CUDA: fix FA tg at long context for CC >= 8.9 (#13852 )

convert : fix tensor naming conflict for llama 4 vision (#13836 )
* convert : fix tensor naming conflict for llama 4 vision * add comment
2026-06-30 09:37:42 +02:00 · 2025-05-28 13:33:37 +02:00 · 2025-05-28 10:05:54 +02:00 · 2025-05-28 11:54:20 +08:00 · 2025-05-27 12:56:08 -07:00 · 2025-05-27 12:53:14 -07:00
102 changed files with 5046 additions and 1683 deletions
@@ -242,7 +242,56 @@ static bool curl_perform_with_retry(const std::string & url, CURL * curl, int ma
 }

 // download one single file from remote URL to local path
-static bool common_download_file_single(const std::string & url, const std::string & path, const std::string & bearer_token) {
+static bool common_download_file_single(const std::string & url, const std::string & path, const std::string & bearer_token, bool offline) {
+    // Check if the file already exists locally
+    auto file_exists = std::filesystem::exists(path);
+
+    // If the file exists, check its JSON metadata companion file.
+    std::string metadata_path = path + ".json";
+    nlohmann::json metadata; // TODO @ngxson : get rid of this json, use regex instead
+    std::string etag;
+    std::string last_modified;
+
+    if (file_exists) {
+        if (offline) {
+            LOG_INF("%s: using cached file (offline mode): %s\n", __func__, path.c_str());
+            return true; // skip verification/downloading
+        }
+        // Try and read the JSON metadata file (note: stream autoclosed upon exiting this block).
+        std::ifstream metadata_in(metadata_path);
+        if (metadata_in.good()) {
+            try {
+                metadata_in >> metadata;
+                LOG_DBG("%s: previous metadata file found %s: %s\n", __func__, metadata_path.c_str(), metadata.dump().c_str());
+                if (metadata.contains("etag") && metadata.at("etag").is_string()) {
+                    etag = metadata.at("etag");
+                }
+                if (metadata.contains("lastModified") && metadata.at("lastModified").is_string()) {
+                    last_modified = metadata.at("lastModified");
+                }
+            } catch (const nlohmann::json::exception & e) {
+                LOG_ERR("%s: error reading metadata file %s: %s\n", __func__, metadata_path.c_str(), e.what());
+            }
+        }
+        // if we cannot open the metadata file, we assume that the downloaded file is not valid (etag and last-modified are left empty, so we will download it again)
+    } else {
+        if (offline) {
+            LOG_ERR("%s: required file is not available in cache (offline mode): %s\n", __func__, path.c_str());
+            return false;
+        }
+        LOG_INF("%s: no previous model file found %s\n", __func__, path.c_str());
+    }
+
+    // Send a HEAD request to retrieve the etag and last-modified headers
+    struct common_load_model_from_url_headers {
+        std::string etag;
+        std::string last_modified;
+    };
+
+    common_load_model_from_url_headers headers;
+    bool head_request_ok = false;
+    bool should_download = !file_exists; // by default, we should download if the file does not exist
+
    // Initialize libcurl
    curl_ptr       curl(curl_easy_init(), &curl_easy_cleanup);
    curl_slist_ptr http_headers;
@@ -269,91 +318,47 @@ static bool common_download_file_single(const std::string & url, const std::stri
    curl_easy_setopt(curl.get(), CURLOPT_SSL_OPTIONS, CURLSSLOPT_NATIVE_CA);
 #endif

-    // Check if the file already exists locally
-    auto file_exists = std::filesystem::exists(path);
+    typedef size_t(*CURLOPT_HEADERFUNCTION_PTR)(char *, size_t, size_t, void *);
+    auto header_callback = [](char * buffer, size_t /*size*/, size_t n_items, void * userdata) -> size_t {
+        common_load_model_from_url_headers * headers = (common_load_model_from_url_headers *) userdata;

-    // If the file exists, check its JSON metadata companion file.
-    std::string metadata_path = path + ".json";
-    nlohmann::json metadata; // TODO @ngxson : get rid of this json, use regex instead
-    std::string etag;
-    std::string last_modified;
+        static std::regex header_regex("([^:]+): (.*)\r\n");
+        static std::regex etag_regex("ETag", std::regex_constants::icase);
+        static std::regex last_modified_regex("Last-Modified", std::regex_constants::icase);

-    if (file_exists) {
-        // Try and read the JSON metadata file (note: stream autoclosed upon exiting this block).
-        std::ifstream metadata_in(metadata_path);
-        if (metadata_in.good()) {
-            try {
-                metadata_in >> metadata;
-                LOG_DBG("%s: previous metadata file found %s: %s\n", __func__, metadata_path.c_str(), metadata.dump().c_str());
-                if (metadata.contains("etag") && metadata.at("etag").is_string()) {
-                    etag = metadata.at("etag");
-                }
-                if (metadata.contains("lastModified") && metadata.at("lastModified").is_string()) {
-                    last_modified = metadata.at("lastModified");
-                }
-            } catch (const nlohmann::json::exception & e) {
-                LOG_ERR("%s: error reading metadata file %s: %s\n", __func__, metadata_path.c_str(), e.what());
+        std::string header(buffer, n_items);
+        std::smatch match;
+        if (std::regex_match(header, match, header_regex)) {
+            const std::string & key = match[1];
+            const std::string & value = match[2];
+            if (std::regex_match(key, match, etag_regex)) {
+                headers->etag = value;
+            } else if (std::regex_match(key, match, last_modified_regex)) {
+                headers->last_modified = value;
            }
        }
-        // if we cannot open the metadata file, we assume that the downloaded file is not valid (etag and last-modified are left empty, so we will download it again)
-    } else {
-        LOG_INF("%s: no previous model file found %s\n", __func__, path.c_str());
-    }
-
-    // Send a HEAD request to retrieve the etag and last-modified headers
-    struct common_load_model_from_url_headers {
-        std::string etag;
-        std::string last_modified;
+        return n_items;
    };

-    common_load_model_from_url_headers headers;
-    bool head_request_ok = false;
-    bool should_download = !file_exists; // by default, we should download if the file does not exist
+    curl_easy_setopt(curl.get(), CURLOPT_NOBODY, 1L); // will trigger the HEAD verb
+    curl_easy_setopt(curl.get(), CURLOPT_NOPROGRESS, 1L); // hide head request progress
+    curl_easy_setopt(curl.get(), CURLOPT_HEADERFUNCTION, static_cast<CURLOPT_HEADERFUNCTION_PTR>(header_callback));
+    curl_easy_setopt(curl.get(), CURLOPT_HEADERDATA, &headers);

-    // get ETag to see if the remote file has changed
-    {
-        typedef size_t(*CURLOPT_HEADERFUNCTION_PTR)(char *, size_t, size_t, void *);
-        auto header_callback = [](char * buffer, size_t /*size*/, size_t n_items, void * userdata) -> size_t {
-            common_load_model_from_url_headers * headers = (common_load_model_from_url_headers *) userdata;
+    // we only allow retrying once for HEAD requests
+    // this is for the use case of using running offline (no internet), retrying can be annoying
+    bool was_perform_successful = curl_perform_with_retry(url, curl.get(), 1, 0, "HEAD");
+    if (!was_perform_successful) {
+        head_request_ok = false;
+    }

-            static std::regex header_regex("([^:]+): (.*)\r\n");
-            static std::regex etag_regex("ETag", std::regex_constants::icase);
-            static std::regex last_modified_regex("Last-Modified", std::regex_constants::icase);
-
-            std::string header(buffer, n_items);
-            std::smatch match;
-            if (std::regex_match(header, match, header_regex)) {
-                const std::string & key = match[1];
-                const std::string & value = match[2];
-                if (std::regex_match(key, match, etag_regex)) {
-                    headers->etag = value;
-                } else if (std::regex_match(key, match, last_modified_regex)) {
-                    headers->last_modified = value;
-                }
-            }
-            return n_items;
-        };
-
-        curl_easy_setopt(curl.get(), CURLOPT_NOBODY, 1L); // will trigger the HEAD verb
-        curl_easy_setopt(curl.get(), CURLOPT_NOPROGRESS, 1L); // hide head request progress
-        curl_easy_setopt(curl.get(), CURLOPT_HEADERFUNCTION, static_cast<CURLOPT_HEADERFUNCTION_PTR>(header_callback));
-        curl_easy_setopt(curl.get(), CURLOPT_HEADERDATA, &headers);
-
-        // we only allow retrying once for HEAD requests
-        // this is for the use case of using running offline (no internet), retrying can be annoying
-        bool was_perform_successful = curl_perform_with_retry(url, curl.get(), 1, 0, "HEAD");
-        if (!was_perform_successful) {
-            head_request_ok = false;
-        }
-
-        long http_code = 0;
-        curl_easy_getinfo(curl.get(), CURLINFO_RESPONSE_CODE, &http_code);
-        if (http_code == 200) {
-            head_request_ok = true;
-        } else {
-            LOG_WRN("%s: HEAD invalid http status code received: %ld\n", __func__, http_code);
-            head_request_ok = false;
-        }
+    long http_code = 0;
+    curl_easy_getinfo(curl.get(), CURLINFO_RESPONSE_CODE, &http_code);
+    if (http_code == 200) {
+        head_request_ok = true;
+    } else {
+        LOG_WRN("%s: HEAD invalid http status code received: %ld\n", __func__, http_code);
+        head_request_ok = false;
    }

    // if head_request_ok is false, we don't have the etag or last-modified headers
@@ -460,12 +465,12 @@ static bool common_download_file_single(const std::string & url, const std::stri

 // download multiple files from remote URLs to local paths
 // the input is a vector of pairs <url, path>
-static bool common_download_file_multiple(const std::vector<std::pair<std::string, std::string>> & urls, const std::string & bearer_token) {
+static bool common_download_file_multiple(const std::vector<std::pair<std::string, std::string>> & urls, const std::string & bearer_token, bool offline) {
    // Prepare download in parallel
    std::vector<std::future<bool>> futures_download;
    for (auto const & item : urls) {
-        futures_download.push_back(std::async(std::launch::async, [bearer_token](const std::pair<std::string, std::string> & it) -> bool {
-            return common_download_file_single(it.first, it.second, bearer_token);
+        futures_download.push_back(std::async(std::launch::async, [bearer_token, offline](const std::pair<std::string, std::string> & it) -> bool {
+            return common_download_file_single(it.first, it.second, bearer_token, offline);
        }, item));
    }

@@ -481,14 +486,15 @@ static bool common_download_file_multiple(const std::vector<std::pair<std::strin

 static bool common_download_model(
        const common_params_model & model,
-        const std::string & bearer_token) {
+        const std::string & bearer_token,
+        bool offline) {
    // Basic validation of the model.url
    if (model.url.empty()) {
        LOG_ERR("%s: invalid model url\n", __func__);
        return false;
    }

-    if (!common_download_file_single(model.url, model.path, bearer_token)) {
+    if (!common_download_file_single(model.url, model.path, bearer_token, offline)) {
        return false;
    }

@@ -547,7 +553,7 @@ static bool common_download_model(
        }

        // Download in parallel
-        common_download_file_multiple(urls, bearer_token);
+        common_download_file_multiple(urls, bearer_token, offline);
    }

    return true;
@@ -608,7 +614,7 @@ std::pair<long, std::vector<char>> common_remote_get_content(const std::string &
 *
 * Note: we use the Ollama-compatible HF API, but not using the blobId. Instead, we use the special "ggufFile" field which returns the value for "hf_file". This is done to be backward-compatible with existing cache files.
 */
-static struct common_hf_file_res common_get_hf_file(const std::string & hf_repo_with_tag, const std::string & bearer_token) {
+static struct common_hf_file_res common_get_hf_file(const std::string & hf_repo_with_tag, const std::string & bearer_token, bool offline) {
    auto parts = string_split<std::string>(hf_repo_with_tag, ':');
    std::string tag = parts.size() > 1 ? parts.back() : "latest";
    std::string hf_repo = parts[0];
@@ -638,20 +644,25 @@ static struct common_hf_file_res common_get_hf_file(const std::string & hf_repo_
    long res_code = 0;
    std::string res_str;
    bool use_cache = false;
-    try {
-        auto res = common_remote_get_content(url, params);
-        res_code = res.first;
-        res_str = std::string(res.second.data(), res.second.size());
-    } catch (const std::exception & e) {
-        LOG_WRN("error: failed to get manifest: %s\n", e.what());
-        LOG_WRN("try reading from cache\n");
-        // try to read from cache
+    if (!offline) {
        try {
+            auto res = common_remote_get_content(url, params);
+            res_code = res.first;
+            res_str = std::string(res.second.data(), res.second.size());
+        } catch (const std::exception & e) {
+            LOG_WRN("error: failed to get manifest at %s: %s\n", url.c_str(), e.what());
+        }
+    }
+    if (res_code == 0) {
+        if (std::filesystem::exists(cached_response_path)) {
+            LOG_WRN("trying to read manifest from cache: %s\n", cached_response_path.c_str());
            res_str = read_file(cached_response_path);
            res_code = 200;
            use_cache = true;
-        } catch (const std::exception & e) {
-            throw std::runtime_error("error: failed to get manifest (check your internet connection)");
+        } else {
+            throw std::runtime_error(
+                offline ? "error: failed to get manifest (offline mode)"
+                : "error: failed to get manifest (check your internet connection)");
        }
    }
    std::string ggufFile;
@@ -698,24 +709,25 @@ bool common_has_curl() {
    return false;
 }

-static bool common_download_file_single(const std::string &, const std::string &, const std::string &) {
+static bool common_download_file_single(const std::string &, const std::string &, const std::string &, bool) {
    LOG_ERR("error: built without CURL, cannot download model from internet\n");
    return false;
 }

-static bool common_download_file_multiple(const std::vector<std::pair<std::string, std::string>> &, const std::string &) {
+static bool common_download_file_multiple(const std::vector<std::pair<std::string, std::string>> &, const std::string &, bool) {
    LOG_ERR("error: built without CURL, cannot download model from the internet\n");
    return false;
 }

 static bool common_download_model(
        const common_params_model &,
-        const std::string &) {
+        const std::string &,
+        bool) {
    LOG_ERR("error: built without CURL, cannot download model from the internet\n");
    return false;
 }

-static struct common_hf_file_res common_get_hf_file(const std::string &, const std::string &) {
+static struct common_hf_file_res common_get_hf_file(const std::string &, const std::string &, bool) {
    LOG_ERR("error: built without CURL, cannot download model from the internet\n");
    return {};
 }
@@ -742,7 +754,8 @@ struct handle_model_result {
 static handle_model_result common_params_handle_model(
        struct common_params_model & model,
        const std::string & bearer_token,
-        const std::string & model_path_default) {
+        const std::string & model_path_default,
+        bool offline) {
    handle_model_result result;
    // handle pre-fill default model path and url based on hf_repo and hf_file
    {
@@ -750,7 +763,7 @@ static handle_model_result common_params_handle_model(
            // short-hand to avoid specifying --hf-file -> default it to --model
            if (model.hf_file.empty()) {
                if (model.path.empty()) {
-                    auto auto_detected = common_get_hf_file(model.hf_repo, bearer_token);
+                    auto auto_detected = common_get_hf_file(model.hf_repo, bearer_token, offline);
                    if (auto_detected.repo.empty() || auto_detected.ggufFile.empty()) {
                        exit(1); // built without CURL, error message already printed
                    }
@@ -791,7 +804,7 @@ static handle_model_result common_params_handle_model(

    // then, download it if needed
    if (!model.url.empty()) {
-        bool ok = common_download_model(model, bearer_token);
+        bool ok = common_download_model(model, bearer_token, offline);
        if (!ok) {
            LOG_ERR("error: failed to download model from %s\n", model.url.c_str());
            exit(1);
@@ -934,7 +947,7 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context

    // handle model and download
    {
-        auto res = common_params_handle_model(params.model, params.hf_token, DEFAULT_MODEL_PATH);
+        auto res = common_params_handle_model(params.model, params.hf_token, DEFAULT_MODEL_PATH, params.offline);
        if (params.no_mmproj) {
            params.mmproj = {};
        } else if (res.found_mmproj && params.mmproj.path.empty() && params.mmproj.url.empty()) {
@@ -944,12 +957,12 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context
        // only download mmproj if the current example is using it
        for (auto & ex : mmproj_examples) {
            if (ctx_arg.ex == ex) {
-                common_params_handle_model(params.mmproj,    params.hf_token, "");
+                common_params_handle_model(params.mmproj,    params.hf_token, "", params.offline);
                break;
            }
        }
-        common_params_handle_model(params.speculative.model, params.hf_token, "");
-        common_params_handle_model(params.vocoder.model,     params.hf_token, "");
+        common_params_handle_model(params.speculative.model, params.hf_token, "", params.offline);
+        common_params_handle_model(params.vocoder.model,     params.hf_token, "", params.offline);
    }

    if (params.escape) {
@@ -2848,15 +2861,24 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
    ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_MAIN}).set_env("LLAMA_ARG_JINJA"));
    add_opt(common_arg(
        {"--reasoning-format"}, "FORMAT",
-        "reasoning format (default: deepseek; allowed values: deepseek, none)\n"
-        "controls whether thought tags are extracted from the response, and in which format they're returned. 'none' leaves thoughts unparsed in `message.content`, 'deepseek' puts them in `message.reasoning_content` (for DeepSeek R1 & Command R7B only).\n"
-        "only supported for non-streamed responses",
+        "controls whether thought tags are allowed and/or extracted from the response, and in which format they're returned; one of:\n"
+        "- none: leaves thoughts unparsed in `message.content`\n"
+        "- deepseek: puts thoughts in `message.reasoning_content` (except in streaming mode, which behaves as `none`)\n"
+        "(default: deepseek)",
        [](common_params & params, const std::string & value) {
            /**/ if (value == "deepseek") { params.reasoning_format = COMMON_REASONING_FORMAT_DEEPSEEK; }
            else if (value == "none") {     params.reasoning_format = COMMON_REASONING_FORMAT_NONE; }
-            else { std::invalid_argument("invalid value"); }
+            else { throw std::invalid_argument("invalid value"); }
        }
    ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_MAIN}).set_env("LLAMA_ARG_THINK"));
+    add_opt(common_arg(
+        {"--reasoning-budget"}, "N",
+        "controls the amount of thinking allowed; currently only one of: -1 for unrestricted thinking budget, or 0 to disable thinking (default: -1)",
+        [](common_params & params, int value) {
+            if (value != 0 && value != -1) { throw std::invalid_argument("invalid value"); }
+            params.reasoning_budget = value;
+        }
+    ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_MAIN}).set_env("LLAMA_ARG_THINK_BUDGET"));
    add_opt(common_arg(
        {"--chat-template"}, "JINJA_TEMPLATE",
        string_format(
@@ -2955,7 +2977,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
        [](common_params & params, const std::string & value) {
            /**/ if (value == "jsonl") { params.batched_bench_output_jsonl = true; }
            else if (value == "md") { params.batched_bench_output_jsonl = false; }
-            else { std::invalid_argument("invalid value"); }
+            else { throw std::invalid_argument("invalid value"); }
        }
    ).set_examples({LLAMA_EXAMPLE_BENCH}));
    add_opt(common_arg(
@@ -2987,6 +3009,13 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
            common_log_set_verbosity_thold(INT_MAX);
        }
    ));
+    add_opt(common_arg(
+        {"--offline"},
+        "Offline mode: forces use of cache, prevents network access",
+        [](common_params & params) {
+            params.offline = true;
+        }
+    ).set_env("LLAMA_OFFLINE"));
    add_opt(common_arg(
        {"-lv", "--verbosity", "--log-verbosity"}, "N",
        "Set the verbosity threshold. Messages with a higher verbosity will be ignored.",
@@ -170,20 +170,23 @@ std::string common_chat_msg_parser::consume_rest() {
 }

 // Tries to find the regex, consumes it (pos right after it) and gives the prelude (right before it) and the groups to the callback.
-std::optional<common_chat_msg_parser::find_regex_result> common_chat_msg_parser::try_find_regex(const common_regex & regex, size_t from) {
+std::optional<common_chat_msg_parser::find_regex_result> common_chat_msg_parser::try_find_regex(const common_regex & regex, size_t from, bool add_prelude_to_content) {
    auto m = regex.search(input_, from == std::string::npos ? pos_ : from);
    if (m.type == COMMON_REGEX_MATCH_TYPE_NONE) {
        return std::nullopt;
    }
+    auto prelude = input_.substr(pos_, m.groups[0].begin - pos_);
+    pos_ = m.groups[0].end;
+
+    if (add_prelude_to_content) {
+        add_content(prelude);
+    }
    if (m.type == COMMON_REGEX_MATCH_TYPE_PARTIAL) {
        if (is_partial()) {
            throw common_chat_msg_partial_exception(regex.str());
        }
        return std::nullopt;
    }
-    auto prelude = input_.substr(pos_, m.groups[0].begin - pos_);
-    pos_ = m.groups[0].end;
-
    return find_regex_result{prelude, m.groups};
 }

@@ -30,6 +30,7 @@ class common_chat_msg_parser {
    const std::string & healing_marker() const { return healing_marker_; }
    const bool & is_partial() const { return is_partial_; }
    const common_chat_msg & result() const { return result_; }
+    const common_chat_syntax & syntax() const { return syntax_; }

    void move_to(size_t pos) {
        if (pos > input_.size()) {
@@ -77,7 +78,7 @@ class common_chat_msg_parser {
        std::vector<common_string_range> groups;
    };

-    std::optional<find_regex_result> try_find_regex(const common_regex & regex, size_t from = std::string::npos);
+    std::optional<find_regex_result> try_find_regex(const common_regex & regex, size_t from = std::string::npos, bool add_prelude_to_content = true);

    bool try_consume_literal(const std::string & literal);

@@ -31,6 +31,11 @@ static std::string string_diff(const std::string & last, const std::string & cur
        return current;
    }
    if (!string_starts_with(current, last)) {
+        if (string_starts_with(last, current)) {
+            // This happens if the last generation ended on a partial stop word (not erased),
+            // and the current ended on a stop word (erased).
+            return "";
+        }
        throw std::runtime_error("Invalid diff: '" + last + "' not found at start of '" + current + "'");
    }
    return current.substr(last.size());
@@ -101,9 +106,9 @@ std::vector<common_chat_msg_diff> common_chat_msg_diff::compute_diffs(const comm
        if (!args_diff.empty() || pref.id != newf.id) {
            auto & diff = diffs.emplace_back();
            diff.tool_call_index = idx;
-            diff.tool_call_delta.name = newf.name;
            if (pref.id != newf.id) {
                diff.tool_call_delta.id = newf.id;
+                diff.tool_call_delta.name = newf.name;
            }
            diff.tool_call_delta.arguments = args_diff;
        }
@@ -133,6 +138,7 @@ struct templates_params {
    bool stream;
    std::string grammar;
    bool add_generation_prompt = true;
+    bool enable_thinking = true;
    std::chrono::system_clock::time_point now = std::chrono::system_clock::now();
 };

@@ -386,22 +392,19 @@ template <> json common_chat_msg_diff_to_json_oaicompat(const common_chat_msg_di
        delta["content"] = diff.content_delta;
    }
    if (diff.tool_call_index != std::string::npos) {
+        json tool_call;
+        tool_call["index"] = diff.tool_call_index;
+        if (!diff.tool_call_delta.id.empty()) {
+            tool_call["id"] = diff.tool_call_delta.id;
+            tool_call["type"] = "function";
+        }
        json function = json::object();
        if (!diff.tool_call_delta.name.empty()) {
            function["name"] = diff.tool_call_delta.name;
        }
-        if (!diff.tool_call_delta.id.empty()) {
-            function["id"] = diff.tool_call_delta.id;
-        }
-        if (!diff.tool_call_delta.arguments.empty()) {
-            function["arguments"] = diff.tool_call_delta.arguments;
-        }
-        delta["tool_calls"] = json::array({
-            json {
-                {"index", diff.tool_call_index},
-                {"function", function}
-            }
-        });
+        function["arguments"] = diff.tool_call_delta.arguments;
+        tool_call["function"] = function;
+        delta["tool_calls"] = json::array({tool_call});
    }
    return delta;
 }
@@ -573,7 +576,7 @@ common_chat_templates_ptr common_chat_templates_init(
    return tmpls;
 }

-std::string common_chat_format_name(common_chat_format format) {
+const char * common_chat_format_name(common_chat_format format) {
    switch (format) {
        case COMMON_CHAT_FORMAT_CONTENT_ONLY: return "Content-only";
        case COMMON_CHAT_FORMAT_GENERIC: return "Generic";
@@ -591,6 +594,15 @@ std::string common_chat_format_name(common_chat_format format) {
    }
 }

+const char * common_reasoning_format_name(common_reasoning_format format) {
+    switch (format) {
+        case COMMON_REASONING_FORMAT_NONE:     return "none";
+        case COMMON_REASONING_FORMAT_DEEPSEEK: return "deepseek";
+        default:
+            throw std::runtime_error("Unknown reasoning format");
+    }
+}
+
 static std::string wrap_code_as_arguments(common_chat_msg_parser & builder, const std::string & code) {
    std::string arguments;
    if (builder.is_partial()) {
@@ -644,7 +656,6 @@ static void parse_json_tool_calls(
                }
                from = std::string::npos;

-                builder.add_content(res->prelude);
                auto maybe_raw_python = name == "python" && allow_raw_python;
                if (builder.input()[builder.pos()] == '{' || !maybe_raw_python) {
                    if (auto arguments = builder.try_consume_json_with_dumped_args({{}})) {
@@ -674,7 +685,6 @@ static void parse_json_tool_calls(
    };
    if (block_open) {
        if (auto res = builder.try_find_regex(*block_open)) {
-            builder.add_content(res->prelude);
            parse_tool_calls();
        } else {
            builder.add_content(builder.consume_rest());
@@ -687,7 +697,6 @@ static void parse_json_tool_calls(
 static void parse_prefixed_json_tool_call_array(common_chat_msg_parser & builder, const common_regex & prefix, size_t rstrip_prefix = 0) {
    static const std::vector<std::vector<std::string>> args_paths = {{"arguments"}};
    if (auto res = builder.try_find_regex(prefix)) {
-        builder.add_content(res->prelude);
        builder.move_back(rstrip_prefix);
        auto tool_calls = builder.consume_json_with_dumped_args(args_paths);
        if (!builder.add_tool_calls(tool_calls.value) || tool_calls.is_partial) {
@@ -823,6 +832,10 @@ static common_chat_params common_chat_params_init_generic(const common_chat_temp
    return data;
 }
 static void common_chat_parse_generic(common_chat_msg_parser & builder) {
+    if (!builder.syntax().parse_tool_calls) {
+        builder.add_content(builder.consume_rest());
+        return;
+    }
    static const std::vector<std::vector<std::string>> content_paths = {
        {"response"},
    };
@@ -895,6 +908,11 @@ static common_chat_params common_chat_params_init_mistral_nemo(const common_chat
    return data;
 }
 static void common_chat_parse_mistral_nemo(common_chat_msg_parser & builder) {
+    if (!builder.syntax().parse_tool_calls) {
+        builder.add_content(builder.consume_rest());
+        return;
+    }
+
    static const common_regex prefix(regex_escape("[TOOL_CALLS]"));
    parse_prefixed_json_tool_call_array(builder, prefix);
 }
@@ -918,7 +936,13 @@ static common_chat_params common_chat_params_init_command_r7b(const common_chat_
    data.prompt = apply(tmpl, adjusted_messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt, {});
    data.format = COMMON_CHAT_FORMAT_COMMAND_R7B;
    if (string_ends_with(data.prompt, "<|START_THINKING|>")) {
-        data.thinking_forced_open = true;
+        if (!inputs.enable_thinking) {
+            data.prompt += "<|END_THINKING|>";
+        } else {
+            data.thinking_forced_open = true;
+        }
+    } else if (!inputs.enable_thinking && string_ends_with(data.prompt, "<|CHATBOT_TOKEN|>")) {
+        data.prompt += "<|START_THINKING|><|END_THINKING|>";
    }

    data.grammar_lazy = inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_REQUIRED;
@@ -983,7 +1007,6 @@ static void common_chat_parse_command_r7b(common_chat_msg_parser & builder) {

    if (auto res = builder.try_find_regex(start_action_regex)) {
        // If we didn't extract thoughts, prelude includes them.
-        builder.add_content(res->prelude);
        auto tool_calls = builder.consume_json_with_dumped_args({{"parameters"}});
        for (const auto & tool_call : tool_calls.value) {
            std::string name = tool_call.contains("tool_name") ? tool_call.at("tool_name") : "";
@@ -998,11 +1021,7 @@ static void common_chat_parse_command_r7b(common_chat_msg_parser & builder) {
        }
        builder.consume_regex(end_action_regex);
    } else if (auto res = builder.try_find_regex(start_response_regex)) {
-        // If we didn't extract thoughts, prelude includes them.
-        builder.add_content(res->prelude);
-        if (auto res = builder.try_find_regex(end_response_regex)) {
-            builder.add_content(res->prelude);
-        } else {
+        if (!builder.try_find_regex(end_response_regex)) {
            builder.add_content(builder.consume_rest());
            throw common_chat_msg_partial_exception(end_response_regex.str());
        }
@@ -1110,6 +1129,11 @@ static common_chat_params common_chat_params_init_llama_3_x(const common_chat_te
    return data;
 }
 static void common_chat_parse_llama_3_1(common_chat_msg_parser & builder, bool with_builtin_tools = false) {
+    if (!builder.syntax().parse_tool_calls) {
+        builder.add_content(builder.consume_rest());
+        return;
+    }
+
    static const common_regex function_regex(
        "\\s*\\{\\s*(?:\"type\"\\s*:\\s*\"function\"\\s*,\\s*)?\"name\"\\s*:\\s*\"([^\"]+)\"\\s*,\\s*\"parameters\"\\s*: ");
    static const common_regex close_regex("\\}\\s*");
@@ -1120,8 +1144,6 @@ static void common_chat_parse_llama_3_1(common_chat_msg_parser & builder, bool w
    if (with_builtin_tools) {
        static const common_regex builtin_call_regex("<\\|python_tag\\|>");
        if (auto res = builder.try_find_regex(builtin_call_regex)) {
-            builder.add_content(res->prelude);
-
            auto fun_res = builder.consume_regex(function_name_regex);
            auto function_name = builder.str(fun_res.groups[1]);

@@ -1186,7 +1208,11 @@ static common_chat_params common_chat_params_init_deepseek_r1(const common_chat_
    data.prompt = prompt;
    data.format = COMMON_CHAT_FORMAT_DEEPSEEK_R1;
    if (string_ends_with(data.prompt, "<think>\n")) {
-        data.thinking_forced_open = true;
+        if (!inputs.enable_thinking) {
+            data.prompt += "</think>";
+        } else {
+            data.thinking_forced_open = true;
+        }
    }

    if (inputs.tools.is_array() && !inputs.tools.empty()) {
@@ -1233,6 +1259,10 @@ static common_chat_params common_chat_params_init_deepseek_r1(const common_chat_
 }
 static void common_chat_parse_deepseek_r1(common_chat_msg_parser & builder) {
    builder.try_parse_reasoning("<think>", "</think>");
+    if (!builder.syntax().parse_tool_calls) {
+        builder.add_content(builder.consume_rest());
+        return;
+    }

    static const common_regex tool_calls_begin("(?:<｜tool▁calls▁begin｜>|<｜tool_calls_begin｜>|<｜tool calls begin｜>|<｜tool\\\\_calls\\\\_begin｜>|<｜tool▁calls｜>)");
    static const common_regex tool_calls_end("<｜tool▁calls▁end｜>");
@@ -1294,6 +1324,10 @@ static common_chat_params common_chat_params_init_firefunction_v2(const common_c
    return data;
 }
 static void common_chat_parse_firefunction_v2(common_chat_msg_parser & builder) {
+    if (!builder.syntax().parse_tool_calls) {
+        builder.add_content(builder.consume_rest());
+        return;
+    }
    static const common_regex prefix(regex_escape(" functools["));
    parse_prefixed_json_tool_call_array(builder, prefix, /* rstrip_prefix= */ 1);
 }
@@ -1435,15 +1469,12 @@ static common_chat_params common_chat_params_init_functionary_v3_1_llama_3_1(con
    return data;
 }
 static void common_chat_parse_functionary_v3_1_llama_3_1(common_chat_msg_parser & builder) {
-    // This version of Functionary still supports the llama 3.1 tool call format for the python tool.
-    static const common_regex python_tag_regex(regex_escape("<|python_tag|>"));
-
-    if (auto res = builder.try_find_regex(python_tag_regex)) {
-        builder.add_content(res->prelude);
-        auto arguments = wrap_code_as_arguments(builder, builder.consume_rest());
-        builder.add_tool_call("python", "", arguments);
+    if (!builder.syntax().parse_tool_calls) {
+        builder.add_content(builder.consume_rest());
        return;
    }
+    // This version of Functionary still supports the llama 3.1 tool call format for the python tool.
+    static const common_regex python_tag_regex(regex_escape("<|python_tag|>"));

    static const common_regex function_regex(R"(<function=(\w+)>)");
    static const common_regex close_regex(R"(</function>)");
@@ -1455,114 +1486,134 @@ static void common_chat_parse_functionary_v3_1_llama_3_1(common_chat_msg_parser
        function_regex,
        close_regex,
        std::nullopt);
+
+    if (auto res = builder.try_find_regex(python_tag_regex)) {
+        auto arguments = wrap_code_as_arguments(builder, builder.consume_rest());
+        builder.add_tool_call("python", "", arguments);
+        return;
+    }
 }

 static common_chat_params common_chat_params_init_hermes_2_pro(const common_chat_template & tmpl, const struct templates_params & inputs) {
    common_chat_params data;

-    data.prompt = apply(tmpl, inputs.messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt);
+    json additional_context = {
+        {"enable_thinking", inputs.enable_thinking},
+    };
+
+    data.prompt = apply(tmpl, inputs.messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt, additional_context);
    data.format = COMMON_CHAT_FORMAT_HERMES_2_PRO;
    if (string_ends_with(data.prompt, "<think>\n")) {
-        data.thinking_forced_open = true;
+        if (!inputs.enable_thinking) {
+            data.prompt += "</think>";
+        } else {
+            data.thinking_forced_open = true;
+        }
    }

-    // (content)?(<tool_call>{"name": "foo", "arguments": {"a": 1}}</tool_call>)*
-    data.grammar_lazy = inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_REQUIRED;
-    data.grammar = build_grammar([&](const common_grammar_builder & builder) {
-        std::vector<std::string> tool_rules;
-        std::vector<std::string> tool_call_alts;
-        std::vector<std::string> escaped_names;
-        foreach_function(inputs.tools, [&](const json & tool) {
-            const auto & function = tool.at("function");
-            std::string name = function.at("name");
-            auto parameters = function.at("parameters");
-            builder.resolve_refs(parameters);
-            tool_rules.push_back(builder.add_schema(name + "-call", {
-                {"type", "object"},
-                {"properties", json {
-                    {"name", json {{"const", name}}},
-                    {"arguments", parameters},
-                }},
-                {"required", json::array({"name", "arguments"})},
-            }));
-            tool_call_alts.push_back(builder.add_rule(
-                name + "-function-tag",
-                "\"<function\" ( \"=" + name + "\" | \" name=\\\"" + name + "\\\"\" ) \">\" space " +
-                builder.add_schema(name + "-args", parameters) + " "
-                "\"</function>\" space"));
+    if (!inputs.tools.is_null()) {
+        // (content)?(<tool_call>{"name": "foo", "arguments": {"a": 1}}</tool_call>)*
+        data.grammar_lazy = inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_REQUIRED;
+        data.grammar = build_grammar([&](const common_grammar_builder & builder) {
+            std::vector<std::string> tool_rules;
+            std::vector<std::string> tool_call_alts;
+            std::vector<std::string> escaped_names;
+            foreach_function(inputs.tools, [&](const json & tool) {
+                const auto & function = tool.at("function");
+                std::string name = function.at("name");
+                auto parameters = function.at("parameters");
+                builder.resolve_refs(parameters);
+                tool_rules.push_back(builder.add_schema(name + "-call", {
+                    {"type", "object"},
+                    {"properties", json {
+                        {"name", json {{"const", name}}},
+                        {"arguments", parameters},
+                    }},
+                    {"required", json::array({"name", "arguments"})},
+                }));
+                tool_call_alts.push_back(builder.add_rule(
+                    name + "-function-tag",
+                    "\"<function\" ( \"=" + name + "\" | \" name=\\\"" + name + "\\\"\" ) \">\" space " +
+                    builder.add_schema(name + "-args", parameters) + " "
+                    "\"</function>\" space"));

-            data.grammar_triggers.push_back({
-                COMMON_GRAMMAR_TRIGGER_TYPE_WORD,
-                "<function=" + name + ">",
+                data.grammar_triggers.push_back({
+                    COMMON_GRAMMAR_TRIGGER_TYPE_WORD,
+                    "<function=" + name + ">",
+                });
+                auto escaped_name = regex_escape(name);
+                data.grammar_triggers.push_back({
+                    COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN,
+                    "<function\\s+name\\s*=\\s*\"" + escaped_name + "\"",
+                });
+                escaped_names.push_back(escaped_name);
            });
-            auto escaped_name = regex_escape(name);
+            auto any_tool_call = builder.add_rule("any_tool_call", "( " + string_join(tool_rules, " | ") + " ) space");
+            std::vector<std::string> alt_tags {
+                any_tool_call,
+                "\"<tool_call>\" space "     + any_tool_call + " \"</tool_call>\"",
+                // The rest is just to accommodate common "good bad" outputs.
+                "\"<function_call>\" space " + any_tool_call + " \"</function_call>\"",
+                "\"<response>\"  space "     + any_tool_call + " \"</response>\"",
+                "\"<tools>\"     space "     + any_tool_call + " \"</tools>\"",
+                "\"<json>\"      space "     + any_tool_call + " \"</json>\"",
+                "\"<xml>\"      space "     + any_tool_call + " \"</xml>\"",
+                "\"<JSON>\"      space "     + any_tool_call + " \"</JSON>\"",
+            };
+            auto wrappable_tool_call = builder.add_rule("wrappable_tool_call", "( " + string_join(alt_tags, " | ") + " ) space");
+            tool_call_alts.push_back(wrappable_tool_call);
+            tool_call_alts.push_back(
+                "( \"```\\n\" | \"```json\\n\" | \"```xml\\n\" ) space " + wrappable_tool_call + " space \"```\" space ");
+            auto tool_call = builder.add_rule("tool_call", string_join(tool_call_alts, " | "));
+            builder.add_rule("root",
+                std::string(data.thinking_forced_open ? "( \"</think>\" space )? " : "") +
+                (inputs.parallel_tool_calls ? "(" + tool_call + ")+" : tool_call));
+            // Trigger on some common known "good bad" outputs (only from the start and with a json that's about a specific argument name to avoid false positives)
            data.grammar_triggers.push_back({
-                COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN,
-                "<function\\s+name\\s*=\\s*\"" + escaped_name + "\"",
+                COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN_FULL,
+                // If thinking_forced_open, then we capture the </think> tag in the grammar,
+                // (important for required tool choice) and in the trigger's first capture (decides what is sent to the grammar)
+                std::string(data.thinking_forced_open ? "[\\s\\S]*?(</think>\\s*)" : "(?:<think>[\\s\\S]*?</think>\\s*)?") + (
+                    "(\\s*"
+                    "(?:<tool_call>"
+                    "|<function"
+                    "|(?:```(?:json|xml)?\n\\s*)?(?:<function_call>|<tools>|<xml><json>|<response>)?"
+                    "\\s*\\{\\s*\"name\"\\s*:\\s*\"(?:" + string_join(escaped_names, "|") + ")\""
+                    ")"
+                    ")[\\s\\S]*"
+                ),
            });
-            escaped_names.push_back(escaped_name);
+            data.preserved_tokens = {
+                "<think>",
+                "</think>",
+                "<tool_call>",
+                "</tool_call>",
+                "<function",
+                "<tools>",
+                "</tools>",
+                "<response>",
+                "</response>",
+                "<function_call>",
+                "</function_call>",
+                "<json>",
+                "</json>",
+                "<JSON>",
+                "</JSON>",
+                "```",
+                "```json",
+                "```xml",
+            };
        });
-        auto any_tool_call = builder.add_rule("any_tool_call", "( " + string_join(tool_rules, " | ") + " ) space");
-        std::vector<std::string> alt_tags {
-            any_tool_call,
-            "\"<tool_call>\" space "     + any_tool_call + " \"</tool_call>\"",
-            // The rest is just to accommodate common "good bad" outputs.
-            "\"<function_call>\" space " + any_tool_call + " \"</function_call>\"",
-            "\"<response>\"  space "     + any_tool_call + " \"</response>\"",
-            "\"<tools>\"     space "     + any_tool_call + " \"</tools>\"",
-            "\"<json>\"      space "     + any_tool_call + " \"</json>\"",
-            "\"<xml>\"      space "     + any_tool_call + " \"</xml>\"",
-            "\"<JSON>\"      space "     + any_tool_call + " \"</JSON>\"",
-        };
-        auto wrappable_tool_call = builder.add_rule("wrappable_tool_call", "( " + string_join(alt_tags, " | ") + " ) space");
-        tool_call_alts.push_back(wrappable_tool_call);
-        tool_call_alts.push_back(
-            "( \"```\\n\" | \"```json\\n\" | \"```xml\\n\" ) space " + wrappable_tool_call + " space \"```\" space ");
-        auto tool_call = builder.add_rule("tool_call", string_join(tool_call_alts, " | "));
-        builder.add_rule("root",
-            std::string(data.thinking_forced_open ? "( \"</think>\" space )? " : "") +
-            (inputs.parallel_tool_calls ? "(" + tool_call + ")+" : tool_call));
-        // Trigger on some common known "good bad" outputs (only from the start and with a json that's about a specific argument name to avoid false positives)
-        data.grammar_triggers.push_back({
-            COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN_FULL,
-            // If thinking_forced_open, then we capture the </think> tag in the grammar,
-            // (important for required tool choice) and in the trigger's first capture (decides what is sent to the grammar)
-            std::string(data.thinking_forced_open ? "[\\s\\S]*?(</think>\\s*)" : "(?:<think>[\\s\\S]*?</think>\\s*)?") + (
-                "(\\s*"
-                "(?:<tool_call>"
-                "|<function"
-                "|(?:```(?:json|xml)?\n\\s*)?(?:<function_call>|<tools>|<xml><json>|<response>)?"
-                 "\\s*\\{\\s*\"name\"\\s*:\\s*\"(?:" + string_join(escaped_names, "|") + ")\""
-                ")"
-                ")[\\s\\S]*"
-            ),
-        });
-        data.preserved_tokens = {
-            "<think>",
-            "</think>",
-            "<tool_call>",
-            "</tool_call>",
-            "<function",
-            "<tools>",
-            "</tools>",
-            "<response>",
-            "</response>",
-            "<function_call>",
-            "</function_call>",
-            "<json>",
-            "</json>",
-            "<JSON>",
-            "</JSON>",
-            "```",
-            "```json",
-            "```xml",
-        };
-    });
+    }

    return data;
 }
 static void common_chat_parse_hermes_2_pro(common_chat_msg_parser & builder) {
    builder.try_parse_reasoning("<think>", "</think>");
+    if (!builder.syntax().parse_tool_calls) {
+        builder.add_content(builder.consume_rest());
+        return;
+    }

    static const common_regex open_regex(
        "(?:"
@@ -1584,8 +1635,6 @@ static void common_chat_parse_hermes_2_pro(common_chat_msg_parser & builder) {
    );

    if (auto res = builder.try_find_regex(open_regex)) {
-        builder.add_content(res->prelude);
-
        const auto & block_start = res->groups[1];
        std::string block_end = block_start.empty() ? "" : "```";

@@ -1669,6 +1718,7 @@ static common_chat_params common_chat_templates_apply_jinja(
    params.messages = common_chat_msgs_to_json_oaicompat<json>(inputs.messages, /* concat_text= */ !tmpl.original_caps().requires_typed_content);
    params.add_generation_prompt = inputs.add_generation_prompt;
    params.tool_choice = inputs.tool_choice;
+    params.enable_thinking = inputs.enable_thinking;
    params.grammar = inputs.grammar;
    params.now = inputs.now;
    if (!inputs.json_schema.empty()) {
@@ -1702,7 +1752,7 @@ static common_chat_params common_chat_templates_apply_jinja(
    }

    // Hermes 2/3 Pro, Qwen 2.5 Instruct (w/ tools)
-    if (src.find("<tool_call>") != std::string::npos && params.json_schema.is_null() && params.tools.is_array() && params.json_schema.is_null()) {
+    if (src.find("<tool_call>") != std::string::npos && params.json_schema.is_null()) {
        return common_chat_params_init_hermes_2_pro(tmpl, params);
    }

@@ -1820,10 +1870,10 @@ static void common_chat_parse_content_only(common_chat_msg_parser & builder) {
    builder.add_content(builder.consume_rest());
 }

-static void common_chat_parse(common_chat_msg_parser & builder, common_chat_format format) {
-    LOG_DBG("Parsing input with format %s: %s\n", common_chat_format_name(format).c_str(), builder.input().c_str());
+static void common_chat_parse(common_chat_msg_parser & builder) {
+    LOG_DBG("Parsing input with format %s: %s\n", common_chat_format_name(builder.syntax().format), builder.input().c_str());

-    switch (format) {
+    switch (builder.syntax().format) {
        case COMMON_CHAT_FORMAT_CONTENT_ONLY:
            common_chat_parse_content_only(builder);
            break;
@@ -1858,7 +1908,7 @@ static void common_chat_parse(common_chat_msg_parser & builder, common_chat_form
            common_chat_parse_command_r7b(builder);
            break;
        default:
-            throw std::runtime_error("Unsupported format: " + common_chat_format_name(format));
+            throw std::runtime_error(std::string("Unsupported format: ") + common_chat_format_name(builder.syntax().format));
    }
    builder.finish();
 }
@@ -1866,7 +1916,7 @@ static void common_chat_parse(common_chat_msg_parser & builder, common_chat_form
 common_chat_msg common_chat_parse(const std::string & input, bool is_partial, const common_chat_syntax & syntax) {
    common_chat_msg_parser builder(input, is_partial, syntax);
    try {
-        common_chat_parse(builder, syntax.format);
+        common_chat_parse(builder);
    } catch (const common_chat_msg_partial_exception & ex) {
        LOG_DBG("Partial parse: %s\n", ex.what());
        if (!is_partial) {
@@ -123,6 +123,7 @@ struct common_chat_templates_inputs {
    common_chat_tool_choice tool_choice = COMMON_CHAT_TOOL_CHOICE_AUTO;
    bool parallel_tool_calls = false;
    common_reasoning_format reasoning_format = COMMON_REASONING_FORMAT_NONE;
+    bool enable_thinking = true;
    std::chrono::system_clock::time_point now = std::chrono::system_clock::now();
 };

@@ -143,6 +144,7 @@ struct common_chat_syntax {
    // Whether reasoning_content should be inlined in the content (e.g. for reasoning_format=deepseek in stream mode)
    bool                     reasoning_in_content  = false;
    bool                     thinking_forced_open  = false;
+    bool                     parse_tool_calls      = true;
 };

 // Check if the template supplied via "--chat-template" is supported or not. Returns true if it's valid
@@ -181,7 +183,8 @@ std::string common_chat_format_example(
    const struct common_chat_templates * tmpls,
    bool use_jinja);

-std::string               common_chat_format_name(common_chat_format format);
+const char*               common_chat_format_name(common_chat_format format);
+const char*               common_reasoning_format_name(common_reasoning_format format);
 common_chat_msg           common_chat_parse(const std::string & input, bool is_partial, const common_chat_syntax & syntax);

 common_chat_tool_choice common_chat_tool_choice_parse_oaicompat(const std::string & tool_choice);
@@ -849,7 +849,7 @@ std::string fs_get_cache_directory() {
    if (getenv("LLAMA_CACHE")) {
        cache_directory = std::getenv("LLAMA_CACHE");
    } else {
-#if defined(__linux__) || defined(__FreeBSD__) || defined(_AIX)
+#if defined(__linux__) || defined(__FreeBSD__) || defined(_AIX) || defined(__OpenBSD__)
        if (std::getenv("XDG_CACHE_HOME")) {
            cache_directory = std::getenv("XDG_CACHE_HOME");
        } else {
@@ -291,6 +291,7 @@ struct common_params {
    int32_t verbosity                  = 0;
    int32_t control_vector_layer_start = -1; // layer range for control vector
    int32_t control_vector_layer_end   = -1; // layer range for control vector
+    bool    offline                    = false;

    int32_t ppl_stride      = 0;     // stride for perplexity calculations. If left at 0, the pre-existing approach will be used.
    int32_t ppl_output_type = 0;     // = 0 -> ppl output is as usual, = 1 -> ppl output is num_tokens, ppl, one per line
@@ -368,6 +369,7 @@ struct common_params {
    bool use_jinja = false;                                                                                 // NOLINT
    bool enable_chat_template = true;
    common_reasoning_format reasoning_format = COMMON_REASONING_FORMAT_DEEPSEEK;
+    int reasoning_budget = -1;
    bool prefill_assistant = true;                                                                          // if true, any trailing assistant message will be prefilled into the response

    std::vector<std::string> api_keys;
@@ -432,6 +432,9 @@ class ModelBase:
                if "llm_config" in config:
                    # rename for InternVL
                    config["text_config"] = config["llm_config"]
+                if "thinker_config" in config:
+                    # rename for Qwen2.5-Omni
+                    config["text_config"] = config["thinker_config"]["text_config"]
                return config

    @classmethod
@@ -1121,18 +1124,21 @@ class MmprojModel(ModelBase):
    preprocessor_config: dict[str, Any]
    global_config: dict[str, Any]

+    n_block_keys = ["n_layers", "num_hidden_layers", "n_layer", "num_layers", "depth"]
+
    has_vision_encoder: bool = True # by default
    has_audio_encoder: bool = False

+    # for models having multiple encoders, we need to separate their hparams
+    hparams_vision: dict[str, Any] | None = None
+    hparams_audio: dict[str, Any] | None = None
+
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)

        if self.model_arch != gguf.MODEL_ARCH.MMPROJ:
            raise TypeError("MmprojModel must be subclassed with model_arch = gguf.MODEL_ARCH.MMPROJ")

-        if self.has_vision_encoder and self.has_audio_encoder:
-            raise NotImplementedError("both vision + audio not supported yet")
-
        # get n_embd of the text model
        if "text_config" not in self.hparams:
            self.hparams["text_config"] = {}
@@ -1143,22 +1149,32 @@ class MmprojModel(ModelBase):
        assert self.n_embd_text > 0, "n_embd not found in hparams"

        # move vision config to the top level, while preserving the original hparams in global_config
-        self.global_config = self.hparams
+        import copy
+        self.global_config = copy.deepcopy(self.hparams)
+        self.hparams_vision = self.get_vision_config()
+        self.hparams_audio = self.get_audio_config()

-        if "vision_config" in self.hparams:
-            self.hparams = self.hparams["vision_config"]
-        elif "audio_config" in self.hparams:
-            self.hparams = self.hparams["audio_config"]
-        else:
+        if self.hparams_vision is None and self.hparams_audio is None:
            raise ValueError("vision_config / audio_config not found in hparams")

-        self.block_count = self.find_hparam(["n_layers", "num_hidden_layers", "n_layer", "num_layers", "depth"])
+        # for compat with vision-only models
+        self.hparams = self.hparams_vision or self.hparams_audio or self.hparams
+
+        # TODO @ngxson : this is a hack to support both vision and audio encoders
+        have_multiple_encoders = self.has_audio_encoder and self.has_vision_encoder
+        self.block_count = 128 if have_multiple_encoders else self.find_hparam(self.n_block_keys, True)
        self.tensor_map = gguf.get_tensor_name_map(gguf.MODEL_ARCH.MMPROJ, self.block_count)

        # load preprocessor config
        with open(self.dir_model / "preprocessor_config.json", "r", encoding="utf-8") as f:
            self.preprocessor_config = json.load(f)

+    def get_vision_config(self) -> dict[str, Any] | None:
+        return self.global_config.get("vision_config")
+
+    def get_audio_config(self) -> dict[str, Any] | None:
+        return self.global_config.get("audio_config")
+
    def set_type(self):
        self.gguf_writer.add_type(gguf.GGUFType.MMPROJ)

@@ -1170,26 +1186,26 @@ class MmprojModel(ModelBase):
            self.gguf_writer.add_vision_projection_dim(self.n_embd_text)

            # vision config
-            self.gguf_writer.add_vision_image_size(self.find_hparam(["image_size"]))
-            self.gguf_writer.add_vision_patch_size(self.find_hparam(["patch_size"]))
-            self.gguf_writer.add_vision_embedding_length(self.find_hparam(["hidden_size"]))
-            self.gguf_writer.add_vision_feed_forward_length(self.find_hparam(["intermediate_size"]))
-            self.gguf_writer.add_vision_block_count(self.block_count)
-            self.gguf_writer.add_vision_head_count(self.find_hparam(["num_attention_heads"]))
+            self.gguf_writer.add_vision_image_size(self.find_vparam(["image_size"]))
+            self.gguf_writer.add_vision_patch_size(self.find_vparam(["patch_size"]))
+            self.gguf_writer.add_vision_embedding_length(self.find_vparam(["hidden_size"]))
+            self.gguf_writer.add_vision_feed_forward_length(self.find_vparam(["intermediate_size"]))
+            self.gguf_writer.add_vision_block_count(self.find_vparam(self.n_block_keys))
+            self.gguf_writer.add_vision_head_count(self.find_vparam(["num_attention_heads"]))

