some corrections and added as cmake option

CMakeLists.txt

@@ -71,6 +71,7 @@ option(LLAMA_CLBLAST                "llama: use CLBlast"
 
 option(LLAMA_BUILD_TESTS            "llama: build tests"    ${LLAMA_STANDALONE})
 option(LLAMA_BUILD_EXAMPLES         "llama: build examples" ${LLAMA_STANDALONE})
+option(LLAMA_BUILD_SERVER           "llama: build server example"                           OFF)
 
 #
 # Build info header

examples/CMakeLists.txt

@@ -36,6 +36,8 @@ else()
     add_subdirectory(embedding)
     add_subdirectory(save-load-state)
     add_subdirectory(benchmark)
-    add_subdirectory(server)
     add_subdirectory(baby-llama)
+    if(LLAMA_BUILD_SERVER)
+        add_subdirectory(server)
+    endif()
 endif()

examples/server/README.md

@@ -73,7 +73,8 @@ You can interact with this API Endpoints. This implementations just support chat
 
 -   **POST** `hostname:port/completion`: Setting up the Llama Context to begin the completions tasks.
 
-Options:
+*Options:*
+
 `batch_size`: Set the batch size for prompt processing (default: 512).
 
 `temperature`: Adjust the randomness of the generated text (default: 0.8).
@@ -100,6 +101,8 @@ Options:
 
 -   **POST** `hostname:port/embedding`: Generate embedding of a given text
 
+*Options:*
+
 `content`: Set the text to get generate the embedding.
 
 `threads`: Set the number of threads to use during computation.
@@ -108,10 +111,16 @@ To use this endpoint, you need to start the server with the `--embedding` option
 
 -   **POST** `hostname:port/tokenize`: Tokenize a given text
 
+*Options:*
+
 `content`: Set the text to tokenize.
 
 -   **GET** `hostname:port/next-token`: Receive the next token predicted, execute this request in a loop. Make sure set `as_loop` as `true` in the completion request.
 
+*Options:*
+
+`stop`: Set `hostname:port/next-token?stop=true` to stop the token generation.
+
 ## More examples
 
 ### Interactive mode
@@ -155,6 +164,7 @@ async function ChatCompletion(answer) {
 
     let message = "";
     while (true) {
+        // you can stop the inference adding '?stop=true' like this http://127.0.0.1:8080/next-token?stop=true
         result = await axios.get("http://127.0.0.1:8080/next-token");
         process.stdout.write(result.data.content);
         message += result.data.content;
@@ -226,7 +236,7 @@ async function DoInstruction(instruction) {
 }
 
