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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+main filter=lfs diff=lfs merge=lfs -text

main

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:349cf33b64ee47606d53bfca53b64b0779f4c6b92c81aeccf04d793265e265fd
+size 1676065

main.cpp

@@ -0,0 +1,954 @@
+#include "common.h"
+
+#include "console.h"
+#include "llama.h"
+
+#include <cassert>
+#include <cinttypes>
+#include <cmath>
+#include <cstdio>
+#include <cstring>
+#include <ctime>
+#include <fstream>
+#include <iostream>
+#include <sstream>
+#include <string>
+#include <vector>
+
+#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__))
+#include <signal.h>
+#include <unistd.h>
+#elif defined (_WIN32)
+#define WIN32_LEAN_AND_MEAN
+#ifndef NOMINMAX
+#define NOMINMAX
+#endif
+#include <windows.h>
+#include <signal.h>
+#endif
+
+#if defined(_MSC_VER)
+#pragma warning(disable: 4244 4267) // possible loss of data
+#endif
+
+static llama_context           ** g_ctx;
+static llama_model             ** g_model;
+static gpt_params               * g_params;
+static std::vector<llama_token> * g_input_tokens;
+static std::ostringstream       * g_output_ss;
+static std::vector<llama_token> * g_output_tokens;
+static bool is_interacting = false;
+
+static bool file_exists(const std::string &path) {
+    std::ifstream f(path.c_str());
+    return f.good();
+}
+
+static bool file_is_empty(const std::string &path) {
+    std::ifstream f;
+    f.exceptions(std::ifstream::failbit | std::ifstream::badbit);
+    f.open(path.c_str(), std::ios::in | std::ios::binary | std::ios::ate);
+    return f.tellg() == 0;
+}
+
+static void write_logfile(
+    const llama_context * ctx, const gpt_params & params, const llama_model * model,
+    const std::vector<llama_token> & input_tokens, const std::string & output,
+    const std::vector<llama_token> & output_tokens
+) {
+    if (params.logdir.empty()) {
+        return;
+    }
+
+    const std::string timestamp = get_sortable_timestamp();
+
+    const bool success = create_directory_with_parents(params.logdir);
+    if (!success) {
+        fprintf(stderr, "%s: warning: failed to create logdir %s, cannot write logfile\n",
+                __func__, params.logdir.c_str());
+        return;
+    }
+
+    const std::string logfile_path = params.logdir + timestamp + ".yml";
+    FILE * logfile = fopen(logfile_path.c_str(), "w");
+
+    if (logfile == NULL) {
+        fprintf(stderr, "%s: failed to open logfile %s\n", __func__, logfile_path.c_str());
+        return;
+    }
+
+    fprintf(logfile, "binary: main\n");
+    char model_desc[128];
+    llama_model_desc(model, model_desc, sizeof(model_desc));
+    dump_non_result_info_yaml(logfile, params, ctx, timestamp, input_tokens, model_desc);
+
+    fprintf(logfile, "\n");
+    fprintf(logfile, "######################\n");
+    fprintf(logfile, "# Generation Results #\n");
+    fprintf(logfile, "######################\n");
+    fprintf(logfile, "\n");
+
+    dump_string_yaml_multiline(logfile, "output", output.c_str());
+    dump_vector_int_yaml(logfile, "output_tokens", output_tokens);
+
+    llama_dump_timing_info_yaml(logfile, ctx);
+    fclose(logfile);
+}
+
+#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) || defined (_WIN32)
+static void sigint_handler(int signo) {
+    if (signo == SIGINT) {
+        if (!is_interacting && g_params->interactive) {
+            is_interacting = true;
+        } else {
+            console::cleanup();
+            printf("\n");
+            // llama_print_timings(*g_ctx);
+            write_logfile(*g_ctx, *g_params, *g_model, *g_input_tokens, g_output_ss->str(), *g_output_tokens);
+            _exit(130);
+        }
+    }
+}
+#endif
+
+static void llama_log_callback_logTee(ggml_log_level level, const char * text, void * user_data) {
+    (void) level;
+    (void) user_data;
+    // LOG_TEE("%s", text);
+}
+
+int main(int argc, char ** argv) {
+    gpt_params params;
+    g_params = &params;
+
+    if (!gpt_params_parse(argc, argv, params)) {
+        return 1;
+    }
+    llama_sampling_params & sparams = params.sparams;
+
+#ifndef LOG_DISABLE_LOGS
+    log_set_target(log_filename_generator("main", "log"));
+    // LOG_TEE("Log start\n");
+    log_dump_cmdline(argc, argv);
+    llama_log_set(llama_log_callback_logTee, nullptr);
+#endif // LOG_DISABLE_LOGS
+
+    // TODO: Dump params ?
