Merge pull request #8 from SlyEcho/server_refactor

Change how the token buffers work.

examples/server/server.cpp

@@ -14,6 +14,12 @@ struct server_params
   bool verbose = false;
 };
 
+static size_t common_part(const std::vector<llama_token> & a, const std::vector<llama_token> & b) {
+  size_t i;
+  for (i = 0; i < a.size() && i < b.size() && a[i] == b[i]; i++);
+  return i;
+}
+
 struct llama_server_context
 {
   bool stream = false;
@@ -28,10 +34,7 @@ struct llama_server_context
 
   std::vector<llama_token> embd;
   std::vector<llama_token> last_n_tokens;
-  std::vector<llama_token> processed_tokens;
-  std::vector<llama_token> embd_inp;
 
-  std::vector<llama_token> last_prompt_tokens;
   llama_context *ctx = nullptr;
   gpt_params params;
 
@@ -55,11 +58,10 @@ struct llama_server_context
     generated_text.reserve(params.n_ctx);
     stopping_word = "";
 
-    //processed_tokens.clear();
-    embd_inp.clear();
     n_remain = 0;
     n_past = 0;
     n_consumed = 0;
+    last_n_tokens.clear();
   }
 
   bool loadModel(const gpt_params &params_)
@@ -80,58 +82,61 @@ struct llama_server_context
   bool loadPrompt() {
     params.prompt.insert(0, 1, ' '); // always add a first space
     std::vector<llama_token> prompt_tokens = ::llama_tokenize(ctx, params.prompt, true);
-    if (prompt_tokens == last_prompt_tokens)
+
-    {
+    if (params.n_keep < 0) {
-        embd.clear();
+      params.n_keep = (int)prompt_tokens.size();
     }
+    params.n_keep = std::min(params.n_ctx - 4, params.n_keep);
+
+    // if input prompt is too big, truncate like normal
+    if (prompt_tokens.size() >= (size_t)params.n_ctx) {
+      const int n_left = (params.n_ctx - params.n_keep)/2;
+      std::vector<llama_token> new_tokens(prompt_tokens.begin(), prompt_tokens.begin() + params.n_keep);
+      new_tokens.insert(new_tokens.end(), prompt_tokens.end() - n_left, prompt_tokens.end());
+      prompt_tokens = new_tokens;
+    }
+
     // compare the evaluated prompt with the new prompt
-    for (n_past = 0; n_past < prompt_tokens.size() - 1 && n_past < processed_tokens.size(); n_past++) {
+    n_past = common_part(embd, prompt_tokens);
-      if (prompt_tokens[n_past] != processed_tokens[n_past]) {
+    embd = prompt_tokens;
-        break;
+    if (n_past == prompt_tokens.size()) {
+      // we have to evaluate at least 1 token to generate logits.
+      n_past--;
     }
-    }
-    processed_tokens.resize(n_past);
-    if (prompt_tokens.size() > n_past) {
-      embd_inp.insert(embd_inp.end(), prompt_tokens.begin() + n_past, prompt_tokens.end());
-    }
-    last_prompt_tokens = prompt_tokens;
     has_next_token = true;
     return true;
   }
 
   void beginCompletion()
   {
-    if(n_remain == 0) {
     // number of tokens to keep when resetting context
-      if (params.n_keep < 0 || params.n_keep > (int)embd_inp.size())
+
-      {
+
-        params.n_keep = (int)embd_inp.size();
-      }
-    }
     n_remain = params.n_predict;
     llama_set_rng_seed(ctx, params.seed);
   }
 
   llama_token nextToken() {
     llama_token result = -1;
-    if (embd.size() > 0)
+
-    {
+    if (embd.size() >= (size_t)params.n_ctx) {
-      if (n_past + embd.size() > (size_t)params.n_ctx)
-      {
       // Reset context
-        const int n_left = n_past - params.n_keep;
+      const int n_left = (params.n_ctx - params.n_keep)/2;
-        n_past = std::max(1, params.n_keep);
+
-        //processed_tokens.erase(processed_tokens.begin() + n_past, processed_tokens.end());
+      std::vector<llama_token> new_tokens(embd.begin(), embd.begin() + params.n_keep);
-        embd.insert(embd.begin(), last_n_tokens.begin() + params.n_ctx - n_left / 2 - embd.size(), last_n_tokens.end() - embd.size());
+      new_tokens.insert(new_tokens.end(), embd.end() - n_left, embd.end());
+      embd = new_tokens;
+      n_past = params.n_keep;
     }
-      for (int i = 0; i < (int)embd.size(); i += params.n_batch)
+
+    while (n_past < embd.size())
     {
-        int n_eval = (int)embd.size() - i;
+      int n_eval = (int)embd.size() - n_past;
       if (n_eval > params.n_batch)
       {
         n_eval = params.n_batch;
       }
-        if (llama_eval(ctx, &embd[i], n_eval, n_past, params.n_threads))
+      if (llama_eval(ctx, &embd[n_past], n_eval, n_past, params.n_threads))
       {
         fprintf(stderr, "%s : failed to eval\n", __func__);
         has_next_token = false;
@@ -139,10 +144,7 @@ struct llama_server_context
       }
       n_past += n_eval;
     }
-    }
+
-    embd.clear();
-    if (embd_inp.size() <= n_consumed)
-    {
     // 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;
@@ -224,7 +226,6 @@ 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++;
     }
 
@@ -233,23 +234,6 @@ struct llama_server_context
     result = id;
     // decrement remaining sampling budget
     --n_remain;
-    }
-    else
-    {
-      // some user input remains from prompt or interaction, forward it to processing
-      while (embd_inp.size() > n_consumed)
-      {
-        embd.push_back(embd_inp[n_consumed]);
-        last_n_tokens.erase(last_n_tokens.begin());
-        last_n_tokens.push_back(embd_inp[n_consumed]);
-        processed_tokens.push_back(embd_inp[n_consumed]);
-        ++n_consumed;
-        if ((int)embd.size() >= params.n_batch)
-        {
-          break;
-        }
-      }
-    }
 
     if (!embd.empty() && embd.back() == llama_token_eos()) {
         stopping_word = llama_token_to_str(ctx, embd.back());