refactor evaluation logic

examples/server/server.cpp

@@ -21,6 +21,7 @@
 
 using namespace httplib;
 using json = nlohmann::json;
+using ordered_json = nlohmann::ordered_json;
 
 struct server_params {
     std::string hostname = "127.0.0.1";
@@ -82,9 +83,19 @@ static std::string tokens_to_str(llama_context * ctx, Iter begin, Iter end) {
     return ret;
 }
 
+#define SERVER_LOG_PRETTY 0
+
 static void server_log(const char * level, const char * function, int line,
-                       const char * message, const nlohmann::ordered_json & extra) {
-    nlohmann::ordered_json log {
+                       const char * message, const ordered_json & extra) {
+
+    #if SERVER_LOG_PRETTY == 1
+    fprintf(stdout, ANSI_COLOR_MAGENTA ANSI_BOLD "[%s] " ANSI_COLOR_RESET " %s@%d:  %s\n", level, function, line, message);
+    for (auto & it : extra.items()) {
+        fprintf(stdout, "%s=" ANSI_COLOR_YELLOW ANSI_BOLD "%s  " ANSI_COLOR_RESET, it.key().c_str(), it.value().dump().c_str());
+    }
+    fprintf(stdout, "\n\n");
+    #else
+    ordered_json log {
         { "timestamp", time(nullptr) },
         { "level", level },
         { "function", function },
@@ -98,6 +109,8 @@ static void server_log(const char * level, const char * function, int line,
 
     const std::string str = log.dump(-1, ' ', false, json::error_handler_t::replace);
     fprintf(stdout, "%.*s\n", (int)str.size(), str.data());
+    #endif
+
     fflush(stdout);
 }
 
@@ -128,8 +141,8 @@ static json probs_vector_to_json(const llama_context * ctx, const std::vector<co
         }
         std::string tok_str = tokens_to_output_formatted_string(ctx, prob.tok);
         out.push_back(json {
-            {"content", tok_str},
-            {"probs", probs_for_token},
+            { "content", tok_str },
+            { "probs", probs_for_token },
         });
     }
     return out;
@@ -152,124 +165,72 @@ static bool server_verbose = false;
 #define LOG_WARNING(MSG, ...) server_log("WARNING", __func__, __LINE__, MSG, __VA_ARGS__)
 #define LOG_INFO(MSG, ...) server_log("INFO", __func__, __LINE__, MSG, __VA_ARGS__)
 
-struct llama_server_context {
-    bool stream = false;
-    bool has_next_token = false;
-    std::string generated_text;
-    std::vector<completion_token_output> generated_token_probs;
-
-    size_t num_prompt_tokens = 0;
-    size_t num_tokens_predicted = 0;
-    size_t n_past = 0;
-    size_t n_past_guidance = 0;
-    int n_keep_guidance = 0;
-    size_t n_remain = 0;
-    bool cfg_enabled = false;
-
+struct prompt_evaluator {
+    llama_context * ctx;
+    size_t n_ctx = 0;
+    //std::string prompt;
     std::vector<llama_token> embd;
-    std::vector<llama_token> embd_guidance;
     std::vector<llama_token> last_n_tokens;
-
-    llama_model * model = nullptr;
-    llama_context * ctx = nullptr;
-    llama_context * ctx_guidance = nullptr;
-    gpt_params params;
-
+    size_t num_prompt_tokens = 0;
+    //size_t num_tokens_predicted = 0;
+    //size_t n_remain = 0;
+    size_t n_past = 0;
+    size_t n_keep = 0;
     bool truncated = false;
-    bool stopped_eos = false;
-    bool stopped_word = false;
-    bool stopped_limit = false;
-    std::string stopping_word;
-    int32_t multibyte_pending = 0;
 
-    std::mutex mutex;
-
-    std::unique_lock<std::mutex> lock() {
-        return std::unique_lock<std::mutex>(mutex);
+    void set_context(llama_context * ctx) {
+        this->ctx = ctx;
+        this->n_ctx = llama_n_ctx(ctx);
     }
 
