llama.cpp/tools/server/server-task.cpp

#include "server-common.h"
#include "server-task.h"

#include "common.h"
#include "llama.h"
#include "chat.h"
#include "sampling.h"
#include "json-schema-to-grammar.h"

using json = nlohmann::ordered_json;

//
// task_params
//

json task_params::format_logit_bias(const std::vector<llama_logit_bias> & logit_bias) const {
    json data = json::array();
    for (const auto & lb : logit_bias) {
        data.push_back(json{
            {"bias", lb.bias},
            {"token", lb.token},
        });
    }
    return data;
}

json task_params::to_json(bool only_metrics) const {
    std::vector<std::string> samplers;
    samplers.reserve(sampling.samplers.size());
    for (const auto & sampler : sampling.samplers) {
        samplers.emplace_back(common_sampler_type_to_str(sampler));
    }

    json lora = json::array();
    for (size_t i = 0; i < this->lora.size(); ++i) {
        lora.push_back({{"id", i}, {"scale", this->lora[i].scale}});
    }

    if (only_metrics) {
        return json {
            {"seed",                      sampling.seed},
            {"temperature",               sampling.temp},
            {"dynatemp_range",            sampling.dynatemp_range},
            {"dynatemp_exponent",         sampling.dynatemp_exponent},
            {"top_k",                     sampling.top_k},
            {"top_p",                     sampling.top_p},
            {"min_p",                     sampling.min_p},
            {"top_n_sigma",               sampling.top_n_sigma},
            {"xtc_probability",           sampling.xtc_probability},
            {"xtc_threshold",             sampling.xtc_threshold},
            {"typical_p",                 sampling.typ_p},
            {"repeat_last_n",             sampling.penalty_last_n},
            {"repeat_penalty",            sampling.penalty_repeat},
            {"presence_penalty",          sampling.penalty_present},
            {"frequency_penalty",         sampling.penalty_freq},
            {"dry_multiplier",            sampling.dry_multiplier},
            {"dry_base",                  sampling.dry_base},
            {"dry_allowed_length",        sampling.dry_allowed_length},
            {"dry_penalty_last_n",        sampling.dry_penalty_last_n},
            {"mirostat",                  sampling.mirostat},
            {"mirostat_tau",              sampling.mirostat_tau},
            {"mirostat_eta",              sampling.mirostat_eta},
            {"max_tokens",                n_predict},
            {"n_predict",                 n_predict}, // TODO: deduplicate?
            {"n_keep",                    n_keep},
            {"n_discard",                 n_discard},
            {"ignore_eos",                sampling.ignore_eos},
            {"stream",                    stream},
            {"n_probs",                   sampling.n_probs},
            {"min_keep",                  sampling.min_keep},
            {"chat_format",               common_chat_format_name(oaicompat_chat_syntax.format)},
            {"reasoning_format",          common_reasoning_format_name(oaicompat_chat_syntax.reasoning_format)},
            {"reasoning_in_content",      oaicompat_chat_syntax.reasoning_in_content},
            {"thinking_forced_open",      oaicompat_chat_syntax.thinking_forced_open},
            {"samplers",                  samplers},
            {"speculative.n_max",         speculative.n_max},
            {"speculative.n_min",         speculative.n_min},
            {"speculative.p_min",         speculative.p_min},
            {"timings_per_token",         timings_per_token},
            {"post_sampling_probs",       post_sampling_probs},
            {"lora",                      lora},
        };
    }

    auto grammar_triggers = json::array();
    for (const auto & trigger : sampling.grammar_triggers) {
        server_grammar_trigger ct(trigger);
        grammar_triggers.push_back(ct.to_json());
    }

    return json {
        {"seed",                      sampling.seed},
        {"temperature",               sampling.temp},
        {"dynatemp_range",            sampling.dynatemp_range},
        {"dynatemp_exponent",         sampling.dynatemp_exponent},
        {"top_k",                     sampling.top_k},
        {"top_p",                     sampling.top_p},
        {"min_p",                     sampling.min_p},
        {"top_n_sigma",               sampling.top_n_sigma},
        {"xtc_probability",           sampling.xtc_probability},
        {"xtc_threshold",             sampling.xtc_threshold},
        {"typical_p",                 sampling.typ_p},
        {"repeat_last_n",             sampling.penalty_last_n},
        {"repeat_penalty",            sampling.penalty_repeat},
        {"presence_penalty",          sampling.penalty_present},
        {"frequency_penalty",         sampling.penalty_freq},
        {"dry_multiplier",            sampling.dry_multiplier},
        {"dry_base",                  sampling.dry_base},
        {"dry_allowed_length",        sampling.dry_allowed_length},
        {"dry_penalty_last_n",        sampling.dry_penalty_last_n},
        {"dry_sequence_breakers",     sampling.dry_sequence_breakers},
        {"mirostat",                  sampling.mirostat},
        {"mirostat_tau",              sampling.mirostat_tau},
        {"mirostat_eta",              sampling.mirostat_eta},
        {"stop",                      antiprompt},
        {"max_tokens",                n_predict},
        {"n_predict",                 n_predict}, // TODO: deduplicate?
        {"n_keep",                    n_keep},
        {"n_discard",                 n_discard},
        {"ignore_eos",                sampling.ignore_eos},
        {"stream",                    stream},
        {"logit_bias",                format_logit_bias(sampling.logit_bias)},
        {"n_probs",                   sampling.n_probs},
        {"min_keep",                  sampling.min_keep},
        {"grammar",                   sampling.grammar},
        {"grammar_lazy",              sampling.grammar_lazy},
        {"grammar_triggers",          grammar_triggers},
        {"preserved_tokens",          sampling.preserved_tokens},
        {"chat_format",               common_chat_format_name(oaicompat_chat_syntax.format)},
        {"reasoning_format",          common_reasoning_format_name(oaicompat_chat_syntax.reasoning_format)},
        {"reasoning_in_content",      oaicompat_chat_syntax.reasoning_in_content},
        {"thinking_forced_open",      oaicompat_chat_syntax.thinking_forced_open},
        {"samplers",                  samplers},
        {"speculative.n_max",         speculative.n_max},
        {"speculative.n_min",         speculative.n_min},
        {"speculative.p_min",         speculative.p_min},
        {"timings_per_token",         timings_per_token},
        {"post_sampling_probs",       post_sampling_probs},
        {"lora",                      lora},
    };
}

