llama-graph: avoid expand_forward for fusion (#17633)

* contributing: update guidelines for AI-generated code

* revise

.devops/vulkan.Dockerfile

@@ -50,6 +50,7 @@ WORKDIR /app
 
 RUN apt-get update \
     && apt-get install -y \
+    build-essential \
     git \
     python3 \
     python3-pip \

CONTRIBUTING.md

@@ -19,6 +19,7 @@ The project differentiates between 3 levels of contributors:
 - If your PR becomes stale, don't hesitate to ping the maintainers in the comments
 - Maintainers will rely on your insights and approval when making a final decision to approve and merge a PR
 - Consider adding yourself to [CODEOWNERS](CODEOWNERS) to indicate your availability for reviewing related PRs
+- Using AI to generate PRs is permitted. However, you must (1) explicitly disclose how AI was used and (2) conduct a thorough manual review before publishing the PR. Note that trivial tab autocompletions do not require disclosure.
 
 # Pull requests (for maintainers)
 

SECURITY.md

@@ -65,4 +65,6 @@ However, If you have discovered a security vulnerability in this project, please
 
 Please disclose it as a private [security advisory](https://github.com/ggml-org/llama.cpp/security/advisories/new).
 
+Please note that using AI to identify vulnerabilities and generate reports is permitted. However, you must (1) explicitly disclose how AI was used and (2) conduct a thorough manual review before submitting the report.
+
 A team of volunteers on a reasonable-effort basis maintains this project. As such, please give us at least 90 days to work on a fix before public exposure.

ci/run.sh

@@ -45,7 +45,7 @@ sd=`dirname $0`
 cd $sd/../
 SRC=`pwd`
 
-CMAKE_EXTRA="-DLLAMA_FATAL_WARNINGS=ON -DLLAMA_CURL=ON"
+CMAKE_EXTRA="-DLLAMA_FATAL_WARNINGS=ON -DLLAMA_CURL=ON -DGGML_SCHED_NO_REALLOC=ON"
 
 if [ ! -z ${GG_BUILD_METAL} ]; then
     CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_METAL=ON"
@@ -428,10 +428,10 @@ function gg_run_qwen3_0_6b {
 
     (time ./bin/llama-imatrix --model ${model_f16} -f ${wiki_test} -ngl 99 -c 1024 -b 512 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-imatrix.log
 
-    (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 10 -c 1024 -fa off ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
-    (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 10 -c 1024 -fa on  ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
-    (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 99 -c 1024 -fa off ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
-    (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 99 -c 1024 -fa on  ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
+    (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 10 -c 1024 -fa off --no-op-offload) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
+    (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 10 -c 1024 -fa on  --no-op-offload) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
+    (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 99 -c 1024 -fa off                ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
+    (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 99 -c 1024 -fa on                 ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
 
     function check_ppl {
         qnt="$1"
@@ -523,8 +523,8 @@ function gg_run_embd_bge_small {
 
     ./bin/llama-quantize ${model_f16} ${model_q8_0} q8_0
 
-    (time ./bin/llama-embedding --model ${model_f16}  -p "I believe the meaning of life is" -ngl 99 -c 0 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
-    (time ./bin/llama-embedding --model ${model_q8_0} -p "I believe the meaning of life is" -ngl 99 -c 0 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
+    (time ./bin/llama-embedding --model ${model_f16}  -p "I believe the meaning of life is" -ngl 99 -c 0 --no-op-offload) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
+    (time ./bin/llama-embedding --model ${model_q8_0} -p "I believe the meaning of life is" -ngl 99 -c 0 --no-op-offload) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
 
     set +e
 }
@@ -564,7 +564,7 @@ function gg_run_rerank_tiny {
     model_f16="${path_models}/ggml-model-f16.gguf"
 
     # for this model, the SEP token is "</s>"
-    (time ./bin/llama-embedding --model ${model_f16} -p "what is panda?\thi\nwhat is panda?\tit's a bear\nwhat is panda?\tThe giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China." -ngl 99 -c 0 --pooling rank --embd-normalize -1 --verbose-prompt) 2>&1 | tee -a $OUT/${ci}-rk-f16.log
+    (time ./bin/llama-embedding --model ${model_f16} -p "what is panda?\thi\nwhat is panda?\tit's a bear\nwhat is panda?\tThe giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China." -ngl 99 -c 0 --pooling rank --embd-normalize -1 --no-op-offload --verbose-prompt) 2>&1 | tee -a $OUT/${ci}-rk-f16.log
 
     # sample output
     # rerank score 0:    0.029

common/arg.cpp

@@ -980,7 +980,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params) {
             params.kv_unified = true;
         }
-    ).set_env("LLAMA_ARG_KV_SPLIT"));
+    ).set_env("LLAMA_ARG_KV_UNIFIED"));
     add_opt(common_arg(
         {"--no-context-shift"},
         string_format("disables context shift on infinite text generation (default: %s)", params.ctx_shift ? "disabled" : "enabled"),
@@ -2639,7 +2639,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params &, const std::string & value) {
             common_log_set_file(common_log_main(), value.c_str());
         }
-    ));
+    ).set_env("LLAMA_LOG_FILE"));
     add_opt(common_arg(
         {"--log-colors"}, "[on|off|auto]",
         "Set colored logging ('on', 'off', or 'auto', default: 'auto')\n"

common/chat-parser.cpp

@@ -13,6 +13,120 @@
 
 using json = nlohmann::ordered_json;
 