-    ~llama_server_context() {
+    ~prompt_evaluator() {
         if (ctx) {
             llama_free(ctx);
             ctx = nullptr;
         }
-        if (ctx_guidance) {
-            llama_free(ctx_guidance);
-            ctx_guidance = nullptr;
-        }
-        if (model) {
-            llama_free_model(model);
-            model = nullptr;
-        }
     }
 
     void rewind() {
-        params.antiprompt.clear();
         num_prompt_tokens = 0;
-        num_tokens_predicted = 0;
-        generated_text = "";
-        generated_text.reserve(params.n_ctx);
-        generated_token_probs.clear();
+        //num_tokens_predicted = 0;
         truncated = false;
-        stopped_eos = false;
-        stopped_word = false;
-        stopped_limit = false;
-        stopping_word = "";
-        multibyte_pending = 0;
-
-        n_remain = 0;
+        //n_remain = 0;
         n_past = 0;
-        cfg_enabled = false;
-        n_past_guidance = 0;
     }
 
-    bool loadModel(const gpt_params & params_) {
-        params = params_;
-        std::tie(model, ctx) = llama_init_from_gpt_params(params);
-        if (model == nullptr) {
-            LOG_ERROR("unable to load model", {{ "model", params_.model }});
-            return false;
-        }
-
-        struct llama_context_params lparams = llama_context_params_from_gpt_params(params);
-        ctx_guidance = llama_new_context_with_model(model, lparams);
-
-        last_n_tokens.resize(params.n_ctx);
-        std::fill(last_n_tokens.begin(), last_n_tokens.end(), 0);
-        return true;
-    }
-
-    void loadPrompt() {
-        params.prompt.insert(0, 1, ' '); // always add a first space
-        std::vector<llama_token> prompt_tokens = ::llama_tokenize(ctx, params.prompt, true);
+    void load_prompt(std::string &prompt, int keep, size_t n_last) {
+        prompt.insert(0, 1, ' '); // always add a first space
+        std::vector<llama_token> prompt_tokens = ::llama_tokenize(ctx, prompt, true);
         num_prompt_tokens = prompt_tokens.size();
 
-        if (params.n_keep < 0) {
-            params.n_keep = (int)num_prompt_tokens;
+        if (keep < 0) {
+            keep = (int)num_prompt_tokens;
         }
-        params.n_keep = std::min(params.n_ctx - 4, params.n_keep);
+        n_keep = std::min(n_ctx - 4, (size_t)keep);
 
         // if input prompt is too big, truncate like normal
-        if (num_prompt_tokens >= (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);
-            const int erased_blocks = (num_prompt_tokens - params.n_keep - n_left - 1) / n_left;
-            new_tokens.insert(new_tokens.end(), prompt_tokens.begin() + params.n_keep + erased_blocks * n_left, prompt_tokens.end());
-            std::copy(prompt_tokens.end() - params.n_ctx, prompt_tokens.end(), last_n_tokens.begin());
+        if (num_prompt_tokens >= n_ctx) {
+            const size_t n_left = (n_ctx - n_keep) / 2;
+            std::vector<llama_token> new_tokens(prompt_tokens.begin(), prompt_tokens.begin() + n_keep);
+            const size_t erased_blocks = (num_prompt_tokens - n_keep - n_left - 1) / n_left;
+            new_tokens.insert(new_tokens.end(), prompt_tokens.begin() + n_keep + erased_blocks * n_left, prompt_tokens.end());
 
