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batched-bench : add mmq CLI arg
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@ -10,7 +10,7 @@ There are 2 modes of operation:
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- `prompt is shared` - there is a common prompt of size `PP` used by all batches (i.e. `N_KV = PP + B*TG`)
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```bash
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./batched-bench MODEL_PATH [N_KV_MAX] [IS_PP_SHARED] [NGL] <PP> <TG> <PL>
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./batched-bench MODEL_PATH [N_KV_MAX] [IS_PP_SHARED] [NGL] [MMQ] <PP> <TG> <PL>
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# LLaMA 7B, F16, N_KV_MAX = 16384 (8GB), prompt not shared
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./batched-bench ./models/llama-7b/ggml-model-f16.gguf 16384 0 99
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@ -19,7 +19,7 @@ There are 2 modes of operation:
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./batched-bench ./models/llama-7b/ggml-model-q8_0.gguf 16384 1 99
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# custom set of batches
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./batched-bench ./models/llama-7b/ggml-model-q8_0.gguf 2048 0 999 128,256,512 128,256 1,2,4,8,16,32
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./batched-bench ./models/llama-7b/ggml-model-q8_0.gguf 2048 0 999 0 128,256,512 128,256 1,2,4,8,16,32
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```
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## Sample results
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@ -32,15 +32,16 @@ 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] <PP> <TG> <PL>\n" , argv[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(" <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 0 999 128,256,512 128,256 1,2,4,8,16,32\n\n", argv[0]);
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printf(" example: %s ggml-model-f16.gguf 2048 0 999 0 128,256,512 128,256 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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@ -64,15 +65,19 @@ int main(int argc, char ** argv) {
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}
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if (argc >= 6) {
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n_pp = parse_list(argv[5]);
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mmq = std::atoi(argv[5]);
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}
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if (argc >= 7) {
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n_tg = parse_list(argv[6]);
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n_pp = parse_list(argv[6]);
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}
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if (argc >= 8) {
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n_pl = parse_list(argv[7]);
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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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}
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// init LLM
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@ -94,9 +99,11 @@ 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 = 512;
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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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