+static void parse_prefixed_json_tool_call_array(common_chat_msg_parser & builder,
+                                                const common_regex &     prefix,
+                                                size_t                   rstrip_prefix = 0) {
+    static const std::vector<std::vector<std::string>> args_paths = { { "arguments" } };
+    if (auto res = builder.try_find_regex(prefix)) {
+        builder.move_back(rstrip_prefix);
+        auto tool_calls = builder.consume_json_with_dumped_args(args_paths);
+        if (!builder.add_tool_calls(tool_calls.value) || tool_calls.is_partial) {
+            throw common_chat_msg_partial_exception("incomplete tool call array");
+        }
+    } else {
+        builder.add_content(builder.consume_rest());
+    }
+}
+
+static std::string wrap_code_as_arguments(common_chat_msg_parser & builder, const std::string & code) {
+    std::string arguments;
+    if (builder.is_partial()) {
+        arguments = (json{
+                         { "code", code + builder.healing_marker() }
+        })
+                        .dump();
+        auto idx = arguments.find(builder.healing_marker());
+        if (idx != std::string::npos) {
+            arguments.resize(idx);
+        }
+    } else {
+        arguments = (json{
+                         { "code", code }
+        })
+                        .dump();
+    }
+    return arguments;
+}
+
+/**
+ * Takes a prefix regex that must have 1 group to capture the function name, a closing suffix, and expects json parameters in between.
+ * Aggregates the prefix, suffix and in-between text into the content.
+ */
+static void parse_json_tool_calls(
+    common_chat_msg_parser &            builder,
+    const std::optional<common_regex> & block_open,
+    const std::optional<common_regex> & function_regex_start_only,
+    const std::optional<common_regex> & function_regex,
+    const common_regex &                close_regex,
+    const std::optional<common_regex> & block_close,
+    bool                                allow_raw_python = false,
+    const std::function<std::string(const common_chat_msg_parser::find_regex_result & fres)> & get_function_name =
+        nullptr) {
+    auto parse_tool_calls = [&]() {
+        size_t from  = std::string::npos;
+        auto   first = true;
+        while (true) {
+            auto start_pos = builder.pos();
+            auto res = function_regex_start_only && first ? builder.try_consume_regex(*function_regex_start_only) :
+                       function_regex                     ? builder.try_find_regex(*function_regex, from) :
+                                                            std::nullopt;
+
+            if (res) {
+                std::string name;
+                if (get_function_name) {
+                    name = get_function_name(*res);
+                } else {
+                    GGML_ASSERT(res->groups.size() == 2);
+                    name = builder.str(res->groups[1]);
+                }
+                first = false;
+                if (name.empty()) {
+                    // get_function_name signalled us that we should skip this match and treat it as content.
+                    from = res->groups[0].begin + 1;
+                    continue;
+                }
+                from = std::string::npos;
+
+                auto maybe_raw_python = name == "python" && allow_raw_python;
+                if (builder.input()[builder.pos()] == '{' || !maybe_raw_python) {
+                    if (auto arguments = builder.try_consume_json_with_dumped_args({ {} })) {
+                        if (!builder.add_tool_call(name, "", arguments->value) || arguments->is_partial) {
+                            throw common_chat_msg_partial_exception("incomplete tool call");
+                        }
+                        builder.consume_regex(close_regex);
+                    }
+                    continue;
+                }
+                if (maybe_raw_python) {
+                    auto arguments = wrap_code_as_arguments(builder, builder.consume_rest());
+                    if (!builder.add_tool_call(name, "", arguments)) {
+                        throw common_chat_msg_partial_exception("incomplete tool call");
+                    }
+                    return;
+                }
+                throw common_chat_msg_partial_exception("incomplete tool call");
+            } else {
+                builder.move_to(start_pos);
+            }
+            break;
+        }
+        if (block_close) {
+            builder.consume_regex(*block_close);
+        }
+        builder.consume_spaces();
+        builder.add_content(builder.consume_rest());
+    };
+    if (block_open) {
+        if (auto res = builder.try_find_regex(*block_open)) {
+            parse_tool_calls();
+        } else {
+            builder.add_content(builder.consume_rest());
+        }
+    } else {
+        parse_tool_calls();
+    }
+}
+
 common_chat_msg_parser::common_chat_msg_parser(const std::string & input, bool is_partial, const common_chat_syntax & syntax)
     : input_(input), is_partial_(is_partial), syntax_(syntax)
 {
@@ -532,3 +646,857 @@ std::optional<common_chat_msg_parser::consume_json_result> common_chat_msg_parse
 void common_chat_msg_parser::clear_tools() {
     result_.tool_calls.clear();
 }
+
+/**
+ * All common_chat_parse_* moved from chat.cpp to chat-parser.cpp below
+ * to reduce incremental compile time for parser changes.
+ */
+static void common_chat_parse_generic(common_chat_msg_parser & builder) {
+    if (!builder.syntax().parse_tool_calls) {
+        builder.add_content(builder.consume_rest());
+        return;
+    }
+    static const std::vector<std::vector<std::string>> content_paths = {
+        {"response"},
+    };
+    static const std::vector<std::vector<std::string>> args_paths = {
+        {"tool_call", "arguments"},
+        {"tool_calls", "arguments"},
+    };
+    auto data = builder.consume_json_with_dumped_args(args_paths, content_paths);
+    if (data.value.contains("tool_calls")) {
+        if (!builder.add_tool_calls(data.value.at("tool_calls")) || data.is_partial) {
+            throw common_chat_msg_partial_exception("incomplete tool calls");
+        }
+    } else if (data.value.contains("tool_call")) {
+        if (!builder.add_tool_call(data.value.at("tool_call")) || data.is_partial) {
+            throw common_chat_msg_partial_exception("incomplete tool call");
+        }
+    } else if (data.value.contains("response")) {
+        const auto & response = data.value.at("response");
+        builder.add_content(response.is_string() ? response.template get<std::string>() : response.dump(2));
+        if (data.is_partial) {
+            throw common_chat_msg_partial_exception("incomplete response");
+        }
+    } else {
+        throw common_chat_msg_partial_exception("Expected 'tool_call', 'tool_calls' or 'response' in JSON");
+    }
+}
+
+static void common_chat_parse_mistral_nemo(common_chat_msg_parser & builder) {
+    if (!builder.syntax().parse_tool_calls) {
+        builder.add_content(builder.consume_rest());
+        return;
+    }
+
+    static const common_regex prefix(regex_escape("[TOOL_CALLS]"));
+    parse_prefixed_json_tool_call_array(builder, prefix);
+}
+
+static void common_chat_parse_magistral(common_chat_msg_parser & builder) {
+    builder.try_parse_reasoning("[THINK]", "[/THINK]");
+
+    if (!builder.syntax().parse_tool_calls) {
+        builder.add_content(builder.consume_rest());
+        return;
+    }
+
+    static const common_regex prefix(regex_escape("[TOOL_CALLS]"));
+    parse_prefixed_json_tool_call_array(builder, prefix);
+}
+
+static void common_chat_parse_command_r7b(common_chat_msg_parser & builder) {
+    builder.try_parse_reasoning("<|START_THINKING|>", "<|END_THINKING|>");
+
+    static const common_regex start_action_regex("<\\|START_ACTION\\|>");
+    static const common_regex end_action_regex("<\\|END_ACTION\\|>");
+    static const common_regex start_response_regex("<\\|START_RESPONSE\\|>");
+    static const common_regex end_response_regex("<\\|END_RESPONSE\\|>");
+
+    if (auto res = builder.try_find_regex(start_action_regex)) {
+        // If we didn't extract thoughts, prelude includes them.
+        auto tool_calls = builder.consume_json_with_dumped_args({{"parameters"}});
+        for (const auto & tool_call : tool_calls.value) {
+            std::string name = tool_call.contains("tool_name") ? tool_call.at("tool_name") : "";
+            std::string id = tool_call.contains("tool_call_id") ? tool_call.at("tool_call_id") : "";
+            std::string arguments = tool_call.contains("parameters") ? tool_call.at("parameters") : "";
+            if (!builder.add_tool_call(name, id, arguments) || tool_calls.is_partial) {
+                throw common_chat_msg_partial_exception("incomplete tool call");
+            }
+        }
+        if (tool_calls.is_partial) {
+            throw common_chat_msg_partial_exception("incomplete tool call");
+        }
+        builder.consume_regex(end_action_regex);
+    } else if (auto res = builder.try_find_regex(start_response_regex)) {
+        if (!builder.try_find_regex(end_response_regex)) {
+            builder.add_content(builder.consume_rest());
+            throw common_chat_msg_partial_exception(end_response_regex.str());
+        }
+    } else {
+        builder.add_content(builder.consume_rest());
+    }
+}
+
+static void common_chat_parse_llama_3_1(common_chat_msg_parser & builder, bool with_builtin_tools = false) {
+    builder.try_parse_reasoning("<think>", "</think>");
+
+    if (!builder.syntax().parse_tool_calls) {
+        builder.add_content(builder.consume_rest());
+        return;
+    }
+
+    static const common_regex function_regex(
+        "\\s*\\{\\s*(?:\"type\"\\s*:\\s*\"function\"\\s*,\\s*)?\"name\"\\s*:\\s*\"([^\"]+)\"\\s*,\\s*\"parameters\"\\s*: ");
+    static const common_regex close_regex("\\}\\s*");
+
+    static const common_regex function_name_regex("\\s*(\\w+)\\s*\\.\\s*call\\(");
+    static const common_regex arg_name_regex("\\s*(\\w+)\\s*=\\s*");
+
+    if (with_builtin_tools) {
+        static const common_regex builtin_call_regex("<\\|python_tag\\|>");
+        if (auto res = builder.try_find_regex(builtin_call_regex)) {
+            auto fun_res = builder.consume_regex(function_name_regex);
+            auto function_name = builder.str(fun_res.groups[1]);
+
+            common_healing_marker healing_marker;
+            json args = json::object();
+            while (true) {
+                if (auto arg_res = builder.try_consume_regex(arg_name_regex)) {
+                    auto arg_name = builder.str(arg_res->groups[1]);
+                    auto partial = builder.consume_json();
+                    args[arg_name] = partial.json;
+                    healing_marker.marker = partial.healing_marker.marker;
+                    healing_marker.json_dump_marker = partial.healing_marker.json_dump_marker;
+                    builder.consume_spaces();
+                    if (!builder.try_consume_literal(",")) {
+                        break;
+                    }
+                } else {
+                    break;
+                }
+            }
+            builder.consume_literal(")");
+            builder.consume_spaces();
+
+            auto arguments = args.dump();
+            if (!builder.add_tool_call(function_name, "", arguments)) {
+                throw common_chat_msg_partial_exception("Incomplete tool call");
+            }
+            return;
+        }
+    }
+    parse_json_tool_calls(
+        builder,
+        /* block_open= */ std::nullopt,
+        /* function_regex_start_only= */ function_regex,
+        /* function_regex= */ std::nullopt,
+        close_regex,
+        std::nullopt);
+
+}
+
+static void common_chat_parse_deepseek_r1(common_chat_msg_parser & builder) {
+    builder.try_parse_reasoning("<think>", "</think>");
+    if (!builder.syntax().parse_tool_calls) {
+        builder.add_content(builder.consume_rest());
+        return;
+    }
+
+    static const common_regex tool_calls_begin("(?:<｜tool▁calls▁begin｜>|<｜tool_calls_begin｜>|<｜tool calls begin｜>|<｜tool\\\\_calls\\\\_begin｜>|<｜tool▁calls｜>)");
+    static const common_regex tool_calls_end("<｜tool▁calls▁end｜>");
+    static const common_regex function_regex("(?:<｜tool▁call▁begin｜>)?function<｜tool▁sep｜>([^\n]+)\n```json\n");
+    static const common_regex close_regex("```[\\s\\r\\n]*<｜tool▁call▁end｜>");
+
+    parse_json_tool_calls(
+        builder,
+        /* block_open= */ tool_calls_begin,
+        /* function_regex_start_only= */ std::nullopt,
+        function_regex,
+        close_regex,
+        tool_calls_end);
+}
+
+static void common_chat_parse_deepseek_v3_1_content(common_chat_msg_parser & builder) {
+    static const common_regex function_regex("(?:<｜tool▁call▁begin｜>)?([^\\n<]+)(?:<｜tool▁sep｜>)");
+
+    static const common_regex close_regex("(?:[\\s]*)?<｜tool▁call▁end｜>");
+    static const common_regex tool_calls_begin("(?:<｜tool▁calls▁begin｜>|<｜tool_calls_begin｜>|<｜tool calls begin｜>|<｜tool\\\\_calls\\\\_begin｜>|<｜tool▁calls｜>)");
+    static const common_regex tool_calls_end("<｜tool▁calls▁end｜>");
+
+    if (!builder.syntax().parse_tool_calls) {
+        LOG_DBG("%s: not parse_tool_calls\n", __func__);
+        builder.add_content(builder.consume_rest());
+        return;
+    }
+
+    LOG_DBG("%s: parse_tool_calls\n", __func__);
+
+    parse_json_tool_calls(
+        builder,
+        /* block_open= */ tool_calls_begin,
+        /* function_regex_start_only= */ std::nullopt,
+        function_regex,
+        close_regex,
+        tool_calls_end);
+}
+
+static void common_chat_parse_deepseek_v3_1(common_chat_msg_parser & builder) {