+    //LOG("Params perplexity: %s\n", LOG_TOSTR(params.perplexity));
+
+    // save choice to use color for later
+    // (note for later: this is a slightly awkward choice)
+    console::init(params.simple_io, params.use_color);
+    atexit([]() { console::cleanup(); });
+
+    if (params.logits_all) {
+        printf("\n************\n");
+        printf("%s: please use the 'perplexity' tool for perplexity calculations\n", __func__);
+        printf("************\n\n");
+
+        return 0;
+    }
+
+    if (params.embedding) {
+        printf("\n************\n");
+        printf("%s: please use the 'embedding' tool for embedding calculations\n", __func__);
+        printf("************\n\n");
+
+        return 0;
+    }
+
+    if (params.n_ctx != 0 && params.n_ctx < 8) {
+        LOG_TEE("%s: warning: minimum context size is 8, using minimum size.\n", __func__);
+        params.n_ctx = 8;
+    }
+
+    if (params.rope_freq_base != 0.0) {
+        LOG_TEE("%s: warning: changing RoPE frequency base to %g.\n", __func__, params.rope_freq_base);
+    }
+
+    if (params.rope_freq_scale != 0.0) {
+        LOG_TEE("%s: warning: scaling RoPE frequency by %g.\n", __func__, params.rope_freq_scale);
+    }
+
+    // LOG_TEE("%s: build = %d (%s)\n",      __func__, LLAMA_BUILD_NUMBER, LLAMA_COMMIT);
+    // LOG_TEE("%s: built with %s for %s\n", __func__, LLAMA_COMPILER, LLAMA_BUILD_TARGET);
+
+    if (params.seed == LLAMA_DEFAULT_SEED) {
+        params.seed = time(NULL);
+    }
+
+    // LOG_TEE("%s: seed  = %u\n", __func__, params.seed);
+
+    std::mt19937 rng(params.seed);
+    if (params.random_prompt) {
+        params.prompt = gpt_random_prompt(rng);
+    }
+
+    LOG("%s: llama backend init\n", __func__);
+    llama_backend_init();
+    llama_numa_init(params.numa);
+
+    llama_model * model;
+    llama_context * ctx;
+    llama_context * ctx_guidance = NULL;
+    g_model = &model;
+    g_ctx = &ctx;
+
+    // load the model and apply lora adapter, if any
+    LOG("%s: load the model and apply lora adapter, if any\n", __func__);
+    std::tie(model, ctx) = llama_init_from_gpt_params(params);
+    if (sparams.cfg_scale > 1.f) {
+        struct llama_context_params lparams = llama_context_params_from_gpt_params(params);
+        ctx_guidance = llama_new_context_with_model(model, lparams);
+    }
+
+    if (model == NULL) {
+        LOG_TEE("%s: error: unable to load model\n", __func__);
+        return 1;
+    }
+
+    const int n_ctx_train = llama_n_ctx_train(model);
+    const int n_ctx = llama_n_ctx(ctx);
+    // LOG("n_ctx: %d\n", n_ctx);
+
+    if (n_ctx > n_ctx_train) {
+        LOG_TEE("%s: warning: model was trained on only %d context tokens (%d specified)\n",
+                __func__, n_ctx_train, n_ctx);
+    }
+
+    // print system information
+    // {
+        // LOG_TEE("\n");
+        // LOG_TEE("%s\n", get_system_info(params).c_str());
+    // }
+
+    std::string path_session = params.path_prompt_cache;
+    std::vector<llama_token> session_tokens;
+
+    if (!path_session.empty()) {
+        // LOG_TEE("%s: attempting to load saved session from '%s'\n", __func__, path_session.c_str());
+        if (!file_exists(path_session)) {
+            // LOG_TEE("%s: session file does not exist, will create.\n", __func__);
+        } else if (file_is_empty(path_session)) {
+            // LOG_TEE("%s: The session file is empty. A new session will be initialized.\n", __func__);
+        } else {
+            // The file exists and is not empty
+            session_tokens.resize(n_ctx);
+            size_t n_token_count_out = 0;
+            if (!llama_load_session_file(ctx, path_session.c_str(), session_tokens.data(), session_tokens.capacity(), &n_token_count_out)) {
+                LOG_TEE("%s: error: failed to load session file '%s'\n", __func__, path_session.c_str());
+                return 1;
+            }
+            session_tokens.resize(n_token_count_out);
+            llama_set_rng_seed(ctx, params.seed);
+            // LOG_TEE("%s: loaded a session with prompt size of %d tokens\n", __func__, (int)session_tokens.size());
+        }
+    }
+
+    const bool add_bos = llama_should_add_bos_token(model);
+    // LOG("add_bos: %d\n", add_bos);
+
+    std::vector<llama_token> embd_inp;
+
+    if (params.interactive_first || params.instruct || params.chatml || !params.prompt.empty() || session_tokens.empty()) {
+        LOG("tokenize the prompt\n");
+        if (params.chatml) {
+            params.prompt = "<|im_start|>system\n" + params.prompt + "<|im_end|>";
+        }
+        embd_inp = ::llama_tokenize(ctx, params.prompt, add_bos, true);
+    } else {
+        LOG("use session tokens\n");
+        embd_inp = session_tokens;
+    }
+
+    LOG("prompt: \"%s\"\n", log_tostr(params.prompt));
+    LOG("tokens: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_inp).c_str());
+
+    // Should not run without any tokens
+    if (embd_inp.empty()) {
+        embd_inp.push_back(llama_token_bos(model));
+        LOG("embd_inp was considered empty and bos was added: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_inp).c_str());
+    }
+
+    // Tokenize negative prompt
