SYCL : support non-contiguous tensors in binary ops (add, sub, etc) (#12399 )

* sycl : support non-contiguous tensors in binary ops * sycl : silence unused variable warning --------- Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
[CANN]MUL_MAT optimization (#12382 )
2026-07-04 19:45:57 +02:00 · 2025-03-15 22:19:30 +08:00 · 2025-03-15 09:31:08 +08:00 · 2025-03-14 16:41:20 +00:00 · 2025-03-14 16:57:05 +01:00 · 2025-03-14 13:47:05 +01:00
103 changed files with 16457 additions and 12997 deletions
@@ -774,7 +774,7 @@ jobs:
    env:
      OPENBLAS_VERSION: 0.3.23
      SDE_VERSION: 9.33.0-2024-01-07
-      VULKAN_VERSION: 1.3.261.1
+      VULKAN_VERSION: 1.4.304.1

    strategy:
      matrix:
@@ -1,4 +1,4 @@
-# date: Tue Feb  4 13:04:05 EET 2025
+# date: Sat Mar  8 18:23:52 EET 2025
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 thement <40525767+thement@users.noreply.github.com>
+theraininsky <76763719+theraininsky@users.noreply.github.com>
 thewh1teagle <61390950+thewh1teagle@users.noreply.github.com>
 tjohnman <tjohnman@users.noreply.github.com>
 toyer <2042519524@qq.com>
 tslmy <tslmy@users.noreply.github.com>
+tv1wnd <55383215+tv1wnd@users.noreply.github.com>
 ubik2 <ubik2@users.noreply.github.com>
 uint256_t <konndennsa@gmail.com>
 uint256_t <maekawatoshiki1017@gmail.com>
@@ -1014,6 +1070,7 @@ valiray <133289098+valiray@users.noreply.github.com>
 vb <vaibhavs10@gmail.com>
 vik <vikhyatk@gmail.com>
 viric <viric@viric.name>
+vmobilis <75476228+vmobilis@users.noreply.github.com>
 vodkaslime <646329483@qq.com>
 vvhg1 <94630311+vvhg1@users.noreply.github.com>
 vxiiduu <73044267+vxiiduu@users.noreply.github.com>
@@ -1028,6 +1085,8 @@ wzy <32936898+Freed-Wu@users.noreply.github.com>
 xaedes <xaedes@gmail.com>
 xaedes <xaedes@googlemail.com>
 xctan <axunlei@gmail.com>
+xiaobing318 <71554036+xiaobing318@users.noreply.github.com>
+xiaofei <hbuxiaofei@gmail.com>
 xloem <0xloem@gmail.com>
 yangli2 <yangli2@gmail.com>
 ymcki <84055651+ymcki@users.noreply.github.com>
@@ -836,7 +836,7 @@ ifdef GGML_MUSA
 	else
 		MUSA_PATH ?= /opt/musa
 	endif
-	MUSA_ARCHITECTURES ?= 21;22
+	MUSA_ARCHITECTURES ?= 21;22;31

 	MK_CPPFLAGS += -DGGML_USE_MUSA -DGGML_USE_CUDA
 	MK_LDFLAGS += -L$(MUSA_PATH)/lib -Wl,-rpath=$(MUSA_PATH)/lib
@@ -172,6 +172,7 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo
 - [eva](https://github.com/ylsdamxssjxxdd/eva) (MIT)
 - [iohub/collama](https://github.com/iohub/coLLaMA) (Apache-2.0)
 - [janhq/jan](https://github.com/janhq/jan) (AGPL)
+- [johnbean393/Sidekick](https://github.com/johnbean393/Sidekick) (MIT)
 - [KanTV](https://github.com/zhouwg/kantv?tab=readme-ov-file) (Apache-2.0)
 - [KodiBot](https://github.com/firatkiral/kodibot) (GPL)
 - [llama.vim](https://github.com/ggml-org/llama.vim) (MIT)
@@ -352,10 +352,10 @@ function gg_run_open_llama_7b_v2 {

    (time ./bin/llama-imatrix --model ${model_f16} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-imatrix.log

-    (time ./bin/llama-save-load-state--model ${model_q4_0} -ngl 10 -c 0     ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
-    (time ./bin/llama-save-load-state--model ${model_q4_0} -ngl 10 -c 0 -fa ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
-    (time ./bin/llama-save-load-state--model ${model_q4_0} -ngl 99 -c 0     ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
-    (time ./bin/llama-save-load-state--model ${model_q4_0} -ngl 99 -c 0 -fa ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
+    (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 10 -c 0     ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
+    (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 10 -c 0 -fa ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
+    (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 99 -c 0     ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
+    (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 99 -c 0 -fa ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log

    function check_ppl {
        qnt="$1"
@@ -764,7 +764,11 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
    ).set_env("LLAMA_ARG_CTX_SIZE"));
    add_opt(common_arg(
        {"-n", "--predict", "--n-predict"}, "N",
-        string_format("number of tokens to predict (default: %d, -1 = infinity, -2 = until context filled)", params.n_predict),
+        string_format(
+            ex == LLAMA_EXAMPLE_MAIN || ex == LLAMA_EXAMPLE_INFILL
+                ? "number of tokens to predict (default: %d, -1 = infinity, -2 = until context filled)"
+                : "number of tokens to predict (default: %d, -1 = infinity)",
+            params.n_predict),
        [](common_params & params, int value) {
            params.n_predict = value;
        }
@@ -849,6 +853,20 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
            }
        }
    ).set_excludes({LLAMA_EXAMPLE_SERVER}));
+    add_opt(common_arg(
+        {"-sysf", "--system-prompt-file"}, "FNAME",
+        "a file containing the system prompt (default: none)",
+        [](common_params & params, const std::string & value) {
+            std::ifstream file(value);
+            if (!file) {
+                throw std::runtime_error(string_format("error: failed to open file '%s'\n", value.c_str()));
+            }
+            std::copy(std::istreambuf_iterator<char>(file), std::istreambuf_iterator<char>(), back_inserter(params.system_prompt));
+            if (!params.system_prompt.empty() && params.system_prompt.back() == '\n') {
+                params.system_prompt.pop_back();
+            }
+        }
+    ).set_examples({LLAMA_EXAMPLE_MAIN}));
    add_opt(common_arg(
        {"--in-file"}, "FNAME",
        "an input file (repeat to specify multiple files)",
@@ -1867,16 +1885,9 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
    ).set_examples({LLAMA_EXAMPLE_PASSKEY}));
    add_opt(common_arg(
        {"-o", "--output", "--output-file"}, "FNAME",
-        string_format("output file (default: '%s')",
-            ex == LLAMA_EXAMPLE_EXPORT_LORA
-                ? params.lora_outfile.c_str()
-                : ex == LLAMA_EXAMPLE_CVECTOR_GENERATOR
-                    ? params.cvector_outfile.c_str()
-                    : params.out_file.c_str()),
+        string_format("output file (default: '%s')", params.out_file.c_str()),
        [](common_params & params, const std::string & value) {
            params.out_file = value;
-            params.cvector_outfile = value;
-            params.lora_outfile = value;
        }
    ).set_examples({LLAMA_EXAMPLE_IMATRIX, LLAMA_EXAMPLE_CVECTOR_GENERATOR, LLAMA_EXAMPLE_EXPORT_LORA}));
    add_opt(common_arg(
@@ -2571,5 +2582,43 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
        }
    ).set_examples({LLAMA_EXAMPLE_SERVER}));

+    add_opt(common_arg(
+        {"--fim-qwen-7b-spec"},
+        string_format("use Qwen 2.5 Coder 7B + 0.5B draft for speculative decoding (note: can download weights from the internet)"),
+        [](common_params & params) {
+            params.hf_repo = "ggml-org/Qwen2.5-Coder-7B-Q8_0-GGUF";
+            params.hf_file = "qwen2.5-coder-7b-q8_0.gguf";
+            params.speculative.hf_repo = "ggml-org/Qwen2.5-Coder-0.5B-Q8_0-GGUF";
+            params.speculative.hf_file = "qwen2.5-coder-0.5b-q8_0.gguf";
+            params.speculative.n_gpu_layers = 99;
+            params.port = 8012;
+            params.n_gpu_layers = 99;
+            params.flash_attn = true;
+            params.n_ubatch = 1024;
+            params.n_batch = 1024;
+            params.n_ctx = 0;
+            params.n_cache_reuse = 256;
+        }
+    ).set_examples({LLAMA_EXAMPLE_SERVER}));
+
+    add_opt(common_arg(
+        {"--fim-qwen-14b-spec"},
+        string_format("use Qwen 2.5 Coder 14B + 0.5B draft for speculative decoding (note: can download weights from the internet)"),
+        [](common_params & params) {
+            params.hf_repo = "ggml-org/Qwen2.5-Coder-14B-Q8_0-GGUF";
+            params.hf_file = "qwen2.5-coder-14b-q8_0.gguf";
+            params.speculative.hf_repo = "ggml-org/Qwen2.5-Coder-0.5B-Q8_0-GGUF";
+            params.speculative.hf_file = "qwen2.5-coder-0.5b-q8_0.gguf";
+            params.speculative.n_gpu_layers = 99;
+            params.port = 8012;
+            params.n_gpu_layers = 99;
+            params.flash_attn = true;
+            params.n_ubatch = 1024;
+            params.n_batch = 1024;
+            params.n_ctx = 0;
+            params.n_cache_reuse = 256;
+        }
+    ).set_examples({LLAMA_EXAMPLE_SERVER}));
+
    return ctx_arg;
 }
@@ -60,7 +60,9 @@ std::vector<common_chat_msg> common_chat_msgs_parse_oaicompat(const json & messa
            }
            msg.role = message.at("role");

-            if (message.contains("content")) {
+            auto has_content = message.contains("content");
+            auto has_tool_calls = message.contains("tool_calls");
+            if (has_content) {
                const auto & content = message.at("content");
                if (content.is_string()) {
                    msg.content = content;
@@ -81,19 +83,8 @@ std::vector<common_chat_msg> common_chat_msgs_parse_oaicompat(const json & messa
                } else if (!content.is_null()) {
                    throw std::runtime_error("Invalid 'content' type: expected string or array, got " + content.dump() + " (ref: https://github.com/ggml-org/llama.cpp/issues/8367)");
                }
-            } else {
-                throw std::runtime_error("Expected 'content' (ref: https://github.com/ggml-org/llama.cpp/issues/8367)");
            }
-            if (message.contains("reasoning_content")) {
-                msg.reasoning_content = message.at("reasoning_content");
-            }
-            if (message.contains("name")) {
-                msg.tool_name = message.at("name");
-            }
-            if (message.contains("tool_call_id")) {
-                msg.tool_call_id = message.at("tool_call_id");
-            }
-            if (message.contains("tool_calls")) {
+            if (has_tool_calls) {
                for (const auto & tool_call : message.at("tool_calls")) {
                    common_chat_tool_call tc;
                    if (!tool_call.contains("type")) {
@@ -118,6 +109,18 @@ std::vector<common_chat_msg> common_chat_msgs_parse_oaicompat(const json & messa
                    msg.tool_calls.push_back(tc);
                }
            }
+            if (!has_content && !has_tool_calls) {
+                throw std::runtime_error("Expected 'content' or 'tool_calls' (ref: https://github.com/ggml-org/llama.cpp/issues/8367 & https://github.com/ggml-org/llama.cpp/issues/12279)");
+            }
+            if (message.contains("reasoning_content")) {
+                msg.reasoning_content = message.at("reasoning_content");
+            }
+            if (message.contains("name")) {
+                msg.tool_name = message.at("name");
+            }
+            if (message.contains("tool_call_id")) {
+                msg.tool_call_id = message.at("tool_call_id");
+            }

            msgs.push_back(msg);
        }
@@ -442,6 +445,7 @@ std::string common_chat_format_name(common_chat_format format) {
        case COMMON_CHAT_FORMAT_FUNCTIONARY_V3_2: return "Functionary v3.2";
        case COMMON_CHAT_FORMAT_FUNCTIONARY_V3_1_LLAMA_3_1: return "Functionary v3.1 Llama 3.1";
        case COMMON_CHAT_FORMAT_HERMES_2_PRO: return "Hermes 2 Pro";
+        case COMMON_CHAT_FORMAT_HERMES_2_PRO_EXTRACT_REASONING: return "Hermes 2 Pro (extract reasoning)";
        case COMMON_CHAT_FORMAT_COMMAND_R7B: return "Command R7B";
        case COMMON_CHAT_FORMAT_COMMAND_R7B_EXTRACT_REASONING: return "Command R7B (extract reasoning)";
        default:
@@ -875,9 +879,9 @@ static common_chat_params common_chat_params_init_command_r7b(const common_chat_
    return data;
 }
 static common_chat_msg common_chat_parse_command_r7b(const std::string & input, bool extract_reasoning) {
-    static std::regex thought_regex("(<\\|START_THINKING\\|>([\\s\\S]*?)<\\|END_THINKING\\|>)([\\s\\S]*)");
-    static std::regex action_regex("<\\|START_ACTION\\|>([\\s\\S]*?)<\\|END_ACTION\\|>");
-    static std::regex response_regex("(?:<\\|START_RESPONSE\\|>)?([\\s\\S]*?)<\\|END_RESPONSE\\|>");
+    static const std::regex thought_regex("(<\\|START_THINKING\\|>([\\s\\S]*?)<\\|END_THINKING\\|>)([\\s\\S]*)");
+    static const std::regex action_regex("<\\|START_ACTION\\|>([\\s\\S]*?)<\\|END_ACTION\\|>");
+    static const std::regex response_regex("(?:<\\|START_RESPONSE\\|>)?([\\s\\S]*?)<\\|END_RESPONSE\\|>");

    std::smatch match;

@@ -1009,10 +1013,10 @@ static common_chat_params common_chat_params_init_llama_3_1_tool_calls(const com
 }
 static common_chat_msg common_chat_parse_llama_3_1(const std::string & input, bool with_builtin_tools = false) {
    // TODO: tighten & simplify the parser, don't accept leading text context.
-    static std::regex function_regex(
+    static const std::regex function_regex(
        "\\s*\\{\\s*(?:\"type\"\\s*:\\s*\"function\"\\s*,\\s*)?\"name\"\\s*:\\s*\"([^\"]+)\"\\s*,\\s*\"parameters\"\\s*: ");
-    static std::regex close_regex("\\}\\s*");
-    static std::regex builtin_call_regex("<\\|python_tag\\|>\\s*([^.(]+)\\s*\\.\\s*call\\s*\\(\\s*([\\w]+)\\s*=\\s*([\\s\\S]*?)\\)");
+    static const std::regex close_regex("\\}\\s*");
+    static const std::regex builtin_call_regex("<\\|python_tag\\|>\\s*([^.(]+)\\s*\\.\\s*call\\s*\\(\\s*([\\w]+)\\s*=\\s*([\\s\\S]*?)\\)");

    if (with_builtin_tools) {
        std::smatch match;
@@ -1102,34 +1106,42 @@ static common_chat_params common_chat_params_init_deepseek_r1(const common_chat_
    data.format = inputs.extract_reasoning ? COMMON_CHAT_FORMAT_DEEPSEEK_R1_EXTRACT_REASONING : COMMON_CHAT_FORMAT_DEEPSEEK_R1;
    return data;
 }
-static common_chat_msg common_chat_parse_deepseek_r1(const std::string & input, bool extract_reasoning) {
-    static std::regex function_regex("<｜tool▁call▁begin｜>function<｜tool▁sep｜>([^\n]+)\n```json\n");
-    static std::regex close_regex("```[\\s\\r\\n]*<｜tool▁call▁end｜>");
-    static std::regex reasoning_content_regex("((?:<think>)?([\\s\\S\\r\\n]*?)</think>)?([\\s\\S\\r\\n]*)");
-    static std::regex tool_calls_regex("[\\s\\r\\n]*(?:<｜tool▁calls▁begin｜>|<｜tool_calls_begin｜>|<｜tool calls begin｜>|<｜tool\\\\_calls\\\\_begin｜>)([\\s\\S\\r\\n]*?)<｜tool▁calls▁end｜>");
-    common_chat_msg msg;
-    msg.role = "assistant";
+static common_chat_msg handle_think_tag_prelude(const std::string & input, bool extract_reasoning, const std::function<common_chat_msg(const std::string &)> & rest_parser) {
    std::smatch match;
+    static const std::regex reasoning_content_regex("((?:<think>)?([\\s\\S\\r\\n]*?)</think>)?([\\s\\S\\r\\n]*)");
    if (std::regex_match(input, match, reasoning_content_regex)) {
-        std::string rest;
+        auto rest = match[3].str();
+        auto msg = rest_parser(rest);
+        auto reasoning_content = string_strip(match[2].str());
        if (extract_reasoning) {
-            msg.reasoning_content = string_strip(match[2].str());
-        } else {
-            msg.content = match[1].str();
+            msg.reasoning_content = reasoning_content;
+        } else if (!reasoning_content.empty()) {
+            std::ostringstream content;
+            content << "<think>" << reasoning_content << "</think>" << msg.content;
+            msg.content = content.str();
        }
-        rest = match[3].str();
+        return msg;
+    }
+    return rest_parser(input);
+}
+static common_chat_msg common_chat_parse_deepseek_r1(const std::string & input, bool extract_reasoning) {
+    return handle_think_tag_prelude(input, extract_reasoning, [](const std::string & input) {
+        static const std::regex function_regex("<｜tool▁call▁begin｜>function<｜tool▁sep｜>([^\n]+)\n```json\n");
+        static const std::regex close_regex("```[\\s\\r\\n]*<｜tool▁call▁end｜>");
+        static const std::regex tool_calls_regex("[\\s\\r\\n]*(?:<｜tool▁calls▁begin｜>|<｜tool_calls_begin｜>|<｜tool calls begin｜>|<｜tool\\\\_calls\\\\_begin｜>)([\\s\\S\\r\\n]*?)<｜tool▁calls▁end｜>");

-        if (std::regex_search(rest, match, tool_calls_regex)) {
+        common_chat_msg msg;
+        msg.role = "assistant";
+        std::smatch match;
+        if (std::regex_search(input, match, tool_calls_regex)) {
            auto tool_calls = match[1].str();
            auto msg2 = parse_json_tool_calls(tool_calls, std::nullopt, function_regex, close_regex);
            msg.tool_calls = std::move(msg2.tool_calls);
        } else {
-            msg.content += std::string(rest.begin() + rest.find_first_not_of(" \r\n"), rest.end());
+            msg.content = input;
        }
-    } else {
-        msg.content = input;
-    }
-    return msg;
+        return msg;
+    });
 }

 static common_chat_params common_chat_params_init_firefunction_v2(const common_chat_template & tmpl, const struct templates_params & inputs) {
@@ -1234,8 +1246,8 @@ static common_chat_params common_chat_params_init_functionary_v3_2(const common_
 }

 static common_chat_msg common_chat_parse_functionary_v3_2(const std::string & input) {
-    static std::regex function_regex(R"((?:>>>)?(?:assistant<|end_header_id|>\n)?(\w+)\n)");
-    static std::regex close_regex(R"($|(?=>>>))");
+    static const std::regex function_regex(R"((?:>>>)?(?:assistant<|end_header_id|>\n)?(\w+)\n)");
+    static const std::regex close_regex(R"($|(?=>>>))");

    std::string content;
    auto it = input.begin();
@@ -1324,7 +1336,7 @@ static common_chat_params common_chat_params_init_functionary_v3_1_llama_3_1(con
 }
 static common_chat_msg common_chat_parse_functionary_v3_1_llama_3_1(const std::string & input) {
    // This version of Functionary still supports the llama 3.1 tool call format for the python tool.
-    static std::regex python_tag_regex(R"(<\|python_tag\|>([\s\S\n]*)$)");
+    static const std::regex python_tag_regex(R"(<\|python_tag\|>([\s\S\n]*)$)");
    std::smatch match;
    if (std::regex_search(input, match, python_tag_regex)) {
        auto code = match[1].str();
@@ -1338,8 +1350,8 @@ static common_chat_msg common_chat_parse_functionary_v3_1_llama_3_1(const std::s
        });
        return msg;
    }
-    static std::regex function_regex(R"(<function=(\w+)>)");
-    static std::regex close_regex(R"(</function>)");
+    static const std::regex function_regex(R"(<function=(\w+)>)");
+    static const std::regex close_regex(R"(</function>)");
    // TODO: tighten & simplify.
    return parse_json_tool_calls(input, std::nullopt, function_regex, close_regex);
 }
@@ -1406,6 +1418,8 @@ static common_chat_params common_chat_params_init_hermes_2_pro(const common_chat
            "(?:```(?:json|xml)?\n\\s*)?(?:<function_call>|<tools>|<xml><json>|<response>)?\\s*\\{\\s*\"", //name\"\\s*:\\s*\"" + escaped_name + "\"",
        });
        data.preserved_tokens = {
+            "<think>",
+            "</think>",
            "<tool_call>",
            "</tool_call>",
            "<function",
@@ -1426,122 +1440,123 @@ static common_chat_params common_chat_params_init_hermes_2_pro(const common_chat
    });

    data.prompt = apply(tmpl, inputs.messages, inputs.tools.empty() ? json() : inputs.tools, inputs.add_generation_prompt);
-    data.format = COMMON_CHAT_FORMAT_HERMES_2_PRO;
+    data.format = inputs.extract_reasoning ? COMMON_CHAT_FORMAT_HERMES_2_PRO_EXTRACT_REASONING : COMMON_CHAT_FORMAT_HERMES_2_PRO;
    return data;
 }
-static common_chat_msg common_chat_parse_hermes_2_pro(const std::string& input) {
-    const static std::regex open_regex(
-        "(?:"
-        "(```(?:xml|json)?\\n\\s*)?"         // match 1 (block_start)
-        "(<tool_call>"                   // match 2 (open_tag)
-        "|<function_call>"
-        "|<tool>"
-        "|<tools>"
-        "|<response>"
-        "|<json>"
-        "|<xml>"
-        "|<JSON>"
-        ")?"
-        "(\\s*\\{\\s*\"name\"\\s*:[\\s\\S]*)"    // match 3 (named tool call + rest)
-        ")"
-        "|"
-        "(?:<function=([^>]+)>"            // match 4 (function name)
-        "|<function name=\"([^\"]+)\">)" // match 5 (function name again)
-        "([\\s\\S]*)"                   // match 6 (function arguments + rest)})"
-    );
+static common_chat_msg common_chat_parse_hermes_2_pro(const std::string& input, bool extract_reasoning) {
+    return handle_think_tag_prelude(input, extract_reasoning, [](const std::string & input) {
+        static const std::regex open_regex(
+            "(?:"
+            "(```(?:xml|json)?\\n\\s*)?"         // match 1 (block_start)
+            "(<tool_call>"                   // match 2 (open_tag)
+            "|<function_call>"
+            "|<tool>"
+            "|<tools>"
+            "|<response>"
+            "|<json>"
+            "|<xml>"
+            "|<JSON>"
+            ")?"
+            "(\\s*\\{\\s*\"name\"\\s*:[\\s\\S]*)"    // match 3 (named tool call + rest)
+            ")"
+            "|"
+            "(?:<function=([^>]+)>"            // match 4 (function name)
+            "|<function name=\"([^\"]+)\">)" // match 5 (function name again)
+            "([\\s\\S]*)"                   // match 6 (function arguments + rest)})"
+        );

-    try {
+        try {
+            common_chat_msg msg;
+            msg.role = "assistant";

-        common_chat_msg msg;
-        msg.role = "assistant";
+            std::string::const_iterator it = input.begin();
+            const std::string::const_iterator end = input.end();
+            std::smatch match;

-        std::string::const_iterator it = input.begin();
-        const std::string::const_iterator end = input.end();
-        std::smatch match;
+            while (it != end) {
+                if (std::regex_search(it, end, match, open_regex)) {
+                    // Add content before the match
+                    msg.content += std::string(it, match[0].first);

-        while (it != end) {
-            if (std::regex_search(it, end, match, open_regex)) {
-                // Add content before the match
-                msg.content += std::string(it, match[0].first);
+                    auto block_start = match[1].str();
+                    std::string block_end = block_start.empty() ? "" : "```";

-                auto block_start = match[1].str();
-                std::string block_end = block_start.empty() ? "" : "```";
+                    auto open_tag = match[2].str();
+                    std::string close_tag;

-                auto open_tag = match[2].str();
-                std::string close_tag;
+                    if (match[3].matched) {
+                        close_tag = open_tag.empty() ? "" : "</" + open_tag.substr(1);
+                        auto json_it = match[3].first;
+                        json tool_call;
+                        if (parse_json(json_it, end, tool_call) && tool_call.contains("name") && tool_call.contains("arguments")) {

-                if (match[3].matched) {
-                    close_tag = open_tag.empty() ? "" : "</" + open_tag.substr(1);
-                    auto json_it = match[3].first;
-                    json tool_call;
-                    if (parse_json(json_it, end, tool_call) && tool_call.contains("name") && tool_call.contains("arguments")) {
+                            msg.tool_calls.emplace_back(process_tool_call(tool_call));
+                            it = json_it;  // Move iterator past parsed JSON

-                        msg.tool_calls.emplace_back(process_tool_call(tool_call));
-                        it = json_it;  // Move iterator past parsed JSON
-
-                        // Handle close tags
-                        consume_spaces(it, end);
-                        if (!close_tag.empty() && !parse_literal(it, end, close_tag)) {
-                            throw std::runtime_error("Failed to parse closing tag");
+                            // Handle close tags
+                            consume_spaces(it, end);
+                            if (!close_tag.empty() && !parse_literal(it, end, close_tag)) {
+                                throw std::runtime_error("Failed to parse closing tag");
+                            }
+                            consume_spaces(it, end);
+                            if (!block_end.empty() && !parse_literal(it, end, block_end)) {
+                                throw std::runtime_error("Failed to parse block end");
+                            }
+                            consume_spaces(it, end);
+                        } else {
+                            // Not a valid tool call, treat as content
+                            msg.content += std::string(match[0].first, match[0].second);
+                            it = match[0].second;
                        }
-                        consume_spaces(it, end);
-                        if (!block_end.empty() && !parse_literal(it, end, block_end)) {
-                            throw std::runtime_error("Failed to parse block end");
-                        }
-                        consume_spaces(it, end);
                    } else {
-                        // Not a valid tool call, treat as content
-                        msg.content += std::string(match[0].first, match[0].second);
-                        it = match[0].second;
+                        auto function_name = match[4].str();
+                        if (function_name.empty()) {
+                            function_name = match[5].str();
+                        }
+                        GGML_ASSERT(!function_name.empty());
+
+                        close_tag = "</function>";
+                        // Start parsing from after the opening tags
+                        auto json_it = match[6].first;
+                        json arguments;
+                        if (parse_json(json_it, end, arguments)) {
+                            msg.tool_calls.emplace_back(process_tool_call({
+                                {"name", function_name},
+                                {"arguments", arguments},
+                            }));
+                            it = json_it;  // Move iterator past parsed JSON
+
+                            // Handle close tags
+                            consume_spaces(it, end);
+                            if (!close_tag.empty() && !parse_literal(it, end, close_tag)) {
+                                throw std::runtime_error("Failed to parse closing tag");
+                            }
+                            consume_spaces(it, end);
+                            if (!block_end.empty() && !parse_literal(it, end, block_end)) {
+                                throw std::runtime_error("Failed to parse block end");
+                            }
+                            consume_spaces(it, end);
+                        } else {
+                            // Not a valid tool call, treat as content
+                            msg.content += std::string(match[0].first, match[0].second);
+                            it = match[0].second;
+                        }
                    }
                } else {
-                    auto function_name = match[4].str();
-                    if (function_name.empty()) {
-                        function_name = match[5].str();
-                    }
-                    GGML_ASSERT(!function_name.empty());
-
-                    close_tag = "</function>";
-                    // Start parsing from after the opening tags
-                    auto json_it = match[6].first;
-                    json arguments;
-                    if (parse_json(json_it, end, arguments)) {
-                        msg.tool_calls.emplace_back(process_tool_call({
-                            {"name", function_name},
-                            {"arguments", arguments},
-                        }));
-                        it = json_it;  // Move iterator past parsed JSON
-
-                        // Handle close tags
-                        consume_spaces(it, end);
-                        if (!close_tag.empty() && !parse_literal(it, end, close_tag)) {
-                            throw std::runtime_error("Failed to parse closing tag");
-                        }
-                        consume_spaces(it, end);
-                        if (!block_end.empty() && !parse_literal(it, end, block_end)) {
-                            throw std::runtime_error("Failed to parse block end");
-                        }
-                        consume_spaces(it, end);
-                    } else {
-                        // Not a valid tool call, treat as content
-                        msg.content += std::string(match[0].first, match[0].second);
-                        it = match[0].second;
-                    }
+                    // Add remaining content
+                    msg.content += std::string(it, end);
+                    break;
                }
-            } else {
-                // Add remaining content
-                msg.content += std::string(it, end);
-                break;
            }
+            return msg;
+        } catch (const std::exception & e) {
+            LOG_ERR("Failed to parse hermes 2 pro input: %s\n", e.what());
+            common_chat_msg msg;
+            msg.role = "assistant";
+            msg.content = input;
+            return msg;
        }
-        return msg;
-    } catch (const std::exception & e) {
-        LOG_ERR("Failed to parse hermes 2 pro input: %s\n", e.what());
-        common_chat_msg msg;
-        msg.role = "assistant";
-        msg.content = input;
-        return msg;
-    }
+    });
 }

 static common_chat_params common_chat_params_init_without_tools(const common_chat_template & tmpl, const struct templates_params & inputs) {
@@ -1606,6 +1621,11 @@ static common_chat_params common_chat_templates_apply_jinja(
        return common_chat_params_init_command_r7b(tmpl, params);
    }

+    // Hermes 2/3 Pro, Qwen 2.5 Instruct (w/ tools)
+    if (src.find("<tool_call>") != std::string::npos && params.json_schema.is_null()) {
+        return common_chat_params_init_hermes_2_pro(tmpl, params);
+    }
+
    // Use generic handler when mixing tools + JSON schema.
    // TODO: support that mix in handlers below.
    if ((params.tools.is_array() && params.json_schema.is_object())) {
@@ -1627,11 +1647,6 @@ static common_chat_params common_chat_templates_apply_jinja(
        return common_chat_params_init_without_tools(tmpl, params);
    }

-    // Hermes 2/3 Pro, Qwen 2.5 Instruct (w/ tools)
-    if (src.find("<tool_call>") != std::string::npos) {
-        return common_chat_params_init_hermes_2_pro(tmpl, params);
-    }
-
    // Functionary v3.1 (w/ tools)
    if (src.find("<|start_header_id|>") != std::string::npos
        && src.find("<function=") != std::string::npos) {
@@ -1749,7 +1764,9 @@ common_chat_msg common_chat_parse(const std::string & input, common_chat_format
        case COMMON_CHAT_FORMAT_FUNCTIONARY_V3_1_LLAMA_3_1:
            return common_chat_parse_functionary_v3_1_llama_3_1(input);
        case COMMON_CHAT_FORMAT_HERMES_2_PRO:
-            return common_chat_parse_hermes_2_pro(input);
+            return common_chat_parse_hermes_2_pro(input, /* extract_reasoning= */ false);
+        case COMMON_CHAT_FORMAT_HERMES_2_PRO_EXTRACT_REASONING:
+            return common_chat_parse_hermes_2_pro(input, /* extract_reasoning= */ true);
        case COMMON_CHAT_FORMAT_FIREFUNCTION_V2:
            return common_chat_parse_firefunction_v2(input);
        case COMMON_CHAT_FORMAT_COMMAND_R7B:
@@ -53,6 +53,7 @@ enum common_chat_format {
    COMMON_CHAT_FORMAT_FUNCTIONARY_V3_2,
    COMMON_CHAT_FORMAT_FUNCTIONARY_V3_1_LLAMA_3_1,
    COMMON_CHAT_FORMAT_HERMES_2_PRO,
+    COMMON_CHAT_FORMAT_HERMES_2_PRO_EXTRACT_REASONING,
    COMMON_CHAT_FORMAT_COMMAND_R7B,
    COMMON_CHAT_FORMAT_COMMAND_R7B_EXTRACT_REASONING,

@@ -955,8 +955,8 @@ struct common_init_result common_init_from_params(common_params & params) {
        return iparams;
    }

-    if (params.ctx_shift && !llama_kv_cache_can_shift(lctx)) {
-        LOG_WRN("%s: KV cache shifting is not supported for this model, disabling KV cache shifting\n", __func__);
+    if (params.ctx_shift && !llama_kv_self_can_shift(lctx)) {
+        LOG_WRN("%s: KV cache shifting is not supported for this context, disabling KV cache shifting\n", __func__);
        params.ctx_shift = false;
    }

@@ -1033,6 +1033,8 @@ struct common_init_result common_init_from_params(common_params & params) {
    if (params.warmup) {
        LOG_WRN("%s: warming up the model with an empty run - please wait ... (--no-warmup to disable)\n", __func__);

+        llama_set_warmup(lctx, true);
+
        std::vector<llama_token> tmp;
        llama_token bos = llama_vocab_bos(vocab);
        llama_token eos = llama_vocab_eos(vocab);
@@ -1060,9 +1062,10 @@ struct common_init_result common_init_from_params(common_params & params) {
        if (llama_model_has_decoder(model)) {
            llama_decode(lctx, llama_batch_get_one(tmp.data(), std::min(tmp.size(), (size_t) params.n_batch)));
        }
-        llama_kv_cache_clear(lctx);
+        llama_kv_self_clear(lctx);
        llama_synchronize(lctx);
        llama_perf_context_reset(lctx);
+        llama_set_warmup(lctx, false);
    }

    iparams.model.reset(model);
@@ -407,8 +407,6 @@ struct common_params {
    int32_t i_pos  = -1;  // position of the passkey in the junk text

    // imatrix params
-    std::string out_file = "imatrix.dat"; // save the resulting imatrix to this file
-
    int32_t n_out_freq  = 10; // output the imatrix every n_out_freq iterations
    int32_t n_save_freq =  0; // save the imatrix every n_save_freq iterations
    int32_t i_chunk     =  0; // start processing from this chunk
@@ -420,16 +418,16 @@ struct common_params {
    int n_pca_batch = 100;
    int n_pca_iterations = 1000;
    dimre_method cvector_dimre_method = DIMRE_METHOD_PCA;
-    std::string cvector_outfile       = "control_vector.gguf";
    std::string cvector_positive_file = "examples/cvector-generator/positive.txt";
    std::string cvector_negative_file = "examples/cvector-generator/negative.txt";

    bool spm_infill = false; // suffix/prefix/middle pattern for infill

-    std::string lora_outfile = "ggml-lora-merged-f16.gguf";
-
    // batched-bench params
    bool batched_bench_output_jsonl = false;
+
+    // common params
+    std::string out_file; // output filename for all example programs
 };

 // call once at the start of a program if it uses libcommon
@@ -173,7 +173,7 @@ llama_tokens common_speculative_gen_draft(
    result.reserve(params.n_draft);

    if (reuse_n == 0) {
-        llama_kv_cache_clear(ctx);
+        llama_kv_self_clear(ctx);

        prompt.clear();
    } else {
@@ -192,14 +192,14 @@ llama_tokens common_speculative_gen_draft(
        }

        if (reuse_i > 0) {
-            llama_kv_cache_seq_rm (ctx, 0, 0, reuse_i);
-            llama_kv_cache_seq_add(ctx, 0, reuse_i, -1, -reuse_i);
+            llama_kv_self_seq_rm (ctx, 0, 0, reuse_i);
+            llama_kv_self_seq_add(ctx, 0, reuse_i, -1, -reuse_i);

            prompt.erase(prompt.begin(), prompt.begin() + reuse_i);
        }

        if (reuse_n < (int) prompt.size()) {
-            llama_kv_cache_seq_rm (ctx, 0, reuse_n, -1);
+            llama_kv_self_seq_rm (ctx, 0, reuse_n, -1);

            prompt.erase(prompt.begin() + reuse_n, prompt.end());
        }
@@ -861,6 +861,9 @@ class Model:
                for token_id, token_data in added_tokens_decoder.items():
                    token_id = int(token_id)
                    token: str = token_data["content"]
+                    if token_id >= vocab_size:
+                        logger.warning(f'ignore token {token_id}: id is out of range, max={vocab_size - 1}')
+                        continue
                    if toktypes[token_id] != SentencePieceTokenTypes.UNUSED:
                        if tokens[token_id] != token.encode("utf-8"):
                            logger.warning(f'replacing token {token_id}: {tokens[token_id].decode("utf-8")!r} -> {token!r}')
@@ -3322,6 +3325,83 @@ class Gemma2Model(Model):
        return [(self.map_tensor_name(name), data_torch)]


+@Model.register("Gemma3ForCausalLM", "Gemma3ForConditionalGeneration")
+class Gemma3Model(Model):
+    model_arch = gguf.MODEL_ARCH.GEMMA3
+    has_vision: bool = False
+
+    # we need to merge the text_config into the root level of hparams
+    def __init__(self, *args, **kwargs):
+        hparams = Model.load_hparams(kwargs["dir_model"])
+        if "text_config" in hparams:
+            hparams = {**hparams, **hparams["text_config"]}
+            kwargs["hparams"] = hparams
+        super().__init__(*args, **kwargs)
+        if "vision_config" in hparams:
+            logger.info("Has vision encoder, but it will be ignored")
+            self.has_vision = True
+
+    def write(self):
+        super().write()
+        if self.has_vision:
+            logger.info("NOTE: this script only convert the language model to GGUF")
+            logger.info("      for the vision model, please use gemma3_convert_encoder_to_gguf.py")
+
+    def set_vocab(self):
+        self._set_vocab_sentencepiece()
+
+        self.gguf_writer.add_add_space_prefix(False)
+
+    def set_gguf_parameters(self):
+        hparams = self.hparams
+        block_count = hparams["num_hidden_layers"]
+
+        # some default values are not specified in the hparams
+        self.gguf_writer.add_context_length(hparams.get("max_position_embeddings", 131072))
+        self.gguf_writer.add_embedding_length(hparams["hidden_size"])
+        self.gguf_writer.add_block_count(block_count)
+        self.gguf_writer.add_feed_forward_length(hparams["intermediate_size"])
+        self.gguf_writer.add_head_count(hparams.get("num_attention_heads", 8))
+        self.gguf_writer.add_layer_norm_rms_eps(self.hparams.get("rms_norm_eps", 1e-6))
+        self.gguf_writer.add_key_length(hparams.get("head_dim", 256))
+        self.gguf_writer.add_value_length(hparams.get("head_dim", 256))
+        self.gguf_writer.add_file_type(self.ftype)
+        self.gguf_writer.add_rope_freq_base(hparams.get("rope_theta", 1_000_000.0)) # for global layers
+        # both attn_logit_softcapping and final_logit_softcapping are removed in Gemma3
+        assert hparams.get("attn_logit_softcapping") is None
+        assert hparams.get("final_logit_softcapping") is None
+        self.gguf_writer.add_sliding_window(hparams["sliding_window"])
+        self.gguf_writer.add_head_count_kv(hparams.get("num_key_value_heads", 4))
+        if hparams.get("rope_scaling") is not None:
+            assert hparams["rope_scaling"]["rope_type"] == "linear"
+            # important: this rope_scaling is only applied for global layers, and not used by 1B model
+            self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR)
+            self.gguf_writer.add_rope_scaling_factor(hparams["rope_scaling"]["factor"])
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        del bid  # unused
+
+        if name.startswith("language_model."):
+            name = name.replace("language_model.", "")
+        elif name.startswith("multi_modal_projector.") or name.startswith("vision_tower.") \
+                or name.startswith("multimodal_projector.") or name.startswith("vision_model."): # this is for old HF model, should be removed later
+            # ignore vision tensors
+            return []
+
+        # remove OOV (out-of-vocabulary) rows in token_embd
+        if "embed_tokens.weight" in name:
+            vocab = self._create_vocab_sentencepiece()
+            tokens = vocab[0]
+            data_torch = data_torch[:len(tokens)]
+
+        # ref code in Gemma3RMSNorm
+        # output = output * (1.0 + self.weight.float())
+        if name.endswith("norm.weight"):
+            data_torch = data_torch + 1
+
+        return [(self.map_tensor_name(name), data_torch)]
+
+
@Model.register("Starcoder2ForCausalLM")
 class StarCoder2Model(Model):
    model_arch = gguf.MODEL_ARCH.STARCODER2
@@ -197,29 +197,53 @@ The following compilation options are also available to tweak performance:

 ## MUSA

-This provides GPU acceleration using the MUSA cores of your Moore Threads MTT GPU. Make sure to have the MUSA SDK installed. You can download it from here: [MUSA SDK](https://developer.mthreads.com/sdk/download/musa).
+This provides GPU acceleration using a Moore Threads GPU. Make sure to have the [MUSA SDK](https://developer.mthreads.com/musa/musa-sdk) installed.

- Using `CMake`:
+#### Download directly from Moore Threads

-  ```bash
-  cmake -B build -DGGML_MUSA=ON
-  cmake --build build --config Release
+You may find the official downloads here: [Moore Threads developer site](https://developer.mthreads.com/sdk/download/musa).
+
+### Compilation
+
+```bash
+cmake -B build -DGGML_MUSA=ON
+cmake --build build --config Release
+```
+
+#### Override Compute Capability Specifications
+
+By default, all supported compute capabilities are enabled. To customize this behavior, you can specify the `MUSA_ARCHITECTURES` option in the CMake command:
+
+```bash
+cmake -B build -DGGML_MUSA=ON -DMUSA_ARCHITECTURES="21"
+```
+
+This configuration enables only compute capability `2.1` (MTT S80) during compilation, which can help reduce compilation time.
+
+#### Compilation options
+
+Most of the compilation options available for CUDA should also be available for MUSA, though they haven't been thoroughly tested yet.
+
+- For static builds, add `-DBUILD_SHARED_LIBS=OFF` and `-DCMAKE_POSITION_INDEPENDENT_CODE=ON`:
  ```
-
-  For static build:
-
-  ```bash
  cmake -B build -DGGML_MUSA=ON \
    -DBUILD_SHARED_LIBS=OFF -DCMAKE_POSITION_INDEPENDENT_CODE=ON
  cmake --build build --config Release
  ```

-The environment variable [`MUSA_VISIBLE_DEVICES`](https://docs.mthreads.com/musa-sdk/musa-sdk-doc-online/programming_guide/Z%E9%99%84%E5%BD%95/) can be used to specify which GPU(s) will be used.
+### Runtime MUSA environmental variables
+
+You may set the [musa environmental variables](https://docs.mthreads.com/musa-sdk/musa-sdk-doc-online/programming_guide/Z%E9%99%84%E5%BD%95/) at runtime.
+
+```bash
+# Use `MUSA_VISIBLE_DEVICES` to hide the first compute device.
+MUSA_VISIBLE_DEVICES="-0" ./build/bin/llama-server --model /srv/models/llama.gguf
+```
+
+### Unified Memory

 The environment variable `GGML_CUDA_ENABLE_UNIFIED_MEMORY=1` can be used to enable unified memory in Linux. This allows swapping to system RAM instead of crashing when the GPU VRAM is exhausted.

