common : refactor cli arg parsing (#7675)

* common : gpt_params_parse do not print usage

* common : rework usage print (wip)

* common : valign

* common : rework print_usage

* infill : remove cfg support

* common : reorder args

* server : deduplicate parameters

ggml-ci

* common : add missing header

ggml-ci

* common : remote --random-prompt usages

ggml-ci

* examples : migrate to gpt_params

ggml-ci

* batched-bench : migrate to gpt_params

* retrieval : migrate to gpt_params

* common : change defaults for escape and n_ctx

* common : remove chatml and instruct params

ggml-ci

* common : passkey use gpt_params

1 files changed, 23 insertions, 43 deletions

diff --git a/examples/passkey/passkey.cpp b/examples/passkey/passkey.cpp
index f2ef9ca1..d03215cd 100644
--- a/examples/passkey/passkey.cpp
+++ b/examples/passkey/passkey.cpp
@@ -6,46 +6,32 @@
 #include <string>
 #include <vector>
 
-int main(int argc, char ** argv) {
-    gpt_params params;
-
-    if (argc == 1 || argv[1][0] == '-') {
-        printf("usage: %s MODEL_PATH N_JUNK N_GRP I_POS SEED\n" , argv[0]);
-        return 1 ;
-    }
-
-    int seed = -1;
+static void print_usage(int argc, char ** argv, const gpt_params & params) {
+    gpt_params_print_usage(argc, argv, params);
 
-    int n_junk = 250; // number of times to repeat the junk text
-    int n_keep = 32;  // number of tokens in the prompt prefix
-    int n_grp  = 1;   // if more than 1 - perform LongLM SelfExtend
-    int i_pos  = -1;  // position of the passkey in the junk text
-
-    if (argc >= 2) {
-        params.model = argv[1];
-    }
-
-    if (argc >= 3) {
-        n_junk = std::stoi(argv[2]);
-    }
+    LOG_TEE("\nexample usage:\n");
+    LOG_TEE("\n    %s -m model.gguf --junk 250 --pos 90 --keep 32 --grp-attn-n 2 [--seed 1234]\n", argv[0]);
+    LOG_TEE("\n");
+}
 
-    if (argc >= 4) {
-        n_grp = std::stoi(argv[3]);
-    }
+int main(int argc, char ** argv) {
+    gpt_params params;
 
-    if (argc >= 5) {
-        i_pos = std::stoi(argv[4]);
-    }
+    params.n_junk = 250;
+    params.n_keep = 32;
+    params.i_pos  = -1;
 
-    if (argc >= 6) {
-        seed = std::stoi(argv[5]);
+    if (!gpt_params_parse(argc, argv, params)) {
+        print_usage(argc, argv, params);
+        return 1;
     }
 
-    if (seed == -1) {
-        seed = time(NULL);
-    }
+    srand(params.seed == LLAMA_DEFAULT_SEED ? time(NULL) : params.seed);
 
-    srand(seed);
+    int n_junk = params.n_junk;
+    int n_keep = params.n_keep;
+    int n_grp  = params.grp_attn_n;
+    int i_pos  = params.i_pos;
 
     if (i_pos == -1) {
         i_pos = rand() % n_junk;
@@ -76,9 +62,7 @@ int main(int argc, char ** argv) {
 
     // initialize the model
 
-    llama_model_params model_params = llama_model_default_params();
-
-    model_params.n_gpu_layers = 99; // offload all layers to the GPU
+    llama_model_params model_params = llama_model_params_from_gpt_params(params);
 
     llama_model * model = llama_load_model_from_file(params.model.c_str(), model_params);
 
@@ -89,13 +73,9 @@ int main(int argc, char ** argv) {
 
     // initialize the context
 
-    llama_context_params ctx_params = llama_context_default_params();
+    llama_context_params ctx_params = llama_context_params_from_gpt_params(params);
 
-    ctx_params.seed    = seed;
-    ctx_params.n_ctx   = llama_n_ctx_train(model)*n_grp + n_keep;
-    ctx_params.n_batch = 512;
-    ctx_params.n_threads       = params.n_threads;
-    ctx_params.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch;
+    ctx_params.n_ctx = llama_n_ctx_train(model)*n_grp + n_keep;
 
     GGML_ASSERT(ctx_params.n_batch % n_grp == 0 && "n_batch must be divisible by n_grp");
 
@@ -135,7 +115,7 @@ int main(int argc, char ** argv) {
     LOG_TEE("prompt tokens: %d\n", n_tokens_all);
     //LOG_TEE("prompt: %s\n", params.prompt.c_str());
 
-    llama_batch batch = llama_batch_init(512, 0, 1);
+    llama_batch batch = llama_batch_init(params.n_batch, 0, 1);
 
     int n_past = 0;