llama : clean-up

ggml-ci

Author: ggerganov

examples/llama.vim

@@ -1,31 +1,55 @@
+" LLM-based code completion using llama.cpp
+"
+" requires:
+"   - neovim
+"   - llama.cpp server instance
+"
 " sample config:
 "
-"   - Ctrl+F - trigger FIM completion manually
+"   - Tab       - accept the current suggestion
+"   - Shift+Tab - accept just the first line
+"   - Ctrl+F    - trigger FIM completion manually
+"
+" make symlink or copy this file to ~/.config/nvim/autoload/llama.vim
+"
+" start the llama.cpp server with a FIM-compatible model. for example:
+"
+"   llama-server -m {model.gguf} --port 8012 -ngl 99 -fa -ub 1024 -b 2048
+"
+" adjust the batch size to control how much of the provided context will be used during the inference
+" lower values will use smaller part of the context, which will result in faster processing
 "
-" run this once to initialise the plugin:
+" run this once to initialise llama.vim:
 "
-" :call llama#init()
+"   :call llama#init()
 "
 
 " color of the suggested text
 highlight llama_hl_hint guifg=#ff772f
 highlight llama_hl_info guifg=#77ff2f
 
+" endpoint:         llama.cpp server endpoint
+" n_prefix:         number of lines to include in the prefix
+" n_suffix:         number of lines to include in the suffix
+" n_predict:        max number of tokens to predict
+" t_max_prompt_ms:  max alloted time for the text generation
+" show_info:        show extra info about the inference
+" auto_fim:         trigger FIM completion automatically on cursor movement
 let s:default_config = {
     \ 'endpoint':         'http://127.0.0.1:8012/infill',
     \ 'n_prefix':         128,
     \ 'n_suffix':         128,
     \ 'n_predict':        64,
     \ 't_max_prompt_ms':  300,
     \ 't_max_predict_ms': 200,
+    \ 'show_info':        v:true,
     \ 'auto_fim':         v:true,
-    \ 'stop':             ["\n"]
     \ }
 
 let g:llama_config = get(g:, 'llama_config', s:default_config)
 
 function! llama#init()
-    let s:pos_x  = 0
+    let s:pos_x  = 0 " cursor position upon start of completion
     let s:pos_y  = 0
     let s:pos_x0 = 0 " pos_x corrected for end-of-line edge case
 
@@ -46,8 +70,8 @@ function! llama#init()
 
     augroup llama
         autocmd!
-        autocmd InsertEnter * inoremap <buffer> <silent> <C-F> <C-O>:call llama#fim(v:false)<CR>
-        autocmd InsertLeave * call llama#fim_cancel()
+        autocmd InsertEnter    * inoremap <buffer> <silent> <C-F> <C-O>:call llama#fim(v:false)<CR>
+        autocmd InsertLeavePre * call llama#fim_cancel()
 
         autocmd CursorMoved * call llama#fim_cancel()
     augroup END
@@ -90,7 +114,6 @@ function! llama#fim(is_auto) abort
         \ 'prompt':           "",
         \ 'input_prefix':     l:prefix,
         \ 'input_suffix':     l:suffix,
-       "\ 'stop':             g:llama_config.stop,
         \ 'n_predict':        g:llama_config.n_predict,
         \ 'penalty_last_n':   0,
         \ 'top_k':            100,
@@ -126,16 +149,23 @@ function! llama#fim(is_auto) abort
     endif
 endfunction
 
-function! llama#fim_accept()
+" if first_line == v:true accept only the first line of the response
+function! llama#fim_accept(first_line)
     " insert the suggestion at the cursor location
     if s:can_accept && len(s:content) > 0
         call setline(s:pos_y, s:line_cur[:(s:pos_x0 - 1)] . s:content[0])
         if len(s:content) > 1
-            call append(s:pos_y, s:content[1:-1])
+            if !a:first_line
+                call append(s:pos_y, s:content[1:-1])
+            endif
         endif
 
         " move the cursor to the end of the accepted text
-        call cursor(s:pos_y + len(s:content) - 1, s:pos_x + s:pos_dx)
+        if !a:first_line
+            call cursor(s:pos_y + len(s:content) - 1, s:pos_x + s:pos_dx)
+        else
+            call cursor(s:pos_y, s:pos_x + len(s:content[0]) - 1)
+        endif
     endif
 
     call llama#fim_cancel()
@@ -146,6 +176,11 @@ function! llama#fim_cancel()
         call jobstop(s:current_job)
     endif
 
