llama : add RobertaForSequenceClassification reranker support (#13875)

* F32-Mamba-SVE

* F32-Mamba-SVE

* Resolve test errors-1

* Resolve test errors-2

* F32-vec-SVE

* F32-vec-SVE

* F32-vec-SVE

.editorconfig

@@ -49,6 +49,6 @@ charset = unset
 trim_trailing_whitespace = unset
 insert_final_newline = unset
 
-[tools/mtmd/miniaudio.h]
+[tools/mtmd/vendor/miniaudio.h]
 trim_trailing_whitespace = unset
 insert_final_newline = unset

.github/workflows/build-linux-cross.yml

@@ -26,12 +26,12 @@ jobs:
           sudo apt-get install -y --no-install-recommends \
                   build-essential \
                   gcc-14-riscv64-linux-gnu \
-                  g++-14-riscv64-linux-gnu \
-                  libcurl4-openssl-dev:riscv64
+                  g++-14-riscv64-linux-gnu
 
       - name: Build
         run: |
-          cmake -B build -DCMAKE_BUILD_TYPE=Release \
+          cmake -B build -DLLAMA_CURL=OFF \
+                         -DCMAKE_BUILD_TYPE=Release \
                          -DGGML_OPENMP=OFF \
                          -DLLAMA_BUILD_EXAMPLES=ON \
                          -DLLAMA_BUILD_TOOLS=ON \
@@ -72,12 +72,12 @@ jobs:
                   glslc \
                   gcc-14-riscv64-linux-gnu \
                   g++-14-riscv64-linux-gnu \
-                  libvulkan-dev:riscv64 \
-                  libcurl4-openssl-dev:riscv64
+                  libvulkan-dev:riscv64
 
       - name: Build
         run: |
-          cmake -B build -DCMAKE_BUILD_TYPE=Release \
+          cmake -B build -DLLAMA_CURL=OFF \
+                         -DCMAKE_BUILD_TYPE=Release \
                          -DGGML_VULKAN=ON \
                          -DGGML_OPENMP=OFF \
                          -DLLAMA_BUILD_EXAMPLES=ON \
@@ -118,12 +118,12 @@ jobs:
                   build-essential \
                   glslc \
                   crossbuild-essential-arm64 \
-                  libvulkan-dev:arm64 \
-                  libcurl4-openssl-dev:arm64
+                  libvulkan-dev:arm64
 
       - name: Build
         run: |
-          cmake -B build -DCMAKE_BUILD_TYPE=Release \
+          cmake -B build -DLLAMA_CURL=OFF \
+                         -DCMAKE_BUILD_TYPE=Release \
                          -DGGML_VULKAN=ON \
                          -DGGML_OPENMP=OFF \
                          -DLLAMA_BUILD_EXAMPLES=ON \
@@ -163,12 +163,12 @@ jobs:
           sudo apt-get install -y --no-install-recommends \
                   build-essential \
                   gcc-14-powerpc64le-linux-gnu \
-                  g++-14-powerpc64le-linux-gnu \
-                  libcurl4-openssl-dev:ppc64el
+                  g++-14-powerpc64le-linux-gnu
 
       - name: Build
         run: |
-          cmake -B build -DCMAKE_BUILD_TYPE=Release \
+          cmake -B build -DLLAMA_CURL=OFF \
+                         -DCMAKE_BUILD_TYPE=Release \
                          -DGGML_OPENMP=OFF \
                          -DLLAMA_BUILD_EXAMPLES=ON \
                          -DLLAMA_BUILD_TOOLS=ON \
@@ -209,12 +209,12 @@ jobs:
                   glslc \
                   gcc-14-powerpc64le-linux-gnu \
                   g++-14-powerpc64le-linux-gnu \
-                  libvulkan-dev:ppc64el \
-                  libcurl4-openssl-dev:ppc64el
+                  libvulkan-dev:ppc64el
 
       - name: Build
         run: |
-          cmake -B build -DCMAKE_BUILD_TYPE=Release \
+          cmake -B build -DLLAMA_CURL=OFF \
+                         -DCMAKE_BUILD_TYPE=Release \
                          -DGGML_VULKAN=ON \
                          -DGGML_OPENMP=OFF \
                          -DLLAMA_BUILD_EXAMPLES=ON \

common/arg.cpp

@@ -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.",

common/chat-parser.cpp

@@ -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};
 }
 

common/chat-parser.h

@@ -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);
 

common/chat.cpp

@@ -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) {

common/chat.h

@@ -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);

common/common.cpp

@@ -903,13 +903,16 @@ struct common_init_result common_init_from_params(common_params & params) {
             ok = false;
         }
 
-        if (llama_vocab_eos(vocab) == LLAMA_TOKEN_NULL) {
-            LOG_WRN("%s: warning: vocab does not have an EOS token, reranking will not work\n", __func__);
-            ok = false;
-        }
+        bool has_eos = llama_vocab_eos(vocab) != LLAMA_TOKEN_NULL;
+        bool has_sep = llama_vocab_sep(vocab) != LLAMA_TOKEN_NULL;
 
-        if (llama_vocab_sep(vocab) == LLAMA_TOKEN_NULL) {
-            LOG_WRN("%s: warning: vocab does not have a  SEP token, reranking will not work\n", __func__);
+        if (!has_eos && !has_sep) {
+            LOG_WRN("%s: warning: vocab does not have an EOS token or SEP token, reranking will not work\n", __func__);
+            ok = false;
+        } else if (!has_eos) {
+            LOG_WRN("%s: warning: vocab does not have an EOS token, using SEP token as fallback\n", __func__);
+        } else if (!has_sep) {
+            LOG_WRN("%s: warning: vocab does not have a SEP token, reranking will not work\n", __func__);
             ok = false;
         }
 

common/common.h

@@ -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;

convert_hf_to_gguf.py

@@ -423,16 +423,19 @@ class ModelBase:
         try:
             # for security reason, we don't allow loading remote code by default
             # if a model need remote code, we will fallback to config.json
-            return AutoConfig.from_pretrained(dir_model, trust_remote_code=False).to_dict()
+            config = AutoConfig.from_pretrained(dir_model, trust_remote_code=False).to_dict()
         except Exception as e:
             logger.warning(f"Failed to load model config from {dir_model}: {e}")
             logger.warning("Trying to load config.json instead")
             with open(dir_model / "config.json", "r", encoding="utf-8") as f:
                 config = json.load(f)
-                if "llm_config" in config:
-                    # rename for InternVL
-                    config["text_config"] = config["llm_config"]
-                return config
+        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
     def register(cls, *names: str) -> Callable[[AnyModel], AnyModel]:
@@ -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)
 
@@ -1170,33 +1186,49 @@ 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:
+        if not self.has_vision_encoder and not self.has_audio_encoder:
             raise ValueError("MmprojModel must have either vision or audio encoder")
 
     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):
@@ -1809,7 +1841,8 @@ class StableLMModel(TextModel):
     "MistralForCausalLM",
     "MixtralForCausalLM",
     "VLlama3ForCausalLM",
-    "LlavaForConditionalGeneration")
+    "LlavaForConditionalGeneration",
+    "LlamaModel")
 class LlamaModel(TextModel):
     model_arch = gguf.MODEL_ARCH.LLAMA
     undo_permute = True
@@ -1889,6 +1922,8 @@ class LlamaModel(TextModel):
 
         if is_vision_tensor:
             return [] # skip vision tensors
+        elif self.hf_arch == "LlamaModel":
+            name = "model." + name
         elif name.startswith("model.text_model"):
             name = name.replace("text_model.", "") # for SmolVLM
         elif name.startswith("language_model."):
@@ -2137,6 +2172,9 @@ 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 []
 
@@ -2674,7 +2712,12 @@ class Qwen2Model(TextModel):
         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
 
@@ -2692,8 +2735,11 @@ 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)]
 
@@ -2702,21 +2748,27 @@ 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")
@@ -2774,6 +2826,66 @@ 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):
@@ -3570,7 +3682,7 @@ class InternLM3Model(TextModel):
         return [(self.map_tensor_name(name), data_torch)]
 
 
-@ModelBase.register("BertModel", "BertForMaskedLM", "CamembertModel")
+@ModelBase.register("BertModel", "BertForMaskedLM", "CamembertModel", "BertForSequenceClassification")
 class BertModel(TextModel):
     model_arch = gguf.MODEL_ARCH.BERT
 
@@ -3583,6 +3695,10 @@ class BertModel(TextModel):
         self.gguf_writer.add_causal_attention(False)
         self._try_set_pooling_type()
 
+        if cls_out_labels := self.hparams.get("id2label"):
+            key_name = gguf.Keys.Classifier.OUTPUT_LABELS.format(arch = gguf.MODEL_ARCH_NAMES[self.model_arch])
+            self.gguf_writer.add_array(key_name, [v for k, v in sorted(cls_out_labels.items())])
+
     def set_vocab(self):
         tokens, toktypes, tokpre = self.get_vocab_base()
         self.vocab_size = len(tokens)
@@ -3633,6 +3749,14 @@ class BertModel(TextModel):
         if name.startswith("cls.seq_relationship"):
             return []
 
+        if self.hparams.get("id2label"):
+            # For BertForSequenceClassification (direct projection layer)
+            if name == "classifier.weight":
+                name = "classifier.out_proj.weight"
+
+            if name == "classifier.bias":
+                name = "classifier.out_proj.bias"
+
         return [(self.map_tensor_name(name), data_torch)]
 
     def _xlmroberta_tokenizer_init(self) -> None:
@@ -3727,7 +3851,7 @@ class BertModel(TextModel):
         self.gguf_writer.add_add_eos_token(True)
 
 
-@ModelBase.register("RobertaModel")
+@ModelBase.register("RobertaModel", "RobertaForSequenceClassification")
 class RobertaModel(BertModel):
     model_arch = gguf.MODEL_ARCH.BERT
 

convert_hf_to_gguf_update.py

@@ -288,7 +288,7 @@ logger.info("+++ convert_hf_to_gguf.py was updated")
 
 tests = [
     "ied 4 ½ months",
-    "Führer",
+    "Äpfel",
     "",
     " ",
     "  ",

docs/backend/CANN.md

@@ -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.

docs/function-calling.md

@@ -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
 

docs/multimodal.md

@@ -98,3 +98,12 @@ NOTE: some models may require large context window, for example: `-c 8192`
 # 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
+```

examples/embedding/embedding.cpp

@@ -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++) {

examples/retrieval/retrieval.cpp

@@ -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);
 

examples/training/README.md

@@ -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.

ggml/CMakeLists.txt

@@ -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)

ggml/include/ggml.h

@@ -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,

ggml/src/ggml-backend.cpp

@@ -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) {

ggml/src/ggml-cann/CMakeLists.txt

@@ -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.

ggml/src/ggml-cann/acl_tensor.cpp

@@ -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:

ggml/src/ggml-cann/aclnn_ops.cpp

@@ -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.");
+    }
+}

ggml/src/ggml-cann/aclnn_ops.h

@@ -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.
  */

ggml/src/ggml-cann/ggml-cann.cpp

@@ -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;
     }

ggml/src/ggml-cpu/CMakeLists.txt

@@ -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()

ggml/src/ggml-cpu/ggml-cpu-aarch64.cpp

@@ -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;
 

ggml/src/ggml-cpu/ggml-cpu-impl.h

@@ -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>

ggml/src/ggml-cpu/ggml-cpu-quants.c

@@ -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) {

ggml/src/ggml-cpu/ops.cpp

@@ -7641,8 +7641,8 @@ static void ggml_compute_forward_ssm_scan_f32(
             const float * A  = (const float *) ((const char *) src3->data + ir0*(src3->nb[1])); // {d_state, d_inner}
             const float * B  = (const float *) ((const char *) src4->data +  i2*(src4->nb[1]) + i3*(src4->nb[2])); // {d_state, n_t, n_s}
             const float * C  = (const float *) ((const char *) src5->data +  i2*(src5->nb[1]) + i3*(src5->nb[2])); // {d_state, n_t, n_s}
-                  float * y  = (      float *) ((      char *) dst->data  + ir0*(src1->nb[0]) + i2*(src1->nb[1]) + i3*(src1->nb[2])); // {d_inner, n_t, n_s}
-                  float * s  = (      float *) ((      char *) dst->data  + ir0*(src0->nb[1]) + i3*(src0->nb[2]) +     src1->nb[3]);  // {d_state, d_inner, n_s}
+                float * y  = (      float *) ((      char *) dst->data  + ir0*(src1->nb[0]) + i2*(src1->nb[1]) + i3*(src1->nb[2])); // {d_inner, n_t, n_s}
+                float * s  = (      float *) ((      char *) dst->data  + ir0*(src0->nb[1]) + i3*(src0->nb[2]) +     src1->nb[3]);  // {d_state, d_inner, n_s}
 