            # preprocessor config
            self.gguf_writer.add_vision_image_mean(self.preprocessor_config["image_mean"])
            self.gguf_writer.add_vision_image_std(self.preprocessor_config["image_std"])

-        elif self.has_audio_encoder:
+        if self.has_audio_encoder:
            self.gguf_writer.add_clip_has_audio_encoder(True)
            self.gguf_writer.add_audio_projection_dim(self.n_embd_text)

            # audio config
-            self.gguf_writer.add_audio_embedding_length(self.find_hparam(["hidden_size"]))
-            self.gguf_writer.add_audio_feed_forward_length(self.find_hparam(["intermediate_size"]))
-            self.gguf_writer.add_audio_block_count(self.block_count)
-            self.gguf_writer.add_audio_head_count(self.find_hparam(["num_attention_heads"]))
+            self.gguf_writer.add_audio_embedding_length(self.find_aparam(["hidden_size"]))
+            self.gguf_writer.add_audio_feed_forward_length(self.find_aparam(["intermediate_size"]))
+            self.gguf_writer.add_audio_block_count(self.find_aparam(self.n_block_keys))
+            self.gguf_writer.add_audio_head_count(self.find_aparam(["num_attention_heads"]))

        else:
            raise ValueError("MmprojModel must have either vision or audio encoder")
@@ -1197,6 +1213,22 @@ class MmprojModel(ModelBase):
    def write_vocab(self):
        raise ValueError("MmprojModel does not support vocab writing")

+    def find_vparam(self, keys: Iterable[str], optional: bool = False) -> Any:
+        assert self.hparams_vision is not None
+        return self._find_param(self.hparams_vision, keys, optional)
+
+    def find_aparam(self, keys: Iterable[str], optional: bool = False) -> Any:
+        assert self.hparams_audio is not None
+        return self._find_param(self.hparams_audio, keys, optional)
+
+    def _find_param(self, obj: dict[str, Any], keys: Iterable[str], optional: bool = False) -> Any:
+        key = next((k for k in keys if k in obj), None)
+        if key is not None:
+            return obj[key]
+        if optional:
+            return None
+        raise KeyError(f"could not find any of: {keys}")
+

@ModelBase.register("GPTNeoXForCausalLM")
 class GPTNeoXModel(TextModel):
@@ -2137,6 +2169,9 @@ class Llama4VisionModel(MmprojModel):
            # process vision tensors
            if "positional_embedding_vlm" in name and ".weight" not in name:
                name += ".weight"
+            if "multi_modal_projector.linear_1" in name:
+                # despite the name with number postfix, this is a single fully connected layer
+                return [(gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.V_MMPROJ_FC], data_torch)]
            return [(self.map_tensor_name(name), data_torch)]
        return []

@@ -2643,7 +2678,7 @@ class QwenModel(TextModel):
        self.gguf_writer.add_file_type(self.ftype)


-@ModelBase.register("Qwen2Model", "Qwen2ForCausalLM")
+@ModelBase.register("Qwen2Model", "Qwen2ForCausalLM", "Qwen2AudioForConditionalGeneration")
 class Qwen2Model(TextModel):
    model_arch = gguf.MODEL_ARCH.QWEN2

@@ -2667,13 +2702,19 @@ class Qwen2Model(TextModel):
            name = f"model.{name}"  # map to Qwen2ForCausalLM tensors
        if "language_model." in name:
            name = name.replace("language_model.", "") # for InternVL
-        if name.startswith("mlp") or name.startswith("vision_model"):
-            # skip visual tensors
+        if name.startswith("mlp") or name.startswith("multi_modal_projector") \
+                or name.startswith("vision_model") or name.startswith("audio_tower"):
+            # skip vision and audio tensors
            return []
        yield from super().modify_tensors(data_torch, name, bid)


-@ModelBase.register("Qwen2VLModel", "Qwen2VLForConditionalGeneration", "Qwen2_5_VLForConditionalGeneration")
+@ModelBase.register(
+    "Qwen2VLModel",
+    "Qwen2VLForConditionalGeneration",
+    "Qwen2_5_VLForConditionalGeneration",
+    "Qwen2_5OmniModel",
+)
 class Qwen2VLModel(TextModel):
    model_arch = gguf.MODEL_ARCH.QWEN2VL

@@ -2691,8 +2732,11 @@ class Qwen2VLModel(TextModel):

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        del bid  # unused
-        if name.startswith("visual."):
-            # skip visual tensors
+        if name.startswith("thinker."):
+            name = name.replace("thinker.", "")
+        if name.startswith("visual") or name.startswith("audio") or \
+                name.startswith("talker") or name.startswith("token2wav"):
+            # skip multimodal tensors
            return []
        return [(self.map_tensor_name(name), data_torch)]

@@ -2701,21 +2745,27 @@ class Qwen2VLModel(TextModel):
 class Qwen2VLVisionModel(MmprojModel):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
-        self.hparams["image_size"] = self.hparams.get("image_size", 560)
+        assert self.hparams_vision is not None
+        self.hparams_vision["image_size"] = self.hparams_vision.get("image_size", 560)
        # rename config.json values
-        self.hparams["num_attention_heads"] = self.hparams.get("num_heads")
-        self.hparams["num_hidden_layers"] = self.hparams.get("depth")
-        if "embed_dim" in self.hparams: # qwen2vl
-            self.hparams["intermediate_size"] = self.hparams.get("hidden_size")
-            self.hparams["hidden_size"] = self.hparams.get("embed_dim")
+        self.hparams_vision["num_attention_heads"] = self.hparams_vision.get("num_heads")
+        self.hparams_vision["num_hidden_layers"] = self.hparams_vision.get("depth")
+        if "embed_dim" in self.hparams_vision: # qwen2vl
+            self.hparams_vision["intermediate_size"] = self.hparams_vision.get("hidden_size")
+            self.hparams_vision["hidden_size"] = self.hparams_vision.get("embed_dim")

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
-        hparams = self.hparams
-        if self.global_config['model_type'] == 'qwen2_vl':
+        assert self.hparams_vision is not None
+        hparams = self.hparams_vision
+        model_type = self.global_config['model_type']
+        if model_type == 'qwen2_vl':
            self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.QWEN2VL)
-        elif self.global_config['model_type'] == 'qwen2_5_vl':
-            self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.QWEN25VL)
+        elif model_type == 'qwen2_5_vl' or model_type == 'qwen2_5_omni':
+            if model_type == 'qwen2_5_omni':
+                self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.QWEN25O)
+            else:
+                self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.QWEN25VL)
            self.gguf_writer.add_vision_use_silu(True)
            # find n_wa_pattern (window attention pattern)
            fullatt_block_indexes = hparams.get("fullatt_block_indexes")
@@ -2773,6 +2823,66 @@ class Qwen2VLVisionModel(MmprojModel):
        return [] # skip other tensors


+@ModelBase.register("Qwen2_5OmniModel")
+class Qwen25OmniModel(Qwen2VLVisionModel):
+    has_vision_encoder = True
+    has_audio_encoder = True
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        assert self.hparams_audio is not None
+        self.hparams_audio["hidden_size"] = self.hparams_audio["d_model"]
+        self.hparams_audio["intermediate_size"] = self.hparams_audio["encoder_ffn_dim"]
+        self.hparams_audio["num_attention_heads"] = self.hparams_audio["encoder_attention_heads"]
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        assert self.hparams_audio is not None
+        self.gguf_writer.add_audio_num_mel_bins(self.hparams_audio["num_mel_bins"])
+        self.gguf_writer.add_audio_attention_layernorm_eps(self.hparams_audio.get("layer_norm_eps", 1e-5))
+
+    def get_vision_config(self) -> dict[str, Any] | None:
+        return self.global_config["thinker_config"].get("vision_config")
+
+    def get_audio_config(self) -> dict[str, Any] | None:
+        return self.global_config["thinker_config"].get("audio_config")
+
+    def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
+        # SinusoidsPositionEmbedding
+        assert self.hparams_audio is not None
+        max_timescale = 10000
+        length = 1500
+        channels = self.hparams_audio["hidden_size"]
+        log_timescale_increment = np.log(max_timescale) / (channels // 2 - 1)
+        inv_timescales = torch.exp(-log_timescale_increment * torch.arange(channels // 2).float())
+        scaled_time = torch.arange(length)[:, np.newaxis] * inv_timescales[np.newaxis, :]
+        pos_embd = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], dim=1).to(dtype=torch.float32)
+        yield ("audio_tower.embed_positions.weight", pos_embd)
+
+    def tensor_force_quant(self, name, new_name, bid, n_dims):
+        del bid, new_name, n_dims  # unused
+        if ".conv" in name and ".weight" in name:
+            return gguf.GGMLQuantizationType.F16
+        return False
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        if name.startswith("thinker."):
+            name = name.replace("thinker.", "")
+
+        if name.startswith("audio_tower"):
+            # process audio tensors
+            if "conv1.bias" in name or "conv2.bias" in name:
+                # transpose conv1 and conv2 bias
+                data_torch = data_torch.unsqueeze(-1)
+            if "audio_bos_eos_token" in name:
+                # this tensor is left unused in transformers code
+                # https://github.com/huggingface/transformers/blob/6e3063422c4b1c014aa60c32b9254fd2902f0f28/src/transformers/models/qwen2_5_omni/modular_qwen2_5_omni.py#L1809
+                return []
+            return [(self.map_tensor_name(name), data_torch)]
+
+        return super().modify_tensors(data_torch, name, bid)
+
+
@ModelBase.register("InternVisionModel")
 class InternVisionModel(MmprojModel):
    def set_gguf_parameters(self):
@@ -5993,11 +6103,11 @@ class UltravoxModel(TextModel):

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
-        raise NotImplementedError("Ultravox does not have text decoder. Please use --mmproj argument")
+        raise NotImplementedError("Ultravox does not have text decoder. Instead, it uses Llama or other models for text. If you want to get the audio encoder, please use --mmproj argument")


-@ModelBase.register("UltravoxModel")
-class UltravoxAudioModel(MmprojModel):
+@ModelBase.register("Qwen2AudioForConditionalGeneration")
+class WhisperEncoderModel(MmprojModel):
    has_vision_encoder = False # no vision encoder
    has_audio_encoder = True

@@ -6009,10 +6119,9 @@ class UltravoxAudioModel(MmprojModel):

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
-        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.ULTRAVOX)
+        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.QWEN2A)
        self.gguf_writer.add_audio_num_mel_bins(self.hparams["num_mel_bins"])
        self.gguf_writer.add_audio_attention_layernorm_eps(self.hparams.get("layer_norm_eps", 1e-5))
-        self.gguf_writer.add_audio_stack_factor(self.global_config["stack_factor"])

    def tensor_force_quant(self, name, new_name, bid, n_dims):
        del bid, new_name, n_dims  # unused
@@ -6023,6 +6132,10 @@ class UltravoxAudioModel(MmprojModel):
    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
        del bid  # unused

+        if name.startswith("language_model."):
+            # skip language model tensors
+            return []
+
        # prevent clash naming with vision tensors
        if name.startswith("multi_modal_projector"):
            name = "audio." + name
@@ -6033,6 +6146,16 @@ class UltravoxAudioModel(MmprojModel):

        return [(self.map_tensor_name(name), data_torch)]

+
+@ModelBase.register("UltravoxModel")
+class UltravoxWhisperEncoderModel(WhisperEncoderModel):
+    has_vision_encoder = False # no vision encoder
+    has_audio_encoder = True
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self.gguf_writer.add_audio_stack_factor(self.global_config["stack_factor"])
+
 ###### CONVERSION LOGIC ######


@@ -280,6 +280,15 @@ cmake --build build --config release
 ### **GitHub contribution**:
 Please add the **[CANN]** prefix/tag in issues/PRs titles to help the CANN-team check/address them without delay.

+## Updates
+### Basic Flash Attention Support
+The basic FA kernel with aclnnops has been added in aclnn_ops.cpp.
+Currently, the FA only supports the cases with FP16 KV tensors and NO logit softcap.
+Since the aclnn interface for flash attention cannot support the logit softcap, we will only update the quantized version in the future.
+
+Authors from Peking University: Bizhao Shi (bshi@pku.edu.cn), Yuxin Yang (yxyang@pku.edu.cn), Ruiyang Ma (ruiyang@stu.pku.edu.cn), and Guojie Luo (gluo@pku.edu.cn).
+
+We would like to thank Tuo Dai, Shanni Li, and all of the project maintainers from Huawei Technologies Co., Ltd for their help during the code development and pull request.

 ## TODO
 - Support more models and data types.
@@ -2,7 +2,6 @@

 [chat.h](../common/chat.h) (https://github.com/ggml-org/llama.cpp/pull/9639) adds support for [OpenAI-style function calling](https://platform.openai.com/docs/guides/function-calling) and is used in:
 - `llama-server` when started w/ `--jinja` flag
- `llama-cli` (WIP: https://github.com/ggml-org/llama.cpp/pull/11556)

 ## Universal support w/ Native & Generic handlers

@@ -33,7 +33,7 @@ llama-server -hf ggml-org/gemma-3-4b-it-GGUF --no-mmproj-offload

 ## Pre-quantized models

-These are ready-to-use models, most of them come with `Q4_K_M` quantization by default. They can be found at the Hugging Face page of the ggml-org: https://huggingface.co/ggml-org
+These are ready-to-use models, most of them come with `Q4_K_M` quantization by default. They can be found at the Hugging Face page of the ggml-org: https://huggingface.co/collections/ggml-org/multimodal-ggufs-68244e01ff1f39e5bebeeedc

 Replaces the `(tool_name)` with the name of binary you want to use. For example, `llama-mtmd-cli` or `llama-server`

@@ -81,6 +81,10 @@ NOTE: some models may require large context window, for example: `-c 8192`

 # Llama 4 Scout
 (tool_name) -hf ggml-org/Llama-4-Scout-17B-16E-Instruct-GGUF
+
+# Moondream2 20250414 version
+(tool_name) -hf ggml-org/moondream2-20250414-GGUF
+
 ```

 **Audio models**:
@@ -89,4 +93,17 @@ NOTE: some models may require large context window, for example: `-c 8192`
 # Ultravox 0.5
 (tool_name) -hf ggml-org/ultravox-v0_5-llama-3_2-1b-GGUF
 (tool_name) -hf ggml-org/ultravox-v0_5-llama-3_1-8b-GGUF
+
+# Qwen2-Audio and SeaLLM-Audio
+# note: no pre-quantized GGUF this model, as they have very poor result
+# ref: https://github.com/ggml-org/llama.cpp/pull/13760
+```
+
+**Mixed modalities**:
+
+```sh
+# Qwen2.5 Omni
+# Capabilities: audio input, vision input
+(tool_name) -hf ggml-org/Qwen2.5-Omni-3B-GGUF
+(tool_name) -hf ggml-org/Qwen2.5-Omni-7B-GGUF
 ```
@@ -41,8 +41,8 @@ static void batch_decode(llama_context * ctx, llama_batch & batch, float * outpu

    // run model
    LOG_INF("%s: n_tokens = %d, n_seq = %d\n", __func__, batch.n_tokens, n_seq);
-    if (llama_encode(ctx, batch) < 0) {
-        LOG_ERR("%s : failed to encode\n", __func__);
+    if (llama_decode(ctx, batch) < 0) {
+        LOG_ERR("%s : failed to process\n", __func__);
    }

    for (int i = 0; i < batch.n_tokens; i++) {
@@ -81,14 +81,14 @@ static void batch_add_seq(llama_batch & batch, const std::vector<int32_t> & toke
    }
 }

-static void batch_encode(llama_context * ctx, llama_batch & batch, float * output, int n_seq, int n_embd) {
+static void batch_process(llama_context * ctx, llama_batch & batch, float * output, int n_seq, int n_embd) {
    // clear previous kv_cache values (irrelevant for embeddings)
    llama_kv_self_clear(ctx);

    // run model
    LOG_INF("%s: n_tokens = %d, n_seq = %d\n", __func__, batch.n_tokens, n_seq);
-    if (llama_encode(ctx, batch) < 0) {
-        LOG_ERR("%s : failed to encode\n", __func__);
+    if (llama_decode(ctx, batch) < 0) {
+        LOG_ERR("%s : failed to process\n", __func__);
    }

    for (int i = 0; i < batch.n_tokens; i++) {
@@ -233,7 +233,7 @@ int main(int argc, char ** argv) {
        // encode if at capacity
        if (batch.n_tokens + n_toks > n_batch) {
            float * out = emb + p * n_embd;
-            batch_encode(ctx, batch, out, s, n_embd);
+            batch_process(ctx, batch, out, s, n_embd);
            common_batch_clear(batch);
            p += s;
            s = 0;
@@ -246,7 +246,7 @@ int main(int argc, char ** argv) {

    // final batch
    float * out = emb + p * n_embd;
-    batch_encode(ctx, batch, out, s, n_embd);
+    batch_process(ctx, batch, out, s, n_embd);

    // save embeddings to chunks
    for (int i = 0; i < n_chunks; i++) {
@@ -267,7 +267,7 @@ int main(int argc, char ** argv) {
        batch_add_seq(query_batch, query_tokens, 0);

        std::vector<float> query_emb(n_embd, 0);
-        batch_encode(ctx, query_batch, query_emb.data(), 1, n_embd);
+        batch_process(ctx, query_batch, query_emb.data(), 1, n_embd);

        common_batch_clear(query_batch);

@@ -10,8 +10,8 @@ Proof of concept:

 ``` sh
 export model_name=llama_3.2-1b && export quantization=f32
-./build/bin/finetune --file wikitext-2-raw/wiki.test.raw -ngl 999 --model models/${model_name}-${quantization}.gguf -c 512 -b 512 -ub 512
-./build/bin/perplexity --file wikitext-2-raw/wiki.test.raw -ngl 999 --model finetuned-model.gguf
+./build/bin/llama-finetune --file wikitext-2-raw/wiki.test.raw -ngl 999 --model models/${model_name}-${quantization}.gguf -c 512 -b 512 -ub 512
+./build/bin/llama-perplexity --file wikitext-2-raw/wiki.test.raw -ngl 999 --model finetuned-model.gguf
 ```

 The perplexity value of the finetuned model should be lower after training on the test set for 2 epochs.
@@ -129,6 +129,7 @@ option(GGML_LASX             "ggml: enable lasx"             ON)
 option(GGML_LSX              "ggml: enable lsx"              ON)
 option(GGML_RVV              "ggml: enable rvv"              ON)
 option(GGML_RV_ZFH           "ggml: enable riscv zfh"        OFF)
+option(GGML_XTHEADVECTOR     "ggml: enable xtheadvector"     OFF)
 option(GGML_VXE              "ggml: enable vxe"              ON)

 option(GGML_CPU_ALL_VARIANTS "ggml: build all variants of the CPU backend (requires GGML_BACKEND_DL)" OFF)
@@ -176,7 +177,6 @@ option(GGML_VULKAN_CHECK_RESULTS            "ggml: run Vulkan op checks"
 option(GGML_VULKAN_DEBUG                    "ggml: enable Vulkan debug output"                OFF)
 option(GGML_VULKAN_MEMORY_DEBUG             "ggml: enable Vulkan memory debug output"         OFF)
 option(GGML_VULKAN_SHADER_DEBUG_INFO        "ggml: enable Vulkan shader debug info"           OFF)
-option(GGML_VULKAN_PERF                     "ggml: enable Vulkan perf output"                 OFF)
 option(GGML_VULKAN_VALIDATE                 "ggml: enable Vulkan validation"                  OFF)
 option(GGML_VULKAN_RUN_TESTS                "ggml: run Vulkan tests"                          OFF)
 option(GGML_KOMPUTE                         "ggml: use Kompute"                               OFF)
@@ -935,6 +935,15 @@ extern "C" {
            struct ggml_tensor  * a,
            struct ggml_tensor  * b);

+    // repeat a to the specified shape
+    GGML_API struct ggml_tensor * ggml_repeat_4d(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+                       int64_t    ne0,
+                       int64_t    ne1,
+                       int64_t    ne2,
+                       int64_t    ne3);
+
    // sums repetitions in a into shape of b
    GGML_API struct ggml_tensor * ggml_repeat_back(
            struct ggml_context * ctx,
@@ -1598,6 +1598,9 @@ void ggml_backend_sched_synchronize(ggml_backend_sched_t sched) {
    for (int i = 0; i < sched->n_backends; i++) {
        ggml_backend_synchronize(sched->backends[i]);
    }
+    // reset the current copy to 0 so that the graphs will be similar during generation
+    // necessary for CUDA graphs
+    sched->cur_copy = 0;
 }

 void ggml_backend_sched_set_eval_callback(ggml_backend_sched_t sched, ggml_backend_sched_eval_callback callback, void * user_data) {
@@ -30,6 +30,7 @@ string(TOLOWER ${SOC_TYPE} SOC_VERSION) # SOC_VERSION need lower
 string(REGEX MATCH "[0-9]+[a-zA-Z]" SOC_TYPE_MAJOR_SN "${SOC_VERSION}")
 set(SOC_TYPE_COMPILE_OPTION "ASCEND_${SOC_TYPE_MAJOR_SN}")
 string(TOUPPER ${SOC_TYPE_COMPILE_OPTION} SOC_TYPE_COMPILE_OPTION)
+message(STATUS "CANN: SOC_VERSION =  ${SOC_VERSION}")

 if (CANN_INSTALL_DIR)
    # Only Support Linux.
@@ -31,6 +31,8 @@ aclDataType ggml_cann_type_mapping(ggml_type type) {
            return ACL_FLOAT;
        case GGML_TYPE_F16:
            return ACL_FLOAT16;
+        case GGML_TYPE_BF16:
+            return ACL_BF16;
        case GGML_TYPE_I8:
            return ACL_INT8;
        case GGML_TYPE_I16:
@@ -66,6 +66,7 @@
 #include <aclnnop/aclnn_gt_scalar.h>
 #include <aclnnop/aclnn_pow.h>
 #include <aclnnop/aclnn_grouped_matmul_v2.h>
+#include <aclnnop/aclnn_fused_infer_attention_score_v2.h>
 #include <float.h>

 #include <cmath>
@@ -74,11 +75,13 @@
 #include <vector>

 #include "ggml-impl.h"
+#include "ggml.h"

 #define GGML_COMMON_DECL_C

 #include "../ggml-common.h"

+
 void bcast_shape(ggml_tensor * src0, ggml_tensor * src1, ggml_tensor * dst, aclTensor ** acl_src0,
                 aclTensor ** acl_src1, aclTensor ** acl_dst) {
    GGML_ASSERT(ggml_are_same_shape(src0, dst) && ggml_can_repeat(src1, src0));
@@ -2861,3 +2864,330 @@ void ggml_cann_mul_mat_id(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
            break;
    }
 }
+
+void ggml_cann_flash_attn_ext(ggml_backend_cann_context& ctx, ggml_tensor* dst){
+
+    ggml_tensor* src0 = dst->src[0]; // q, fp32
+    ggml_tensor* src1 = dst->src[1]; // k, fp16
+    ggml_tensor* src2 = dst->src[2]; // v, fp16
+    ggml_tensor* src3 = dst->src[3]; // mask, fp16
+
+    float maxBias = 0.0f;
+    float scaleValue = 1.0f;
+    float logitSoftcap = 0.0f;
+    memcpy(&scaleValue,    (float*)dst->op_params + 0, sizeof(float));
+    memcpy(&maxBias,       (float*)dst->op_params + 1, sizeof(float));
+    memcpy(&logitSoftcap,  (float*)dst->op_params + 2, sizeof(float));
+
+    if(logitSoftcap == 0.0f){
+        size_t faElemSize = sizeof(uint16_t);
+        auto   faDataType = ACL_FLOAT16; //ACL_BF16;
+
+        aclTensor* acl_src0_f16_tensor = nullptr;
+        aclTensor* acl_src1_f16_tensor = nullptr;
+        aclTensor* acl_src2_f16_tensor = nullptr;
+        aclTensor* acl_dst_f16_tensor  = nullptr;
+
+        // Step 1: cast the src0 (Query) to fp16 if needed
+        ggml_cann_pool_alloc src0_f16_allocator(ctx.pool());
+        void* src0_f16_buffer = nullptr;
+
+        if(ggml_cann_type_mapping(src0->type) != faDataType){
+            aclTensor* acl_src0_f32_tensor = ggml_cann_create_tensor(src0);
+            src0_f16_buffer = src0_f16_allocator.alloc(
+                                    ggml_nelements(src0) * faElemSize);
+
+            int64_t* src0_f16_ne = src0->ne;
+            size_t   src0_f16_nb[GGML_MAX_DIMS];
+            src0_f16_nb[0] = sizeof(uint16_t);
+            for(int i = 1; i < GGML_MAX_DIMS; ++i){
+                src0_f16_nb[i] = src0_f16_nb[i - 1] * src0_f16_ne[i - 1];
+            }
+
+            acl_src0_f16_tensor = ggml_cann_create_tensor(
+                src0_f16_buffer, faDataType, faElemSize,
+                src0_f16_ne, src0_f16_nb, GGML_MAX_DIMS
+            );
+            aclnn_cast(ctx, acl_src0_f32_tensor, acl_src0_f16_tensor, faDataType);
+            ggml_cann_release_resources(ctx, acl_src0_f32_tensor);
+        }else{
+            acl_src0_f16_tensor = ggml_cann_create_tensor(src0);
+        }
+
+        // Step 2: create the acl tensors for src1 (Key), src2 (Value),
+        //         and the direct output from FusedInferAttention
+
+        acl_src1_f16_tensor = ggml_cann_create_tensor(src1);
+        acl_src2_f16_tensor = ggml_cann_create_tensor(src2);
+
+        ggml_cann_pool_alloc out_f16_allocator(ctx.pool());
+        void* out_f16_buffer = out_f16_allocator.alloc(
+                                    ggml_nelements(dst) * faElemSize);
+
+        int64_t* out_f16_ne = src0->ne;
+        size_t out_f16_nb[GGML_MAX_DIMS];
+        out_f16_nb[0] = faElemSize;
+        for(int i = 1; i < GGML_MAX_DIMS; ++i){
+            out_f16_nb[i] = out_f16_nb[i - 1] * out_f16_ne[i - 1];
+        }
+
+        acl_dst_f16_tensor = ggml_cann_create_tensor(
+            out_f16_buffer, faDataType, faElemSize,
+            out_f16_ne, out_f16_nb, GGML_MAX_DIMS
+        );
+
+        // Step 3: create the PSEShift tensor if needed
+        //         this tensor is considered as mask (f16) in the llama.cpp
+
+        aclTensor* bcast_pse_tensor = nullptr;
+        int64_t bcast_pse_ne[GGML_MAX_DIMS];
+        size_t bcast_pse_nb[GGML_MAX_DIMS];
+        ggml_cann_pool_alloc bcast_pse_allocator(ctx.pool());
+        void* bcast_pse_buffer = nullptr;
+
+        if(src3 != nullptr){
+            bcast_pse_buffer = bcast_pse_allocator.alloc(
+                            ggml_nelements(src3) * src0->ne[2] * sizeof(uint16_t));
+
+            if(src0->ne[1] > 1){
+                // Case 1: broadcast pse for prefill stage with multiple head
+                aclTensor* acl_mask_f16_tensor = ggml_cann_create_tensor(src3);
+                bcast_pse_ne[0] = src3->ne[0];
+                bcast_pse_ne[1] = src3->ne[1];
+                bcast_pse_ne[2] = src0->ne[2];
+                bcast_pse_ne[3] = src3->ne[3];
+
+                bcast_pse_nb[0] = sizeof(uint16_t);
+                for(int i = 1; i < GGML_MAX_DIMS; ++i){
+                    bcast_pse_nb[i] = bcast_pse_nb[i - 1] * bcast_pse_ne[i - 1];
+                }
+
+                bcast_pse_tensor = ggml_cann_create_tensor(
+                    bcast_pse_buffer, ACL_FLOAT16, sizeof(uint16_t),
+                    bcast_pse_ne, bcast_pse_nb, GGML_MAX_DIMS);
+
+                int64_t repeats[] = {1, src0->ne[2], 1, 1};
+                aclnn_repeat(ctx, acl_mask_f16_tensor, bcast_pse_tensor, repeats);
+
+                ggml_cann_release_resources(ctx, acl_mask_f16_tensor);
+            }else{
+                // Case 2: trunc the first row and broadcast pse for decode stage with multiple head
+                int64_t trunc_pse_ne[GGML_MAX_DIMS] = {src3->ne[0], src0->ne[1], src3->ne[2], src3->ne[3]};
+                size_t* trunc_pse_nb = src3->nb;
+
+                aclTensor* acl_mask_f16_trunc_tensor = ggml_cann_create_tensor(
+                    src3->data, ACL_FLOAT16, sizeof(uint16_t),
+                    trunc_pse_ne, trunc_pse_nb, GGML_MAX_DIMS);
+
+                bcast_pse_ne[0] = src3->ne[0];
+                bcast_pse_ne[1] = src0->ne[1];
+                bcast_pse_ne[2] = src0->ne[2];
+                bcast_pse_ne[3] = src3->ne[3];
+
+                bcast_pse_nb[0] = sizeof(uint16_t);
+                for(int i = 1; i < GGML_MAX_DIMS; ++i){
+                    bcast_pse_nb[i] = bcast_pse_nb[i - 1] * bcast_pse_ne[i - 1];
+                }
+
+                bcast_pse_tensor = ggml_cann_create_tensor(
+                    bcast_pse_buffer, ACL_FLOAT16, sizeof(uint16_t),
+                    bcast_pse_ne, bcast_pse_nb, GGML_MAX_DIMS);
+
+                int64_t repeats[] = {1, src0->ne[2], 1, 1};
+                aclnn_repeat(ctx, acl_mask_f16_trunc_tensor, bcast_pse_tensor, repeats);
+
+                ggml_cann_release_resources(ctx, acl_mask_f16_trunc_tensor);
+            }
+
+            // Compute the slope if needed. Derived from ggml_cann_softmax().
+            if(maxBias != 0.0f){
+                // alibi
+                const int64_t ne2_ne3 = src0->ne[2] * src0->ne[3];
+                const int64_t n_head = src0->ne[2];
+                const int n_heads_log2_floor = 1u << (uint32_t)floor(log2(n_head));
+                float m0 = powf(2.0f, -(maxBias) / n_heads_log2_floor);
+                float m1 = powf(2.0f, -(maxBias / 2.0f) / n_heads_log2_floor);
+                // init arange
+                ggml_cann_pool_alloc arange_allocator(ctx.pool(),
+                                                    ne2_ne3 * faElemSize);
+                void* tmp_arange_buffer = arange_allocator.get();
+
+                // arange1: [1, ..., n_heads_log2_floor+1)
+                float start = 1;
+                float stop = n_heads_log2_floor + 1;
+                float step = 1;
+                int64_t n_elements_arange = n_heads_log2_floor;
+
+                int64_t tmp_arange1_ne[] = {n_heads_log2_floor};
+                size_t tmp_arange1_nb[] = {faElemSize};
+                aclTensor* tmp_arange1_tensor = ggml_cann_create_tensor(
+                    tmp_arange_buffer, faDataType, faElemSize,
+                    tmp_arange1_ne, tmp_arange1_nb,
+                    GGML_MAX_DIMS - 3, ACL_FORMAT_ND);
+
+                aclnn_arange(ctx, tmp_arange1_tensor, start, stop, step, n_elements_arange);
+
+                aclTensor* tmp_arange2_tensor = nullptr;
+                if (n_heads_log2_floor < ne2_ne3) {
+                    // arange2: [1, ..., 2 * (k - n_heads_log2_floor) + 1)
+                    start = 1;
+                    stop = 2 * (ne2_ne3 - n_heads_log2_floor) + 1;
+                    step = 2;
+                    n_elements_arange = ne2_ne3 - n_heads_log2_floor;
+                    int64_t tmp_arange2_ne[] = {ne2_ne3 - n_heads_log2_floor};
+                    size_t tmp_arange2_nb[] = {faElemSize};
+
+                    aclTensor* tmp_arange2_tensor = ggml_cann_create_tensor(
+                        (char*)tmp_arange_buffer +
+                            n_heads_log2_floor * faElemSize,
+                        faDataType, faElemSize,
+                        tmp_arange2_ne, tmp_arange2_nb, GGML_MAX_DIMS - 3, ACL_FORMAT_ND);
+                    aclnn_arange(ctx, tmp_arange2_tensor, start, stop, step,
+                                n_elements_arange);
+                }
+
+                // init mk_base
+                ggml_cann_pool_alloc mk_base_allocator(ctx.pool(),
+                                                    ne2_ne3 * faElemSize);
+                void* tmp_mk_base_buffer = mk_base_allocator.get();
+                int64_t tmp_mk_base1_ne[] = {n_heads_log2_floor};
+                size_t tmp_mk_base1_nb[] = {faElemSize};
+                aclTensor* tmp_mk_base1_tensor = ggml_cann_create_tensor(
+                    tmp_mk_base_buffer, faDataType, faElemSize,
+                    tmp_mk_base1_ne, tmp_mk_base1_nb,
+                    GGML_MAX_DIMS - 3, ACL_FORMAT_ND);
+
+                aclnn_fill_scalar(ctx, m0, tmp_mk_base1_tensor);
+
+                aclTensor* tmp_mk_base2_tensor = nullptr;
+                if (n_heads_log2_floor < ne2_ne3) {
+                    int64_t tmp_mk_base2_ne[] = {ne2_ne3 - n_heads_log2_floor};
+                    size_t tmp_mk_base2_nb[] = {faElemSize};
+                    aclTensor* tmp_mk_base2_tensor = ggml_cann_create_tensor(
+                        (char*)tmp_mk_base_buffer +
+                            n_heads_log2_floor * faElemSize,
+                        faDataType, faElemSize,
+                        tmp_mk_base2_ne, tmp_mk_base2_nb, GGML_MAX_DIMS - 3, ACL_FORMAT_ND);
+                    aclnn_fill_scalar(ctx, m1, tmp_mk_base2_tensor);
+                }
+
+                // init mk
+                int64_t tmp_mk_base_ne[] = {ne2_ne3};
+                size_t tmp_mk_base_nb[] = {faElemSize};
+                aclTensor* tmp_mk_base_tensor = ggml_cann_create_tensor(
+                    tmp_mk_base_buffer, faDataType, faElemSize,
+                    tmp_mk_base_ne, tmp_mk_base_nb,
+                    GGML_MAX_DIMS - 3, ACL_FORMAT_ND);
+                aclTensor* tmp_arange_tensor = ggml_cann_create_tensor(
+                    tmp_arange_buffer, faDataType, faElemSize,
+                    tmp_mk_base_ne, tmp_mk_base_nb,
+                    GGML_MAX_DIMS - 3, ACL_FORMAT_ND);
+                aclnn_pow_tensor_tensor(ctx, tmp_mk_base_tensor, tmp_arange_tensor);
+
+                // reshape mk
+                int64_t tmp_mk_ne[] = {1, 1, src0->ne[2], src0->ne[3]};
+                size_t tmp_mk_nb[GGML_MAX_DIMS];
+                tmp_mk_nb[0] = faElemSize;
+                for (int i = 1; i < GGML_MAX_DIMS; i++) {
+                    tmp_mk_nb[i] = tmp_mk_nb[i - 1] * tmp_mk_ne[i - 1];
+                }
+                aclTensor* tmp_mk_tensor = ggml_cann_create_tensor(
+                    tmp_mk_base_buffer, faDataType, faElemSize,
+                    tmp_mk_ne, tmp_mk_nb, GGML_MAX_DIMS,
+                    ACL_FORMAT_ND);
+                GGML_CANN_CALL_ACLNN_OP(ctx, InplaceMul, bcast_pse_tensor, tmp_mk_tensor);
+
+                ggml_cann_release_resources(ctx, tmp_arange1_tensor, tmp_arange2_tensor,
+                    tmp_mk_base1_tensor, tmp_mk_base2_tensor, tmp_mk_base_tensor,
+                    tmp_arange_tensor, tmp_mk_tensor);
+            }
+        }
+
+        // Step 4: set the inputs for FusedInferAttention.
+        int kvTensorNum = 1;
+        aclTensor* acl_q_tensor = acl_src0_f16_tensor;
+        aclTensor* acl_k_tensors[] = {acl_src1_f16_tensor};
+        aclTensor* acl_v_tensors[] = {acl_src2_f16_tensor};
+        auto acl_k_tensor_list = aclCreateTensorList(acl_k_tensors, kvTensorNum);
+        auto acl_v_tensor_list = aclCreateTensorList(acl_v_tensors, kvTensorNum);
+
+        int64_t numHeads = src0->ne[2]; // N
+        int64_t numKeyValueHeads = src1->ne[2];
+        // double  scaleValue = 1 / sqrt(src0->ne[0]); // 1/sqrt(d)
+        int64_t preTokens = 65535;
+        int64_t nextTokens = 65535;
+        char layout[5] = {'B', 'N', 'S', 'D', 0};
+        int64_t sparseMode = 0;
+        int64_t innerPrecise = (src0->ne[1] == 1) ? 0 : 2;
+        int64_t blockSize = 0;
+        int64_t antiquantMode = 0;
+        bool softmaxLseFlag = false;
+        int64_t keyAntiquantMode = 0;
+        int64_t valueAntiquantMode = 0;
+
+        // Step 5: launch the FusedInferAttentionScoreV2 kernel.
+        // Refer to https://gitee.com/ascend/cann-ops-adv/blob/master/docs/FusedInferAttentionScoreV2.md
+
+        GGML_CANN_CALL_ACLNN_OP(ctx, FusedInferAttentionScoreV2,
+            acl_q_tensor, acl_k_tensor_list, acl_v_tensor_list, // q, k, v
+            bcast_pse_tensor, nullptr, // pse, mask
+            nullptr, nullptr, // actSeqLen, actSeqLenkv
+            nullptr, nullptr, // deqScale1, quantScale1
+            nullptr, nullptr, nullptr, // deqScale2, quantScale2, quantOffset2
+            nullptr, nullptr, // antiquantScale, antiquantOffset
+            nullptr, // blockTable
+            nullptr, nullptr, // qPadSize, kvPadSize
+            nullptr, nullptr, // kAntiquantScale, kAntiQuantOffset
+            nullptr, nullptr, // vAntiquantScale, vAntiQuantOffset
+            nullptr, nullptr, nullptr, // kSharedPrefix, vSharedPrefix, actSharedLen
+            numHeads, scaleValue, // heads, scaleValue
+            preTokens, nextTokens, // preTokens, nextTokens
+            layout, // inputLayout
+            numKeyValueHeads, // numKVHeads
+            sparseMode, innerPrecise, // sparseMode, innerPrecise
+            blockSize, antiquantMode, // blockSize, antiquantMode
+            softmaxLseFlag, // softmaxLseFlag
+            keyAntiquantMode, valueAntiquantMode, // keyAntiqMode, valueAntiqMode
+            acl_dst_f16_tensor, // attentionOut
+            nullptr // softmaxLse
+        );
+
+        // Step 6: post-processing, permute and cast to f32
+
+        int64_t new_dim[] = {0, 2, 1, 3};
+        aclTensor* acl_dst_tensor = ggml_cann_create_tensor(dst);
+
+        if(ggml_cann_type_mapping(dst->type) != faDataType){
+            ggml_cann_pool_alloc perm_out_f16_allocator(ctx.pool());
+            perm_out_f16_allocator.alloc(ggml_nelements(dst) * faElemSize);
+            void* perm_out_f16_buffer = perm_out_f16_allocator.get();
+
+            int64_t* perm_out_f16_ne = dst->ne;
+            size_t  perm_out_f16_nb[GGML_MAX_DIMS];
+            perm_out_f16_nb[0] = faElemSize;
+            for(int i = 1; i < GGML_MAX_DIMS; ++i){
+                perm_out_f16_nb[i] = perm_out_f16_nb[i - 1] * perm_out_f16_ne[i - 1];
+            }
+            aclTensor* acl_perm_out_f16_tensor = ggml_cann_create_tensor(
+                perm_out_f16_buffer, faDataType, faElemSize,
+                perm_out_f16_ne, perm_out_f16_nb, GGML_MAX_DIMS);
+            aclnn_permute(ctx, acl_dst_f16_tensor, acl_perm_out_f16_tensor, new_dim, GGML_MAX_DIMS);
+            aclnn_cast(ctx,
+                acl_perm_out_f16_tensor, acl_dst_tensor, ggml_cann_type_mapping(dst->type));
+            ggml_cann_release_resources(ctx, acl_perm_out_f16_tensor);
+        }else{
+            // only need to permute
+            aclnn_permute(ctx, acl_dst_f16_tensor, acl_dst_tensor, new_dim, GGML_MAX_DIMS);
+        }
+        ggml_cann_release_resources(ctx, acl_src0_f16_tensor,
+                                         acl_src1_f16_tensor,
+                                         acl_src2_f16_tensor,
+                                         acl_dst_f16_tensor,
+                                         acl_dst_tensor);
+        if(src3 != nullptr){
+            ggml_cann_release_resources(ctx, bcast_pse_tensor);
+        }
+    }else{
+        GGML_ABORT("Function is not implemented.");
+    }
+}
@@ -714,6 +714,21 @@ void ggml_cann_count_equal(ggml_backend_cann_context& ctx, ggml_tensor* dst);
 */
 void ggml_cann_step(ggml_backend_cann_context& ctx, ggml_tensor* dst);

+/**
+ * @brief   Performs the Flash Attention extended operator using the CANN backend.
+ *
+ * @details This function implements the memory-efficient Flash Attention algorithm
+ *          for computing scaled dot-product attention with hardware acceleration.
+ *          The result is stored in the destination tensor `dst`.
+ *
+ *          This operation is accelerated using the CANN backend to improve runtime performance.
+ *
+ * @param ctx The CANN context used for operations.
+ * @param dst The destination tensor where the result will be stored.
+ *            dst->op is expected to be `GGML_OP_FLASH_ATTN_EXT`.
+ */
+void ggml_cann_flash_attn_ext(ggml_backend_cann_context& ctx, ggml_tensor* dst);
+
 /*
 * @brief A generic wrapper for ACL resources with custom deleter support.
 */
@@ -36,6 +36,7 @@
 #include "ggml-backend-impl.h"
 #include "ggml-cann/aclnn_ops.h"
 #include "ggml-cann/common.h"
+#include "ggml.h"

 #define GGML_COMMON_DECL_C

@@ -1748,6 +1749,9 @@ static bool ggml_cann_compute_forward(ggml_backend_cann_context& ctx,
        case GGML_OP_COUNT_EQUAL:
            ggml_cann_count_equal(ctx, dst);
            break;
+        case GGML_OP_FLASH_ATTN_EXT:
+            ggml_cann_flash_attn_ext(ctx, dst);
+            break;
        default:
            return false;
    }
@@ -2177,6 +2181,38 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev,
        case GGML_OP_PAD_REFLECT_1D:
        case GGML_OP_COUNT_EQUAL:
            return true;
+        case GGML_OP_FLASH_ATTN_EXT:{
+            // derived from [ggml-cuda.cu]
+            if(op->src[1]->type != GGML_TYPE_F16 || op->src[2]->type != GGML_TYPE_F16){
+                return false;
+            }
+            if(op->src[1]->type != GGML_TYPE_F16 && op->src[1]->type != GGML_TYPE_F32 && op->src[1]->type != GGML_TYPE_BF16){
+                return false;
+            }
+            if(op->type != GGML_TYPE_F16 && op->type != GGML_TYPE_F32 && op->type != GGML_TYPE_BF16){
+                return false;
+            }
+            if (op->src[1]->ne[0] != op->src[2]->ne[0]) {
+                // different head sizes of K and V are not supported yet
+                return false;
+            }
+            if (op->src[0]->ne[0] == 192) {
+                return false;
+            }
+            if (op->src[0]->ne[0] == 576) {
+                // DeepSeek MLA
+                return false;
+            }
+            if (op->src[0]->ne[3] != 1) {
+                return false;
+            }
+            float logitSoftcap = 0.0f;
+            memcpy(&logitSoftcap,  (float*)op->op_params + 2, sizeof(float));
+            if(logitSoftcap != 0.0f) {
+                return false;
+            }
+            return true;
+        }
        default:
            return false;
    }
@@ -299,6 +299,25 @@ function(ggml_add_cpu_backend_variant_impl tag_name)
                endif()
            endif()
        endif()
+
+        if (GGML_BACKEND_DL)
+            if (GGML_NATIVE)
+                # the feature check relies on ARCH_DEFINITIONS, but it is not set with GGML_NATIVE
+                message(FATAL_ERROR "GGML_NATIVE is not compatible with GGML_BACKEND_DL, consider using GGML_CPU_ALL_VARIANTS")
+            endif()
+
+            # The feature detection code is compiled as a separate target so that
+            # it can be built without the architecture flags
+            # Since multiple variants of the CPU backend may be included in the same
+            # build, using set_source_files_properties() to set the arch flags is not possible
+            set(GGML_CPU_FEATS_NAME ${GGML_CPU_NAME}-feats)
+            add_library(${GGML_CPU_FEATS_NAME} OBJECT ggml-cpu/cpu-feats-x86.cpp)
+            target_include_directories(${GGML_CPU_FEATS_NAME} PRIVATE . .. ../include)
+            target_compile_definitions(${GGML_CPU_FEATS_NAME} PRIVATE ${ARCH_DEFINITIONS})
+            target_compile_definitions(${GGML_CPU_FEATS_NAME} PRIVATE GGML_BACKEND_DL GGML_BACKEND_BUILD GGML_BACKEND_SHARED)
+            set_target_properties(${GGML_CPU_FEATS_NAME} PROPERTIES POSITION_INDEPENDENT_CODE ON)
+            target_link_libraries(${GGML_CPU_NAME} PRIVATE ${GGML_CPU_FEATS_NAME})
+        endif()
    elseif ("${CMAKE_SYSTEM_PROCESSOR} " STREQUAL "ppc64le " OR "${CMAKE_SYSTEM_PROCESSOR} " STREQUAL "powerpc ")
        message(STATUS "PowerPC detected")
        if (GGML_NATIVE)
@@ -338,8 +357,10 @@ function(ggml_add_cpu_backend_variant_impl tag_name)
    elseif (${CMAKE_SYSTEM_PROCESSOR} MATCHES "riscv64")
        message(STATUS "RISC-V detected")
        if (GGML_RVV)
-            if (GGML_RV_ZFH)
-                list(APPEND ARCH_FLAGS -march=rv64gcv_zfhmin -DGGML_RV_ZFH -mabi=lp64d)
+            if (GGML_XTHEADVECTOR)
+                list(APPEND ARCH_FLAGS -march=rv64gc_xtheadvector -mabi=lp64d)
+            elseif (GGML_RV_ZFH)
+                list(APPEND ARCH_FLAGS -march=rv64gcv_zfhmin -mabi=lp64d)
            else()
                list(APPEND ARCH_FLAGS -march=rv64gcv -mabi=lp64d)
            endif()
@@ -477,25 +498,6 @@ function(ggml_add_cpu_backend_variant_impl tag_name)
    target_compile_options(${GGML_CPU_NAME} PRIVATE ${ARCH_FLAGS})
    target_compile_definitions(${GGML_CPU_NAME} PRIVATE ${ARCH_DEFINITIONS})

-    if (GGML_BACKEND_DL)
-        if (GGML_NATIVE)
-            # the feature check relies on ARCH_DEFINITIONS, but it is not set with GGML_NATIVE
-            message(FATAL_ERROR "GGML_NATIVE is not compatible with GGML_BACKEND_DL, consider using GGML_CPU_ALL_VARIANTS")
-        endif()
-
-        # The feature detection code is compiled as a separate target so that
-        # it can be built without the architecture flags
-        # Since multiple variants of the CPU backend may be included in the same
-        # build, using set_source_files_properties() to set the arch flags is not possible
-        set(GGML_CPU_FEATS_NAME ${GGML_CPU_NAME}-feats)
-        add_library(${GGML_CPU_FEATS_NAME} OBJECT ggml-cpu/cpu-feats-x86.cpp)
-        target_include_directories(${GGML_CPU_FEATS_NAME} PRIVATE . .. ../include)
-        target_compile_definitions(${GGML_CPU_FEATS_NAME} PRIVATE ${ARCH_DEFINITIONS})
-        target_compile_definitions(${GGML_CPU_FEATS_NAME} PRIVATE GGML_BACKEND_DL GGML_BACKEND_BUILD GGML_BACKEND_SHARED)
-        set_target_properties(${GGML_CPU_FEATS_NAME} PROPERTIES POSITION_INDEPENDENT_CODE ON)
-        target_link_libraries(${GGML_CPU_NAME} PRIVATE ${GGML_CPU_FEATS_NAME})
-    endif()
-
    if (EMSCRIPTEN)
        set_target_properties(${GGML_CPU_NAME} PROPERTIES COMPILE_FLAGS "-msimd128")
    endif()
@@ -1191,7 +1191,7 @@ static void ggml_gemv_q4_0_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, c
        }
    }
    return;
-#elif defined(__riscv_v_intrinsic)
+#elif defined __riscv_v
    if (__riscv_vlenb() >= QK4_0) {
        const size_t vl = QK4_0;

@@ -3783,7 +3783,7 @@ static void ggml_gemm_q4_0_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, c
        }
        return;
    }
-#elif defined(__riscv_v_intrinsic)
+#elif defined __riscv_v
    if (__riscv_vlenb() >= QK4_0) {
        const size_t vl = QK4_0;

@@ -320,21 +320,17 @@ inline static int32x4_t ggml_vdotq_s32(int32x4_t acc, int8x16_t a, int8x16_t b)

 #ifdef __wasm_simd128__
 #include <wasm_simd128.h>
-#else
+#endif
+
 #ifdef __POWER9_VECTOR__
 #include <altivec.h>
-#else
+#endif
+
 #if defined(_MSC_VER) || defined(__MINGW32__)
 #include <intrin.h>
-#else
-#if defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) || defined(__SSSE3__) || defined(__SSE3__) || defined(__SSE__)
-#if !defined(__riscv)
+#elif defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) || defined(__SSSE3__) || defined(__SSE3__) || defined(__SSE__)
 #include <immintrin.h>
 #endif
-#endif
-#endif
-#endif
-#endif

 #ifdef __riscv_v_intrinsic
 #include <riscv_vector.h>
@@ -883,7 +883,7 @@ void quantize_row_q8_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, i
        _mm_storeu_si128((__m128i *)(y[i].qs + 16), ni4);
 #endif
    }
-#elif defined(__riscv_v_intrinsic)
+#elif defined(__riscv_v)

    size_t vl = QK8_0;

@@ -1221,7 +1221,7 @@ void quantize_row_q8_1(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, i
        _mm_storeu_si128((__m128i *)(y[i].qs + 16), ni4);
 #endif
    }
-#elif defined(__riscv_v_intrinsic)
+#elif defined(__riscv_v)

    size_t vl = QK8_1;

@@ -2384,7 +2384,7 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const voi
    }

    sumf = hsum_float_4x4(acc_0, acc_1, acc_2, acc_3);
-#elif defined(__riscv_v_intrinsic)
+#elif defined(__riscv_v)
    size_t vl = qk / 2;

    for (; ib < nb; ++ib) {
@@ -2774,7 +2774,7 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * GGML_RESTRICT s, size_t bs, const voi
    }

    sumf = hsum_float_8(acc) + summs;
-#elif defined(__riscv_v_intrinsic)
+#elif defined(__riscv_v)
    size_t vl = qk / 2;

    for (; ib < nb; ++ib) {
@@ -3121,7 +3121,7 @@ void ggml_vec_dot_q5_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const voi
    }

    sumf = hsum_float_8(acc);
-#elif defined(__riscv_v_intrinsic)
+#elif defined(__riscv_v)
    size_t vl;
    size_t vlenb = __riscv_vlenb();

@@ -3460,7 +3460,7 @@ void ggml_vec_dot_q5_1_q8_1(int n, float * GGML_RESTRICT s, size_t bs, const voi
    }

    sumf = hsum_float_8(acc) + summs;
-#elif defined(__riscv_v_intrinsic)
+#elif defined(__riscv_v)
    size_t vl;
    size_t vlenb = __riscv_vlenb();

@@ -3897,7 +3897,7 @@ void ggml_vec_dot_q8_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const voi
    }

    sumf = hsum_float_8(accum);
-#elif defined(__riscv_v_intrinsic)
+#elif defined(__riscv_v)
    size_t vl = qk;

    for (; ib < nb; ++ib) {
@@ -5100,14 +5100,111 @@ void ggml_vec_dot_q2_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi

    *s = sumf;