-Most of the compilation options available for CUDA should also be available for MUSA, though they haven't been thoroughly tested yet.
-
 ## HIP

 This provides GPU acceleration on HIP-supported AMD GPUs.
@@ -132,7 +132,7 @@ int main(int argc, char ** argv) {

                const auto t_pp_start = ggml_time_us();

-                llama_kv_cache_clear(ctx);
+                llama_kv_self_clear(ctx);

                if (!decode_helper(ctx, batch, ctx_params.n_batch)) {
                    LOG_ERR("%s: llama_decode() failed\n", __func__);
@@ -141,7 +141,7 @@ int main(int argc, char ** argv) {

                if (is_pp_shared) {
                    for (int32_t i = 1; i < pl; ++i) {
-                        llama_kv_cache_seq_cp(ctx, 0, i, -1, -1);
+                        llama_kv_self_seq_cp(ctx, 0, i, -1, -1);
                    }
                }

@@ -116,7 +116,7 @@ if llama_decode(context, batch) != 0 {
 }

 for i in 1 ..< n_parallel {
-    llama_kv_cache_seq_cp(context, 0, Int32(i), 0, batch.n_tokens)
+    llama_kv_self_seq_cp(context, 0, Int32(i), 0, batch.n_tokens)
 }

 if n_parallel > 1 {
@@ -342,7 +342,7 @@ static bool cb_eval(struct ggml_tensor * t, bool ask, void * user_data) {
 }

 static bool get_hidden_layers(llama_context * ctx, std::vector<llama_token> & tokens) {
-    llama_kv_cache_clear(ctx);
+    llama_kv_self_clear(ctx);
    if (llama_decode(ctx, llama_batch_get_one(tokens.data(), tokens.size()))) {
        fprintf(stderr, "%s : failed to eval\n", __func__);
        return false;
@@ -394,6 +394,8 @@ static int prepare_entries(common_params & params, train_context & ctx_train) {
 int main(int argc, char ** argv) {
    common_params params;

+    params.out_file = "control_vector.gguf";
+
    if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_CVECTOR_GENERATOR, print_usage)) {
        return 1;
    }
@@ -498,7 +500,7 @@ int main(int argc, char ** argv) {
    }

    // write output vectors to gguf
-    export_gguf(ctx_train.v_final, params.cvector_outfile, model_hint);
+    export_gguf(ctx_train.v_final, params.out_file, model_hint);

    llama_backend_free();

@@ -38,7 +38,7 @@ static void batch_decode(llama_context * ctx, llama_batch & batch, float * outpu
    const struct llama_model * model = llama_get_model(ctx);

    // clear previous kv_cache values (irrelevant for embeddings)
-    llama_kv_cache_clear(ctx);
+    llama_kv_self_clear(ctx);

    // run model
    LOG_INF("%s: n_tokens = %d, n_seq = %d\n", __func__, batch.n_tokens, n_seq);
@@ -413,20 +413,22 @@ static void print_usage(int, char ** argv) {
 int main(int argc, char ** argv) {
    common_params params;

+    params.out_file = "ggml-lora-merged-f16.gguf";
+
    if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_EXPORT_LORA, print_usage)) {
        return 1;
    }

    g_verbose = (params.verbosity > 1);
    try {
-        lora_merge_ctx ctx(params.model, params.lora_adapters, params.lora_outfile, params.cpuparams.n_threads);
+        lora_merge_ctx ctx(params.model, params.lora_adapters, params.out_file, params.cpuparams.n_threads);
        ctx.run_merge();
    } catch (const std::exception & err) {
        fprintf(stderr, "%s\n", err.what());
        exit(EXIT_FAILURE);
    }

-    printf("done, output file is %s\n", params.lora_outfile.c_str());
+    printf("done, output file is %s\n", params.out_file.c_str());

    return 0;
 }
@@ -45,7 +45,7 @@ static std::vector<std::vector<float>> encode(llama_context * ctx, const std::ve
        }

        // clear previous kv_cache values (irrelevant for embeddings)
-        llama_kv_cache_clear(ctx);
+        llama_kv_self_clear(ctx);
        llama_set_embeddings(ctx, true);
        llama_set_causal_attn(ctx, false);

@@ -102,7 +102,7 @@ static std::string generate(llama_context * ctx, llama_sampler * smpl, const std

    llama_token eos_token = llama_vocab_eos(vocab);

-    llama_kv_cache_clear(ctx);
+    llama_kv_self_clear(ctx);
    llama_set_embeddings(ctx, false);
    llama_set_causal_attn(ctx, true);

@@ -206,9 +206,6 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *

 void IMatrixCollector::save_imatrix(int ncall) const {
    auto fname = m_params.out_file;
-    if (fname.empty()) {
-        fname = "imatrix.dat";
-    }

    if (ncall > 0) {
        fname += ".at_";
@@ -498,7 +495,7 @@ static bool compute_imatrix(llama_context * ctx, const common_params & params) {
        const auto t_start = std::chrono::high_resolution_clock::now();

        // clear the KV cache
-        llama_kv_cache_clear(ctx);
+        llama_kv_self_clear(ctx);

        llama_batch batch = llama_batch_init(n_batch, 0, 1);

@@ -583,6 +580,8 @@ static bool compute_imatrix(llama_context * ctx, const common_params & params) {
 int main(int argc, char ** argv) {
    common_params params;

+    params.out_file = "imatrix.dat" ;
+
    params.n_ctx = 512;
    params.logits_all = true;
    params.escape = false;
@@ -332,8 +332,8 @@ int main(int argc, char ** argv) {
                LOG_DBG("context full, swapping: n_past = %d, n_left = %d, n_ctx = %d, n_keep = %d, n_discard = %d\n",
                    n_past, n_left, n_ctx, params.n_keep, n_discard);

-                llama_kv_cache_seq_rm (ctx, 0, params.n_keep + 1            , params.n_keep + n_discard + 1);
-                llama_kv_cache_seq_add(ctx, 0, params.n_keep + 1 + n_discard, n_past, -n_discard);
+                llama_kv_self_seq_rm (ctx, 0, params.n_keep + 1            , params.n_keep + n_discard + 1);
+                llama_kv_self_seq_add(ctx, 0, params.n_keep + 1 + n_discard, n_past, -n_discard);

                n_past -= n_discard;

@@ -1578,7 +1578,7 @@ int main(int argc, char ** argv) {

        test t(inst, lmodel, ctx);

-        llama_kv_cache_clear(ctx);
+        llama_kv_self_clear(ctx);

        // cool off before the test
        if (params.delay) {
@@ -1618,7 +1618,7 @@ int main(int argc, char ** argv) {
        }

        for (int i = 0; i < params.reps; i++) {
-            llama_kv_cache_clear(ctx);
+            llama_kv_self_clear(ctx);

            uint64_t t_start = get_time_ns();

@@ -194,7 +194,7 @@ Java_android_llama_cpp_LLamaAndroid_bench_1model(
        }

        batch->logits[batch->n_tokens - 1] = true;
-        llama_kv_cache_clear(context);
+        llama_kv_self_clear(context);

        const auto t_pp_start = ggml_time_us();
        if (llama_decode(context, *batch) != 0) {
@@ -206,7 +206,7 @@ Java_android_llama_cpp_LLamaAndroid_bench_1model(

        LOGi("Benchmark text generation (tg)");

-        llama_kv_cache_clear(context);
+        llama_kv_self_clear(context);
        const auto t_tg_start = ggml_time_us();
        for (i = 0; i < tg; i++) {

@@ -223,7 +223,7 @@ Java_android_llama_cpp_LLamaAndroid_bench_1model(

        const auto t_tg_end = ggml_time_us();

-        llama_kv_cache_clear(context);
+        llama_kv_self_clear(context);

        const auto t_pp = double(t_pp_end - t_pp_start) / 1000000.0;
        const auto t_tg = double(t_tg_end - t_tg_start) / 1000000.0;
@@ -448,5 +448,5 @@ Java_android_llama_cpp_LLamaAndroid_completion_1loop(
 extern "C"
 JNIEXPORT void JNICALL
 Java_android_llama_cpp_LLamaAndroid_kv_1cache_1clear(JNIEnv *, jobject, jlong context) {
-    llama_kv_cache_clear(reinterpret_cast<llama_context *>(context));
+    llama_kv_self_clear(reinterpret_cast<llama_context *>(context));
 }
@@ -16,7 +16,7 @@ Open `llama.swiftui.xcodeproj` project in Xcode and you should be able to build
 a simulator or a real device.

 To use the framework with a different project, the XCFramework can be added to the project by
-adding `build-ios/llama.xcframework` by dragging and dropping it into the project navigator, or
+adding `build-apple/llama.xcframework` by dragging and dropping it into the project navigator, or
 by manually selecting the framework in the "Frameworks, Libraries, and Embedded Content" section
 of the project settings.

@@ -210,7 +210,7 @@ actor LlamaContext {
            }
            batch.logits[Int(batch.n_tokens) - 1] = 1 // true

-            llama_kv_cache_clear(context)
+            llama_kv_self_clear(context)

            let t_pp_start = DispatchTime.now().uptimeNanoseconds / 1000;

@@ -223,7 +223,7 @@ actor LlamaContext {

            // bench text generation

-            llama_kv_cache_clear(context)
+            llama_kv_self_clear(context)

            let t_tg_start = DispatchTime.now().uptimeNanoseconds / 1000;

@@ -242,7 +242,7 @@ actor LlamaContext {

            let t_tg_end = DispatchTime.now().uptimeNanoseconds / 1000;

-            llama_kv_cache_clear(context)
+            llama_kv_self_clear(context)

            let t_pp = Double(t_pp_end - t_pp_start) / 1000000.0
            let t_tg = Double(t_tg_end - t_tg_start) / 1000000.0
@@ -292,7 +292,7 @@ actor LlamaContext {
    func clear() {
        tokens_list.removeAll()
        temporary_invalid_cchars.removeAll()
-        llama_kv_cache_clear(context)
+        llama_kv_self_clear(context)
    }

    private func tokenize(text: String, add_bos: Bool) -> [llama_token] {
@@ -51,6 +51,13 @@ install(TARGETS ${TARGET} RUNTIME)
 target_link_libraries(${TARGET} PRIVATE common llava ${CMAKE_THREAD_LIBS_INIT})
 target_compile_features(${TARGET} PRIVATE cxx_std_17)

+set(TARGET llama-gemma3-cli)
+add_executable(${TARGET} gemma3-cli.cpp)
+set_target_properties(${TARGET} PROPERTIES OUTPUT_NAME llama-gemma3-cli)
+install(TARGETS ${TARGET} RUNTIME)
+target_link_libraries(${TARGET} PRIVATE common llava ${CMAKE_THREAD_LIBS_INIT})
+target_compile_features(${TARGET} PRIVATE cxx_std_17)
+
 set(TARGET llama-llava-clip-quantize-cli)
 add_executable(${TARGET} clip-quantize-cli.cpp)
 set_target_properties(${TARGET} PROPERTIES OUTPUT_NAME llama-llava-clip-quantize-cli)
@@ -0,0 +1,30 @@
+# Gemma 3 vision
+
+> [!IMPORTANT]
+>
+> This is very experimental, only used for demo purpose.
+
+## How to get mmproj.gguf?
+
+```bash
+cd gemma-3-4b-it
+python ../llama.cpp/examples/llava/gemma3_convert_encoder_to_gguf.py .
+
+# output file is mmproj.gguf
+```
+
+## How to run it?
+
+What you need:
+- The text model GGUF, can be converted using `convert_hf_to_gguf.py`
+- The mmproj file from step above
+- An image file
+
+```bash
+# build
+cmake -B build
+cmake --build build --target llama-gemma3-cli
+
+# run it
+./build/bin/llama-gemma3-cli -m {text_model}.gguf --mmproj mmproj.gguf --image your_image.jpg
+```
@@ -5,13 +5,25 @@ Currently, this readme only supports minicpm-omni's image capabilities, and we w

 Download [MiniCPM-o-2_6](https://huggingface.co/openbmb/MiniCPM-o-2_6) PyTorch model from huggingface to "MiniCPM-o-2_6" folder.

+
+### Build llama.cpp
+Readme modification time: 20250206
+
+If there are differences in usage, please refer to the official build [documentation](https://github.com/ggerganov/llama.cpp/blob/master/docs/build.md)
+
 Clone llama.cpp:
 ```bash
-git clone git@github.com:OpenBMB/llama.cpp.git
+git clone https://github.com/ggerganov/llama.cpp
 cd llama.cpp
-git checkout minicpm-omni
 ```

+Build llama.cpp using `CMake`:
+```bash
+cmake -B build
+cmake --build build --config Release
+```
+
+
 ### Usage of MiniCPM-o 2.6

 Convert PyTorch model to gguf files (You can also download the converted [gguf](https://huggingface.co/openbmb/MiniCPM-o-2_6-gguf) by us)
@@ -22,25 +34,15 @@ python ./examples/llava/minicpmv-convert-image-encoder-to-gguf.py -m ../MiniCPM-
 python ./convert_hf_to_gguf.py ../MiniCPM-o-2_6/model

 # quantize int4 version
-./llama-quantize ../MiniCPM-o-2_6/model/ggml-model-f16.gguf ../MiniCPM-o-2_6/model/ggml-model-Q4_K_M.gguf Q4_K_M
+./build/bin/llama-quantize ../MiniCPM-o-2_6/model/ggml-model-f16.gguf ../MiniCPM-o-2_6/model/ggml-model-Q4_K_M.gguf Q4_K_M
 ```

-Build llama.cpp using `CMake`:
-https://github.com/ggml-org/llama.cpp/blob/master/docs/build.md
-
-```bash
-cmake -B build
-cmake --build build --config Release
-```

 Inference on Linux or Mac
-```
+```bash
 # run f16 version
-./llama-minicpmv-cli -m ../MiniCPM-o-2_6/model/ggml-model-f16.gguf --mmproj ../MiniCPM-o-2_6/mmproj-model-f16.gguf -c 4096 --temp 0.7 --top-p 0.8 --top-k 100 --repeat-penalty 1.05 --image xx.jpg -p "What is in the image?"
+./build/bin/llama-minicpmv-cli -m ../MiniCPM-o-2_6/model/ggml-model-f16.gguf --mmproj ../MiniCPM-o-2_6/mmproj-model-f16.gguf -c 4096 --temp 0.7 --top-p 0.8 --top-k 100 --repeat-penalty 1.05 --image xx.jpg -p "What is in the image?"

 # run quantized int4 version
-./llama-minicpmv-cli -m ../MiniCPM-o-2_6/model/ggml-model-Q4_K_M.gguf --mmproj ../MiniCPM-o-2_6/mmproj-model-f16.gguf -c 4096 --temp 0.7 --top-p 0.8 --top-k 100 --repeat-penalty 1.05 --image xx.jpg  -p "What is in the image?"
-
-# or run in interactive mode
-./llama-minicpmv-cli -m ../MiniCPM-o-2_6/model/ggml-model-Q4_K_M.gguf --mmproj ../MiniCPM-o-2_6/mmproj-model-f16.gguf -c 4096 --temp 0.7 --top-p 0.8 --top-k 100 --repeat-penalty 1.05 --image xx.jpg -i
+./build/bin/llama-minicpmv-cli -m ../MiniCPM-o-2_6/model/ggml-model-Q4_K_M.gguf --mmproj ../MiniCPM-o-2_6/mmproj-model-f16.gguf -c 4096 --temp 0.7 --top-p 0.8 --top-k 100 --repeat-penalty 1.05 --image xx.jpg  -p "What is in the image?"
 ```
@@ -4,13 +4,26 @@

 Download [MiniCPM-Llama3-V-2_5](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5) PyTorch model from huggingface to "MiniCPM-Llama3-V-2_5" folder.

+
+### Build llama.cpp
+Readme modification time: 20250206
+
+If there are differences in usage, please refer to the official build [documentation](https://github.com/ggerganov/llama.cpp/blob/master/docs/build.md)
+
 Clone llama.cpp:
 ```bash
 git clone https://github.com/ggml-org/llama.cpp
 cd llama.cpp
 ```

-### Usage
+Build llama.cpp using `CMake`:
+```bash
+cmake -B build
+cmake --build build --config Release
+```
+
+
+### Usage of MiniCPM-Llama3-V 2.5

 Convert PyTorch model to gguf files (You can also download the converted [gguf](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5-gguf) by us)

@@ -20,80 +33,15 @@ python ./examples/llava/minicpmv-convert-image-encoder-to-gguf.py -m ../MiniCPM-
 python ./convert_hf_to_gguf.py ../MiniCPM-Llama3-V-2_5/model

 # quantize int4 version
-./llama-quantize ../MiniCPM-Llama3-V-2_5/model/model-8B-F16.gguf ../MiniCPM-Llama3-V-2_5/model/ggml-model-Q4_K_M.gguf Q4_K_M
+./build/bin/llama-quantize ../MiniCPM-Llama3-V-2_5/model/model-8B-F16.gguf ../MiniCPM-Llama3-V-2_5/model/ggml-model-Q4_K_M.gguf Q4_K_M
 ```

-Build for Linux or Mac
-
-```bash
-make
-make llama-minicpmv-cli
-```

 Inference on Linux or Mac
-```
+```bash
 # run f16 version
-./llama-minicpmv-cli -m ../MiniCPM-Llama3-V-2_5/model/model-8B-F16.gguf --mmproj ../MiniCPM-Llama3-V-2_5/mmproj-model-f16.gguf -c 4096 --temp 0.7 --top-p 0.8 --top-k 100 --repeat-penalty 1.05 --image xx.jpg -p "What is in the image?"
+./build/bin/llama-minicpmv-cli -m ../MiniCPM-Llama3-V-2_5/model/model-8B-F16.gguf --mmproj ../MiniCPM-Llama3-V-2_5/mmproj-model-f16.gguf -c 4096 --temp 0.7 --top-p 0.8 --top-k 100 --repeat-penalty 1.05 --image xx.jpg -p "What is in the image?"

 # run quantized int4 version
-./llama-minicpmv-cli -m ../MiniCPM-Llama3-V-2_5/model/ggml-model-Q4_K_M.gguf --mmproj ../MiniCPM-Llama3-V-2_5/mmproj-model-f16.gguf -c 4096 --temp 0.7 --top-p 0.8 --top-k 100 --repeat-penalty 1.05 --image xx.jpg  -p "What is in the image?"
-
-# or run in interactive mode
-./llama-minicpmv-cli -m ../MiniCPM-Llama3-V-2_5/model/ggml-model-Q4_K_M.gguf --mmproj ../MiniCPM-Llama3-V-2_5/mmproj-model-f16.gguf -c 4096 --temp 0.7 --top-p 0.8 --top-k 100 --repeat-penalty 1.05 --image xx.jpg -i
-```
-
-### Android
-
-#### Build on Android device using Termux
-We found that build on Android device would bring better runtime performance, so we recommend to build on device.
-
-[Termux](https://github.com/termux/termux-app#installation) is a terminal app on Android device (no root required).
-
-Install tools in Termux:
-```
-apt update && apt upgrade -y
-apt install git make cmake
-```
-
-It's recommended to move your model inside the `~/` directory for best performance:
-```
-cd storage/downloads
-mv model.gguf ~/
-```
-
-#### Building the Project using Android NDK
-Obtain the [Android NDK](https://developer.android.com/ndk) and then build with CMake.
-
-Execute the following commands on your computer to avoid downloading the NDK to your mobile. Alternatively, you can also do this in Termux:
-
-```bash
-mkdir build-android
-cd build-android
-export NDK=/your_ndk_path
-cmake -DCMAKE_TOOLCHAIN_FILE=$NDK/build/cmake/android.toolchain.cmake -DANDROID_ABI=arm64-v8a -DANDROID_PLATFORM=android-23 -DCMAKE_C_FLAGS=-march=armv8.4a+dotprod ..
-make
-```
-
-Install [termux](https://github.com/termux/termux-app#installation) on your device and run `termux-setup-storage` to get access to your SD card (if Android 11+ then run the command twice).
-
-Finally, copy these built `llama` binaries and the model file to your device storage. Because the file permissions in the Android sdcard cannot be changed, you can copy the executable files to the `/data/data/com.termux/files/home/bin` path, and then execute the following commands in Termux to add executable permission:
-
-(Assumed that you have pushed the built executable files to the /sdcard/llama.cpp/bin path using `adb push`)
-```
-$cp -r /sdcard/llama.cpp/bin /data/data/com.termux/files/home/
-$cd /data/data/com.termux/files/home/bin
-$chmod +x ./*
-```
-
-Download models and push them to `/sdcard/llama.cpp/`, then move it to `/data/data/com.termux/files/home/model/`
-
-```
-$mv /sdcard/llama.cpp/ggml-model-Q4_K_M.gguf /data/data/com.termux/files/home/model/
-$mv /sdcard/llama.cpp/mmproj-model-f16.gguf /data/data/com.termux/files/home/model/
-```
-
-Now, you can start chatting:
-```
-$cd /data/data/com.termux/files/home/bin
-$./llama-minicpmv-cli -m ../model/ggml-model-Q4_K_M.gguf --mmproj ../model/mmproj-model-f16.gguf -c 4096 --temp 0.7 --top-p 0.8 --top-k 100 --repeat-penalty 1.05 --image xx.jpg  -p "What is in the image?"
+./build/bin/llama-minicpmv-cli -m ../MiniCPM-Llama3-V-2_5/model/ggml-model-Q4_K_M.gguf --mmproj ../MiniCPM-Llama3-V-2_5/mmproj-model-f16.gguf -c 4096 --temp 0.7 --top-p 0.8 --top-k 100 --repeat-penalty 1.05 --image xx.jpg  -p "What is in the image?"
 ```
@@ -4,13 +4,25 @@

 Download [MiniCPM-V-2_6](https://huggingface.co/openbmb/MiniCPM-V-2_6) PyTorch model from huggingface to "MiniCPM-V-2_6" folder.

+
+### Build llama.cpp
+Readme modification time: 20250206
+
+If there are differences in usage, please refer to the official build [documentation](https://github.com/ggerganov/llama.cpp/blob/master/docs/build.md)
+
 Clone llama.cpp:
 ```bash
-git clone git@github.com:OpenBMB/llama.cpp.git
+git clone https://github.com/ggerganov/llama.cpp
 cd llama.cpp
-git checkout minicpmv-main
 ```

+Build llama.cpp using `CMake`:
+```bash
+cmake -B build
+cmake --build build --config Release
+```
+
+
 ### Usage of MiniCPM-V 2.6

 Convert PyTorch model to gguf files (You can also download the converted [gguf](https://huggingface.co/openbmb/MiniCPM-V-2_6-gguf) by us)
@@ -21,87 +33,15 @@ python ./examples/llava/minicpmv-convert-image-encoder-to-gguf.py -m ../MiniCPM-
 python ./convert_hf_to_gguf.py ../MiniCPM-V-2_6/model

 # quantize int4 version
-./llama-quantize ../MiniCPM-V-2_6/model/ggml-model-f16.gguf ../MiniCPM-V-2_6/model/ggml-model-Q4_K_M.gguf Q4_K_M
+./build/bin/llama-quantize ../MiniCPM-V-2_6/model/ggml-model-f16.gguf ../MiniCPM-V-2_6/model/ggml-model-Q4_K_M.gguf Q4_K_M
 ```

-Build for Linux or Mac
-
-```bash
-make
-make llama-minicpmv-cli
-```

 Inference on Linux or Mac
-```
+```bash
 # run f16 version
-./llama-minicpmv-cli -m ../MiniCPM-V-2_6/model/ggml-model-f16.gguf --mmproj ../MiniCPM-V-2_6/mmproj-model-f16.gguf -c 4096 --temp 0.7 --top-p 0.8 --top-k 100 --repeat-penalty 1.05 --image xx.jpg -p "What is in the image?"
+./build/bin/llama-minicpmv-cli -m ../MiniCPM-V-2_6/model/ggml-model-f16.gguf --mmproj ../MiniCPM-V-2_6/mmproj-model-f16.gguf -c 4096 --temp 0.7 --top-p 0.8 --top-k 100 --repeat-penalty 1.05 --image xx.jpg -p "What is in the image?"

 # run quantized int4 version
-./llama-minicpmv-cli -m ../MiniCPM-V-2_6/model/ggml-model-Q4_K_M.gguf --mmproj ../MiniCPM-V-2_6/mmproj-model-f16.gguf -c 4096 --temp 0.7 --top-p 0.8 --top-k 100 --repeat-penalty 1.05 --image xx.jpg  -p "What is in the image?"
-
-# or run in interactive mode
-./llama-minicpmv-cli -m ../MiniCPM-V-2_6/model/ggml-model-Q4_K_M.gguf --mmproj ../MiniCPM-V-2_6/mmproj-model-f16.gguf -c 4096 --temp 0.7 --top-p 0.8 --top-k 100 --repeat-penalty 1.05 --image xx.jpg -i
-```
-
-### Video
-Install FFmpeg
-```
-brew install ffmpeg
-brew install pkg-config
-```
-
-### Android
-
-#### Build on Android device using Termux
-We found that build on Android device would bring better runtime performance, so we recommend to build on device.
-
-[Termux](https://github.com/termux/termux-app#installation) is a terminal app on Android device (no root required).
-
-Install tools in Termux:
-```
-apt update && apt upgrade -y
-apt install git make cmake
-```
-
-It's recommended to move your model inside the `~/` directory for best performance:
-```
-cd storage/downloads
-mv model.gguf ~/
-```
-
-#### Building the Project using Android NDK
-Obtain the [Android NDK](https://developer.android.com/ndk) and then build with CMake.
-
-Execute the following commands on your computer to avoid downloading the NDK to your mobile. Alternatively, you can also do this in Termux:
-
-```bash
-mkdir build-android
-cd build-android
-export NDK=/your_ndk_path
-cmake -DCMAKE_TOOLCHAIN_FILE=$NDK/build/cmake/android.toolchain.cmake -DANDROID_ABI=arm64-v8a -DANDROID_PLATFORM=android-23 -DCMAKE_C_FLAGS=-march=armv8.4a+dotprod ..
-make
-```
-
-Install [termux](https://github.com/termux/termux-app#installation) on your device and run `termux-setup-storage` to get access to your SD card (if Android 11+ then run the command twice).
-
-Finally, copy these built `llama` binaries and the model file to your device storage. Because the file permissions in the Android sdcard cannot be changed, you can copy the executable files to the `/data/data/com.termux/files/home/bin` path, and then execute the following commands in Termux to add executable permission:
-
-(Assumed that you have pushed the built executable files to the /sdcard/llama.cpp/bin path using `adb push`)
-```
-$cp -r /sdcard/llama.cpp/bin /data/data/com.termux/files/home/
-$cd /data/data/com.termux/files/home/bin
-$chmod +x ./*
-```
-
-Download models and push them to `/sdcard/llama.cpp/`, then move it to `/data/data/com.termux/files/home/model/`
-
-```
-$mv /sdcard/llama.cpp/ggml-model-Q4_K_M.gguf /data/data/com.termux/files/home/model/
-$mv /sdcard/llama.cpp/mmproj-model-f16.gguf /data/data/com.termux/files/home/model/
-```
-
-Now, you can start chatting:
-```
-$cd /data/data/com.termux/files/home/bin
-$./llama-minicpmv-cli -m ../model/ggml-model-Q4_K_M.gguf --mmproj ../model/mmproj-model-f16.gguf -c 4096 --temp 0.7 --top-p 0.8 --top-k 100 --repeat-penalty 1.05 --image xx.jpg  -p "What is in the image?"
+./build/bin/llama-minicpmv-cli -m ../MiniCPM-V-2_6/model/ggml-model-Q4_K_M.gguf --mmproj ../MiniCPM-V-2_6/mmproj-model-f16.gguf -c 4096 --temp 0.7 --top-p 0.8 --top-k 100 --repeat-penalty 1.05 --image xx.jpg  -p "What is in the image?"
 ```
@@ -4,31 +4,12 @@
 // Note: Even when using identical normalized image inputs (see normalize_image_u8_to_f32()) we have a significant difference in resulting embeddings compared to pytorch
 #include "clip.h"
 #include "ggml.h"
+#include "ggml-cpp.h"
 #include "ggml-cpu.h"
 #include "ggml-alloc.h"
 #include "ggml-backend.h"
 #include "gguf.h"

-//#ifdef GGML_USE_CUDA
-//#include "ggml-cuda.h"
-//#endif
-//
-//#ifdef GGML_USE_SYCL
-//#include "ggml-sycl.h"
-//#endif
-//
-//#ifdef GGML_USE_METAL
-//#include "ggml-metal.h"
-//#endif
-//
-//#ifdef GGML_USE_CANN
-//#include "ggml-cann.h"
-//#endif
-//
-//#ifdef GGML_USE_VULKAN
-//#include "ggml-vulkan.h"
-//#endif
-
 #define STB_IMAGE_IMPLEMENTATION
 #include "stb_image.h"

@@ -155,6 +136,8 @@ static std::string format(const char * fmt, ...) {
 #define TN_MVLM_PROJ_BLOCK "mm.model.mb_block.%d.block.%d.%s"
 #define TN_MVLM_PROJ_PEG   "mm.model.peg.%d.%s"
 #define TN_IMAGE_NEWLINE   "model.image_newline"
+#define TN_MM_INP_PROJ     "mm.input_projection.weight" // gemma3
+#define TN_MM_SOFT_EMB_N   "mm.soft_emb_norm.weight"    // gemma3

 #define TN_MINICPMV_POS_EMBD_K "resampler.pos_embed_k"
 #define TN_MINICPMV_QUERY "resampler.query"
@@ -181,6 +164,7 @@ enum projector_type {
    PROJECTOR_TYPE_RESAMPLER,
    PROJECTOR_TYPE_GLM_EDGE,
    PROJECTOR_TYPE_MERGER,
+    PROJECTOR_TYPE_GEMMA3,
    PROJECTOR_TYPE_UNKNOWN,
 };

@@ -191,6 +175,7 @@ static std::map<projector_type, std::string> PROJECTOR_TYPE_NAMES = {
    { PROJECTOR_TYPE_RESAMPLER, "resampler"},
    { PROJECTOR_TYPE_GLM_EDGE, "adapter"},
    { PROJECTOR_TYPE_MERGER, "qwen2vl_merger"},
+    { PROJECTOR_TYPE_GEMMA3, "gemma3"},
 };


@@ -317,7 +302,7 @@ static projector_type clip_projector_type_from_string(const std::string & name)
            return kv.first;
        }
    }
-    return PROJECTOR_TYPE_UNKNOWN;
+    throw std::runtime_error(format("Unknown projector type: %s", name.c_str()));
 }

 #ifdef CLIP_DEBUG_FUNCTIONS
@@ -574,6 +559,10 @@ struct clip_vision_model {
    struct ggml_tensor * mm_model_ln_kv_b;
    struct ggml_tensor * mm_model_ln_post_w;
    struct ggml_tensor * mm_model_ln_post_b;
+
+    // gemma3
+    struct ggml_tensor * mm_input_proj_w;
+    struct ggml_tensor * mm_soft_emb_norm_w;
 };

 struct clip_ctx {
@@ -588,7 +577,7 @@ struct clip_ctx {
    struct clip_vision_model vision_model;
    projector_type proj_type = PROJECTOR_TYPE_MLP;

-    int32_t max_feature_layer;
+    int32_t max_feature_layer; // unused in newer models like gemma3
    float image_mean[3];
    float image_std[3];
    bool use_gelu = false;
@@ -600,21 +589,209 @@ struct clip_ctx {
    bool has_post_norm = false;
    bool has_patch_bias = false;

-    struct gguf_context * ctx_gguf;
-    struct ggml_context * ctx_data;
+    struct gguf_context * ctx_gguf = nullptr;
+    struct ggml_context * ctx_data = nullptr;

    std::vector<uint8_t> buf_compute_meta;

-    // memory buffers to evaluate the model
-    ggml_backend_buffer_t params_buffer  = NULL;
+    std::vector<ggml_backend_t> backend_ptrs;
+    std::vector<ggml_backend_buffer_type_t> backend_buft;

-    ggml_backend_t backend       = NULL;
-    ggml_gallocr_t compute_alloc = NULL;
+    ggml_backend_t backend     = nullptr;
+    ggml_backend_t backend_cpu = nullptr;
+    ggml_backend_buffer_t buf  = nullptr;

-    struct clip_image_size * load_image_size;
+    ggml_backend_sched_ptr sched;
+
+    struct clip_image_size * load_image_size = nullptr;
+
+    clip_ctx(clip_context_params & ctx_params) {
+        backend_cpu = ggml_backend_init_by_type(GGML_BACKEND_DEVICE_TYPE_CPU, nullptr);
+        backend     = ctx_params.use_gpu
+                        ? ggml_backend_init_by_type(GGML_BACKEND_DEVICE_TYPE_GPU, nullptr)
+                        : nullptr;
+
+        if (backend) {
+            LOG_INF("%s: CLIP using %s backend\n", __func__, ggml_backend_name(backend));
+            backend_ptrs.push_back(backend);
+            backend_buft.push_back(ggml_backend_get_default_buffer_type(backend));
+        } else {
+            backend = backend_cpu;
+            LOG_INF("%s: CLIP using CPU backend\n", __func__);
+        }
+
+        backend_ptrs.push_back(backend_cpu);
+        backend_buft.push_back(ggml_backend_get_default_buffer_type(backend_cpu));
+
+        sched.reset(
+            ggml_backend_sched_new(backend_ptrs.data(), backend_buft.data(), backend_ptrs.size(), 8192, false)
+        );
+    }
+
+    ~clip_ctx() {
+        ggml_free(ctx_data);
+        gguf_free(ctx_gguf);
+        ggml_backend_buffer_free(buf);
+        ggml_backend_free(backend);
+        if (backend_cpu != backend) {
+            ggml_backend_free(backend_cpu);
+        }
+    }
 };

-static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32_batch * imgs, struct clip_image_size * load_image_size, bool is_inf = false) {
+static ggml_cgraph * clip_image_build_graph_siglip(clip_ctx * ctx, const clip_image_f32_batch * imgs) {
+    const auto & model = ctx->vision_model;
+    const auto & hparams = model.hparams;
+
+    const int image_size = hparams.image_size;
+    int image_size_width  = image_size;
+    int image_size_height = image_size;
+
+    const int patch_size           = hparams.patch_size;
+    const int num_patches          = ((image_size_width / patch_size) * (image_size_height / patch_size));
+    const int hidden_size          = hparams.hidden_size;
+    const int n_head               = hparams.n_head;
+    const int d_head               = hidden_size / n_head;
+    const int n_layer              = hparams.n_layer;
+    const float eps                = hparams.eps;
+
+    GGML_ASSERT(imgs->size == 1); // batch_size == 1
+
+    struct ggml_init_params params = {
+        /*.mem_size   =*/ ctx->buf_compute_meta.size(),
+        /*.mem_buffer =*/ ctx->buf_compute_meta.data(),
+        /*.no_alloc   =*/ true,
+    };
+
+    struct ggml_context * ctx0 = ggml_init(params);
+    struct ggml_cgraph * gf = ggml_new_graph(ctx0);
+
+    // input raw
+    struct ggml_tensor * inp_raw = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, image_size_width, image_size_height, 3);
+    ggml_set_name(inp_raw, "inp_raw");
+    ggml_set_input(inp_raw);
+
+    struct ggml_tensor * inp = ggml_conv_2d(ctx0, model.patch_embeddings_0, inp_raw, patch_size, patch_size, 0, 0, 1, 1);
+    inp = ggml_reshape_2d(ctx0, inp, num_patches, hidden_size);
+    inp = ggml_cont(ctx0, ggml_transpose(ctx0, inp));
+    inp = ggml_add(ctx0, inp, model.patch_bias);
+
+    // position embeddings
+    struct ggml_tensor * embeddings = ggml_add(ctx0, inp, model.position_embeddings);
+
+    // loop over layers
+    for (int il = 0; il < n_layer; il++) {
+        struct ggml_tensor * cur = embeddings; // embeddings = residual, cur = hidden_states
+
+        // layernorm1
+        {
+            cur = ggml_norm(ctx0, cur, eps);
+            cur = ggml_add(ctx0, ggml_mul(ctx0, cur, model.layers[il].ln_1_w), model.layers[il].ln_1_b);
+        }
+
+        // self-attention
+        {
+
+            struct ggml_tensor * Q =
+                ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].q_w, cur), model.layers[il].q_b);
+
+            Q = ggml_reshape_3d(ctx0, Q, d_head, n_head, num_patches);
+            Q = ggml_cont(ctx0, ggml_permute(ctx0, Q, 0, 2, 1, 3));
+
+            struct ggml_tensor * K =
+                ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].k_w, cur), model.layers[il].k_b);
+
+            K = ggml_reshape_3d(ctx0, K, d_head, n_head, num_patches);
+            K = ggml_cont(ctx0, ggml_permute(ctx0, K, 0, 2, 1, 3));
+
+            struct ggml_tensor * V =
+                ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].v_w, cur), model.layers[il].v_b);
+
+            V = ggml_reshape_3d(ctx0, V, d_head, n_head, num_patches);
+            V = ggml_cont(ctx0, ggml_permute(ctx0, V, 1, 2, 0, 3));
+
+            struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
+            KQ = ggml_scale_inplace(ctx0, KQ, 1.0f / sqrtf((float)d_head));
+            KQ = ggml_soft_max_inplace(ctx0, KQ);
+
+            struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ);
+            KQV = ggml_reshape_3d(ctx0, KQV, d_head, num_patches, n_head);
+            KQV = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
+
+            cur = ggml_cont_2d(ctx0, KQV, hidden_size, num_patches);
+        }
+
+        // attention output
+        cur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].o_w, cur), model.layers[il].o_b);
+
+        // re-add the layer input, e.g., residual
+        cur = ggml_add(ctx0, cur, embeddings);
+
+        embeddings = cur; // embeddings = residual, cur = hidden_states
+
+        // layernorm2
+        {
+            cur = ggml_norm(ctx0, cur, eps);
+            cur = ggml_add(ctx0, ggml_mul(ctx0, cur, model.layers[il].ln_2_w), model.layers[il].ln_2_b);
+        }
+
+        cur = ggml_mul_mat(ctx0, model.layers[il].ff_i_w, cur);
+        cur = ggml_add(ctx0, cur, model.layers[il].ff_i_b);
+
+        // siglip uses gelu
+        cur = ggml_gelu(ctx0, cur);
+
+        cur = ggml_mul_mat(ctx0, model.layers[il].ff_o_w, cur);
+        cur = ggml_add(ctx0, cur, model.layers[il].ff_o_b);
+
+        // residual 2
+        cur = ggml_add(ctx0, embeddings, cur);
+
+        embeddings = cur;
+    }
+
+    // post-layernorm
+    if (ctx->has_post_norm) {
+        embeddings = ggml_norm(ctx0, embeddings, eps);
+        ggml_set_name(embeddings, "post_ln");
+
+        embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.post_ln_w), model.post_ln_b);
+    }
+
+    if (ctx->proj_type == PROJECTOR_TYPE_GEMMA3) {
+        const int batch_size = 1;
+        const int mm_tokens_per_image = 256; // default value for gemma3
+        const int tokens_per_side = sqrt(mm_tokens_per_image);
+        const int patches_per_image = sqrt(num_patches);
+        const int kernel_size = patches_per_image / tokens_per_side;
+
+        embeddings = ggml_cont(ctx0, ggml_transpose(ctx0, embeddings));
+        embeddings = ggml_reshape_4d(ctx0, embeddings, patches_per_image, patches_per_image, hidden_size, batch_size);
+
+        // doing a pool2d to reduce the number of output tokens to 256
+        embeddings = ggml_pool_2d(ctx0, embeddings, GGML_OP_POOL_AVG, kernel_size, kernel_size, kernel_size, kernel_size, 0, 0);
+        embeddings = ggml_reshape_3d(ctx0, embeddings, embeddings->ne[0] * embeddings->ne[0], hidden_size, batch_size);
+        embeddings = ggml_cont(ctx0, ggml_transpose(ctx0, embeddings));
+
+        // apply norm before projection
+        embeddings = ggml_rms_norm(ctx0, embeddings, eps);
+        embeddings = ggml_mul(ctx0, embeddings, model.mm_soft_emb_norm_w);
+
+        // apply projection
+        embeddings = ggml_mul_mat(ctx0,
+            ggml_cont(ctx0, ggml_transpose(ctx0, model.mm_input_proj_w)),
+            embeddings);
+    }
+
+    // build the graph
+    ggml_build_forward_expand(gf, embeddings);
+
+    ggml_free(ctx0);
+
+    return gf;
+}
+
+static ggml_cgraph * clip_image_build_graph_legacy(clip_ctx * ctx, const clip_image_f32_batch * imgs, struct clip_image_size * load_image_size, bool is_inf = false) {
    if (!ctx->has_vision_encoder) {
        LOG_ERR("This gguf file seems to have no vision encoder\n");
        return nullptr;
@@ -1160,7 +1337,8 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
        } else {
            GGML_ABORT("fatel error");
        }
-    } else if (ctx->proj_type == PROJECTOR_TYPE_MERGER) {
+    }
+    else if (ctx->proj_type == PROJECTOR_TYPE_MERGER) {
        embeddings = ggml_reshape_3d(ctx0, embeddings, hidden_size * 4, num_positions / 4, batch_size);

        embeddings = ggml_mul_mat(ctx0, model.mm_0_w, embeddings);
@@ -1182,8 +1360,25 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
    return gf;
 }

+static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32_batch * imgs, struct clip_image_size * load_image_size, bool is_inf = false) {
+    if (ctx->proj_type == PROJECTOR_TYPE_GEMMA3) {
+        return clip_image_build_graph_siglip(ctx, imgs);
+    } else {
+        // TODO: we should have one build_* function per model
+        return clip_image_build_graph_legacy(ctx, imgs, load_image_size, is_inf);
+    }
+}
+
 // read and create ggml_context containing the tensors and their data
 struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
+    return clip_init(fname, clip_context_params{
+        /* use_gpu */   true,
+        /* verbosity */ verbosity,
+    });
+}
+
+struct clip_ctx * clip_init(const char * fname, struct clip_context_params ctx_params) {
+    int verbosity = ctx_params.verbosity;
    struct ggml_context * meta = NULL;

    struct gguf_init_params params = {
@@ -1277,7 +1472,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
        }
    }

-    clip_ctx * new_clip = new clip_ctx{};
+    clip_ctx * new_clip = new clip_ctx(ctx_params);

    // update projector type
    {
@@ -1296,36 +1491,6 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
        }
    }

-//#ifdef GGML_USE_CUDA
-//    new_clip->backend = ggml_backend_cuda_init(0);
-//    LOG_INF("%s: CLIP using CUDA backend\n", __func__);
-//#endif
-//
-//#ifdef GGML_USE_METAL
-//    new_clip->backend = ggml_backend_metal_init();
-//    LOG_INF("%s: CLIP using Metal backend\n", __func__);
-//#endif
-//
-//#ifdef GGML_USE_CANN
-//    new_clip->backend = ggml_backend_cann_init(0);
-//    LOG_INF("%s: CLIP using CANN backend\n", __func__);
-//#endif
-//
-//#ifdef GGML_USE_VULKAN
-//    new_clip->backend = ggml_backend_vk_init(0);
-//    LOG_INF("%s: CLIP using Vulkan backend\n", __func__);
-//#endif
-//
-//#ifdef GGML_USE_SYCL
-//    new_clip->backend = ggml_backend_sycl_init(0);
-//    LOG_INF("%s: CLIP using SYCL backend\n", __func__);
-//#endif
-
-    if (!new_clip->backend) {
-        new_clip->backend = ggml_backend_cpu_init();
-        LOG_INF("%s: CLIP using CPU backend\n", __func__);
-    }
-
    // model size and capabilities
    {
        int idx = get_key_idx(ctx, KEY_HAS_TEXT_ENC);
@@ -1363,8 +1528,12 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
        GGML_ASSERT(new_clip->has_vision_encoder);
        GGML_ASSERT(!new_clip->has_text_encoder);

-        idx = get_key_idx(ctx, KEY_USE_GELU);
-        new_clip->use_gelu = gguf_get_val_bool(ctx, idx);
+        try {
+            idx = get_key_idx(ctx, KEY_USE_GELU);
+            new_clip->use_gelu = gguf_get_val_bool(ctx, idx);
+        } catch (std::runtime_error & /*e*/) {
+            new_clip->use_gelu = false;
+        }

        try {
            idx = get_key_idx(ctx, KEY_USE_SILU);
@@ -1378,6 +1547,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
            LOG_INF("%s: vision_encoder: %d\n", __func__, new_clip->has_vision_encoder);
            LOG_INF("%s: llava_projector:  %d\n", __func__, new_clip->has_llava_projector);
            LOG_INF("%s: minicpmv_projector:  %d\n", __func__, new_clip->has_minicpmv_projector);
+            LOG_INF("%s: minicpmv_version:  %d\n", __func__, new_clip->minicpmv_version);
            LOG_INF("%s: glm_projector:  %d\n", __func__, new_clip->has_glm_projector);
            LOG_INF("%s: model size:     %.2f MB\n", __func__, model_size / 1024.0 / 1024.0);
            LOG_INF("%s: metadata size:  %.2f MB\n", __func__, ggml_get_mem_size(meta) / 1024.0 / 1024.0);
@@ -1420,7 +1590,9 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
        }

        // alloc memory and offload data
-        new_clip->params_buffer = ggml_backend_alloc_ctx_tensors(new_clip->ctx_data, new_clip->backend);
+        ggml_backend_buffer_type_t buft = ggml_backend_get_default_buffer_type(new_clip->backend);
+        new_clip->buf = ggml_backend_alloc_ctx_tensors_from_buft(new_clip->ctx_data, buft);
+        ggml_backend_buffer_set_usage(new_clip->buf, GGML_BACKEND_BUFFER_USAGE_WEIGHTS);
        for (int i = 0; i < n_tensors; ++i) {
            const char * name = gguf_get_tensor_name(ctx, i);
            struct ggml_tensor * cur = ggml_get_tensor(new_clip->ctx_data, name);
@@ -1433,7 +1605,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
                return nullptr;
            }
            int num_bytes = ggml_nbytes(cur);
-            if (ggml_backend_buffer_is_host(new_clip->params_buffer)) {
+            if (ggml_backend_buft_is_host(buft)) {
                // for the CPU and Metal backend, we can read directly into the tensor
                fin.read(reinterpret_cast<char *>(cur->data), num_bytes);
            } else {
@@ -1569,11 +1741,17 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
        }

        try {
-            vision_model.patch_embeddings_0    = get_tensor(new_clip->ctx_data, TN_PATCH_EMBD);
+            vision_model.patch_embeddings_0 = get_tensor(new_clip->ctx_data, TN_PATCH_EMBD);
+        } catch(const std::exception& /*e*/) {
+            vision_model.patch_embeddings_0 = nullptr;
+        }
+
+        try {
            vision_model.position_embeddings = get_tensor(new_clip->ctx_data, format(TN_POS_EMBD, "v"));
        } catch(const std::exception& /*e*/) {
-            LOG_ERR("%s: failed to load vision model tensors\n", __func__);
+            vision_model.position_embeddings = nullptr;
        }
+
        try {
            vision_model.patch_embeddings_1    = get_tensor(new_clip->ctx_data, TN_PATCH_EMBD_1);
        } catch(const std::exception& /*e*/) {
@@ -1684,6 +1862,10 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
            vision_model.mm_1_w = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 2, "weight"));
            vision_model.mm_1_b = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 2, "bias"));
        }
+        else if (new_clip->proj_type == PROJECTOR_TYPE_GEMMA3) {
+            vision_model.mm_input_proj_w    = get_tensor(new_clip->ctx_data, TN_MM_INP_PROJ);
+            vision_model.mm_soft_emb_norm_w = get_tensor(new_clip->ctx_data, TN_MM_SOFT_EMB_N);
+        }
        else {
            std::string proj_type = PROJECTOR_TYPE_NAMES[new_clip->proj_type];
            throw std::runtime_error(format("%s: don't support projector with: %s currently\n", __func__, proj_type.c_str()));
@@ -1719,14 +1901,21 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
    // measure mem requirement and allocate
    {
        new_clip->buf_compute_meta.resize(GGML_DEFAULT_GRAPH_SIZE * ggml_tensor_overhead() + ggml_graph_overhead());
-        new_clip->compute_alloc = ggml_gallocr_new(ggml_backend_get_default_buffer_type(new_clip->backend));
        clip_image_f32_batch batch;
        batch.size = 1;
        batch.data = nullptr;
        ggml_cgraph * gf = clip_image_build_graph(new_clip, &batch, nullptr, false);
-        ggml_gallocr_reserve(new_clip->compute_alloc, gf);
-        size_t compute_memory_buffer_size = ggml_gallocr_get_buffer_size(new_clip->compute_alloc, 0);
-        LOG_INF("%s: compute allocated memory: %.2f MB\n", __func__, compute_memory_buffer_size /1024.0/1024.0);
+        ggml_backend_sched_reserve(new_clip->sched.get(), gf);
+        for (size_t i = 0; i < new_clip->backend_ptrs.size(); ++i) {
+            ggml_backend_t backend = new_clip->backend_ptrs[i];
+            ggml_backend_buffer_type_t buft = new_clip->backend_buft[i];
+            size_t size = ggml_backend_sched_get_buffer_size(new_clip->sched.get(), backend);
+            if (size > 1) {
+                LOG_INF("%s: %10s compute buffer size = %8.2f MiB\n", __func__,
+                        ggml_backend_buft_name(buft),
+                        size / 1024.0 / 1024.0);
+            }
+        }
    }

    return new_clip;
@@ -2218,7 +2407,7 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, cli
        return true;
    }

