, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_M, get_rows_iq1_m, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_NL, get_rows_iq4_nl, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_XS, get_rows_iq4_xs, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_I32, get_rows_i32, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_RMS_NORM, rms_norm, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GROUP_NORM, group_norm, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_NORM, norm, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F32_F32, mul_mv_f32_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F16, mul_mv_f16_f16, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32, mul_mv_f16_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_1ROW, mul_mv_f16_f32_1row, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_L4, mul_mv_f16_f32_l4, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_0_F32, mul_mv_q4_0_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_1_F32, mul_mv_q4_1_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_0_F32, mul_mv_q5_0_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_1_F32, mul_mv_q5_1_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q8_0_F32, mul_mv_q8_0_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q2_K_F32, mul_mv_q2_K_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q3_K_F32, mul_mv_q3_K_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_K_F32, mul_mv_q4_K_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_K_F32, mul_mv_q5_K_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q6_K_F32, mul_mv_q6_K_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XXS_F32, mul_mv_iq2_xxs_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XS_F32, mul_mv_iq2_xs_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_XXS_F32, mul_mv_iq3_xxs_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_S_F32, mul_mv_iq3_s_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_S_F32, mul_mv_iq2_s_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_S_F32, mul_mv_iq1_s_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_M_F32, mul_mv_iq1_m_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_NL_F32, mul_mv_iq4_nl_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_XS_F32, mul_mv_iq4_xs_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F32_F32, mul_mv_id_f32_f32, ctx->support_simdgroup_reduction);
|
||||
//GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F16, mul_mv_id_f16_f16, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32, mul_mv_id_f16_f32, ctx->support_simdgroup_reduction);
|
||||
//GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32_1ROW, mul_mv_id_f16_f32_1row, ctx->support_simdgroup_reduction);
|
||||
//GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32_L4, mul_mv_id_f16_f32_l4, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_0_F32, mul_mv_id_q4_0_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_1_F32, mul_mv_id_q4_1_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_0_F32, mul_mv_id_q5_0_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_1_F32, mul_mv_id_q5_1_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q8_0_F32, mul_mv_id_q8_0_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q2_K_F32, mul_mv_id_q2_K_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q3_K_F32, mul_mv_id_q3_K_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_K_F32, mul_mv_id_q4_K_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_K_F32, mul_mv_id_q5_K_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q6_K_F32, mul_mv_id_q6_K_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XXS_F32, mul_mv_id_iq2_xxs_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XS_F32, mul_mv_id_iq2_xs_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_XXS_F32, mul_mv_id_iq3_xxs_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_S_F32, mul_mv_id_iq3_s_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_S_F32, mul_mv_id_iq2_s_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_S_F32, mul_mv_id_iq1_s_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_M_F32, mul_mv_id_iq1_m_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_NL_F32, mul_mv_id_iq4_nl_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_XS_F32, mul_mv_id_iq4_xs_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_F32_F32, mul_mm_f32_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_F16_F32, mul_mm_f16_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_0_F32, mul_mm_q4_0_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_1_F32, mul_mm_q4_1_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_0_F32, mul_mm_q5_0_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_1_F32, mul_mm_q5_1_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q8_0_F32, mul_mm_q8_0_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q2_K_F32, mul_mm_q2_K_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q3_K_F32, mul_mm_q3_K_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_K_F32, mul_mm_q4_K_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_K_F32, mul_mm_q5_K_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q6_K_F32, mul_mm_q6_K_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XXS_F32, mul_mm_iq2_xxs_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XS_F32, mul_mm_iq2_xs_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_XXS_F32, mul_mm_iq3_xxs_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_S_F32, mul_mm_iq3_s_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_S_F32, mul_mm_iq2_s_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_S_F32, mul_mm_iq1_s_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_M_F32, mul_mm_iq1_m_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_NL_F32, mul_mm_iq4_nl_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_XS_F32, mul_mm_iq4_xs_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F32_F32, mul_mm_id_f32_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F16_F32, mul_mm_id_f16_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_0_F32, mul_mm_id_q4_0_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_1_F32, mul_mm_id_q4_1_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_0_F32, mul_mm_id_q5_0_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_1_F32, mul_mm_id_q5_1_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q8_0_F32, mul_mm_id_q8_0_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q2_K_F32, mul_mm_id_q2_K_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q3_K_F32, mul_mm_id_q3_K_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_K_F32, mul_mm_id_q4_K_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_K_F32, mul_mm_id_q5_K_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q6_K_F32, mul_mm_id_q6_K_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XXS_F32, mul_mm_id_iq2_xxs_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XS_F32, mul_mm_id_iq2_xs_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_XXS_F32, mul_mm_id_iq3_xxs_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_S_F32, mul_mm_id_iq3_s_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_S_F32, mul_mm_id_iq2_s_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_S_F32, mul_mm_id_iq1_s_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_M_F32, mul_mm_id_iq1_m_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_NL_F32, mul_mm_id_iq4_nl_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_XS_F32, mul_mm_id_iq4_xs_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_F32, rope_f32, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_F16, rope_f16, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ALIBI_F32, alibi_f32, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_IM2COL_F16, im2col_f16, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_IM2COL_F32, im2col_f32, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_UPSCALE_F32, upscale_f32, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_PAD_F32, pad_f32, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_TIMESTEP_EMBEDDING_F32, timestep_embedding_f32, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARANGE_F32, arange_f32, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_ASC, argsort_f32_i32_asc, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_DESC, argsort_f32_i32_desc, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_LEAKY_RELU_F32, leaky_relu_f32, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_F16, cpy_f32_f16, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_F32, cpy_f32_f32, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q8_0, cpy_f32_q8_0, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_0, cpy_f32_q4_0, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_1, cpy_f32_q4_1, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_0, cpy_f32_q5_0, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_1, cpy_f32_q5_1, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_IQ4_NL, cpy_f32_iq4_nl, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F16_F16, cpy_f16_f16, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F16_F32, cpy_f16_f32, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CONCAT, concat, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SQR, sqr, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SUM_ROWS, sum_rows, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ADD, add, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ADD_ROW, add_row, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL, mul, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_ROW, mul_row, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIV, div, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIV_ROW, div_row, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SCALE, scale, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SCALE_4, scale_4, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CLAMP, clamp, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_TANH, tanh, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_RELU, relu, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU, gelu, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU_4, gelu_4, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU_QUICK, gelu_quick, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU_QUICK_4, gelu_quick_4, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SILU, silu, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SILU_4, silu_4, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SOFT_MAX_F16, soft_max_f16, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SOFT_MAX_F16_4, soft_max_f16_4, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SOFT_MAX_F32, soft_max_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SOFT_MAX_F32_4, soft_max_f32_4, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF, diag_mask_inf, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF_8, diag_mask_inf_8, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_F32, get_rows_f32, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_F16, get_rows_f16, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_0, get_rows_q4_0, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_1, get_rows_q4_1, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_0, get_rows_q5_0, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_1, get_rows_q5_1, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q8_0, get_rows_q8_0, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q2_K, get_rows_q2_K, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q3_K, get_rows_q3_K, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_K, get_rows_q4_K, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_K, get_rows_q5_K, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q6_K, get_rows_q6_K, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XXS, get_rows_iq2_xxs, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XS, get_rows_iq2_xs, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_XXS, get_rows_iq3_xxs, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_S, get_rows_iq3_s, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_S, get_rows_iq2_s, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_S, get_rows_iq1_s, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_M, get_rows_iq1_m, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_NL, get_rows_iq4_nl, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_XS, get_rows_iq4_xs, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_I32, get_rows_i32, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_RMS_NORM, rms_norm, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GROUP_NORM, group_norm, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_NORM, norm, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F32_F32, mul_mv_f32_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F16, mul_mv_f16_f16, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32, mul_mv_f16_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_1ROW, mul_mv_f16_f32_1row, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_L4, mul_mv_f16_f32_l4, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_0_F32, mul_mv_q4_0_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_1_F32, mul_mv_q4_1_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_0_F32, mul_mv_q5_0_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_1_F32, mul_mv_q5_1_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q8_0_F32, mul_mv_q8_0_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q2_K_F32, mul_mv_q2_K_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q3_K_F32, mul_mv_q3_K_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_K_F32, mul_mv_q4_K_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_K_F32, mul_mv_q5_K_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q6_K_F32, mul_mv_q6_K_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XXS_F32, mul_mv_iq2_xxs_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XS_F32, mul_mv_iq2_xs_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_XXS_F32, mul_mv_iq3_xxs_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_S_F32, mul_mv_iq3_s_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_S_F32, mul_mv_iq2_s_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_S_F32, mul_mv_iq1_s_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_M_F32, mul_mv_iq1_m_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_NL_F32, mul_mv_iq4_nl_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_XS_F32, mul_mv_iq4_xs_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F32_F32, mul_mv_id_f32_f32, ctx->support_simdgroup_reduction);
|
||||
//GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F16, mul_mv_id_f16_f16, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32, mul_mv_id_f16_f32, ctx->support_simdgroup_reduction);
|
||||
//GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32_1ROW, mul_mv_id_f16_f32_1row, ctx->support_simdgroup_reduction);
|
||||
//GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32_L4, mul_mv_id_f16_f32_l4, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_0_F32, mul_mv_id_q4_0_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_1_F32, mul_mv_id_q4_1_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_0_F32, mul_mv_id_q5_0_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_1_F32, mul_mv_id_q5_1_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q8_0_F32, mul_mv_id_q8_0_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q2_K_F32, mul_mv_id_q2_K_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q3_K_F32, mul_mv_id_q3_K_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_K_F32, mul_mv_id_q4_K_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_K_F32, mul_mv_id_q5_K_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q6_K_F32, mul_mv_id_q6_K_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XXS_F32, mul_mv_id_iq2_xxs_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XS_F32, mul_mv_id_iq2_xs_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_XXS_F32, mul_mv_id_iq3_xxs_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_S_F32, mul_mv_id_iq3_s_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_S_F32, mul_mv_id_iq2_s_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_S_F32, mul_mv_id_iq1_s_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_M_F32, mul_mv_id_iq1_m_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_NL_F32, mul_mv_id_iq4_nl_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_XS_F32, mul_mv_id_iq4_xs_f32, ctx->support_simdgroup_reduction);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_F32_F32, mul_mm_f32_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_F16_F32, mul_mm_f16_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_0_F32, mul_mm_q4_0_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_1_F32, mul_mm_q4_1_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_0_F32, mul_mm_q5_0_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_1_F32, mul_mm_q5_1_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q8_0_F32, mul_mm_q8_0_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q2_K_F32, mul_mm_q2_K_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q3_K_F32, mul_mm_q3_K_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_K_F32, mul_mm_q4_K_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_K_F32, mul_mm_q5_K_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q6_K_F32, mul_mm_q6_K_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XXS_F32, mul_mm_iq2_xxs_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XS_F32, mul_mm_iq2_xs_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_XXS_F32, mul_mm_iq3_xxs_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_S_F32, mul_mm_iq3_s_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_S_F32, mul_mm_iq2_s_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_S_F32, mul_mm_iq1_s_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_M_F32, mul_mm_iq1_m_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_NL_F32, mul_mm_iq4_nl_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_XS_F32, mul_mm_iq4_xs_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F32_F32, mul_mm_id_f32_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F16_F32, mul_mm_id_f16_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_0_F32, mul_mm_id_q4_0_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_1_F32, mul_mm_id_q4_1_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_0_F32, mul_mm_id_q5_0_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_1_F32, mul_mm_id_q5_1_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q8_0_F32, mul_mm_id_q8_0_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q2_K_F32, mul_mm_id_q2_K_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q3_K_F32, mul_mm_id_q3_K_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_K_F32, mul_mm_id_q4_K_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_K_F32, mul_mm_id_q5_K_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q6_K_F32, mul_mm_id_q6_K_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XXS_F32, mul_mm_id_iq2_xxs_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XS_F32, mul_mm_id_iq2_xs_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_XXS_F32, mul_mm_id_iq3_xxs_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_S_F32, mul_mm_id_iq3_s_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_S_F32, mul_mm_id_iq2_s_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_S_F32, mul_mm_id_iq1_s_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_M_F32, mul_mm_id_iq1_m_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_NL_F32, mul_mm_id_iq4_nl_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_XS_F32, mul_mm_id_iq4_xs_f32, ctx->support_simdgroup_mm);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_F32, rope_f32, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_F16, rope_f16, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ALIBI_F32, alibi_f32, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_IM2COL_F16, im2col_f16, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_IM2COL_F32, im2col_f32, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_UPSCALE_F32, upscale_f32, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_PAD_F32, pad_f32, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_TIMESTEP_EMBEDDING_F32, timestep_embedding_f32, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARANGE_F32, arange_f32, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_ASC, argsort_f32_i32_asc, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_DESC, argsort_f32_i32_desc, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_LEAKY_RELU_F32, leaky_relu_f32, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H64, flash_attn_ext_f16_h64, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H80, flash_attn_ext_f16_h80, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H96, flash_attn_ext_f16_h96, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H112, flash_attn_ext_f16_h112, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H128, flash_attn_ext_f16_h128, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H256, flash_attn_ext_f16_h256, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H128, flash_attn_ext_vec_f16_h128, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H256, flash_attn_ext_vec_f16_h256, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_F16, cpy_f32_f16, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_F32, cpy_f32_f32, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q8_0, cpy_f32_q8_0, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_0, cpy_f32_q4_0, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_1, cpy_f32_q4_1, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_0, cpy_f32_q5_0, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_1, cpy_f32_q5_1, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_IQ4_NL, cpy_f32_iq4_nl, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F16_F16, cpy_f16_f16, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F16_F32, cpy_f16_f32, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CONCAT, concat, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SQR, sqr, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SUM_ROWS, sum_rows, true);
|
||||
}
|
||||
|
||||
[metal_library release];
|
||||
@ -743,6 +763,7 @@ static bool ggml_metal_supports_op(const struct ggml_metal_context * ctx, const
|
||||
case GGML_OP_TIMESTEP_EMBEDDING:
|
||||
case GGML_OP_ARGSORT:
|
||||
case GGML_OP_LEAKY_RELU:
|
||||
case GGML_OP_FLASH_ATTN_EXT:
|
||||
return true;
|
||||
case GGML_OP_MUL_MAT:
|
||||
case GGML_OP_MUL_MAT_ID:
|
||||
@ -1326,20 +1347,33 @@ static enum ggml_status ggml_metal_graph_compute(
|
||||
} break;
|
||||
case GGML_OP_SOFT_MAX:
|
||||
{
|
||||
GGML_ASSERT(!src1 || src1->type == GGML_TYPE_F16 || src1->type == GGML_TYPE_F32);
|
||||
GGML_ASSERT(!src2 || src2->type == GGML_TYPE_F16 || src2->type == GGML_TYPE_F32);
|
||||
|
||||
int nth = 32; // SIMD width
|
||||
|
||||
id<MTLComputePipelineState> pipeline = nil;
|
||||
|
||||
const bool use_f16 = (src1 && src1->type == GGML_TYPE_F16) || (src2 && src2->type == GGML_TYPE_F16);
|
||||
|
||||
if (ne00%4 == 0) {
|
||||
while (nth < ne00/4 && nth < 256) {
|
||||
nth *= 2;
|
||||
}
|
||||
pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SOFT_MAX_4].pipeline;
|
||||
if (use_f16) {
|
||||
pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SOFT_MAX_F16_4].pipeline;
|
||||
} else {
|
||||
pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SOFT_MAX_F32_4].pipeline;
|
||||
}
|
||||
} else {
|
||||
while (nth < ne00 && nth < 1024) {
|
||||
nth *= 2;
|
||||
}
|
||||
pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SOFT_MAX].pipeline;
|
||||
if (use_f16) {
|
||||
pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SOFT_MAX_F16].pipeline;
|
||||
} else {
|
||||
pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SOFT_MAX_F32].pipeline;
|
||||
}
|
||||
}
|
||||
|
||||
float scale;
|
||||
@ -2503,6 +2537,161 @@ static enum ggml_status ggml_metal_graph_compute(
|
||||
|
||||
[encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
|
||||
} break;
|
||||
case GGML_OP_FLASH_ATTN_EXT:
|
||||
{
|
||||
GGML_ASSERT(ne00 % 4 == 0);
|
||||
GGML_ASSERT(src0->type == GGML_TYPE_F32);
|
||||
|
||||
struct ggml_tensor * src3 = gf->nodes[i]->src[3];
|
||||
|
||||
GGML_ASSERT(ggml_are_same_shape(src1, src2));
|
||||
GGML_ASSERT(src3);
|
||||
|
||||
size_t offs_src3 = 0;
|
||||
|
||||
id<MTLBuffer> id_src3 = src3 ? ggml_metal_get_buffer(src3, &offs_src3) : nil;
|
||||
|
||||
GGML_ASSERT(!src3 || src3->type == GGML_TYPE_F16);
|
||||
GGML_ASSERT(!src3 || src3->ne[1] >= GGML_PAD(src0->ne[1], 8) &&
|
||||
"the Flash-Attention Metal kernel requires the mask to be padded to 8 and at least n_queries big");
|
||||
|
||||
const int64_t ne30 = src3 ? src3->ne[0] : 0; GGML_UNUSED(ne30);
|
||||
const int64_t ne31 = src3 ? src3->ne[1] : 0;
|
||||
const int64_t ne32 = src3 ? src3->ne[2] : 0; GGML_UNUSED(ne32);
|
||||
const int64_t ne33 = src3 ? src3->ne[3] : 0; GGML_UNUSED(ne33);
|
||||
|
||||
const uint64_t nb30 = src3 ? src3->nb[0] : 0; GGML_UNUSED(nb30);
|
||||
const uint64_t nb31 = src3 ? src3->nb[1] : 0;
|
||||
const uint64_t nb32 = src3 ? src3->nb[2] : 0; GGML_UNUSED(nb32);
|
||||
const uint64_t nb33 = src3 ? src3->nb[3] : 0; GGML_UNUSED(nb33);
|
||||
|
||||
const enum ggml_type src2t = src2 ? src2->type : GGML_TYPE_COUNT; GGML_UNUSED(src2t);
|
||||
|
||||
float scale;
|
||||
memcpy(&scale, dst->op_params, sizeof(float));
|
||||
|
||||
id<MTLComputePipelineState> pipeline = nil;
|
||||
|
||||
bool use_vec_kernel = false;
|
||||
|
||||
if (ne01 >= 4 || (ne00%128 != 0)) {
|
||||
switch (ne00) {
|
||||
case 64: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H64 ].pipeline; break;
|
||||
case 80: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H80 ].pipeline; break;
|
||||
case 96: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H96 ].pipeline; break;
|
||||
case 112: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H112].pipeline; break;
|
||||
case 128: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H128].pipeline; break;
|
||||
case 256: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H256].pipeline; break;
|
||||
default:
|
||||
{
|
||||
GGML_METAL_LOG_ERROR("unsupported size: %lld\n", ne00);
|
||||
GGML_METAL_LOG_ERROR("add template specialization for this size\n");
|
||||
GGML_ASSERT(false && "add template specialization for this size");
|
||||
}
|
||||
}
|
||||
} else {
|
||||
use_vec_kernel = true;
|
||||
|
||||
switch (ne00) {
|
||||
case 128: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H128].pipeline; break;
|
||||
case 256: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H256].pipeline; break;
|
||||
default:
|
||||
{
|
||||
GGML_METAL_LOG_ERROR("unsupported size: %lld\n", ne00);
|
||||
GGML_METAL_LOG_ERROR("add template specialization for this size\n");
|
||||
GGML_ASSERT(false && "add template specialization for this size");
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
[encoder setComputePipelineState:pipeline];
|
||||
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
|
||||
[encoder setBuffer:id_src1 offset:offs_src1 atIndex:1];
|
||||
[encoder setBuffer:id_src2 offset:offs_src2 atIndex:2];
|
||||
[encoder setBuffer:id_src3 offset:offs_src3 atIndex:3];
|
||||
[encoder setBuffer:id_dst offset:offs_dst atIndex:4];
|
||||
[encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:5];
|
||||
[encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:6];
|
||||
[encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:7];
|
||||
[encoder setBytes:&ne03 length:sizeof( int64_t) atIndex:8];
|
||||
[encoder setBytes:&nb00 length:sizeof(uint64_t) atIndex:9];
|
||||
[encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:10];
|
||||
[encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:11];
|
||||
[encoder setBytes:&nb03 length:sizeof(uint64_t) atIndex:12];
|
||||
[encoder setBytes:&ne10 length:sizeof( int64_t) atIndex:13];
|
||||
[encoder setBytes:&ne11 length:sizeof( int64_t) atIndex:14];
|
||||
[encoder setBytes:&ne12 length:sizeof( int64_t) atIndex:15];
|
||||
[encoder setBytes:&ne13 length:sizeof( int64_t) atIndex:16];
|
||||
[encoder setBytes:&nb10 length:sizeof(uint64_t) atIndex:17];
|
||||
[encoder setBytes:&nb11 length:sizeof(uint64_t) atIndex:18];
|
||||
[encoder setBytes:&nb12 length:sizeof(uint64_t) atIndex:19];
|
||||
[encoder setBytes:&nb13 length:sizeof(uint64_t) atIndex:20];
|
||||
[encoder setBytes:&ne31 length:sizeof( int64_t) atIndex:21];
|
||||
[encoder setBytes:&nb31 length:sizeof(uint64_t) atIndex:22];
|
||||
[encoder setBytes:&ne0 length:sizeof( int64_t) atIndex:23];
|
||||
[encoder setBytes:&ne1 length:sizeof( int64_t) atIndex:24];
|
||||
[encoder setBytes:&ne2 length:sizeof( int64_t) atIndex:25];
|
||||
[encoder setBytes:&ne3 length:sizeof( int64_t) atIndex:26];
|
||||
[encoder setBytes:&scale length:sizeof( float) atIndex:27];
|
||||
|
||||
if (!use_vec_kernel) {
|
||||
// half8x8 kernel
|
||||
const int64_t nqptg = 8; // queries per threadgroup !! sync with kernel template arguments !!
|
||||
const int64_t ncpsg = 32; // cache values per simdgroup !! sync with kernel template arguments !!
|
||||
|
||||
GGML_ASSERT(nqptg <= 32);
|
||||
GGML_ASSERT(nqptg % 8 == 0);
|
||||
GGML_ASSERT(ncpsg % 32 == 0);
|
||||
|
||||
int64_t nsgmax = 2;
|
||||
|
||||
while (true) {
|
||||
const size_t smem = nqptg*(ne00 + 2*nsgmax*(ncpsg + nqptg))*(sizeof(float)/2);
|
||||
if (smem > ctx->device.maxThreadgroupMemoryLength) {
|
||||
break;
|
||||
}
|
||||
nsgmax *= 2;
|
||||
}
|
||||
nsgmax /= 2;
|
||||
|
||||
// simdgroups per threadgroup (a.k.a. warps)
|
||||
const int64_t nsg = ne01 <= nqptg ? MAX(4, MIN(nsgmax, MIN(ne11/ncpsg, (int64_t) pipeline.maxTotalThreadsPerThreadgroup/32))) : 4;
|
||||
|
||||
const size_t smem = nqptg*(ne00 + 2*nsg*(ncpsg + nqptg))*(sizeof(float)/2);
|
||||
|
||||
//printf("smem: %zu, max: %zu\n", smem, ctx->device.maxThreadgroupMemoryLength);
|
||||
GGML_ASSERT(smem <= ctx->device.maxThreadgroupMemoryLength);
|
||||
|
||||
[encoder setThreadgroupMemoryLength:GGML_PAD(smem, 16) atIndex:0];
|
||||
|
||||
[encoder dispatchThreadgroups:MTLSizeMake((ne01 + nqptg - 1)/nqptg, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(32, nsg, 1)];
|
||||
} else {
|
||||
// half1x4 kernel
|
||||
const int64_t nqptg = 1; // queries per threadgroup !! sync with kernel template arguments !!
|
||||
const int64_t ncpsg = 32; // cache values per simdgroup !! sync with kernel template arguments !!