//
// server_task
//

task_params server_task::params_from_json_cmpl(
        const llama_context * ctx,
        const common_params & params_base,
        const json & data) {
    const llama_model * model = llama_get_model(ctx);
    const llama_vocab * vocab = llama_model_get_vocab(model);

    task_params params;

    // Sampling parameter defaults are loaded from the global server context (but individual requests can still them)
    task_params defaults;
    defaults.sampling    = params_base.sampling;
    defaults.speculative = params_base.speculative;
    defaults.n_keep      = params_base.n_keep;
    defaults.n_predict   = params_base.n_predict;
    defaults.antiprompt  = params_base.antiprompt;

    // enabling this will output extra debug information in the HTTP responses from the server
    params.verbose           = params_base.verbosity > 9;
    params.timings_per_token = json_value(data, "timings_per_token", false);

    params.stream           = json_value(data,       "stream",             false);
    auto stream_opt         = json_value(data,       "stream_options",     json::object());
    params.include_usage    = json_value(stream_opt, "include_usage",      false);
    params.cache_prompt     = json_value(data,       "cache_prompt",       true);
    params.return_tokens    = json_value(data,       "return_tokens",      false);
    params.return_progress  = json_value(data,       "return_progress",    false);
    params.n_predict        = json_value(data,       "n_predict",          json_value(data, "max_tokens", defaults.n_predict));
    params.n_indent         = json_value(data,       "n_indent",           defaults.n_indent);
    params.n_keep           = json_value(data,       "n_keep",             defaults.n_keep);
    params.n_discard        = json_value(data,       "n_discard",          defaults.n_discard);
    //params.t_max_prompt_ms  = json_value(data,       "t_max_prompt_ms",    defaults.t_max_prompt_ms); // TODO: implement
    params.t_max_predict_ms = json_value(data,       "t_max_predict_ms",   defaults.t_max_predict_ms);
    params.response_fields  = json_value(data,       "response_fields",    std::vector<std::string>());

    params.sampling.top_k              = json_value(data, "top_k",               defaults.sampling.top_k);
    params.sampling.top_p              = json_value(data, "top_p",               defaults.sampling.top_p);
    params.sampling.min_p              = json_value(data, "min_p",               defaults.sampling.min_p);
    params.sampling.top_n_sigma        = json_value(data, "top_n_sigma",         defaults.sampling.top_n_sigma);
    params.sampling.xtc_probability    = json_value(data, "xtc_probability",     defaults.sampling.xtc_probability);
    params.sampling.xtc_threshold      = json_value(data, "xtc_threshold",       defaults.sampling.xtc_threshold);
    params.sampling.typ_p              = json_value(data, "typical_p",           defaults.sampling.typ_p);
    params.sampling.temp               = json_value(data, "temperature",         defaults.sampling.temp);
    params.sampling.dynatemp_range     = json_value(data, "dynatemp_range",      defaults.sampling.dynatemp_range);
    params.sampling.dynatemp_exponent  = json_value(data, "dynatemp_exponent",   defaults.sampling.dynatemp_exponent);
    params.sampling.penalty_last_n     = json_value(data, "repeat_last_n",       defaults.sampling.penalty_last_n);
    params.sampling.penalty_repeat     = json_value(data, "repeat_penalty",      defaults.sampling.penalty_repeat);
    params.sampling.penalty_freq       = json_value(data, "frequency_penalty",   defaults.sampling.penalty_freq);
    params.sampling.penalty_present    = json_value(data, "presence_penalty",    defaults.sampling.penalty_present);
    params.sampling.dry_multiplier     = json_value(data, "dry_multiplier",      defaults.sampling.dry_multiplier);
    params.sampling.dry_base           = json_value(data, "dry_base",            defaults.sampling.dry_base);
    params.sampling.dry_allowed_length = json_value(data, "dry_allowed_length",  defaults.sampling.dry_allowed_length);
    params.sampling.dry_penalty_last_n = json_value(data, "dry_penalty_last_n",  defaults.sampling.dry_penalty_last_n);
    params.sampling.mirostat           = json_value(data, "mirostat",            defaults.sampling.mirostat);
    params.sampling.mirostat_tau       = json_value(data, "mirostat_tau",        defaults.sampling.mirostat_tau);
    params.sampling.mirostat_eta       = json_value(data, "mirostat_eta",        defaults.sampling.mirostat_eta);
    params.sampling.seed               = json_value(data, "seed",                defaults.sampling.seed);
    params.sampling.n_probs            = json_value(data, "n_probs",             defaults.sampling.n_probs);
    params.sampling.min_keep           = json_value(data, "min_keep",            defaults.sampling.min_keep);
    params.post_sampling_probs         = json_value(data, "post_sampling_probs", defaults.post_sampling_probs);

    params.speculative.n_min = json_value(data, "speculative.n_min", defaults.speculative.n_min);
    params.speculative.n_max = json_value(data, "speculative.n_max", defaults.speculative.n_max);
    params.speculative.p_min = json_value(data, "speculative.p_min", defaults.speculative.p_min);

    params.speculative.n_min = std::min(params.speculative.n_max, params.speculative.n_min);
    params.speculative.n_min = std::max(params.speculative.n_min, 0);
    params.speculative.n_max = std::max(params.speculative.n_max, 0);