+    // DeepSeek V3.1 outputs reasoning content between "<think>" and "</think>" tags, followed by regular content
+    // First try to parse using the standard reasoning parsing method
+    LOG_DBG("%s: thinking_forced_open: %s\n", __func__, std::to_string(builder.syntax().thinking_forced_open).c_str());
+
+    auto start_pos = builder.pos();
+    auto found_end_think = builder.try_find_literal("</think>");
+    builder.move_to(start_pos);
+
+    if (builder.syntax().thinking_forced_open && !builder.is_partial() && !found_end_think) {
+        LOG_DBG("%s: no end_think, not partial, adding content\n", __func__);
+        common_chat_parse_deepseek_v3_1_content(builder);
+    } else if (builder.try_parse_reasoning("<think>", "</think>")) {
+        // If reasoning was parsed successfully, the remaining content is regular content
+        LOG_DBG("%s: parsed reasoning, adding content\n", __func__);
+        // </think><｜tool▁calls▁begin｜><｜tool▁call▁begin｜>function<｜tool▁sep｜>NAME\n```json\nJSON\n```<｜tool▁call▁end｜><｜tool▁calls▁end｜>
+        common_chat_parse_deepseek_v3_1_content(builder);
+    } else {
+        if (builder.syntax().reasoning_format == COMMON_REASONING_FORMAT_NONE) {
+          LOG_DBG("%s: reasoning_format none, adding content\n", __func__);
+          common_chat_parse_deepseek_v3_1_content(builder);
+          return;
+        }
+        // If no reasoning tags found, check if we should treat everything as reasoning
+        if (builder.syntax().thinking_forced_open) {
+            // If thinking is forced open but no tags found, treat everything as reasoning
+            LOG_DBG("%s: thinking_forced_open, adding reasoning content\n", __func__);
+            builder.add_reasoning_content(builder.consume_rest());
+        } else {
+            LOG_DBG("%s: no thinking_forced_open, adding content\n", __func__);
+            // <｜tool▁call▁begin｜>NAME<｜tool▁sep｜>JSON<｜tool▁call▁end｜>
+            common_chat_parse_deepseek_v3_1_content(builder);
+        }
+    }
+}
+
+static void common_chat_parse_minimax_m2(common_chat_msg_parser & builder) {
+    static const xml_tool_call_format form {
+        /* form.scope_start = */ "<minimax:tool_call>",
+        /* form.tool_start  = */ "<invoke name=\"",
+        /* form.tool_sep    = */ "\">",
+        /* form.key_start   = */ "<parameter name=\"",
+        /* form.key_val_sep = */ "\">",
+        /* form.val_end     = */ "</parameter>",
+        /* form.tool_end    = */ "</invoke>",
+        /* form.scope_end   = */ "</minimax:tool_call>",
+    };
+    builder.consume_reasoning_with_xml_tool_calls(form, "<think>", "</think>");
+}
+
+static void common_chat_parse_qwen3_coder_xml(common_chat_msg_parser & builder) {
+    static const xml_tool_call_format form = ([]() {
+        xml_tool_call_format form {};
+        form.scope_start = "<tool_call>";
+        form.tool_start  = "<function=";
+        form.tool_sep    = ">";
+        form.key_start   = "<parameter=";
+        form.key_val_sep = ">";
+        form.val_end     = "</parameter>";
+        form.tool_end    = "</function>";
+        form.scope_end   = "</tool_call>";
+        form.trim_raw_argval = true;
+        return form;
+    })();
+    builder.consume_reasoning_with_xml_tool_calls(form);
+}
+
+static void common_chat_parse_kimi_k2(common_chat_msg_parser & builder) {
+    static const xml_tool_call_format form = ([]() {
+        xml_tool_call_format form {};
+        form.scope_start = "<|tool_calls_section_begin|>";
+        form.tool_start  = "<|tool_call_begin|>";
+        form.tool_sep    = "<|tool_call_argument_begin|>{";
+        form.key_start   = "\"";
+        form.key_val_sep = "\": ";
+        form.val_end     = ", ";
+        form.tool_end    = "}<|tool_call_end|>";
+        form.scope_end   = "<|tool_calls_section_end|>";
+        form.raw_argval  = false;
+        form.last_val_end = "";
+        return form;
+    })();
+    builder.consume_reasoning_with_xml_tool_calls(form, "<think>", "</think>");
+}
+
+static void common_chat_parse_apriel_1_5(common_chat_msg_parser & builder) {
+    static const xml_tool_call_format form = ([]() {
+        xml_tool_call_format form {};
+        form.scope_start = "<tool_calls>[";
+        form.tool_start  = "{\"name\": \"";
+        form.tool_sep    = "\", \"arguments\": {";
+        form.key_start   = "\"";
+        form.key_val_sep = "\": ";
+        form.val_end     = ", ";
+        form.tool_end    = "}, ";
+        form.scope_end   = "]</tool_calls>";
+        form.raw_argval  = false;
+        form.last_val_end = "";
+        form.last_tool_end = "}";
+        return form;
+    })();
+    builder.consume_reasoning_with_xml_tool_calls(form, "<thinking>", "</thinking>");
+}
+
+static void common_chat_parse_xiaomi_mimo(common_chat_msg_parser & builder) {
+    static const xml_tool_call_format form = ([]() {
+        xml_tool_call_format form {};
+        form.scope_start = "";
+        form.tool_start  = "<tool_call>\n{\"name\": \"";
+        form.tool_sep    = "\", \"arguments\": {";
+        form.key_start   = "\"";
+        form.key_val_sep = "\": ";
+        form.val_end     = ", ";
+        form.tool_end    = "}\n</tool_call>";
+        form.scope_end   = "";
+        form.raw_argval  = false;
+        form.last_val_end = "";
+        return form;
+    })();
+    builder.consume_reasoning_with_xml_tool_calls(form);
+}
+
+static void common_chat_parse_gpt_oss(common_chat_msg_parser & builder) {
+    static const std::string constraint = "(?: (<\\|constrain\\|>)?([a-zA-Z0-9_-]+))";
+    static const std::string recipient("(?: to=functions\\.([^<\\s]+))");
+
+    static const common_regex start_regex("<\\|start\\|>assistant");
+    static const common_regex analysis_regex("<\\|channel\\|>analysis");
+    static const common_regex final_regex("<\\|channel\\|>final" + constraint + "?");
+    static const common_regex preamble_regex("<\\|channel\\|>commentary");
+    static const common_regex tool_call1_regex(recipient + "<\\|channel\\|>(analysis|commentary)" + constraint + "?");
+    static const common_regex tool_call2_regex("<\\|channel\\|>(analysis|commentary)" + recipient + constraint + "?");
+
+    auto consume_end = [&](bool include_end = false) {
+        if (auto res = builder.try_find_literal("<|end|>")) {
+            return res->prelude + (include_end ? builder.str(res->groups[0]) : "");
+        }
+        return builder.consume_rest();
+    };
+
+    auto handle_tool_call = [&](const std::string & name) {
+        if (auto args = builder.try_consume_json_with_dumped_args({{}})) {
+            if (builder.syntax().parse_tool_calls) {
+                if (!builder.add_tool_call(name, "", args->value) || args->is_partial) {
+                    throw common_chat_msg_partial_exception("incomplete tool call");
+                }
+            } else if (args->is_partial) {
+                throw common_chat_msg_partial_exception("incomplete tool call");
+            }
+        }
+    };
+
+    auto regex_match = [](const common_regex & regex, const std::string & input) -> std::optional<common_regex_match> {
+        auto match = regex.search(input, 0, true);
+        if (match.type == COMMON_REGEX_MATCH_TYPE_FULL) {
+            return match;
+        }
+        return std::nullopt;
+    };
+
+    do {
+        auto header_start_pos = builder.pos();
+        auto content_start = builder.try_find_literal("<|message|>");
+        if (!content_start) {
+            throw common_chat_msg_partial_exception("incomplete header");
+        }
+
+        auto header = content_start->prelude;
+
+        if (auto match = regex_match(tool_call1_regex, header)) {
+            auto group = match->groups[1];
+            auto name = header.substr(group.begin, group.end - group.begin);
+            handle_tool_call(name);
+            continue;
+        }
+
+        if (auto match = regex_match(tool_call2_regex, header)) {
+            auto group = match->groups[2];
+            auto name = header.substr(group.begin, group.end - group.begin);
+            handle_tool_call(name);
+            continue;
+        }
+
+        if (regex_match(analysis_regex, header)) {
+            builder.move_to(header_start_pos);
+            if (builder.syntax().reasoning_format == COMMON_REASONING_FORMAT_NONE || builder.syntax().reasoning_in_content) {
+                builder.add_content(consume_end(true));
+            } else {
+                builder.try_parse_reasoning("<|channel|>analysis<|message|>", "<|end|>");
+            }
+            continue;
+        }
+
+        if(regex_match(final_regex, header) || regex_match(preamble_regex, header)) {
+            builder.add_content(consume_end());
+            continue;
+        }
+
+        // Possibly a malformed message, attempt to recover by rolling
+        // back to pick up the next <|start|>
+        LOG_DBG("%s: unknown header from message: %s\n", __func__, header.c_str());
+        builder.move_to(header_start_pos);
+    } while (builder.try_find_regex(start_regex, std::string::npos, false));
+
+    auto remaining = builder.consume_rest();
+    if (!remaining.empty()) {
+        LOG_DBG("%s: content after last message: %s\n", __func__, remaining.c_str());
+    }
+}
+
+static void common_chat_parse_glm_4_5(common_chat_msg_parser & builder) {
+    static const xml_tool_call_format form {
+        /* form.scope_start  = */ "",
+        /* form.tool_start   = */ "<tool_call>",
+        /* form.tool_sep     = */ "",
+        /* form.key_start    = */ "<arg_key>",
+        /* form.key_val_sep  = */ "</arg_key>",
+        /* form.val_end      = */ "</arg_value>",
+        /* form.tool_end     = */ "</tool_call>",
+        /* form.scope_end    = */ "",
+        /* form.key_val_sep2 = */ "<arg_value>",
+    };
+    builder.consume_reasoning_with_xml_tool_calls(form, "<think>", "</think>");
+}
+
+static void common_chat_parse_firefunction_v2(common_chat_msg_parser & builder) {
+    if (!builder.syntax().parse_tool_calls) {
+        builder.add_content(builder.consume_rest());
+        return;
+    }
+    static const common_regex prefix(regex_escape(" functools["));
+    parse_prefixed_json_tool_call_array(builder, prefix, /* rstrip_prefix= */ 1);
+}
+
+static void common_chat_parse_functionary_v3_2(common_chat_msg_parser & builder) {
+    static const common_regex function_regex_start_only(R"((\w+\n\{|python\n|all\n))");
+    static const common_regex function_regex(R"(>>>(\w+\n\{|python\n|all\n))");
+    static const common_regex close_regex(R"(\s*)");
+
+    parse_json_tool_calls(
+        builder,
+        std::nullopt,
+        function_regex_start_only,
+        function_regex,
+        close_regex,
+        std::nullopt,
+        /* allow_raw_python= */ true,
+        /* get_function_name= */ [&](const auto & res) -> std::string {
+            auto at_start = res.groups[0].begin == 0;
+            auto name = builder.str(res.groups[1]);
+            if (!name.empty() && name.back() == '{') {
+                // Unconsume the opening brace '{' to ensure the JSON parsing goes well.
+                builder.move_back(1);
+            }
+            auto idx = name.find_last_not_of("\n{");
+            name = name.substr(0, idx + 1);
+            if (at_start && name == "all") {
+                return "";
+            }
+            return name;
+        });
+}
+
+static void common_chat_parse_functionary_v3_1_llama_3_1(common_chat_msg_parser & builder) {
+    if (!builder.syntax().parse_tool_calls) {
+        builder.add_content(builder.consume_rest());
+        return;
+    }
+    // This version of Functionary still supports the llama 3.1 tool call format for the python tool.
+    static const common_regex python_tag_regex(regex_escape("<|python_tag|>"));
+
+    static const common_regex function_regex(R"(<function=(\w+)>)");
+    static const common_regex close_regex(R"(</function>)");
+
+    parse_json_tool_calls(
+        builder,
+        /* block_open= */ std::nullopt,
+        /* function_regex_start_only= */ std::nullopt,
+        function_regex,
+        close_regex,
+        std::nullopt);
+
+    if (auto res = builder.try_find_regex(python_tag_regex)) {
+        auto arguments = wrap_code_as_arguments(builder, builder.consume_rest());
+        builder.add_tool_call("python", "", arguments);
+        return;
+    }
+}
+
+static void common_chat_parse_hermes_2_pro(common_chat_msg_parser & builder) {
+    builder.try_parse_reasoning("<think>", "</think>");
+    if (!builder.syntax().parse_tool_calls) {
+        builder.add_content(builder.consume_rest());
+        return;
+    }
+
+    static const common_regex open_regex(
+        "(?:"
+            "(```(?:xml|json)?\\n\\s*)?" // match 1 (block_start)
+            "("                          // match 2 (open_tag)
+                "<tool_call>"
+                "|<function_call>"
+                "|<tool>"
+                "|<tools>"
+                "|<response>"
+                "|<json>"
+                "|<xml>"
+                "|<JSON>"
+            ")?"
+            "(\\s*\\{\\s*\"name\")" // match 3 (named tool call)
+        ")"
+        "|<function=([^>]+)>"            // match 4 (function name)
+        "|<function name=\"([^\"]+)\">"  // match 5 (function name again)
+    );
+
+    while (auto res = builder.try_find_regex(open_regex)) {
+        const auto & block_start = res->groups[1];
+        std::string block_end = block_start.empty() ? "" : "```";
+
+        const auto & open_tag = res->groups[2];
+        std::string close_tag;
+
+        if (!res->groups[3].empty()) {
+            builder.move_to(res->groups[3].begin);
+            close_tag = open_tag.empty() ? "" : "</" + builder.str(open_tag).substr(1);