             LOG_VERBOSE("input truncated", {
-                { "n_ctx", params.n_ctx },
-                { "n_keep", params.n_keep },
+                { "n_ctx", n_ctx },
+                { "n_keep", n_keep },
                 { "n_left", n_left },
                 { "new_tokens", tokens_to_str(ctx, new_tokens.cbegin(), new_tokens.cend()) },
             });
 
             truncated = true;
             prompt_tokens = new_tokens;
-        } else {
-            const size_t ps = num_prompt_tokens;
-            std::fill(last_n_tokens.begin(), last_n_tokens.end() - ps, 0);
-            std::copy(prompt_tokens.begin(), prompt_tokens.end(), last_n_tokens.end() - ps);
+        }
+
+        last_n_tokens.resize(n_last);
+        if (n_last > 0) {
+            const size_t s = std::min(n_last, num_prompt_tokens);
+            std::fill(last_n_tokens.begin(), last_n_tokens.end() - s, 0);
+            std::copy(prompt_tokens.end() - s, prompt_tokens.end(), last_n_tokens.begin());
         }
 
         // compare the evaluated prompt with the new prompt
@@ -285,50 +246,141 @@ struct llama_server_context {
             { "cached", tokens_to_str(ctx, embd.cbegin(), embd.cbegin() + n_past) },
             { "to_eval", tokens_to_str(ctx, embd.cbegin() + n_past, embd.cend()) },
         });
+    }
 
+    bool evaluate(size_t n_threads, size_t n_batch) {
+        if (embd.size() >= n_ctx) {
+            // Reset context
+            const size_t n_left = (n_ctx - n_keep) / 2;
+
+            std::vector<llama_token> new_tokens(embd.begin(), embd.begin() + n_keep);
+            new_tokens.insert(new_tokens.end(), embd.end() - n_left, embd.end());
+            embd = new_tokens;
+            n_past = n_keep;
+            truncated = true;
+            LOG_VERBOSE("input truncated", {
+                { "n_ctx", n_ctx },
+                { "n_keep", n_keep },
+                { "n_left", n_left },
+                { "new_tokens", tokens_to_str(ctx, new_tokens.cbegin(), new_tokens.cend()) },
+            });
+        }
+
+        while (n_past < embd.size()) {
+            size_t n_eval = embd.size() - n_past;
+            if (n_eval > n_batch) {
+                n_eval = n_batch;
+            }
+
+            //LOG_VERBOSE("eval", {
+            //    { "n_eval", n_eval },
+            //    { "n_past", n_past },
+            //    { "n_threads", n_threads },
+            //    { "embd", tokens_to_str(ctx, embd.cbegin() + n_past, embd.cend()) },
+            //});
+
+            if (llama_eval(ctx, &embd[n_past], n_eval, n_past, n_threads)) {
+                LOG_ERROR("failed to eval", {
+                    { "n_eval", n_eval },
+                    { "n_past", n_past },
+                    { "n_threads", n_threads },
+                    { "embd", tokens_to_str(ctx, embd.cbegin() + n_past, embd.cend()) },
+                });
+                return false;
+            }
+            n_past += n_eval;
+        }
+
+        return true;
+    }
+
+    void append_token(llama_token id) {
+        if (last_n_tokens.size() > 0) {
+            last_n_tokens.erase(last_n_tokens.begin());
+            last_n_tokens.push_back(id);
+        }
+        embd.push_back(id);
+    }
+};
+
+struct llama_server_context {
+    bool stream = false;
+    bool has_next_token = false;
+    std::string generated_text;
+    std::vector<completion_token_output> generated_token_probs;
+
+    size_t num_tokens_predicted = 0;
+    int n_keep_guidance = 0;
+    size_t n_remain = 0;
+    bool cfg_enabled = false;
+
+    llama_model * model = nullptr;
+    llama_context * ctx = nullptr;
+    gpt_params params;
+
+    prompt_evaluator evaluator;
+    prompt_evaluator evaluator_guidance;
+
+    bool stopped_eos = false;
+    bool stopped_word = false;
+    bool stopped_limit = false;
+    std::string stopping_word;
+    int32_t multibyte_pending = 0;
+
+    std::mutex mutex;
+
+    std::unique_lock<std::mutex> lock() {
+        return std::unique_lock<std::mutex>(mutex);
+    }
+
+    ~llama_server_context() {
+        if (model) {
+            llama_free_model(model);
+            model = nullptr;
+        }
+    }
+
+    void rewind() {
+        params.antiprompt.clear();
+        num_tokens_predicted = 0;
+        generated_text = "";
+        generated_text.reserve(params.n_ctx);
+        generated_token_probs.clear();
+        stopped_eos = false;
+        stopped_word = false;
+        stopped_limit = false;
+        stopping_word = "";
+        multibyte_pending = 0;
+
+        n_remain = 0;
+        cfg_enabled = false;
+        evaluator.rewind();
+        evaluator_guidance.rewind();
+    }
+
+    bool loadModel(const gpt_params & params_) {
+        params = params_;
+        std::tie(model, ctx) = llama_init_from_gpt_params(params);
+        if (model == nullptr) {
+            LOG_ERROR("unable to load model", {{ "model", params_.model }});
+            return false;
+        }
+        evaluator.set_context(ctx);
+
+        struct llama_context_params lparams = llama_context_params_from_gpt_params(params);
+        llama_context * ctx_guidance = llama_new_context_with_model(model, lparams);
+        evaluator_guidance.set_context(ctx_guidance);
+
+        return true;
+    }
+
+    void loadPrompt() {
+        evaluator.load_prompt(params.prompt, params.n_keep, params.repeat_last_n);
         has_next_token = true;
     }
 
