llama.cpp/tools/server/server-context.cpp

#include "server-context.h"
#include "server-common.h"
#include "server-http.h"
#include "server-task.h"
#include "server-queue.h"

#include "arg.h"
#include "common.h"
#include "llama.h"
#include "log.h"
#include "sampling.h"
#include "speculative.h"
#include "mtmd.h"
#include "mtmd-helper.h"

#include <cstddef>
#include <cinttypes>
#include <memory>
#include <unordered_set>

// fix problem with std::min and std::max
#if defined(_WIN32)
#define WIN32_LEAN_AND_MEAN
#ifndef NOMINMAX
#   define NOMINMAX
#endif
#include <windows.h>
#endif

using json = nlohmann::ordered_json;

constexpr int HTTP_POLLING_SECONDS = 1;

// state diagram: https://github.com/ggml-org/llama.cpp/pull/9283
enum slot_state {
    SLOT_STATE_IDLE,
    SLOT_STATE_STARTED, // TODO: this state is only used for setting up the initial prompt processing; maybe merge it with launch_slot_with_task in the future
    SLOT_STATE_PROCESSING_PROMPT,
    SLOT_STATE_DONE_PROMPT,
    SLOT_STATE_GENERATING,
};

enum server_state {
    SERVER_STATE_LOADING_MODEL,  // Server is starting up, model not fully loaded yet
    SERVER_STATE_READY,          // Server is ready and model is loaded
};

static bool server_task_type_need_embd(server_task_type task_type) {
    switch (task_type) {
        case SERVER_TASK_TYPE_EMBEDDING:
        case SERVER_TASK_TYPE_RERANK:
            return true;
        default:
            return false;
    }
}

static bool server_task_type_need_logits(server_task_type task_type) {
    switch (task_type) {
        case SERVER_TASK_TYPE_COMPLETION:
        case SERVER_TASK_TYPE_INFILL:
            return true;
        default:
            return false;
    }
}

struct server_slot {
    int id;

    llama_batch batch_spec = {};

    // TODO: change to unique_ptrs for consistency:
    llama_context * ctx = nullptr;
    llama_context * ctx_dft = nullptr;

    // multimodal
    mtmd_context * mctx = nullptr;

    common_speculative * spec = nullptr;

    std::unique_ptr<const server_task> task;
    std::unique_ptr<const server_task> task_prev; // used for debugging

    // used to determine the slot that has been used the longest
    int64_t t_last_used = -1;

    // generation props
    int32_t n_ctx       = 0;  // context size per slot
    int32_t n_keep      = 0;
    int32_t n_decoded   = 0;
    int32_t n_remaining = -1;
    int32_t i_batch     = -1;

    int32_t n_prompt_tokens_cache     = 0;
    int32_t n_prompt_tokens_processed = 0;

    size_t last_nl_pos = 0;

    std::string  generated_text;
    llama_tokens generated_tokens;

    common_chat_msg chat_msg;

    std::vector<completion_token_output> generated_token_probs;

    bool has_next_token = true;
    bool has_new_line   = false;
    bool truncated      = false;

    stop_type stop;

    std::string stopping_word;

    // state
    slot_state state = SLOT_STATE_IDLE;

    server_prompt prompt;

    void prompt_save(server_prompt_cache & prompt_cache) const {
        GGML_ASSERT(prompt.data.size() == 0);

        const size_t cur_size = llama_state_seq_get_size_ext(ctx, id, 0);

        SRV_WRN(" - saving prompt with length %d, total state size = %.3f MiB\n",
                (int) prompt.tokens.size(), cur_size / (1024.0 * 1024.0));

        auto * cur = prompt_cache.alloc(prompt, cur_size);
        if (cur == nullptr) {
            return;
        }

        llama_state_seq_get_data_ext(ctx, cur->data.data(), cur_size, id, 0);
    }

    bool prompt_load(server_prompt_cache & prompt_cache, const server_tokens & tokens) {
        bool res = prompt_cache.load(prompt, tokens, ctx, id);
        if (!res) {
            SLT_WRN(*this, "%s", "failed to load prompt from cache\n");
        }

        return res;
    }

    std::vector<common_adapter_lora_info> lora;
    int32_t alora_invocation_start = -1;

    // sampling
    json json_schema;

    struct common_sampler * smpl = nullptr;

    llama_token sampled;

    common_chat_format chat_format = COMMON_CHAT_FORMAT_CONTENT_ONLY;
    std::vector<std::string> generated_tool_call_ids;

    // stats
    size_t n_sent_text = 0; // number of sent text character

    int64_t t_start_process_prompt;
    int64_t t_start_generation;

    double t_prompt_processing; // ms
    double t_token_generation;  // ms

    std::function<void(int)> callback_on_release;

    // Speculative decoding stats
    int32_t n_draft_total = 0;      // Total draft tokens generated
    int32_t n_draft_accepted = 0;   // Draft tokens actually accepted

    void reset() {
        SLT_DBG(*this, "%s", "\n");

        n_prompt_tokens_cache = 0;

        last_nl_pos    = 0;
        generated_text = "";
        has_new_line   = false;
        truncated      = false;
        stop           = STOP_TYPE_NONE;
        stopping_word  = "";
        n_sent_text    = 0;
        chat_format    = COMMON_CHAT_FORMAT_CONTENT_ONLY;

        generated_tokens.clear();
        generated_token_probs.clear();
        chat_msg = {};
        json_schema = json();
        generated_tool_call_ids.clear();

        // clear speculative decoding stats
        n_draft_total = 0;
        n_draft_accepted = 0;

        task.reset();
        task_prev.reset();

        // clear alora start
        alora_invocation_start = -1;
    }

    bool need_embd() const {
        GGML_ASSERT(task);

        return server_task_type_need_embd(task->type);
    }

    bool need_logits() const {
        GGML_ASSERT(task);

        return server_task_type_need_logits(task->type);
    }

    // if the context does not have a memory module then all embeddings have to be computed within a single ubatch
    // also we cannot split if the pooling would require any past tokens
    bool can_split() const {
        return
            !need_embd() ||
            (llama_get_memory(ctx) && llama_pooling_type(ctx) == LLAMA_POOLING_TYPE_LAST);
    }

    bool can_batch_with(server_slot & other_slot) const {
        GGML_ASSERT(task);

        return task->type == other_slot.task->type && are_lora_equal(lora, other_slot.lora);
    }

    bool has_budget(const common_params & global_params) {
        GGML_ASSERT(task);

        if (task->params.n_predict == -1 && global_params.n_predict == -1) {
            return true; // limitless
        }

        n_remaining = -1;

        if (task->params.n_predict != -1) {
            n_remaining = task->params.n_predict - n_decoded;
        } else if (global_params.n_predict != -1) {
            n_remaining = global_params.n_predict - n_decoded;
        }

        return n_remaining > 0; // no budget
    }

    bool is_processing() const {
        return state != SLOT_STATE_IDLE;
    }

    bool can_speculate() const {
        return ctx_dft;
    }

    void add_token(const completion_token_output & token) {
        if (!is_processing()) {
            SLT_WRN(*this, "%s", "slot is not processing\n");
            return;
        }
        generated_token_probs.push_back(token);
    }

    void release() {
        if (is_processing()) {
            GGML_ASSERT(task);

            SLT_INF(*this, "stop processing: n_tokens = %d, truncated = %d\n", prompt.n_tokens(), truncated);

            t_last_used = ggml_time_us();
            t_token_generation = (ggml_time_us() - t_start_generation) / 1e3;
            state = SLOT_STATE_IDLE;

            task_prev = std::move(task);
            task.reset();

            callback_on_release(id);
        }
    }

    result_timings get_timings() const {
        result_timings timings;
        timings.cache_n = n_prompt_tokens_cache;

        timings.prompt_n            = n_prompt_tokens_processed;
        timings.prompt_ms           = t_prompt_processing;
        timings.prompt_per_token_ms = t_prompt_processing / n_prompt_tokens_processed;
        timings.prompt_per_second   = 1e3 / t_prompt_processing * n_prompt_tokens_processed;

        timings.predicted_n            = n_decoded;
        timings.predicted_ms           = t_token_generation;
        timings.predicted_per_token_ms = t_token_generation / n_decoded;
        timings.predicted_per_second   = 1e3 / t_token_generation * n_decoded;

        // Add speculative metrics
        if (n_draft_total > 0) {
            timings.draft_n          = n_draft_total;
            timings.draft_n_accepted = n_draft_accepted;
        }

        return timings;
    }

    const common_chat_msg & update_chat_msg(std::vector<common_chat_msg_diff> & diffs) {
        GGML_ASSERT(task);

        auto previous_msg = chat_msg;
        SRV_DBG("Parsing chat message: %s\n", generated_text.c_str());
        auto new_msg = common_chat_parse(
            generated_text,
            /* is_partial= */ stop != STOP_TYPE_EOS,
            task->params.oaicompat_chat_syntax);
        if (!new_msg.empty()) {
            new_msg.set_tool_call_ids(generated_tool_call_ids, gen_tool_call_id);
            chat_msg = new_msg;
            diffs = common_chat_msg_diff::compute_diffs(previous_msg, new_msg.empty() ? previous_msg : new_msg);
        }
        return chat_msg;
    }

    size_t find_stopping_strings(const std::string & text, const size_t last_token_size, bool is_full_stop) {
        GGML_ASSERT(task);

        size_t stop_pos = std::string::npos;

        for (const std::string & word : task->params.antiprompt) {
            size_t pos;

            if (is_full_stop) {
                const size_t tmp      = word.size() + last_token_size;
                const size_t from_pos = text.size() > tmp ? text.size() - tmp : 0;

                pos = text.find(word, from_pos);
            } else {
                // otherwise, partial stop
                pos = string_find_partial_stop(text, word);
            }

            if (pos != std::string::npos && (stop_pos == std::string::npos || pos < stop_pos)) {
                if (is_full_stop) {
                    stop           = STOP_TYPE_WORD;
                    stopping_word  = word;
                    has_next_token = false;
                }
                stop_pos = pos;
            }
        }

        return stop_pos;
    }

    void print_timings() const {
        const double t_prompt        =       t_prompt_processing / n_prompt_tokens_processed;
        const double n_prompt_second = 1e3 / t_prompt_processing * n_prompt_tokens_processed;

        const double t_gen        =       t_token_generation / n_decoded;
        const double n_gen_second = 1e3 / t_token_generation * n_decoded;

        SLT_INF(*this,
                "\n"
                "prompt eval time = %10.2f ms / %5d tokens (%8.2f ms per token, %8.2f tokens per second)\n"
                "       eval time = %10.2f ms / %5d tokens (%8.2f ms per token, %8.2f tokens per second)\n"
                "      total time = %10.2f ms / %5d tokens\n",
                t_prompt_processing, n_prompt_tokens_processed, t_prompt, n_prompt_second,
                t_token_generation, n_decoded, t_gen, n_gen_second,
                t_prompt_processing + t_token_generation, n_prompt_tokens_processed + n_decoded);

        if (n_draft_total > 0) {
            const float draft_ratio = (float) n_draft_accepted / n_draft_total;
            SLT_INF(*this,
                    "\n"
                    "draft acceptance rate = %0.5f (%5d accepted / %5d generated)\n",
                    draft_ratio, n_draft_accepted, n_draft_total
            );
        }
    }

    json to_json(bool only_metrics = false) const {
        json res;

        res = {
            {"id",            id},
            {"n_ctx",         n_ctx},
            {"speculative",   can_speculate()},
            {"is_processing", is_processing()},
        };

        const auto & ptask = task ? task : task_prev;

        if (ptask) {
            res["id_task"] = ptask->id;
            res["params"] = ptask->params.to_json(only_metrics);
            res["next_token"] = {
                {
                    {"has_next_token", has_next_token},
                    {"has_new_line",   has_new_line},
                    {"n_remain",       n_remaining},
                    {"n_decoded",      n_decoded},
                }
            };

            if (!only_metrics) {
                res["prompt"] = ptask->tokens.detokenize(ctx, true);
                res["generated"] = generated_text;
            }
        }

        return res;
    }
};



//
// server_metrics
//

struct server_metrics {
    int64_t t_start = 0;

    uint64_t n_prompt_tokens_processed_total = 0;
    uint64_t t_prompt_processing_total       = 0;
    uint64_t n_tokens_predicted_total        = 0;
    uint64_t t_tokens_generation_total       = 0;

    uint64_t n_tokens_max = 0;

    uint64_t n_prompt_tokens_processed = 0;
    uint64_t t_prompt_processing       = 0;

    uint64_t n_tokens_predicted  = 0;
    uint64_t t_tokens_generation = 0;

    uint64_t n_decode_total     = 0;
    uint64_t n_busy_slots_total = 0;

    void init() {
        t_start = ggml_time_us();
    }

    void on_prompt_eval(const server_slot & slot) {
        n_prompt_tokens_processed_total += slot.n_prompt_tokens_processed;
        n_prompt_tokens_processed       += slot.n_prompt_tokens_processed;
        t_prompt_processing             += slot.t_prompt_processing;
        t_prompt_processing_total       += slot.t_prompt_processing;

        n_tokens_max = std::max(n_tokens_max, (uint64_t) slot.prompt.n_tokens());
    }

    void on_prediction(const server_slot & slot) {
        n_tokens_predicted_total   += slot.n_decoded;
        n_tokens_predicted         += slot.n_decoded;
        t_tokens_generation        += slot.t_token_generation;
        t_tokens_generation_total  += slot.t_token_generation;
    }

    void on_decoded(const std::vector<server_slot> & slots) {
        n_decode_total++;
        for (const auto & slot : slots) {
            if (slot.is_processing()) {
                n_busy_slots_total++;
            }
            n_tokens_max = std::max(n_tokens_max, (uint64_t) slot.prompt.n_tokens());
        }
    }

    void reset_bucket() {
        n_prompt_tokens_processed = 0;
        t_prompt_processing       = 0;
        n_tokens_predicted        = 0;
        t_tokens_generation       = 0;
    }
};


//
// server_context_impl (private implementation)
//

struct server_context_impl {
    common_params params_base;

    // note: keep these alive - they determine the lifetime of the model, context, etc.
    common_init_result llama_init;
    common_init_result llama_init_dft;

    llama_model * model = nullptr;
    llama_context * ctx = nullptr;

    // multimodal
    mtmd_context * mctx = nullptr;

    const llama_vocab * vocab = nullptr;
    bool vocab_dft_compatible = true;

    llama_model * model_dft = nullptr;

    llama_context_params cparams_dft;

    llama_batch batch {};

    bool add_bos_token  = true;

    int32_t n_ctx; // total context for all clients / slots

    // slots / clients
    std::vector<server_slot> slots;

    int slots_debug = 0;

    server_queue    queue_tasks;
    server_response queue_results;

    std::unique_ptr<server_prompt_cache> prompt_cache;

    server_metrics metrics;

    // Necessary similarity of prompt for slot selection
    float slot_prompt_similarity = 0.0f;

    common_chat_templates_ptr chat_templates;
    oaicompat_parser_options  oai_parser_opt;

    ~server_context_impl() {
        mtmd_free(mctx);