-#elif defined __riscv_v_intrinsic
+#elif defined __riscv_xtheadvector
+
+    float sumf = 0;
+    uint8_t atmp[16];
+
+    for (int i = 0; i < nb; ++i) {
+        const uint8_t * q2 = x[i].qs;
+        const  int8_t * q8 = y[i].qs;
+        const uint8_t * sc = x[i].scales;
+        const float dall = y[i].d * GGML_FP16_TO_FP32(x[i].d);
+        const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin);
+        uint8_t *patmp = atmp;
+        int vsums;
+        int tmp;
+        __asm__ __volatile__(
+            "th.vsetvli zero, %[vl16], e8, m1\n\t"
+            "th.vmv.v.x v8, zero\n\t"
+            "th.vlb.v v1, (%[sc])\n\t"
+            "th.vand.vi v0, v1, 0xF\n\t"
+            "th.vsrl.vi v1, v1, 4\n\t"
+            "th.vsb.v v0, (%[scale])\n\t"
+            "th.vwaddu.vx v16, v1, zero\n\t"
+            "th.vsetvli zero, %[vl16], e16, m2\n\t"
+            "th.vlh.v v2, (%[bsums])\n\t"
+            "th.vwmul.vv v4, v16, v2\n\t"
+            "th.vsetvli zero, %[vl16], e32, m4\n\t"
+            "th.vredsum.vs v8, v4, v8\n\t"
+            "th.vmv.x.s %[vsums], v8"
+            : [tmp] "=&r" (tmp), [vsums] "=&r" (vsums)
+            : [sc] "r" (sc), [scale] "r" (atmp), [bsums] "r" (y[i].bsums)
+            , [vl16] "r" (16)
+            : "memory"
+            , "v0", "v1", "v2", "v3", "v4", "v5", "v6", "v7"
+            , "v8", "v9", "v10", "v11", "v12", "v13", "v14", "v15"
+            , "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23"
+            , "v24", "v25", "v26", "v27", "v28", "v29", "v30", "v31"
+        );
+        sumf += dmin * vsums;
+        int isum = 0;
+
+        for (int j = 0; j < QK_K/128; ++j) {
+            __asm__ __volatile__(
+                "th.vsetvli zero, %[vl32], e8, m2\n\t"
+                "th.vlb.v v0, (%[q2])\n\t"
+                "th.vsrl.vi v2, v0, 2\n\t"
+                "th.vsrl.vi v4, v0, 4\n\t"
+                "th.vsrl.vi v6, v0, 6\n\t"
+                "th.vand.vi v0, v0, 0x3\n\t"
+                "th.vand.vi v2, v2, 0x3\n\t"
+                "th.vand.vi v4, v4, 0x3\n\t"
+                "th.vsetvli zero, %[vl128], e8, m8\n\t"
+                "th.vlb.v v8, (%[q8])\n\t"
+                "th.vsetvli zero, %[vl64], e8, m4\n\t"
+                "th.vwmul.vv v16, v0, v8\n\t"
+                "th.vwmul.vv v24, v4, v12\n\t"
+                "th.vsetvli zero, %[vl16], e16, m2\n\t"
+                "th.vmv.v.x v0, zero\n\t"
+                "th.vwredsum.vs v10, v16, v0\n\t"
+                "th.vwredsum.vs v9, v18, v0\n\t"
+                "th.vwredsum.vs v8, v20, v0\n\t"
+                "th.vwredsum.vs v7, v22, v0\n\t"
+                "th.vwredsum.vs v11, v24, v0\n\t"
+                "th.vwredsum.vs v12, v26, v0\n\t"
+                "th.vwredsum.vs v13, v28, v0\n\t"
+                "th.vwredsum.vs v14, v30, v0\n\t"
+                "li %[tmp], 4\n\t"
+                "th.vsetvli zero, %[tmp], e32, m1\n\t"
+                "th.vslideup.vi v10, v9, 1\n\t"
+                "th.vslideup.vi v8, v7, 1\n\t"
+                "th.vslideup.vi v11, v12, 1\n\t"
+                "th.vslideup.vi v13, v14, 1\n\t"
+                "th.vslideup.vi v10, v8, 2\n\t"
+                "th.vslideup.vi v11, v13, 2\n\t"
+                "li %[tmp], 8\n\t"
+                "th.vsetvli zero, %[tmp], e32, m2\n\t"
+                "th.vlbu.v v12, (%[scale])\n\t"
+                "th.vmul.vv v10, v10, v12\n\t"
+                "th.vredsum.vs v0, v10, v0\n\t"
+                "th.vmv.x.s %[tmp], v0\n\t"
+                "add %[isum], %[isum], %[tmp]"
+                : [tmp] "=&r" (tmp), [isum] "+&r" (isum)
+                : [q2] "r" (q2), [scale] "r" (patmp), [q8] "r" (q8)
+                , [vl16] "r" (16), [vl32] "r" (32), [vl64] "r" (64), [vl128] "r" (128)
+                : "memory"
+                , "v0", "v1", "v2", "v3", "v4", "v5", "v6", "v7"
+                , "v8", "v9", "v10", "v11", "v12", "v13", "v14", "v15"
+                , "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23"
+                , "v24", "v25", "v26", "v27", "v28", "v29", "v30", "v31"
+            );
+            q2 += 32; q8 += 128; patmp += 8;
+        }
+
+        sumf += dall * isum;
+    }
+
+    *s = sumf;
+
+#elif defined __riscv_v
+
+    float sumf = 0;
+    uint8_t atmp[16];

    const int vector_length = __riscv_vlenb() * 8;
-    float sumf = 0;
-
    uint8_t temp_01[32] = { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
                            1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 };
-    uint8_t atmp[16];

    switch (vector_length) {
    case 256:
@@ -6137,14 +6234,141 @@ void ggml_vec_dot_q3_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi

    *s = sumf;

-#elif defined __riscv_v_intrinsic
+#elif defined __riscv_xtheadvector

-    uint32_t aux[3];
    uint32_t utmp[4];
-
-    const int vector_length = __riscv_vlenb() * 8;
    float sumf = 0;

+    for (int i = 0; i < nb; ++i) {
+        const uint8_t * restrict q3 = x[i].qs;
+        const uint8_t * restrict qh = x[i].hmask;
+        const  int8_t * restrict q8 = y[i].qs;
+
+        int8_t * scale = (int8_t *)utmp;
+        int tmp;
+        __asm__ __volatile__(
+            "li %[tmp], 12\n\t"
+            "th.vsetvli zero, %[tmp], e8, m1\n\t"
+            "th.vlb.v v0, (%[s6b])\n\t"
+            "th.vmv.v.v v2, v0\n\t"
+            "li %[tmp], 2\n\t"
+            "th.vsetvli zero, %[tmp], e64, m1\n\t"
+            "th.vmv.v.x v9, %[sh]\n\t"\
+            "th.vslidedown.vi v1, v0, 1\n\t"
+            "th.vslide1up.vx v8, v9, zero\n\t" // {0, 0, 4, 4}
+            "th.vslideup.vi v0, v2, 1\n\t" // {aux[0], aux[1], aux[0], aux[1]}
+            "li %[tmp], 4\n\t"
+            "th.vsetvli zero, %[tmp], e32, m1\n\t"
+            "th.vid.v v9\n\t"
+            "th.vmv.x.s %[tmp], v1\n\t"
+            "th.vsll.vi v9, v9, 1\n\t" // {0, 2, 4, 6}
+            "th.vmv.v.x v1, %[tmp]\n\t" // {aux[2], aux[2], aux[2], aux[2]}
+            "th.vsrl.vv v4, v1, v9\n\t"
+            "th.vsrl.vv v2, v0, v8\n\t"
+            "th.vand.vx v5, v4, %[kmask1]\n\t"
+            "th.vand.vx v3, v2, %[kmask2]\n\t"
+            "th.vsll.vi v6, v5, 4\n\t"
+            "th.vor.vv v7, v6, v3\n\t"
+            "li %[tmp], 16\n\t"
+            "th.vsetvli zero, %[tmp], e8, m1\n\t"
+            "th.vsub.vx v0, v7, %[c]\n\t"
+            "th.vsb.v v0, (%[scale])"
+            : [tmp] "=&r" (tmp)
+            : [sh] "r" (0x0000000400000004), [s6b] "r" (x[i].scales), [c] "r" (32)
+            , [scale] "r" (scale), [kmask1] "r" (kmask1), [kmask2] "r" (kmask2)
+            : "memory"
+            , "v0", "v1", "v2", "v3", "v4", "v5", "v6", "v7"
+            , "v8", "v9", "v10", "v11", "v12", "v13", "v14", "v15"
+            , "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23"
+            , "v24", "v25", "v26", "v27", "v28", "v29", "v30", "v31"
+        );
+
+        uint8_t m = 1;
+        int isum = 0;
+        for (int j = 0; j < QK_K; j += 128) {
+            __asm__ __volatile__(
+                // fixme: use v0p7 mask layout directly
+                "th.vsetvli zero, %[vl32], e8, m2\n\t"
+                "th.vlb.v v8, (%[q3])\n\t"
+                "th.vsrl.vi v10, v8, 2\n\t"
+                "th.vsrl.vi v12, v8, 4\n\t"
+                "th.vsrl.vi v14, v8, 6\n\t"
+                "th.vand.vi v8, v8, 3\n\t"
+                "th.vand.vi v10, v10, 3\n\t"
+                "th.vand.vi v12, v12, 3\n\t"
+                "th.vlb.v v2, (%[qh])\n\t"
+                "th.vand.vx v4, v2, %[m]\n\t"
+                "slli %[m], %[m], 1\n\t"
+                "th.vmseq.vx v0, v4, zero\n\t"
+                "th.vadd.vi v8, v8, -4, v0.t\n\t"
+                "th.vand.vx v4, v2, %[m]\n\t"
+                "slli %[m], %[m], 1\n\t"
+                "th.vmseq.vx v0, v4, zero\n\t"
+                "th.vadd.vi v10, v10, -4, v0.t\n\t"
+                "th.vand.vx v4, v2, %[m]\n\t"
+                "slli %[m], %[m], 1\n\t"
+                "th.vmseq.vx v0, v4, zero\n\t"
+                "th.vadd.vi v12, v12, -4, v0.t\n\t"
+                "th.vand.vx v4, v2, %[m]\n\t"
+                "slli %[m], %[m], 1\n\t"
+                "th.vmseq.vx v0, v4, zero\n\t"
+                "th.vadd.vi v14, v14, -4, v0.t\n\t"
+                "th.vsetvli zero, %[vl128], e8, m8\n\t"
+                "th.vlb.v v0, (%[q8])\n\t"
+                "th.vsetvli zero, %[vl64], e8, m4\n\t"
+                "th.vwmul.vv v16, v0, v8\n\t"
+                "th.vwmul.vv v24, v4, v12\n\t"
+                "li %[tmp], 16\n\t"
+                "th.vsetvli zero, %[tmp], e16, m2\n\t"
+                "th.vmv.v.x v0, zero\n\t"
+                "th.vwredsum.vs v10, v16, v0\n\t"
+                "th.vwredsum.vs v9, v18, v0\n\t"
+                "th.vwredsum.vs v8, v20, v0\n\t"
+                "th.vwredsum.vs v7, v22, v0\n\t"
+                "th.vwredsum.vs v11, v24, v0\n\t"
+                "th.vwredsum.vs v12, v26, v0\n\t"
+                "th.vwredsum.vs v13, v28, v0\n\t"
+                "th.vwredsum.vs v14, v30, v0\n\t"
+                "li %[tmp], 4\n\t"
+                "th.vsetvli zero, %[tmp], e32, m1\n\t"
+                "th.vslideup.vi v10, v9, 1\n\t"
+                "th.vslideup.vi v8, v7, 1\n\t"
+                "th.vslideup.vi v11, v12, 1\n\t"
+                "th.vslideup.vi v13, v14, 1\n\t"
+                "th.vslideup.vi v10, v8, 2\n\t"
+                "th.vslideup.vi v11, v13, 2\n\t"
+                "li %[tmp], 8\n\t"
+                "th.vsetvli zero, %[tmp], e32, m2\n\t"
+                "th.vlb.v v12, (%[scale])\n\t"
+                "th.vmul.vv v10, v10, v12\n\t"
+                "th.vredsum.vs v0, v10, v0\n\t"
+                "th.vmv.x.s %[tmp], v0\n\t"
+                "add %[isum], %[isum], %[tmp]"
+                : [tmp] "=&r" (tmp), [m] "+&r" (m), [isum] "+&r" (isum)
+                : [vl128] "r" (128), [vl64] "r" (64), [vl32] "r" (32)
+                , [q3] "r" (q3), [qh] "r" (qh), [scale] "r" (scale), [q8] "r" (q8)
+                : "memory"
+                , "v0", "v1", "v2", "v3", "v4", "v5", "v6", "v7"
+                , "v8", "v9", "v10", "v11", "v12", "v13", "v14", "v15"
+                , "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23"
+                , "v24", "v25", "v26", "v27", "v28", "v29", "v30", "v31"
+            );
+            q3 += 32;    q8 += 128;   scale += 8;
+        }
+
+        const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d;
+        sumf += d * isum;
+    }
+
+    *s = sumf;
+
+#elif defined __riscv_v
+
+    uint32_t utmp[4];
+    float sumf = 0;
+    uint32_t aux[3];
+    const int vector_length = __riscv_vlenb() * 8;
+
    switch (vector_length) {
    case 256:
        for (int i = 0; i < nb; ++i) {
@@ -6331,7 +6555,7 @@ void ggml_vec_dot_q3_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
                    "vslideup.vi v13, v14, 1\n\t"
                    "vslideup.vi v10, v8, 2\n\t"
                    "vslideup.vi v11, v13, 2\n\t"
-                    "vsetivli zero, 8, e32, m2\n\t"\
+                    "vsetivli zero, 8, e32, m2\n\t"
                    "vle8.v v15, (%[scale])\n\t"
                    "vsext.vf4 v12, v15\n\t"
                    "vmul.vv v10, v10, v12\n\t"
@@ -7180,14 +7404,130 @@ void ggml_vec_dot_q4_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi

    *s = hsum_float_8(acc) + _mm_cvtss_f32(acc_m);

-#elif defined __riscv_v_intrinsic
+#elif defined __riscv_xtheadvector

    const uint8_t * scales = (const uint8_t*)&utmp[0];
    const uint8_t * mins   = (const uint8_t*)&utmp[2];

-    const int vector_length = __riscv_vlenb() * 8;
    float sumf = 0;

+    for (int i = 0; i < nb; ++i) {
+        const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d);
+        const float dmin = y[i].d * GGML_FP16_TO_FP32(x[i].dmin);
+
+        int tmp, tmp2, sumi;
+        __asm__ __volatile__(
+            "li %[t1], 12\n\t"
+            "th.vsetvli zero, %[t1], e8, m1\n\t"
+            "th.vlb.v v1, (%[s6b])\n\t" // {aux[0], aux[1], aux[2]}
+            "li %[t1], 4\n\t"
+            "th.vsetvli zero, %[t1], e32, m1\n\t"
+            "th.vslidedown.vi v2, v1, 2\n\t"
+            "th.vmv.v.v v3, v2\n\t"
+            "th.vslideup.vi v2, v3, 1\n\t" // {aux[2], aux[2]}
+            "li %[t1], 2\n\t"
+            "th.vsetvli zero, %[t1], e32, m1\n\t"
+            "th.vmv.v.i v4, 4\n\t"
+            "th.vand.vx v8, v1, %[kmask1]\n\t"
+            "th.vslide1up.vx v5, v4, zero\n\t" // {0, 4}
+            "th.vsrl.vi v6, v1, 6\n\t"
+            "th.vsrl.vv v7, v2, v5\n\t"
+            "th.vand.vx v0, v6, %[kmask3]\n\t"
+            "th.vand.vx v2, v7, %[kmask2]\n\t"
+            "th.vsll.vi v6, v0, 4\n\t"
+            "li %[t2], 8\n\t"
+            "addi %[t1], %[utmp], 4\n\t"
+            "th.vor.vv v1, v6, v2\n\t"
+            "th.vssw.v v8, (%[utmp]), %[t2]\n\t"
+            "th.vssw.v v1, (%[t1]), %[t2]\n\t"
+            "th.vsetvli zero, zero, e32, m2\n\t" // vl == 8
+            "th.vlw.v v2, (%[bsums])\n\t"
+            "th.vsetvli zero, %[t2], e16, m1\n\t"
+            "th.vnsrl.vi v0, v2, 0\n\t"
+            "th.vnsrl.vi v1, v2, 16\n\t"
+            "th.vadd.vv v2, v0, v1\n\t"
+            "th.vlbu.v v4, (%[mins])\n\t"
+            "th.vwmul.vv v6, v4, v2\n\t"
+            "th.vmv.v.x v0, zero\n\t"
+            "th.vsetvli zero, %[t2], e32, m2\n\t"
+            "th.vredsum.vs v0, v6, v0\n\t"
+            "th.vmv.x.s %[sumi], v0"
+            : [t1] "=&r" (tmp), [t2] "=&r" (tmp2), [sumi] "=&r" (sumi)
+            : [bsums] "r" (y[i].bsums), [mins] "r" (mins), [utmp] "r" (utmp)
+            , [s6b] "r" (x[i].scales), [kmask1] "r" (kmask1)
+            , [kmask2] "r" (kmask2), [kmask3] "r" (kmask3)
+            : "memory"
+            , "v0", "v1", "v2", "v3", "v4", "v5", "v6", "v7"
+            , "v8", "v9", "v10", "v11", "v12", "v13", "v14", "v15"
+            , "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23"
+            , "v24", "v25", "v26", "v27", "v28", "v29", "v30", "v31"
+        );
+        sumf -= dmin * sumi;
+
+        const uint8_t * restrict q4 = x[i].qs;
+        const int8_t  * restrict q8 = y[i].qs;
+
+        sumi = 0;
+        const uint8_t * scale = scales;
+
+        for (int j = 0; j < QK_K/128; ++j) {
+            int vl128 = 128, vl64 = 64, vl32 = 32;
+            __asm__ __volatile__(
+                "th.vsetvli zero, %[vl128], e8, m8\n\t"
+                "th.vlb.v v8, (%[q8])\n\t"
+                "th.vsetvli zero, %[vl64], e8, m4\n\t"
+                "th.vlb.v v0, (%[q4])\n\t"
+                "th.vsrl.vi v4, v0, 4\n\t"
+                "th.vand.vi v0, v0, 0xF\n\t"
+                "th.vsetvli zero, %[vl32], e8, m2\n\t"
+                "th.vwmul.vv v28, v6, v14\n\t"
+                "th.vwmul.vv v20, v4, v10\n\t"
+                "th.vwmul.vv v24, v2, v12\n\t"
+                "th.vwmul.vv v16, v0, v8\n\t"
+                "li %[tmp], 4\n\t"
+                "th.vsetvli zero, %[tmp], e32, m1\n\t"
+                "th.vlbu.v v1, (%[scale])\n\t"
+                "th.vmv.v.x v0, zero\n\t"
+                "th.vsetvli zero, %[vl32], e16, m4\n\t"
+                "th.vwredsum.vs v6, v24, v0\n\t"
+                "th.vwredsum.vs v7, v28, v0\n\t"
+                "th.vwredsum.vs v4, v16, v0\n\t"
+                "th.vwredsum.vs v5, v20, v0\n\t"
+                "th.vsetvli zero, %[tmp], e32, m1\n\t"
+                "th.vslideup.vi v6, v7, 1\n\t"
+                "th.vslideup.vi v4, v5, 1\n\t"
+                "th.vslideup.vi v4, v6, 2\n\t"
+                "th.vmul.vv v8, v4, v1\n\t"
+                "th.vredsum.vs v0, v8, v0\n\t"
+                "th.vmv.x.s %[tmp], v0\n\t"
+                "add %[sumi], %[sumi], %[tmp]"
+                : [tmp] "=&r" (tmp), [sumi] "+&r" (sumi)
+                : [vl128] "r" (vl128), [vl64] "r" (vl64), [vl32] "r" (vl32)
+                , [q4] "r" (q4), [q8] "r" (q8), [scale] "r" (scale)
+                : "memory"
+                , "v0", "v1", "v2", "v3", "v4", "v5", "v6", "v7"
+                , "v8", "v9", "v10", "v11", "v12", "v13", "v14", "v15"
+                , "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23"
+                , "v24", "v25", "v26", "v27", "v28", "v29", "v30", "v31"
+            );
+
+            q4 += 64;    q8 += 128;    scale += 4;
+        }
+
+        sumf += d * sumi;
+
+    }
+
+    *s = sumf;
+
+#elif defined __riscv_v
+
+    const uint8_t * scales = (const uint8_t*)&utmp[0];
+    const uint8_t * mins   = (const uint8_t*)&utmp[2];
+
+    float sumf = 0;
+    const int vector_length = __riscv_vlenb() * 8;
+
    switch (vector_length) {
    case 256:
        for (int i = 0; i < nb; ++i) {
@@ -8074,7 +8414,7 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi

    *s = sumf;

-#elif defined __riscv_v_intrinsic
+#elif defined __riscv_v

    const uint8_t * scales = (const uint8_t*)&utmp[0];
    const uint8_t * mins   = (const uint8_t*)&utmp[2];
@@ -9232,11 +9572,92 @@ void ggml_vec_dot_q6_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
    }
    *s = sumf;

-#elif defined __riscv_v_intrinsic
+#elif defined __riscv_xtheadvector

-    const int vector_length = __riscv_vlenb() * 8;
    float sumf = 0;

+    for (int i = 0; i < nb; ++i) {
+
+        const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d;
+
+        const uint8_t * restrict q6 = x[i].ql;
+        const uint8_t * restrict qh = x[i].qh;
+        const  int8_t * restrict q8 = y[i].qs;
+
+        const int8_t * restrict scale = x[i].scales;
+
+        int sum_t = 0;
+        int t0;
+
+        for (int j = 0; j < QK_K/128; ++j) {
+            __asm__ __volatile__(
+                "th.vsetvli zero, %[vl32], e8, m2\n\t" // vl == 32
+                "th.vlb.v v4, (%[qh])\n\t"
+                "th.vsll.vi v0, v4, 4\n\t"
+                "th.vsll.vi v2, v4, 2\n\t"
+                "th.vsrl.vi v6, v4, 2\n\t"
+                "th.vsetvli zero, %[vl64], e8, m4\n\t" // vl == 64
+                "th.vlb.v v8, (%[q6])\n\t"
+                "th.vsrl.vi v12, v8, 4\n\t"
+                "th.vand.vi v8, v8, 0xF\n\t"
+                "th.vsetvli zero, %[vl128], e8, m8\n\t" // vl == 128
+                "th.vand.vx v0, v0, %[mask]\n\t"
+                "th.vor.vv v8, v8, v0\n\t"
+                "th.vlb.v v0, (%[q8])\n\t"
+                "th.vsub.vx v8, v8, %[vl32]\n\t"
+                "th.vsetvli zero, %[vl64], e8, m4\n\t" // vl == 64
+                "th.vwmul.vv v16, v0, v8\n\t"
+                "th.vwmul.vv v24, v4, v12\n\t"
+                "li %[t0], 16\n\t"
+                "th.vsetvli zero, %[t0], e16, m2\n\t" // vl == 16
+                "th.vmv.v.x v0, zero\n\t"
+                "th.vwredsum.vs v10, v16, v0\n\t"
+                "th.vwredsum.vs v9, v18, v0\n\t"
+                "th.vwredsum.vs v8, v20, v0\n\t"
+                "th.vwredsum.vs v7, v22, v0\n\t"
+                "th.vwredsum.vs v11, v24, v0\n\t"
+                "th.vwredsum.vs v12, v26, v0\n\t"
+                "th.vwredsum.vs v13, v28, v0\n\t"
+                "th.vwredsum.vs v14, v30, v0\n\t"
+                "li %[t0], 4\n\t"
+                "th.vsetvli zero, %[t0], e32, m1\n\t" // vl == 4
+                "th.vslideup.vi v10, v9, 1\n\t"
+                "th.vslideup.vi v8, v7, 1\n\t"
+                "th.vslideup.vi v11, v12, 1\n\t"
+                "th.vslideup.vi v13, v14, 1\n\t"
+                "th.vslideup.vi v10, v8, 2\n\t"
+                "th.vslideup.vi v11, v13, 2\n\t"
+                "li %[t0], 8\n\t"
+                "th.vsetvli zero, %[t0], e32, m2\n\t" // vl == 8
+                "th.vlb.v v4, (%[scale])\n\t"
+                "th.vmul.vv v2, v4, v10\n\t"
+                "th.vredsum.vs v0, v2, v0\n\t"
+                "th.vmv.x.s %[t0], v0\n\t"
+                "add %[sumi], %[sumi], %[t0]"
+                : [sumi] "+&r" (sum_t), [t0] "=&r" (t0)
+                : [qh] "r" (qh), [q6] "r" (q6), [q8] "r" (q8), [scale] "r" (scale)
+                , [vl32] "r" (32), [vl64] "r" (64), [vl128] "r" (128)
+                , [mask] "r" (0x30)
+                : "memory"
+                , "v0", "v1", "v2", "v3", "v4", "v5", "v6", "v7"
+                , "v8", "v9", "v10", "v11", "v12", "v13", "v14", "v15"
+                , "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23"
+                , "v24", "v25", "v26", "v27", "v28", "v29", "v30", "v31"
+            );
+            q6 += 64;   qh += 32;   q8 += 128;   scale += 8;
+        }
+
+        sumf += d * sum_t;
+
+    }
+
+    *s = sumf;
+
+#elif defined __riscv_v
+
+    float sumf = 0;
+    const int vector_length = __riscv_vlenb() * 8;
+
    switch (vector_length) {
    case 256:
        for (int i = 0; i < nb; ++i) {
@@ -168,7 +168,7 @@ void ggml_cuda_error(const char * stmt, const char * func, const char * file, in

 #define CUBLAS_CHECK(err) CUDA_CHECK_GEN(err, CUBLAS_STATUS_SUCCESS, cublas_get_error_str)

-#if !defined(GGML_USE_HIP)
+#if !defined(GGML_USE_HIP) && !defined(GGML_CUDA_NO_VMM)
 static const char * cu_get_error_str(CUresult err) {
    const char * err_str;
    cuGetErrorString(err, &err_str);
@@ -623,8 +623,8 @@ static __global__ void flash_attn_combine_results(
    __builtin_assume(tid < D);

    extern __shared__ float2 meta[];
-    if (tid < 2*parallel_blocks) {
-        ((float *) meta)[threadIdx.x] = ((const float *)VKQ_meta) [blockIdx.z*(2*parallel_blocks) + tid];
+    for (int i = tid; i < 2*parallel_blocks; i += D) {
+        ((float *) meta)[i] = ((const float *)VKQ_meta) [blockIdx.z*(2*parallel_blocks) + i];
    }

    __syncthreads();
@@ -386,7 +386,7 @@ GGML_API void ggml_aligned_free(void * ptr, size_t size);
        return r;
    }

-#elif defined(__riscv) && defined(GGML_RV_ZFH)
+#elif defined(__riscv) && defined(__riscv_zfhmin)

    static inline float ggml_compute_fp16_to_fp32(ggml_fp16_t h) {
        float f;
@@ -55,14 +55,17 @@ endfunction()

 set(GGML_OPENCL_KERNELS
    add
+    argsort
    clamp
    cpy
    cvt
    diag_mask_inf
+    div
    gelu
    gemv_noshuffle_general
    gemv_noshuffle
    get_rows
+    group_norm
    im2col_f32
    im2col_f16
    mul_mat_Ab_Bi_8x4
@@ -83,11 +86,14 @@ set(GGML_OPENCL_KERNELS
    rms_norm
    rope
    scale
+    sigmoid
    silu
    softmax_4_f32
    softmax_4_f16
    softmax_f32
    softmax_f16
+    sub
+    sum_rows
    transpose
 )

@@ -299,27 +299,37 @@ struct ggml_backend_opencl_context {
    cl_program program_mul_mv_f16_f32;
    cl_program program_mul_mv_f32_f32;
    cl_program program_mul;
+    cl_program program_div;
+    cl_program program_sub;
    cl_program program_norm;
    cl_program program_relu;
    cl_program program_rms_norm;
+    cl_program program_group_norm;
    cl_program program_rope;
    cl_program program_scale;
    cl_program program_silu;
+    cl_program program_sigmoid;
    cl_program program_softmax_f32;
    cl_program program_softmax_f16;
    cl_program program_softmax_4_f32;
    cl_program program_softmax_4_f16;
+    cl_program program_argsort_f32_i32;
+    cl_program program_sum_rows_f32;

    cl_kernel kernel_add, kernel_add_row;
    cl_kernel kernel_mul, kernel_mul_row;
+    cl_kernel kernel_div, kernel_div_row;
+    cl_kernel kernel_sub, kernel_sub_row;
    cl_kernel kernel_scale;
    cl_kernel kernel_silu, kernel_silu_4;
    cl_kernel kernel_gelu, kernel_gelu_4;
    cl_kernel kernel_gelu_quick, kernel_gelu_quick_4;
    cl_kernel kernel_relu;
+    cl_kernel kernel_sigmoid_f32, kernel_sigmoid_f16;
    cl_kernel kernel_clamp;
    cl_kernel kernel_norm;
    cl_kernel kernel_rms_norm;
+    cl_kernel kernel_group_norm;
    cl_kernel kernel_diag_mask_inf, kernel_diag_mask_inf_8;
    cl_kernel kernel_soft_max, kernel_soft_max_4;
    cl_kernel kernel_soft_max_f16, kernel_soft_max_4_f16;
@@ -339,6 +349,8 @@ struct ggml_backend_opencl_context {
    cl_kernel kernel_mul_mat_q4_0_f32_1d_8x_flat, kernel_mul_mat_q4_0_f32_1d_16x_flat;
    cl_kernel kernel_mul_mv_q6_K_f32;
    cl_kernel kernel_im2col_f32, kernel_im2col_f16;
+    cl_kernel kernel_argsort_f32_i32;
+    cl_kernel kernel_sum_rows_f32;

 #ifdef GGML_OPENCL_USE_ADRENO_KERNELS
    // Transpose kernels
@@ -986,6 +998,105 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve
        GGML_LOG_CONT(".");
    }

+    // argsort
+    {
+#ifdef GGML_OPENCL_EMBED_KERNELS
+        const std::string kernel_src {
+            #include "argsort.cl.h"
+        };
+#else
+        const std::string kernel_src = read_file("argsort.cl");
+#endif
+        backend_ctx->program_argsort_f32_i32 =
+            build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts);
+
+        CL_CHECK((backend_ctx->kernel_argsort_f32_i32 = clCreateKernel(backend_ctx->program_argsort_f32_i32, "kernel_argsort_f32_i32", &err), err));
+        GGML_LOG_CONT(".");
+    }
+
+    // div
+    {
+#ifdef GGML_OPENCL_EMBED_KERNELS
+        const std::string kernel_src {
+            #include "div.cl.h"
+        };
+#else
+        const std::string kernel_src = read_file("div.cl");
+#endif
+        backend_ctx->program_div =
+            build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts);
+
+        CL_CHECK((backend_ctx->kernel_div     = clCreateKernel(backend_ctx->program_div, "kernel_div", &err), err));
+        CL_CHECK((backend_ctx->kernel_div_row = clCreateKernel(backend_ctx->program_div, "kernel_div_row", &err), err));
+        GGML_LOG_CONT(".");
+    }
+
+    // sub
+    {
+#ifdef GGML_OPENCL_EMBED_KERNELS
+        const std::string kernel_src {
+            #include "sub.cl.h"
+        };
+#else
+        const std::string kernel_src = read_file("sub.cl");
+#endif
+        backend_ctx->program_sub =
+            build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts);
+
+        CL_CHECK((backend_ctx->kernel_sub     = clCreateKernel(backend_ctx->program_sub, "kernel_sub", &err), err));
+        CL_CHECK((backend_ctx->kernel_sub_row = clCreateKernel(backend_ctx->program_sub, "kernel_sub_row", &err), err));
+        GGML_LOG_CONT(".");
+    }
+
+    // sum_rows
+    {
+#ifdef GGML_OPENCL_EMBED_KERNELS
+        const std::string kernel_src {
+            #include "sum_rows.cl.h"
+        };
+#else
+        const std::string kernel_src = read_file("sum_rows.cl");
+#endif
+        backend_ctx->program_sum_rows_f32 =
+            build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts);
+
+        CL_CHECK((backend_ctx->kernel_sum_rows_f32 = clCreateKernel(backend_ctx->program_sum_rows_f32, "kernel_sum_rows_f32", &err), err));
+        GGML_LOG_CONT(".");
+    }
+
+    // sigmoid
+    {
+#ifdef GGML_OPENCL_EMBED_KERNELS
+        const std::string kernel_src {
+            #include "sigmoid.cl.h"
+        };
+#else
+        const std::string kernel_src = read_file("sigmoid.cl");
+#endif
+        backend_ctx->program_sigmoid =
+            build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts);
+
+        CL_CHECK((backend_ctx->kernel_sigmoid_f32 = clCreateKernel(backend_ctx->program_sigmoid, "kernel_sigmoid_f32", &err), err));
+        CL_CHECK((backend_ctx->kernel_sigmoid_f16 = clCreateKernel(backend_ctx->program_sigmoid, "kernel_sigmoid_f16", &err), err));
+        GGML_LOG_CONT(".");
+    }
+
+    // group_norm
+    {
+#ifdef GGML_OPENCL_EMBED_KERNELS
+        const std::string kernel_src {
+            #include "group_norm.cl.h"
+        };
+#else
+        const std::string kernel_src = read_file("group_norm.cl");
+#endif
+        backend_ctx->program_group_norm =
+            build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts);
+
+        CL_CHECK((backend_ctx->kernel_group_norm = clCreateKernel(backend_ctx->program_group_norm, "kernel_group_norm", &err), err));
+        GGML_LOG_CONT(".");
+    }
+
    // Adreno kernels
 #ifdef GGML_OPENCL_USE_ADRENO_KERNELS
    // transpose
@@ -1856,6 +1967,8 @@ static bool ggml_opencl_supports_op(ggml_backend_dev_t dev, const struct ggml_te
        case GGML_OP_ADD:
        case GGML_OP_SCALE:
        case GGML_OP_MUL:
+        case GGML_OP_DIV:
+        case GGML_OP_SUB:
            return op->src[0]->type == GGML_TYPE_F32;
        case GGML_OP_UNARY:
            switch (ggml_get_unary_op(op)) {
@@ -1863,7 +1976,9 @@ static bool ggml_opencl_supports_op(ggml_backend_dev_t dev, const struct ggml_te
                case GGML_UNARY_OP_SILU:
                case GGML_UNARY_OP_RELU:
                case GGML_UNARY_OP_GELU_QUICK:
-                   return ggml_is_contiguous(op->src[0]) && op->src[0]->type == GGML_TYPE_F32;
+                    return ggml_is_contiguous(op->src[0]) && op->src[0]->type == GGML_TYPE_F32;
+                case GGML_UNARY_OP_SIGMOID:
+                    return ggml_is_contiguous(op->src[0]);
                default:
                    return false;
            }
@@ -1873,11 +1988,13 @@ static bool ggml_opencl_supports_op(ggml_backend_dev_t dev, const struct ggml_te
        case GGML_OP_NORM:
        case GGML_OP_RMS_NORM:
            return true;
+        case GGML_OP_GROUP_NORM:
+            return ggml_is_contiguous(op->src[0]);
        case GGML_OP_MUL_MAT:
            if (op->src[0]->type == GGML_TYPE_F16) {
                return true;
            } else if (op->src[0]->type == GGML_TYPE_F32) {
-                return op->src[1]->type == GGML_TYPE_F32 && ggml_is_contiguous(op->src[0]) && ggml_is_contiguous(op->src[1]);
+                return op->src[1]->type == GGML_TYPE_F32;
            } else if (op->src[0]->type == GGML_TYPE_Q4_0 ||
                       op->src[0]->type == GGML_TYPE_Q6_K) {
                return op->src[1]->type == GGML_TYPE_F32 && ggml_is_contiguous(op->src[0]) && ggml_is_contiguous(op->src[1]);
@@ -1912,6 +2029,10 @@ static bool ggml_opencl_supports_op(ggml_backend_dev_t dev, const struct ggml_te
        }
        case GGML_OP_IM2COL:
            return true;
+        case GGML_OP_ARGSORT:
+            return op->src[0]->type == GGML_TYPE_F32;
+        case GGML_OP_SUM_ROWS:
+            return op->src[0]->type == GGML_TYPE_F32 && ggml_is_contiguous(op->src[0]);
        default:
            return false;
    }
@@ -3238,6 +3359,256 @@ static void ggml_cl_mul(ggml_backend_t backend, const ggml_tensor * src0, const
    }
 }

+static void ggml_cl_div(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_ASSERT(src0);
+    GGML_ASSERT(src0->extra);
+    GGML_ASSERT(src1);
+    GGML_ASSERT(src1->extra);
+    GGML_ASSERT(dst);
+    GGML_ASSERT(dst->extra);
+
+    const int ne00 = src0->ne[0];
+    const int ne01 = src0->ne[1];
+    const int ne02 = src0->ne[2];
+    const int ne03 = src0->ne[3];
+
+    const cl_ulong nb00 = src0->nb[0];
+    const cl_ulong nb01 = src0->nb[1];
+    const cl_ulong nb02 = src0->nb[2];
+    const cl_ulong nb03 = src0->nb[3];
+
+    const int ne10 = src1->ne[0];
+    const int ne11 = src1->ne[1];
+    const int ne12 = src1->ne[2];
+    const int ne13 = src1->ne[3];
+
+    const cl_ulong nb10 = src1->nb[0];
+    const cl_ulong nb11 = src1->nb[1];
+    const cl_ulong nb12 = src1->nb[2];
+    const cl_ulong nb13 = src1->nb[3];
+
+    const int ne0  = dst->ne[0];
+
+    const cl_ulong nb0  = dst->nb[0];
+    const cl_ulong nb1  = dst->nb[1];
+    const cl_ulong nb2  = dst->nb[2];
+    const cl_ulong nb3  = dst->nb[3];
+
+    ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context;
+    cl_command_queue queue = backend_ctx->queue;
+
+    ggml_tensor_extra_cl * extra0 = (ggml_tensor_extra_cl *)src0->extra;
+    ggml_tensor_extra_cl * extra1 = (ggml_tensor_extra_cl *)src1->extra;
+    ggml_tensor_extra_cl * extrad = (ggml_tensor_extra_cl *)dst->extra;
+
+    cl_ulong offset0 = extra0->offset + src0->view_offs;
+    cl_ulong offset1 = extra1->offset + src1->view_offs;
+    cl_ulong offsetd = extrad->offset + dst->view_offs;
+
+    bool bcast_row = false;
+    cl_kernel kernel;
+
+    if (ggml_nelements(src1) == ne10 && ggml_is_contiguous(src1) && ne00 % 4 == 0 && ne10 % 4 == 0) {
+        GGML_ASSERT(ggml_is_contiguous(src0));
+
+        // src1 is a row
+        GGML_ASSERT(ne11 == 1);
+
+        bcast_row = true;
+        int ne = ne00 / 4;
+        kernel = backend_ctx->kernel_div_row;
+
+        CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem),   &extra0->data_device));
+        CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_ulong), &offset0));
+        CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem),   &extra1->data_device));
+        CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &offset1));
+        CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_mem),   &extrad->data_device));
+        CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_ulong), &offsetd));
+        CL_CHECK(clSetKernelArg(kernel, 6, sizeof(int),      &ne));
+    } else {
+        kernel = backend_ctx->kernel_div;
+
+        CL_CHECK(clSetKernelArg(kernel,  0, sizeof(cl_mem),   &extra0->data_device));
+        CL_CHECK(clSetKernelArg(kernel,  1, sizeof(cl_ulong), &offset0));
+        CL_CHECK(clSetKernelArg(kernel,  2, sizeof(cl_mem),   &extra1->data_device));
+        CL_CHECK(clSetKernelArg(kernel,  3, sizeof(cl_ulong), &offset1));
+        CL_CHECK(clSetKernelArg(kernel,  4, sizeof(cl_mem),   &extrad->data_device));
+        CL_CHECK(clSetKernelArg(kernel,  5, sizeof(cl_ulong), &offsetd));
+        CL_CHECK(clSetKernelArg(kernel,  6, sizeof(cl_ulong), &nb00));
+        CL_CHECK(clSetKernelArg(kernel,  7, sizeof(cl_ulong), &nb01));
+        CL_CHECK(clSetKernelArg(kernel,  8, sizeof(cl_ulong), &nb02));
+        CL_CHECK(clSetKernelArg(kernel,  9, sizeof(cl_ulong), &nb03));
+        CL_CHECK(clSetKernelArg(kernel, 10, sizeof(int),      &ne10));
+        CL_CHECK(clSetKernelArg(kernel, 11, sizeof(int),      &ne11));
+        CL_CHECK(clSetKernelArg(kernel, 12, sizeof(int),      &ne12));
+        CL_CHECK(clSetKernelArg(kernel, 13, sizeof(int),      &ne13));
+        CL_CHECK(clSetKernelArg(kernel, 14, sizeof(cl_ulong), &nb10));
+        CL_CHECK(clSetKernelArg(kernel, 15, sizeof(cl_ulong), &nb11));
+        CL_CHECK(clSetKernelArg(kernel, 16, sizeof(cl_ulong), &nb12));
+        CL_CHECK(clSetKernelArg(kernel, 17, sizeof(cl_ulong), &nb13));
+        CL_CHECK(clSetKernelArg(kernel, 18, sizeof(int),      &ne0));
+        CL_CHECK(clSetKernelArg(kernel, 19, sizeof(cl_ulong), &nb0));
+        CL_CHECK(clSetKernelArg(kernel, 20, sizeof(cl_ulong), &nb1));
+        CL_CHECK(clSetKernelArg(kernel, 21, sizeof(cl_ulong), &nb2));
+        CL_CHECK(clSetKernelArg(kernel, 22, sizeof(cl_ulong), &nb3));
+    }
+
+    if (bcast_row) {
+        int n = ggml_nelements(dst)/4;
+        size_t global_work_size[] = {(size_t)n, 1, 1};
+        size_t local_work_size[] = {64, 1, 1};
+
+#ifdef GGML_OPENCL_PROFILING
+        cl_event evt;
+        CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, &evt));
+
+        g_profiling_info.emplace_back();
+        populateProfilingInfo(g_profiling_info.back(), evt, kernel, global_work_size, local_work_size, dst);
+#else
+        CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, NULL));
+#endif
+    } else {
+        unsigned int nth = MIN(64, ne0);
+        size_t global_work_size[] = {ne01*nth, (size_t)ne02, (size_t)ne03};
+        size_t local_work_size[] = {nth, 1, 1};
+
+#ifdef GGML_OPENCL_PROFILING
+        cl_event evt;
+        CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, &evt));
+
+        g_profiling_info.emplace_back();
+        populateProfilingInfo(g_profiling_info.back(), evt, kernel, global_work_size, local_work_size, dst);
+#else
+        CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, NULL));
+#endif
+    }
+}
+
+static void ggml_cl_sub(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_ASSERT(src0);
+    GGML_ASSERT(src0->extra);
+    GGML_ASSERT(src1);
+    GGML_ASSERT(src1->extra);
+    GGML_ASSERT(dst);
+    GGML_ASSERT(dst->extra);
+
+    const int ne00 = src0->ne[0];
+    const int ne01 = src0->ne[1];
+    const int ne02 = src0->ne[2];
+    const int ne03 = src0->ne[3];
+
+    const cl_ulong nb00 = src0->nb[0];
+    const cl_ulong nb01 = src0->nb[1];
+    const cl_ulong nb02 = src0->nb[2];
+    const cl_ulong nb03 = src0->nb[3];
+
+    const int ne10 = src1->ne[0];
+    const int ne11 = src1->ne[1];
+    const int ne12 = src1->ne[2];
+    const int ne13 = src1->ne[3];
+
+    const cl_ulong nb10 = src1->nb[0];
+    const cl_ulong nb11 = src1->nb[1];
+    const cl_ulong nb12 = src1->nb[2];
+    const cl_ulong nb13 = src1->nb[3];
+
+    const int ne0  = dst->ne[0];
+
+    const cl_ulong nb0  = dst->nb[0];
+    const cl_ulong nb1  = dst->nb[1];
+    const cl_ulong nb2  = dst->nb[2];
+    const cl_ulong nb3  = dst->nb[3];
+
+    ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context;
+    cl_command_queue queue = backend_ctx->queue;
+
+    ggml_tensor_extra_cl * extra0 = (ggml_tensor_extra_cl *)src0->extra;
+    ggml_tensor_extra_cl * extra1 = (ggml_tensor_extra_cl *)src1->extra;
+    ggml_tensor_extra_cl * extrad = (ggml_tensor_extra_cl *)dst->extra;
+
+    cl_ulong offset0 = extra0->offset + src0->view_offs;
+    cl_ulong offset1 = extra1->offset + src1->view_offs;
+    cl_ulong offsetd = extrad->offset + dst->view_offs;
+
+    bool bcast_row = false;
+    cl_kernel kernel;
+
+    if (ggml_nelements(src1) == ne10 && ggml_is_contiguous(src1) && ne00 % 4 == 0 && ne10 % 4 == 0) {
+        GGML_ASSERT(ggml_is_contiguous(src0));
+
+        // src1 is a row
+        GGML_ASSERT(ne11 == 1);
+
+        bcast_row = true;
+        int ne = ne00 / 4;
+        kernel = backend_ctx->kernel_sub_row;
+
+        CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem),   &extra0->data_device));
+        CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_ulong), &offset0));
+        CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem),   &extra1->data_device));
+        CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &offset1));
+        CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_mem),   &extrad->data_device));
+        CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_ulong), &offsetd));
+        CL_CHECK(clSetKernelArg(kernel, 6, sizeof(int),      &ne));
+    } else {
+        kernel = backend_ctx->kernel_sub;
+
+        CL_CHECK(clSetKernelArg(kernel,  0, sizeof(cl_mem),   &extra0->data_device));
+        CL_CHECK(clSetKernelArg(kernel,  1, sizeof(cl_ulong), &offset0));
+        CL_CHECK(clSetKernelArg(kernel,  2, sizeof(cl_mem),   &extra1->data_device));
+        CL_CHECK(clSetKernelArg(kernel,  3, sizeof(cl_ulong), &offset1));
+        CL_CHECK(clSetKernelArg(kernel,  4, sizeof(cl_mem),   &extrad->data_device));
+        CL_CHECK(clSetKernelArg(kernel,  5, sizeof(cl_ulong), &offsetd));
+        CL_CHECK(clSetKernelArg(kernel,  6, sizeof(cl_ulong), &nb00));
+        CL_CHECK(clSetKernelArg(kernel,  7, sizeof(cl_ulong), &nb01));
+        CL_CHECK(clSetKernelArg(kernel,  8, sizeof(cl_ulong), &nb02));
+        CL_CHECK(clSetKernelArg(kernel,  9, sizeof(cl_ulong), &nb03));
+        CL_CHECK(clSetKernelArg(kernel, 10, sizeof(int),      &ne10));
+        CL_CHECK(clSetKernelArg(kernel, 11, sizeof(int),      &ne11));
+        CL_CHECK(clSetKernelArg(kernel, 12, sizeof(int),      &ne12));
+        CL_CHECK(clSetKernelArg(kernel, 13, sizeof(int),      &ne13));
+        CL_CHECK(clSetKernelArg(kernel, 14, sizeof(cl_ulong), &nb10));
+        CL_CHECK(clSetKernelArg(kernel, 15, sizeof(cl_ulong), &nb11));
+        CL_CHECK(clSetKernelArg(kernel, 16, sizeof(cl_ulong), &nb12));
+        CL_CHECK(clSetKernelArg(kernel, 17, sizeof(cl_ulong), &nb13));
+        CL_CHECK(clSetKernelArg(kernel, 18, sizeof(int),      &ne0));
+        CL_CHECK(clSetKernelArg(kernel, 19, sizeof(cl_ulong), &nb0));
+        CL_CHECK(clSetKernelArg(kernel, 20, sizeof(cl_ulong), &nb1));
+        CL_CHECK(clSetKernelArg(kernel, 21, sizeof(cl_ulong), &nb2));
+        CL_CHECK(clSetKernelArg(kernel, 22, sizeof(cl_ulong), &nb3));
+    }
+
+    if (bcast_row) {
+        int n = ggml_nelements(dst)/4;
+        size_t global_work_size[] = {(size_t)n, 1, 1};
+        size_t local_work_size[] = {64, 1, 1};
+
+#ifdef GGML_OPENCL_PROFILING
+        cl_event evt;
+        CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, &evt));
+
+        g_profiling_info.emplace_back();
+        populateProfilingInfo(g_profiling_info.back(), evt, kernel, global_work_size, local_work_size, dst);
+#else
+        CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, NULL));
+#endif
+    } else {
+        unsigned int nth = MIN(64, ne0);
+        size_t global_work_size[] = {ne01*nth, (size_t)ne02, (size_t)ne03};
+        size_t local_work_size[] = {nth, 1, 1};
+
+#ifdef GGML_OPENCL_PROFILING
+        cl_event evt;
+        CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, &evt));
+
+        g_profiling_info.emplace_back();
+        populateProfilingInfo(g_profiling_info.back(), evt, kernel, global_work_size, local_work_size, dst);
+#else
+        CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, NULL));
+#endif
+    }
+}
+
 static void ggml_cl_gelu(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
    GGML_ASSERT(src0);
    GGML_ASSERT(src0->extra);
@@ -3429,6 +3800,58 @@ static void ggml_cl_relu(ggml_backend_t backend, const ggml_tensor * src0, const
 #endif
 }

+static void ggml_cl_sigmoid(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_ASSERT(src0);
+    GGML_ASSERT(src0->extra);
+    GGML_ASSERT(dst);
+    GGML_ASSERT(dst->extra);
+
+    UNUSED(src1);
+
+    ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context;
+    cl_command_queue queue = backend_ctx->queue;
+
+    ggml_tensor_extra_cl * extra0 = (ggml_tensor_extra_cl *)src0->extra;
+    ggml_tensor_extra_cl * extrad = (ggml_tensor_extra_cl *)dst->extra;
+
+    cl_ulong offset0 = extra0->offset + src0->view_offs;
+    cl_ulong offsetd = extrad->offset + dst->view_offs;
+
+    cl_kernel kernel;
+    if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
+        kernel = backend_ctx->kernel_sigmoid_f32;
+    } else if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F16) {
+        kernel = backend_ctx->kernel_sigmoid_f16;
+    } else {
+        GGML_ASSERT(false && "Unsupported data types for sigmoid (input and output must be both f32 or f16)");
+    }
+
+    CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem),   &extra0->data_device));
+    CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_ulong), &offset0));
+    CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem),   &extrad->data_device));
+    CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &offsetd));
+
+    const int64_t n = ggml_nelements(dst);
+
+    size_t global_work_size[] = {(size_t)n, 1, 1};
+    size_t local_work_size[] = {64, 1, 1};
+
+    size_t * local_work_size_ptr = local_work_size;
+    if (n % 64 != 0 && !backend_ctx->non_uniform_workgroups) {
+        local_work_size_ptr = nullptr;  // Let driver choose the work-group sizes.
+    }
+
+#ifdef GGML_OPENCL_PROFILING
+    cl_event evt;
+    CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size_ptr, 0, NULL, &evt));
+
+    g_profiling_info.emplace_back();
+    populateProfilingInfo(g_profiling_info.back(), evt, kernel, global_work_size, local_work_size_ptr, dst);
+#else
+    CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size_ptr, 0, NULL, NULL));
+#endif
+}
+
 static void ggml_cl_clamp(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
    GGML_ASSERT(src0);
    GGML_ASSERT(src0->extra);
@@ -3626,6 +4049,65 @@ static void ggml_cl_rms_norm(ggml_backend_t backend, const ggml_tensor * src0, c
 #endif
 }

+static void ggml_cl_group_norm(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_ASSERT(src0);
+    GGML_ASSERT(src0->extra);
+    GGML_ASSERT(dst);
+    GGML_ASSERT(dst->extra);
+
+    UNUSED(src1);
+
+    ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context;
+    cl_command_queue queue = backend_ctx->queue;
+
+    ggml_tensor_extra_cl * extra0 = (ggml_tensor_extra_cl *)src0->extra;
+    ggml_tensor_extra_cl * extrad = (ggml_tensor_extra_cl *)dst->extra;
+
+    cl_ulong offset0 = extra0->offset + src0->view_offs;
+    cl_ulong offsetd = extrad->offset + dst->view_offs;
+
+    int32_t n_groups   = ((const int32_t *) dst->op_params)[0];
+    int32_t group_size = src0->ne[0] * src0->ne[1] * ((src0->ne[2] + n_groups - 1) / n_groups);
+    float   eps        = ((const float *) dst->op_params)[1];
+
+    const int ne00 = src0->ne[0];
+    const int ne01 = src0->ne[1];
+    const int ne02 = src0->ne[2];
+    const int ne = ne00*ne01*ne02;
+
+    cl_kernel kernel = backend_ctx->kernel_group_norm;
+
+    size_t sgs = 64;
+    if (backend_ctx->gpu_family == ADRENO) {
+        sgs = 64;
+    } else if (backend_ctx->gpu_family == INTEL) {
+        sgs = 32;
+    } else {
+        GGML_ASSERT(false && "Unsupported GPU");
+    }
+
+    CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem),   &extra0->data_device));
+    CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_ulong), &offset0));
+    CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem),   &extrad->data_device));
+    CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &offsetd));
+    CL_CHECK(clSetKernelArg(kernel, 4, sizeof(int),      &ne));
+    CL_CHECK(clSetKernelArg(kernel, 5, sizeof(int),      &group_size));
+    CL_CHECK(clSetKernelArg(kernel, 6, sizeof(float),    &eps));
+
+    size_t global_work_size[] = {(size_t)n_groups*sgs, 1, 1};
+    size_t local_work_size[] = {(size_t)sgs, 1, 1};
+
+#ifdef GGML_OPENCL_PROFILING
+    cl_event evt;
+    CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, &evt));
+
+    g_profiling_info.emplace_back();
+    populateProfilingInfo(g_profiling_info.back(), evt, kernel, global_work_size, local_work_size, dst);
+#else
+    CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, NULL));
+#endif
+}
+
 static void ggml_cl_mul_mat(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
    GGML_ASSERT(src0);
    GGML_ASSERT(src0->extra);
@@ -4975,6 +5457,124 @@ static void ggml_cl_im2col(ggml_backend_t backend, const ggml_tensor * src0, con
 #endif
 }