     void loadGuidancePrompt() {
-        params.cfg_negative_prompt.insert(0, 1, ' '); // always add a first space
-        std::vector<llama_token> prompt_tokens = ::llama_tokenize(ctx_guidance, params.cfg_negative_prompt, true);
-        num_prompt_tokens = prompt_tokens.size();
-
-        if (n_keep_guidance < 0) {
-            n_keep_guidance = (int)num_prompt_tokens;
-        }
-        n_keep_guidance = std::min(params.n_ctx - 4, n_keep_guidance);
-
-        // if input prompt is too big, truncate like normal
-        if (num_prompt_tokens >= (size_t)params.n_ctx) {
-            const int n_left = (params.n_ctx - n_keep_guidance) / 2;
-            std::vector<llama_token> new_tokens(prompt_tokens.begin(), prompt_tokens.begin() + n_keep_guidance);
-            const int erased_blocks = (num_prompt_tokens - n_keep_guidance - n_left - 1) / n_left;
-            new_tokens.insert(new_tokens.end(), prompt_tokens.begin() + n_keep_guidance + erased_blocks * n_left, prompt_tokens.end());
-
-            LOG_VERBOSE("guidance truncated", {
-                { "n_ctx", params.n_ctx },
-                { "n_keep", n_keep_guidance },
-                { "n_left", n_left },
-                { "new_tokens", tokens_to_str(ctx_guidance, new_tokens.cbegin(), new_tokens.cend()) },
-            });
-
-            prompt_tokens = new_tokens;
-        }
-
-        // compare the evaluated prompt with the new prompt
-        n_past_guidance = common_part(embd_guidance, prompt_tokens);
-        embd_guidance = prompt_tokens;
-        if (n_past_guidance == num_prompt_tokens) {
-            // we have to evaluate at least 1 token to generate logits.
-            n_past_guidance--;
-        }
-
-        LOG_VERBOSE("guidance prompt ingested", {
-            { "n_past", n_past_guidance },
-            { "cached", tokens_to_str(ctx_guidance, embd.cbegin(), embd.cbegin() + n_past) },
-            { "to_eval", tokens_to_str(ctx_guidance, embd.cbegin() + n_past, embd.cend()) },
-        });
+        evaluator_guidance.load_prompt(params.cfg_negative_prompt, n_keep_guidance, 0);
     }
 
     void beginCompletion() {
@@ -341,80 +393,14 @@ struct llama_server_context {
         completion_token_output result;
         result.tok = -1;
 
