mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-24 13:28:50 +01:00
llama : cleanup unused mmq flags (#5772)
* cleanup unused --no-mul-mat-q,-nommq, -mmq, --mul-mat-q, mul_mat_q * remove: mul_mat_q in compare llama bench and usage * update llama-bench --------- Co-authored-by: slaren <slarengh@gmail.com>
This commit is contained in:
Pierrick Hymbert
GitHub
parent
9600d59e01
commit
3ab8b3a92e
@ -1281,7 +1281,6 @@ struct llama_context_params llama_context_params_from_gpt_params(const gpt_param
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cparams.n_batch = params.n_batch;
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cparams.n_threads = params.n_threads;
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cparams.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch;
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cparams.mul_mat_q = params.mul_mat_q;
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cparams.seed = params.seed;
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cparams.logits_all = params.logits_all;
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cparams.embedding = params.embedding;
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@ -1725,7 +1724,6 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l
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fprintf(stream, "n_predict: %d # default: -1 (unlimited)\n", params.n_predict);
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fprintf(stream, "n_probs: %d # only used by server binary, default: 0\n", sparams.n_probs);
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fprintf(stream, "no_mmap: %s # default: false\n", !params.use_mmap ? "true" : "false");
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fprintf(stream, "no_mul_mat_q: %s # default: false\n", !params.mul_mat_q ? "true" : "false");
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fprintf(stream, "no_penalize_nl: %s # default: false\n", !sparams.penalize_nl ? "true" : "false");
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fprintf(stream, "ppl_output_type: %d # default: 0\n", params.ppl_output_type);
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fprintf(stream, "ppl_stride: %d # default: 0\n", params.ppl_stride);
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@ -115,7 +115,6 @@ struct gpt_params {
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bool kl_divergence = false; // compute KL-divergence
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bool mul_mat_q = true; // if true, use mul_mat_q kernels instead of cuBLAS
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bool random_prompt = false; // do not randomize prompt if none provided
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bool use_color = false; // use color to distinguish generations and inputs
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bool interactive = false; // interactive mode
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@ -32,16 +32,15 @@ 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] [IS_PP_SHARED] [NGL] [MMQ] <PP> <TG> <PL>\n" , argv[0]);
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printf("usage: %s MODEL_PATH [N_KV_MAX] [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 0 999 0 128,256,512 128,256 1,2,4,8,16,32\n\n", argv[0]);
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printf(" example: %s ggml-model-f16.gguf 2048 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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}
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int n_kv_max = 2048;
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int is_pp_shared = 0;
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int n_gpu_layers = 0;
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int mmq = 0;
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std::vector<int> n_pp = { 128, 256, 512, 1024, 2048, 3584, 7680, };
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std::vector<int> n_tg = { 128, 256, };
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@ -65,19 +64,15 @@ int main(int argc, char ** argv) {
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}
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if (argc >= 6) {
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mmq = std::atoi(argv[5]);
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n_pp = parse_list(argv[5]);
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}
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if (argc >= 7) {
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n_pp = parse_list(argv[6]);
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n_tg = parse_list(argv[6]);
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}
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if (argc >= 8) {
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n_tg = parse_list(argv[7]);
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}
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if (argc >= 9) {
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n_pl = parse_list(argv[8]);
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n_pl = parse_list(argv[7]);
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}
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// init LLM
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@ -106,7 +101,6 @@ int main(int argc, char ** argv) {
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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 = 512;
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ctx_params.mul_mat_q = mmq;
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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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@ -159,7 +153,7 @@ 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, is_pp_shared = %d, n_gpu_layers = %d, mmq = %d, n_threads = %u, n_threads_batch = %u\n", __func__, n_kv_max, is_pp_shared, n_gpu_layers, mmq, ctx_params.n_threads, ctx_params.n_threads_batch);
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LOG_TEE("%s: n_kv_max = %d, is_pp_shared = %d, n_gpu_layers = %d, n_threads = %u, n_threads_batch = %u\n", __func__, n_kv_max, 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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@ -35,7 +35,6 @@ options:
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-mg, --main-gpu <i> (default: 0)
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-nkvo, --no-kv-offload <0|1> (default: 0)
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-mmp, --mmap <0|1> (default: 1)
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-mmq, --mul-mat-q <0|1> (default: 1)
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-ts, --tensor_split <ts0/ts1/..> (default: 0)
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-r, --repetitions <n> (default: 5)
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-o, --output <csv|json|md|sql> (default: md)
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@ -176,7 +176,6 @@ 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> mul_mat_q;
