server : update /props with "total_slots" value (#5373 )

* include total "num_slots" in default_generation_settings_for_props * cleanup total_slots return value in /props endpoint * update /props endpoint docs with total_slots * remove num_slots from default_generation_settings_for_props * update /props endpoint section
convert : fix TypeError on GPT-2 vocab.json (#5288 )
2026-07-14 00:15:54 +02:00 · 2024-02-07 08:15:19 +02:00 · 2024-02-06 23:28:00 -05:00 · 2024-02-06 20:08:38 +02:00
5 changed files with 251 additions and 223 deletions
@@ -334,9 +334,9 @@ class Params:
 class BpeVocab:
    def __init__(self, fname_tokenizer: Path, fname_added_tokens: Path | None) -> None:
        self.bpe_tokenizer = json.loads(open(str(fname_tokenizer), encoding="utf-8").read())
-        try:
+        if isinstance(self.bpe_tokenizer.get('model'), dict):
            self.vocab = self.bpe_tokenizer["model"]["vocab"]
-        except KeyError:
+        else:
            self.vocab = self.bpe_tokenizer
        added_tokens: dict[str, int]
        if fname_added_tokens is not None:
@@ -276,13 +276,15 @@ Notice that each `probs` is an array of length `n_probs`.
 {
  "assistant_name": "",
  "user_name": "",
-  "default_generation_settings": { ... }
+  "default_generation_settings": { ... },
+  "total_slots": 1
 }
 ```

 - `assistant_name` - the required assistant name to generate the prompt in case you have specified a system prompt for all slots.
 - `user_name` - the required anti-prompt to generate the prompt in case you have specified a system prompt for all slots.
 - `default_generation_settings` - the default generation settings for the `/completion` endpoint, has the same fields as the `generation_settings` response object from the `/completion` endpoint.
+- `total_slots` - the total number of slots for process requests (defined by `--parallel` option)

 - **POST** `/v1/chat/completions`: OpenAI-compatible Chat Completions API. Given a ChatML-formatted json description in `messages`, it returns the predicted completion. Both synchronous and streaming mode are supported, so scripted and interactive applications work fine. While no strong claims of compatibility with OpenAI API spec is being made, in our experience it suffices to support many apps. Only ChatML-tuned models, such as Dolphin, OpenOrca, OpenHermes, OpenChat-3.5, etc can be used with this endpoint. Compared to `api_like_OAI.py` this API implementation does not require a wrapper to be served.

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 };
-unsigned int completion_js_len = 5346;
+unsigned int completion_js_len = 5782;
@@ -195,7 +195,8 @@ export const llamaComplete = async (params, controller, callback) => {
 // Get the model info from the server. This is useful for getting the context window and so on.
 export const llamaModelInfo = async () => {
  if (!generation_settings) {
-    generation_settings = await fetch("/model.json").then(r => r.json());
+    const props = await fetch("/props").then(r => r.json());
+    generation_settings = props.default_generation_settings;
  }
  return generation_settings;
 }
@@ -432,7 +432,6 @@ struct llama_server_context
        }

        default_generation_settings_for_props = get_formated_generation(slots.front());
-        default_generation_settings_for_props["num_slots"] = params.n_parallel;
        default_generation_settings_for_props["seed"] = -1;

        batch = llama_batch_init(n_ctx, 0, params.n_parallel);
@@ -990,11 +989,6 @@ struct llama_server_context
        queue_results.send(res);
    }

-    json get_model_props()
-    {
-        return get_formated_generation(slots[0]);
-    }
-
    json get_formated_generation(llama_client_slot &slot)
    {
        const auto eos_bias = slot.sparams.logit_bias.find(llama_token_eos(model));
@@ -2644,7 +2638,8 @@ int main(int argc, char **argv)
                json data = {
                    { "user_name",      llama.name_user.c_str() },
                    { "assistant_name", llama.name_assistant.c_str() },
-                    { "default_generation_settings", llama.default_generation_settings_for_props }
+                    { "default_generation_settings", llama.default_generation_settings_for_props },
+                    { "total_slots",    llama.params.n_parallel }
                };
                res.set_content(data.dump(), "application/json; charset=utf-8");
            });
@@ -2895,12 +2890,6 @@ int main(int argc, char **argv)
                }
            });

-    svr.Get("/model.json", [&llama](const httplib::Request &, httplib::Response &res)
-            {
-                const json data = llama.get_model_props();
-                return res.set_content(data.dump(), "application/json; charset=utf-8");
-            });
-
    svr.Options(R"(/.*)", [](const httplib::Request &, httplib::Response &res)
                { return res.set_content("", "application/json; charset=utf-8"); });
Author	SHA1	Message	Date
Justin Parker	f3e2b4fa3f	server : update `/props` with "total_slots" value (#5373 ) * include total "num_slots" in default_generation_settings_for_props * cleanup total_slots return value in /props endpoint * update /props endpoint docs with total_slots * remove num_slots from default_generation_settings_for_props * update /props endpoint section	2024-02-07 08:15:19 +02:00
Sang-Kil Park	f68664ac24	convert : fix TypeError on GPT-2 vocab.json (#5288 )	2024-02-06 23:28:00 -05:00
Alexey Parfenov	213d1439fa	server : remove model.json endpoint (#5371 )	2024-02-06 20:08:38 +02:00