-        if (embd.size() >= (size_t)params.n_ctx) {
-            // Reset context
-            const int n_left = (params.n_ctx - params.n_keep) / 2;
-
-            std::vector<llama_token> new_tokens(embd.begin(), embd.begin() + params.n_keep);
-            new_tokens.insert(new_tokens.end(), embd.end() - n_left, embd.end());
-            embd = new_tokens;
-            n_past = params.n_keep;
-            truncated = true;
-            LOG_VERBOSE("input truncated", {
-                { "n_ctx", params.n_ctx },
-                { "n_keep", params.n_keep },
-                { "n_left", n_left },
-                { "new_tokens", tokens_to_str(ctx, new_tokens.cbegin(), new_tokens.cend()) },
-            });
-        }
-
-        while (n_past < embd.size()) {
-            int n_eval = (int)embd.size() - n_past;
-            if (n_eval > params.n_batch) {
-                n_eval = params.n_batch;
-            }
-            if (llama_eval(ctx, &embd[n_past], n_eval, n_past, params.n_threads)) {
-                LOG_ERROR("failed to eval", {
-                    { "n_eval", n_eval },
-                    { "n_past", n_past },
-                    { "n_threads", params.n_threads },
-                    { "embd", tokens_to_str(ctx, embd.cbegin() + n_past, embd.cend()) },
-                });
-                has_next_token = false;
-                return result;
-            }
-            n_past += n_eval;
-        }
+        evaluator.evaluate(params.n_threads, params.n_batch);
 
         if (cfg_enabled) {
-            if (embd_guidance.size() >= (size_t)params.n_ctx) {
-                // Reset context
-                const int n_left = (params.n_ctx - n_keep_guidance) / 2;
-
-                std::vector<llama_token> new_tokens(embd.begin(), embd.begin() + n_keep_guidance);
-                new_tokens.insert(new_tokens.end(), embd_guidance.end() - n_left, embd_guidance.end());
-                embd_guidance = new_tokens;
-                n_past_guidance = n_keep_guidance;
-                LOG_VERBOSE("guidance truncated", {
-                    { "n_ctx", params.n_ctx },
-                    { "n_keep", n_keep_guidance },
-                    { "n_left", n_left },
-                    { "new_tokens", tokens_to_str(ctx, new_tokens.cbegin(), new_tokens.cend()) },
-                });
-            }
-
-            while (n_past_guidance < embd_guidance.size()) {
-                int n_eval = (int)embd_guidance.size() - n_past_guidance;
-                if (n_eval > params.n_batch) {
-                    n_eval = params.n_batch;
-                }
-                if (llama_eval(ctx_guidance, &embd_guidance[n_past_guidance], n_eval, n_past_guidance, params.n_threads)) {
-                    LOG_ERROR("failed to eval", {
-                        { "n_eval", n_eval },
-                        { "n_past", n_past_guidance },
-                        { "n_threads", params.n_threads },
-                        { "embd", tokens_to_str(ctx_guidance, embd_guidance.cbegin() + n_past_guidance, embd_guidance.cend()) },
-                    });
-                    has_next_token = false;
-                    return result;
-                }
-                n_past_guidance += n_eval;
-            }
+           evaluator_guidance.evaluate(params.n_threads, params.n_batch);
         }
 
         if (params.n_predict == 0) {
             has_next_token = false;
-            //result.tok = llama_token_eos();
             return result;
         }
 
@@ -424,7 +410,7 @@ struct llama_server_context {
         const float top_p = params.top_p;
         const float tfs_z = params.tfs_z;
         const float typical_p = params.typical_p;
-        const int32_t repeat_last_n = params.repeat_last_n < 0 ? params.n_ctx : params.repeat_last_n;
+        //const int32_t repeat_last_n = params.repeat_last_n < 0 ? params.n_ctx : params.repeat_last_n;
         const float repeat_penalty = params.repeat_penalty;
         const float alpha_presence = params.presence_penalty;
         const float alpha_frequency = params.frequency_penalty;
@@ -435,7 +421,7 @@ struct llama_server_context {
         const int32_t n_probs = params.n_probs;
 
         {
-            auto * logits = llama_get_logits(ctx);
+            auto logits = llama_get_logits(ctx);
             auto n_vocab = llama_n_vocab(ctx);
 