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std::vector<std::vector<float>> tensor_split;
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std::vector<bool> use_mmap;
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int reps;
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@ -196,7 +195,6 @@ 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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/* mul_mat_q */ {true},
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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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/* reps */ 5,
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@ -221,7 +219,6 @@ static void print_usage(int /* argc */, char ** argv) {
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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(" -mmp, --mmap <0|1> (default: %s)\n", join(cmd_params_defaults.use_mmap, ",").c_str());
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printf(" -mmq, --mul-mat-q <0|1> (default: %s)\n", join(cmd_params_defaults.mul_mat_q, ",").c_str());
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printf(" -ts, --tensor_split <ts0/ts1/..> (default: 0)\n");
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printf(" -r, --repetitions <n> (default: %d)\n", cmd_params_defaults.reps);
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printf(" -o, --output <csv|json|md|sql> (default: %s)\n", output_format_str(cmd_params_defaults.output_format));
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@ -383,13 +380,6 @@ 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 == "-mmq" || arg == "--mul-mat-q") {
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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.mul_mat_q.insert(params.mul_mat_q.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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@ -466,7 +456,6 @@ 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.mul_mat_q.empty()) { params.mul_mat_q = cmd_params_defaults.mul_mat_q; }
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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.n_threads.empty()) { params.n_threads = cmd_params_defaults.n_threads; }
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@ -486,7 +475,6 @@ 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 mul_mat_q;
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std::vector<float> tensor_split;
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bool use_mmap;
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@ -518,7 +506,6 @@ struct cmd_params_instance {
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cparams.n_batch = n_batch;
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cparams.type_k = type_k;
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cparams.type_v = type_v;
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cparams.mul_mat_q = mul_mat_q;
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cparams.offload_kqv = !no_kv_offload;
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return cparams;
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@ -538,7 +525,6 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
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for (const auto & nb : params.n_batch)
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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 & mmq : params.mul_mat_q)
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for (const auto & nkvo : params.no_kv_offload)
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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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@ -557,7 +543,6 @@ 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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/* .mul_mat_q = */ mmq,
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/* .tensor_split = */ ts,
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/* .use_mmap = */ mmp,
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};
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@ -580,7 +565,6 @@ 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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/* .mul_mat_q = */ mmq,
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/* .tensor_split = */ ts,
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/* .use_mmap = */ mmp,
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};
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@ -616,7 +600,6 @@ 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 mul_mat_q;
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std::vector<float> tensor_split;
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bool use_mmap;
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int n_prompt;
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@ -639,7 +622,6 @@ 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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mul_mat_q = inst.mul_mat_q;
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tensor_split = inst.tensor_split;
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use_mmap = inst.use_mmap;
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n_prompt = inst.n_prompt;
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@ -713,7 +695,7 @@ struct test {
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"n_batch", "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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"mul_mat_q", "tensor_split", "use_mmap",
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"tensor_split", "use_mmap",
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"n_prompt", "n_gen", "test_time",
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"avg_ns", "stddev_ns",
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"avg_ts", "stddev_ts"
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@ -733,7 +715,7 @@ 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" ||
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field == "mul_mat_q" || field == "use_mmap") {
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field == "use_mmap") {
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return BOOL;
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}
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if (field == "avg_ts" || field == "stddev_ts") {
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@ -767,7 +749,7 @@ struct test {
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std::to_string(n_batch), std::to_string(n_threads), ggml_type_name(type_k), ggml_type_name(type_v),
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std::to_string(n_gpu_layers), split_mode_str(split_mode),
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std::to_string(main_gpu), std::to_string(no_kv_offload),
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std::to_string(mul_mat_q), tensor_split_str, std::to_string(use_mmap),