        // Clear any sampling context
        for (server_slot & slot : slots) {
            common_sampler_free(slot.smpl);
            slot.smpl = nullptr;

            llama_free(slot.ctx_dft);
            slot.ctx_dft = nullptr;

            common_speculative_free(slot.spec);
            slot.spec = nullptr;

            llama_batch_free(slot.batch_spec);
        }

        llama_batch_free(batch);
    }

    // load the model and initialize llama_context
    bool load_model(const common_params & params) {
        SRV_INF("loading model '%s'\n", params.model.path.c_str());

        params_base = params;

        llama_init = common_init_from_params(params_base);

        model = llama_init.model.get();
        ctx   = llama_init.context.get();

        if (model == nullptr) {
            SRV_ERR("failed to load model, '%s'\n", params_base.model.path.c_str());
            return false;
        }

        vocab = llama_model_get_vocab(model);

        n_ctx = llama_n_ctx(ctx);

        add_bos_token = llama_vocab_get_add_bos(vocab);

        if (params_base.has_speculative()) {
            SRV_INF("loading draft model '%s'\n", params_base.speculative.model.path.c_str());

            auto params_dft = params_base;

            params_dft.devices      = params_base.speculative.devices;
            params_dft.model        = params_base.speculative.model;
            params_dft.n_ctx        = params_base.speculative.n_ctx == 0 ? llama_n_ctx_seq(ctx) : params_base.speculative.n_ctx;
            params_dft.n_gpu_layers = params_base.speculative.n_gpu_layers;
            params_dft.n_parallel   = 1;
            params_dft.cache_type_k = params_base.speculative.cache_type_k;
            params_dft.cache_type_v = params_base.speculative.cache_type_v;

            params_dft.cpuparams.n_threads = params_base.speculative.cpuparams.n_threads;
            params_dft.cpuparams_batch.n_threads = params_base.speculative.cpuparams_batch.n_threads;
            params_dft.tensor_buft_overrides = params_base.speculative.tensor_buft_overrides;

            llama_init_dft = common_init_from_params(params_dft);

            model_dft = llama_init_dft.model.get();

            if (model_dft == nullptr) {
                SRV_ERR("failed to load draft model, '%s'\n", params_base.speculative.model.path.c_str());
                return false;
            }

            vocab_dft_compatible = common_speculative_are_compatible(ctx, llama_init_dft.context.get());
            if (!vocab_dft_compatible) {
                SRV_INF("the draft model '%s' is not compatible with the target model '%s'. tokens will be translated between the draft and target models.\n", params_base.speculative.model.path.c_str(), params_base.model.path.c_str());
            }

            const int n_ctx_dft = llama_n_ctx(llama_init_dft.context.get());

            cparams_dft = common_context_params_to_llama(params_dft);
            cparams_dft.n_batch = n_ctx_dft;

            // the context is not needed - we will create one for each slot
            llama_init_dft.context.reset();
        }

        chat_templates = common_chat_templates_init(model, params_base.chat_template);
        try {
            common_chat_format_example(chat_templates.get(), params.use_jinja, params.default_template_kwargs);
        } catch (const std::exception & e) {
            SRV_WRN("%s: Chat template parsing error: %s\n", __func__, e.what());
            SRV_WRN("%s: The chat template that comes with this model is not yet supported, falling back to chatml. This may cause the model to output suboptimal responses\n", __func__);
            chat_templates = common_chat_templates_init(model, "chatml");
        }

        std::string & mmproj_path = params_base.mmproj.path;
        if (!mmproj_path.empty()) {
            mtmd_helper_log_set(common_log_default_callback, nullptr);

            mtmd_context_params mparams = mtmd_context_params_default();
            mparams.use_gpu          = params_base.mmproj_use_gpu;
            mparams.print_timings    = false;
            mparams.n_threads        = params_base.cpuparams.n_threads;
            mparams.flash_attn_type  = params_base.flash_attn_type;
            mparams.image_min_tokens = params_base.image_min_tokens;
            mparams.image_max_tokens = params_base.image_max_tokens;
            mctx = mtmd_init_from_file(mmproj_path.c_str(), model, mparams);
            if (mctx == nullptr) {
                SRV_ERR("failed to load multimodal model, '%s'\n", mmproj_path.c_str());
                return false;
            }
            SRV_INF("loaded multimodal model, '%s'\n", mmproj_path.c_str());

            if (params_base.ctx_shift) {
                params_base.ctx_shift = false;
                SRV_WRN("%s\n", "ctx_shift is not supported by multimodal, it will be disabled");
            }

            if (params_base.n_cache_reuse) {
                params_base.n_cache_reuse = 0;
                SRV_WRN("%s\n", "cache_reuse is not supported by multimodal, it will be disabled");
            }

            if (params_base.has_speculative()) {
                SRV_ERR("%s\n", "err: speculative decode is not supported by multimodal");
                return false;
            }
        }

        if (!llama_memory_can_shift(llama_get_memory(ctx))) {
            if (params_base.ctx_shift) {
                params_base.ctx_shift = false;
                SRV_WRN("%s\n", "ctx_shift is not supported by this context, it will be disabled");
            }

            if (params_base.n_cache_reuse) {
                params_base.n_cache_reuse = 0;
                SRV_WRN("%s\n", "cache_reuse is not supported by this context, it will be disabled");
            }
        }

        return true;
    }

    // initialize slots and server-related data
    void init() {
        // wiring up server queues
        queue_tasks.on_new_task([this](server_task && task) {
            process_single_task(std::move(task));
        });
        queue_tasks.on_update_slots([this]() {
            update_slots();
        });

        // Necessary similarity of prompt for slot selection
        slot_prompt_similarity = params_base.slot_prompt_similarity;

        // setup slots
        SRV_INF("initializing slots, n_slots = %d\n", params_base.n_parallel);

        const int n_ctx_train = llama_model_n_ctx_train(model);

        int n_ctx_slot = llama_n_ctx_seq(ctx);
        if (n_ctx_slot > n_ctx_train) {
            SRV_WRN("the slot context (%d) exceeds the training context of the model (%d) - capping\n", n_ctx_slot, n_ctx_train);
            n_ctx_slot = n_ctx_train;
        }

        for (int i = 0; i < params_base.n_parallel; i++) {
            server_slot slot;

            slot.id = i;
            slot.ctx = ctx;
            slot.n_ctx = n_ctx_slot;
            slot.mctx = mctx;
            slot.prompt.tokens.has_mtmd = mctx != nullptr;

            if (model_dft) {
                slot.batch_spec = llama_batch_init(params_base.speculative.n_max + 1, 0, 1);

                // TODO: rework speculative decoding [TAG_SERVER_SPEC_REWORK]
                slot.ctx_dft = llama_init_from_model(model_dft, cparams_dft);
                if (slot.ctx_dft == nullptr) {
                    SRV_ERR("%s", "failed to create draft context\n");
                    return;
                }

                slot.spec = common_speculative_init(slot.ctx, slot.ctx_dft);
                if (slot.spec == nullptr) {
                    SRV_ERR("%s", "failed to create speculator\n");
                    return;
                }
                for (auto & pair : params_base.speculative.replacements) {
                    common_speculative_add_replacement_tgt_dft(slot.spec, pair.first.c_str(), pair.second.c_str());
                }
            }

            SLT_INF(slot, "new slot, n_ctx = %d\n", slot.n_ctx);

            slot.callback_on_release = [this](int) {
                queue_tasks.pop_deferred_task();
            };

            slot.reset();

            slots.push_back(std::move(slot));
        }

        {
            const char * LLAMA_SERVER_SLOTS_DEBUG = getenv("LLAMA_SERVER_SLOTS_DEBUG");
            slots_debug = LLAMA_SERVER_SLOTS_DEBUG ? atoi(LLAMA_SERVER_SLOTS_DEBUG) : 0;

            if (slots_debug) {
                SRV_WRN("slots debug = %d\n", slots_debug);
            }
        }

        // the update_slots() logic will always submit a maximum of n_batch or n_parallel tokens
        // note that n_batch can be > n_ctx (e.g. for non-causal attention models such as BERT where the KV cache is not used)
        {
            const int32_t n_batch = llama_n_batch(ctx);
            batch = llama_batch_init(std::max(n_batch, params_base.n_parallel), 0, 1);
        }

        metrics.init();

        if (params_base.cache_ram_mib != 0) {
            if (params_base.cache_ram_mib < 0) {
                SRV_WRN("prompt cache is enabled, size limit: %s\n", "no limit");
            } else {
                SRV_WRN("prompt cache is enabled, size limit: %d MiB\n", params_base.cache_ram_mib);
            }
            SRV_WRN("%s", "use `--cache-ram 0` to disable the prompt cache\n");

            prompt_cache = std::make_unique<server_prompt_cache>(params_base.cache_ram_mib, n_ctx);
        } else {
            SRV_WRN("%s", "prompt cache is disabled - use `--cache-ram N` to enable it\n");
        }
        SRV_WRN("%s", "for more info see https://github.com/ggml-org/llama.cpp/pull/16391\n");

        // thinking is enabled if:
        // 1. It's not explicitly disabled (reasoning_budget == 0)
        // 2. The chat template supports it
        const bool enable_thinking = params_base.use_jinja && params_base.reasoning_budget != 0 && common_chat_templates_support_enable_thinking(chat_templates.get());
        SRV_INF("thinking = %d\n", enable_thinking);

        oai_parser_opt = {
            /* use_jinja             */ params_base.use_jinja,
            /* prefill_assistant     */ params_base.prefill_assistant,
            /* reasoning_format      */ params_base.reasoning_format,
            /* chat_template_kwargs  */ params_base.default_template_kwargs,
            /* common_chat_templates */ chat_templates.get(),
            /* allow_image           */ mctx ? mtmd_support_vision(mctx) : false,
            /* allow_audio           */ mctx ? mtmd_support_audio (mctx) : false,
            /* enable_thinking       */ enable_thinking,
        };

        // print sample chat example to make it clear which template is used
        LOG_INF("%s: chat template, chat_template: %s, example_format: '%s'\n", __func__,
            common_chat_templates_source(chat_templates.get()),
            common_chat_format_example(chat_templates.get(), params_base.use_jinja, params_base.default_template_kwargs).c_str());
    }

    server_slot * get_slot_by_id(int id) {
        for (server_slot & slot : slots) {
            if (slot.id == id) {
                return &slot;
            }
        }

        return nullptr;
    }

    server_slot * get_available_slot(const server_task & task) {
        server_slot * ret = nullptr;

        bool update_cache = false;

        // find the slot that has at least n% prompt similarity
        if (ret == nullptr && slot_prompt_similarity != 0.0f) {
            float sim_best = 0;

            for (server_slot & slot : slots) {
                // skip the slot if it is not available
                if (slot.is_processing()) {
                    continue;
                }

                const auto & tokens = slot.prompt.tokens;

                // skip the slot if it does not contains cached tokens
                if (tokens.empty()) {
                    continue;
                }

                // fraction of the Longest Common Prefix length with respect to the input prompt length
                const float sim_cur = float(tokens.get_common_prefix(task.tokens)) / task.tokens.size();

                // select the current slot if the criteria match
                if (sim_cur > sim_best && sim_cur > slot_prompt_similarity) {
                    sim_best = sim_cur;

                    ret = &slot;
                }
            }

            if (ret != nullptr) {
                const float f_keep = (sim_best*task.tokens.size()) / ret->prompt.tokens.size();

                SLT_INF(*ret, "selected slot by LCP similarity, sim_best = %.3f (> %.3f thold), f_keep = %.3f\n",
                        sim_best, slot_prompt_similarity, f_keep);

                // if we are about to lose a large portion of the existing context - save it in the prompt cache
                if (f_keep < 0.5f) {
                    update_cache = true;
                }
            }
        }

        // find the slot that has been least recently used
        if (ret == nullptr) {
            int64_t t_last = -1;

            for (server_slot & slot : slots) {
                // skip the slot if it is not available
                if (slot.is_processing()) {
                    continue;
                }

                // select the current slot if the criteria match
                if (!ret || slot.t_last_used <= t_last) {
                    t_last = slot.t_last_used;
                    ret = &slot;
                }
            }

            if (ret != nullptr) {
                SLT_INF(*ret, "selected slot by LRU, t_last = %" PRId64 "\n", t_last);

                update_cache = true;
            }
        }

        if (ret) {
            const auto & tokens = ret->prompt.tokens;

            update_cache = update_cache && prompt_cache;

            // cache prompts only for completion tasks
            update_cache = update_cache && task.type == SERVER_TASK_TYPE_COMPLETION;

            // don't update the cache if the slot's context is empty
            update_cache = update_cache && tokens.size() > 0;

            // TODO: mtmd does not support prompt cache
            update_cache = update_cache && (ret->mctx == nullptr);

            if (update_cache) {
                SRV_WRN("%s", "updating prompt cache\n");

                const int64_t t_start = ggml_time_us();

                ret->prompt_save(*prompt_cache);

                if (!ret->prompt_load(*prompt_cache, task.tokens)) {
                    clear_slot(*ret);
                }

                prompt_cache->update();

                SRV_WRN("prompt cache update took %.2f ms\n", (ggml_time_us() - t_start) / 1000.0);
            }
        }

        return ret;
    }

    void clear_slot(server_slot & slot) const {
        GGML_ASSERT(!slot.is_processing());

        SLT_WRN(slot, "clearing slot with %zu tokens\n", slot.prompt.tokens.size());

        llama_memory_seq_rm(llama_get_memory(ctx), slot.id, -1, -1);
        slot.prompt.tokens.clear();
    }

    // return true if at least one slot has been cleared
    // TODO: improve logic
    //       - smarter decision which slot to clear (LRU or longest prompt?)
    //       - move slot to level 2 cache instead of removing?
    //       - instead of purging, try to store and resume later?
    bool try_clear_idle_slots() {
        bool res = false;

        if (!params_base.kv_unified) {
            return res;
        }

        for (auto & slot : slots) {
            if (slot.is_processing()) {
                continue;
            }

            if (slot.prompt.n_tokens() > 0) {
                SRV_WRN("purging slot %d with %zu tokens\n", slot.id, slot.prompt.tokens.size());

                clear_slot(slot);

                res = true;

                // clear slots one by one
                break;
            }
        }

        return res;
    }

    bool launch_slot_with_task(server_slot & slot, server_task && task) {
        slot.reset();

        if (!are_lora_equal(task.params.lora, slot.lora)) {
            // if lora has changed, check to see if the cache should be cleared
            if (lora_should_clear_cache(slot.lora, task.params.lora)) {
                SLT_INF(slot, "clearing cache for lora change. %zu loras -> %zu loras\n", slot.lora.size(), task.params.lora.size());
                slot.prompt.tokens.clear();
            } else {
                SLT_INF(slot, "keeping cache for alora. %zu target loras\n", task.params.lora.size());
            }
            slot.lora = task.params.lora;
        }

        // if using alora, make sure it's only a single one requested and active
        size_t alora_invocation_start = task.tokens.size();
        if (lora_all_alora(slot.lora)) {
            const auto & enabled_ids = lora_get_enabled_ids(slot.lora);
            // TODO: This will error out if a user requests two aloras, but only
            // provides the activation string for one. We could, instead search
            // for all requested alora activation strings and then either keep
            // only the last one, or reject if multiple are found.
            if (enabled_ids.size() != 1) {
                send_error(task, "Cannot run multiple aLoRAs in a single request", ERROR_TYPE_INVALID_REQUEST);
                return false;
            }
            const auto & lora = slot.lora[enabled_ids[0]].ptr;

            // get the pointer and count for the invocation tokens
            const uint64_t      n_invocation_tokens = llama_adapter_get_alora_n_invocation_tokens(lora);
            const llama_token * invocation_tokens   = llama_adapter_get_alora_invocation_tokens  (lora);

            // scan backwards through the prompt tokens to find the last
            // occurrence of the invocation sequence
            int match_idx = static_cast<int>(n_invocation_tokens) - 1;
            for (int i = task.tokens.size() - 1; i >= 0; --i) {
                // the token in this position matches the next token to find in
                // the invocation sequence
                if (task.tokens[i] == invocation_tokens[match_idx]) {
                    // if it's a full match, we've found the start
                    if (match_idx == 0) {
                        alora_invocation_start = i;
                        break;
                    }
                    // otherwise, check the next token in the sequence
                    --match_idx;
                } else {
                    // no match in this position, so start looking over again
                    match_idx = static_cast<int>(n_invocation_tokens) - 1;
                }
            }

            // if the activation string is not found, disable the alora
            if (alora_invocation_start == task.tokens.size()) {
                SLT_DBG(slot, "alora %zu requested, but not found. deactivating\n", enabled_ids[0]);
                slot.lora[enabled_ids[0]].scale = 0.0f;
            } else {
                SLT_DBG(slot, "alora %zu activated starting at %zu\n", enabled_ids[0], alora_invocation_start);
                slot.alora_invocation_start = alora_invocation_start;
            }
        }

        if (!task.tokens.validate(ctx)) {
            send_error(task, "Prompt contains invalid tokens", ERROR_TYPE_INVALID_REQUEST);
            return false;
        }

        SLT_DBG(slot, "launching slot : %s\n", safe_json_to_str(slot.to_json()).c_str());