+    std::vector<llama_token> guidance_inp;
+    int guidance_offset = 0;
+    int original_prompt_len = 0;
+    if (ctx_guidance) {
+        LOG("cfg_negative_prompt: \"%s\"\n", log_tostr(sparams.cfg_negative_prompt));
+
+        guidance_inp = ::llama_tokenize(ctx_guidance, sparams.cfg_negative_prompt, add_bos, true);
+        LOG("guidance_inp tokenized: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx_guidance, guidance_inp).c_str());
+
+        std::vector<llama_token> original_inp = ::llama_tokenize(ctx, params.prompt, add_bos, true);
+        LOG("original_inp tokenized: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, original_inp).c_str());
+
+        original_prompt_len = original_inp.size();
+        guidance_offset = (int)guidance_inp.size() - original_prompt_len;
+        LOG("original_prompt_len: %s", log_tostr(original_prompt_len));
+        LOG("guidance_offset:     %s", log_tostr(guidance_offset));
+    }
+
+    if ((int) embd_inp.size() > n_ctx - 4) {
+        LOG_TEE("%s: error: prompt is too long (%d tokens, max %d)\n", __func__, (int) embd_inp.size(), n_ctx - 4);
+        return 1;
+    }
+
+    // debug message about similarity of saved session, if applicable
+    size_t n_matching_session_tokens = 0;
+    if (!session_tokens.empty()) {
+        for (llama_token id : session_tokens) {
+            if (n_matching_session_tokens >= embd_inp.size() || id != embd_inp[n_matching_session_tokens]) {
+                break;
+            }
+            n_matching_session_tokens++;
+        }
+        // if (params.prompt.empty() && n_matching_session_tokens == embd_inp.size()) {
+        //     LOG_TEE("%s: using full prompt from session file\n", __func__);
+        // } else if (n_matching_session_tokens >= embd_inp.size()) {
+        //     LOG_TEE("%s: session file has exact match for prompt!\n", __func__);
+        // } else if (n_matching_session_tokens < (embd_inp.size() / 2)) {
+        //     LOG_TEE("%s: warning: session file has low similarity to prompt (%zu / %zu tokens); will mostly be reevaluated\n",
+        //         __func__, n_matching_session_tokens, embd_inp.size());
+        // } else {
+        //     LOG_TEE("%s: session file matches %zu / %zu tokens of prompt\n",
+        //         __func__, n_matching_session_tokens, embd_inp.size());
+        // }
+
+        // remove any "future" tokens that we might have inherited from the previous session
+        llama_kv_cache_seq_rm(ctx, -1, n_matching_session_tokens, -1);
+    }
+
+    LOGLN(
+            "recalculate the cached logits (check): embd_inp.empty() %s, n_matching_session_tokens %zu, embd_inp.size() %zu, session_tokens.size() %zu, embd_inp.size() %zu",
+            log_tostr(embd_inp.empty()), n_matching_session_tokens, embd_inp.size(), session_tokens.size(), embd_inp.size());
+
+    // if we will use the cache for the full prompt without reaching the end of the cache, force
+    // reevaluation of the last token token to recalculate the cached logits
+    if (!embd_inp.empty() && n_matching_session_tokens == embd_inp.size() && session_tokens.size() > embd_inp.size()) {
+        LOGLN("recalculate the cached logits (do): session_tokens.resize( %zu )", embd_inp.size() - 1);
+
+        session_tokens.resize(embd_inp.size() - 1);
+    }
+
+    // number of tokens to keep when resetting context
+    if (params.n_keep < 0 || params.n_keep > (int) embd_inp.size() || params.instruct || params.chatml) {
+        params.n_keep = (int)embd_inp.size();
+    } else {
+        params.n_keep += add_bos; // always keep the BOS token
+    }
+
+    // prefix & suffix for instruct mode
+    const auto inp_pfx = ::llama_tokenize(ctx, "\n\n### Instruction:\n\n", add_bos, true);
+    const auto inp_sfx = ::llama_tokenize(ctx, "\n\n### Response:\n\n",    false,   true);
+
+    // LOG("inp_pfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, inp_pfx).c_str());
+    // LOG("inp_sfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, inp_sfx).c_str());
+
+    // chatml prefix & suffix
+    const auto cml_pfx = ::llama_tokenize(ctx, "\n<|im_start|>user\n", add_bos, true);
+    const auto cml_sfx = ::llama_tokenize(ctx, "<|im_end|>\n<|im_start|>assistant\n", false, true);
+
+    // LOG("cml_pfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, cml_pfx).c_str());
+    // LOG("cml_sfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, cml_sfx).c_str());
+
+    // in instruct mode, we inject a prefix and a suffix to each input by the user
+    if (params.instruct) {
+        params.interactive_first = true;
+        params.antiprompt.emplace_back("### Instruction:\n\n");
+    }
+    // similar for chatml mode
+    else if (params.chatml) {
+        params.interactive_first = true;
+        params.antiprompt.emplace_back("<|im_start|>user\n");
+    }
+
+    // enable interactive mode if interactive start is specified
+    if (params.interactive_first) {
+        params.interactive = true;
+    }
+
+    // if (params.verbose_prompt) {
+    //     LOG_TEE("\n");
+    //     LOG_TEE("%s: prompt: '%s'\n", __func__, params.prompt.c_str());
+    //     LOG_TEE("%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size());