    // Use OpenAI API logprobs only if n_probs wasn't provided
    if (data.contains("logprobs") && params.sampling.n_probs == defaults.sampling.n_probs){
        params.sampling.n_probs = json_value(data, "logprobs", defaults.sampling.n_probs);
    }

    if (data.contains("lora")) {
        if (data.at("lora").is_array()) {
            params.lora = parse_lora_request(params_base.lora_adapters, data.at("lora"));
        } else {
            throw std::runtime_error("Error: 'lora' must be an array of objects with 'id' and 'scale' fields");
        }
    } else {
        params.lora = params_base.lora_adapters;
    }

    // TODO: add more sanity checks for the input parameters

    if (params.sampling.penalty_last_n < -1) {
        throw std::runtime_error("Error: repeat_last_n must be >= -1");
    }

    if (params.sampling.dry_penalty_last_n < -1) {
        throw std::runtime_error("Error: dry_penalty_last_n must be >= -1");
    }

    if (params.sampling.penalty_last_n == -1) {
        // note: should be the slot's context and not the full context, but it's ok
        params.sampling.penalty_last_n = llama_n_ctx(ctx);
    }

    if (params.sampling.dry_penalty_last_n == -1) {
        params.sampling.dry_penalty_last_n = llama_n_ctx(ctx);
    }

    if (params.sampling.dry_base < 1.0f) {
        params.sampling.dry_base = defaults.sampling.dry_base;
    }

    // sequence breakers for DRY
    {
        // Currently, this is not compatible with TextGen WebUI, Koboldcpp and SillyTavern format
        // Ref: https://github.com/oobabooga/text-generation-webui/blob/d1af7a41ade7bd3c3a463bfa640725edb818ebaf/extensions/openai/typing.py#L39

        if (data.contains("dry_sequence_breakers")) {
            params.sampling.dry_sequence_breakers = json_value(data, "dry_sequence_breakers", std::vector<std::string>());
            if (params.sampling.dry_sequence_breakers.empty()) {
                throw std::runtime_error("Error: dry_sequence_breakers must be a non-empty array of strings");
            }
        }
    }

    // process "json_schema" and "grammar"
    if (data.contains("json_schema") && !data.contains("grammar")) {
        try {
            auto schema                  = json_value(data, "json_schema", json::object());
            SRV_DBG("JSON schema: %s\n", schema.dump(2).c_str());
            params.sampling.grammar      = json_schema_to_grammar(schema);
            SRV_DBG("Converted grammar: %s\n", params.sampling.grammar.c_str());
        } catch (const std::exception & e) {
            throw std::runtime_error(std::string("\"json_schema\": ") + e.what());
        }
    } else {
        params.sampling.grammar      = json_value(data, "grammar", defaults.sampling.grammar);
        SRV_DBG("Grammar: %s\n", params.sampling.grammar.c_str());
        params.sampling.grammar_lazy = json_value(data, "grammar_lazy", defaults.sampling.grammar_lazy);
        SRV_DBG("Grammar lazy: %s\n", params.sampling.grammar_lazy ? "true" : "false");
    }

    {
        auto it = data.find("chat_format");
        if (it != data.end()) {
            params.oaicompat_chat_syntax.format = static_cast<common_chat_format>(it->get<int>());
            SRV_INF("Chat format: %s\n", common_chat_format_name(params.oaicompat_chat_syntax.format));
        } else {
            params.oaicompat_chat_syntax.format = defaults.oaicompat_chat_syntax.format;
        }
        common_reasoning_format reasoning_format = params_base.reasoning_format;
        if (data.contains("reasoning_format")) {
            reasoning_format = common_reasoning_format_from_name(data.at("reasoning_format").get<std::string>());
        }
        params.oaicompat_chat_syntax.reasoning_format = reasoning_format;
        params.oaicompat_chat_syntax.reasoning_in_content = params.stream && (reasoning_format == COMMON_REASONING_FORMAT_DEEPSEEK_LEGACY);
        params.oaicompat_chat_syntax.thinking_forced_open = json_value(data, "thinking_forced_open", false);
        params.oaicompat_chat_syntax.parse_tool_calls = json_value(data, "parse_tool_calls", false);
    }

