llama.cpp : split llama_context_params into model and context params (#3301)

* llama.cpp : split llama_context_params into model and context params

ggml-ci

* fix metal build

* fix freq_base/scale default to model value

* llama-bench : keep the same model between tests when possible

* move n_threads to llama_context_params, add n_threads_batch

* fix mpi build

* remove kv_size(), cuda scratch fixes

* remove low-vram option

* add n_threads_batch to system info, refactor to get_system_info()

* add documentation about --threads-batch to the READMEs

* llama-bench fix

* main : fix rope freq/scale warning

* llama.cpp : add llama_get_model
common : add llama_tokenize from model

* remove duplicated ctx/model functions

ggml-ci

* cuda : print total VRAM used

1 files changed, 24 insertions, 15 deletions

diff --git a/examples/batched/batched.cpp b/examples/batched/batched.cpp
index 4dd1d553..688ef221 100644
--- a/examples/batched/batched.cpp
+++ b/examples/batched/batched.cpp
@@ -40,20 +40,35 @@ int main(int argc, char ** argv) {
 
     llama_backend_init(params.numa);
 
-    llama_context_params ctx_params = llama_context_default_params();
+    // initialize the model
 
-    ctx_params.seed  = 1234;
-    ctx_params.n_ctx = n_len*n_parallel; // FIXME: use n_kv_req instead (tokenize with model after #3301)
-    ctx_params.n_batch = std::max(n_len, n_parallel);
-    // ctx_params.n_gpu_layers = 99; // offload all layers to the GPU
+    llama_model_params model_params = llama_model_default_params();
 
-    llama_model * model = llama_load_model_from_file(params.model.c_str(), ctx_params);
+    // model_params.n_gpu_layers = 99; // offload all layers to the GPU
+
+    llama_model * model = llama_load_model_from_file(params.model.c_str(), model_params);
 
     if (model == NULL) {
         fprintf(stderr , "%s: error: unable to load model\n" , __func__);
         return 1;
     }
 
+    // tokenize the prompt
+
+    std::vector<llama_token> tokens_list;
+    tokens_list = ::llama_tokenize(model, params.prompt, true);
+    const int n_kv_req = tokens_list.size() + (n_len - tokens_list.size())*n_parallel;
+
+    // initialize the context
+
+    llama_context_params ctx_params = llama_context_default_params();
+
+    ctx_params.seed  = 1234;
+    ctx_params.n_ctx = n_kv_req;
+    ctx_params.n_batch = std::max(n_len, n_parallel);
+    ctx_params.n_threads = params.n_threads;
+    ctx_params.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch;
+
     llama_context * ctx = llama_new_context_with_model(model, ctx_params);
 
     if (ctx == NULL) {
@@ -61,13 +76,7 @@ int main(int argc, char ** argv) {
         return 1;
     }
 
-    // tokenize the prompt
-
-    std::vector<llama_token> tokens_list;
-    tokens_list = ::llama_tokenize(ctx, params.prompt, true);
-
     const int n_ctx    = llama_n_ctx(ctx);
-    const int n_kv_req = tokens_list.size() + (n_len - tokens_list.size())*n_parallel;
 
     LOG_TEE("\n%s: n_len = %d, n_ctx = %d, n_batch = %d, n_parallel = %d, n_kv_req = %d\n", __func__, n_len, n_ctx, ctx_params.n_batch, n_parallel, n_kv_req);
 
@@ -106,7 +115,7 @@ int main(int argc, char ** argv) {
     // llama_decode will output logits only for the last token of the prompt
     batch.logits[batch.n_tokens - 1] = true;
 
-    if (llama_decode(ctx, batch, params.n_threads) != 0) {
+    if (llama_decode(ctx, batch) != 0) {
         LOG_TEE("%s: llama_decode() failed\n", __func__);
         return 1;
     }
@@ -146,7 +155,7 @@ int main(int argc, char ** argv) {
                 continue;
             }
 
-            auto   n_vocab = llama_n_vocab(ctx);
+            auto   n_vocab = llama_n_vocab(model);
             auto * logits  = llama_get_logits_ith(ctx, i_batch[i]);
 
             std::vector<llama_token_data> candidates;
@@ -210,7 +219,7 @@ int main(int argc, char ** argv) {
         n_cur += 1;
 
         // evaluate the current batch with the transformer model
-        if (llama_decode(ctx, batch, params.n_threads)) {
+        if (llama_decode(ctx, batch)) {
             fprintf(stderr, "%s : failed to eval, return code %d\n", __func__, 1);
             return 1;
         }