+static void ggml_cl_argsort(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_ASSERT(src0);
+    GGML_ASSERT(src0->extra);
+    GGML_ASSERT(dst);
+    GGML_ASSERT(dst->extra);
+    GGML_UNUSED(src1);
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_I32);
+    GGML_ASSERT(ggml_is_contiguous(src0));
+
+    ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context;
+    cl_command_queue queue = backend_ctx->queue;
+
+    ggml_tensor_extra_cl * extra0 = (ggml_tensor_extra_cl *)src0->extra;
+    ggml_tensor_extra_cl * extrad = (ggml_tensor_extra_cl *)dst->extra;
+
+    cl_ulong offset0 = extra0->offset + src0->view_offs;
+    cl_ulong offsetd = extrad->offset + dst->view_offs;
+
+    const int ne00  = src0->ne[0];
+    const int nrows = ggml_nrows(src0);
+
+    int ne00_padded = 1;
+    while (ne00_padded < ne00) {
+        ne00_padded *= 2;
+    }
+
+    int order = (enum ggml_sort_order) dst->op_params[0];
+
+    cl_kernel kernel = backend_ctx->kernel_argsort_f32_i32;
+
+    CL_CHECK(clSetKernelArg(kernel,   0, sizeof(cl_mem),            &extra0->data_device));
+    CL_CHECK(clSetKernelArg(kernel,   1, sizeof(cl_ulong),          &offset0));
+    CL_CHECK(clSetKernelArg(kernel,   2, sizeof(cl_mem),            &extrad->data_device));
+    CL_CHECK(clSetKernelArg(kernel,   3, sizeof(cl_ulong),          &offsetd));
+    CL_CHECK(clSetKernelArg(kernel,   4, sizeof(int),               &ne00));
+    CL_CHECK(clSetKernelArg(kernel,   5, sizeof(int),               &ne00_padded));
+    CL_CHECK(clSetKernelArg(kernel,   6, sizeof(int),               &order));
+    CL_CHECK(clSetKernelArg(kernel,   7, ne00_padded*sizeof(int),   NULL));
+
+    size_t global_work_size[] = {(size_t)ne00_padded, (size_t)nrows, (size_t)1};
+    size_t local_work_size[] = {(size_t)ne00_padded, 1, 1};
+
+#ifdef GGML_OPENCL_PROFILING
+    cl_event evt;
+    CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, &evt));
+
+    g_profiling_info.emplace_back();
+    populateProfilingInfo(g_profiling_info.back(), evt, kernel, global_work_size, local_work_size, dst);
+#else
+    CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, NULL));
+#endif
+}
+
+static void ggml_cl_sum_rows(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_ASSERT(src0);
+    GGML_ASSERT(src0->extra);
+    GGML_ASSERT(dst);
+    GGML_ASSERT(dst->extra);
+    GGML_UNUSED(src1);
+
+    GGML_ASSERT(src0->nb[0] == ggml_type_size(src0->type));
+    GGML_ASSERT(ggml_is_contiguous(src0));
+
+    ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context;
+    cl_command_queue queue = backend_ctx->queue;
+
+    ggml_tensor_extra_cl * extra0 = (ggml_tensor_extra_cl *)src0->extra;
+    ggml_tensor_extra_cl * extrad = (ggml_tensor_extra_cl *)dst->extra;
+
+    cl_ulong offset0 = extra0->offset + src0->view_offs;
+    cl_ulong offsetd = extrad->offset + dst->view_offs;
+
+    const int ne00 = src0->ne[0];
+    const int ne01 = src0->ne[1];
+    const int ne02 = src0->ne[2];
+    const int ne03 = src0->ne[3];
+
+    const cl_ulong nb01 = src0->nb[1];
+    const cl_ulong nb02 = src0->nb[2];
+    const cl_ulong nb03 = src0->nb[3];
+
+    const cl_ulong nb1  = dst->nb[1];
+    const cl_ulong nb2  = dst->nb[2];
+    const cl_ulong nb3  = dst->nb[3];
+
+    cl_kernel kernel = backend_ctx->kernel_sum_rows_f32;
+
+    CL_CHECK(clSetKernelArg(kernel,   0, sizeof(cl_mem),   &extra0->data_device));
+    CL_CHECK(clSetKernelArg(kernel,   1, sizeof(cl_ulong), &offset0));
+    CL_CHECK(clSetKernelArg(kernel,   2, sizeof(cl_mem),   &extrad->data_device));
+    CL_CHECK(clSetKernelArg(kernel,   3, sizeof(cl_ulong), &offsetd));
+    CL_CHECK(clSetKernelArg(kernel,   4, sizeof(int),      &ne00));
+    CL_CHECK(clSetKernelArg(kernel,   5, sizeof(int),      &ne01));
+    CL_CHECK(clSetKernelArg(kernel,   6, sizeof(int),      &ne02));
+    CL_CHECK(clSetKernelArg(kernel,   7, sizeof(int),      &ne03));
+    CL_CHECK(clSetKernelArg(kernel,   8, sizeof(cl_ulong), &nb01));
+    CL_CHECK(clSetKernelArg(kernel,   9, sizeof(cl_ulong), &nb02));
+    CL_CHECK(clSetKernelArg(kernel,  10, sizeof(cl_ulong), &nb03));
+    CL_CHECK(clSetKernelArg(kernel,  11, sizeof(cl_ulong), &nb1));
+    CL_CHECK(clSetKernelArg(kernel,  12, sizeof(cl_ulong), &nb2));
+    CL_CHECK(clSetKernelArg(kernel,  13, sizeof(cl_ulong), &nb3));
+
+    size_t global_work_size[] = {(size_t)ne01, (size_t)ne02, (size_t)ne03};
+    size_t local_work_size[] = {(size_t)64, 1, 1};
+
+#ifdef GGML_OPENCL_PROFILING
+    cl_event evt;
+    CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, &evt));
+
+    g_profiling_info.emplace_back();
+    populateProfilingInfo(g_profiling_info.back(), evt, kernel, global_work_size, local_work_size, dst);
+#else
+    CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, NULL));
+#endif
+}
+
 //------------------------------------------------------------------------------
 // Op offloading
 //------------------------------------------------------------------------------
@@ -5023,6 +5623,18 @@ bool ggml_cl_compute_forward(ggml_backend_t backend, struct ggml_tensor * tensor
            }
            func = ggml_cl_mul;
            break;
+        case GGML_OP_DIV:
+            if (!any_on_device) {
+                return false;
+            }
+            func = ggml_cl_div;
+            break;
+        case GGML_OP_SUB:
+            if (!any_on_device) {
+                return false;
+            }
+            func = ggml_cl_sub;
+            break;
        case GGML_OP_UNARY:
            switch (ggml_get_unary_op(tensor)) {
                case GGML_UNARY_OP_GELU:
@@ -5049,6 +5661,12 @@ bool ggml_cl_compute_forward(ggml_backend_t backend, struct ggml_tensor * tensor
                    }
                    func = ggml_cl_relu;
                    break;
+                case GGML_UNARY_OP_SIGMOID:
+                    if (!any_on_device) {
+                        return false;
+                    }
+                    func = ggml_cl_sigmoid;
+                    break;
                default:
                    return false;
            } break;
@@ -5070,6 +5688,12 @@ bool ggml_cl_compute_forward(ggml_backend_t backend, struct ggml_tensor * tensor
            }
            func = ggml_cl_rms_norm;
            break;
+        case GGML_OP_GROUP_NORM:
+            if (!any_on_device) {
+                return false;
+            }
+            func = ggml_cl_group_norm;
+            break;
        case GGML_OP_MUL_MAT:
            if (!any_on_device && !ggml_cl_can_mul_mat(tensor->src[0], tensor->src[1], tensor)) {
                return false;
@@ -5115,6 +5739,18 @@ bool ggml_cl_compute_forward(ggml_backend_t backend, struct ggml_tensor * tensor
            }
            func = ggml_cl_im2col;
            break;
+        case GGML_OP_ARGSORT:
+            if (!any_on_device) {
+                return false;
+            }
+            func = ggml_cl_argsort;
+            break;
+        case GGML_OP_SUM_ROWS:
+            if (!any_on_device) {
+                return false;
+            }
+            func = ggml_cl_sum_rows;
+            break;
        default:
            return false;
    }
@@ -0,0 +1,86 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+
+#ifdef cl_intel_subgroups
+#pragma OPENCL EXTENSION cl_intel_subgroups : enable
+#else
+#pragma OPENCL EXTENSION cl_khr_subgroups : enable
+#endif
+
+#ifdef cl_intel_required_subgroup_size
+#pragma OPENCL EXTENSION cl_intel_required_subgroup_size : enable
+#define INTEL_GPU 1
+#define REQD_SUBGROUP_SIZE_16 __attribute__((intel_reqd_sub_group_size(16)))
+#define REQD_SUBGROUP_SIZE_32 __attribute__((intel_reqd_sub_group_size(32)))
+#elif defined(cl_qcom_reqd_sub_group_size)
+#pragma OPENCL EXTENSION cl_qcom_reqd_sub_group_size : enable
+#define ADRENO_GPU 1
+#define REQD_SUBGROUP_SIZE_64  __attribute__((qcom_reqd_sub_group_size("half")))
+#define REQD_SUBGROUP_SIZE_128 __attribute__((qcom_reqd_sub_group_size("full")))
+#endif
+
+#define SWAP(x, y, T) { T tmp = (x); (x) = (y); (y) = tmp; }
+
+enum ggml_sort_order {
+    GGML_SORT_ORDER_ASC,
+    GGML_SORT_ORDER_DESC,
+};
+
+kernel void kernel_argsort_f32_i32(
+    global float * src0,
+    ulong          offset0,
+    global int   * dst,
+    ulong          offsetd,
+    const int      ne00,
+    const int      ne00_pad,
+    const int      order,
+    local int    * dst_row
+) {
+    // bitonic sort
+    int col = get_local_id(0);
+    int row = get_group_id(1);
+
+    if (col >= ne00_pad) {
+        return;
+    }
+
+    src0 = (global char  *)((global char *)src0 + offset0);
+    dst  = (global float *)((global char *)dst  + offsetd);
+
+    global float * x_row = src0 + row * ne00;
+
+    // initialize indices
+    dst_row[col] = col;
+
+    barrier(CLK_LOCAL_MEM_FENCE);
+
+    for (int k = 2; k <= ne00_pad; k *= 2) {
+        for (int j = k / 2; j > 0; j /= 2) {
+            int ixj = col ^ j;
+            if (ixj > col) {
+                if ((col & k) == 0) {
+                    if (dst_row[col] >= ne00 ||
+                        (dst_row[ixj] < ne00 && (order == GGML_SORT_ORDER_ASC ?
+                            x_row[dst_row[col]] > x_row[dst_row[ixj]] :
+                            x_row[dst_row[col]] < x_row[dst_row[ixj]]))
+                    ) {
+                        SWAP(dst_row[col], dst_row[ixj], int);
+                    }
+                } else {
+                    if (dst_row[ixj] >= ne00 ||
+                        (dst_row[col] < ne00 && (order == GGML_SORT_ORDER_ASC ?
+                            x_row[dst_row[col]] < x_row[dst_row[ixj]] :
+                            x_row[dst_row[col]] > x_row[dst_row[ixj]]))
+                    ) {
+                        SWAP(dst_row[col], dst_row[ixj], int);
+                    }
+                }
+            }
+            barrier(CLK_LOCAL_MEM_FENCE);
+        }
+    }
+
+    // copy the result to dst without the padding
+    if (col < ne00) {
+        dst[row * ne00 + col] = dst_row[col];
+    }
+}
@@ -0,0 +1,72 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+
+//------------------------------------------------------------------------------
+// div
+//------------------------------------------------------------------------------
+kernel void kernel_div(
+        global char * src0,
+        ulong offset0,
+        global char * src1,
+        ulong offset1,
+        global char * dst,
+        ulong offsetd,
+        ulong nb00,
+        ulong nb01,
+        ulong nb02,
+        ulong nb03,
+        int ne10,
+        int ne11,
+        int ne12,
+        int ne13,
+        ulong nb10,
+        ulong nb11,
+        ulong nb12,
+        ulong nb13,
+        int ne0,
+        ulong nb0,
+        ulong nb1,
+        ulong nb2,
+        ulong nb3
+) {
+    src0 = src0 + offset0;
+    src1 = src1 + offset1;
+    dst  = dst + offsetd;
+
+    int i03 = get_group_id(2);
+    int i02 = get_group_id(1);
+    int i01 = get_group_id(0);
+
+    int i13 = i03 % ne13;
+    int i12 = i02 % ne12;
+    int i11 = i01 % ne11;
+
+    global char * src0_ptr = src0 + i03*nb03 + i02*nb02 + i01*nb01;
+    global char * src1_ptr = src1 + i13*nb13 + i12*nb12 + i11*nb11;
+    global char * dst_ptr  = dst  + i03*nb3  + i02*nb2  + i01*nb1;
+
+    for (int i0 = get_local_id(0); i0 < ne0; i0 += get_local_size(0)) {
+        const int i10 = i0 % ne10;
+        *((global float *)(dst_ptr + i0*nb0)) = *((global float *)(src0_ptr + i0*nb00)) / *((global float *)(src1_ptr + i10*nb10));
+    }
+}
+
+// assumption: src1 is a row
+// broadcast src1 into src0
+kernel void kernel_div_row(
+        global float4 * src0,
+        ulong offset0,
+        global float4 * src1,
+        ulong offset1,
+        global float4 * dst,
+        ulong offsetd,
+        int ne
+) {
+    src0 = (global float4*)((global char*)src0 + offset0);
+    src1 = (global float4*)((global char*)src1 + offset1);
+    dst = (global float4*)((global char*)dst + offsetd);
+
+    // This performs better than using %.
+    uint gid = get_global_id(0);
+    uint idx1 = gid - (gid/ne)*ne; // get_global_id(0) % ne
+    dst[gid] = src0[gid] / src1[idx1];
+}
@@ -0,0 +1,72 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+
+#ifdef cl_intel_subgroups
+#pragma OPENCL EXTENSION cl_intel_subgroups : enable
+#else
+#pragma OPENCL EXTENSION cl_khr_subgroups : enable
+#endif
+
+#ifdef cl_intel_required_subgroup_size
+#pragma OPENCL EXTENSION cl_intel_required_subgroup_size : enable
+#define INTEL_GPU 1
+#define REQD_SUBGROUP_SIZE_16 __attribute__((intel_reqd_sub_group_size(16)))
+#define REQD_SUBGROUP_SIZE_32 __attribute__((intel_reqd_sub_group_size(32)))
+#elif defined(cl_qcom_reqd_sub_group_size)
+#pragma OPENCL EXTENSION cl_qcom_reqd_sub_group_size : enable
+#define ADRENO_GPU 1
+#define REQD_SUBGROUP_SIZE_64  __attribute__((qcom_reqd_sub_group_size("half")))
+#define REQD_SUBGROUP_SIZE_128 __attribute__((qcom_reqd_sub_group_size("full")))
+#endif
+
+// Workgroup must be a subgroup
+#ifdef INTEL_GPU
+REQD_SUBGROUP_SIZE_32
+#elif defined (ADRENO_GPU)
+REQD_SUBGROUP_SIZE_64
+#endif
+kernel void kernel_group_norm(
+        global float * src0,
+        ulong offset0,
+        global float * dst,
+        ulong offsetd,
+        int ne,
+        int group_size,
+        float eps
+) {
+    src0 = (global float  *)((global char *)src0 + offset0);
+    dst  = (global float *)((global char *)dst  + offsetd);
+
+    int start = get_group_id(0) * group_size;
+    int end   = start + group_size;
+
+    start += get_local_id(0);
+
+    if (end >= ne) {
+        end = ne;
+    }
+
+    float tmp = 0.0f;
+
+    for (int j = start; j < end; j += get_local_size(0)) {
+        tmp += src0[j];
+    }
+
+    tmp = sub_group_reduce_add(tmp);
+
+    const float mean = tmp / group_size;
+    tmp = 0.0f;
+
+    for (int j = start; j < end; j += get_local_size(0)) {
+        float xi = src0[j] - mean;
+        dst[j] = xi;
+        tmp += xi * xi;
+    }
+
+    tmp = sub_group_reduce_add(tmp);
+
+    const float variance = tmp / group_size;
+    const float scale = 1.0f/sqrt(variance + eps);
+    for (int j = start; j < end; j += get_local_size(0)) {
+        dst[j] *= scale;
+    }
+}
@@ -0,0 +1,29 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+
+//------------------------------------------------------------------------------
+// sigmoid
+//------------------------------------------------------------------------------
+
+kernel void kernel_sigmoid_f32(
+        global float * src0,
+        ulong offset0,
+        global float * dst,
+        ulong offsetd
+) {
+    src0 = (global float*)((global char*)src0 + offset0);
+    dst = (global float*)((global char*)dst + offsetd);
+
+    dst[get_global_id(0)] = 1.0f / (1.0f + exp(-src0[get_global_id(0)]));
+}
+
+kernel void kernel_sigmoid_f16(
+        global half * src0,
+        ulong offset0,
+        global half * dst,
+        ulong offsetd
+) {
+    src0 = (global half*)((global char*)src0 + offset0);
+    dst = (global half*)((global char*)dst + offsetd);
+
+    dst[get_global_id(0)] = 1.0f / (1.0f + exp(-src0[get_global_id(0)]));
+}
@@ -0,0 +1,72 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+
+//------------------------------------------------------------------------------
+// div
+//------------------------------------------------------------------------------
+kernel void kernel_sub(
+        global char * src0,
+        ulong offset0,
+        global char * src1,
+        ulong offset1,
+        global char * dst,
+        ulong offsetd,
+        ulong nb00,
+        ulong nb01,
+        ulong nb02,
+        ulong nb03,
+        int ne10,
+        int ne11,
+        int ne12,
+        int ne13,
+        ulong nb10,
+        ulong nb11,
+        ulong nb12,
+        ulong nb13,
+        int ne0,
+        ulong nb0,
+        ulong nb1,
+        ulong nb2,
+        ulong nb3
+) {
+    src0 = src0 + offset0;
+    src1 = src1 + offset1;
+    dst  = dst + offsetd;
+
+    int i03 = get_group_id(2);
+    int i02 = get_group_id(1);
+    int i01 = get_group_id(0);
+
+    int i13 = i03 % ne13;
+    int i12 = i02 % ne12;
+    int i11 = i01 % ne11;
+
+    global char * src0_ptr = src0 + i03*nb03 + i02*nb02 + i01*nb01;
+    global char * src1_ptr = src1 + i13*nb13 + i12*nb12 + i11*nb11;
+    global char * dst_ptr  = dst  + i03*nb3  + i02*nb2  + i01*nb1;
+
+    for (int i0 = get_local_id(0); i0 < ne0; i0 += get_local_size(0)) {
+        const int i10 = i0 % ne10;
+        *((global float *)(dst_ptr + i0*nb0)) = *((global float *)(src0_ptr + i0*nb00)) - *((global float *)(src1_ptr + i10*nb10));
+    }
+}
+
+// assumption: src1 is a row
+// broadcast src1 into src0
+kernel void kernel_sub_row(
+        global float4 * src0,
+        ulong offset0,
+        global float4 * src1,
+        ulong offset1,
+        global float4 * dst,
+        ulong offsetd,
+        int ne
+) {
+    src0 = (global float4*)((global char*)src0 + offset0);
+    src1 = (global float4*)((global char*)src1 + offset1);
+    dst = (global float4*)((global char*)dst + offsetd);
+
+    // This performs better than using %.
+    uint gid = get_global_id(0);
+    uint idx1 = gid - (gid/ne)*ne; // get_global_id(0) % ne
+    dst[gid] = src0[gid] - src1[idx1];
+}
@@ -0,0 +1,39 @@
+
+kernel void kernel_sum_rows_f32(
+    global float *  src0,
+    ulong           offset0,
+    global float *  dst,
+    ulong           offsetd,
+    int             ne00,
+    int             ne01,
+    int             ne02,
+    int             ne03,
+    ulong           nb01,
+    ulong           nb02,
+    ulong           nb03,
+    ulong           nb1,
+    ulong           nb2,
+    ulong           nb3
+) {
+    src0 = (global float *)((global char *)src0 + offset0);
+    dst  = (global float *)((global char *)dst  + offsetd);
+
+    int i3 = get_global_id(2);
+    int i2 = get_global_id(1);
+    int i1 = get_global_id(0);
+
+    if (i3 >= ne03 || i2 >= ne02 || i1 >= ne01) {
+        return;
+    }
+
+    global float * src_row = (global float *) ((global char *) src0 + i1*nb01 + i2*nb02 + i3*nb03);
+    global float * dst_row = (global float *) ((global char *) dst  + i1*nb1  + i2*nb2  + i3*nb3);
+
+    float row_sum = 0;
+
+    for (int i0 = 0; i0 < ne00; i0++) {
+        row_sum += src_row[i0];
+    }
+
+    dst_row[0] = row_sum;
+}
@@ -319,32 +319,27 @@ inline void ggml_sycl_op_repeat(ggml_backend_sycl_context & ctx, ggml_tensor *ds


 void ggml_sycl_add(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
-    GGML_SYCL_DEBUG("call %s\n", __func__);
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/2);
    ggml_sycl_op_add(ctx, dst);
-    GGML_SYCL_DEBUG("call %s done\n", __func__);
 }

 void ggml_sycl_sub(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
-    GGML_SYCL_DEBUG("call %s\n", __func__);
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/2);
    ggml_sycl_op_sub(ctx, dst);
-    GGML_SYCL_DEBUG("call %s done\n", __func__);
 }

 void ggml_sycl_mul(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
-    GGML_SYCL_DEBUG("call %s\n", __func__);
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/2);
    ggml_sycl_op_mul(ctx, dst);
-    GGML_SYCL_DEBUG("call %s done\n", __func__);
 }

 void ggml_sycl_div(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
-    GGML_SYCL_DEBUG("call %s\n", __func__);
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/2);
    ggml_sycl_op_div(ctx, dst);
-    GGML_SYCL_DEBUG("call %s done\n", __func__);
 }

 void ggml_sycl_repeat(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
-    GGML_SYCL_DEBUG("call %s\n", __func__);
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/1);
    ggml_sycl_op_repeat(ctx, dst);
-    GGML_SYCL_DEBUG("call %s done\n", __func__);
 }

@@ -13,8 +13,10 @@
 #ifndef GGML_SYCL_COMMON_HPP
 #define GGML_SYCL_COMMON_HPP

+#include <cstddef>
 #include <fstream>
 #include <iostream>
+#include <string>

 #include "dpct/helper.hpp"
 #include "ggml-sycl.h"
@@ -44,11 +46,20 @@ extern int g_ggml_sycl_debug;
 extern int g_ggml_sycl_disable_optimize;
 extern int g_ggml_sycl_prioritize_dmmv;

-#define GGML_SYCL_DEBUG(...)        \
-  do {                              \
-    if (g_ggml_sycl_debug)          \
-      fprintf(stderr, __VA_ARGS__); \
-  } while (0)
+#if defined(__clang__) && __has_builtin(__builtin_expect)
+// Hint the optimizer to pipeline the more likely following instruction in branches
+#    define LIKELY(expr)   __builtin_expect(expr, true)
+#    define UNLIKELY(expr) __builtin_expect(expr, false)
+#else
+#    define LIKELY(expr)   (expr)
+#    define UNLIKELY(expr) (expr)
+#endif
+
+#define GGML_SYCL_DEBUG(...)              \
+    do {                                  \
+        if (UNLIKELY(g_ggml_sycl_debug))  \
+            fprintf(stderr, __VA_ARGS__); \
+    } while (0)

 #define CHECK_TRY_ERROR(expr)                                            \
  [&]() {                                                                \
@@ -471,6 +482,19 @@ static __dpct_inline__ float warp_reduce_max(float x,
    return x;
 }

+/* Helper for Computing the linear offset of a ggml_tensor given
+per-dimension sizes, strides, and indices */
+template<int N>
+__dpct_inline__ size_t calculate_offset(const std::array<int, N> & strides, const std::array<int, N> & indices) {
+    size_t offset = 0;
+#pragma unroll
+    for (int i = 0; i < N; i++) {
+        auto index_i = indices[i];
+        offset += strides[i] * index_i;
+    }
+    return offset;
+}
+
 // Helper for vec loading aligned data
 template <typename Tp, int n>
 inline sycl::vec<Tp, n> vec_aligned_load(const Tp* aligned_ptr) {
@@ -490,4 +514,76 @@ constexpr size_t ceil_div(const size_t m, const size_t n) {
 }

 bool gpu_has_xmx(sycl::device &dev);
+
+template <int N, class T> void debug_print_array(const std::string & prefix, const T array[N]) {
+    if (LIKELY(!g_ggml_sycl_debug)) {
+        return;
+    }
+    std::stringstream ss;
+    ss << prefix << "=[";
+    for (std::size_t i = 0; i < N - 1; ++i) {
+        ss << array[i] << ", ";
+    }
+    if constexpr (N > 0) {
+        ss << array[N - 1];
+    }
+    ss << "]";
+    GGML_SYCL_DEBUG("%s", ss.str().c_str());
+}
+
+inline void debug_print_tensor(const std::string & prefix, const ggml_tensor * tensor,
+                               const std::string & suffix = "") {
+    if (LIKELY(!g_ggml_sycl_debug)) {
+        return;
+    }
+    GGML_SYCL_DEBUG("%s=", prefix.c_str());
+    if (tensor) {
+        GGML_SYCL_DEBUG("'%s':type=%s", tensor->name, ggml_type_name(tensor->type));
+        debug_print_array<GGML_MAX_DIMS>(";ne", tensor->ne);
+        debug_print_array<GGML_MAX_DIMS>(";nb", tensor->nb);
+        if (!ggml_is_contiguous(tensor)) {
+            GGML_SYCL_DEBUG(";strided");
+        }
+        if (ggml_is_permuted(tensor)) {
+            GGML_SYCL_DEBUG(";permuted");
+        }
+    } else {
+        GGML_SYCL_DEBUG("nullptr");
+    }
+    GGML_SYCL_DEBUG("%s", suffix.c_str());
+}
+
+// Use scope_op_debug_print to log operations coming from running a model
+struct scope_op_debug_print {
+    // Use string_views to avoid the cost of creating a string and concatenating them
+    // string_views must be alive for as long as the object is alive
+    // scope_op_debug_print are used with string literals in practice which are stored in constant space so always accessible
+    scope_op_debug_print(const std::string_view & func, const std::string_view & func_suffix, const ggml_tensor * dst,
+                         std::size_t num_src, const std::string_view & suffix = "") :
+        func(func),
+        func_suffix(func_suffix) {
+        if (LIKELY(!g_ggml_sycl_debug)) {
+            return;
+        }
+        GGML_SYCL_DEBUG("[SYCL][OP] call %s%s:", func.data(), func_suffix.data());
+        debug_print_tensor(" dst", dst);
+        if (dst) {
+            for (std::size_t i = 0; i < num_src; ++i) {
+                debug_print_tensor("\tsrc" + std::to_string(i), dst->src[i]);
+            }
+        }
+        GGML_SYCL_DEBUG("%s\n", suffix.data());
+    }
+
+    scope_op_debug_print(const std::string_view & func, const ggml_tensor * dst, std::size_t num_src,
+                         const std::string_view & suffix = "") :
+        scope_op_debug_print(func, "", dst, num_src, suffix) {}
+
+    ~scope_op_debug_print() { GGML_SYCL_DEBUG("[SYCL][OP] call %s%s done\n", func.data(), func_suffix.data()); }
+
+  private:
+    std::string_view func;
+    std::string_view func_suffix;
+};
+
 #endif // GGML_SYCL_COMMON_HPP
@@ -159,39 +159,37 @@ static void concat_f32_sycl_non_cont(
 }

 void ggml_sycl_op_concat(ggml_backend_sycl_context & ctx, ggml_tensor *dst) {
-  const ggml_tensor *src0 = dst->src[0];
-  const ggml_tensor *src1 = dst->src[1];
-  queue_ptr stream = ctx.stream();
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/2);
+    const ggml_tensor *  src0   = dst->src[0];
+    const ggml_tensor *  src1   = dst->src[1];
+    queue_ptr            stream = ctx.stream();

-  const int32_t dim = ((int32_t *)dst->op_params)[0];
+    const int32_t dim = ((int32_t *) dst->op_params)[0];

-  if (ggml_is_contiguous(src0) && ggml_is_contiguous(src1)) {
-    const float *src0_d = (const float *)src0->data;
-    const float *src1_d = (const float *)src1->data;
+    if (ggml_is_contiguous(src0) && ggml_is_contiguous(src1)) {
+        const float * src0_d = (const float *) src0->data;
+        const float * src1_d = (const float *) src1->data;

-    float *dst_d = (float *)dst->data;
+        float * dst_d = (float *) dst->data;

-    if (dim != 3) {
-      for (int i3 = 0; i3 < dst->ne[3]; i3++) {
-        concat_f32_sycl(
-            src0_d + i3 * (src0->nb[3] / 4), src1_d + i3 * (src1->nb[3] / 4),
-            dst_d + i3 * (dst->nb[3] / 4), src0->ne[0], src0->ne[1],
-            src0->ne[2], dst->ne[0], dst->ne[1], dst->ne[2], dim, stream);
-      }
+        if (dim != 3) {
+            for (int i3 = 0; i3 < dst->ne[3]; i3++) {
+                concat_f32_sycl(src0_d + i3 * (src0->nb[3] / 4), src1_d + i3 * (src1->nb[3] / 4),
+                                dst_d + i3 * (dst->nb[3] / 4), src0->ne[0], src0->ne[1], src0->ne[2], dst->ne[0],
+                                dst->ne[1], dst->ne[2], dim, stream);
+            }
+        } else {
+            const size_t size0 = ggml_nbytes(src0);
+            const size_t size1 = ggml_nbytes(src1);
+
+            SYCL_CHECK(CHECK_TRY_ERROR(stream->memcpy(dst_d, src0_d, size0).wait()));
+            SYCL_CHECK(CHECK_TRY_ERROR(stream->memcpy(dst_d + size0 / 4, src1_d, size1).wait()));
+        }
    } else {
-      const size_t size0 = ggml_nbytes(src0);
-      const size_t size1 = ggml_nbytes(src1);
-
-      SYCL_CHECK(CHECK_TRY_ERROR(stream->memcpy(dst_d, src0_d, size0).wait()));
-      SYCL_CHECK(CHECK_TRY_ERROR(
-          stream->memcpy(dst_d + size0 / 4, src1_d, size1).wait()));
+        concat_f32_sycl_non_cont(stream, (const char *) src0->data, (const char *) src1->data, (char *) dst->data,
+                                 src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3], src0->nb[0], src0->nb[1],
+                                 src0->nb[2], src0->nb[3], src1->ne[0], src1->ne[1], src1->ne[2], src1->ne[3],
+                                 src1->nb[0], src1->nb[1], src1->nb[2], src1->nb[3], dst->ne[0], dst->ne[1], dst->ne[2],
+                                 dst->ne[3], dst->nb[0], dst->nb[1], dst->nb[2], dst->nb[3], dim);
    }
-  } else
-    concat_f32_sycl_non_cont(
-        stream, (const char *)src0->data, (const char *)src1->data,
-        (char *)dst->data, src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3],
-        src0->nb[0], src0->nb[1], src0->nb[2], src0->nb[3], src1->ne[0],
-        src1->ne[1], src1->ne[2], src1->ne[3], src1->nb[0], src1->nb[1],
-        src1->nb[2], src1->nb[3], dst->ne[0], dst->ne[1], dst->ne[2],
-        dst->ne[3], dst->nb[0], dst->nb[1], dst->nb[2], dst->nb[3], dim);
 }
@@ -72,6 +72,7 @@ static void conv_transpose_1d_f32_f32_sycl(
 }

 void ggml_sycl_op_conv_transpose_1d(ggml_backend_sycl_context & ctx, ggml_tensor *dst) {
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/2);
    const ggml_tensor *src0 = dst->src[0];
    const ggml_tensor *src1 = dst->src[1];
    const float * src0_d = (const float *)src0->data;
@@ -616,6 +616,9 @@ static void ggml_cpy_i32_i32_sycl(const char * cx, char * cdst, const int ne, co
 }

 void ggml_sycl_cpy(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1) try {
+    // Unlike other operators ggml_sycl_cpy takes 2 distinct tensors instead of a dst ggml_tensor and rely on its src field
+    scope_op_debug_print scope_dbg_print(__func__, src1, /*num_src=*/0,
+                                         std::string(" src0 type=") + ggml_type_name(src0->type));
    const int64_t ne = ggml_nelements(src0);
    GGML_ASSERT(ne == ggml_nelements(src1));

@@ -629,8 +632,6 @@ void ggml_sycl_cpy(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, co

    char * src0_ddc = (char *) src0->data;
    char * src1_ddc = (char *) src1->data;
-    GGML_SYCL_DEBUG("[SYCL] %s: Tensor supplied: %s to %s\n", __func__, ggml_type_name(src0->type),
-                    ggml_type_name(src1->type));

    if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32) {
        ggml_cpy_f32_f32_sycl(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10,
@@ -694,8 +695,6 @@ void ggml_sycl_cpy(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, co
 }

 void ggml_sycl_dup(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
-    // TODO: why do we pass dst as src1 here?
-    GGML_SYCL_DEBUG("[SYCL] call %s\n", __func__);
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/1);
    ggml_sycl_cpy(ctx, dst->src[0], dst);
-    GGML_SYCL_DEBUG("[SYCL] call %s done\n", __func__);
 }
@@ -1092,6 +1092,8 @@ void ggml_sycl_op_dequantize_mul_mat_vec(
        src0->type == GGML_TYPE_Q8_0 || src0->type == GGML_TYPE_F16;

    if (src1_convert_f16) {
+        scope_op_debug_print scope_dbg_print(__func__, "/to_fp16_sycl", dst, /*num_src=*/2,
+                                             " : converting src1 to fp16");
        src1_dfloat = src1_dfloat_a.alloc(ne00);
        const to_fp16_sycl_t to_fp16_sycl = ggml_get_to_fp16_sycl(src1->type, dst);
        GGML_ASSERT(to_fp16_sycl != nullptr);
@@ -84,6 +84,15 @@ static void gelu_quick(const T *x, T *dst, int k,
    dst[i] = x[i] * (static_cast<T>(1.0f) / (static_cast<T>(1.0f) + sycl::native::exp(GELU_QUICK_COEF * x[i])));
 }

+template<typename T>
+static void gelu_erf(const T * x, T * dst, const int k, const sycl::nd_item<3> &item_ct1) {
+    const T SQRT_2_INV = static_cast<T>(0.70710678118654752440084436210484f);
+    for(auto i = item_ct1.get_global_id(2); i < (const size_t)k; i += item_ct1.get_global_range(2)) {
+       auto x_i = x[i];
+        dst[i] = static_cast<T>(0.5f) * x_i * (static_cast<T>(1.0f) + sycl::erf(x_i * SQRT_2_INV));
+    }
+}
+
 template<typename T>
 static void tanh(const T *x, T *dst, int k,
                     const sycl::nd_item<3> &item_ct1) {
@@ -400,6 +409,20 @@ static void gelu_quick_sycl(const T *x, T *dst, const int k,
        });
 }

+
+template<typename T>
+static void gelu_erf_sycl(const T *x, T *dst, const int k,
+                                queue_ptr stream) {
+    const int num_blocks = ceil_div(k, SYCL_GELU_BLOCK_SIZE);
+    stream->parallel_for(
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) *
+                              sycl::range<3>(1, 1, SYCL_GELU_BLOCK_SIZE),
+                          sycl::range<3>(1, 1, SYCL_GELU_BLOCK_SIZE)),
+        [=](sycl::nd_item<3> item_ct1) {
+            gelu_erf(x, dst, k, item_ct1);
+        });
+}
+
 template<typename T>
 static void tanh_sycl(const T *x, T *dst, const int k,
                          queue_ptr stream) {
@@ -816,6 +839,38 @@ inline void ggml_sycl_op_gelu_quick(ggml_backend_sycl_context & ctx, ggml_tensor
    }
 }

+inline void ggml_sycl_op_gelu_erf(ggml_backend_sycl_context & ctx, ggml_tensor *dst) {
+#if defined (GGML_SYCL_F16)
+    GGML_ASSERT(dst->src[0]->type == GGML_TYPE_F32 || dst->src[0]->type == GGML_TYPE_F16);
+    GGML_ASSERT(dst->type == GGML_TYPE_F32 || dst->type == GGML_TYPE_F16);
+#else
+    GGML_ASSERT(dst->src[0]->type == GGML_TYPE_F32);
+    GGML_ASSERT(dst->type == GGML_TYPE_F32);
+#endif
+    GGML_ASSERT(dst->src[0]->type == dst->type);
+    dpct::queue_ptr main_stream = ctx.stream();
+    SYCL_CHECK(ggml_sycl_set_device(ctx.device));
+    switch (dst->type) {
+#if defined (GGML_SYCL_F16)
+        case GGML_TYPE_F16:
+            {
+                auto data_pts = cast_data<sycl::half>(dst);
+                gelu_erf_sycl(data_pts.src, data_pts.dst, ggml_nelements(dst->src[0]), main_stream);
+                break;
+            }
+#endif
+        case GGML_TYPE_F32:
+            {
+                auto data_pts = cast_data<float>(dst);
+                gelu_erf_sycl(data_pts.src, data_pts.dst, ggml_nelements(dst->src[0]), main_stream);
+                break;
+            }
+        default:
+            GGML_ABORT("GGML tensor type not supported!\n");
+    }
+}
+
+
 inline void ggml_sycl_op_tanh(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
 #if defined (GGML_SYCL_F16)
    GGML_ASSERT(dst->src[0]->type == GGML_TYPE_F32 || dst->src[0]->type == GGML_TYPE_F16);
@@ -1391,146 +1446,126 @@ inline void ggml_sycl_op_acc(ggml_backend_sycl_context & ctx, ggml_tensor *dst)


 void ggml_sycl_sqrt(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
-    GGML_SYCL_DEBUG("call %s: DST Tensor type: %s\n", __func__, ggml_type_name(dst->type));
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/1);
    ggml_sycl_op_sqrt(ctx, dst);
-    GGML_SYCL_DEBUG("call %s done\n", __func__);
 }

 void ggml_sycl_sin(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
-    GGML_SYCL_DEBUG("call %s: DST Tensor type: %s\n", __func__, ggml_type_name(dst->type));
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/1);
    ggml_sycl_op_sin(ctx, dst);
-    GGML_SYCL_DEBUG("call %s done\n", __func__);
 }

 void ggml_sycl_cos(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
-    GGML_SYCL_DEBUG("call %s: DST Tensor type: %s\n", __func__, ggml_type_name(dst->type));
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/1);
    ggml_sycl_op_cos(ctx, dst);
-    GGML_SYCL_DEBUG("call %s done\n", __func__);
 }

 void ggml_sycl_acc(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
-    GGML_SYCL_DEBUG("call %s: DST Tensor type: %s\n", __func__, ggml_type_name(dst->type));
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/2);
    ggml_sycl_op_acc(ctx, dst);
-    GGML_SYCL_DEBUG("call %s done\n", __func__);
 }

 void ggml_sycl_gelu(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
-    GGML_SYCL_DEBUG("call %s: DST Tensor type: %s\n", __func__, ggml_type_name(dst->type));
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/1);
    ggml_sycl_op_gelu(ctx, dst);
-    GGML_SYCL_DEBUG("call %s done\n", __func__);
 }

 void ggml_sycl_silu(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
-    GGML_SYCL_DEBUG("call %s: DST Tensor type: %s\n", __func__, ggml_type_name(dst->type));
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/1);
    ggml_sycl_op_silu(ctx, dst);
-    GGML_SYCL_DEBUG("call %s done\n", __func__);
 }

 void ggml_sycl_gelu_quick(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
-    GGML_SYCL_DEBUG("call %s: DST Tensor type: %s\n", __func__, ggml_type_name(dst->type));
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/1);
    ggml_sycl_op_gelu_quick(ctx, dst);
-    GGML_SYCL_DEBUG("call %s done\n", __func__);
+}
+
+void ggml_sycl_gelu_erf(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/1);
+    ggml_sycl_op_gelu_erf(ctx, dst);
 }

 void ggml_sycl_tanh(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
-    GGML_SYCL_DEBUG("call %s: DST Tensor type: %s\n", __func__, ggml_type_name(dst->type));
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/1);
    ggml_sycl_op_tanh(ctx, dst);
-    GGML_SYCL_DEBUG("call %s done\n", __func__);
 }

 void ggml_sycl_relu(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
-    GGML_SYCL_DEBUG("call %s: DST Tensor type: %s\n", __func__, ggml_type_name(dst->type));
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/1);
    ggml_sycl_op_relu(ctx, dst);
-    GGML_SYCL_DEBUG("call %s done\n", __func__);
 }

 void ggml_sycl_sigmoid(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
-    GGML_SYCL_DEBUG("call %s: DST Tensor type: %s\n", __func__, ggml_type_name(dst->type));
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/1);
    ggml_sycl_op_sigmoid(ctx, dst);
-    GGML_SYCL_DEBUG("call %s done\n", __func__);
 }

 void ggml_sycl_hardsigmoid(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
-    GGML_SYCL_DEBUG("call %s: DST Tensor type: %s\n", __func__, ggml_type_name(dst->type));
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/1);
    ggml_sycl_op_hardsigmoid(ctx, dst);
-    GGML_SYCL_DEBUG("call %s done\n", __func__);
 }

 void ggml_sycl_hardswish(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
-    GGML_SYCL_DEBUG("call %s: DST Tensor type: %s\n", __func__, ggml_type_name(dst->type));
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/1);
    ggml_sycl_op_hardswish(ctx, dst);
-    GGML_SYCL_DEBUG("call %s done\n", __func__);
 }

-
 void ggml_sycl_exp(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
-    GGML_SYCL_DEBUG("call %s: DST Tensor type: %s\n", __func__, ggml_type_name(dst->type));
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/1);
    ggml_sycl_op_exp(ctx, dst);
-    GGML_SYCL_DEBUG("call %s done\n", __func__);
 }

 void ggml_sycl_log(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
-    GGML_SYCL_DEBUG("call %s: DST Tensor type: %s\n", __func__, ggml_type_name(dst->type));
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/1);
    ggml_sycl_op_log(ctx, dst);
-    GGML_SYCL_DEBUG("call %s done\n", __func__);
 }

 void ggml_sycl_neg(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
-    GGML_SYCL_DEBUG("call %s: DST Tensor type: %s\n", __func__, ggml_type_name(dst->type));
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/1);
    ggml_sycl_op_neg(ctx, dst);
-    GGML_SYCL_DEBUG("call %s done\n", __func__);
 }

 void ggml_sycl_step(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
-    GGML_SYCL_DEBUG("call %s: DST Tensor type: %s\n", __func__, ggml_type_name(dst->type));
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/1);
    ggml_sycl_op_step(ctx, dst);
-    GGML_SYCL_DEBUG("call %s done\n", __func__);
 }

 void ggml_sycl_leaky_relu(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
-    GGML_SYCL_DEBUG("call %s: DST Tensor type: %s\n", __func__, ggml_type_name(dst->type));
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/1);
    ggml_sycl_op_leaky_relu(ctx, dst);
-    GGML_SYCL_DEBUG("call %s done\n", __func__);
 }

 void ggml_sycl_sqr(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
-    GGML_SYCL_DEBUG("call %s: DST Tensor type: %s\n", __func__, ggml_type_name(dst->type));
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/1);
    ggml_sycl_op_sqr(ctx, dst);
-    GGML_SYCL_DEBUG("call %s done\n", __func__);
 }

 void ggml_sycl_upscale(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
-    GGML_SYCL_DEBUG("call %s: DST Tensor type: %s\n", __func__, ggml_type_name(dst->type));
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/1);
    ggml_sycl_op_upscale(ctx, dst);
-    GGML_SYCL_DEBUG("call %s done\n", __func__);
 }

 void ggml_sycl_pad(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
-    GGML_SYCL_DEBUG("call %s: DST Tensor type: %s\n", __func__, ggml_type_name(dst->type));
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/1);
    ggml_sycl_op_pad(ctx, dst);
-    GGML_SYCL_DEBUG("call %s done\n", __func__);
 }

 void ggml_sycl_clamp(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
-    GGML_SYCL_DEBUG("call %s: DST Tensor type: %s\n", __func__, ggml_type_name(dst->type));
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/1);
    ggml_sycl_op_clamp(ctx, dst);
-    GGML_SYCL_DEBUG("call %s done\n", __func__);
 }

 void ggml_sycl_sgn(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
-    GGML_SYCL_DEBUG("call %s: DST Tensor type: %s\n", __func__, ggml_type_name(dst->type));
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/1);
    ggml_sycl_op_sgn(ctx, dst);
-    GGML_SYCL_DEBUG("call %s done\n", __func__);
 }

 void ggml_sycl_abs(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
-    GGML_SYCL_DEBUG("call %s: DST Tensor type: %s\n", __func__, ggml_type_name(dst->type));
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/1);
    ggml_sycl_op_abs(ctx, dst);
-    GGML_SYCL_DEBUG("call %s done\n", __func__);
 }

 void ggml_sycl_elu(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
-    GGML_SYCL_DEBUG("call %s: DST Tensor type: %s\n", __func__, ggml_type_name(dst->type));
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/1);
    ggml_sycl_op_elu(ctx, dst);
-    GGML_SYCL_DEBUG("call %s done\n", __func__);
 }
@@ -38,6 +38,8 @@ void ggml_sycl_silu(ggml_backend_sycl_context & ctx, ggml_tensor * dst);

 void ggml_sycl_gelu_quick(ggml_backend_sycl_context & ctx, ggml_tensor * dst);

+void ggml_sycl_gelu_erf(ggml_backend_sycl_context & ctx, ggml_tensor * dst);
+
 void ggml_sycl_tanh(ggml_backend_sycl_context & ctx, ggml_tensor * dst);

 void ggml_sycl_relu(ggml_backend_sycl_context & ctx, ggml_tensor * dst);
@@ -257,8 +257,7 @@ static void get_rows_sycl_float(ggml_backend_sycl_context & ctx, const ggml_tens
    GGML_UNUSED(ctx);
 }

-void ggml_sycl_op_get_rows(ggml_backend_sycl_context & ctx, ggml_tensor *dst) {
-
+void ggml_sycl_op_get_rows(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
    GGML_ASSERT(dst->src[1]->type == GGML_TYPE_I32);
    GGML_ASSERT(dst->type == GGML_TYPE_F32);

@@ -308,4 +307,3 @@ void ggml_sycl_op_get_rows(ggml_backend_sycl_context & ctx, ggml_tensor *dst) {
            GGML_ABORT("fatal error");
    }
 }
-
@@ -346,6 +346,8 @@ static void * ggml_backend_sycl_buffer_get_base(ggml_backend_buffer_t buffer) {
 static enum ggml_status
 ggml_backend_sycl_buffer_init_tensor(ggml_backend_buffer_t buffer,
                                     ggml_tensor *tensor) try {
+    GGML_SYCL_DEBUG("[SYCL] call %s", __func__);
+    debug_print_tensor(": tensor=", tensor, "\n");
    ggml_backend_sycl_buffer_context * ctx = (ggml_backend_sycl_buffer_context *)buffer->context;

    if (tensor->view_src != NULL) {
@@ -381,7 +383,9 @@ static void ggml_backend_sycl_buffer_set_tensor(ggml_backend_buffer_t buffer,
                                                ggml_tensor *tensor,
                                                const void *data, size_t offset,
                                                size_t size) try {
-
+    GGML_SYCL_DEBUG("[SYCL] call %s", __func__);
+    debug_print_tensor(": tensor=", tensor);
+    GGML_SYCL_DEBUG(" size=%zu offset=%zu\n", size, offset);
    ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context;
    ggml_sycl_set_device(ctx->device);
    auto stream = &(dpct::dev_mgr::instance().get_device(ctx->device).default_queue());
@@ -407,7 +411,9 @@ static void ggml_backend_sycl_buffer_get_tensor(ggml_backend_buffer_t buffer,
                                                const ggml_tensor *tensor,
                                                void *data, size_t offset,
                                                size_t size) try {
-
+    GGML_SYCL_DEBUG("[SYCL] call %s", __func__);
+    debug_print_tensor(": tensor=", tensor);
+    GGML_SYCL_DEBUG(" size=%zu offset=%zu\n", size, offset);
    ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context;

    ggml_sycl_set_device(ctx->device);
@@ -435,7 +441,12 @@ static bool
 ggml_backend_sycl_buffer_cpy_tensor(ggml_backend_buffer_t buffer,
                                    const ggml_tensor *src,
                                    ggml_tensor *dst) try {
-    if (ggml_backend_buffer_is_sycl(src->buffer)) {
+    bool is_cpy_supported = ggml_backend_buffer_is_sycl(src->buffer);
+    GGML_SYCL_DEBUG("[SYCL] call %s", __func__);
+    debug_print_tensor(": dst=", dst);
+    debug_print_tensor(" src=", src);
+    GGML_SYCL_DEBUG(" is_cpy_supported=%d\n", is_cpy_supported);
+    if (is_cpy_supported) {
        ggml_backend_sycl_buffer_context * src_ctx = (ggml_backend_sycl_buffer_context *)src->buffer->context;
        ggml_backend_sycl_buffer_context * dst_ctx = (ggml_backend_sycl_buffer_context *)dst->buffer->context;