        // initialize samplers
        {
            if (slot.smpl != nullptr) {
                common_sampler_free(slot.smpl);
            }

            slot.smpl = common_sampler_init(model, task.params.sampling);
            if (slot.smpl == nullptr) {
                // for now, the only error that may happen here is invalid grammar
                send_error(task, "Failed to parse grammar", ERROR_TYPE_INVALID_REQUEST);
                return false;
            }

            SLT_INF(slot, "sampler chain: %s\n", common_sampler_print(slot.smpl).c_str());
        }

        // initialize draft batch
        // TODO: rework speculative decoding [TAG_SERVER_SPEC_REWORK]
        if (slot.ctx_dft) {
            llama_batch_free(slot.batch_spec);

            slot.batch_spec = llama_batch_init(task.params.speculative.n_max + 1, 0, 1);
        }

        slot.task = std::make_unique<const server_task>(std::move(task));

        slot.state = SLOT_STATE_STARTED;

        SLT_INF(slot, "%s", "processing task\n");

        return true;
    }

    bool process_token(completion_token_output & result, server_slot & slot) {
        // remember which tokens were sampled - used for repetition penalties during sampling
        const std::string token_str = result.text_to_send;
        slot.sampled = result.tok;

        slot.generated_text += token_str;
        if (slot.task->params.return_tokens) {
            slot.generated_tokens.push_back(result.tok);
        }
        slot.has_next_token = true;

        // check if there is incomplete UTF-8 character at the end
        bool incomplete = validate_utf8(slot.generated_text) < slot.generated_text.size();

        // search stop word and delete it
        if (!incomplete) {
            size_t pos = std::min(slot.n_sent_text, slot.generated_text.size());

            const std::string str_test = slot.generated_text.substr(pos);
            bool send_text = true;

            size_t stop_pos = slot.find_stopping_strings(str_test, token_str.size(), true);
            if (stop_pos != std::string::npos) {
                slot.generated_text.erase(
                    slot.generated_text.begin() + pos + stop_pos,
                    slot.generated_text.end());
                pos = std::min(slot.n_sent_text, slot.generated_text.size());
            } else if (slot.has_next_token && !llama_vocab_is_eog(vocab, result.tok) ) {
                stop_pos = slot.find_stopping_strings(str_test, token_str.size(), false);
                send_text = stop_pos == std::string::npos;
            }

            // check if there is any token to predict
            if (send_text) {
                // no send the stop word in the response
                result.text_to_send = slot.generated_text.substr(pos, std::string::npos);
                slot.n_sent_text += result.text_to_send.size();
                // add the token to slot queue and cache
            } else {
                result.text_to_send = "";
            }

            slot.add_token(result);
            if (slot.task->params.stream) {
                send_partial_response(slot, result, false);
            }
        }

        if (incomplete) {
            slot.has_next_token = true;
        }

        // if context shifting is disabled, make sure that we don't run out of context
        if (!params_base.ctx_shift && slot.prompt.n_tokens() + 1 >= slot.n_ctx) {
            slot.truncated      = true;
            slot.stop           = STOP_TYPE_LIMIT;
            slot.has_next_token = false;

            SLT_DBG(slot, "stopped due to running out of context capacity, prompt.n_tokens() = %d, task.n_tokens = %d, n_decoded = %d, n_ctx = %d\n",
                    slot.prompt.n_tokens(), slot.task->n_tokens(), slot.n_decoded, slot.n_ctx);
        }

        // check the limits
        if (slot.n_decoded > 0 && slot.has_next_token && !slot.has_budget(params_base)) {
            slot.stop           = STOP_TYPE_LIMIT;
            slot.has_next_token = false;

            SLT_DBG(slot, "stopped by limit, n_decoded = %d, n_predict = %d\n", slot.n_decoded, slot.task->params.n_predict);
        }

        if (slot.has_new_line) {
            // require that each new line has a whitespace prefix (i.e. indentation) of at least slot.params.n_indent
            if (slot.task->params.n_indent > 0) {
                // check the current indentation
                // TODO: improve by not doing it more than once for each new line
                if (slot.last_nl_pos > 0) {
                    size_t pos = slot.last_nl_pos;

                    int n_indent = 0;
                    while (pos < slot.generated_text.size() && (slot.generated_text[pos] == ' ' || slot.generated_text[pos] == '\t')) {
                        n_indent++;
                        pos++;
                    }

                    if (pos < slot.generated_text.size() && n_indent < slot.task->params.n_indent) {
                        slot.stop           = STOP_TYPE_LIMIT;
                        slot.has_next_token = false;

                        // cut the last line
                        slot.generated_text.erase(pos, std::string::npos);

                        SLT_DBG(slot, "stopped by indentation limit, n_decoded = %d, n_indent = %d\n", slot.n_decoded, n_indent);
                    }
                }

                // find the next new line
                {
                    const size_t pos = slot.generated_text.find('\n', slot.last_nl_pos);

                    if (pos != std::string::npos) {
                        slot.last_nl_pos = pos + 1;
                    }
                }
            }
        }

        // check if there is a new line in the generated text
        if (result.text_to_send.find('\n') != std::string::npos) {
            slot.has_new_line = true;

            // if we have seen a new line, we stop after a certain time limit, but only upon another new line
            if (slot.task->params.t_max_predict_ms > 0 && (ggml_time_us() - slot.t_start_generation > 1000.0f*slot.task->params.t_max_predict_ms)) {
                slot.stop           = STOP_TYPE_LIMIT;
                slot.has_next_token = false;

                SLT_DBG(slot, "stopped by time limit, n_decoded = %d, t_max_predict_ms = %d ms\n", slot.n_decoded, (int) slot.task->params.t_max_predict_ms);
            }
        }

        if (llama_vocab_is_eog(vocab, result.tok)) {
            slot.stop           = STOP_TYPE_EOS;
            slot.has_next_token = false;

            SLT_DBG(slot, "%s", "stopped by EOS\n");
        }

        SLT_DBG(slot, "n_decoded = %d, n_remaining = %d, next token: %5d '%s'\n", slot.n_decoded, slot.n_remaining, result.tok, token_str.c_str());

        return slot.has_next_token; // continue
    }

    void populate_token_probs(const server_slot & slot, completion_token_output & result, bool post_sampling, bool special, int idx) const {
        size_t n_probs = slot.task->params.sampling.n_probs;
        size_t n_vocab = llama_vocab_n_tokens(vocab);

        if (post_sampling) {
            const auto * cur_p = common_sampler_get_candidates(slot.smpl, true);
            const size_t max_probs = cur_p->size;

            // set probability for sampled token
            for (size_t i = 0; i < max_probs; i++) {
                if (cur_p->data[i].id == result.tok) {
                    result.prob = cur_p->data[i].p;
                    break;
                }
            }

            // set probability for top n_probs tokens
            result.probs.reserve(max_probs);
            for (size_t i = 0; i < std::min(max_probs, n_probs); i++) {
                result.probs.push_back({
                    cur_p->data[i].id,
                    common_token_to_piece(ctx, cur_p->data[i].id, special),
                    cur_p->data[i].p
                });
            }
        } else {
            // TODO: optimize this with min-p optimization
            std::vector<llama_token_data> cur = get_token_probabilities(ctx, idx);

            // set probability for sampled token
            for (size_t i = 0; i < n_vocab; i++) {
                // set probability for sampled token
                if (cur[i].id == result.tok) {
                    result.prob = cur[i].p;
                    break;
                }
            }

            // set probability for top n_probs tokens
            result.probs.reserve(n_probs);
            for (size_t i = 0; i < std::min(n_vocab, n_probs); i++) {
                result.probs.push_back({
                    cur[i].id,
                    common_token_to_piece(ctx, cur[i].id, special),
                    cur[i].p
                });
            }
        }
    }

    void send_error(const server_task & task, const std::string & error, const enum error_type type = ERROR_TYPE_SERVER) {
        send_error(task.id, error, type);
    }

    void send_error(const server_slot & slot, const std::string & error, const enum error_type type = ERROR_TYPE_SERVER) {
        send_error(slot.task->id, error, type, slot.task->n_tokens(), slot.n_ctx);
    }

    void send_error(const int id_task, const std::string & error, const enum error_type type = ERROR_TYPE_SERVER, const int32_t n_prompt_tokens = 0, const int32_t n_ctx = 0) {
        SRV_ERR("task id = %d, error: %s\n", id_task, error.c_str());

        if (type == ERROR_TYPE_EXCEED_CONTEXT_SIZE) {
            GGML_ASSERT(n_ctx > 0 && n_prompt_tokens > 0);
        }

        auto res = std::make_unique<server_task_result_error>();
        res->id              = id_task;
        res->err_type        = type;
        res->err_msg         = error;
        res->n_prompt_tokens = n_prompt_tokens;
        res->n_ctx           = n_ctx;

        queue_results.send(std::move(res));
    }

    // if multimodal is enabled, send an error and return false
    bool check_no_mtmd(const int id_task) {
        if (mctx) {
            send_error(id_task, "This feature is not supported by multimodal", ERROR_TYPE_NOT_SUPPORTED);
            return false;
        }
        return true;
    }

    void send_partial_response(server_slot & slot, const completion_token_output & tkn, bool is_progress) {
        auto res = std::make_unique<server_task_result_cmpl_partial>();

        res->id    = slot.task->id;
        res->index = slot.task->index;

        if (is_progress) {
            res->is_progress        = true;
            res->progress.total     = slot.task->n_tokens();
            res->progress.cache     = slot.n_prompt_tokens_cache;
            res->progress.processed = slot.prompt.tokens.size();
            res->progress.time_ms   = (ggml_time_us() - slot.t_start_process_prompt) / 1000;
        } else {
            res->content = tkn.text_to_send;
            res->tokens  = { tkn.tok };

            slot.update_chat_msg(res->oaicompat_msg_diffs);
        }

        res->n_decoded           = slot.n_decoded;
        res->n_prompt_tokens     = slot.task->n_tokens();
        res->post_sampling_probs = slot.task->params.post_sampling_probs;

        res->verbose           = slot.task->params.verbose;
        res->res_type          = slot.task->params.res_type;
        res->oaicompat_model   = slot.task->params.oaicompat_model;
        res->oaicompat_cmpl_id = slot.task->params.oaicompat_cmpl_id;

        // populate res.probs_output
        if (slot.task->params.sampling.n_probs > 0) {
            res->prob_output = tkn; // copy the token probs
        }

        // populate timings if this is final response or timings_per_token is enabled
        if (slot.stop != STOP_TYPE_NONE || slot.task->params.timings_per_token) {
            res->timings = slot.get_timings();
        }

        queue_results.send(std::move(res));
    }

    void send_final_response(server_slot & slot) {
        auto res = std::make_unique<server_task_result_cmpl_final>();

        res->id      = slot.task->id;
        res->id_slot = slot.id;

        res->index           = slot.task->index;
        res->content         = slot.generated_text;
        res->tokens          = std::move(slot.generated_tokens);
        res->timings         = slot.get_timings();
        res->prompt          = slot.task->tokens.detokenize(ctx, true);
        res->response_fields = std::move(slot.task->params.response_fields);

        res->truncated           = slot.truncated;
        res->n_decoded           = slot.n_decoded;
        res->n_prompt_tokens     = slot.task->n_tokens();
        res->n_tokens_cached     = slot.prompt.n_tokens();
        res->has_new_line        = slot.has_new_line;
        res->stopping_word       = slot.stopping_word;
        res->stop                = slot.stop;
        res->post_sampling_probs = slot.task->params.post_sampling_probs;

        res->verbose           = slot.task->params.verbose;
        res->stream            = slot.task->params.stream;
        res->include_usage     = slot.task->params.include_usage;
        res->res_type          = slot.task->params.res_type;
        res->oaicompat_model   = slot.task->params.oaicompat_model;
        res->oaicompat_cmpl_id = slot.task->params.oaicompat_cmpl_id;
        res->oaicompat_msg     = slot.update_chat_msg(res->oaicompat_msg_diffs);

        // populate res.probs_output
        if (slot.task->params.sampling.n_probs > 0) {
            if (!slot.task->params.stream && slot.stop == STOP_TYPE_WORD) {
                const llama_tokens stop_word_toks = common_tokenize(ctx, slot.stopping_word, false);

                size_t safe_offset = std::min(slot.generated_token_probs.size(), stop_word_toks.size());
                res->probs_output = std::vector<completion_token_output>(
                        slot.generated_token_probs.begin(),
                        slot.generated_token_probs.end() - safe_offset);
            } else {
                res->probs_output = std::vector<completion_token_output>(
                        slot.generated_token_probs.begin(),
                        slot.generated_token_probs.end());
            }
        }

        res->generation_params = slot.task->params; // copy the parameters

        queue_results.send(std::move(res));
    }

    void send_embedding(const server_slot & slot, const llama_batch & batch) {
        auto res = std::make_unique<server_task_result_embd>();
        res->id        = slot.task->id;
        res->index     = slot.task->index;
        res->n_tokens  = slot.task->n_tokens();
        res->res_type  = slot.task->params.res_type;

        const int n_embd = llama_model_n_embd(model);

        std::vector<float> embd_res(n_embd, 0.0f);

        for (int i = 0; i < batch.n_tokens; ++i) {
            if (!batch.logits[i] || batch.seq_id[i][0] != slot.id) {
                continue;
            }

            const float * embd = nullptr;
            if (llama_pooling_type(slot.ctx) == LLAMA_POOLING_TYPE_NONE) {
                embd = llama_get_embeddings_ith(ctx, i);
            } else {
                embd = llama_get_embeddings_seq(ctx, batch.seq_id[i][0]);
            }

            if (embd == nullptr) {
                SLT_ERR(slot, "failed to get embeddings, token = %d, seq_id = %d\n", batch.token[i], batch.seq_id[i][0]);

                res->embedding.push_back(std::vector<float>(n_embd, 0.0f));
                continue;
            }

            // normalize only when there is pooling
            if (llama_pooling_type(slot.ctx) != LLAMA_POOLING_TYPE_NONE) {
                common_embd_normalize(embd, embd_res.data(), n_embd, slot.task->params.embd_normalize);
                res->embedding.push_back(embd_res);
                break;
            }

            res->embedding.emplace_back(embd, embd + n_embd);
        }

        SLT_DBG(slot, "%s", "sending embeddings\n");

        queue_results.send(std::move(res));
    }

    void send_rerank(const server_slot & slot, const llama_batch & batch) {
        auto res = std::make_unique<server_task_result_rerank>();
        res->id       = slot.task->id;
        res->index    = slot.task->index;
        res->n_tokens = slot.task->n_tokens();

        for (int i = 0; i < batch.n_tokens; ++i) {
            if (!batch.logits[i] || batch.seq_id[i][0] != slot.id) {
                continue;
            }

            const float * embd = llama_get_embeddings_seq(ctx, batch.seq_id[i][0]);
            if (embd == NULL) {
                embd = llama_get_embeddings_ith(ctx, i);
            }

            if (embd == NULL) {
                SLT_ERR(slot, "failed to get embeddings, token = %d, seq_id = %d\n", batch.token[i], batch.seq_id[i][0]);

                res->score = -1e6;
                continue;
            }

            res->score = embd[0];
        }

        SLT_DBG(slot, "sending rerank result, res.score = %f\n", res->score);

        queue_results.send(std::move(res));
    }

    //
    // Functions to process the task
    //

    void process_single_task(server_task && task) {
        switch (task.type) {
            case SERVER_TASK_TYPE_COMPLETION:
            case SERVER_TASK_TYPE_INFILL:
            case SERVER_TASK_TYPE_EMBEDDING:
            case SERVER_TASK_TYPE_RERANK:
                {
                    const int id_slot = task.id_slot;

                    server_slot * slot = id_slot != -1 ? get_slot_by_id(id_slot) : get_available_slot(task);

                    if (slot == nullptr) {
                        // if no slot is available, we defer this task for processing later
                        SRV_DBG("no slot is available, defer task, id_task = %d\n", task.id);
                        queue_tasks.defer(std::move(task));
                        break;
                    }

                    if (slot->is_processing()) {
                        // if requested slot is unavailable, we defer this task for processing later
                        SRV_DBG("requested slot is unavailable, defer task, id_task = %d\n", task.id);
                        queue_tasks.defer(std::move(task));
                        break;
                    }

                    if (!launch_slot_with_task(*slot, std::move(task))) {
                        SRV_ERR("failed to launch slot with task, id_task = %d\n", task.id);
                        break;
                    }
                } break;
            case SERVER_TASK_TYPE_CANCEL:
                {
                    // release slot linked with the task id
                    for (auto & slot : slots) {
                        if (slot.task && slot.task->id == task.id_target) {
                            slot.release();
                            break;
                        }
                    }
                } break;
            case SERVER_TASK_TYPE_NEXT_RESPONSE:
                {
                    // do nothing
                } break;
            case SERVER_TASK_TYPE_METRICS:
                {
                    json slots_data = json::array();

                    int n_idle_slots       = 0;
                    int n_processing_slots = 0;

                    for (server_slot & slot : slots) {
                        json slot_data = slot.to_json(slots_debug == 0);