+    //     for (int i = 0; i < (int) embd_inp.size(); i++) {
+    //         LOG_TEE("%6d -> '%s'\n", embd_inp[i], llama_token_to_piece(ctx, embd_inp[i]).c_str());
+    //     }
+
+    //     if (ctx_guidance) {
+    //         LOG_TEE("\n");
+    //         LOG_TEE("%s: negative prompt: '%s'\n", __func__, sparams.cfg_negative_prompt.c_str());
+    //         LOG_TEE("%s: number of tokens in negative prompt = %zu\n", __func__, guidance_inp.size());
+    //         for (int i = 0; i < (int) guidance_inp.size(); i++) {
+    //             LOG_TEE("%6d -> '%s'\n", guidance_inp[i], llama_token_to_piece(ctx, guidance_inp[i]).c_str());
+    //         }
+    //     }
+
+    //     if (params.n_keep > add_bos) {
+    //         LOG_TEE("%s: static prompt based on n_keep: '", __func__);
+    //         for (int i = 0; i < params.n_keep; i++) {
+    //             LOG_TEE("%s", llama_token_to_piece(ctx, embd_inp[i]).c_str());
+    //         }
+    //         LOG_TEE("'\n");
+    //     }
+    //     LOG_TEE("\n");
+    // }
+
+    // ctrl+C handling
+    {
+#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__))
+        struct sigaction sigint_action;
+        sigint_action.sa_handler = sigint_handler;
+        sigemptyset (&sigint_action.sa_mask);
+        sigint_action.sa_flags = 0;
+        sigaction(SIGINT, &sigint_action, NULL);
+#elif defined (_WIN32)
+        auto console_ctrl_handler = +[](DWORD ctrl_type) -> BOOL {
+            return (ctrl_type == CTRL_C_EVENT) ? (sigint_handler(SIGINT), true) : false;
+        };
+        SetConsoleCtrlHandler(reinterpret_cast<PHANDLER_ROUTINE>(console_ctrl_handler), true);
+#endif
+    }
+
+    if (params.interactive) {
+        // LOG_TEE("%s: interactive mode on.\n", __func__);
+
+        if (!params.antiprompt.empty()) {
+            for (const auto & antiprompt : params.antiprompt) {
+                // LOG_TEE("Reverse prompt: '%s'\n", antiprompt.c_str());
+                if (params.verbose_prompt) {
+                    auto tmp = ::llama_tokenize(ctx, antiprompt, false, true);
+                    for (int i = 0; i < (int) tmp.size(); i++) {
+                        // LOG_TEE("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx, tmp[i]).c_str());
+                    }
+                }
+            }
+        }
+
+        // if (params.input_prefix_bos) {
+        //     LOG_TEE("Input prefix with BOS\n");
+        // }
+
+        // if (!params.input_prefix.empty()) {
+        //     LOG_TEE("Input prefix: '%s'\n", params.input_prefix.c_str());
+        //     if (params.verbose_prompt) {
+        //         auto tmp = ::llama_tokenize(ctx, params.input_prefix, true, true);
+        //         for (int i = 0; i < (int) tmp.size(); i++) {
+        //             LOG_TEE("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx, tmp[i]).c_str());
+        //         }
+        //     }
+        // }
+
+    //     if (!params.input_suffix.empty()) {
+    //         LOG_TEE("Input suffix: '%s'\n", params.input_suffix.c_str());
+    //         if (params.verbose_prompt) {
+    //             auto tmp = ::llama_tokenize(ctx, params.input_suffix, false, true);
+    //             for (int i = 0; i < (int) tmp.size(); i++) {
+    //                 LOG_TEE("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx, tmp[i]).c_str());
+    //             }
+    //         }
+    //     }
+    }
+    // LOG_TEE("sampling: \n%s\n", llama_sampling_print(sparams).c_str());
+    // LOG_TEE("sampling order: \n%s\n", llama_sampling_order_print(sparams).c_str());
+    // LOG_TEE("generate: n_ctx = %d, n_batch = %d, n_predict = %d, n_keep = %d\n", n_ctx, params.n_batch, params.n_predict, params.n_keep);
+
+    // group-attention state
+    // number of grouped KV tokens so far (used only if params.grp_attn_n > 1)
+    int ga_i = 0;
+
+    const int ga_n = params.grp_attn_n;
+    const int ga_w = params.grp_attn_w;
+
+    if (ga_n != 1) {
+        GGML_ASSERT(ga_n > 0                    && "grp_attn_n must be positive");                     // NOLINT
+        GGML_ASSERT(ga_w % ga_n == 0            && "grp_attn_w must be a multiple of grp_attn_n");     // NOLINT
+      //GGML_ASSERT(n_ctx_train % ga_w == 0     && "n_ctx_train must be a multiple of grp_attn_w");    // NOLINT
+      //GGML_ASSERT(n_ctx >= n_ctx_train * ga_n && "n_ctx must be at least n_ctx_train * grp_attn_n"); // NOLINT
+        // LOG_TEE("self-extend: n_ctx_train = %d, grp_attn_n = %d, grp_attn_w = %d\n", n_ctx_train, ga_n, ga_w);
+    }
+    // LOG_TEE("\n\n");
+
+    if (params.interactive) {
+        const char *control_message;
+        if (params.multiline_input) {
+            control_message = " - To return control to LLaMa, end your input with '\\'.\n"
+                              " - To return control without starting a new line, end your input with '/'.\n";
+        } else {
+            control_message = " - Press Return to return control to LLaMa.\n"
+                              " - To return control without starting a new line, end your input with '/'.\n"
+                              " - If you want to submit another line, end your input with '\\'.\n";
+        }
+        // LOG_TEE("== Running in interactive mode. ==\n");