@@ -492,7 +503,8 @@ ggml_backend_sycl_buffer_cpy_tensor(ggml_backend_buffer_t buffer,

 static void ggml_backend_sycl_buffer_clear(ggml_backend_buffer_t buffer,
                                           uint8_t value) try {
-     ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context;
+    GGML_SYCL_DEBUG("[SYCL] call %s: size=%zu\n", __func__, buffer->size);
+    ggml_backend_sycl_buffer_context * ctx = (ggml_backend_sycl_buffer_context *) buffer->context;

    ggml_sycl_set_device(ctx->device);
    queue_ptr stream = ctx->stream;
@@ -511,7 +523,9 @@ catch (sycl::exception const &exc) {

 static void ggml_backend_sycl_buffer_memset_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, uint8_t value,
                                                   size_t offset, size_t size) {
-    GGML_SYCL_DEBUG(" [SYCL] call %s\n", __func__);
+    GGML_SYCL_DEBUG("[SYCL] call %s", __func__);
+    debug_print_tensor(": tensor=", tensor);
+    GGML_SYCL_DEBUG(" size=%zu offset=%zu value=%u\n", size, offset, value);
    ggml_backend_sycl_buffer_context * ctx = (ggml_backend_sycl_buffer_context *) buffer->context;
    SYCL_CHECK(ggml_sycl_set_device(ctx->device));
    auto stream = &(dpct::dev_mgr::instance().get_device(ctx->device).default_queue());
@@ -789,6 +803,8 @@ static void * ggml_backend_sycl_split_buffer_get_base(ggml_backend_buffer_t buff
 static enum ggml_status
 ggml_backend_sycl_split_buffer_init_tensor(ggml_backend_buffer_t buffer,
                                           ggml_tensor *tensor) try {
+    GGML_SYCL_DEBUG("[SYCL] call %s", __func__);
+    debug_print_tensor(": tensor=", tensor, "\n");
    GGML_ASSERT(tensor->view_src == nullptr); // views of split tensors are not supported

    ggml_backend_sycl_split_buffer_context * ctx = (ggml_backend_sycl_split_buffer_context *)buffer->context;
@@ -873,6 +889,9 @@ static void
 ggml_backend_sycl_split_buffer_set_tensor(ggml_backend_buffer_t buffer,
                                          ggml_tensor *tensor, const void *data,
                                          size_t offset, size_t size) try {
+    GGML_SYCL_DEBUG("[SYCL] call %s", __func__);
+    debug_print_tensor(": tensor=", tensor);
+    GGML_SYCL_DEBUG(" size=%zu offset=%zu\n", size, offset);
    // split tensors must always be set in their entirety at once
    GGML_ASSERT(offset == 0);
    GGML_ASSERT(size == ggml_nbytes(tensor));
@@ -926,6 +945,9 @@ static void
 ggml_backend_sycl_split_buffer_get_tensor(ggml_backend_buffer_t buffer,
                                          const ggml_tensor *tensor, void *data,
                                          size_t offset, size_t size) try {
+    GGML_SYCL_DEBUG("[SYCL] call %s", __func__);
+    debug_print_tensor(": tensor=", tensor);
+    GGML_SYCL_DEBUG(" size=%zu offset=%zu\n", size, offset);
    // split tensors must always be set in their entirety at once
    GGML_ASSERT(offset == 0);
    GGML_ASSERT(size == ggml_nbytes(tensor));
@@ -2015,12 +2037,12 @@ inline void ggml_sycl_op_mul_mat_sycl(
 #else
    bool use_fp16 = false;
 #endif
-    if ((src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type)) &&
-        use_fp16 && ggml_is_contiguous(src0) && row_diff == src0->ne[1] &&
-        dst->op_params[0] == GGML_PREC_DEFAULT) {
-        // GGML_SYCL_DEBUG("ggml_sycl_op_mul_mat_sycl - fp16 path\n");
+    if ((src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type)) && use_fp16 && ggml_is_contiguous(src0) &&
+        row_diff == src0->ne[1] && dst->op_params[0] == GGML_PREC_DEFAULT) {
        ggml_sycl_pool_alloc<sycl::half> src0_as_f16(ctx.pool());
        if (src0->type != GGML_TYPE_F16) {
+            scope_op_debug_print scope_dbg_print(__func__, "/to_fp16_sycl", dst, /*num_src=*/2,
+                                                 " : converting src0 to fp16");
            const to_fp16_sycl_t to_fp16_sycl = ggml_get_to_fp16_sycl(src0->type, dst);
            GGML_ASSERT(to_fp16_sycl != nullptr);
            size_t ne = row_diff*ne00;
@@ -2033,6 +2055,8 @@ inline void ggml_sycl_op_mul_mat_sycl(

        ggml_sycl_pool_alloc<sycl::half> src1_as_f16(ctx.pool());
        if (src1->type != GGML_TYPE_F16) {
+            scope_op_debug_print scope_dbg_print(__func__, "/to_fp16_sycl", dst, /*num_src=*/2,
+                                                 " : converting src1 to fp16");
            const to_fp16_sycl_t to_fp16_sycl = ggml_get_to_fp16_sycl(src1->type, dst);
            GGML_ASSERT(to_fp16_sycl != nullptr);
            size_t ne = src1_ncols*ne10;
@@ -2049,6 +2073,8 @@ inline void ggml_sycl_op_mul_mat_sycl(
            DnnlGemmWrapper::row_gemm(ctx, src1_ncols, row_diff, ne10, src1_ptr,
                                      DnnlGemmWrapper::to_dt<sycl::half>(), src0_ptr, DnnlGemmWrapper::to_dt<sycl::half>(),
                                      dst_f16.get(), DnnlGemmWrapper::to_dt<sycl::half>(), stream);
+            scope_op_debug_print scope_dbg_print(__func__, "/to_fp32_sycl", dst, /*num_src=*/2,
+                                                 " : converting dst to fp32");
            const to_fp32_sycl_t to_fp32_sycl = ggml_get_to_fp32_sycl(GGML_TYPE_F16, dst);
            to_fp32_sycl(dst_f16.get(), dst_dd_i, row_diff* src1_ncols, stream);
        }
@@ -2064,21 +2090,25 @@ inline void ggml_sycl_op_mul_mat_sycl(
                src1_ptr, dpct::library_data_t::real_half, ne10, &beta_f16,
                dst_f16.get(), dpct::library_data_t::real_half, ldc,
                dpct::library_data_t::real_half)));
+            scope_op_debug_print scope_dbg_print(__func__, "/to_fp32_sycl", dst, /*num_src=*/2,
+                                                 " : converting dst to fp32");
            const to_fp32_sycl_t to_fp32_sycl = ggml_get_to_fp32_sycl(GGML_TYPE_F16, dst);
            to_fp32_sycl(dst_f16.get(), dst_dd_i, row_diff*src1_ncols, stream);
        }
-    }
-    else {
-        // GGML_SYCL_DEBUG("ggml_sycl_op_mul_mat_sycl - fp32 path\n");
+    } else {
        ggml_sycl_pool_alloc<float> src0_ddq_as_f32(ctx.pool());
        ggml_sycl_pool_alloc<float> src1_ddq_as_f32(ctx.pool());
        if (src0->type != GGML_TYPE_F32) {
+            scope_op_debug_print scope_dbg_print(__func__, "/to_fp32_sycl", dst, /*num_src=*/2,
+                                                 " : converting src0 to fp32");
            const to_fp32_sycl_t to_fp32_sycl = ggml_get_to_fp32_sycl(src0->type, dst);
            GGML_ASSERT(to_fp32_sycl != nullptr);
            src0_ddq_as_f32.alloc(row_diff*ne00);
            to_fp32_sycl(src0_dd_i, src0_ddq_as_f32.get(), row_diff*ne00, stream);
        }
        if (src1->type != GGML_TYPE_F32) {
+            scope_op_debug_print scope_dbg_print(__func__, "/to_fp32_sycl", dst, /*num_src=*/2,
+                                                 " : converting src1 to fp32");
            const to_fp32_sycl_t to_fp32_sycl = ggml_get_to_fp32_sycl(src1->type, dst);
            GGML_ASSERT(to_fp32_sycl != nullptr);
            src1_ddq_as_f32.alloc(src1_ncols*ne10);
@@ -2114,8 +2144,7 @@ catch (sycl::exception const &exc) {
  std::exit(1);
 }

-static void ggml_sycl_op_pool2d(ggml_backend_sycl_context & ctx, ggml_tensor *dst) {
-
+static void ggml_sycl_op_pool2d(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
    GGML_ASSERT(dst->src[0]->type == GGML_TYPE_F32);
    GGML_ASSERT( dst->type == GGML_TYPE_F32);
    dpct::queue_ptr main_stream = ctx.stream();
@@ -2167,8 +2196,7 @@ inline void ggml_sycl_op_sum(ggml_backend_sycl_context & ctx, ggml_tensor *dst)
    sum_rows_f32_sycl(src0_dd, dst_dd, ne, 1, main_stream);
 }

-inline void ggml_sycl_op_sum_rows(ggml_backend_sycl_context & ctx, ggml_tensor *dst) {
-
+inline void ggml_sycl_op_sum_rows(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
    GGML_ASSERT(dst->src[0]->type == GGML_TYPE_F32);
    GGML_ASSERT( dst->type == GGML_TYPE_F32);
    dpct::queue_ptr main_stream = ctx.stream();
@@ -2199,8 +2227,7 @@ inline void ggml_sycl_op_argsort(ggml_backend_sycl_context & ctx, ggml_tensor *
    argsort_f32_i32_sycl(src0_dd, (int *) dst_dd, ncols, nrows, order, main_stream);
 }

-inline void ggml_sycl_op_argmax(ggml_backend_sycl_context & ctx, ggml_tensor *dst) {
-
+inline void ggml_sycl_op_argmax(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
    GGML_ASSERT(dst->src[0]->type == GGML_TYPE_F32);
    GGML_ASSERT( dst->type == GGML_TYPE_I32);

@@ -2215,8 +2242,7 @@ inline void ggml_sycl_op_argmax(ggml_backend_sycl_context & ctx, ggml_tensor *ds
    argmax_f32_i32_sycl(src0_dd, dst_dd, ncols, nrows, main_stream);
 }

-inline void ggml_sycl_op_diag_mask_inf(ggml_backend_sycl_context & ctx,ggml_tensor *dst) {
-
+inline void ggml_sycl_op_diag_mask_inf(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
    GGML_ASSERT(dst->src[0]->type == GGML_TYPE_F32);
    GGML_ASSERT( dst->type == GGML_TYPE_F32);
    dpct::queue_ptr main_stream = ctx.stream();
@@ -2233,8 +2259,7 @@ inline void ggml_sycl_op_diag_mask_inf(ggml_backend_sycl_context & ctx,ggml_tens
    diag_mask_inf_f32_sycl(src0_dd, dst_dd, ne00, nrows0, ne01, n_past, main_stream);
 }

-inline void ggml_sycl_op_scale(ggml_backend_sycl_context & ctx, ggml_tensor *dst) {
-
+inline void ggml_sycl_op_scale(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
    GGML_ASSERT(dst->src[0]->type == GGML_TYPE_F32);
    GGML_ASSERT( dst->type == GGML_TYPE_F32);
    dpct::queue_ptr main_stream = ctx.stream();
@@ -2421,6 +2446,8 @@ static void ggml_sycl_op_mul_mat(ggml_backend_sycl_context & ctx, const ggml_ten
            dev[i].src1_ddq = dev[i].src1_ddq_alloc.alloc(ctx.pool(i), nrows1*src1_padded_col_size*q8_1_ts/q8_1_bs);

            if (src1_on_device && src1_is_contiguous) {
+                scope_op_debug_print scope_dbg_print(__func__, "/quantize_row_q8_1_sycl", dst,
+                                                     /*num_src=*/2, " : converting src1 to Q8_1");
                quantize_row_q8_1_sycl(dev[i].src1_ddf, dev[i].src1_ddq, ne10, nrows1, src1_padded_col_size, stream);
                /*
                DPCT1010:90: SYCL uses exceptions to report errors and does not
@@ -2525,6 +2552,8 @@ static void ggml_sycl_op_mul_mat(ggml_backend_sycl_context & ctx, const ggml_ten
                }

                if (convert_src1_to_q8_1 && !src1_is_contiguous) {
+                    scope_op_debug_print scope_dbg_print(__func__, "/quantize_row_q8_1_sycl", dst,
+                                                         /*num_src=*/2, " : converting src1 to Q8_1");
                    quantize_row_q8_1_sycl(src1_ddf_i, src1_ddq_i, ne10, src1_ncols, src1_padded_col_size, stream);
                    /*
                    DPCT1010:92: SYCL uses exceptions to report errors and does
@@ -2619,33 +2648,28 @@ catch (sycl::exception const &exc) {


 static void ggml_sycl_get_rows(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
-    GGML_SYCL_DEBUG("call %s\n", __func__);
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/2);
    ggml_sycl_op_get_rows(ctx, dst);
-    GGML_SYCL_DEBUG("call %s done\n", __func__);
 }

 static void ggml_sycl_norm(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
-    GGML_SYCL_DEBUG("call %s\n", __func__);
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/1);
    ggml_sycl_op_norm(ctx, dst);
-    GGML_SYCL_DEBUG("call %s done\n", __func__);
 }

 static void ggml_sycl_rms_norm(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
-    GGML_SYCL_DEBUG("call %s\n", __func__);
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/1);
    ggml_sycl_op_rms_norm(ctx, dst);
-    GGML_SYCL_DEBUG("call %s done\n", __func__);
 }

 static void ggml_sycl_l2_norm(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
-    GGML_SYCL_DEBUG("call %s\n", __func__);
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/1);
    ggml_sycl_op_l2_norm(ctx, dst);
-    GGML_SYCL_DEBUG("call %s done\n", __func__);
 }

 static void ggml_sycl_group_norm(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
-    GGML_SYCL_DEBUG("call %s\n", __func__);
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/1);
    ggml_sycl_op_group_norm(ctx, dst);
-    GGML_SYCL_DEBUG("call %s done\n", __func__);
 }

 static void ggml_sycl_mul_mat_vec_p021(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
@@ -2773,6 +2797,8 @@ static void ggml_sycl_mul_mat_batched_sycl(ggml_backend_sycl_context & ctx, cons

    // convert src1 to fp16
    if (src1->type != GGML_TYPE_F16) {
+        scope_op_debug_print    scope_dbg_print(__func__, "/to_fp16_nc_sycl", dst, /*num_src=*/2,
+                                                " : converting src1 to fp16");
        const to_fp16_nc_sycl_t to_fp16_nc_sycl = get_to_fp16_nc_sycl(src1->type);
        GGML_ASSERT(to_fp16_nc_sycl != nullptr);
        const int64_t ne_src1 = ggml_nelements(src1);
@@ -3076,6 +3102,7 @@ static bool can_use_mul_mat_vec_q(const ggml_tensor * src0, const ggml_tensor *
 }

 static void ggml_sycl_mul_mat(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/2);
    const bool split = ggml_backend_buffer_is_sycl_split(src0->buffer);
    int64_t min_compute_capability = INT_MAX;

@@ -3153,7 +3180,6 @@ static void ggml_sycl_mul_mat(ggml_backend_sycl_context & ctx, const ggml_tensor
        constexpr bool convert_src1_to_q8_1 = false;
        ggml_sycl_op_mul_mat(ctx, src0, src1, dst, ggml_sycl_op_mul_mat_sycl, convert_src1_to_q8_1);
    }
-    GGML_SYCL_DEBUG("call %s done\n", __func__);
 }


@@ -3224,6 +3250,7 @@ __dpct_inline__ static void k_copy_dst_from_contiguous(

 static void ggml_sycl_mul_mat_id(ggml_backend_sycl_context & ctx,
                                 ggml_tensor *dst) try {
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/3);
    const ggml_tensor *src0 = dst->src[0];
    const ggml_tensor *src1 = dst->src[1];
    GGML_ASSERT(!ggml_backend_buffer_is_sycl_split(src0->buffer) && "mul_mat_id does not support split buffers");
@@ -3392,37 +3419,45 @@ catch (sycl::exception const &exc) {
 }

 static void ggml_sycl_scale(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/1);
    ggml_sycl_op_scale(ctx, dst);
 }

 static void ggml_sycl_diag_mask_inf(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/1);
    ggml_sycl_op_diag_mask_inf(ctx, dst);
 }

 static void ggml_sycl_pool2d(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/1);
    ggml_sycl_op_pool2d(ctx, dst);
 }

 static void ggml_sycl_im2col(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/2);
    ggml_sycl_op_im2col(ctx, dst);
 }

 static void ggml_sycl_sum(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/1);
    GGML_ASSERT(ggml_is_contiguous(dst->src[0]));
    ggml_sycl_op_sum(ctx, dst);
 }

 static void ggml_sycl_sum_rows(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/1);
    GGML_ASSERT(ggml_is_contiguous(dst->src[0]));
    ggml_sycl_op_sum_rows(ctx, dst);
 }

 static void ggml_sycl_argsort(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/1);
    GGML_ASSERT(ggml_is_contiguous(dst->src[0]));
    ggml_sycl_op_argsort(ctx, dst);
 }

 static void ggml_sycl_argmax(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/1);
    GGML_ASSERT(ggml_is_contiguous(dst->src[0]));
    ggml_sycl_op_argmax(ctx, dst);
 }
@@ -3508,6 +3543,9 @@ static bool ggml_sycl_compute_forward(ggml_backend_sycl_context & ctx, struct gg
                case GGML_UNARY_OP_GELU_QUICK:
                    ggml_sycl_gelu_quick(ctx, dst);
                    break;
+                case GGML_UNARY_OP_GELU_ERF:
+                    ggml_sycl_gelu_erf(ctx, dst);
+                    break;
                case GGML_UNARY_OP_TANH:
                    ggml_sycl_tanh(ctx, dst);
                    break;
@@ -3716,6 +3754,9 @@ static void ggml_backend_sycl_set_tensor_async(ggml_backend_t backend,
                                               ggml_tensor *tensor,
                                               const void *data, size_t offset,
                                               size_t size) try {
+    GGML_SYCL_DEBUG("[SYCL] call %s", __func__);
+    debug_print_tensor(": tensor=", tensor);
+    GGML_SYCL_DEBUG(" size=%zu offset=%zu\n", size, offset);
    ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context;
    ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer;

@@ -3734,6 +3775,9 @@ static void ggml_backend_sycl_get_tensor_async(ggml_backend_t backend,
                                               const ggml_tensor *tensor,
                                               void *data, size_t offset,
                                               size_t size) try {
+    GGML_SYCL_DEBUG("[SYCL] call %s", __func__);
+    debug_print_tensor(": tensor=", tensor);
+    GGML_SYCL_DEBUG(" size=%zu offset=%zu\n", size, offset);
    ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context;
    ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer;

@@ -3752,7 +3796,13 @@ static bool ggml_backend_sycl_cpy_tensor_async(ggml_backend_t backend,
                                               const ggml_tensor *src,
                                               ggml_tensor *dst) try {
    ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context;
-    if (dst->buffer->buft == ggml_backend_sycl_buffer_type(sycl_ctx->device) && ggml_backend_buffer_is_sycl(src->buffer)) {
+    bool is_cpy_supported                = dst->buffer->buft == ggml_backend_sycl_buffer_type(sycl_ctx->device) &&
+                            ggml_backend_buffer_is_sycl(src->buffer);
+    GGML_SYCL_DEBUG("[SYCL] call %s", __func__);
+    debug_print_tensor(": dst=", dst);
+    debug_print_tensor(" src=", src);
+    GGML_SYCL_DEBUG(" is_cpy_supported=%d\n", is_cpy_supported);
+    if (is_cpy_supported) {
        /*
        DPCT1009:215: SYCL uses exceptions to report errors and does not use the
        error codes. The original code was commented out and a warning string
@@ -3773,6 +3823,7 @@ catch (sycl::exception const &exc) {
 }

 static void ggml_backend_sycl_synchronize(ggml_backend_t backend) try {
+    GGML_SYCL_DEBUG("[SYCL] call %s\n", __func__);
    ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context;
    const queue_ptr stream = sycl_ctx->stream(sycl_ctx->device, 0);
    SYCL_CHECK(CHECK_TRY_ERROR((stream)->wait()));
@@ -3906,7 +3957,7 @@ catch (sycl::exception const &exc)
 }

 static void ggml_backend_sycl_event_wait(ggml_backend_t backend, ggml_backend_event_t event) try {
-
+    GGML_SYCL_DEBUG("[SYCL] call %s\n", __func__);
    sycl::event* sycl_event = static_cast<sycl::event*>(event->context);

    if (ggml_backend_is_sycl(backend)) {
@@ -4048,6 +4099,7 @@ static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const g
                case GGML_UNARY_OP_HARDSIGMOID:
                case GGML_UNARY_OP_HARDSWISH:
                case GGML_UNARY_OP_GELU_QUICK:
+                case GGML_UNARY_OP_GELU_ERF:
                case GGML_UNARY_OP_TANH:
                case GGML_UNARY_OP_EXP:
                case GGML_UNARY_OP_SGN:
@@ -4193,6 +4245,7 @@ static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const g
 #endif
        case GGML_OP_NORM:
        case GGML_OP_RMS_NORM:
+            return true;
        case GGML_OP_L2_NORM:
        case GGML_OP_GROUP_NORM:
            return ggml_is_contiguous(op->src[0]);
@@ -4301,6 +4354,7 @@ static void ggml_backend_sycl_device_event_free(ggml_backend_dev_t dev, ggml_bac

 static void ggml_backend_sycl_device_event_synchronize(ggml_backend_dev_t dev, ggml_backend_event_t event) try {
  GGML_UNUSED(dev);
+  GGML_SYCL_DEBUG("[SYCL] call %s\n", __func__);

  sycl::event *sycl_event = static_cast<sycl::event *>(event->context);
  SYCL_CHECK(CHECK_TRY_ERROR(sycl_event->wait()));
@@ -76,6 +76,7 @@ static void gated_linear_attn_f32_kernel(const dpct::queue_ptr stream, u_int B,
 }

 void ggml_sycl_op_gated_linear_attn(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/5);
    const float * k_d  = static_cast<const float *>(dst->src[0]->data);
    const float * v_d  = static_cast<const float *>(dst->src[1]->data);
    const float * r_d  = static_cast<const float *>(dst->src[2]->data);
@@ -1059,8 +1059,10 @@ void ggml_sycl_op_mul_mat_vec_q(ggml_backend_sycl_context & ctx, const ggml_tens
            case GGML_TYPE_Q4_K:
                if ((ggml_tensor_extra_gpu *) dst->src[0]->extra &&
                    ((ggml_tensor_extra_gpu *) dst->src[0]->extra)->optimized_feature.reorder) {
+                    GGML_SYCL_DEBUG("Calling reorder_mul_mat_vec_q4_k_q8_1_sycl\n");
                    reorder_mul_mat_vec_q4_k_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
                } else {
+                    GGML_SYCL_DEBUG("Calling mul_mat_vec_q4_K_q8_1_sycl\n");
                    mul_mat_vec_q4_K_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
                }
                break;
@@ -1,40 +1,50 @@
 #include "norm.hpp"
+#include "ggml-sycl/common.hpp"
+#include "ggml-sycl/presets.hpp"

-static void norm_f32(const float* x, float* dst, const int ncols, const float eps,
-    const sycl::nd_item<3>& item_ct1, sycl::float2* s_sum, int block_size) {
-    const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) +
-        item_ct1.get_local_id(1);
-    const int tid = item_ct1.get_local_id(2);
+static void norm_f32(const float* x, float* dst, const int ncols, const int64_t stride_row, const int64_t stride_channel,
+        const int64_t stride_sample, const float eps, const sycl::nd_item<3>& item_ct1, sycl::float2* s_sum, int block_size) {
+
+    const int nrows = item_ct1.get_group_range(2);
+    const int nchannels = item_ct1.get_group_range(1);

    const int nthreads = item_ct1.get_local_range(2);
+    const int sample  = item_ct1.get_group(0);
+    const int channel = item_ct1.get_group(1);
+    const int row     = item_ct1.get_group(2);
+
+    const int tid = item_ct1.get_local_id(2);
    const int nwarps = nthreads / WARP_SIZE;
+
+    const auto strided_offset = calculate_offset<3>({stride_sample, stride_channel, stride_row}, {sample, channel, row});
+    const auto packed_offset = calculate_offset<3>({nchannels * nrows * ncols, nrows * ncols, ncols}, {sample, channel, row});
+
+    x += strided_offset;
+    dst += packed_offset;
+
    sycl::float2 mean_var = sycl::float2(0.f, 0.f);

    for (int col = tid; col < ncols; col += block_size) {
-        const float xi = x[row * ncols + col];
+        const float xi = x[col];
        mean_var.x() += xi;
        mean_var.y() += xi * xi;
    }

    // sum up partial sums
    mean_var = warp_reduce_sum(mean_var, item_ct1);
-    if (block_size > WARP_SIZE) {
-
-        int warp_id = item_ct1.get_local_id(2) / WARP_SIZE;
-        int lane_id = item_ct1.get_local_id(2) % WARP_SIZE;
-        if (lane_id == 0) {
-            s_sum[warp_id] = mean_var;
+    if  (block_size > WARP_SIZE) {
+        const auto sub_group = item_ct1.get_sub_group();
+        const auto sg_id = sub_group.get_group_linear_id();
+        const auto wi_in_sg = sub_group.get_local_linear_id();
+        if (wi_in_sg == 0) {
+            s_sum[sg_id] = mean_var;
        }
-        /*
-        DPCT1118:0: SYCL group functions and algorithms must be encountered in
-        converged control flow. You may need to adjust the code.
-        */
        item_ct1.barrier(sycl::access::fence_space::local_space);
        mean_var = 0.f;
-        size_t nreduce = nwarps / WARP_SIZE;
+        const size_t nreduce = ceil_div(nwarps, WARP_SIZE);
        for (size_t i = 0; i < nreduce; i += 1)
        {
-            mean_var += s_sum[lane_id + i * WARP_SIZE];
+            mean_var += s_sum[wi_in_sg + i * WARP_SIZE];
        }
        mean_var = warp_reduce_sum(mean_var, item_ct1);
    }
@@ -44,7 +54,7 @@ static void norm_f32(const float* x, float* dst, const int ncols, const float ep
    const float inv_std = sycl::rsqrt(var + eps);

    for (int col = tid; col < ncols; col += block_size) {
-        dst[row * ncols + col] = (x[row * ncols + col] - mean) * inv_std;
+        dst[col] = (x[col] - mean) * inv_std;
    }
 }

@@ -135,39 +145,51 @@ static void group_norm_f32(const float* x, float* dst, const int group_size, con
    }
 }

-static void rms_norm_f32(const float* x, float* dst, const int ncols, const float eps,
-    const sycl::nd_item<3>& item_ct1, float* s_sum, int block_size) {
-    const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) +
-        item_ct1.get_local_id(1);
-    const int tid = item_ct1.get_local_id(2);
+static void rms_norm_f32(const float* x, float* dst, const int ncols, const int64_t stride_row, const int64_t stride_channel,
+        const int64_t stride_sample, const float eps, const sycl::nd_item<3>& item_ct1, float* s_sum, int block_size) {
+
+    const int nrows = item_ct1.get_group_range(2);
+    const int nchannels = item_ct1.get_group_range(1);
+
+    const int sample  = item_ct1.get_group(0);
+    const int channel = item_ct1.get_group(1);
+    const int row     = item_ct1.get_group(2);
+
    const int nthreads = item_ct1.get_local_range(2);
+
+    const int tid = item_ct1.get_local_id(2);
    const int nwarps = nthreads / WARP_SIZE;
+
+    const auto strided_offset = calculate_offset<3>({stride_sample, stride_channel, stride_row}, {sample, channel, row});
+    const auto packed_offset = calculate_offset<3>({nchannels * nrows * ncols, nrows * ncols, ncols}, {sample, channel, row});
+
+    x   += strided_offset;
+    dst += packed_offset;
+
+
    float tmp = 0.0f; // partial sum for thread in warp

    for (int col = tid; col < ncols; col += block_size) {
-        const float xi = x[row * ncols + col];
+        const float xi = x[col];
        tmp += xi * xi;
    }

    // sum up partial sums
    tmp = warp_reduce_sum(tmp, item_ct1);
    if (block_size > WARP_SIZE) {
-
-        int warp_id = item_ct1.get_local_id(2) / WARP_SIZE;
-        int lane_id = item_ct1.get_local_id(2) % WARP_SIZE;
-        if (lane_id == 0) {
-            s_sum[warp_id] = tmp;
+        const auto sub_group = item_ct1.get_sub_group();
+        const auto sg_id = sub_group.get_group_linear_id();
+        const auto wi_in_sg = sub_group.get_local_linear_id();
+        if (wi_in_sg == 0) {
+            s_sum[sg_id] = tmp;
        }
-        /*
-        DPCT1118:3: SYCL group functions and algorithms must be encountered in
-        converged control flow. You may need to adjust the code.
-        */
+
        item_ct1.barrier(sycl::access::fence_space::local_space);
-        size_t nreduce = nwarps / WARP_SIZE;
+        const size_t nreduce = ceil_div(nwarps, WARP_SIZE);
        tmp = 0.f;
        for (size_t i = 0; i < nreduce; i += 1)
        {
-            tmp += s_sum[lane_id + i * WARP_SIZE];
+            tmp += s_sum[wi_in_sg + i * WARP_SIZE];
        }
        tmp = warp_reduce_sum(tmp, item_ct1);
    }
@@ -176,7 +198,7 @@ static void rms_norm_f32(const float* x, float* dst, const int ncols, const floa
    const float scale = sycl::rsqrt(mean + eps);

    for (int col = tid; col < ncols; col += block_size) {
-        dst[row * ncols + col] = scale * x[row * ncols + col];
+        dst[col] = scale * x[col];
    }
 }

@@ -224,20 +246,20 @@ static void l2_norm_f32(const float* x, float* dst, const int ncols, const float
    }
 }

-static void norm_f32_sycl(const float* x, float* dst, const int ncols,
-    const int nrows, const float eps,
-    queue_ptr stream, int device) {
+static void norm_f32_sycl(const float * x, float * dst, const int ncols, const int nrows, const int nchannels, const int nsamples,
+        const int64_t stride_row, const int64_t stride_channel, const int64_t stride_sample,
+        const float eps, queue_ptr stream, int device) {
+
+    const sycl::range<3> global_dims(nsamples, nchannels, nrows);
    GGML_ASSERT(ncols % WARP_SIZE == 0);
    if (ncols < 1024) {
        const sycl::range<3> block_dims(1, 1, WARP_SIZE);
        stream->submit([&](sycl::handler& cgh) {
            cgh.parallel_for(
-                sycl::nd_range<3>(sycl::range<3>(1, 1, nrows) * block_dims,
-                    block_dims),
+                sycl::nd_range<3>(global_dims * block_dims, block_dims),
                [=](sycl::nd_item<3> item_ct1)
                [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
-                    norm_f32(x, dst, ncols, eps, item_ct1,
-                        nullptr, WARP_SIZE);
+                    norm_f32(x, dst, ncols, stride_row, stride_channel, stride_sample, eps, item_ct1, nullptr, WARP_SIZE);
                });
            });
    }
@@ -252,15 +274,12 @@ static void norm_f32_sycl(const float* x, float* dst, const int ncols,
        */
        stream->submit([&](sycl::handler& cgh) {
            sycl::local_accessor<sycl::float2, 1> s_sum_acc_ct1(
-                sycl::range<1>(work_group_size / WARP_SIZE), cgh);
-
+                            sycl::range<1>(work_group_size / WARP_SIZE), cgh);
            cgh.parallel_for(
-                sycl::nd_range<3>(sycl::range<3>(1, 1, nrows) * block_dims,
-                    block_dims),
+                sycl::nd_range<3>(global_dims * block_dims, block_dims),
                [=](sycl::nd_item<3> item_ct1)
                [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
-                    norm_f32(x, dst, ncols, eps, item_ct1,
-                        get_pointer(s_sum_acc_ct1), work_group_size);
+                    norm_f32(x, dst, ncols, stride_row, stride_channel, stride_sample, eps, item_ct1, get_pointer(s_sum_acc_ct1), work_group_size);
                });
            });
    }
@@ -313,21 +332,20 @@ static void group_norm_f32_sycl(const float* x, float* dst,
    }
 }

-static void rms_norm_f32_sycl(const float* x, float* dst, const int ncols,
-    const int nrows, const float eps,
-    queue_ptr stream, int device) {
+static void rms_norm_f32_sycl(const float* x, float* dst, const int ncols, const int nrows, const int nchannels, const int nsamples,
+        const int64_t stride_row, const int64_t stride_channel, const int64_t stride_sample, const float eps, queue_ptr stream, int device) {
    GGML_ASSERT(ncols % WARP_SIZE == 0);
    // printf("%s ncols=%d, nrows=%d, WARP_SIZE=%d\n", __func__, ncols, nrows, WARP_SIZE);
+
+    const sycl::range<3> global_dims(nsamples, nchannels, nrows);
    if (ncols < 1024) {
        const sycl::range<3> block_dims(1, 1, WARP_SIZE);
        stream->submit([&](sycl::handler& cgh) {
            cgh.parallel_for(
-                sycl::nd_range<3>(sycl::range<3>(1, 1, nrows) * block_dims,
-                    block_dims),
+                sycl::nd_range<3>(global_dims * block_dims, block_dims),
                [=](sycl::nd_item<3> item_ct1)
                [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
-                    rms_norm_f32(x, dst, ncols, eps, item_ct1,
-                        nullptr, WARP_SIZE);
+                    rms_norm_f32(x, dst, ncols, stride_row, stride_channel, stride_sample, eps, item_ct1, nullptr, WARP_SIZE);
                });
            });
    }
@@ -344,12 +362,10 @@ static void rms_norm_f32_sycl(const float* x, float* dst, const int ncols,
            sycl::local_accessor<float, 1> s_sum_acc_ct1(sycl::range<1>(work_group_size / WARP_SIZE),
                cgh);
            cgh.parallel_for(
-                sycl::nd_range<3>(sycl::range<3>(1, 1, nrows) * block_dims,
-                    block_dims),
+                sycl::nd_range<3>(global_dims * block_dims, block_dims),
                [=](sycl::nd_item<3> item_ct1)
                [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
-                    rms_norm_f32(x, dst, ncols, eps, item_ct1,
-                        get_pointer(s_sum_acc_ct1), work_group_size);
+                    rms_norm_f32(x, dst, ncols, stride_row, stride_channel, stride_sample, eps, item_ct1, get_pointer(s_sum_acc_ct1), work_group_size);
                });
            });
    }
@@ -398,12 +414,12 @@ static void l2_norm_f32_sycl(const float* x, float* dst, const int ncols,
 }

 void ggml_sycl_op_norm(ggml_backend_sycl_context& ctx, ggml_tensor* dst) {
+    const ggml_tensor * src0 = dst->src[0];

    GGML_ASSERT(dst->src[0]->type == GGML_TYPE_F32);
    GGML_ASSERT(dst->type == GGML_TYPE_F32);

-    const int64_t ne00 = dst->src[0]->ne[0];
-    const int64_t nrows = ggml_nrows(dst->src[0]);
+    GGML_TENSOR_UNARY_OP_LOCALS
    dpct::queue_ptr main_stream = ctx.stream();
    SYCL_CHECK(ggml_sycl_set_device(ctx.device));
    const float * src0_dd = static_cast<const float *>(dst->src[0]->data);
@@ -411,8 +427,14 @@ void ggml_sycl_op_norm(ggml_backend_sycl_context& ctx, ggml_tensor* dst) {

    float eps;
    memcpy(&eps, dst->op_params, sizeof(float));
+    GGML_ASSERT(eps >= 0.0f);
+    const size_t ts0 = ggml_type_size(src0->type);
+    GGML_ASSERT(nb00 == ts0);
+    const int64_t s01 = nb01 / ts0;
+    const int64_t s02 = nb02 / ts0;
+    const int64_t s03 = nb03 / ts0;

-    norm_f32_sycl(src0_dd, dst_dd, ne00, nrows, eps, main_stream, ctx.device);
+    norm_f32_sycl(src0_dd, dst_dd, ne00, ne01, ne02, ne03, s01, s02, s03, eps, main_stream, ctx.device);
 }

 void ggml_sycl_op_group_norm(ggml_backend_sycl_context& ctx, ggml_tensor* dst) {
@@ -436,11 +458,10 @@ void ggml_sycl_op_group_norm(ggml_backend_sycl_context& ctx, ggml_tensor* dst) {

 void ggml_sycl_op_rms_norm(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {

+    const ggml_tensor * src0 = dst->src[0];
    GGML_ASSERT(dst->src[0]->type == GGML_TYPE_F32);
    GGML_ASSERT(dst->type == GGML_TYPE_F32);

-    const int64_t ne00 = dst->src[0]->ne[0];
-    const int64_t nrows = ggml_nrows(dst->src[0]);
    dpct::queue_ptr main_stream = ctx.stream();
    SYCL_CHECK(ggml_sycl_set_device(ctx.device));

@@ -450,7 +471,13 @@ void ggml_sycl_op_rms_norm(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
    float eps;
    memcpy(&eps, dst->op_params, sizeof(float));

-    rms_norm_f32_sycl(src0_dd, dst_dd, ne00, nrows, eps, main_stream, ctx.device);
+    GGML_TENSOR_UNARY_OP_LOCALS
+    const size_t ts0 = ggml_type_size(src0->type);
+    GGML_ASSERT(nb00 == ts0);
+    const int64_t s01 = nb01 / ts0;
+    const int64_t s02 = nb02 / ts0;
+    const int64_t s03 = nb03 / ts0;
+    rms_norm_f32_sycl(src0_dd, dst_dd, ne00, ne01, ne02, ne03, s01, s02, s03, eps, main_stream, ctx.device);
 }

 void ggml_sycl_op_l2_norm(ggml_backend_sycl_context& ctx, ggml_tensor* dst) {
@@ -1,6 +1,7 @@
 #include "outprod.hpp"

 void ggml_sycl_op_out_prod(ggml_backend_sycl_context& ctx, ggml_tensor* dst) {
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/2);
    const ggml_tensor *src0 = dst->src[0];
    const ggml_tensor *src1 = dst->src[1];

@@ -355,8 +355,7 @@ inline void ggml_sycl_op_rope(ggml_backend_sycl_context & ctx, ggml_tensor *dst)
 }

 void ggml_sycl_rope(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
-    GGML_SYCL_DEBUG("call %s\n", __func__);
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/3);
    ggml_sycl_op_rope(ctx, dst);
-    GGML_SYCL_DEBUG("call %s done\n", __func__);
 }

@@ -225,7 +225,7 @@ static void soft_max_f32_sycl(const float * x, const T * mask,
 }

 void ggml_sycl_op_soft_max(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
-
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/2);
    GGML_ASSERT(dst->src[0]->type == GGML_TYPE_F32);
    GGML_ASSERT( dst->type == GGML_TYPE_F32);

@@ -249,16 +249,13 @@ void ggml_sycl_op_soft_max(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {

    if (dst->src[1] && dst->src[1]->type == GGML_TYPE_F16) {
        const sycl::half * src1_dd = static_cast<sycl::half *>(dst->src[1]->data);
-        GGML_SYCL_DEBUG("%s: F16 mask\n", __func__);
        soft_max_f32_sycl<sycl::half>(src0_dd, src1_dd, dst_dd, ne00, nrows_x, nrows_y, scale, max_bias,
                          main_stream, ctx.device);
    } else if (dst->src[1] && dst->src[1]->type == GGML_TYPE_F32) {
        const float * src1_dd = static_cast<const float *>(dst->src[1]->data);
-        GGML_SYCL_DEBUG("%s: F32 mask\n", __func__);
        soft_max_f32_sycl<float>(src0_dd, src1_dd, dst_dd, ne00, nrows_x, nrows_y, scale, max_bias, main_stream, ctx.device);
    } else {
        /* mask unavailable */
-        GGML_SYCL_DEBUG("%s: No mask\n", __func__);
        soft_max_f32_sycl<float>(src0_dd, nullptr, dst_dd, ne00, nrows_x, nrows_y, scale, max_bias, main_stream, ctx.device);
    }
 }
@@ -56,8 +56,8 @@ static void timestep_embedding_f32_sycl(
 }

 void ggml_sycl_op_timestep_embedding(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
-    const ggml_tensor *src0 = dst->src[0];
-    const ggml_tensor *src1 = dst->src[1];
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/1);
+    const ggml_tensor *  src0   = dst->src[0];
    const float * src0_d = (const float *)src0->data;
    float * dst_d = (float *)dst->data;
    dpct::queue_ptr stream = ctx.stream();
@@ -69,5 +69,4 @@ void ggml_sycl_op_timestep_embedding(ggml_backend_sycl_context & ctx, ggml_tenso
    const int max_period = dst->op_params[1];

    timestep_embedding_f32_sycl(src0_d, dst_d, src0->ne[0], dst->nb[1], dim, max_period, stream);
-    GGML_UNUSED(src1);
 }
@@ -180,10 +180,7 @@ static void rwkv_wkv7_f32_kernel(
 }

 void ggml_sycl_op_rwkv_wkv6(ggml_backend_sycl_context& ctx, ggml_tensor* dst) {
-
-    const ggml_tensor *src0 = dst->src[0];
-    const ggml_tensor *src1 = dst->src[1];
-
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/6);
    const float* k_d = (const float*)dst->src[0]->data;
    const float* v_d = (const float*)dst->src[1]->data;
    const float* r_d = (const float*)dst->src[2]->data;
@@ -236,16 +233,10 @@ void ggml_sycl_op_rwkv_wkv6(ggml_backend_sycl_context& ctx, ggml_tensor* dst) {
                });
        });
    }
-
-    GGML_UNUSED(src0);
-    GGML_UNUSED(src1);
 }

 void ggml_sycl_op_rwkv_wkv7(ggml_backend_sycl_context& ctx, ggml_tensor* dst) {
-
-    const ggml_tensor *src0 = dst->src[0];
-    const ggml_tensor *src1 = dst->src[1];
-
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/7);
    const float* r_d = (const float*)dst->src[0]->data;
    const float* w_d = (const float*)dst->src[1]->data;
    const float* k_d = (const float*)dst->src[2]->data;
@@ -299,7 +290,4 @@ void ggml_sycl_op_rwkv_wkv7(ggml_backend_sycl_context& ctx, ggml_tensor* dst) {
                });
        });
    }
-
-    GGML_UNUSED(src0);
-    GGML_UNUSED(src1);
 }
@@ -109,10 +109,6 @@ if (Vulkan_FOUND)
        add_compile_definitions(GGML_VULKAN_SHADER_DEBUG_INFO)
    endif()

-    if (GGML_VULKAN_PERF)
-        add_compile_definitions(GGML_VULKAN_PERF)
-    endif()
-
    if (GGML_VULKAN_VALIDATE)
        add_compile_definitions(GGML_VULKAN_VALIDATE)
    endif()
@@ -1,6 +1,6 @@
 #include "ggml-vulkan.h"
 #include <vulkan/vulkan_core.h>
-#if defined(GGML_VULKAN_RUN_TESTS) || defined(GGML_VULKAN_PERF) || defined(GGML_VULKAN_CHECK_RESULTS)
+#if defined(GGML_VULKAN_RUN_TESTS) || defined(GGML_VULKAN_CHECK_RESULTS)
 #include <chrono>
 #include "ggml-cpu.h"
 #endif
@@ -184,9 +184,7 @@ static ggml_backend_buffer_type_i ggml_backend_vk_buffer_type_interface = {
 #ifdef GGML_VULKAN_MEMORY_DEBUG
 class vk_memory_logger;
 #endif
-#ifdef GGML_VULKAN_PERF
 class vk_perf_logger;
-#endif
 static void ggml_vk_destroy_buffer(vk_buffer& buf);

 static constexpr uint32_t mul_mat_vec_max_cols = 8;
@@ -442,9 +440,11 @@ struct vk_device_struct {
 #ifdef GGML_VULKAN_MEMORY_DEBUG
    std::unique_ptr<vk_memory_logger> memory_logger;
 #endif
-#ifdef GGML_VULKAN_PERF
+
+    // for GGML_VK_PERF_LOGGER
    std::unique_ptr<vk_perf_logger> perf_logger;
-#endif
+    vk::QueryPool query_pool;
+    uint32_t num_queries;

    ~vk_device_struct() {
        VK_LOG_DEBUG("destroy device " << name);
@@ -828,8 +828,6 @@ private:
 #define VK_LOG_MEMORY(msg) ((void) 0)
 #endif // GGML_VULKAN_MEMORY_DEBUG

-#if defined(GGML_VULKAN_PERF)
-
 class vk_perf_logger {
 public:
    void print_timings() {
@@ -839,7 +837,7 @@ public:
            for (const auto& time : t.second) {
                total += time;
            }
-            std::cerr << t.first << ": " << t.second.size() << " x " << (total / t.second.size() / 1000.0) << " ms" << std::endl;
+            std::cerr << t.first << ": " << t.second.size() << " x " << (total / t.second.size() / 1000.0) << " us" << std::endl;
        }

        timings.clear();
@@ -868,7 +866,6 @@ public:
 private:
    std::map<std::string, std::vector<uint64_t>> timings;
 };
-#endif // GGML_VULKAN_PERF

 struct ggml_backend_vk_context {
    std::string name;
@@ -958,6 +955,8 @@ struct vk_instance_t {
 static bool vk_instance_initialized = false;
 static vk_instance_t vk_instance;

+static bool vk_perf_logger_enabled = false;
+
 #ifdef GGML_VULKAN_CHECK_RESULTS
 static size_t vk_skip_checks;
 static size_t vk_output_tensor;
@@ -2757,9 +2756,9 @@ static vk_device ggml_vk_get_device(size_t idx) {
 #ifdef GGML_VULKAN_MEMORY_DEBUG
        device->memory_logger = std::unique_ptr<vk_memory_logger>(new vk_memory_logger());
 #endif
-#ifdef GGML_VULKAN_PERF
-        device->perf_logger = std::unique_ptr<vk_perf_logger>(new vk_perf_logger());
-#endif
+        if (vk_perf_logger_enabled) {
+            device->perf_logger = std::unique_ptr<vk_perf_logger>(new vk_perf_logger());
+        }

        size_t dev_num = vk_instance.device_indices[idx];

@@ -3547,6 +3546,8 @@ static void ggml_vk_instance_init() {
    vk_instance.instance = vk::createInstance(instance_create_info);
    vk_instance_initialized = true;

+    vk_perf_logger_enabled = getenv("GGML_VK_PERF_LOGGER") != nullptr;
+
    size_t num_available_devices = vk_instance.instance.enumeratePhysicalDevices().size();

    // Emulate behavior of CUDA_VISIBLE_DEVICES for Vulkan
@@ -6452,6 +6453,7 @@ static bool ggml_vk_op_supports_incontiguous(ggml_op op) {
    case GGML_OP_ROPE:
    case GGML_OP_RMS_NORM:
    case GGML_OP_CONV_2D_DW:
+    case GGML_OP_IM2COL:
        return true;
    default:
        return false;
@@ -8884,7 +8886,7 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod

    ctx->tensor_ctxs[node_idx] = compute_ctx;

-#if defined(GGML_VULKAN_CHECK_RESULTS) || defined(GGML_VULKAN_PERF)
+#if defined(GGML_VULKAN_CHECK_RESULTS)
    // Force context reset on each node so that each tensor ends up in its own context
    // and can be run and compared to its CPU equivalent separately
    last_node = true;
@@ -9504,6 +9506,29 @@ static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cg
    bool first_node_in_batch = true; // true if next node will be first node in a batch
    int submit_node_idx = 0; // index to first node in a batch

+    vk_context compute_ctx;
+    if (vk_perf_logger_enabled) {
+        // allocate/resize the query pool
+        if (ctx->device->num_queries < cgraph->n_nodes + 1) {
+            if (ctx->device->query_pool) {
+                ctx->device->device.destroyQueryPool(ctx->device->query_pool);
+            }
+            VkQueryPoolCreateInfo query_create_info = { VK_STRUCTURE_TYPE_QUERY_POOL_CREATE_INFO };
+            query_create_info.queryType = VK_QUERY_TYPE_TIMESTAMP;
+            query_create_info.queryCount = cgraph->n_nodes + 100;
+            ctx->device->query_pool = ctx->device->device.createQueryPool(query_create_info);
+            ctx->device->num_queries = query_create_info.queryCount;
+        }
+
+        ctx->device->device.resetQueryPool(ctx->device->query_pool, 0, cgraph->n_nodes+1);
+
+        GGML_ASSERT(ctx->compute_ctx.expired());
+        compute_ctx = ggml_vk_create_context(ctx, ctx->device->compute_queue);
+        ctx->compute_ctx = compute_ctx;
+        ggml_vk_ctx_begin(ctx->device, compute_ctx);
+        compute_ctx->s->buffer.writeTimestamp(vk::PipelineStageFlagBits::eAllCommands, ctx->device->query_pool, 0);
+    }
+
    // Submit after enough work has accumulated, to overlap CPU cmdbuffer generation with GPU execution.
    // Estimate the amount of matmul work by looking at the weight matrix size, and submit every 100MB
    // (and scaled down based on model size, so smaller models submit earlier).
@@ -9531,6 +9556,17 @@ static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cg

        bool enqueued = ggml_vk_build_graph(ctx, cgraph->nodes[i], i, cgraph->nodes[submit_node_idx], submit_node_idx, false, i == last_node, almost_ready, submit);

+        if (vk_perf_logger_enabled) {
+            if (ctx->compute_ctx.expired()) {
+                compute_ctx = ggml_vk_create_context(ctx, ctx->device->compute_queue);
+                ctx->compute_ctx = compute_ctx;
+                ggml_vk_ctx_begin(ctx->device, compute_ctx);
+            } else {
+                compute_ctx = ctx->compute_ctx.lock();
+            }
+            compute_ctx->s->buffer.writeTimestamp(vk::PipelineStageFlagBits::eAllCommands, ctx->device->query_pool, i+1);
+        }
+
        if (enqueued) {
            ++submitted_nodes;

@@ -9552,9 +9588,27 @@ static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cg
        }
    }

-#ifdef GGML_VULKAN_PERF
-    ctx->device->perf_logger->print_timings();
-#endif
+    if (vk_perf_logger_enabled) {
+        // End the command buffer and submit/wait
+        GGML_ASSERT(!ctx->compute_ctx.expired());
+        compute_ctx = ctx->compute_ctx.lock();
+        ggml_vk_ctx_end(compute_ctx);
+
+        ggml_vk_submit(compute_ctx, ctx->device->fence);
+        VK_CHECK(ctx->device->device.waitForFences({ ctx->device->fence }, true, UINT64_MAX), "GGML_VULKAN_PERF waitForFences");
+        ctx->device->device.resetFences({ ctx->device->fence });
+
+        // Get the results and pass them to the logger
+        std::vector<uint64_t> timestamps(cgraph->n_nodes + 1);
+        ctx->device->device.getQueryPoolResults(ctx->device->query_pool, 0, cgraph->n_nodes + 1, (cgraph->n_nodes + 1)*sizeof(uint64_t), timestamps.data(), sizeof(uint64_t), vk::QueryResultFlagBits::e64 | vk::QueryResultFlagBits::eWait);
+        for (int i = 0; i < cgraph->n_nodes; i++) {
+            if (!ggml_vk_is_empty(cgraph->nodes[i])) {
+                ctx->device->perf_logger->log_timing(cgraph->nodes[i], uint64_t((timestamps[i+1] - timestamps[i]) * ctx->device->properties.limits.timestampPeriod));
+            }
+        }
+
+        ctx->device->perf_logger->print_timings();
+    }

    ggml_vk_graph_cleanup(ctx);

@@ -2312,6 +2312,26 @@ struct ggml_tensor * ggml_repeat(
    return result;
 }

+struct ggml_tensor * ggml_repeat_4d(
+        struct ggml_context * ctx,
+        struct ggml_tensor * a,
+        int64_t ne0, int64_t ne1, int64_t ne2, int64_t ne3) {
+    const bool can_repeat = ggml_is_empty(a) || (
+        (ne0 % a->ne[0] == 0) &&
+        (ne1 % a->ne[1] == 0) &&
+        (ne2 % a->ne[2] == 0) &&
+        (ne3 % a->ne[3] == 0)
+    );
+    GGML_ASSERT(can_repeat);
+
+    struct ggml_tensor * result = ggml_new_tensor_4d(ctx, a->type, ne0, ne1, ne2, ne3);
+
+    result->op     = GGML_OP_REPEAT;
+    result->src[0] = a;
+
+    return result;
+}
+
 // ggml_repeat_back

 struct ggml_tensor * ggml_repeat_back(
@@ -546,6 +546,7 @@ class MODEL_TENSOR(IntEnum):
    A_ENC_FFN_GATE       = auto()
    A_ENC_FFN_DOWN       = auto()
    A_MMPROJ             = auto()
+    A_MMPROJ_FC          = auto()
    A_MM_NORM_PRE        = auto()
    A_MM_NORM_MID        = auto()

@@ -825,6 +826,7 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
    MODEL_TENSOR.A_ENC_FFN_GATE:            "a.blk.{bid}.ffn_gate",
    MODEL_TENSOR.A_ENC_FFN_DOWN:            "a.blk.{bid}.ffn_down",
    MODEL_TENSOR.A_MMPROJ:                  "mm.a.mlp.{bid}",
+    MODEL_TENSOR.A_MMPROJ_FC:               "mm.a.fc",
    MODEL_TENSOR.A_MM_NORM_PRE:             "mm.a.norm_pre",
    MODEL_TENSOR.A_MM_NORM_MID:             "mm.a.norm_mid",
 }
@@ -885,6 +887,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
        MODEL_TENSOR.A_ENC_FFN_GATE,
        MODEL_TENSOR.A_ENC_FFN_DOWN,
        MODEL_TENSOR.A_MMPROJ,
+        MODEL_TENSOR.A_MMPROJ_FC,
        MODEL_TENSOR.A_MM_NORM_PRE,
        MODEL_TENSOR.A_MM_NORM_MID,
    ],
@@ -2256,6 +2259,8 @@ class VisionProjectorType:
    QWEN25VL = "qwen2.5vl_merger"
    ULTRAVOX = "ultravox"
    INTERNVL = "internvl"
+    QWEN2A = "qwen2a" # audio
+    QWEN25O = "qwen2.5o" # omni


 # Items here are (block size, type size)
@@ -902,7 +902,6 @@ class TensorNameMap:

        MODEL_TENSOR.V_MMPROJ_FC: (
            "model.connector.modality_projection.proj", # SmolVLM
-            "multi_modal_projector.linear_1", # llama 4
        ),

        MODEL_TENSOR.V_MMPROJ_MLP: (
@@ -1125,6 +1124,7 @@ class TensorNameMap:

        MODEL_TENSOR.A_POST_NORM: (
            "audio_tower.layer_norm", # ultravox
+            "audio_tower.ln_post", # qwen2omni
        ),

        MODEL_TENSOR.A_ENC_ATTN_Q: (
@@ -1161,10 +1161,18 @@ class TensorNameMap:
            "audio_tower.layers.{bid}.fc2", # ultravox
        ),

+        # note: some tensors below has "audio." pseudo-prefix, to prevent conflicts with vision tensors
+        # this prefix is added in the conversion code in modify_tensors()
+
        MODEL_TENSOR.A_MMPROJ: (
            "audio.multi_modal_projector.linear_{bid}", # ultravox
        ),

+        MODEL_TENSOR.A_MMPROJ_FC: (
+            "audio.multi_modal_projector.linear", # qwen2audio
+            "audio_tower.proj", # qwen2omni
+        ),
+
        MODEL_TENSOR.A_MM_NORM_PRE: (
            "audio.multi_modal_projector.ln_pre", # ultravox
        ),
@@ -471,6 +471,7 @@ extern "C" {
    LLAMA_API int64_t llama_time_us(void);

    LLAMA_API size_t llama_max_devices(void);
+    LLAMA_API size_t llama_max_parallel_sequences(void);

    LLAMA_API bool llama_supports_mmap       (void);
    LLAMA_API bool llama_supports_mlock      (void);
@@ -611,11 +612,11 @@ extern "C" {
    // Returns the number of tokens in the KV cache (slow, use only for debug)
    // If a KV cell has multiple sequences assigned to it, it will be counted multiple times
    DEPRECATED(LLAMA_API int32_t llama_kv_self_n_tokens(const struct llama_context * ctx),
-               "Use llama_kv_self_seq_pos_max() instead");
+               "Use llama_kv_self_seq_pos_max() and llama_kv_self_seq_pos_min() instead (https://github.com/ggml-org/llama.cpp/issues/13793)");