                        if (slot.is_processing()) {
                            n_processing_slots++;
                        } else {
                            n_idle_slots++;
                        }

                        slots_data.push_back(slot_data);
                    }
                    SRV_DBG("n_idle_slots = %d, n_processing_slots = %d\n", n_idle_slots, n_processing_slots);

                    auto res = std::make_unique<server_task_result_metrics>();
                    res->id                  = task.id;
                    res->slots_data          = std::move(slots_data);
                    res->n_idle_slots        = n_idle_slots;
                    res->n_processing_slots  = n_processing_slots;
                    res->n_tasks_deferred    = queue_tasks.queue_tasks_deferred_size();
                    res->t_start             = metrics.t_start;

                    res->n_prompt_tokens_processed_total = metrics.n_prompt_tokens_processed_total;
                    res->t_prompt_processing_total       = metrics.t_prompt_processing_total;
                    res->n_tokens_predicted_total        = metrics.n_tokens_predicted_total;
                    res->t_tokens_generation_total       = metrics.t_tokens_generation_total;

                    res->n_tokens_max = metrics.n_tokens_max;

                    res->n_prompt_tokens_processed = metrics.n_prompt_tokens_processed;
                    res->t_prompt_processing       = metrics.t_prompt_processing;
                    res->n_tokens_predicted        = metrics.n_tokens_predicted;
                    res->t_tokens_generation       = metrics.t_tokens_generation;

                    res->n_decode_total          = metrics.n_decode_total;
                    res->n_busy_slots_total      = metrics.n_busy_slots_total;

                    if (task.metrics_reset_bucket) {
                        metrics.reset_bucket();
                    }
                    queue_results.send(std::move(res));
                } break;
            case SERVER_TASK_TYPE_SLOT_SAVE:
                {
                    if (!check_no_mtmd(task.id)) {
                        break;
                    }

                    int id_slot = task.slot_action.slot_id;
                    server_slot * slot = get_slot_by_id(id_slot);
                    if (slot == nullptr) {
                        send_error(task, "Invalid slot ID", ERROR_TYPE_INVALID_REQUEST);
                        break;
                    }
                    if (slot->is_processing()) {
                        // if requested slot is unavailable, we defer this task for processing later
                        SRV_DBG("requested slot is unavailable, defer task, id_task = %d\n", task.id);
                        queue_tasks.defer(std::move(task));
                        break;
                    }

                    const size_t token_count = slot->prompt.tokens.size();
                    const int64_t t_start = ggml_time_us();

                    std::string filename = task.slot_action.filename;
                    std::string filepath = task.slot_action.filepath;

                    const llama_tokens & tokens = slot->prompt.tokens.get_text_tokens();
                    const size_t nwrite = llama_state_seq_save_file(ctx, filepath.c_str(), slot->id, tokens.data(), token_count);

                    const int64_t t_end = ggml_time_us();
                    const double t_save_ms = (t_end - t_start) / 1000.0;

                    auto res = std::make_unique<server_task_result_slot_save_load>();
                    res->id       = task.id;
                    res->id_slot  = id_slot;
                    res->filename = filename;
                    res->is_save  = true;
                    res->n_tokens = token_count;
                    res->n_bytes  = nwrite;
                    res->t_ms     = t_save_ms;
                    queue_results.send(std::move(res));
                } break;
            case SERVER_TASK_TYPE_SLOT_RESTORE:
                {
                    if (!check_no_mtmd(task.id)) break;
                    int id_slot = task.slot_action.slot_id;
                    server_slot * slot = get_slot_by_id(id_slot);
                    if (slot == nullptr) {
                        send_error(task, "Invalid slot ID", ERROR_TYPE_INVALID_REQUEST);
                        break;
                    }
                    if (slot->is_processing()) {
                        // if requested slot is unavailable, we defer this task for processing later
                        SRV_DBG("requested slot is unavailable, defer task, id_task = %d\n", task.id);
                        queue_tasks.defer(std::move(task));
                        break;
                    }

                    const int64_t t_start = ggml_time_us();

                    std::string filename = task.slot_action.filename;
                    std::string filepath = task.slot_action.filepath;

                    llama_tokens tokens;
                    tokens.resize(slot->n_ctx);
                    size_t token_count = 0;
                    size_t nread = llama_state_seq_load_file(ctx, filepath.c_str(), slot->id, tokens.data(), tokens.size(), &token_count);
                    if (nread == 0) {
                        slot->prompt.tokens.clear(); // KV may already been invalidated?
                        send_error(task, "Unable to restore slot, no available space in KV cache or invalid slot save file", ERROR_TYPE_INVALID_REQUEST);
                        break;
                    }
                    tokens.resize(token_count);
                    slot->prompt.tokens.clear();
                    slot->prompt.tokens.insert(tokens);

                    const int64_t t_end = ggml_time_us();
                    const double t_restore_ms = (t_end - t_start) / 1000.0;

                    auto res = std::make_unique<server_task_result_slot_save_load>();
                    res->id       = task.id;
                    res->id_slot  = id_slot;
                    res->filename = filename;
                    res->is_save  = false;
                    res->n_tokens = token_count;
                    res->n_bytes  = nread;
                    res->t_ms     = t_restore_ms;
                    queue_results.send(std::move(res));
                } break;
            case SERVER_TASK_TYPE_SLOT_ERASE:
                {
                    if (!check_no_mtmd(task.id)) {
                        break;
                    }
                    int id_slot = task.slot_action.slot_id;
                    server_slot * slot = get_slot_by_id(id_slot);
                    if (slot == nullptr) {
                        send_error(task, "Invalid slot ID", ERROR_TYPE_INVALID_REQUEST);
                        break;
                    }
                    if (slot->is_processing()) {
                        // if requested slot is unavailable, we defer this task for processing later
                        SRV_DBG("requested slot is unavailable, defer task, id_task = %d\n", task.id);
                        queue_tasks.defer(std::move(task));
                        break;
                    }

                    // Erase token cache
                    const size_t n_erased = slot->prompt.tokens.size();

                    clear_slot(*slot);

                    auto res = std::make_unique<server_task_result_slot_erase>();
                    res->id       = task.id;
                    res->id_slot  = id_slot;
                    res->n_erased = n_erased;
                    queue_results.send(std::move(res));
                } break;
            case SERVER_TASK_TYPE_SET_LORA:
                {
                    params_base.lora_adapters = std::move(task.set_lora);
                    auto res = std::make_unique<server_task_result_apply_lora>();
                    res->id = task.id;
                    queue_results.send(std::move(res));
                } break;

        }
    }

    void update_slots() {
        // check if all slots are idle
        {
            bool all_idle = true;

            for (auto & slot : slots) {
                if (slot.is_processing()) {
                    all_idle = false;
                    break;
                }
            }

            if (all_idle) {
                SRV_INF("%s", "all slots are idle\n");

                return;
            }
        }

        {
            SRV_DBG("%s", "posting NEXT_RESPONSE\n");

            server_task task(SERVER_TASK_TYPE_NEXT_RESPONSE);
            task.id = queue_tasks.get_new_id();
            queue_tasks.post(std::move(task));
        }

        // apply context-shift if needed
        // TODO: simplify and improve
        for (server_slot & slot : slots) {
            if (slot.state == SLOT_STATE_GENERATING && slot.prompt.n_tokens() + 1 >= slot.n_ctx) {
                if (!params_base.ctx_shift) {
                    // this check is redundant (for good)
                    // we should never get here, because generation should already stopped in process_token()
                    send_error(slot, "context shift is disabled", ERROR_TYPE_SERVER);
                    slot.release();
                    continue;
                }

                if (mctx) {
                    // we should never reach this because params_base.ctx_shift is automatically disabled if mmproj is loaded
                    // we don't support ctx_shift because an image chunk may contains multiple tokens
                    GGML_ABORT("not supported by multimodal");
                }

                // Shift context
                int n_keep = slot.task->params.n_keep < 0 ? slot.task->n_tokens() : slot.task->params.n_keep;

                if (add_bos_token) {
                    n_keep += 1;
                }

                n_keep = std::min(slot.n_ctx - 4, n_keep);

                const int n_left    = slot.prompt.n_tokens() - n_keep;
                const int n_discard = slot.task->params.n_discard ? slot.task->params.n_discard : (n_left / 2);

                SLT_WRN(slot, "slot context shift, n_keep = %d, n_left = %d, n_discard = %d\n", n_keep, n_left, n_discard);

                llama_memory_seq_rm (llama_get_memory(ctx), slot.id, n_keep            , n_keep + n_discard);
                llama_memory_seq_add(llama_get_memory(ctx), slot.id, n_keep + n_discard, slot.prompt.n_tokens(), -n_discard);

                // add generated tokens to cache
                // ref: https://github.com/ggml-org/llama.cpp/pull/16818#discussion_r2473269481
                {
                    GGML_ASSERT(!slot.prompt.tokens.has_mtmd);

                    llama_tokens new_tokens = slot.prompt.tokens.get_text_tokens(); // copy
                    for (size_t i = n_keep + n_discard; i < new_tokens.size(); i++) {
                        new_tokens[i - n_discard] = new_tokens[i];
                    }

                    new_tokens.resize(slot.prompt.tokens.size() - n_discard);

                    slot.prompt.tokens.clear();
                    slot.prompt.tokens.insert(new_tokens);
                }

                slot.truncated = true;
            }
        }

        // start populating the batch for this iteration
        common_batch_clear(batch);

        // track if given slot can be batched with slots already in the batch
        server_slot * slot_batched = nullptr;

        auto accept_special_token = [&](server_slot & slot, llama_token token) {
            return params_base.special ||
                slot.task->params.sampling.preserved_tokens.find(token) != slot.task->params.sampling.preserved_tokens.end();
        };

        // first, add sampled tokens from any ongoing sequences
        for (auto & slot : slots) {
            if (slot.state != SLOT_STATE_GENERATING) {
                continue;
            }

            // check if we can batch this slot with the previous one
            if (!slot_batched) {
                slot_batched = &slot;
            } else if (!slot_batched->can_batch_with(slot)) {
                continue;
            }

            slot.i_batch = batch.n_tokens;

            common_batch_add(batch, slot.sampled, slot.prompt.tokens.pos_next(), { slot.id }, true);

            slot.prompt.tokens.push_back(slot.sampled);

            SLT_DBG(slot, "slot decode token, n_ctx = %d, n_tokens = %d, truncated = %d\n",
                    slot.n_ctx, slot.prompt.n_tokens(), slot.truncated);
        }

        // process in chunks of params.n_batch
        int32_t n_batch  = llama_n_batch(ctx);
        int32_t n_ubatch = llama_n_ubatch(ctx);

        float  alora_scale       = -1.0f;
        size_t alora_disabled_id = 0;

        // next, batch any pending prompts without exceeding n_batch
        if (params_base.cont_batching || batch.n_tokens == 0) {
            for (auto & slot : slots) {
                if (!slot.is_processing()) {
                    continue;
                }

                // check if we can batch this slot with the previous one
                if (slot_batched && !slot_batched->can_batch_with(slot)) {
                    continue;
                }

                // this slot still has a prompt to be processed
                if (slot.state == SLOT_STATE_PROCESSING_PROMPT || slot.state == SLOT_STATE_STARTED) {
                    const auto & input_tokens = slot.task->tokens;

                    // TODO: maybe move branch to outside of this loop in the future
                    if (slot.state == SLOT_STATE_STARTED) {
                        slot.t_start_process_prompt = ggml_time_us();
                        slot.t_start_generation = 0;

                        slot.state = SLOT_STATE_PROCESSING_PROMPT;

                        SLT_INF(slot, "new prompt, n_ctx_slot = %d, n_keep = %d, task.n_tokens = %d\n",
                                slot.n_ctx, slot.task->params.n_keep, slot.task->n_tokens());

                        // print prompt tokens (for debugging)
                        /*if (1) {
                            // first 16 tokens (avoid flooding logs)
                            for (int i = 0; i < std::min<int>(16, input_tokens.size()); i++) {
                                SLT_DBG(slot, "prompt token %3d: %6d '%s'\n", i, input_tokens[i], common_token_to_piece(ctx, input_tokens[i]).c_str());
                            }
                        } else {
                            // all
                            for (int i = 0; i < (int) input_tokens.size(); i++) {
                                SLT_DBG(slot, "prompt token %3d: %6d '%s'\n", i, input_tokens[i], common_token_to_piece(ctx, input_tokens[i]).c_str());
                            }
                        }*/

                        // keep track how many tokens we can reuse from the previous state
                        int n_past = 0;

                        // empty prompt passed -> release the slot and send empty response
                        if (input_tokens.empty()) {
                            SLT_WRN(slot, "%s", "empty prompt - releasing slot\n");

                            slot.print_timings();
                            send_final_response(slot);
                            slot.release();

                            continue;
                        }

                        // TODO: support memory-less logits computation
                        if (slot.need_logits() && !llama_get_memory(ctx)) {
                            send_error(slot, "the current context does not logits computation. skipping", ERROR_TYPE_SERVER);
                            slot.release();
                            continue;
                        }

                        if (!slot.can_split()) {
                            if (slot.task->n_tokens() > n_ubatch) {
                                send_error(slot, "input is too large to process. increase the physical batch size", ERROR_TYPE_SERVER);
                                slot.release();
                                continue;
                            }

                            if (slot.task->n_tokens() > slot.n_ctx) {
                                send_error(slot, "input is larger than the max context size. skipping", ERROR_TYPE_EXCEED_CONTEXT_SIZE);
                                slot.release();
                                continue;
                            }
                        } else {
                            if (slot.task->n_tokens() >= slot.n_ctx) {
                                send_error(slot, "the request exceeds the available context size, try increasing it", ERROR_TYPE_EXCEED_CONTEXT_SIZE);
                                slot.release();
                                continue;
                            }

                            if (slot.task->params.cache_prompt) {
                                // reuse any previously computed tokens that are common with the new prompt
                                n_past = slot.prompt.tokens.get_common_prefix(input_tokens);

                                // if there is an alora invoked, don't cache after the invocation start
                                if (slot.alora_invocation_start > 0) {
                                    SLT_DBG(slot, "only caching to alora invocation start (n_past = %d, alora_invocation_start = %d)\n", n_past, slot.alora_invocation_start);
                                    n_past = std::min(n_past, slot.alora_invocation_start - 1);
                                }

                                // reuse chunks from the cached prompt by shifting their KV cache in the new position
                                if (params_base.n_cache_reuse > 0) {
                                    GGML_ASSERT(!slot.prompt.tokens.has_mtmd);

                                    size_t head_c = n_past; // cache
                                    size_t head_p = n_past; // current prompt

                                    if (mctx) {
                                        // we should never reach this
                                        GGML_ABORT("not supported by multimodal");
                                    }

                                    SLT_DBG(slot, "trying to reuse chunks with size > %d, n_past = %d\n", params_base.n_cache_reuse, n_past);

                                    while (head_c < slot.prompt.tokens.size() &&
                                           head_p < input_tokens.size()) {

                                        size_t n_match = 0;
                                        while (head_c + n_match < slot.prompt.tokens.size() &&
                                               head_p + n_match < input_tokens.size()       &&
                                               slot.prompt.tokens[head_c + n_match] == input_tokens[head_p + n_match]) {