+// #if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) || defined (_WIN32)
+//         // LOG_TEE(       " - Press Ctrl+C to interject at any time.\n");
+// #endif
+        // LOG_TEE(       "%s\n", control_message);
+
+        is_interacting = params.interactive_first;
+    }
+
+    bool is_antiprompt        = false;
+    bool input_echo           = true;
+    bool display              = true;
+    bool need_to_save_session = !path_session.empty() && n_matching_session_tokens < embd_inp.size();
+
+    int n_past             = 0;
+    int n_remain           = params.n_predict;
+    int n_consumed         = 0;
+    int n_session_consumed = 0;
+    int n_past_guidance    = 0;
+
+    std::vector<int>   input_tokens;  g_input_tokens  = &input_tokens;
+    std::vector<int>   output_tokens; g_output_tokens = &output_tokens;
+    std::ostringstream output_ss;     g_output_ss     = &output_ss;
+
+    // the first thing we will do is to output the prompt, so set color accordingly
+    console::set_display(console::prompt);
+    display = params.display_prompt;
+
+    std::vector<llama_token> embd;
+    std::vector<llama_token> embd_guidance;
+
+    // tokenized antiprompts
+    std::vector<std::vector<llama_token>> antiprompt_ids;
+
+    antiprompt_ids.reserve(params.antiprompt.size());
+    for (const std::string & antiprompt : params.antiprompt) {
+        antiprompt_ids.emplace_back(::llama_tokenize(ctx, antiprompt, false, true));
+    }
+
+    struct llama_sampling_context * ctx_sampling = llama_sampling_init(sparams);
+
+    while ((n_remain != 0 && !is_antiprompt) || params.interactive) {
+        // predict
+        if (!embd.empty()) {
+            // Note: (n_ctx - 4) here is to match the logic for commandline prompt handling via
+            // --prompt or --file which uses the same value.
+            int max_embd_size = n_ctx - 4;
+
+            // Ensure the input doesn't exceed the context size by truncating embd if necessary.
+            if ((int) embd.size() > max_embd_size) {
+                const int skipped_tokens = (int) embd.size() - max_embd_size;
+                embd.resize(max_embd_size);
+
+                console::set_display(console::error);
+                printf("<<input too long: skipped %d token%s>>", skipped_tokens, skipped_tokens != 1 ? "s" : "");
+                console::set_display(console::reset);
+                fflush(stdout);
+            }
+
+            if (ga_n == 1) {
+                // infinite text generation via context shifting
+                // if we run out of context:
+                // - take the n_keep first tokens from the original prompt (via n_past)
+                // - take half of the last (n_ctx - n_keep) tokens and recompute the logits in batches
+                if (n_past + (int) embd.size() + std::max<int>(0, guidance_offset) > n_ctx) {
+                    if (params.n_predict == -2) {
+                        // LOG_TEE("\n\n%s: context full and n_predict == -%d => stopping\n", __func__, params.n_predict);
+                        break;
+                    }
+
+                    const int n_left    = n_past - params.n_keep;
+                    const int n_discard = n_left/2;
+
+                    LOG("context full, swapping: n_past = %d, n_left = %d, n_ctx = %d, n_keep = %d, n_discard = %d\n",
+                            n_past, n_left, n_ctx, params.n_keep, n_discard);
+
+                    llama_kv_cache_seq_rm (ctx, 0, params.n_keep            , params.n_keep + n_discard);
+                    llama_kv_cache_seq_add(ctx, 0, params.n_keep + n_discard, n_past, -n_discard);
+
+                    n_past -= n_discard;
+
+                    if (ctx_guidance) {
+                        n_past_guidance -= n_discard;
+                    }
+
+                    LOG("after swap: n_past = %d, n_past_guidance = %d\n", n_past, n_past_guidance);
+
+                    LOG("embd: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd).c_str());
+
+                    LOG("clear session path\n");
+                    path_session.clear();
+                }
+            } else {
+                // context extension via Self-Extend
+                while (n_past >= ga_i + ga_w) {
+                    const int ib = (ga_n*ga_i)/ga_w;
+                    const int bd = (ga_w/ga_n)*(ga_n - 1);
+                    const int dd = (ga_w/ga_n) - ib*bd - ga_w;
+
+                    LOG("\n");
+                    LOG("shift: [%6d, %6d] + %6d -> [%6d, %6d]\n", ga_i, n_past, ib*bd, ga_i + ib*bd, n_past + ib*bd);
+                    LOG("div:   [%6d, %6d] / %6d -> [%6d, %6d]\n", ga_i + ib*bd, ga_i + ib*bd + ga_w, ga_n, (ga_i + ib*bd)/ga_n, (ga_i + ib*bd + ga_w)/ga_n);
+                    LOG("shift: [%6d, %6d] + %6d -> [%6d, %6d]\n", ga_i + ib*bd + ga_w, n_past + ib*bd, dd, ga_i + ib*bd + ga_w + dd, n_past + ib*bd + dd);
+
+                    llama_kv_cache_seq_add(ctx, 0, ga_i,                n_past,              ib*bd);
+                    llama_kv_cache_seq_div(ctx, 0, ga_i + ib*bd,        ga_i + ib*bd + ga_w, ga_n);
+                    llama_kv_cache_seq_add(ctx, 0, ga_i + ib*bd + ga_w, n_past + ib*bd,      dd);