+    if s:timer_fim != -1
+        call timer_stop(s:timer_fim)
+        let s:timer_fim = -1
+    endif
+
     " clear the virtual text
     let l:bufnr = bufnr('%')
 
@@ -155,7 +190,9 @@ function! llama#fim_cancel()
     call nvim_buf_clear_namespace(l:bufnr, l:id_vt_fim,  0, -1)
     call nvim_buf_clear_namespace(l:bufnr, l:id_vt_info, 0, -1)
 
+    " remove the mappings
     silent! iunmap <buffer> <Tab>
+    silent! iunmap <buffer> <S-Tab>
     silent! iunmap <buffer> <Esc>
 
     augroup llama_insert
@@ -173,6 +210,8 @@ function! s:fim_auto_enable()
     augroup END
 endfunction
 
+" auto-start a fim job a short time after the cursor has moved
+" if there is already a job queued - cancel it
 function! s:fim_auto()
     if s:current_job != v:null
         call jobstop(s:current_job)
@@ -189,7 +228,7 @@ function! s:fim_auto()
     let s:timer_fim = timer_start(500, {-> llama#fim(v:true)})
 endfunction
 
-
+" callback that processes the result from the server
 function! s:fim_on_stdout(job_id, data, event) dict
     let l:raw = join(a:data, "\n")
     if len(l:raw) == 0
@@ -199,6 +238,13 @@ function! s:fim_on_stdout(job_id, data, event) dict
     let s:can_accept = v:true
     let l:has_info   = v:false
 
+    if s:can_accept && v:shell_error
+        if !self.is_auto
+            call add(s:content, "<| curl error: is the server on? |>")
+        endif
+        let s:can_accept = v:false
+    endif
+
     let l:n_prompt    = 0
     let l:t_prompt_ms = 1.0
     let l:s_prompt    = 0
@@ -207,13 +253,6 @@ function! s:fim_on_stdout(job_id, data, event) dict
     let l:t_predict_ms = 1.0
     let l:s_predict    = 0
 
-    if s:can_accept && v:shell_error
-        if !self.is_auto
-            call add(s:content, "<| curl error: is the server on? |>")
-        endif
-        let s:can_accept = v:false
-    endif
-
     " get the generated suggestion
     if s:can_accept
         let l:response = json_decode(l:raw)
@@ -227,7 +266,7 @@ function! s:fim_on_stdout(job_id, data, event) dict
             call remove(s:content, -1)
         endwhile
 
-        " if response.timings
+        " if response.timings is available
         if len(get(l:response, 'timings', {})) > 0
             let l:has_info = v:true
             let l:timings  = get(l:response, 'timings', {})
@@ -264,8 +303,8 @@ function! s:fim_on_stdout(job_id, data, event) dict
     let l:id_vt_fim  = nvim_create_namespace('vt_fim')
     let l:id_vt_info = nvim_create_namespace('vt_info')
 
-    " construct the info message:
-    if l:has_info
+    " construct the info message and display it to the right of the current line
+    if g:llama_config.show_info && l:has_info
         " prefix the info string with whitespace in order to offset it to the right of the fim overlay
         let l:prefix = repeat(' ', len(s:content[0]) - len(s:line_cur_suffix) + 3)
 
@@ -282,6 +321,7 @@ function! s:fim_on_stdout(job_id, data, event) dict
             \ })
     endif
 
+    " display the suggestion
     call nvim_buf_set_extmark(l:bufnr, l:id_vt_fim, s:pos_y - 1, s:pos_x - 1, {
         \ 'virt_text': [[s:content[0], 'llama_hl_hint']],
         \ 'virt_text_win_col': virtcol('.') - 1
@@ -293,8 +333,8 @@ function! s:fim_on_stdout(job_id, data, event) dict
         \ })
 
     " setup accept/cancel events
-    inoremap <buffer> <Tab> <C-O>:call llama#fim_accept()<CR>
-    inoremap <buffer> <Esc> <C-O>:call llama#fim_cancel()<CR><Esc>
+    inoremap <buffer> <Tab>   <C-O>:call llama#fim_accept(v:false)<CR>
+    inoremap <buffer> <S-Tab> <C-O>:call llama#fim_accept(v:true)<CR>
 
     augroup llama_insert
         autocmd!