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tensor_split_str, std::to_string(use_mmap),
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std::to_string(n_prompt), std::to_string(n_gen), test_time,
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std::to_string(avg_ns()), std::to_string(stdev_ns()),
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std::to_string(avg_ts()), std::to_string(stdev_ts())
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@ -931,9 +913,6 @@ struct markdown_printer : public printer {
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if (field == "n_threads") {
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return "threads";
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}
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if (field == "mul_mat_q") {
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return "mmq";
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}
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if (field == "no_kv_offload") {
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return "nkvo";
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}
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@ -974,9 +953,6 @@ struct markdown_printer : public printer {
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if (params.split_mode.size() > 1 || params.split_mode != cmd_params_defaults.split_mode) {
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fields.emplace_back("split_mode");
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}
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if (params.mul_mat_q.size() > 1 || params.mul_mat_q != cmd_params_defaults.mul_mat_q) {
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fields.emplace_back("mul_mat_q");
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}
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if (params.no_kv_offload.size() > 1 || params.no_kv_offload != cmd_params_defaults.no_kv_offload) {
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fields.emplace_back("no_kv_offload");
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}
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@ -2390,14 +2390,6 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
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}
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#else
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LOG_WARNING("llama.cpp was compiled without cuBLAS. It is not possible to set a tensor split.\n", {});
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#endif // GGML_USE_CUBLAS
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}
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else if (arg == "--no-mul-mat-q" || arg == "-nommq")
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{
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#if defined(GGML_USE_CUBLAS) || defined(GGML_USE_SYCL)
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params.mul_mat_q = false;
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#else
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LOG_WARNING("warning: llama.cpp was compiled without cuBLAS. Disabling mul_mat_q kernels has no effect.\n", {});
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#endif // GGML_USE_CUBLAS
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}
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else if (arg == "--main-gpu" || arg == "-mg")
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@ -1645,7 +1645,6 @@ struct llama_cparams {
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float yarn_beta_slow;
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float defrag_thold;
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bool mul_mat_q;
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bool offload_kqv;
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bool do_pooling;
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@ -11633,7 +11632,6 @@ struct llama_context_params llama_context_default_params() {
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/*.cb_eval_user_data =*/ nullptr,
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/*.type_k =*/ GGML_TYPE_F16,
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/*.type_v =*/ GGML_TYPE_F16,
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/*.mul_mat_q =*/ true,
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/*.logits_all =*/ false,
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/*.embedding =*/ false,
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/*.offload_kqv =*/ true,
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@ -11785,7 +11783,6 @@ struct llama_context * llama_new_context_with_model(
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cparams.yarn_beta_fast = params.yarn_beta_fast;
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cparams.yarn_beta_slow = params.yarn_beta_slow;
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cparams.defrag_thold = params.defrag_thold;
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cparams.mul_mat_q = params.mul_mat_q;
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cparams.offload_kqv = params.offload_kqv;
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cparams.do_pooling = params.do_pooling;
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1
llama.h
1
llama.h
@ -255,7 +255,6 @@ extern "C" {
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enum ggml_type type_v; // data type for V cache
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// Keep the booleans together to avoid misalignment during copy-by-value.
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bool mul_mat_q; // if true, use experimental mul_mat_q kernels (DEPRECATED - always true)
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bool logits_all; // the llama_eval() call computes all logits, not just the last one (DEPRECATED - set llama_batch.logits instead)
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bool embedding; // embedding mode only
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bool offload_kqv; // whether to offload the KQV ops (including the KV cache) to GPU
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@ -31,7 +31,7 @@ PRETTY_NAMES = {
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"model_size": "Model Size [GiB]", "model_n_params": "Num. of Parameters",
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"n_batch": "Batch size", "n_threads": "Threads", "type_k": "K type", "type_v": "V type",
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"n_gpu_layers": "GPU layers", "main_gpu": "Main GPU", "no_kv_offload": "NKVO",
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"mul_mat_q": "MMQ", "tensor_split": "Tensor split"
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"tensor_split": "Tensor split"
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}
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DEFAULT_SHOW = ["model_type"] # Always show these properties by default.
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