-    if (ctx->has_glm_projector) {
+    if (ctx->has_glm_projector || ctx->proj_type == PROJECTOR_TYPE_GEMMA3) {
        res_imgs->size = 1;
        res_imgs->data = new clip_image_f32[res_imgs->size];
        clip_image_u8 resized_image;
@@ -2407,12 +2596,6 @@ ggml_tensor * clip_get_newline_tensor(const struct clip_ctx * ctx) {
 }

 void clip_free(clip_ctx * ctx) {
-    ggml_free(ctx->ctx_data);
-    gguf_free(ctx->ctx_gguf);
-
-    ggml_backend_buffer_free(ctx->params_buffer);
-    ggml_backend_free(ctx->backend);
-    ggml_gallocr_free(ctx->compute_alloc);
    delete ctx;
 }

@@ -2608,8 +2791,9 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
    }

    // build the inference graph
+    ggml_backend_sched_reset(ctx->sched.get());
    ggml_cgraph * gf = clip_image_build_graph(ctx, imgs, ctx->load_image_size, true);
-    ggml_gallocr_alloc_graph(ctx->compute_alloc, gf);
+    ggml_backend_sched_alloc_graph(ctx->sched.get(), gf);

    // set inputs
    const auto & model = ctx->vision_model;
@@ -2748,6 +2932,9 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
            ggml_backend_tensor_set(positions, positions_data, 0, ggml_nbytes(positions));
            free(positions_data);
        }
+        else if (ctx->proj_type == PROJECTOR_TYPE_GEMMA3) {
+            // do nothing
+        }
        else {
            struct ggml_tensor * positions = ggml_graph_get_tensor(gf, "positions");

@@ -2774,11 +2961,13 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
        }
    }

-    if (ggml_backend_is_cpu(ctx->backend)) {
-        ggml_backend_cpu_set_n_threads(ctx->backend, n_threads);
-    }
+    ggml_backend_cpu_set_n_threads(ctx->backend_cpu, n_threads);

-    ggml_backend_graph_compute(ctx->backend, gf);
+    auto status = ggml_backend_sched_graph_compute(ctx->sched.get(), gf);
+    if (status != GGML_STATUS_SUCCESS) {
+        LOG_ERR("%s: ggml_backend_sched_graph_compute failed with error %d\n", __func__, status);
+        return false;
+    }

    // the last node is the embedding tensor
    struct ggml_tensor * embeddings = ggml_graph_node(gf, -1);
@@ -2958,6 +3147,9 @@ int clip_n_mmproj_embd(const struct clip_ctx * ctx) {
    if (ctx->proj_type == PROJECTOR_TYPE_MERGER) {
        return ctx->vision_model.mm_1_b->ne[0];
    }
+    if (ctx->proj_type == PROJECTOR_TYPE_GEMMA3) {
+        return ctx->vision_model.mm_input_proj_w->ne[0];
+    }

    std::string proj_type = PROJECTOR_TYPE_NAMES[ctx->proj_type];
    throw std::runtime_error(format("%s: don't support projector with: %s currently\n", __func__, proj_type.c_str()));
@@ -39,8 +39,15 @@ struct clip_image_f32_batch {
    size_t size;
 };

-CLIP_API struct clip_ctx * clip_model_load    (const char * fname, int verbosity);
-CLIP_API struct clip_ctx * clip_model_load_cpu(const char * fname, int verbosity);
+struct clip_context_params {
+    bool use_gpu;
+    int verbosity;
+};
+
+// deprecated, use clip_init
+CLIP_API struct clip_ctx * clip_model_load(const char * fname, int verbosity);
+
+CLIP_API struct clip_ctx * clip_init(const char * fname, struct clip_context_params ctx_params);

 CLIP_API void clip_free(struct clip_ctx * ctx);

@@ -0,0 +1,341 @@
+#include "arg.h"
+#include "log.h"
+#include "common.h"
+#include "sampling.h"
+#include "clip.h"
+#include "stb_image.h"
+#include "llama.h"
+#include "ggml.h"
+#include "console.h"
+
+#include <vector>
+#include <limits.h>
+#include <inttypes.h>
+
+#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__))
+#include <signal.h>
+#include <unistd.h>
+#elif defined (_WIN32)
+#define WIN32_LEAN_AND_MEAN
+#ifndef NOMINMAX
+#define NOMINMAX
+#endif
+#include <windows.h>
+#include <signal.h>
+#endif
+
+static bool g_is_generating = false;
+
+/**
+ * Please note that this is NOT a production-ready stuff.
+ * It is a playground for trying Gemma 3 vision capabilities.
+ * For contributors: please keep this code simple and easy to understand.
+ */
+
+static void show_additional_info(int /*argc*/, char ** argv) {
+    LOG(
+        "Experimental CLI for using Gemma 3 vision model\n\n"
+        "Usage: %s [options] -m <model> --mmproj <mmproj> --image <image> -p <prompt>\n\n"
+        "  -m and --mmproj are required\n"
+        "  --image and -p are optional, if NOT provided, the CLI will run in chat mode\n",
+        argv[0]
+    );
+}
+
+#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) || defined (_WIN32)
+static void sigint_handler(int signo) {
+    if (signo == SIGINT) {
+        if (g_is_generating) {
+            g_is_generating = false;
+        } else {
+            console::cleanup();
+            LOG("\nInterrupted by user\n");
+            _exit(130);
+        }
+    }
+}
+#endif
+
+struct gemma3_context {
+    struct clip_ctx    * ctx_clip = NULL;
+    common_init_result   llama_init;
+
+    llama_model       * model;
+    llama_context     * lctx;
+    const llama_vocab * vocab;
+    llama_batch         batch;
+
+    int n_threads    = 1;
+    llama_pos n_past = 0;
+
+    gemma3_context(common_params & params) : llama_init(common_init_from_params(params)) {
+        model = llama_init.model.get();
+        lctx = llama_init.context.get();
+        vocab = llama_model_get_vocab(model);
+        n_threads = params.cpuparams.n_threads;
+        batch = llama_batch_init(params.n_batch, 0, 1);
+        init_clip_model(params);
+    }
+
+    void init_clip_model(common_params & params) {
+        const char * clip_path = params.mmproj.c_str();
+        ctx_clip = clip_model_load(clip_path, params.verbosity > 1);
+    }
+
+    ~gemma3_context() {
+        clip_free(ctx_clip);
+    }
+};
+
+struct decode_embd_batch {
+    std::vector<llama_pos>      pos;
+    std::vector<int32_t>        n_seq_id;
+    std::vector<llama_seq_id>   seq_id_0;
+    std::vector<llama_seq_id *> seq_ids;
+    std::vector<int8_t>         logits;
+    llama_batch batch;
+    decode_embd_batch(float * embd, int32_t n_tokens, llama_pos pos_0, llama_seq_id seq_id) {
+        pos     .resize(n_tokens);
+        n_seq_id.resize(n_tokens);
+        seq_ids .resize(n_tokens + 1);
+        logits  .resize(n_tokens);
+        seq_id_0.resize(1);
+        seq_id_0[0] = seq_id;
+        seq_ids [n_tokens] = nullptr;
+        batch = {
+            /*n_tokens       =*/ n_tokens,
+            /*tokens         =*/ nullptr,
+            /*embd           =*/ embd,
+            /*pos            =*/ pos.data(),
+            /*n_seq_id       =*/ n_seq_id.data(),
+            /*seq_id         =*/ seq_ids.data(),
+            /*logits         =*/ logits.data(),
+        };
+        for (int i = 0; i < n_tokens; i++) {
+            batch.pos     [i] = pos_0 + i;
+            batch.n_seq_id[i] = 1;
+            batch.seq_id  [i] = seq_id_0.data();
+            batch.logits  [i] = false;
+        }
+    }
+};
+
+static int eval_text(gemma3_context & ctx, std::string input, bool logits_last = false) {
+    llama_tokens tokens = common_tokenize(ctx.lctx, input, false, true);
+    common_batch_clear(ctx.batch);
+    for (llama_token & t : tokens) {
+        common_batch_add(ctx.batch, t, ctx.n_past++, {0}, false);
+    }
+    if (logits_last) {
+        ctx.batch.logits[ctx.batch.n_tokens - 1] = true;
+    }
+    // LOG("eval_text (n_tokens = %d): %s\n", (int)tokens.size(), input.c_str());
+    if (llama_decode(ctx.lctx, ctx.batch)) {
+        LOG_ERR("Failed to decode text\n");
+        return 1;
+    }
+    return 0;
+}
+
+static int eval_image(gemma3_context & ctx, std::string & fname) {
+    std::vector<float> image_embd_v;
+    int n_embd = llama_model_n_embd(ctx.model);
+    int n_tokens = 256;
+    image_embd_v.resize(n_tokens * n_embd);
+
+    bool ok;
+    struct clip_image_u8 * img_u8 = clip_image_u8_init();
+    ok = clip_image_load_from_file(fname.c_str(), img_u8);
+    if (!ok) {
+        LOG_ERR("Unable to load image %s\n", fname.c_str());
+        clip_image_u8_free(img_u8);
+        return 2; // non-fatal error
+    }
+
+    clip_image_f32_batch batch_f32;
+    ok = clip_image_preprocess(ctx.ctx_clip, img_u8, &batch_f32);
+    if (!ok) {
+        LOG_ERR("Unable to preprocess image\n");
+        clip_image_f32_batch_free(&batch_f32);
+        clip_image_u8_free(img_u8);
+        return 1;
+    }
+
+    int64_t t0 = ggml_time_ms();
+    LOG("Encoding image %s\n", fname.c_str());
+    ok = clip_image_batch_encode(ctx.ctx_clip, ctx.n_threads, &batch_f32, image_embd_v.data());
+    if (!ok) {
+        LOG_ERR("Unable to encode image\n");
+        clip_image_f32_batch_free(&batch_f32);
+        clip_image_u8_free(img_u8);
+        return 1;
+    }
+    LOG("Image encoded in %" PRId64 " ms\n", ggml_time_ms() - t0);
+
+    clip_image_f32_batch_free(&batch_f32);
+    clip_image_u8_free(img_u8);
+
+    // decode image embeddings
+    int64_t t1 = ggml_time_ms();
+    eval_text(ctx, "<start_of_image>");
+    llama_set_causal_attn(ctx.lctx, false);
+    decode_embd_batch batch_img(image_embd_v.data(), n_tokens, ctx.n_past, 0);
+    if (llama_decode(ctx.lctx, batch_img.batch)) {
+        LOG_ERR("failed to decode image\n");
+        return 1;
+    }
+    ctx.n_past += n_tokens;
+    llama_set_causal_attn(ctx.lctx, true);
+    eval_text(ctx, "<end_of_image>");
+    LOG("Image decoded in %" PRId64 " ms\n", ggml_time_ms() - t1);
+    return 0;
+}
+
+static int generate_response(gemma3_context & ctx, common_sampler * smpl, int n_predict) {
+    for (int i = 0; i < n_predict; i++) {
+        if (i > n_predict || !g_is_generating) {
+            printf("\n");
+            break;
+        }
+
+        llama_token token_id = common_sampler_sample(smpl, ctx.lctx, -1);
+        common_sampler_accept(smpl, token_id, true);
+
+        if (llama_vocab_is_eog(ctx.vocab, token_id)) {
+            printf("\n");
+            break; // end of generation
+        }
+
+        printf("%s", common_token_to_piece(ctx.lctx, token_id).c_str());
+        fflush(stdout);
+
+        // eval the token
+        common_batch_clear(ctx.batch);
+        common_batch_add(ctx.batch, token_id, ctx.n_past++, {0}, true);
+        if (llama_decode(ctx.lctx, ctx.batch)) {
+            LOG_ERR("failed to decode token\n");
+            return 1;
+        }
+    }
+    return 0;
+}
+
+int main(int argc, char ** argv) {
+    ggml_time_init();
+
+    common_params params;
+    params.sampling.temp = 0.2; // lower temp by default for better quality
+
+    if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_LLAVA, show_additional_info)) {
+        return 1;
+    }
+
+    common_init();
+
+    if (params.mmproj.empty()) {
+        show_additional_info(argc, argv);
+        return 1;
+    }
+
+    gemma3_context ctx(params);
+    printf("%s: %s\n", __func__, params.model.c_str());
+
+    bool is_single_turn = !params.prompt.empty() && !params.image.empty();
+
+    struct common_sampler * smpl = common_sampler_init(ctx.model, params.sampling);
+    int n_predict = params.n_predict < 0 ? INT_MAX : params.n_predict;
+
+    // ctrl+C handling
+    {
+#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__))
+        struct sigaction sigint_action;
+        sigint_action.sa_handler = sigint_handler;
+        sigemptyset (&sigint_action.sa_mask);
+        sigint_action.sa_flags = 0;
+        sigaction(SIGINT, &sigint_action, NULL);
+#elif defined (_WIN32)
+        auto console_ctrl_handler = +[](DWORD ctrl_type) -> BOOL {
+            return (ctrl_type == CTRL_C_EVENT) ? (sigint_handler(SIGINT), true) : false;
+        };
+        SetConsoleCtrlHandler(reinterpret_cast<PHANDLER_ROUTINE>(console_ctrl_handler), true);
+#endif
+    }
+
+    if (eval_text(ctx, "<bos>")) {
+        return 1;
+    }
+
+    if (is_single_turn) {
+        g_is_generating = true;
+        if (eval_text(ctx, "<start_of_turn>user\n")) {
+            return 1;
+        }
+        for (auto & fname : params.image) {
+            if (eval_image(ctx, fname)) {
+                return 1;
+            }
+        }
+        if (eval_text(ctx, params.prompt + "<end_of_turn><start_of_turn>model\n", true)) {
+            return 1;
+        }
+        if (generate_response(ctx, smpl, n_predict)) {
+            return 1;
+        }
+
+    } else {
+        LOG("\n Running in chat mode, available commands:");
+        LOG("\n   /image <path>    load an image");
+        LOG("\n   /clear           clear the chat history");
+        LOG("\n   /quit or /exit   exit the program");
+        LOG("\n");
+
+        if (eval_text(ctx, "<start_of_turn>user\n")) {
+            return 1;
+        }
+
+        while (true) {
+            g_is_generating = false;
+            LOG("\n> ");
+            console::set_display(console::user_input);
+            std::string line;
+            console::readline(line, false);
+            console::set_display(console::reset);
+            line = string_strip(line);
+            if (line.empty()) {
+                continue;
+            }
+            if (line == "/quit" || line == "/exit") {
+                break;
+            }
+            if (line == "/clear") {
+                ctx.n_past = 0;
+                llama_kv_self_seq_rm(ctx.lctx, 0, 1, -1); // keep BOS
+                LOG("Chat history cleared\n\n");
+                continue;
+            }
+            g_is_generating = true;
+            if (line.find("/image") == 0) {
+                std::string image = line.substr(7);
+                int res = eval_image(ctx, image);
+                if (res == 2) {
+                    continue; // image not found
+                }
+                if (res) {
+                    return 1;
+                }
+                continue;
+            }
+            if (eval_text(ctx, line + "<end_of_turn><start_of_turn>model\n", true)) {
+                return 1;
+            }
+            if (generate_response(ctx, smpl, n_predict)) {
+                return 1;
+            }
+            if (eval_text(ctx, "<end_of_turn><start_of_turn>user\n")) {
+                return 1;
+            }
+        }
+    }
+
+    return 0;
+}
@@ -0,0 +1,307 @@
+import gguf
+import argparse
+import logging
+import sys
+import torch
+import json
+import os
+import numpy as np
+from typing import cast, ContextManager, Any, Iterator
+from pathlib import Path
+from torch import Tensor
+
+logger = logging.getLogger("gemma3-mmproj")
+
+
+# (copied from convert_hf_to_gguf.py)
+# tree of lazy tensors
+class LazyTorchTensor(gguf.LazyBase):
+    _tensor_type = torch.Tensor
+    # to keep the type-checker happy
+    dtype: torch.dtype
+    shape: torch.Size
+
+    # only used when converting a torch.Tensor to a np.ndarray
+    _dtype_map: dict[torch.dtype, type] = {
+        torch.float16: np.float16,
+        torch.float32: np.float32,
+    }
+
+    # used for safetensors slices
+    # ref: https://github.com/huggingface/safetensors/blob/079781fd0dc455ba0fe851e2b4507c33d0c0d407/bindings/python/src/lib.rs#L1046
+    # TODO: uncomment U64, U32, and U16, ref: https://github.com/pytorch/pytorch/issues/58734
+    _dtype_str_map: dict[str, torch.dtype] = {
+        "F64": torch.float64,
+        "F32": torch.float32,
+        "BF16": torch.bfloat16,
+        "F16": torch.float16,
+        # "U64": torch.uint64,
+        "I64": torch.int64,
+        # "U32": torch.uint32,
+        "I32": torch.int32,
+        # "U16": torch.uint16,
+        "I16": torch.int16,
+        "U8": torch.uint8,
+        "I8": torch.int8,
+        "BOOL": torch.bool,
+        "F8_E4M3": torch.float8_e4m3fn,
+        "F8_E5M2": torch.float8_e5m2,
+    }
+
+    def numpy(self) -> gguf.LazyNumpyTensor:
+        dtype = self._dtype_map[self.dtype]
+        return gguf.LazyNumpyTensor(
+            meta=gguf.LazyNumpyTensor.meta_with_dtype_and_shape(dtype, self.shape),
+            args=(self,),
+            func=(lambda s: s.numpy())
+        )
+
+    @classmethod
+    def meta_with_dtype_and_shape(cls, dtype: torch.dtype, shape: tuple[int, ...]) -> Tensor:
+        return torch.empty(size=shape, dtype=dtype, device="meta")
+
+    @classmethod
+    def from_safetensors_slice(cls, st_slice: Any) -> Tensor:
+        dtype = cls._dtype_str_map[st_slice.get_dtype()]
+        shape: tuple[int, ...] = tuple(st_slice.get_shape())
+        lazy = cls(meta=cls.meta_with_dtype_and_shape(dtype, shape), args=(st_slice,), func=lambda s: s[:])
+        return cast(torch.Tensor, lazy)
+
+    @classmethod
+    def __torch_function__(cls, func, types, args=(), kwargs=None):
+        del types  # unused
+
+        if kwargs is None:
+            kwargs = {}
+
+        if func is torch.Tensor.numpy:
+            return args[0].numpy()
+
+        return cls._wrap_fn(func)(*args, **kwargs)
+
+
+class Gemma3VisionTower:
+    hparams: dict
+    gguf_writer: gguf.GGUFWriter
+    fname_out: Path
+    ftype: gguf.LlamaFileType
+
+    @staticmethod
+    def load_hparams(dir_model: Path):
+        with open(dir_model / "config.json", "r", encoding="utf-8") as f:
+            return json.load(f)
+
+    @staticmethod
+    def get_model_part_names(dir_model: Path, prefix: str, suffix: str) -> list[str]:
+        part_names: list[str] = []
+        for filename in os.listdir(dir_model):
+            if filename.startswith(prefix) and filename.endswith(suffix):
+                part_names.append(filename)
+        part_names.sort()
+        return part_names
+
+    def __init__(self,
+                 dir_model: Path,
+                 fname_out: Path,
+                 ftype: gguf.LlamaFileType,
+                 is_big_endian: bool,):
+        hparams = Gemma3VisionTower.load_hparams(dir_model)
+        self.hparams = hparams
+        self.fname_out = fname_out
+        self.ftype = ftype
+        endianess = gguf.GGUFEndian.BIG if is_big_endian else gguf.GGUFEndian.LITTLE
+        self.gguf_writer = gguf.GGUFWriter(path=None, arch="clip", endianess=endianess)
+
+        text_config = hparams["text_config"]
+        vision_config = hparams["vision_config"]
+
+        assert hparams["architectures"][0] == "Gemma3ForConditionalGeneration"
+        assert text_config is not None
+        assert vision_config is not None
+
+        self.gguf_writer.add_string ("clip.projector_type",              "gemma3")
+        self.gguf_writer.add_bool   ("clip.has_text_encoder",            False)
+        self.gguf_writer.add_bool   ("clip.has_vision_encoder",          True)
+        self.gguf_writer.add_bool   ("clip.has_llava_projector",         False) # legacy
+        self.gguf_writer.add_uint32 ("clip.vision.image_size",           vision_config["image_size"])
+        self.gguf_writer.add_uint32 ("clip.vision.patch_size",           vision_config["patch_size"])
+        self.gguf_writer.add_uint32 ("clip.vision.embedding_length",     vision_config["hidden_size"])
+        self.gguf_writer.add_uint32 ("clip.vision.feed_forward_length",  vision_config["intermediate_size"])
+        self.gguf_writer.add_uint32 ("clip.vision.projection_dim",       text_config["hidden_size"])
+        self.gguf_writer.add_uint32 ("clip.vision.block_count",          vision_config["num_hidden_layers"])
+        self.gguf_writer.add_uint32 ("clip.vision.attention.head_count", vision_config["num_attention_heads"])
+        self.gguf_writer.add_float32("clip.vision.attention.layer_norm_epsilon", vision_config.get("layer_norm_eps", 1e-6))
+        # default values taken from HF tranformers code
+        self.gguf_writer.add_array  ("clip.vision.image_mean", [0.5, 0.5, 0.5])
+        self.gguf_writer.add_array  ("clip.vision.image_std",  [0.5, 0.5, 0.5])
+        self.gguf_writer.add_bool   ("clip.use_gelu", True)
+
+        # load tensors
+        for name, data_torch in self.get_tensors(dir_model):
+            # convert any unsupported data types to float32
+            if data_torch.dtype not in (torch.float16, torch.float32):
+                data_torch = data_torch.to(torch.float32)
+            self.add_tensor(name, data_torch)
+
+    def get_tensors(self, dir_model: Path) -> Iterator[tuple[str, Tensor]]:
+        part_names = Gemma3VisionTower.get_model_part_names(dir_model, "model", ".safetensors")
+        tensor_names_from_parts: set[str] = set()
+        for part_name in part_names:
+            logger.info(f"gguf: loading model part '{part_name}'")
+            from safetensors import safe_open
+            ctx = cast(ContextManager[Any], safe_open(dir_model / part_name, framework="pt", device="cpu"))
+            with ctx as model_part:
+                tensor_names_from_parts.update(model_part.keys())
+
+                for name in model_part.keys():
+                    data = model_part.get_slice(name)
+                    data = LazyTorchTensor.from_safetensors_slice(data)
+                    yield name, data
+
+    def add_tensor(self, name: str, data_torch: Tensor):
+        is_1d = len(data_torch.shape) == 1
+        is_embd = ".embeddings." in name
+        old_dtype = data_torch.dtype
+        can_quantize = not is_1d and not is_embd
+        data_qtype = gguf.GGMLQuantizationType.F32
+
+        # this is to support old checkpoint
+        # TODO: remove this when we have the final model
+        name = name.replace("vision_model.vision_model.", "vision_tower.vision_model.")
+        name = name.replace("multimodal_projector.", "multi_modal_projector.")
+
+        # filter only vision tensors
+        if not name.startswith("vision_tower.vision_model.") and not name.startswith("multi_modal_projector."):
+            return
+        # prefix
+        name = name.replace("vision_tower.vision_model.encoder.layers.", "v.blk.")
+        name = name.replace("vision_tower.vision_model.", "v.")
+        # projector and input embd
+        name = name.replace(".embeddings.patch_embedding.", ".patch_embd.")
+        name = name.replace(".embeddings.position_embedding.", ".position_embd.")
+        name = name.replace(
+            "multi_modal_projector.mm_input_projection_weight",
+            "mm.input_projection.weight"
+        )
+        name = name.replace(
+            "multi_modal_projector.mm_soft_emb_norm.weight",
+            "mm.soft_emb_norm.weight"
+        )
+        name = name.replace("post_layernorm.", "post_ln.")
+        # each block
+        name = name.replace(".self_attn.k_proj.", ".attn_k.")
+        name = name.replace(".self_attn.v_proj.", ".attn_v.")
+        name = name.replace(".self_attn.q_proj.", ".attn_q.")
+        name = name.replace(".self_attn.out_proj.", ".attn_out.")
+        name = name.replace(".layer_norm1.", ".ln1.")
+        name = name.replace(".layer_norm2.", ".ln2.")
+        name = name.replace(".mlp.fc1.", ".ffn_down.")
+        name = name.replace(".mlp.fc2.", ".ffn_up.")
+
+        if can_quantize:
+            if self.ftype == gguf.LlamaFileType.ALL_F32:
+                data_qtype = gguf.GGMLQuantizationType.F32
+            elif self.ftype == gguf.LlamaFileType.MOSTLY_F16:
+                data_qtype = gguf.GGMLQuantizationType.F16
+            elif self.ftype == gguf.LlamaFileType.MOSTLY_BF16:
+                data_qtype = gguf.GGMLQuantizationType.BF16
+            elif self.ftype == gguf.LlamaFileType.MOSTLY_Q8_0:
+                data_qtype = gguf.GGMLQuantizationType.Q8_0
+            else:
+                raise ValueError(f"Unsupported file type: {self.ftype}")
+
+        # corrent norm value ; only this "soft_emb_norm" need to be corrected as it's part of Gemma projector
+        # the other norm values are part of SigLIP model, and they are already correct
+        # ref code: Gemma3RMSNorm
+        if "soft_emb_norm.weight" in name:
+            logger.info(f"Correcting norm value for '{name}'")
+            data_torch = data_torch + 1
+
+        data = data_torch.numpy()
+
+        try:
+            data = gguf.quants.quantize(data, data_qtype)
+        except Exception as e:
+            logger.error(f"Error quantizing tensor '{name}': {e}, fallback to F16")
+            data_qtype = gguf.GGMLQuantizationType.F16
+            data = gguf.quants.quantize(data, data_qtype)
+
+        # reverse shape to make it similar to the internal ggml dimension order
+        shape_str = f"{{{', '.join(str(n) for n in reversed(data_torch.shape))}}}"
+        logger.info(f"{f'%-32s' % f'{name},'} {old_dtype} --> {data_qtype.name}, shape = {shape_str}")
+
+        self.gguf_writer.add_tensor(name, data, raw_dtype=data_qtype)
+
+    def write(self):
+        self.gguf_writer.write_header_to_file(path=self.fname_out)
+        self.gguf_writer.write_kv_data_to_file()
+        self.gguf_writer.write_tensors_to_file(progress=True)
+        self.gguf_writer.close()
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(
+        description="Convert Gemma 3 vision tower safetensors to GGUF format",)
+    parser.add_argument(
+        "--outfile", type=Path, default="mmproj.gguf",
+        help="path to write to",
+    )
+    parser.add_argument(
+        "--outtype", type=str, choices=["f32", "f16", "bf16", "q8_0"], default="f16",
+        help="output format",
+    )
+    parser.add_argument(
+        "--bigendian", action="store_true",
+        help="model is executed on big endian machine",
+    )
+    parser.add_argument(
+        "model", type=Path,
+        help="directory containing model file",
+        nargs="?",
+    )
+    parser.add_argument(
+        "--verbose", action="store_true",
+        help="increase output verbosity",
+    )
+
+    args = parser.parse_args()
+    if args.model is None:
+        parser.error("the following arguments are required: model")
+    return args
+
+
+def main() -> None:
+    args = parse_args()
+
+    if args.verbose:
+        logging.basicConfig(level=logging.DEBUG)
+    else:
+        logging.basicConfig(level=logging.INFO)
+
+    dir_model = args.model
+
+    if not dir_model.is_dir():
+        logger.error(f'Error: {args.model} is not a directory')
+        sys.exit(1)
+
+    ftype_map: dict[str, gguf.LlamaFileType] = {
+        "f32": gguf.LlamaFileType.ALL_F32,
+        "f16": gguf.LlamaFileType.MOSTLY_F16,
+        "bf16": gguf.LlamaFileType.MOSTLY_BF16,
+        "q8_0": gguf.LlamaFileType.MOSTLY_Q8_0,
+    }
+
+    logger.info(f"Loading model: {dir_model.name}")
+
+    with torch.inference_mode():
+        gemma3_vision_tower = Gemma3VisionTower(
+            dir_model=dir_model,
+            fname_out=args.outfile,
+            ftype=ftype_map[args.outtype],
+            is_big_endian=args.bigendian,
+        )
+        gemma3_vision_tower.write()
+
+
+if __name__ == '__main__':
+    main()
+
@@ -86,7 +86,11 @@ static struct clip_ctx * clip_init_context(common_params * params) {
    if (prompt.empty()) {
        prompt = "describe the image in detail.";
    }
-    auto * ctx_clip = clip_model_load(clip_path, /*verbosity=*/ 1);
+    struct clip_context_params clip_params = {
+        /* use_gpu */   params->n_gpu_layers != 0,
+        /* verbosity */ params->verbosity,
+    };
+    auto * ctx_clip = clip_init(clip_path, clip_params);
    return ctx_clip;
 }

@@ -148,19 +152,34 @@ static void process_image(struct llava_context * ctx_llava, struct llava_image_e
    process_eval_image_embed(ctx_llava, embeds, params->n_batch, &n_past, idx++);
    eval_string(ctx_llava->ctx_llama, std::string("</image>").c_str(), params->n_batch, &n_past, false);
    if (num_image_embeds > 1) {
-        size_t num_image_embeds_col = clip_uhd_num_image_embeds_col(ctx_llava->ctx_clip);
-        eval_string(ctx_llava->ctx_llama, std::string("<slice>").c_str(), params->n_batch, &n_past, false);
-        for (size_t i = 0; i < (num_image_embeds-1)/num_image_embeds_col; ++i) {
-            for (size_t j = 0; j < num_image_embeds_col; ++j) {
-                eval_string(ctx_llava->ctx_llama, std::string("<image>").c_str(), params->n_batch, &n_past, false);
-                process_eval_image_embed(ctx_llava, embeds, params->n_batch, &n_past, idx++);
-                eval_string(ctx_llava->ctx_llama, std::string("</image>").c_str(), params->n_batch, &n_past, false);
-                if (j == num_image_embeds_col - 1) {
-                    eval_string(ctx_llava->ctx_llama, std::string("\n").c_str(), params->n_batch, &n_past, false);
+        if (has_minicpmv_projector == 2) {
+            size_t num_image_embeds_col = clip_uhd_num_image_embeds_col(ctx_llava->ctx_clip);
+            eval_string(ctx_llava->ctx_llama, std::string("<slice>").c_str(), params->n_batch, &n_past, false);
+            for (size_t i = 0; i < (num_image_embeds-1)/num_image_embeds_col; ++i) {
+                for (size_t j = 0; j < num_image_embeds_col; ++j) {
+                    eval_string(ctx_llava->ctx_llama, std::string("<image>").c_str(), params->n_batch, &n_past, false);
+                    process_eval_image_embed(ctx_llava, embeds, params->n_batch, &n_past, idx++);
+                    eval_string(ctx_llava->ctx_llama, std::string("</image>").c_str(), params->n_batch, &n_past, false);
+                    if (j == num_image_embeds_col - 1) {
+                        eval_string(ctx_llava->ctx_llama, std::string("\n").c_str(), params->n_batch, &n_past, false);
+                    }
+                }
+            }
+            eval_string(ctx_llava->ctx_llama, std::string("</slice>").c_str(), params->n_batch, &n_past, false);
+        }
+        else if (has_minicpmv_projector == 3 || has_minicpmv_projector == 4) {
+            size_t num_image_embeds_col = clip_uhd_num_image_embeds_col(ctx_llava->ctx_clip);
+            for (size_t i = 0; i < (num_image_embeds-1)/num_image_embeds_col; ++i) {
+                for (size_t j = 0; j < num_image_embeds_col; ++j) {
+                    eval_string(ctx_llava->ctx_llama, std::string("<slice>").c_str(), params->n_batch, &n_past, false);
+                    process_eval_image_embed(ctx_llava, embeds, params->n_batch, &n_past, idx++);
+                    eval_string(ctx_llava->ctx_llama, std::string("</slice>").c_str(), params->n_batch, &n_past, false);
+                    if (j == num_image_embeds_col - 1) {
+                        eval_string(ctx_llava->ctx_llama, std::string("\n").c_str(), params->n_batch, &n_past, false);
+                    }
                }
            }
        }
-        eval_string(ctx_llava->ctx_llama, std::string("</slice>").c_str(), params->n_batch, &n_past, false);
    }
    LOG_INF("%s: image token past: %d\n", __func__, n_past);
 }
@@ -597,7 +597,6 @@ elif args.minicpmv_projector is not None:
    fname_middle = "mmproj-"
    has_text_encoder = False
    has_minicpmv_projector = True
-    minicpmv_version = 4
 elif args.vision_only:
    fname_middle = "vision-"
    has_text_encoder = False
@@ -96,7 +96,7 @@ int main(int argc, char ** argv) {
    llama_decode(ctx, llama_batch_get_one(&inp.back(),           1));

    for (int s = 1; s < W + G + 1; ++s) {
-        llama_kv_cache_seq_cp(ctx, 0, s, -1, -1);
+        llama_kv_self_seq_cp(ctx, 0, s, -1, -1);
    }

    const auto t_enc_end = ggml_time_us();
@@ -438,17 +438,17 @@ int main(int argc, char ** argv) {

        // KV cache management
        // if no verification token matched, we simply remove all cells from this batch -> no fragmentation
-        llama_kv_cache_seq_rm(ctx, -1, n_past, -1);
+        llama_kv_self_seq_rm(ctx, -1, n_past, -1);

        if (seq_id_best != 0) {
            // if a verification token matched, we keep the best sequence and remove the rest
            // this leads to some KV cache fragmentation
-            llama_kv_cache_seq_keep(ctx, seq_id_best);
-            llama_kv_cache_seq_cp  (ctx, seq_id_best, 0, -1, -1);
-            llama_kv_cache_seq_rm  (ctx, seq_id_best,    -1, -1);
+            llama_kv_self_seq_keep(ctx, seq_id_best);
+            llama_kv_self_seq_cp  (ctx, seq_id_best, 0, -1, -1);
+            llama_kv_self_seq_rm  (ctx, seq_id_best,    -1, -1);

            for (int s = 1; s < W + G + 1; ++s) {
-                llama_kv_cache_seq_cp(ctx, 0, s, -1, -1);
+                llama_kv_self_seq_cp(ctx, 0, s, -1, -1);
            }
        }
    }
@@ -192,7 +192,7 @@ int main(int argc, char ** argv){

        // KV cache management
        // clean the cache of draft tokens that weren't accepted
-        llama_kv_cache_seq_rm(ctx, 0, n_past, -1);
+        llama_kv_self_seq_rm(ctx, 0, n_past, -1);

        common_batch_clear(batch_tgt);
        common_batch_add(batch_tgt, draft[0], n_past, { 0 }, true);
@@ -354,7 +354,7 @@ int main(int argc, char ** argv) {
        }

        // remove any "future" tokens that we might have inherited from the previous session
-        llama_kv_cache_seq_rm(ctx, -1, n_matching_session_tokens, -1);
+        llama_kv_self_seq_rm(ctx, -1, n_matching_session_tokens, -1);
    }

    LOG_DBG("recalculate the cached logits (check): embd_inp.size() %zu, n_matching_session_tokens %zu, embd_inp.size() %zu, session_tokens.size() %zu\n",
@@ -602,8 +602,8 @@ int main(int argc, char ** argv) {
                    LOG_DBG("context full, swapping: n_past = %d, n_left = %d, n_ctx = %d, n_keep = %d, n_discard = %d\n",
                            n_past, n_left, n_ctx, params.n_keep, n_discard);

-                    llama_kv_cache_seq_rm (ctx, 0, params.n_keep            , params.n_keep + n_discard);
-                    llama_kv_cache_seq_add(ctx, 0, params.n_keep + n_discard, n_past, -n_discard);
+                    llama_kv_self_seq_rm (ctx, 0, params.n_keep            , params.n_keep + n_discard);
+                    llama_kv_self_seq_add(ctx, 0, params.n_keep + n_discard, n_past, -n_discard);

                    n_past -= n_discard;

@@ -626,9 +626,9 @@ int main(int argc, char ** argv) {
                    LOG_DBG("div:   [%6d, %6d] / %6d -> [%6d, %6d]\n", ga_i + ib*bd, ga_i + ib*bd + ga_w, ga_n, (ga_i + ib*bd)/ga_n, (ga_i + ib*bd + ga_w)/ga_n);
                    LOG_DBG("shift: [%6d, %6d] + %6d -> [%6d, %6d]\n", ga_i + ib*bd + ga_w, n_past + ib*bd, dd, ga_i + ib*bd + ga_w + dd, n_past + ib*bd + dd);

-                    llama_kv_cache_seq_add(ctx, 0, ga_i,                n_past,              ib*bd);
-                    llama_kv_cache_seq_div(ctx, 0, ga_i + ib*bd,        ga_i + ib*bd + ga_w, ga_n);
-                    llama_kv_cache_seq_add(ctx, 0, ga_i + ib*bd + ga_w, n_past + ib*bd,      dd);
+                    llama_kv_self_seq_add(ctx, 0, ga_i,                n_past,              ib*bd);
+                    llama_kv_self_seq_div(ctx, 0, ga_i + ib*bd,        ga_i + ib*bd + ga_w, ga_n);
+                    llama_kv_self_seq_add(ctx, 0, ga_i + ib*bd + ga_w, n_past + ib*bd,      dd);

                    n_past -= bd;

@@ -202,7 +202,7 @@ int main(int argc, char ** argv) {

        // assign the system KV cache to all parallel sequences
        for (int32_t i = 1; i <= n_clients; ++i) {
-            llama_kv_cache_seq_cp(ctx, 0, i, -1, -1);
+            llama_kv_self_seq_cp(ctx, 0, i, -1, -1);
        }

        LOG_INF("\n");
@@ -234,9 +234,9 @@ int main(int argc, char ** argv) {
        if (batch.n_tokens == 0) {
            // all sequences have ended - clear the entire KV cache
            for (int i = 1; i <= n_clients; ++i) {
-                llama_kv_cache_seq_rm(ctx, i, -1, -1);
+                llama_kv_self_seq_rm(ctx, i, -1, -1);
                // but keep the system prompt
-                llama_kv_cache_seq_cp(ctx, 0, i, -1, -1);
+                llama_kv_self_seq_cp(ctx, 0, i, -1, -1);
            }

            LOG_INF("%s: clearing the KV cache\n", __func__);
@@ -372,8 +372,8 @@ int main(int argc, char ** argv) {
                    }

                    // delete only the generated part of the sequence, i.e. keep the system prompt in the cache
-                    llama_kv_cache_seq_rm(ctx,    client.id + 1, -1, -1);
-                    llama_kv_cache_seq_cp(ctx, 0, client.id + 1, -1, -1);
+                    llama_kv_self_seq_rm(ctx,    client.id + 1, -1, -1);
+                    llama_kv_self_seq_cp(ctx, 0, client.id + 1, -1, -1);

                    const auto t_main_end = ggml_time_us();

@@ -133,11 +133,11 @@ int main(int argc, char ** argv) {
            const int ib = i/n_batch - 1;
            const int bd = n_batch_grp*(n_grp - 1);

-            llama_kv_cache_seq_add (ctx, 0, n_past - n_batch,         n_past,         ib*bd);
-            llama_kv_cache_seq_div (ctx, 0, n_past - n_batch + ib*bd, n_past + ib*bd, n_grp);
-            llama_kv_cache_update  (ctx);
+            llama_kv_self_seq_add (ctx, 0, n_past - n_batch,         n_past,         ib*bd);
+            llama_kv_self_seq_div (ctx, 0, n_past - n_batch + ib*bd, n_past + ib*bd, n_grp);
+            llama_kv_self_update  (ctx);

-            n_past = llama_kv_cache_seq_pos_max(ctx, 0) + 1;
+            n_past = llama_kv_self_seq_pos_max(ctx, 0) + 1;
        }

        common_batch_clear(batch);
@@ -167,12 +167,12 @@ int main(int argc, char ** argv) {

        LOG_INF("%s: shifting KV cache with %d\n", __func__, n_discard);

-        llama_kv_cache_seq_rm (ctx, 0, n_keep            , n_keep + n_discard);
-        llama_kv_cache_seq_add(ctx, 0, n_keep + n_discard, n_ctx,  -n_discard);
-      //llama_kv_cache_defrag (ctx);
-        llama_kv_cache_update (ctx);
+        llama_kv_self_seq_rm (ctx, 0, n_keep            , n_keep + n_discard);
+        llama_kv_self_seq_add(ctx, 0, n_keep + n_discard, n_ctx,  -n_discard);
+      //llama_kv_self_defrag (ctx);
+        llama_kv_self_update (ctx);

-        n_past = llama_kv_cache_seq_pos_max(ctx, 0) + 1;
+        n_past = llama_kv_self_seq_pos_max(ctx, 0) + 1;

        common_batch_clear(batch);

@@ -198,12 +198,12 @@ int main(int argc, char ** argv) {
        if (n_discard > 0) {
            LOG_INF("%s: shifting KV cache with %d to free space for the answer\n", __func__, n_discard);

-            llama_kv_cache_seq_rm (ctx, 0, n_keep            , n_keep + n_discard);
-            llama_kv_cache_seq_add(ctx, 0, n_keep + n_discard, n_ctx,  -n_discard);
-          //llama_kv_cache_defrag (ctx);
-            llama_kv_cache_update (ctx);
+            llama_kv_self_seq_rm (ctx, 0, n_keep            , n_keep + n_discard);
+            llama_kv_self_seq_add(ctx, 0, n_keep + n_discard, n_ctx,  -n_discard);
+          //llama_kv_self_defrag (ctx);
+            llama_kv_self_update (ctx);

-            n_past = llama_kv_cache_seq_pos_max(ctx, 0) + 1;
+            n_past = llama_kv_self_seq_pos_max(ctx, 0) + 1;
        }
    }

@@ -361,7 +361,7 @@ static results_perplexity perplexity_v2(llama_context * ctx, const common_params
        const auto t_start = std::chrono::high_resolution_clock::now();

        // clear the KV cache
-        llama_kv_cache_clear(ctx);
+        llama_kv_self_clear(ctx);

        llama_batch batch = llama_batch_init(n_batch, 0, 1);

@@ -547,7 +547,7 @@ static results_perplexity perplexity(llama_context * ctx, const common_params &
        const auto t_start = std::chrono::high_resolution_clock::now();

        // clear the KV cache
-        llama_kv_cache_clear(ctx);
+        llama_kv_self_clear(ctx);

        for (int j = 0; j < num_batches; ++j) {
            const int batch_start = start + j * n_batch;
@@ -924,7 +924,7 @@ static void hellaswag_score(llama_context * ctx, const common_params & params) {
            return;
        }

-        llama_kv_cache_clear(ctx);
+        llama_kv_self_clear(ctx);

        // decode all tasks [i0, i1)
        if (!decode_helper(ctx, batch, batch_logits, n_batch, n_vocab)) {
@@ -1203,7 +1203,7 @@ static void winogrande_score(llama_context * ctx, const common_params & params)
            return;
        }

-        llama_kv_cache_clear(ctx);
+        llama_kv_self_clear(ctx);

        // decode all tasks [i0, i1)
        if (!decode_helper(ctx, batch, batch_logits, n_batch, n_vocab)) {
@@ -1575,7 +1575,7 @@ static void multiple_choice_score(llama_context * ctx, const common_params & par
            return;
        }

-        llama_kv_cache_clear(ctx);
+        llama_kv_self_clear(ctx);

        // decode all tasks [i0, i1)
        if (!decode_helper(ctx, batch, batch_logits, n_batch, n_vocab)) {
@@ -1765,7 +1765,7 @@ static void kl_divergence(llama_context * ctx, const common_params & params) {
        }

        // clear the KV cache
-        llama_kv_cache_clear(ctx);
+        llama_kv_self_clear(ctx);

        llama_batch batch = llama_batch_init(n_batch, 0, 1);

@@ -1,6 +1,6 @@
 #include "ggml.h"
 #include "llama.h"
-#include "llama-context.h"
+#include "llama-model.h"
 #include "common.h"

 #include <algorithm>
@@ -328,7 +328,7 @@ int main(int argc, char ** argv) {
        }
    }

-    const auto & tensors = llama_internal_get_tensor_map(ctx);
+    const auto & tensors = llama_internal_get_tensor_map(model);

    // check layer tensors
    int included_layers = 0;
@@ -83,7 +83,7 @@ static void batch_add_seq(llama_batch & batch, const std::vector<int32_t> & toke