|
||||
|
||||
GGML_ASSERT(nqptg <= 32);
|
||||
GGML_ASSERT(nqptg % 1 == 0);
|
||||
GGML_ASSERT(ncpsg % 32 == 0);
|
||||
|
||||
// simdgroups per threadgroup (a.k.a. warps)
|
||||
const int64_t nsgt = MAX(2, MIN(ne11/ncpsg, (int64_t) pipeline.maxTotalThreadsPerThreadgroup/32));
|
||||
|
||||
int64_t nsg = 1;
|
||||
while (nsg <= nsgt) {
|
||||
nsg *= 2;
|
||||
}
|
||||
nsg /= 2;
|
||||
|
||||
const size_t smem = (nqptg*(ne00 + 2*nsg*(ncpsg + nqptg)) + nsg*ne00)*(sizeof(float)/2);
|
||||
|
||||
//printf("smem: %zu, max: %zu\n", smem, ctx->device.maxThreadgroupMemoryLength);
|
||||
GGML_ASSERT(smem <= ctx->device.maxThreadgroupMemoryLength);
|
||||
[encoder setThreadgroupMemoryLength:GGML_PAD(smem, 16) atIndex:0];
|
||||
|
||||
[encoder dispatchThreadgroups:MTLSizeMake((ne01 + nqptg - 1)/nqptg, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(32, nsg, 1)];
|
||||
}
|
||||
} break;
|
||||
case GGML_OP_DUP:
|
||||
case GGML_OP_CPY:
|
||||
case GGML_OP_CONT:
|
||||
@ -2706,10 +2895,13 @@ GGML_CALL static const char * ggml_backend_metal_buffer_type_get_name(ggml_backe
|
||||
UNUSED(buft);
|
||||
}
|
||||
|
||||
static void ggml_backend_metal_log_allocated_size(id<MTLDevice> device) {
|
||||
static void ggml_backend_metal_log_allocated_size(id<MTLDevice> device, size_t size_aligned) {
|
||||
#ifndef GGML_METAL_NDEBUG
|
||||
#if TARGET_OS_OSX || (TARGET_OS_IOS && __clang_major__ >= 15)
|
||||
if (@available(macOS 10.12, iOS 16.0, *)) {
|
||||
GGML_METAL_LOG_INFO(", (%8.2f / %8.2f)",
|
||||
GGML_METAL_LOG_INFO("%s: allocated buffer, size = %8.2f MiB, (%8.2f / %8.2f)",
|
||||
__func__,
|
||||
size_aligned / 1024.0 / 1024.0,
|
||||
device.currentAllocatedSize / 1024.0 / 1024.0,
|
||||
device.recommendedMaxWorkingSetSize / 1024.0 / 1024.0);
|
||||
|
||||
@ -2719,10 +2911,15 @@ static void ggml_backend_metal_log_allocated_size(id<MTLDevice> device) {
|
||||
GGML_METAL_LOG_INFO("\n");
|
||||
}
|
||||
} else {
|
||||
GGML_METAL_LOG_INFO(", (%8.2f)\n", device.currentAllocatedSize / 1024.0 / 1024.0);
|
||||
GGML_METAL_LOG_INFO("%s: allocated buffer, size = %8.2f MiB, (%8.2f)\n",
|
||||
__func__,
|
||||
size_aligned / 1024.0 / 1024.0,
|
||||
device.currentAllocatedSize / 1024.0 / 1024.0);
|
||||
}
|
||||
#endif
|
||||
#endif
|
||||
UNUSED(device);
|
||||
UNUSED(size_aligned);
|
||||
}
|
||||
|
||||
GGML_CALL static ggml_backend_buffer_t ggml_backend_metal_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
|
||||
@ -2756,8 +2953,7 @@ GGML_CALL static ggml_backend_buffer_t ggml_backend_metal_buffer_type_alloc_buff
|
||||
return NULL;
|
||||
}
|
||||
|
||||
GGML_METAL_LOG_INFO("%s: allocated buffer, size = %8.2f MiB", __func__, size_aligned / 1024.0 / 1024.0);
|
||||
ggml_backend_metal_log_allocated_size(device);
|
||||
//ggml_backend_metal_log_allocated_size(device, size_aligned);
|
||||
|
||||
return ggml_backend_buffer_init(buft, ggml_backend_metal_buffer_i, ctx, size);
|
||||
}
|
||||
@ -2844,7 +3040,7 @@ GGML_CALL ggml_backend_buffer_t ggml_backend_metal_buffer_from_ptr(void * data,
|
||||
return false;
|
||||
}
|
||||
|
||||
GGML_METAL_LOG_INFO("%s: allocated buffer, size = %8.2f MiB", __func__, size_aligned / 1024.0 / 1024.0);
|
||||
ggml_backend_metal_log_allocated_size(device, size_aligned);
|
||||
|
||||
++ctx->n_buffers;
|
||||
} else {
|
||||
@ -2867,7 +3063,8 @@ GGML_CALL ggml_backend_buffer_t ggml_backend_metal_buffer_from_ptr(void * data,
|
||||
return false;
|
||||
}
|
||||
|
||||
GGML_METAL_LOG_INFO("%s: allocated buffer, size = %8.2f MiB, offs = %12ld", __func__, size_step_aligned / 1024.0 / 1024.0, i);
|
||||
ggml_backend_metal_log_allocated_size(device, size_step_aligned);
|
||||
|
||||
if (i + size_step < size) {
|
||||
GGML_METAL_LOG_INFO("\n");
|
||||
}
|
||||
@ -2876,8 +3073,6 @@ GGML_CALL ggml_backend_buffer_t ggml_backend_metal_buffer_from_ptr(void * data,
|
||||
}
|
||||
}
|
||||
|
||||
ggml_backend_metal_log_allocated_size(device);
|
||||
|
||||
return ggml_backend_buffer_init(ggml_backend_metal_buffer_type(), ggml_backend_metal_buffer_i, ctx, size);
|
||||
}
|
||||
|
||||
|
||||
672
ggml-metal.metal
672
ggml-metal.metal
@ -352,11 +352,12 @@ kernel void kernel_sum_rows(
|
||||
dst_row[0] = row_sum;
|
||||
}
|
||||
|
||||
template<typename T>
|
||||
kernel void kernel_soft_max(
|
||||
device const float * src0,
|
||||
device const float * src1,
|
||||
device const float * src2,
|
||||
device float * dst,
|
||||
device const char * src0,
|
||||
device const char * src1,
|
||||
device const char * src2,
|
||||
device char * dst,
|
||||
constant int64_t & ne00,
|
||||
constant int64_t & ne01,
|
||||
constant int64_t & ne02,
|
||||
@ -375,10 +376,10 @@ kernel void kernel_soft_max(
|
||||
const int64_t i02 = (tgpig - i03*ne02*ne01) / ne01;
|
||||
const int64_t i01 = (tgpig - i03*ne02*ne01 - i02*ne01);
|
||||
|
||||
device const float * psrc0 = src0 + i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00;
|
||||
device const float * pmask = src1 != src0 ? src1 + i01*ne00 : nullptr;
|
||||
device const float * ppos = src2 != src0 ? src2 : nullptr;
|
||||
device float * pdst = dst + i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00;
|
||||
device const float * psrc0 = (device const float *) src0 + (i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00);
|
||||
device const T * pmask = src1 != src0 ? (device const T *) src1 + i01*ne00 : nullptr;
|
||||
device const T * ppos = src2 != src0 ? (device const T *) src2 : nullptr;
|
||||
device float * pdst = (device float *) dst + (i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00);
|
||||
|
||||
float slope = 0.0f;
|
||||
|
||||
@ -456,11 +457,12 @@ kernel void kernel_soft_max(
|
||||
}
|
||||
}
|
||||
|
||||
template<typename T>
|
||||
kernel void kernel_soft_max_4(
|
||||
device const float * src0,
|
||||
device const float * src1,
|
||||
device const float * src2,
|
||||
device float * dst,
|
||||
device const char * src0,
|
||||
device const char * src1,
|
||||
device const char * src2,
|
||||
device char * dst,
|
||||
constant int64_t & ne00,
|
||||
constant int64_t & ne01,
|
||||
constant int64_t & ne02,
|
||||
@ -479,10 +481,10 @@ kernel void kernel_soft_max_4(
|
||||
const int64_t i02 = (tgpig - i03*ne02*ne01) / ne01;
|
||||
const int64_t i01 = (tgpig - i03*ne02*ne01 - i02*ne01);
|
||||
|
||||
device const float4 * psrc4 = (device const float4 *)(src0 + i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00);
|
||||
device const float4 * pmask = src1 != src0 ? (device const float4 *)(src1 + i01*ne00) : nullptr;
|
||||
device const float4 * ppos = src2 != src0 ? (device const float4 *)(src2) : nullptr;
|
||||
device float4 * pdst4 = (device float4 *)(dst + i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00);
|
||||
device const float4 * psrc4 = (device const float4 *) src0 + (i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00)/4;
|
||||
device const T * pmask = src1 != src0 ? (device const T *) src1 + i01*ne00/4 : nullptr;
|
||||
device const T * ppos = src2 != src0 ? (device const T *) src2 : nullptr;
|
||||
device float4 * pdst4 = (device float4 *) dst + (i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00)/4;
|
||||
|
||||
float slope = 0.0f;
|
||||
|
||||
@ -499,7 +501,7 @@ kernel void kernel_soft_max_4(
|
||||
float4 lmax4 = -INFINITY;
|
||||
|
||||
for (int i00 = tpitg; i00 < ne00/4; i00 += ntg) {
|
||||
lmax4 = fmax(lmax4, psrc4[i00]*scale + (pmask ? pmask[i00] : 0.0f) + (ppos ? slope*ppos[i00] : 0.0f));
|
||||
lmax4 = fmax(lmax4, psrc4[i00]*scale + (float4)((pmask ? pmask[i00] : 0.0f) + (ppos ? slope*ppos[i00] : 0.0f)));
|
||||
}
|
||||
|
||||
const float lmax = MAX(MAX(lmax4[0], lmax4[1]), MAX(lmax4[2], lmax4[3]));
|
||||
@ -525,7 +527,7 @@ kernel void kernel_soft_max_4(
|
||||
// parallel sum
|
||||
float4 lsum4 = 0.0f;
|
||||
for (int i00 = tpitg; i00 < ne00/4; i00 += ntg) {
|
||||
const float4 exp_psrc4 = exp((psrc4[i00]*scale + (pmask ? pmask[i00] : 0.0f) + (ppos ? slope*ppos[i00] : 0.0f)) - max_val);
|
||||
const float4 exp_psrc4 = exp((psrc4[i00]*scale + (float4)((pmask ? pmask[i00] : 0.0f) + (ppos ? slope*ppos[i00] : 0.0f))) - max_val);
|
||||
lsum4 += exp_psrc4;
|
||||
pdst4[i00] = exp_psrc4;
|
||||
}
|
||||
@ -562,6 +564,14 @@ kernel void kernel_soft_max_4(
|
||||
}
|
||||
}
|
||||
|
||||
typedef decltype(kernel_soft_max<float>) kernel_soft_max_t;
|
||||
typedef decltype(kernel_soft_max_4<float4>) kernel_soft_max_4_t;
|
||||
|
||||
template [[host_name("kernel_soft_max_f16")]] kernel kernel_soft_max_t kernel_soft_max<half>;
|
||||
template [[host_name("kernel_soft_max_f32")]] kernel kernel_soft_max_t kernel_soft_max<float>;
|
||||
template [[host_name("kernel_soft_max_f16_4")]] kernel kernel_soft_max_4_t kernel_soft_max_4<half4>;
|
||||
template [[host_name("kernel_soft_max_f32_4")]] kernel kernel_soft_max_4_t kernel_soft_max_4<float4>;
|
||||
|
||||
kernel void kernel_diag_mask_inf(
|
||||
device const float * src0,
|
||||
device float * dst,
|
||||
@ -2084,6 +2094,632 @@ kernel void kernel_leaky_relu_f32(
|
||||
dst[tpig] = src0[tpig] > 0.0f ? src0[tpig] : src0[tpig] * slope;
|
||||
}
|
||||
|
||||
typedef void (flash_attn_ext_f16_t)(
|
||||
device const char * q,
|
||||
device const char * k,
|
||||
device const char * v,
|
||||
device const char * mask,
|
||||
device float * dst,
|
||||
constant int64_t & ne00,
|
||||
constant int64_t & ne01,
|
||||
constant int64_t & ne02,
|
||||
constant int64_t & ne03,
|
||||
constant uint64_t & nb00,
|
||||
constant uint64_t & nb01,
|
||||
constant uint64_t & nb02,
|
||||
constant uint64_t & nb03,
|
||||
constant int64_t & ne10,
|
||||
constant int64_t & ne11,
|
||||
constant int64_t & ne12,
|
||||
constant int64_t & ne13,
|
||||
constant uint64_t & nb10,
|
||||
constant uint64_t & nb11,
|
||||
constant uint64_t & nb12,
|
||||
constant uint64_t & nb13,
|
||||
constant int64_t & ne31,
|
||||
constant uint64_t & nb31,
|
||||
constant int64_t & ne0,
|
||||
constant int64_t & ne1,
|
||||
constant int64_t & ne2,
|
||||
constant int64_t & ne3,
|
||||
constant float & scale,
|
||||
threadgroup half * shared,
|
||||
uint3 tgpig[[threadgroup_position_in_grid]],
