Add embedding mode with arg flag. Currently working

llama.cpp

@@ -598,6 +598,18 @@ static bool llama_model_load(
     return true;
 }
 
+// Prints the provided embedding vector to stdout
+// in a neat format
+void display_embedding(const std::vector<float> & embedding_representation){
+        fprintf(stdout, "\n[\n");
+        for (int j = 0; j < embedding_representation.size()-1 ; j++){
+            fprintf(stdout, "%f, ", embedding_representation[j]);
+        }
+        fprintf(stdout, "%f", embedding_representation[embedding_representation.size()-1]);
+        fprintf(stdout, "\n]\n");
+}
+
+
 // evaluate the transformer
 //
 //   - lctx:      llama context
@@ -610,7 +622,8 @@ static bool llama_eval_internal(
     const llama_token * tokens,
             const int   n_tokens,
             const int   n_past,
-            const int   n_threads) {
+            const int   n_threads,
+            const bool embedding_mode = false) {
     const int64_t t_start_us = ggml_time_us();
 
     const int N = n_tokens;
@@ -798,6 +811,18 @@ static bool llama_eval_internal(
                     inpL);
     }
 
+    if(embedding_mode){
+            // capture input sentence embedding
+            ggml_build_forward_expand(&gf, inpL);
+            ggml_graph_compute       (ctx0, &gf);
+            std::vector<float> embedding_representation;    
+            embedding_representation.resize(n_embd);
+            memcpy(embedding_representation.data(), (float *) ggml_get_data(inpL) + (n_embd * (N - 1)), sizeof(float) * n_embd);
+            display_embedding(embedding_representation);
+            ggml_free(ctx0);
+            return true;
+    }
+
     // lm_head
     {
         inpL = ggml_mul_mat(ctx0, model.output, inpL);
@@ -1440,8 +1465,9 @@ int llama_eval(
            const llama_token * tokens,
                          int   n_tokens,
                          int   n_past,
-                         int   n_threads) {
-    if (!llama_eval_internal(*ctx, tokens, n_tokens, n_past, n_threads)) {
+                         int   n_threads,
+                         bool embedding_mode = false) {
+    if (!llama_eval_internal(*ctx, tokens, n_tokens, n_past, n_threads, embedding_mode)) {
         fprintf(stderr, "%s: failed to eval\n", __func__);
         return 1;
     }

llama.h

@@ -84,7 +84,8 @@ extern "C" {
                const llama_token * tokens,
                              int   n_tokens,
                              int   n_past,
-                             int   n_threads);
+                             int   n_threads,
+                             bool embedding_mode);
 
     // Convert the provided text into tokens.
     // The tokens pointer must be large enough to hold the resulting tokens.

main.cpp

@@ -98,7 +98,7 @@ void perplexity(llama_context * ctx, const gpt_params & params) {
         int end = start + params.n_ctx - 1;
         std::vector<llama_token> embd(tokens.begin() + start, tokens.begin() + end);
         auto start_t = std::chrono::high_resolution_clock::now();
-        if (llama_eval(ctx, embd.data(), embd.size(), 0, params.n_threads)) {
+        if (llama_eval(ctx, embd.data(), embd.size(), 0, params.n_threads, false)) {
             fprintf(stderr, "%s : failed to eval\n", __func__);
             return;
         }
@@ -219,7 +219,7 @@ int main(int argc, char ** argv) {
     // TODO: better way to do that
     {
         const std::vector<llama_token> tmp = { 0, 1, 2, 3 };
-        llama_eval(ctx, tmp.data(), tmp.size(), 0, params.n_threads);
+        llama_eval(ctx, tmp.data(), tmp.size(), 0, params.n_threads, false);
     }
 
     if (params.perplexity) {
@@ -285,6 +285,7 @@ int main(int argc, char ** argv) {
 
     std::vector<llama_token> embd;
 
+
     int last_n_size = params.repeat_last_n;
     std::vector<llama_token> last_n_tokens(last_n_size);
     std::fill(last_n_tokens.begin(), last_n_tokens.end(), 0);
@@ -317,10 +318,25 @@ int main(int argc, char ** argv) {
     // the first thing we will do is to output the prompt, so set color accordingly
     set_console_state(CONSOLE_STATE_PROMPT);
 
+    if (params.embedding){
+        embd = embd_inp;
+        if (embd.size() > 0) {
+            if (llama_eval(ctx, embd.data(), embd.size(), n_past, params.n_threads, params.embedding)) {
+                fprintf(stderr, "%s : failed to eval\n", __func__);
+                return 1;
+            }
+        }
+
+        if (params.use_color) {
+            printf(ANSI_COLOR_RESET);
+        }
+        return 0;
+    }
+
     while (remaining_tokens > 0 || params.interactive) {
         // predict
         if (embd.size() > 0) {
-            if (llama_eval(ctx, embd.data(), embd.size(), n_past, params.n_threads)) {
+            if (llama_eval(ctx, embd.data(), embd.size(), n_past, params.n_threads, params.embedding)) {
                 fprintf(stderr, "%s : failed to eval\n", __func__);
                 return 1;
             }

utils.cpp

@@ -63,6 +63,11 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
             params.model = argv[++i];
         } else if (arg == "-i" || arg == "--interactive") {
             params.interactive = true;
+        } else if (arg == "--embedding") {
+            params.embedding = true;
+        } else if (arg == "--interactive-start") {
+            params.interactive = true;
+            params.interactive_start = true;
         } else if (arg == "-ins" || arg == "--instruct") {
             params.instruct = true;
         } else if (arg == "--color") {

utils.h

@@ -32,12 +32,14 @@ struct gpt_params {
     std::string model  = "models/lamma-7B/ggml-model.bin"; // model path
     std::string prompt = "";
 
+
     std::vector<std::string> antiprompt; // string upon seeing which more user input is prompted
 
     bool memory_f16        = false; // use f16 instead of f32 for memory kv
     bool random_prompt     = false; // do not randomize prompt if none provided
     bool use_color         = false; // use color to distinguish generations and inputs
     bool interactive       = false; // interactive mode
+    bool embedding         = false; // get only sentence embedding
     bool interactive_start = false; // reverse prompt immediately
     bool instruct          = false; // instruction mode (used for Alpaca models)
     bool ignore_eos        = false; // do not stop generating after eos