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

    // run model
    LOG_INF("%s: n_tokens = %d, n_seq = %d\n", __func__, batch.n_tokens, n_seq);
@@ -79,6 +79,7 @@ class Opt {
        ctx_params           = llama_context_default_params();
        model_params         = llama_model_default_params();
        context_size_default = ctx_params.n_batch;
+        n_threads_default    = ctx_params.n_threads;
        ngl_default          = model_params.n_gpu_layers;
        common_params_sampling sampling;
        temperature_default = sampling.temp;
@@ -104,6 +105,7 @@ class Opt {

        ctx_params.n_batch        = context_size >= 0 ? context_size : context_size_default;
        ctx_params.n_ctx          = ctx_params.n_batch;
+        ctx_params.n_threads = ctx_params.n_threads_batch = n_threads >= 0 ? n_threads : n_threads_default;
        model_params.n_gpu_layers = ngl >= 0 ? ngl : ngl_default;
        temperature               = temperature >= 0 ? temperature : temperature_default;

@@ -116,12 +118,12 @@ class Opt {
    std::string chat_template_file;
    std::string          user;
    bool                 use_jinja   = false;
-    int                  context_size = -1, ngl = -1;
+    int                  context_size = -1, ngl = -1, n_threads = -1;
    float                temperature = -1;
    bool                 verbose     = false;

  private:
-    int   context_size_default = -1, ngl_default = -1;
+    int   context_size_default = -1, ngl_default = -1, n_threads_default = -1;
    float temperature_default = -1;
    bool  help                = false;

@@ -159,53 +161,94 @@ class Opt {
        return 0;
    }

+    int parse_options_with_value(int argc, const char ** argv, int & i, bool & options_parsing) {
+        if (options_parsing && (strcmp(argv[i], "-c") == 0 || strcmp(argv[i], "--context-size") == 0)) {
+            if (handle_option_with_value(argc, argv, i, context_size) == 1) {
+                return 1;
+            }
+        } else if (options_parsing &&
+                   (strcmp(argv[i], "-n") == 0 || strcmp(argv[i], "-ngl") == 0 || strcmp(argv[i], "--ngl") == 0)) {
+            if (handle_option_with_value(argc, argv, i, ngl) == 1) {
+                return 1;
+            }
+        } else if (options_parsing && (strcmp(argv[i], "-t") == 0 || strcmp(argv[i], "--threads") == 0)) {
+            if (handle_option_with_value(argc, argv, i, n_threads) == 1) {
+                return 1;
+            }
+        } else if (options_parsing && strcmp(argv[i], "--temp") == 0) {
+            if (handle_option_with_value(argc, argv, i, temperature) == 1) {
+                return 1;
+            }
+        } else if (options_parsing && strcmp(argv[i], "--chat-template-file") == 0) {
+            if (handle_option_with_value(argc, argv, i, chat_template_file) == 1) {
+                return 1;
+            }
+            use_jinja = true;
+        } else {
+            return 2;
+        }
+
+        return 0;
+    }
+
+    int parse_options(const char ** argv, int & i, bool & options_parsing) {
+        if (options_parsing && (parse_flag(argv, i, "-v", "--verbose") || parse_flag(argv, i, "-v", "--log-verbose"))) {
+            verbose = true;
+        } else if (options_parsing && strcmp(argv[i], "--jinja") == 0) {
+            use_jinja = true;
+        } else if (options_parsing && parse_flag(argv, i, "-h", "--help")) {
+            help = true;
+            return 0;
+        } else if (options_parsing && strcmp(argv[i], "--") == 0) {
+            options_parsing = false;
+        } else {
+            return 2;
+        }
+
+        return 0;
+    }
+
+    int parse_positional_args(const char ** argv, int & i, int & positional_args_i) {
+        if (positional_args_i == 0) {
+            if (!argv[i][0] || argv[i][0] == '-') {
+                return 1;
+            }
+
+            ++positional_args_i;
+            model_ = argv[i];
+        } else if (positional_args_i == 1) {
+            ++positional_args_i;
+            user = argv[i];
+        } else {
+            user += " " + std::string(argv[i]);
+        }
+
+        return 0;
+    }
+
    int parse(int argc, const char ** argv) {
        bool options_parsing   = true;
        for (int i = 1, positional_args_i = 0; i < argc; ++i) {
-            if (options_parsing && (strcmp(argv[i], "-c") == 0 || strcmp(argv[i], "--context-size") == 0)) {
-                if (handle_option_with_value(argc, argv, i, context_size) == 1) {
-                    return 1;
-                }
-            } else if (options_parsing &&
-                       (strcmp(argv[i], "-n") == 0 || strcmp(argv[i], "-ngl") == 0 || strcmp(argv[i], "--ngl") == 0)) {
-                if (handle_option_with_value(argc, argv, i, ngl) == 1) {
-                    return 1;
-                }
-            } else if (options_parsing && strcmp(argv[i], "--temp") == 0) {
-                if (handle_option_with_value(argc, argv, i, temperature) == 1) {
-                    return 1;
-                }
-            } else if (options_parsing &&
-                       (parse_flag(argv, i, "-v", "--verbose") || parse_flag(argv, i, "-v", "--log-verbose"))) {
-                verbose = true;
-            } else if (options_parsing && strcmp(argv[i], "--jinja") == 0) {
-                use_jinja = true;
-            } else if (options_parsing && strcmp(argv[i], "--chat-template-file") == 0){
-                if (handle_option_with_value(argc, argv, i, chat_template_file) == 1) {
-                    return 1;
-                }
-                use_jinja = true;
-            } else if (options_parsing && parse_flag(argv, i, "-h", "--help")) {
-                help = true;
-                return 0;
-            } else if (options_parsing && strcmp(argv[i], "--") == 0) {
-                options_parsing = false;
-            } else if (positional_args_i == 0) {
-                if (!argv[i][0] || argv[i][0] == '-') {
-                    return 1;
-                }
+            int ret = parse_options_with_value(argc, argv, i, options_parsing);
+            if (ret == 0) {
+                continue;
+            } else if (ret == 1) {
+                return ret;
+            }

-                ++positional_args_i;
-                model_ = argv[i];
-            } else if (positional_args_i == 1) {
-                ++positional_args_i;
-                user = argv[i];
-            } else {
-                user += " " + std::string(argv[i]);
+            ret = parse_options(argv, i, options_parsing);
+            if (ret == 0) {
+                continue;
+            } else if (ret == 1) {
+                return ret;
+            }
+
+            if (parse_positional_args(argv, i, positional_args_i)) {
+                return 1;
            }
        }

-        if (model_.empty()){
+        if (model_.empty()) {
            return 1;
        }

@@ -232,6 +275,8 @@ class Opt {
            "      Number of GPU layers (default: %d)\n"
            "  --temp <value>\n"
            "      Temperature (default: %.1f)\n"
+            "  -t, --threads <value>\n"
+            "      Number of threads to use during generation (default: %d)\n"
            "  -v, --verbose, --log-verbose\n"
            "      Set verbosity level to infinity (i.e. log all messages, useful for debugging)\n"
            "  -h, --help\n"
@@ -260,7 +305,7 @@ class Opt {
            "  llama-run file://some-file3.gguf\n"
            "  llama-run --ngl 999 some-file4.gguf\n"
            "  llama-run --ngl 999 some-file5.gguf Hello World\n",
-            context_size_default, ngl_default, temperature_default);
+            context_size_default, ngl_default, temperature_default, n_threads_default);
    }
 };

@@ -891,7 +936,7 @@ static int apply_chat_template(const struct common_chat_templates * tmpls, Llama
 // Function to tokenize the prompt
 static int tokenize_prompt(const llama_vocab * vocab, const std::string & prompt,
                           std::vector<llama_token> & prompt_tokens, const LlamaData & llama_data) {
-    const bool is_first = llama_get_kv_cache_used_cells(llama_data.context.get()) == 0;
+    const bool is_first = llama_kv_self_used_cells(llama_data.context.get()) == 0;

    const int n_prompt_tokens = -llama_tokenize(vocab, prompt.c_str(), prompt.size(), NULL, 0, is_first, true);
    prompt_tokens.resize(n_prompt_tokens);
@@ -907,7 +952,7 @@ static int tokenize_prompt(const llama_vocab * vocab, const std::string & prompt
 // Check if we have enough space in the context to evaluate this batch
 static int check_context_size(const llama_context_ptr & ctx, const llama_batch & batch) {
    const int n_ctx      = llama_n_ctx(ctx.get());
-    const int n_ctx_used = llama_get_kv_cache_used_cells(ctx.get());
+    const int n_ctx_used = llama_kv_self_used_cells(ctx.get());
    if (n_ctx_used + batch.n_tokens > n_ctx) {
        printf(LOG_COL_DEFAULT "\n");
        printe("context size exceeded\n");
@@ -15,7 +15,7 @@ int main(int argc, char ** argv) {
        return 1;
    }

-    print_build_info();
+    common_init();

    if (params.n_predict < 0) {
        params.n_predict = 16;
@@ -196,7 +196,7 @@ int main(int argc, char ** argv) {
        fprintf(stderr, "%s : seq 0 copied, %zd bytes\n", __func__, ncopy);

        // erase whole kv
-        llama_kv_cache_clear(ctx3);
+        llama_kv_self_clear(ctx3);
        fprintf(stderr, "%s : kv cache cleared\n", __func__);

        // restore kv into seq 1
@@ -384,8 +384,9 @@ struct server_task {
                            SRV_DBG("Grammar trigger token: %d (`%s`)\n", token, word.c_str());
                            common_grammar_trigger trigger;
                            trigger.type = COMMON_GRAMMAR_TRIGGER_TYPE_TOKEN;
-                            trigger.value = (llama_token) token;
-                            params.sampling.grammar_triggers.push_back(trigger);
+                            trigger.value = word;
+                            trigger.token = token;
+                            params.sampling.grammar_triggers.push_back(std::move(trigger));
                        } else {
                            SRV_DBG("Grammar trigger word: `%s`\n", word.c_str());
                            params.sampling.grammar_triggers.push_back({COMMON_GRAMMAR_TRIGGER_TYPE_WORD, word});
@@ -750,7 +751,10 @@ struct server_task_result_cmpl_final : server_task_result {
                        {"name", tc.name},
                        {"arguments", tc.arguments},
                    }},
-                    {"id", tc.id},
+                    // Some templates generate and require an id (sometimes in a very specific format, e.g. Mistral Nemo).
+                    // We only generate a random id for the ones that don't generate one by themselves
+                    // (they also won't get to see it as their template likely doesn't use it, so it's all for the client)
+                    {"id", tc.id.empty() ? gen_tool_call_id() : tc.id},
                });
            }
            message["tool_calls"] = tool_calls;
@@ -1312,7 +1316,7 @@ struct server_slot {
        return task_type == SERVER_TASK_TYPE_EMBEDDING || task_type == SERVER_TASK_TYPE_RERANK;
    }

-    bool can_batch_with(server_slot & other_slot) {
+    bool can_batch_with(server_slot & other_slot) const {
        return is_non_causal() == other_slot.is_non_causal()
            && are_lora_equal(lora, other_slot.lora);
    }
@@ -1900,6 +1904,7 @@ struct server_context {
        try {
            common_chat_format_example(chat_templates.get(), params.use_jinja);
        } catch (const std::exception & e) {
+            SRV_WRN("%s: Chat template parsing error: %s\n", __func__, e.what());
            SRV_WRN("%s: The chat template that comes with this model is not yet supported, falling back to chatml. This may cause the model to output suboptimal responses\n", __func__);
            chat_templates = common_chat_templates_init(model, "chatml");
        }
@@ -2035,6 +2040,18 @@ struct server_context {
        return ret;
    }

+    bool can_be_detokenized(const struct llama_context * ctx, const std::vector<llama_token> & tokens) {
+        const llama_model * model = llama_get_model(ctx);
+        const llama_vocab * vocab = llama_model_get_vocab(model);
+        const int32_t n_vocab = llama_vocab_n_tokens(vocab);
+        for (const auto & token : tokens) {
+            if (token < 0 || token >= n_vocab) {
+                return false;
+            }
+        }
+        return true;
+    }
+
    bool launch_slot_with_task(server_slot & slot, const server_task & task) {
        slot.reset();
        slot.id_task       = task.id;
@@ -2049,6 +2066,11 @@ struct server_context {
            slot.lora = task.params.lora;
        }

+        bool can_detokenize = can_be_detokenized(ctx, slot.prompt_tokens);
+        if (!can_detokenize) {
+            send_error(task, "Prompt contains invalid tokens", ERROR_TYPE_INVALID_REQUEST);
+            return false;
+        }
        SLT_DBG(slot, "launching slot : %s\n", safe_json_to_str(slot.to_json()).c_str());

        if (slot.n_predict > 0 && slot.params.n_predict > slot.n_predict) {
@@ -2091,7 +2113,7 @@ struct server_context {
        SRV_DBG("%s", "clearing KV cache\n");

        // clear the entire KV cache
-        llama_kv_cache_clear(ctx);
+        llama_kv_self_clear(ctx);
        clean_kv_cache = false;
    }

@@ -2156,14 +2178,6 @@ struct server_context {
        }

        if (slot.has_new_line) {
-            // if we have already seen a new line, we stop after a certain time limit
-            if (slot.params.t_max_predict_ms > 0 && (ggml_time_us() - slot.t_start_generation > 1000.0f*slot.params.t_max_predict_ms)) {
-                slot.stop           = STOP_TYPE_LIMIT;
-                slot.has_next_token = false;
-
-                SLT_DBG(slot, "stopped by time limit, n_decoded = %d, t_max_predict_ms = %d ms\n", slot.n_decoded, (int) slot.params.t_max_predict_ms);
-            }
-
            // require that each new line has a whitespace prefix (i.e. indentation) of at least slot.params.n_indent
            if (slot.params.n_indent > 0) {
                // check the current indentation
@@ -2202,6 +2216,14 @@ struct server_context {
        // check if there is a new line in the generated text
        if (result.text_to_send.find('\n') != std::string::npos) {
            slot.has_new_line = true;
+
+            // if we have seen a new line, we stop after a certain time limit, but only upon another new line
+            if (slot.params.t_max_predict_ms > 0 && (ggml_time_us() - slot.t_start_generation > 1000.0f*slot.params.t_max_predict_ms)) {
+                slot.stop           = STOP_TYPE_LIMIT;
+                slot.has_next_token = false;
+
+                SLT_DBG(slot, "stopped by time limit, n_decoded = %d, t_max_predict_ms = %d ms\n", slot.n_decoded, (int) slot.params.t_max_predict_ms);
+            }
        }

        // if context shift is disabled, we stop when it reaches the context limit
@@ -2633,8 +2655,8 @@ struct server_context {
                    res->n_tasks_deferred    = queue_tasks.queue_tasks_deferred.size();
                    res->t_start             = metrics.t_start;

-                    res->kv_cache_tokens_count = llama_get_kv_cache_token_count(ctx);
-                    res->kv_cache_used_cells   = llama_get_kv_cache_used_cells(ctx);
+                    res->kv_cache_tokens_count = llama_kv_self_n_tokens(ctx);
+                    res->kv_cache_used_cells   = llama_kv_self_used_cells(ctx);

                    res->n_prompt_tokens_processed_total = metrics.n_prompt_tokens_processed_total;
                    res->t_prompt_processing_total       = metrics.t_prompt_processing_total;
@@ -2750,7 +2772,7 @@ struct server_context {

                    // Erase token cache
                    const size_t n_erased = slot->cache_tokens.size();
-                    llama_kv_cache_seq_rm(ctx, slot->id, -1, -1);
+                    llama_kv_self_seq_rm(ctx, slot->id, -1, -1);
                    slot->cache_tokens.clear();

                    auto res = std::make_unique<server_task_result_slot_erase>();
@@ -2818,8 +2840,8 @@ struct server_context {

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

-                llama_kv_cache_seq_rm (ctx, slot.id, n_keep            , n_keep + n_discard);
-                llama_kv_cache_seq_add(ctx, slot.id, n_keep + n_discard, slot.n_past,        -n_discard);
+                llama_kv_self_seq_rm (ctx, slot.id, n_keep            , n_keep + n_discard);
+                llama_kv_self_seq_add(ctx, slot.id, n_keep + n_discard, slot.n_past,        -n_discard);

                if (slot.params.cache_prompt) {
                    for (size_t i = n_keep + n_discard; i < slot.cache_tokens.size(); i++) {
@@ -3010,8 +3032,8 @@ struct server_context {

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

-                                            llama_kv_cache_seq_rm (ctx, slot.id, head_p, head_c);
-                                            llama_kv_cache_seq_add(ctx, slot.id, head_c, head_c + n_match, kv_shift);
+                                            llama_kv_self_seq_rm (ctx, slot.id, head_p, head_c);
+                                            llama_kv_self_seq_add(ctx, slot.id, head_c, head_c + n_match, kv_shift);

                                            for (size_t i = 0; i < n_match; i++) {
                                                slot.cache_tokens[head_p + i] = slot.cache_tokens[head_c + i];
@@ -3049,9 +3071,9 @@ struct server_context {
                    }

                    // keep only the common part
-                    if (!llama_kv_cache_seq_rm(ctx, slot.id, slot.n_past, -1)) {
+                    if (!llama_kv_self_seq_rm(ctx, slot.id, slot.n_past, -1)) {
                        // could not partially delete (likely using a non-Transformer model)
-                        llama_kv_cache_seq_rm(ctx, slot.id, -1, -1);
+                        llama_kv_self_seq_rm(ctx, slot.id, -1, -1);

                        // there is no common part left
                        slot.n_past = 0;
@@ -3291,7 +3313,7 @@ struct server_context {
                slot.cache_tokens.push_back(id);
                slot.cache_tokens.insert(slot.cache_tokens.end(), ids.begin(), ids.end() - 1);

-                llama_kv_cache_seq_rm(ctx, slot.id, slot.n_past, -1);
+                llama_kv_self_seq_rm(ctx, slot.id, slot.n_past, -1);

                for (size_t i = 0; i < ids.size(); ++i) {
                    completion_token_output result;
@@ -92,6 +92,7 @@ def do_test_completion_with_required_tool_tiny(server: ServerProcess, tool: dict
    assert tool_calls and len(tool_calls) == 1, f'Expected 1 tool call in {choice["message"]}'
    tool_call = tool_calls[0]
    assert choice["message"].get("content") in (None, ""), f'Expected no content in {choice["message"]}'
+    assert len(tool_call.get("id", "")) > 0, f'Expected non empty tool call id in {tool_call}'
    expected_function_name = "python" if tool["type"] == "code_interpreter" else tool["function"]["name"]
    assert expected_function_name == tool_call["function"]["name"]
    actual_arguments = tool_call["function"]["arguments"]
@@ -373,6 +374,7 @@ def do_test_weather(server: ServerProcess, **kwargs):
    tool_call = tool_calls[0]
    # assert choice["message"].get("content") in (None, ""), f'Expected no content in {choice["message"]}'
    assert tool_call["function"]["name"] == WEATHER_TOOL["function"]["name"], f'Expected weather tool call, got {tool_call["function"]["name"]}'
+    assert len(tool_call.get("id", "")) > 0, f'Expected non empty tool call id in {tool_call}'
    actual_arguments = json.loads(tool_call["function"]["arguments"])
    assert 'location' in actual_arguments, f"location not found in {json.dumps(actual_arguments)}"
    location = actual_arguments["location"]
@@ -596,6 +598,7 @@ def do_test_hello_world(server: ServerProcess, **kwargs):
    tool_call = tool_calls[0]
    # assert choice["message"].get("content") in (None, ""), f'Expected no content in {choice["message"]}'
    assert tool_call["function"]["name"] == PYTHON_TOOL["function"]["name"]
+    assert len(tool_call.get("id", "")) > 0, f'Expected non empty tool call id in {tool_call}'
    actual_arguments = json.loads(tool_call["function"]["arguments"])
    assert 'code' in actual_arguments, f"code not found in {json.dumps(actual_arguments)}"
    code = actual_arguments["code"]
@@ -302,7 +302,7 @@ class ServerPreset:
        server.model_hf_repo = "ggml-org/models"
        server.model_hf_file = "tinyllamas/stories260K.gguf"
        server.model_alias = "tinyllama-2"
-        server.n_ctx = 256
+        server.n_ctx = 512
        server.n_batch = 32
        server.n_slots = 2
        server.n_predict = 64
@@ -435,6 +435,10 @@ static std::string gen_chatcmplid() {
    return "chatcmpl-" + random_string();
 }

+static std::string gen_tool_call_id() {
+    return random_string();
+}
+
 //
 // other common utils
 //
@@ -617,7 +621,9 @@ static json oaicompat_completion_params_parse(

    llama_params["chat_format"]      = static_cast<int>(chat_params.format);
    llama_params["prompt"]           = chat_params.prompt;
-    llama_params["grammar"]          = chat_params.grammar;
+    if (!chat_params.grammar.empty()) {
+        llama_params["grammar"] = chat_params.grammar;
+    }
    llama_params["grammar_lazy"]     = chat_params.grammar_lazy;
    auto grammar_triggers = json::array();
    for (const auto & trigger : chat_params.grammar_triggers) {
@@ -98,7 +98,7 @@ int main(int argc, char ** argv) {
    auto generate = [&](const std::string & prompt) {
        std::string response;

-        const bool is_first = llama_get_kv_cache_used_cells(ctx) == 0;
+        const bool is_first = llama_kv_self_used_cells(ctx) == 0;

        // tokenize the prompt
        const int n_prompt_tokens = -llama_tokenize(vocab, prompt.c_str(), prompt.size(), NULL, 0, is_first, true);
@@ -113,7 +113,7 @@ int main(int argc, char ** argv) {
        while (true) {
            // check if we have enough space in the context to evaluate this batch
            int n_ctx = llama_n_ctx(ctx);
-            int n_ctx_used = llama_get_kv_cache_used_cells(ctx);
+            int n_ctx_used = llama_kv_self_used_cells(ctx);
            if (n_ctx_used + batch.n_tokens > n_ctx) {
                printf("\033[0m\n");
                fprintf(stderr, "context size exceeded\n");
@@ -217,7 +217,7 @@ int main(int argc, char ** argv) {
        {
            LOG_DBG("clear kv cache from any extra tokens, n_past = %d\n", n_past);

-            llama_kv_cache_seq_rm(ctx_tgt, 0, n_past, -1);
+            llama_kv_self_seq_rm(ctx_tgt, 0, n_past, -1);
        }

        if ((params.n_predict >= 0 && n_predict > params.n_predict) || has_eos) {
@@ -420,14 +420,14 @@ int main(int argc, char ** argv) {
            {
                LOG_DBG("keeping sequence %d, n_past_tgt = %d, n_past_dft = %d\n", s_keep, n_past_tgt, n_past_dft);

-                llama_kv_cache_seq_keep(ctx_dft, s_keep);
-                llama_kv_cache_seq_cp  (ctx_dft, s_keep, 0, -1, -1);
-                llama_kv_cache_seq_keep(ctx_dft, 0);
+                llama_kv_self_seq_keep(ctx_dft, s_keep);
+                llama_kv_self_seq_cp  (ctx_dft, s_keep, 0, -1, -1);
+                llama_kv_self_seq_keep(ctx_dft, 0);

-                llama_kv_cache_seq_rm  (ctx_tgt, s_keep, n_past_tgt, -1);
-                llama_kv_cache_seq_keep(ctx_tgt, s_keep);
-                llama_kv_cache_seq_cp  (ctx_tgt, s_keep, 0, -1, -1);
-                llama_kv_cache_seq_keep(ctx_tgt, 0);
+                llama_kv_self_seq_rm  (ctx_tgt, s_keep, n_past_tgt, -1);
+                llama_kv_self_seq_keep(ctx_tgt, s_keep);
+                llama_kv_self_seq_cp  (ctx_tgt, s_keep, 0, -1, -1);
+                llama_kv_self_seq_keep(ctx_tgt, 0);
            }

            for (int s = 0; s < n_seq_dft; ++s) {
@@ -444,7 +444,7 @@ int main(int argc, char ** argv) {
            common_batch_clear(batch_dft);
            common_batch_add  (batch_dft, token_id, n_past_dft, { 0 }, true);

-            llama_kv_cache_seq_rm(ctx_dft, 0, n_past_dft, -1);
+            llama_kv_self_seq_rm(ctx_dft, 0, n_past_dft, -1);
            // LOG_DBG("dft batch: %s\n", LOG_BATCH_TOSTR_PRETTY(ctx_dft, batch_dft).c_str());
            llama_decode(ctx_dft, batch_dft);

@@ -503,8 +503,8 @@ int main(int argc, char ** argv) {
                    if (n_seq_cur < n_seq_dft && cur_p->data[f].p > p_draft_split) {
                        LOG_DBG("splitting seq %3d into %3d\n", s, n_seq_cur);

-                        llama_kv_cache_seq_rm(ctx_dft,    n_seq_cur, -1, -1);
-                        llama_kv_cache_seq_cp(ctx_dft, s, n_seq_cur, -1, -1);
+                        llama_kv_self_seq_rm(ctx_dft,    n_seq_cur, -1, -1);
+                        llama_kv_self_seq_cp(ctx_dft, s, n_seq_cur, -1, -1);

                        // all previous tokens from this branch are now also part of the new branch
                        for (int t = 0; t < batch_tgt.n_tokens; ++t) {
@@ -585,9 +585,9 @@ int main(int argc, char ** argv) {

        // evaluate the target model on the drafted tokens
        {
-            llama_kv_cache_seq_keep(ctx_tgt, 0);
+            llama_kv_self_seq_keep(ctx_tgt, 0);
            for (int s = 1; s < n_seq_dft; ++s) {
-                llama_kv_cache_seq_cp(ctx_tgt, 0, s, -1, -1);
+                llama_kv_self_seq_cp(ctx_tgt, 0, s, -1, -1);
            }

            // LOG_DBG("target batch: %s\n", LOG_BATCH_TOSTR_PRETTY(ctx_tgt, batch_tgt).c_str());
@@ -195,6 +195,8 @@ option(GGML_OPENCL                          "ggml: use OpenCL"
 option(GGML_OPENCL_PROFILING                "ggml: use OpenCL profiling (increases overhead)" OFF)
 option(GGML_OPENCL_EMBED_KERNELS            "ggml: embed kernels"                             ON)
 option(GGML_OPENCL_USE_ADRENO_KERNELS       "ggml: use optimized kernels for Adreno"          ON)
+set   (GGML_OPENCL_TARGET_VERSION "300" CACHE STRING
+                                            "gmml: OpenCL API version to target")

 # toolchain for vulkan-shaders-gen
 set   (GGML_VULKAN_SHADERS_GEN_TOOLCHAIN "" CACHE FILEPATH "ggml: toolchain file for vulkan-shaders-gen")
@@ -76,7 +76,14 @@ namespace fs = std::filesystem;
 static std::string path_str(const fs::path & path) {
    std::string u8path;
    try {
+#if defined(__cpp_lib_char8_t)
+        // C++20 and later: u8string() returns std::u8string
+        std::u8string u8str = path.u8string();
+        u8path = std::string(reinterpret_cast<const char*>(u8str.c_str()));
+#else
+        // C++17: u8string() returns std::string
        u8path = path.u8string();
+#endif
    } catch (...) {
    }
    return u8path;
@@ -490,7 +497,7 @@ static ggml_backend_reg_t ggml_backend_load_best(const char * name, bool silent,
        search_paths.push_back(get_executable_path());
        search_paths.push_back(fs::current_path());
    } else {
-        search_paths.push_back(user_search_path);
+        search_paths.push_back(fs::u8path(user_search_path));
    }

    int best_score = 0;
@@ -504,9 +511,9 @@ static ggml_backend_reg_t ggml_backend_load_best(const char * name, bool silent,
        fs::directory_iterator dir_it(search_path, fs::directory_options::skip_permission_denied);
        for (const auto & entry : dir_it) {
            if (entry.is_regular_file()) {
-                auto filename = entry.path().filename().native();
-                auto ext = entry.path().extension().native();
-                if (filename.find(file_prefix) == 0 && ext == file_extension) {
+                auto filename = entry.path().filename();
+                auto ext = entry.path().extension();
+                if (filename.native().find(file_prefix) == 0 && ext == file_extension) {
                    dl_handle_ptr handle { dl_load_library(entry) };
                    if (!handle && !silent) {
                        GGML_LOG_ERROR("%s: failed to load %s\n", __func__, path_str(entry.path()).c_str());
@@ -537,7 +544,7 @@ static ggml_backend_reg_t ggml_backend_load_best(const char * name, bool silent,
        // try to load the base backend
        for (const auto & search_path : search_paths) {
            fs::path filename = backend_filename_prefix().native() + name_path.native() + backend_filename_extension().native();
-            fs::path path = search_path.native() + filename.native();
+            fs::path path = search_path / filename;
            if (fs::exists(path)) {
                return get_reg().load_backend(path, silent);
            }
@@ -2790,10 +2790,14 @@ static void ggml_cann_mul_mat_quant(ggml_backend_cann_context& ctx,
                (char*)output_buffer + batch1 * output_stride, ACL_FLOAT16,
                output_elem_size, output_ne, output_nb, 2, ACL_FORMAT_ND,
                output_ne_offset);
+            int64_t antiquantGroupSize = 0;
+            if (src0->ne[0] > QK8_0) {
+                antiquantGroupSize = QK8_0;
+            }

            ACL_CHECK(aclnnWeightQuantBatchMatmulV2GetWorkspaceSize(
                acl_input_tensor, acl_weight_tensor, acl_scale_tensor, nullptr,
-                nullptr, nullptr, nullptr, QK8_0, acl_output_tensor,
+                nullptr, nullptr, nullptr, antiquantGroupSize, acl_output_tensor,
                &workspaceSize, &executor));
            if (workspaceAddr == nullptr) {
                workspaceAddr = workspace_allocator.alloc(workspaceSize);
@@ -2833,7 +2837,7 @@ static void ggml_cann_mul_mat_quant(ggml_backend_cann_context& ctx,

                ACL_CHECK(aclnnWeightQuantBatchMatmulV2GetWorkspaceSize(
                    acl_input_tensor, acl_weight_tensor, acl_scale_tensor,
-                    nullptr, nullptr, nullptr, nullptr, QK8_0,
+                    nullptr, nullptr, nullptr, nullptr, antiquantGroupSize,
                    acl_output_tensor, &workspaceSize, &executor));
                ACL_CHECK(aclnnWeightQuantBatchMatmulV2(
                    workspaceAddr, workspaceSize, executor, ctx.stream()));
@@ -1689,11 +1689,6 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev,
        case GGML_OP_MUL_MAT: {
            switch (op->src[0]->type) {
                case GGML_TYPE_Q8_0:
-                    // Current groupsize should not be greater than k-1 in
-                    // aclnnWeightQuantBatchMatmulV2GetWorkspaceSize
-                    if (op->src[0]->ne[0] <= QK8_0) {
-                        return false;
-                    }
                case GGML_TYPE_F16:
                case GGML_TYPE_F32:
                case GGML_TYPE_Q4_0:
@@ -11718,9 +11718,12 @@ void ggml_vec_dot_iq1_m_q8_K  (int n, float * GGML_RESTRICT s, size_t bs, const

 #elif defined __AVX2__

-    const __m256i mask = _mm256_set1_epi16(2 * 0x7);
+    const __m256i mask = _mm256_set1_epi16(0x7);
    const __m256i mone = _mm256_set1_epi16(1);
    const __m256i mone8 = _mm256_set1_epi8(1);
+    const __m256i mtwo8 = _mm256_set1_epi8(2);
+    // VPSHUFB cannot cross 128-bit lanes so odd shifts go to upper half.
+    const __m256i scales_shift = _mm256_set_epi64x(9, 3, 6, 0);

    __m256 accum1 = _mm256_setzero_ps();
    __m256 accum2 = _mm256_setzero_ps();
@@ -11732,6 +11735,14 @@ void ggml_vec_dot_iq1_m_q8_K  (int n, float * GGML_RESTRICT s, size_t bs, const
        const uint16_t * sc = (const uint16_t *)x[i].scales;

        scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000);
+        // Extract 3-bit scales (16 values)
+        __m256i scales = _mm256_set1_epi64x(*(const uint64_t*)sc);
+        scales = _mm256_srlv_epi64(scales, scales_shift);
+        scales = _mm256_add_epi16(_mm256_slli_epi16(_mm256_and_si256(scales, mask), 1), mone);
+
+        // Indices to repeat each scale 8 times.
+        __m256i scales_idx1 = _mm256_set1_epi16(0x0100);
+        __m256i scales_idx2 = _mm256_add_epi8(scales_idx1, _mm256_set1_epi8(8));

        __m256i sumi1 = _mm256_setzero_si256();
        __m256i sumi2 = _mm256_setzero_si256();
@@ -11777,11 +11788,12 @@ void ggml_vec_dot_iq1_m_q8_K  (int n, float * GGML_RESTRICT s, size_t bs, const
            const __m256i dot3 = _mm256_maddubs_epi16(mone8, _mm256_sign_epi8(q8b_1, delta1));
            const __m256i dot4 = _mm256_maddubs_epi16(mone8, _mm256_sign_epi8(q8b_2, delta2));

-            __m256i scale1 = MM256_SET_M128I(_mm_set1_epi16(sc[ib/2] >> 2), _mm_set1_epi16(sc[ib/2] << 1));
-            __m256i scale2 = MM256_SET_M128I(_mm_set1_epi16(sc[ib/2] >> 8), _mm_set1_epi16(sc[ib/2] >> 5));
+            __m256i scale1 = _mm256_shuffle_epi8(scales, scales_idx1);
+            __m256i scale2 = _mm256_shuffle_epi8(scales, scales_idx2);
+
+            scales_idx1 = _mm256_add_epi8(scales_idx1, mtwo8);
+            scales_idx2 = _mm256_add_epi8(scales_idx2, mtwo8);

-            scale1 = _mm256_add_epi16(_mm256_and_si256(scale1, mask), mone);
-            scale2 = _mm256_add_epi16(_mm256_and_si256(scale2, mask), mone);
            const __m256i p1 = _mm256_madd_epi16(dot1, scale1);
            const __m256i p2 = _mm256_madd_epi16(dot2, scale2);
            const __m256i p3 = _mm256_madd_epi16(dot3, scale1);
@@ -6648,6 +6648,135 @@ static void ggml_compute_forward_repeat_back(

 // ggml_compute_forward_concat

+static void ggml_compute_forward_concat_any(
+    const struct ggml_compute_params * params,
+    struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
+    const size_t len = ggml_type_size(src0->type);
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    GGML_TENSOR_BINARY_OP_LOCALS
+
+    const int32_t dim = ggml_get_op_params_i32(dst, 0);
+
+    GGML_ASSERT(dim >= 0 && dim < 4);
+
+    int64_t o[4] = {0, 0, 0, 0};
+    o[dim] = src0->ne[dim];
+
+    const char * x;
+
+    // TODO: smarter multi-theading
+    for (int i3 = 0; i3 < ne3; i3++) {
+        for (int i2 = ith; i2 < ne2; i2 += nth) {
+            for (int i1 = 0; i1 < ne1; i1++) {
+                for (int i0 = 0; i0 < ne0; i0++) {
+                    if (i0 < ne00 && i1 < ne01 && i2 < ne02 && i3 < ne03) {
+                        x = (const char *)src0->data + (i0       )*nb00 + (i1       )*nb01 + (i2       )*nb02 + (i3       )*nb03;
+                    } else {
+                        x = (const char *)src1->data + (i0 - o[0])*nb10 + (i1 - o[1])*nb11 + (i2 - o[2])*nb12 + (i3 - o[3])*nb13;
+                    }
+
+                    char * y = (char *)dst->data + i0*nb0 + i1*nb1 + i2*nb2 + i3*nb3;
+
+                    memcpy(y, x, len);
+                }
+            }
+        }
+    }
+}
+
+static void ggml_compute_forward_concat_i8(
+    const struct ggml_compute_params * params,
+    struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
+    GGML_ASSERT(ggml_type_size(src0->type) == sizeof(int8_t));
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    GGML_TENSOR_BINARY_OP_LOCALS
+
+    const int32_t dim = ggml_get_op_params_i32(dst, 0);
+
+    GGML_ASSERT(dim >= 0 && dim < 4);
+
+    int64_t o[4] = {0, 0, 0, 0};
+    o[dim] = src0->ne[dim];
+
+    const int8_t * x;
+
+    // TODO: smarter multi-theading
+    for (int i3 = 0; i3 < ne3; i3++) {
+        for (int i2 = ith; i2 < ne2; i2 += nth) {
+            for (int i1 = 0; i1 < ne1; i1++) {
+                for (int i0 = 0; i0 < ne0; i0++) {
+                    if (i0 < ne00 && i1 < ne01 && i2 < ne02 && i3 < ne03) {
+                        x = (const int8_t *) ((const char *)src0->data + (i0       )*nb00 + (i1       )*nb01 + (i2       )*nb02 + (i3       )*nb03);
+                    } else {
+                        x = (const int8_t *) ((const char *)src1->data + (i0 - o[0])*nb10 + (i1 - o[1])*nb11 + (i2 - o[2])*nb12 + (i3 - o[3])*nb13);
+                    }
+
+                    int8_t * y = (int8_t *)((char *)dst->data + i0*nb0 + i1*nb1 + i2*nb2 + i3*nb3);
+
+                    *y = *x;
+                }
+            }
+        }
+    }
+}
+
+static void ggml_compute_forward_concat_f16(
+    const struct ggml_compute_params * params,
+    struct ggml_tensor * dst) {
+
+    const struct ggml_tensor * src0 = dst->src[0];
+    const struct ggml_tensor * src1 = dst->src[1];
+
+    GGML_ASSERT(ggml_type_size(src0->type) == sizeof(ggml_fp16_t));
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    GGML_TENSOR_BINARY_OP_LOCALS
+
+    const int32_t dim = ggml_get_op_params_i32(dst, 0);
+
+    GGML_ASSERT(dim >= 0 && dim < 4);
+
+    int64_t o[4] = {0, 0, 0, 0};
+    o[dim] = src0->ne[dim];
+
+    const ggml_fp16_t * x;
+
+    // TODO: smarter multi-theading
+    for (int i3 = 0; i3 < ne3; i3++) {
+        for (int i2 = ith; i2 < ne2; i2 += nth) {
+            for (int i1 = 0; i1 < ne1; i1++) {
+                for (int i0 = 0; i0 < ne0; i0++) {
+                    if (i0 < ne00 && i1 < ne01 && i2 < ne02 && i3 < ne03) {
+                        x = (const ggml_fp16_t *) ((const char *)src0->data + (i0       )*nb00 + (i1       )*nb01 + (i2       )*nb02 + (i3       )*nb03);
+                    } else {
+                        x = (const ggml_fp16_t *) ((const char *)src1->data + (i0 - o[0])*nb10 + (i1 - o[1])*nb11 + (i2 - o[2])*nb12 + (i3 - o[3])*nb13);
+                    }
+
+                    ggml_fp16_t * y = (ggml_fp16_t *)((char *)dst->data + i0*nb0 + i1*nb1 + i2*nb2 + i3*nb3);
+
+                    *y = *x;
+                }
+            }
+        }
+    }
+}
+
 static void ggml_compute_forward_concat_f32(
    const struct ggml_compute_params * params,
    struct ggml_tensor * dst) {
@@ -6655,7 +6784,7 @@ static void ggml_compute_forward_concat_f32(
    const struct ggml_tensor * src0 = dst->src[0];
    const struct ggml_tensor * src1 = dst->src[1];

-    GGML_ASSERT(src0->nb[0] == sizeof(float));
+    GGML_ASSERT(ggml_type_size(src0->type) == sizeof(float));

    const int ith = params->ith;
    const int nth = params->nth;
@@ -6698,6 +6827,16 @@ static void ggml_compute_forward_concat(
    const struct ggml_tensor * src0 = dst->src[0];

    switch (src0->type) {
+        case GGML_TYPE_F16:
+        case GGML_TYPE_BF16:
+        case GGML_TYPE_I16:
+            {
+                ggml_compute_forward_concat_f16(params, dst);
+            } break;
+        case GGML_TYPE_I8:
+            {
+                ggml_compute_forward_concat_i8(params, dst);
+            } break;
        case GGML_TYPE_F32:
        case GGML_TYPE_I32:
            {
@@ -6705,7 +6844,7 @@ static void ggml_compute_forward_concat(
            } break;
        default:
            {
-                GGML_ABORT("fatal error");
+                ggml_compute_forward_concat_any(params, dst);
            }
    }
 }
@@ -395,11 +395,11 @@ static __device__ __forceinline__ uint32_t __hgt2_mask(const half2 a, const half

 static __device__ __forceinline__ int ggml_cuda_dp4a(const int a, const int b, int c) {
 #if defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)
-#if defined(__gfx906__) || defined(__gfx908__) || defined(__gfx90a__) || defined(RDNA2)
+#if defined(CDNA) || defined(RDNA2) || defined(__gfx906__)
    c = __builtin_amdgcn_sdot4(a, b, c, false);
 #elif defined(RDNA3)
    c = __builtin_amdgcn_sudot4( true, a, true, b, c, false);
-#elif defined(__gfx1010__) || defined(__gfx900__)
+#elif defined(RDNA1) || defined(__gfx900__)
    int tmp1;
    int tmp2;
    asm("\n \
@@ -52,12 +52,11 @@ typedef half (*vec_dot_KQ_f16_t)(
 typedef float (*vec_dot_KQ_f32_t)(
    const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8 , const void * __restrict__ Q_ds);

-template<typename T, int D>
+template<typename T, int D, int warp_size>
 static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q4_0(
    const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8, const void * __restrict__ Q_ds_v) {

    const block_q4_0 * K_q4_0 = (const block_q4_0 *) K_c;
-    constexpr int warp_size = ggml_cuda_get_physical_warp_size();
    GGML_UNUSED(Q_v);

    T sum = 0.0f;
@@ -93,12 +92,11 @@ static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q4_0(
    return sum;
 }

-template<typename T, int D>
+template<typename T, int D, int warp_size>
 static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q4_1(
    const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8, const void * __restrict__ Q_ds_v) {

    const block_q4_1 * K_q4_1 = (const block_q4_1 *) K_c;
-    constexpr int warp_size = ggml_cuda_get_physical_warp_size();
    GGML_UNUSED(Q_v);

    T sum = 0.0f;
@@ -138,12 +136,11 @@ static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q4_1(
    return sum;
 }

-template<typename T, int D>
+template<typename T, int D, int warp_size>
 static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q5_0(
    const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8, const void * __restrict__ Q_ds_v) {

    const block_q5_0 * K_q5_0 = (const block_q5_0 *) K_c;
-    constexpr int warp_size = ggml_cuda_get_physical_warp_size();
    GGML_UNUSED(Q_v);

    T sum = 0.0f;
@@ -186,12 +183,11 @@ static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q5_0(
    return sum;
 }

-template<typename T, int D>
+template<typename T, int D, int warp_size>
 static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q5_1(
    const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8, const void * __restrict__ Q_ds_v) {

    const block_q5_1 * K_q5_1 = (const block_q5_1 *) K_c;
-    constexpr int warp_size = ggml_cuda_get_physical_warp_size();
    GGML_UNUSED(Q_v);

    T sum = 0.0f;
@@ -238,12 +234,11 @@ static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q5_1(
    return sum;
 }

-template <typename T, int D>
+template <typename T, int D, int warp_size>
 static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q8_0(
    const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8, const void * __restrict__ Q_ds_v) {

    const block_q8_0 * K_q8_0 = (const block_q8_0 *) K_c;
-    constexpr int warp_size = ggml_cuda_get_physical_warp_size();
    GGML_UNUSED(Q_v);

    T sum = 0.0f;
@@ -272,12 +267,11 @@ static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q8_0(
    return sum;
 }

-template <typename T, int D>
+template <typename T, int D, int warp_size>
 static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_f16(
    const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8 , const void * __restrict__ Q_ds_v) {

    const half2 * K_h2 = (const half2 *) K_c;
-    constexpr int warp_size = ggml_cuda_get_physical_warp_size();
    GGML_UNUSED(Q_q8);
    GGML_UNUSED(Q_ds_v);

@@ -480,25 +474,25 @@ static __device__ __forceinline__ T dequantize_1_f16(const void * __restrict__ v
    return x[i];
 }

-template <int D>
+template <int D, int warp_size = WARP_SIZE>
 constexpr __device__ vec_dot_KQ_f16_t get_vec_dot_KQ_f16(ggml_type type_K) {
-    return type_K == GGML_TYPE_Q4_0 ? vec_dot_fattn_vec_KQ_q4_0<half, D> :
-        type_K == GGML_TYPE_Q4_1 ? vec_dot_fattn_vec_KQ_q4_1<half, D> :
-        type_K == GGML_TYPE_Q5_0 ? vec_dot_fattn_vec_KQ_q5_0<half, D> :
-        type_K == GGML_TYPE_Q5_1 ? vec_dot_fattn_vec_KQ_q5_1<half, D> :
-        type_K == GGML_TYPE_Q8_0 ? vec_dot_fattn_vec_KQ_q8_0<half, D> :
-        type_K == GGML_TYPE_F16 ? vec_dot_fattn_vec_KQ_f16<half, D> :
+    return type_K == GGML_TYPE_Q4_0 ? vec_dot_fattn_vec_KQ_q4_0<half, D, warp_size> :
+        type_K == GGML_TYPE_Q4_1 ? vec_dot_fattn_vec_KQ_q4_1<half, D, warp_size> :
+        type_K == GGML_TYPE_Q5_0 ? vec_dot_fattn_vec_KQ_q5_0<half, D, warp_size> :
+        type_K == GGML_TYPE_Q5_1 ? vec_dot_fattn_vec_KQ_q5_1<half, D, warp_size> :
+        type_K == GGML_TYPE_Q8_0 ? vec_dot_fattn_vec_KQ_q8_0<half, D, warp_size> :
+        type_K == GGML_TYPE_F16 ? vec_dot_fattn_vec_KQ_f16<half, D, warp_size> :
        nullptr;
 }