                                            n_match++;
                                        }

                                        if (n_match >= (size_t) params_base.n_cache_reuse) {
                                            SLT_INF(slot, "reusing chunk with size %zu, shifting KV cache [%zu, %zu) -> [%zu, %zu)\n", n_match, head_c, head_c + n_match, head_p, head_p + n_match);
                                            //for (size_t i = head_p; i < head_p + n_match; i++) {
                                            //    SLT_DBG(slot, "cache token %3zu: %6d '%s'\n", i, prompt_tokens[i], common_token_to_piece(ctx, prompt_tokens[i]).c_str());
                                            //}

                                            const int64_t kv_shift = (int64_t) head_p - (int64_t) head_c;

                                            llama_memory_seq_rm (llama_get_memory(ctx), slot.id, head_p, head_c);
                                            llama_memory_seq_add(llama_get_memory(ctx), slot.id, head_c, head_c + n_match, kv_shift);

                                            for (size_t i = 0; i < n_match; i++) {
                                                slot.prompt.tokens.set_token(head_p + i, slot.prompt.tokens[head_c + i]);
                                                n_past++;
                                            }

                                            head_c += n_match;
                                            head_p += n_match;
                                        } else {
                                            head_c += 1;
                                        }
                                    }

                                    SLT_DBG(slot, "after context reuse, new n_past = %d\n", n_past);
                                }
                            } else {
                                // if we don't cache the prompt, we have to remove all previous tokens
                                n_past = 0;
                            }

                            // note: when n_swa == 0, the model does not use SWA, which is equivalent to a window of 1
                            const auto n_swa = std::max(1, llama_model_n_swa(model));

                            // the largest pos_min required for a checkpoint to be useful
                            const auto pos_min_thold = std::max(0, n_past - n_swa);

                            // note: disallow with mtmd contexts for now
                            //       https://github.com/ggml-org/llama.cpp/issues/17043
                            if (!mctx && n_past > 0 && n_past < slot.prompt.n_tokens()) {
                                const auto pos_min = llama_memory_seq_pos_min(llama_get_memory(ctx), slot.id);
                                if (pos_min == -1) {
                                    SLT_ERR(slot, "n_past = %d, slot.prompt.tokens.size() = %d, seq_id = %d, pos_min = %d\n", n_past, (int) slot.prompt.tokens.size(), slot.id, pos_min);
                                    GGML_ABORT("pos_min == -1, but n_past > 0 - should not happen: https://github.com/ggml-org/llama.cpp/pull/13833#discussion_r2116181237");
                                }

                                // when the prompt prefix does not match, print the tokens around the mismatch
                                // this is useful for debugging prompt caching
                                if (slots_debug) {
                                    const int np0 = std::max<int>(n_past - 4, 0);
                                    const int np1 = std::min<int>(n_past + 6, std::min(slot.prompt.tokens.size(), slot.task->tokens.size()));

                                    std::stringstream ss0;
                                    std::stringstream ss1;

                                    std::stringstream st0;
                                    std::stringstream st1;

                                    ss0 << "old: ... ";
                                    ss1 << "new: ... ";

                                    for (int i = np0; i < np1; i++) {
                                        if (i == n_past) {
                                            ss0 << " | ";
                                            ss1 << " | ";
                                        }

                                        {
                                            const auto token = slot.prompt.tokens[i];
                                            const auto piece = token != LLAMA_TOKEN_NULL ? common_token_to_piece(ctx, token) : "[mtmd]";
                                            ss0 << piece;
                                            st0 << std::setw(8) << token;
                                        }

                                        {
                                            const auto token = slot.task->tokens[i];
                                            const auto piece = token != LLAMA_TOKEN_NULL ? common_token_to_piece(ctx, token) : "[mtmd]";
                                            ss1 << piece;
                                            st1 << std::setw(8) << token;
                                        }
                                    }

                                    SLT_WRN(slot, "%s\n", ss0.str().c_str());
                                    SLT_WRN(slot, "%s\n", ss1.str().c_str());

                                    SLT_WRN(slot, "%s\n", st0.str().c_str());
                                    SLT_WRN(slot, "%s\n", st1.str().c_str());
                                }

                                if (pos_min > pos_min_thold) {
                                    // TODO: support can be added in the future when corresponding vision models get released
                                    GGML_ASSERT(!slot.prompt.tokens.has_mtmd);

                                    SLT_WRN(slot, "n_past = %d, slot.prompt.tokens.size() = %d, seq_id = %d, pos_min = %d, n_swa = %d\n", n_past, (int) slot.prompt.tokens.size(), slot.id, pos_min, n_swa);

                                    // search for a context checkpoint
                                    const auto it = std::find_if(
                                        slot.prompt.checkpoints.rbegin(),
                                        slot.prompt.checkpoints.rend(),
                                        [&](const auto & cur) {
                                            // guarantee that a checkpoint will result in at least one token being processed [TAG_PROMPT_LOGITS]
                                            return cur.pos_min < pos_min_thold;
                                        }
                                    );

                                    bool do_reset = it == slot.prompt.checkpoints.rend();

                                    if (!do_reset) {
                                        // restore the context checkpoint
                                        const size_t checkpoint_size = it->data.size();
                                        const size_t n = llama_state_seq_set_data_ext(ctx, it->data.data(), checkpoint_size, slot.id, LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY);

                                        if (n != checkpoint_size) {
                                            SLT_ERR(slot, "failed to restore context checkpoint (pos_min = %d, pos_max = %d, size = %.3f MiB)\n", it->pos_min, it->pos_max, (float) checkpoint_size / 1024 / 1024);
                                            do_reset = true;
                                            //printf("[DEBUG] `do_reset` was set to `true` after failing to restore a checkpoint");
                                        } else {
                                            n_past = std::min(n_past, std::max(it->pos_min + 1, it->pos_max));
                                            SLT_WRN(slot, "restored context checkpoint (pos_min = %d, pos_max = %d, size = %.3f MiB)\n", it->pos_min, it->pos_max, (float) checkpoint_size / 1024 / 1024);
                                        }
                                    }

                                    if (do_reset) {
                                        SLT_WRN(slot, "forcing full prompt re-processing due to lack of cache data (likely due to SWA or hybrid/recurrent memory, see %s)\n",
                                                "https://github.com/ggml-org/llama.cpp/pull/13194#issuecomment-2868343055");
                                        n_past = 0;
                                    }
                                }
                            }

                            {
                                // erase any checkpoints with pos_min > pos_min_thold
                                for (auto it = slot.prompt.checkpoints.begin(); it != slot.prompt.checkpoints.end();) {
                                    const auto & cur = *it;
                                    if (cur.pos_min > pos_min_thold) {
                                        SLT_WRN(slot, "erased invalidated context checkpoint (pos_min = %d, pos_max = %d, n_swa = %d, size = %.3f MiB)\n", cur.pos_min, cur.pos_max, n_swa, (float) cur.data.size() / 1024 / 1024);
                                        it = slot.prompt.checkpoints.erase(it);
                                    } else {
                                        ++it;
                                    }
                                }
                            }
                        }

                        // [TAG_PROMPT_LOGITS]
                        if (n_past == slot.task->n_tokens() && n_past > 0) {
                            SLT_WRN(slot, "need to evaluate at least 1 token for each active slot (n_past = %d, task.n_tokens() = %d)\n", n_past, slot.task->n_tokens());
                            n_past--;
                            SLT_WRN(slot, "n_past was set to %d\n", n_past);
                        }

                        slot.n_prompt_tokens_cache     = n_past;
                        slot.n_prompt_tokens_processed = 0;

                        slot.prompt.tokens.keep_first(n_past);
                    }

                    if (!slot.can_split()) {
                        // cannot fit the prompt in the current batch - will try next iter
                        if (batch.n_tokens + slot.task->n_tokens() > n_batch) {
                            continue;
                        }
                    }

                    // truncate any tokens that are beyond n_past for this slot
                    const llama_pos p0 = slot.prompt.tokens.pos_next();

                    SLT_INF(slot, "n_tokens = %d, memory_seq_rm [%d, end)\n", slot.prompt.n_tokens(), p0);

                    if (!llama_memory_seq_rm(llama_get_memory(ctx), slot.id, p0, -1)) {
                        SLT_WRN(slot, "failed to truncate tokens with position >= %d - clearing the memory\n", p0);

                        clear_slot(slot);

                        // there is no common part left
                        slot.n_prompt_tokens_cache = 0;
                    }

                    // check if we should process the image
                    if (slot.prompt.n_tokens() < slot.task->n_tokens() && input_tokens[slot.prompt.n_tokens()] == LLAMA_TOKEN_NULL) {
                        // process the image
                        size_t n_tokens_out = 0;
                        int32_t res = input_tokens.process_chunk(ctx, mctx, slot.prompt.n_tokens(), slot.prompt.tokens.pos_next(), slot.id, n_tokens_out);
                        if (res != 0) {
                            SLT_ERR(slot, "failed to process image, res = %d\n", res);
                            send_error(slot, "failed to process image", ERROR_TYPE_SERVER);
                            slot.release();
                            continue;
                        }

                        slot.n_prompt_tokens_processed += n_tokens_out;

                        // add the image chunk to cache
                        {
                            const auto & chunk = input_tokens.find_chunk(slot.prompt.n_tokens());
                            slot.prompt.tokens.push_back(chunk.get()); // copy
                        }
                    }

                    // If using an alora, there may be uncached tokens that come
                    // before the invocation sequence. When this happens, the
                    // tokens before the invocation sequence need to be
                    // processed without the adapter in a separate batch, then
                    // the adapter needs to be enabled for the remaining tokens.
                    if (lora_all_alora(slot.lora) && slot.alora_invocation_start - 1 > slot.prompt.n_tokens()) {
                        SLT_DBG(slot, "processing pre-alora tokens without the adapter (n_tokens = %d, alora_invocation_start = %d)\n", slot.prompt.n_tokens(), slot.alora_invocation_start);
                        const auto & enabled_loras = lora_get_enabled_ids(slot.lora);
                        GGML_ASSERT(enabled_loras.size() == 1);
                        alora_scale = slot.lora[enabled_loras[0]].scale;
                        slot.lora[enabled_loras[0]].scale = 0.0f;
                        alora_disabled_id = enabled_loras[0];
                    }

                    bool do_checkpoint = params_base.n_ctx_checkpoints > 0;

                    // make checkpoints only for completion tasks
                    do_checkpoint = do_checkpoint && slot.task->type == SERVER_TASK_TYPE_COMPLETION;

                    // make a checkpoint of the parts of the memory that cannot be rolled back.
                    // checkpoints are created only if:
                    // - the model uses SWA and we are not using `swa_full`
                    // - the model architecture is marked as recurrent or hybrid
                    //
                    // TODO: try to make this conditional on the context or the memory module, instead of the model type
                    do_checkpoint = do_checkpoint && (
                            llama_model_is_recurrent(model) ||
                            llama_model_is_hybrid(model) ||
                            (llama_model_n_swa(model) > 0 && !params_base.swa_full)
                            );

                    // add prompt tokens for processing in the current batch
                    while (slot.prompt.n_tokens() < slot.task->n_tokens() && batch.n_tokens < n_batch) {
                        // get next token to process
                        llama_token cur_tok = input_tokens[slot.prompt.n_tokens()];
                        if (cur_tok == LLAMA_TOKEN_NULL) {
                            break; // end of text chunk
                        }

                        // if this is an alora request with pre-invocation
                        // tokens that are not cached, we need to stop filling
                        // this batch at those pre-invocation tokens.
                        if (alora_scale > 0 && slot.prompt.n_tokens() == slot.alora_invocation_start - 1) {
                            SLT_DBG(slot, "stop prompt batch filling at (n_tokens = %d, alora_invocation_start = %d)\n", slot.prompt.n_tokens(), slot.alora_invocation_start);
                            break;
                        }

                        // embedding requires all tokens in the batch to be output
                        common_batch_add(batch,
                            cur_tok,
                            slot.prompt.tokens.pos_next(),
                            { slot.id },
                            slot.need_embd());
                        slot.prompt.tokens.push_back(cur_tok);

                        slot.n_prompt_tokens_processed++;

                        // process the last few tokens of the prompt separately in order to allow for a checkpoint to be created.
                        if (do_checkpoint && slot.task->n_tokens() - slot.prompt.n_tokens() == 64) {
                            break;
                        }
                    }

                    // SLT_INF(slot, "new slot.prompt.tokens: %s\n", slot.slot.prompt.tokens.str().c_str());

                    SLT_INF(slot, "prompt processing progress, n_tokens = %d, batch.n_tokens = %d, progress = %f\n", slot.prompt.n_tokens(), batch.n_tokens, (float) slot.prompt.n_tokens() / slot.task->n_tokens());

                    // entire prompt has been processed
                    if (slot.prompt.n_tokens() == slot.task->n_tokens()) {
                        slot.state = SLOT_STATE_DONE_PROMPT;

                        GGML_ASSERT(batch.n_tokens > 0);

                        common_sampler_reset(slot.smpl);

                        // Process all prompt tokens through sampler system
                        for (int i = 0; i < slot.task->n_tokens(); ++i) {
                            llama_token id = input_tokens[i];
                            if (id != LLAMA_TOKEN_NULL) {
                                common_sampler_accept(slot.smpl, id, false);
                            }
                        }

                        // extract the logits only for the last token
                        batch.logits[batch.n_tokens - 1] = true;

                        slot.n_decoded = 0;
                        slot.i_batch   = batch.n_tokens - 1;

                        SLT_INF(slot, "prompt done, n_tokens = %d, batch.n_tokens = %d\n", slot.prompt.n_tokens(), batch.n_tokens);

                        const auto pos_min = llama_memory_seq_pos_min(llama_get_memory(ctx), slot.id);
                        const auto pos_max = llama_memory_seq_pos_max(llama_get_memory(ctx), slot.id);

                        // no need for empty or small checkpoints
                        do_checkpoint = do_checkpoint && (pos_min >= 0 && pos_max >= 64);

                        // no need to create checkpoints that are too close together
                        do_checkpoint = do_checkpoint && (slot.prompt.checkpoints.empty() || pos_max > slot.prompt.checkpoints.back().pos_max + 64);

                        if (do_checkpoint) {
                            while (slot.prompt.checkpoints.size() >= (size_t) params_base.n_ctx_checkpoints) {
                                // make room for the new checkpoint, if needed
                                const auto & cur = slot.prompt.checkpoints.front();

                                SLT_WRN(slot, "erasing old context checkpoint (pos_min = %d, pos_max = %d, size = %.3f MiB)\n",
                                        cur.pos_min, cur.pos_max, (float) cur.data.size() / 1024 / 1024);

                                slot.prompt.checkpoints.erase(slot.prompt.checkpoints.begin());
                            }

                            const size_t checkpoint_size = llama_state_seq_get_size_ext(ctx, slot.id, LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY);

                            auto & cur = slot.prompt.checkpoints.emplace_back(server_prompt_checkpoint{
                                /*.pos_min = */ pos_min,
                                /*.pos_max = */ pos_max,
                                /*.data    = */ std::vector<uint8_t>(checkpoint_size),
                            });

                            llama_state_seq_get_data_ext(ctx, cur.data.data(), checkpoint_size, slot.id, LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY);

                            SLT_WRN(slot, "created context checkpoint %d of %d (pos_min = %d, pos_max = %d, size = %.3f MiB)\n",
                                    (int) slot.prompt.checkpoints.size(), params_base.n_ctx_checkpoints, cur.pos_min, cur.pos_max, (float) cur.data.size() / 1024 / 1024);
                        }
                    }
                }

                if (!slot_batched) {
                    slot_batched = &slot;
                }

                if (batch.n_tokens >= n_batch) {
                    break;
                }
            }
        }

        if (batch.n_tokens == 0) {
            SRV_WRN("%s", "no tokens to decode\n");
            return;
        }

        SRV_DBG("decoding batch, n_tokens = %d\n", batch.n_tokens);

        if (slot_batched) {
            // apply lora, only need to do it once per batch
            common_set_adapter_lora(ctx, slot_batched->lora);