    {
        const auto preserved_tokens = data.find("preserved_tokens");
        if (preserved_tokens != data.end()) {
            for (const auto & t : *preserved_tokens) {
                auto ids = common_tokenize(vocab, t.get<std::string>(), /* add_special= */ false, /* parse_special= */ true);
                if (ids.size() == 1) {
                    SRV_DBG("Preserved token: %d\n", ids[0]);
                    params.sampling.preserved_tokens.insert(ids[0]);
                } else {
                    // This may happen when using a tool call style meant for a model with special tokens to preserve on a model without said tokens.
                    SRV_DBG("Not preserved because more than 1 token: %s\n", t.get<std::string>().c_str());
                }
            }
        }
        const auto grammar_triggers = data.find("grammar_triggers");
        if (grammar_triggers != data.end()) {
            for (const auto & t : *grammar_triggers) {
                server_grammar_trigger ct(t);
                if (ct.value.type == COMMON_GRAMMAR_TRIGGER_TYPE_WORD) {
                    const auto & word = ct.value.value;
                    auto ids = common_tokenize(vocab, word, /* add_special= */ false, /* parse_special= */ true);
                    if (ids.size() == 1) {
                        auto token = ids[0];
                        if (std::find(params.sampling.preserved_tokens.begin(), params.sampling.preserved_tokens.end(), (llama_token) token) == params.sampling.preserved_tokens.end()) {
                            throw std::runtime_error("Grammar trigger word should be marked as preserved token: " + word);
                        }
                        SRV_DBG("Grammar trigger token: %d (`%s`)\n", token, word.c_str());
                        common_grammar_trigger trigger;
                        trigger.type = COMMON_GRAMMAR_TRIGGER_TYPE_TOKEN;
                        trigger.value = word;
                        trigger.token = token;
                        params.sampling.grammar_triggers.push_back(std::move(trigger));
                    } else {
                        SRV_DBG("Grammar trigger word: `%s`\n", word.c_str());
                        params.sampling.grammar_triggers.push_back({COMMON_GRAMMAR_TRIGGER_TYPE_WORD, word});
                    }
                } else {
                    if (ct.value.type == COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN) {
                        SRV_DBG("Grammar trigger pattern: `%s`\n", ct.value.value.c_str());
                    } else if (ct.value.type == COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN_FULL) {
                        SRV_DBG("Grammar trigger pattern full: `%s`\n", ct.value.value.c_str());
                    } else {
                        throw std::runtime_error("Unknown grammar trigger type");
                    }
                    params.sampling.grammar_triggers.emplace_back(std::move(ct.value));
                }
            }
        }
        if (params.sampling.grammar_lazy && params.sampling.grammar_triggers.empty()) {
            throw std::runtime_error("Error: no triggers set for lazy grammar!");
        }
    }

    {
        params.sampling.logit_bias.clear();

        const auto & logit_bias = data.find("logit_bias");
        if (logit_bias != data.end() && logit_bias->is_array()) {
            const int n_vocab = llama_vocab_n_tokens(vocab);
            for (const auto & el : *logit_bias) {
                // TODO: we may want to throw errors here, in case "el" is incorrect
                if (el.is_array() && el.size() == 2) {
                    float bias;
                    if (el[1].is_number()) {
                        bias = el[1].get<float>();
                    } else if (el[1].is_boolean() && !el[1].get<bool>()) {
                        bias = -INFINITY;
                    } else {
                        continue;
                    }

                    if (el[0].is_number_integer()) {
                        llama_token tok = el[0].get<llama_token>();
                        if (tok >= 0 && tok < n_vocab) {
                            params.sampling.logit_bias.push_back({tok, bias});
                        }
                    } else if (el[0].is_string()) {
                        auto toks = common_tokenize(vocab, el[0].get<std::string>(), false);
                        for (auto tok : toks) {
                            params.sampling.logit_bias.push_back({tok, bias});
                        }
                    }
                }
            }
        } else if (logit_bias != data.end() && logit_bias->is_object()) {
            const int n_vocab = llama_vocab_n_tokens(vocab);
            for (const auto & el : logit_bias->items()) {
                float bias;
                const auto & key = el.key();
                const auto & value = el.value();
                if (value.is_number()) {
                    bias = value.get<float>();
                } else if (value.is_boolean() && !value.get<bool>()) {
                    bias = -INFINITY;
                } else {
                    continue;
                }

                char *end;
                llama_token tok = strtol(key.c_str(), &end, 10);
                if (*end == 0) {
                    if (tok >= 0 && tok < n_vocab) {
                        params.sampling.logit_bias.push_back({tok, bias});
                    }
                } else {
                    auto toks = common_tokenize(vocab, key, false);
                    for (auto tok : toks) {
                        params.sampling.logit_bias.push_back({tok, bias});
                    }
                }
            }
        }

        params.sampling.ignore_eos = json_value(data, "ignore_eos", params_base.sampling.ignore_eos);
        if (params.sampling.ignore_eos) {
            params.sampling.logit_bias.insert(
                    params.sampling.logit_bias.end(),
                    defaults.sampling.logit_bias_eog.begin(), defaults.sampling.logit_bias_eog.end());
        }
    }

    {
        params.antiprompt.clear();

        const auto & stop = data.find("stop");
        if (stop != data.end() && stop->is_array()) {
            for (const auto & word : *stop) {
                if (!word.empty()) {
                    params.antiprompt.push_back(word);
                }
            }
        }
        // set reverse prompt from cli args if not set in the request
        if (params.antiprompt.empty()) {
            params.antiprompt = defaults.antiprompt;
        }
    }

    {
        const auto samplers = data.find("samplers");
        if (samplers != data.end()) {
            if (samplers->is_array()) {
                params.sampling.samplers = common_sampler_types_from_names(*samplers, false);
            } else if (samplers->is_string()){
                params.sampling.samplers = common_sampler_types_from_chars(samplers->get<std::string>());
            }
        } else {
            params.sampling.samplers = defaults.sampling.samplers;
        }
    }

    std::string model_name = params_base.model_alias.empty() ? DEFAULT_OAICOMPAT_MODEL : params_base.model_alias;
    params.oaicompat_model = json_value(data, "model", model_name);

    return params;
}

//
// result_timings
//

json result_timings::to_json() const {
    json base = {
        {"cache_n",                cache_n},