-template <int D>
+template <int D, int warp_size = WARP_SIZE>
 constexpr __device__ vec_dot_KQ_f32_t get_vec_dot_KQ_f32(ggml_type type_K) {
-    return type_K == GGML_TYPE_Q4_0 ? vec_dot_fattn_vec_KQ_q4_0<float, D> :
-        type_K == GGML_TYPE_Q4_1 ? vec_dot_fattn_vec_KQ_q4_1<float, D> :
-        type_K == GGML_TYPE_Q5_0 ? vec_dot_fattn_vec_KQ_q5_0<float, D> :
-        type_K == GGML_TYPE_Q5_1 ? vec_dot_fattn_vec_KQ_q5_1<float, D> :
-        type_K == GGML_TYPE_Q8_0 ? vec_dot_fattn_vec_KQ_q8_0<float, D> :
-        type_K == GGML_TYPE_F16 ? vec_dot_fattn_vec_KQ_f16<float, D> :
+    return type_K == GGML_TYPE_Q4_0 ? vec_dot_fattn_vec_KQ_q4_0<float, D, warp_size> :
+        type_K == GGML_TYPE_Q4_1 ? vec_dot_fattn_vec_KQ_q4_1<float, D, warp_size> :
+        type_K == GGML_TYPE_Q5_0 ? vec_dot_fattn_vec_KQ_q5_0<float, D, warp_size> :
+        type_K == GGML_TYPE_Q5_1 ? vec_dot_fattn_vec_KQ_q5_1<float, D, warp_size> :
+        type_K == GGML_TYPE_Q8_0 ? vec_dot_fattn_vec_KQ_q8_0<float, D, warp_size> :
+        type_K == GGML_TYPE_F16 ? vec_dot_fattn_vec_KQ_f16<float, D, warp_size> :
        nullptr;
 }

@@ -681,7 +675,8 @@ static void on_no_fattn_vec_case(const int D) {
 template <int D, int ncols1, int ncols2, int parallel_blocks, int KQ_stride>
 void launch_fattn(
    ggml_backend_cuda_context & ctx, ggml_tensor * dst, fattn_kernel_t fattn_kernel,
-    const int nwarps, const size_t nbytes_shared, const bool need_f16_K, const bool need_f16_V
+    const int nwarps, const size_t nbytes_shared, const bool need_f16_K, const bool need_f16_V,
+    const int warp_size = WARP_SIZE
 ) {
    constexpr int ncols = ncols1 * ncols2;

@@ -704,8 +699,6 @@ void launch_fattn(

    GGML_ASSERT(Q->ne[3] == 1);

-    const int warp_size = ggml_cuda_info().devices[ctx.device].warp_size;
-
    ggml_cuda_pool & pool = ctx.pool();
    cudaStream_t main_stream = ctx.stream();
    const int id  = ggml_cuda_get_device();
@@ -805,7 +798,6 @@ void launch_fattn(
    const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2);

    GGML_ASSERT(block_dim.x % warp_size == 0);
-    GGML_ASSERT(!GGML_CUDA_CC_IS_AMD(cc) || block_dim.x * block_dim.y <= 4 * (unsigned int)warp_size);
    fattn_kernel<<<blocks_num, block_dim, nbytes_shared, main_stream>>>(
        (const char *) Q->data,
        K_data,
@@ -469,6 +469,7 @@ void ggml_cuda_flash_attn_ext_wmma_f16_case(ggml_backend_cuda_context & ctx, ggm
    constexpr int frag_m = cols_per_block == 8 && D % 32 == 0 ? 32 : 16;
    const int blocks_num_pb1 = ((Q->ne[1] + cols_per_block - 1) / cols_per_block)*Q->ne[2]*Q->ne[3];
    const int nsm = ggml_cuda_info().devices[ggml_cuda_get_device()].nsm;
+    const int warp_size = ggml_cuda_info().devices[ggml_cuda_get_device()].warp_size;

    float logit_softcap;
    memcpy(&logit_softcap, (const float *) KQV->op_params + 2, sizeof(float));
@@ -485,7 +486,7 @@ void ggml_cuda_flash_attn_ext_wmma_f16_case(ggml_backend_cuda_context & ctx, ggm
            fattn_kernel = flash_attn_ext_f16<
                D, cols_per_block, nwarps, get_VKQ_stride(D, nwarps, frag_m), parallel_blocks, KQ_acc_t, use_logit_softcap>;
        }
-        launch_fattn<D, cols_per_block, 1, parallel_blocks, -1>(ctx, dst, fattn_kernel, nwarps, 0, true, true);
+        launch_fattn<D, cols_per_block, 1, parallel_blocks, -1>(ctx, dst, fattn_kernel, nwarps, 0, true, true, warp_size);
        return;
    }
    if (2*blocks_num_pb1 < 2*nsm) {
@@ -500,7 +501,7 @@ void ggml_cuda_flash_attn_ext_wmma_f16_case(ggml_backend_cuda_context & ctx, ggm
            fattn_kernel = flash_attn_ext_f16<
                D, cols_per_block, nwarps, get_VKQ_stride(D, nwarps, frag_m), parallel_blocks, KQ_acc_t, use_logit_softcap>;
        }
-        launch_fattn<D, cols_per_block, 1, parallel_blocks, -1>(ctx, dst, fattn_kernel, nwarps, 0, true, true);
+        launch_fattn<D, cols_per_block, 1, parallel_blocks, -1>(ctx, dst, fattn_kernel, nwarps, 0, true, true, warp_size);
        return;
    }
    constexpr int parallel_blocks = 1;
@@ -514,7 +515,7 @@ void ggml_cuda_flash_attn_ext_wmma_f16_case(ggml_backend_cuda_context & ctx, ggm
        fattn_kernel = flash_attn_ext_f16<
            D, cols_per_block, nwarps, get_VKQ_stride(D, nwarps, frag_m), parallel_blocks, KQ_acc_t, use_logit_softcap>;
    }
-    launch_fattn<D, cols_per_block, 1, parallel_blocks, -1>(ctx, dst, fattn_kernel, nwarps, 0, true, true);
+    launch_fattn<D, cols_per_block, 1, parallel_blocks, -1>(ctx, dst, fattn_kernel, nwarps, 0, true, true, warp_size);
 }

 void ggml_cuda_flash_attn_ext_wmma_f16(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
@@ -47,11 +47,89 @@ static constexpr __device__ int get_vdr_mmvq(ggml_type type) {
        1;
 }

+enum mmvq_parameter_table_id {
+    MMVQ_PARAMETERS_GENERIC = 0,
+    MMVQ_PARAMETERS_GCN,
+    MMVQ_PARAMETERS_RDNA2
+};
+
+static constexpr __device__ mmvq_parameter_table_id get_device_table_id() {
+#if defined(RDNA2) || defined(RDNA3)
+    return MMVQ_PARAMETERS_RDNA2;
+#elif defined(GCN) || defined(CDNA)
+    return MMVQ_PARAMETERS_GCN;
+#else
+    return MMVQ_PARAMETERS_GENERIC;
+#endif
+}
+
+static __host__ mmvq_parameter_table_id get_device_table_id(int cc) {
+    if (GGML_CUDA_CC_IS_RDNA2(cc) || GGML_CUDA_CC_IS_RDNA3(cc)) {
+        return MMVQ_PARAMETERS_RDNA2;
+    }
+    if (GGML_CUDA_CC_IS_GCN(cc) || GGML_CUDA_CC_IS_CDNA(cc)) {
+        return MMVQ_PARAMETERS_GCN;
+    }
+    return MMVQ_PARAMETERS_GENERIC;
+}
+
+static constexpr __host__ __device__ int calc_nwarps(int ncols_y,  mmvq_parameter_table_id table_id) {
+    if (table_id == MMVQ_PARAMETERS_GENERIC) {
+        switch (ncols_y) {
+            case 1:
+            case 2:
+            case 3:
+            case 4:
+                return 4;
+            case 5:
+            case 6:
+            case 7:
+            case 8:
+                return 2;
+            default:
+                return 1;
+        }
+    } else if (table_id == MMVQ_PARAMETERS_GCN) {
+        switch (ncols_y) {
+            case 1:
+            case 2:
+            case 3:
+            case 4:
+                return 2;
+            case 5:
+            case 6:
+            case 7:
+            case 8:
+            default:
+                return 1;
+        }
+    }
+    return 1;
+}
+
+static constexpr __host__ __device__ int calc_rows_per_block(int ncols_y, int table_id) {
+    if (table_id == MMVQ_PARAMETERS_GENERIC || table_id == MMVQ_PARAMETERS_GCN) {
+        switch (ncols_y) {
+            case 1:
+                return 1;
+            case 2:
+            case 3:
+            case 4:
+            case 5:
+            case 6:
+            case 7:
+            case 8:
+                return 2;
+            default:
+                return 1;
+        }
+    }
+    return 1;
+}
+
 template <ggml_type type, int ncols_y>
-#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__))
 // tell the compiler to use as many registers as it wants, see nwarps definition below
-__launch_bounds__((ncols_y <= 4 ? 4 : 2)*WARP_SIZE, 1)
-#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__))
+__launch_bounds__(calc_nwarps(ncols_y, get_device_table_id())*ggml_cuda_get_physical_warp_size(), 1)
 static __global__ void mul_mat_vec_q(
    const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
    const int ncols_x, const int nrows_x, const int nrows_y, const int nrows_dst) {
@@ -59,24 +137,20 @@ static __global__ void mul_mat_vec_q(
    constexpr int qk  = ggml_cuda_type_traits<type>::qk;
    constexpr int qi  = ggml_cuda_type_traits<type>::qi;
    constexpr int vdr = get_vdr_mmvq(type);
+    constexpr mmvq_parameter_table_id table_id = get_device_table_id();
+    constexpr int nwarps = calc_nwarps(ncols_y, table_id);
+    constexpr int rows_per_cuda_block = calc_rows_per_block(ncols_y, table_id);
+    constexpr int warp_size = ggml_cuda_get_physical_warp_size();

    constexpr vec_dot_q_cuda_t vec_dot_q_cuda = get_vec_dot_q_cuda(type);

-#if defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__) && (defined(RDNA2) || defined(RDNA3))
-    constexpr int nwarps              = 1;
-    constexpr int rows_per_cuda_block = 1;
-#else
-    constexpr int nwarps              = ncols_y <= 4 ? 4 : 2;
-    constexpr int rows_per_cuda_block = ncols_y == 1 ? 1 : 2;
-#endif // defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__) && !defined(RDNA2) && !defined(RDNA3)
-
-    const     int tid = WARP_SIZE*threadIdx.y + threadIdx.x;
+    const     int tid = warp_size*threadIdx.y + threadIdx.x;
    const     int row0 = rows_per_cuda_block*blockIdx.x;
    const     int blocks_per_row_x = ncols_x / qk;
    const     int blocks_per_col_y = nrows_y / QK8_1;
-    constexpr int blocks_per_iter = vdr * nwarps*WARP_SIZE / qi;
+    constexpr int blocks_per_iter = vdr * nwarps*warp_size / qi;

-// partial sum for each thread
+    // partial sum for each thread
    float tmp[ncols_y][rows_per_cuda_block] = {0.0f};

    const block_q8_1 * y = (const block_q8_1 *) vy;
@@ -96,7 +170,7 @@ static __global__ void mul_mat_vec_q(
        }
    }

-    __shared__ float tmp_shared[nwarps-1 > 0 ? nwarps-1 : 1][ncols_y][rows_per_cuda_block][WARP_SIZE];
+    __shared__ float tmp_shared[nwarps-1 > 0 ? nwarps-1 : 1][ncols_y][rows_per_cuda_block][warp_size];
    if (threadIdx.y > 0) {
 #pragma unroll
        for (int j = 0; j < ncols_y; ++j) {
@@ -120,7 +194,7 @@ static __global__ void mul_mat_vec_q(
            for (int l = 0; l < nwarps-1; ++l) {
                tmp[j][i] += tmp_shared[l][j][i][threadIdx.x];
            }
-            tmp[j][i] = warp_reduce_sum(tmp[j][i]);
+            tmp[j][i] = warp_reduce_sum<warp_size>(tmp[j][i]);
        }

        if (threadIdx.x < rows_per_cuda_block && (rows_per_cuda_block == 1 || row0 + threadIdx.x < nrows_dst)) {
@@ -129,6 +203,13 @@ static __global__ void mul_mat_vec_q(
    }
 }

+static std::pair<dim3, dim3> calc_launch_params(const int ncols_y, const int nrows_x, const int warp_size, const mmvq_parameter_table_id table_id) {
+    const int64_t nblocks = (nrows_x + calc_rows_per_block(ncols_y, table_id) - 1) / calc_rows_per_block(ncols_y, table_id);
+    const dim3 block_nums(nblocks, 1, 1);
+    const dim3 block_dims(warp_size, calc_nwarps(ncols_y, table_id), 1);
+    return {block_nums, block_dims};
+}
+
 template <ggml_type type>
 static void mul_mat_vec_q_cuda(
    const void * vx, const void * vy, float * dst,
@@ -137,65 +218,67 @@ static void mul_mat_vec_q_cuda(
    GGML_ASSERT(ncols_x % ggml_blck_size(type) == 0);
    GGML_ASSERT(ncols_y <= MMVQ_MAX_BATCH_SIZE);

-    int id = ggml_cuda_get_device();
-
-    int64_t nwarps = 1;
-    int64_t rows_per_cuda_block = 1;
-
-    if (ggml_cuda_info().devices[id].cc < GGML_CUDA_CC_RDNA2) { // NVIDIA and AMD older than RDNA2
-        switch(ncols_y) {
-            case 1:
-                nwarps = 4;
-                rows_per_cuda_block = 1;
-                break;
-            case 2:
-            case 3:
-            case 4:
-                nwarps = 4;
-                rows_per_cuda_block = 2;
-                break;
-            case 5:
-            case 6:
-            case 7:
-            case 8:
-                nwarps = 2;
-                rows_per_cuda_block = 2;
-                break;
-            default:
-                GGML_ABORT("fatal error");
-                break;
-        }
-    }
-
-    const int64_t nblocks = (nrows_x + rows_per_cuda_block - 1) / rows_per_cuda_block;
-    const dim3 block_nums(nblocks, 1, 1);
-    const dim3 block_dims(WARP_SIZE, nwarps, 1);
+    const int device = ggml_cuda_get_device();
+    const int warp_size = ggml_cuda_info().devices[device].warp_size;
+    const mmvq_parameter_table_id table_id = get_device_table_id(ggml_cuda_info().devices[device].cc);

    switch (ncols_y) {
        case 1:
-            mul_mat_vec_q<type, 1><<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst);
+        {
+            constexpr int c_ncols_y = 1;
+            std::pair<dim3, dim3> dims = calc_launch_params(c_ncols_y, nrows_x, warp_size, table_id);
+            mul_mat_vec_q<type, c_ncols_y><<<dims.first, dims.second, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst);
            break;
+        }
        case 2:
-            mul_mat_vec_q<type, 2><<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst);
+        {
+            constexpr int c_ncols_y = 2;
+            std::pair<dim3, dim3> dims = calc_launch_params(c_ncols_y, nrows_x, warp_size, table_id);
+            mul_mat_vec_q<type, c_ncols_y><<<dims.first, dims.second, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst);
            break;
+        }
        case 3:
-            mul_mat_vec_q<type, 3><<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst);
+        {
+            constexpr int c_ncols_y = 3;
+            std::pair<dim3, dim3> dims = calc_launch_params(c_ncols_y, nrows_x, warp_size, table_id);
+            mul_mat_vec_q<type, c_ncols_y><<<dims.first, dims.second, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst);
            break;
+        }
        case 4:
-            mul_mat_vec_q<type, 4><<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst);
+        {
+            constexpr int c_ncols_y = 4;
+            std::pair<dim3, dim3> dims = calc_launch_params(c_ncols_y, nrows_x, warp_size, table_id);
+            mul_mat_vec_q<type, c_ncols_y><<<dims.first, dims.second, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst);
            break;
+        }
        case 5:
-            mul_mat_vec_q<type, 5><<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst);
+        {
+            constexpr int c_ncols_y = 5;
+            std::pair<dim3, dim3> dims = calc_launch_params(c_ncols_y, nrows_x, warp_size, table_id);
+            mul_mat_vec_q<type, c_ncols_y><<<dims.first, dims.second, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst);
            break;
+        }
        case 6:
-            mul_mat_vec_q<type, 6><<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst);
+        {
+            constexpr int c_ncols_y = 6;
+            std::pair<dim3, dim3> dims = calc_launch_params(c_ncols_y, nrows_x, warp_size, table_id);
+            mul_mat_vec_q<type, c_ncols_y><<<dims.first, dims.second, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst);
            break;
+        }
        case 7:
-            mul_mat_vec_q<type, 7><<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst);
+        {
+            constexpr int c_ncols_y = 7;
+            std::pair<dim3, dim3> dims = calc_launch_params(c_ncols_y, nrows_x, warp_size, table_id);
+            mul_mat_vec_q<type, c_ncols_y><<<dims.first, dims.second, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst);
            break;
+        }
        case 8:
-            mul_mat_vec_q<type, 8><<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst);
+        {
+            constexpr int c_ncols_y = 8;
+            std::pair<dim3, dim3> dims = calc_launch_params(c_ncols_y, nrows_x, warp_size, table_id);
+            mul_mat_vec_q<type, c_ncols_y><<<dims.first, dims.second, 0, stream>>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst);
            break;
+        }
        default:
            GGML_ABORT("fatal error");
            break;
@@ -88,9 +88,8 @@ else()

    add_custom_command(
        OUTPUT ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/default.metallib
-        COMMAND xcrun -sdk macosx metal    ${XC_FLAGS} -c ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.metal -o ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.air
-        COMMAND xcrun -sdk macosx metallib                ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.air   -o ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/default.metallib
-        COMMAND rm -f ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.air
+        COMMAND xcrun -sdk macosx metal ${XC_FLAGS} -c ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.metal -o - |
+            xcrun -sdk macosx metallib - -o ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/default.metallib
        COMMAND rm -f ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-common.h
        COMMAND rm -f ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.metal
        DEPENDS ggml-metal.metal ${METALLIB_COMMON}
@@ -46,6 +46,7 @@ static struct ggml_backend_device g_ggml_backend_metal_device;
 static struct ggml_backend_metal_device_context {
    id<MTLDevice> mtl_device;
    int           mtl_device_ref_count;
+    id<MTLLibrary> mtl_library;

    bool has_simdgroup_reduction;
    bool has_simdgroup_mm;
@@ -57,6 +58,7 @@ static struct ggml_backend_metal_device_context {
 } g_ggml_ctx_dev_main = {
    /*.mtl_device              =*/ nil,
    /*.mtl_device_ref_count    =*/ 0,
+    /*.mtl_library             =*/ nil,
    /*.has_simdgroup_reduction =*/ false,
    /*.has_simdgroup_mm        =*/ false,
    /*.has_residency_sets      =*/ false,
@@ -108,6 +110,11 @@ static void ggml_backend_metal_device_rel(struct ggml_backend_metal_device_conte
    ctx->mtl_device_ref_count--;

    if (ctx->mtl_device_ref_count == 0) {
+        if (ctx->mtl_library) {
+            [ctx->mtl_library release];
+            ctx->mtl_library = nil;
+        }
+
        if (ctx->mtl_device) {
            [ctx->mtl_device release];
            ctx->mtl_device = nil;
@@ -495,6 +502,139 @@ static void * ggml_metal_host_malloc(size_t n) {
    return data;
 }

+// load library
+//
+// - first check if the library is embedded
+// - then check if the library is in the bundle
+// - if not found, load the source and compile it
+// - if that fails, return NULL
+static id<MTLLibrary> ggml_metal_load_library(id<MTLDevice> device, bool use_bfloat) {
+    id<MTLLibrary> metal_library = nil;
+    NSError * error = nil;
+    NSString * src = nil;
+
+#if GGML_METAL_EMBED_LIBRARY
+    GGML_LOG_INFO("%s: using embedded metal library\n", __func__);
+
+    extern const char ggml_metallib_start[];
+    extern const char ggml_metallib_end[];
+
+    src = [[NSString alloc] initWithBytes:ggml_metallib_start length:(ggml_metallib_end-ggml_metallib_start) encoding:NSUTF8StringEncoding];
+
+#else
+
+#ifdef SWIFT_PACKAGE
+    NSBundle * bundle = SWIFTPM_MODULE_BUNDLE;
+#else
+    NSBundle * bundle = [NSBundle bundleForClass:[GGMLMetalClass class]];
+#endif
+
+    NSString * path_lib = [bundle pathForResource:@"default" ofType:@"metallib"];
+    if (path_lib == nil) {
+        // Try to find the resource in the directory where the current binary located.
+        NSString * current_binary = [[NSProcessInfo processInfo] arguments][0];
+        NSString * bin_dir = [current_binary stringByDeletingLastPathComponent];
+        NSString * default_metallib_path = [NSString pathWithComponents:@[bin_dir, @"default.metallib"]];
+        if ([[NSFileManager defaultManager] isReadableFileAtPath:default_metallib_path]) {
+            GGML_LOG_INFO("%s: found '%s'\n", __func__, [default_metallib_path UTF8String]);
+            NSDictionary * atts = [[NSFileManager defaultManager] attributesOfItemAtPath:default_metallib_path error:&error];
+            if (atts && atts[NSFileType] == NSFileTypeSymbolicLink) {
+                // Optionally, if this is a symlink, try to resolve it.
+                default_metallib_path = [[NSFileManager defaultManager] destinationOfSymbolicLinkAtPath:default_metallib_path error:&error];
+                if (default_metallib_path && [default_metallib_path length] > 0 && ![[default_metallib_path substringToIndex:1] isEqualToString:@"/"]) {
+                    // It is a relative path, adding the binary directory as directory prefix.
+                    default_metallib_path = [NSString pathWithComponents:@[bin_dir, default_metallib_path]];
+                }
+                if (!default_metallib_path || ![[NSFileManager defaultManager] isReadableFileAtPath:default_metallib_path]) {
+                    // Link to the resource could not be resolved.
+                    default_metallib_path = nil;
+                } else {
+                    GGML_LOG_INFO("%s: symlink resolved '%s'\n", __func__, [default_metallib_path UTF8String]);
+                }
+            }
+        } else {
+            // The resource couldn't be found in the binary's directory.
+            default_metallib_path = nil;
+        }
+        path_lib = default_metallib_path;
+    }
+
+    if (path_lib != nil) {
+        // pre-compiled library found
+        NSURL * libURL = [NSURL fileURLWithPath:path_lib];
+        GGML_LOG_INFO("%s: loading '%s'\n", __func__, [path_lib UTF8String]);
+
+        metal_library = [device newLibraryWithURL:libURL error:&error];
+        if (error) {
+            GGML_LOG_ERROR("%s: error: %s\n", __func__, [[error description] UTF8String]);
+            return NULL;
+        }
+    } else {
+        GGML_LOG_INFO("%s: default.metallib not found, loading from source\n", __func__);
+
+        NSString * path_source;
+        NSString * path_resource = [[NSProcessInfo processInfo].environment objectForKey:@"GGML_METAL_PATH_RESOURCES"];
+
+        GGML_LOG_INFO("%s: GGML_METAL_PATH_RESOURCES = %s\n", __func__, path_resource ? [path_resource UTF8String] : "nil");
+
+        if (path_resource) {
+            path_source = [path_resource stringByAppendingPathComponent:@"ggml-metal.metal"];
+        } else {
+            path_source = [bundle pathForResource:@"ggml-metal" ofType:@"metal"];
+        }
+
+        if (path_source == nil) {
+            GGML_LOG_WARN("%s: error: could not use bundle path to find ggml-metal.metal, falling back to trying cwd\n", __func__);
+            path_source = @"ggml-metal.metal";
+        }
+
+        GGML_LOG_INFO("%s: loading '%s'\n", __func__, [path_source UTF8String]);
+
+        src = [NSString stringWithContentsOfFile:path_source encoding:NSUTF8StringEncoding error:&error];
+        if (error) {
+            GGML_LOG_ERROR("%s: error: %s\n", __func__, [[error description] UTF8String]);
+            return NULL;
+        }
+    }
+#endif
+
+    if (!metal_library) {
+        @autoreleasepool {
+            // dictionary of preprocessor macros
+            NSMutableDictionary * prep = [NSMutableDictionary dictionary];
+
+            if (use_bfloat) {
+                [prep setObject:@"1" forKey:@"GGML_METAL_USE_BF16"];
+            }
+
+#if GGML_METAL_EMBED_LIBRARY
+            [prep setObject:@"1" forKey:@"GGML_METAL_EMBED_LIBRARY"];
+#endif
+
+            MTLCompileOptions * options = [MTLCompileOptions new];
+            options.preprocessorMacros = prep;
+
+            //[options setFastMathEnabled:false];
+
+            metal_library = [device newLibraryWithSource:src options:options error:&error];
+            if (error) {
+                GGML_LOG_ERROR("%s: error: %s\n", __func__, [[error description] UTF8String]);
+                return NULL;
+            }
+
+#if !__has_feature(objc_arc)
+            [options release];
+#endif
+        }
+    }
+
+#if GGML_METAL_EMBED_LIBRARY
+    [src release];
+#endif // GGML_METAL_EMBED_LIBRARY
+
+    return metal_library;
+}
+
 static struct ggml_backend_metal_context * ggml_metal_init(ggml_backend_dev_t dev) {
    GGML_LOG_INFO("%s: allocating\n", __func__);

@@ -522,136 +662,14 @@ static struct ggml_backend_metal_context * ggml_metal_init(ggml_backend_dev_t de

    ctx->d_queue = dispatch_queue_create("ggml-metal", DISPATCH_QUEUE_CONCURRENT);

-    id<MTLLibrary> metal_library = nil;
-
    // load library
-    //
-    // - first check if the library is embedded
-    // - then check if the library is in the bundle
-    // - if not found, load the source and compile it
-    // - if that fails, return NULL
-    {
-        NSError * error = nil;
-        NSString * src = nil;
-
-#if GGML_METAL_EMBED_LIBRARY
-        GGML_LOG_INFO("%s: using embedded metal library\n", __func__);
-
-        extern const char ggml_metallib_start[];
-        extern const char ggml_metallib_end[];
-
-        src = [[NSString alloc] initWithBytes:ggml_metallib_start length:(ggml_metallib_end-ggml_metallib_start) encoding:NSUTF8StringEncoding];
-
-#else
-
-#ifdef SWIFT_PACKAGE
-        NSBundle * bundle = SWIFTPM_MODULE_BUNDLE;
-#else
-        NSBundle * bundle = [NSBundle bundleForClass:[GGMLMetalClass class]];
-#endif
-
-        NSString * path_lib = [bundle pathForResource:@"default" ofType:@"metallib"];
-        if (path_lib == nil) {
-            // Try to find the resource in the directory where the current binary located.
-            NSString * current_binary = [[NSProcessInfo processInfo] arguments][0];
-            NSString * bin_dir = [current_binary stringByDeletingLastPathComponent];
-            NSString * default_metallib_path = [NSString pathWithComponents:@[bin_dir, @"default.metallib"]];
-            if ([[NSFileManager defaultManager] isReadableFileAtPath:default_metallib_path]) {
-                GGML_LOG_INFO("%s: found '%s'\n", __func__, [default_metallib_path UTF8String]);
-                NSDictionary * atts = [[NSFileManager defaultManager] attributesOfItemAtPath:default_metallib_path error:&error];
-                if (atts && atts[NSFileType] == NSFileTypeSymbolicLink) {
-                    // Optionally, if this is a symlink, try to resolve it.
-                    default_metallib_path = [[NSFileManager defaultManager] destinationOfSymbolicLinkAtPath:default_metallib_path error:&error];
-                    if (default_metallib_path && [default_metallib_path length] > 0 && ![[default_metallib_path substringToIndex:1] isEqualToString:@"/"]) {
-                        // It is a relative path, adding the binary directory as directory prefix.
-                        default_metallib_path = [NSString pathWithComponents:@[bin_dir, default_metallib_path]];
-                    }
-                    if (!default_metallib_path || ![[NSFileManager defaultManager] isReadableFileAtPath:default_metallib_path]) {
-                        // Link to the resource could not be resolved.
-                        default_metallib_path = nil;
-                    } else {
-                        GGML_LOG_INFO("%s: symlink resolved '%s'\n", __func__, [default_metallib_path UTF8String]);
-                    }
-                }
-            } else {
-                // The resource couldn't be found in the binary's directory.
-                default_metallib_path = nil;
-            }
-            path_lib = default_metallib_path;
-        }
-
-        if (path_lib != nil) {
-            // pre-compiled library found
-            NSURL * libURL = [NSURL fileURLWithPath:path_lib];
-            GGML_LOG_INFO("%s: loading '%s'\n", __func__, [path_lib UTF8String]);
-
-            metal_library = [device newLibraryWithURL:libURL error:&error];
-            if (error) {
-                GGML_LOG_ERROR("%s: error: %s\n", __func__, [[error description] UTF8String]);
-                return NULL;
-            }
-        } else {
-            GGML_LOG_INFO("%s: default.metallib not found, loading from source\n", __func__);
-
-            NSString * path_source;
-            NSString * path_resource = [[NSProcessInfo processInfo].environment objectForKey:@"GGML_METAL_PATH_RESOURCES"];
-
-            GGML_LOG_INFO("%s: GGML_METAL_PATH_RESOURCES = %s\n", __func__, path_resource ? [path_resource UTF8String] : "nil");
-
-            if (path_resource) {
-                path_source = [path_resource stringByAppendingPathComponent:@"ggml-metal.metal"];
-            } else {
-                path_source = [bundle pathForResource:@"ggml-metal" ofType:@"metal"];
-            }
-
-            if (path_source == nil) {
-                GGML_LOG_WARN("%s: error: could not use bundle path to find ggml-metal.metal, falling back to trying cwd\n", __func__);
-                path_source = @"ggml-metal.metal";
-            }
-
-            GGML_LOG_INFO("%s: loading '%s'\n", __func__, [path_source UTF8String]);
-
-            src = [NSString stringWithContentsOfFile:path_source encoding:NSUTF8StringEncoding error:&error];
-            if (error) {
-                GGML_LOG_ERROR("%s: error: %s\n", __func__, [[error description] UTF8String]);
-                return NULL;
-            }
-        }
-#endif
-
-        if (!metal_library) {
-            @autoreleasepool {
-                // dictionary of preprocessor macros
-                NSMutableDictionary * prep = [NSMutableDictionary dictionary];
-
-                if (ctx_dev->use_bfloat) {
-                    [prep setObject:@"1" forKey:@"GGML_METAL_USE_BF16"];
-                }
-
-#if GGML_METAL_EMBED_LIBRARY
-                [prep setObject:@"1" forKey:@"GGML_METAL_EMBED_LIBRARY"];
-#endif
-
-                MTLCompileOptions * options = [MTLCompileOptions new];
-                options.preprocessorMacros = prep;
-
-                //[options setFastMathEnabled:false];
-
-                metal_library = [device newLibraryWithSource:src options:options error:&error];
-                if (error) {
-                    GGML_LOG_ERROR("%s: error: %s\n", __func__, [[error description] UTF8String]);
-                    return NULL;
-                }
-
-#if !__has_feature(objc_arc)
-                [options release];
-#endif
-            }
-        }
-
-#if GGML_METAL_EMBED_LIBRARY
-        [src release];
-#endif // GGML_METAL_EMBED_LIBRARY
+    if (ctx_dev->mtl_library == nil) {
+        ctx_dev->mtl_library = ggml_metal_load_library(device, ctx_dev->use_bfloat);
+    }
+    id<MTLLibrary> metal_library = ctx_dev->mtl_library;
+    if (metal_library == nil) {
+        GGML_LOG_ERROR("%s: error: metal library is nil\n", __func__);
+        return NULL;
    }

    // print MTL GPU family:
@@ -725,7 +743,6 @@ static struct ggml_backend_metal_context * ggml_metal_init(ggml_backend_dev_t de
            [metal_function release]; \
            if (error) { \
                GGML_LOG_ERROR("%s: error: load pipeline error: %s\n", __func__, [[error description] UTF8String]); \
-                [metal_library release]; \
                return NULL; \
            } \
        } else { \
@@ -1044,8 +1061,6 @@ static struct ggml_backend_metal_context * ggml_metal_init(ggml_backend_dev_t de
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_POOL_2D_MAX_F32,               pool_2d_max_f32,                true);
    }

-    [metal_library release];
-
    return ctx;
 }

@@ -21,7 +21,7 @@ if (MUSAToolkit_FOUND)
    message(STATUS "MUSA Toolkit found")

    if (NOT DEFINED MUSA_ARCHITECTURES)
-        set(MUSA_ARCHITECTURES "21;22")
+        set(MUSA_ARCHITECTURES "21;22;31")
    endif()
    message(STATUS "Using MUSA architectures: ${MUSA_ARCHITECTURES}")

@@ -15,6 +15,7 @@ if (GGML_OPENCL_PROFILING)
 endif ()

 add_compile_definitions(GGML_OPENCL_SOA_Q)
+add_compile_definitions(GGML_OPENCL_TARGET_VERSION=${GGML_OPENCL_TARGET_VERSION})

 if (GGML_OPENCL_USE_ADRENO_KERNELS)
    message(STATUS "OpenCL will use matmul kernels optimized for Adreno")
@@ -1,4 +1,4 @@
-#define CL_TARGET_OPENCL_VERSION 220
+#define CL_TARGET_OPENCL_VERSION GGML_OPENCL_TARGET_VERSION
 #define CL_USE_DEPRECATED_OPENCL_1_2_APIS

 // suppress warnings in CL headers for GCC and Clang
@@ -25,6 +25,8 @@
 #include <vector>
 #include <string>
 #include <cmath>
+#include <memory>
+#include <charconv>

 #undef MIN
 #undef MAX
@@ -62,6 +64,97 @@ enum ADRENO_GPU_GEN {
    X1E,
 };

+struct ggml_cl_version {
+    cl_uint major = 0;
+    cl_uint minor = 0;
+};
+
+// Parses a version string of form "XX.YY ". On an error returns ggml_cl_version with all zeroes.
+static ggml_cl_version parse_cl_version(std::string_view str) {
+    size_t major_str_begin = 0;
+    size_t major_str_end   = str.find(".", major_str_begin);
+    if (major_str_end == std::string::npos) {
+        return {};
+    }
+
+    size_t minor_str_begin = major_str_end + 1;
+    size_t minor_str_end   = str.find(" ", minor_str_begin);
+    if (minor_str_end == std::string::npos) {
+        return {};
+    }
+
+    cl_uint version_major;
+    if (std::from_chars(str.data() + major_str_begin, str.data() + major_str_end, version_major).ec != std::errc{}) {
+        return {};
+    }
+
+    cl_uint version_minor;
+    if (std::from_chars(str.data() + minor_str_begin, str.data() + minor_str_end, version_minor).ec != std::errc{}) {
+        return {};
+    }
+    return { version_major, version_minor };
+}
+
+// Returns OpenCL platform's version. On an error returns ggml_cl_version with all zeroes.
+static ggml_cl_version get_opencl_platform_version(cl_platform_id platform) {
+    size_t param_size;
+    CL_CHECK(clGetPlatformInfo(platform, CL_PLATFORM_VERSION, 0, nullptr, &param_size));
+    std::unique_ptr<char[]> param_storage(new char[param_size]);
+    CL_CHECK(clGetPlatformInfo(platform, CL_PLATFORM_VERSION, param_size, param_storage.get(), nullptr));
+
+    auto              param_value    = std::string_view(param_storage.get(), param_size);
+    const std::string version_prefix = "OpenCL ";  // Suffix: "XX.YY <platform-specific-info>"
+    if (param_value.find(version_prefix) != 0) {
+        return {};
+    }
+    param_value.remove_prefix(version_prefix.length());
+    return parse_cl_version(param_value);
+}
+
+// Return a version to use in OpenCL C compilation. On an error returns ggml_cl_version with all zeroes.
+static ggml_cl_version get_opencl_c_version(ggml_cl_version platform_version, cl_device_id device) {
+    size_t param_size;
+
+#if CL_TARGET_OPENCL_VERSION >= 300
+    if (platform_version.major >= 3) {
+        CL_CHECK(clGetDeviceInfo(device, CL_DEVICE_OPENCL_C_ALL_VERSIONS, 0, nullptr, &param_size));
+        if (!param_size) {
+            return {};
+        }
+
+        std::unique_ptr<cl_name_version[]> versions(new cl_name_version[param_size]);
+        CL_CHECK(clGetDeviceInfo(device, CL_DEVICE_OPENCL_C_ALL_VERSIONS, param_size, versions.get(), nullptr));
+        unsigned versions_count = param_size / sizeof(cl_name_version);
+
+        cl_version version_max = 0;
+        for (unsigned i = 0; i < versions_count; i++) {
+            version_max = std::max<cl_version>(versions[i].version, version_max);
+        }
+
+        return { CL_VERSION_MAJOR(version_max), CL_VERSION_MINOR(version_max) };
+    }
+#else
+    GGML_UNUSED(platform_version);
+#endif  // CL_TARGET_OPENCL_VERSION >= 300
+
+    CL_CHECK(clGetDeviceInfo(device, CL_DEVICE_OPENCL_C_VERSION, 0, nullptr, &param_size));
+    if (!param_size) {
+        return {};
+    }
+
+    std::unique_ptr<char[]> param_storage(new char[param_size]);
+    CL_CHECK(clGetDeviceInfo(device, CL_DEVICE_OPENCL_C_VERSION, param_size, param_storage.get(), nullptr));
+    auto param_value = std::string_view(param_storage.get(), param_size);
+
+    const std::string version_prefix = "OpenCL C ";  // Suffix: "XX.YY <platform-specific-info>"
+    if (param_value.find(version_prefix) != 0) {
+        return {};
+    }
+    param_value.remove_prefix(version_prefix.length());
+
+    return parse_cl_version(param_value);
+}
+
 static ADRENO_GPU_GEN get_adreno_gpu_gen(const char *device_name) {
    if (strstr(device_name, "730") ||
        strstr(device_name, "740") ||
@@ -470,16 +563,11 @@ static ggml_backend_opencl_context * ggml_cl2_init(ggml_backend_dev_t dev) {
    // A local ref of cl_device_id for convenience
    cl_device_id device = backend_ctx->device;

-    // Check device OpenCL version, OpenCL 2.0 or above is required
-    size_t device_ver_str_size;
-    clGetDeviceInfo(device, CL_DEVICE_VERSION, 0, NULL, &device_ver_str_size);
-    char *device_ver_buffer = (char *)alloca(device_ver_str_size + 1);
-    clGetDeviceInfo(device, CL_DEVICE_VERSION, device_ver_str_size, device_ver_buffer, NULL);
-    device_ver_buffer[device_ver_str_size] = '\0';
-    GGML_LOG_INFO("ggml_opencl: device OpenCL version: %s\n", device_ver_buffer);
+    ggml_cl_version platform_version = get_opencl_platform_version(default_device->platform->id);

-    if (strstr(device_ver_buffer, "OpenCL 2") == NULL &&
-        strstr(device_ver_buffer, "OpenCL 3") == NULL) {
+    // Check device OpenCL version, OpenCL 2.0 or above is required
+    ggml_cl_version opencl_c_version = get_opencl_c_version(platform_version, device);
+    if (opencl_c_version.major < 2) {
        GGML_LOG_ERROR("ggml_opencl: OpenCL 2.0 or above is required\n");
        return backend_ctx;
    }
@@ -516,8 +604,7 @@ static ggml_backend_opencl_context * ggml_cl2_init(ggml_backend_dev_t dev) {

    // If OpenCL 3.0 is supported, then check for cl_khr_subgroups, which becomes
    // optional in OpenCL 3.0 (cl_khr_subgroup is mandatory in OpenCL 2.x)
-    if (strstr(device_ver_buffer, "OpenCL 3") &&
-        strstr(ext_buffer, "cl_khr_subgroups") == NULL &&
+    if (opencl_c_version.major == 3 && strstr(ext_buffer, "cl_khr_subgroups") == NULL &&
        strstr(ext_buffer, "cl_intel_subgroups") == NULL) {
        GGML_LOG_ERROR("ggml_opencl: device does not support subgroups (cl_khr_subgroups or cl_intel_subgroups) "
            "(note that subgroups is an optional feature in OpenCL 3.0)\n");
@@ -581,9 +668,12 @@ static ggml_backend_opencl_context * ggml_cl2_init(ggml_backend_dev_t dev) {
    const std::string kernel_src = read_file("ggml-opencl.cl");
 #endif

-    std::string compile_opts =
-        "-cl-std=CL2.0 -cl-mad-enable -cl-unsafe-math-optimizations "
-        "-cl-finite-math-only -cl-fast-relaxed-math ";
+    auto opencl_c_std =
+        std::string("CL") + std::to_string(opencl_c_version.major) + "." + std::to_string(opencl_c_version.minor);
+
+    std::string compile_opts = std::string("-cl-std=") + opencl_c_std +
+                               " -cl-mad-enable -cl-unsafe-math-optimizations"
+                               " -cl-finite-math-only -cl-fast-relaxed-math";
    backend_ctx->program = build_program_from_source(context, device, kernel_src.c_str(), compile_opts);

    // Non matmul kernels.
@@ -693,10 +783,10 @@ static ggml_backend_opencl_context * ggml_cl2_init(ggml_backend_dev_t dev) {
    CL_CHECK((backend_ctx->kernel_transpose_16 = clCreateKernel(backend_ctx->program_transpose_16, "kernel_transpose_16", &err), err));

    // Gemv general
-    std::string CL_gemv_compile_opts =
-        " -cl-std=CL2.0 "
-        " -cl-mad-enable "
-        " -DSIMDGROUP_WIDTH=" + std::to_string(backend_ctx->adreno_wave_size);
+    std::string CL_gemv_compile_opts = std::string("-cl-std=") + opencl_c_std +
+                                       " -cl-mad-enable "
+                                       " -DSIMDGROUP_WIDTH=" +
+                                       std::to_string(backend_ctx->adreno_wave_size);
    if (has_vector_subgroup_broadcast) {
        CL_gemv_compile_opts += " -DVECTOR_SUB_GROUP_BROADCAT ";
    }
@@ -713,12 +803,12 @@ static ggml_backend_opencl_context * ggml_cl2_init(ggml_backend_dev_t dev) {
    CL_CHECK((backend_ctx->CL_mul_mat_vec_q4_0_f32_1d_4x_flat_general = clCreateKernel(backend_ctx->program_CL_gemv_general, "kernel_gemv_noshuffle", &err), err));

    // Gemv 2048, 16384
-    CL_gemv_compile_opts =
-        " -cl-std=CL2.0 "
-        " -cl-mad-enable "
-        " -DLINE_STRIDE_A=2048 "
-        " -DBLOCK_STRIDE_A=16384 "
-        " -DSIMDGROUP_WIDTH=" + std::to_string(backend_ctx->adreno_wave_size);
+    CL_gemv_compile_opts = std::string("-cl-std=") + opencl_c_std +
+                           " -cl-mad-enable "
+                           " -DLINE_STRIDE_A=2048 "
+                           " -DBLOCK_STRIDE_A=16384 "
+                           " -DSIMDGROUP_WIDTH=" +
+                           std::to_string(backend_ctx->adreno_wave_size);
    if (has_vector_subgroup_broadcast) {
        CL_gemv_compile_opts += " -DVECTOR_SUB_GROUP_BROADCAT ";
    }
@@ -735,12 +825,12 @@ static ggml_backend_opencl_context * ggml_cl2_init(ggml_backend_dev_t dev) {
    CL_CHECK((backend_ctx->CL_mul_mat_vec_q4_0_f32_1d_4x_flat_4096_1_4096 = clCreateKernel(backend_ctx->program_CL_gemv_4096_1_4096, "kernel_gemv_noshuffle", &err), err));

    // Gemv 2048, 16384
-    CL_gemv_compile_opts =
-        " -cl-std=CL2.0 "
-        " -cl-mad-enable "
-        " -DLINE_STRIDE_A=2048 "
-        " -DBLOCK_STRIDE_A=16384 "
-        " -DSIMDGROUP_WIDTH=" + std::to_string(backend_ctx->adreno_wave_size);
+    CL_gemv_compile_opts = std::string("-cl-std=") + opencl_c_std +
+                           " -cl-mad-enable "
+                           " -DLINE_STRIDE_A=2048 "
+                           " -DBLOCK_STRIDE_A=16384 "
+                           " -DSIMDGROUP_WIDTH=" +
+                           std::to_string(backend_ctx->adreno_wave_size);
    if (has_vector_subgroup_broadcast) {
        CL_gemv_compile_opts += " -DVECTOR_SUB_GROUP_BROADCAT ";
    }
@@ -750,12 +840,12 @@ static ggml_backend_opencl_context * ggml_cl2_init(ggml_backend_dev_t dev) {
    CL_CHECK((backend_ctx->CL_mul_mat_vec_q4_0_f32_1d_4x_flat_4096_1_11008 = clCreateKernel(backend_ctx->program_CL_gemv_4096_1_11008, "kernel_gemv_noshuffle", &err), err));