+
+                    n_past -= bd;
+
+                    ga_i += ga_w/ga_n;
+
+                    LOG("\nn_past_old = %d, n_past = %d, ga_i = %d\n\n", n_past + bd, n_past, ga_i);
+                }
+            }
+
+            // try to reuse a matching prefix from the loaded session instead of re-eval (via n_past)
+            if (n_session_consumed < (int) session_tokens.size()) {
+                size_t i = 0;
+                for ( ; i < embd.size(); i++) {
+                    if (embd[i] != session_tokens[n_session_consumed]) {
+                        session_tokens.resize(n_session_consumed);
+                        break;
+                    }
+
+                    n_past++;
+                    n_session_consumed++;
+
+                    if (n_session_consumed >= (int) session_tokens.size()) {
+                        ++i;
+                        break;
+                    }
+                }
+                if (i > 0) {
+                    embd.erase(embd.begin(), embd.begin() + i);
+                }
+            }
+
+            // evaluate tokens in batches
+            // embd is typically prepared beforehand to fit within a batch, but not always
+            if (ctx_guidance) {
+                int input_size = 0;
+                llama_token * input_buf = NULL;
+
+                if (n_past_guidance < (int) guidance_inp.size()) {
+                    // Guidance context should have the same data with these modifications:
+                    //
+                    // * Replace the initial prompt
+                    // * Shift everything by guidance_offset
+                    embd_guidance = guidance_inp;
+                    if (embd.begin() + original_prompt_len < embd.end()) {
+                        embd_guidance.insert(
+                            embd_guidance.end(),
+                            embd.begin() + original_prompt_len,
+                            embd.end()
+                        );
+                    }
+
+                    input_buf  = embd_guidance.data();
+                    input_size = embd_guidance.size();
+
+                    LOG("guidance context: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_guidance).c_str());
+                } else {
+                    input_buf  = embd.data();
+                    input_size = embd.size();
+                }
+
+                for (int i = 0; i < input_size; i += params.n_batch) {
+                    int n_eval = std::min(input_size - i, params.n_batch);
+                    if (llama_decode(ctx_guidance, llama_batch_get_one(input_buf + i, n_eval, n_past_guidance, 0))) {
+                        // LOG_TEE("%s : failed to eval\n", __func__);
+                        return 1;
+                    }
+
+                    n_past_guidance += n_eval;
+                }
+            }
+
+            for (int i = 0; i < (int) embd.size(); i += params.n_batch) {
+                int n_eval = (int) embd.size() - i;
+                if (n_eval > params.n_batch) {
+                    n_eval = params.n_batch;
+                }
+
+                LOG("eval: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd).c_str());
+
+                if (llama_decode(ctx, llama_batch_get_one(&embd[i], n_eval, n_past, 0))) {
+                    LOG_TEE("%s : failed to eval\n", __func__);
+                    return 1;
+                }
+
+                n_past += n_eval;
+
+                // LOG("n_past = %d\n", n_past);
+                // Display total tokens alongside total time
+                // if (params.n_print > 0 && n_past % params.n_print == 0) {
+                //     LOG_TEE("\n\033[31mTokens consumed so far = %d / %d \033[0m\n", n_past, n_ctx);
+                // }
+            }
+
+            if (!embd.empty() && !path_session.empty()) {
+                session_tokens.insert(session_tokens.end(), embd.begin(), embd.end());
+                n_session_consumed = session_tokens.size();
+            }
+        }
+
+        embd.clear();
+        embd_guidance.clear();
+
+        if ((int) embd_inp.size() <= n_consumed && !is_interacting) {
+            // optionally save the session on first sample (for faster prompt loading next time)
+            if (!path_session.empty() && need_to_save_session && !params.prompt_cache_ro) {
+                need_to_save_session = false;
+                llama_save_session_file(ctx, path_session.c_str(), session_tokens.data(), session_tokens.size());
+
+                LOG("saved session to %s\n", path_session.c_str());
+            }
+
+            const llama_token id = llama_sampling_sample(ctx_sampling, ctx, ctx_guidance);
+
+            llama_sampling_accept(ctx_sampling, ctx, id, true);
+
+            LOG("last: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, ctx_sampling->prev).c_str());
+
+            embd.push_back(id);
+
+            // echo this to console
+            input_echo = true;
+
+            // decrement remaining sampling budget
+            --n_remain;
+
+            LOG("n_remain: %d\n", n_remain);
+        } else {
+            // some user input remains from prompt or interaction, forward it to processing