|
||||
uint3 tpitg[[thread_position_in_threadgroup]],
|
||||
uint3 ntg[[threads_per_threadgroup]],
|
||||
ushort tiisg[[thread_index_in_simdgroup]],
|
||||
ushort sgitg[[simdgroup_index_in_threadgroup]]);
|
||||
|
||||
// ref: https://arxiv.org/pdf/2307.08691.pdf
|
||||
template<int64_t D, int64_t Q = 8, int64_t C = 32> // head size, queries per threadgroup, cache items per threadgroup
|
||||
kernel void kernel_flash_attn_ext_f16(
|
||||
device const char * q,
|
||||
device const char * k,
|
||||
device const char * v,
|
||||
device const char * mask,
|
||||
device float * dst,
|
||||
constant int64_t & ne00,
|
||||
constant int64_t & ne01,
|
||||
constant int64_t & ne02,
|
||||
constant int64_t & ne03,
|
||||
constant uint64_t & nb00,
|
||||
constant uint64_t & nb01,
|
||||
constant uint64_t & nb02,
|
||||
constant uint64_t & nb03,
|
||||
constant int64_t & ne10,
|
||||
constant int64_t & ne11,
|
||||
constant int64_t & ne12,
|
||||
constant int64_t & ne13,
|
||||
constant uint64_t & nb10,
|
||||
constant uint64_t & nb11,
|
||||
constant uint64_t & nb12,
|
||||
constant uint64_t & nb13,
|
||||
constant int64_t & ne31,
|
||||
constant uint64_t & nb31,
|
||||
constant int64_t & ne0,
|
||||
constant int64_t & ne1,
|
||||
constant int64_t & ne2,
|
||||
constant int64_t & ne3,
|
||||
constant float & scale,
|
||||
threadgroup half * shared [[threadgroup(0)]],
|
||||
uint3 tgpig[[threadgroup_position_in_grid]],
|
||||
uint3 tpitg[[thread_position_in_threadgroup]],
|
||||
uint3 ntg[[threads_per_threadgroup]],
|
||||
ushort tiisg[[thread_index_in_simdgroup]],
|
||||
ushort sgitg[[simdgroup_index_in_threadgroup]]) {
|
||||
const short nsg = ntg.y; // number of simdgroups
|
||||
|
||||
const short iq3 = tgpig[2];
|
||||
const short iq2 = tgpig[1];
|
||||
const short iq1 = tgpig[0]*Q;
|
||||
|
||||
const short D4 = D/4;
|
||||
const short D8 = D/8;
|
||||
const short Q8 = Q/8;
|
||||
const short NW = N_SIMDWIDTH;
|
||||
const short SH = (C + Q); // shared memory per simdgroup in (half)
|
||||
|
||||
const short T = D + 2*nsg*SH; // shared memory size per query in (half)
|
||||
const short TF = T/2; // shared memory size per query in (float)
|
||||
const short T4 = T/4; // shared memory size per query in (half4)
|
||||
|
||||
threadgroup half * sq = (threadgroup half *) (shared + 0*D); // holds the query data
|
||||
threadgroup half4 * sq4 = (threadgroup half4 *) (shared + 0*D); // same as above but in half4
|
||||
threadgroup float * ss = (threadgroup float *) (shared + 2*sgitg*SH + 1*D); // scratch buffer for attention and diagonal matrix
|
||||
|
||||
// store the result for all queries in local memory in 8x8 matrices (the O matrix from the paper)
|
||||
simdgroup_half8x8 lo[D8];
|
||||
|
||||
// load heads from Q to shared memory
|
||||
for (short j = sgitg; j < Q; j += nsg) {
|
||||
device const float4 * q4 = (device const float4 *) ((device const char *) q + ((iq1 + j)*nb01 + iq2*nb02 + iq3*nb03));
|
||||
|
||||
for (short i = tiisg; i < D4; i += NW) {
|
||||
if (iq1 + j < ne01) {
|
||||
sq4[j*T4 + i] = (half4) q4[i];
|
||||
} else {
|
||||
sq4[j*T4 + i] = 0.0h;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// zero out lo
|
||||
for (short i = 0; i < D8; ++i) {
|
||||
lo[i] = make_filled_simdgroup_matrix<half, 8>(0.0h);
|
||||
}
|
||||
|
||||
// zero out shared memory SH
|
||||
for (short j = 0; j < Q; ++j) {
|
||||
for (short i = tiisg; i < SH; i += NW) {
|
||||
ss[j*TF + i] = 0.0f;
|
||||
}
|
||||
}
|
||||
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
{
|
||||
float S[Q] = { [0 ... Q-1] = 0.0h };
|
||||
float M[Q] = { [0 ... Q-1] = -FLT_MAX/2 };
|
||||
|
||||
// assume K and V are same shape
|
||||
const short ne22 = ne12;
|
||||
const short ne23 = ne13;
|
||||
|
||||
const uint nb21 = nb11;
|
||||
const uint nb22 = nb12;
|
||||
const uint nb23 = nb13;
|
||||
|
||||
// broadcast
|
||||
const short rk2 = ne02/ne12;
|
||||
const short rk3 = ne03/ne13;
|
||||
|
||||
const short rv2 = ne02/ne22;
|
||||
const short rv3 = ne03/ne23;
|
||||
|
||||
// k indices
|
||||
const short ik2 = iq2/rk2;
|
||||
const short ik3 = iq3/rk3;
|
||||
|
||||
// v indices
|
||||
const short iv2 = iq2/rv2;
|
||||
const short iv3 = iq3/rv3;
|
||||
|
||||
// load the queries from shared memory into local memory
|
||||
simdgroup_half8x8 mq[D8];
|
||||
|
||||
for (short i = 0; i < D8; ++i) {
|
||||
simdgroup_load(mq[i], sq + i*8, T);
|
||||
}
|
||||
|
||||
// pointer to the mask
|
||||
device const half * mp = (device const half *) (mask + iq1*nb31);
|
||||
|
||||
// prepare diagonal scale matrix
|
||||
simdgroup_float8x8 mscale(scale);
|
||||
|
||||
// loop over the KV cache
|
||||
// each simdgroup handles blocks of Q rows and C columns
|
||||
for (int ic0 = 0; ic0 < ne11; ic0 += C*nsg) {
|
||||
const int ic = ic0 + C*sgitg;
|
||||
if (ic >= ne11) {
|
||||
break;
|
||||
}
|
||||
|
||||
// Q*K^T
|
||||
{
|
||||
for (short cc = 0; cc < C/8; ++cc) {
|
||||
simdgroup_float8x8 mqk = make_filled_simdgroup_matrix<float, 8>(0.h);
|
||||
|
||||
device const half * pk = (device const half *) ((device const char *) k + ((ic + 8*cc)*nb11 + ik2*nb12 + ik3*nb13));
|
||||
|
||||
for (short i = 0; i < D8; ++i) {
|
||||
simdgroup_half8x8 mk;
|
||||
simdgroup_load(mk, pk + i*8, nb11/sizeof(half), 0, true); // transpose
|
||||
|
||||
simdgroup_multiply_accumulate(mqk, mq[i], mk, mqk);
|
||||
}
|
||||
|
||||
// mqk = mqk*scale + mask
|
||||
simdgroup_half8x8 mm;
|
||||
simdgroup_load(mm, mp + ic + 8*cc, nb31/sizeof(half), 0, false);
|
||||
simdgroup_multiply_accumulate(mqk, mqk, mscale, mm);
|
||||
|
||||
simdgroup_store(mqk, ss + 8*cc, TF, 0, false);
|
||||
}
|
||||
}
|
||||
|
||||
// used to detect blocks full of -INF
|
||||
float smax = -INFINITY;
|
||||
|
||||
// online softmax
|
||||
{
|
||||
float ms[Q];
|
||||
|
||||
for (short j = 0; j < Q; ++j) {
|
||||
const short p = tiisg;
|
||||
|
||||
const float m = M[j];
|
||||
const float s = ss[j*TF + p];
|
||||
|
||||
smax = simd_max(max(smax, s));
|
||||
M[j] = simd_max(max(M[j], s));
|
||||
|
||||
ms[j] = exp(m - M[j]);
|
||||
const float vs = exp(s - M[j]);
|
||||
|
||||
S[j] = S[j]*ms[j] + simd_sum(vs);
|
||||
|
||||
// the P matrix from the paper (Q rows, C columns)
|
||||
ss[j*TF + p] = vs;
|
||||
}
|
||||
|
||||
// create a QxQ diagonal matrix for rescaling the output
|
||||
if (tiisg < Q) {
|
||||
ss[tiisg*TF + C + tiisg] = ms[tiisg];
|
||||
}
|
||||
}
|
||||
|
||||
// skip -INF blocks
|
||||
if (smax == -INFINITY) {
|
||||
continue;
|
||||
}
|
||||
|
||||
// O = diag(ms)*O
|
||||
{
|
||||
simdgroup_float8x8 mm;
|
||||
simdgroup_load(mm, ss + C, TF, 0, false);
|
||||
|
||||
for (short i = 0; i < D8; ++i) {
|
||||
simdgroup_multiply(lo[i], mm, lo[i]);
|
||||
}
|
||||
}
|
||||
|
||||
// O = O + (Q*K^T)*V
|
||||
{
|
||||
for (short cc = 0; cc < C/8; ++cc) {
|
||||
device const half * pv = (device const half *) ((device const char *) v + ((ic + 8*cc)*nb21 + iv2*nb22 + iv3*nb23));
|
||||
|
||||
for (short i = 0; i < D8; ++i) {
|
||||
simdgroup_half8x8 mk;
|
||||
simdgroup_load(mk, pv + i*8, nb21/sizeof(half), 0, false);
|
||||
|
||||
simdgroup_float8x8 mv;
|
||||
simdgroup_load(mv, ss + 8*cc, TF, 0, false);
|
||||
|
||||
simdgroup_multiply_accumulate(lo[i], mv, mk, lo[i]);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// these are needed for reducing the results from the simdgroups (reuse the ss buffer)
|
||||
for (short j = 0; j < Q; ++j) {
|
||||
if (tiisg == 0) {
|
||||
ss[j*TF + 0] = S[j];
|
||||
ss[j*TF + 1] = M[j];
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// reduce the warps sequentially
|
||||
for (short sg = 1; sg < nsg; ++sg) {
|
||||
float S = { 0.0h };
|
||||
float M = { -FLT_MAX/2 };
|
||||
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
// each simdgroup stores its output to shared memory, reusing sq
|
||||
if (sgitg == sg) {
|
||||
for (short i = 0; i < D8; ++i) {
|
||||
simdgroup_store(lo[i], sq + i*8, T, 0, false);
|
||||
}
|
||||
}
|
||||
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
// the first simdgroup accumulates the results from the other simdgroups
|
||||
if (sgitg == 0) {
|
||||
for (short j = 0; j < Q; ++j) {
|
||||
const float S0 = ss[j*TF + 0];
|
||||
const float S1 = ss[j*TF + sg*SH + 0];
|
||||
|
||||
const float M0 = ss[j*TF + 1];
|
||||
const float M1 = ss[j*TF + sg*SH + 1];
|
||||
|
||||
M = max(M0, M1);
|
||||
|
||||
const float ms0 = exp(M0 - M);
|
||||
const float ms1 = exp(M1 - M);
|
||||
|
||||
S = S0*ms0 + S1*ms1;
|
||||
|
||||
if (tiisg == 0) {
|
||||
ss[j*TF + 0] = S;
|
||||
ss[j*TF + 1] = M;
|
||||
|
||||
ss[j*TF + C + j ] = ms0;
|
||||
ss[j*TF + C + j + sg*SH] = ms1;
|
||||
}
|
||||
}
|
||||
|
||||
// O_0 = diag(ms0)*O_0 + diag(ms1)*O_1
|
||||
{
|
||||
simdgroup_half8x8 t;
|
||||
simdgroup_float8x8 ms0;
|
||||
simdgroup_float8x8 ms1;
|
||||
|
||||
simdgroup_load(ms0, ss + C, TF, 0, false);
|
||||
simdgroup_load(ms1, ss + C + sg*SH, TF, 0, false);
|
||||
|
||||
for (short i = 0; i < D8; ++i) {
|
||||
simdgroup_load (t, sq + i*8, T, 0, false);
|
||||
simdgroup_multiply(t, ms1, t);
|
||||
|
||||
simdgroup_multiply_accumulate(lo[i], ms0, lo[i], t);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// store result to shared memory (reuse sq)
|
||||
if (sgitg == 0) {
|
||||
for (short i = 0; i < D8; ++i) {
|
||||
simdgroup_store(lo[i], sq + i*8, T, 0, false);
|
||||
}
|
||||
}
|
||||
|
||||
device float4 * dst4 = (device float4 *) dst;
|
||||
|
||||
// final rescale with 1/S and store to global memory
|
||||
if (sgitg == 0) {
|
||||
for (short j = 0; j < Q && iq1 + j < ne01; ++j) {
|
||||
const float S = ss[j*TF + 0];
|
||||
|
||||
for (short i = tiisg; i < D4; i += NW) {
|
||||
dst4[(iq3*ne2*ne1 + iq2 + (iq1 + j)*ne1)*D4 + i] = (float4) sq4[j*T4 + i]/S;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
template [[host_name("kernel_flash_attn_ext_f16_h64" )]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_f16<64>;
|
||||
template [[host_name("kernel_flash_attn_ext_f16_h80" )]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_f16<80>;
|
||||