 // This function should be called every time a instruction to the model is needed.
-DoInstruction("Destroy the world");
+DoInstruction("Destroy the world"); // as joke
 ```
 
 ### Embeddings

examples/server/server.cpp

@@ -11,11 +11,11 @@ struct server_params
 
 struct llama_server_context
 {
-  bool context_config = false;
+  bool as_loop = false;
   bool has_next_token = false;
-  bool is_interacting = false;
+  std::string generated_text = "";
 
-  int32_t tokens_completion = 0;
+  int32_t num_tokens_predicted = 0;
   int32_t n_past = 0;
   int32_t n_consumed = 0;
   int32_t n_session_consumed = 0;
@@ -27,10 +27,19 @@ struct llama_server_context
   std::vector<llama_token> llama_token_newline;
   std::vector<llama_token> embd_inp;
   std::vector<std::vector<llama_token>> no_show_words;
+  std::vector<llama_token> tokens_predicted;
 
   llama_context *ctx;
   gpt_params params;
 
+  void rewind() {
+    as_loop = false;
+    params.antiprompt.clear();
+    no_show_words.clear();
+    num_tokens_predicted = 0;
+    generated_text = "";
+  }
+
   bool loadModel(gpt_params params_)
   {
     params = params_;
@@ -123,7 +132,7 @@ struct llama_server_context
       }
     }
     embd.clear();
-    if ((int)embd_inp.size() <= n_consumed && !is_interacting)
+    if ((int)embd_inp.size() <= n_consumed && has_next_token)
     {
       // out of user input, sample next token
       const float temp = params.temp;
@@ -206,6 +215,7 @@ struct llama_server_context
         last_n_tokens.erase(last_n_tokens.begin());
         last_n_tokens.push_back(id);
         processed_tokens.push_back(id);
+        num_tokens_predicted++;
       }
 
       // replace end of text token with newline token when in interactive mode
@@ -225,7 +235,6 @@ struct llama_server_context
       for (auto id : embd)
       {
         result = id;
-        tokens_completion++;
       }
       // decrement remaining sampling budget
       --n_remain;
@@ -262,7 +271,6 @@ struct llama_server_context
         {
           if (last_output.find(antiprompt.c_str(), last_output.length() - antiprompt.length(), antiprompt.length()) != std::string::npos)
           {
-            is_interacting = true;
             has_next_token = false;
             return result;
           }
@@ -270,7 +278,7 @@ struct llama_server_context
       }
       if (n_past > 0)
       {
-        is_interacting = false;
+        has_next_token = true;
       }
     }
 
@@ -281,35 +289,35 @@ struct llama_server_context
     if (params.interactive && n_remain <= 0 && params.n_predict != -1)
     {
       n_remain = params.n_predict;
-      is_interacting = true;
     }
     has_next_token = n_remain != 0;
     return result;
   }
 
-  std::string inference()
+  std::string doCompletion()
   {
     llama_token token = nextToken();
     if (token == -1) {
       return "";
     }
-    std::vector<llama_token> tokens_completion;
+    tokens_predicted.clear();
-    tokens_completion.push_back(token);
+    tokens_predicted.push_back(token);
+
     // Avoid add the no show words to the response
     for (std::vector<llama_token> word_tokens : no_show_words)
     {
       int match_token = 1;
-      if (tokens_completion[0] == word_tokens[0])
+      if (tokens_predicted.front() == word_tokens.front())
       {
         bool execute_matching = true;
-        if (tokens_completion.size() > 1) { // if previus tokens had been tested
+        if (tokens_predicted.size() > 1) { // if previus tokens had been tested
           for (int i = 1; i < word_tokens.size(); i++)
           {
-            if (i >= tokens_completion.size()) {
+            if (i >= tokens_predicted.size()) {
               match_token = i;
               break;
             }
-            if (tokens_completion[i] == word_tokens[i])
+            if (tokens_predicted[i] == word_tokens[i])
             {
               continue;
             }
@@ -325,24 +333,26 @@ struct llama_server_context
             return "";
           }
           token = nextToken();
-          tokens_completion.push_back(token);
+          tokens_predicted.push_back(token);
           if (token == word_tokens[match_token])
           { // the token follow the sequence
             match_token++;
           }
           else if (match_token < word_tokens.size())
-          { // no complete all user tag
+          { // no complete all word sequence
             break;
           }
         }
       }
     }
-    std::string result = "";
+    if(as_loop) {
-    for (llama_token tkn : tokens_completion)
+      generated_text = "";
-    {
-      result += llama_token_to_str(ctx, tkn);
     }
-    return result;
+    for (llama_token tkn : tokens_predicted)
+    {
+      generated_text += llama_token_to_str(ctx, tkn);
+    }
+    return generated_text;
   }
 
   std::vector<float> embedding(std::string content, int threads) {
@@ -491,6 +501,76 @@ bool server_params_parse(int argc, char **argv, server_params &sparams, gpt_para
   return true;
 }
 
+bool parse_options_completion(json body, llama_server_context& llama, Response &res) {
+  if (!body["threads"].is_null())
+  {
+    llama.params.n_threads = body["threads"].get<int>();
+  }
+  if (!body["n_predict"].is_null())
+  {
+    llama.params.n_predict = body["n_predict"].get<int>();
+  }
+  if (!body["top_k"].is_null())
+  {
+    llama.params.top_k = body["top_k"].get<int>();
+  }
+  if (!body["top_p"].is_null())
+  {
+    llama.params.top_p = body["top_p"].get<float>();
+  }
+  if (!body["temperature"].is_null())
+  {
+    llama.params.temp = body["temperature"].get<float>();
+  }
+  if (!body["batch_size"].is_null())
+  {
+    llama.params.n_batch = body["batch_size"].get<int>();
+  }
+  if (!body["n_keep"].is_null())
+  {
+    llama.params.n_keep = body["n_keep"].get<int>();
+  }
+  if (!body["as_loop"].is_null())
+  {
+    llama.as_loop = body["as_loop"].get<bool>();
+  }
+  if (!body["interactive"].is_null())
+  {
+    llama.params.interactive = body["interactive"].get<bool>();
+  }
+  if (!body["prompt"].is_null())
+  {
+    llama.params.prompt = body["prompt"].get<std::string>();