+
+            if (auto tool_call = builder.try_consume_json_with_dumped_args({{"arguments"}})) {
+                if (!builder.add_tool_call(tool_call->value) || tool_call->is_partial) {
+                    throw common_chat_msg_partial_exception("incomplete tool call");
+                }
+                builder.consume_spaces();
+                builder.consume_literal(close_tag);
+                builder.consume_spaces();
+                if (!block_end.empty()) {
+                    builder.consume_literal(block_end);
+                    builder.consume_spaces();
+                }
+            } else {
+                throw common_chat_msg_partial_exception("failed to parse tool call");
+            }
+        } else {
+            auto function_name = builder.str(res->groups[4]);
+            if (function_name.empty()) {
+                function_name = builder.str(res->groups[5]);
+            }
+            GGML_ASSERT(!function_name.empty());
+
+            close_tag = "</function>";
+
+            if (auto arguments = builder.try_consume_json_with_dumped_args({{}})) {
+                if (!builder.add_tool_call(function_name, "", arguments->value) || arguments->is_partial) {
+                    throw common_chat_msg_partial_exception("incomplete tool call");
+                }
+                builder.consume_spaces();
+                builder.consume_literal(close_tag);
+                builder.consume_spaces();
+                if (!block_end.empty()) {
+                    builder.consume_literal(block_end);
+                    builder.consume_spaces();
+                }
+            }
+        }
+    }
+
+    builder.add_content(builder.consume_rest());
+}
+
+static void common_chat_parse_granite(common_chat_msg_parser & builder) {
+    // Parse thinking tags
+    static const common_regex start_think_regex(regex_escape("<think>"));
+    static const common_regex end_think_regex(regex_escape("</think>"));
+    // Granite models output partial tokens such as "<" and "<think".
+    // By leveraging try_consume_regex()/try_find_regex() throwing
+    // common_chat_msg_partial_exception for these partial tokens,
+    // processing is interrupted and the tokens are not passed to add_content().
+    if (auto res = builder.try_consume_regex(start_think_regex)) {
+        // Restore position for try_parse_reasoning()
+        builder.move_to(res->groups[0].begin);
+        builder.try_find_regex(end_think_regex, std::string::npos, false);
+        // Restore position for try_parse_reasoning()
+        builder.move_to(res->groups[0].begin);
+    }
+    builder.try_parse_reasoning("<think>", "</think>");
+
+    // Parse response tags
+    static const common_regex start_response_regex(regex_escape("<response>"));
+    static const common_regex end_response_regex(regex_escape("</response>"));
+    // Granite models output partial tokens such as "<" and "<response".
+    // Same hack as reasoning parsing.
+    if (builder.try_consume_regex(start_response_regex)) {
+        builder.try_find_regex(end_response_regex);
+    }
+
+    if (!builder.syntax().parse_tool_calls) {
+        builder.add_content(builder.consume_rest());
+        return;
+    }
+
+    // Look for tool calls
+    static const common_regex tool_call_regex(regex_escape("<|tool_call|>"));
+    if (auto res = builder.try_find_regex(tool_call_regex)) {
+        builder.move_to(res->groups[0].end);
+
+        // Expect JSON array of tool calls
+        if (auto tool_call = builder.try_consume_json_with_dumped_args({{{"arguments"}}})) {
+            if (!builder.add_tool_calls(tool_call->value) || tool_call->is_partial) {
+                throw common_chat_msg_partial_exception("incomplete tool call");
+            }
+        }
+    } else {
+        builder.add_content(builder.consume_rest());
+    }
+}
+
+static void common_chat_parse_nemotron_v2(common_chat_msg_parser & builder) {
+    // Parse thinking tags
+    builder.try_parse_reasoning("<think>", "</think>");
+    if (!builder.syntax().parse_tool_calls) {
+        builder.add_content(builder.consume_rest());
+        return;
+    }
+
+    // Look for tool calls
+    static const common_regex tool_call_regex(regex_escape("<TOOLCALL>"));
+    if (auto res = builder.try_find_regex(tool_call_regex)) {
+        builder.move_to(res->groups[0].end);
+
+        // Expect JSON array of tool calls
+        auto tool_calls_data = builder.consume_json();
+        if (tool_calls_data.json.is_array()) {
+            if (!builder.try_consume_literal("</TOOLCALL>")) {
+                throw common_chat_msg_partial_exception("Incomplete tool call");
+            }
+            builder.add_tool_calls(tool_calls_data.json);
+        } else {
+            throw common_chat_msg_partial_exception("Incomplete tool call");
+        }
+    }
+    builder.add_content(builder.consume_rest());
+}
+
+static void common_chat_parse_apertus(common_chat_msg_parser & builder) {
+    // Parse thinking tags
+    builder.try_parse_reasoning("<|inner_prefix|>", "<|inner_suffix|>");
+    if (!builder.syntax().parse_tool_calls) {
+        builder.add_content(builder.consume_rest());
+        return;
+    }
+
+    // Look for tool calls
+    static const common_regex tool_call_regex(regex_escape("<|tools_prefix|>"));
+    if (auto res = builder.try_find_regex(tool_call_regex)) {
+        builder.move_to(res->groups[0].end);
+
+        auto tool_calls_data = builder.consume_json();
+        if (tool_calls_data.json.is_array()) {
+            builder.consume_spaces();
+            if (!builder.try_consume_literal("<|tools_suffix|>")) {
+                throw common_chat_msg_partial_exception("Incomplete tool call");
+            }
+            for (const auto & value : tool_calls_data.json) {
+                if (value.is_object()) {
+                    builder.add_tool_call_short_form(value);
+                }
+            }
+        } else {
+            throw common_chat_msg_partial_exception("Incomplete tool call");
+        }
+    }
+    builder.add_content(builder.consume_rest());
+}
+
+
+static void common_chat_parse_lfm2(common_chat_msg_parser & builder) {
+    if (!builder.syntax().parse_tool_calls) {
+        builder.add_content(builder.consume_rest());
+        return;
+    }
+
+    // LFM2 format: <|tool_call_start|>[{"name": "get_current_time", "arguments": {"location": "Paris"}}]<|tool_call_end|>
+    static const common_regex tool_call_start_regex(regex_escape("<|tool_call_start|>"));
+    static const common_regex tool_call_end_regex(regex_escape("<|tool_call_end|>"));
+
+    // Loop through all tool calls
+    while (auto res = builder.try_find_regex(tool_call_start_regex, std::string::npos, /* add_prelude_to_content= */ true)) {
+        builder.move_to(res->groups[0].end);
+
+        // Parse JSON array format: [{"name": "...", "arguments": {...}}]
+        auto tool_calls_data = builder.consume_json();
+
+        // Consume end marker
+        builder.consume_spaces();
+        if (!builder.try_consume_regex(tool_call_end_regex)) {
+            throw common_chat_msg_partial_exception("Expected <|tool_call_end|>");
+        }
+
+        // Process each tool call in the array
+        if (tool_calls_data.json.is_array()) {
+            for (const auto & tool_call : tool_calls_data.json) {
+                if (!tool_call.is_object()) {
+                    throw common_chat_msg_partial_exception("Tool call must be an object");
+                }
+
+                if (!tool_call.contains("name")) {
+                    throw common_chat_msg_partial_exception("Tool call missing 'name' field");
+                }
+
+                std::string function_name = tool_call.at("name");
+                std::string arguments = "{}";
+
+                if (tool_call.contains("arguments")) {
+                    if (tool_call.at("arguments").is_object()) {
+                        arguments = tool_call.at("arguments").dump();
+                    } else if (tool_call.at("arguments").is_string()) {
+                        arguments = tool_call.at("arguments");
+                    }
+                }
+
+                if (!builder.add_tool_call(function_name, "", arguments)) {
+                    throw common_chat_msg_partial_exception("Incomplete tool call");
+                }
+            }
+        } else {
+            throw common_chat_msg_partial_exception("Expected JSON array for tool calls");
+        }
+
+        // Consume any trailing whitespace after this tool call
+        builder.consume_spaces();
+    }
+
+    // Consume any remaining content after all tool calls
+    auto remaining = builder.consume_rest();
+    if (!string_strip(remaining).empty()) {
+        builder.add_content(remaining);
+    }
+}
+
+static void common_chat_parse_seed_oss(common_chat_msg_parser & builder) {
+    static const xml_tool_call_format form {
+        /* form.scope_start = */ "<seed:tool_call>",
+        /* form.tool_start  = */ "<function=",
+        /* form.tool_sep    = */ ">",
+        /* form.key_start   = */ "<parameter=",
+        /* form.key_val_sep = */ ">",
+        /* form.val_end     = */ "</parameter>",
+        /* form.tool_end    = */ "</function>",
+        /* form.scope_end   = */ "</seed:tool_call>",
+    };
+    builder.consume_reasoning_with_xml_tool_calls(form, "<seed:think>", "</seed:think>");
+}
+
+static void common_chat_parse_content_only(common_chat_msg_parser & builder) {
+    builder.try_parse_reasoning("<think>", "</think>");
+    builder.add_content(builder.consume_rest());
+}
+
+static void common_chat_parse(common_chat_msg_parser & builder) {
+    LOG_DBG("Parsing input with format %s: %s\n", common_chat_format_name(builder.syntax().format), builder.input().c_str());
+
+    switch (builder.syntax().format) {
+        case COMMON_CHAT_FORMAT_CONTENT_ONLY:
+            common_chat_parse_content_only(builder);
+            break;
+        case COMMON_CHAT_FORMAT_GENERIC:
+            common_chat_parse_generic(builder);
+            break;
+        case COMMON_CHAT_FORMAT_MISTRAL_NEMO:
+            common_chat_parse_mistral_nemo(builder);
+            break;
+        case COMMON_CHAT_FORMAT_MAGISTRAL:
+            common_chat_parse_magistral(builder);
+            break;
+        case COMMON_CHAT_FORMAT_LLAMA_3_X:
+            common_chat_parse_llama_3_1(builder);
+            break;
+        case COMMON_CHAT_FORMAT_LLAMA_3_X_WITH_BUILTIN_TOOLS:
+            common_chat_parse_llama_3_1(builder, /* with_builtin_tools= */ true);
+            break;
+        case COMMON_CHAT_FORMAT_DEEPSEEK_R1:
+            common_chat_parse_deepseek_r1(builder);
+            break;
+        case COMMON_CHAT_FORMAT_DEEPSEEK_V3_1:
+            common_chat_parse_deepseek_v3_1(builder);
+            break;
+        case COMMON_CHAT_FORMAT_FUNCTIONARY_V3_2:
+            common_chat_parse_functionary_v3_2(builder);
+            break;
+        case COMMON_CHAT_FORMAT_FUNCTIONARY_V3_1_LLAMA_3_1:
+            common_chat_parse_functionary_v3_1_llama_3_1(builder);
+            break;
+        case COMMON_CHAT_FORMAT_HERMES_2_PRO:
+            common_chat_parse_hermes_2_pro(builder);
+            break;
+        case COMMON_CHAT_FORMAT_FIREFUNCTION_V2:
+            common_chat_parse_firefunction_v2(builder);
+            break;
+        case COMMON_CHAT_FORMAT_COMMAND_R7B:
+            common_chat_parse_command_r7b(builder);
+            break;
+        case COMMON_CHAT_FORMAT_GRANITE:
+            common_chat_parse_granite(builder);
+            break;
+        case COMMON_CHAT_FORMAT_GPT_OSS:
+            common_chat_parse_gpt_oss(builder);
+            break;
+        case COMMON_CHAT_FORMAT_SEED_OSS:
+            common_chat_parse_seed_oss(builder);
+            break;
+        case COMMON_CHAT_FORMAT_NEMOTRON_V2:
+            common_chat_parse_nemotron_v2(builder);
+            break;
+        case COMMON_CHAT_FORMAT_APERTUS:
+            common_chat_parse_apertus(builder);
+            break;
+        case COMMON_CHAT_FORMAT_LFM2_WITH_JSON_TOOLS:
+            common_chat_parse_lfm2(builder);
+            break;
+        case COMMON_CHAT_FORMAT_MINIMAX_M2:
+            common_chat_parse_minimax_m2(builder);
+            break;
+        case COMMON_CHAT_FORMAT_GLM_4_5:
+            common_chat_parse_glm_4_5(builder);
+            break;
+        case COMMON_CHAT_FORMAT_KIMI_K2:
+            common_chat_parse_kimi_k2(builder);
+            break;
+        case COMMON_CHAT_FORMAT_QWEN3_CODER_XML:
+            common_chat_parse_qwen3_coder_xml(builder);
+            break;
+        case COMMON_CHAT_FORMAT_APRIEL_1_5:
+            common_chat_parse_apriel_1_5(builder);
+            break;
+        case COMMON_CHAT_FORMAT_XIAOMI_MIMO:
+            common_chat_parse_xiaomi_mimo(builder);
+            break;
+        default:
+            throw std::runtime_error(std::string("Unsupported format: ") + common_chat_format_name(builder.syntax().format));
+    }
+    builder.finish();
+}
+
+common_chat_msg common_chat_parse(const std::string & input, bool is_partial, const common_chat_syntax & syntax) {
+    common_chat_msg_parser builder(input, is_partial, syntax);
+    try {
+        common_chat_parse(builder);
+    } catch (const common_chat_msg_partial_exception & ex) {
+        LOG_DBG("Partial parse: %s\n", ex.what());
+        if (!is_partial) {
+            builder.clear_tools();
+            builder.move_to(0);
+            common_chat_parse_content_only(builder);
+        }
+    }
+    auto msg = builder.result();
+    if (!is_partial) {
+        LOG_DBG("Parsed message: %s\n", common_chat_msgs_to_json_oaicompat<json>({msg}).at(0).dump().c_str());
+    }
+    return msg;
+}