    // Gemv 5504, 44032
-    CL_gemv_compile_opts =
-        " -cl-std=CL2.0 "
-        " -cl-mad-enable "
-        " -DLINE_STRIDE_A=5504 "
-        " -DBLOCK_STRIDE_A=44032 "
-        " -DSIMDGROUP_WIDTH=" + std::to_string(backend_ctx->adreno_wave_size);
+    CL_gemv_compile_opts = std::string("-cl-std=") + opencl_c_std +
+                           " -cl-mad-enable "
+                           " -DLINE_STRIDE_A=5504 "
+                           " -DBLOCK_STRIDE_A=44032 "
+                           " -DSIMDGROUP_WIDTH=" +
+                           std::to_string(backend_ctx->adreno_wave_size);
    if (has_vector_subgroup_broadcast) {
        CL_gemv_compile_opts += " -DVECTOR_SUB_GROUP_BROADCAT ";
    }
@@ -765,12 +855,12 @@ static ggml_backend_opencl_context * ggml_cl2_init(ggml_backend_dev_t dev) {
    CL_CHECK((backend_ctx->CL_mul_mat_vec_q4_0_f32_1d_4x_flat_11008_1_4096 = clCreateKernel(backend_ctx->program_CL_gemv_11008_1_4096, "kernel_gemv_noshuffle", &err), err));

    // Gemv 16000, 128000
-    CL_gemv_compile_opts =
-        " -cl-std=CL2.0 "
-        " -cl-mad-enable "
-        " -DLINE_STRIDE_A=16000 "
-        " -DBLOCK_STRIDE_A=128000 "
-        " -DSIMDGROUP_WIDTH=" + std::to_string(backend_ctx->adreno_wave_size);
+    CL_gemv_compile_opts = std::string("-cl-std=") + opencl_c_std +
+                           " -cl-mad-enable "
+                           " -DLINE_STRIDE_A=16000 "
+                           " -DBLOCK_STRIDE_A=128000 "
+                           " -DSIMDGROUP_WIDTH=" +
+                           std::to_string(backend_ctx->adreno_wave_size);
    if (has_vector_subgroup_broadcast) {
        CL_gemv_compile_opts += " -DVECTOR_SUB_GROUP_BROADCAT ";
    }
@@ -474,6 +474,7 @@ static void k_bin_bcast(const src0_t * src0, const src1_t * src1, dst_t * dst,
        int ne0, int ne1, int ne2, int ne3,
        int ne10, int ne11, int ne12, int ne13,
        /*int s0, */ int s1,  int s2,  int s3,
+        /*int s00,*/ int s01, int s02, int s03,
        /*int s10,*/ int s11, int s12, int s13,
        const sycl::nd_item<3> &item_ct1) {
    const int i0s = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
@@ -495,9 +496,9 @@ static void k_bin_bcast(const src0_t * src0, const src1_t * src1, dst_t * dst,
    const int i12 = i2 % ne12;
    const int i13 = i3 % ne13;

-    const size_t i_src0 = i3*s3 + i2*s2 + i1*s1;
+    const size_t i_src0 =  i3*s03 +  i2*s02 +  i1*s01;
    const size_t i_src1 = i13*s13 + i12*s12 + i11*s11;
-    const size_t i_dst  = i_src0;
+    const size_t i_dst  =  i3*s3  +  i2*s2  +  i1*s1;

    const src0_t * src0_row = src0 + i_src0;
    const src1_t * src1_row = src1 + i_src1;
@@ -515,6 +516,7 @@ static void k_bin_bcast_unravel(const src0_t * src0, const src1_t * src1, dst_t
        int ne0, int ne1, int ne2, int ne3,
        int ne10, int ne11, int ne12, int ne13,
        /*int s0, */ int s1,  int s2,  int s3,
+        /*int s00,*/ int s01, int s02, int s03,
        /*int s10,*/ int s11, int s12, int s13,
        const sycl::nd_item<3> &item_ct1) {

@@ -534,9 +536,9 @@ static void k_bin_bcast_unravel(const src0_t * src0, const src1_t * src1, dst_t
    const int i12 = i2 % ne12;
    const int i13 = i3 % ne13;

-    const size_t i_src0 = i3*s3 + i2*s2 + i1*s1;
+    const size_t i_src0 =  i3*s03 +  i2*s02 +  i1*s01;
    const size_t i_src1 = i13*s13 + i12*s12 + i11*s11;
-    const size_t i_dst  = i_src0;
+    const size_t i_dst  =  i3*s3  +  i2*s2  +  i1*s1;

    const src0_t * src0_row = src0 + i_src0;
    const src1_t * src1_row = src1 + i_src1;
@@ -566,9 +568,11 @@ struct bin_bcast_sycl {
        int nr[4] = { nr0, nr1, nr2, nr3 };

        // collapse dimensions until first broadcast dimension
-        int64_t cne0[] = {ne0, ne1, ne2, ne3};
+        int64_t cne[] = {ne0, ne1, ne2, ne3};
+        int64_t cne0[] = {ne00, ne01, ne02, ne03};
        int64_t cne1[] = {ne10, ne11, ne12, ne13};
-        size_t cnb0[] = {nb0, nb1, nb2, nb3};
+        size_t cnb[] = {nb0, nb1, nb2, nb3};
+        size_t cnb0[] = {nb00, nb01, nb02, nb03};
        size_t cnb1[] = {nb10, nb11, nb12, nb13};
        auto collapse = [](int64_t cne[]) {
            cne[0] *= cne[1];
@@ -583,32 +587,41 @@ struct bin_bcast_sycl {
            cnb[3] *= cne[3];
        };

-        for (int i = 0; i < 4; i++) {
-            if (nr[i] != 1) {
-                break;
-            }
-            if (i > 0) {
-                collapse_nb(cnb0, cne0);
-                collapse_nb(cnb1, cne1);
-                collapse(cne0);
-                collapse(cne1);
+        if (ggml_is_contiguous(src0) && ggml_is_contiguous(src1) && ggml_is_contiguous(dst)) {
+            for (int i = 0; i < 4; i++) {
+                if (nr[i] != 1) {
+                    break;
+                }
+                if (i > 0) {
+                    collapse_nb(cnb, cne);
+                    collapse_nb(cnb0, cne0);
+                    collapse_nb(cnb1, cne1);
+                    collapse(cne);
+                    collapse(cne0);
+                    collapse(cne1);
+                }
            }
        }
        {
-            int64_t ne0 = cne0[0];
-            int64_t ne1 = cne0[1];
-            int64_t ne2 = cne0[2];
-            int64_t ne3 = cne0[3];
+            int64_t ne0 = cne[0];
+            int64_t ne1 = cne[1];
+            int64_t ne2 = cne[2];
+            int64_t ne3 = cne[3];

            int64_t ne10 = cne1[0];
            int64_t ne11 = cne1[1];
            int64_t ne12 = cne1[2];
            int64_t ne13 = cne1[3];

-            size_t nb0 = cnb0[0];
-            size_t nb1 = cnb0[1];
-            size_t nb2 = cnb0[2];
-            size_t nb3 = cnb0[3];
+            size_t nb0 = cnb[0];
+            size_t nb1 = cnb[1];
+            size_t nb2 = cnb[2];
+            size_t nb3 = cnb[3];
+
+            size_t nb00 = cnb0[0];
+            size_t nb01 = cnb0[1];
+            size_t nb02 = cnb0[2];
+            size_t nb03 = cnb0[3];

            size_t nb10 = cnb1[0];
            size_t nb11 = cnb1[1];
@@ -625,6 +638,28 @@ struct bin_bcast_sycl {
            size_t s12 = nb12 / sizeof(src1_t);
            size_t s13 = nb13 / sizeof(src1_t);

+            size_t s00 = nb00 / sizeof(src0_t);
+            size_t s01 = nb01 / sizeof(src0_t);
+            size_t s02 = nb02 / sizeof(src0_t);
+            size_t s03 = nb03 / sizeof(src0_t);
+
+            GGML_UNUSED(s00);
+
+            GGML_ASSERT(nb0 % sizeof(dst_t) == 0);
+            GGML_ASSERT(nb1 % sizeof(dst_t) == 0);
+            GGML_ASSERT(nb2 % sizeof(dst_t) == 0);
+            GGML_ASSERT(nb3 % sizeof(dst_t) == 0);
+
+            GGML_ASSERT(nb00 % sizeof(src0_t) == 0);
+            GGML_ASSERT(nb01 % sizeof(src0_t) == 0);
+            GGML_ASSERT(nb02 % sizeof(src0_t) == 0);
+            GGML_ASSERT(nb03 % sizeof(src0_t) == 0);
+
+            GGML_ASSERT(nb10 % sizeof(src1_t) == 0);
+            GGML_ASSERT(nb11 % sizeof(src1_t) == 0);
+            GGML_ASSERT(nb12 % sizeof(src1_t) == 0);
+            GGML_ASSERT(nb13 % sizeof(src1_t) == 0);
+
            GGML_ASSERT(s0 == 1);
            GGML_ASSERT(s10 == 1);

@@ -661,8 +696,8 @@ struct bin_bcast_sycl {
                        [=](sycl::nd_item<3> item_ct1) {
                            k_bin_bcast_unravel<bin_op>(
                                src0_dd, src1_dd, dst_dd, ne0, ne1, ne2, ne3,
-                                ne10, ne11, ne12, ne13, s1, s2, s3, s11, s12,
-                                s13, item_ct1);
+                                ne10, ne11, ne12, ne13, s1, s2, s3, s01, s02,
+                                s03, s11, s12, s13, item_ct1);
                        });
                }
            } else {
@@ -680,7 +715,7 @@ struct bin_bcast_sycl {
                    [=](sycl::nd_item<3> item_ct1) {
                        k_bin_bcast<bin_op>(src0_dd, src1_dd, dst_dd, ne0, ne1,
                                            ne2, ne3, ne10, ne11, ne12, ne13,
-                                            s1, s2, s3, s11, s12, s13,
+                                            s1, s2, s3, s01, s02, s03, s11, s12, s13,
                                            item_ct1);
                    });
            }
@@ -3,44 +3,42 @@
 #include <cassert>

 template <int qk, int qi, typename block_q_t, int vdr, vec_dot_q_sycl_t vec_dot_q_sycl>
-static void mul_mat_vec_q(const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, const int ncols, const int nrows,
-                          const sycl::nd_item<3> &item_ct1) {
-    const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) +
-                    item_ct1.get_local_id(1);
+static void mul_mat_vec_q(const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst,
+                          const int ncols, const int nrows, const sycl::nd_item<3> & item_ct1) {
+    const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) + item_ct1.get_local_id(1);

    if (row >= nrows) {
        return;
    }

-    const int blocks_per_row = ncols / qk;
-    const int blocks_per_warp = vdr * QK_WARP_SIZE / qi;
-    assert(blocks_per_warp>0);
+    const int     blocks_per_row  = ncols / qk;
+    constexpr int blocks_per_warp = (vdr * WARP_SIZE + qi - 1) / qi;  // Ensuring blocks_per_warp > 0

-// partial sum for each thread
+    assert(blocks_per_warp > 0);
+
+    // partial sum for each thread
    float tmp = 0.0f;

-    const block_q_t  * x = (const block_q_t  *) vx;
+    const block_q_t *  x = (const block_q_t *) vx;
    const block_q8_1 * y = (const block_q8_1 *) vy;

-    for (int i = item_ct1.get_local_id(2) / (qi / vdr); i < blocks_per_row;
-         i += blocks_per_warp) {
-        const int ibx = row*blocks_per_row + i; // x block index
+    for (int i = item_ct1.get_local_id(2) / (qi / vdr); i < blocks_per_row; i += blocks_per_warp) {
+        const int ibx = row * blocks_per_row + i;  // x block index

-        const int iby = i * (qk/QK8_1); // y block index that aligns with ibx
+        const int iby = i * (qk / QK8_1);          // y block index that aligns with ibx

-        const int iqs =
-            vdr *
-            (item_ct1.get_local_id(2) %
-             (qi / vdr)); // x block quant index when casting the quants to int
+        for (size_t elem = 0; elem < qi / vdr; elem += WARP_SIZE) {
+            const int iqs = elem + vdr * (item_ct1.get_local_id(2) %
+                                          (qi / vdr));  // x block quant index when casting the quants to int

-        tmp += vec_dot_q_sycl(&x[ibx], &y[iby], iqs);
+            tmp += vec_dot_q_sycl(&x[ibx], &y[iby], iqs);
+        }
    }

    // sum up partial sums and write back result
 #pragma unroll
-    for (int mask = QK_WARP_SIZE / 2; mask > 0; mask >>= 1) {
-        tmp +=
-            dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
+    for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
+        tmp += dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
    }

    if (item_ct1.get_local_id(2) == 0) {
@@ -62,7 +60,7 @@ static void mul_mat_vec_q_iq2_xxs_q8_1(const void *__restrict__ vx,
    }

    const int blocks_per_row = ncols / qk;
-    const int blocks_per_warp = vdr * QK_WARP_SIZE / qi;
+    const int blocks_per_warp = vdr * WARP_SIZE / qi;
    assert(blocks_per_warp>0);

 // partial sum for each thread
@@ -87,7 +85,7 @@ static void mul_mat_vec_q_iq2_xxs_q8_1(const void *__restrict__ vx,

    // sum up partial sums and write back result
 #pragma unroll
-    for (int mask = QK_WARP_SIZE / 2; mask > 0; mask >>= 1) {
+    for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
        tmp +=
            dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
    }
@@ -111,7 +109,7 @@ static void mul_mat_vec_q_iq2_xs_q8_1(const void *__restrict__ vx,
    }

    const int blocks_per_row = ncols / qk;
-    const int blocks_per_warp = vdr * QK_WARP_SIZE / qi;
+    const int blocks_per_warp = vdr * WARP_SIZE / qi;
    assert(blocks_per_warp>0);
 // partial sum for each thread
    float tmp = 0.0f;
@@ -135,7 +133,7 @@ static void mul_mat_vec_q_iq2_xs_q8_1(const void *__restrict__ vx,

    // sum up partial sums and write back result
 #pragma unroll
-    for (int mask = QK_WARP_SIZE / 2; mask > 0; mask >>= 1) {
+    for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
        tmp +=
            dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
    }
@@ -159,7 +157,7 @@ static void mul_mat_vec_q_iq2_s_q8_1(const void *__restrict__ vx,
    }

    const int blocks_per_row = ncols / qk;
-    const int blocks_per_warp = vdr * QK_WARP_SIZE / qi;
+    const int blocks_per_warp = vdr * WARP_SIZE / qi;
    assert(blocks_per_warp>0);
 // partial sum for each thread
    float tmp = 0.0f;
@@ -183,7 +181,7 @@ static void mul_mat_vec_q_iq2_s_q8_1(const void *__restrict__ vx,

    // sum up partial sums and write back result
 #pragma unroll
-    for (int mask = QK_WARP_SIZE / 2; mask > 0; mask >>= 1) {
+    for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
        tmp +=
            dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
    }
@@ -207,7 +205,7 @@ static void mul_mat_vec_q_iq3_xxs_q8_1(const void *__restrict__ vx,
    }

    const int blocks_per_row = ncols / qk;
-    const int blocks_per_warp = vdr * QK_WARP_SIZE / qi;
+    const int blocks_per_warp = vdr * WARP_SIZE / qi;
    assert(blocks_per_warp>0);
 // partial sum for each thread
    float tmp = 0.0f;
@@ -231,7 +229,7 @@ static void mul_mat_vec_q_iq3_xxs_q8_1(const void *__restrict__ vx,

    // sum up partial sums and write back result
 #pragma unroll
-    for (int mask = QK_WARP_SIZE / 2; mask > 0; mask >>= 1) {
+    for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
        tmp +=
            dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
    }
@@ -255,7 +253,7 @@ static void mul_mat_vec_q_iq3_s_q8_1(const void *__restrict__ vx,
    }

    const int blocks_per_row = ncols / qk;
-    const int blocks_per_warp = vdr * QK_WARP_SIZE / qi;
+    const int blocks_per_warp = vdr * WARP_SIZE / qi;
    assert(blocks_per_warp>0);
 // partial sum for each thread
    float tmp = 0.0f;
@@ -279,7 +277,7 @@ static void mul_mat_vec_q_iq3_s_q8_1(const void *__restrict__ vx,

    // sum up partial sums and write back result
 #pragma unroll
-    for (int mask = QK_WARP_SIZE / 2; mask > 0; mask >>= 1) {
+    for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
        tmp +=
            dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
    }
@@ -303,7 +301,7 @@ static void mul_mat_vec_q_iq1_s_q8_1(const void *__restrict__ vx,
    }

    const int blocks_per_row = ncols / qk;
-    const int blocks_per_warp = vdr * QK_WARP_SIZE / qi;
+    const int blocks_per_warp = vdr * WARP_SIZE / qi;
    assert(blocks_per_warp>0);
 // partial sum for each thread
    float tmp = 0.0f;
@@ -327,7 +325,7 @@ static void mul_mat_vec_q_iq1_s_q8_1(const void *__restrict__ vx,

    // sum up partial sums and write back result
 #pragma unroll
-    for (int mask = QK_WARP_SIZE / 2; mask > 0; mask >>= 1) {
+    for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
        tmp +=
            dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
    }
@@ -351,7 +349,7 @@ static void mul_mat_vec_q_iq1_m_q8_1(const void *__restrict__ vx,
    }

    const int blocks_per_row = ncols / qk;
-    const int blocks_per_warp = vdr * QK_WARP_SIZE / qi;
+    const int blocks_per_warp = vdr * WARP_SIZE / qi;
    assert(blocks_per_warp>0);
 // partial sum for each thread
    float tmp = 0.0f;
@@ -375,7 +373,7 @@ static void mul_mat_vec_q_iq1_m_q8_1(const void *__restrict__ vx,

    // sum up partial sums and write back result
 #pragma unroll
-    for (int mask = QK_WARP_SIZE / 2; mask > 0; mask >>= 1) {
+    for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
        tmp +=
            dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
    }
@@ -399,7 +397,7 @@ static void mul_mat_vec_q_iq4_nl_q8_1(const void *__restrict__ vx,
    }

    const int blocks_per_row = ncols / qk;
-    const int blocks_per_warp = vdr * QK_WARP_SIZE / qi;
+    const int blocks_per_warp = vdr * WARP_SIZE / qi;
    assert(blocks_per_warp>0);
 // partial sum for each thread
    float tmp = 0.0f;
@@ -423,7 +421,7 @@ static void mul_mat_vec_q_iq4_nl_q8_1(const void *__restrict__ vx,

    // sum up partial sums and write back result
 #pragma unroll
-    for (int mask = QK_WARP_SIZE / 2; mask > 0; mask >>= 1) {
+    for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
        tmp +=
            dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
    }
@@ -448,7 +446,7 @@ static void mul_mat_vec_q_iq4_xs_q8_1(const void *__restrict__ vx,
    }

    const int blocks_per_row = ncols / qk;
-    const int blocks_per_warp = vdr * QK_WARP_SIZE / qi;
+    const int blocks_per_warp = vdr * WARP_SIZE / qi;
    assert(blocks_per_warp>0);
 // partial sum for each thread
    float tmp = 0.0f;
@@ -472,7 +470,7 @@ static void mul_mat_vec_q_iq4_xs_q8_1(const void *__restrict__ vx,

    // sum up partial sums and write back result
 #pragma unroll
-    for (int mask = QK_WARP_SIZE / 2; mask > 0; mask >>= 1) {
+    for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
        tmp +=
            dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
    }
@@ -489,7 +487,7 @@ static void mul_mat_vec_q4_0_q8_1_sycl(const void *vx, const void *vy,
    GGML_ASSERT(ncols % QK4_0 == 0);
    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
    const sycl::range<3> block_nums(1, 1, block_num_y);
-    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, QK_WARP_SIZE);
+    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
    {

        stream->submit([&](sycl::handler &cgh) {
@@ -497,7 +495,7 @@ static void mul_mat_vec_q4_0_q8_1_sycl(const void *vx, const void *vy,
            cgh.parallel_for(
                sycl::nd_range<3>(block_nums * block_dims, block_dims),
                [=](sycl::nd_item<3> item_ct1)
-                    [[intel::reqd_sub_group_size(QK_WARP_SIZE)]] {
+                    [[intel::reqd_sub_group_size(WARP_SIZE)]] {
                        mul_mat_vec_q<QK4_0, QI4_0, block_q4_0,
                                      VDR_Q4_0_Q8_1_MMVQ, vec_dot_q4_0_q8_1>(
                            vx, vy, dst, ncols, nrows, item_ct1);
@@ -513,7 +511,7 @@ static void mul_mat_vec_q4_1_q8_1_sycl(const void *vx, const void *vy,
    GGML_ASSERT(ncols % QK4_1 == 0);
    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
    const sycl::range<3> block_nums(1, 1, block_num_y);
-    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, QK_WARP_SIZE);
+    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
    {

        stream->submit([&](sycl::handler &cgh) {
@@ -521,7 +519,7 @@ static void mul_mat_vec_q4_1_q8_1_sycl(const void *vx, const void *vy,
            cgh.parallel_for(
                sycl::nd_range<3>(block_nums * block_dims, block_dims),
                [=](sycl::nd_item<3> item_ct1)
-                    [[intel::reqd_sub_group_size(QK_WARP_SIZE)]] {
+                    [[intel::reqd_sub_group_size(WARP_SIZE)]] {
                        mul_mat_vec_q<QK4_0, QI4_1, block_q4_1,
                                      VDR_Q4_1_Q8_1_MMVQ, vec_dot_q4_1_q8_1>(
                            vx, vy, dst, ncols, nrows, item_ct1);
@@ -537,7 +535,7 @@ static void mul_mat_vec_q5_0_q8_1_sycl(const void *vx, const void *vy,
    GGML_ASSERT(ncols % QK5_0 == 0);
    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
    const sycl::range<3> block_nums(1, 1, block_num_y);
-    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, QK_WARP_SIZE);
+    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
    {

        stream->submit([&](sycl::handler &cgh) {
@@ -545,7 +543,7 @@ static void mul_mat_vec_q5_0_q8_1_sycl(const void *vx, const void *vy,
            cgh.parallel_for(
                sycl::nd_range<3>(block_nums * block_dims, block_dims),
                [=](sycl::nd_item<3> item_ct1)
-                    [[intel::reqd_sub_group_size(QK_WARP_SIZE)]] {
+                    [[intel::reqd_sub_group_size(WARP_SIZE)]] {
                        mul_mat_vec_q<QK5_0, QI5_0, block_q5_0,
                                      VDR_Q5_0_Q8_1_MMVQ, vec_dot_q5_0_q8_1>(
                            vx, vy, dst, ncols, nrows, item_ct1);
@@ -561,7 +559,7 @@ static void mul_mat_vec_q5_1_q8_1_sycl(const void *vx, const void *vy,
    GGML_ASSERT(ncols % QK5_1 == 0);
    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
    const sycl::range<3> block_nums(1, 1, block_num_y);
-    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, QK_WARP_SIZE);
+    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
    {

        stream->submit([&](sycl::handler &cgh) {
@@ -569,7 +567,7 @@ static void mul_mat_vec_q5_1_q8_1_sycl(const void *vx, const void *vy,
            cgh.parallel_for(
                sycl::nd_range<3>(block_nums * block_dims, block_dims),
                [=](sycl::nd_item<3> item_ct1)
-                    [[intel::reqd_sub_group_size(QK_WARP_SIZE)]] {
+                    [[intel::reqd_sub_group_size(WARP_SIZE)]] {
                        mul_mat_vec_q<QK5_1, QI5_1, block_q5_1,
                                      VDR_Q5_1_Q8_1_MMVQ, vec_dot_q5_1_q8_1>(
                            vx, vy, dst, ncols, nrows, item_ct1);
@@ -585,7 +583,7 @@ static void mul_mat_vec_q8_0_q8_1_sycl(const void *vx, const void *vy,
    GGML_ASSERT(ncols % QK8_0 == 0);
    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
    const sycl::range<3> block_nums(1, 1, block_num_y);
-    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, QK_WARP_SIZE);
+    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
    {

        stream->submit([&](sycl::handler &cgh) {
@@ -593,7 +591,7 @@ static void mul_mat_vec_q8_0_q8_1_sycl(const void *vx, const void *vy,
            cgh.parallel_for(
                sycl::nd_range<3>(block_nums * block_dims, block_dims),
                [=](sycl::nd_item<3> item_ct1)
-                    [[intel::reqd_sub_group_size(QK_WARP_SIZE)]] {
+                    [[intel::reqd_sub_group_size(WARP_SIZE)]] {
                        mul_mat_vec_q<QK8_0, QI8_0, block_q8_0,
                                      VDR_Q8_0_Q8_1_MMVQ, vec_dot_q8_0_q8_1>(
                            vx, vy, dst, ncols, nrows, item_ct1);
@@ -609,7 +607,7 @@ static void mul_mat_vec_q2_K_q8_1_sycl(const void *vx, const void *vy,
    GGML_ASSERT(ncols % QK_K == 0);
    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
    const sycl::range<3> block_nums(1, 1, block_num_y);
-    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, QK_WARP_SIZE);
+    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
    {

        stream->submit([&](sycl::handler &cgh) {
@@ -617,7 +615,7 @@ static void mul_mat_vec_q2_K_q8_1_sycl(const void *vx, const void *vy,
            cgh.parallel_for(
                sycl::nd_range<3>(block_nums * block_dims, block_dims),
                [=](sycl::nd_item<3> item_ct1)
-                    [[intel::reqd_sub_group_size(QK_WARP_SIZE)]] {
+                    [[intel::reqd_sub_group_size(WARP_SIZE)]] {
                        mul_mat_vec_q<QK_K, QI2_K, block_q2_K,
                                      VDR_Q2_K_Q8_1_MMVQ, vec_dot_q2_K_q8_1>(
                            vx, vy, dst, ncols, nrows, item_ct1);
@@ -633,7 +631,7 @@ static void mul_mat_vec_q3_K_q8_1_sycl(const void *vx, const void *vy,
    GGML_ASSERT(ncols % QK_K == 0);
    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
    const sycl::range<3> block_nums(1, 1, block_num_y);
-    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, QK_WARP_SIZE);
+    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
    {

        stream->submit([&](sycl::handler &cgh) {
@@ -641,7 +639,7 @@ static void mul_mat_vec_q3_K_q8_1_sycl(const void *vx, const void *vy,
            cgh.parallel_for(
                sycl::nd_range<3>(block_nums * block_dims, block_dims),
                [=](sycl::nd_item<3> item_ct1)
-                    [[intel::reqd_sub_group_size(QK_WARP_SIZE)]] {
+                    [[intel::reqd_sub_group_size(WARP_SIZE)]] {
                        mul_mat_vec_q<QK_K, QI3_K, block_q3_K,
                                      VDR_Q3_K_Q8_1_MMVQ, vec_dot_q3_K_q8_1>(
                            vx, vy, dst, ncols, nrows, item_ct1);
@@ -657,7 +655,7 @@ static void mul_mat_vec_q4_K_q8_1_sycl(const void *vx, const void *vy,
    GGML_ASSERT(ncols % QK_K == 0);
    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
    const sycl::range<3> block_nums(1, 1, block_num_y);
-    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, QK_WARP_SIZE);
+    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
    {

        stream->submit([&](sycl::handler &cgh) {
@@ -665,7 +663,7 @@ static void mul_mat_vec_q4_K_q8_1_sycl(const void *vx, const void *vy,
            cgh.parallel_for(
                sycl::nd_range<3>(block_nums * block_dims, block_dims),
                [=](sycl::nd_item<3> item_ct1)
-                    [[intel::reqd_sub_group_size(QK_WARP_SIZE)]] {
+                    [[intel::reqd_sub_group_size(WARP_SIZE)]] {
                        mul_mat_vec_q<QK_K, QI4_K, block_q4_K,
                                      VDR_Q4_K_Q8_1_MMVQ, vec_dot_q4_K_q8_1>(
                            vx, vy, dst, ncols, nrows, item_ct1);
@@ -681,7 +679,7 @@ static void mul_mat_vec_q5_K_q8_1_sycl(const void *vx, const void *vy,
    GGML_ASSERT(ncols % QK_K == 0);
    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
    const sycl::range<3> block_nums(1, 1, block_num_y);
-    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, QK_WARP_SIZE);
+    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
    {

        stream->submit([&](sycl::handler &cgh) {
@@ -689,7 +687,7 @@ static void mul_mat_vec_q5_K_q8_1_sycl(const void *vx, const void *vy,
            cgh.parallel_for(
                sycl::nd_range<3>(block_nums * block_dims, block_dims),
                [=](sycl::nd_item<3> item_ct1)
-                    [[intel::reqd_sub_group_size(QK_WARP_SIZE)]] {
+                    [[intel::reqd_sub_group_size(WARP_SIZE)]] {
                        mul_mat_vec_q<QK_K, QI5_K, block_q5_K,
                                      VDR_Q5_K_Q8_1_MMVQ, vec_dot_q5_K_q8_1>(
                            vx, vy, dst, ncols, nrows, item_ct1);
@@ -705,7 +703,7 @@ static void mul_mat_vec_q6_K_q8_1_sycl(const void *vx, const void *vy,
    GGML_ASSERT(ncols % QK_K == 0);
    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
    const sycl::range<3> block_nums(1, 1, block_num_y);
-    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, QK_WARP_SIZE);
+    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
    {

        stream->submit([&](sycl::handler &cgh) {
@@ -713,7 +711,7 @@ static void mul_mat_vec_q6_K_q8_1_sycl(const void *vx, const void *vy,
            cgh.parallel_for(
                sycl::nd_range<3>(block_nums * block_dims, block_dims),
                [=](sycl::nd_item<3> item_ct1)
-                    [[intel::reqd_sub_group_size(QK_WARP_SIZE)]] {
+                    [[intel::reqd_sub_group_size(WARP_SIZE)]] {
                        mul_mat_vec_q<QK_K, QI6_K, block_q6_K,
                                      VDR_Q6_K_Q8_1_MMVQ, vec_dot_q6_K_q8_1>(
                            vx, vy, dst, ncols, nrows, item_ct1);
@@ -730,13 +728,13 @@ static void mul_mat_vec_iq2_xxs_q8_1_sycl(const void *vx, const void *vy,
    GGML_ASSERT(ncols % QK_K == 0);
    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
    const sycl::range<3> block_nums(1, 1, block_num_y);
-    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, QK_WARP_SIZE);
+    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
    {
        stream->submit([&](sycl::handler &cgh) {
            cgh.parallel_for(
                sycl::nd_range<3>(block_nums * block_dims, block_dims),
                [=](sycl::nd_item<3> item_ct1)
-                    [[intel::reqd_sub_group_size(QK_WARP_SIZE)]] {
+                    [[intel::reqd_sub_group_size(WARP_SIZE)]] {
                        mul_mat_vec_q_iq2_xxs_q8_1<QK_K, QI2_XXS/2, block_iq2_xxs, 1>(
                            vx, vy, dst, ncols, nrows, item_ct1);
                    });
@@ -751,13 +749,13 @@ static void mul_mat_vec_iq2_xs_q8_1_sycl(const void *vx, const void *vy,
    GGML_ASSERT(ncols % QK_K == 0);
    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
    const sycl::range<3> block_nums(1, 1, block_num_y);
-    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, QK_WARP_SIZE);
+    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
    {
        stream->submit([&](sycl::handler & cgh) {
            cgh.parallel_for(
                sycl::nd_range<3>(block_nums * block_dims, block_dims),
                [=](sycl::nd_item<3> item_ct1)
-                    [[intel::reqd_sub_group_size(QK_WARP_SIZE)]] {
+                    [[intel::reqd_sub_group_size(WARP_SIZE)]] {
                        mul_mat_vec_q_iq2_xs_q8_1<QK_K, QI2_XS/2, block_iq2_xs, 1>(
                            vx, vy, dst, ncols, nrows, item_ct1);
                    });
@@ -772,14 +770,14 @@ static void mul_mat_vec_iq2_s_q8_1_sycl(const void *vx, const void *vy,
    GGML_ASSERT(ncols % QK_K == 0);
    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
    const sycl::range<3> block_nums(1, 1, block_num_y);
-    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, QK_WARP_SIZE);
+    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
    {

        stream->submit([&](sycl::handler &cgh) {
            cgh.parallel_for(
                sycl::nd_range<3>(block_nums * block_dims, block_dims),
                [=](sycl::nd_item<3> item_ct1)
-                    [[intel::reqd_sub_group_size(QK_WARP_SIZE)]] {
+                    [[intel::reqd_sub_group_size(WARP_SIZE)]] {
                        mul_mat_vec_q_iq2_s_q8_1<QK_K, QI2_S/2, block_iq2_s, 1>(
                            vx, vy, dst, ncols, nrows, item_ct1);
                    });
@@ -794,14 +792,14 @@ static void mul_mat_vec_iq3_xxs_q8_1_sycl(const void *vx, const void *vy,
    GGML_ASSERT(ncols % QK_K == 0);
    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
    const sycl::range<3> block_nums(1, 1, block_num_y);
-    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, QK_WARP_SIZE);
+    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
    {

        stream->submit([&](sycl::handler &cgh) {
            cgh.parallel_for(
                sycl::nd_range<3>(block_nums * block_dims, block_dims),
                [=](sycl::nd_item<3> item_ct1)
-                    [[intel::reqd_sub_group_size(QK_WARP_SIZE)]] {
+                    [[intel::reqd_sub_group_size(WARP_SIZE)]] {
                        mul_mat_vec_q_iq3_xxs_q8_1<QK_K, QI3_XXS/2, block_iq3_xxs, 1>(
                            vx, vy, dst, ncols, nrows, item_ct1);
                    });
@@ -816,14 +814,14 @@ static void mul_mat_vec_iq3_s_q8_1_sycl(const void *vx, const void *vy,
    GGML_ASSERT(ncols % QK_K == 0);
    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
    const sycl::range<3> block_nums(1, 1, block_num_y);
-    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, QK_WARP_SIZE);
+    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
    {

        stream->submit([&](sycl::handler &cgh) {
            cgh.parallel_for(
                sycl::nd_range<3>(block_nums * block_dims, block_dims),
                [=](sycl::nd_item<3> item_ct1)
-                    [[intel::reqd_sub_group_size(QK_WARP_SIZE)]] {
+                    [[intel::reqd_sub_group_size(WARP_SIZE)]] {
                        mul_mat_vec_q_iq3_s_q8_1<QK_K, QI3_S/2, block_iq3_s, 1>(
                            vx, vy, dst, ncols, nrows, item_ct1);
                    });
@@ -838,14 +836,14 @@ static void mul_mat_vec_iq1_s_q8_1_sycl(const void *vx, const void *vy,
    GGML_ASSERT(ncols % QK_K == 0);
    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
    const sycl::range<3> block_nums(1, 1, block_num_y);
-    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, QK_WARP_SIZE);
+    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
    {

        stream->submit([&](sycl::handler &cgh) {
            cgh.parallel_for(
                sycl::nd_range<3>(block_nums * block_dims, block_dims),
                [=](sycl::nd_item<3> item_ct1)
-                    [[intel::reqd_sub_group_size(QK_WARP_SIZE)]] {
+                    [[intel::reqd_sub_group_size(WARP_SIZE)]] {
                        mul_mat_vec_q_iq1_s_q8_1<QK_K, QI1_S, block_iq1_s, 1>(
                            vx, vy, dst, ncols, nrows, item_ct1);
                    });
@@ -860,13 +858,13 @@ static void mul_mat_vec_iq1_m_q8_1_sycl(const void *vx, const void *vy,
    GGML_ASSERT(ncols % QK_K == 0);
    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
    const sycl::range<3> block_nums(1, 1, block_num_y);
-    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, QK_WARP_SIZE);
+    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
    {
        stream->submit([&](sycl::handler &cgh) {
            cgh.parallel_for(
                sycl::nd_range<3>(block_nums * block_dims, block_dims),
                [=](sycl::nd_item<3> item_ct1)
-                    [[intel::reqd_sub_group_size(QK_WARP_SIZE)]] {
+                    [[intel::reqd_sub_group_size(WARP_SIZE)]] {
                        mul_mat_vec_q_iq1_m_q8_1<QK_K, QI1_S, block_iq1_m, 1>(
                            vx, vy, dst, ncols, nrows, item_ct1);
                    });
@@ -881,14 +879,14 @@ static void mul_mat_vec_iq4_nl_q8_1_sycl(const void *vx, const void *vy,
    GGML_ASSERT(ncols % QK4_NL == 0);
    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
    const sycl::range<3> block_nums(1, 1, block_num_y);
-    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, QK_WARP_SIZE);
+    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
    {

        stream->submit([&](sycl::handler &cgh) {
            cgh.parallel_for(
                sycl::nd_range<3>(block_nums * block_dims, block_dims),
                [=](sycl::nd_item<3> item_ct1)
-                    [[intel::reqd_sub_group_size(QK_WARP_SIZE)]] {
+                    [[intel::reqd_sub_group_size(WARP_SIZE)]] {
                        mul_mat_vec_q_iq4_nl_q8_1<QK4_NL, QI4_NL, block_iq4_nl, 2>(
                            vx, vy, dst, ncols, nrows, item_ct1);
                    });
@@ -903,14 +901,14 @@ static void mul_mat_vec_iq4_xs_q8_1_sycl(const void *vx, const void *vy,
    GGML_ASSERT(ncols % QK_K == 0);
    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
    const sycl::range<3> block_nums(1, 1, block_num_y);
-    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, QK_WARP_SIZE);
+    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
    {

        stream->submit([&](sycl::handler &cgh) {
            cgh.parallel_for(
                sycl::nd_range<3>(block_nums * block_dims, block_dims),
                [=](sycl::nd_item<3> item_ct1)
-                    [[intel::reqd_sub_group_size(QK_WARP_SIZE)]] {
+                    [[intel::reqd_sub_group_size(WARP_SIZE)]] {
                        mul_mat_vec_q_iq4_xs_q8_1<QK_K, QI4_XS/4, block_iq4_xs, 1>(
                            vx, vy, dst, ncols, nrows, item_ct1);
                    });
@@ -5,23 +5,24 @@

 layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in;

-shared FLOAT_TYPE sccache1[BLOCK_SIZE/16][16];
-shared FLOAT_TYPE sccache2[BLOCK_SIZE/16][16];
+shared FLOAT_TYPE sccache1[2][BLOCK_SIZE/16][16];
+shared FLOAT_TYPE sccache2[2][BLOCK_SIZE/16][16];

 FLOAT_TYPE temp[NUM_COLS][NUM_ROWS];
+uint csel = 0;

 void calc_superblock(const uint a_offset, const uint b_offset, const uint itid, const uint v_im, const uint ix, const uint q_offset, const uint y_offset, const uint i, const uint num_blocks_per_row, const uint first_row, const uint num_rows, const bool all_threads) {
    const uint y_idx = i * QUANT_K + y_offset;

    [[unroll]] for (uint n = 0; n < num_rows; ++n) {
        const uint ib0 = a_offset / QUANT_K + (first_row+n)*num_blocks_per_row;
+        csel ^= 1;

-        barrier();
        if (!all_threads) { // when we don't have enough blocks to use all threads
            if (i < num_blocks_per_row) {
                const uint32_t scale = uint32_t(data_a[ib0 + i].scales[itid]);
-                sccache1[ix][itid] = FLOAT_TYPE(scale & 0xF);
-                sccache2[ix][itid] = FLOAT_TYPE((scale >> 4) & 0xF);
+                sccache1[csel][ix][itid] = FLOAT_TYPE(scale & 0xF);
+                sccache2[csel][ix][itid] = FLOAT_TYPE((scale >> 4) & 0xF);
            }
            barrier();

@@ -29,8 +30,8 @@ void calc_superblock(const uint a_offset, const uint b_offset, const uint itid,
                continue;
        } else {
            const uint32_t scale = uint32_t(data_a[ib0 + i].scales[itid]);
-            sccache1[ix][itid] = FLOAT_TYPE(scale & 0xF);
-            sccache2[ix][itid] = FLOAT_TYPE((scale >> 4) & 0xF);
+            sccache1[csel][ix][itid] = FLOAT_TYPE(scale & 0xF);
+            sccache2[csel][ix][itid] = FLOAT_TYPE((scale >> 4) & 0xF);
            barrier();
        }

@@ -57,22 +58,22 @@ void calc_superblock(const uint a_offset, const uint b_offset, const uint itid,
            FLOAT_TYPE sum1 = FLOAT_TYPE(0.0);
            FLOAT_TYPE sum2 = FLOAT_TYPE(0.0);
            [[unroll]] for (int l = 0; l < 2; ++l) {
-                sum1 = fma(FLOAT_TYPE(b0[l]),   sccache1[ix][    8*v_im] * qs_u32_0[l  ],
-                       fma(FLOAT_TYPE(b16[l]),  sccache1[ix][1 + 8*v_im] * qs_u32_0[l+2],
-                       fma(FLOAT_TYPE(b32[l]),  sccache1[ix][2 + 8*v_im] * qs_u32_2[l  ],
-                       fma(FLOAT_TYPE(b48[l]),  sccache1[ix][3 + 8*v_im] * qs_u32_2[l+2],
-                       fma(FLOAT_TYPE(b64[l]),  sccache1[ix][4 + 8*v_im] * qs_u32_4[l  ],
-                       fma(FLOAT_TYPE(b80[l]),  sccache1[ix][5 + 8*v_im] * qs_u32_4[l+2],
-                       fma(FLOAT_TYPE(b96[l]),  sccache1[ix][6 + 8*v_im] * qs_u32_6[l  ],
-                       fma(FLOAT_TYPE(b112[l]), sccache1[ix][7 + 8*v_im] * qs_u32_6[l+2], sum1))))))));
-                sum2 = fma(FLOAT_TYPE(b0[l]),   sccache2[ix][    8*v_im],
-                       fma(FLOAT_TYPE(b16[l]),  sccache2[ix][1 + 8*v_im],
-                       fma(FLOAT_TYPE(b32[l]),  sccache2[ix][2 + 8*v_im],
-                       fma(FLOAT_TYPE(b48[l]),  sccache2[ix][3 + 8*v_im],
-                       fma(FLOAT_TYPE(b64[l]),  sccache2[ix][4 + 8*v_im],
-                       fma(FLOAT_TYPE(b80[l]),  sccache2[ix][5 + 8*v_im],
-                       fma(FLOAT_TYPE(b96[l]),  sccache2[ix][6 + 8*v_im],
-                       fma(FLOAT_TYPE(b112[l]), sccache2[ix][7 + 8*v_im], sum2))))))));
+                sum1 = fma(FLOAT_TYPE(b0[l]),   sccache1[csel][ix][    8*v_im] * qs_u32_0[l  ],
+                       fma(FLOAT_TYPE(b16[l]),  sccache1[csel][ix][1 + 8*v_im] * qs_u32_0[l+2],
+                       fma(FLOAT_TYPE(b32[l]),  sccache1[csel][ix][2 + 8*v_im] * qs_u32_2[l  ],
+                       fma(FLOAT_TYPE(b48[l]),  sccache1[csel][ix][3 + 8*v_im] * qs_u32_2[l+2],
+                       fma(FLOAT_TYPE(b64[l]),  sccache1[csel][ix][4 + 8*v_im] * qs_u32_4[l  ],
+                       fma(FLOAT_TYPE(b80[l]),  sccache1[csel][ix][5 + 8*v_im] * qs_u32_4[l+2],
+                       fma(FLOAT_TYPE(b96[l]),  sccache1[csel][ix][6 + 8*v_im] * qs_u32_6[l  ],
+                       fma(FLOAT_TYPE(b112[l]), sccache1[csel][ix][7 + 8*v_im] * qs_u32_6[l+2], sum1))))))));
+                sum2 = fma(FLOAT_TYPE(b0[l]),   sccache2[csel][ix][    8*v_im],
+                       fma(FLOAT_TYPE(b16[l]),  sccache2[csel][ix][1 + 8*v_im],
+                       fma(FLOAT_TYPE(b32[l]),  sccache2[csel][ix][2 + 8*v_im],
+                       fma(FLOAT_TYPE(b48[l]),  sccache2[csel][ix][3 + 8*v_im],
+                       fma(FLOAT_TYPE(b64[l]),  sccache2[csel][ix][4 + 8*v_im],
+                       fma(FLOAT_TYPE(b80[l]),  sccache2[csel][ix][5 + 8*v_im],
+                       fma(FLOAT_TYPE(b96[l]),  sccache2[csel][ix][6 + 8*v_im],
+                       fma(FLOAT_TYPE(b112[l]), sccache2[csel][ix][7 + 8*v_im], sum2))))))));
            }
            temp[j][n] = fma(dall, sum1, fma(-dmin, sum2, temp[j][n]));
        }
@@ -5,20 +5,21 @@

 layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in;

-shared FLOAT_TYPE sccache[BLOCK_SIZE/16][2][8];
+shared FLOAT_TYPE sccache[2][BLOCK_SIZE/16][2][8];

 FLOAT_TYPE temp[NUM_COLS][NUM_ROWS];
+uint csel = 0;

 void calc_superblock(const uint a_offset, const uint b_offset, const uint ix, const uint itid8, const uint v_im, const uint v_im4, const uint v_in, const uint32_t hm_m[4], const uint q_offset, const uint y_offset, const uint s_shift, const uint i, const uint num_blocks_per_row, const uint first_row, const uint num_rows, const bool all_threads) {
    const uint y_idx = i * QUANT_K + y_offset;