    // Returns the number of used KV cells (i.e. have at least one sequence assigned to them)
    DEPRECATED(LLAMA_API int32_t llama_kv_self_used_cells(const struct llama_context * ctx),
-               "Use llama_kv_self_seq_pos_max() instead");
+               "Use llama_kv_self_seq_pos_max() and llama_kv_self_seq_pos_min() instead (https://github.com/ggml-org/llama.cpp/issues/13793)");

    // Clear the KV cache - both cell info is erased and KV data is zeroed
    LLAMA_API void llama_kv_self_clear(
@@ -0,0 +1,112 @@
+ied 4 ½ months
+__ggml_vocab_test__
+Führer
+__ggml_vocab_test__
+
+__ggml_vocab_test__
+ 
+__ggml_vocab_test__
+  
+__ggml_vocab_test__
+   
+__ggml_vocab_test__
+	
+__ggml_vocab_test__
+
+
+__ggml_vocab_test__
+
+
+
+__ggml_vocab_test__
+
+
+
+
+__ggml_vocab_test__
+	
+
+__ggml_vocab_test__
+Hello world
+__ggml_vocab_test__
+ Hello world
+__ggml_vocab_test__
+Hello World
+__ggml_vocab_test__
+ Hello World
+__ggml_vocab_test__
+ Hello World!
+__ggml_vocab_test__
+Hello, world!
+__ggml_vocab_test__
+ Hello, world!
+__ggml_vocab_test__
+ this is 🦙.cpp
+__ggml_vocab_test__
+w048 7tuijk dsdfhu
+__ggml_vocab_test__
+нещо на Български
+__ggml_vocab_test__
+កាន់តែពិសេសអាចខលចេញ
+__ggml_vocab_test__
+🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token)
+__ggml_vocab_test__
+Hello
+__ggml_vocab_test__
+ Hello
+__ggml_vocab_test__
+  Hello
+__ggml_vocab_test__
+   Hello
+__ggml_vocab_test__
+    Hello
+__ggml_vocab_test__
+    Hello
+    Hello
+__ggml_vocab_test__
+ (
+__ggml_vocab_test__
+
+ =
+__ggml_vocab_test__
+' era
+__ggml_vocab_test__
+Hello, y'all! How are you 😁 ?我想在apple工作1314151天～
+__ggml_vocab_test__
+!!!!!!
+__ggml_vocab_test__
+3
+__ggml_vocab_test__
+33
+__ggml_vocab_test__
+333
+__ggml_vocab_test__
+3333
+__ggml_vocab_test__
+33333
+__ggml_vocab_test__
+333333
+__ggml_vocab_test__
+3333333
+__ggml_vocab_test__
+33333333
+__ggml_vocab_test__
+333333333
+__ggml_vocab_test__
+Cửa Việt
+__ggml_vocab_test__
+ discards
+__ggml_vocab_test__
+
+ 
+
+ 
+
+
+ 	 		 	
+  
+   
+    
+     
+🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ 🦙🦙 3 33 333 3333 33333 333333 3333333 33333333 3.3 3..3 3...3 កាន់តែពិសេសអាច😁 ?我想在apple工作1314151天～ ------======= нещо на Български ''''''```````""""......!!!!!!?????? I've been 'told he's there, 'RE you sure? 'M not sure I'll make it, 'D you like some tea? We'Ve a'lL
+__ggml_vocab_test__
@@ -0,0 +1,46 @@
+ 17 297 201 78660 21775
+ 72805 4097 56
+
+
+
+
+
+
+
+
+
+ 35378 8999
+ 35378 8999
+ 35378 6661
+ 35378 6661
+ 35378 6661 38
+ 35378 4 8999 38
+ 35378 4 8999 38
+ 903 83 6 3 5 238 6366
+ 148 7709 1019 361 458 134362 104 7 71 420 1132
+ 14271 29 117152
+ 6 149561 78270 48967 64254 7616 81705
+ 6 247206 15 33176 16 6 247442 6 3 15755 15 144227 8705 18255 40292 158 4460 33 27686 16 6 142325 15 191 538 28 121505 450 1556 6863 10002 47 1098 16
+ 35378
+ 35378
+ 35378
+ 35378
+ 35378
+ 35378 35378
+ 15
+ 2203
+ 242 1615
+ 35378 4 113 25 5584 38 11249 621 398 6 201344 705 23638 213 9007 133 1879 2681 2592 135224 1906 6087
+ 6 90827
+ 138
+ 3912
+ 6 66000
+ 138 66000
+ 3912 66000
+ 6 66000 66000
+ 138 66000 66000
+ 3912 66000 66000
+ 6 66000 66000 66000
+ 199152 3763
+ 17116 99397
+ 6 247206 15 33176 16 6 247442 6 3 15755 15 144227 8705 18255 40292 158 4460 33 27686 16 6 142325 6 3 138 3912 6 66000 138 66000 3912 66000 6 66000 66000 138 66000 66000 3912 66000 66000 80308 1031 5 363 138 27 363 6 149561 78270 48967 201344 705 23638 213 9007 133 1879 2681 2592 135224 1906 6087 6 110405 1369 69112 69112 69112 14271 29 117152 5106 4765 4765 1135 164721 164721 164721 58 58 58 58 2551 90827 32 85908 87 25 272 2809 242 18 18345 764 25 7 2685 4 242 11766 398 9077 32 242 594 959 9077 87 25 1181 3249 442 4 242 397 398 1884 3060 26156 32 1401 25 26455 10 25 141 866
@@ -0,0 +1,85 @@
+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0].role == 'system' %}
+        {{- messages[0].content + '\n\n' }}
+    {%- endif %}
+    {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
+{%- for message in messages[::-1] %}
+    {%- set index = (messages|length - 1) - loop.index0 %}
+    {%- if ns.multi_step_tool and message.role == "user" and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
+        {%- set ns.multi_step_tool = false %}
+        {%- set ns.last_query_index = index %}
+    {%- endif %}
+{%- endfor %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {%- set content = message.content %}
+        {%- set reasoning_content = '' %}
+        {%- if message.reasoning_content is defined and message.reasoning_content is not none %}
+            {%- set reasoning_content = message.reasoning_content %}
+        {%- else %}
+            {%- if '</think>' in message.content %}
+                {%- set content = message.content.split('</think>')[-1].lstrip('\n') %}
+                {%- set reasoning_content = message.content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
+            {%- endif %}
+        {%- endif %}
+        {%- if loop.index0 > ns.last_query_index %}
+            {%- if loop.last or (not loop.last and reasoning_content) %}
+                {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
+            {%- else %}
+                {{- '<|im_start|>' + message.role + '\n' + content }}
+            {%- endif %}
+        {%- else %}
+            {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- endif %}
+        {%- if message.tool_calls %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if (loop.first and content) or (not loop.first) %}
+                    {{- '\n' }}
+                {%- endif %}
+                {%- if tool_call.function %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {{- '<tool_call>\n{"name": "' }}
+                {{- tool_call.name }}
+                {{- '", "arguments": ' }}
+                {%- if tool_call.arguments is string %}
+                    {{- tool_call.arguments }}
+                {%- else %}
+                    {{- tool_call.arguments | tojson }}
+                {%- endif %}
+                {{- '}\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+    {%- if enable_thinking is defined and enable_thinking is false %}
+        {{- '<think>\n\n</think>\n\n' }}
+    {%- endif %}
+{%- endif %}
@@ -20,4 +20,5 @@ These templates can be updated with the following commands:
 ./scripts/get_chat_template.py NousResearch/Hermes-3-Llama-3.1-8B tool_use   > models/templates/NousResearch-Hermes-3-Llama-3.1-8B-tool_use.jinja
 ./scripts/get_chat_template.py Qwen/Qwen2.5-7B-Instruct                      > models/templates/Qwen-Qwen2.5-7B-Instruct.jinja
 ./scripts/get_chat_template.py Qwen/QwQ-32B                                  > models/templates/Qwen-QwQ-32B.jinja
+./scripts/get_chat_template.py Qwen/Qwen3-0.6B                               > models/templates/Qwen-Qwen3-0.6B.jinja
 ```
@@ -17,14 +17,14 @@ rm -f llama-bench.sqlite > /dev/null

 # to test a backend, call the script with the corresponding environment variable (e.g. GGML_CUDA=1 ./scripts/compare-commits.sh ...)
 if [ -n "$GGML_CUDA" ]; then
-    cmake_opts="-DGGML_CUDA=ON"
+    CMAKE_OPTS="${CMAKE_OPTS} -DGGML_CUDA=ON"
 fi

 dir="build-bench"

 function run {
    rm -fr ${dir} > /dev/null
-    cmake -B ${dir} -S . $cmake_opts > /dev/null
+    cmake -B ${dir} -S . ${CMAKE_OPTS} > /dev/null
    cmake --build ${dir} -t llama-bench > /dev/null
    ${dir}/bin/llama-bench -o sql -oe md $bench_args | sqlite3 llama-bench.sqlite
 }
@@ -1 +1 @@
-7c06c10c532a6cda913c17fc56341e8880ae341d
+06b715f4c170232af261425240914fa49c44f982
@@ -14,6 +14,7 @@ add_library(llama
            llama-batch.cpp
            llama-chat.cpp
            llama-context.cpp
+            llama-cparams.cpp
            llama-grammar.cpp
            llama-graph.cpp
            llama-hparams.cpp
@@ -25,7 +25,11 @@ llama_context::llama_context(

    const auto & hparams = model.hparams;

-    cparams.n_seq_max        = std::max(1u, params.n_seq_max);
+    cparams.n_seq_max = std::max(1u, params.n_seq_max);
+    if (cparams.n_seq_max > LLAMA_MAX_PARALLEL_SEQUENCES) {
+        throw std::runtime_error("n_seq_max must be <= " + std::to_string(LLAMA_MAX_PARALLEL_SEQUENCES));
+    }
+
    cparams.n_threads        = params.n_threads;
    cparams.n_threads_batch  = params.n_threads_batch;
    cparams.yarn_ext_factor  = params.yarn_ext_factor;
@@ -689,12 +693,18 @@ int llama_context::encode(llama_batch & inp_batch) {

    GGML_ASSERT((!batch.token && batch.embd) || (batch.token && !batch.embd)); // NOLINT

+    // TODO: move the validation to the llama_batch_allocr
    if (batch.token) {
        for (int32_t i = 0; i < n_tokens; ++i) {
            if (batch.token[i] < 0 || (uint32_t) batch.token[i] >= model.vocab.n_tokens()) {
                LLAMA_LOG_ERROR("%s: invalid token[%d] = %d\n", __func__, i, batch.token[i]);
                return -1;
            }
+
+            if (batch.seq_id && (batch.seq_id[i][0] < 0 || batch.seq_id[i][0] >= LLAMA_MAX_PARALLEL_SEQUENCES)) {
+                LLAMA_LOG_ERROR("%s: invalid seq_id[%d] = %d > %d\n", __func__, i, batch.seq_id[i][0], LLAMA_MAX_PARALLEL_SEQUENCES);
+                throw -1;
+            }
        }
    }

@@ -848,7 +858,7 @@ int llama_context::encode(llama_batch & inp_batch) {

 int llama_context::decode(llama_batch & inp_batch) {
    if (!memory) {
-        LLAMA_LOG_WARN("%s: cannot decode batches with this context (use llama_encode() instead)\n", __func__);
+        LLAMA_LOG_DEBUG("%s: cannot decode batches with this context (calling encode() instead)\n", __func__);
        return encode(inp_batch);
    }

@@ -883,11 +893,17 @@ int llama_context::decode(llama_batch & inp_batch) {

    GGML_ASSERT((!batch.token && batch.embd) || (batch.token && !batch.embd)); // NOLINT

+    // TODO: move the validation to the llama_batch_allocr
    if (batch.token) {
        for (int64_t i = 0; i < n_tokens_all; ++i) {
            if (batch.token[i] < 0 || (uint32_t) batch.token[i] >= model.vocab.n_tokens()) {
                LLAMA_LOG_ERROR("%s: invalid token[%" PRId64 "] = %d\n", __func__, i, batch.token[i]);
-                throw std::runtime_error("invalid token");
+                return -1;
+            }
+
+            if (batch.seq_id && (batch.seq_id[i][0] < 0 || batch.seq_id[i][0] >= LLAMA_MAX_PARALLEL_SEQUENCES)) {
+                LLAMA_LOG_ERROR("%s: invalid seq_id[%" PRId64 "] = %d >= %d\n", __func__, i, batch.seq_id[i][0], LLAMA_MAX_PARALLEL_SEQUENCES);
+                return -1;
            }
        }
    }
@@ -1 +1,5 @@
 #include "llama-cparams.h"
+
+size_t llama_max_parallel_sequences(void) {
+    return LLAMA_MAX_PARALLEL_SEQUENCES;
+}
@@ -4,6 +4,8 @@

 #include <cstdint>

+#define LLAMA_MAX_PARALLEL_SEQUENCES 64
+
 struct llama_cparams {
    uint32_t n_ctx;           // context size used during inference
    uint32_t n_batch;
@@ -65,8 +65,6 @@ llama_kv_cache_unified::llama_kv_cache_unified(
    };

    head = 0;
-    size = kv_size;
-    used = 0;

    cells.resize(kv_size);

@@ -138,13 +136,9 @@ llama_kv_cache_unified::llama_kv_cache_unified(
 }

 void llama_kv_cache_unified::clear() {
-    for (uint32_t i = 0; i < size; ++i) {
-        cells[i].pos = -1;
-        cells[i].seq_id.clear();
-    }
+    cells.reset();

    head = 0;
-    used = 0;

    for (auto & buf : bufs) {
        ggml_backend_buffer_clear(buf.get(), 0);
@@ -152,7 +146,7 @@ void llama_kv_cache_unified::clear() {
 }

 bool llama_kv_cache_unified::seq_rm(llama_seq_id seq_id, llama_pos p0, llama_pos p1) {
-    uint32_t new_head = size;
+    uint32_t new_head = cells.size();

    if (p0 < 0) {
        p0 = 0;
@@ -162,33 +156,20 @@ bool llama_kv_cache_unified::seq_rm(llama_seq_id seq_id, llama_pos p0, llama_pos
        p1 = std::numeric_limits<llama_pos>::max();
    }

-    for (uint32_t i = 0; i < size; ++i) {
-        if (cells[i].pos >= p0 && cells[i].pos < p1) {
-            if (seq_id < 0) {
-                cells[i].seq_id.clear();
-            } else if (cells[i].has_seq_id(seq_id)) {
-                cells[i].seq_id.erase(seq_id);
-            } else {
-                continue;
-            }
+    for (uint32_t i = 0; i < cells.size(); ++i) {
+        if (!cells.pos_in(i, p0, p1)) {
+            continue;
+        }

-            if (cells[i].is_empty()) {
-                // keep count of the number of used cells
-                if (cells[i].pos >= 0) {
-                    used--;
-                }
-
-                cells[i].pos = -1;
-
-                if (new_head == size) {
-                    new_head = i;
-                }
+        if (cells.seq_has(i, seq_id) && cells.seq_rm(i, seq_id)) {
+            if (new_head == cells.size()) {
+                new_head = i;
            }
        }
    }

    // If we freed up a slot, set head to it so searching can start there.
-    if (new_head != size && new_head < head) {
+    if (new_head != cells.size() && new_head < head) {
        head = new_head;
    }

@@ -208,49 +189,40 @@ void llama_kv_cache_unified::seq_cp(llama_seq_id seq_id_src, llama_seq_id seq_id
        p1 = std::numeric_limits<llama_pos>::max();
    }

-    // otherwise, this is the KV of a Transformer-like model
-    head = 0;
+    for (uint32_t i = 0; i < cells.size(); ++i) {
+        if (!cells.pos_in(i, p0, p1)) {
+            continue;
+        }

-    for (uint32_t i = 0; i < size; ++i) {
-        if (cells[i].has_seq_id(seq_id_src) && cells[i].pos >= p0 && cells[i].pos < p1) {
-            cells[i].seq_id.insert(seq_id_dst);
+        if (cells.seq_has(i, seq_id_src)) {
+            cells.seq_add(i, seq_id_dst);
        }
    }
 }

 void llama_kv_cache_unified::seq_keep(llama_seq_id seq_id) {
-    uint32_t new_head = size;
+    uint32_t new_head = cells.size();

-    for (uint32_t i = 0; i < size; ++i) {
-        if (!cells[i].has_seq_id(seq_id)) {
-            if (cells[i].pos >= 0) {
-                used--;
-            }
-
-            cells[i].pos = -1;
-            cells[i].seq_id.clear();
-
-            if (new_head == size){
+    for (uint32_t i = 0; i < cells.size(); ++i) {
+        if (cells.seq_keep(i, seq_id)) {
+            if (new_head == cells.size()) {
                new_head = i;
            }
-        } else {
-            cells[i].seq_id.clear();
-            cells[i].seq_id.insert(seq_id);
        }
    }

    // If we freed up a slot, set head to it so searching can start there.
-    if (new_head != size && new_head < head) {
+    if (new_head != cells.size() && new_head < head) {
        head = new_head;
    }
 }

-void llama_kv_cache_unified::seq_add(llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos delta) {
-    if (delta == 0) {
+void llama_kv_cache_unified::seq_add(llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos shift) {
+    if (shift == 0) {
        return;
    }

-    uint32_t new_head = size;
+    uint32_t new_head = cells.size();

    if (p0 < 0) {
        p0 = 0;
@@ -260,25 +232,19 @@ void llama_kv_cache_unified::seq_add(llama_seq_id seq_id, llama_pos p0, llama_po
        p1 = std::numeric_limits<llama_pos>::max();
    }

-    // If there is no range then return early to avoid looping over the
+    // If there is no range then return early to avoid looping over all cells.
    if (p0 == p1) {
        return;
    }

-    for (uint32_t i = 0; i < size; ++i) {
-        if (cells[i].has_seq_id(seq_id) && cells[i].pos >= p0 && cells[i].pos < p1) {
-            has_shift = true;
+    for (uint32_t i = 0; i < cells.size(); ++i) {
+        if (!cells.pos_in(i, p0, p1)) {
+            continue;
+        }

-            cells[i].pos   += delta;
-            cells[i].delta += delta;
-
-            if (cells[i].pos < 0) {
-                if (!cells[i].is_empty()) {
-                    used--;
-                }
-                cells[i].pos = -1;
-                cells[i].seq_id.clear();
-                if (new_head == size) {
+        if (cells.seq_has(i, seq_id)) {
+            if (cells.pos_add(i, shift)) {
+                if (new_head == cells.size()) {
                    new_head = i;
                }
            }
@@ -287,7 +253,7 @@ void llama_kv_cache_unified::seq_add(llama_seq_id seq_id, llama_pos p0, llama_po

    // If we freed up a slot, set head to it so searching can start there.
    // Otherwise we just start the next search from the beginning.
-    head = new_head != size ? new_head : 0;
+    head = new_head != cells.size() ? new_head : 0;
 }

 void llama_kv_cache_unified::seq_div(llama_seq_id seq_id, llama_pos p0, llama_pos p1, int d) {
@@ -308,67 +274,35 @@ void llama_kv_cache_unified::seq_div(llama_seq_id seq_id, llama_pos p0, llama_po
        return;
    }

-    for (uint32_t i = 0; i < size; ++i) {
-        if (cells[i].has_seq_id(seq_id) && cells[i].pos >= p0 && cells[i].pos < p1) {
-            has_shift = true;
+    for (uint32_t i = 0; i < cells.size(); ++i) {
+        if (!cells.pos_in(i, p0, p1)) {
+            continue;
+        }

-            {
-                llama_pos p_old = cells[i].pos;
-                cells[i].pos   /= d;
-                cells[i].delta += cells[i].pos - p_old;
-            }
+        if (cells.seq_has(i, seq_id)) {
+            cells.pos_div(i, d);
        }
    }
 }

 llama_pos llama_kv_cache_unified::seq_pos_min(llama_seq_id seq_id) const {
-    llama_pos result = std::numeric_limits<llama_pos>::max();
-
-    for (uint32_t i = 0; i < size; ++i) {
-        if (cells[i].has_seq_id(seq_id)) {
-            result = std::min(result, cells[i].pos);
-        }
-    }
-
-    if (result == std::numeric_limits<llama_pos>::max()) {
-        result = -1;
-    }
-
-    return result;
+    return cells.seq_pos_min(seq_id);
 }

 llama_pos llama_kv_cache_unified::seq_pos_max(llama_seq_id seq_id) const {
-    llama_pos result = -1;
-
-    for (uint32_t i = 0; i < size; ++i) {
-        if (cells[i].has_seq_id(seq_id)) {
-            result = std::max(result, cells[i].pos);
-        }
-    }
-
-    return result;
+    return cells.seq_pos_max(seq_id);
 }

 void llama_kv_cache_unified::restore() {
-    for (const auto & [id, cell] : recovery.cells) {
-        // TODO: move to new `struct kv_cells`
-        const bool is_empty0 = cells[id].is_empty();
-        const bool is_empty1 = cell.is_empty();
-
-        if (!is_empty0 && is_empty1) {
-            used--;
-        } else if (is_empty0 && !is_empty1) {
-            used++;
-        }
-
-        cells[id] = cell;
+    for (auto & state : recovery.states) {
+        cells.set(state.i, state.cells);
    }

    recovery.clear();
 }

 void llama_kv_cache_unified::commit() {
-    if (recovery.cells.empty()) {
+    if (recovery.states.empty()) {
        LLAMA_LOG_WARN("%s: the recovery information upon a commit was empty - might indicate a bug (ref: %s)\n",
                __func__, "https://github.com/ggml-org/llama.cpp/pull/13194");
        return;
@@ -382,7 +316,7 @@ bool llama_kv_cache_unified::update(llama_context & lctx) {

    auto * sched = lctx.get_sched();

-    if (has_shift) {
+    if (cells.get_has_shift()) {
        if (!get_can_shift()) {
            GGML_ABORT("The current KV cache / model configuration does not support K-shift");
        }
@@ -406,13 +340,7 @@ bool llama_kv_cache_unified::update(llama_context & lctx) {
            need_reserve = true;
        }

-        {
-            has_shift = false;
-
-            for (uint32_t i = 0; i < size; ++i) {
-                cells[i].delta = 0;
-            }
-        }
+        cells.reset_shift();
    }

    if (do_defrag) {
@@ -443,7 +371,7 @@ bool llama_kv_cache_unified::update(llama_context & lctx) {
 void llama_kv_cache_unified::defrag_sched(float thold) {
    // - do not defrag small contexts (i.e. < 2048 tokens)
    // - count the padding towards the number of used tokens
-    const float fragmentation = n >= 2048 ? std::max(0.0f, 1.0f - (float(used + n_pad)/n)) : 0.0f;
+    const float fragmentation = n >= 2048 ? std::max(0.0f, 1.0f - (float(cells.get_used() + n_pad)/n)) : 0.0f;

    // queue defragmentation for next llama_kv_cache_update
    if (fragmentation > thold) {
@@ -454,7 +382,7 @@ void llama_kv_cache_unified::defrag_sched(float thold) {
 }

 void llama_kv_cache_unified::set_full() {
-    n = size;
+    n = cells.size();

    // when simulating a full KV cache, the specific value of the "head" pointer is not important because it does not
    //   affect the shapes of the tensors in the compute graph - it only affects the offsets of the K/V views.
@@ -478,14 +406,14 @@ bool llama_kv_cache_unified::find_slot(const llama_ubatch & ubatch) {

    // if we have enough unused cells before the current head ->
    //   better to start searching from the beginning of the cache, hoping to fill it
-    if (head > used + 2*ubatch.n_tokens) {
+    if (head > cells.get_used() + 2*ubatch.n_tokens) {
        head = 0;
    }

    // otherwise, one cell per token.

-    if (n_tokens > size) {
-        LLAMA_LOG_ERROR("%s: n_tokens = %d > size = %d\n", __func__, n_tokens, size);
+    if (n_tokens > cells.size()) {
+        LLAMA_LOG_ERROR("%s: n_tokens = %d > size = %u\n", __func__, n_tokens, cells.size());
        return false;
    }

@@ -498,10 +426,10 @@ bool llama_kv_cache_unified::find_slot(const llama_ubatch & ubatch) {
        std::string ss;
        if (n_swa > 0) {
            for (uint32_t i = 0; i < size; ++i) {
-                if (cells[i].pos == -1) {
+                if (cells.is_empty(i)) {
                    ss += '.';
                } else {
-                    ss += std::to_string(*cells[i].seq_id.begin());
+                    ss += 'x';
                }
                if (i%256 == 255) {
                    ss += '\n';
@@ -515,15 +443,16 @@ bool llama_kv_cache_unified::find_slot(const llama_ubatch & ubatch) {
    uint32_t n_tested = 0;

    while (true) {
-        if (head + n_tokens > size) {
-            n_tested += size - head;
+        if (head + n_tokens > cells.size()) {
+            n_tested += cells.size() - head;
            head = 0;
            continue;
        }

        bool found = true;
        for (uint32_t i = 0; i < n_tokens; i++) {
-            if (cells[head + i].pos >= 0) {
+            // TODO: improve to accept cells that are masked by the SWA
+            if (!cells.is_empty(head + i)) {
                found = false;
                head     += i + 1;
                n_tested += i + 1;
@@ -535,31 +464,27 @@ bool llama_kv_cache_unified::find_slot(const llama_ubatch & ubatch) {
            break;
        }

-        if (n_tested >= size) {
+        if (n_tested >= cells.size()) {
            //LLAMA_LOG_ERROR("%s: failed to find a slot for %d tokens\n", __func__, n_tokens);
            return false;
        }
    }

-    for (uint32_t i = 0; i < n_tokens; ++i) {
-        // remember the original state
-        if (recovery.cells.find(head + i) == recovery.cells.end()) {
-            recovery.cells[head + i] = cells[head + i];
-        }
+    // store the old state of the cells in the recovery stack
+    recovery.states.push_back({head, cells.cp(head, n_tokens)});

-        cells[head + i].pos = ubatch.pos[i];
+    for (uint32_t i = 0; i < n_tokens; ++i) {
+        cells.pos_set(head + i, ubatch.pos[i]);

        for (int32_t j = 0; j < ubatch.n_seq_id[i]; j++) {
-            cells[head + i].seq_id.insert(ubatch.seq_id[i][j]);
+            cells.seq_add(head + i, ubatch.seq_id[i][j]);
        }
    }

-    used += n_tokens;
-
    // a heuristic, to avoid attending the full cache if it is not yet utilized
    // after enough generations, the benefit from this heuristic disappears
    // if we start defragmenting the cache, the benefit from this will be more important
-    n = std::min(size, std::max(n_pad, GGML_PAD(cell_max(), n_pad)));
+    n = std::min(cells.size(), std::max(n_pad, GGML_PAD(cells.used_max_p1(), n_pad)));

 #ifdef FIND_SLOT_DEBUG
    LLAMA_LOG_WARN("end:   n = %5d, used = %5d, head = %5d, n_swa = %5d\n", n, used, head, n_swa);
@@ -577,7 +502,7 @@ uint32_t llama_kv_cache_unified::get_n() const {
 }

 uint32_t llama_kv_cache_unified::get_size() const {
-    return size;
+    return cells.size();
 }

 ggml_tensor * llama_kv_cache_unified::get_k(ggml_context * ctx, int32_t il) const {
@@ -661,30 +586,19 @@ void llama_kv_cache_unified::prune_swa(llama_seq_id seq_id, llama_pos pmin, llam

    int n_attended = 0;

-    for (uint32_t i = 0; i < size; ++i) {
-        const llama_pos p0 = cells[i].pos;
+    for (uint32_t i = 0; i < cells.size(); ++i) {
+        if (!cells.seq_has(i, seq_id)) {
+            continue;
+        }
+
+        const llama_pos p0 = cells.pos_get(i);

        if (p0 <= pmin && !is_masked_swa(p0, pmin)) {
            n_attended++;
        }

        if (is_masked_swa(p0, pmax)) {
-            if (seq_id < 0) {
-                cells[i].seq_id.clear();
-            } else if (cells[i].has_seq_id(seq_id)) {
-                cells[i].seq_id.erase(seq_id);
-            } else {
-                continue;
-            }
-
-            if (cells[i].is_empty()) {
-                // keep count of the number of used cells
-                if (cells[i].pos >= 0) {
-                    used--;
-                }
-
-                cells[i].pos = -1;
-            }
+            cells.seq_rm(i, seq_id);
        }
    }

@@ -723,25 +637,31 @@ void llama_kv_cache_unified::set_input_kq_mask(ggml_tensor * dst, const llama_ub
                const llama_pos p1 = ubatch->pos[s*n_seq_tokens + j];

                for (int i = 0; i < n_kv; ++i) {
-                    const llama_pos p0 = cells[i].pos;
+                    float f = 0.0f;

                    bool masked = false;

-                    // mask the token if not the same sequence
-                    masked = masked || (!cells[i].has_seq_id(seq_id));
+                    if (cells.is_empty(i)) {
+                        masked = true;
+                    } else {
+                        const llama_pos p0 = cells.pos_get(i);

-                    // mask future tokens
-                    masked = masked || (causal_attn && p0 > p1);
+                        // mask the token if not the same sequence
+                        masked = masked || (!cells.seq_has(i, seq_id));

-                    // apply SWA if any
-                    masked = masked || (is_masked_swa(p0, p1));
+                        // mask future tokens
+                        masked = masked || (causal_attn && p0 > p1);

-                    float f = 0.0f;
+                        // apply SWA if any
+                        masked = masked || (is_masked_swa(p0, p1));
+
+                        if (!masked && hparams.use_alibi) {
+                            f = -std::abs(p0 - p1);
+                        }
+                    }

                    if (masked) {
                        f = -INFINITY;
-                    } else if (hparams.use_alibi) {
-                        f = -std::abs(p0 - p1);
                    }

                    data[h*(n_kv*n_tokens) + s*(n_kv*n_seq_tokens) + j*n_kv + i] = f;
@@ -765,8 +685,8 @@ void llama_kv_cache_unified::set_input_k_shift(ggml_tensor * dst) const {

    int32_t * data = (int32_t *) dst->data;

-    for (uint32_t i = 0; i < size; ++i) {
-        data[i] = cells[i].delta;
+    for (uint32_t i = 0; i < cells.size(); ++i) {
+        data[i] = cells.is_empty(i) ? 0 : cells.get_shift(i);
    }
 }

@@ -783,7 +703,10 @@ void llama_kv_cache_unified::set_input_pos_bucket(ggml_tensor * dst, const llama
    for (int h = 0; h < 1; ++h) {
        for (int j = 0; j < n_tokens; ++j) {
            for (int i = 0; i < n_kv; ++i) {
-                data[h*(n_kv*n_tokens) + j*n_kv + i] = llama_relative_position_bucket(cells[i].pos, ubatch->pos[j], hparams.n_rel_attn_bkts, false);
+                // the position when the cells is empty is irrelevant - it will be masked out later in the attention
+                const llama_pos p0 = cells.is_empty(i) ? -1 : cells.pos_get(i);
+
+                data[h*(n_kv*n_tokens) + j*n_kv + i] = llama_relative_position_bucket(p0, ubatch->pos[j], hparams.n_rel_attn_bkts, false);
            }
        }
    }
@@ -910,7 +833,7 @@ llm_graph_result_ptr llama_kv_cache_unified::build_graph_shift(

        ggml_tensor * k =
            ggml_view_3d(ctx, layer.k,
-                n_embd_head_k, n_head_kv, size,
+                n_embd_head_k, n_head_kv, cells.size(),
                ggml_row_size(layer.k->type, n_embd_head_k),
                ggml_row_size(layer.k->type, n_embd_k_gqa),
                0);
@@ -1050,12 +973,12 @@ llm_graph_result_ptr llama_kv_cache_unified::build_graph_defrag(
            } else {
                view_v_src = ggml_view_2d(ctx, layer.v,
                        nm, n_embd_v_gqa,
-                        ggml_row_size(layer.v->type, size),
+                        ggml_row_size(layer.v->type, cells.size()),
                        ggml_row_size(layer.v->type, i));

                view_v_dst = ggml_view_2d(ctx, layer.v,
                        nm, n_embd_v_gqa,
-                        ggml_row_size(layer.v->type, size),
+                        ggml_row_size(layer.v->type, cells.size()),
                        ggml_row_size(layer.v->type, id));
            }

@@ -1075,8 +998,8 @@ llm_graph_result_ptr llama_kv_cache_unified::build_graph_defrag(
 bool llama_kv_cache_unified::defrag_prepare(int32_t n_max_nodes) {
    const uint32_t n_layer = layers.size();

-    const uint32_t n_kv   = cell_max();
-    const uint32_t n_used = used;
+    const uint32_t n_kv   = cells.used_max_p1();
+    const uint32_t n_used = cells.get_used();

    assert(n_used <= n_kv);

@@ -1104,9 +1027,7 @@ bool llama_kv_cache_unified::defrag_prepare(int32_t n_max_nodes) {
    ids.resize(n_kv, n_kv);

    for (uint32_t i0 = 0; i0 < n_used; ++i0) {
-        const auto & cell0 = cells[i0];
-
-        if (!cell0.is_empty()) {
+        if (!cells.is_empty(i0)) {
            ids[i0] = i0;

            continue;
@@ -1117,7 +1038,7 @@ bool llama_kv_cache_unified::defrag_prepare(int32_t n_max_nodes) {
        uint32_t nh = 1;

        // determine the size of the hole
-        while (i0 + nh < n_used && cells[i0 + nh].is_empty()) {
+        while (i0 + nh < n_used && cells.is_empty(i0 + nh)) {
            nh++;
        }

@@ -1126,9 +1047,7 @@ bool llama_kv_cache_unified::defrag_prepare(int32_t n_max_nodes) {

        // starting from the end, find nh non-empty cells
        for (; is > i0; --is) {
-            const auto & cell1 = cells[is];
-
-            if (cell1.is_empty() || ids[is] != n_kv) {
+            if (cells.is_empty(is) || ids[is] != n_kv) {
                continue;
            }

@@ -1155,9 +1074,7 @@ bool llama_kv_cache_unified::defrag_prepare(int32_t n_max_nodes) {

        // go back and move the nf cells to the hole
        for (; i1 < n_kv; ++i1) {
-            auto & cell1 = cells[i1];
-
-            if (cell1.is_empty() || ids[i1] != n_kv) {
+            if (cells.is_empty(i1) || ids[i1] != n_kv) {
                if (n_moves == max_moves) {
                    stop = true;
                    break;
@@ -1171,10 +1088,8 @@ bool llama_kv_cache_unified::defrag_prepare(int32_t n_max_nodes) {
            ids[i1] = i0 + nf;

            // move the cell meta data
-            cells[i0 + nf] = cell1;
+            cells.mv(i1, i0 + nf);

-            // clear the old cell and move the head there
-            cell1 = kv_cell();
            head = n_used;

            if (!cont) {
@@ -1209,22 +1124,8 @@ bool llama_kv_cache_unified::defrag_prepare(int32_t n_max_nodes) {
    return true;
 }

-uint32_t llama_kv_cache_unified::cell_max() const {
-    for (uint32_t i = size; i > 0; --i) {
-        const kv_cell & cell = cells[i - 1];
-
-        if (cell.pos >= 0 && !cell.is_empty()) {
-            return i;
-        }
-    }
-
-    return 0;
-}
-
 bool llama_kv_cache_unified::is_masked_swa(llama_pos p0, llama_pos p1) const {
-    if (p0 < 0) {
-        return true;
-    }
+    assert(p0 >= 0 && p1 >= 0);

    switch (swa_type) {
        case LLAMA_SWA_TYPE_NONE:
@@ -1255,23 +1156,24 @@ void llama_kv_cache_unified::state_write(llama_io_write_i & io, llama_seq_id seq

    // Count the number of cells with the specified seq_id
    // Find all the ranges of cells with this seq id (or all, when -1)
-    uint32_t cell_range_begin = size;
-    for (uint32_t i = 0; i < size; ++i) {
-        const auto & cell = cells[i];
-        if ((seq_id == -1 && !cell.is_empty()) || cell.has_seq_id(seq_id)) {
+    uint32_t cell_range_begin = cells.size();
+
+    for (uint32_t i = 0; i < cells.size(); ++i) {
+        if (!cells.is_empty(i) && (seq_id == -1 || cells.seq_has(i, seq_id))) {
            ++cell_count;
-            if (cell_range_begin == size) {
+            if (cell_range_begin == cells.size()) {
                cell_range_begin = i;
            }
        } else {
-            if (cell_range_begin != size) {
+            if (cell_range_begin != cells.size()) {
                cell_ranges.emplace_back(cell_range_begin, i);
-                cell_range_begin = size;
+                cell_range_begin = cells.size();
            }
        }
    }
-    if (cell_range_begin != size) {
-        cell_ranges.emplace_back(cell_range_begin, size);
+
+    if (cell_range_begin != cells.size()) {
+        cell_ranges.emplace_back(cell_range_begin, cells.size());
    }

    // DEBUG CHECK: Sum of cell counts in ranges should equal the total cell count
@@ -1308,17 +1210,24 @@ void llama_kv_cache_unified::state_read(llama_io_read_i & io, llama_seq_id seq_i
 void llama_kv_cache_unified::state_write_meta(llama_io_write_i & io, const std::vector<std::pair<uint32_t, uint32_t>> & cell_ranges, llama_seq_id seq_id) const {
    for (const auto & range : cell_ranges) {
        for (uint32_t i = range.first; i < range.second; ++i) {
-            const auto & cell = cells[i];
-            const llama_pos pos      = cell.pos;
-            const uint32_t  n_seq_id = seq_id == -1 ? cell.seq_id.size() : 0;
+            std::vector<llama_seq_id> seq_ids;
+
+            for (llama_seq_id cur = 0; cur < (int) n_seq_max; ++cur) {
+                if (cur == seq_id || seq_id == -1) {
+                    if (cells.seq_has(i, cur)) {
+                        seq_ids.push_back(cur);
+                    }
+                }
+            }
+
+            const llama_pos pos     = cells.pos_get(i);
+            const uint32_t n_seq_id = seq_ids.size();

            io.write(&pos,      sizeof(pos));
            io.write(&n_seq_id, sizeof(n_seq_id));

-            if (n_seq_id) {
-                for (auto seq_id : cell.seq_id) {
-                    io.write(&seq_id, sizeof(seq_id));
-                }
+            for (const auto & seq_id : seq_ids) {
+                io.write(&seq_id, sizeof(seq_id));
            }
        }
    }
@@ -1379,7 +1288,7 @@ void llama_kv_cache_unified::state_write_data(llama_io_write_i & io, const std::
        }
    } else {
        // When v is transposed, we also need the element size and get the element ranges from each row
-        const uint32_t kv_size = size;
+        const uint32_t kv_size = cells.size();

        for (const auto & layer : layers) {
            const uint32_t il = layer.il;
@@ -1429,14 +1338,20 @@ bool llama_kv_cache_unified::state_read_meta(llama_io_read_i & io, uint32_t cell
            io.read_to(&pos,      sizeof(pos));
            io.read_to(&n_seq_id, sizeof(n_seq_id));

-            if (n_seq_id != 0) {
+            if (n_seq_id != 1) {
                LLAMA_LOG_ERROR("%s: invalid seq_id-agnostic kv cell\n", __func__);
                return false;
            }

-            batch.pos[i] = pos;
-            batch.n_seq_id[i] = 1;
-            batch.seq_id[i] = &dest_seq_id;
+            // read the sequence id, but directly discard it - we will use dest_seq_id instead
+            {
+                llama_seq_id seq_id;
+                io.read_to(&seq_id, sizeof(seq_id));
+            }
+
+            batch.pos[i]      = pos;
+            batch.n_seq_id[i] = n_seq_id;
+            batch.seq_id[i]   = &dest_seq_id;
        }

        if (!find_slot(batch)) {
@@ -1448,15 +1363,15 @@ bool llama_kv_cache_unified::state_read_meta(llama_io_read_i & io, uint32_t cell

        // DEBUG CHECK: kv.head should be our first cell, kv.head + cell_count - 1 should be our last cell (verify seq_id and pos values)
        // Assume that this is one contiguous block of cells
-        GGML_ASSERT(head + cell_count <= size);
-        GGML_ASSERT(cells[head].pos == batch.pos[0]);
-        GGML_ASSERT(cells[head + cell_count - 1].pos == batch.pos[cell_count - 1]);
-        GGML_ASSERT(cells[head].has_seq_id(dest_seq_id));
-        GGML_ASSERT(cells[head + cell_count - 1].has_seq_id(dest_seq_id));
+        GGML_ASSERT(head + cell_count <= cells.size());
+        GGML_ASSERT(cells.pos_get(head)                  == batch.pos[0]);
+        GGML_ASSERT(cells.pos_get(head + cell_count - 1) == batch.pos[cell_count - 1]);
+        GGML_ASSERT(cells.seq_has(head,                  dest_seq_id));
+        GGML_ASSERT(cells.seq_has(head + cell_count - 1, dest_seq_id));
    } else {
        // whole KV cache restore

-        if (cell_count > size) {
+        if (cell_count > cells.size()) {
            LLAMA_LOG_ERROR("%s: not enough cells in kv cache\n", __func__);
            return false;
        }
@@ -1464,15 +1379,13 @@ bool llama_kv_cache_unified::state_read_meta(llama_io_read_i & io, uint32_t cell
        clear();

        for (uint32_t i = 0; i < cell_count; ++i) {
-            kv_cell & cell = cells[i];
-
            llama_pos pos;
            uint32_t  n_seq_id;

            io.read_to(&pos,      sizeof(pos));
            io.read_to(&n_seq_id, sizeof(n_seq_id));

-            cell.pos = pos;
+            cells.pos_set(i, pos);

            for (uint32_t j = 0; j < n_seq_id; ++j) {
                llama_seq_id seq_id;
@@ -1483,12 +1396,11 @@ bool llama_kv_cache_unified::state_read_meta(llama_io_read_i & io, uint32_t cell
                    return false;
                }

-                cell.seq_id.insert(seq_id);
+                cells.seq_add(i, seq_id);
            }
        }

        head = 0;
-        used = cell_count;
    }

    return true;
@@ -1505,8 +1417,8 @@ bool llama_kv_cache_unified::state_read_data(llama_io_read_i & io, uint32_t cell
        LLAMA_LOG_ERROR("%s: mismatched layer count (%u instead of %u)\n", __func__, n_layer, (uint32_t) layers.size());
        return false;
    }
-    if (cell_count > size) {
-        LLAMA_LOG_ERROR("%s: not enough cells in kv cache to restore state (%u > %u)\n", __func__, cell_count, size);
+    if (cell_count > cells.size()) {
+        LLAMA_LOG_ERROR("%s: not enough cells in kv cache to restore state (%u > %u)\n", __func__, cell_count, cells.size());
        return false;
    }
    if (this->v_trans != (bool) v_trans) {
@@ -1609,7 +1521,7 @@ bool llama_kv_cache_unified::state_read_data(llama_io_read_i & io, uint32_t cell
            if (cell_count) {
                // For each row in the transposed matrix, read the values for the whole cell range
                for (uint32_t j = 0; j < n_embd_v_gqa; ++j) {
-                    const size_t dst_offset = (head + j * size) * v_size_el;
+                    const size_t dst_offset = (head + j * cells.size()) * v_size_el;
                    ggml_backend_tensor_set(layer.v, io.read(cell_count * v_size_el), dst_offset, cell_count * v_size_el);
                }
            }
@@ -1689,9 +1601,9 @@ void llama_kv_cache_unified_iswa::seq_keep(llama_seq_id seq_id) {
    kv_swa ->seq_keep(seq_id);
 }

-void llama_kv_cache_unified_iswa::seq_add(llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos delta) {
-    kv_base->seq_add(seq_id, p0, p1, delta);
-    kv_swa ->seq_add(seq_id, p0, p1, delta);
+void llama_kv_cache_unified_iswa::seq_add(llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos shift) {
+    kv_base->seq_add(seq_id, p0, p1, shift);
+    kv_swa ->seq_add(seq_id, p0, p1, shift);
 }

 void llama_kv_cache_unified_iswa::seq_div(llama_seq_id seq_id, llama_pos p0, llama_pos p1, int d) {
@@ -2063,8 +1975,8 @@ void llama_kv_cache_recurrent::seq_keep(llama_seq_id seq_id) {
    }
 }

-void llama_kv_cache_recurrent::seq_add(llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos delta) {
-    if (delta == 0) {
+void llama_kv_cache_recurrent::seq_add(llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos shift) {
+    if (shift == 0) {
        return;
    }

@@ -2087,7 +1999,7 @@ void llama_kv_cache_recurrent::seq_add(llama_seq_id seq_id, llama_pos p0, llama_
        if (tail_id >= 0) {
            kv_cell & cell = cells[tail_id];
            if (cell.has_seq_id(seq_id) && p0 <= cell.pos && cell.pos < p1) {
-                cell.pos += delta;
+                cell.pos += shift;
            }
        }
    }
@@ -4,6 +4,7 @@
 #include "llama-io.h"
 #include "llama-graph.h"
 #include "llama-memory.h"
+#include "llama-kv-cells.h"

 #include "ggml-cpp.h"

@@ -35,6 +36,7 @@ struct llama_kv_cache : public llama_memory_i {
    virtual void defrag_sched(float thold) = 0;

    // simulate full cache, used for allocating worst-case compute buffers
+    // TODO: remove
    virtual void set_full() = 0;

    //
@@ -42,7 +44,7 @@ struct llama_kv_cache : public llama_memory_i {
    //

    // =============================================================================================================
-    // TODO: refactor  and simplify this
+    // TODO: refactor and simplify this [TAG: KV_API]

    virtual llama_sbatch sbatch_init(const llama_batch & batch, bool logits_all) = 0;

@@ -121,7 +123,7 @@ public:
    bool seq_rm  (llama_seq_id seq_id,                              llama_pos p0, llama_pos p1) override;
    void seq_cp  (llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) override;
    void seq_keep(llama_seq_id seq_id)                                                          override;
-    void seq_add (llama_seq_id seq_id,                              llama_pos p0, llama_pos p1, llama_pos delta) override;
+    void seq_add (llama_seq_id seq_id,                              llama_pos p0, llama_pos p1, llama_pos shift) override;
    void seq_div (llama_seq_id seq_id,                              llama_pos p0, llama_pos p1, int d) override;

    llama_pos seq_pos_min(llama_seq_id seq_id) const override;
@@ -159,7 +161,7 @@ public:
    // llama_kv_cache_unified specific API
    //

-    uint32_t get_n() const;
+    uint32_t get_n()    const;
    uint32_t get_size() const;

    // get views of the current state of the cache
@@ -180,26 +182,6 @@ private:
    const llama_model & model;
    const llama_hparams & hparams;

-    struct kv_cell {
-        llama_pos pos   = -1;
-        llama_pos delta =  0;
-
-        // TODO: replace with bitset uint64_t
-        std::set<llama_seq_id> seq_id;
-
-        bool has_seq_id(const llama_seq_id & id) const {
-            return seq_id.find(id) != seq_id.end();
-        }
-
-        bool is_empty() const {
-            return seq_id.empty();
-        }
-
-        bool is_same_seq(const kv_cell & other) const {
-            return seq_id == other.seq_id;
-        }
-    };
-
    struct kv_layer {
        // layer index in the model
        // note: can be different from the layer index in the KV cache
@@ -209,15 +191,13 @@ private:
        ggml_tensor * v;
    };

-    bool has_shift = false;
    bool do_defrag = false;
    bool v_trans   = true;  // the value tensor is transposed

    uint32_t head = 0; // the location where the batch will be placed in the cache (see find_slot())
-    uint32_t size = 0; // total number of cells, shared across all sequences
-    uint32_t used = 0; // used cells (i.e. at least one seq_id) (TODO: add `struct kv_cells` and keep track automaticallt)

    // computed before each graph build
+    // TODO: cells should start to maintain this value dynamically based on the edits
    uint32_t n = 0;

    const uint32_t n_seq_max = 1;
@@ -233,19 +213,29 @@ private:
    std::vector<ggml_context_ptr>        ctxs;
    std::vector<ggml_backend_buffer_ptr> bufs;

-    std::vector<kv_cell>  cells;  // TODO: replace with `struct kv_cells`
+    llama_kv_cells_unified cells;
+
    std::vector<kv_layer> layers;

    // model layer id -> KV cache layer id
    std::unordered_map<int32_t, int32_t> map_layer_ids;

    // recovery information used to restore the KV cells to their original state in case of a failure
+    // TODO: do not store as a state in the llama_kv_cache object, instead return upon batch preparation
+    //       to achieve that, first need to refactor the llama_kv_cache interface [TAG: KV_API]
    struct {
        void clear() {
-            cells.clear();
+            states.clear();
        }

-        std::unordered_map<uint32_t, kv_cell> cells;
+        struct state {
+            uint32_t i;
+
+            llama_kv_cells_unified cells;
+        };
+
+        // stack with the partial states before each ubatch
+        std::vector<state> states;
    } recovery;

    // defrag
@@ -256,9 +246,6 @@ private:
    // return true if cells have been moved
    bool defrag_prepare(int32_t n_max_nodes);

-    // find how many cells are currently in use
-    uint32_t cell_max() const;
-
    size_t total_size() const;

    size_t size_k_bytes() const;
@@ -325,7 +312,7 @@ public:
    bool seq_rm  (llama_seq_id seq_id,                              llama_pos p0, llama_pos p1) override;
    void seq_cp  (llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) override;
    void seq_keep(llama_seq_id seq_id)                                                          override;
-    void seq_add (llama_seq_id seq_id,                              llama_pos p0, llama_pos p1, llama_pos delta) override;
+    void seq_add (llama_seq_id seq_id,                              llama_pos p0, llama_pos p1, llama_pos shift) override;
    void seq_div (llama_seq_id seq_id,                              llama_pos p0, llama_pos p1, int d) override;

    llama_pos seq_pos_min(llama_seq_id seq_id) const override;
@@ -431,7 +418,7 @@ public:
    bool seq_rm  (llama_seq_id seq_id,                              llama_pos p0, llama_pos p1) override;
    void seq_cp  (llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) override;
    void seq_keep(llama_seq_id seq_id)                                                          override;
-    void seq_add (llama_seq_id seq_id,                              llama_pos p0, llama_pos p1, llama_pos delta) override;
+    void seq_add (llama_seq_id seq_id,                              llama_pos p0, llama_pos p1, llama_pos shift) override;
    void seq_div (llama_seq_id seq_id,                              llama_pos p0, llama_pos p1, int d) override;

    llama_pos seq_pos_min(llama_seq_id seq_id) const override;
@@ -0,0 +1,379 @@
+#pragma once
+
+#include "llama.h"
+#include "llama-cparams.h"
+
+#include <bitset>
+#include <cassert>
+#include <vector>
+#include <set>
+
+// meta information about KV cells that can be part of multiple sequences at the same time
+// TODO: add unit tests
+class llama_kv_cells_unified {
+public:
+    void reset() {
+        for (uint32_t i = 0; i < pos.size(); ++i) {
+            pos[i]   = -1;
+            shift[i] =  0;
+            seq[i].reset();
+        }
+
+        has_shift = false;
+
+        used.clear();
+
+        for (uint32_t s = 0; s < LLAMA_MAX_PARALLEL_SEQUENCES; ++s) {
+            seq_pos[s].clear();
+        }
+    }
+
+    void reset_shift() {
+        has_shift = false;
+
+        for (uint32_t i = 0; i < shift.size(); ++i) {
+            shift[i] = 0;
+        }
+    }
+
+    uint32_t size() const {
+        return pos.size();
+    }
+
+    void resize(uint32_t n) {
+        pos.resize(n);
+        shift.resize(n);
+        seq.resize(n);
+
+        reset();
+    }
+
+    bool is_empty(uint32_t i) const {
+        assert(i < pos.size());
+        assert((pos[i] < 0 && pos[i] == -1) || pos[i] >= 0);
+
+        return pos[i] == -1;
+    }
+
+    uint32_t get_used() const {
+        return used.size();
+    }
+
+    // the index of the first cell that is used
+    // return 0 if no cells are used
+    uint32_t used_min() const {
+        return used.empty() ? 0 : *used.begin();
+    }
+
+    // the index of the last cell that is used + 1
+    // return 0 if no cells are used
+    uint32_t used_max_p1() const {
+#if 0
+        if (!seq_pos[0].empty()) printf("kv_cells: min[0] = %5d, max[0] = %5d\n", *seq_pos[0].begin(), *seq_pos[0].rbegin());
+        if (!seq_pos[1].empty()) printf("kv_cells: min[1] = %5d, max[1] = %5d\n", *seq_pos[1].begin(), *seq_pos[1].rbegin());
+        if (!seq_pos[2].empty()) printf("kv_cells: min[2] = %5d, max[2] = %5d\n", *seq_pos[2].begin(), *seq_pos[2].rbegin());
+#endif
+
+        return used.empty() ? 0 : *used.rbegin() + 1;
+    }
+
+    bool get_has_shift() const {
+        return has_shift;
+    }
+
+    // move cell isrc to idst (used during defrag)
+    void mv(uint32_t isrc, uint32_t idst) {
+        assert(isrc < pos.size());
+        assert(idst < pos.size());
+
+        pos  [idst] = pos  [isrc];
+        shift[idst] = shift[isrc];
+        seq  [idst] = seq  [isrc];
+
+        pos  [isrc] = -1;
+        shift[isrc] =  0;
+        seq  [isrc].reset();
+
+        used.erase (isrc);
+        used.insert(idst);
+    }
+
+    // copy the state of cells [i, i + n) (used for save/restore the state of the cells)
+    llama_kv_cells_unified cp(uint32_t i, uint32_t n) const {
+        assert(i + n <= pos.size());
+
+        llama_kv_cells_unified res;