        {"prompt_n",               prompt_n},
        {"prompt_ms",              prompt_ms},
        {"prompt_per_token_ms",    prompt_per_token_ms},
        {"prompt_per_second",      prompt_per_second},

        {"predicted_n",            predicted_n},
        {"predicted_ms",           predicted_ms},
        {"predicted_per_token_ms", predicted_per_token_ms},
        {"predicted_per_second",   predicted_per_second},
    };

    if (draft_n > 0) {
        base["draft_n"] = draft_n;
        base["draft_n_accepted"] = draft_n_accepted;
    }

    return base;
}

//
// result_prompt_progress
//
json result_prompt_progress::to_json() const {
    return json {
        {"total",     total},
        {"cache",     cache},
        {"processed", processed},
        {"time_ms",   time_ms},
    };
}

static inline std::string stop_type_to_str(stop_type type) {
    switch (type) {
        case STOP_TYPE_EOS:   return "eos";
        case STOP_TYPE_WORD:  return "word";
        case STOP_TYPE_LIMIT: return "limit";
        default:              return "none";
    }
}

//
// completion_token_output
//

json completion_token_output::to_json(bool post_sampling_probs) const {
    json probs_for_token = json::array();
    for (const auto & p : probs) {
        std::string txt(p.txt);
        txt.resize(validate_utf8(txt));
        probs_for_token.push_back(json {
            {"id",      p.tok},
            {"token",   txt},
            {"bytes",   str_to_bytes(p.txt)},
            {
                post_sampling_probs ? "prob" : "logprob",
                post_sampling_probs ? p.prob : logarithm(p.prob)
            },
        });
    }
    return probs_for_token;
}

json completion_token_output::probs_vector_to_json(const std::vector<completion_token_output> & probs, bool post_sampling_probs) {
    json out = json::array();
    for (const auto & p : probs) {
        std::string txt(p.text_to_send);
        txt.resize(validate_utf8(txt));
        out.push_back(json {
            {"id",           p.tok},
            {"token",        txt},
            {"bytes",        str_to_bytes(p.text_to_send)},
            {
                post_sampling_probs ? "prob" : "logprob",
                post_sampling_probs ? p.prob : logarithm(p.prob)
            },
            {
                post_sampling_probs ? "top_probs" : "top_logprobs",
                p.to_json(post_sampling_probs)
            },
        });
    }
    return out;
}

float completion_token_output::logarithm(float x) {
    // nlohmann::json converts -inf to null, so we need to prevent that
    return x == 0.0f ? std::numeric_limits<float>::lowest() : std::log(x);
}

std::vector<unsigned char> completion_token_output::str_to_bytes(const std::string & str) {
    std::vector<unsigned char> bytes;
    for (unsigned char c : str) {
        bytes.push_back(c);
    }
    return bytes;
}

//
// server_task_result_cmpl_final
//
json server_task_result_cmpl_final::to_json() {
    switch (oaicompat) {
        case OAICOMPAT_TYPE_NONE:
            return to_json_non_oaicompat();
        case OAICOMPAT_TYPE_COMPLETION:
            return to_json_oaicompat();
        case OAICOMPAT_TYPE_CHAT:
            return stream ? to_json_oaicompat_chat_stream() : to_json_oaicompat_chat();
        default:
            GGML_ASSERT(false && "Invalid oaicompat_type");
    }
}

json server_task_result_cmpl_final::to_json_non_oaicompat() {
    json res = json {
        {"index",               index},
        {"content",             stream ? "" : content}, // in stream mode, content is already in last partial chunk
        {"tokens",              stream ? llama_tokens {} : tokens},
        {"id_slot",             id_slot},
        {"stop",                true},
        {"model",               oaicompat_model},
        {"tokens_predicted",    n_decoded},
        {"tokens_evaluated",    n_prompt_tokens},
        {"generation_settings", generation_params.to_json()},
        {"prompt",              prompt},
        {"has_new_line",        has_new_line},
        {"truncated",           truncated},
        {"stop_type",           stop_type_to_str(stop)},
        {"stopping_word",       stopping_word},
        {"tokens_cached",       n_tokens_cached},
        {"timings",             timings.to_json()},
    };
    if (!stream && !probs_output.empty()) {
        res["completion_probabilities"] = completion_token_output::probs_vector_to_json(probs_output, post_sampling_probs);
    }
    return response_fields.empty() ? res : json_get_nested_values(response_fields, res);
}

json server_task_result_cmpl_final::to_json_oaicompat() {
    std::time_t t = std::time(0);
    json logprobs = json(nullptr); // OAI default to null
    if (!stream && probs_output.size() > 0) {
        logprobs = json{
            {"content", completion_token_output::probs_vector_to_json(probs_output, post_sampling_probs)},
        };
    }
    json finish_reason = "length";
    if (stop == STOP_TYPE_WORD || stop == STOP_TYPE_EOS) {
        finish_reason = "stop";
    }
    json res = json {
        {"choices",            json::array({
            json{
                {"text",          stream ? "" : content}, // in stream mode, content is already in last partial chunk
                {"index",         index},
                {"logprobs",      logprobs},
                {"finish_reason", finish_reason},
            }
        })},
        {"created",            t},
        {"model",              oaicompat_model},
        {"system_fingerprint", build_info},
        {"object",             "text_completion"},
        {"usage", json {
            {"completion_tokens", n_decoded},
            {"prompt_tokens",     n_prompt_tokens},
            {"total_tokens",      n_decoded + n_prompt_tokens}
        }},
        {"id", oaicompat_cmpl_id}
    };