            // if the lora is temporarily disabled for an alora, re-enable it
            // for next time
            if (alora_scale > 0.0f) {
                SRV_DBG("re-enabling alora with scale %f\n", alora_scale);
                slot_batched->lora[alora_disabled_id].scale = alora_scale;
            }

            llama_set_embeddings(ctx, slot_batched->need_embd());
        }

        int32_t i_next = 0;

        // process the created batch of tokens
        for (int32_t i = 0; i < batch.n_tokens; i = i_next) {
            const int32_t n_tokens = std::min(n_batch, batch.n_tokens - i);

            llama_batch batch_view = {
                n_tokens,
                batch.token    + i,
                nullptr,
                batch.pos      + i,
                batch.n_seq_id + i,
                batch.seq_id   + i,
                batch.logits   + i,
            };

            const int ret = llama_decode(ctx, batch_view);

            metrics.on_decoded(slots);

            if (ret != 0) {
                {
                    std::string err;

                    if (n_batch == 1 && ret == 1) {
                        // TODO: try to terminate only the largest active slot/sequence and continue with the rest
                        //       need to remove the tokens from the current batch too
                        err = "Context size has been exceeded.";
                    }

                    if (ret == -1) {
                        err = "Invalid input batch.";
                    }

                    if (ret < -1) {
                        // TODO: update slot state based on llama_memory_seq_pos_min() and llama_memory_seq_pos_max()
                        err = "Compute error.";
                    }

                    // TODO: handle ret == 2 (abort) when we start aborting

                    if (!err.empty()) {
                        SRV_ERR("%s i = %d, n_batch = %d, ret = %d\n", err.c_str(), i, n_batch, ret);

                        for (auto & slot : slots) {
                            if (slot.is_processing()) {
                                send_error(slot, err);
                                slot.release();

                                // note: it's complicated to keep track of how much of the current batch has been
                                //       processed before the error occurred, so we simply clear the entire context
                                clear_slot(slot);
                            }
                        }

                        break;
                    }
                }

                // retry with half the batch size to try to find a free slot in the KV cache
                if (!try_clear_idle_slots()) {
                    n_batch /= 2;
                }

                SRV_WRN("failed to find free space in the KV cache, retrying with smaller batch size, i = %d, n_batch = %d, ret = %d\n", i, n_batch, ret);

                continue; // continue loop of n_batch
            }

            // move the head of the batch forward with the number of tokens we just processed
            i_next = i + n_tokens;

            // on successful decode, restore the original batch size
            n_batch = llama_n_batch(ctx);

            for (auto & slot : slots) {
                // optionally send prompt processing progress
                if (slot.state == SLOT_STATE_PROCESSING_PROMPT || slot.state == SLOT_STATE_DONE_PROMPT) {
                    if (slot.task->params.stream && slot.task->params.return_progress) {
                        send_partial_response(slot, {}, true);
                    }
                }

                if (slot.i_batch < (int) i || slot.i_batch >= (int) (i + n_tokens)) {
                    continue; // continue loop of slots
                }

                if (slot.state == SLOT_STATE_DONE_PROMPT) {
                    if (slot.task->type == SERVER_TASK_TYPE_EMBEDDING) {
                        // prompt evaluated for embedding
                        send_embedding(slot, batch_view);
                        slot.release();
                        slot.i_batch = -1;
                        continue; // continue loop of slots
                    }

                    if (slot.task->type == SERVER_TASK_TYPE_RERANK) {
                        send_rerank(slot, batch_view);
                        slot.release();
                        slot.i_batch = -1;
                        continue; // continue loop of slots
                    }

                    // prompt evaluated for next-token prediction
                    slot.state = SLOT_STATE_GENERATING;
                } else if (slot.state != SLOT_STATE_GENERATING) {
                    continue; // continue loop of slots
                }

                const int tok_idx = slot.i_batch - i;

                llama_token id = common_sampler_sample(slot.smpl, ctx, tok_idx);

                slot.i_batch = -1;

                common_sampler_accept(slot.smpl, id, true);

                slot.n_decoded += 1;

                const int64_t t_current = ggml_time_us();

                if (slot.n_decoded == 1) {
                    slot.t_start_generation = t_current;
                    slot.t_prompt_processing = (slot.t_start_generation - slot.t_start_process_prompt) / 1e3;
                    metrics.on_prompt_eval(slot);
                }

                slot.t_token_generation = std::max<int64_t>(1, t_current - slot.t_start_generation) / 1e3;

                completion_token_output result;
                result.tok          = id;
                result.text_to_send = common_token_to_piece(ctx, result.tok, accept_special_token(slot, result.tok));
                result.prob         = 1.0f; // TODO: set it here instead of doing inside populate_token_probs

                if (slot.task->params.sampling.n_probs > 0) {
                    populate_token_probs(slot, result, slot.task->params.post_sampling_probs, params_base.special, tok_idx);
                }

                if (!process_token(result, slot)) {
                    // release slot because of stop condition
                    slot.print_timings();
                    send_final_response(slot);
                    metrics.on_prediction(slot);
                    slot.release();

                    continue;
                }
            }

            // do speculative decoding
            // TODO: rework to have a single draft llama_context shared across all slots [TAG_SERVER_SPEC_REWORK]
            //       perform the speculative drafting for all sequences at the same time in a single batch
            for (auto & slot : slots) {
                if (!slot.is_processing() || !slot.can_speculate()) {
                    continue;
                }

                if (slot.state != SLOT_STATE_GENERATING) {
                    continue;
                }

                if (mctx) {
                    // we should never reach this, as speculative is automatically disabled if mmproj is loaded
                    GGML_ABORT("not supported by multimodal");
                }

                // determine the max draft that fits the current slot state
                int n_draft_max = slot.task->params.speculative.n_max;

                // note: slot.prompt is not yet expanded with the `id` token sampled above
                //       also, need to leave space for 1 extra token to allow context shifts
                n_draft_max = std::min(n_draft_max, slot.n_ctx - slot.prompt.n_tokens() - 2);

                if (slot.n_remaining > 0) {
                    n_draft_max = std::min(n_draft_max, slot.n_remaining - 1);
                }

                SLT_DBG(slot, "max possible draft: %d\n", n_draft_max);

                if (n_draft_max < slot.task->params.speculative.n_min) {
                    SLT_DBG(slot, "the max possible draft is too small: %d < %d - skipping speculative decoding\n", n_draft_max, slot.task->params.speculative.n_min);

                    continue;
                }

                llama_token id = slot.sampled;

                struct common_speculative_params params_spec;
                params_spec.n_draft = n_draft_max;
                params_spec.n_reuse = llama_n_ctx(slot.ctx_dft) - slot.task->params.speculative.n_max;
                params_spec.p_min   = slot.task->params.speculative.p_min;

                const llama_tokens & cached_text_tokens = slot.prompt.tokens.get_text_tokens();
                llama_tokens draft = common_speculative_gen_draft(slot.spec, params_spec, cached_text_tokens, id);

                // ignore small drafts
                if (slot.task->params.speculative.n_min > (int) draft.size()) {
                    SLT_DBG(slot, "ignoring small draft: %d < %d\n", (int) draft.size(), slot.task->params.speculative.n_min);

                    continue;
                }

                // keep track of total number of drafted tokens tested
                slot.n_draft_total += draft.size();

                // construct the speculation batch
                common_batch_clear(slot.batch_spec);
                common_batch_add  (slot.batch_spec, id, slot.prompt.tokens.pos_next(), { slot.id }, true);

                for (size_t i = 0; i < draft.size(); ++i) {
                    common_batch_add(slot.batch_spec, draft[i], slot.prompt.tokens.pos_next() + 1 + i, { slot.id }, true);
                }

                SLT_DBG(slot, "decoding speculative batch, size = %d\n", slot.batch_spec.n_tokens);

                llama_decode(ctx, slot.batch_spec);

                // the accepted tokens from the speculation
                const auto ids = common_sampler_sample_and_accept_n(slot.smpl, ctx, draft);

                slot.n_decoded += ids.size();

                // update how many tokens out of those tested were accepted
                slot.n_draft_accepted += ids.size() - 1;

                slot.prompt.tokens.push_back(id);
                slot.prompt.tokens.insert({ids.begin(), ids.end() - 1});

                llama_memory_seq_rm(llama_get_memory(ctx), slot.id, slot.prompt.n_tokens(), -1);

                for (size_t i = 0; i < ids.size(); ++i) {
                    completion_token_output result;

                    result.tok          = ids[i];
                    result.text_to_send = common_token_to_piece(ctx, result.tok, accept_special_token(slot, result.tok));
                    result.prob         = 1.0f; // set later

                    // TODO: set result.probs

                    if (!process_token(result, slot)) {
                        slot.print_timings();
                        send_final_response(slot);
                        metrics.on_prediction(slot);
                        slot.release();

                        break;
                    }
                }

                SLT_DBG(slot, "accepted %d/%d draft tokens, new n_tokens = %d\n", (int) ids.size() - 1, (int) draft.size(), slot.prompt.n_tokens());
            }
        }

        SRV_DBG("%s", "run slots completed\n");
    }

    json model_meta() const {
        return json {
            {"vocab_type",  llama_vocab_type       (vocab)},
            {"n_vocab",     llama_vocab_n_tokens   (vocab)},
            {"n_ctx_train", llama_model_n_ctx_train(model)},
            {"n_embd",      llama_model_n_embd     (model)},
            {"n_params",    llama_model_n_params   (model)},
            {"size",        llama_model_size       (model)},
        };
    }

    int get_slot_n_ctx() {
        return slots.back().n_ctx;
    }
};

//
// server_context (public API)
//

server_context::server_context() : impl(new server_context_impl()) {}
server_context::~server_context() = default;

void server_context::init() {
    impl->init();
}

bool server_context::load_model(const common_params & params) {
    return impl->load_model(params);
}

void server_context::start_loop() {
    impl->queue_tasks.start_loop();
}

void server_context::terminate() {
    impl->queue_tasks.terminate();
}

llama_context * server_context::get_llama_context() const {
    return impl->ctx;
}

std::pair<server_queue &, server_response &> server_context::get_queues() {
    return { impl->queue_tasks, impl->queue_results };
}



// generator-like API for HTTP response generation
struct server_res_generator : server_http_res {
    server_response_reader rd;
    server_res_generator(server_context_impl & ctx_server)
        : rd({ctx_server.queue_tasks, ctx_server.queue_results}, HTTP_POLLING_SECONDS) {}
    void ok(const json & response_data) {
        status = 200;
        data = safe_json_to_str(response_data);
    }
    void error(const json & error_data) {
        status = json_value(error_data, "code", 500);
        data = safe_json_to_str({{ "error", error_data }});
    }
};



//
// server_routes
//

static std::unique_ptr<server_res_generator> handle_completions_impl(
            server_context_impl & ctx_server,
            server_task_type type,
            const json & data,
            const std::vector<raw_buffer> & files,
            const std::function<bool()> & should_stop,
            task_response_type res_type) {
    GGML_ASSERT(type == SERVER_TASK_TYPE_COMPLETION || type == SERVER_TASK_TYPE_INFILL);

    auto res = std::make_unique<server_res_generator>(ctx_server);
    auto completion_id = gen_chatcmplid();
    auto & rd = res->rd;

    try {
        std::vector<server_task> tasks;

        const auto & prompt = data.at("prompt");
        // TODO: this log can become very long, put it behind a flag or think about a more compact format
        //SRV_DBG("Prompt: %s\n", prompt.is_string() ? prompt.get<std::string>().c_str() : prompt.dump(2).c_str());

        // process prompt
        std::vector<server_tokens> inputs;

        if (res_type != TASK_RESPONSE_TYPE_NONE && ctx_server.mctx != nullptr) {
            // This is the case used by OAI compatible chat path with MTMD. TODO It can be moved to the path below.
            inputs.push_back(process_mtmd_prompt(ctx_server.mctx, prompt.get<std::string>(), files));
        } else {
            // Everything else, including multimodal completions.
            inputs = tokenize_input_prompts(ctx_server.vocab, ctx_server.mctx, prompt, true, true);
        }
        tasks.reserve(inputs.size());
        for (size_t i = 0; i < inputs.size(); i++) {
            server_task task = server_task(type);

            task.id    = ctx_server.queue_tasks.get_new_id();
            task.index = i;

            task.tokens = std::move(inputs[i]);
            task.params = server_task::params_from_json_cmpl(
                    ctx_server.ctx,
                    ctx_server.params_base,
                    data);
            task.id_slot = json_value(data, "id_slot", -1);

            // OAI-compat
            task.params.res_type          = res_type;
            task.params.oaicompat_cmpl_id = completion_id;
            // oaicompat_model is already populated by params_from_json_cmpl

            tasks.push_back(std::move(task));
        }

        rd.post_tasks(std::move(tasks));
    } catch (const std::exception & e) {
        res->error(format_error_response(e.what(), ERROR_TYPE_INVALID_REQUEST));
        return res;
    }

    bool stream = json_value(data, "stream", false);

    if (!stream) {
        // non-stream, wait for the results
        auto all_results = rd.wait_for_all(should_stop);
        if (all_results.is_terminated) {
            return res; // connection is closed
        } else if (all_results.error) {
            res->error(all_results.error->to_json());
            return res;
        } else {
            json arr = json::array();
            for (auto & res : all_results.results) {
                GGML_ASSERT(dynamic_cast<server_task_result_cmpl_final*>(res.get()) != nullptr);
                arr.push_back(res->to_json());
            }
            // if single request, return single object instead of array
            res->ok(arr.size() == 1 ? arr[0] : arr);
        }

    } else {
        // in streaming mode, the first error must be treated as non-stream response
        // this is to match the OAI API behavior
        // ref: https://github.com/ggml-org/llama.cpp/pull/16486#discussion_r2419657309
        server_task_result_ptr first_result = rd.next(should_stop);
        if (first_result == nullptr) {
            return res; // connection is closed
        } else if (first_result->is_error()) {
            res->error(first_result->to_json());
            return res;
        } else {
            GGML_ASSERT(
                dynamic_cast<server_task_result_cmpl_partial*>(first_result.get()) != nullptr
                || dynamic_cast<server_task_result_cmpl_final*>(first_result.get()) != nullptr
            );
        }

        // next responses are streamed
        if (res_type == TASK_RESPONSE_TYPE_ANTHROPIC) {
            res->data = format_anthropic_sse(first_result->to_json());
        } else {
            res->data = format_oai_sse(first_result->to_json()); // to be sent immediately
        }
        res->status = 200;
        res->content_type = "text/event-stream";
        res->next = [res_this = res.get(), res_type, &should_stop](std::string & output) -> bool {
            if (should_stop()) {
                SRV_DBG("%s", "stopping streaming due to should_stop condition\n");
                return false; // should_stop condition met
            }

            if (!res_this->data.empty()) {
                // flush the first chunk
                output = std::move(res_this->data);
                res_this->data.clear();
                return true;
            }

            server_response_reader & rd = res_this->rd;

            // check if there is more data
            if (!rd.has_next()) {
                if (res_type == TASK_RESPONSE_TYPE_ANTHROPIC) {
                    // Anthropic doesn't send [DONE], message_stop was already sent
                    output = "";
                } else if (res_type != TASK_RESPONSE_TYPE_NONE) {
                    output = "data: [DONE]\n\n";
                } else {
                    output = "";
                }
                SRV_DBG("%s", "all results received, terminating stream\n");
                return false; // no more data, terminate
            }

            // receive subsequent results
            auto result = rd.next(should_stop);
            if (result == nullptr) {
                SRV_DBG("%s", "stopping streaming due to should_stop condition\n");
                return false; // should_stop condition met
            }

            // send the results
            json res_json = result->to_json();
            if (result->is_error()) {
                if (res_type == TASK_RESPONSE_TYPE_ANTHROPIC) {
                    output = format_anthropic_sse({
                        {"event", "error"},
                        {"data", res_json},
                    });
                } else {
                    output = format_oai_sse(json {{ "error", res_json }});
                }
                SRV_DBG("%s", "error received during streaming, terminating stream\n");
                return false; // terminate on error
            } else {
                GGML_ASSERT(
                    dynamic_cast<server_task_result_cmpl_partial*>(result.get()) != nullptr
                    || dynamic_cast<server_task_result_cmpl_final*>(result.get()) != nullptr
                );
                if (res_type == TASK_RESPONSE_TYPE_ANTHROPIC) {
                    output = format_anthropic_sse(res_json);
                } else {
                    output = format_oai_sse(res_json);
                }
            }