+
+        res.resize(n);
+
+        for (uint32_t j = 0; j < n; ++j) {
+            res.pos[j] = pos[i + j];
+            res.seq[j] = seq[i + j];
+
+            assert(shift[i + j] == 0);
+        }
+
+        return res;
+    }
+
+    // set the state of cells [i, i + other.pos.size()) (used for save/restore the state of the cells)
+    void set(uint32_t i, const llama_kv_cells_unified & other) {
+        assert(i + other.pos.size() <= pos.size());
+
+        for (uint32_t j = 0; j < other.pos.size(); ++j) {
+            if (pos[i + j] == -1 && other.pos[j] != -1) {
+                used.insert(i + j);
+            }
+
+            if (pos[i + j] != -1 && other.pos[j] == -1) {
+                used.erase(i + j);
+            }
+
+            if (pos[i + j] != -1) {
+                seq_pos_rm(i + j);
+            }
+
+            pos[i + j] = other.pos[j];
+            seq[i + j] = other.seq[j];
+
+            if (pos[i + j] != -1) {
+                seq_pos_add(i + j);
+            }
+
+            assert(shift[i + j] == 0);
+        }
+    }
+
+    // note: call only if the cell has seq_id
+    // return true if the cell becomes empty
+    bool seq_rm(uint32_t i, llama_seq_id seq_id) {
+        assert(i < pos.size());
+        assert(seq[i].test(seq_id));
+        assert(pos[i] != -1);
+        assert(seq_id >= 0);
+
+        seq[i].reset(seq_id);
+        seq_pos[seq_id].erase(pos[i]);
+
+        if (seq[i].none()) {
+            pos[i] = -1;
+
+            used.erase(i);
+
+            return true;
+        }
+
+        return false;
+    }
+
+    // return true if the cell becomes empty (i.e. it did not contain seq_id before the call)
+    bool seq_keep(uint32_t i, llama_seq_id seq_id) {
+        assert(i < pos.size());
+
+        if (seq[i].test(seq_id)) {
+            seq_pos_rm(i);
+            seq[i].reset();
+
+            seq[i].set(seq_id);
+            seq_pos[seq_id].insert(pos[i]);
+
+            return false;
+        }
+
+        if (seq[i].any()) {
+            seq_pos_rm(i);
+            seq[i].reset();
+
+            pos[i] = -1;
+
+            used.erase(i);
+
+            return true;
+        }
+
+        assert(pos[i] == -1);
+
+        return false;
+    }
+
+    bool seq_has(uint32_t i, llama_seq_id seq_id) const {
+        assert(i < pos.size());
+        assert(seq_id >= 0);
+
+        return seq[i].test(seq_id);
+    }
+
+    // note: call only if the cell is not empty and the seq_id is not in the cell
+    void seq_add(uint32_t i, llama_seq_id seq_id) {
+        assert(i < pos.size());
+        assert(pos[i] != -1);
+        assert(!seq[i].test(seq_id));
+
+        seq[i].set(seq_id);
+        seq_pos[seq_id].insert(pos[i]);
+    }
+
+    // the minimum position of sequence seq_id currently present in any of the cells
+    // return -1 if the sequence is not present
+    llama_pos seq_pos_min(llama_seq_id seq_id) const {
+        assert(seq_id >= 0);
+        assert(seq_id < LLAMA_MAX_PARALLEL_SEQUENCES);
+
+        if (seq_pos[seq_id].empty()) {
+            return -1;
+        }
+
+        return *seq_pos[seq_id].begin();
+    }
+
+    // the maximum position of sequence seq_id currently present in any of the cells
+    // return -1 if the sequence is not present
+    llama_pos seq_pos_max(llama_seq_id seq_id) const {
+        assert(seq_id >= 0);
+        assert(seq_id < LLAMA_MAX_PARALLEL_SEQUENCES);
+
+        if (seq_pos[seq_id].empty()) {
+            return -1;
+        }
+
+        return *seq_pos[seq_id].rbegin();
+    }
+
+    // note: call only if the cell is not empty
+    llama_pos pos_get(uint32_t i) const {
+        assert(i < pos.size());
+        assert(pos[i] != -1);
+
+        return pos[i];
+    }
+
+    // note: call only if the cell is not empty
+    llama_pos get_shift(uint32_t i) const {
+        assert(i < pos.size());
+        assert(pos[i] != -1);
+
+        return shift[i];
+    }
+
+    // check if a cell is not empty and its position is within [p0, p1)
+    bool pos_in(uint32_t i, llama_pos p0, llama_pos p1) const {
+        assert(i < pos.size());
+
+        return pos[i] >= p0 && pos[i] < p1;
+    }
+
+    // set the position of an empty cell
+    // does not modify "has_shift"
+    // note: call only if the cell is empty
+    void pos_set(uint32_t i, llama_pos p) {
+        assert(i < pos.size());
+        assert(pos[i] == -1);
+
+        pos[i] = p;
+
+        used.insert(i);
+    }
+
+    // pos[i] = pos[i] + d
+    // sets "has_shift" to true
+    // note: call only if the cell is not empty
+    bool pos_add(uint32_t i, llama_pos d) {
+        assert(i < pos.size());
+        assert(pos[i] != -1);
+
+        seq_pos_rm(i);
+
+        pos[i]   += d;
+        shift[i] += d;
+
+        seq_pos_add(i);
+
+        has_shift = true;
+
+        if (pos[i] < 0) {
+            seq_pos_rm(i);
+
+            seq[i].reset();
+            pos[i] = -1;
+
+            used.erase(i);
+
+            return true;
+        }
+
+        return false;
+    }
+
+    // pos[i] = pos[i] / d
+    // sets "has_shift" to true
+    // note: call only if the cell is not empty
+    void pos_div(uint32_t i, int d) {
+        assert(i < pos.size());
+        assert(pos[i] != -1);
+
+        const llama_pos p_old = pos[i];
+
+        seq_pos_rm(i);
+
+        pos[i]   /= d;
+        shift[i] += p_old - pos[i];
+
+        seq_pos_add(i);
+
+        has_shift = true;
+    }
+
+private:
+    bool has_shift = false;
+
+    // set of indices of used cells (i.e. pos[i] != -1, allowed to not have any seq_id)
+    std::set<uint32_t> used;
+
+    std::vector<llama_pos> pos;
+
+    // this array accumulates any applied shifts to the pos array since the last reset_shift() call
+    // this is used to queue multiple updates to the pos array, which in the end can be applied in one go:
+    //
+    //   cells.pos_add(x, shift_x);
+    //   cells.pos_div(y, shift_y);
+    //   ...
+    //
+    //   if (cells.has_shift()) {
+    //      for (int i = 0; i < n; ++i) {
+    //          auto shift_i = cells.get_shift(i);
+    //          ...
+    //      }
+    //      cells.reset_shift();
+    //   }
+    //
+    std::vector<llama_pos> shift;
+
+    using bits_t = std::bitset<LLAMA_MAX_PARALLEL_SEQUENCES>;
+
+    // the bitset seq[i] tells us which sequences are currently occupying the i-th cell
+    std::vector<bits_t> seq;
+
+    // the set seq_pos[s] tells us which positions are currently present for sequence s
+    // this way seq_pos[s].begin() and seq_pos[s].rbegin() give us the min/max positions currently in the cache
+    std::set<llama_pos> seq_pos[LLAMA_MAX_PARALLEL_SEQUENCES];
+
+    // helper functions for updating `seq_pos`, once cell at a time:
+
+    // remove cell i
+    void seq_pos_rm(uint32_t i) {
+        for (int s = 0; s < LLAMA_MAX_PARALLEL_SEQUENCES; ++s) {
+            if (seq[i].test(s)) {
+                seq_pos[s].erase(pos[i]);
+            }
+        }
+    }
+
+    // add cell i
+    void seq_pos_add(uint32_t i) {
+        for (int s = 0; s < LLAMA_MAX_PARALLEL_SEQUENCES; ++s) {
+            if (seq[i].test(s)) {
+                seq_pos[s].insert(pos[i]);
+            }
+        }
+    }
+};
@@ -22,7 +22,7 @@ public:
    virtual bool seq_rm  (llama_seq_id seq_id,                              llama_pos p0, llama_pos p1) = 0;
    virtual void seq_cp  (llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) = 0;
    virtual void seq_keep(llama_seq_id seq_id) = 0;
-    virtual void seq_add (llama_seq_id seq_id,                              llama_pos p0, llama_pos p1, llama_pos delta) = 0;
+    virtual void seq_add (llama_seq_id seq_id,                              llama_pos p0, llama_pos p1, llama_pos shift) = 0;
    virtual void seq_div (llama_seq_id seq_id,                              llama_pos p0, llama_pos p1, int d) = 0;

    virtual llama_pos seq_pos_min(llama_seq_id seq_id) const = 0;
@@ -2489,7 +2489,11 @@ bool llama_model::load_tensors(llama_model_loader & ml) {

                    // output
                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
+                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+                    // if output is NULL, init from the input tok embed
+                    if (output == NULL) {
+                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+                    }

                    for (int i = 0; i < n_layer; ++i) {
                        auto & layer = layers[i];
@@ -798,7 +798,7 @@ static void llama_sampler_min_p_apply(struct llama_sampler * smpl, llama_token_d
        }

        // if we have enough values the operation was a success
-        if (filtered_tokens.size() >= ctx->min_keep) {
+        if (!filtered_tokens.empty() && filtered_tokens.size() >= ctx->min_keep) {
            memcpy(cur_p->data, filtered_tokens.data(), filtered_tokens.size()*sizeof(llama_token_data));
            cur_p->size = filtered_tokens.size();
            min_p_applied = true;
@@ -909,7 +909,7 @@ static void llama_sampler_typical_apply(struct llama_sampler * smpl, llama_token
        cum_sum += cur_p->data[idx].p;

        // Check if the running sum is greater than typical or if we have kept at least min_keep tokens
-        if (cum_sum > ctx->p && i >= ctx->min_keep - 1) {
+        if (cum_sum > ctx->p && (ctx->min_keep == 0 || i >= ctx->min_keep - 1)) {
            last_idx = i + 1;
            break;
        }
@@ -92,6 +92,7 @@ llama_test(test-tokenizer-0 NAME test-tokenizer-0-gpt-2             ARGS ${CMAKE
 llama_test(test-tokenizer-0 NAME test-tokenizer-0-llama-bpe         ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama-bpe.gguf)
 llama_test(test-tokenizer-0 NAME test-tokenizer-0-llama-spm         ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama-spm.gguf)
 llama_test(test-tokenizer-0 NAME test-tokenizer-0-mpt               ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-mpt.gguf)
+llama_test(test-tokenizer-0 NAME test-tokenizer-0-nomic-bert-moe    ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-nomic-bert-moe.gguf)
 llama_test(test-tokenizer-0 NAME test-tokenizer-0-phi-3             ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-phi-3.gguf)
 llama_test(test-tokenizer-0 NAME test-tokenizer-0-qwen2             ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-qwen2.gguf)
 llama_test(test-tokenizer-0 NAME test-tokenizer-0-refact            ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-refact.gguf)
@@ -401,9 +401,12 @@ static common_chat_msg simple_assist_msg(const std::string & content, const std:
    }
    return msg;
 }
-const common_chat_msg message_assist                             = simple_assist_msg("Hello, world!\nWhat's up?");
-const common_chat_msg message_assist_empty                       = simple_assist_msg("");
-const common_chat_msg message_assist_thoughts_unparsed_deepseek  = simple_assist_msg("<think>I'm\nthinking</think>Hello, world!\nWhat's up?");
+const common_chat_msg message_assist                              = simple_assist_msg("Hello, world!\nWhat's up?");
+const common_chat_msg message_assist_empty                        = simple_assist_msg("");
+const common_chat_msg message_assist_thoughts_unparsed_deepseek   = simple_assist_msg("<think>I'm\nthinking</think>Hello, world!\nWhat's up?");
+const common_chat_msg message_assist_thoughts_unparsed_md         = simple_assist_msg("<think>I'm\nthinking</think>Hello, world!\nWhat's up?\n```json\n{}```");
+const common_chat_msg message_assist_thoughts_unparsed_md_partial = simple_assist_msg("<think>I'm\nthinking</think>Hello, world!\nWhat's up?\n```json\n{}");
+
 const common_chat_msg message_assist_thoughts_unparsed_r7b       = simple_assist_msg("<|START_THINKING|>I'm\nthinking<|END_THINKING|>Hello, world!\nWhat's up?");
 const common_chat_msg message_assist_thoughts                    = simple_assist_msg("Hello, world!\nWhat's up?", "I'm\nthinking");
 const common_chat_msg message_assist_thoughts_unopened_unparsed  = simple_assist_msg("I'm\nthinking</think>Hello, world!\nWhat's up?");
@@ -591,8 +594,6 @@ static void test_template_output_parsers() {
                {
                    /* .format = */ COMMON_CHAT_FORMAT_COMMAND_R7B,
                    /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK,
-                    /* .reasoning_in_content = */ false,
-                    /* .thinking_forced_open = */ false,
                }));
        assert_msg_equals(message_assist_thoughts_unparsed_deepseek,
            common_chat_parse(
@@ -619,8 +620,6 @@ static void test_template_output_parsers() {
                {
                    /* .format = */ COMMON_CHAT_FORMAT_COMMAND_R7B,
                    /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK,
-                    /* .reasoning_in_content = */ false,
-                    /* .thinking_forced_open = */ false,
                }));
        assert_msg_equals(message_assist_thoughts_call_idx,
            common_chat_parse(
@@ -632,8 +631,6 @@ static void test_template_output_parsers() {
                {
                    /* .format = */ COMMON_CHAT_FORMAT_COMMAND_R7B,
                    /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK,
-                    /* .reasoning_in_content = */ false,
-                    /* .thinking_forced_open = */ false,
                }));
        assert_msg_equals(message_assist_thoughts_no_content,
            common_chat_parse(
@@ -644,8 +641,6 @@ static void test_template_output_parsers() {
                {
                    /* .format = */ COMMON_CHAT_FORMAT_COMMAND_R7B,
                    /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK,
-                    /* .reasoning_in_content = */ false,
-                    /* .thinking_forced_open = */ false,
                }));

        test_templates(tmpls.get(), end_tokens, message_assist_call_idx, tools,
@@ -675,6 +670,18 @@ static void test_template_output_parsers() {

        // Generic tool calls doesn't generate / parse content-only messages symmetrically.

+        assert_equals(
+            simple_assist_msg("{ \"tool_call\" : { \"name\" : \"t"),
+            common_chat_parse(
+                "{ \"tool_call\" : { \"name\" : \"t",
+                /* is_partial= */ true,
+                {
+                    /* .format = */ COMMON_CHAT_FORMAT_GENERIC,
+                    /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK,
+                    /* .reasoning_in_content = */ false,
+                    /* .thinking_forced_open = */ true,
+                    /* .parse_tool_calls = */ false,
+                }));
        assert_equals(
            message_assist_empty,
            common_chat_parse(
@@ -737,14 +744,14 @@ static void test_template_output_parsers() {
        auto tmpls = read_templates("models/templates/Qwen-QwQ-32B.jinja");
        std::vector<std::string> end_tokens{ "<|im_end|>" };

-        assert_equals(COMMON_CHAT_FORMAT_CONTENT_ONLY, common_chat_templates_apply(tmpls.get(), inputs_no_tools).format);
+        assert_equals(COMMON_CHAT_FORMAT_HERMES_2_PRO, common_chat_templates_apply(tmpls.get(), inputs_no_tools).format);
        assert_equals(COMMON_CHAT_FORMAT_HERMES_2_PRO, common_chat_templates_apply(tmpls.get(), inputs_tools).format);
    }
    {
        auto tmpls = read_templates("models/templates/NousResearch-Hermes-2-Pro-Llama-3-8B-tool_use.jinja");
        std::vector<std::string> end_tokens{ "<|im_end|>" };

-        assert_equals(COMMON_CHAT_FORMAT_CONTENT_ONLY, common_chat_templates_apply(tmpls.get(), inputs_no_tools).format);
+        assert_equals(COMMON_CHAT_FORMAT_HERMES_2_PRO, common_chat_templates_apply(tmpls.get(), inputs_no_tools).format);
        assert_equals(COMMON_CHAT_FORMAT_HERMES_2_PRO, common_chat_templates_apply(tmpls.get(), inputs_tools).format);
        assert_equals(
            COMMON_CHAT_FORMAT_HERMES_2_PRO,
@@ -776,11 +783,9 @@ static void test_template_output_parsers() {
                {
                    /* .format = */ COMMON_CHAT_FORMAT_HERMES_2_PRO,
                    /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK,
-                    /* .reasoning_in_content = */ false,
-                    /* .thinking_forced_open = */ false,
                }));
        assert_msg_equals(
-            simple_assist_msg(""),
+            simple_assist_msg("Let's call something\n"),
            common_chat_parse(
                "Let's call something\n"
                "<tool_call>{\"name",
@@ -788,8 +793,6 @@ static void test_template_output_parsers() {
                {
                    /* .format = */ COMMON_CHAT_FORMAT_HERMES_2_PRO,
                    /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK,
-                    /* .reasoning_in_content = */ false,
-                    /* .thinking_forced_open = */ false,
                }));
        assert_msg_equals(message_assist_call_thoughts,
            common_chat_parse(
@@ -979,7 +982,34 @@ static void test_template_output_parsers() {
                {
                    /* .format = */ COMMON_CHAT_FORMAT_HERMES_2_PRO,
                    /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK,
-                    /* .reasoning_in_content = */ false,
+                }));
+        assert_msg_equals(message_assist_thoughts,
+            common_chat_parse(
+                "<think>I'm\nthinking</think>Hello, world!\nWhat's up?",
+                /* is_partial= */ true,
+                {
+                    /* .format = */ COMMON_CHAT_FORMAT_HERMES_2_PRO,
+                    /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK,
+                }));
+        assert_msg_equals(message_assist_thoughts_unparsed_md,
+            common_chat_parse(
+                "<think>I'm\nthinking</think>Hello, world!\nWhat's up?\n```json\n{}```",
+                /* is_partial= */ false,
+                {
+                    /* .format = */ COMMON_CHAT_FORMAT_HERMES_2_PRO,
+                    /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK,
+                    /* .reasoning_in_content = */ true,
+                    /* .thinking_forced_open = */ false,
+                    /* .parse_tool_calls = */ false,
+                }));
+        assert_msg_equals(message_assist_thoughts_unparsed_md_partial,
+            common_chat_parse(
+                "<think>I'm\nthinking</think>Hello, world!\nWhat's up?\n```json\n{}```",
+                /* is_partial= */ true,
+                {
+                    /* .format = */ COMMON_CHAT_FORMAT_HERMES_2_PRO,
+                    /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK,
+                    /* .reasoning_in_content = */ true,
                    /* .thinking_forced_open = */ false,
                }));
        assert_msg_equals(message_assist_thoughts_unopened_unparsed,
@@ -989,8 +1019,6 @@ static void test_template_output_parsers() {
                {
                    /* .format = */ COMMON_CHAT_FORMAT_HERMES_2_PRO,
                    /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK,
-                    /* .reasoning_in_content = */ false,
-                    /* .thinking_forced_open = */ false,
                }));
        assert_msg_equals(message_assist_thoughts,
            common_chat_parse(
@@ -1073,6 +1101,13 @@ static void test_template_output_parsers() {
                    {COMMON_CHAT_FORMAT_FUNCTIONARY_V3_1_LLAMA_3_1}));
        }

+        assert_equals(
+            message_assist_call,
+            common_chat_parse(
+                "<function=special_function>{\"arg1\": 1}<",
+                /* is_partial= */ true,
+                {COMMON_CHAT_FORMAT_FUNCTIONARY_V3_1_LLAMA_3_1}));
+
        test_templates(tmpls.get(), end_tokens, message_assist, tools, "Hello, world!\nWhat's up?", /* expect_grammar_triggered= */ false);
        test_templates(tmpls.get(), end_tokens, message_assist_call, tools,
                      "<function=special_function>{\"arg1\": 1}</function>");
@@ -1187,8 +1222,6 @@ static void test_template_output_parsers() {
                {
                    /* .format = */ COMMON_CHAT_FORMAT_DEEPSEEK_R1,
                    /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK,
-                    /* .reasoning_in_content = */ false,
-                    /* .thinking_forced_open = */ false,
                }));
        assert_msg_equals(message_assist_thoughts_unopened_unparsed,
            common_chat_parse(
@@ -1197,8 +1230,6 @@ static void test_template_output_parsers() {
                {
                    /* .format = */ COMMON_CHAT_FORMAT_DEEPSEEK_R1,
                    /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK,
-                    /* .reasoning_in_content = */ false,
-                    /* .thinking_forced_open = */ false,
                }));
        assert_msg_equals(message_assist_thoughts,
            common_chat_parse(
@@ -1252,8 +1283,6 @@ static void test_template_output_parsers() {
                {
                    /* .format = */ COMMON_CHAT_FORMAT_DEEPSEEK_R1,
                    /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK,
-                    /* .reasoning_in_content = */ false,
-                    /* .thinking_forced_open = */ false,
                }));
        assert_msg_equals(message_assist_thoughts,
            common_chat_parse(
@@ -1295,8 +1324,6 @@ static void test_template_output_parsers() {
                {
                    /* .format = */ COMMON_CHAT_FORMAT_DEEPSEEK_R1,
                    /* .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK,
-                    /* .reasoning_in_content = */ false,
-                    /* .thinking_forced_open = */ false,
                }));
        test_templates(tmpls.get(), end_tokens, message_assist_call, tools,
                "<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>function<｜tool▁sep｜>special_function\n"
@@ -1356,8 +1383,7 @@ static void test_msg_diffs_compute() {

        common_chat_msg_diff diff12;
        diff12.tool_call_index = 0;
-        diff12.tool_call_delta.name = "special_function";
-        // Note: id doesnt change here.
+        // Note: neither id nor name change here.
        diff12.tool_call_delta.arguments = "g1\": 1}";

        assert_equals(
@@ -98,7 +98,7 @@ static void test_top_p(const std::vector<float> & probs, const std::vector<float
    sampler_tester tester(probs, probs_expected);

    DUMP(&tester.cur_p);
-    tester.apply(llama_sampler_init_top_p(p, 1));
+    tester.apply(llama_sampler_init_top_p(p, 0));
    tester.apply(llama_sampler_init_dist (0));
    DUMP(&tester.cur_p);

@@ -109,7 +109,7 @@ static void test_min_p(const std::vector<float> & probs, const std::vector<float
    sampler_tester tester(probs, probs_expected);

    DUMP(&tester.cur_p);
-    tester.apply(llama_sampler_init_min_p(p, 1));
+    tester.apply(llama_sampler_init_min_p(p, 0));
    tester.apply(llama_sampler_init_dist (0));
    DUMP(&tester.cur_p);

@@ -130,7 +130,7 @@ static void test_typical(const std::vector<float> & probs, const std::vector<flo
    sampler_tester tester(probs, probs_expected);

    DUMP(&tester.cur_p);
-    tester.apply(llama_sampler_init_typical(p, 1));
+    tester.apply(llama_sampler_init_typical(p, 0));
    DUMP(&tester.cur_p);

    tester.check();
@@ -332,6 +332,7 @@ int main(void) {
    test_min_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f/0.7f, 0.3f/0.7f},                       0.74f);
    test_min_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f/0.4f},                                  0.76f);
    test_min_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f/0.4f},                                  1.00f);
+    test_min_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f/0.4f},                                  1.05f);

    printf("XTC should:\n");
    test_xtc({0.4f, 0.3f, 0.2f, 0.1f},   {0.1f},                                0.99f, 0.09f);
@@ -341,8 +342,8 @@ int main(void) {
    printf("XTC should not:\n");
    test_xtc({0.4f, 0.3f, 0.2f, 0.1f},   {0.4f, 0.3f, 0.2f, 0.1f},              0.99f, 0.39f);

-    test_typical({0.97f, 0.01f, 0.01f, 0.01f}, {0.97f}, 0.5f);
-    test_typical({0.4f, 0.2f, 0.2f, 0.2f}, {0.2f, 0.2f, 0.2f}, 0.5f);
+    test_typical({0.97f, 0.01f, 0.01f, 0.01f}, {0.97f},            0.5f);
+    test_typical({0.4f, 0.2f, 0.2f, 0.2f},     {0.2f, 0.2f, 0.2f}, 0.5f);

    test_penalties({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0}, {0.25f, 0.25f, 0.25f, 0.25f, 0},   50.0f, 0.0f, 0.0f);
    test_penalties({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2}, {0.5f, 0.5f, 0, 0, 0},       50.0f, 0.0f, 0.0f);
@@ -107,6 +107,7 @@
 // ultravox
 #define TN_CONV1D       "a.conv1d.%d.%s"
 #define TN_MM_AUDIO_MLP "mm.a.mlp.%d.%s"
+#define TN_MM_AUDIO_FC  "mm.a.fc.%s" // fully connected layer
 #define TN_MM_NORM_PRE  "mm.a.norm_pre.%s"
 #define TN_MM_NORM_MID  "mm.a.norm_mid.%s"

@@ -128,6 +129,8 @@ enum projector_type {
    PROJECTOR_TYPE_ULTRAVOX,
    PROJECTOR_TYPE_INTERNVL,
    PROJECTOR_TYPE_LLAMA4,
+    PROJECTOR_TYPE_QWEN2A,
+    PROJECTOR_TYPE_QWEN25O, // will be replaced by QWEN2A or QWEN25VL depending on clip_ctx
    PROJECTOR_TYPE_UNKNOWN,
 };

@@ -145,6 +148,8 @@ static std::map<projector_type, std::string> PROJECTOR_TYPE_NAMES = {
    { PROJECTOR_TYPE_ULTRAVOX,  "ultravox"},
    { PROJECTOR_TYPE_INTERNVL,  "internvl"},
    { PROJECTOR_TYPE_LLAMA4,    "llama4"},
+    { PROJECTOR_TYPE_QWEN2A,    "qwen2a"},
+    { PROJECTOR_TYPE_QWEN25O,   "qwen2.5o"},
 };

 static projector_type clip_projector_type_from_string(const std::string & str) {
@@ -4,6 +4,8 @@
 #include <stddef.h>
 #include <stdint.h>

+// !!! Internal header, to be used by mtmd only !!!
+
 struct clip_ctx;

 struct clip_image_size {
@@ -15,12 +17,22 @@ struct clip_image_f32;
 struct clip_image_u8_batch;
 struct clip_image_f32_batch;

+enum clip_modality {
+    CLIP_MODALITY_VISION,
+    CLIP_MODALITY_AUDIO,
+};
+
 struct clip_context_params {
    bool use_gpu;
    enum ggml_log_level verbosity;
 };

-struct clip_ctx * clip_init(const char * fname, struct clip_context_params ctx_params);
+struct clip_init_result {
+    struct clip_ctx * ctx_v; // vision context
+    struct clip_ctx * ctx_a; // audio context
+};
+
+struct clip_init_result clip_init(const char * fname, struct clip_context_params ctx_params);

 void clip_free(struct clip_ctx * ctx);

@@ -99,3 +111,4 @@ void clip_image_f32_batch_add_mel(struct clip_image_f32_batch * batch, int n_mel

 bool clip_has_vision_encoder(const struct clip_ctx * ctx);
 bool clip_has_audio_encoder(const struct clip_ctx * ctx);
+bool clip_has_whisper_encoder(const struct clip_ctx * ctx);
@@ -284,7 +284,9 @@ int main(int argc, char ** argv) {
    if (is_single_turn) {
        g_is_generating = true;
        if (params.prompt.find(mtmd_default_marker()) == std::string::npos) {
-            params.prompt += mtmd_default_marker();
+            for (size_t i = 0; i < params.image.size(); i++) {
+                params.prompt += mtmd_default_marker();
+            }
        }
        common_chat_msg msg;
        msg.role = "user";
@@ -66,7 +66,8 @@ struct decode_embd_batch {
        }
    }

-    void set_position_mrope(llama_pos pos_0, int nx, int ny, llama_seq_id seq_id) {
+    // M-RoPE for image
+    void set_position_mrope_2d(llama_pos pos_0, int nx, int ny, llama_seq_id seq_id) {
        GGML_ASSERT(n_pos_per_embd == 4);
        seq_id_0[0] = seq_id;
        for (int y = 0; y < ny; y++) {
@@ -85,6 +86,23 @@ struct decode_embd_batch {
        }
    }

+    // M-RoPE for audio
+    void set_position_mrope_1d(llama_pos pos_0, llama_seq_id seq_id) {
+        GGML_ASSERT(n_pos_per_embd == 4);
+        seq_id_0[0] = seq_id;
+        for (int i = 0; i < batch.n_tokens; i++) {
+            pos[i                     ] = pos_0 + i;
+            pos[i + batch.n_tokens    ] = pos_0 + i;
+            pos[i + batch.n_tokens * 2] = pos_0 + i;
+            pos[i + batch.n_tokens * 3] = 0; // last pos dim is unused
+        }
+        for (int i = 0; i < batch.n_tokens; i++) {
+            batch.n_seq_id[i] = 1;
+            batch.seq_id  [i] = seq_id_0.data();
+            batch.logits  [i] = false;
+        }
+    }
+
    llama_batch get_view(int offset, int n_tokens) {
        llama_pos * pos_ptr;
        pos_view.clear();
@@ -146,18 +164,20 @@ int32_t mtmd_helper_decode_image_chunk(
    decode_embd_batch batch_embd(encoded_embd, n_tokens, n_pos_per_embd, n_mmproj_embd);

    if (mtmd_decode_use_mrope(ctx)) {
-        const auto image_tokens = mtmd_input_chunk_get_tokens_image(chunk);
-        if (chunk_type != MTMD_INPUT_CHUNK_TYPE_IMAGE) {
-            LOG_ERR("failed to decode chunk: M-RoPE only accepts image chunk\n");
-            return -1;
+        if (chunk_type == MTMD_INPUT_CHUNK_TYPE_IMAGE) {
+            const auto image_tokens = mtmd_input_chunk_get_tokens_image(chunk);
+            if (!image_tokens) {
+                LOG_ERR("failed to decode chunk: image tokens are null\n");
+                return -1;
+            }
+            const int nx = mtmd_image_tokens_get_nx(image_tokens);
+            const int ny = mtmd_image_tokens_get_ny(image_tokens);
+            batch_embd.set_position_mrope_2d(n_past, nx, ny, seq_id);
+        } else if (chunk_type == MTMD_INPUT_CHUNK_TYPE_AUDIO) {
+            batch_embd.set_position_mrope_1d(n_past, seq_id);
+        } else {
+            GGML_ABORT("invalid chunk type for M-RoPE");
        }
-        if (!image_tokens) {
-            LOG_ERR("failed to decode chunk: image tokens are null\n");
-            return -1;
-        }
-        const int nx = mtmd_image_tokens_get_nx(image_tokens);
-        const int ny = mtmd_image_tokens_get_ny(image_tokens);
-        batch_embd.set_position_mrope(n_past, nx, ny, seq_id);
    } else {
        batch_embd.set_position_normal(n_past, seq_id);
    }
@@ -95,15 +95,21 @@ mtmd_context_params mtmd_context_params_default() {
 }

 struct mtmd_context {
-    struct clip_ctx * ctx_clip;
+    struct clip_ctx * ctx_v; // vision
+    struct clip_ctx * ctx_a; // audio
    const struct llama_model * text_model;
    std::vector<float> image_embd_v; // image embedding vector

    bool print_timings;
    int n_threads;
    std::string media_marker;
-    bool has_vision;
-    bool has_audio;
+    const int n_embd_text;
+
+    // these are not token, but strings used to mark the beginning and end of image/audio embeddings
+    std::string img_beg;
+    std::string img_end;
+    std::string aud_beg;
+    std::string aud_end;

    // for llava-uhd style models, we need special tokens in-between slices
    // minicpmv calls them "slices", llama 4 calls them "tiles"
@@ -132,26 +138,61 @@ struct mtmd_context {
        text_model   (text_model),
        print_timings(ctx_params.print_timings),
        n_threads    (ctx_params.n_threads),
-        media_marker (ctx_params.media_marker)
+        media_marker (ctx_params.media_marker),
+        n_embd_text  (llama_model_n_embd(text_model))
    {
        if (std::string(ctx_params.image_marker) != MTMD_DEFAULT_IMAGE_MARKER) {
            throw std::runtime_error("custom image_marker is not supported anymore, use media_marker instead");
        }

+        if (media_marker.empty()) {
+            throw std::runtime_error("media_marker must not be empty");
+        }
+
        clip_context_params ctx_clip_params;
        ctx_clip_params.use_gpu   = ctx_params.use_gpu;
        ctx_clip_params.verbosity = ctx_params.verbosity;
-        ctx_clip = clip_init(mmproj_fname, ctx_clip_params);
-        if (!ctx_clip) {
+        auto res = clip_init(mmproj_fname, ctx_clip_params);
+        ctx_v = res.ctx_v;
+        ctx_a = res.ctx_a;
+        if (!ctx_v && !ctx_a) {
            throw std::runtime_error(string_format("Failed to load CLIP model from %s\n", mmproj_fname));
        }

-        has_vision = clip_has_vision_encoder(ctx_clip);
-        has_audio  = clip_has_audio_encoder(ctx_clip);
-        use_mrope  = clip_is_qwen2vl(ctx_clip);
+        // if both vision and audio mmproj are present, we need to validate their n_embd
+        if (ctx_v && ctx_a) {
+            int n_embd_v = clip_n_mmproj_embd(ctx_v);
+            int n_embd_a = clip_n_mmproj_embd(ctx_a);
+            if (n_embd_v != n_embd_a) {
+                throw std::runtime_error(string_format(
+                    "mismatch between vision and audio mmproj (n_embd_v = %d, n_embd_a = %d)\n",
+                    n_embd_v, n_embd_a));
+            }
+        }

-        projector_type proj = clip_get_projector_type(ctx_clip);
-        int minicpmv_version = clip_is_minicpmv(ctx_clip);
+        // since we already validate n_embd of vision and audio mmproj,
+        // we can safely assume that they are the same
+        int n_embd_clip = clip_n_mmproj_embd(ctx_v ? ctx_v : ctx_a);
+        if (n_embd_text != n_embd_clip) {
+            throw std::runtime_error(string_format(
+                "mismatch between text model (n_embd = %d) and mmproj (n_embd = %d)\n"
+                "hint: you may be using wrong mmproj\n",
+                n_embd_text, n_embd_clip));
+        }
+        if (ctx_v) {
+            init_vision();
+        }
+        if (ctx_a) {
+            init_audio();
+        }
+    }
+
+    void init_vision() {
+        GGML_ASSERT(ctx_v != nullptr);
+        use_mrope = clip_is_qwen2vl(ctx_v);
+
+        projector_type proj = clip_get_projector_type(ctx_v);
+        int minicpmv_version = clip_is_minicpmv(ctx_v);
        if (minicpmv_version == 2) {
            // minicpmv 2.5 format:
            // <image> (overview) </image><slice><image> (slice) </image><image> (slice) </image>\n ... </slice>
@@ -196,24 +237,82 @@ struct mtmd_context {
            ov_img_first      = false; // overview image is last
        }

-        if (proj == PROJECTOR_TYPE_ULTRAVOX) {
+        // set boi/eoi
+        if (proj == PROJECTOR_TYPE_GEMMA3) {
+            // <start_of_image> ... (image embeddings) ... <end_of_image>
+            img_beg = "<start_of_image>";
+            img_end = "<end_of_image>";
+
+        } else if (proj == PROJECTOR_TYPE_IDEFICS3) {
+            // https://github.com/huggingface/transformers/blob/a42ba80fa520c784c8f11a973ca9034e5f859b79/src/transformers/models/idefics3/processing_idefics3.py#L192-L215
+            img_beg = "<fake_token_around_image><global-img>";
+            img_end = "<fake_token_around_image>";
+
+        } else if (proj == PROJECTOR_TYPE_PIXTRAL) {
+            // https://github.com/huggingface/transformers/blob/1cd110c6cb6a6237614130c470e9a902dbc1a4bd/docs/source/en/model_doc/pixtral.md
+            img_end = "[IMG_END]";
+
+        } else if (proj == PROJECTOR_TYPE_QWEN2VL || proj == PROJECTOR_TYPE_QWEN25VL) {
+            // <|vision_start|> ... (image embeddings) ... <|vision_end|>
+            img_beg = "<|vision_start|>";
+            img_end = "<|vision_end|>";
+
+        } else if (proj == PROJECTOR_TYPE_LLAMA4) {
+            // (more details in mtmd_context constructor)
+            img_beg = "<|image_start|>";
+            img_end = "<|image_end|>";
+            LOG_WRN("%s: llama 4 vision is known to have degraded quality:\n"
+                    "    https://github.com/ggml-org/llama.cpp/pull/13282\n", __func__);
+
+        } else if (proj == PROJECTOR_TYPE_INTERNVL) {
+            // <img> ... (image embeddings) ... </img>
+            img_beg = "<img>";
+            img_end = "</img>";
+
+        }
+    }
+
+    void init_audio() {
+        GGML_ASSERT(ctx_a != nullptr);
+        projector_type proj = clip_get_projector_type(ctx_a);
+
+        if (clip_has_whisper_encoder(ctx_a)) {
            // TODO @ngxson : check if model n_mel is 128 or 80
            w_filters = whisper_precalc_filters::get_128_bins();
        }

-        // warning messages
-        if (proj == PROJECTOR_TYPE_LLAMA4) {
-            LOG_WRN("%s: llama 4 vision is known to have degraded quality:\n"
-                    "    https://github.com/ggml-org/llama.cpp/pull/13282\n", __func__);
-        }
-        if (has_audio) {
-            LOG_WRN("%s: audio input is in experimental stage and may have reduced quality:\n"
-                    "    https://github.com/ggml-org/llama.cpp/pull/13623\n", __func__);
+        LOG_WRN("%s: audio input is in experimental stage and may have reduced quality:\n"
+                "    https://github.com/ggml-org/llama.cpp/discussions/13759\n", __func__);
+
+        if (proj == PROJECTOR_TYPE_QWEN2A) {
+            // <|audio_bos|> ... (embeddings) ... <|audio_eos|>
+            aud_beg = "<|audio_bos|>";
+            aud_end = "<|audio_eos|>";
+
        }
    }

+    // get clip ctx based on chunk type
+    clip_ctx * get_clip_ctx(const mtmd_input_chunk * chunk) const {
+        if (chunk->type == MTMD_INPUT_CHUNK_TYPE_IMAGE) {
+            return ctx_v;
+        } else if (chunk->type == MTMD_INPUT_CHUNK_TYPE_AUDIO) {
+            return ctx_a;
+        }
+        GGML_ABORT("unknown chunk type");
+    }
+
+    projector_type proj_type_v() const {
+        return ctx_v ? clip_get_projector_type(ctx_v) : PROJECTOR_TYPE_UNKNOWN;
+    }
+
+    projector_type proj_type_a() const {
+        return ctx_a ? clip_get_projector_type(ctx_a) : PROJECTOR_TYPE_UNKNOWN;
+    }
+
    ~mtmd_context() {
-        clip_free(ctx_clip);
+        clip_free(ctx_a);
+        clip_free(ctx_v);
    }

 private:
@@ -260,102 +359,315 @@ void mtmd_free(mtmd_context * ctx) {
    }
 }

-// copied from common_tokenize
-static std::vector<llama_token> mtmd_tokenize_text_internal(
-    const struct llama_vocab * vocab,
-           const std::string & text,
-                        bool   add_special,
-                        bool   parse_special) {
-    // upper limit for the number of tokens
-    int n_tokens = text.length() + 2 * add_special;
-    std::vector<llama_token> result(n_tokens);
-    n_tokens = llama_tokenize(vocab, text.data(), text.length(), result.data(), result.size(), add_special, parse_special);
-    if (n_tokens < 0) {
-        result.resize(-n_tokens);
-        int check = llama_tokenize(vocab, text.data(), text.length(), result.data(), result.size(), add_special, parse_special);
-        GGML_ASSERT(check == -n_tokens);
-    } else {
-        result.resize(n_tokens);
-    }
-    return result;
-}
+struct mtmd_tokenizer {
+    mtmd_context * ctx;
+    std::vector<const mtmd_bitmap *> bitmaps;

-int32_t mtmd_tokenize(mtmd_context * ctx,
-            mtmd_input_chunks * output,
+    std::string input_text;
+    bool add_special;
+    bool parse_special;
+    const llama_vocab * vocab;
+
+    mtmd_input_chunks cur;
+
+    mtmd_tokenizer(mtmd_context * ctx,
            const mtmd_input_text * text,
            const mtmd_bitmap ** bitmaps,
-            size_t n_bitmaps) {
-    auto vocab = llama_model_get_vocab(ctx->text_model);
-
-    std::string prompt_modified(text->text);
-    std::string marker_modified(ctx->media_marker);
-    projector_type proj_type = clip_get_projector_type(ctx->ctx_clip);
-
-    // for compatibility, we convert image marker to media marker
-    string_replace_all(prompt_modified, MTMD_DEFAULT_IMAGE_MARKER, ctx->media_marker);
-
-    // a bit hacky here, but works for now
-    // for some models, we need to add prefix and suffix to the image embeddings
-    if (clip_is_gemma3(ctx->ctx_clip)) {
-        // gemma 3
-        // <start_of_image> ... (image embeddings) ... <end_of_image>
-        marker_modified = "<start_of_image>" + ctx->media_marker + "<end_of_image>";
-        string_replace_all(prompt_modified, ctx->media_marker, marker_modified);
-
-    } else if (proj_type == PROJECTOR_TYPE_IDEFICS3) {
-        // https://github.com/huggingface/transformers/blob/a42ba80fa520c784c8f11a973ca9034e5f859b79/src/transformers/models/idefics3/processing_idefics3.py#L192-L215
-        marker_modified = "<fake_token_around_image><global-img>" + ctx->media_marker + "<fake_token_around_image>";
-        string_replace_all(prompt_modified, ctx->media_marker, marker_modified);
-
-    } else if (proj_type == PROJECTOR_TYPE_PIXTRAL) {
-        // https://github.com/huggingface/transformers/blob/1cd110c6cb6a6237614130c470e9a902dbc1a4bd/docs/source/en/model_doc/pixtral.md
-        marker_modified = ctx->media_marker + "[IMG_END]";
-        string_replace_all(prompt_modified, ctx->media_marker, marker_modified);
-
-    } else if (proj_type == PROJECTOR_TYPE_QWEN2VL || proj_type == PROJECTOR_TYPE_QWEN25VL) {
-        // <|vision_start|> ... (image embeddings) ... <|vision_end|>
-        marker_modified = "<|vision_start|>" + ctx->media_marker + "<|vision_end|>";
-        string_replace_all(prompt_modified, ctx->media_marker, marker_modified);
-
-    } else if (proj_type == PROJECTOR_TYPE_LLAMA4) {
-        // (more details in mtmd_context constructor)
-        marker_modified = "<|image_start|>" + ctx->media_marker + "<|image_end|>";
-        string_replace_all(prompt_modified, ctx->media_marker, marker_modified);
-
-    } else if (proj_type == PROJECTOR_TYPE_INTERNVL) {
-        // <img> ... (image embeddings) ... </img>
-        marker_modified = "<img>" + ctx->media_marker + "</img>";
-        string_replace_all(prompt_modified, ctx->media_marker, marker_modified);
+            size_t n_bitmaps) : ctx(ctx), bitmaps(bitmaps, bitmaps + n_bitmaps) {
+        add_special   = text->add_special;
+        parse_special = text->parse_special;
+        input_text    = text->text;
+        vocab         = llama_model_get_vocab(ctx->text_model);

+        // for compatibility, we convert image marker to media marker
+        string_replace_all(input_text, MTMD_DEFAULT_IMAGE_MARKER, ctx->media_marker);
    }

-    // llava-1.5, llava-1.6, Yi-VL, Yi-34B, granite: don't need to add prefix and suffix
-    // for glm-edge, BOI and EOI token's embeddings are not present in the text model
+    int32_t tokenize(mtmd_input_chunks * output) {
+        cur.entries.clear();
+        std::vector<std::string> parts = split_text(input_text, ctx->media_marker);
+        size_t i_bm = 0; // index of the current bitmap
+        for (auto & part : parts) {
+            if (part == ctx->media_marker) {
+                // this is a marker, we should add the next bitmap
+                if (i_bm >= bitmaps.size()) {
+                    LOG_ERR("%s: error: number of bitmaps (%zu) does not match number of markers (%zu)\n",
+                            __func__, bitmaps.size(), parts.size() - 1);
+                    return 1;
+                }
+                const mtmd_bitmap * bitmap = bitmaps[i_bm++];
+                int32_t res = add_media(bitmap);
+                if (res != 0) {
+                    return res;
+                }
+            } else {
+                // this is a text part, we should add it as text
+                add_text(part, parse_special);
+            }
+        }

-    std::vector<std::string> parts = string_split_str(prompt_modified, ctx->media_marker);
-    output->entries.clear();
-    output->entries.reserve(parts.size());
+        if (add_special && llama_vocab_get_add_bos(vocab)) {
+            // if first chunk is text, we add BOS token to first text chunk
+            // otherwise, create a new text chunk with BOS token
+            if (!cur.entries.empty() && cur.entries[0].type == MTMD_INPUT_CHUNK_TYPE_TEXT) {
+                // add BOS token to the beginning of first text chunk
+                cur.entries[0].tokens_text.insert(cur.entries[0].tokens_text.begin(), llama_vocab_bos(vocab));
+            } else {
+                // create a new text chunk with BOS token at the beginning
+                mtmd_input_chunk bos_chunk{
+                    MTMD_INPUT_CHUNK_TYPE_TEXT,
+                    {llama_vocab_bos(vocab)},
+                    nullptr, // image tokens
+                    nullptr, // audio tokens
+                };
+                cur.entries.insert(cur.entries.begin(), std::move(bos_chunk));
+            }
+        }

-    size_t i_bm = 0;
+        if (add_special && llama_vocab_get_add_eos(vocab)) {
+            // if last chunk is text, we add EOS token to it
+            add_text({llama_vocab_eos(vocab)});
+        }

-    // utility for adding raw tokens
-    auto add_text_chunk = [&output](std::vector<llama_token> && tokens) {
-        mtmd_input_chunk chunk{
-            MTMD_INPUT_CHUNK_TYPE_TEXT,
-            std::move(tokens),
-            nullptr, // image tokens
-            nullptr, // audio tokens
-        };
-        output->entries.emplace_back(std::move(chunk));
-    };
+        if (i_bm != bitmaps.size()) {
+            LOG_ERR("%s: error: number of bitmaps (%zu) does not match number of markers (%zu)\n",
+                    __func__, bitmaps.size(), parts.size() - 1);
+            return 1;
+        }

-    // utility for splitting batch of multiple images into chunks of batch having single images
-    auto split_batch_to_chunk = [&ctx](clip_image_f32_batch && batch_f32, const std::string & id) {
+        *output = std::move(cur);
+
+        return 0;
+    }
+
+    void add_text(const std::string & txt, bool parse_special) {
+        LOG_DBG("%s: %s\n", __func__, txt.c_str());
+        auto tokens = mtmd_tokenize_text_internal(vocab, txt, /* add_special */ false, parse_special);
+        add_text(tokens);
+    }
+
+    void add_text(const std::vector<llama_token> & tokens) {
+        if (tokens.empty()) {
+            return;
+        }
+        // if last entry is also a text chunk, add tokens to it instead of creating new chunk
+        if (!cur.entries.empty() && cur.entries.back().type == MTMD_INPUT_CHUNK_TYPE_TEXT) {
+            cur.entries.back().tokens_text.insert(
+                                            cur.entries.back().tokens_text.end(),
+                                            tokens.begin(),
+                                            tokens.end());
+        } else {
+            mtmd_input_chunk chunk{
+                MTMD_INPUT_CHUNK_TYPE_TEXT,
+                tokens,
+                nullptr, // image tokens
+                nullptr, // audio tokens
+            };
+            cur.entries.emplace_back(std::move(chunk));
+        }
+    }
+
+    int32_t add_media(const mtmd_bitmap * bitmap) {
+        if (!bitmap->is_audio) {
+            // handle image
+
+            if (!ctx->ctx_v) {
+                LOG_ERR("%s: error: model does not support vision input\n", __func__);
+                return 2;
+            }
+
+            if (!ctx->img_beg.empty()) {
+                add_text(ctx->img_beg, true); // add image begin token
+            }
+
+            // convert mtmd_bitmap to clip_image_u8
+            clip_image_u8_ptr img_u8(clip_image_u8_init());
+            img_u8->nx = bitmap->nx;
+            img_u8->ny = bitmap->ny;
+            img_u8->buf.resize(bitmap->data.size());
+            std::memcpy(img_u8->buf.data(), bitmap->data.data(), img_u8->nx * img_u8->ny * 3);
+
+            // preprocess image
+            clip_image_f32_batch batch_f32;
+            bool ok = clip_image_preprocess(ctx->ctx_v, img_u8.get(), &batch_f32);
+            if (!ok) {
+                LOG_ERR("Unable to preprocess image\n");
+                return 2;
+            }
+
+            // handle llava-uhd style preprocessing
+            if (
+                ctx->slice_tmpl == MTMD_SLICE_TMPL_MINICPMV_2_5
+                || ctx->slice_tmpl == MTMD_SLICE_TMPL_MINICPMV_2_6
+                || ctx->slice_tmpl == MTMD_SLICE_TMPL_LLAMA4
+            ) {
+                // split batch into chunks of single images
+                auto chunks = split_batch_to_chunk(std::move(batch_f32), bitmap->id);
+                GGML_ASSERT(chunks.size() > 0);
+
+                auto ov_chunk = std::move(chunks.front());
+                chunks.erase(chunks.begin());
+
+                // add overview image (first)
+                if (ctx->ov_img_first) {
+                    if (ctx->tok_ov_img_start != LLAMA_TOKEN_NULL) {
+                        add_text({ctx->tok_ov_img_start});
+                    }
+                    cur.entries.emplace_back(std::move(ov_chunk));
+                    if (ctx->tok_ov_img_end != LLAMA_TOKEN_NULL) {
+                        add_text({ctx->tok_ov_img_end});
+                    }
+                }
+
+                // add slices (or tiles)
+                if (!chunks.empty()) {
+                    const int n_col = batch_f32.grid_x;
+                    const int n_row = batch_f32.grid_y;
+                    if (ctx->tok_slices_start != LLAMA_TOKEN_NULL) {
+                        add_text({ctx->tok_slices_start});
+                    }
+                    for (int y = 0; y < n_row; y++) {
+                        for (int x = 0; x < n_col; x++) {
+                            const bool is_last_in_row = (x == n_col - 1);
+                            if (ctx->tok_sli_img_start != LLAMA_TOKEN_NULL) {
+                                add_text({ctx->tok_sli_img_start});
+                            }
+                            cur.entries.emplace_back(std::move(chunks[y * n_col + x]));
+                            if (ctx->tok_sli_img_end != LLAMA_TOKEN_NULL) {
+                                add_text({ctx->tok_sli_img_end});
+                            }
+                            if (!is_last_in_row && ctx->tok_sli_img_mid != LLAMA_TOKEN_NULL) {
+                                add_text({ctx->tok_sli_img_mid});
+                            }
+                        }
+                        if ((y != n_row - 1 || ctx->tok_row_end_trail) && ctx->tok_row_end != LLAMA_TOKEN_NULL) {