             // use the output as the source for the next token-wise iterations
             if (i2 > 0) { s0 = s; }
@@ -8070,6 +8070,14 @@ static void ggml_compute_forward_rwkv_wkv6_f32(
         #define GGML_F32X_MUL GGML_F32x16_MUL
         #define GGML_F32X_FMA GGML_F32x16_FMA
         #define WKV_VECTOR_SIZE 16
+    #elif defined(__ARM_FEATURE_SVE) && defined(__aarch64__)
+        #define GGML_F32X GGML_F32xt
+        #define GGML_F32X_SET1 GGML_F32xt_SET1
+        #define GGML_F32X_LOAD GGML_F32xt_LOAD
+        #define GGML_F32X_STORE GGML_F32xt_STORE
+        #define GGML_F32X_MUL GGML_F32xt_MUL
+        #define GGML_F32X_FMA GGML_F32xt_FMA
+        #define WKV_VECTOR_SIZE 8
     #elif defined(__ARM_NEON) && defined(__aarch64__)
         #define GGML_F32X GGML_F32x4
         #define GGML_F32X_SET1 GGML_F32x4_SET1
@@ -8080,8 +8088,14 @@ static void ggml_compute_forward_rwkv_wkv6_f32(
         #define WKV_VECTOR_SIZE 4
     #endif
 
+    int wkv_vector_size;
     #ifdef WKV_VECTOR_SIZE
-        const int64_t vec_count = head_size / WKV_VECTOR_SIZE;
+        #if defined(__ARM_FEATURE_SVE)
+            wkv_vector_size = svcntw();
+        #else
+            wkv_vector_size = WKV_VECTOR_SIZE;
+        #endif
+        const int64_t vec_count = head_size / wkv_vector_size;
 
         for (int64_t t = 0; t < T; t++) {
             size_t t_offset = t * t_stride;
@@ -8111,7 +8125,7 @@ static void ggml_compute_forward_rwkv_wkv6_f32(
                     GGML_F32X time_decay_vec = GGML_F32X_SET1(time_decay_val);
 
                     for (int64_t j = 0; j < vec_count; j++) {
-                        size_t base_j = j * WKV_VECTOR_SIZE;
+                        size_t base_j = j * wkv_vector_size;
                         size_t t_h_j_offset = t_h_offset + base_j;
                         size_t h_2d_i_j_offset = h_2d_i_offset + base_j;
 
@@ -8136,7 +8150,7 @@ static void ggml_compute_forward_rwkv_wkv6_f32(
                     }
 
                     // Handle remaining elements, this will not be used.
-                    for (int64_t j = vec_count * WKV_VECTOR_SIZE; j < head_size; j++) {
+                    for (int64_t j = vec_count * wkv_vector_size; j < head_size; j++) {
                         size_t t_h_j_offset = t_h_offset + j;
                         size_t h_2d_i_j_offset = h_2d_i_offset + j;
                         float v_val = v[t_h_j_offset];
@@ -8272,6 +8286,14 @@ static void ggml_compute_forward_gla_f32(
         #define GGML_F32X_MUL GGML_F32x16_MUL
         #define GGML_F32X_FMA GGML_F32x16_FMA
         #define GLA_VECTOR_SIZE 16
+    #elif defined(__ARM_FEATURE_SVE) && defined(__aarch64__)
+        #define GGML_F32X GGML_F32xt
+        #define GGML_F32X_SET1 GGML_F32xt_SET1
+        #define GGML_F32X_LOAD GGML_F32xt_LOAD
+        #define GGML_F32X_STORE GGML_F32xt_STORE
+        #define GGML_F32X_MUL GGML_F32xt_MUL
+        #define GGML_F32X_FMA GGML_F32xt_FMA
+        #define GLA_VECTOR_SIZE 8
     #elif defined(__ARM_NEON) && defined(__aarch64__)
         #define GGML_F32X GGML_F32x4
         #define GGML_F32X_SET1 GGML_F32x4_SET1
@@ -8282,8 +8304,14 @@ static void ggml_compute_forward_gla_f32(
         #define GLA_VECTOR_SIZE 4
     #endif
 
+    int gla_vector_size;
     #ifdef GLA_VECTOR_SIZE
-        const int64_t vec_count = head_size / GLA_VECTOR_SIZE;
+        #if defined(__ARM_FEATURE_SVE)
+            gla_vector_size = svcntw();
+        #else
+            gla_vector_size = GLA_VECTOR_SIZE;
+        #endif
+        const int64_t vec_count = head_size / gla_vector_size;
 
         for (int64_t t = 0; t < T; t++) {
             size_t t_offset = t * t_stride;
@@ -8310,7 +8338,7 @@ static void ggml_compute_forward_gla_f32(
                     GGML_F32X g_vec = GGML_F32X_SET1(g_val);
 
                     for (int64_t j = 0; j < vec_count; j++) {
-                        size_t base_j = j * GLA_VECTOR_SIZE;
+                        size_t base_j = j * gla_vector_size;
                         size_t t_h_j_offset = t_h_offset + base_j;
                         size_t h_2d_i_j_offset = h_2d_i_offset + base_j;
 
@@ -8334,7 +8362,7 @@ static void ggml_compute_forward_gla_f32(
                     }
 
                     // Handle remaining elements, this will not be used.
-                    for (int64_t j = vec_count * GLA_VECTOR_SIZE; j < head_size; j++) {
+                    for (int64_t j = vec_count * gla_vector_size; j < head_size; j++) {
                         size_t t_h_j_offset = t_h_offset + j;
                         size_t h_2d_i_j_offset = h_2d_i_offset + j;
                         float v_val = v[t_h_j_offset];
@@ -8443,83 +8471,126 @@ static void ggml_compute_forward_rwkv_wkv7_f32(
     int64_t h_stride_2d = head_size * head_size;
 
     #if defined(GGML_SIMD)
-        for (int64_t t = 0; t < T; t++) {
-            int64_t t_offset = t * t_stride;
-            int64_t state_offset = head_size * C * (t / (T / n_seqs));
-            float * state_cur = state + state_offset;
-            float * state_prev = t % (T / n_seqs) ? state_cur : (float*)dst->src[6]->data + state_offset;
+        #if defined(__ARM_FEATURE_SVE)
+            // scalar Route to scalar implementation       //TODO: Write SVE code
+            for (int64_t t = 0; t < T; t++) {
+                int64_t t_offset = t * t_stride;
+                int64_t state_offset = head_size * C * (t / (T / n_seqs));
+                float * state_cur = state + state_offset;
+                float * state_prev = t % (T / n_seqs) ? state_cur : (float*)dst->src[6]->data + state_offset;
 
-            for (int64_t h = h_start; h < h_end; h++) {
-                int64_t h_offset = h * h_stride;
-                int64_t t_h_offset = t_offset + h_offset;
-                int64_t h_2d_offset = h * h_stride_2d;
+                for (int64_t h = h_start; h < h_end; h++) {
+                    int64_t h_offset = h * h_stride;
+                    int64_t t_h_offset = t_offset + h_offset;
+                    int64_t h_2d_offset = h * h_stride_2d;
 
-                for (int64_t ii = 0; ii < head_size; ii++) {
-                    int64_t t_h_i_offset = t_h_offset + ii;
-                    int64_t h_2d_i_offset = h_2d_offset + ii * h_stride;
+                    for (int64_t i = 0; i < head_size; i++) {
+                        int64_t t_h_i_offset = t_h_offset + i;
+                        int64_t h_2d_i_offset = h_2d_offset + i * h_stride;
 
-                    GGML_F32_VEC v_vec = GGML_F32_VEC_SET1(v[t_h_i_offset]);
+                        float v_val = v[t_h_i_offset];
 
-                    float sa = 0;
-                    {
-                        GGML_F32_VEC sum[GGML_F32_ARR] = { GGML_F32_VEC_ZERO };
-                        GGML_F32_VEC ax[GGML_F32_ARR];
-                        GGML_F32_VEC ay[GGML_F32_ARR];
-                        for (int64_t j = 0; j < head_size; j += GGML_F32_STEP) {
-                            for (int64_t kk = 0; kk < GGML_F32_ARR; kk++) {
-                                ax[kk] = GGML_F32_VEC_LOAD(&a[t_h_offset + j + kk * GGML_F32_EPR]);
-                                ay[kk] = GGML_F32_VEC_LOAD(&state_prev[h_2d_i_offset + j + kk * GGML_F32_EPR]);
-                                sum[kk] = GGML_F32_VEC_FMA(sum[kk], ax[kk], ay[kk]);
-                            }
+                        float sa = 0, result = 0;
+                        for (int64_t j = 0; j < head_size; j++) {
+                            sa += a[t_h_offset + j] * state_prev[h_2d_i_offset + j];
                         }
-                        GGML_F32_VEC_REDUCE(sa, sum);
-                    }
 
-                    GGML_F32_VEC sa_vec = GGML_F32_VEC_SET1(sa);
+                        for (int64_t j = 0; j < head_size; j++) {
+                            int64_t t_h_j_offset = t_h_offset + j;
+                            int64_t h_2d_i_j_offset = h_2d_i_offset + j;
 
-                    int64_t j = 0;
-                    GGML_F32_VEC result_vec[GGML_F32_ARR] = { GGML_F32_VEC_ZERO };
-                    for (; j < head_size; j += GGML_F32_STEP) {
-                        for (int64_t kk = 0; kk < GGML_F32_ARR; kk++) {
-                            int64_t t_h_j_offset = t_h_offset + j + kk * GGML_F32_EPR;
-                            int64_t h_2d_i_j_offset = h_2d_i_offset + j + kk * GGML_F32_EPR;
-
-                            GGML_F32_VEC r_vec = GGML_F32_VEC_LOAD(&r[t_h_j_offset]);
-                            GGML_F32_VEC w_vec = GGML_F32_VEC_LOAD(&w[t_h_j_offset]);
-                            GGML_F32_VEC k_vec = GGML_F32_VEC_LOAD(&k[t_h_j_offset]);
-                            GGML_F32_VEC b_vec = GGML_F32_VEC_LOAD(&b[t_h_j_offset]);
-
-                            k_vec = GGML_F32_VEC_MUL(v_vec, k_vec);
-
-                            GGML_F32_VEC state_vec = GGML_F32_VEC_LOAD(&state_prev[h_2d_i_j_offset]);
-                            // kv + s * decay + sa * b
-                            state_vec = GGML_F32_VEC_FMA(k_vec, state_vec, w_vec);
-                            state_vec = GGML_F32_VEC_FMA(state_vec, sa_vec, b_vec);
-                            GGML_F32_VEC_STORE(&state_cur[h_2d_i_j_offset], state_vec);
-
-                            result_vec[kk] = GGML_F32_VEC_FMA(result_vec[kk], state_vec, r_vec);
+                            float r_val = r[t_h_j_offset];
+                            float w_val = w[t_h_j_offset];
+                            float k_val = k[t_h_j_offset];
+                            float b_val = b[t_h_j_offset];
+                            float kv_val = v_val * k_val;
+                            float prev_state_val = state_prev[h_2d_i_j_offset];
+                            state_cur[h_2d_i_j_offset] = prev_state_val * w_val + kv_val + sa * b_val;
+                            result += state_cur[h_2d_i_j_offset] * r_val;
                         }
-                    }
-                    GGML_F32_VEC_REDUCE(dst_data[t_h_i_offset], result_vec);
-
-                    // There shouldn't be left-overs though.
-                    for (; j < head_size; j++) {
-                        int64_t t_h_j_offset = t_h_offset + j;
-                        int64_t h_2d_i_j_offset = h_2d_i_offset + j;
-
-                        float r_val = r[t_h_j_offset];
-                        float w_val = w[t_h_j_offset];
-                        float k_val = k[t_h_j_offset];
-                        float b_val = b[t_h_j_offset];
-                        float kv_val = v[t_h_i_offset] * k_val;
-
-                        float prev_state_val = state_prev[h_2d_i_j_offset];
-                        state_cur[h_2d_i_j_offset] = prev_state_val * w_val + kv_val + sa * b_val;
-                        dst_data[t_h_i_offset] += state_cur[h_2d_i_j_offset] * r_val;
+                        dst_data[t_h_i_offset] = result;
                     }
                 }
             }
-        }
+        #else
+            for (int64_t t = 0; t < T; t++) {
+                int64_t t_offset = t * t_stride;
+                int64_t state_offset = head_size * C * (t / (T / n_seqs));
+                float * state_cur = state + state_offset;
+                float * state_prev = t % (T / n_seqs) ? state_cur : (float*)dst->src[6]->data + state_offset;
+
+                for (int64_t h = h_start; h < h_end; h++) {
+                    int64_t h_offset = h * h_stride;
+                    int64_t t_h_offset = t_offset + h_offset;
+                    int64_t h_2d_offset = h * h_stride_2d;
+
+                    for (int64_t ii = 0; ii < head_size; ii++) {
+                        int64_t t_h_i_offset = t_h_offset + ii;
+                        int64_t h_2d_i_offset = h_2d_offset + ii * h_stride;
+
+                        GGML_F32_VEC v_vec = GGML_F32_VEC_SET1(v[t_h_i_offset]);
+
+                        float sa = 0;
+                        {
+                            GGML_F32_VEC sum[GGML_F32_ARR] = { GGML_F32_VEC_ZERO };
+                            GGML_F32_VEC ax[GGML_F32_ARR];
+                            GGML_F32_VEC ay[GGML_F32_ARR];
+                            for (int64_t j = 0; j < head_size; j += GGML_F32_STEP) {
+                                for (int64_t kk = 0; kk < GGML_F32_ARR; kk++) {
+                                    ax[kk] = GGML_F32_VEC_LOAD(&a[t_h_offset + j + kk * GGML_F32_EPR]);
+                                    ay[kk] = GGML_F32_VEC_LOAD(&state_prev[h_2d_i_offset + j + kk * GGML_F32_EPR]);
+                                    sum[kk] = GGML_F32_VEC_FMA(sum[kk], ax[kk], ay[kk]);
+                                }
+                            }
+                            GGML_F32_VEC_REDUCE(sa, sum);
+                        }
+
+                        GGML_F32_VEC sa_vec = GGML_F32_VEC_SET1(sa);
+
+                        int64_t j = 0;
+                        GGML_F32_VEC result_vec[GGML_F32_ARR] = { GGML_F32_VEC_ZERO };
+                        for (; j < head_size; j += GGML_F32_STEP) {
+                            for (int64_t kk = 0; kk < GGML_F32_ARR; kk++) {
+                                int64_t t_h_j_offset = t_h_offset + j + kk * GGML_F32_EPR;
+                                int64_t h_2d_i_j_offset = h_2d_i_offset + j + kk * GGML_F32_EPR;
+
+                                GGML_F32_VEC r_vec = GGML_F32_VEC_LOAD(&r[t_h_j_offset]);
+                                GGML_F32_VEC w_vec = GGML_F32_VEC_LOAD(&w[t_h_j_offset]);
+                                GGML_F32_VEC k_vec = GGML_F32_VEC_LOAD(&k[t_h_j_offset]);
+                                GGML_F32_VEC b_vec = GGML_F32_VEC_LOAD(&b[t_h_j_offset]);
+
+                                k_vec = GGML_F32_VEC_MUL(v_vec, k_vec);
+
+                                GGML_F32_VEC state_vec = GGML_F32_VEC_LOAD(&state_prev[h_2d_i_j_offset]);
+                                // kv + s * decay + sa * b
+                                state_vec = GGML_F32_VEC_FMA(k_vec, state_vec, w_vec);
+                                state_vec = GGML_F32_VEC_FMA(state_vec, sa_vec, b_vec);
+                                GGML_F32_VEC_STORE(&state_cur[h_2d_i_j_offset], state_vec);
+
+                                result_vec[kk] = GGML_F32_VEC_FMA(result_vec[kk], state_vec, r_vec);
+                            }
+                        }
+                        GGML_F32_VEC_REDUCE(dst_data[t_h_i_offset], result_vec);
+
+                        // There shouldn't be left-overs though.
+                        for (; j < head_size; j++) {
+                            int64_t t_h_j_offset = t_h_offset + j;
+                            int64_t h_2d_i_j_offset = h_2d_i_offset + j;
+
+                            float r_val = r[t_h_j_offset];
+                            float w_val = w[t_h_j_offset];
+                            float k_val = k[t_h_j_offset];
+                            float b_val = b[t_h_j_offset];
+                            float kv_val = v[t_h_i_offset] * k_val;
+
+                            float prev_state_val = state_prev[h_2d_i_j_offset];
+                            state_cur[h_2d_i_j_offset] = prev_state_val * w_val + kv_val + sa * b_val;
+                            dst_data[t_h_i_offset] += state_cur[h_2d_i_j_offset] * r_val;
+                        }
+                    }
+                }
+            }
+        #endif
     #else
         for (int64_t t = 0; t < T; t++) {
             int64_t t_offset = t * t_stride;