common/download.cpp

@@ -517,16 +517,18 @@ static bool common_pull_file(httplib::Client & cli,
         headers.emplace("Range", "bytes=" + std::to_string(existing_size) + "-");
     }
 
-    std::atomic<size_t> downloaded{existing_size};
+    const char * func = __func__; // avoid __func__ inside a lambda
+    size_t downloaded = existing_size;
+    size_t progress_step = 0;
 
     auto res = cli.Get(resolve_path, headers,
         [&](const httplib::Response &response) {
             if (existing_size > 0 && response.status != 206) {
-                LOG_WRN("%s: server did not respond with 206 Partial Content for a resume request. Status: %d\n", __func__, response.status);
+                LOG_WRN("%s: server did not respond with 206 Partial Content for a resume request. Status: %d\n", func, response.status);
                 return false;
             }
             if (existing_size == 0 && response.status != 200) {
-                LOG_WRN("%s: download received non-successful status code: %d\n", __func__, response.status);
+                LOG_WRN("%s: download received non-successful status code: %d\n", func, response.status);
                 return false;
             }
             if (total_size == 0 && response.has_header("Content-Length")) {
@@ -534,7 +536,7 @@ static bool common_pull_file(httplib::Client & cli,
                     size_t content_length = std::stoull(response.get_header_value("Content-Length"));
                     total_size = existing_size + content_length;
                 } catch (const std::exception &e) {
-                    LOG_WRN("%s: invalid Content-Length header: %s\n", __func__, e.what());
+                    LOG_WRN("%s: invalid Content-Length header: %s\n", func, e.what());
                 }
             }
             return true;
@@ -542,11 +544,16 @@ static bool common_pull_file(httplib::Client & cli,
         [&](const char *data, size_t len) {
             ofs.write(data, len);
             if (!ofs) {
-                LOG_ERR("%s: error writing to file: %s\n", __func__, path_tmp.c_str());
+                LOG_ERR("%s: error writing to file: %s\n", func, path_tmp.c_str());
                 return false;
             }
             downloaded += len;
-            print_progress(downloaded, total_size);
+            progress_step += len;
+
+            if (progress_step >= total_size / 1000 || downloaded == total_size) {
+                print_progress(downloaded, total_size);
+                progress_step = 0;
+            }
             return true;
         },
         nullptr

common/json-schema-to-grammar.cpp

@@ -268,10 +268,10 @@ static bool is_reserved_name(const std::string & name) {
 }
 
 std::regex INVALID_RULE_CHARS_RE("[^a-zA-Z0-9-]+");
-std::regex GRAMMAR_LITERAL_ESCAPE_RE("[\r\n\"]");
+std::regex GRAMMAR_LITERAL_ESCAPE_RE("[\r\n\"\\\\]");
 std::regex GRAMMAR_RANGE_LITERAL_ESCAPE_RE("[\r\n\"\\]\\-\\\\]");
 std::unordered_map<char, std::string> GRAMMAR_LITERAL_ESCAPES = {
-    {'\r', "\\r"}, {'\n', "\\n"}, {'"', "\\\""}, {'-', "\\-"}, {']', "\\]"}
+    {'\r', "\\r"}, {'\n', "\\n"}, {'"', "\\\""}, {'-', "\\-"}, {']', "\\]"}, {'\\', "\\\\"}
 };
 
 std::unordered_set<char> NON_LITERAL_SET = {'|', '.', '(', ')', '[', ']', '{', '}', '*', '+', '?'};

convert_hf_to_gguf.py

@@ -4183,6 +4183,36 @@ class Qwen3MoeModel(Qwen2MoeModel):
         super().set_vocab()
 
 
+@ModelBase.register("Qwen3NextForCausalLM")
+class Qwen3NextModel(Qwen2MoeModel):
+    model_arch = gguf.MODEL_ARCH.QWEN3NEXT
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self.gguf_writer.add_ssm_conv_kernel(self.hparams["linear_conv_kernel_dim"])
+        self.gguf_writer.add_ssm_state_size(self.hparams["linear_key_head_dim"])
+        self.gguf_writer.add_ssm_group_count(self.hparams["linear_num_key_heads"])
+        self.gguf_writer.add_ssm_time_step_rank(self.hparams["linear_num_value_heads"])
+        self.gguf_writer.add_ssm_inner_size(self.hparams["linear_value_head_dim"] * self.hparams["linear_num_value_heads"])
+        if (rope_dim := self.hparams.get("head_dim")) is None:
+            rope_dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
+        self.gguf_writer.add_rope_dimension_count(int(rope_dim * self.hparams.get("partial_rotary_factor", 0.25)))
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        if name.startswith("mtp"):
+            return [] # ignore MTP layers for now
+        if name.endswith(".A_log"):
+            data_torch = -torch.exp(data_torch)
+        elif name.endswith(".dt_bias"):
+            name = name.rpartition(".dt_bias")[0] + ".dt_proj.bias"
+        elif "conv1d" in name:
+            data_torch = data_torch.squeeze()
+        elif name.endswith("norm.weight") and not name.endswith("linear_attn.norm.weight"):
+            data_torch = data_torch + 1
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
+
 @ModelBase.register("RND1")
 class RND1Model(Qwen2MoeModel):
     model_arch = gguf.MODEL_ARCH.RND1

docs/backend/SYCL.md

@@ -42,6 +42,9 @@ The following releases are verified and recommended:
 
 ## News
 
+- 2025.11
+  - Support malloc memory on device more than 4GB.
+
 - 2025.2
   - Optimize MUL_MAT Q4_0 on Intel GPU for all dGPUs and built-in GPUs since MTL. Increase the performance of LLM (llama-2-7b.Q4_0.gguf) 21%-87% on Intel GPUs (MTL, ARL-H, Arc, Flex, PVC).
     |GPU|Base tokens/s|Increased tokens/s|Percent|
@@ -789,6 +792,8 @@ use 1 SYCL GPUs: [0] with Max compute units:512
 | GGML_SYCL_DISABLE_GRAPH | 0 or 1 (default) | Disable running computations through SYCL Graphs feature. Disabled by default because graph performance isn't yet better than non-graph performance. |
 | GGML_SYCL_DISABLE_DNN | 0 (default) or 1 | Disable running computations through oneDNN and always use oneMKL. |
 | ZES_ENABLE_SYSMAN | 0 (default) or 1 | Support to get free memory of GPU by sycl::aspect::ext_intel_free_memory.<br>Recommended to use when --split-mode = layer |
+| UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS | 0 (default) or 1 | Support malloc device memory more than 4GB.|
+
 
 
 ## Known Issues
@@ -835,6 +840,14 @@ use 1 SYCL GPUs: [0] with Max compute units:512
   | The default context is too big. It leads to excessive memory usage.|Set `-c 8192` or a smaller value.|
   | The model is too big and requires more memory than what is available.|Choose a smaller model or change to a smaller quantization, like Q5 -> Q4;<br>Alternatively, use more than one device to load model.|
 
+- `ggml_backend_sycl_buffer_type_alloc_buffer: can't allocate 5000000000 Bytes of memory on device`
+
+  You need to enable to support 4GB memory malloc by:
+  ```
+    export UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS=1
+    set UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS=1
+  ```
+
 ### **GitHub contribution**:
 Please add the `SYCL :` prefix/tag in issues/PRs titles to help the SYCL contributors to check/address them without delay.
 

examples/embedding/embedding.cpp

@@ -104,12 +104,16 @@ int main(int argc, char ** argv) {
 
     params.embedding = true;
 
+    // get max number of sequences per batch
+    const int n_seq_max = llama_max_parallel_sequences();
+
     // if the number of prompts that would be encoded is known in advance, it's more efficient to specify the
     //   --parallel argument accordingly. for convenience, if not specified, we fallback to unified KV cache
     //   in order to support any number of prompts
     if (params.n_parallel == 1) {
         LOG_INF("%s: n_parallel == 1 -> unified KV cache is enabled\n", __func__);
         params.kv_unified = true;
+        params.n_parallel = n_seq_max;
     }
 
     // utilize the full context
@@ -123,9 +127,6 @@ int main(int argc, char ** argv) {
         params.n_ubatch = params.n_batch;
     }
 
-    // get max number of sequences per batch
-    const int n_seq_max = llama_max_parallel_sequences();
-
     llama_backend_init();
     llama_numa_init(params.numa);
 

examples/json_schema_to_grammar.py

@@ -231,9 +231,9 @@ DOT = '[^\\x0A\\x0D]'
 RESERVED_NAMES = set(["root", "dot", *PRIMITIVE_RULES.keys(), *STRING_FORMAT_RULES.keys()])
 