+                            add_text({ctx->tok_row_end});
+                        }
+                    }
+                    if (ctx->tok_slices_end != LLAMA_TOKEN_NULL) {
+                        add_text({ctx->tok_slices_end});
+                    }
+                }
+
+                // add overview image (last)
+                if (!ctx->ov_img_first) {
+                    if (ctx->tok_ov_img_start != LLAMA_TOKEN_NULL) {
+                        add_text({ctx->tok_ov_img_start});
+                    }
+                    cur.entries.emplace_back(std::move(ov_chunk));
+                    if (ctx->tok_ov_img_end != LLAMA_TOKEN_NULL) {
+                        add_text({ctx->tok_ov_img_end});
+                    }
+                }
+
+            } else {
+                size_t n_tokens = 0;
+                for (const auto & entry : batch_f32.entries) {
+                    n_tokens += clip_n_output_tokens(ctx->ctx_v, entry.get());
+                }
+
+                mtmd_image_tokens_ptr image_tokens(new mtmd_image_tokens);
+                if (ctx->use_mrope) {
+                    // for Qwen2VL, we need this information for M-RoPE decoding positions
+                    image_tokens->nx = clip_n_output_tokens_x(ctx->ctx_v, batch_f32.entries[0].get());
+                    image_tokens->ny = clip_n_output_tokens_y(ctx->ctx_v, batch_f32.entries[0].get());
+                    image_tokens->use_mrope_pos = true;
+                } else {
+                    // other models, we only need the total number of tokens
+                    image_tokens->nx = n_tokens;
+                    image_tokens->ny = 1;
+                }
+                image_tokens->batch_f32 = std::move(batch_f32);
+                image_tokens->id = bitmap->id; // optional
+
+                LOG_DBG("image_tokens->nx = %d\n", image_tokens->nx);
+                LOG_DBG("image_tokens->ny = %d\n", image_tokens->ny);
+                LOG_DBG("batch_f32 size = %d\n", (int)image_tokens->batch_f32.entries.size());
+
+                mtmd_input_chunk chunk{
+                    MTMD_INPUT_CHUNK_TYPE_IMAGE,
+                    {}, // text tokens
+                    std::move(image_tokens),
+                    nullptr, // audio tokens
+                };
+                cur.entries.emplace_back(std::move(chunk));
+            }
+
+            if (!ctx->img_end.empty()) {
+                add_text(ctx->img_end, true); // add image end token
+            }
+
+        } else {
+            // handle audio
+
+            if (!ctx->ctx_a) {
+                LOG_ERR("%s: error: model does not support audio input\n", __func__);
+                return 2;
+            }
+
+            if (bitmap->data.size() == 0) {
+                LOG_ERR("%s: error: empty audio data\n", __func__);
+                return 2;
+            }
+
+            if (!ctx->aud_beg.empty()) {
+                add_text(ctx->aud_beg, true); // add audio begin token
+            }
+
+            // preprocess audio
+            GGML_ASSERT(ctx->w_filters.n_mel); // make sure we have filter preloaded
+            std::vector<whisper_preprocessor::whisper_mel> mel_spec_chunks;
+            const float * samples = (const float *)bitmap->data.data();
+            size_t n_samples = bitmap->data.size() / sizeof(float);
+            bool ok = whisper_preprocessor::preprocess_audio(samples, n_samples, ctx->w_filters, mel_spec_chunks);
+            if (!ok) {
+                LOG_ERR("Unable to preprocess audio\n");
+                return 2;
+            }
+
+            // consider each mel_spec as a separate audio chunk
+            // TODO: maybe support batching, but this may come with memory cost
+            for (auto & mel_spec : mel_spec_chunks) {
+                clip_image_f32_ptr mel_f32(clip_image_f32_init());
+                mel_f32->nx  = mel_spec.n_len;
+                mel_f32->ny  = mel_spec.n_mel;
+                mel_f32->buf = std::move(mel_spec.data);
+                size_t n_tokens = clip_n_output_tokens(ctx->ctx_a, mel_f32.get());
+
+                clip_image_f32_batch batch_f32;
+                batch_f32.is_audio = true;
+                batch_f32.entries.push_back(std::move(mel_f32));
+
+                mtmd_audio_tokens_ptr audio_tokens(new mtmd_audio_tokens);
+                audio_tokens->n_tokens = n_tokens;
+                audio_tokens->batch_f32 = std::move(batch_f32);
+                audio_tokens->id = bitmap->id; // optional
+
+                LOG_DBG("audio_tokens->n_tokens = %d\n", audio_tokens->n_tokens);
+
+                mtmd_input_chunk chunk{
+                    MTMD_INPUT_CHUNK_TYPE_AUDIO,
+                    {}, // text tokens
+                    nullptr, // image tokens
+                    std::move(audio_tokens),
+                };
+                cur.entries.emplace_back(std::move(chunk));
+            }
+
+            if (!ctx->aud_end.empty()) {
+                add_text(ctx->aud_end, true); // add audio end token
+            }
+        }
+
+        return 0;
+    }
+
+    std::vector<mtmd_input_chunk> split_batch_to_chunk(clip_image_f32_batch && batch_f32, const std::string & id) {
        std::vector<mtmd_input_chunk> chunks;

        for (auto & entry : batch_f32.entries) {
            mtmd_image_tokens_ptr image_tokens(new mtmd_image_tokens);
-            image_tokens->nx = clip_n_output_tokens(ctx->ctx_clip, entry.get());
+            image_tokens->nx = clip_n_output_tokens(ctx->ctx_v, entry.get());
            image_tokens->ny = 1;
            image_tokens->batch_f32.entries.push_back(std::move(entry));
            image_tokens->id = id;
@@ -370,222 +682,57 @@ int32_t mtmd_tokenize(mtmd_context * ctx,
        }

        return chunks;
-    };
-
-    for (const auto & part : parts) {
-        // printf("tokenizing part: %s\n", part.c_str());
-        bool add_bos = &parts.front() == &part;
-        auto tokens = mtmd_tokenize_text_internal(vocab, part, text->add_special && add_bos, text->parse_special);
-        if (tokens.empty()) {
-            continue;
-        }
-        mtmd_input_chunk chunk{
-            MTMD_INPUT_CHUNK_TYPE_TEXT,
-            std::move(tokens),
-            nullptr, // image tokens
-            nullptr, // audio tokens
-        };
-        output->entries.emplace_back(std::move(chunk));
-
-        // only add image/audio tokens to middle of 2 parts
-        // therefore, we skip handling image/audio if this is the last part
-        if (&parts.back() == &part) {
-            continue;
-        }
-
-        if (!bitmaps[i_bm]->is_audio) {
-            // handle image
-
-            if (i_bm >= n_bitmaps) {
-                LOG_ERR("%s: error: not enough images for %d parts\n", __func__, (int)parts.size());
-                return 1;
-            }
-
-            if (!ctx->has_vision) {
-                LOG_ERR("%s: error: model does not support vision input\n", __func__);
-                return 2;
-            }
-
-            // convert mtmd_bitmap to clip_image_u8
-            clip_image_u8_ptr img_u8(clip_image_u8_init());
-            img_u8->nx = bitmaps[i_bm]->nx;
-            img_u8->ny = bitmaps[i_bm]->ny;
-            img_u8->buf.resize(bitmaps[i_bm]->data.size());
-            std::memcpy(img_u8->buf.data(), bitmaps[i_bm]->data.data(), img_u8->nx * img_u8->ny * 3);
-
-            // preprocess image
-            clip_image_f32_batch batch_f32;
-            bool ok = clip_image_preprocess(ctx->ctx_clip, img_u8.get(), &batch_f32);
-            if (!ok) {
-                LOG_ERR("Unable to preprocess image\n");
-                return 2;
-            }
-
-            // handle llava-uhd style preprocessing
-            if (
-                ctx->slice_tmpl == MTMD_SLICE_TMPL_MINICPMV_2_5
-                || ctx->slice_tmpl == MTMD_SLICE_TMPL_MINICPMV_2_6
-                || ctx->slice_tmpl == MTMD_SLICE_TMPL_LLAMA4
-            ) {
-                // split batch into chunks of single images
-                auto chunks = split_batch_to_chunk(std::move(batch_f32), bitmaps[i_bm]->id);
-                GGML_ASSERT(chunks.size() > 0);
-
-                auto ov_chunk = std::move(chunks.front());
-                chunks.erase(chunks.begin());
-
-                // add overview image (first)
-                if (ctx->ov_img_first) {
-                    if (ctx->tok_ov_img_start != LLAMA_TOKEN_NULL) {
-                        add_text_chunk({ctx->tok_ov_img_start});
-                    }
-                    output->entries.emplace_back(std::move(ov_chunk));
-                    if (ctx->tok_ov_img_end != LLAMA_TOKEN_NULL) {
-                        add_text_chunk({ctx->tok_ov_img_end});
-                    }
-                }
-
-                // add slices (or tiles)
-                if (!chunks.empty()) {
-                    const int n_col = batch_f32.grid_x;
-                    const int n_row = batch_f32.grid_y;
-                    if (ctx->tok_slices_start != LLAMA_TOKEN_NULL) {
-                        add_text_chunk({ctx->tok_slices_start});
-                    }
-                    for (int y = 0; y < n_row; y++) {
-                        for (int x = 0; x < n_col; x++) {
-                            const bool is_last_in_row = (x == n_col - 1);
-                            if (ctx->tok_sli_img_start != LLAMA_TOKEN_NULL) {
-                                add_text_chunk({ctx->tok_sli_img_start});
-                            }
-                            output->entries.emplace_back(std::move(chunks[y * n_col + x]));
-                            if (ctx->tok_sli_img_end != LLAMA_TOKEN_NULL) {
-                                add_text_chunk({ctx->tok_sli_img_end});
-                            }
-                            if (!is_last_in_row && ctx->tok_sli_img_mid != LLAMA_TOKEN_NULL) {
-                                add_text_chunk({ctx->tok_sli_img_mid});
-                            }
-                        }
-                        if ((y != n_row - 1 || ctx->tok_row_end_trail) && ctx->tok_row_end != LLAMA_TOKEN_NULL) {
-                            add_text_chunk({ctx->tok_row_end});
-                        }
-                    }
-                    if (ctx->tok_slices_end != LLAMA_TOKEN_NULL) {
-                        add_text_chunk({ctx->tok_slices_end});
-                    }
-                }
-
-                // add overview image (last)
-                if (!ctx->ov_img_first) {
-                    if (ctx->tok_ov_img_start != LLAMA_TOKEN_NULL) {
-                        add_text_chunk({ctx->tok_ov_img_start});
-                    }
-                    output->entries.emplace_back(std::move(ov_chunk));
-                    if (ctx->tok_ov_img_end != LLAMA_TOKEN_NULL) {
-                        add_text_chunk({ctx->tok_ov_img_end});
-                    }
-                }
-
-            } else {
-                size_t n_tokens = 0;
-                for (const auto & entry : batch_f32.entries) {
-                    n_tokens += clip_n_output_tokens(ctx->ctx_clip, entry.get());
-                }
-
-                mtmd_image_tokens_ptr image_tokens(new mtmd_image_tokens);
-                if (ctx->use_mrope) {
-                    // for Qwen2VL, we need this information for M-RoPE decoding positions
-                    image_tokens->nx = clip_n_output_tokens_x(ctx->ctx_clip, batch_f32.entries[0].get());
-                    image_tokens->ny = clip_n_output_tokens_y(ctx->ctx_clip, batch_f32.entries[0].get());
-                    image_tokens->use_mrope_pos = true;
-                } else {
-                    // other models, we only need the total number of tokens
-                    image_tokens->nx = n_tokens;
-                    image_tokens->ny = 1;
-                }
-                image_tokens->batch_f32 = std::move(batch_f32);
-                image_tokens->id = bitmaps[i_bm]->id; // optional
-
-                LOG_DBG("image_tokens->nx = %d\n", image_tokens->nx);
-                LOG_DBG("image_tokens->ny = %d\n", image_tokens->ny);
-                LOG_DBG("batch_f32 size = %d\n", (int)image_tokens->batch_f32.entries.size());
-
-                mtmd_input_chunk chunk{
-                    MTMD_INPUT_CHUNK_TYPE_IMAGE,
-                    {}, // text tokens
-                    std::move(image_tokens),
-                    nullptr, // audio tokens
-                };
-                output->entries.emplace_back(std::move(chunk));
-            }
-
-            i_bm++; // move to next image
-            continue;
-
-        } else {
-            // handle audio
-
-            if (i_bm >= n_bitmaps) {
-                LOG_ERR("%s: error: not enough images for %d parts\n", __func__, (int)parts.size());
-                return 1;
-            }
-
-            if (!ctx->has_audio) {
-                LOG_ERR("%s: error: model does not support audio input\n", __func__);
-                return 2;
-            }
-
-            if (bitmaps[i_bm]->data.size() == 0) {
-                LOG_ERR("%s: error: empty audio data\n", __func__);
-                return 2;
-            }
-
-            // preprocess audio
-            GGML_ASSERT(ctx->w_filters.n_mel); // make sure we have filter preloaded
-            std::vector<whisper_preprocessor::whisper_mel> mel_spec_chunks;
-            const float * samples = (const float *)bitmaps[i_bm]->data.data();
-            size_t n_samples = bitmaps[i_bm]->data.size() / sizeof(float);
-            bool ok = whisper_preprocessor::preprocess_audio(samples, n_samples, ctx->w_filters, mel_spec_chunks);
-            if (!ok) {
-                LOG_ERR("Unable to preprocess audio\n");
-                return 2;
-            }
-
-            // consider each mel_spec as a separate audio chunk
-            // TODO: maybe support batching, but this may come with memory cost
-            for (auto & mel_spec : mel_spec_chunks) {
-                clip_image_f32_ptr mel_f32(clip_image_f32_init());
-                mel_f32->nx  = mel_spec.n_len;
-                mel_f32->ny  = mel_spec.n_mel;
-                mel_f32->buf = std::move(mel_spec.data);
-                size_t n_tokens = clip_n_output_tokens(ctx->ctx_clip, mel_f32.get());
-
-                clip_image_f32_batch batch_f32;
-                batch_f32.is_audio = true;
-                batch_f32.entries.push_back(std::move(mel_f32));
-
-                mtmd_audio_tokens_ptr audio_tokens(new mtmd_audio_tokens);
-                audio_tokens->n_tokens = n_tokens;
-                audio_tokens->batch_f32 = std::move(batch_f32);
-                audio_tokens->id = bitmaps[i_bm]->id; // optional
-
-                LOG_DBG("audio_tokens->n_tokens = %d\n", audio_tokens->n_tokens);
-
-                mtmd_input_chunk chunk{
-                    MTMD_INPUT_CHUNK_TYPE_AUDIO,
-                    {}, // text tokens
-                    nullptr, // image tokens
-                    std::move(audio_tokens),
-                };
-                output->entries.emplace_back(std::move(chunk));
-            }
-
-            i_bm++;
-            continue;
-        }
    }

-    return 0;
+    // for example: "a <__media__> b <__media__> c" --> "a", "<__media__>", "b", "<__media__>", "c"
+    static std::vector<std::string> split_text(const std::string & input, const std::string & delimiter) {
+        std::vector<std::string> result;
+        if (input.empty()) {
+            return result;
+        }
+        size_t start = 0;
+        size_t pos = 0;
+        while ((pos = input.find(delimiter, start)) != std::string::npos) {
+            if (pos > start) {
+                result.push_back(input.substr(start, pos - start));
+            }
+            result.push_back(delimiter);
+            start = pos + delimiter.length();
+        }
+        if (start < input.length()) {
+            result.push_back(input.substr(start));
+        }
+        return result;
+    }
+
+    // copied from common_tokenize
+    static std::vector<llama_token> mtmd_tokenize_text_internal(
+        const struct llama_vocab * vocab,
+               const std::string & text,
+                            bool   add_special,
+                            bool   parse_special) {
+        // upper limit for the number of tokens
+        int n_tokens = text.length() + 2 * add_special;
+        std::vector<llama_token> result(n_tokens);
+        n_tokens = llama_tokenize(vocab, text.data(), text.length(), result.data(), result.size(), add_special, parse_special);
+        if (n_tokens < 0) {
+            result.resize(-n_tokens);
+            int check = llama_tokenize(vocab, text.data(), text.length(), result.data(), result.size(), add_special, parse_special);
+            GGML_ASSERT(check == -n_tokens);
+        } else {
+            result.resize(n_tokens);
+        }
+        return result;
+    }
+};
+
+int32_t mtmd_tokenize(mtmd_context * ctx,
+            mtmd_input_chunks * output,
+            const mtmd_input_text * text,
+            const mtmd_bitmap ** bitmaps,
+            size_t n_bitmaps) {
+    mtmd_tokenizer tokenizer(ctx, text, bitmaps, n_bitmaps);
+    return tokenizer.tokenize(output);
 }

 int32_t mtmd_encode_chunk(mtmd_context * ctx, const mtmd_input_chunk * chunk) {
@@ -593,41 +740,54 @@ int32_t mtmd_encode_chunk(mtmd_context * ctx, const mtmd_input_chunk * chunk) {
        LOG_WRN("mtmd_encode_chunk has no effect for text chunks\n");
        return 0;
    } else if (chunk->type == MTMD_INPUT_CHUNK_TYPE_IMAGE) {
+        if (!ctx->ctx_v) {
+            LOG_ERR("%s: model does not support vision input\n", __func__);
+            return 1;
+        }
        return mtmd_encode(ctx, chunk->tokens_image.get());
    } else if (chunk->type == MTMD_INPUT_CHUNK_TYPE_AUDIO) {
-        int n_mmproj_embd = clip_n_mmproj_embd(ctx->ctx_clip);
+        if (!ctx->ctx_a) {
+            LOG_ERR("%s: model does not support audio input\n", __func__);
+            return 1;
+        }
+        int n_mmproj_embd = ctx->n_embd_text;
        ctx->image_embd_v.resize(chunk->tokens_audio->n_tokens * n_mmproj_embd);
        bool ok = clip_image_batch_encode(
-            ctx->ctx_clip,
+            ctx->ctx_a,
            ctx->n_threads,
            &chunk->tokens_audio->batch_f32,
            ctx->image_embd_v.data());
        return ok ? 0 : 1;
    }

-    LOG_ERR("mtmd_encode_chunk: unknown chunk type %d\n", (int)chunk->type);
+    LOG_ERR("%s: unknown chunk type %d\n", __func__, (int)chunk->type);
    return 1;
 }

 int32_t mtmd_encode(mtmd_context * ctx, const mtmd_image_tokens * image_tokens) {
-    int n_mmproj_embd = clip_n_mmproj_embd(ctx->ctx_clip);
+    clip_ctx * ctx_clip = ctx->ctx_v;
+    if (!ctx_clip) {
+        LOG_ERR("%s: this API does not support non-vision input, please use mtmd_encode_chunk instead\n", __func__);
+        return 1;
+    }
+    int n_mmproj_embd = clip_n_mmproj_embd(ctx_clip);
    ctx->image_embd_v.resize(image_tokens->n_tokens() * n_mmproj_embd);
    bool ok = false;

-    if (clip_is_llava(ctx->ctx_clip) || clip_is_minicpmv(ctx->ctx_clip) || clip_is_glm(ctx->ctx_clip)) {
+    if (clip_is_llava(ctx_clip) || clip_is_minicpmv(ctx_clip) || clip_is_glm(ctx_clip)) {
        // TODO @ngxson : llava does not support batched encoding ; this should be fixed inside clip_image_batch_encode()
        const auto & entries = image_tokens->batch_f32.entries;
        for (size_t i = 0; i < entries.size(); i++) {
-            int n_tokens_per_image = clip_n_output_tokens(ctx->ctx_clip, entries[i].get());
+            int n_tokens_per_image = clip_n_output_tokens(ctx_clip, entries[i].get());
            ok = clip_image_encode(
-                ctx->ctx_clip,
+                ctx_clip,
                ctx->n_threads,
                entries[i].get(),
                ctx->image_embd_v.data() + i*n_mmproj_embd*n_tokens_per_image);
        }
    } else {
        ok = clip_image_batch_encode(
-            ctx->ctx_clip,
+            ctx_clip,
            ctx->n_threads,
            &image_tokens->batch_f32,
            ctx->image_embd_v.data());
@@ -641,8 +801,7 @@ float * mtmd_get_output_embd(mtmd_context * ctx) {
 }

 bool mtmd_decode_use_non_causal(mtmd_context * ctx) {
-    projector_type proj_type = clip_get_projector_type(ctx->ctx_clip);
-    if (proj_type == PROJECTOR_TYPE_GEMMA3) {
+    if (ctx->ctx_v && clip_get_projector_type(ctx->ctx_v) == PROJECTOR_TYPE_GEMMA3) {
        return true;
    }
    return false;
@@ -653,11 +812,11 @@ bool mtmd_decode_use_mrope(mtmd_context * ctx) {
 }

 bool mtmd_support_vision(mtmd_context * ctx) {
-    return ctx->has_vision;
+    return ctx->ctx_v != nullptr;
 }

 bool mtmd_support_audio(mtmd_context * ctx) {
-    return ctx->has_audio;
+    return ctx->ctx_a != nullptr;
 }

 // these 2 helpers below use internal clip_image_u8_ptr,
@@ -203,6 +203,8 @@ MTMD_API int32_t mtmd_encode_chunk(mtmd_context * ctx,
                                   const mtmd_input_chunk * chunk);

 // get output embeddings from the last encode pass
+// the reading size (in bytes) is equal to:
+// llama_model_n_embd(model) * mtmd_input_chunk_get_n_tokens(chunk) * sizeof(float)
 MTMD_API float * mtmd_get_output_embd(mtmd_context * ctx);

 /////////////////////////////////////////
@@ -25,80 +25,99 @@ RUN_HUGE_TESTS=false
 if [ "${1:-}" = "huge" ]; then
    RUN_HUGE_TESTS=true
    RUN_BIG_TESTS=true
-    echo "Include BIG models..."
+    echo "Include BIG and HUGE models..."
 fi

 ###############

-arr_bin=()
+arr_prefix=()
 arr_hf=()
 arr_tmpl=() # chat template
+arr_file=()

-add_test() {
-    local bin=$1
-    local hf=$2
-    local tmpl=${3:-""} # default to empty string if not provided
-    arr_bin+=("$bin")
+add_test_vision() {
+    local hf=$1
+    local tmpl=${2:-""} # default to empty string if not provided
+    arr_prefix+=("[vision]")
    arr_hf+=("$hf")
    arr_tmpl+=("$tmpl")
+    arr_file+=("test-1.jpeg")
 }

-add_test "llama-mtmd-cli"  "ggml-org/SmolVLM-500M-Instruct-GGUF:Q8_0"
-add_test "llama-mtmd-cli"  "ggml-org/SmolVLM2-2.2B-Instruct-GGUF:Q4_K_M"
-add_test "llama-mtmd-cli"  "ggml-org/SmolVLM2-500M-Video-Instruct-GGUF:Q8_0"
-add_test "llama-mtmd-cli"  "ggml-org/gemma-3-4b-it-GGUF:Q4_K_M"
-add_test "llama-mtmd-cli"  "THUDM/glm-edge-v-5b-gguf:Q4_K_M"
-add_test "llama-mtmd-cli"  "second-state/Llava-v1.5-7B-GGUF:Q2_K"            "vicuna"
-add_test "llama-mtmd-cli"  "cjpais/llava-1.6-mistral-7b-gguf:Q3_K_M"         "vicuna"
-add_test "llama-mtmd-cli"  "ibm-research/granite-vision-3.2-2b-GGUF:Q4_K_M"
-add_test "llama-mtmd-cli"  "second-state/MiniCPM-Llama3-V-2_5-GGUF:Q2_K"  # model from openbmb is corrupted
-add_test "llama-mtmd-cli"  "openbmb/MiniCPM-V-2_6-gguf:Q2_K"
-add_test "llama-mtmd-cli"  "openbmb/MiniCPM-o-2_6-gguf:Q4_0"
-add_test "llama-mtmd-cli"  "bartowski/Qwen2-VL-2B-Instruct-GGUF:Q4_K_M"
-add_test "llama-mtmd-cli"  "ggml-org/Qwen2.5-VL-3B-Instruct-GGUF:Q4_K_M"
-add_test "llama-mtmd-cli"  "ggml-org/InternVL2_5-1B-GGUF:Q8_0"
-add_test "llama-mtmd-cli"  "ggml-org/InternVL3-1B-Instruct-GGUF:Q8_0"
+add_test_audio() {
+    local hf=$1
+    arr_prefix+=("[audio] ")
+    arr_hf+=("$hf")
+    arr_tmpl+=("") # no need for chat tmpl
+    arr_file+=("test-2.mp3")
+}
+
+add_test_vision "ggml-org/SmolVLM-500M-Instruct-GGUF:Q8_0"
+add_test_vision "ggml-org/SmolVLM2-2.2B-Instruct-GGUF:Q4_K_M"
+add_test_vision "ggml-org/SmolVLM2-500M-Video-Instruct-GGUF:Q8_0"
+add_test_vision "ggml-org/gemma-3-4b-it-GGUF:Q4_K_M"
+add_test_vision "THUDM/glm-edge-v-5b-gguf:Q4_K_M"
+add_test_vision "second-state/Llava-v1.5-7B-GGUF:Q2_K"            "vicuna"
+add_test_vision "cjpais/llava-1.6-mistral-7b-gguf:Q3_K_M"         "vicuna"
+add_test_vision "ibm-research/granite-vision-3.2-2b-GGUF:Q4_K_M"
+add_test_vision "second-state/MiniCPM-Llama3-V-2_5-GGUF:Q2_K"  # model from openbmb is corrupted
+add_test_vision "openbmb/MiniCPM-V-2_6-gguf:Q2_K"
+add_test_vision "openbmb/MiniCPM-o-2_6-gguf:Q4_0"
+add_test_vision "bartowski/Qwen2-VL-2B-Instruct-GGUF:Q4_K_M"
+add_test_vision "ggml-org/Qwen2.5-VL-3B-Instruct-GGUF:Q4_K_M"
+add_test_vision "ggml-org/InternVL2_5-1B-GGUF:Q8_0"
+add_test_vision "ggml-org/InternVL3-1B-Instruct-GGUF:Q8_0"
+add_test_vision "ggml-org/Qwen2.5-Omni-3B-GGUF:Q4_K_M"
+
+add_test_audio  "ggml-org/ultravox-v0_5-llama-3_2-1b-GGUF:Q8_0"
+add_test_audio  "ggml-org/Qwen2.5-Omni-3B-GGUF:Q4_K_M"

 # to test the big models, run: ./tests.sh big
 if [ "$RUN_BIG_TESTS" = true ]; then
-    add_test "llama-mtmd-cli" "ggml-org/pixtral-12b-GGUF:Q4_K_M"
-    add_test "llama-mtmd-cli" "ggml-org/Mistral-Small-3.1-24B-Instruct-2503-GGUF" "mistral-v7"
-    add_test "llama-mtmd-cli" "ggml-org/Qwen2-VL-2B-Instruct-GGUF:Q4_K_M"
-    add_test "llama-mtmd-cli" "ggml-org/Qwen2-VL-7B-Instruct-GGUF:Q4_K_M"
-    add_test "llama-mtmd-cli" "ggml-org/Qwen2.5-VL-3B-Instruct-GGUF:Q4_K_M"
-    add_test "llama-mtmd-cli" "ggml-org/Qwen2.5-VL-7B-Instruct-GGUF:Q4_K_M"
-    add_test "llama-mtmd-cli" "ggml-org/InternVL3-8B-Instruct-GGUF:Q4_K_M"
-    add_test "llama-mtmd-cli" "ggml-org/InternVL3-14B-Instruct-GGUF:Q4_K_M"
-    # add_test "llama-mtmd-cli" "ggml-org/Qwen2.5-VL-32B-Instruct-GGUF:Q4_K_M" # does not work on my mac M3 Ultra
+    add_test_vision "ggml-org/pixtral-12b-GGUF:Q4_K_M"
+    add_test_vision "ggml-org/Mistral-Small-3.1-24B-Instruct-2503-GGUF" "mistral-v7"
+    add_test_vision "ggml-org/Qwen2-VL-2B-Instruct-GGUF:Q4_K_M"
+    add_test_vision "ggml-org/Qwen2-VL-7B-Instruct-GGUF:Q4_K_M"
+    add_test_vision "ggml-org/Qwen2.5-VL-3B-Instruct-GGUF:Q4_K_M"
+    add_test_vision "ggml-org/Qwen2.5-VL-7B-Instruct-GGUF:Q4_K_M"
+    add_test_vision "ggml-org/InternVL3-8B-Instruct-GGUF:Q4_K_M"
+    add_test_vision "ggml-org/InternVL3-14B-Instruct-GGUF:Q4_K_M"
+    add_test_vision "ggml-org/Qwen2.5-Omni-7B-GGUF:Q4_K_M"
+    # add_test_vision "ggml-org/Qwen2.5-VL-32B-Instruct-GGUF:Q4_K_M" # does not work on my mac M3 Ultra
+
+    add_test_audio  "ggml-org/ultravox-v0_5-llama-3_1-8b-GGUF:Q4_K_M"
+    add_test_audio  "ggml-org/Qwen2.5-Omni-7B-GGUF:Q4_K_M"
 fi

 # to test the huge models, run: ./tests.sh huge
 # this will run both the big and huge models
 # huge models are > 32B parameters
 if [ "$RUN_HUGE_TESTS" = true ]; then
-    add_test "llama-mtmd-cli" "ggml-org/Qwen2.5-VL-72B-Instruct-GGUF:Q4_K_M"
-    add_test "llama-mtmd-cli" "ggml-org/Llama-4-Scout-17B-16E-Instruct-GGUF:IQ1_S"
+    add_test_vision "ggml-org/Qwen2.5-VL-72B-Instruct-GGUF:Q4_K_M"
+    add_test_vision "ggml-org/Llama-4-Scout-17B-16E-Instruct-GGUF:IQ1_S"
 fi

 # these models always give the wrong answer, not sure why
-# add_test "llama-mtmd-cli"  "ggml-org/SmolVLM-Instruct-GGUF:Q4_K_M"
-# add_test "llama-mtmd-cli"  "ggml-org/SmolVLM-256M-Instruct-GGUF:Q8_0"
-# add_test "llama-mtmd-cli"  "ggml-org/SmolVLM2-256M-Video-Instruct-GGUF:Q8_0"
+# add_test_vision "ggml-org/SmolVLM-Instruct-GGUF:Q4_K_M"
+# add_test_vision "ggml-org/SmolVLM-256M-Instruct-GGUF:Q8_0"
+# add_test_vision "ggml-org/SmolVLM2-256M-Video-Instruct-GGUF:Q8_0"

 # this model has broken chat template, not usable
-# add_test "llama-mtmd-cli"  "cmp-nct/Yi-VL-6B-GGUF:Q5_K"
-# add_test "llama-mtmd-cli"  "guinmoon/MobileVLM-3B-GGUF:Q4_K_M" "deepseek"
+# add_test_vision "cmp-nct/Yi-VL-6B-GGUF:Q5_K"
+# add_test_vision "guinmoon/MobileVLM-3B-GGUF:Q4_K_M" "deepseek"

 ###############

-cmake --build build -j --target "${arr_bin[@]}"
+cmake --build build -j --target llama-mtmd-cli

 arr_res=()

-for i in "${!arr_bin[@]}"; do
-    bin="${arr_bin[$i]}"
+for i in "${!arr_hf[@]}"; do
+    bin="llama-mtmd-cli"
+    prefix="${arr_prefix[$i]}"
    hf="${arr_hf[$i]}"
    tmpl="${arr_tmpl[$i]}"
+    inp_file="${arr_file[$i]}"

    echo "Running test with binary: $bin and HF model: $hf"
    echo ""
@@ -107,7 +126,7 @@ for i in "${!arr_bin[@]}"; do
    output=$(\
        "$PROJ_ROOT/build/bin/$bin" \
        -hf "$hf" \
-        --image $SCRIPT_DIR/test-1.jpeg \
+        --image $SCRIPT_DIR/$inp_file \
        -p "what is the publisher name of the newspaper?" \
        --temp 0 -n 128 \
        ${tmpl:+--chat-template "$tmpl"} \
@@ -116,9 +135,9 @@ for i in "${!arr_bin[@]}"; do
    echo "$output" > $SCRIPT_DIR/output/$bin-$(echo "$hf" | tr '/' '-').log

    if echo "$output" | grep -iq "new york"; then
-        result="\033[32mOK\033[0m:   $bin $hf"
+        result="$prefix \033[32mOK\033[0m:   $bin $hf"
    else
-        result="\033[31mFAIL\033[0m: $bin $hf"
+        result="$prefix \033[31mFAIL\033[0m: $bin $hf"
    fi
    echo -e "$result"
    arr_res+=("$result")
@@ -111,7 +111,7 @@ static std::string fs_get_cache_directory() {
    if (getenv("LLAMA_CACHE")) {
        cache_directory = std::getenv("LLAMA_CACHE");
    } else {
-#if defined(__linux__) || defined(__FreeBSD__) || defined(_AIX)
+#if defined(__linux__) || defined(__FreeBSD__) || defined(_AIX) || defined(__OpenBSD__)
        if (std::getenv("XDG_CACHE_HOME")) {
            cache_directory = std::getenv("XDG_CACHE_HOME");
        } else {
@@ -173,7 +173,8 @@ The project is under active development, and we are [looking for feedback and co
 | `--no-slots` | disables slots monitoring endpoint<br/>(env: LLAMA_ARG_NO_ENDPOINT_SLOTS) |
 | `--slot-save-path PATH` | path to save slot kv cache (default: disabled) |
 | `--jinja` | use jinja template for chat (default: disabled)<br/>(env: LLAMA_ARG_JINJA) |
-| `--reasoning-format FORMAT` | reasoning format (default: deepseek; allowed values: deepseek, none)<br/>controls whether thought tags are extracted from the response, and in which format they're returned. 'none' leaves thoughts unparsed in `message.content`, 'deepseek' puts them in `message.reasoning_content` (for DeepSeek R1 & Command R7B only).<br/>only supported for non-streamed responses<br/>(env: LLAMA_ARG_THINK) |
+| `--reasoning-format FORMAT` | controls whether thought tags are allowed and/or extracted from the response, and in which format they're returned; one of:<br/>- none: leaves thoughts unparsed in `message.content`<br/>- deepseek: puts thoughts in `message.reasoning_content` (except in streaming mode, which behaves as `none`)<br/>(default: deepseek)<br/>(env: LLAMA_ARG_THINK) |
+| `--reasoning-budget N` | controls the amount of thinking allowed; currently only one of: -1 for unrestricted thinking budget, or 0 to disable thinking (default: -1)<br/>(env: LLAMA_ARG_THINK_BUDGET) |
 | `--chat-template JINJA_TEMPLATE` | set custom jinja chat template (default: template taken from model's metadata)<br/>if suffix/prefix are specified, template will be disabled<br/>only commonly used templates are accepted (unless --jinja is set before this flag):<br/>list of built-in templates:<br/>bailing, chatglm3, chatglm4, chatml, command-r, deepseek, deepseek2, deepseek3, exaone3, falcon3, gemma, gigachat, glmedge, granite, llama2, llama2-sys, llama2-sys-bos, llama2-sys-strip, llama3, llama4, megrez, minicpm, mistral-v1, mistral-v3, mistral-v3-tekken, mistral-v7, mistral-v7-tekken, monarch, openchat, orion, phi3, phi4, rwkv-world, smolvlm, vicuna, vicuna-orca, yandex, zephyr<br/>(env: LLAMA_ARG_CHAT_TEMPLATE) |
 | `--chat-template-file JINJA_TEMPLATE_FILE` | set custom jinja chat template file (default: template taken from model's metadata)<br/>if suffix/prefix are specified, template will be disabled<br/>only commonly used templates are accepted (unless --jinja is set before this flag):<br/>list of built-in templates:<br/>bailing, chatglm3, chatglm4, chatml, command-r, deepseek, deepseek2, deepseek3, exaone3, falcon3, gemma, gigachat, glmedge, granite, llama2, llama2-sys, llama2-sys-bos, llama2-sys-strip, llama3, llama4, megrez, minicpm, mistral-v1, mistral-v3, mistral-v3-tekken, mistral-v7, mistral-v7-tekken, monarch, openchat, orion, phi3, phi4, rwkv-world, smolvlm, vicuna, vicuna-orca, yandex, zephyr<br/>(env: LLAMA_ARG_CHAT_TEMPLATE_FILE) |
 | `--no-prefill-assistant` | whether to prefill the assistant's response if the last message is an assistant message (default: prefill enabled)<br/>when this flag is set, if the last message is an assistant message then it will be treated as a full message and not prefilled<br/>(env: LLAMA_ARG_NO_PREFILL_ASSISTANT) |
@@ -178,7 +178,7 @@ struct slot_params {
            {"grammar_triggers",          grammar_triggers},
            {"preserved_tokens",          sampling.preserved_tokens},
            {"chat_format",               common_chat_format_name(oaicompat_chat_syntax.format)},
-            {"reasoning_format",          (oaicompat_chat_syntax.reasoning_format == COMMON_REASONING_FORMAT_DEEPSEEK ? "deepseek" : "none")},
+            {"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},
@@ -357,13 +357,14 @@ struct server_task {
            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).c_str());
+                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;
            }
            params.oaicompat_chat_syntax.reasoning_format = params_base.reasoning_format;
            params.oaicompat_chat_syntax.reasoning_in_content = params.stream;
            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);
        }

        {
@@ -2089,6 +2090,7 @@ struct server_context {
            /* common_chat_templates */ chat_templates.get(),
            /* allow_image           */ mctx ? mtmd_support_vision(mctx) : false,
            /* allow_audio           */ mctx ? mtmd_support_audio (mctx) : false,
+            /* enable_thinking       */ params_base.reasoning_budget != 0,
        };
    }

@@ -3393,13 +3395,7 @@ struct server_context {
                batch.logits   + i,
            };

-            int ret = 0;
-
-            if (do_encode) {
-                ret = llama_encode(ctx, batch_view);
-            } else {
-                ret = llama_decode(ctx, batch_view);
-            }
+            const int ret = llama_decode(ctx, batch_view);

            metrics.on_decoded(slots);

@@ -121,6 +121,30 @@ def test_completion_stream_with_openai_library():
    assert match_regex("(going|bed)+", output_text)


+# Test case from https://github.com/ggml-org/llama.cpp/issues/13780
+@pytest.mark.slow
+def test_completion_stream_with_openai_library_stops():
+    global server
+    server.model_hf_repo = "bartowski/Phi-3.5-mini-instruct-GGUF:Q4_K_M"
+    server.model_hf_file = None
+    server.start()
+    client = OpenAI(api_key="dummy", base_url=f"http://{server.server_host}:{server.server_port}/v1")
+    res = client.completions.create(
+        model="davinci-002",
+        prompt="System: You are helpfull assistant.\nAssistant:\nHey! How could I help?\nUser:\nTell me a joke.\nAssistant:\n",
+        stop=["User:\n", "Assistant:\n"],
+        max_tokens=200,
+        stream=True,
+    )
+    output_text = ''
+    for data in res:
+        choice = data.choices[0]
+        if choice.finish_reason is None:
+            assert choice.text is not None
+            output_text += choice.text
+    assert match_regex("Sure, here's one for[\\s\\S]*", output_text), f'Unexpected output: {output_text}'
+
+
@pytest.mark.parametrize("n_slots", [1, 2])
 def test_consistent_result_same_seed(n_slots: int):
    global server
@@ -25,6 +25,40 @@ def create_server():
    server.n_slots = 1


+@pytest.mark.parametrize("tools", [None, [], [TEST_TOOL]])
+@pytest.mark.parametrize("template_name,reasoning_budget,expected_end", [
+    ("deepseek-ai-DeepSeek-R1-Distill-Qwen-32B", None, "<think>\n"),
+    ("deepseek-ai-DeepSeek-R1-Distill-Qwen-32B",   -1, "<think>\n"),
+    ("deepseek-ai-DeepSeek-R1-Distill-Qwen-32B",    0, "<think>\n</think>"),
+
+    ("Qwen-Qwen3-0.6B", -1, "<|im_start|>assistant\n"),
+    ("Qwen-Qwen3-0.6B",  0, "<|im_start|>assistant\n<think>\n\n</think>\n\n"),
+
+    ("Qwen-QwQ-32B", -1, "<|im_start|>assistant\n<think>\n"),
+    ("Qwen-QwQ-32B",  0, "<|im_start|>assistant\n<think>\n</think>"),
+
+    ("CohereForAI-c4ai-command-r7b-12-2024-tool_use", -1, "<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>"),
+    ("CohereForAI-c4ai-command-r7b-12-2024-tool_use",  0, "<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|><|START_THINKING|><|END_THINKING|>"),
+])
+def test_reasoning_budget(template_name: str, reasoning_budget: int | None, expected_end: str, tools: list[dict]):
+    global server
+    server.jinja = True
+    server.reasoning_budget = reasoning_budget
+    server.chat_template_file = f'../../../models/templates/{template_name}.jinja'
+    server.start(timeout_seconds=TIMEOUT_SERVER_START)
+
+    res = server.make_request("POST", "/apply-template", data={
+        "messages": [
+            {"role": "user", "content": "What is today?"},
+        ],
+        "tools": tools,
+    })
+    assert res.status_code == 200
+    prompt = res.body["prompt"]
+
+    assert prompt.endswith(expected_end), f"Expected prompt to end with '{expected_end}', got '{prompt}'"
+
+
@pytest.mark.parametrize("tools", [None, [], [TEST_TOOL]])
@pytest.mark.parametrize("template_name,format", [
    ("meta-llama-Llama-3.3-70B-Instruct",    "%d %b %Y"),
@@ -47,3 +81,28 @@ def test_date_inside_prompt(template_name: str, format: str, tools: list[dict]):

    today_str = datetime.date.today().strftime(format)
    assert today_str in prompt, f"Expected today's date ({today_str}) in content ({prompt})"
+
+
+@pytest.mark.parametrize("add_generation_prompt", [False, True])
+@pytest.mark.parametrize("template_name,expected_generation_prompt", [
+    ("meta-llama-Llama-3.3-70B-Instruct",    "<|start_header_id|>assistant<|end_header_id|>"),
+])
+def test_add_generation_prompt(template_name: str, expected_generation_prompt: str, add_generation_prompt: bool):
+    global server
+    server.jinja = True
+    server.chat_template_file = f'../../../models/templates/{template_name}.jinja'
+    server.start(timeout_seconds=TIMEOUT_SERVER_START)
+
+    res = server.make_request("POST", "/apply-template", data={
+        "messages": [
+            {"role": "user", "content": "What is today?"},
+        ],
+        "add_generation_prompt": add_generation_prompt,
+    })
+    assert res.status_code == 200
+    prompt = res.body["prompt"]
+
+    if add_generation_prompt:
+        assert expected_generation_prompt in prompt, f"Expected generation prompt ({expected_generation_prompt}) in content ({prompt})"
+    else:
+        assert expected_generation_prompt not in prompt, f"Did not expect generation prompt ({expected_generation_prompt}) in content ({prompt})"
@@ -84,7 +84,8 @@ class ServerProcess:
    draft_max: int | None = None
    no_webui: bool | None = None
    jinja: bool | None = None
-    reasoning_format: Literal['deepseek', 'none'] | None = None
+    reasoning_format: Literal['deepseek', 'none', 'nothink'] | None = None
+    reasoning_budget: int | None = None
    chat_template: str | None = None
    chat_template_file: str | None = None
    server_path: str | None = None
@@ -191,6 +192,8 @@ class ServerProcess:
            server_args.append("--jinja")
        if self.reasoning_format is not None:
            server_args.extend(("--reasoning-format", self.reasoning_format))
+        if self.reasoning_budget is not None:
+            server_args.extend(("--reasoning-budget", self.reasoning_budget))
        if self.chat_template:
            server_args.extend(["--chat-template", self.chat_template])
        if self.chat_template_file:
@@ -325,6 +328,10 @@ class ServerProcess:
                    if 'function' not in tc:
                        raise ValueError(f"Expected function type, got {tc['type']}")
                    if tc['index'] >= len(tool_calls):
+                        assert 'id' in tc
+                        assert tc.get('type') == 'function'
+                        assert 'function' in tc and 'name' in tc['function'] and len(tc['function']['name']) > 0, \
+                            f"Expected function call with name, got {tc.get('function')}"
                        tool_calls.append(dict(
                            id="",
                            type="function",
@@ -337,10 +344,10 @@ class ServerProcess:
                    if tc.get('id') is not None:
                        tool_call['id'] = tc['id']
                    fct = tc['function']
+                    assert 'id' not in fct, f"Function call should not have id: {fct}"
                    if fct.get('name') is not None:
-                        tool_call['function']['name'] = fct['name']
+                        tool_call['function']['name'] = tool_call['function'].get('name', '') + fct['name']
                    if fct.get('arguments') is not None:
-                        assert len(fct['arguments']) > 0, f'Expected non empty arguments delta!'
                        tool_call['function']['arguments'] += fct['arguments']

            print(f'Streamed response had {content_parts} content parts, {tool_call_parts} tool call parts incl. {arguments_parts} arguments parts')
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
Johannes Gäßler	a68247439b	CUDA: fix FA tg at long context for CC >= 8.9 (#13852 )	2025-05-28 13:33:37 +02:00
Xuan-Son Nguyen	26b79b6cb3	convert : fix tensor naming conflict for llama 4 vision (#13836 ) * convert : fix tensor naming conflict for llama 4 vision * add comment	2025-05-28 10:05:54 +02:00
leo-pony	1e8659e65a	CANN: Add SOC TYPE printing in cmake configuration (#13837 )	2025-05-28 11:54:20 +08:00
lhez	a3c30846e4	opencl: add new ops - `argsort`, `div`, `sub`, `addrows`, `sigmoid`, `group_norm` (#13787 ) * opencl: add `argsort` * opencl: add `div` * opencl: add `add_rows` * opencl: add `sub` * opencl: add `sigmoid`, both `f16` and `f32` * opencl: add `group_norm`	2025-05-27 12:56:08 -07:00
lhez	1701d4c54f	opencl: mark `mul_mat` `f32f32` as supporting non-contiguous tensors (#13790 )	2025-05-27 12:53:14 -07:00
Jeff Bolz	bef8176387	vulkan: use timestamp queries for GGML_VULKAN_PERF (#13817 ) Also change it to be controlled by an env var rather than cmake flag	2025-05-27 18:39:07 +02:00
Georgi Gerganov	34b7c0439e	cmake : add llama-cparams.cpp to build (#13832 )	2025-05-27 19:08:44 +03:00
Akarshan Biswas	f3101a8cc6	SYCL: add gelu_erf kernel (#13749 ) * SYCL: add gelu_erf kernel * refactor code Co-authored-by: Atharva Dubey <atharva.dubey@codeplay.com> * Use scope_op_debug_print --------- Co-authored-by: Atharva Dubey <atharva.dubey@codeplay.com>	2025-05-27 20:52:59 +05:30
Georgi Gerganov	1c49c70d07	sync : ggml	2025-05-27 18:05:33 +03:00
Xuan-Son Nguyen	a8ea03d8ad	ggml : add ggml_repeat_4d (#13824 )	2025-05-27 15:53:55 +02:00
xctan	05f6ac6283	ggml : riscv: add xtheadvector support (#13720 ) * ggml : riscv: add xtheadvector support * ggml : clean up some macro usage	2025-05-27 16:21:36 +03:00
Xuan-Son Nguyen	bc583e3c63	mtmd : support Qwen 2.5 Omni (input audio+vision, no audio output) (#13784 ) * mtmd : allow multiple modalities at the same time * refactor mtmd tokenizer * fix compile * ok, missing SinusoidsPositionEmbedding * first working version * fix style * more strict validate of n_embd * refactor if..else to switch * fix regression * add test for 3B * update docs * fix tokenizing with add_special * add more tests * fix test case "huge" * rm redundant code * set_position_mrope_1d rm n_tokens	2025-05-27 14:06:10 +02:00
bandoti	72b090da2c	docs: remove link for llama-cli function calling (#13810 )	2025-05-27 08:52:40 -03:00
Christian Kastner	7fe03e7446	ggml-cpu: x86 feature detection is specific to x86 (#13811 )	2025-05-27 13:18:39 +02:00
Diego Devesa	952f3953c1	ggml : allow CUDA graphs when using pipeline parallelism (#13814 )	2025-05-27 13:05:18 +02:00
Georgi Gerganov	81713121ee	kv-cells : track min/max used cells and per-sequence positions (#13808 ) * kv-cells : track min/max used cells and per-sequence positions ggml-ci * kv-cells : fix pos-modification updates for seq_pos ggml-ci * kv-cells : add comments ggml-ci	2025-05-27 13:49:41 +03:00
Georgi Gerganov	f9cd68398b	sampling : make sure samplers return at least 1 token (#13822 ) * sampling : min-p should always return at least one token ggml-ci * sampling : same for typical sampling * tests : sampling tests use min_keep == 0 ggml-ci	2025-05-27 12:07:52 +03:00
Georgi Gerganov	4f81b33e32	llama : validate seq id batch input (#13809 ) * llama : validate seq id batch input ggml-ci * cont : fix the fix ggml-ci	2025-05-27 09:40:59 +03:00
Olivier Chafik	cdf94a1802	server: --offline mode (#13804 ) * server: --offline mode (env: LLAMA_OFFLINE) --------- Co-authored-by: Xuan-Son Nguyen <thichthat@gmail.com>	2025-05-26 22:34:27 +01:00
Georgi Gerganov	a26c4cc11e	scripts : add option to compare commits in Debug (#13806 ) * scripts : add option to compare commits in Debug * cont : reuse existing CMAKE_OPTS	2025-05-26 22:24:01 +03:00
Georgi Gerganov	4265a87b59	cuda : avoid cuGetErrorString (#13791 ) ggml-ci	2025-05-26 22:14:52 +03:00
Akarshan Biswas	6f180b915c	SYCL: Add non contiguous support in RMS_NORM and NORM kernels (#13611 ) * SYCL: Add non contiguous input support to norm kernel * refactor and add RMS_NORM non contiguous input support ggml-ci * restore subgroup reduction for multi-subgroup thread blocks in norm kernels * Swap grid dims of nsamples and nrows ggml-ci * Revert "Swap grid dims of nsamples and nrows" This reverts commit 43be2d657fec7f7fba54e2cd154106bc0fc45adf. * restore not required changes ggml-ci * address review comments: change it to more like SYCL * Use a common function to calculate offset * remove wrap around logic for handling broadcasts * remove static from calculate_offset fn and use ceil_div	2025-05-26 21:10:36 +05:30
Olivier Chafik	03f582ae8f	server: fix streaming crashes (#13786 ) * add preludes to content on partial regex match * allow all parsers to parse non-tool-call content. * tweak order of <\|python_tag\|> vs <function= parsing for functionary v3.1 format. still not ideal but hopefully less prone to crash	2025-05-26 16:03:57 +01:00
standby24x7	88c125f2ac	examples/training: Fix file name in README (#13803 ) This patch fixes binary file names in README.md. Signed-off-by: Masanari Iida <standby24x7@gmail.com>	2025-05-26 16:55:24 +02:00
Olivier Chafik	d74e94c1b3	`server`: fix format of streamed tool call deltas (diff name, fix id location) (#13800 ) * fix deltas of tool_call.function.name * fix tool_call.id (was in tool_call.function.id!) + add function type * add tool_call.type * populate empty tool_call.function.arguments on first delta	2025-05-26 14:56:49 +01:00
Olivier Chafik	f13847cfb5	server: fix regression on streamed non-chat completion w/ stops (#13785 ) * more forgiving message diffs: partial stop words aren't erased, full stops are * Add (slow) server test for completion + stream + stop	2025-05-26 14:16:37 +01:00
Georgi Gerganov	79c137f776	examples : allow extracting embeddings from decoder contexts (#13797 ) ggml-ci	2025-05-26 14:03:54 +03:00
Georgi Gerganov	22229314fc	llama : clarify deprecation message (#13794 )	2025-05-26 12:57:50 +03:00
Romain Biessy	9012eb9b45	sycl: Add more debug prints (#13640 )	2025-05-26 10:28:53 +02:00
Jeff Bolz	fef693dc6b	vulkan: mark IM2COL as supporting non-contig (#13783 )	2025-05-26 06:02:07 +02:00
Bizhao Shi	2d38b6e400	CANN: Add the basic supports of Flash Attention kernel (#13627 ) * cann: add the basic FA support * cann: update the readme * cann: update the FlashAttention with PSEShift * cann: update the input parameters in FA * cann: update the alibi with max_bias * cann: add the constrints of softcap * cann: update the docs CANN.md * cann: update the docs CANN.md * cann: fix typo of CANN.md * cann: add some comments and update the CANN.md * cann: update the CANN.md * cann: update the inner precise for fusedInferAttention * cann: update the constraints of flash_attn_ext on ggml-cann.cpp * cann: clean the whitespace * cann: clean the whitespace * cann: add a new endline	2025-05-26 10:20:18 +08:00
Olivier Chafik	e121edc432	`server`: add `--reasoning-budget 0` to disable thinking (incl. qwen3 w/ enable_thinking:false) (#13771 ) --------- Co-authored-by: ochafik <ochafik@google.com> Co-authored-by: Xuan-Son Nguyen <thichthat@gmail.com>	2025-05-26 00:30:51 +01:00
Xuan-Son Nguyen	2f099b510f	webui : bump max upload file size to 500MB (#13779 )	2025-05-25 18:02:18 +01:00
Sigbjørn Skjæret	aa50ba462f	tests : improve UGM tokenizer test coverage (#13773 )	2025-05-25 16:22:29 +02:00
Georgi Gerganov	de2ef53a4b	kv-cache : rework kv_cell (#13706 ) * kv-cache : rework kv_cell ggml-ci * kv-cells : use "shift" instead of "delta" consistently ggml-ci * llama : add llama_max_parallel_sequences() ggml-ci * kv-cells : update comments [no ci] * context : fail upon construction if sequences exceed max value ggml-ci * kv-cells : get_pos() -> pos_get() + comments ggml-ci * kv-cells : fix tracking of "used" cells ggml-ci	2025-05-25 16:34:36 +03:00
Percy Piper	c508256db2	rpc : Fix build on OpenBSD (#13541 )	2025-05-25 15:35:53 +03:00
Xuan-Son Nguyen	40aaa8a403	mtmd : add support for Qwen2-Audio and SeaLLM-Audio (#13760 ) * mtmd : add Qwen2-Audio support * small clean up * update discussion link * clarify mtmd_get_output_embd * clarification in multimodal.md * fix ultravox bug * ggml_cont	2025-05-25 14:06:32 +02:00
ddpasa	a08c1d2845	docs : add Moondream2 pre-quantized link (#13745 ) * Multimodal: Added Moondream2 model and fixed ggml.org link * Apply suggestions from code review --------- Co-authored-by: name <none@none.com> Co-authored-by: Xuan-Son Nguyen <thichthat@gmail.com>	2025-05-25 14:04:49 +02:00
Olivier Chafik	d785f9c1fd	server: fix/test add_generation_prompt (#13770 ) Co-authored-by: ochafik <ochafik@google.com>	2025-05-25 10:45:49 +01:00
Piotr Jasiukajtis	4032ca4066	llama : add support for Qwen3 MoE tied word embeddings (#13768 )	2025-05-25 10:29:43 +02:00