ggml/src/ggml-cpu/simd-mappings.h

@@ -17,7 +17,123 @@
 //   number of elements to fit in a single register
 //
 
-#if defined(__ARM_NEON) && defined(__ARM_FEATURE_FMA)
+#if defined(__ARM_FEATURE_SVE) && defined(__ARM_FEATURE_FMA)
+
+#define GGML_SIMD
+
+// F32 SVE
+#define GGML_F32_EPR 8
+#define DEFAULT_PG svptrue_b32()
+
+#define GGML_F32xt                        svfloat32_t
+#define GGML_F32xt_ZERO                   svdup_n_f32(0.0f)
+#define GGML_F32xt_SET1(x)                svdup_n_f32(x)
+#define GGML_F32xt_LOAD_IMPL(pg, a, ...)  svld1_f32(pg, a)
+#define GGML_F32xt_LOAD(...)              GGML_F32xt_LOAD_IMPL(DEFAULT_PG, __VA_ARGS__)
+#define GGML_F32xt_STORE_IMPL(pg,a,b)     svst1_f32(pg, a, b)
+#define GGML_F32xt_STORE(...)             GGML_F32xt_STORE_IMPL(DEFAULT_PG, __VA_ARGS__)
+#define GGML_F32xt_FMA_IMPL(pg, a, b, c)  svmad_f32_m(pg, a, b, c)
+#define GGML_F32xt_FMA(...)               GGML_F32xt_FMA_IMPL(DEFAULT_PG, __VA_ARGS__)
+#define GGML_F32xt_ADD_IMPL(pg, a, b)     svadd_f32_m(pg, a, b)
+#define GGML_F32xt_ADD(...)               GGML_F32xt_ADD_IMPL(DEFAULT_PG, __VA_ARGS__)
+#define GGML_F32xt_MUL_IMPL(pg, a, b)     svmul_f32_m(pg, a, b)
+#define GGML_F32xt_MUL(...)               GGML_F32xt_MUL_IMPL(DEFAULT_PG, __VA_ARGS__)
+#define GGML_F32xt_REDUCE_ONE_IMPL(pg, a) svaddv(pg, a)
+#define GGML_F32xt_REDUCE_ONE(...)        GGML_F32xt_REDUCE_ONE_IMPL(DEFAULT_PG, __VA_ARGS__)
+#define GGML_F32xt_REDUCE_IMPL(pg, res, sum1, sum2, sum3, sum4, sum5, sum6, sum7, sum8)  \
+{                                                      \
+    sum1 = svadd_f32_m(DEFAULT_PG, sum1, sum2);        \
+    sum3 = svadd_f32_m(DEFAULT_PG, sum3, sum4);        \
+    sum5 = svadd_f32_m(DEFAULT_PG, sum5, sum6);        \
+    sum7 = svadd_f32_m(DEFAULT_PG, sum7, sum8);        \
+    sum1 = svadd_f32_m(DEFAULT_PG, sum1, sum3);        \
+    sum5 = svadd_f32_m(DEFAULT_PG, sum5, sum7);        \
+    sum1 = svadd_f32_m(DEFAULT_PG, sum1, sum5);        \
+    (res) = (ggml_float) GGML_F32xt_REDUCE_ONE(sum1);  \
+}
+#define GGML_F32xt_REDUCE(...) GGML_F32xt_REDUCE_IMPL(DEFAULT_PG, __VA_ARGS__)
+
+#define GGML_F32_VEC        GGML_F32xt
+#define GGML_F32_VEC_ZERO   GGML_F32xt_ZERO
+#define GGML_F32_VEC_SET1   GGML_F32xt_SET1
+#define GGML_F32_VEC_LOAD   GGML_F32xt_LOAD
+#define GGML_F32_VEC_STORE  GGML_F32xt_STORE
+#define GGML_F32_VEC_FMA    GGML_F32xt_FMA
+#define GGML_F32_VEC_ADD    GGML_F32xt_ADD
+#define GGML_F32_VEC_MUL    GGML_F32xt_MUL
+#define GGML_F32_VEC_REDUCE GGML_F32xt_REDUCE
+
+// F16 NEON
+
+#if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC)
+    #define GGML_F16_STEP 32
+    #define GGML_F16_EPR  8
+
+    #define GGML_F16x8              float16x8_t
+    #define GGML_F16x8_ZERO         vdupq_n_f16(0.0f)
+    #define GGML_F16x8_SET1(x)      vdupq_n_f16(x)
+    #define GGML_F16x8_LOAD(x)      vld1q_f16((const __fp16 *)(x))
+    #define GGML_F16x8_STORE        vst1q_f16
+    #define GGML_F16x8_FMA(a, b, c) vfmaq_f16(a, b, c)
+    #define GGML_F16x8_ADD          vaddq_f16
+    #define GGML_F16x8_MUL          vmulq_f16
+    #define GGML_F16x8_REDUCE(res, x)                               \
+    do {                                                            \
+        int offset = GGML_F16_ARR >> 1;                             \
+        for (int i = 0; i < offset; ++i) {                          \
+            (x)[i] = vaddq_f16((x)[i], (x)[offset+i]);              \
+        }                                                           \
+        offset >>= 1;                                               \
+        for (int i = 0; i < offset; ++i) {                          \
+            (x)[i] = vaddq_f16((x)[i], (x)[offset+i]);              \
+        }                                                           \
+        offset >>= 1;                                               \
+        for (int i = 0; i < offset; ++i) {                          \
+            (x)[i] = vaddq_f16((x)[i], (x)[offset+i]);              \
+        }                                                           \
+        const float32x4_t t0 = vcvt_f32_f16(vget_low_f16 ((x)[0])); \
+        const float32x4_t t1 = vcvt_f32_f16(vget_high_f16((x)[0])); \
+        (res) = (ggml_float) vaddvq_f32(vaddq_f32(t0, t1));         \
+    } while (0)
+
+    #define GGML_F16_VEC                GGML_F16x8
+    #define GGML_F16_VEC_ZERO           GGML_F16x8_ZERO
+    #define GGML_F16_VEC_SET1           GGML_F16x8_SET1
+    #define GGML_F16_VEC_LOAD(p, i)     GGML_F16x8_LOAD(p)
+    #define GGML_F16_VEC_STORE(p, r, i) GGML_F16x8_STORE((__fp16 *)(p), (r)[i])
+    #define GGML_F16_VEC_FMA            GGML_F16x8_FMA
+    #define GGML_F16_VEC_ADD            GGML_F16x8_ADD
+    #define GGML_F16_VEC_MUL            GGML_F16x8_MUL
+    #define GGML_F16_VEC_REDUCE         GGML_F16x8_REDUCE
+#else
+    // if FP16 vector arithmetic is not supported, we use FP32 instead
+    // and take advantage of the vcvt_ functions to convert to/from FP16
+
+    #define GGML_F16_STEP 16
+    #define GGML_F16_EPR  4
+
+    #define GGML_F32Cx4              float32x4_t
+    #define GGML_F32Cx4_ZERO         vdupq_n_f32(0.0f)
+    #define GGML_F32Cx4_SET1(x)      vdupq_n_f32(x)
+    #define GGML_F32Cx4_LOAD(x)      vcvt_f32_f16(vld1_f16((const __fp16 *)(x)))
+    #define GGML_F32Cx4_STORE(x, y)  vst1_f16(x, vcvt_f16_f32(y))
+    #define GGML_F32Cx4_FMA(a, b, c) vfmaq_f32(a, b, c)
+    #define GGML_F32Cx4_ADD          vaddq_f32
+    #define GGML_F32Cx4_MUL          vmulq_f32
+    #define GGML_F32Cx4_REDUCE       GGML_F32x4_REDUCE
+
+    #define GGML_F16_VEC                GGML_F32Cx4
+    #define GGML_F16_VEC_ZERO           GGML_F32Cx4_ZERO
+    #define GGML_F16_VEC_SET1           GGML_F32Cx4_SET1
+    #define GGML_F16_VEC_LOAD(p, i)     GGML_F32Cx4_LOAD(p)
+    #define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx4_STORE((__fp16 *)(p), r[i])
+    #define GGML_F16_VEC_FMA            GGML_F32Cx4_FMA
+    #define GGML_F16_VEC_ADD            GGML_F32Cx4_ADD
+    #define GGML_F16_VEC_MUL            GGML_F32Cx4_MUL
+    #define GGML_F16_VEC_REDUCE         GGML_F32Cx4_REDUCE
+#endif
+
+#elif defined(__ARM_NEON) && defined(__ARM_FEATURE_FMA)
 
 #define GGML_SIMD
 

ggml/src/ggml-cpu/vec.cpp

@@ -17,29 +17,98 @@ void ggml_vec_dot_f32(int n, float * GGML_RESTRICT s, size_t bs, const float * G
 
 #if defined(GGML_SIMD)
     float sumf = 0.0f;
-    const int np = (n & ~(GGML_F32_STEP - 1));
 
-    GGML_F32_VEC sum[GGML_F32_ARR] = { GGML_F32_VEC_ZERO };
+    #if defined(__ARM_FEATURE_SVE)
+        const int sve_register_length = ggml_cpu_get_sve_cnt() * 8;
+        const int ggml_f32_epr = sve_register_length / 32;//8;//svcntw(); // SVE128:4, SVE256:8, SVE512:16
+        const int ggml_f32_step = 8 * ggml_f32_epr; // choose 8 SVE registers
 