    [[unroll]] for (uint n = 0; n < num_rows; ++n) {
        const uint ib0 = a_offset / QUANT_K + (first_row+n)*num_blocks_per_row;
+        csel ^= 1;

        if (!all_threads) { // when we don't have enough blocks to use all threads
-            barrier();
            if (i < num_blocks_per_row)
-                sccache[ix][v_im][itid8] = FLOAT_TYPE(int8_t(((data_a[ib0+i].scales[itid8] >> v_im4) & 0xF) | (((data_a[ib0+i].scales[itid8%4+8] >> s_shift) & 3) << 4)) - 32);
+                sccache[csel][ix][v_im][itid8] = FLOAT_TYPE(int8_t(((data_a[ib0+i].scales[itid8] >> v_im4) & 0xF) | (((data_a[ib0+i].scales[itid8%4+8] >> s_shift) & 3) << 4)) - 32);
            barrier();

            if (i >= num_blocks_per_row)
@@ -40,8 +41,7 @@ void calc_superblock(const uint a_offset, const uint b_offset, const uint ix, co
        const vec4 qs_u32_6 = vec4(unpack8((qs_u32 >> 6) & 0x03030303));

        if (all_threads) {
-            barrier();
-            sccache[ix][v_im][itid8] = FLOAT_TYPE(int8_t(((data_a[ib0+i].scales[itid8] >> v_im4) & 0xF) | (((data_a[ib0+i].scales[itid8%4+8] >> s_shift) & 3) << 4)) - 32);
+            sccache[csel][ix][v_im][itid8] = FLOAT_TYPE(int8_t(((data_a[ib0+i].scales[itid8] >> v_im4) & 0xF) | (((data_a[ib0+i].scales[itid8%4+8] >> s_shift) & 3) << 4)) - 32);
            barrier();
        }

@@ -59,14 +59,14 @@ void calc_superblock(const uint a_offset, const uint b_offset, const uint ix, co

            FLOAT_TYPE sum = FLOAT_TYPE(0.0);
            [[unroll]] for (int l = 0; l < 2; ++l) {
-                sum = fma(FLOAT_TYPE(  b0[l]) * sccache[ix][v_im][0], qs_u32_0[l  ] - hmk_0[l  ],
-                      fma(FLOAT_TYPE( b16[l]) * sccache[ix][v_im][1], qs_u32_0[l+2] - hmk_0[l+2],
-                      fma(FLOAT_TYPE( b32[l]) * sccache[ix][v_im][2], qs_u32_2[l  ] - hmk_1[l  ],
-                      fma(FLOAT_TYPE( b48[l]) * sccache[ix][v_im][3], qs_u32_2[l+2] - hmk_1[l+2],
-                      fma(FLOAT_TYPE( b64[l]) * sccache[ix][v_im][4], qs_u32_4[l  ] - hmk_2[l  ],
-                      fma(FLOAT_TYPE( b80[l]) * sccache[ix][v_im][5], qs_u32_4[l+2] - hmk_2[l+2],
-                      fma(FLOAT_TYPE( b96[l]) * sccache[ix][v_im][6], qs_u32_6[l  ] - hmk_3[l  ],
-                      fma(FLOAT_TYPE(b112[l]) * sccache[ix][v_im][7], qs_u32_6[l+2] - hmk_3[l+2], sum))))))));
+                sum = fma(FLOAT_TYPE(  b0[l]) * sccache[csel][ix][v_im][0], qs_u32_0[l  ] - hmk_0[l  ],
+                      fma(FLOAT_TYPE( b16[l]) * sccache[csel][ix][v_im][1], qs_u32_0[l+2] - hmk_0[l+2],
+                      fma(FLOAT_TYPE( b32[l]) * sccache[csel][ix][v_im][2], qs_u32_2[l  ] - hmk_1[l  ],
+                      fma(FLOAT_TYPE( b48[l]) * sccache[csel][ix][v_im][3], qs_u32_2[l+2] - hmk_1[l+2],
+                      fma(FLOAT_TYPE( b64[l]) * sccache[csel][ix][v_im][4], qs_u32_4[l  ] - hmk_2[l  ],
+                      fma(FLOAT_TYPE( b80[l]) * sccache[csel][ix][v_im][5], qs_u32_4[l+2] - hmk_2[l+2],
+                      fma(FLOAT_TYPE( b96[l]) * sccache[csel][ix][v_im][6], qs_u32_6[l  ] - hmk_3[l  ],
+                      fma(FLOAT_TYPE(b112[l]) * sccache[csel][ix][v_im][7], qs_u32_6[l+2] - hmk_3[l+2], sum))))))));
            }
            temp[j][n] = fma(d, sum, temp[j][n]);
        }
@@ -6,20 +6,21 @@

 layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in;

-shared FLOAT_TYPE sccache[BLOCK_SIZE/16][16];
+shared FLOAT_TYPE sccache[2][BLOCK_SIZE/16][16];

 FLOAT_TYPE temp[NUM_COLS][NUM_ROWS];
+uint csel = 0;

 void calc_superblock(const uint a_offset, const uint b_offset, const uint itid, const uint ix, const uint ql_offset, const uint qh_offset, const uint s_offset, const uint y_offset, const uint i, const uint num_blocks_per_row, const uint first_row, const uint num_rows, const bool all_threads) {
    const uint y_idx = i * QUANT_K + y_offset;

    [[unroll]] for (uint n = 0; n < num_rows; ++n) {
        const uint ib0 = a_offset / QUANT_K + (first_row+n)*num_blocks_per_row;
+        csel ^= 1;

        if (!all_threads) { // when we don't have enough blocks to use all threads
-            barrier();
            if (i < num_blocks_per_row)
-                sccache[ix][itid] = FLOAT_TYPE(data_a[ib0 + i].scales[itid]);
+                sccache[csel][ix][itid] = FLOAT_TYPE(data_a[ib0 + i].scales[itid]);
            barrier();

            if (i >= num_blocks_per_row)
@@ -51,8 +52,7 @@ void calc_superblock(const uint a_offset, const uint b_offset, const uint itid,
        const vec4 q3 = vec4(unpack8(q3_u32)) - 32;

        if (all_threads) {
-            barrier();
-            sccache[ix][itid] = FLOAT_TYPE(data_a[ib0 + i].scales[itid]);
+            sccache[csel][ix][itid] = FLOAT_TYPE(data_a[ib0 + i].scales[itid]);
            barrier();
        }

@@ -71,7 +71,7 @@ void calc_superblock(const uint a_offset, const uint b_offset, const uint itid,
                sum[2] = fma(FLOAT_TYPE(by64[l]), q2[l], sum[2]);
                sum[3] = fma(FLOAT_TYPE(by96[l]), q3[l], sum[3]);
            }
-            temp[j][n] = fma(fma(sum[0], sccache[ix][s_offset], fma(sum[1], sccache[ix][s_offset + 2], fma(sum[2], sccache[ix][s_offset + 4], sum[3] * sccache[ix][s_offset + 6]))), d, temp[j][n]);
+            temp[j][n] = fma(fma(sum[0], sccache[csel][ix][s_offset], fma(sum[1], sccache[csel][ix][s_offset + 2], fma(sum[2], sccache[csel][ix][s_offset + 4], sum[3] * sccache[csel][ix][s_offset + 6]))), d, temp[j][n]);
        }
    }
 }
@@ -777,7 +777,7 @@ void main() {
        [[unroll]] for (uint cm_col = 0; cm_col < cms_per_col; cm_col++) {
            coopMatStore(sums[cm_col * cms_per_row + cm_row], coopmat_stage, warp_i * TM * TN, TM, gl_CooperativeMatrixLayoutColumnMajor);

-            [[unroll]] for (uint col = 0; col < BN; col += storestride) {
+            [[unroll]] for (uint col = 0; col < TN; col += storestride) {
                const uint row_i = dc + cm_col * TN + col + store_c;
                if (row_i >= _ne1) break;

@@ -2332,6 +2332,7 @@ struct ggml_tensor * ggml_concat(
    struct ggml_tensor  * b,
    int                   dim) {
    GGML_ASSERT(dim >= 0 && dim < GGML_MAX_DIMS);
+    GGML_ASSERT(a->type == b->type);

    int64_t ne[GGML_MAX_DIMS];
    for (int d = 0; d < GGML_MAX_DIMS; ++d) {
@@ -253,6 +253,7 @@ class MODEL_ARCH(IntEnum):
    MINICPM3         = auto()
    GEMMA            = auto()
    GEMMA2           = auto()
+    GEMMA3           = auto()
    STARCODER2       = auto()
    RWKV6            = auto()
    RWKV6QWEN2       = auto()
@@ -440,6 +441,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
    MODEL_ARCH.MINICPM3:         "minicpm3",
    MODEL_ARCH.GEMMA:            "gemma",
    MODEL_ARCH.GEMMA2:           "gemma2",
+    MODEL_ARCH.GEMMA3:           "gemma3",
    MODEL_ARCH.STARCODER2:       "starcoder2",
    MODEL_ARCH.RWKV6:            "rwkv6",
    MODEL_ARCH.RWKV6QWEN2:       "rwkv6qwen2",
@@ -1077,6 +1079,23 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
        MODEL_TENSOR.FFN_PRE_NORM,
        MODEL_TENSOR.FFN_POST_NORM,
    ],
+    MODEL_ARCH.GEMMA3: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.ATTN_Q,
+        MODEL_TENSOR.ATTN_Q_NORM,
+        MODEL_TENSOR.ATTN_K,
+        MODEL_TENSOR.ATTN_K_NORM,
+        MODEL_TENSOR.ATTN_V,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.FFN_GATE,
+        MODEL_TENSOR.FFN_DOWN,
+        MODEL_TENSOR.FFN_UP,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_POST_NORM,
+        MODEL_TENSOR.FFN_PRE_NORM,
+        MODEL_TENSOR.FFN_POST_NORM,
+    ],
    MODEL_ARCH.STARCODER2: [
        MODEL_TENSOR.TOKEN_EMBD,
        MODEL_TENSOR.OUTPUT_NORM,
@@ -60,6 +60,7 @@ extern "C" {
    struct llama_model;
    struct llama_context;
    struct llama_sampler;
+    struct llama_kv_cache;

    typedef int32_t llama_pos;
    typedef int32_t llama_token;
@@ -469,7 +470,8 @@ extern "C" {
    DEPRECATED(LLAMA_API int32_t llama_n_vocab    (const struct llama_vocab * vocab), "use llama_vocab_n_tokens instead");

    LLAMA_API const struct llama_model * llama_get_model   (const struct llama_context * ctx);
-    LLAMA_API enum llama_pooling_type    llama_pooling_type(const struct llama_context * ctx);
+    LLAMA_API    struct llama_kv_cache * llama_get_kv_self (      struct llama_context * ctx);
+    LLAMA_API  enum llama_pooling_type   llama_pooling_type(const struct llama_context * ctx); // TODO: rename to llama_get_pooling_type

    LLAMA_API const struct llama_vocab * llama_model_get_vocab(const struct llama_model * model);
    LLAMA_API enum llama_rope_type       llama_model_rope_type(const struct llama_model * model);
@@ -586,7 +588,7 @@ extern "C" {
    // KV cache
    //

-    // TODO: remove llama_kv_cache_view_* API
+    // TODO: start using struct llama_kv_cache

    // Information associated with an individual cell in the KV cache view.
    struct llama_kv_cache_view_cell {
@@ -641,13 +643,19 @@ 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
-    LLAMA_API int32_t llama_get_kv_cache_token_count(const struct llama_context * ctx);
+    LLAMA_API int32_t llama_kv_self_n_tokens(const struct llama_context * ctx);
+
+    DEPRECATED(LLAMA_API int32_t llama_get_kv_cache_token_count(const struct llama_context * ctx),
+            "use llama_kv_self_n_tokens instead");

    // Returns the number of used KV cells (i.e. have at least one sequence assigned to them)
-    LLAMA_API int32_t llama_get_kv_cache_used_cells(const struct llama_context * ctx);
+    LLAMA_API int32_t llama_kv_self_used_cells(const struct llama_context * ctx);
+
+    DEPRECATED(LLAMA_API int32_t llama_get_kv_cache_used_cells(const struct llama_context * ctx),
+            "use llama_kv_self_used_cells instead");

    // Clear the KV cache - both cell info is erased and KV data is zeroed
-    LLAMA_API void llama_kv_cache_clear(
+    LLAMA_API void llama_kv_self_clear(
            struct llama_context * ctx);

    // Removes all tokens that belong to the specified sequence and have positions in [p0, p1)
@@ -655,7 +663,7 @@ extern "C" {
    // seq_id < 0 : match any sequence
    // p0 < 0     : [0,  p1]
    // p1 < 0     : [p0, inf)
-    LLAMA_API bool llama_kv_cache_seq_rm(
+    LLAMA_API bool llama_kv_self_seq_rm(
            struct llama_context * ctx,
                    llama_seq_id   seq_id,
                       llama_pos   p0,
@@ -665,7 +673,7 @@ extern "C" {
    // Note that this does not allocate extra KV cache memory - it simply assigns the tokens to the new sequence
    // p0 < 0 : [0,  p1]
    // p1 < 0 : [p0, inf)
-    LLAMA_API void llama_kv_cache_seq_cp(
+    LLAMA_API void llama_kv_self_seq_cp(
            struct llama_context * ctx,
                    llama_seq_id   seq_id_src,
                    llama_seq_id   seq_id_dst,
@@ -673,17 +681,17 @@ extern "C" {
                       llama_pos   p1);

    // Removes all tokens that do not belong to the specified sequence
-    LLAMA_API void llama_kv_cache_seq_keep(
+    LLAMA_API void llama_kv_self_seq_keep(
            struct llama_context * ctx,
                    llama_seq_id   seq_id);

    // Adds relative position "delta" to all tokens that belong to the specified sequence and have positions in [p0, p1)
    // If the KV cache is RoPEd, the KV data is updated accordingly:
    //   - lazily on next llama_decode()
-    //   - explicitly with llama_kv_cache_update()
+    //   - explicitly with llama_kv_self_update()
    // p0 < 0 : [0,  p1]
    // p1 < 0 : [p0, inf)
-    LLAMA_API void llama_kv_cache_seq_add(
+    LLAMA_API void llama_kv_self_seq_add(
            struct llama_context * ctx,
                    llama_seq_id   seq_id,
                       llama_pos   p0,
@@ -693,10 +701,10 @@ extern "C" {
    // Integer division of the positions by factor of `d > 1`
    // If the KV cache is RoPEd, the KV data is updated accordingly:
    //   - lazily on next llama_decode()
-    //   - explicitly with llama_kv_cache_update()
+    //   - explicitly with llama_kv_self_update()
    // p0 < 0 : [0,  p1]
    // p1 < 0 : [p0, inf)
-    LLAMA_API void llama_kv_cache_seq_div(
+    LLAMA_API void llama_kv_self_seq_div(
            struct llama_context * ctx,
                    llama_seq_id   seq_id,
                       llama_pos   p0,
@@ -704,24 +712,76 @@ extern "C" {
                             int   d);

    // Returns the largest position present in the KV cache for the specified sequence
-    LLAMA_API llama_pos llama_kv_cache_seq_pos_max(
+    LLAMA_API llama_pos llama_kv_self_seq_pos_max(
            struct llama_context * ctx,
-                    llama_seq_id   seq_id);
-
-    // TODO: the llama_kv_cache_defrag and llama_kv_cache_update API tightly couples llama_context with llama_kv_cache
-    //       how to avoid this?
+                     llama_seq_id   seq_id);

    // Defragment the KV cache
    // This will be applied:
    //   - lazily on next llama_decode()
-    //   - explicitly with llama_kv_cache_update()
-    LLAMA_API void llama_kv_cache_defrag(struct llama_context * ctx);
-
-    // Apply the KV cache updates (such as K-shifts, defragmentation, etc.)
-    LLAMA_API void llama_kv_cache_update(struct llama_context * ctx);
+    //   - explicitly with llama_kv_self_update()
+    LLAMA_API void llama_kv_self_defrag(struct llama_context * ctx);

    // Check if the context supports KV cache shifting
-    LLAMA_API bool llama_kv_cache_can_shift(struct llama_context * ctx);
+    LLAMA_API bool llama_kv_self_can_shift(const struct llama_context * ctx);
+
+    // Apply the KV cache updates (such as K-shifts, defragmentation, etc.)
+    LLAMA_API void llama_kv_self_update(struct llama_context * ctx);
+
+    DEPRECATED(LLAMA_API void llama_kv_cache_clear(
+            struct llama_context * ctx),
+            "use llama_kv_self_clear instead");
+
+    DEPRECATED(LLAMA_API bool llama_kv_cache_seq_rm(
+            struct llama_context * ctx,
+                    llama_seq_id   seq_id,
+                       llama_pos   p0,
+                       llama_pos   p1),
+            "use llama_kv_self_seq_rm instead");
+
+    DEPRECATED(LLAMA_API void llama_kv_cache_seq_cp(
+            struct llama_context * ctx,
+                    llama_seq_id   seq_id_src,
+                    llama_seq_id   seq_id_dst,
+                       llama_pos   p0,
+                       llama_pos   p1),
+            "use llama_kv_self_seq_cp instead");
+
+    DEPRECATED(LLAMA_API void llama_kv_cache_seq_keep(
+            struct llama_context * ctx,
+                    llama_seq_id   seq_id),
+            "use llama_kv_self_seq_keep instead");
+
+    DEPRECATED(LLAMA_API void llama_kv_cache_seq_add(
+            struct llama_context * ctx,
+                    llama_seq_id   seq_id,
+                       llama_pos   p0,
+                       llama_pos   p1,
+                       llama_pos   delta),
+            "use llama_kv_self_seq_add instead");
+
+    DEPRECATED(LLAMA_API void llama_kv_cache_seq_div(
+            struct llama_context * ctx,
+                    llama_seq_id   seq_id,
+                       llama_pos   p0,
+                       llama_pos   p1,
+                             int   d),
+            "use llama_kv_self_seq_div instead");
+
+    DEPRECATED(LLAMA_API llama_pos llama_kv_cache_seq_pos_max(
+            struct llama_context * ctx,
+                    llama_seq_id   seq_id),
+            "use llama_kv_self_seq_pos_max instead");
+
+    DEPRECATED(LLAMA_API void llama_kv_cache_defrag(struct llama_context * ctx),
+            "use llama_kv_self_defrag instead");
+
+    DEPRECATED(LLAMA_API bool llama_kv_cache_can_shift(const struct llama_context * ctx),
+            "use llama_kv_self_can_shift instead");
+
+    DEPRECATED(LLAMA_API void llama_kv_cache_update(struct llama_context * ctx),
+            "use llama_kv_self_update instead");
+

    //
    // State / sessions
@@ -885,6 +945,10 @@ extern "C" {
    // If set to true, the model will only attend to the past tokens
    LLAMA_API void llama_set_causal_attn(struct llama_context * ctx, bool causal_attn);

+    // Set whether the model is in warmup mode or not
+    // If true, all model tensors are activated during llama_decode() to load and cache their weights.
+    LLAMA_API void llama_set_warmup(struct llama_context * ctx, bool warmup);
+
    // Set abort callback
    LLAMA_API void llama_set_abort_callback(struct llama_context * ctx, ggml_abort_callback abort_callback, void * abort_callback_data);

@@ -1 +1 @@
-58ecf6b96d887e408b6869915863fa1126483d51
+c7dfe3d174f98b14801f9ed12f129179d3e7b638
@@ -15,18 +15,21 @@ add_library(llama
            llama-chat.cpp
            llama-context.cpp
            llama-grammar.cpp
+            llama-graph.cpp
            llama-hparams.cpp
            llama-impl.cpp
+            llama-io.cpp
            llama-kv-cache.cpp
+            llama-memory.cpp
            llama-mmap.cpp
            llama-model-loader.cpp
            llama-model.cpp
            llama-quant.cpp
            llama-sampling.cpp
            llama-vocab.cpp
-            unicode.h
-            unicode.cpp
            unicode-data.cpp
+            unicode.cpp
+            unicode.h
            )

 target_include_directories(llama PUBLIC . ../include ../common)
@@ -4,14 +4,13 @@
 #include "llama-mmap.h"
 #include "llama-model.h"

-#include <algorithm>
 #include <map>
 #include <cassert>
 #include <stdexcept>

 // vec

-struct ggml_tensor * llama_adapter_cvec::tensor_for(int il) const {
+ggml_tensor * llama_adapter_cvec::tensor_for(int il) const {
    if (il < 0 || il < layer_start || il > layer_end || (size_t) il >= tensors.size()) {
        return nullptr;
    }
@@ -19,7 +18,7 @@ struct ggml_tensor * llama_adapter_cvec::tensor_for(int il) const {
    return tensors[il];
 }

-struct ggml_tensor * llama_adapter_cvec::apply_to(struct ggml_context * ctx, struct ggml_tensor * cur, int  il) const {
+ggml_tensor * llama_adapter_cvec::apply_to(ggml_context * ctx, ggml_tensor * cur, int  il) const {
    ggml_tensor * layer_dir = tensor_for(il);
    if (layer_dir != nullptr) {
        cur = ggml_add(ctx, cur, layer_dir);
@@ -40,7 +39,7 @@ bool llama_adapter_cvec::init(const llama_model & model) {
    auto ctx_for_buft = [&](ggml_backend_buffer_type_t buft) -> ggml_context * {
        auto it = ctx_map.find(buft);
        if (it == ctx_map.end()) {
-            struct ggml_init_params params = {
+            ggml_init_params params = {
                /*.mem_size   =*/ hparams.n_layer*ggml_tensor_overhead(),
                /*.mem_buffer =*/ NULL,
                /*.no_alloc   =*/ true,
@@ -91,7 +90,7 @@ bool llama_adapter_cvec::init(const llama_model & model) {
    return true;
 }

-int32_t llama_adapter_cvec::apply(
+bool llama_adapter_cvec::apply(
        const llama_model & model,
        const float * data,
        size_t len,
@@ -104,17 +103,17 @@ int32_t llama_adapter_cvec::apply(
        // disable the current control vector (but leave allocated for later)
        layer_start = -1;
        layer_end   = -1;
-        return 0;
+        return true;
    }

    if (n_embd != (int) hparams.n_embd) {
        LLAMA_LOG_ERROR("%s: control vector n_embd does not match model\n", __func__);
-        return 1;
+        return false;
    }

    if (tensors.empty()) {
        if (!init(model)) {
-            return 1;
+            return false;
        }
    }

@@ -130,12 +129,12 @@ int32_t llama_adapter_cvec::apply(
        }
    }

-    return 0;
+    return true;
 }

 // lora

-llama_adapter_lora_weight * llama_adapter_lora::get_weight(struct ggml_tensor * w) {
+llama_adapter_lora_weight * llama_adapter_lora::get_weight(ggml_tensor * w) {
    const std::string name(w->name);

    const auto pos = ab_map.find(name);
@@ -146,11 +145,11 @@ llama_adapter_lora_weight * llama_adapter_lora::get_weight(struct ggml_tensor *
    return nullptr;
 }

-static void llama_adapter_lora_init_impl(struct llama_model & model, const char * path_lora, struct llama_adapter_lora & adapter) {
+static void llama_adapter_lora_init_impl(llama_model & model, const char * path_lora, llama_adapter_lora & adapter) {
    LLAMA_LOG_INFO("%s: loading lora adapter from '%s' ...\n", __func__, path_lora);

    ggml_context * ctx_init;
-    struct gguf_init_params meta_gguf_params = {
+    gguf_init_params meta_gguf_params = {
        /* .no_alloc = */ true,
        /* .ctx      = */ &ctx_init,
    };
@@ -201,7 +200,7 @@ static void llama_adapter_lora_init_impl(struct llama_model & model, const char
        auto it = ctx_map.find(buft);
        if (it == ctx_map.end()) {
            // add a new context
-            struct ggml_init_params params = {
+            ggml_init_params params = {
                /*.mem_size   =*/ n_tensors*ggml_tensor_overhead(),
                /*.mem_buffer =*/ NULL,
                /*.no_alloc   =*/ true,
@@ -264,7 +263,7 @@ static void llama_adapter_lora_init_impl(struct llama_model & model, const char
            throw std::runtime_error("LoRA tensor '" + name + "' does not exist in base model (hint: maybe wrong base model?)");
        }

-        struct ggml_context * dev_ctx = ctx_for_buft(ggml_backend_buffer_get_type(model_tensor->buffer));
+        ggml_context * dev_ctx = ctx_for_buft(ggml_backend_buffer_get_type(model_tensor->buffer));
        // validate tensor shape
        if (is_token_embd) {
            // expect B to be non-transposed, A and B are flipped; see llm_build_inp_embd()
@@ -281,8 +280,8 @@ static void llama_adapter_lora_init_impl(struct llama_model & model, const char
        }

        // save tensor to adapter
-        struct ggml_tensor * tensor_a = ggml_dup_tensor(dev_ctx, w.a);
-        struct ggml_tensor * tensor_b = ggml_dup_tensor(dev_ctx, w.b);
+        ggml_tensor * tensor_a = ggml_dup_tensor(dev_ctx, w.a);
+        ggml_tensor * tensor_b = ggml_dup_tensor(dev_ctx, w.b);
        ggml_set_name(tensor_a, w.a->name);
        ggml_set_name(tensor_b, w.b->name);
        adapter.ab_map[name] = llama_adapter_lora_weight(tensor_a, tensor_b);
@@ -308,7 +307,7 @@ static void llama_adapter_lora_init_impl(struct llama_model & model, const char
    {
        llama_file gguf_file(path_lora, "rb");
        std::vector<uint8_t> read_buf;
-        auto set_tensor = [&](struct ggml_tensor * orig, struct ggml_tensor * dev) {
+        auto set_tensor = [&](ggml_tensor * orig, ggml_tensor * dev) {
            size_t offs = gguf_get_data_offset(ctx_gguf.get()) + gguf_get_tensor_offset(ctx_gguf.get(), gguf_find_tensor(ctx_gguf.get(), orig->name));
            size_t size = ggml_nbytes(orig);
            read_buf.resize(size);
@@ -327,8 +326,8 @@ static void llama_adapter_lora_init_impl(struct llama_model & model, const char
    LLAMA_LOG_INFO("%s: loaded %zu tensors from lora file\n", __func__, adapter.ab_map.size()*2);
 }

-struct llama_adapter_lora * llama_adapter_lora_init(struct llama_model * model, const char * path_lora) {
-    struct llama_adapter_lora * adapter = new llama_adapter_lora();
+llama_adapter_lora * llama_adapter_lora_init(llama_model * model, const char * path_lora) {
+    llama_adapter_lora * adapter = new llama_adapter_lora();

    try {
        llama_adapter_lora_init_impl(*model, path_lora, *adapter);
@@ -342,6 +341,6 @@ struct llama_adapter_lora * llama_adapter_lora_init(struct llama_model * model,
    return nullptr;
 }

-void llama_adapter_lora_free(struct llama_adapter_lora * adapter) {
+void llama_adapter_lora_free(llama_adapter_lora * adapter) {
    delete adapter;
 }
@@ -15,11 +15,11 @@
 //

 struct llama_adapter_cvec {
-    struct ggml_tensor * tensor_for(int il) const;
+    ggml_tensor * tensor_for(int il) const;

-    struct ggml_tensor * apply_to(struct ggml_context * ctx, struct ggml_tensor * cur, int  il) const;
+    ggml_tensor * apply_to(ggml_context * ctx, ggml_tensor * cur, int  il) const;

-    int32_t apply(
+    bool apply(
            const llama_model & model,
            const float * data,
            size_t len,
@@ -36,7 +36,7 @@ private:
    std::vector<ggml_context_ptr> ctxs;
    std::vector<ggml_backend_buffer_ptr> bufs;

-    std::vector<struct ggml_tensor *> tensors; // per layer
+    std::vector<ggml_tensor *> tensors; // per layer
 };

 //
@@ -44,8 +44,8 @@ private:
 //

 struct llama_adapter_lora_weight {
-    struct ggml_tensor * a = nullptr;
-    struct ggml_tensor * b = nullptr;
+    ggml_tensor * a = nullptr;
+    ggml_tensor * b = nullptr;

    // get actual scale based on rank and alpha
    float get_scale(float alpha, float adapter_scale) const {
@@ -55,12 +55,12 @@ struct llama_adapter_lora_weight {
    }

    llama_adapter_lora_weight() = default;
-    llama_adapter_lora_weight(struct ggml_tensor * a, struct ggml_tensor * b) : a(a), b(b) {}
+    llama_adapter_lora_weight(ggml_tensor * a, ggml_tensor * b) : a(a), b(b) {}
 };

 struct llama_adapter_lora {
    // map tensor name to lora_a_b
-    std::unordered_map<std::string, struct llama_adapter_lora_weight> ab_map;
+    std::unordered_map<std::string, llama_adapter_lora_weight> ab_map;

    std::vector<ggml_context_ptr> ctxs;
    std::vector<ggml_backend_buffer_ptr> bufs;
@@ -70,5 +70,7 @@ struct llama_adapter_lora {
    llama_adapter_lora() = default;
    ~llama_adapter_lora() = default;

-    llama_adapter_lora_weight * get_weight(struct ggml_tensor * w);
+    llama_adapter_lora_weight * get_weight(ggml_tensor * w);
 };
+
+using llama_adapter_loras = std::unordered_map<llama_adapter_lora *, float>;
@@ -36,6 +36,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
    { LLM_ARCH_MINICPM3,         "minicpm3"         },
    { LLM_ARCH_GEMMA,            "gemma"            },
    { LLM_ARCH_GEMMA2,           "gemma2"           },
+    { LLM_ARCH_GEMMA3,           "gemma3"           },
    { LLM_ARCH_STARCODER2,       "starcoder2"       },
    { LLM_ARCH_MAMBA,            "mamba"            },
    { LLM_ARCH_XVERSE,           "xverse"           },
@@ -766,6 +767,26 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
            { LLM_TENSOR_FFN_POST_NORM,   "blk.%d.post_ffw_norm" },
        },
    },
+    {
+        LLM_ARCH_GEMMA3,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_Q_NORM,     "blk.%d.attn_q_norm" },
+            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_K_NORM,     "blk.%d.attn_k_norm" },
+            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_ATTN_POST_NORM,  "blk.%d.post_attention_norm" },
+            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+            { LLM_TENSOR_FFN_POST_NORM,   "blk.%d.post_ffw_norm" },
+        },
+    },
    {
        LLM_ARCH_STARCODER2,
        {
@@ -40,6 +40,7 @@ enum llm_arch {
    LLM_ARCH_MINICPM3,
    LLM_ARCH_GEMMA,
    LLM_ARCH_GEMMA2,
+    LLM_ARCH_GEMMA3,
    LLM_ARCH_STARCODER2,
    LLM_ARCH_MAMBA,
    LLM_ARCH_XVERSE,
@@ -42,9 +42,9 @@ struct llama_sbatch {
    bool logits_all; // TODO: remove once lctx.logits_all is removed too

    // sorted indices into the batch
-    std::vector<size_t> ids;
+    std::vector<int64_t> ids;
    // batch indices of the output
-    std::vector<size_t> out_ids;
+    std::vector<int64_t> out_ids;
    std::vector<llama_sbatch_seq> seq;

    const llama_batch * batch = nullptr;
@@ -3,66 +3,213 @@
 #include "llama.h"
 #include "llama-batch.h"
 #include "llama-cparams.h"
-#include "llama-model.h"
-#include "llama-kv-cache.h"
+#include "llama-graph.h"
 #include "llama-adapter.h"

 #include "ggml-cpp.h"

 #include <map>
-#include <unordered_map>
 #include <vector>
-#include <set>
+
+struct llama_model;
+struct llama_kv_cache;
+
+class llama_io_read_i;
+class llama_io_write_i;

 struct llama_context {
-    llama_context(const llama_model & model)
-        : model(model)
-        , t_start_us(model.t_start_us)
-        , t_load_us(model.t_load_us) {}
+    // init scheduler and compute buffers, reserve worst-case graphs
+    llama_context(
+            const llama_model & model,
+                  llama_context_params params);

-    const struct llama_model & model;
+    ~llama_context();

-    struct llama_cparams      cparams;
-    struct llama_sbatch       sbatch;  // TODO: revisit if needed
-    struct llama_kv_cache     kv_self;
-    struct llama_adapter_cvec cvec;
+    void synchronize();

-    std::unordered_map<struct llama_adapter_lora *, float> lora;
+    const llama_model & get_model() const;

-    std::vector<ggml_backend_ptr> backends;
-    std::vector<std::pair<ggml_backend_t, ggml_backend_set_n_threads_t>> set_n_threads_fns;
+    uint32_t n_ctx()         const;
+    uint32_t n_ctx_per_seq() const;
+    uint32_t n_batch()       const;
+    uint32_t n_ubatch()      const;
+    uint32_t n_seq_max()     const;

-    ggml_backend_t backend_cpu = nullptr;
+    uint32_t n_threads()       const;
+    uint32_t n_threads_batch() const;

-    ggml_threadpool_t threadpool       = nullptr;
-    ggml_threadpool_t threadpool_batch = nullptr;
+          llama_kv_cache * get_kv_self();
+    const llama_kv_cache * get_kv_self() const;

-    bool has_evaluated_once = false;
+    void kv_self_update();

-    mutable int64_t t_start_us;
-    mutable int64_t t_load_us;
-    mutable int64_t t_p_eval_us = 0;
-    mutable int64_t t_eval_us   = 0;
+    enum llama_pooling_type pooling_type() const;

-    mutable int64_t t_compute_start_us = 0;
-    mutable int64_t n_queued_tokens = 0;
+    float * get_logits();
+    float * get_logits_ith(int32_t i);

-    mutable int32_t n_p_eval = 0; // number of tokens in eval calls for the prompt (with batch size > 1)
-    mutable int32_t n_eval   = 0; // number of eval calls
+    float * get_embeddings();
+    float * get_embeddings_ith(int32_t i);
+    float * get_embeddings_seq(llama_seq_id seq_id);

-    // host buffer for the model output (logits and embeddings)
-    ggml_backend_buffer_ptr buf_output;
+    void attach_threadpool(
+            ggml_threadpool_t threadpool,
+            ggml_threadpool_t threadpool_batch);
+
+    void detach_threadpool();
+
+    void set_n_threads(int32_t n_threads, int32_t n_threads_batch);
+
+    void set_abort_callback(bool (*abort_callback)(void * data), void * abort_callback_data);
+
+    void set_embeddings (bool value);
+    void set_causal_attn(bool value);
+    void set_warmup(bool value);
+
+    void set_adapter_lora(
+            llama_adapter_lora * adapter,
+            float scale);
+
+    bool rm_adapter_lora(
+            llama_adapter_lora * adapter);
+
+    void clear_adapter_lora();
+
+    bool apply_adapter_cvec(
+            const float * data,
+                 size_t   len,
+                int32_t   n_embd,
+                int32_t   il_start,
+                int32_t   il_end);
+
+    int encode(llama_batch & inp_batch);
+    int decode(llama_batch & inp_batch);
+
+    //
+    // state save/load
+    //
+
+    size_t state_get_size();
+    size_t state_get_data(      uint8_t * dst, size_t size);
+    size_t state_set_data(const uint8_t * src, size_t size);
+
+    size_t state_seq_get_size(llama_seq_id seq_id);
+    size_t state_seq_get_data(llama_seq_id seq_id,       uint8_t * dst, size_t size);
+    size_t state_seq_set_data(llama_seq_id seq_id, const uint8_t * src, size_t size);
+
+    bool state_load_file(
+            const char * filepath,
+           llama_token * tokens_out,
+                size_t   n_token_capacity,
+                size_t * n_token_count_out);
+
+    bool state_save_file(
+            const char * filepath,
+     const llama_token * tokens,
+                size_t   n_token_count);
+
+    size_t state_seq_load_file(
+          llama_seq_id   seq_id,
+            const char * filepath,
+           llama_token * tokens_out,
+                size_t   n_token_capacity,
+                size_t * n_token_count_out);
+
+    size_t state_seq_save_file(
+          llama_seq_id   seq_id,
+            const char * filepath,
+     const llama_token * tokens,
+                size_t   n_token_count);
+
+    //
+    // perf
+    //
+
+    llama_perf_context_data perf_get_data() const;
+    void perf_reset();
+
+private:
+    //
+    // output
+    //
+
+    // Make sure enough space is available for outputs.
+    // Returns max number of outputs for which space was reserved.
+    int32_t output_reserve(int32_t n_outputs);
+
+    // make the outputs have the same order they had in the user-provided batch
+    // TODO: maybe remove this
+    void output_reorder();
+
+    //
+    // graph
+    //
+
+    int32_t graph_max_nodes() const;
+
+    // zero-out inputs and create the ctx_compute for the compute graph
+    ggml_cgraph * graph_init();
+
+    llm_graph_result_ptr graph_build(
+            ggml_context * ctx,
+             ggml_cgraph * gf,
+      const llama_ubatch & ubatch,
+          llm_graph_type   gtype);
+
+    // returns the result of ggml_backend_sched_graph_compute_async execution
+    ggml_status graph_compute(
+            ggml_cgraph * gf,
+                   bool   batched);
+
+    llm_graph_cb graph_get_cb() const;
+
+    // used by kv_self_update()
+    ggml_tensor * build_rope_shift(
+        ggml_context * ctx0,
+        ggml_tensor * cur,
+        ggml_tensor * shift,
+        ggml_tensor * factors,
+              float   freq_base,
+              float   freq_scale,
+        ggml_backend_buffer * bbuf) const;
+
+    llm_graph_result_ptr build_kv_self_shift(
+            ggml_context * ctx0,
+            ggml_cgraph * gf) const;
+
+    llm_graph_result_ptr build_kv_self_defrag(
+            ggml_context * ctx0,
+            ggml_cgraph * gf) const;
+
+    // TODO: read/write lora adapters and cvec
+    size_t state_write_data(llama_io_write_i & io);
+    size_t state_read_data (llama_io_read_i  & io);
+
+    size_t state_seq_write_data(llama_io_write_i & io, llama_seq_id seq_id);
+    size_t state_seq_read_data (llama_io_read_i  & io, llama_seq_id seq_id);
+
+    //
+    // members
+    //
+
+    const llama_model & model;
+
+    llama_cparams       cparams;
+    llama_adapter_cvec  cvec;
+    llama_adapter_loras loras;
+    llama_sbatch        sbatch;
+
+    llama_cross cross; // TODO: tmp for handling cross-attention - need something better probably
+
+    std::unique_ptr<llama_kv_cache_unified> kv_self;
+
+    // TODO: remove
+    bool logits_all = false;

    // decode output (2-dimensional array: [n_outputs][n_vocab])
    size_t  logits_size = 0; // capacity (of floats) for logits
    float * logits      = nullptr;

-    std::vector<int32_t> output_ids; // map batch token positions to ids of the logits and embd buffers
-    size_t  output_size = 0; // capacity (of tokens positions) for the output buffers
-    int32_t n_outputs   = 0; // number of actually-used outputs in the current ubatch or last logical batch
-
-    bool logits_all = false;
-
    // embeddings output (2-dimensional array: [n_outputs][n_embd])
    // populated only when pooling_type == LLAMA_POOLING_TYPE_NONE
    size_t  embd_size = 0; // capacity (of floats) for embeddings
@@ -72,57 +219,47 @@ struct llama_context {
    // populated only when pooling_type != LLAMA_POOLING_TYPE_NONE
    std::map<llama_seq_id, std::vector<float>> embd_seq;

-    // whether we are computing encoder output or decoder output
-    bool is_encoding = false;
+    int32_t n_outputs     = 0; // number of actually-used outputs in the current ubatch or last logical batch
+    int32_t n_outputs_max = 0; // capacity (of tokens positions) for the output buffers

-    // TODO: find a better way to accommodate mutli-dimension position encoding methods
-    // number of position id each token get, 1 for each token in most cases.
-    // when using m-rope, it will be 3 position ids per token to representing 3 dimension coordinate.
-    int n_pos_per_token = 1;
+    std::vector<int32_t> output_ids; // map batch token positions to ids of the logits and embd buffers

-    // output of the encoder part of the encoder-decoder models
-    std::vector<float> embd_enc;
-    std::vector<std::set<llama_seq_id>> seq_ids_enc;
-
-    // memory buffers used to evaluate the model
-    std::vector<uint8_t> buf_compute_meta;
    ggml_backend_sched_ptr sched;

+    ggml_backend_t backend_cpu = nullptr;
+    std::vector<ggml_backend_ptr> backends;
+
+    ggml_context_ptr ctx_compute;
+
+    ggml_threadpool_t threadpool       = nullptr;
+    ggml_threadpool_t threadpool_batch = nullptr;
+
    ggml_abort_callback abort_callback      = nullptr;
    void *              abort_callback_data = nullptr;

-    // input tensors
-    struct ggml_tensor * inp_tokens;        // I32 [n_batch]
-    struct ggml_tensor * inp_embd;          // F32 [n_embd, n_batch]
-    struct ggml_tensor * inp_pos;           // I32 [n_batch]
-    struct ggml_tensor * inp_out_ids;       // I32 [n_outputs]
-    struct ggml_tensor * inp_KQ_mask;       // F32 [kv_size, n_batch]
-    struct ggml_tensor * inp_KQ_mask_swa;   // F32 [kv_size, n_batch]
-    struct ggml_tensor * inp_K_shift;       // I32 [kv_size]
-    struct ggml_tensor * inp_mean;          // F32 [n_batch, n_batch]
-    struct ggml_tensor * inp_cls;           // I32 [n_batch]
-    struct ggml_tensor * inp_s_copy;        // I32 [kv_size]
-    struct ggml_tensor * inp_s_mask;        // F32 [1, n_kv]
-    struct ggml_tensor * inp_s_seq;         // I32 [n_kv, n_batch]
-    struct ggml_tensor * inp_pos_bucket;    // I32 [n_batch|n_kv, n_batch]
-    struct ggml_tensor * inp_embd_enc;      // F32 [n_embd, n_outputs_enc]
-    struct ggml_tensor * inp_KQ_mask_cross; // F32 [n_outputs_enc, n_batch]
+    std::vector<std::pair<ggml_backend_t, ggml_backend_set_n_threads_t>> set_n_threads_fns;
+
+    // buffer types used for the compute buffer of each backend
+    std::vector<ggml_backend_t>             backend_ptrs;
+    std::vector<ggml_backend_buffer_type_t> backend_buft;
+
+    // memory buffers used to evaluate the model
+    std::vector<uint8_t> buf_compute_meta;
+
+    // host buffer for the model output (logits and embeddings)
+    ggml_backend_buffer_ptr buf_output;
+
+    bool has_evaluated_once = false;
+
+    // perf
+    mutable int64_t t_start_us  = 0;
+    mutable int64_t t_load_us   = 0;
+    mutable int64_t t_p_eval_us = 0;
+    mutable int64_t t_eval_us   = 0;
+
+    mutable int64_t t_compute_start_us = 0;
+    mutable int64_t n_queued_tokens    = 0;
+
+    mutable int32_t n_p_eval = 0; // number of tokens in eval calls for the prompt (with batch size > 1)
+    mutable int32_t n_eval   = 0; // number of eval calls
 };
-
-// TODO: make these methods of llama_context
-void llama_set_k_shift(struct llama_context & lctx);
-
-void llama_set_s_copy(struct llama_context & lctx);
-
-void llama_set_inputs(llama_context & lctx, const llama_ubatch & ubatch);
-
-// Make sure enough space is available for outputs.
-// Returns max number of outputs for which space was reserved.
-size_t llama_output_reserve(struct llama_context & lctx, size_t n_outputs);
-
-// make the outputs have the same order they had in the user-provided batch
-void llama_output_reorder(struct llama_context & ctx);
-
-// For internal test use
-// TODO: remove
-const std::vector<std::pair<std::string, struct ggml_tensor *>> & llama_internal_get_tensor_map(struct llama_context * ctx);
@@ -29,6 +29,7 @@ struct llama_cparams {
    bool offload_kqv;
    bool flash_attn;
    bool no_perf;
+    bool warmup;

    enum llama_pooling_type pooling_type;