 INVALID_RULE_CHARS_RE = re.compile(r'[^a-zA-Z0-9-]+')
-GRAMMAR_LITERAL_ESCAPE_RE = re.compile(r'[\r\n"]')
+GRAMMAR_LITERAL_ESCAPE_RE = re.compile(r'[\r\n"\\]')
 GRAMMAR_RANGE_LITERAL_ESCAPE_RE = re.compile(r'[\r\n"\]\-\\]')
-GRAMMAR_LITERAL_ESCAPES = {'\r': '\\r', '\n': '\\n', '"': '\\"', '-': '\\-', ']': '\\]'}
+GRAMMAR_LITERAL_ESCAPES = {'\r': '\\r', '\n': '\\n', '"': '\\"', '-': '\\-', ']': '\\]', '\\': '\\\\'}
 
 NON_LITERAL_SET = set('|.()[]{}*+?')
 ESCAPED_IN_REGEXPS_BUT_NOT_IN_LITERALS = set('^$.[]()|{}*+?')

examples/model-conversion/scripts/causal/run-converted-model.sh

@@ -4,6 +4,11 @@ set -e
 
 # First try command line argument, then environment variable, then file
 CONVERTED_MODEL="${1:-"$CONVERTED_MODEL"}"
+MODEL_TESTING_PROMPT="${2:-"$MODEL_TESTING_PROMPT"}"
+
+if [ -z "$MODEL_TESTING_PROMPT"]; then
+    MODEL_TESTING_PROMPT="Hello, my name is"
+fi
 
 # Final check if we have a model path
 if [ -z "$CONVERTED_MODEL" ]; then
@@ -14,7 +19,8 @@ if [ -z "$CONVERTED_MODEL" ]; then
 fi
 
 echo $CONVERTED_MODEL
+echo $MODEL_TESTING_PROMPT
 
 cmake --build ../../build --target llama-logits -j8
 
-../../build/bin/llama-logits -m "$CONVERTED_MODEL" "Hello, my name is"
+../../build/bin/llama-logits -m "$CONVERTED_MODEL" "$MODEL_TESTING_PROMPT"

examples/model-conversion/scripts/causal/run-org-model.py

@@ -184,8 +184,12 @@ model_name = os.path.basename(model_path)
 # of using AutoModelForCausalLM.
 print(f"Model class: {model.__class__.__name__}")
 
-prompt = "Hello, my name is"
-input_ids = tokenizer(prompt, return_tensors="pt").input_ids
+device = next(model.parameters()).device
+if os.getenv("MODEL_TESTING_PROMPT"):
+    prompt = os.getenv("MODEL_TESTING_PROMPT")
+else:
+    prompt = "Hello, my name is"
+input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(device)
 
 print(f"Input tokens: {input_ids}")
 print(f"Input text: {repr(prompt)}")

examples/sycl/run-llama2.sh

@@ -15,6 +15,9 @@ MODEL_FILE=models/llama-2-7b.Q4_0.gguf
 NGL=99
 CONTEXT=4096
 
+#support malloc device memory more than 4GB.
+export UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS=1
+
 if [ $# -gt 0 ]; then
     GGML_SYCL_DEVICE=$1
     echo "use $GGML_SYCL_DEVICE as main GPU"

examples/sycl/run-llama3.sh

@@ -6,7 +6,7 @@
 
 # If you want more control, DPC++ Allows selecting a specific device through the
 # following environment variable
-#export ONEAPI_DEVICE_SELECTOR="level_zero:0"
+export ONEAPI_DEVICE_SELECTOR="level_zero:0"
 source /opt/intel/oneapi/setvars.sh
 
 #export GGML_SYCL_DEBUG=1
@@ -18,11 +18,14 @@ MODEL_FILE=models/Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf
 NGL=99 # Layers offloaded to the GPU. If the device runs out of memory, reduce this value according to the model you are using.
 CONTEXT=4096
 
+#support malloc device memory more than 4GB.
+export UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS=1
+
 if [ $# -gt 0 ]; then
     GGML_SYCL_DEVICE=$1
     echo "Using $GGML_SYCL_DEVICE as the main GPU"
-    ZES_ENABLE_SYSMAN=1 ./build/bin/llama-cli -m ${MODEL_FILE} -p "${INPUT_PROMPT}" -n 400 -e -ngl ${NGL} -c ${CONTEXT} -mg $GGML_SYCL_DEVICE -sm none
+    ZES_ENABLE_SYSMAN=1 ./build/bin/llama-cli -m ${MODEL_FILE} -p "${INPUT_PROMPT}" -n 400 -e -ngl ${NGL} -s 0 -c ${CONTEXT} -mg $GGML_SYCL_DEVICE -sm none
 else
     #use multiple GPUs with same max compute units
-    ZES_ENABLE_SYSMAN=1 ./build/bin/llama-cli -m ${MODEL_FILE} -p "${INPUT_PROMPT}" -n 400 -e -ngl ${NGL} -c ${CONTEXT}
+    ZES_ENABLE_SYSMAN=1 ./build/bin/llama-cli -m ${MODEL_FILE} -p "${INPUT_PROMPT}" -n 400 -e -ngl ${NGL} -s 0 -c ${CONTEXT}
 fi

examples/sycl/win-run-llama2.bat

@@ -5,5 +5,7 @@
 set INPUT2="Building a website can be done in 10 simple steps:\nStep 1:"
 @call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64 --force
 
+:: support malloc device memory more than 4GB.
+set UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS=1
 
 .\build\bin\llama-cli.exe -m models\llama-2-7b.Q4_0.gguf -p %INPUT2% -n 400 -e -ngl 99 -s 0

examples/sycl/win-run-llama3.bat

@@ -5,5 +5,7 @@
 set INPUT2="Building a website can be done in 10 simple steps:\nStep 1:"
 @call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64 --force
 
+:: support malloc device memory more than 4GB.
+set UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS=1
 
-.\build\bin\llama-cli.exe -m models\Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf -p %INPUT2% -n 400 -e -ngl 99
+.\build\bin\llama-cli.exe -m models\Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf -p %INPUT2% -n 400 -s 0 -e -ngl 99

ggml/CMakeLists.txt

@@ -183,6 +183,7 @@ endif()
 # ggml core
 set(GGML_SCHED_MAX_COPIES  "4" CACHE STRING "ggml: max input copies for pipeline parallelism")
 option(GGML_CPU                             "ggml: enable CPU backend"                        ON)
+option(GGML_SCHED_NO_REALLOC                "ggml: disallow reallocations in ggml-alloc (for debugging)" OFF)
 
 # 3rd party libs / backends
 option(GGML_ACCELERATE                      "ggml: enable Accelerate framework"               ON)

ggml/include/ggml-rpc.h

@@ -8,7 +8,7 @@ extern "C" {
 #endif
 
 #define RPC_PROTO_MAJOR_VERSION    3
-#define RPC_PROTO_MINOR_VERSION    0
+#define RPC_PROTO_MINOR_VERSION    5
 #define RPC_PROTO_PATCH_VERSION    0
 #define GGML_RPC_MAX_SERVERS       16
 

ggml/include/ggml.h

@@ -2148,7 +2148,8 @@ extern "C" {
     };
 
     enum ggml_scale_flag {
-        GGML_SCALE_FLAG_ALIGN_CORNERS = (1 << 8)
+        GGML_SCALE_FLAG_ALIGN_CORNERS = (1 << 8),
+        GGML_SCALE_FLAG_ANTIALIAS     = (1 << 9),
     };
 
     // interpolate

ggml/src/CMakeLists.txt

@@ -221,6 +221,10 @@ if (GGML_BACKEND_DL)
     target_compile_definitions(ggml-base PUBLIC GGML_BACKEND_DL)
 endif()
 
+if (GGML_SCHED_NO_REALLOC)
+    target_compile_definitions(ggml-base PUBLIC GGML_SCHED_NO_REALLOC)
+endif()
+
 add_library(ggml
             ggml-backend-reg.cpp)
 add_library(ggml::ggml ALIAS ggml)
@@ -270,10 +274,13 @@ function(ggml_add_backend_library backend)
     endif()
 
     # Set versioning properties for all backend libraries
-    set_target_properties(${backend} PROPERTIES
-        VERSION ${GGML_VERSION}
-        SOVERSION ${GGML_VERSION_MAJOR}
-    )
+    # Building a MODULE library with a version is not supported on macOS (https://gitlab.kitware.com/cmake/cmake/-/issues/20782)
+    if (NOT (APPLE AND GGML_BACKEND_DL))
+        set_target_properties(${backend} PROPERTIES
+            VERSION ${GGML_VERSION}
+            SOVERSION ${GGML_VERSION_MAJOR}
+        )
+    endif()
 
     if(NOT GGML_AVAILABLE_BACKENDS)
         set(GGML_AVAILABLE_BACKENDS "${backend}"

ggml/src/ggml-alloc.c

@@ -921,10 +921,15 @@ bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, c
         }
         if (realloc) {
 #ifndef NDEBUG
-            size_t cur_size = galloc->buffers[i] ? ggml_vbuffer_size(galloc->buffers[i]) : 0;
-            GGML_LOG_DEBUG("%s: reallocating %s buffer from size %.02f MiB to %.02f MiB\n", __func__, ggml_backend_buft_name(galloc->bufts[i]), cur_size / 1024.0 / 1024.0, new_size / 1024.0 / 1024.0);
+            {
+                size_t cur_size = galloc->buffers[i] ? ggml_vbuffer_size(galloc->buffers[i]) : 0;
+                if (cur_size > 0) {
+                    GGML_LOG_DEBUG("%s: reallocating %s buffer from size %.02f MiB to %.02f MiB\n",
+                        __func__, ggml_backend_buft_name(galloc->bufts[i]),
+                        cur_size / 1024.0 / 1024.0, new_size / 1024.0 / 1024.0);
+                }
+            }
 #endif
-
             ggml_vbuffer_free(galloc->buffers[i]);
             galloc->buffers[i] = ggml_vbuffer_alloc(galloc->bufts[i], galloc->buf_tallocs[i], GGML_BACKEND_BUFFER_USAGE_COMPUTE);
             if (galloc->buffers[i] == NULL) {

ggml/src/ggml-backend.cpp

@@ -1395,14 +1395,20 @@ static bool ggml_backend_sched_alloc_splits(ggml_backend_sched_t sched) {
 
     // allocate graph
     if (backend_ids_changed || !ggml_gallocr_alloc_graph(sched->galloc, &sched->graph)) {
+#ifdef GGML_SCHED_NO_REALLOC
+        GGML_ABORT("%s: failed to allocate graph, but graph re-allocation is disabled by GGML_SCHED_NO_REALLOC\n", __func__);
+#endif
+
+#ifndef NDEBUG
+        GGML_LOG_DEBUG("%s: failed to allocate graph, reserving (backend_ids_changed = %d)\n", __func__, backend_ids_changed);
+#endif
+
         // the re-allocation may cause the split inputs to be moved to a different address
         // synchronize without ggml_backend_sched_synchronize to avoid changing cur_copy
         for (int i = 0; i < sched->n_backends; i++) {
             ggml_backend_synchronize(sched->backends[i]);
         }
-#ifndef NDEBUG
-        GGML_LOG_DEBUG("%s: failed to allocate graph, reserving (backend_ids_changed = %d)\n", __func__, backend_ids_changed);
-#endif
+
         ggml_gallocr_reserve_n(sched->galloc, &sched->graph, sched->node_backend_ids, sched->leaf_backend_ids);
         if (!ggml_gallocr_alloc_graph(sched->galloc, &sched->graph)) {
             GGML_LOG_ERROR("%s: failed to allocate graph\n", __func__);