template [[host_name("kernel_flash_attn_ext_f16_h96" )]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_f16<96>;
|
||||
template [[host_name("kernel_flash_attn_ext_f16_h112")]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_f16<112>;
|
||||
template [[host_name("kernel_flash_attn_ext_f16_h128")]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_f16<128>;
|
||||
template [[host_name("kernel_flash_attn_ext_f16_h256")]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_f16<256>;
|
||||
|
||||
template<int64_t D, int64_t Q = 1, int64_t C = 32> // head size, queries per threadgroup, cache items per threadgroup
|
||||
kernel void kernel_flash_attn_ext_vec_f16(
|
||||
device const char * q,
|
||||
device const char * k,
|
||||
device const char * v,
|
||||
device const char * mask,
|
||||
device float * dst,
|
||||
constant int64_t & ne00,
|
||||
constant int64_t & ne01,
|
||||
constant int64_t & ne02,
|
||||
constant int64_t & ne03,
|
||||
constant uint64_t & nb00,
|
||||
constant uint64_t & nb01,
|
||||
constant uint64_t & nb02,
|
||||
constant uint64_t & nb03,
|
||||
constant int64_t & ne10,
|
||||
constant int64_t & ne11,
|
||||
constant int64_t & ne12,
|
||||
constant int64_t & ne13,
|
||||
constant uint64_t & nb10,
|
||||
constant uint64_t & nb11,
|
||||
constant uint64_t & nb12,
|
||||
constant uint64_t & nb13,
|
||||
constant int64_t & ne31,
|
||||
constant uint64_t & nb31,
|
||||
constant int64_t & ne0,
|
||||
constant int64_t & ne1,
|
||||
constant int64_t & ne2,
|
||||
constant int64_t & ne3,
|
||||
constant float & scale,
|
||||
threadgroup half * shared [[threadgroup(0)]],
|
||||
uint3 tgpig[[threadgroup_position_in_grid]],
|
||||
uint3 tpitg[[thread_position_in_threadgroup]],
|
||||
uint3 ntg[[threads_per_threadgroup]],
|
||||
ushort tiisg[[thread_index_in_simdgroup]],
|
||||
ushort sgitg[[simdgroup_index_in_threadgroup]]) {
|
||||
const short nsg = ntg.y; // number of simdgroups
|
||||
|
||||
const short iq3 = tgpig[2];
|
||||
const short iq2 = tgpig[1];
|
||||
const short iq1 = tgpig[0];
|
||||
|
||||
const short D4 = D/4;
|
||||
const short NW = N_SIMDWIDTH;
|
||||
const short SH = (C + Q); // shared memory per simdgroup in (half)
|
||||
|
||||
const short T = D + 2*nsg*SH; // shared memory size per query in (half)
|
||||
|
||||
//threadgroup half * sq = (threadgroup half *) (shared + 0*D); // holds the query data
|
||||
threadgroup half4 * sq4 = (threadgroup half4 *) (shared + 0*D); // same as above but in half4
|
||||
threadgroup float * ss = (threadgroup float *) (shared + 2*sgitg*SH + 1*D); // scratch buffer for attention and diagonal matrix
|
||||
threadgroup float4 * ss4 = (threadgroup float4 *) (shared + 2*sgitg*SH + 1*D); // same as above but in half4
|
||||
threadgroup half4 * sr4 = (threadgroup half4 *) (shared + sgitg*D + 1*T); // scratch buffer for the results
|
||||
|
||||
// store the result for all queries in local memory in 8x8 matrices (the O matrix from the paper)
|
||||
half4 lo[D4/NW];
|
||||
|
||||
// load heads from Q to shared memory
|
||||
device const float4 * q4 = (device const float4 *) ((device const char *) q + (iq1*nb01 + iq2*nb02 + iq3*nb03));
|
||||
|
||||
for (short i = tiisg; i < D4; i += NW) {
|
||||
if (iq1 < ne01) {
|
||||
sq4[i] = (half4) q4[i];
|
||||
} else {
|
||||
sq4[i] = 0.0h;
|
||||
}
|
||||
}
|
||||
|
||||
// zero out lo
|
||||
for (short i = tiisg; i < D4; i += NW) {
|
||||
lo[i/NW] = 0.0h;
|
||||
}
|
||||
|
||||
// zero out shared memory SH
|
||||
for (short i = tiisg; i < SH/4; i += NW) {
|
||||
ss4[i] = 0.0h;
|
||||
}
|
||||
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
{
|
||||
float S = { 0.0h };
|
||||
float M = { -FLT_MAX/2 };
|
||||
|
||||
// assume K and V are same shape
|
||||
const short ne22 = ne12;
|
||||
const short ne23 = ne13;
|
||||
|
||||
const uint nb21 = nb11;
|
||||
const uint nb22 = nb12;
|
||||
const uint nb23 = nb13;
|
||||
|
||||
// broadcast
|
||||
const short rk2 = ne02/ne12;
|
||||
const short rk3 = ne03/ne13;
|
||||
|
||||
const short rv2 = ne02/ne22;
|
||||
const short rv3 = ne03/ne23;
|
||||
|
||||
// k indices
|
||||
const short ik2 = iq2 / rk2;
|
||||
const short ik3 = iq3 / rk3;
|
||||
|
||||
// v indices
|
||||
const short iv2 = iq2 / rv2;
|
||||
const short iv3 = iq3 / rv3;
|
||||
|
||||
// load the queries from shared memory into local memory
|
||||
half4 mq[D4];
|
||||
|
||||
for (short ii = 0; ii < D4; ii += NW) {
|
||||
short i = ii + tiisg;
|
||||
mq[i] = sq4[i];
|
||||
}
|
||||
|
||||
// pointer to the mask
|
||||
device const half4 * mp4 = (device const half4 *) (mask + iq1*nb31);
|
||||
|
||||
// loop over the KV cache
|
||||
// each simdgroup handles blocks of Q rows and C columns
|
||||
for (int ic0 = 0; ic0 < ne11; ic0 += C*nsg) {
|
||||
const int ic = ic0 + C*sgitg;
|
||||
if (ic >= ne11) {
|
||||
break;
|
||||
}
|
||||
|
||||
// Q*K^T
|
||||
{
|
||||
#pragma unroll
|
||||
for (short cc = 0; cc < C/4; ++cc) {
|
||||
float4 mqk = { 0.0h };
|
||||
|
||||
device const half4 * pk4 = (device const half4 *) ((device const char *) k + ((ic + 4*cc)*nb11 + ik2*nb12 + ik3*nb13));
|
||||
|
||||
#pragma unroll
|
||||
for (short ii = 0; ii < D4; ii += NW) {
|
||||
const short i = ii + tiisg;
|
||||
|
||||
half4x4 mk;
|
||||
mk[0] = pk4[i + 0*(nb11/8)];
|
||||
mk[1] = pk4[i + 1*(nb11/8)];
|
||||
mk[2] = pk4[i + 2*(nb11/8)];
|
||||
mk[3] = pk4[i + 3*(nb11/8)];
|
||||
|
||||
mqk += (float4) (mq[i] * mk);
|
||||
}
|
||||
|
||||
// reduce the results from the threads in the simdgroup
|
||||
mqk += simd_shuffle_down(mqk, 16);
|
||||
mqk += simd_shuffle_down(mqk, 8);
|
||||
mqk += simd_shuffle_down(mqk, 4);
|
||||
mqk += simd_shuffle_down(mqk, 2);
|
||||
mqk += simd_shuffle_down(mqk, 1);
|
||||
|
||||
// mqk = mqk*scale + mask
|
||||
if (tiisg == 0) {
|
||||
float4 mm = (float4) mp4[ic/4 + cc];
|
||||
mqk = mqk*scale + mm;
|
||||
|
||||
ss4[cc] = mqk;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// online softmax
|
||||
{
|
||||
const short p = tiisg;
|
||||
|
||||
const float m = M;
|
||||
const float s = ss[p];
|
||||
|
||||
M = simd_max(max(M, s));
|
||||
|
||||
const float ms = exp(m - M);
|
||||
const float vs = exp(s - M);
|
||||
|
||||
S = S*ms + simd_sum(vs);
|
||||
|
||||
// the P matrix from the paper (Q rows, C columns)
|
||||
ss[p] = vs;
|
||||
|
||||
// O = diag(ms)*O
|
||||
#pragma unroll
|
||||
for (short ii = 0; ii < D4; ii += NW) {
|
||||
const short i = ii + tiisg;
|
||||
lo[i/NW] *= ms;
|
||||
}
|
||||
}
|
||||
|
||||
// O = O + (Q*K^T)*V
|
||||
{
|
||||
#pragma unroll
|
||||
for (short cc = 0; cc < C/4; ++cc) {
|
||||
device const half4 * pv4 = (device const half4 *) ((device const char *) v + ((ic + 4*cc)*nb21 + iv2*nb22 + iv3*nb23));
|
||||
|
||||
#pragma unroll
|
||||
for (short ii = 0; ii < D4; ii += NW) {
|
||||
const short i = ii + tiisg;
|
||||
|
||||
lo[i/NW] += pv4[i + 0*(nb21/8)] * ss[4*cc + 0];
|
||||
lo[i/NW] += pv4[i + 1*(nb21/8)] * ss[4*cc + 1];
|
||||
lo[i/NW] += pv4[i + 2*(nb21/8)] * ss[4*cc + 2];
|
||||
lo[i/NW] += pv4[i + 3*(nb21/8)] * ss[4*cc + 3];
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
// these are needed for reducing the results from the simdgroups (reuse the ss buffer)
|
||||
if (tiisg == 0) {
|
||||
ss[0] = S;
|
||||
ss[1] = M;
|
||||
}
|
||||
}
|
||||
|
||||
// store results to shared memory
|
||||
for (short ii = 0; ii < D4; ii += NW) {
|
||||
short i = ii + tiisg;
|
||||
sr4[i] = lo[ii/NW];
|
||||
}
|
||||
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
// parallel reduce
|
||||
for (short r = nsg/2; r > 0; r >>= 1) {
|
||||
if (sgitg < r) {
|
||||
const float S0 = ss[ 0];
|
||||
const float S1 = ss[r*SH + 0];
|
||||
|
||||
const float M0 = ss[ 1];
|
||||
const float M1 = ss[r*SH + 1];
|
||||
|
||||
const float M = max(M0, M1);
|
||||
|
||||
const float ms0 = exp(M0 - M);
|
||||
const float ms1 = exp(M1 - M);
|
||||
|
||||
const float S = S0*ms0 + S1*ms1;
|
||||
|
||||
if (tiisg == 0) {
|
||||
ss[0] = S;
|
||||
ss[1] = M;
|
||||
}
|
||||
|
||||
// O_0 = diag(ms0)*O_0 + diag(ms1)*O_1
|
||||
for (short ii = 0; ii < D4; ii += NW) {
|
||||
short i = ii + tiisg;
|
||||
sr4[i] = sr4[i]*ms0 + sr4[i + r*D4]*ms1;
|
||||
}
|
||||
}
|
||||
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
}
|
||||
|
||||
device float4 * dst4 = (device float4 *) dst;
|
||||
|
||||
// final rescale with 1/S and store to global memory
|
||||
if (sgitg == 0) {
|
||||
const float S = ss[0];
|
||||
|
||||
for (short ii = 0; ii < D4; ii += NW) {
|
||||
short i = ii + tiisg;
|
||||
dst4[(iq3*ne2*ne1 + iq2 + (iq1)*ne1)*D4 + i] = (float4) sr4[i]/S;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
template [[host_name("kernel_flash_attn_ext_vec_f16_h128")]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_vec_f16<128>;
|
||||
template [[host_name("kernel_flash_attn_ext_vec_f16_h256")]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_vec_f16<256>;
|
||||
|
||||
kernel void kernel_cpy_f16_f16(
|
||||
device const half * src0,
|
||||
device half * dst,
|
||||
|
||||
@ -14744,7 +14744,12 @@ inline void ggml_sycl_op_soft_max(const ggml_tensor *src0,
|
||||
GGML_ASSERT(src0->type == GGML_TYPE_F32);
|
||||
GGML_ASSERT( dst->type == GGML_TYPE_F32);
|
||||
|
||||
const ggml_tensor * src2 = dst->src[2];
|
||||
|
||||
#pragma message("TODO: add ggml_sycl_op_soft_max() F16 src1 and src2 support")
|
||||
#pragma message("ref: https://github.com/ggerganov/llama.cpp/pull/5021")
|
||||
GGML_ASSERT(!src1 || src1->type == GGML_TYPE_F32); // src1 contains mask and it is optional
|
||||
GGML_ASSERT(!src2 || src2->type == GGML_TYPE_F32); // src2 contains positions and it is optional
|
||||
|
||||
const int64_t ne00 = src0->ne[0];
|
||||
const int64_t nrows_x = ggml_nrows(src0);
|
||||
@ -14760,7 +14765,6 @@ inline void ggml_sycl_op_soft_max(const ggml_tensor *src0,
|
||||
float * src2_dd = nullptr;
|
||||
sycl_pool_alloc<float> src2_f;
|
||||
|
||||
ggml_tensor * src2 = dst->src[2];
|
||||
const bool use_src2 = src2 != nullptr;
|
||||
|
||||
if (use_src2) {
|
||||
|
||||
@ -3178,6 +3178,11 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const
|
||||
}
|
||||
return nullptr;
|
||||
case GGML_OP_SOFT_MAX:
|
||||
#pragma message("TODO: add ggml_vk_soft_max() F16 src1 and src2 support")
|
||||
#pragma message("ref: https://github.com/ggerganov/llama.cpp/pull/5021")
|
||||
GGML_ASSERT(!src1 || src1->type == GGML_TYPE_F32);
|
||||
GGML_ASSERT(!src2 || src2->type == GGML_TYPE_F32);
|
||||
|
||||
if (src0->type == GGML_TYPE_F32 && (src1 == nullptr || src1->type == GGML_TYPE_F32) && (src2 == nullptr || src2->type == GGML_TYPE_F32) && dst->type == GGML_TYPE_F32) {
|
||||
return ctx->device->pipeline_soft_max_f32;
|
||||
}
|
||||
|
||||
375
ggml.c
375
ggml.c
@ -951,7 +951,7 @@ ggml_type_traits_t ggml_internal_get_type_traits(enum ggml_type type) {
|
||||
#define GGML_F16_VEC_ZERO GGML_F16x8_ZERO
|
||||
#define GGML_F16_VEC_SET1 GGML_F16x8_SET1
|
||||
#define GGML_F16_VEC_LOAD(p, i) GGML_F16x8_LOAD(p)
|
||||
#define GGML_F16_VEC_STORE(p, r, i) GGML_F16x8_STORE(p, r[i])
|
||||
#define GGML_F16_VEC_STORE(p, r, i) GGML_F16x8_STORE((ggml_fp16_internal_t *)(p), r[i])
|
||||
#define GGML_F16_VEC_FMA GGML_F16x8_FMA
|
||||
#define GGML_F16_VEC_ADD GGML_F16x8_ADD
|
||||
#define GGML_F16_VEC_MUL GGML_F16x8_MUL
|
||||
@ -977,7 +977,7 @@ ggml_type_traits_t ggml_internal_get_type_traits(enum ggml_type type) {
|
||||
#define GGML_F16_VEC_ZERO GGML_F32Cx4_ZERO
|
||||
#define GGML_F16_VEC_SET1 GGML_F32Cx4_SET1
|
||||
#define GGML_F16_VEC_LOAD(p, i) GGML_F32Cx4_LOAD(p)
|
||||
#define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx4_STORE(p, r[i])
|
||||
#define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx4_STORE((ggml_fp16_internal_t *)(p), r[i])
|
||||
#define GGML_F16_VEC_FMA GGML_F32Cx4_FMA
|
||||
#define GGML_F16_VEC_ADD GGML_F32Cx4_ADD
|
||||
#define GGML_F16_VEC_MUL GGML_F32Cx4_MUL
|
||||
@ -1046,7 +1046,7 @@ do { \
|
||||
|
||||
// unlike _mm256_cvt intrinsics that require F16C, _mm512_cvt is defined in AVX512F
|
||||
// so F16C guard isn't required
|
||||
#define GGML_F32Cx16_LOAD(x) _mm512_cvtph_ps(_mm256_loadu_si256((__m256i *)(x)))
|
||||
#define GGML_F32Cx16_LOAD(x) _mm512_cvtph_ps(_mm256_loadu_si256((const __m256i *)(x)))
|
||||
#define GGML_F32Cx16_STORE(x, y) _mm256_storeu_si256((__m256i *)(x), _mm512_cvtps_ph(y, 0))
|
||||
|
||||
#define GGML_F32Cx16_FMA(a, b, c) _mm512_fmadd_ps(b, c, a)
|
||||
@ -1144,7 +1144,7 @@ do { \
|
||||
|
||||
#if defined(__F16C__)
|
||||
// the _mm256_cvt intrinsics require F16C
|
||||
#define GGML_F32Cx8_LOAD(x) _mm256_cvtph_ps(_mm_loadu_si128((__m128i *)(x)))
|
||||
#define GGML_F32Cx8_LOAD(x) _mm256_cvtph_ps(_mm_loadu_si128((const __m128i *)(x)))
|
||||
#define GGML_F32Cx8_STORE(x, y) _mm_storeu_si128((__m128i *)(x), _mm256_cvtps_ph(y, 0))
|
||||
#else
|
||||
static inline __m256 __avx_f32cx8_load(ggml_fp16_t *x) {
|
||||
@ -1662,6 +1662,37 @@ inline static void ggml_vec_mad_f32(const int n, float * restrict y, const float
|
||||
#endif
|
||||
}
|
||||
|
||||
inline static void ggml_vec_mad_f16(const int n, ggml_fp16_t * restrict y, const ggml_fp16_t * restrict x, const float v) {
|
||||
#if defined(GGML_SIMD)
|
||||
const int np = (n & ~(GGML_F16_STEP - 1));
|
||||
|
||||
GGML_F16_VEC vx = GGML_F16_VEC_SET1(v);
|
||||
|
||||
GGML_F16_VEC ax[GGML_F16_ARR];
|
||||
GGML_F16_VEC ay[GGML_F16_ARR];
|
||||
|
||||
for (int i = 0; i < np; i += GGML_F16_STEP) {
|
||||
for (int j = 0; j < GGML_F16_ARR; j++) {
|
||||
ax[j] = GGML_F16_VEC_LOAD(x + i + j*GGML_F16_EPR, j);
|
||||
ay[j] = GGML_F16_VEC_LOAD(y + i + j*GGML_F16_EPR, j);
|
||||
ay[j] = GGML_F16_VEC_FMA(ay[j], ax[j], vx);
|
||||
|
||||
GGML_F16_VEC_STORE(y + i + j*GGML_F16_EPR, ay, j);
|
||||
}
|
||||
}
|
||||
|
||||
// leftovers
|
||||
for (int i = np; i < n; ++i) {
|
||||
y[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(y[i]) + GGML_FP16_TO_FP32(x[i])*v);
|
||||
}
|
||||
#else
|
||||
// scalar
|
||||
for (int i = 0; i < n; ++i) {
|
||||
y[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(y[i]) + GGML_FP16_TO_FP32(x[i])*v);
|
||||
}
|
||||
#endif
|
||||
}
|
||||
|
||||
// xs and vs are byte strides of x and v
|
||||
inline static void ggml_vec_mad_f32_unroll(const int n, const int xs, const int vs, float * restrict y, const float * restrict xv, const float * restrict vv) {
|
||||
|
||||
@ -1746,6 +1777,35 @@ inline static void ggml_vec_scale_f32(const int n, float * y, const float v) {
|
||||
#endif
|
||||
}
|
||||
|
||||
inline static void ggml_vec_scale_f16(const int n, ggml_fp16_t * y, const float v) {
|
||||
#if defined(GGML_SIMD)
|
||||
const int np = (n & ~(GGML_F16_STEP - 1));
|
||||
|
||||
GGML_F16_VEC vx = GGML_F16_VEC_SET1(v);
|
||||
|
||||
GGML_F16_VEC ay[GGML_F16_ARR];
|
||||
|
||||
for (int i = 0; i < np; i += GGML_F16_STEP) {
|
||||
for (int j = 0; j < GGML_F16_ARR; j++) {
|
||||
ay[j] = GGML_F16_VEC_LOAD(y + i + j*GGML_F16_EPR, j);
|
||||
ay[j] = GGML_F16_VEC_MUL(ay[j], vx);
|
||||
|
||||
GGML_F16_VEC_STORE(y + i + j*GGML_F16_EPR, ay, j);
|
||||
}
|
||||
}
|
||||
|
||||
// leftovers
|
||||
for (int i = np; i < n; ++i) {
|
||||
y[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(y[i])*v);
|
||||
}
|
||||
#else
|
||||
// scalar
|
||||
for (int i = 0; i < n; ++i) {
|
||||
y[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(y[i])*v);
|
||||
}
|
||||
#endif
|
||||
}
|
||||
|
||||
inline static void ggml_vec_norm_f32 (const int n, float * s, const float * x) { ggml_vec_dot_f32(n, s, 0, x, 0, x, 0, 1); *s = sqrtf(*s); }
|
||||
inline static void ggml_vec_sqr_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = x[i]*x[i]; }
|
||||
inline static void ggml_vec_sqrt_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = sqrtf(x[i]); }
|
||||
@ -2000,6 +2060,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
|
||||
"LEAKY_RELU",
|
||||
|
||||
"FLASH_ATTN",
|
||||
"FLASH_ATTN_EXT",
|
||||
"FLASH_FF",
|
||||
"FLASH_ATTN_BACK",
|
||||
"SSM_CONV",
|
||||
@ -2026,7 +2087,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
|
||||
"CROSS_ENTROPY_LOSS_BACK",
|
||||
};
|
||||
|
||||
static_assert(GGML_OP_COUNT == 76, "GGML_OP_COUNT != 76");
|
||||
static_assert(GGML_OP_COUNT == 77, "GGML_OP_COUNT != 77");
|
||||
|
||||
static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
|
||||
"none",
|
||||
@ -2090,6 +2151,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
|
||||
"leaky_relu(x)",
|
||||
|
||||
"flash_attn(x)",
|
||||
"flash_attn_ext(x)",
|
||||
"flash_ff(x)",
|
||||
"flash_attn_back(x)",
|
||||
"ssm_conv(x)",
|
||||
@ -2116,7 +2178,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
|
||||
"cross_entropy_loss_back(x,y)",
|
||||
};
|
||||
|
||||
static_assert(GGML_OP_COUNT == 76, "GGML_OP_COUNT != 76");
|
||||
static_assert(GGML_OP_COUNT == 77, "GGML_OP_COUNT != 77");
|
||||
|
||||
static_assert(GGML_OP_POOL_COUNT == 2, "GGML_OP_POOL_COUNT != 2");
|
||||
|
||||
@ -4559,6 +4621,8 @@ struct ggml_tensor * ggml_mul_mat(
|
||||
void ggml_mul_mat_set_prec(
|
||||
struct ggml_tensor * a,
|
||||
enum ggml_prec prec) {
|
||||
GGML_ASSERT(a->op == GGML_OP_MUL_MAT);
|
||||
|
||||
const int32_t prec_i32 = (int32_t) prec;
|
||||
|
||||
ggml_set_op_params_i32(a, 0, prec_i32);
|
||||
@ -5397,17 +5461,23 @@ static struct ggml_tensor * ggml_soft_max_impl(
|
||||
GGML_ASSERT(ggml_is_contiguous(a));
|
||||
|
||||
if (mask) {
|
||||
GGML_ASSERT(mask->type == GGML_TYPE_F16 || mask->type == GGML_TYPE_F32);
|
||||
GGML_ASSERT(ggml_is_contiguous(mask));
|
||||
GGML_ASSERT(ggml_is_matrix(mask));
|
||||
GGML_ASSERT(ggml_can_repeat_rows(mask, a));
|
||||
GGML_ASSERT(mask->ne[0] == a->ne[0]);
|
||||
GGML_ASSERT(mask->ne[1] >= a->ne[1]);
|
||||
}
|
||||
|
||||
if (pos) {
|
||||
GGML_ASSERT(ggml_is_vector(pos));
|
||||
GGML_ASSERT(pos->type == GGML_TYPE_F32);
|
||||
GGML_ASSERT(pos->type == GGML_TYPE_F16 || pos->type == GGML_TYPE_F32);
|
||||
GGML_ASSERT(pos->ne[0] == a->ne[0]);
|
||||
}
|
||||
|
||||
if (pos && mask) {
|
||||
GGML_ASSERT(pos->type == mask->type);
|
||||
}
|
||||
|
||||
if (max_bias > 0.0f) {
|
||||
GGML_ASSERT(pos);
|
||||
}
|
||||
@ -6216,6 +6286,59 @@ struct ggml_tensor * ggml_flash_attn(
|
||||
return result;
|
||||
}
|
||||
|
||||
// ggml_flash_attn_ext
|
||||
|
||||
struct ggml_tensor * ggml_flash_attn_ext(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * q,
|
||||
struct ggml_tensor * k,
|
||||
struct ggml_tensor * v,
|
||||
struct ggml_tensor * mask,
|
||||
float scale) {
|
||||
GGML_ASSERT(ggml_can_mul_mat(k, q));
|
||||
// TODO: check if vT can be multiplied by (k*qT)
|
||||
if (mask) {
|
||||
GGML_ASSERT(ggml_is_contiguous(mask));
|
||||
GGML_ASSERT(mask->ne[2] == 1);
|
||||
GGML_ASSERT(mask->ne[3] == 1);
|
||||
GGML_ASSERT(mask->ne[1] >= GGML_PAD(q->ne[1], GGML_KQ_MASK_PAD) &&
|
||||
"the Flash-Attention kernel requires the mask to be padded to GGML_KQ_MASK_PAD and at least n_queries big");
|
||||
//GGML_ASSERT(ggml_can_repeat_rows(mask, qk));
|
||||
}
|
||||
|
||||
bool is_node = false;
|
||||
|
||||
if (q->grad || k->grad || v->grad) {
|
||||
is_node = true;
|
||||
}
|
||||
|
||||
// permute(0, 2, 1, 3)
|
||||
int64_t ne[4] = { q->ne[0], q->ne[2], q->ne[1], q->ne[3] };
|
||||
struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne);
|
||||
|
||||
float params[] = { scale };
|
||||
ggml_set_op_params(result, params, sizeof(params));
|
||||
|
||||
result->op = GGML_OP_FLASH_ATTN_EXT;
|
||||
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
|
||||
result->src[0] = q;
|
||||
result->src[1] = k;
|
||||
result->src[2] = v;
|
||||
result->src[3] = mask;
|
||||
|
||||
return result;
|
||||
}
|
||||
|
||||
void ggml_flash_attn_ext_set_prec(
|
||||
struct ggml_tensor * a,
|
||||
enum ggml_prec prec) {
|
||||
GGML_ASSERT(a->op == GGML_OP_FLASH_ATTN_EXT);
|
||||
|
||||
const int32_t prec_i32 = (int32_t) prec;
|
||||
|
||||
ggml_set_op_params_i32(a, 1, prec_i32); // scale is on first pos
|
||||
}
|
||||
|
||||
// ggml_flash_ff
|
||||
|
||||
struct ggml_tensor * ggml_flash_ff(
|
||||
@ -12255,7 +12378,7 @@ static void ggml_compute_forward_soft_max_f32(
|
||||
|
||||
GGML_TENSOR_UNARY_OP_LOCALS
|
||||
|
||||
const int64_t ne11 = src1 ? src1->ne[1] : 1;
|
||||
//const int64_t ne11 = src1 ? src1->ne[1] : 1;
|
||||
|
||||
// TODO: is this supposed to be ceil instead of floor?