             // Apply params.logit_bias map
@@ -453,18 +439,18 @@ struct llama_server_context {
 
             if (cfg_enabled) {
                 llama_sample_classifier_free_guidance(
-                    ctx, &candidates_p, ctx_guidance, params.cfg_scale, params.cfg_smooth_factor);
+                    ctx, &candidates_p, evaluator_guidance.ctx, params.cfg_scale, params.cfg_smooth_factor);
             }
 
             // Apply penalties
             float nl_logit = logits[llama_token_nl()];
-            auto last_n_repeat = std::min(std::min((int)last_n_tokens.size(), repeat_last_n), params.n_ctx);
+            //auto last_n_repeat = std::min(std::min((int)last_n_tokens.size(), repeat_last_n), params.n_ctx);
             llama_sample_repetition_penalty(ctx, &candidates_p,
-                last_n_tokens.data() + last_n_tokens.size() - last_n_repeat,
-                last_n_repeat, repeat_penalty);
+                evaluator.last_n_tokens.data(), evaluator.last_n_tokens.size(),
+                repeat_penalty);
             llama_sample_frequency_and_presence_penalties(ctx, &candidates_p,
-                last_n_tokens.data() + last_n_tokens.size() - last_n_repeat,
-                last_n_repeat, alpha_frequency, alpha_presence);
+                evaluator.last_n_tokens.data(), evaluator.last_n_tokens.size(),
+                alpha_frequency, alpha_presence);
             if (!penalize_nl) {
                 logits[llama_token_nl()] = nl_logit;
             }
@@ -500,21 +486,19 @@ struct llama_server_context {
             for (size_t i = 0; i < std::min(candidates_p.size, (size_t) n_probs); ++i) {
                 result.probs.push_back({candidates_p.data[i].id, candidates_p.data[i].p});
             }
-            last_n_tokens.erase(last_n_tokens.begin());
-            last_n_tokens.push_back(result.tok);
             num_tokens_predicted++;
         }
 
         // add it to the context
-        embd.push_back(result.tok);
+        evaluator.append_token(result.tok);
         if (cfg_enabled) {
-            embd_guidance.push_back(result.tok);
+            evaluator_guidance.append_token(result.tok);
         }
         // decrement remaining sampling budget
         --n_remain;
 
-        if (!embd.empty() && embd.back() == llama_token_eos()) {
-            //stopping_word = llama_token_to_str(ctx, embd.back());
+        if (result.tok == llama_token_eos()) {
+            stopping_word = "";
             has_next_token = false;
             stopped_eos = true;
             LOG_VERBOSE("eos token found", {});
@@ -591,6 +575,7 @@ struct llama_server_context {
         LOG_VERBOSE("next token", {
             { "token", token_with_probs.tok },
             { "token_text", tokens_to_output_formatted_string(ctx, token_with_probs.tok) },
+            { "n_past", evaluator.n_past },
             { "has_next_token", has_next_token },
             { "n_remain", n_remain },
             { "num_tokens_predicted", num_tokens_predicted },
@@ -884,16 +869,16 @@ static json format_final_response(llama_server_context & llama, const std::strin
         { "stop", true },
         { "model", llama.params.model_alias },
         { "tokens_predicted", llama.num_tokens_predicted },
-        { "tokens_evaluated", llama.num_prompt_tokens },
+        { "tokens_evaluated", llama.evaluator.num_prompt_tokens },
         { "generation_settings", format_generation_settings(llama) },
         { "prompt", llama.params.prompt },
         { "cfg_negative_prompt", llama.params.cfg_negative_prompt },
-        { "truncated", llama.truncated },
+        { "truncated", llama.evaluator.truncated },
         { "stopped_eos", llama.stopped_eos },
         { "stopped_word", llama.stopped_word },
         { "stopped_limit", llama.stopped_limit },
         { "stopping_word", llama.stopping_word },
-        { "tokens_cached", llama.n_past },
+        { "tokens_cached", llama.evaluator.n_past },
         { "timings", format_timings(llama) },
     };