+            LOG("embd_inp.size(): %d, n_consumed: %d\n", (int) embd_inp.size(), n_consumed);
+            while ((int) embd_inp.size() > n_consumed) {
+                embd.push_back(embd_inp[n_consumed]);
+
+                // push the prompt in the sampling context in order to apply repetition penalties later
+                // for the prompt, we don't apply grammar rules
+                llama_sampling_accept(ctx_sampling, ctx, embd_inp[n_consumed], false);
+
+                ++n_consumed;
+                if ((int) embd.size() >= params.n_batch) {
+                    break;
+                }
+            }
+        }
+
+        // display text
+        if (input_echo && display) {
+            for (auto id : embd) {
+                const std::string token_str = llama_token_to_piece(ctx, id);
+                printf("%s", token_str.c_str());
+
+                if (embd.size() > 1) {
+                    input_tokens.push_back(id);
+                } else {
+                    output_tokens.push_back(id);
+                    output_ss << token_str;
+                }
+            }
+            fflush(stdout);
+        }
+        // reset color to default if there is no pending user input
+        if (input_echo && (int) embd_inp.size() == n_consumed) {
+            console::set_display(console::reset);
+            display = true;
+        }
+
+        // if not currently processing queued inputs;
+        if ((int) embd_inp.size() <= n_consumed) {
+            // check for reverse prompt in the last n_prev tokens
+            if (!params.antiprompt.empty()) {
+                const int n_prev = 32;
+                const std::string last_output = llama_sampling_prev_str(ctx_sampling, ctx, n_prev);
+
+                is_antiprompt = false;
+                // Check if each of the reverse prompts appears at the end of the output.
+                // If we're not running interactively, the reverse prompt might be tokenized with some following characters
+                // so we'll compensate for that by widening the search window a bit.
+                for (std::string & antiprompt : params.antiprompt) {
+                    size_t extra_padding = params.interactive ? 0 : 2;
+                    size_t search_start_pos = last_output.length() > static_cast<size_t>(antiprompt.length() + extra_padding)
+                        ? last_output.length() - static_cast<size_t>(antiprompt.length() + extra_padding)
+                        : 0;
+
+                    if (last_output.find(antiprompt, search_start_pos) != std::string::npos) {
+                        if (params.interactive) {
+                            is_interacting = true;
+                        }
+                        is_antiprompt = true;
+                        break;
+                    }
+                }
+
+                // check for reverse prompt using special tokens
+                llama_token last_token = llama_sampling_last(ctx_sampling);
+                for (std::vector<llama_token> ids : antiprompt_ids) {
+                    if (ids.size() == 1 && last_token == ids[0]) {
+                        if (params.interactive) {
+                            is_interacting = true;
+                        }
+                        is_antiprompt = true;
+                        break;
+                    }
+                }
+
+                if (is_antiprompt) {
+                    LOG("found antiprompt: %s\n", last_output.c_str());
+                }
+            }
+
+            // deal with end of text token in interactive mode
+            if (llama_sampling_last(ctx_sampling) == llama_token_eos(model)) {
+                LOG("found EOS token\n");
+
+                if (params.interactive) {
+                    if (!params.antiprompt.empty()) {
+                        // tokenize and inject first reverse prompt
+                        const auto first_antiprompt = ::llama_tokenize(ctx, params.antiprompt.front(), false, true);
+                        embd_inp.insert(embd_inp.end(), first_antiprompt.begin(), first_antiprompt.end());
+                        is_antiprompt = true;
+                    }
+
+                    is_interacting = true;
+                    printf("\n");
+                } else if (params.instruct || params.chatml) {
+                    is_interacting = true;
+                }
+            }
+
+            if (n_past > 0 && is_interacting) {
+                LOG("waiting for user input\n");
+
+                if (params.instruct || params.chatml) {
+                    printf("\n> ");
+                }
+
+                if (params.input_prefix_bos) {
+                    LOG("adding input prefix BOS token\n");
+                    embd_inp.push_back(llama_token_bos(model));
+                }
+
+                std::string buffer;
+                if (!params.input_prefix.empty()) {
+                    LOG("appending input prefix: '%s'\n", params.input_prefix.c_str());
+                    printf("%s", params.input_prefix.c_str());
+                }
+
+                // color user input only
+                console::set_display(console::user_input);