|
||||
// https://huggingface.co/mosaicml/mpt-7b/blob/main/attention.py#L370
|
||||
@ -12278,19 +12401,31 @@ static void ggml_compute_forward_soft_max_f32(
|
||||
float * wp = (float *) params->wdata + (nc + CACHE_LINE_SIZE_F32) * ith;
|
||||
|
||||
// when max_bias <= 0.0f, src2 is not used and we default it to src0 to avoid branching
|
||||
float * pos = src2 ? (float *) src2->data : src0->data;
|
||||
ggml_fp16_t * pos_f16 = src2 ? (ggml_fp16_t *) src2->data : src0->data;
|
||||
float * pos_f32 = src2 ? (float *) src2->data : src0->data;
|
||||
|
||||
const bool use_f16 = (src1 && src1->type == GGML_TYPE_F16) || (src2 && src2->type == GGML_TYPE_F16);
|
||||
|
||||
for (int i1 = ir0; i1 < ir1; i1++) {
|
||||
float * sp = (float *)((char *) src0->data + i1*src0->nb[1]);
|
||||
float * dp = (float *)((char *) dst->data + i1*dst->nb[1]);
|
||||
|
||||
// broadcast the mask across rows
|
||||
float * mp = src1 ? (float *)((char *) src1->data + (i1%ne11)*src1->nb[1]) : NULL;
|
||||
ggml_fp16_t * mp_f16 = src1 ? (ggml_fp16_t *)((char *) src1->data) + (i1%ne01)*ne00 : NULL;
|
||||
float * mp_f32 = src1 ? (float *)((char *) src1->data) + (i1%ne01)*ne00 : NULL;
|
||||
|
||||
ggml_vec_cpy_f32 (nc, wp, sp);
|
||||
ggml_vec_scale_f32(nc, wp, scale);
|
||||
if (mp) {
|
||||
ggml_vec_acc_f32(nc, wp, mp);
|
||||
if (mp_f32) {
|
||||
if (use_f16) {
|
||||
for (int i = 0; i < nc; ++i) {
|
||||
wp[i] += GGML_FP16_TO_FP32(mp_f16[i]);
|
||||
}
|
||||
} else {
|
||||
for (int i = 0; i < nc; ++i) {
|
||||
wp[i] += mp_f32[i];
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// ALiBi bias
|
||||
@ -12298,8 +12433,14 @@ static void ggml_compute_forward_soft_max_f32(
|
||||
const uint32_t h = (i1/ne01)%ne02; // head
|
||||
const float slope = h < n_head_log2 ? powf(m0, h + 1) : powf(m1, 2*(h - n_head_log2) + 1);
|
||||
|
||||
for (int i = 0; i < nc; i++) {
|
||||
wp[i] = wp[i] + slope*pos[i];
|
||||
if (use_f16) {
|
||||
for (int i = 0; i < nc; ++i) {
|
||||
wp[i] += slope*GGML_FP16_TO_FP32(pos_f16[i]);
|
||||
}
|
||||
} else {
|
||||
for (int i = 0; i < nc; ++i) {
|
||||
wp[i] += slope*pos_f32[i];
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@ -14569,6 +14710,198 @@ static void ggml_compute_forward_flash_attn(
|
||||
}
|
||||
}
|
||||
|
||||
// ggml_compute_forward_flash_attn_ext
|
||||
|
||||
static void ggml_compute_forward_flash_attn_ext_f16(
|
||||
const struct ggml_compute_params * params,
|
||||
const struct ggml_tensor * q,
|
||||
const struct ggml_tensor * k,
|
||||
const struct ggml_tensor * v,
|
||||
const struct ggml_tensor * mask,
|
||||
struct ggml_tensor * dst) {
|
||||
int64_t t0 = ggml_perf_time_us();
|
||||
UNUSED(t0);
|
||||
|
||||
GGML_TENSOR_LOCALS(int64_t, neq, q, ne)
|
||||
GGML_TENSOR_LOCALS(size_t, nbq, q, nb)
|
||||
GGML_TENSOR_LOCALS(int64_t, nek, k, ne)
|
||||
GGML_TENSOR_LOCALS(size_t, nbk, k, nb)
|
||||
GGML_TENSOR_LOCALS(int64_t, nev, v, ne)
|
||||
GGML_TENSOR_LOCALS(size_t, nbv, v, nb)
|
||||
GGML_TENSOR_LOCALS(int64_t, ne, dst, ne)
|
||||
GGML_TENSOR_LOCALS(size_t, nb, dst, nb)
|
||||
|
||||
const int ith = params->ith;
|
||||
const int nth = params->nth;
|
||||
|
||||
const int64_t D = neq0;
|
||||
const int64_t N = neq1;
|
||||
|
||||
GGML_ASSERT(ne0 == D);
|
||||
GGML_ASSERT(ne2 == N);
|
||||
|
||||
GGML_ASSERT(nbq0 == sizeof(float));
|
||||
GGML_ASSERT(nbk0 == sizeof(ggml_fp16_t));
|
||||
GGML_ASSERT(nbv0 == sizeof(ggml_fp16_t));
|
||||
|
||||
GGML_ASSERT(neq0 == D);
|
||||
GGML_ASSERT(nek0 == D);
|
||||
GGML_ASSERT(nev0 == D);
|
||||
|
||||
GGML_ASSERT(neq1 == N);
|
||||
GGML_ASSERT(nev0 == D);
|
||||
|
||||
// dst cannot be transposed or permuted
|
||||
GGML_ASSERT(nb0 == sizeof(float));
|
||||
GGML_ASSERT(nb0 <= nb1);
|
||||
GGML_ASSERT(nb1 <= nb2);
|
||||
GGML_ASSERT(nb2 <= nb3);
|
||||
|
||||
// broadcast factors
|
||||
const int64_t rk2 = neq2/nek2;
|
||||
const int64_t rk3 = neq3/nek3;
|
||||
|
||||
const int64_t rv2 = neq2/nev2;
|
||||
const int64_t rv3 = neq3/nev3;
|
||||
|
||||
if (params->type == GGML_TASK_TYPE_INIT) {
|
||||
return;
|
||||
}
|
||||
|
||||
if (params->type == GGML_TASK_TYPE_FINALIZE) {
|
||||
return;
|
||||
}
|
||||
|
||||
// parallelize by q rows using ggml_vec_dot_f32
|
||||
|
||||
// total rows in q
|
||||
const int nr = neq1*neq2*neq3;
|
||||
|
||||
// rows per thread
|
||||
const int dr = (nr + nth - 1)/nth;
|
||||
|
||||
// row range for this thread
|
||||
const int ir0 = dr*ith;
|
||||
const int ir1 = MIN(ir0 + dr, nr);
|
||||
|
||||
float scale = 1.0f;
|
||||
memcpy(&scale, (float *) dst->op_params + 0, sizeof(float));
|
||||
|
||||
// loop over n_batch and n_head
|
||||
for (int ir = ir0; ir < ir1; ++ir) {
|
||||
// q indices
|
||||
const int iq3 = ir/(neq2*neq1);
|
||||
const int iq2 = (ir - iq3*neq2*neq1)/neq1;
|
||||
const int iq1 = (ir - iq3*neq2*neq1 - iq2*neq1);
|
||||
|
||||
float S = 0.0f;
|
||||
float M = -INFINITY;
|
||||
|
||||
float * V32 = (float *) params->wdata + ith*(2*D + CACHE_LINE_SIZE_F32);
|
||||
ggml_fp16_t * Q16 = (ggml_fp16_t *) (V32); // reuse memory
|
||||
ggml_fp16_t * V16 = (ggml_fp16_t *) (V32 + D);
|
||||
|
||||
memset(V16, 0, D*sizeof(ggml_fp16_t));
|
||||
|
||||
const ggml_fp16_t * mp = mask ? (ggml_fp16_t *)((char *) mask->data + iq1*mask->nb[1]) : NULL;
|
||||
|
||||
// k indices
|
||||
const int ik3 = iq3 / rk3;
|
||||
const int ik2 = iq2 / rk2;
|
||||
|
||||
// v indices
|
||||
const int iv3 = iq3 / rv3;
|
||||
const int iv2 = iq2 / rv2;
|
||||
|
||||
// online softmax / attention
|
||||
// loop over n_kv and n_head_kv
|
||||
// ref: https://arxiv.org/pdf/2112.05682.pdf
|
||||
for (int64_t ic = 0; ic < nek1; ++ic) {
|
||||
const float mv = mp ? GGML_FP16_TO_FP32(mp[ic]) : 0.0f;
|
||||
if (mv == -INFINITY) {
|
||||
continue;
|
||||
}
|
||||
|
||||
float s;
|
||||
|
||||
// convert Q to F16 in V32
|
||||
{
|
||||
const float * pq = (const float *) ((char *) q->data + (iq1*nbq1 + iq2*nbq2 + iq3*nbq3));
|
||||
|
||||
for (int64_t d = 0; d < D; ++d) {
|
||||
Q16[d] = GGML_FP32_TO_FP16(pq[d]);
|
||||
}
|
||||
}
|
||||
|
||||
ggml_vec_dot_f16(D,
|
||||
&s, 0,
|
||||
(ggml_fp16_t *) ((char *) k->data + ( ic*nbk1 + ik2*nbk2 + ik3*nbk3)), 0,
|
||||
Q16, 0, 1);
|
||||
|
||||
s = s*scale + mv;
|
||||
|
||||
const float Mold = M;
|
||||
|
||||
float ms = 1.0f;
|
||||
float vs = 1.0f;
|
||||
|
||||
if (s > M) {
|
||||
M = s;
|
||||
ms = expf(Mold - M);
|
||||
|
||||
// V = V*expf(Mold - M)
|
||||
ggml_vec_scale_f16(D, V16, ms);
|
||||
} else {
|
||||
vs = expf(s - M);
|
||||
}
|
||||
|
||||
const ggml_fp16_t * v16 = (const ggml_fp16_t *) ((char *) v->data + (ic*nbv1 + iv2*nbv2 + iv3*nbv3));
|
||||
|
||||
// V += v*expf(s - M)
|
||||
ggml_vec_mad_f16(D, V16, v16, vs);
|
||||
|
||||
S = S*ms + vs;
|
||||
}
|
||||
|
||||
// V /= S
|
||||
for (int64_t d = 0; d < D; ++d) {
|
||||
V32[d] = GGML_FP16_TO_FP32(V16[d])/S;
|
||||
}
|
||||
|
||||
// dst indices
|
||||
const int i1 = iq1;
|
||||
const int i2 = iq2;
|
||||
const int i3 = iq3;
|
||||
|
||||
// original
|
||||
//memcpy((char *) dst->data + (i1*nb1 + i2*nb2 + i3*nb3), V, nev0*sizeof(float));
|
||||
|
||||
// permute(0, 2, 1, 3)
|
||||
memcpy((char *) dst->data + (i3*ne2*ne1 + i2 + i1*ne1)*nb1, V32, nb1);
|
||||
}
|
||||
}
|
||||
|
||||
static void ggml_compute_forward_flash_attn_ext(
|
||||
const struct ggml_compute_params * params,
|
||||
const struct ggml_tensor * q,
|
||||
const struct ggml_tensor * k,
|
||||
const struct ggml_tensor * v,
|
||||
const struct ggml_tensor * mask,
|
||||
struct ggml_tensor * dst) {
|
||||
switch (dst->op_params[1]) {
|
||||
case GGML_PREC_DEFAULT:
|
||||
case GGML_PREC_F32:
|
||||
{
|
||||
// uses F32 accumulators
|
||||
ggml_compute_forward_flash_attn_ext_f16(params, q, k, v, mask, dst);
|
||||
} break;
|
||||
default:
|
||||
{
|
||||
GGML_ASSERT(false);
|
||||
} break;
|
||||
}
|
||||
}
|
||||
|
||||
// ggml_compute_forward_flash_ff
|
||||
|
||||
static void ggml_compute_forward_flash_ff_f16(
|
||||
@ -16376,6 +16709,10 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
|
||||
const bool masked = t != 0;
|
||||
ggml_compute_forward_flash_attn(params, masked, tensor);
|
||||
} break;
|
||||
case GGML_OP_FLASH_ATTN_EXT:
|
||||
{
|
||||
ggml_compute_forward_flash_attn_ext(params, tensor->src[0], tensor->src[1], tensor->src[2], tensor->src[3], tensor);
|
||||