examples/server/server.cpp

@@ -132,8 +132,8 @@ struct slot_params {
     int32_t n_discard =  0; // number of tokens after n_keep that may be discarded when shifting context, 0 defaults to half
     int32_t n_predict = -1; // new tokens to predict
 
-    int64_t t_max_prompt_ms  = -1;
-    int64_t t_max_predict_ms = -1;
+    int64_t t_max_prompt_ms  = -1; // TODO: not implemented
+    int64_t t_max_predict_ms = -1; // if positive, limit the generation phase to this time limit
 
     std::vector<std::string> antiprompt;
 
@@ -2028,8 +2028,8 @@ struct server_context {
                                     auto prefix_tokens = tokenize(slot.params.input_prefix, false, false);
                                     auto suffix_tokens = tokenize(slot.params.input_suffix, false, false);
 
-                                    // for now pick context to fit in a single batch
-                                    const int n_suffix_take = std::min<int>(suffix_tokens.size(), n_batch/2);
+                                    // for now pick context to fit in a single batch (ratio prefix:suffix = 3:1, TODO: configurable?)
+                                    const int n_suffix_take = std::min<int>(suffix_tokens.size(), n_batch/4);
                                     const int n_prefix_take = std::min<int>(prefix_tokens.size(), (n_batch - 3) - n_suffix_take);
 
                                     prefix_tokens.erase(prefix_tokens.begin(), prefix_tokens.begin() + prefix_tokens.size() - n_prefix_take);
@@ -2057,9 +2057,17 @@ struct server_context {
 
                         SLT_INF(slot, "prompt tokenized, n_ctx_slot = %d, n_keep = %d, n_prompt_tokens = %d\n", slot.n_ctx, slot.params.n_keep, slot.n_prompt_tokens);
 
-                        // print prompt tokens:
-                        for (int i = 0; i < (int) prompt_tokens.size(); i++) {
-                            SLT_DBG(slot, "prompt token %3d: %6d '%s'\n", i, prompt_tokens[i], common_token_to_piece(ctx, prompt_tokens[i]).c_str());
+                        // print prompt tokens (for debugging)
+                        if (1) {
+                            // first 16 tokens (avoid flooding logs)
+                            for (int i = 0; i < std::min<int>(16, prompt_tokens.size()); i++) {
+                                SLT_DBG(slot, "prompt token %3d: %6d '%s'\n", i, prompt_tokens[i], common_token_to_piece(ctx, prompt_tokens[i]).c_str());
+                            }
+                        } else {
+                            // all
+                            for (int i = 0; i < prompt_tokens.size(); i++) {
+                                SLT_DBG(slot, "prompt token %3d: %6d '%s'\n", i, prompt_tokens[i], common_token_to_piece(ctx, prompt_tokens[i]).c_str());
+                            }
                         }
 
                         // empty prompt passed -> release the slot and send empty response

src/llama-sampling.cpp

@@ -1646,6 +1646,8 @@ struct llama_sampler * llama_sampler_init_logit_bias(
 
 // infill
 
+//#define GGML_DEBUG_SAMPLER_INFILL
+
 struct llama_sampler_infill {
     const struct llama_vocab * vocab;
 };
@@ -1659,10 +1661,11 @@ static void llama_sampler_infill_apply(struct llama_sampler * smpl, llama_token_
 
     llama_sampler_softmax_impl(cur_p);
 
-    // print cur_p:
+#if defined(GGML_DEBUG_SAMPLER_INFILL)
     for (size_t i = 0; i < cur_p->size; ++i) {
         LLAMA_LOG_DEBUG("infill: cur_p[%zu] = { id: %d, p: %f, logit: %f }\n", i, cur_p->data[i].id, cur_p->data[i].p, cur_p->data[i].logit);
     }
+#endif
 
     float p_max     = 0.0f;
     float p_txt_sum = 0.0f;
@@ -1746,6 +1749,7 @@ static void llama_sampler_infill_apply(struct llama_sampler * smpl, llama_token_
         return;
     }
 
+    // pick the best token
     cur_p->size = 1;
     cur_p->data[0] = cur_p->data[i_max];