Fix merge

Author: ochafik

common/common.cpp

@@ -1929,8 +1929,9 @@ minja::chat_template llama_chat_template_from_model(
             chat_template = _llama_model_meta_val_str(model, "tokenizer.chat_template");
         }
     }
-    auto bos_token = _common_token_to_piece(model, llama_token_bos(model), true);
-    auto eos_token = _common_token_to_piece(model, llama_token_eos(model), true);
+    const auto vocab = llama_model_get_vocab(model);
+    auto bos_token = common_token_to_piece(vocab, llama_vocab_bos(vocab), true);
+    auto eos_token = common_token_to_piece(vocab, llama_vocab_eos(vocab), true);
     return {std::move(chat_template), bos_token, eos_token};
 }
 

common/sampling.h

@@ -100,7 +100,7 @@ std::string common_sampler_prev_str(common_sampler * gsmpl, llama_context * ctx,
 char        common_sampler_type_to_chr(enum common_sampler_type cnstr);
 std::string common_sampler_type_to_str(enum common_sampler_type cnstr);
 
-bool        common_sampler_trigger_grammar(const struct llama_model * model, common_sampler * gsmpl, const std::string & trigger);
+bool        common_sampler_trigger_grammar(const struct llama_vocab * vocab, common_sampler * gsmpl, const std::string & trigger);
 
 std::vector<enum common_sampler_type> common_sampler_types_from_names(const std::vector<std::string> & names, bool allow_alt_names);
 std::vector<enum common_sampler_type> common_sampler_types_from_chars(const std::string & chars);

examples/server/server.cpp

@@ -3729,7 +3729,7 @@ int main(int argc, char ** argv) {
     const auto handle_props = [&ctx_server, &res_ok, &get_chat_templates](const httplib::Request &, httplib::Response & res) {
         // this endpoint is publicly available, please only return what is safe to be exposed
         const auto & templates = get_chat_templates();
-        const auto vocab = llama_vocab_from_model(ctx_server.model);
+        const auto vocab = llama_model_get_vocab(ctx_server.model);
         json data = {
             { "default_generation_settings", ctx_server.default_generation_settings_for_props },
             { "total_slots",                 ctx_server.params_base.n_parallel },
@@ -3765,7 +3765,6 @@ int main(int argc, char ** argv) {
             json & data,
             httplib::Response & res,
             oaicompat_type oaicompat,
-            bool oaicompat_chat = false,
             llama_tool_call_style tool_call_style = llama_tool_call_style::None) {
         GGML_ASSERT(type == SERVER_TASK_TYPE_COMPLETION || type == SERVER_TASK_TYPE_INFILL);
 
@@ -3976,7 +3975,8 @@ int main(int argc, char ** argv) {
             SERVER_TASK_TYPE_COMPLETION,
             data,
             res,
-            OAICOMPAT_TYPE_CHAT);
+            OAICOMPAT_TYPE_CHAT,
+            tool_call_style);
     };
 
     const auto handle_models = [&params, &ctx_server, &res_ok](const httplib::Request &, httplib::Response & res) {

examples/server/tests/unit/test_chat_completion.py

@@ -241,7 +241,7 @@ def test_chat_completion_with_timings_per_token():
 ])
 def test_completion_with_required_tool(template_name: str, n_predict: int, tool: dict, expected_arguments: dict):
     global server
-    server.use_jinja = True
+    server.jinja = True
     server.n_predict = n_predict
     server.chat_template_file = f'../../../tests/chat/templates/{template_name}.jinja'
     server.start()
@@ -278,7 +278,7 @@ def test_completion_with_required_tool(template_name: str, n_predict: int, tool:
 ])
 def test_completion_without_tool_call(template_name: str, n_predict: int, tools: list[dict], tool_choice: str | None):
     global server
-    server.use_jinja = True
+    server.jinja = True
     server.n_predict = n_predict
     server.chat_template_file = f'../../../tests/chat/templates/{template_name}.jinja'
     server.start()
@@ -322,7 +322,7 @@ def test_completion_without_tool_call(template_name: str, n_predict: int, tools:
 ])
 def test_hello_world_tool_call(tool: dict, expected_arguments: dict, hf_repo: str, hf_file: str, template_override: Tuple[str, str | None] | None):
     global server
-    server.use_jinja = True
+    server.jinja = True
     server.n_ctx = 8192
     server.n_predict = 128
     server.model_hf_repo = hf_repo

examples/server/tests/utils.py

@@ -157,10 +157,6 @@ def start(self, timeout_seconds: int = 10) -> None:
         if self.lora_files:
             for lora_file in self.lora_files:
                 server_args.extend(["--lora", lora_file])
-        if self.chat_template_file:
-            server_args.extend(["--chat-template-file", self.chat_template_file])
-        if self.use_jinja:
-            server_args.append("--jinja")
         if self.disable_ctx_shift:
             server_args.extend(["--no-context-shift"])
         if self.api_key:

examples/server/utils.hpp

@@ -595,7 +595,7 @@ static json oaicompat_completion_params_parse(
     if (has_tools) {
         if (stream) {
             throw std::runtime_error("Cannot use tools with stream");
-        }    
+        }
         if (use_jinja) {
             if (tool_call_style == llama_tool_call_style::UnknownToolCallStyle) {
                 throw std::runtime_error("Chat template does not seem to support tools. Override the model template with --chat-template.");

include/llama.h

@@ -1193,8 +1193,6 @@ extern "C" {
                           const char * grammar_str,
                           const char * grammar_root);
 
-    LLAMA_API bool llama_sampler_is_grammar_empty(struct llama_sampler * gsmpl);
-
     /// NOTE: Avoid using on the full vocabulary as searching for repeated tokens can become slow. For example, apply top-k or top-p sampling first.
     LLAMA_API struct llama_sampler * llama_sampler_init_penalties(
                              int32_t   penalty_last_n,   // last n tokens to penalize (0 = disable penalty, -1 = context size)
@@ -1256,6 +1254,8 @@ extern "C" {
     // Returns the sampled token
     LLAMA_API llama_token llama_sampler_sample(struct llama_sampler * smpl, struct llama_context * ctx, int32_t idx);
 
+    LLAMA_API bool llama_sampler_is_grammar_empty(struct llama_sampler * smpl);
+
     // TODO: extend in the future
     //LLAMA_API void llama_decode_with_sampler(struct llama_context * ctx, struct llama_sampler * smpl, struct llama_batch batch, ...);
 

src/llama-sampling.cpp

@@ -1511,11 +1511,6 @@ static struct llama_sampler_i llama_sampler_grammar_i = {
     /* .free   = */ llama_sampler_grammar_free,
 };
 
-bool llama_sampler_is_grammar_empty(struct llama_sampler * gsmpl) {
-    struct llama_sampler_grammar * ctx = (struct llama_sampler_grammar *) gsmpl->ctx;
-    return ctx->grammar == nullptr;
-}
-
 struct llama_sampler * llama_sampler_init_grammar(const struct llama_vocab * vocab, const char * grammar_str, const char * grammar_root) {
     auto * ctx = new llama_sampler_grammar;
 

src/llama.cpp

@@ -1130,8 +1130,7 @@ struct llm_build_context {
         rope_type        (hparams.rope_type),
         cb               (cb),
         buf_compute_meta (lctx.buf_compute_meta) {
-            // all 
-            ializations should be done in init()
+            // all initializations should be done in init()
         }
 
     void init() {