            // has next data, continue
            return true;
        };
    }

    return res;
}

void server_routes::init_routes() {
    this->get_health = [this](const server_http_req &) {
        // error and loading states are handled by middleware
        auto res = std::make_unique<server_res_generator>(ctx_server);
        res->ok({{"status", "ok"}});
        return res;
    };

    this->get_metrics = [this](const server_http_req &) {
        auto res = std::make_unique<server_res_generator>(ctx_server);
        if (!params.endpoint_metrics) {
            res->error(format_error_response("This server does not support metrics endpoint. Start it with `--metrics`", ERROR_TYPE_NOT_SUPPORTED));
            return res;
        }

        // request slots data using task queue
        // TODO: use server_response_reader
        int task_id = ctx_server.queue_tasks.get_new_id();
        {
            server_task task(SERVER_TASK_TYPE_METRICS);
            task.id = task_id;
            ctx_server.queue_results.add_waiting_task_id(task_id);
            ctx_server.queue_tasks.post(std::move(task), true); // high-priority task
        }

        // get the result
        server_task_result_ptr result = ctx_server.queue_results.recv(task_id);
        ctx_server.queue_results.remove_waiting_task_id(task_id);

        if (result->is_error()) {
            res->error(result->to_json());
            return res;
        }

        // TODO: get rid of this dynamic_cast
        auto res_task = dynamic_cast<server_task_result_metrics*>(result.get());
        GGML_ASSERT(res_task != nullptr);

        // metrics definition: https://prometheus.io/docs/practices/naming/#metric-names
        json all_metrics_def = json {
            {"counter", {{
                    {"name",  "prompt_tokens_total"},
                    {"help",  "Number of prompt tokens processed."},
                    {"value",  (uint64_t) res_task->n_prompt_tokens_processed_total}
            }, {
                    {"name",  "prompt_seconds_total"},
                    {"help",  "Prompt process time"},
                    {"value",  (uint64_t) res_task->t_prompt_processing_total / 1.e3}
            }, {
                    {"name",  "tokens_predicted_total"},
                    {"help",  "Number of generation tokens processed."},
                    {"value",  (uint64_t) res_task->n_tokens_predicted_total}
            }, {
                    {"name",  "tokens_predicted_seconds_total"},
                    {"help",  "Predict process time"},
                    {"value",  (uint64_t) res_task->t_tokens_generation_total / 1.e3}
            }, {
                    {"name",  "n_decode_total"},
                    {"help",  "Total number of llama_decode() calls"},
                    {"value",  res_task->n_decode_total}
            }, {
                    {"name",  "n_tokens_max"},
                    {"help",  "Largest observed n_tokens."},
                    {"value",  res_task->n_tokens_max}
            }, {
                    {"name",  "n_busy_slots_per_decode"},
                    {"help",  "Average number of busy slots per llama_decode() call"},
                    {"value",  (float) res_task->n_busy_slots_total / std::max((float) res_task->n_decode_total, 1.f)}
            }}},
            {"gauge", {{
                    {"name",  "prompt_tokens_seconds"},
                    {"help",  "Average prompt throughput in tokens/s."},
                    {"value",  res_task->n_prompt_tokens_processed ? 1.e3 / res_task->t_prompt_processing * res_task->n_prompt_tokens_processed : 0.}
            },{
                    {"name",  "predicted_tokens_seconds"},
                    {"help",  "Average generation throughput in tokens/s."},
                    {"value",  res_task->n_tokens_predicted ? 1.e3 / res_task->t_tokens_generation * res_task->n_tokens_predicted : 0.}
            },{
                    {"name",  "requests_processing"},
                    {"help",  "Number of requests processing."},
                    {"value",  (uint64_t) res_task->n_processing_slots}
            },{
                    {"name",  "requests_deferred"},
                    {"help",  "Number of requests deferred."},
                    {"value",  (uint64_t) res_task->n_tasks_deferred}
            }}}
        };

        std::stringstream prometheus;

        for (const auto & el : all_metrics_def.items()) {
            const auto & type        = el.key();
            const auto & metrics_def = el.value();

            for (const auto & metric_def : metrics_def) {
                const std::string name = metric_def.at("name");
                const std::string help = metric_def.at("help");

                auto value = json_value(metric_def, "value", 0.);
                prometheus << "# HELP llamacpp:" << name << " " << help  << "\n"
                            << "# TYPE llamacpp:" << name << " " << type  << "\n"
                            << "llamacpp:"        << name << " " << value << "\n";
            }
        }

        res->headers["Process-Start-Time-Unix"] = std::to_string(res_task->t_start);
        res->content_type = "text/plain; version=0.0.4";
        res->status = 200;
        res->data = prometheus.str();
        return res;
    };

    this->get_slots = [this](const server_http_req & req) {
        auto res = std::make_unique<server_res_generator>(ctx_server);
        if (!params.endpoint_slots) {
            res->error(format_error_response("This server does not support slots endpoint. Start it with `--slots`", ERROR_TYPE_NOT_SUPPORTED));
            return res;
        }

        // request slots data using task queue
        int task_id = ctx_server.queue_tasks.get_new_id();
        {
            server_task task(SERVER_TASK_TYPE_METRICS);
            task.id = task_id;
            ctx_server.queue_results.add_waiting_task_id(task_id);
            ctx_server.queue_tasks.post(std::move(task), true); // high-priority task
        }

        // get the result
        server_task_result_ptr result = ctx_server.queue_results.recv(task_id);
        ctx_server.queue_results.remove_waiting_task_id(task_id);

        if (result->is_error()) {
            res->error(result->to_json());
            return res;
        }

        // TODO: get rid of this dynamic_cast
        auto res_task = dynamic_cast<server_task_result_metrics*>(result.get());
        GGML_ASSERT(res_task != nullptr);

        // optionally return "fail_on_no_slot" error
        if (!req.get_param("fail_on_no_slot").empty()) {
            if (res_task->n_idle_slots == 0) {
                res->error(format_error_response("no slot available", ERROR_TYPE_UNAVAILABLE));
                return res;
            }
        }

        res->ok(res_task->slots_data);
        return res;
    };

    this->post_slots = [this](const server_http_req & req) {
        auto res = std::make_unique<server_res_generator>(ctx_server);
        if (params.slot_save_path.empty()) {
            res->error(format_error_response("This server does not support slots action. Start it with `--slot-save-path`", ERROR_TYPE_NOT_SUPPORTED));
            return res;
        }

        std::string id_slot_str = req.get_param("id_slot");
        int id_slot;

        try {
            id_slot = std::stoi(id_slot_str);
        } catch (const std::exception &) {
            res->error(format_error_response("Invalid slot ID", ERROR_TYPE_INVALID_REQUEST));
            return res;
        }

        std::string action = req.get_param("action");

        if (action == "save") {
            return handle_slots_save(req, id_slot);
        } else if (action == "restore") {
            return handle_slots_restore(req, id_slot);
        } else if (action == "erase") {
            return handle_slots_erase(req, id_slot);
        } else {
            res->error(format_error_response("Invalid action", ERROR_TYPE_INVALID_REQUEST));
            return res;
        }
    };

    this->get_props = [this](const server_http_req &) {
        auto res = std::make_unique<server_res_generator>(ctx_server);
        json default_generation_settings_for_props;

        {
            task_params params;

            params.sampling = ctx_server.params_base.sampling;

            default_generation_settings_for_props = json {
                {"params", params.to_json(true)},
                {"n_ctx",  ctx_server.get_slot_n_ctx()},
            };
        }

        // this endpoint is publicly available, please only return what is safe to be exposed
        json data = {
            { "default_generation_settings", default_generation_settings_for_props },
            { "total_slots",                 ctx_server.params_base.n_parallel },
            { "model_alias",                 ctx_server.params_base.model_alias },
            { "model_path",                  ctx_server.params_base.model.path },
            { "modalities",                  json {
                {"vision", ctx_server.oai_parser_opt.allow_image},
                {"audio",  ctx_server.oai_parser_opt.allow_audio},
            } },
            { "endpoint_slots",              params.endpoint_slots },
            { "endpoint_props",              params.endpoint_props },
            { "endpoint_metrics",            params.endpoint_metrics },
            { "webui",                       params.webui },
            { "chat_template",               common_chat_templates_source(ctx_server.chat_templates.get()) },
            { "bos_token",                   common_token_to_piece(ctx_server.ctx, llama_vocab_bos(ctx_server.vocab), /* special= */ true)},
            { "eos_token",                   common_token_to_piece(ctx_server.ctx, llama_vocab_eos(ctx_server.vocab), /* special= */ true)},
            { "build_info",                  build_info },
        };
        if (ctx_server.params_base.use_jinja) {
            if (auto tool_use_src = common_chat_templates_source(ctx_server.chat_templates.get(), "tool_use")) {
                data["chat_template_tool_use"] = tool_use_src;
            }
        }

        res->ok(data);
        return res;
    };

    this->post_props = [this](const server_http_req &) {
        auto res = std::make_unique<server_res_generator>(ctx_server);
        if (!params.endpoint_props) {
            res->error(format_error_response("This server does not support changing global properties. Start it with `--props`", ERROR_TYPE_NOT_SUPPORTED));
            return res;
        }
        // update any props here

        res->ok({{ "success", true }});
        return res;
    };

    this->get_api_show = [this](const server_http_req &) {
        auto res = std::make_unique<server_res_generator>(ctx_server);
        bool has_mtmd = ctx_server.mctx != nullptr;
        json data = {
            {
                "template", common_chat_templates_source(ctx_server.chat_templates.get()),
            },
            {
                "model_info", {
                    { "llama.context_length", ctx_server.get_slot_n_ctx() },
                }
            },
            {"modelfile", ""},
            {"parameters", ""},
            {"template", common_chat_templates_source(ctx_server.chat_templates.get())},
            {"details", {
                {"parent_model", ""},
                {"format", "gguf"},
                {"family", ""},
                {"families", {""}},
                {"parameter_size", ""},
                {"quantization_level", ""}
            }},
            {"model_info", ""},
            {"capabilities", has_mtmd ? json({"completion","multimodal"}) : json({"completion"})}
        };

        res->ok(data);
        return res;
    };

    this->post_infill = [this](const server_http_req & req) {
        auto res = std::make_unique<server_res_generator>(ctx_server);
        // check model compatibility
        std::string err;
        if (llama_vocab_fim_pre(ctx_server.vocab) == LLAMA_TOKEN_NULL) {
            err += "prefix token is missing. ";
        }
        if (llama_vocab_fim_suf(ctx_server.vocab) == LLAMA_TOKEN_NULL) {
            err += "suffix token is missing. ";
        }
        if (llama_vocab_fim_mid(ctx_server.vocab) == LLAMA_TOKEN_NULL) {
            err += "middle token is missing. ";
        }
        if (!err.empty()) {
            res->error(format_error_response(string_format("Infill is not supported by this model: %s", err.c_str()), ERROR_TYPE_NOT_SUPPORTED));
            return res;
        }

        // validate input
        json data = json::parse(req.body);
        if (data.contains("prompt") && !data.at("prompt").is_string()) {
            // prompt is optional
            res->error(format_error_response("\"prompt\" must be a string", ERROR_TYPE_INVALID_REQUEST));
        }

        if (!data.contains("input_prefix")) {
            res->error(format_error_response("\"input_prefix\" is required", ERROR_TYPE_INVALID_REQUEST));
        }

        if (!data.contains("input_suffix")) {
            res->error(format_error_response("\"input_suffix\" is required", ERROR_TYPE_INVALID_REQUEST));
        }

        if (data.contains("input_extra") && !data.at("input_extra").is_array()) {
            // input_extra is optional
            res->error(format_error_response("\"input_extra\" must be an array of {\"filename\": string, \"text\": string}", ERROR_TYPE_INVALID_REQUEST));
            return res;
        }

        json input_extra = json_value(data, "input_extra", json::array());
        for (const auto & chunk : input_extra) {
            // { "text": string, "filename": string }
            if (!chunk.contains("text") || !chunk.at("text").is_string()) {
                res->error(format_error_response("extra_context chunk must contain a \"text\" field with a string value", ERROR_TYPE_INVALID_REQUEST));
                return res;
            }
            // filename is optional
            if (chunk.contains("filename") && !chunk.at("filename").is_string()) {
                res->error(format_error_response("extra_context chunk's \"filename\" field must be a string", ERROR_TYPE_INVALID_REQUEST));
                return res;
            }
        }
        data["input_extra"] = input_extra; // default to empty array if it's not exist

        std::string prompt = json_value(data, "prompt", std::string());
        std::vector<server_tokens> tokenized_prompts = tokenize_input_prompts(ctx_server.vocab, ctx_server.mctx, prompt, false, true);
        SRV_DBG("creating infill tasks, n_prompts = %d\n", (int) tokenized_prompts.size());
        data["prompt"] = format_prompt_infill(
            ctx_server.vocab,
            data.at("input_prefix"),
            data.at("input_suffix"),
            data.at("input_extra"),
            ctx_server.params_base.n_batch,
            ctx_server.params_base.n_predict,
            ctx_server.get_slot_n_ctx(),
            ctx_server.params_base.spm_infill,
            tokenized_prompts[0].get_text_tokens() // TODO: this could maybe be multimodal.
        );

        std::vector<raw_buffer> files; // dummy
        return handle_completions_impl(
            ctx_server,
            SERVER_TASK_TYPE_INFILL,
            data,
            files,
            req.should_stop,
            TASK_RESPONSE_TYPE_NONE); // infill is not OAI compatible
    };

    this->post_completions = [this](const server_http_req & req) {
        std::vector<raw_buffer> files; // dummy
        const json body = json::parse(req.body);
        return handle_completions_impl(
            ctx_server,
            SERVER_TASK_TYPE_COMPLETION,
            body,
            files,
            req.should_stop,
            TASK_RESPONSE_TYPE_NONE);
    };

    this->post_completions_oai = [this](const server_http_req & req) {
        std::vector<raw_buffer> files; // dummy
        const json body = json::parse(req.body);
        return handle_completions_impl(
            ctx_server,
            SERVER_TASK_TYPE_COMPLETION,
            body,
            files,
            req.should_stop,
            TASK_RESPONSE_TYPE_OAI_CMPL);
    };

    this->post_chat_completions = [this](const server_http_req & req) {
        std::vector<raw_buffer> files;
        json body = json::parse(req.body);
        json body_parsed = oaicompat_chat_params_parse(
            body,
            ctx_server.oai_parser_opt,
            files);
        return handle_completions_impl(
            ctx_server,
            SERVER_TASK_TYPE_COMPLETION,
            body_parsed,
            files,
            req.should_stop,
            TASK_RESPONSE_TYPE_OAI_CHAT);
    };

    this->post_anthropic_messages = [this](const server_http_req & req) {
        std::vector<raw_buffer> files;
        json body = convert_anthropic_to_oai(json::parse(req.body));
        json body_parsed = oaicompat_chat_params_parse(
            body,
            ctx_server.oai_parser_opt,
            files);
        return handle_completions_impl(
            ctx_server,
            SERVER_TASK_TYPE_COMPLETION,
            body_parsed,
            files,
            req.should_stop,
            TASK_RESPONSE_TYPE_ANTHROPIC);
    };

    this->post_anthropic_count_tokens = [this](const server_http_req & req) {
        auto res = std::make_unique<server_res_generator>(ctx_server);
        std::vector<raw_buffer> files;
        json body = convert_anthropic_to_oai(json::parse(req.body));
        json body_parsed = oaicompat_chat_params_parse(
            body,
            ctx_server.oai_parser_opt,
            files);

        json prompt = body_parsed.at("prompt");
        llama_tokens tokens = tokenize_mixed(ctx_server.vocab, prompt, true, true);

        res->ok({{"input_tokens", static_cast<int>(tokens.size())}});
        return res;
    };

