Merge branch 'LostRuins:concedo' into koboldcpp-rocm

examples/metal/metal.cpp

@@ -40,8 +40,10 @@ int main(int argc, char ** argv) {
  // this allocates all Metal resources and memory buffers
  auto * ctx_metal = ggml_metal_init();
 
- ggml_metal_add_buffer(ctx_metal, "data", ggml_get_mem_buffer(ctx_data), ggml_get_mem_size(ctx_data));
- ggml_metal_add_buffer(ctx_metal, "eval", ggml_get_mem_buffer(ctx_eval), ggml_get_mem_size(ctx_eval));
+ const size_t max_size_data = ggml_get_max_tensor_size(ctx_data);
+ const size_t max_size_eval = ggml_get_max_tensor_size(ctx_eval);
+ ggml_metal_add_buffer(ctx_metal, "data", ggml_get_mem_buffer(ctx_data), ggml_get_mem_size(ctx_data), max_size_data);
+ ggml_metal_add_buffer(ctx_metal, "eval", ggml_get_mem_buffer(ctx_eval), ggml_get_mem_size(ctx_eval), max_size_eval);
 
  // main
  {

examples/server/README.md

@@ -21,6 +21,7 @@ Command line options:
 - `-to N`, `--timeout N`: Server read/write timeout in seconds. Default `600`.
 - `--host`: Set the hostname or ip address to listen. Default `127.0.0.1`.
 - `--port`: Set the port to listen. Default: `8080`.
+- `--embedding`: Enable embedding extraction, Default: disabled.
 
 ## Build
 
@@ -119,14 +120,14 @@ node .
 
  `top_p`: Limit the next token selection to a subset of tokens with a cumulative probability above a threshold P (default: 0.9).
 
- `n_predict`: Set the number of tokens to predict when generating text. **Note:** May exceed the set limit slightly if the last token is a partial multibyte character. (default: 128, -1 = infinity).
+ `n_predict`: Set the number of tokens to predict when generating text. **Note:** May exceed the set limit slightly if the last token is a partial multibyte character. When 0, no tokens will be generated but the prompt is evaluated into the cache. (default: 128, -1 = infinity).
 
  `n_keep`: Specify the number of tokens from the initial prompt to retain when the model resets its internal context.
  By default, this value is set to 0 (meaning no tokens are kept). Use `-1` to retain all tokens from the initial prompt.
 
  `stream`: It allows receiving each predicted token in real-time instead of waiting for the completion to finish. To enable this, set to `true`.
 
- `prompt`: Provide a prompt. Internally, the prompt is compared, and it detects if a part has already been evaluated, and the remaining part will be evaluate.
+ `prompt`: Provide a prompt. Internally, the prompt is compared, and it detects if a part has already been evaluated, and the remaining part will be evaluate. A space is inserted in the front like main.cpp does.
 
  `stop`: Specify a JSON array of stopping strings.
  These words will not be included in the completion, so make sure to add them to the prompt for the next iteration (default: []).
@@ -163,6 +164,14 @@ node .
 
  `content`: Set the text to tokenize.
 
+ Note that the special `BOS` token is not added in fron of the text and also a space character is not inserted automatically as it is for `/completion`.
+
+- **POST** `/embedding`: Generate embedding of a given text just as [the embedding example](../embedding) does.
+
+ *Options:*
+
+ `content`: Set the text to process.
+
 ## More examples
 
 ### Interactive mode

examples/server/server.cpp

@@ -254,6 +254,11 @@ struct llama_server_context {
  n_past += n_eval;
  }
 
+ if (params.n_predict == 0) {
+ has_next_token = false;
+ return llama_token_eos();
+ }
+
  // out of user input, sample next token
  const float temp = params.temp;
  const int32_t top_k = params.top_k <= 0 ? llama_n_vocab(ctx) : params.top_k;
@@ -419,6 +424,19 @@ struct llama_server_context {
 
  return token_text;
  }
+
+ std::vector<float> getEmbedding() {
+ static const int n_embd = llama_n_embd(ctx);
+ if (!params.embedding) {
+ LOG_WARNING("embedding disabled", {
+ { "params.embedding", params.embedding },
+ });
+ return std::vector<float>(n_embd, 0.0f);
+ }
+ const float * data = llama_get_embeddings(ctx);
+ std::vector<float> embedding(data, data + n_embd);
+ return embedding;
+ }
 };
 
 static void server_print_usage(const char * argv0, const gpt_params & params,
@@ -457,6 +475,7 @@ static void server_print_usage(const char * argv0, const gpt_params & params,
  fprintf(stderr, " --host ip address to listen (default (default: %s)\n", sparams.hostname.c_str());
  fprintf(stderr, " --port PORT port to listen (default (default: %d)\n", sparams.port);
  fprintf(stderr, " -to N, --timeout N server read/write timeout in seconds (default: %d)\n", sparams.read_timeout);
+ fprintf(stderr, " --embedding enable embedding vector output (default: %s)\n", params.embedding ? "enabled" : "disabled");
  fprintf(stderr, "\n");
 }
 
@@ -603,6 +622,8 @@ static void server_params_parse(int argc, char ** argv, server_params & sparams,
  params.use_mlock = true;
  } else if (arg == "--no-mmap") {
  params.use_mmap = false;
+ } else if (arg == "--embedding") {
+ params.embedding = true;
  } else {
  fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
  server_print_usage(argv[0], default_params, default_sparams);
@@ -646,6 +667,12 @@ static json format_generation_settings(llama_server_context & llama) {
  };
 }
 
+static json format_embedding_response(llama_server_context & llama) {
+ return json {
+ { "embedding", llama.getEmbedding() },
+ };
+}
+
 static json format_final_response(llama_server_context & llama, const std::string & content) {
  return json {
  { "content", content },
@@ -881,12 +908,27 @@ int main(int argc, char ** argv) {
 
  svr.Post("/tokenize", [&llama](const Request & req, Response & res) {
  const json body = json::parse(req.body);
- const std::string content = body["content"].get<std::string>();
+ const std::string content = body.value("content", "");
  const std::vector<llama_token> tokens = llama_tokenize(llama.ctx, content, false);
  const json data = format_tokenizer_response(tokens);
  return res.set_content(data.dump(), "application/json");
  });
 
+ svr.Post("/embedding", [&llama](const Request & req, Response & res) {
+ const json body = json::parse(req.body);
+
+ llama.rewind();
+ llama_reset_timings(llama.ctx);
+ llama.params.prompt = body.value("content", "");
+ llama.params.n_predict = 0;
+ llama.loadPrompt();
+ llama.beginCompletion();
+ llama.doCompletion();
+
+ const json data = format_embedding_response(llama);
+ return res.set_content(data.dump(), "application/json");
+ });
+
  svr.set_logger(log_server_request);
 
  svr.set_exception_handler([](const Request &, Response & res, std::exception_ptr ep) {