-    GGML_F32_VEC ax[GGML_F32_ARR];
-    GGML_F32_VEC ay[GGML_F32_ARR];
+        const int np = (n & ~(ggml_f32_step - 1));
+        svfloat32_t sum1 = svdup_n_f32(0.0f);
+        svfloat32_t sum2 = svdup_n_f32(0.0f);
+        svfloat32_t sum3 = svdup_n_f32(0.0f);
+        svfloat32_t sum4 = svdup_n_f32(0.0f);
+        svfloat32_t sum5 = svdup_n_f32(0.0f);
+        svfloat32_t sum6 = svdup_n_f32(0.0f);
+        svfloat32_t sum7 = svdup_n_f32(0.0f);
+        svfloat32_t sum8 = svdup_n_f32(0.0f);
+        svfloat32_t ax1,ax2,ax3,ax4,ax5,ax6,ax7,ax8;
+        svfloat32_t ay1,ay2,ay3,ay4,ay5,ay6,ay7,ay8;
+        for (int i = 0; i < np; i += ggml_f32_step) {
+            ax1 = GGML_F32_VEC_LOAD(x + i);
+            ay1 = GGML_F32_VEC_LOAD(y + i);
+            sum1 = GGML_F32_VEC_FMA(ax1, ay1, sum1);
 
-    for (int i = 0; i < np; i += GGML_F32_STEP) {
-        for (int j = 0; j < GGML_F32_ARR; j++) {
-            ax[j] = GGML_F32_VEC_LOAD(x + i + j*GGML_F32_EPR);
-            ay[j] = GGML_F32_VEC_LOAD(y + i + j*GGML_F32_EPR);
+            ax2 = GGML_F32_VEC_LOAD(x + i + 1*ggml_f32_epr);
+            ay2 = GGML_F32_VEC_LOAD(y + i + 1*ggml_f32_epr);
+            sum2 = GGML_F32_VEC_FMA(ax2, ay2, sum2);
 
-            sum[j] = GGML_F32_VEC_FMA(sum[j], ax[j], ay[j]);
+            ax3 = GGML_F32_VEC_LOAD(x + i + 2*ggml_f32_epr);
+            ay3 = GGML_F32_VEC_LOAD(y + i + 2*ggml_f32_epr);
+            sum3 = GGML_F32_VEC_FMA(ax3, ay3, sum3);
+
+            ax4 = GGML_F32_VEC_LOAD(x + i + 3*ggml_f32_epr);
+            ay4 = GGML_F32_VEC_LOAD(y + i + 3*ggml_f32_epr);
+            sum4 = GGML_F32_VEC_FMA(ax4, ay4, sum4);
+
+            ax5 = GGML_F32_VEC_LOAD(x + i + 4*ggml_f32_epr);
+            ay5 = GGML_F32_VEC_LOAD(y + i + 4*ggml_f32_epr);
+            sum5 = GGML_F32_VEC_FMA(ax5, ay5, sum5);
+
+            ax6 = GGML_F32_VEC_LOAD(x + i + 5*ggml_f32_epr);
+            ay6 = GGML_F32_VEC_LOAD(y + i + 5*ggml_f32_epr);
+            sum6 = GGML_F32_VEC_FMA(ax6, ay6, sum6);
+
+            ax7 = GGML_F32_VEC_LOAD(x + i + 6*ggml_f32_epr);
+            ay7 = GGML_F32_VEC_LOAD(y + i + 6*ggml_f32_epr);
+            sum7 = GGML_F32_VEC_FMA(ax7, ay7, sum7);
+
+            ax8 = GGML_F32_VEC_LOAD(x + i + 7*ggml_f32_epr);
+            ay8 = GGML_F32_VEC_LOAD(y + i + 7*ggml_f32_epr);
+            sum8 = GGML_F32_VEC_FMA(ax8, ay8, sum8);
         }
-    }
+        // leftovers
+        // Since 8 unrolls are done in above loop, leftovers lie in range [0, ggml_f32_step] which is handled in below loop
+        const int np2 = (n & ~(ggml_f32_epr - 1));
+        for (int i = np; i < np2; i += ggml_f32_epr) {
+            ax1 = GGML_F32_VEC_LOAD(x + i);
+            ay1 = GGML_F32_VEC_LOAD(y + i);
+            sum1 = GGML_F32_VEC_FMA(ax1, ay1, sum1);
+        }
+        // maximum number of leftover elements will be less that ggml_f32_epr. Apply predicated svmad on available elements only
+        if (np2 < n) {
+            svbool_t pg = svwhilelt_b32(np2, n);
+            ax1 = svld1_f32(pg, x + np2);
+            ay1 = svld1_f32(pg, y + np2);
+            sum1 = svmad_f32_m(pg, ax1, ay1, sum1);
+        }
+        // reduce sum1,sum2 to sum1
+        GGML_F32_VEC_REDUCE(sumf, sum1, sum2, sum3, sum4, sum5, sum6, sum7, sum8);
+    #else
+        const int np = (n & ~(GGML_F32_STEP - 1));
 
-    // reduce sum0..sum3 to sum0
-    GGML_F32_VEC_REDUCE(sumf, sum);
+        GGML_F32_VEC sum[GGML_F32_ARR] = { GGML_F32_VEC_ZERO };
 
-    // leftovers
-    for (int i = np; i < n; ++i) {
-        sumf += x[i]*y[i];
-    }
+        GGML_F32_VEC ax[GGML_F32_ARR];
+        GGML_F32_VEC ay[GGML_F32_ARR];
+
+        for (int i = 0; i < np; i += GGML_F32_STEP) {
+            for (int j = 0; j < GGML_F32_ARR; j++) {
+                ax[j] = GGML_F32_VEC_LOAD(x + i + j*GGML_F32_EPR);
+                ay[j] = GGML_F32_VEC_LOAD(y + i + j*GGML_F32_EPR);
+
+                sum[j] = GGML_F32_VEC_FMA(sum[j], ax[j], ay[j]);
+            }
+        }
+
+        // reduce sum0..sum3 to sum0
+        GGML_F32_VEC_REDUCE(sumf, sum);
+
+        // leftovers
+        for (int i = np; i < n; ++i) {
+            sumf += x[i]*y[i];
+        }
+    #endif
 #else
     // scalar
     ggml_float sumf = 0.0;

ggml/src/ggml-cpu/vec.h

@@ -5,6 +5,7 @@
 #include "ggml-impl.h"
 #include "simd-mappings.h"
 #include "ggml.h"
+#include "ggml-cpu.h"
 
 #if defined(GGML_USE_ACCELERATE)
 #include <Accelerate/Accelerate.h>
@@ -148,27 +149,108 @@ inline static void ggml_vec_dot_f16_unroll(const int n, const int xs, float * GG
 
 inline static void ggml_vec_mad_f32(const int n, float * GGML_RESTRICT y, const float * GGML_RESTRICT x, const float v) {
 #if defined(GGML_SIMD)
-    const int np = (n & ~(GGML_F32_STEP - 1));
+    #if defined(__ARM_FEATURE_SVE)
 
-    GGML_F32_VEC vx = GGML_F32_VEC_SET1(v);
+        const int sve_register_length = ggml_cpu_get_sve_cnt() * 8;
+        const int ggml_f32_epr = sve_register_length / 32;//8;//svcntw(); // SVE128:4, SVE256:8, SVE512:16
+        const int ggml_f32_step = 8 * ggml_f32_epr; // choose 8 SVE registers
+        GGML_F32_VEC vx = GGML_F32_VEC_SET1(v);
 
-    GGML_F32_VEC ax[GGML_F32_ARR];
-    GGML_F32_VEC ay[GGML_F32_ARR];
+        const int np = (n & ~(ggml_f32_step - 1));
+        svfloat32_t ax1, ax2, ax3, ax4, ax5, ax6, ax7, ax8;
+        svfloat32_t ay1, ay2, ay3, ay4, ay5, ay6, ay7, ay8;
+        for (int i = 0; i < np; i += ggml_f32_step) {
 
-    for (int i = 0; i < np; i += GGML_F32_STEP) {
-        for (int j = 0; j < GGML_F32_ARR; j++) {
-            ax[j] = GGML_F32_VEC_LOAD(x + i + j*GGML_F32_EPR);
-            ay[j] = GGML_F32_VEC_LOAD(y + i + j*GGML_F32_EPR);
-            ay[j] = GGML_F32_VEC_FMA(ay[j], ax[j], vx);
+            ax1 = GGML_F32_VEC_LOAD(x + i);
+            ay1 = GGML_F32_VEC_LOAD(y + i);
+            ay1 = GGML_F32_VEC_FMA(ax1, vx, ay1);
 
-            GGML_F32_VEC_STORE(y + i + j*GGML_F32_EPR, ay[j]);
+            GGML_F32_VEC_STORE(y + i, ay1);
+
+            ax2 = GGML_F32_VEC_LOAD(x + i + 1*ggml_f32_epr);
+            ay2 = GGML_F32_VEC_LOAD(y + i + 1*ggml_f32_epr);
+            ay2 = GGML_F32_VEC_FMA(ax2, vx, ay2);
+
+            GGML_F32_VEC_STORE(y + i + 1*ggml_f32_epr, ay2);
+
+            ax3 = GGML_F32_VEC_LOAD(x + i + 2*ggml_f32_epr);
+            ay3 = GGML_F32_VEC_LOAD(y + i + 2*ggml_f32_epr);
+            ay3 = GGML_F32_VEC_FMA(ax3, vx, ay3);
+
+            GGML_F32_VEC_STORE(y + i + 2*ggml_f32_epr, ay3);
+
+            ax4 = GGML_F32_VEC_LOAD(x + i + 3*ggml_f32_epr);
+            ay4 = GGML_F32_VEC_LOAD(y + i + 3*ggml_f32_epr);
+            ay4 = GGML_F32_VEC_FMA(ax4, vx, ay4);
+
+            GGML_F32_VEC_STORE(y + i + 3*ggml_f32_epr, ay4);
+
+            ax5 = GGML_F32_VEC_LOAD(x + i + 4*ggml_f32_epr);
+            ay5 = GGML_F32_VEC_LOAD(y + i + 4*ggml_f32_epr);
+            ay5 = GGML_F32_VEC_FMA(ax5, vx, ay5);
+
+            GGML_F32_VEC_STORE(y + i + 4*ggml_f32_epr, ay5);
+
+            ax6 = GGML_F32_VEC_LOAD(x + i + 5*ggml_f32_epr);
+            ay6 = GGML_F32_VEC_LOAD(y + i + 5*ggml_f32_epr);
+            ay6 = GGML_F32_VEC_FMA(ax6, vx, ay6);
+
+            GGML_F32_VEC_STORE(y + i + 5*ggml_f32_epr, ay6);
+
+            ax7 = GGML_F32_VEC_LOAD(x + i + 6*ggml_f32_epr);
+            ay7 = GGML_F32_VEC_LOAD(y + i + 6*ggml_f32_epr);
+            ay7 = GGML_F32_VEC_FMA(ax7, vx, ay7);
+
+            GGML_F32_VEC_STORE(y + i + 6*ggml_f32_epr, ay7);
+
+            ax8 = GGML_F32_VEC_LOAD(x + i + 7*ggml_f32_epr);
+            ay8 = GGML_F32_VEC_LOAD(y + i + 7*ggml_f32_epr);
+            ay8 = GGML_F32_VEC_FMA(ax8, vx, ay8);
+
+            GGML_F32_VEC_STORE(y + i + 7*ggml_f32_epr, ay8);
         }
-    }
+        // leftovers
+        // Since 8 unrolls are done in above loop, leftovers lie in range [0, ggml_f32_step] which is handled in below loop
+        const int np2 = (n & ~(ggml_f32_epr - 1));
+        for (int i = np; i < np2; i += ggml_f32_epr) {
+            ax1 = GGML_F32_VEC_LOAD(x + i);
+            ay1 = GGML_F32_VEC_LOAD(y + i);
+            ay1 = GGML_F32_VEC_FMA(ax1, vx, ay1);
 
-    // leftovers
-    for (int i = np; i < n; ++i) {
-        y[i] += x[i]*v;
-    }
+            GGML_F32_VEC_STORE(y + i, ay1);
+        }
+        // maximum number of leftover elements will be less that ggml_f32_epr. Apply predicated svmad on available elements only
+        if (np2 < n) {
+            svbool_t pg =svwhilelt_b32(np2, n);
+            ax1 = svld1_f32(pg, x + np2);
+            ay1 = svld1_f32(pg, y + np2);
+            ay1 = svmad_f32_m(pg, ax1, vx, ay1);
+
+            svst1_f32(pg, y + np2, ay1);
+        }
+    #else
+        const int np = (n & ~(GGML_F32_STEP - 1));
+
+        GGML_F32_VEC vx = GGML_F32_VEC_SET1(v);
+
+        GGML_F32_VEC ax[GGML_F32_ARR];
+        GGML_F32_VEC ay[GGML_F32_ARR];
+
+        for (int i = 0; i < np; i += GGML_F32_STEP) {
+            for (int j = 0; j < GGML_F32_ARR; j++) {
+                ax[j] = GGML_F32_VEC_LOAD(x + i + j*GGML_F32_EPR);
+                ay[j] = GGML_F32_VEC_LOAD(y + i + j*GGML_F32_EPR);
+                ay[j] = GGML_F32_VEC_FMA(ay[j], ax[j], vx);
+
+                GGML_F32_VEC_STORE(y + i + j*GGML_F32_EPR, ay[j]);
+            }
+        }
+
+        // leftovers
+        for (int i = np; i < n; ++i) {
+            y[i] += x[i]*v;
+        }
+    #endif
 #else
     // scalar
     for (int i = 0; i < n; ++i) {
@@ -220,36 +302,45 @@ inline static void ggml_vec_mad_f32_unroll(const int n, const int xs, const int
     }
 