    // same with handle_chat_completions, but without inference part
    this->post_apply_template = [this](const server_http_req & req) {
        auto res = std::make_unique<server_res_generator>(ctx_server);
        std::vector<raw_buffer> files; // dummy, unused
        json body = json::parse(req.body);
        json data = oaicompat_chat_params_parse(
            body,
            ctx_server.oai_parser_opt,
            files);
        res->ok({{ "prompt", std::move(data.at("prompt")) }});
        return res;
    };

    this->get_models = [this](const server_http_req &) {
        auto res = std::make_unique<server_res_generator>(ctx_server);
        json model_meta = nullptr;
        if (is_ready()) {
            model_meta = ctx_server.model_meta();
        }
        bool has_mtmd = ctx_server.mctx != nullptr;
        json models = {
            {"models", {
                {
                    {"name", params.model_alias.empty() ? params.model.path : params.model_alias},
                    {"model", params.model_alias.empty() ? params.model.path : params.model_alias},
                    {"modified_at", ""},
                    {"size", ""},
                    {"digest", ""}, // dummy value, llama.cpp does not support managing model file's hash
                    {"type", "model"},
                    {"description", ""},
                    {"tags", {""}},
                    {"capabilities", has_mtmd ? json({"completion","multimodal"}) : json({"completion"})},
                    {"parameters", ""},
                    {"details", {
                        {"parent_model", ""},
                        {"format", "gguf"},
                        {"family", ""},
                        {"families", {""}},
                        {"parameter_size", ""},
                        {"quantization_level", ""}
                    }}
                }
            }},
            {"object", "list"},
            {"data", {
                {
                    {"id",       params.model_alias.empty() ? params.model.path : params.model_alias},
                    {"object",   "model"},
                    {"created",  std::time(0)},
                    {"owned_by", "llamacpp"},
                    {"meta",     model_meta},
                },
            }}
        };

        res->ok(models);
        return res;
    };

    this->post_tokenize = [this](const server_http_req & req) {
        auto res = std::make_unique<server_res_generator>(ctx_server);
        const json body = json::parse(req.body);
        json tokens_response = json::array();
        if (body.count("content") != 0) {
            const bool add_special = json_value(body, "add_special", false);
            const bool parse_special = json_value(body, "parse_special", true);
            const bool with_pieces = json_value(body, "with_pieces", false);

            llama_tokens tokens = tokenize_mixed(ctx_server.vocab, body.at("content"), add_special, parse_special);

            if (with_pieces) {
                for (const auto& token : tokens) {
                    std::string piece = common_token_to_piece(ctx_server.ctx, token);
                    json piece_json;

                    // Check if the piece is valid UTF-8
                    if (is_valid_utf8(piece)) {
                        piece_json = piece;
                    } else {
                        // If not valid UTF-8, store as array of byte values
                        piece_json = json::array();
                        for (unsigned char c : piece) {
                            piece_json.push_back(static_cast<int>(c));
                        }
                    }

                    tokens_response.push_back({
                        {"id", token},
                        {"piece", piece_json}
                    });
                }
            } else {
                tokens_response = tokens;
            }
        }

        res->ok(json{{"tokens", std::move(tokens_response)}});
        return res;
    };

    this->post_detokenize = [this](const server_http_req & req) {
        auto res = std::make_unique<server_res_generator>(ctx_server);
        const json body = json::parse(req.body);

        std::string content;
        if (body.count("tokens") != 0) {
            const llama_tokens tokens = body.at("tokens");
            content = tokens_to_str(ctx_server.ctx, tokens);
        }

        res->ok(json{{"content", std::move(content)}});
        return res;
    };

    this->post_embeddings = [this](const server_http_req & req) {
        return handle_embeddings_impl(req, TASK_RESPONSE_TYPE_NONE);
    };

    this->post_embeddings_oai = [this](const server_http_req & req) {
        return handle_embeddings_impl(req, TASK_RESPONSE_TYPE_OAI_EMBD);
    };

    this->post_rerank = [this](const server_http_req & req) {
        auto res = std::make_unique<server_res_generator>(ctx_server);
        if (!ctx_server.params_base.embedding || ctx_server.params_base.pooling_type != LLAMA_POOLING_TYPE_RANK) {
            res->error(format_error_response("This server does not support reranking. Start it with `--reranking`", ERROR_TYPE_NOT_SUPPORTED));
            return res;
        }

        const json body = json::parse(req.body);

        // if true, use TEI API format, otherwise use Jina API format
        // Jina: https://jina.ai/reranker/
        // TEI: https://huggingface.github.io/text-embeddings-inference/#/Text%20Embeddings%20Inference/rerank
        bool is_tei_format = body.contains("texts");

        json query;
        if (body.count("query") == 1) {
            query = body.at("query");
            if (!query.is_string()) {
                res->error(format_error_response("\"query\" must be a string", ERROR_TYPE_INVALID_REQUEST));
                return res;
            }
        } else {
            res->error(format_error_response("\"query\" must be provided", ERROR_TYPE_INVALID_REQUEST));
            return res;
        }

        std::vector<std::string> documents = json_value(body, "documents",
                                             json_value(body, "texts", std::vector<std::string>()));
        if (documents.empty()) {
            res->error(format_error_response("\"documents\" must be a non-empty string array", ERROR_TYPE_INVALID_REQUEST));
            return res;
        }

        int top_n = json_value(body, "top_n", (int)documents.size());

        // create and queue the task
        json responses = json::array();
        server_response_reader rd({ctx_server.queue_tasks, ctx_server.queue_results}, HTTP_POLLING_SECONDS);
        {
            std::vector<server_task> tasks;
            tasks.reserve(documents.size());
            for (size_t i = 0; i < documents.size(); i++) {
                auto tmp = format_prompt_rerank(ctx_server.model, ctx_server.vocab, ctx_server.mctx, query, documents[i]);
                server_task task = server_task(SERVER_TASK_TYPE_RERANK);
                task.id     = ctx_server.queue_tasks.get_new_id();
                task.index  = i;
                task.tokens = std::move(tmp);
                tasks.push_back(std::move(task));
            }
            rd.post_tasks(std::move(tasks));
        }

        // wait for the results
        auto all_results = rd.wait_for_all(req.should_stop);

        // collect results
        if (all_results.is_terminated) {
            return res; // connection is closed
        } else if (all_results.error) {
            res->error(all_results.error->to_json());
            return res;
        } else {
            for (auto & res : all_results.results) {
                GGML_ASSERT(dynamic_cast<server_task_result_rerank*>(res.get()) != nullptr);
                responses.push_back(res->to_json());
            }
        }

        // write JSON response
        json root = format_response_rerank(
            body,
            responses,
            is_tei_format,
            documents,
            top_n);

        res->ok(root);
        return res;
    };

    this->get_lora_adapters = [this](const server_http_req &) {
        auto res = std::make_unique<server_res_generator>(ctx_server);
        json result = json::array();
        const auto & loras = ctx_server.params_base.lora_adapters;
        for (size_t i = 0; i < loras.size(); ++i) {
            auto & lora = loras[i];
            json entry = {
                {"id", i},
                {"path", lora.path},
                {"scale", lora.scale},
                {"task_name", lora.task_name},
                {"prompt_prefix", lora.prompt_prefix},
            };
            std::string alora_invocation_string = "";
            const uint64_t n_alora_tokens = llama_adapter_get_alora_n_invocation_tokens(lora.ptr);
            std::vector<llama_token> alora_invocation_tokens;
            if (n_alora_tokens) {
                const llama_token * alora_tokens = llama_adapter_get_alora_invocation_tokens(lora.ptr);
                for (uint64_t i = 0; i < n_alora_tokens; ++i) {
                    alora_invocation_string += common_token_to_piece(ctx_server.ctx, alora_tokens[i]);
                    alora_invocation_tokens.push_back(alora_tokens[i]);
                }
                entry["alora_invocation_string"] = alora_invocation_string;
                entry["alora_invocation_tokens"] = alora_invocation_tokens;
            }
            result.push_back(std::move(entry));
        }
        res->ok(result);
        return res;
    };

    this->post_lora_adapters = [this](const server_http_req & req) {
        auto res = std::make_unique<server_res_generator>(ctx_server);
        const json body = json::parse(req.body);
        if (!body.is_array()) {
            res->error(format_error_response("Request body must be an array", ERROR_TYPE_INVALID_REQUEST));
            return res;
        }

        int task_id = ctx_server.queue_tasks.get_new_id();
        {
            server_task task(SERVER_TASK_TYPE_SET_LORA);
            task.id = task_id;
            task.set_lora = parse_lora_request(ctx_server.params_base.lora_adapters, body);
            ctx_server.queue_results.add_waiting_task_id(task_id);
            ctx_server.queue_tasks.post(std::move(task));
        }

        // get the result
        server_task_result_ptr result = ctx_server.queue_results.recv(task_id);
        ctx_server.queue_results.remove_waiting_task_id(task_id);

        if (result->is_error()) {
            res->error(result->to_json());
            return res;
        }

        GGML_ASSERT(dynamic_cast<server_task_result_apply_lora*>(result.get()) != nullptr);
        res->ok(result->to_json());
        return res;
    };
}

std::unique_ptr<server_res_generator> server_routes::handle_slots_save(const server_http_req & req, int id_slot) {
    auto res = std::make_unique<server_res_generator>(ctx_server);
    const json request_data = json::parse(req.body);
    std::string filename = request_data.at("filename");
    if (!fs_validate_filename(filename)) {
        res->error(format_error_response("Invalid filename", ERROR_TYPE_INVALID_REQUEST));
        return res;
    }
    std::string filepath = params.slot_save_path + filename;

    int task_id = ctx_server.queue_tasks.get_new_id();
    {
        server_task task(SERVER_TASK_TYPE_SLOT_SAVE);
        task.id = task_id;
        task.slot_action.slot_id  = id_slot;
        task.slot_action.filename = filename;
        task.slot_action.filepath = filepath;

        // TODO: use server_response_reader
        ctx_server.queue_results.add_waiting_task_id(task_id);
        ctx_server.queue_tasks.post(std::move(task));
    }

    server_task_result_ptr result = ctx_server.queue_results.recv(task_id);
    ctx_server.queue_results.remove_waiting_task_id(task_id);

    if (result->is_error()) {
        res->error(result->to_json());
        return res;
    }

    res->ok(result->to_json());
    return res;
}

std::unique_ptr<server_res_generator> server_routes::handle_slots_restore(const server_http_req & req, int id_slot) {
    auto res = std::make_unique<server_res_generator>(ctx_server);
    const json request_data = json::parse(req.body);
    std::string filename = request_data.at("filename");
    if (!fs_validate_filename(filename)) {
        res->error(format_error_response("Invalid filename", ERROR_TYPE_INVALID_REQUEST));
        return res;
    }
    std::string filepath = params.slot_save_path + filename;

    int task_id = ctx_server.queue_tasks.get_new_id();
    {
        server_task task(SERVER_TASK_TYPE_SLOT_RESTORE);
        task.id = task_id;
        task.slot_action.slot_id  = id_slot;
        task.slot_action.filename = filename;
        task.slot_action.filepath = filepath;

        // TODO: use server_response_reader
        ctx_server.queue_results.add_waiting_task_id(task_id);
        ctx_server.queue_tasks.post(std::move(task));
    }

    server_task_result_ptr result = ctx_server.queue_results.recv(task_id);
    ctx_server.queue_results.remove_waiting_task_id(task_id);

    if (result->is_error()) {
        res->error(result->to_json());
        return res;
    }

    GGML_ASSERT(dynamic_cast<server_task_result_slot_save_load*>(result.get()) != nullptr);
    res->ok(result->to_json());
    return res;
}

std::unique_ptr<server_res_generator> server_routes::handle_slots_erase(const server_http_req &, int id_slot) {
    auto res = std::make_unique<server_res_generator>(ctx_server);
    int task_id = ctx_server.queue_tasks.get_new_id();
    {
        server_task task(SERVER_TASK_TYPE_SLOT_ERASE);
        task.id = task_id;
        task.slot_action.slot_id = id_slot;

        // TODO: use server_response_reader
        ctx_server.queue_results.add_waiting_task_id(task_id);
        ctx_server.queue_tasks.post(std::move(task));
    }

    server_task_result_ptr result = ctx_server.queue_results.recv(task_id);
    ctx_server.queue_results.remove_waiting_task_id(task_id);

    if (result->is_error()) {
        res->error(result->to_json());
        return res;
    }

    GGML_ASSERT(dynamic_cast<server_task_result_slot_erase*>(result.get()) != nullptr);
    res->ok(result->to_json());
    return res;
}

std::unique_ptr<server_res_generator> server_routes::handle_embeddings_impl(const server_http_req & req, task_response_type res_type) {
    auto res = std::make_unique<server_res_generator>(ctx_server);
    if (!ctx_server.params_base.embedding) {
        res->error(format_error_response("This server does not support embeddings. Start it with `--embeddings`", ERROR_TYPE_NOT_SUPPORTED));
        return res;
    }

    if (res_type != TASK_RESPONSE_TYPE_NONE && llama_pooling_type(ctx_server.ctx) == LLAMA_POOLING_TYPE_NONE) {
        res->error(format_error_response("Pooling type 'none' is not OAI compatible. Please use a different pooling type", ERROR_TYPE_INVALID_REQUEST));
        return res;
    }

    const json body = json::parse(req.body);

    // for the shape of input/content, see tokenize_input_prompts()
    json prompt;
    if (body.count("input") != 0) {
        prompt = body.at("input");
    } else if (body.contains("content")) {
        res_type = TASK_RESPONSE_TYPE_NONE; // "content" field is not OAI compatible
        prompt = body.at("content");
    } else {
        res->error(format_error_response("\"input\" or \"content\" must be provided", ERROR_TYPE_INVALID_REQUEST));
        return res;
    }

    bool use_base64 = false;
    if (body.count("encoding_format") != 0) {
        const std::string& format = body.at("encoding_format");
        if (format == "base64") {
            use_base64 = true;
        } else if (format != "float") {
            res->error(format_error_response("The format to return the embeddings in. Can be either float or base64", ERROR_TYPE_INVALID_REQUEST));
            return res;
        }
    }

    auto tokenized_prompts = tokenize_input_prompts(ctx_server.vocab, ctx_server.mctx, prompt, true, true);
    for (const auto & tokens : tokenized_prompts) {
        // this check is necessary for models that do not add BOS token to the input
        if (tokens.empty()) {
            res->error(format_error_response("Input content cannot be empty", ERROR_TYPE_INVALID_REQUEST));
            return res;
        }
    }

    int embd_normalize = 2; // default to Euclidean/L2 norm
    if (body.count("embd_normalize") != 0) {
        embd_normalize = body.at("embd_normalize");
        if (llama_pooling_type(ctx_server.ctx) == LLAMA_POOLING_TYPE_NONE) {
            SRV_DBG("embd_normalize is not supported by pooling type %d, ignoring it\n", llama_pooling_type(ctx_server.ctx));
        }
    }

    // create and queue the task
    json responses = json::array();
    server_response_reader rd({ctx_server.queue_tasks, ctx_server.queue_results}, HTTP_POLLING_SECONDS);
    {
        std::vector<server_task> tasks;
        for (size_t i = 0; i < tokenized_prompts.size(); i++) {
            server_task task = server_task(SERVER_TASK_TYPE_EMBEDDING);

            task.id     = ctx_server.queue_tasks.get_new_id();
            task.index  = i;
            task.tokens = std::move(tokenized_prompts[i]);

            // OAI-compat
            task.params.res_type = res_type;
            task.params.embd_normalize = embd_normalize;

            tasks.push_back(std::move(task));
        }
        rd.post_tasks(std::move(tasks));
    }

    // wait for the results
    auto all_results = rd.wait_for_all(req.should_stop);

    // collect results
    if (all_results.is_terminated) {
        return res; // connection is closed
    } else if (all_results.error) {
        res->error(all_results.error->to_json());
        return res;
    } else {
        for (auto & res : all_results.results) {
            GGML_ASSERT(dynamic_cast<server_task_result_embd*>(res.get()) != nullptr);
            responses.push_back(res->to_json());
        }
    }

    // write JSON response
    json root = res_type == TASK_RESPONSE_TYPE_OAI_EMBD
        ? format_embeddings_response_oaicompat(body, responses, use_base64)
        : json(responses);
    res->ok(root);
    return res;
}