    // extra fields for debugging purposes
    if (verbose) {
        res["__verbose"] = to_json_non_oaicompat();
    }
    if (timings.prompt_n >= 0) {
        res.push_back({"timings", timings.to_json()});
    }

    return res;
}

json server_task_result_cmpl_final::to_json_oaicompat_chat() {
    std::string finish_reason = "length";
    common_chat_msg msg;
    if (!oaicompat_msg.empty()) {
        msg = oaicompat_msg;
    } else {
        msg.role = "assistant";
        msg.content = content;
    }
    if (stop == STOP_TYPE_WORD || stop == STOP_TYPE_EOS) {
        finish_reason = msg.tool_calls.empty() ? "stop" : "tool_calls";
    }

    json choice {
        {"finish_reason", finish_reason},
        {"index", 0},
        {"message", msg.to_json_oaicompat<json>()},
    };

    if (!stream && probs_output.size() > 0) {
        choice["logprobs"] = json{
            {"content", completion_token_output::probs_vector_to_json(probs_output, post_sampling_probs)},
        };
    }

    std::time_t t = std::time(0);

    json res = json {
        {"choices",            json::array({choice})},
        {"created",            t},
        {"model",              oaicompat_model},
        {"system_fingerprint", build_info},
        {"object",             "chat.completion"},
        {"usage", json {
            {"completion_tokens", n_decoded},
            {"prompt_tokens",     n_prompt_tokens},
            {"total_tokens",      n_decoded + n_prompt_tokens}
        }},
        {"id", oaicompat_cmpl_id}
    };

    // extra fields for debugging purposes
    if (verbose) {
        res["__verbose"] = to_json_non_oaicompat();
    }
    if (timings.prompt_n >= 0) {
        res.push_back({"timings", timings.to_json()});
    }

    return res;
}

json server_task_result_cmpl_final::to_json_oaicompat_chat_stream() {
    std::time_t t = std::time(0);
    std::string finish_reason = "length";
    if (stop == STOP_TYPE_WORD || stop == STOP_TYPE_EOS) {
        finish_reason = oaicompat_msg.tool_calls.empty() ? "stop" : "tool_calls";
    }

    json deltas = json::array();
    for (const auto & diff : oaicompat_msg_diffs) {
        deltas.push_back({
            {"choices", json::array({
                json {
                    {"finish_reason", nullptr},
                    {"index", 0},
                    {"delta", common_chat_msg_diff_to_json_oaicompat<json>(diff)},
                },
            })},
            {"created", t},
            {"id", oaicompat_cmpl_id},
            {"model", oaicompat_model},
            {"system_fingerprint", build_info},
            {"object", "chat.completion.chunk"},
        });
    }

    deltas.push_back({
        {"choices", json::array({
            json {
                {"finish_reason", finish_reason},
                {"index", 0},
                {"delta", json::object()},
            },
        })},
        {"created",            t},
        {"id",                 oaicompat_cmpl_id},
        {"model",              oaicompat_model},
        {"system_fingerprint", build_info},
        {"object",             "chat.completion.chunk"},
    });

    if (include_usage) {
        // OpenAI API spec for chat.completion.chunks specifies an empty `choices` array for the last chunk when including usage
        // https://platform.openai.com/docs/api-reference/chat_streaming/streaming#chat_streaming/streaming-choices
        deltas.push_back({
            {"choices", json::array()},
            {"created",            t},
            {"id",                 oaicompat_cmpl_id},
            {"model",              oaicompat_model},
            {"system_fingerprint", build_info},
            {"object",             "chat.completion.chunk"},
            {"usage", json {
                {"completion_tokens", n_decoded},
                {"prompt_tokens",     n_prompt_tokens},
                {"total_tokens",      n_decoded + n_prompt_tokens},
            }},
        });
    }

    if (timings.prompt_n >= 0) {
        deltas.back().push_back({"timings", timings.to_json()});
    }

    // extra fields for debugging purposes
    if (verbose && !deltas.empty()) {
        deltas.front()["__verbose"] = to_json_non_oaicompat();
    }

    return deltas;
}

//
// server_task_result_cmpl_partial
//
json server_task_result_cmpl_partial::to_json() {
    switch (oaicompat) {
        case OAICOMPAT_TYPE_NONE:
            return to_json_non_oaicompat();
        case OAICOMPAT_TYPE_COMPLETION:
            return to_json_oaicompat();
        case OAICOMPAT_TYPE_CHAT:
            return to_json_oaicompat_chat();
        default:
            GGML_ASSERT(false && "Invalid oaicompat_type");
    }
}

json server_task_result_cmpl_partial::to_json_non_oaicompat() {
    // non-OAI-compat JSON
    json res = json {
        {"index",            index},
        {"content",          content},
        {"tokens",           tokens},
        {"stop",             false},
        {"id_slot",          id_slot},
        {"tokens_predicted", n_decoded},
        {"tokens_evaluated", n_prompt_tokens},
    };
    // populate the timings object when needed (usually for the last response or with timings_per_token enabled)
    if (timings.prompt_n > 0) {
        res.push_back({"timings", timings.to_json()});
    }
    if (is_progress) {
        res.push_back({"prompt_progress", progress.to_json()});
    }
    if (!prob_output.probs.empty()) {
        res["completion_probabilities"] = completion_token_output::probs_vector_to_json({prob_output}, post_sampling_probs);
    }
    return res;
}

json server_task_result_cmpl_partial::to_json_oaicompat() {
    std::time_t t = std::time(0);
    json logprobs = json(nullptr); // OAI default to null
    if (prob_output.probs.size() > 0) {
        logprobs = json{
            {"content", completion_token_output::probs_vector_to_json({prob_output}, post_sampling_probs)},
        };
    }
    json res = json {
        {"choices",            json::array({
            json{
                {"text",          content},
                {"index",         index},
                {"logprobs",      logprobs},
                {"finish_reason", nullptr},
            }
        })},
        {"created",            t},
        {"model",              oaicompat_model},
        {"system_fingerprint", build_info},
        {"object",             "text_completion"},
        {"id",                 oaicompat_cmpl_id}
    };