 #if defined(GGML_SIMD)
-    const int np = (n & ~(GGML_F32_STEP - 1));
-
-    GGML_F32_VEC vx[GGML_VEC_MAD_UNROLL];
-
-    for (int k = 0; k < GGML_VEC_MAD_UNROLL; ++k) {
-        vx[k] = GGML_F32_VEC_SET1(v[k][0]);
-    }
-
-    GGML_F32_VEC ax[GGML_VEC_MAD_UNROLL][GGML_F32_ARR];
-    GGML_F32_VEC ay[GGML_F32_ARR];
-
-    for (int i = 0; i < np; i += GGML_F32_STEP) {
-        for (int j = 0; j < GGML_F32_ARR; j++) {
-            ay[j] = GGML_F32_VEC_LOAD(y + i + j*GGML_F32_EPR);
-
-            for (int k = 0; k < GGML_VEC_MAD_UNROLL; ++k) {
-                ax[k][j] = GGML_F32_VEC_LOAD(x[k] + i + j*GGML_F32_EPR);
-                ay[j] = GGML_F32_VEC_FMA(ay[j], ax[k][j], vx[k]);
+    #if defined(__ARM_FEATURE_SVE)
+        // scalar Route to scalar implementation       //TODO: Write SVE code
+        for (int k = 0; k < GGML_VEC_MAD_UNROLL; ++k) {
+            for (int i = 0; i < n; ++i) {
+                y[i] += x[k][i]*v[k][0];
             }
-
-            GGML_F32_VEC_STORE(y + i + j*GGML_F32_EPR, ay[j]);
         }
-    }
+    #else
+        const int np = (n & ~(GGML_F32_STEP - 1));
 
-    // leftovers
-    for (int k = 0; k < GGML_VEC_MAD_UNROLL; ++k) {
-        for (int i = np; i < n; ++i) {
-            y[i] += x[k][i]*v[k][0];
+        GGML_F32_VEC vx[GGML_VEC_MAD_UNROLL];
+
+        for (int k = 0; k < GGML_VEC_MAD_UNROLL; ++k) {
+            vx[k] = GGML_F32_VEC_SET1(v[k][0]);
         }
-    }
+
+        GGML_F32_VEC ax[GGML_VEC_MAD_UNROLL][GGML_F32_ARR];
+        GGML_F32_VEC ay[GGML_F32_ARR];
+
+        for (int i = 0; i < np; i += GGML_F32_STEP) {
+            for (int j = 0; j < GGML_F32_ARR; j++) {
+                ay[j] = GGML_F32_VEC_LOAD(y + i + j*GGML_F32_EPR);
+
+                for (int k = 0; k < GGML_VEC_MAD_UNROLL; ++k) {
+                    ax[k][j] = GGML_F32_VEC_LOAD(x[k] + i + j*GGML_F32_EPR);
+                    ay[j] = GGML_F32_VEC_FMA(ay[j], ax[k][j], vx[k]);
+                }
+
+                GGML_F32_VEC_STORE(y + i + j*GGML_F32_EPR, ay[j]);
+            }
+        }
+
+        // leftovers
+        for (int k = 0; k < GGML_VEC_MAD_UNROLL; ++k) {
+            for (int i = np; i < n; ++i) {
+                y[i] += x[k][i]*v[k][0];
+            }
+        }
+    #endif
 #else
     // scalar
     for (int k = 0; k < GGML_VEC_MAD_UNROLL; ++k) {
@@ -265,25 +356,53 @@ inline static void ggml_vec_scale_f32(const int n, float * y, const float   v) {
 #if defined(GGML_USE_ACCELERATE)
     vDSP_vsmul(y, 1, &v, y, 1, n);
 #elif defined(GGML_SIMD)
-    const int np = (n & ~(GGML_F32_STEP - 1));
+    #if defined(__ARM_FEATURE_SVE)
+        const int sve_register_length = ggml_cpu_get_sve_cnt() * 8;
+        const int ggml_f32_epr = sve_register_length / 32;//8;//svcntw(); // SVE128:4, SVE256:8, SVE512:16
+        const int ggml_f32_step = 2 * ggml_f32_epr;
 
-    GGML_F32_VEC vx = GGML_F32_VEC_SET1(v);
+        GGML_F32_VEC vx = GGML_F32_VEC_SET1(v);
+        const int np = (n & ~(ggml_f32_step - 1));
+        svfloat32_t ay1;
+        svfloat32_t ay2;
+        for (int i = 0; i < np; i += ggml_f32_step) {
+            ay1 = GGML_F32_VEC_LOAD(y + i);
+            ay1 = GGML_F32_VEC_MUL(ay1, vx);
+            GGML_F32_VEC_STORE(y + i, ay1);
 
-    GGML_F32_VEC ay[GGML_F32_ARR];
-
-    for (int i = 0; i < np; i += GGML_F32_STEP) {
-        for (int j = 0; j < GGML_F32_ARR; j++) {
-            ay[j] = GGML_F32_VEC_LOAD(y + i + j*GGML_F32_EPR);
-            ay[j] = GGML_F32_VEC_MUL(ay[j], vx);
-
-            GGML_F32_VEC_STORE(y + i + j*GGML_F32_EPR, ay[j]);
+            ay2 = GGML_F32_VEC_LOAD(y + i + 1*ggml_f32_epr);
+            ay2 = GGML_F32_VEC_MUL(ay2, vx);
+            GGML_F32_VEC_STORE(y + i + 1*ggml_f32_epr, ay2);
         }
-    }
+        // leftovers
+        // maximum number of leftover elements will be less that ggml_f32_epr. Apply predicated svmad on available elements only
+        if (np < n) {
+            svbool_t pg = svwhilelt_b32(np, n);
+            ay1 = svld1_f32(pg, y + np);
+            ay1 = svmul_f32_m(pg, ay1, vx);
+            svst1_f32(pg, y + np, ay1);
+        }
+    #else
+        const int np = (n & ~(GGML_F32_STEP - 1));
 
-    // leftovers
-    for (int i = np; i < n; ++i) {
-        y[i] *= v;
-    }
+        GGML_F32_VEC vx = GGML_F32_VEC_SET1(v);
+
+        GGML_F32_VEC ay[GGML_F32_ARR];
+
+        for (int i = 0; i < np; i += GGML_F32_STEP) {
+            for (int j = 0; j < GGML_F32_ARR; j++) {
+                ay[j] = GGML_F32_VEC_LOAD(y + i + j*GGML_F32_EPR);
+                ay[j] = GGML_F32_VEC_MUL(ay[j], vx);
+
+                GGML_F32_VEC_STORE(y + i + j*GGML_F32_EPR, ay[j]);
+            }
+        }
+
+        // leftovers
+        for (int i = np; i < n; ++i) {
+            y[i] *= v;
+        }
+    #endif
 #else
     // scalar
     for (int i = 0; i < n; ++i) {

ggml/src/ggml-cuda/common.cuh

@@ -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);

ggml/src/ggml-cuda/fattn-common.cuh

@@ -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();

ggml/src/ggml-impl.h

@@ -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;

ggml/src/ggml-opencl/CMakeLists.txt

@@ -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
 )
 

ggml/src/ggml-opencl/ggml-opencl.cpp

@@ -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;
     }

ggml/src/ggml-opencl/kernels/argsort.cl

@@ -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];
+    }
+}

ggml/src/ggml-opencl/kernels/div.cl

@@ -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];
+}

ggml/src/ggml-opencl/kernels/group_norm.cl

@@ -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;
+    }
+}

ggml/src/ggml-opencl/kernels/sigmoid.cl

@@ -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)]));
+}

ggml/src/ggml-opencl/kernels/sub.cl

@@ -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];
+}

ggml/src/ggml-opencl/kernels/sum_rows.cl

@@ -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;
+}

ggml/src/ggml-sycl/binbcast.cpp

@@ -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__);
 }
 

ggml/src/ggml-sycl/common.hpp

@@ -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

ggml/src/ggml-sycl/concat.cpp

@@ -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);
 }

ggml/src/ggml-sycl/conv.cpp

@@ -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;

ggml/src/ggml-sycl/cpy.cpp

@@ -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__);
 }

ggml/src/ggml-sycl/dmmv.cpp

@@ -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);

ggml/src/ggml-sycl/element_wise.cpp

@@ -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__);
 }

ggml/src/ggml-sycl/element_wise.hpp

@@ -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);

ggml/src/ggml-sycl/getrows.cpp

@@ -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");
     }
 }
-

ggml/src/ggml-sycl/ggml-sycl.cpp

@@ -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()));

ggml/src/ggml-sycl/gla.cpp

@@ -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);

ggml/src/ggml-sycl/mmvq.cpp

@@ -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;

ggml/src/ggml-sycl/norm.cpp

@@ -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) {

ggml/src/ggml-sycl/outprod.cpp

@@ -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];
 

ggml/src/ggml-sycl/rope.cpp

@@ -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__);
 }
 

ggml/src/ggml-sycl/softmax.cpp

@@ -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);
     }
 }

ggml/src/ggml-sycl/tsembd.cpp

@@ -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);
 }

ggml/src/ggml-sycl/wkv.cpp

@@ -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);
 }

ggml/src/ggml-vulkan/CMakeLists.txt

@@ -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()

ggml/src/ggml-vulkan/ggml-vulkan.cpp

@@ -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);
 

ggml/src/ggml.c

@@ -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(

gguf-py/gguf/constants.py

@@ -177,6 +177,9 @@ class Keys:
         EMBEDDING_LENGTH = "{arch}.convnext.embedding_length"
         BLOCK_COUNT      = "{arch}.convnext.block_count"
 
+    class Classifier:
+        OUTPUT_LABELS = "{arch}.classifier.output_labels"
+
     class Tokenizer:
         MODEL                = "tokenizer.ggml.model"
         PRE                  = "tokenizer.ggml.pre"
@@ -2260,6 +2263,7 @@ class VisionProjectorType:
     ULTRAVOX = "ultravox"
     INTERNVL = "internvl"
     QWEN2A = "qwen2a" # audio
+    QWEN25O = "qwen2.5o" # omni
 
 
 # Items here are (block size, type size)

gguf-py/gguf/tensor_mapping.py

@@ -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: (
@@ -1161,12 +1161,16 @@ 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: (

gguf-py/gguf/utility.py

@@ -231,7 +231,7 @@ class SafetensorRemote:
         response.raise_for_status()
 
         # Get raw byte data
-        return response.content[:size]
+        return response.content[slice(size if size > -1 else None)]
 
     @classmethod
     def check_file_exist(cls, url: str) -> bool:

include/llama.h

@@ -612,11 +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(

models/templates/Qwen-Qwen3-0.6B.jinja

@@ -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 %}

models/templates/README.md

@@ -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
 ```

scripts/compare-commits.sh

@@ -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
 }

scripts/sync-ggml.last

@@ -1 +1 @@
-7c06c10c532a6cda913c17fc56341e8880ae341d
+06b715f4c170232af261425240914fa49c44f982

src/CMakeLists.txt

@@ -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

src/llama-arch.cpp

@@ -174,6 +174,8 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
     { LLM_KV_CONVNEXT_EMBEDDING_LENGTH, "%s.convnext.embedding_length" },
     { LLM_KV_CONVNEXT_BLOCK_COUNT,      "%s.convnext.block_count"      },
 
+    { LLM_KV_CLASSIFIER_OUTPUT_LABELS, "%s.classifier.output_labels" },
+
     { LLM_KV_TOKENIZER_MODEL,                "tokenizer.ggml.model"                    },
     { LLM_KV_TOKENIZER_PRE,                  "tokenizer.ggml.pre"                      },
     { LLM_KV_TOKENIZER_LIST,                 "tokenizer.ggml.tokens"                   },

src/llama-arch.h

@@ -213,6 +213,8 @@ enum llm_kv {
     LLM_KV_CONVNEXT_EMBEDDING_LENGTH,
     LLM_KV_CONVNEXT_BLOCK_COUNT,
 
+    LLM_KV_CLASSIFIER_OUTPUT_LABELS,
+
     // deprecated:
     LLM_KV_TOKENIZER_PREFIX_ID,
     LLM_KV_TOKENIZER_SUFFIX_ID,

src/llama-context.cpp

@@ -693,12 +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;
+            }
         }
     }
 
@@ -852,7 +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);
     }
 
@@ -887,11 +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;
             }
         }
     }

src/llama-graph.cpp

@@ -455,7 +455,7 @@ llm_graph_context::llm_graph_context(const llm_graph_params & params) :
     }
 
 int64_t llm_graph_context::n_pos_per_embd() const {
-    return arch == LLM_ARCH_QWEN2VL ? 4 : 1;
+    return hparams.rope_type == LLAMA_ROPE_TYPE_MROPE ? 4 : 1;
 }
 
 void llm_graph_context::cb(ggml_tensor * cur, const char * name, int il) const {
@@ -1562,20 +1562,25 @@ void llm_graph_context::build_pooling(
                 ggml_tensor * inp_cls = build_inp_cls();
                 inp = ggml_get_rows(ctx0, inp, inp_cls);
 
-                // classification head
-                // https://github.com/huggingface/transformers/blob/5af7d41e49bbfc8319f462eb45253dcb3863dfb7/src/transformers/models/roberta/modeling_roberta.py#L1566
-                GGML_ASSERT(cls   != nullptr);
-                GGML_ASSERT(cls_b != nullptr);
+                if (cls != nullptr && cls_b != nullptr) {
+                    // classification head
+                    // https://github.com/huggingface/transformers/blob/5af7d41e49bbfc8319f462eb45253dcb3863dfb7/src/transformers/models/roberta/modeling_roberta.py#L1566
+                    cur = ggml_add(ctx0, ggml_mul_mat(ctx0, cls, inp), cls_b);
+                    cur = ggml_tanh(ctx0, cur);
 
-                cur = ggml_add (ctx0, ggml_mul_mat(ctx0, cls, inp), cls_b);
-                cur = ggml_tanh(ctx0, cur);
-
-                // some models don't have `cls_out`, for example: https://huggingface.co/jinaai/jina-reranker-v1-tiny-en
-                // https://huggingface.co/jinaai/jina-reranker-v1-tiny-en/blob/cb5347e43979c3084a890e3f99491952603ae1b7/modeling_bert.py#L884-L896
-                if (cls_out) {
+                    // some models don't have `cls_out`, for example: https://huggingface.co/jinaai/jina-reranker-v1-tiny-en
+                    // https://huggingface.co/jinaai/jina-reranker-v1-tiny-en/blob/cb5347e43979c3084a890e3f99491952603ae1b7/modeling_bert.py#L884-L896
+                    if (cls_out) {
+                        GGML_ASSERT(cls_out_b != nullptr);
+                        cur = ggml_add(ctx0, ggml_mul_mat(ctx0, cls_out, cur), cls_out_b);
+                    }
+                } else if (cls_out) {
+                    // Single layer classification head (direct projection)
+                    // https://github.com/huggingface/transformers/blob/f4fc42216cd56ab6b68270bf80d811614d8d59e4/src/transformers/models/bert/modeling_bert.py#L1476
                     GGML_ASSERT(cls_out_b != nullptr);
-
-                    cur = ggml_add (ctx0, ggml_mul_mat(ctx0, cls_out, cur), cls_out_b);
+                    cur = ggml_add(ctx0, ggml_mul_mat(ctx0, cls_out, inp), cls_out_b);
+                } else {
+                    GGML_ABORT("RANK pooling requires either cls+cls_b or cls_out+cls_out_b");
                 }
             } break;
         default:

src/llama-hparams.h

@@ -131,6 +131,9 @@ struct llama_hparams {
     bool attn_soft_cap = false;
     bool use_kq_norm   = true;
 
+    // for Classifiers
+    uint32_t n_cls_out = 1;
+
     // llama4
     uint32_t n_moe_layer_step        = 0;
     uint32_t n_no_rope_layer_step    = 4;

src/llama-kv-cache.cpp

@@ -286,31 +286,11 @@ void llama_kv_cache_unified::seq_div(llama_seq_id seq_id, llama_pos p0, llama_po
 }
 
 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 < cells.size(); ++i) {
-        if (cells.seq_has(i, seq_id)) {
-            result = std::min(result, cells.pos_get(i));
-        }
-    }
-
-    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 < cells.size(); ++i) {
-        if (cells.seq_has(i, seq_id)) {
-            result = std::max(result, cells.pos_get(i));
-        }
-    }
-
-    return result;
+    return cells.seq_pos_max(seq_id);
 }
 
 void llama_kv_cache_unified::restore() {
@@ -504,7 +484,7 @@ bool llama_kv_cache_unified::find_slot(const llama_ubatch & ubatch) {
     // 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(cells.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);
@@ -777,11 +757,19 @@ ggml_tensor * llama_kv_cache_unified::build_rope_shift(
     const auto & yarn_beta_slow  = cparams.yarn_beta_slow;
 
     const auto & n_rot     = hparams.n_rot;
-    const auto & rope_type = hparams.rope_type;
+    const auto & rope_type = hparams.rope_type == LLAMA_ROPE_TYPE_MROPE
+                                // @ngxson : this is a workaround
+                                // for M-RoPE, we want to rotate the whole vector when doing KV shift
+                                // a normal RoPE should work, we just need to use the correct ordering
+                                // ref: https://github.com/ggml-org/llama.cpp/pull/13870
+                                ? LLAMA_ROPE_TYPE_NEOX
+                                : hparams.rope_type;
 
     // See llm_build_deepseek2() for why attn_factor has to be scaled for YaRN RoPE to work correctly.
     // See https://github.com/ggerganov/llama.cpp/discussions/7416 for detailed explanation.
-    const float yarn_attn_factor = model.arch == LLM_ARCH_DEEPSEEK2 ? 1.0f / (1.0f + 0.1f * logf(1.0f / freq_scale)) : cparams.yarn_attn_factor;
+    const float yarn_attn_factor = model.arch == LLM_ARCH_DEEPSEEK2
+                                    ? 1.0f / (1.0f + 0.1f * logf(1.0f / freq_scale))
+                                    : cparams.yarn_attn_factor;
 
     ggml_tensor * tmp;
 
@@ -1018,7 +1006,7 @@ 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_kv   = cells.used_max_p1();
     const uint32_t n_used = cells.get_used();
 
     assert(n_used <= n_kv);
@@ -1144,16 +1132,6 @@ 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 = cells.size(); i > 0; --i) {
-        if (!cells.is_empty(i - 1)) {
-            return i;
-        }
-    }
-
-    return 0;
-}
-
 bool llama_kv_cache_unified::is_masked_swa(llama_pos p0, llama_pos p1) const {
     assert(p0 >= 0 && p1 >= 0);
 

src/llama-kv-cache.h

@@ -246,10 +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
-    // TODO: optimize
-    uint32_t cell_max() const;
-
     size_t total_size() const;
 
     size_t size_k_bytes() const;

src/llama-kv-cells.h

@@ -6,6 +6,7 @@
 #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
@@ -18,8 +19,13 @@ public:
             seq[i].reset();
         }
 
-        used      = 0;
         has_shift = false;
+
+        used.clear();
+
+        for (uint32_t s = 0; s < LLAMA_MAX_PARALLEL_SEQUENCES; ++s) {
+            seq_pos[s].clear();
+        }
     }
 
     void reset_shift() {
@@ -50,7 +56,25 @@ public:
     }
 
     uint32_t get_used() const {
-        return used;
+        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 {
@@ -69,6 +93,9 @@ public:
         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)
@@ -95,16 +122,24 @@ public:
 
         for (uint32_t j = 0; j < other.pos.size(); ++j) {
             if (pos[i + j] == -1 && other.pos[j] != -1) {
-                used++;
+                used.insert(i + j);
             }
 
             if (pos[i + j] != -1 && other.pos[j] == -1) {
-                used--;
+                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);
         }
     }
@@ -118,11 +153,12 @@ public:
         assert(seq_id >= 0);
 
         seq[i].reset(seq_id);
+        seq_pos[seq_id].erase(pos[i]);
 
         if (seq[i].none()) {
             pos[i] = -1;
 
-            used--;
+            used.erase(i);
 
             return true;
         }
@@ -135,17 +171,22 @@ public:
         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--;
+            used.erase(i);
 
             return true;
         }
@@ -169,6 +210,33 @@ public:
         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
@@ -202,7 +270,8 @@ public:
         assert(pos[i] == -1);
 
         pos[i] = p;
-        used++;
+
+        used.insert(i);
     }
 
     // pos[i] = pos[i] + d
@@ -212,16 +281,22 @@ public:
         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) {
-            pos[i] = -1;
-            seq[i].reset();
+            seq_pos_rm(i);
 
-            used--;
+            seq[i].reset();
+            pos[i] = -1;
+
+            used.erase(i);
 
             return true;
         }
@@ -238,17 +313,22 @@ public:
 
         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:
-    uint32_t used = 0; // used cells (i.e. pos[i] != -1, allowed to not have any seq_id)
-
     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
@@ -268,6 +348,32 @@ private:
     //
     std::vector<llama_pos> shift;
 
-    std::vector<std::bitset<LLAMA_MAX_PARALLEL_SEQUENCES>> seq;
-};
+    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]);
+            }
+        }
+    }
+};

src/llama-model.cpp

@@ -683,6 +683,7 @@ void llama_model::load_hparams(llama_model_loader & ml) {
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS,    hparams.f_norm_eps);
                 ml.get_key(LLM_KV_ATTENTION_CAUSAL,           hparams.causal_attn);
                 ml.get_key(LLM_KV_POOLING_TYPE,               hparams.pooling_type, false);
+                ml.get_arr_n(LLM_KV_CLASSIFIER_OUTPUT_LABELS, hparams.n_cls_out, false);
 
                 switch (hparams.n_layer) {
                     case 3:
@@ -2121,8 +2122,8 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                         cls   = create_tensor(tn(LLM_TENSOR_CLS, "weight"), {n_embd, n_embd}, TENSOR_NOT_REQUIRED);
                         cls_b = create_tensor(tn(LLM_TENSOR_CLS, "bias"),   {n_embd},         TENSOR_NOT_REQUIRED);
 
-                        cls_out   = create_tensor(tn(LLM_TENSOR_CLS_OUT, "weight"), {n_embd, 1}, TENSOR_NOT_REQUIRED);
-                        cls_out_b = create_tensor(tn(LLM_TENSOR_CLS_OUT, "bias"),   {1},         TENSOR_NOT_REQUIRED);
+                        cls_out   = create_tensor(tn(LLM_TENSOR_CLS_OUT, "weight"), {n_embd, hparams.n_cls_out}, TENSOR_NOT_REQUIRED);
+                        cls_out_b = create_tensor(tn(LLM_TENSOR_CLS_OUT, "bias"),   {hparams.n_cls_out},         TENSOR_NOT_REQUIRED);
                     }
 
                     tok_norm   = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd}, 0);

src/llama-sampling.cpp

@@ -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;
         }

tests/test-chat.cpp

@@ -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(

tests/test-sampling.cpp

@@ -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);

tools/mtmd/CMakeLists.txt

@@ -1,48 +1,54 @@
 # mtmd
 
-# compile mtmd-audio separately to avoid long compile times with miniaudio.h
-# TODO @ngxson : move miniaudio.h and stb_image.h to mtmd-helper.cpp, then compile the helper as a separate library
-add_library(mtmd_audio STATIC mtmd-audio.cpp mtmd-audio.h)
-if (BUILD_SHARED_LIBS)
-    set_target_properties(mtmd_audio PROPERTIES POSITION_INDEPENDENT_CODE ON)
-endif()
-target_link_libraries(mtmd_audio PRIVATE ggml ${CMAKE_THREAD_LIBS_INIT})
-target_compile_features(mtmd_audio PRIVATE cxx_std_17)
-target_include_directories(mtmd_audio PRIVATE .)