@@ -0,0 +1,574 @@
+#pragma once
+
+#include "llama-arch.h"
+#include "llama-hparams.h"
+#include "llama-adapter.h"
+
+#include <cstdint>
+#include <vector>
+#include <memory>
+#include <set>
+#include <functional>
+
+struct ggml_cgraph;
+struct ggml_context;
+struct ggml_tensor;
+
+struct llama_ubatch;
+struct llama_cparams;
+
+class llama_memory_i;
+class llama_kv_cache_unified;
+
+// certain models (typically multi-modal) can produce different types of graphs
+enum llm_graph_type {
+    LLM_GRAPH_TYPE_DEFAULT,
+    LLM_GRAPH_TYPE_ENCODER,
+    LLM_GRAPH_TYPE_DECODER,
+};
+
+enum llm_ffn_op_type {
+    LLM_FFN_SILU,
+    LLM_FFN_GELU,
+    LLM_FFN_RELU,
+    LLM_FFN_RELU_SQR,
+    LLM_FFN_SWIGLU,
+};
+
+enum llm_ffn_gate_type {
+    LLM_FFN_SEQ,
+    LLM_FFN_PAR, // ffn_gate is parallel to ffn_up
+};
+
+enum llm_norm_type {
+    LLM_NORM,
+    LLM_NORM_RMS,
+    LLM_NORM_GROUP,
+};
+
+// TODO: tmp - need something better to pass the data from the encoder to the decoder
+struct llama_cross {
+    // the output embeddings from the encoder as a ggml tensor
+    // TODO: this needs more work to be correct, for now copy the embeddings data to host memory
+    //       ref: https://github.com/ggml-org/llama.cpp/pull/11213#discussion_r1969892524
+    //ggml_tensor * t_embd = nullptr;
+
+    int64_t n_embd = 0;
+    int64_t n_enc  = 0;
+
+    // embeddings data copied to host memory (tmp)
+    std::vector<float> v_embd;
+
+    // needed to construct the cross-attention mask in the decoder
+    std::vector<std::set<llama_seq_id>> seq_ids_enc;
+};
+
+//
+// llm_graph_input
+//
+
+class llm_graph_input_i {
+public:
+    virtual ~llm_graph_input_i() = default;
+
+    virtual void set_input(const llama_ubatch * ubatch) = 0;
+};
+
+using llm_graph_input_ptr = std::unique_ptr<llm_graph_input_i>;
+
+
+class llm_graph_input_embd : public llm_graph_input_i {
+public:
+    llm_graph_input_embd()          = default;
+    virtual ~llm_graph_input_embd() = default;
+
+    void set_input(const llama_ubatch * ubatch) override;
+
+    ggml_tensor * tokens = nullptr; // I32 [n_batch]
+    ggml_tensor * embd   = nullptr; // F32 [n_embd, n_batch]
+};
+
+class llm_graph_input_pos : public llm_graph_input_i {
+public:
+    llm_graph_input_pos(int64_t n_pos_per_token) : n_pos_per_token(n_pos_per_token) {}
+    virtual ~llm_graph_input_pos() = default;
+
+    void set_input(const llama_ubatch * ubatch) override;
+
+    ggml_tensor * pos = nullptr; // I32 [n_batch]
+
+    const int64_t n_pos_per_token = 1;
+};
+
+class llm_graph_input_pos_bucket : public llm_graph_input_i {
+public:
+    llm_graph_input_pos_bucket(const llama_hparams & hparams) : hparams(hparams) {}
+    virtual ~llm_graph_input_pos_bucket() = default;
+
+    void set_input(const llama_ubatch * ubatch) override;
+
+    ggml_tensor * pos_bucket = nullptr; // I32 [n_batch, n_batch]
+
+    const llama_hparams & hparams;
+};
+
+class llm_graph_input_pos_bucket_kv : public llm_graph_input_i {
+public:
+    llm_graph_input_pos_bucket_kv(
+            const llama_hparams & hparams,
+            const llama_kv_cache_unified * kv_self) : hparams(hparams), kv_self(kv_self) {}
+    virtual ~llm_graph_input_pos_bucket_kv() = default;
+
+    void set_input(const llama_ubatch * ubatch) override;
+
+    ggml_tensor * pos_bucket = nullptr; // I32 [n_kv, n_batch]
+
+    const llama_hparams & hparams;
+    const llama_kv_cache_unified * kv_self;
+};
+
+class llm_graph_input_out_ids : public llm_graph_input_i {
+public:
+    llm_graph_input_out_ids(
+            const llama_hparams & hparams,
+            const llama_cparams & cparams,
+            int32_t n_outputs) : hparams(hparams), cparams(cparams), n_outputs(n_outputs) {}
+    virtual ~llm_graph_input_out_ids() = default;
+
+    void set_input(const llama_ubatch * ubatch) override;
+
+    ggml_tensor * out_ids; // I32 [n_outputs]
+
+    const llama_hparams & hparams;
+    const llama_cparams & cparams;
+
+    const int32_t n_outputs;
+};
+
+class llm_graph_input_mean : public llm_graph_input_i {
+public:
+    llm_graph_input_mean(const llama_cparams & cparams) : cparams(cparams) {}
+    virtual ~llm_graph_input_mean() = default;
+
+    void set_input(const llama_ubatch * ubatch) override;
+
+    ggml_tensor * mean; // F32 [n_batch, n_batch]
+
+    const llama_cparams & cparams;
+};
+
+class llm_graph_input_cls : public llm_graph_input_i {
+public:
+    llm_graph_input_cls(const llama_cparams & cparams) : cparams(cparams) {}
+    virtual ~llm_graph_input_cls() = default;
+
+    void set_input(const llama_ubatch * ubatch) override;
+
+    ggml_tensor * cls; // I32 [n_batch]
+
+    const llama_cparams & cparams;
+};
+
+class llm_graph_input_s_copy : public llm_graph_input_i {
+public:
+    llm_graph_input_s_copy(const llama_kv_cache_unified * kv_self) : kv_self(kv_self) {}
+    virtual ~llm_graph_input_s_copy() = default;
+
+    void set_input(const llama_ubatch * ubatch) override;
+
+    ggml_tensor * s_copy; // I32 [kv_size]
+
+    const llama_kv_cache_unified * kv_self;
+};
+
+class llm_graph_input_s_mask : public llm_graph_input_i {
+public:
+    llm_graph_input_s_mask(const llama_kv_cache_unified * kv_self) : kv_self(kv_self) {}
+    virtual ~llm_graph_input_s_mask() = default;
+
+    void set_input(const llama_ubatch * ubatch) override;
+
+    ggml_tensor * s_mask; // F32 [1, n_kv]
+
+    const llama_kv_cache_unified * kv_self;
+};
+
+class llm_graph_input_cross_embd : public llm_graph_input_i {
+public:
+    llm_graph_input_cross_embd(
+            const llama_cross * cross) : cross(cross) {}
+    virtual ~llm_graph_input_cross_embd() = default;
+
+    void set_input(const llama_ubatch * ubatch) override;
+
+    ggml_tensor * cross_embd; // F32 [n_embd, n_outputs_enc]
+
+    const llama_cross * cross;
+};
+
+class llm_graph_input_attn_no_cache : public llm_graph_input_i {
+public:
+    llm_graph_input_attn_no_cache(const llama_hparams & hparams, const llama_cparams & cparams) :
+        hparams(hparams),
+        cparams(cparams) {
+    }
+    ~llm_graph_input_attn_no_cache() = default;
+
+    void set_input(const llama_ubatch * ubatch) override;
+
+    ggml_tensor * get_kq_mask() const { return kq_mask_cnv; }
+
+    ggml_tensor * kq_mask     = nullptr; // F32 [n_tokens, n_batch]
+    ggml_tensor * kq_mask_cnv = nullptr; //     [n_tokens, n_batch]
+
+    const llama_hparams & hparams;
+    const llama_cparams & cparams;
+};
+
+class llm_graph_input_attn_kv_unified : public llm_graph_input_i {
+public:
+    llm_graph_input_attn_kv_unified(
+            const llama_hparams & hparams,
+            const llama_cparams & cparams,
+            const llama_kv_cache_unified * kv_self) :
+        hparams(hparams),
+        cparams(cparams),
+        kv_self(kv_self) {
+    }
+    ~llm_graph_input_attn_kv_unified() = default;
+
+    void set_input(const llama_ubatch * ubatch) override;
+
+    ggml_tensor * get_kq_mask()     const { return self_kq_mask_cnv; }
+    ggml_tensor * get_kq_mask_swa() const { return self_kq_mask_swa_cnv; }
+
+    ggml_tensor * self_kq_mask         = nullptr; // F32 [n_kv, n_batch]
+    ggml_tensor * self_kq_mask_cnv     = nullptr; //     [n_kv, n_batch]
+    ggml_tensor * self_kq_mask_swa     = nullptr; // F32 [n_kv, n_batch]
+    ggml_tensor * self_kq_mask_swa_cnv = nullptr; //     [n_kv, n_batch]
+
+    const llama_hparams & hparams;
+    const llama_cparams & cparams;
+
+    const llama_kv_cache_unified * kv_self;
+};
+
+class llm_graph_input_attn_cross : public llm_graph_input_i {
+public:
+    llm_graph_input_attn_cross(const llama_cross * cross) : cross(cross) {}
+    ~llm_graph_input_attn_cross() = default;
+
+    void set_input(const llama_ubatch * ubatch) override;
+
+    ggml_tensor * get_kq_mask_cross() const { return cross_kq_mask_cnv; }
+
+    ggml_tensor * cross_kq_mask     = nullptr; // F32 [n_outputs_enc, n_batch]
+    ggml_tensor * cross_kq_mask_cnv = nullptr; // F32 [n_outputs_enc, n_batch]
+
+    const llama_cross * cross = nullptr;
+};
+
+//
+// llm_graph_result
+//
+
+// these objects deliver the result from the graph build process back to the llama_context
+// note that the input tensors created for the graph are referenced here - the goal is to be able to populate their
+//   specific data, by calling the set_inputs() method
+// along with the input tensors, the object also provides commonly used outputs tensors, such as logits, embeddings, etc.
+//   these are used by the llama_context to extact the relevant data, based on the compute parameters
+
+class llm_graph_result_i {
+public:
+    virtual ~llm_graph_result_i() = default;
+
+    virtual ggml_tensor * get_logits()      = 0;
+    virtual ggml_tensor * get_embd()        = 0;
+    virtual ggml_tensor * get_embd_pooled() = 0;
+
+    virtual void set_inputs(const llama_ubatch * ubatch) = 0;
+};
+
+using llm_graph_result_ptr = std::unique_ptr<llm_graph_result_i>;
+
+
+class llm_graph_result : public llm_graph_result_i {
+public:
+    virtual ~llm_graph_result() = default;
+
+    ggml_tensor * get_logits()      override { return t_logits; }
+    ggml_tensor * get_embd()        override { return t_embd; }
+    ggml_tensor * get_embd_pooled() override { return t_embd_pooled; }
+
+    void set_inputs(const llama_ubatch * ubatch) override {
+        for (auto & input : inputs) {
+            input->set_input(ubatch);
+        }
+    }
+
+    llm_graph_input_i * add_input(llm_graph_input_ptr input) {
+        inputs.emplace_back(std::move(input));
+        return inputs.back().get();
+    }
+
+    // important graph nodes
+    ggml_tensor * t_logits      = nullptr;
+    ggml_tensor * t_embd        = nullptr;
+    ggml_tensor * t_embd_pooled = nullptr;
+
+    std::vector<llm_graph_input_ptr> inputs;
+};
+
+//
+// llm_graph_context
+//
+
+// callback that allows us to apply custom logic to each tensor (e.g. ggml-alloc, offloading, etc.)
+using llm_graph_cb = std::function<void(const llama_ubatch & ubatch, ggml_tensor * cur, const char * name, int il)>;
+
+struct llm_graph_params {
+    ggml_context * ctx;
+
+    const llm_arch arch;
+
+    const llama_hparams & hparams;
+    const llama_cparams & cparams;
+    const llama_ubatch  & ubatch;
+
+    ggml_backend_sched * sched;
+    ggml_backend * backend_cpu;
+
+    const llama_adapter_cvec  * cvec;
+    const llama_adapter_loras * loras;
+    const llama_memory_i      * memory;
+    const llama_cross         * cross;
+
+    int32_t n_outputs;
+
+    const llm_graph_cb & cb;
+};
+
+struct llm_graph_context {
+    const llm_arch arch;
+
+    const llama_hparams & hparams;
+    const llama_cparams & cparams;
+    const llama_ubatch  & ubatch;
+
+    const int64_t n_embd;
+    const int64_t n_layer;
+    const int64_t n_rot;
+    const int64_t n_ctx;       // user-specified context size (can be different from n_ctx_train)
+    const int64_t n_ctx_per_seq;
+    const int64_t n_head;
+    const int64_t n_head_kv;
+    const int64_t n_embd_head_k;
+    const int64_t n_embd_k_gqa;
+    const int64_t n_embd_head_v;
+    const int64_t n_embd_v_gqa;
+    const int64_t n_expert;
+    const int64_t n_expert_used;
+
+    const float freq_base;
+    const float freq_scale;
+    const float ext_factor;
+    const float attn_factor;
+    const float beta_fast;
+    const float beta_slow;
+    const float norm_eps;
+    const float norm_rms_eps;
+
+    const int32_t n_tokens;
+    const int32_t n_outputs;
+    const int32_t n_ctx_orig; // yarn
+
+    const enum llama_pooling_type pooling_type;
+    const enum llama_rope_type    rope_type;
+
+    ggml_context * ctx0 = nullptr;
+
+    ggml_backend_sched * sched;
+
+    ggml_backend * backend_cpu; // TODO: needed by build_attn_mha, figure out a way to remove?
+
+    const llama_adapter_cvec  * cvec;
+    const llama_adapter_loras * loras;
+    const llama_memory_i      * memory;
+    const llama_cross         * cross;
+
+    const llm_graph_cb & cb_func;
+
+    std::unique_ptr<llm_graph_result> res;
+
+    llm_graph_context(const llm_graph_params & params);
+
+    int64_t n_pos_per_token() const;
+
+    void cb(ggml_tensor * cur, const char * name, int il) const;
+
+    //
+    // common
+    //
+
+    ggml_tensor * build_cvec(
+             ggml_tensor * cur,
+                     int   il) const;
+
+    // do mat_mul, while optionally apply lora
+    ggml_tensor * build_lora_mm(
+              ggml_tensor * w,
+              ggml_tensor * cur) const;
+
+    // do mat_mul_id, while optionally apply lora
+    ggml_tensor * build_lora_mm_id(
+              ggml_tensor * w,   // ggml_tensor * as
+              ggml_tensor * cur, // ggml_tensor * b
+              ggml_tensor * ids) const;
+
+    ggml_tensor * build_norm(
+             ggml_tensor * cur,
+             ggml_tensor * mw,
+             ggml_tensor * mb,
+           llm_norm_type   type,
+                     int   il) const;
+
+    ggml_tensor * build_ffn(
+             ggml_tensor * cur,
+             ggml_tensor * up,
+             ggml_tensor * up_b,
+             ggml_tensor * up_s,
+             ggml_tensor * gate,
+             ggml_tensor * gate_b,
+             ggml_tensor * gate_s,
+             ggml_tensor * down,
+             ggml_tensor * down_b,
+             ggml_tensor * down_s,
+             ggml_tensor * act_scales,
+         llm_ffn_op_type   type_op,
+       llm_ffn_gate_type   type_gate,
+                     int   il) const;
+
+    ggml_tensor * build_moe_ffn(
+             ggml_tensor * cur,
+             ggml_tensor * gate_inp,
+             ggml_tensor * up_exps,
+             ggml_tensor * gate_exps,
+             ggml_tensor * down_exps,
+             ggml_tensor * exp_probs_b,
+                 int64_t   n_expert,
+                 int64_t   n_expert_used,
+         llm_ffn_op_type   type_op,
+                    bool   norm_w,
+                    bool   scale_w,
+                   float   w_scale,
+            llama_expert_gating_func_type gating_op,
+                     int   il) const;
+
+    //
+    // inputs
+    //
+
+    ggml_tensor * build_inp_embd(ggml_tensor * tok_embd) const;
+    ggml_tensor * build_inp_pos() const;
+    ggml_tensor * build_inp_out_ids() const;
+    ggml_tensor * build_inp_mean() const;
+    ggml_tensor * build_inp_cls() const;
+    ggml_tensor * build_inp_s_copy() const;
+    ggml_tensor * build_inp_s_mask() const;
+
+    ggml_tensor * build_inp_cross_embd() const;
+    ggml_tensor * build_inp_pos_bucket_enc() const;
+    ggml_tensor * build_inp_pos_bucket_dec() const;
+    ggml_tensor * build_pos_bias(ggml_tensor * pos_bucket, ggml_tensor * attn_rel_b) const;
+
+    //
+    // attention
+    //
+
+    ggml_tensor * build_attn_mha(
+             ggml_cgraph * gf,
+             ggml_tensor * q,
+             ggml_tensor * k,
+             ggml_tensor * v,
+             ggml_tensor * kq_b,
+             ggml_tensor * kq_mask,
+                    bool   v_trans,
+                   float   kq_scale) const;
+
+    llm_graph_input_attn_no_cache * build_attn_inp_no_cache() const;
+
+    ggml_tensor * build_attn(
+            llm_graph_input_attn_no_cache * inp,
+            ggml_cgraph * gf,
+            ggml_tensor * wo,
+            ggml_tensor * wo_b,
+            ggml_tensor * q_cur,
+            ggml_tensor * k_cur,
+            ggml_tensor * v_cur,
+            ggml_tensor * kq_b,
+                  float   kq_scale,
+                    int   il) const;
+
+    llm_graph_input_attn_kv_unified * build_attn_inp_kv_unified() const;
+
+    ggml_tensor * build_attn(
+            llm_graph_input_attn_kv_unified * inp,
+            ggml_cgraph * gf,
+            ggml_tensor * wo,
+            ggml_tensor * wo_b,
+            ggml_tensor * q_cur,
+            ggml_tensor * k_cur,
+            ggml_tensor * v_cur,
+            ggml_tensor * kq_b,
+                  float   kq_scale,
+                    int   il) const;
+
+    llm_graph_input_attn_cross * build_attn_inp_cross() const;
+
+    ggml_tensor * build_attn(
+            llm_graph_input_attn_cross * inp,
+            ggml_cgraph * gf,
+            ggml_tensor * wo,
+            ggml_tensor * wo_b,
+            ggml_tensor * q_cur,
+            ggml_tensor * k_cur,
+            ggml_tensor * v_cur,
+            ggml_tensor * kq_b,
+                  float   kq_scale,
+                    int   il) const;
+
+    //
+    // recurrent
+    //
+
+    ggml_tensor * build_copy_mask_state(
+             ggml_cgraph * gf,
+             ggml_tensor * s,
+             ggml_tensor * state_copy,
+             ggml_tensor * state_mask,
+                 int32_t   n_state,
+                 int32_t   n_seqs) const;
+
+    ggml_tensor * build_rwkv_token_shift_load(
+             ggml_cgraph * gf,
+             ggml_tensor * state_copy,
+             ggml_tensor * state_mask,
+      const llama_ubatch & ubatch,
+                     int   il) const;
+
+    ggml_tensor * build_rwkv_token_shift_store(
+             ggml_tensor * token_shift,
+      const llama_ubatch & ubatch,
+                     int   il) const;
+
+    //
+    // pooling
+    //
+
+    void build_pooling(
+            ggml_cgraph * gf,
+            ggml_tensor * cls,
+            ggml_tensor * cls_b,
+            ggml_tensor * cls_out,
+            ggml_tensor * cls_out_b) const;
+};
@@ -69,3 +69,11 @@ uint32_t llama_hparams::n_embd_v_s() const {
    // corresponds to Mamba's ssm_states size
    return ssm_d_state * ssm_d_inner;
 }
+
+bool llama_hparams::is_swa(uint32_t il) const {
+    if (il < n_layer) {
+        return n_swa > 0 && n_swa_pattern > 0 && il % n_swa_pattern < (n_swa_pattern - 1);
+    }
+
+    GGML_ABORT("fatal error");
+}
@@ -36,6 +36,7 @@ struct llama_hparams {
    uint32_t n_layer;
    uint32_t n_rot;
    uint32_t n_swa = 0; // sliding window attention (SWA)
+    uint32_t n_swa_pattern = 1; // by default, all layers use non-sliding-window attention
    uint32_t n_embd_head_k; // dimension of keys (d_k). d_q is assumed to be the same, but there are n_head q heads, and only n_head_kv k-v heads
    uint32_t n_embd_head_v; // dimension of values (d_v) aka n_embd_head
    uint32_t n_expert = 0;
@@ -78,7 +79,9 @@ struct llama_hparams {

    float    rope_attn_factor = 1.0f;
    float    rope_freq_base_train;
+    float    rope_freq_base_train_swa;
    float    rope_freq_scale_train;
+    float    rope_freq_scale_train_swa;
    uint32_t n_ctx_orig_yarn;
    float    rope_yarn_log_mul;

@@ -133,6 +136,8 @@ struct llama_hparams {

    // dimension of the recurrent state embeddings
    uint32_t n_embd_v_s() const;
+
+    bool is_swa(uint32_t il) const;
 };

 static_assert(std::is_trivially_copyable<llama_hparams>::value, "llama_hparams must be trivially copyable");
@@ -0,0 +1,15 @@
+#include "llama-io.h"
+
+void llama_io_write_i::write_string(const std::string & str) {
+    uint32_t str_size = str.size();
+
+    write(&str_size,  sizeof(str_size));
+    write(str.data(), str_size);
+}
+
+void llama_io_read_i::read_string(std::string & str) {
+    uint32_t str_size;
+    read_to(&str_size, sizeof(str_size));
+
+    str.assign((const char *) read(str_size), str_size);
+}
@@ -0,0 +1,35 @@
+#pragma once
+
+#include <cstddef>
+#include <cstdint>
+#include <string>
+
+struct ggml_tensor;
+
+class llama_io_write_i {
+public:
+    llama_io_write_i() = default;
+    virtual ~llama_io_write_i() = default;
+
+    virtual void write(const void * src, size_t size) = 0;
+    virtual void write_tensor(const ggml_tensor * tensor, size_t offset, size_t size) = 0;
+
+    // bytes written so far
+    virtual size_t n_bytes() = 0;
+
+    void write_string(const std::string & str);
+};
+
+class llama_io_read_i {
+public:
+    llama_io_read_i() = default;
+    virtual ~llama_io_read_i() = default;
+
+    virtual const uint8_t * read(size_t size) = 0;
+    virtual void read_to(void * dst, size_t size) = 0;
+
+    // bytes read so far
+    virtual size_t n_bytes() = 0;
+
+    void read_string(std::string & str);
+};
@@ -1,12 +1,29 @@
 #pragma once

 #include "llama.h"
+#include "llama-io.h"
+#include "llama-memory.h"

 #include "ggml-cpp.h"

+#include <functional>
 #include <set>
 #include <vector>
-#include <algorithm>
+
+struct llama_cparams;
+struct llama_hparams;
+struct llama_ubatch;
+
+struct llama_kv_cache : public llama_memory_i {
+    using llama_memory_i::llama_memory_i;
+
+    virtual int32_t  get_n_tokens()   const = 0;
+    virtual uint32_t get_used_cells() const = 0; // TODO: remove, this is too-specific to the unified cache
+
+    virtual bool get_can_shift() const = 0;
+
+    bool get_can_edit() const override { return get_can_shift(); }
+};

 struct llama_kv_cell {
    llama_pos pos   = -1;
@@ -29,11 +46,105 @@ struct llama_kv_cell {
    }
 };

+// a structure holds information about the slot found in llama_kv_cache_find_slot
+struct llama_kv_cache_slot_info {
+    std::pair<uint32_t, uint32_t> boundaries; // slot boundaries [begin, end)
+    bool found = false;                       // the slot was found
+
+    explicit llama_kv_cache_slot_info(bool found_) : found{found_} {}
+    llama_kv_cache_slot_info(uint32_t begin, uint32_t end) : boundaries{begin, end}, found{true} {}
+
+    operator bool() const { return found; }
+};
+
 // ring-buffer of cached KV data
-struct llama_kv_cache {
+// TODO: pimpl
+// TODO: add notion of max sequences
+class llama_kv_cache_unified : public llama_kv_cache {
+public:
+    // can be used to query data from the model if needed
+    struct callbacks {
+        std::function<ggml_tensor * (uint32_t n_ctx_per_seq, int il)> get_rope_factors;
+    };
+
+    llama_kv_cache_unified(
+            const llama_hparams & hparams,
+            callbacks             cbs);
+
+    virtual ~llama_kv_cache_unified() = default;
+
+    // TODO: become constructor
+    bool init(
+            const llama_model & model,   // TODO: do not reference the model
+          const llama_cparams & cparams,
+                    ggml_type   type_k,
+                    ggml_type   type_v,
+                     uint32_t   kv_size,
+                         bool   offload);
+
+    int32_t  get_n_tokens()   const override;
+    uint32_t get_used_cells() const override;
+
+    size_t total_size() const;
+
+    // TODO: better data structures to reduce the cost of this operation
+    llama_pos pos_max() const;
+
+    void clear() override;
+    void defrag() override;
+
+    bool seq_rm  (llama_seq_id seq_id,                              llama_pos p0, llama_pos p1) override;
+    void seq_cp  (llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) override;
+    void seq_keep(llama_seq_id seq_id) override;
+    void seq_add (llama_seq_id seq_id,                              llama_pos p0, llama_pos p1, llama_pos delta) override;
+    void seq_div (llama_seq_id seq_id,                              llama_pos p0, llama_pos p1, int d) override;
+
+    llama_pos seq_pos_max(llama_seq_id seq_id) override;
+
+    bool get_can_shift() const override;
+
+    // find an empty slot of size "n_tokens" in the cache
+    // updates the cache head
+    // returns a structure holding information about the slot found
+    // Note: On success, it's important that cache.head points
+    // to the first cell of the slot.
+    llama_kv_cache_slot_info find_slot(const llama_ubatch & batch);
+
+    // TODO: maybe not needed
+    uint32_t get_padding(const llama_cparams & cparams) const;
+
+    // find how many cells are currently in use
+    uint32_t cell_max() const;
+
+    size_t size_k_bytes() const;
+    size_t size_v_bytes() const;
+
+    // defrag
+
+    struct {
+        std::vector<uint32_t> ids;
+    } defrag_info;
+
+    // return true if cells have been moved
+    bool defrag_prepare(int32_t n_max_nodes);
+
+    // state save/load
+
+    void state_write(llama_io_write_i & io, llama_seq_id seq_id = -1) const;
+    void state_read (llama_io_read_i  & io, llama_seq_id seq_id = -1);
+
+    // members
+
+    const llama_hparams & hparams;
+
+    callbacks cbs;
+
    bool has_shift = false;
    bool do_defrag = false;
+
+    // TODO: remove this and implement llama_kv_cache_recurrent instead
    bool recurrent = false; // with recurrent state models, a cell can hold the state for more than one past token
+
    bool v_trans   = true;  // the value tensor is transposed
    bool can_shift = false;

@@ -47,124 +158,30 @@ struct llama_kv_cache {
    // computed before each graph build
    uint32_t n = 0;

+    std::vector<llama_kv_cell> cells;
+
+    std::vector<ggml_tensor *> k_l; // per layer
+    std::vector<ggml_tensor *> v_l;
+
+private:
    ggml_type type_k = GGML_TYPE_F16;
    ggml_type type_v = GGML_TYPE_F16;

-    std::vector<llama_kv_cell> cells;
-
-    std::vector<struct ggml_tensor *> k_l; // per layer
-    std::vector<struct ggml_tensor *> v_l;
-
-    std::vector<ggml_context_ptr> ctxs;
+    std::vector<ggml_context_ptr>        ctxs;
    std::vector<ggml_backend_buffer_ptr> bufs;

-    size_t total_size() const {
-        size_t size = 0;
-        for (const auto & buf : bufs) {
-            size += ggml_backend_buffer_get_size(buf.get());
-        }
+    void state_write_meta(llama_io_write_i & io, const std::vector<std::pair<uint32_t, uint32_t>> & cell_ranges, llama_seq_id seq_id = -1) const;
+    void state_write_data(llama_io_write_i & io, const std::vector<std::pair<uint32_t, uint32_t>> & cell_ranges) const;

-        return size;
-    }
-
-    // TODO: better data structures to reduce the cost of this operation
-    llama_pos max_pos() const {
-        llama_pos max_pos = -1;
-        for (const auto & cell : cells) {
-            max_pos = std::max(max_pos, cell.pos);
-        }
-
-        return max_pos;
-    }
+    bool state_read_meta(llama_io_read_i & io, uint32_t cell_count, llama_seq_id dest_seq_id = -1);
+    bool state_read_data(llama_io_read_i & io, uint32_t cell_count);
 };

-// a structure holds information about the slot found in llama_kv_cache_find_slot
-struct llama_kv_cache_slot_info {
-    std::pair<uint32_t, uint32_t> boundaries; // slot boundaries [begin, end)
-    bool found = false;                       // the slot was found
-
-    explicit llama_kv_cache_slot_info(bool found_) : found{found_} {}
-    llama_kv_cache_slot_info(uint32_t begin, uint32_t end) : boundaries{begin, end}, found{true} {}
-
-    operator bool() const { return found; }
-};
-
-// TODO: maybe not needed
-uint32_t llama_kv_cache_get_padding(const struct llama_cparams & cparams);
-
-bool llama_kv_cache_init(
-        struct llama_kv_cache & cache,
-            const llama_model & model,
-          const llama_cparams & cparams,
-                    ggml_type   type_k,
-                    ggml_type   type_v,
-                     uint32_t   kv_size,
-                         bool   offload);
-
-// find an empty slot of size "n_tokens" in the cache
-// updates the cache head
-// returns a structure holding information about the slot found
-// Note: On success, it's important that cache.head points
-// to the first cell of the slot.
-struct llama_kv_cache_slot_info llama_kv_cache_find_slot(
-           struct llama_kv_cache & cache,
-       const struct llama_ubatch & batch);
-
-// find how many cells are currently in use
-uint32_t llama_kv_cache_cell_max(const struct llama_kv_cache & cache);
-
-void llama_kv_cache_clear(struct llama_kv_cache & cache);
-
-bool llama_kv_cache_seq_rm(
-        struct llama_kv_cache & cache,
-                 llama_seq_id   seq_id,
-                    llama_pos   p0,
-                    llama_pos   p1);
-
-void llama_kv_cache_seq_cp(
-        struct llama_kv_cache & cache,
-                 llama_seq_id   seq_id_src,
-                 llama_seq_id   seq_id_dst,
-                    llama_pos   p0,
-                    llama_pos   p1);
-
-void llama_kv_cache_seq_keep(
-        struct llama_kv_cache & cache,
-                 llama_seq_id   seq_id);
-
-void llama_kv_cache_seq_add(
-        struct llama_kv_cache & cache,
-                 llama_seq_id   seq_id,
-                    llama_pos   p0,
-                    llama_pos   p1,
-                    llama_pos   delta);
-
-void llama_kv_cache_seq_div(
-        struct llama_kv_cache & cache,
-                 llama_seq_id   seq_id,
-                    llama_pos   p0,
-                    llama_pos   p1,
-                          int   d);
-
-llama_pos llama_kv_cache_seq_pos_max(
-        struct llama_kv_cache & cache,
-                 llama_seq_id   seq_id);
-
-void llama_kv_cache_defrag(struct llama_kv_cache & cache);
-
-int32_t llama_get_kv_cache_token_count(const struct llama_kv_cache & kv);
-
-int32_t llama_get_kv_cache_used_cells(const struct llama_kv_cache & kv);
-
-bool llama_kv_cache_can_shift(const struct llama_kv_cache & kv);
-
-//
-// kv cache view
-//
-
-struct llama_kv_cache_view llama_kv_cache_view_init(const struct llama_kv_cache & kv, int32_t n_seq_max);
-
-void llama_kv_cache_view_update(struct llama_kv_cache_view * view, const struct llama_kv_cache & kv);
+// TODO: temporary reusing llama_kv_cache_unified -- implement recurrent cache and simplify llama_kv_cache_unified
+//class llama_kv_cache_recurrent : public llama_kv_cache_unified {
+//public:
+//    using llama_kv_cache_unified::llama_kv_cache_unified;
+//};

 //
 // kv cache restore
@@ -184,13 +201,15 @@ struct llama_kv_slot_restorer {

    bool do_restore = false;

-    explicit llama_kv_slot_restorer(const struct llama_kv_cache & cache) {
+    llama_kv_cache_unified & cache;
+
+    explicit llama_kv_slot_restorer(llama_kv_cache_unified & cache) : cache(cache) {
        old_state.head = cache.head;
        old_state.n    = cache.n;
    }

    // saves a slot information for future restoration
-    void save(const struct llama_kv_cache_slot_info & slot) {
+    void save(const llama_kv_cache_slot_info & slot) {
        if (slot) {
            do_restore = true;
            if (slot.boundaries.first != slot.boundaries.second) {
@@ -201,19 +220,68 @@ struct llama_kv_slot_restorer {

    // must be explicitly called to restore the kv_cache state
    // and rollback changes from all llama_kv_cache_find_slot calls
-    void restore(struct llama_kv_cache & cache) {
+    void restore() {
        if (do_restore) {
            cache.head = old_state.head;
            cache.n    = old_state.n;

            if (cache.recurrent) { // recurrent models like Mamba or RWKV can't have a state partially erased
-                llama_kv_cache_seq_rm(cache, -1, -1, -1);
+                cache.seq_rm(-1, -1, -1);
            } else {
                for (auto & slot : slot_boundaries) {
-                    llama_kv_cache_seq_rm(cache, -1, slot.first, slot.second);
+                    cache.seq_rm(-1, slot.first, slot.second);
                }
            }
        }
    }
 };

+// TODO: maybe become part of the public llama_kv_cache in the future
+int32_t llama_kv_cache_n_tokens(const llama_kv_cache * kv);
+
+int32_t llama_kv_cache_used_cells(const llama_kv_cache * kv);
+
+void llama_kv_cache_clear(llama_kv_cache * kv);
+
+bool llama_kv_cache_seq_rm(
+        llama_kv_cache * kv,
+          llama_seq_id   seq_id,
+             llama_pos   p0,
+             llama_pos   p1);
+
+void llama_kv_cache_seq_cp(
+        llama_kv_cache * kv,
+          llama_seq_id   seq_id_src,
+          llama_seq_id   seq_id_dst,
+             llama_pos   p0,
+             llama_pos   p1);
+
+void llama_kv_cache_seq_keep(llama_kv_cache * kv, llama_seq_id seq_id);
+
+void llama_kv_cache_seq_add(
+        llama_kv_cache * kv,
+          llama_seq_id   seq_id,
+             llama_pos   p0,
+             llama_pos   p1,
+             llama_pos   delta);
+
+void llama_kv_cache_seq_div(
+        llama_kv_cache * kv,
+          llama_seq_id   seq_id,
+             llama_pos   p0,
+             llama_pos   p1,
+                   int   d);
+
+llama_pos llama_kv_cache_seq_pos_max(llama_kv_cache * kv, llama_seq_id seq_id);
+
+void llama_kv_cache_defrag(llama_kv_cache * kv);
+
+bool llama_kv_cache_can_shift(const llama_kv_cache * kv);
+
+//
+// kv cache view
+//
+
+llama_kv_cache_view llama_kv_cache_view_init(const llama_kv_cache & kv, int32_t n_seq_max);
+
+void llama_kv_cache_view_update(llama_kv_cache_view * view, const llama_kv_cache * kv);
@@ -0,0 +1 @@
+#include "llama-memory.h"
@@ -0,0 +1,21 @@
+#pragma once
+
+#include "llama.h"
+
+// general concept of LLM memory
+// the KV cache is a type of LLM memory, but there can be other types
+class llama_memory_i {
+public:
+    virtual void clear() = 0;
+    virtual void defrag() = 0;
+
+    virtual bool seq_rm  (llama_seq_id seq_id,                              llama_pos p0, llama_pos p1) = 0;
+    virtual void seq_cp  (llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) = 0;
+    virtual void seq_keep(llama_seq_id seq_id) = 0;
+    virtual void seq_add (llama_seq_id seq_id,                              llama_pos p0, llama_pos p1, llama_pos delta) = 0;
+    virtual void seq_div (llama_seq_id seq_id,                              llama_pos p0, llama_pos p1, int d) = 0;
+
+    virtual llama_pos seq_pos_max(llama_seq_id seq_id) = 0;
+
+    virtual bool get_can_edit() const = 0;
+};
@@ -2,7 +2,9 @@

 #include "llama.h"
 #include "llama-arch.h"
+#include "llama-graph.h"
 #include "llama-hparams.h"
+#include "llama-memory.h"
 #include "llama-vocab.h"

 #include <memory>
@@ -10,6 +12,8 @@
 #include <unordered_map>
 #include <vector>

+struct llama_cparams;
+struct llama_ubatch;
 struct llama_model_loader;

 // available models
@@ -347,7 +351,7 @@ struct llama_model {
    std::string desc() const;

    size_t size() const;
-    size_t max_nodes() const;
+    size_t n_tensors() const;
    size_t n_devices() const;

    // total number of parameters in the model
@@ -362,9 +366,22 @@ struct llama_model {

    const struct ggml_tensor * get_tensor(const char * name) const;

+    // TODO: move this to new llm_arch_model_i interface
+    llama_memory_i * create_memory() const; // TODO: params
+
+    // TODO: move this to new llm_arch_model_i interface
+    llm_graph_result_ptr build_graph(
+            const llm_graph_params & params,
+                       ggml_cgraph * gf,
+                    llm_graph_type   type) const;
+
 private:
    struct impl;
    std::unique_ptr<impl> pimpl;
 };

 const char * llm_type_name(llm_type type);
+
+// For internal test use
+// TODO: remove
+const std::vector<std::pair<std::string, ggml_tensor *>> & llama_internal_get_tensor_map(const llama_model * model);
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
fairydreaming	b19bd064c0	SYCL : support non-contiguous tensors in binary ops (add, sub, etc) (#12399 ) * sycl : support non-contiguous tensors in binary ops * sycl : silence unused variable warning --------- Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>	2025-03-15 22:19:30 +08:00
Chenguang Li	92a391327e	[CANN]MUL_MAT optimization (#12382 )	2025-03-15 09:31:08 +08:00
Eric Curtin	9f2250ba72	Add CLI arg to llama-run to adjust the number of threads used (#12370 ) We default to 4, sometimes we want to manually adjust this Signed-off-by: Eric Curtin <ecurtin@redhat.com>	2025-03-14 16:41:20 +00:00
Sigbjørn Skjæret	774973b8f3	main : add -sysf / --system-prompt-file (#12249 ) (#12250 ) * add system_prompt_file * add -sysf / --system-prompt-file * remove system_prompt_file	2025-03-14 16:57:05 +01:00
fairydreaming	8fcb563613	Load all MoE experts during warmup (#11571 ) * llama : introduce llama_set_warmup() API call that controls warmup mode; use all MoE experts during warmup * common : use new API to enable warmup mode during model warmup --------- Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>	2025-03-14 13:47:05 +01:00
Victor	add2a3aa5a	server: fix "--grammar-file" parameter (#12285 )	2025-03-14 11:21:17 +01:00
Georgi Gerganov	c522ce4143	graph : simplify attn input build for unified KV cache (#12381 ) ggml-ci	2025-03-14 10:47:44 +02:00
Georgi Gerganov	081bee8c64	hparams : add SWA rope parameters (#12374 ) ggml-ci	2025-03-14 09:03:24 +02:00
Georgi Gerganov	84d5475541	llama : fix Gemma3 SWA KV cache shift (#12373 ) * llama : fix Gemma3 SWA KV cache shift ggml-ci * hparams : add comment [no ci]	2025-03-13 19:08:07 +02:00
Xuan-Son Nguyen	be7c303410	arg : no n_predict = -2 for examples except for main and infill (#12364 )	2025-03-13 12:34:54 +01:00
Georgi Gerganov	e0dbec0bc6	llama : refactor llama_context, llama_kv_cache, llm_build_context (#12181 ) * llama : refactor llama_context, llama_kv_cache, llm_build_context ggml-ci * graph : don't mutate the KV cache during defrag ggml-ci * context : reduce virtuals + remove test function ggml-ci * context : move interface implementation to source file + factory ggml-ci * graph : move KV cache build functions to llama_context impl ggml-ci * graph : remove model reference from build_pooling ggml-ci * graph : remove llama_model reference ggml-ci * kv_cache : provide rope factors ggml-ci * graph : rework inputs to use only unique_ptr, remove attn input abstraction ggml-ci * context : remove llama_context_i abstraction ggml-ci * context : clean-up ggml-ci * graph : clean-up ggml-ci * llama : remove redundant keywords (struct, enum) ggml-ci * model : adapt gemma3 ggml-ci * graph : restore same attention ops as on master ggml-ci * llama : remove TODO + fix indent ggml-ci	2025-03-13 12:35:44 +02:00
Ishaan Gandhi	2048b5913d	server : fix crash when using verbose output with input tokens that are not in printable range (#12178 ) (#12338 ) * Fix DOS index bug * Remove new APIs * remove extra line * Remove from API * Add extra newline * Update examples/server/server.cpp --------- Co-authored-by: Xuan-Son Nguyen <thichthat@gmail.com>	2025-03-13 11:10:05 +01:00
Oscar Barenys	f08f4b3187	Update build.yml for Windows Vulkan builder to use Vulkan 1.4.304 SDK for VK_NV_cooperative_matrix2 support (#12301 )	2025-03-12 20:06:58 +01:00
Daniel Bevenius	80a02aa858	llama.swiftui : fix xcframework dir in README [no ci] (#12353 ) This commit fixes the path to the xcframework in the README file which I had forgotten to change after renaming the build directory.	2025-03-12 13:45:32 +01:00
Alberto Cabrera Pérez	363f8c5d67	sycl : variable sg_size support for mmvq kernels (#12336 )	2025-03-12 09:57:32 +00:00
uvos	34c961b181	CUDA/HIP: Fix fattn-vec-* when device warp size is not 32 (#12315 ) When fattn-wmma was ported over to warp64 various bits that also touch fattn-vec where converted to selectable warp size, however the fattn-vec kernels dont work with 64 wide warps for now, so we need to avoid launching them with parameters for warp64	2025-03-12 10:14:11 +01:00
Xuan-Son Nguyen	7841fc723e	llama : Add Gemma 3 support (+ experimental vision capability) (#12343 ) * llama : Add Gemma 3 text-only support * fix python coding style * fix compile on ubuntu * python: fix style * fix ubuntu compile * fix build on ubuntu (again) * fix ubuntu build, finally * clip : Experimental support for Gemma 3 vision (#12344) * clip : Experimental support for Gemma 3 vision * fix build * PRId64	2025-03-12 09:30:24 +01:00
Jeff Bolz	bf69cfe62f	vulkan: fix bug in coopmat1 mul_mat_id (#12316 ) * tests: run mul_mat_id with a larger N * vulkan: fix bug in coopmat1 mul_mat_id	2025-03-12 06:59:19 +01:00
uvos	10f2e81809	CUDA/HIP: refractor mmqv to unify the calculation of nwarps and rows per block between host and device code. (#12177 ) refactor mmqv to unify the calculation of nwarps and rows per block between host and device code. --------- Co-authored-by: Johannes Gäßler <johannesg@5d6.de>	2025-03-11 20:16:03 +01:00
jklincn	ba7654380a	ggml-backend : fix backend search path (#12330 ) * Fix backend search path * replace .native() with '/' * reverted .native()	2025-03-11 14:25:17 +01:00
BB-fat	6ab2e4765a	metal : Cache the Metal library at the device context level (#12265 )	2025-03-11 13:45:02 +02:00
Xuan-Son Nguyen	96e1280839	clip : bring back GPU support (#12322 ) * clip : bring back GPU support * use n_gpu_layers param * fix double free * ggml_backend_init_by_type * clean up	2025-03-11 09:20:16 +01:00
Eve	2c9f833d17	mat vec double buffer (#12188 )	2025-03-10 19:28:11 +00:00
R0CKSTAR	251364549f	musa: support new arch mp_31 and update doc (#12296 ) Signed-off-by: Xiaodong Ye <xiaodong.ye@mthreads.com>	2025-03-10 18:18:25 +01:00
Henry Linjamäki	8acdacb3ea	opencl: use OpenCL C standard supported by the device (#12221 ) This patch nudges the llama.cpp a bit to be supported on PoCL which doesn't support OpenCL C CL2.0. The issue is solved by querying the device for the supported OpenCL C versions and using the highest one available.	2025-03-10 09:57:00 -07:00
John Bean	89b2b56e86	readme: added Sidekick to available UIs (#12311 )	2025-03-10 16:13:09 +02:00
Georgi Gerganov	e128a1bf5b	tests : fix test-quantize-fns to init the CPU backend (#12306 ) ggml-ci	2025-03-10 14:07:15 +02:00
marcoStocchi	6ef79a67ca	common : refactor '-o' option (#12278 ) As discussed in PR 'llama-tts : add -o option' (#12042): * common_params : 'out_file' string is the only output file name parameter left in common_params. It's intended to be used in all example programs implementing an '-o' option. * cvector-generator, export-lora, imatrix : default output filenames moved from 'common_params' to the 'main()' of each example program.	2025-03-10 13:34:13 +02:00
Olivier Chafik	4e39a3c332	`server`: extract <think> tags from qwq outputs (#12297 ) * extract <think> tags from qwq outputs * const for all static regexes in chat.cpp	2025-03-10 10:59:03 +00:00
Olivier Chafik	be421fc429	`tool-call`: ensure there's always a non-empty tool call id (#12292 )	2025-03-10 09:45:29 +00:00
Olivier Chafik	87c2630546	allow missing content in message if tool_calls provided (#12293 )	2025-03-10 09:45:07 +00:00
Olivier Chafik	2b3a25c212	`sampler`: fixes trigger tokens + lazy grammars (fix typo cast from token to string) (#12291 ) * Fix typo in lazy grammar handling (fixes trigger tokens) Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>	2025-03-10 09:44:42 +00:00
tc-mb	8352cdc87b	llava : fix bug in minicpm-v code (#11513 ) * fix bug in minicpm-v code * update readme of minicpm-v	2025-03-10 10:33:24 +02:00
Georgi Gerganov	1e2f78a004	server : add speculative decoding presets for FIM (#12287 )	2025-03-09 19:08:20 +02:00
Georgi Gerganov	0fd7ca7a21	authors : update (#12271 )	2025-03-08 18:26:00 +02:00
Jason C.H	6fefc05a7a	ggml-backend : make path_str compatible with C++20 (#12269 )	2025-03-08 17:02:39 +01:00
Georgi Gerganov	7ab364390f	server : infill gen ends on new line (#12254 )	2025-03-07 20:54:30 +02:00
Daniel Bevenius	7c7f3b7f43	ggml : skip intermediate .air file when compiling .metallib (#12247 ) This commit updates the compilation of default.metallib to skip the intermediate .air (Apple Intermediate Representation) file. The motivation for this change is to simplify the custom command a little and avoid generating and then removing the .air file.	2025-03-07 14:15:27 +01:00
Georgi Gerganov	102ac1891d	sync : ggml ggml-ci	2025-03-07 14:49:44 +02:00
vmobilis	d6ae2fa061	ggml : ggml_compute_forward_concat() for arbitrary tensor type (ggml/1118) * ggml_compute_forward_concat() for arbitrary tensor type * Check that tensors' type match * ggml-cpu.c: check type of source tensors * ggml-cpu.c: move tensor type check to ggml_compute_forward_concat() * ggml.c: check concatenated tensor type * Remove tensor type check from ggml_compute_forward_concat() in ggml-cpu.c ..., as it was moved to ggml.c.	2025-03-07 14:49:44 +02:00
Rémy O	68d0027f3d	ggml-cpu: faster AVX2 variant for IQ1_M (#12216 )	2025-03-07 13:54:22 +02:00
Georgi Gerganov	ea002810a2	ci : fix save-load test invocations (#12245 )	2025-03-07 12:19:31 +02:00
Sigbjørn Skjæret	8fad3c7a7c	server : Log original chat template parsing error (#12233 )	2025-03-07 11:15:33 +01:00