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

@@ -2500,6 +2500,9 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev, const ggml_ten
                 if (op->op_params[0] != GGML_SCALE_MODE_NEAREST) {
                     return false;
                 }
+                if (op->op_params[0] & GGML_SCALE_FLAG_ANTIALIAS) {
+                    return false;
+                }
                 return true;
             }
         case GGML_OP_POOL_2D:

ggml/src/ggml-cpu/arch-fallback.h

@@ -33,10 +33,12 @@
 // repack.cpp
 #define ggml_quantize_mat_q8_0_4x4_generic ggml_quantize_mat_q8_0_4x4
 #define ggml_quantize_mat_q8_0_4x8_generic ggml_quantize_mat_q8_0_4x8
+#define ggml_quantize_mat_q8_K_4x4_generic ggml_quantize_mat_q8_K_4x4
 #define ggml_quantize_mat_q8_K_4x8_generic ggml_quantize_mat_q8_K_4x8
 #define ggml_gemv_q4_0_4x4_q8_0_generic ggml_gemv_q4_0_4x4_q8_0
 #define ggml_gemv_q4_0_4x8_q8_0_generic ggml_gemv_q4_0_4x8_q8_0
 #define ggml_gemv_q4_0_8x8_q8_0_generic ggml_gemv_q4_0_8x8_q8_0
+#define ggml_gemv_q4_K_8x4_q8_K_generic ggml_gemv_q4_K_8x4_q8_K
 #define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K
 #define ggml_gemv_q2_K_8x8_q8_K_generic ggml_gemv_q2_K_8x8_q8_K
 #define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
@@ -44,12 +46,14 @@
 #define ggml_gemm_q4_0_4x4_q8_0_generic ggml_gemm_q4_0_4x4_q8_0
 #define ggml_gemm_q4_0_4x8_q8_0_generic ggml_gemm_q4_0_4x8_q8_0
 #define ggml_gemm_q4_0_8x8_q8_0_generic ggml_gemm_q4_0_8x8_q8_0
+#define ggml_gemm_q4_K_8x4_q8_K_generic ggml_gemm_q4_K_8x4_q8_K
 #define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K
 #define ggml_gemm_q2_K_8x8_q8_K_generic ggml_gemm_q2_K_8x8_q8_K
 #define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
 #define ggml_gemm_iq4_nl_8x8_q8_0_generic ggml_gemm_iq4_nl_8x8_q8_0
 #elif defined(__aarch64__) || defined(__arm__) || defined(_M_ARM) || defined(_M_ARM64)
 // repack.cpp
+#define ggml_quantize_mat_q8_K_4x4_generic ggml_quantize_mat_q8_K_4x4
 #define ggml_quantize_mat_q8_K_4x8_generic ggml_quantize_mat_q8_K_4x8
 #define ggml_gemv_iq4_nl_8x8_q8_0_generic ggml_gemv_iq4_nl_8x8_q8_0
 #define ggml_gemv_q2_K_8x8_q8_K_generic ggml_gemv_q2_K_8x8_q8_K
@@ -58,11 +62,14 @@
 #elif defined(__x86_64__) || defined(__i386__) || defined(_M_IX86) || defined(_M_X64)
 // repack.cpp
 #define ggml_quantize_mat_q8_0_4x4_generic ggml_quantize_mat_q8_0_4x4
+#define ggml_quantize_mat_q8_K_4x4_generic ggml_quantize_mat_q8_K_4x4
 #define ggml_gemv_q4_0_4x4_q8_0_generic ggml_gemv_q4_0_4x4_q8_0
 #define ggml_gemv_q4_0_4x8_q8_0_generic ggml_gemv_q4_0_4x8_q8_0
+#define ggml_gemv_q4_K_8x4_q8_K_generic ggml_gemv_q4_K_8x4_q8_K
 #define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
 #define ggml_gemm_q4_0_4x4_q8_0_generic ggml_gemm_q4_0_4x4_q8_0
 #define ggml_gemm_q4_0_4x8_q8_0_generic ggml_gemm_q4_0_4x8_q8_0
+#define ggml_gemm_q4_K_8x4_q8_K_generic ggml_gemm_q4_K_8x4_q8_K
 #define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
 #elif defined(__POWERPC__) || defined(__powerpc__)
 // ref: https://github.com/ggml-org/llama.cpp/pull/14146#issuecomment-2972561679
@@ -74,10 +81,12 @@
 // repack.cpp
 #define ggml_quantize_mat_q8_0_4x4_generic ggml_quantize_mat_q8_0_4x4
 #define ggml_quantize_mat_q8_0_4x8_generic ggml_quantize_mat_q8_0_4x8
+#define ggml_quantize_mat_q8_K_4x4_generic ggml_quantize_mat_q8_K_4x4
 #define ggml_quantize_mat_q8_K_4x8_generic ggml_quantize_mat_q8_K_4x8
 #define ggml_gemv_q4_0_4x4_q8_0_generic ggml_gemv_q4_0_4x4_q8_0
 #define ggml_gemv_q4_0_4x8_q8_0_generic ggml_gemv_q4_0_4x8_q8_0
 #define ggml_gemv_q4_0_8x8_q8_0_generic ggml_gemv_q4_0_8x8_q8_0
+#define ggml_gemv_q4_K_8x4_q8_K_generic ggml_gemv_q4_K_8x4_q8_K
 #define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K
 #define ggml_gemv_q2_K_8x8_q8_K_generic ggml_gemv_q2_K_8x8_q8_K
 #define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
@@ -85,6 +94,7 @@
 #define ggml_gemm_q4_0_4x4_q8_0_generic ggml_gemm_q4_0_4x4_q8_0
 #define ggml_gemm_q4_0_4x8_q8_0_generic ggml_gemm_q4_0_4x8_q8_0
 #define ggml_gemm_q4_0_8x8_q8_0_generic ggml_gemm_q4_0_8x8_q8_0
+#define ggml_gemm_q4_K_8x4_q8_K_generic ggml_gemm_q4_K_8x4_q8_K
 #define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K
 #define ggml_gemm_q2_K_8x8_q8_K_generic ggml_gemm_q2_K_8x8_q8_K
 #define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
@@ -99,10 +109,12 @@
 // repack.cpp
 #define ggml_quantize_mat_q8_0_4x4_generic ggml_quantize_mat_q8_0_4x4
 #define ggml_quantize_mat_q8_0_4x8_generic ggml_quantize_mat_q8_0_4x8
+#define ggml_quantize_mat_q8_K_4x4_generic ggml_quantize_mat_q8_K_4x4
 #define ggml_quantize_mat_q8_K_4x8_generic ggml_quantize_mat_q8_K_4x8
 #define ggml_gemv_q4_0_4x4_q8_0_generic ggml_gemv_q4_0_4x4_q8_0
 #define ggml_gemv_q4_0_4x8_q8_0_generic ggml_gemv_q4_0_4x8_q8_0
 #define ggml_gemv_q4_0_8x8_q8_0_generic ggml_gemv_q4_0_8x8_q8_0
+#define ggml_gemv_q4_K_8x4_q8_K_generic ggml_gemv_q4_K_8x4_q8_K
 #define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K
 #define ggml_gemv_q2_K_8x8_q8_K_generic ggml_gemv_q2_K_8x8_q8_K
 #define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
@@ -110,6 +122,7 @@
 #define ggml_gemm_q4_0_4x4_q8_0_generic ggml_gemm_q4_0_4x4_q8_0
 #define ggml_gemm_q4_0_4x8_q8_0_generic ggml_gemm_q4_0_4x8_q8_0
 #define ggml_gemm_q4_0_8x8_q8_0_generic ggml_gemm_q4_0_8x8_q8_0
+#define ggml_gemm_q4_K_8x4_q8_K_generic ggml_gemm_q4_K_8x4_q8_K
 #define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K
 #define ggml_gemm_q2_K_8x8_q8_K_generic ggml_gemm_q2_K_8x8_q8_K
 #define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
@@ -132,15 +145,18 @@
 // repack.cpp
 #define ggml_quantize_mat_q8_0_4x4_generic ggml_quantize_mat_q8_0_4x4
 #define ggml_quantize_mat_q8_0_4x8_generic ggml_quantize_mat_q8_0_4x8
+#define ggml_quantize_mat_q8_K_4x4_generic ggml_quantize_mat_q8_K_4x4
 #define ggml_quantize_mat_q8_K_4x8_generic ggml_quantize_mat_q8_K_4x8
 #define ggml_gemv_q4_0_4x4_q8_0_generic ggml_gemv_q4_0_4x4_q8_0
 #define ggml_gemv_q4_0_4x8_q8_0_generic ggml_gemv_q4_0_4x8_q8_0
+#define ggml_gemv_q4_K_8x4_q8_K_generic ggml_gemv_q4_K_8x4_q8_K
 #define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K
 #define ggml_gemv_q2_K_8x8_q8_K_generic ggml_gemv_q2_K_8x8_q8_K
 #define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
 #define ggml_gemv_iq4_nl_8x8_q8_0_generic ggml_gemv_iq4_nl_8x8_q8_0
 #define ggml_gemm_q4_0_4x4_q8_0_generic ggml_gemm_q4_0_4x4_q8_0
 #define ggml_gemm_q4_0_4x8_q8_0_generic ggml_gemm_q4_0_4x8_q8_0
+#define ggml_gemm_q4_K_8x4_q8_K_generic ggml_gemm_q4_K_8x4_q8_K
 #define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K
 #define ggml_gemm_q2_K_8x8_q8_K_generic ggml_gemm_q2_K_8x8_q8_K
 #define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
@@ -161,10 +177,12 @@
 // repack.cpp
 #define ggml_quantize_mat_q8_0_4x4_generic ggml_quantize_mat_q8_0_4x4
 #define ggml_quantize_mat_q8_0_4x8_generic ggml_quantize_mat_q8_0_4x8
+#define ggml_quantize_mat_q8_K_4x4_generic ggml_quantize_mat_q8_K_4x4
 #define ggml_quantize_mat_q8_K_4x8_generic ggml_quantize_mat_q8_K_4x8
 #define ggml_gemv_q4_0_4x4_q8_0_generic ggml_gemv_q4_0_4x4_q8_0
 #define ggml_gemv_q4_0_4x8_q8_0_generic ggml_gemv_q4_0_4x8_q8_0
 #define ggml_gemv_q4_0_8x8_q8_0_generic ggml_gemv_q4_0_8x8_q8_0
+#define ggml_gemv_q4_K_8x4_q8_K_generic ggml_gemv_q4_K_8x4_q8_K
 #define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K
 #define ggml_gemv_q2_K_8x8_q8_K_generic ggml_gemv_q2_K_8x8_q8_K
 #define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
@@ -172,6 +190,7 @@
 #define ggml_gemm_q4_0_4x4_q8_0_generic ggml_gemm_q4_0_4x4_q8_0
 #define ggml_gemm_q4_0_4x8_q8_0_generic ggml_gemm_q4_0_4x8_q8_0
 #define ggml_gemm_q4_0_8x8_q8_0_generic ggml_gemm_q4_0_8x8_q8_0
+#define ggml_gemm_q4_K_8x4_q8_K_generic ggml_gemm_q4_K_8x4_q8_K
 #define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K
 #define ggml_gemm_q2_K_8x8_q8_K_generic ggml_gemm_q2_K_8x8_q8_K
 #define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
@@ -194,10 +213,12 @@
 // repack.cpp
 #define ggml_quantize_mat_q8_0_4x4_generic ggml_quantize_mat_q8_0_4x4
 #define ggml_quantize_mat_q8_0_4x8_generic ggml_quantize_mat_q8_0_4x8
+#define ggml_quantize_mat_q8_K_4x4_generic ggml_quantize_mat_q8_K_4x4
 #define ggml_quantize_mat_q8_K_4x8_generic ggml_quantize_mat_q8_K_4x8
 #define ggml_gemv_q4_0_4x4_q8_0_generic ggml_gemv_q4_0_4x4_q8_0
 #define ggml_gemv_q4_0_4x8_q8_0_generic ggml_gemv_q4_0_4x8_q8_0
 #define ggml_gemv_q4_0_8x8_q8_0_generic ggml_gemv_q4_0_8x8_q8_0
+#define ggml_gemv_q4_K_8x4_q8_K_generic ggml_gemv_q4_K_8x4_q8_K
 #define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K
 #define ggml_gemv_q2_K_8x8_q8_K_generic ggml_gemv_q2_K_8x8_q8_K
 #define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
@@ -205,6 +226,7 @@
 #define ggml_gemm_q4_0_4x4_q8_0_generic ggml_gemm_q4_0_4x4_q8_0
 #define ggml_gemm_q4_0_4x8_q8_0_generic ggml_gemm_q4_0_4x8_q8_0
 #define ggml_gemm_q4_0_8x8_q8_0_generic ggml_gemm_q4_0_8x8_q8_0
+#define ggml_gemm_q4_K_8x4_q8_K_generic ggml_gemm_q4_K_8x4_q8_K
 #define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K
 #define ggml_gemm_q2_K_8x8_q8_K_generic ggml_gemm_q2_K_8x8_q8_K
 #define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0