    // extra fields for debugging purposes
    if (verbose) {
        res["__verbose"] = to_json_non_oaicompat();
    }
    if (timings.prompt_n >= 0) {
        res.push_back({"timings", timings.to_json()});
    }
    if (is_progress) {
        res.push_back({"prompt_progress", progress.to_json()});
    }

    return res;
}

json server_task_result_cmpl_partial::to_json_oaicompat_chat() {
    bool first = n_decoded == 1;
    std::time_t t = std::time(0);
    json choices;

    std::vector<json> deltas;
    auto add_delta = [&](const json & delta) {
        deltas.push_back({
            {"choices", json::array({
                json {
                    {"finish_reason", nullptr},
                    {"index", 0},
                    {"delta", delta},
                },
            })},
            {"created", t},
            {"id", oaicompat_cmpl_id},
            {"model", oaicompat_model},
            {"system_fingerprint", build_info},
            {"object", "chat.completion.chunk"},
        });
    };
    // We have to send an initial update to conform to openai behavior
    if (first || is_progress) {
        add_delta({
            {"role", "assistant"},
            {"content", nullptr},
        });
    }

    for (const auto & diff : oaicompat_msg_diffs) {
        add_delta(common_chat_msg_diff_to_json_oaicompat<json>(diff));
    }

    if (!deltas.empty()) {
        auto & last_json = deltas[deltas.size() - 1];
        GGML_ASSERT(last_json.at("choices").size() >= 1);

        if (prob_output.probs.size() > 0) {
            last_json.at("choices").at(0)["logprobs"] = json {
                {"content", completion_token_output::probs_vector_to_json({prob_output}, post_sampling_probs)},
            };
        }

        if (timings.prompt_n >= 0) {
            last_json.push_back({"timings", timings.to_json()});
        }
        if (is_progress) {
            last_json.push_back({"prompt_progress", progress.to_json()});
        }
    }

    return deltas;
}

//
// server_task_result_embd
//
json server_task_result_embd::to_json() {
    return oaicompat == OAICOMPAT_TYPE_EMBEDDING
        ? to_json_oaicompat()
        : to_json_non_oaicompat();
}

json server_task_result_embd::to_json_non_oaicompat() {
    return json {
        {"index",     index},
        {"embedding", embedding},
    };
}

json server_task_result_embd::to_json_oaicompat() {
    return json {
        {"index",            index},
        {"embedding",        embedding[0]},
        {"tokens_evaluated", n_tokens},
    };
}

//
// server_task_result_rerank
//
json server_task_result_rerank::to_json() {
    return json {
        {"index",            index},
        {"score",            score},
        {"tokens_evaluated", n_tokens},
    };
}

//
// server_task_result_error
//
json server_task_result_error::to_json() {
    json res = format_error_response(err_msg, err_type);
    if (err_type == ERROR_TYPE_EXCEED_CONTEXT_SIZE) {
        res["n_prompt_tokens"] = n_prompt_tokens;
        res["n_ctx"]           = n_ctx;
    }
    return res;
}

//
// server_task_result_metrics
//
json server_task_result_metrics::to_json() {
    return json {
        { "idle",                            n_idle_slots },
        { "processing",                      n_processing_slots },
        { "deferred",                        n_tasks_deferred },
        { "t_start",                         t_start },

        { "n_prompt_tokens_processed_total", n_prompt_tokens_processed_total },
        { "t_tokens_generation_total",       t_tokens_generation_total },
        { "n_tokens_predicted_total",        n_tokens_predicted_total },
        { "t_prompt_processing_total",       t_prompt_processing_total },

        { "n_tokens_max",                    n_tokens_max },

        { "n_prompt_tokens_processed",       n_prompt_tokens_processed },
        { "t_prompt_processing",             t_prompt_processing },
        { "n_tokens_predicted",              n_tokens_predicted },
        { "t_tokens_generation",             t_tokens_generation },

        { "n_decode_total",                  n_decode_total },
        { "n_busy_slots_total",              n_busy_slots_total },

        { "slots",                           slots_data },
    };
}

//
// server_task_result_slot_save_load
//
json server_task_result_slot_save_load::to_json() {
    if (is_save) {
        return json {
            { "id_slot",   id_slot },
            { "filename",  filename },
            { "n_saved",   n_tokens },
            { "n_written", n_bytes },
            { "timings", {
                { "save_ms", t_ms }
            }},
        };
    }

    return json {
        { "id_slot",    id_slot },
        { "filename",   filename },
        { "n_restored", n_tokens },
        { "n_read",     n_bytes },
        { "timings", {
            { "restore_ms", t_ms }
        }},
    };
}

//
// server_task_result_slot_erase
//
json server_task_result_slot_erase::to_json() {
    return json {
        { "id_slot",  id_slot },
        { "n_erased", n_erased },
    };
}

//
// server_task_result_apply_lora
//

json server_task_result_apply_lora::to_json() {
    return json {{ "success", true }};
}