-
 add_library(mtmd OBJECT
             mtmd.cpp
-            mtmd-helper.cpp
+            mtmd-audio.cpp
             mtmd.h
             clip.cpp
             clip.h
             clip-impl.h
             )
 
-target_link_libraries(mtmd PRIVATE ggml llama mtmd_audio ${CMAKE_THREAD_LIBS_INIT})
-
+target_link_libraries(mtmd PRIVATE ggml llama ${CMAKE_THREAD_LIBS_INIT})
 target_include_directories(mtmd PUBLIC .)
 target_include_directories(mtmd PRIVATE ../..)
-target_include_directories(mtmd PRIVATE ../../common) # for stb_image.h
-
 target_compile_features(mtmd PRIVATE cxx_std_17)
 
-add_library(mtmd_static STATIC $<TARGET_OBJECTS:mtmd>)
+# compile the helper separately, to avoid long compile times with miniaudio.h and stb_image.h
+
+add_library(mtmd_helper OBJECT
+            mtmd-helper.cpp
+            mtmd-helper.h
+            )
+
+target_link_libraries(mtmd_helper PRIVATE ggml llama mtmd ${CMAKE_THREAD_LIBS_INIT})
+target_include_directories(mtmd_helper PUBLIC .)
+target_include_directories(mtmd_helper PRIVATE ./vendor)
+target_include_directories(mtmd_helper PRIVATE ../..)
+target_compile_features(mtmd_helper PRIVATE cxx_std_17)
+
 if (BUILD_SHARED_LIBS)
     set_target_properties(mtmd PROPERTIES POSITION_INDEPENDENT_CODE ON)
     target_compile_definitions(mtmd PRIVATE LLAMA_SHARED LLAMA_BUILD)
     add_library(mtmd_shared SHARED $<TARGET_OBJECTS:mtmd>)
-    target_link_libraries(mtmd_shared PRIVATE ggml llama mtmd_audio ${CMAKE_THREAD_LIBS_INIT})
+    target_link_libraries(mtmd_shared PRIVATE ggml llama ${CMAKE_THREAD_LIBS_INIT})
     install(TARGETS mtmd_shared LIBRARY)
+
+    set_target_properties(mtmd_helper PROPERTIES POSITION_INDEPENDENT_CODE ON)
+    target_compile_definitions(mtmd_helper PRIVATE LLAMA_SHARED LLAMA_BUILD)
+    add_library(mtmd_helper_shared SHARED $<TARGET_OBJECTS:mtmd>)
+    target_link_libraries(mtmd_helper_shared PRIVATE ggml llama mtmd ${CMAKE_THREAD_LIBS_INIT})
+    install(TARGETS mtmd_helper_shared LIBRARY)
 endif()
 
 if (NOT MSVC)
-    target_compile_options(mtmd PRIVATE -Wno-cast-qual) # stb_image.h
-    target_compile_options(mtmd_audio PRIVATE -Wno-cast-qual) # miniaudio.h
+    # for stb_image.h and miniaudio.h
+    target_compile_options(mtmd_helper PRIVATE -Wno-cast-qual)
 endif()
 
 if(TARGET BUILD_INFO)
     add_dependencies(mtmd BUILD_INFO)
+    add_dependencies(mtmd_helper BUILD_INFO)
 endif()
 
 add_executable(llama-llava-cli    deprecation-warning.cpp)
@@ -54,5 +60,5 @@ set(TARGET llama-mtmd-cli)
 add_executable(${TARGET} mtmd-cli.cpp)
 set_target_properties(${TARGET} PROPERTIES OUTPUT_NAME llama-mtmd-cli)
 install(TARGETS ${TARGET} RUNTIME)
-target_link_libraries(${TARGET} PRIVATE common mtmd ${CMAKE_THREAD_LIBS_INIT})
+target_link_libraries(${TARGET} PRIVATE common mtmd mtmd_helper ${CMAKE_THREAD_LIBS_INIT})
 target_compile_features(${TARGET} PRIVATE cxx_std_17)

tools/mtmd/clip-impl.h

@@ -130,6 +130,7 @@ enum projector_type {
     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,
 };
 
@@ -148,6 +149,7 @@ static std::map<projector_type, std::string> PROJECTOR_TYPE_NAMES = {
     { 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) {

tools/mtmd/clip.h

@@ -17,12 +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);
 

tools/mtmd/mtmd-audio.cpp

@@ -1,28 +1,5 @@
-// fix problem with std::min and std::max
-#if defined(_WIN32)
-#define WIN32_LEAN_AND_MEAN
-#ifndef NOMINMAX
-#   define NOMINMAX
-#endif
-#include <windows.h>
-#endif
-
 #include "mtmd-audio.h"
 
-//#define MTMD_AUDIO_DEBUG
-
-#define MINIAUDIO_IMPLEMENTATION
-#ifndef MTMD_AUDIO_DEBUG
-#   define MA_NO_ENCODING
-#endif
-#define MA_NO_DEVICE_IO
-#define MA_NO_RESOURCE_MANAGER
-#define MA_NO_NODE_GRAPH
-#define MA_NO_ENGINE
-#define MA_NO_GENERATION
-#define MA_API static
-#include "miniaudio.h"
-
 #define _USE_MATH_DEFINES // for M_PI
 #include <cmath>
 #include <cstdint>
@@ -359,69 +336,6 @@ bool preprocess_audio(
 } // namespace whisper_preprocessor
 
 
-namespace audio_helpers {
-
-bool is_audio_file(const char * buf, size_t len) {
-    if (len < 12) {
-        return false;
-    }
-
-    // RIFF ref: https://en.wikipedia.org/wiki/Resource_Interchange_File_Format
-    // WAV ref: https://www.mmsp.ece.mcgill.ca/Documents/AudioFormats/WAVE/WAVE.html
-    bool is_wav = memcmp(buf, "RIFF", 4) == 0 && memcmp(buf + 8, "WAVE", 4) == 0;
-    bool is_mp3 = len >= 3 && (
-        memcmp(buf, "ID3", 3) == 0 ||
-        // Check for MPEG sync word (simplified check)
-        ((unsigned char)buf[0] == 0xFF && ((unsigned char)buf[1] & 0xE0) == 0xE0)
-    );
-    bool is_flac = memcmp(buf, "fLaC", 4) == 0;
-
-    return is_wav || is_mp3 || is_flac;
-}
-
-// returns true if the buffer is a valid audio file
-bool decode_audio_from_buf(const unsigned char * buf_in, size_t len, int target_sampler_rate, std::vector<float> & pcmf32_mono) {
-    ma_result result;
-    const int channels = 1;
-    ma_decoder_config decoder_config = ma_decoder_config_init(ma_format_f32, channels, target_sampler_rate);
-    ma_decoder decoder;
-
-    result = ma_decoder_init_memory(buf_in, len, &decoder_config, &decoder);
-    if (result != MA_SUCCESS) {
-        return false;
-    }
-
-    ma_uint64 frame_count;
-    ma_uint64 frames_read;
-    result = ma_decoder_get_length_in_pcm_frames(&decoder, &frame_count);
-    if (result != MA_SUCCESS) {
-        ma_decoder_uninit(&decoder);
-        return false;
-    }
-
-    pcmf32_mono.resize(frame_count);
-    result = ma_decoder_read_pcm_frames(&decoder, pcmf32_mono.data(), frame_count, &frames_read);
-    if (result != MA_SUCCESS) {
-        ma_decoder_uninit(&decoder);
-        return false;
-    }
-
-#ifdef MTMD_AUDIO_DEBUG
-    // save audio to wav file
-    ma_encoder_config config = ma_encoder_config_init(ma_encoding_format_wav, ma_format_f32, 1, target_sampler_rate);
-    ma_encoder encoder;
-    ma_encoder_init_file("output.wav", &config, &encoder);
-    ma_encoder_write_pcm_frames(&encoder, pcmf32_mono.data(), pcmf32_mono.size(), &frames_read);
-    ma_encoder_uninit(&encoder);
-#endif
-
-    ma_decoder_uninit(&decoder);
-    return true;
-}
-
-} // namespace wav_utils
-
-
 // precalculated mel filter banks
 // values are multiplied by 1000.0 to save space, and will be divided by 1000.0 in the end of the function
 //

tools/mtmd/mtmd-audio.h

@@ -32,7 +32,7 @@ struct whisper_filters {
     std::vector<float> data;
 };
 
-extern bool preprocess_audio(
+bool preprocess_audio(
         const float * samples,
         size_t n_samples,
         const whisper_filters & filters,
@@ -40,23 +40,8 @@ extern bool preprocess_audio(
 
 } // namespace whisper_preprocessor
 
-
-// TODO @ngxson : move this helper to mtmd-helpers.cpp
-namespace audio_helpers {
-
-extern bool is_audio_file(const char * buf, size_t len);
-
-extern bool decode_audio_from_buf(
-        const unsigned char * buf_in,
-        size_t len,
-        int target_sampler_rate,
-        std::vector<float> & pcmf32_mono);
-
-} // namespace audio_helpers
-
-
 namespace whisper_precalc_filters {
 
-extern whisper_preprocessor::whisper_filters get_128_bins();
+whisper_preprocessor::whisper_filters get_128_bins();
 
 } // namespace whisper_precalc_filters

tools/mtmd/mtmd-cli.cpp

@@ -7,6 +7,7 @@
 #include "console.h"
 #include "chat.h"
 #include "mtmd.h"
+#include "mtmd-helper.h"
 
 #include <vector>
 #include <limits.h>
@@ -143,7 +144,7 @@ struct mtmd_cli_context {
     }
 
     bool load_media(const std::string & fname) {
-        mtmd::bitmap bmp(mtmd_helper_bitmap_init_from_file(fname.c_str()));
+        mtmd::bitmap bmp(mtmd_helper_bitmap_init_from_file(ctx_vision.get(), fname.c_str()));
         if (!bmp.ptr) {
             return false;
         }
@@ -284,7 +285,9 @@ 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";

tools/mtmd/mtmd-helper.cpp

@@ -1,10 +1,37 @@
+// fix problem with std::min and std::max
+#if defined(_WIN32)
+#define WIN32_LEAN_AND_MEAN
+#ifndef NOMINMAX
+#   define NOMINMAX
+#endif
+#include <windows.h>
+#endif
+
 #include "mtmd.h"
+#include "mtmd-helper.h"
 #include "llama.h"
 
 #include <algorithm>
 #include <cinttypes>
 #include <vector>
 
+//#define MTMD_AUDIO_DEBUG
+
+#define MINIAUDIO_IMPLEMENTATION
+#ifndef MTMD_AUDIO_DEBUG
+#   define MA_NO_ENCODING
+#endif
+#define MA_NO_DEVICE_IO
+#define MA_NO_RESOURCE_MANAGER
+#define MA_NO_NODE_GRAPH
+#define MA_NO_ENGINE
+#define MA_NO_GENERATION
+#define MA_API static
+#include "vendor/miniaudio.h"
+
+#define STB_IMAGE_IMPLEMENTATION
+#include "vendor/stb_image.h"
+
 #define LOG_INF(...) fprintf(stdout, __VA_ARGS__)
 #define LOG_ERR(...) fprintf(stderr, __VA_ARGS__)
 
@@ -66,7 +93,8 @@ 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 +113,23 @@ 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 +191,20 @@ 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);
     }
@@ -295,3 +342,118 @@ int32_t mtmd_helper_eval_chunks(mtmd_context * ctx,
 
     return 0;
 }
+
+namespace audio_helpers {
+
+static bool is_audio_file(const char * buf, size_t len) {
+    if (len < 12) {
+        return false;
+    }
+
+    // RIFF ref: https://en.wikipedia.org/wiki/Resource_Interchange_File_Format
+    // WAV ref: https://www.mmsp.ece.mcgill.ca/Documents/AudioFormats/WAVE/WAVE.html
+    bool is_wav = memcmp(buf, "RIFF", 4) == 0 && memcmp(buf + 8, "WAVE", 4) == 0;
+    bool is_mp3 = len >= 3 && (
+        memcmp(buf, "ID3", 3) == 0 ||
+        // Check for MPEG sync word (simplified check)
+        ((unsigned char)buf[0] == 0xFF && ((unsigned char)buf[1] & 0xE0) == 0xE0)
+    );
+    bool is_flac = memcmp(buf, "fLaC", 4) == 0;
+
+    return is_wav || is_mp3 || is_flac;
+}
+
+// returns true if the buffer is a valid audio file
+static bool decode_audio_from_buf(const unsigned char * buf_in, size_t len, int target_sampler_rate, std::vector<float> & pcmf32_mono) {
+    ma_result result;
+    const int channels = 1;
+    ma_decoder_config decoder_config = ma_decoder_config_init(ma_format_f32, channels, target_sampler_rate);
+    ma_decoder decoder;
+
+    result = ma_decoder_init_memory(buf_in, len, &decoder_config, &decoder);
+    if (result != MA_SUCCESS) {
+        return false;
+    }
+
+    ma_uint64 frame_count;
+    ma_uint64 frames_read;
+    result = ma_decoder_get_length_in_pcm_frames(&decoder, &frame_count);
+    if (result != MA_SUCCESS) {
+        ma_decoder_uninit(&decoder);
+        return false;
+    }
+
+    pcmf32_mono.resize(frame_count);
+    result = ma_decoder_read_pcm_frames(&decoder, pcmf32_mono.data(), frame_count, &frames_read);
+    if (result != MA_SUCCESS) {
+        ma_decoder_uninit(&decoder);
+        return false;
+    }
+
+#ifdef MTMD_AUDIO_DEBUG
+    // save audio to wav file
+    ma_encoder_config config = ma_encoder_config_init(ma_encoding_format_wav, ma_format_f32, 1, target_sampler_rate);
+    ma_encoder encoder;
+    ma_encoder_init_file("output.wav", &config, &encoder);
+    ma_encoder_write_pcm_frames(&encoder, pcmf32_mono.data(), pcmf32_mono.size(), &frames_read);
+    ma_encoder_uninit(&encoder);
+#endif
+
+    ma_decoder_uninit(&decoder);
+    return true;
+}
+
+} // namespace audio_helpers
+
+mtmd_bitmap * mtmd_helper_bitmap_init_from_buf(mtmd_context * ctx, const unsigned char * buf, size_t len) {
+    if (audio_helpers::is_audio_file((const char *)buf, len)) {
+        std::vector<float> pcmf32;
+        int bitrate = mtmd_get_audio_bitrate(ctx);
+        if (bitrate < 0) {
+            LOG_ERR("This model does not support audio input\n");
+            return nullptr;
+        }
+        if (!audio_helpers::decode_audio_from_buf(buf, len, bitrate, pcmf32)) {
+            LOG_ERR("Unable to read WAV audio file from buffer\n");
+            return nullptr;
+        }
+        return mtmd_bitmap_init_from_audio(pcmf32.size(), pcmf32.data());
+    }
+
+    // otherwise, we assume it's an image
+    mtmd_bitmap * result = nullptr;
+    {
+        int nx, ny, nc;
+        auto * data = stbi_load_from_memory(buf, len, &nx, &ny, &nc, 3);
+        if (!data) {
+            LOG_ERR("%s: failed to decode image bytes\n", __func__);
+            return nullptr;
+        }
+        result = mtmd_bitmap_init(nx, ny, data);
+        stbi_image_free(data);
+    }
+    return result;
+}
+
+mtmd_bitmap * mtmd_helper_bitmap_init_from_file(mtmd_context * ctx, const char * fname) {
+    std::vector<unsigned char> buf;
+    FILE * f = fopen(fname, "rb");
+    if (!f) {
+        LOG_ERR("Unable to open file %s: %s\n", fname, strerror(errno));
+        return nullptr;
+    }
+
+    fseek(f, 0, SEEK_END);
+    long file_size = ftell(f);
+    fseek(f, 0, SEEK_SET);
+    buf.resize(file_size);
+
+    size_t n_read = fread(buf.data(), 1, file_size, f);
+    fclose(f);