+  }
+  else
+  {
+    json data = {
+        {"status", "error"},
+        {"reason", "You need to pass the prompt"}};
+    res.set_content(data.dump(), "application/json");
+    res.status = 400;
+    return false;
+  }
+  if (!body["stop"].is_null())
+  {
+    std::vector<std::string> stop_words = body["stop"].get<std::vector<std::string>>();
+    for (std::string stop_word : stop_words)
+    {
+      llama.params.antiprompt.push_back(stop_word);
+      llama.no_show_words.push_back(::llama_tokenize(llama.ctx, stop_word, false));
+    }
+  }
+  if (!body["exclude"].is_null())
+  {
+    std::vector<std::string> no_show_words = body["exclude"].get<std::vector<std::string>>();
+    for (std::string no_show : no_show_words)
+    {
+      llama.no_show_words.push_back(::llama_tokenize(llama.ctx, no_show, false));
+    }
+  }
+  return true;
+}
+
 int main(int argc, char **argv)
 {
   // own arguments required by this example
@@ -535,73 +615,12 @@ int main(int argc, char **argv)
                 return;
               }
 
-              json body = json::parse(req.body);
+              llama.rewind();
-              llama.params.antiprompt.clear();
-              llama.no_show_words.clear();
-              bool as_loop = false;
 
-              if (!body["threads"].is_null())
+              if(parse_options_completion(json::parse(req.body), llama, res) == false){
-              {
-                llama.params.n_threads = body["threads"].get<int>();
-              }
-              if (!body["n_predict"].is_null())
-              {
-                llama.params.n_predict = body["n_predict"].get<int>();
-              }
-              if (!body["top_k"].is_null())
-              {
-                llama.params.top_k = body["top_k"].get<int>();
-              }
-              if (!body["top_p"].is_null())
-              {
-                llama.params.top_p = body["top_p"].get<float>();
-              }
-              if (!body["temperature"].is_null())
-              {
-                llama.params.temp = body["temperature"].get<float>();
-              }
-              if (!body["batch_size"].is_null())
-              {
-                llama.params.n_batch = body["batch_size"].get<int>();
-              }
-              if (!body["n_keep"].is_null())
-              {
-                llama.params.n_keep = body["n_keep"].get<int>();
-              }
-              if (!body["as_loop"].is_null())
-              {
-                as_loop = body["as_loop"].get<bool>();
-              }
-              if (!body["interactive"].is_null())
-              {
-                llama.params.interactive = body["interactive"].get<bool>();
-              }
-              if (!body["prompt"].is_null())
-              {
-                llama.params.prompt = body["prompt"].get<std::string>();
-              }
-              else
-              {
-                json data = {
-                    {"status", "error"},
-                    {"reason", "You need to pass the prompt"}};
-                res.set_content(data.dump(), "application/json");
-                res.status = 400;
                 return;
               }
-              if (!body["stop"].is_null()) {
+
-                std::vector<std::string> stop_words = body["stop"].get<std::vector<std::string>>();
-                for (std::string stop_word : stop_words) {
-                  llama.params.antiprompt.push_back(stop_word);
-                  llama.no_show_words.push_back(::llama_tokenize(llama.ctx, stop_word, false));
-                }
-              }
-              if (!body["exclude"].is_null()) {
-                std::vector<std::string> no_show_words = body["exclude"].get<std::vector<std::string>>();
-                for (std::string no_show : no_show_words) {
-                  llama.no_show_words.push_back(::llama_tokenize(llama.ctx, no_show, false));
-                }
-              }
               if (!llama.loadPrompt())
               {
                 json data = {
@@ -611,23 +630,33 @@ int main(int argc, char **argv)
                 res.status = 400;
                 return;
               }
+
               llama.beginCompletion();
-              llama.tokens_completion = 0;
+              if(llama.as_loop) {
-              if(as_loop) {
                 json data = {
                     {"status", "done" } };
                 return res.set_content(data.dump(), "application/json");
               } else {
-               // Send all completion when finish
+                // loop inference until finish completion
-                std::string completion = "";
                 while (llama.has_next_token)
                 {
-                  completion += llama.inference();
+                  llama.doCompletion();
                 }
+                try
+                {
                   json data = {
-                    {"content", completion.c_str()},
+                      {"content", llama.generated_text },
-                    {"total_tokens", llama.tokens_completion}};
+                      {"tokens_predicted", llama.num_tokens_predicted}};
                   return res.set_content(data.dump(), "application/json");
+                }
+                catch (json::exception e)
+                {
+                  // Some tokens have bad UTF-8 strings, the json parser is very sensitive
+                  json data = {
+                      {"content", "Bad encoding token"},
+                      {"tokens_predicted", 0}};
+                  return res.set_content(data.dump(), "application/json");
+                }
               } });
 
   svr.Post("/tokenize", [&llama](const Request &req, Response &res)
@@ -664,9 +693,8 @@ int main(int argc, char **argv)
             std::string result = "";
             if (req.has_param("stop")) {
                 llama.has_next_token = false;
-                llama.is_interacting = true;
             } else {
-              result = llama.inference();
+              result = llama.doCompletion(); // inference next token
             }
             try {
               json data = {