} break;
|
||||
case GGML_OP_FLASH_FF:
|
||||
{
|
||||
ggml_compute_forward_flash_ff(params, tensor);
|
||||
@ -17388,6 +17725,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
|
||||
GGML_ASSERT(false); // TODO: not implemented
|
||||
} break;
|
||||
case GGML_OP_FLASH_ATTN:
|
||||
case GGML_OP_FLASH_ATTN_EXT:
|
||||
{
|
||||
struct ggml_tensor * flash_grad = NULL;
|
||||
if (src0->grad || src1->grad || tensor->src[2]->grad) {
|
||||
@ -18160,6 +18498,7 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads, int n_cur_
|
||||
n_tasks = n_threads;
|
||||
} break;
|
||||
case GGML_OP_FLASH_ATTN:
|
||||
case GGML_OP_FLASH_ATTN_EXT:
|
||||
{
|
||||
n_tasks = n_threads;
|
||||
} break;
|
||||
@ -18563,6 +18902,12 @@ struct ggml_cplan ggml_graph_plan(const struct ggml_cgraph * cgraph, int n_threa
|
||||
cur += sizeof(float)*ne11*n_tasks; // this is overestimated by x2
|
||||
}
|
||||
} break;
|
||||
case GGML_OP_FLASH_ATTN_EXT:
|
||||
{
|
||||
const int64_t ne00 = node->src[0]->ne[0]; // D
|
||||
|
||||
cur = 2*sizeof(float)*ne00*n_tasks; // 2x head size
|
||||
} break;
|
||||
case GGML_OP_FLASH_FF:
|
||||
{
|
||||
if (node->src[1]->type == GGML_TYPE_F32) {
|
||||
|
||||
20
ggml.h
20
ggml.h
@ -475,6 +475,7 @@ extern "C" {
|
||||
GGML_OP_LEAKY_RELU,
|
||||
|
||||
GGML_OP_FLASH_ATTN,
|
||||
GGML_OP_FLASH_ATTN_EXT,
|
||||
GGML_OP_FLASH_FF,
|
||||
GGML_OP_FLASH_ATTN_BACK,
|
||||
GGML_OP_SSM_CONV,
|
||||
@ -1722,6 +1723,25 @@ extern "C" {
|
||||
struct ggml_tensor * v,
|
||||
bool masked);
|
||||
|
||||
#define GGML_KQ_MASK_PAD 32
|
||||
|
||||
// q: [n_embd, n_batch, n_head, 1]
|
||||
// k: [n_embd, n_kv, n_head_kv, 1]
|
||||
// v: [n_embd, n_kv, n_head_kv, 1] !! not transposed !!
|
||||
// mask: [n_kv, n_batch_pad, 1, 1] !! n_batch_pad = GGML_PAD(n_batch, GGML_KQ_MASK_PAD) !!
|
||||
// res: [n_embd, n_head, n_batch, 1] !! permuted !!
|
||||
GGML_API struct ggml_tensor * ggml_flash_attn_ext(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * q,
|
||||
struct ggml_tensor * k,
|
||||
struct ggml_tensor * v,
|
||||
struct ggml_tensor * mask,
|
||||
float scale);
|
||||
|
||||
GGML_API void ggml_flash_attn_ext_set_prec(
|
||||
struct ggml_tensor * a,
|
||||
enum ggml_prec prec);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_flash_attn_back(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * q,
|
||||
|
||||
5
llama.h
5
llama.h
@ -40,7 +40,7 @@
|
||||
#define LLAMA_FILE_MAGIC_GGSQ 0x67677371u // 'ggsq'
|
||||
|
||||
#define LLAMA_SESSION_MAGIC LLAMA_FILE_MAGIC_GGSN
|
||||
#define LLAMA_SESSION_VERSION 5
|
||||
#define LLAMA_SESSION_VERSION 6
|
||||
|
||||
#define LLAMA_STATE_SEQ_MAGIC LLAMA_FILE_MAGIC_GGSQ
|
||||
#define LLAMA_STATE_SEQ_VERSION 1
|
||||
@ -287,6 +287,7 @@ extern "C" {
|
||||
bool logits_all; // the llama_decode() call computes all logits, not just the last one (DEPRECATED - set llama_batch.logits instead)
|
||||
bool embeddings; // if true, extract embeddings (together with logits)
|
||||
bool offload_kqv; // whether to offload the KQV ops (including the KV cache) to GPU
|
||||
bool flash_attn; // whether to use flash attention
|
||||
|
||||
// Abort callback
|
||||
// if it returns true, execution of llama_decode() will be aborted
|
||||
@ -542,7 +543,7 @@ extern "C" {
|
||||
// Returns the number of used KV cells (i.e. have at least one sequence assigned to them)
|
||||
LLAMA_API int32_t llama_get_kv_cache_used_cells(const struct llama_context * ctx);
|
||||
|
||||
// Clear the KV cache
|
||||
// Clear the KV cache - both cell info is erased and KV data is zeroed
|
||||
LLAMA_API void llama_kv_cache_clear(
|
||||
struct llama_context * ctx);
|
||||
|
||||
|
||||
@ -1090,6 +1090,12 @@ struct test_soft_max : public test_case {
|
||||
return VARS_TO_STR5(type, ne, mask, scale, max_bias);
|
||||
}
|
||||
|
||||
// the 1024 test with bias occasionally fails:
|
||||
// SOFT_MAX(type=f32,ne=[1024,16,1,1],mask=1,scale=1.000000,max_bias=8.000000): [SOFT_MAX] NMSE = 0.000000103 > 0.000000100 FAIL
|
||||
virtual double max_nmse_err() override {
|
||||
return 1e-6;
|
||||
}
|
||||
|
||||
test_soft_max(ggml_type type = GGML_TYPE_F32,
|
||||
std::array<int64_t, 4> ne = {10, 10, 10, 10},
|
||||
bool mask = false,
|
||||
@ -1101,7 +1107,7 @@ struct test_soft_max : public test_case {
|
||||
ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
|
||||
ggml_tensor * mask = nullptr;
|
||||
if (this->mask) {
|
||||
mask = ggml_new_tensor_2d(ctx, type, ne[0], ne[1]);
|
||||
mask = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, ne[0], ne[1]);
|
||||
}
|
||||
ggml_tensor * pos = nullptr;
|
||||
if (max_bias > 0.0f) {
|
||||
@ -1475,6 +1481,34 @@ struct test_leaky_relu : public test_case {
|
||||
}
|
||||
};
|
||||
|
||||
// GGML_OP_FLASH_ATTN_EXT
|
||||
struct test_flash_attn_ext : public test_case {
|
||||
const int64_t hs; // head size
|
||||
const int64_t nh; // num heads
|
||||
const int64_t kv; // kv size
|
||||
const int64_t nb; // batch size
|
||||
|
||||
std::string vars() override {
|
||||
return VARS_TO_STR4(hs, nh, kv, nb);
|
||||
}
|
||||
|
||||
double max_nmse_err() override {
|
||||
return 5e-4;
|
||||
}
|
||||
|
||||
test_flash_attn_ext(int64_t hs = 128, int64_t nh = 32, int64_t kv = 96, int64_t nb = 8)
|
||||
: hs(hs), nh(nh), kv(kv), nb(nb) {}
|
||||
|
||||
ggml_tensor * build_graph(ggml_context * ctx) override {
|
||||
ggml_tensor * q = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, hs, nb, nh, 1);
|
||||
ggml_tensor * k = ggml_new_tensor_4d(ctx, GGML_TYPE_F16, hs, kv, nh, 1);
|
||||
ggml_tensor * v = ggml_new_tensor_4d(ctx, GGML_TYPE_F16, hs, kv, nh, 1);
|
||||
ggml_tensor * mask = ggml_new_tensor_4d(ctx, GGML_TYPE_F16, kv, GGML_PAD(nb, GGML_KQ_MASK_PAD), 1, 1);
|
||||
ggml_tensor * out = ggml_flash_attn_ext(ctx, q, k, v, mask, 1.0f/sqrtf(hs));
|
||||
return out;
|
||||
}
|
||||
};
|
||||
|
||||
enum llm_norm_type {
|
||||
LLM_NORM,
|
||||
LLM_NORM_RMS,
|
||||
@ -1661,7 +1695,7 @@ struct test_llama : public test_llm {
|
||||
struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, hp.n_tokens);
|
||||
|
||||
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
|
||||
struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, hp.n_kv, hp.n_tokens, 1);
|
||||
struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx, GGML_TYPE_F16, hp.n_kv, hp.n_tokens, 1);
|
||||
|
||||
ggml_tensor * k_l = ggml_new_tensor_1d(ctx, GGML_TYPE_F16, 1638400);
|
||||
ggml_tensor * v_l = ggml_new_tensor_1d(ctx, GGML_TYPE_F16, 1638400);
|
||||
@ -1783,7 +1817,7 @@ struct test_falcon : public test_llm {
|
||||
struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, hp.n_tokens);
|
||||
|
||||
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
|
||||
struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, hp.n_kv, hp.n_tokens, 1);
|
||||
struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx, GGML_TYPE_F16, hp.n_kv, hp.n_tokens, 1);
|
||||
|
||||
ggml_tensor * k_l = ggml_new_tensor_1d(ctx, GGML_TYPE_F16, 1638400);
|
||||
ggml_tensor * v_l = ggml_new_tensor_1d(ctx, GGML_TYPE_F16, 1638400);
|
||||
@ -2095,7 +2129,7 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
|
||||
for (float scale : {1.0f, 0.1f}) {
|
||||
for (int64_t ne0 : {16, 1024}) {
|
||||
for (int64_t ne1 : {16, 1024}) {
|
||||
test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {ne0, ne1, 1, 1}, mask, scale, max_bias));
|
||||
test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {ne0, ne1, 1, 1}, mask, scale, max_bias));
|
||||
test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {ne0-1, ne1-1, 1, 1}, mask, scale, max_bias));
|
||||
}
|
||||
}
|
||||
@ -2139,6 +2173,16 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
|
||||
test_cases.emplace_back(new test_timestep_embedding());
|
||||
test_cases.emplace_back(new test_leaky_relu());
|
||||
|
||||
for (int hs : { 64, 80, 128, 256, }) {
|
||||
for (int nh : { 32, }) {
|
||||
for (int kv : { 512, 1024, }) {
|
||||
for (int nb : { 1, 2, 4, 8, }) {
|
||||
test_cases.emplace_back(new test_flash_attn_ext(hs, nh, kv, nb));
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// these tests are disabled to save execution time, but they can be handy for debugging
|
||||
#if 0
|
||||
test_cases.emplace_back(new test_llama(1));
|
||||
|
||||
Loading…
Reference in New Issue
Block a user