+    if (n_read != (size_t)file_size) {
+        LOG_ERR("Failed to read entire file %s", fname);
+        return nullptr;
+    }
+
+    return mtmd_helper_bitmap_init_from_buf(ctx, buf.data(), buf.size());
+}

tools/mtmd/mtmd-helper.h

@@ -0,0 +1,91 @@
+#ifndef MTMD_HELPER_H
+#define MTMD_HELPER_H
+
+#include "ggml.h"
+#include "llama.h"
+#include "mtmd.h"
+
+#include <stddef.h>
+#include <stdint.h>
+#include <stdbool.h>
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+//
+// libmtmd helper functions
+//
+// Please note that these helpers are not guaranteed to be stable.
+// BREAKING CHANGES are expected.
+//
+
+// helper function to construct a mtmd_bitmap from a file
+// it calls mtmd_helper_bitmap_init_from_buf() internally
+// returns nullptr on failure
+// this function is thread-safe
+MTMD_API mtmd_bitmap * mtmd_helper_bitmap_init_from_file(mtmd_context * ctx, const char * fname);
+
+// helper function to construct a mtmd_bitmap from a buffer containing a file
+// supported formats:
+//     image: formats supported by stb_image: jpg, png, bmp, gif, etc.
+//     audio: formats supported by miniaudio: wav, mp3, flac
+// note: audio files will be auto-detected based on magic bytes
+// returns nullptr on failure
+// this function is thread-safe
+MTMD_API mtmd_bitmap * mtmd_helper_bitmap_init_from_buf(mtmd_context * ctx, const unsigned char * buf, size_t len);
+
+// helper to count the total number of tokens from a list of chunks, useful to keep track of KV cache
+MTMD_API size_t mtmd_helper_get_n_tokens(const mtmd_input_chunks * chunks);
+
+// helper to count the total position of tokens from a list of chunks, useful to keep track of n_past
+// normally, n_pos is equal to n_tokens, but for M-RoPE it is different
+MTMD_API llama_pos mtmd_helper_get_n_pos(const mtmd_input_chunks * chunks);
+
+// helper function that automatically:
+// 1. run llama_decode() on text chunks
+// 2. run mtmd_encode() on image chunks, then mtmd_get_output_embd() and then llama_decode()
+// if any of the mtmd_encode() or llama_decode() calls return non-zero, stop and forward the error
+// otherwise, returns 0 on success
+// this function is NOT thread-safe
+MTMD_API int32_t mtmd_helper_eval_chunks(mtmd_context * ctx,
+                                         struct llama_context * lctx,
+                                         const mtmd_input_chunks * chunks,
+                                         llama_pos n_past,
+                                         llama_seq_id seq_id,
+                                         int32_t n_batch,
+                                         bool logits_last,
+                                         llama_pos * new_n_past);
+
+// works like mtmd_helper_eval_chunks(), but only for a single chunk
+// this function is NOT thread-safe
+MTMD_API int32_t mtmd_helper_eval_chunk_single(mtmd_context * ctx,
+                                               struct llama_context * lctx,
+                                               const mtmd_input_chunk * chunk,
+                                               llama_pos n_past,
+                                               llama_seq_id seq_id,
+                                               int32_t n_batch,
+                                               bool logits_last,
+                                               llama_pos * new_n_past);
+
+// helper function to decode an image whose embeddings have already been calculated
+// this helper will handle batching and pre/post decoding setup (for ex. gemma 3 requires non-causal attention)
+// ret 0 on success, -1 on chunk not being a valid image chunk, 1 on decode failure
+MTMD_API int32_t mtmd_helper_decode_image_chunk(mtmd_context * ctx,
+                                                struct llama_context * lctx,
+                                                const mtmd_input_chunk * chunk,
+                                                float * encoded_embd,
+                                                llama_pos n_past,
+                                                llama_seq_id seq_id,
+                                                int32_t n_batch,
+                                                llama_pos * new_n_past);
+
+#ifdef __cplusplus
+} // extern "C"
+#endif
+
+//
+// C++ wrappers
+//
+
+#endif

tools/mtmd/mtmd.cpp

@@ -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"
@@ -132,33 +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));
         }
 
-        if (llama_model_n_embd(text_model) != clip_n_mmproj_embd(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));
+            }
+        }
+
+        // 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",
-                llama_model_n_embd(text_model), clip_n_mmproj_embd(ctx_clip)));
+                n_embd_text, n_embd_clip));
         }
+        if (ctx_v) {
+            init_vision();
+        }
+        if (ctx_a) {
+            init_audio();
+        }
+    }
 
-        has_vision = clip_has_vision_encoder(ctx_clip);
-        has_audio  = clip_has_audio_encoder(ctx_clip);
-        use_mrope  = clip_is_qwen2vl(ctx_clip);
+    void init_vision() {
+        GGML_ASSERT(ctx_v != nullptr);
+        use_mrope = clip_is_qwen2vl(ctx_v);
 
-        projector_type proj = clip_get_projector_type(ctx_clip);
-        int minicpmv_version = clip_is_minicpmv(ctx_clip);
+        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>
@@ -203,24 +237,82 @@ struct mtmd_context {
             ov_img_first      = false; // overview image is last
         }
 
-        if (clip_has_whisper_encoder(ctx_clip)) {
+        // 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/discussions/13759\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:
@@ -267,107 +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);
-
-    } else if (proj_type == PROJECTOR_TYPE_QWEN2A) {
-        // <|audio_bos|> ... (embeddings) ... <|audio_eos|>
-        marker_modified = "<|audio_bos|>" + ctx->media_marker + "<|audio_eos|>";
-        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;
@@ -382,222 +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) {
@@ -605,41 +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());
@@ -653,8 +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;
@@ -665,60 +812,19 @@ 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,
-// so unfortunately they cannot moved to mtmd-helper.h
-// however, in theory, user can decode image file to bitmap using
-// whichever library they want, and then use mtmd_bitmap_init() to create bitmap
-
-mtmd_bitmap * mtmd_helper_bitmap_init_from_buf(const unsigned char * buf, size_t len) {
-    if (audio_helpers::is_audio_file((const char *)buf, len)) {
-        std::vector<float> pcmf32;
-        if (!audio_helpers::decode_audio_from_buf(buf, len, COMMON_SAMPLE_RATE, pcmf32)) {
-            LOG_ERR("Unable to read WAV audio file from buffer\n");
-            return nullptr;
-        }
-        return mtmd_bitmap_init_from_audio(pcmf32.size(), pcmf32.data());
+int mtmd_get_audio_bitrate(mtmd_context * ctx) {
+    if (!ctx->ctx_a) {
+        return -1;
     }
-
-    clip_image_u8_ptr img_u8(clip_image_u8_init());
-    bool ok = clip_image_load_from_bytes(buf, len, img_u8.get());
-    if (!ok) {
-        LOG_ERR("Unable to load image from buffer\n");
-        return nullptr;
-    }
-    uint32_t nx, ny;
-    unsigned char * data = clip_image_u8_get_data(img_u8.get(), &nx, &ny);
-    return mtmd_bitmap_init(nx, ny, data);
-}
-
-mtmd_bitmap * mtmd_helper_bitmap_init_from_file(const char * fname) {
-    std::vector<unsigned char> buf;
-    FILE * f = fopen(fname, "rb");
-    if (!f) {
-        LOG_ERR("Unable to open file %s: %s\n", fname, strerror(errno));
-        return nullptr;
-    }
-
-    fseek(f, 0, SEEK_END);
-    long file_size = ftell(f);
-    fseek(f, 0, SEEK_SET);
-    buf.resize(file_size);
-
-    size_t n_read = fread(buf.data(), 1, file_size, f);
-    fclose(f);
-    if (n_read != (size_t)file_size) {
-        LOG_ERR("Failed to read entire file %s", fname);
-        return nullptr;
-    }
-
-    return mtmd_helper_bitmap_init_from_buf(buf.data(), buf.size());
+    // for now, we assume that all audio models have the same bitrate
+    return 16000; // 16kHz
 }
 
 //

tools/mtmd/mtmd.h

@@ -109,6 +109,10 @@ MTMD_API bool mtmd_support_vision(mtmd_context * ctx);
 // whether the current model supports audio input
 MTMD_API bool mtmd_support_audio(mtmd_context * ctx);
 
+// get audio bitrate in Hz, for example 16000 for Whisper
+// return -1 if audio is not supported
+MTMD_API int mtmd_get_audio_bitrate(mtmd_context * ctx);
+
 // mtmd_bitmap
 //
 // if bitmap is image:
@@ -209,75 +213,6 @@ MTMD_API float * mtmd_get_output_embd(mtmd_context * ctx);
 
 /////////////////////////////////////////
 
-//
-// Helper functions (can be implemented based on other functions)
-//
-// Please note that these helpers are not guaranteed to be stable.
-// BREAKING CHANGES are expected.
-//
-
-// helper function to construct a mtmd_bitmap from a file
-// it calls mtmd_helper_bitmap_init_from_buf() internally
-// returns nullptr on failure
-// this function is thread-safe
-MTMD_API mtmd_bitmap * mtmd_helper_bitmap_init_from_file(const char * fname);
-
-// helper function to construct a mtmd_bitmap from a buffer containing a file
-// supported formats:
-//     image: formats supported by stb_image: jpg, png, bmp, gif, etc.
-//     audio: formats supported by miniaudio: wav, mp3, flac
-// note: audio files will be auto-detected based on magic bytes
-// returns nullptr on failure
-// this function is thread-safe
-MTMD_API mtmd_bitmap * mtmd_helper_bitmap_init_from_buf(const unsigned char * buf, size_t len);
-
-// helper to count the total number of tokens from a list of chunks, useful to keep track of KV cache
-MTMD_API size_t mtmd_helper_get_n_tokens(const mtmd_input_chunks * chunks);
-
-// helper to count the total position of tokens from a list of chunks, useful to keep track of n_past
-// normally, n_pos is equal to n_tokens, but for M-RoPE it is different
-MTMD_API llama_pos mtmd_helper_get_n_pos(const mtmd_input_chunks * chunks);
-
-// helper function that automatically:
-// 1. run llama_decode() on text chunks
-// 2. run mtmd_encode() on image chunks, then mtmd_get_output_embd() and then llama_decode()
-// if any of the mtmd_encode() or llama_decode() calls return non-zero, stop and forward the error
-// otherwise, returns 0 on success
-// this function is NOT thread-safe
-MTMD_API int32_t mtmd_helper_eval_chunks(mtmd_context * ctx,
-                                         struct llama_context * lctx,
-                                         const mtmd_input_chunks * chunks,
-                                         llama_pos n_past,
-                                         llama_seq_id seq_id,
-                                         int32_t n_batch,
-                                         bool logits_last,
-                                         llama_pos * new_n_past);
-
-// works like mtmd_helper_eval_chunks(), but only for a single chunk
-// this function is NOT thread-safe
-MTMD_API int32_t mtmd_helper_eval_chunk_single(mtmd_context * ctx,
-                                               struct llama_context * lctx,
-                                               const mtmd_input_chunk * chunk,
-                                               llama_pos n_past,
-                                               llama_seq_id seq_id,
-                                               int32_t n_batch,
-                                               bool logits_last,
-                                               llama_pos * new_n_past);
-
-// helper function to decode an image whose embeddings have already been calculated
-// this helper will handle batching and pre/post decoding setup (for ex. gemma 3 requires non-causal attention)
-// ret 0 on success, -1 on chunk not being a valid image chunk, 1 on decode failure
-MTMD_API int32_t mtmd_helper_decode_image_chunk(mtmd_context * ctx,
-                                                struct llama_context * lctx,
-                                                const mtmd_input_chunk * chunk,
-                                                float * encoded_embd,
-                                                llama_pos n_past,
-                                                llama_seq_id seq_id,
-                                                int32_t n_batch,
-                                                llama_pos * new_n_past);
-
-/////////////////////////////////////////
-
 // test function, to be used in test-mtmd-c-api.c
 MTMD_API mtmd_input_chunks * mtmd_test_create_input_chunks(void);