//
// server_prompt_cache
//
size_t server_prompt_cache::size() const {
    size_t res = 0;

    for (const auto & state : states) {
        res += state.size();
    }

    return res;
}

size_t server_prompt_cache::n_tokens() const {
    size_t res = 0;

    for (const auto & state : states) {
        res += state.n_tokens();
    }

    return res;
}

server_prompt * server_prompt_cache::alloc(const server_prompt & prompt, size_t state_size) {
    // first check if the current state is contained fully in the cache
    for (auto it = states.begin(); it != states.end(); ++it) {
        const int cur_lcp_len = it->tokens.get_common_prefix(prompt.tokens);

        if (cur_lcp_len == (int) prompt.tokens.size()) {
            SRV_WRN("%s", " - prompt is already in the cache, skipping\n");
            return nullptr;
        }
    }

    // next, remove any cached prompts that are fully contained in the current prompt
    for (auto it = states.begin(); it != states.end();) {
        const int len = it->tokens.get_common_prefix(prompt.tokens);

        if (len == (int) it->tokens.size()) {
            SRV_WRN(" - removing obsolete cached prompt with length %d\n", len);

            it = states.erase(it);
        } else {
            ++it;
        }
    }

    std::vector<uint8_t> state_data;

    // check if we can allocate enough memory for the new state
    try {
        state_data.resize(state_size);
    } catch (const std::bad_alloc & e) {
        SRV_ERR("failed to allocate memory for prompt cache state: %s\n", e.what());

        limit_size = std::max<size_t>(1, 0.4*size());

        SRV_WRN(" - cache size limit reduced to %.3f MiB\n", limit_size / (1024.0 * 1024.0));

        update();

        return nullptr;
    }

    // TODO: for some reason we can't copy server_tokens, so we have to do this workaround
    auto & cur = states.emplace_back();
    cur = {
        /*.tokens      =*/ server_tokens(prompt.tokens.get_text_tokens(), false),
        /*.data        =*/ std::move(state_data),
        /*.checkpoints =*/ prompt.checkpoints,
    };

    return &cur;
}

bool server_prompt_cache::load(server_prompt & prompt, const server_tokens & tokens_new, llama_context * ctx, int32_t id_slot) {
    const int lcp_best = prompt.tokens.get_common_prefix(tokens_new);

    float f_keep_best = float(lcp_best) / prompt.tokens.size();
    float sim_best    = float(lcp_best) / tokens_new.size();

    SRV_WRN(" - looking for better prompt, base f_keep = %.3f, sim = %.3f\n", f_keep_best, sim_best);

    auto it_best = states.end();

    // find the most similar cached prompt, that would also preserve the most context
    for (auto it = states.begin(); it != states.end(); ++it) {
        const int lcp_cur = it->tokens.get_common_prefix(tokens_new);

        const float f_keep_cur = float(lcp_cur) / it->tokens.size();
        const float sim_cur    = float(lcp_cur) / tokens_new.size();

        // don't trash large prompts
        if (f_keep_cur < 0.25f) {
            continue;
        }

        if (f_keep_best < f_keep_cur && sim_best < sim_cur) {
            f_keep_best = f_keep_cur;
            sim_best    = sim_cur;

            it_best = it;
        }
    }

    if (it_best != states.end()) {
        SRV_WRN(" - found better prompt with f_keep = %.3f, sim = %.3f\n", f_keep_best, sim_best);

        const size_t size = it_best->data.size();
        const size_t n = llama_state_seq_set_data_ext(ctx, it_best->data.data(), size, id_slot, 0);
        if (n != size) {
            SRV_WRN("failed to restore state with size %zu\n", size);

            return false;
        }

        it_best->data.clear();
        it_best->data.shrink_to_fit();

        prompt = std::move(*it_best);

        states.erase(it_best);
    }

    return true;
}

void server_prompt_cache::update() {
    if (limit_size > 0) {
        // always keep at least one state, regardless of the limits
        while (states.size() > 1 && size() > limit_size) {
            if (states.empty()) {
                break;
            }

            SRV_WRN(" - cache size limit reached, removing oldest entry (size = %.3f MiB)\n", states.front().size() / (1024.0 * 1024.0));

            states.pop_front();
        }
    }

    // average size per token
    const float size_per_token = std::max<float>(1.0f, float(size()) / (std::max<size_t>(1, n_tokens())));

    // dynamically increase the token limit if it can fit in the memory limit
    const size_t limit_tokens_cur = limit_size > 0 ? std::max<size_t>(limit_tokens, limit_size/size_per_token) : limit_tokens;

    if (limit_tokens > 0) {
        while (states.size() > 1 && n_tokens() > limit_tokens_cur) {
            if (states.empty()) {
                break;
            }

            SRV_WRN(" - cache token limit (%zu, est: %zu) reached, removing oldest entry (size = %.3f MiB)\n",
                    limit_tokens, limit_tokens_cur, states.front().size() / (1024.0 * 1024.0));

            states.pop_front();
        }
    }

    SRV_WRN(" - cache state: %zu prompts, %.3f MiB (limits: %.3f MiB, %zu tokens, %zu est)\n",
            states.size(), size() / (1024.0 * 1024.0), limit_size / (1024.0 * 1024.0), limit_tokens, limit_tokens_cur);

    for (const auto & state : states) {
        SRV_WRN("   - prompt %p: %7d tokens, checkpoints: %2zu, %9.3f MiB\n",
                (const void *)&state, state.n_tokens(), state.checkpoints.size(), state.size() / (1024.0 * 1024.0));
    }
}