+                display = params.display_prompt;
+
+                std::string line;
+                bool another_line = true;
+                do {
+                    another_line = console::readline(line, params.multiline_input);
+                    buffer += line;
+                } while (another_line);
+
+                // done taking input, reset color
+                console::set_display(console::reset);
+                display = true;
+
+                // Add tokens to embd only if the input buffer is non-empty
+                // Entering a empty line lets the user pass control back
+                if (buffer.length() > 1) {
+                    // append input suffix if any
+                    if (!params.input_suffix.empty()) {
+                        LOG("appending input suffix: '%s'\n", params.input_suffix.c_str());
+                        printf("%s", params.input_suffix.c_str());
+                    }
+
+                    LOG("buffer: '%s'\n", buffer.c_str());
+
+                    const size_t original_size = embd_inp.size();
+
+                    // instruct mode: insert instruction prefix
+                    if (params.instruct && !is_antiprompt) {
+                        LOG("inserting instruction prefix\n");
+                        n_consumed = embd_inp.size();
+                        embd_inp.insert(embd_inp.end(), inp_pfx.begin(), inp_pfx.end());
+                    }
+                    // chatml mode: insert user chat prefix
+                    if (params.chatml && !is_antiprompt) {
+                        LOG("inserting chatml prefix\n");
+                        n_consumed = embd_inp.size();
+                        embd_inp.insert(embd_inp.end(), cml_pfx.begin(), cml_pfx.end());
+                    }
+                    if (params.escape) {
+                        process_escapes(buffer);
+                    }
+
+                    const auto line_pfx = ::llama_tokenize(ctx, params.input_prefix, false, true);
+                    const auto line_inp = ::llama_tokenize(ctx, buffer,              false, false);
+                    const auto line_sfx = ::llama_tokenize(ctx, params.input_suffix, false, true);
+
+                    LOG("input tokens: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, line_inp).c_str());
+
+                    embd_inp.insert(embd_inp.end(), line_pfx.begin(), line_pfx.end());
+                    embd_inp.insert(embd_inp.end(), line_inp.begin(), line_inp.end());
+                    embd_inp.insert(embd_inp.end(), line_sfx.begin(), line_sfx.end());
+
+                    // instruct mode: insert response suffix
+                    if (params.instruct) {
+                        LOG("inserting instruction suffix\n");
+                        embd_inp.insert(embd_inp.end(), inp_sfx.begin(), inp_sfx.end());
+                    }
+                    // chatml mode: insert assistant chat suffix
+                    if (params.chatml) {
+                        LOG("inserting chatml suffix\n");
+                        embd_inp.insert(embd_inp.end(), cml_sfx.begin(), cml_sfx.end());
+                    }
+
+                    for (size_t i = original_size; i < embd_inp.size(); ++i) {
+                        const llama_token token = embd_inp[i];
+                        output_tokens.push_back(token);
+                        output_ss << llama_token_to_piece(ctx, token);
+                    }
+
+                    n_remain -= line_inp.size();
+                    LOG("n_remain: %d\n", n_remain);
+                } else {
+                    LOG("empty line, passing control back\n");
+                }
+
+                input_echo = false; // do not echo this again
+            }
+
+            if (n_past > 0) {
+                if (is_interacting) {
+                    llama_sampling_reset(ctx_sampling);
+                }
+                is_interacting = false;
+            }
+        }
+
+        // end of text token
+        if (!embd.empty() && embd.back() == llama_token_eos(model) && !(params.instruct || params.interactive || params.chatml)) {
+            // LOG_TEE(" [end of text]\n");
+            break;
+        }
+
+        // In interactive mode, respect the maximum number of tokens and drop back to user input when reached.
+        // We skip this logic when n_predict == -1 (infinite) or -2 (stop at context size).
+        if (params.interactive && n_remain <= 0 && params.n_predict >= 0) {
+            n_remain = params.n_predict;
+            is_interacting = true;
+        }
+    }
+
+    if (!path_session.empty() && params.prompt_cache_all && !params.prompt_cache_ro) {
+        // LOG_TEE("\n%s: saving final output to session file '%s'\n", __func__, path_session.c_str());
+        llama_save_session_file(ctx, path_session.c_str(), session_tokens.data(), session_tokens.size());
+    }
+
+    // llama_print_timings(ctx);
+    write_logfile(ctx, params, model, input_tokens, output_ss.str(), output_tokens);
+
+    if (ctx_guidance) { llama_free(ctx_guidance); }
+    llama_free(ctx);
+    llama_free_model(model);
+
+    llama_sampling_free(ctx_sampling);
+    llama_backend_free();
+
+
+
+    return 0;
+}