ggml/src/ggml-cpu/arch/arm/repack.cpp

@@ -497,6 +497,140 @@ void ggml_gemv_iq4_nl_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const
     ggml_gemv_iq4_nl_4x4_q8_0_generic(n, s, bs, vx, vy, nr, nc);
 }
 
+void ggml_gemv_q4_K_8x4_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
+    constexpr int qk = QK_K;
+    const int     nb = n / qk;
+
+    constexpr int ncols_interleaved = 8;
+    constexpr int blocklen          = 8;
+
+    assert(n % qk == 0);
+    assert(nr % 4 == 0);
+    assert(nc % ncols_interleaved == 0);
+
+    UNUSED(nb);
+    UNUSED(ncols_interleaved);
+    UNUSED(blocklen);
+
+#if defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_DOTPROD)
+    constexpr int    col_groups = ncols_interleaved / 4; // 0123 and 4567
+    const uint8x16_t m4b        = vdupq_n_u8(0x0f);
+
+    // 1x8 tile = 2 x 4
+    float32x4_t acc_f32[col_groups];
+
+    const block_q8_K * GGML_RESTRICT q8_ptr = (const block_q8_K *) vy;
+
+    for (int x = 0; x < nc / ncols_interleaved; x++) {
+        const block_q4_Kx8 * GGML_RESTRICT q4_ptr = (const block_q4_Kx8 *) vx + (x * nb);
+
+        for (int i = 0; i < col_groups; i++) {
+            acc_f32[i] = vdupq_n_f32(0);
+        }
+
+        for (int b = 0; b < nb; b++) {
+            float32x4_t q4_d_0        = vcvt_f32_f16(vld1_f16((const __fp16 *) q4_ptr[b].d));      // d0 d1 d2 d3
+            float32x4_t q4_d_1        = vcvt_f32_f16(vld1_f16((const __fp16 *) q4_ptr[b].d + 4));  // d4 d5 d6 d7
+            float32x4_t q8_d          = vdupq_n_f32(q8_ptr[b].d);
+            float32x4_t sb_scale_0123 = vmulq_f32(q4_d_0, q8_d);
+            float32x4_t sb_scale_4567 = vmulq_f32(q4_d_1, q8_d);
+            float32x4_t q4_dmin_0     = vcvt_f32_f16(vld1_f16((const __fp16 *) q4_ptr[b].dmin));      // dmin 0..3
+            float32x4_t q4_dmin_1     = vcvt_f32_f16(vld1_f16((const __fp16 *) q4_ptr[b].dmin + 4));  // dmin 4..7
+            float32x4_t sb_min_0123   = vmulq_f32(q4_dmin_0, q8_d);
+            float32x4_t sb_min_4567   = vmulq_f32(q4_dmin_1, q8_d);
+
+            // interleaved bias_acc: [0]->r0 0123, [1]->r0 4567
+            int32x4_t bias_acc[2] = { vdupq_n_s32(0), vdupq_n_s32(0) };
+            int32x4_t acc_lo[col_groups];
+            int32x4_t acc_hi[col_groups];
+
+            // Each bsum is 16 elements, pairwise add leaves us with the 8 bsums of the entire block
+            const int16x8_t bsums = vpaddq_s16(vld1q_s16(q8_ptr[b].bsums), vld1q_s16(q8_ptr[b].bsums + 8));
+            int16_t         bsums_arr[8];
+            vst1q_s16(bsums_arr, bsums);
+            for (int sb = 0; sb < QK_K / 64; sb++) {
+                for (int i = 0; i < col_groups; i++) {
+                    acc_lo[i] = vdupq_n_s32(0);
+                    acc_hi[i] = vdupq_n_s32(0);
+                }
+                // Need scales for the low and high nibbles
+                // 2 * 12 = 24 bytes per subblock, 4 sbs -> 4 * 24 = 96 bytes total
+                int16x8_t q4sb_mins[2];
+                int16x8_t q4sb_scales[2];
+                for (int i = 0; i < 2; i++) {
+                    int8_t    aux_q4sb[8];
+                    const int offset = sb * 24 + i * 12;
+                    decode_q4_Kx8_scales_mins(&q4_ptr[b].scales[offset], &q4sb_mins[i], aux_q4sb);
+                    q4sb_scales[i] = vmovl_s8(vld1_s8(aux_q4sb));
+                }
+
+                int8x16_t q8_qs[64 / 16];
+                for (int i = 0; i < 64 / 16; i++) {
+                    q8_qs[i] = vld1q_s8(q8_ptr[b].qs + sb * 64 + i * 16);
+                }
+
+                for (int c = 0; c < col_groups; c++) {
+                    uint8x16_t q4_cols[8];
+                    for (int i = 0; i < 8; i++) {
+                        q4_cols[i] = vld1q_u8(q4_ptr[b].qs + sb * QK_K + i * 32 + 16 * c);
+                    }
+
+                    acc_lo[c] = vdotq_laneq_s32(acc_lo[c], vreinterpretq_s8_u8(vandq_u8(q4_cols[0], m4b)), q8_qs[0], 0);
+                    acc_lo[c] = vdotq_laneq_s32(acc_lo[c], vreinterpretq_s8_u8(vandq_u8(q4_cols[1], m4b)), q8_qs[0], 1);
+                    acc_lo[c] = vdotq_laneq_s32(acc_lo[c], vreinterpretq_s8_u8(vandq_u8(q4_cols[2], m4b)), q8_qs[0], 2);
+                    acc_lo[c] = vdotq_laneq_s32(acc_lo[c], vreinterpretq_s8_u8(vandq_u8(q4_cols[3], m4b)), q8_qs[0], 3);
+                    acc_lo[c] = vdotq_laneq_s32(acc_lo[c], vreinterpretq_s8_u8(vandq_u8(q4_cols[4], m4b)), q8_qs[1], 0);
+                    acc_lo[c] = vdotq_laneq_s32(acc_lo[c], vreinterpretq_s8_u8(vandq_u8(q4_cols[5], m4b)), q8_qs[1], 1);
+                    acc_lo[c] = vdotq_laneq_s32(acc_lo[c], vreinterpretq_s8_u8(vandq_u8(q4_cols[6], m4b)), q8_qs[1], 2);
+                    acc_lo[c] = vdotq_laneq_s32(acc_lo[c], vreinterpretq_s8_u8(vandq_u8(q4_cols[7], m4b)), q8_qs[1], 3);
+
+                    acc_hi[c] = vdotq_laneq_s32(acc_hi[c], vreinterpretq_s8_u8(vshrq_n_u8(q4_cols[0], 4)), q8_qs[2], 0);
+                    acc_hi[c] = vdotq_laneq_s32(acc_hi[c], vreinterpretq_s8_u8(vshrq_n_u8(q4_cols[1], 4)), q8_qs[2], 1);
+                    acc_hi[c] = vdotq_laneq_s32(acc_hi[c], vreinterpretq_s8_u8(vshrq_n_u8(q4_cols[2], 4)), q8_qs[2], 2);
+                    acc_hi[c] = vdotq_laneq_s32(acc_hi[c], vreinterpretq_s8_u8(vshrq_n_u8(q4_cols[3], 4)), q8_qs[2], 3);
+                    acc_hi[c] = vdotq_laneq_s32(acc_hi[c], vreinterpretq_s8_u8(vshrq_n_u8(q4_cols[4], 4)), q8_qs[3], 0);
+                    acc_hi[c] = vdotq_laneq_s32(acc_hi[c], vreinterpretq_s8_u8(vshrq_n_u8(q4_cols[5], 4)), q8_qs[3], 1);
+                    acc_hi[c] = vdotq_laneq_s32(acc_hi[c], vreinterpretq_s8_u8(vshrq_n_u8(q4_cols[6], 4)), q8_qs[3], 2);
+                    acc_hi[c] = vdotq_laneq_s32(acc_hi[c], vreinterpretq_s8_u8(vshrq_n_u8(q4_cols[7], 4)), q8_qs[3], 3);
+                }
+
+                // Scales
+                // row c0123 blk0 and blk1
+                const int16x4_t   sc_0123_lo = vget_low_s16(q4sb_scales[0]);
+                const int16x4_t   sc_0123_hi = vget_low_s16(q4sb_scales[1]);
+                const float32x4_t sumf_0123  = vcvtq_f32_s32(vaddq_s32(vmulq_s32(vmovl_s16(sc_0123_lo), acc_lo[0]),
+                                                                       vmulq_s32(vmovl_s16(sc_0123_hi), acc_hi[0])));
+                acc_f32[0]                   = vfmaq_f32(acc_f32[0], sb_scale_0123, sumf_0123);
+                // row c4567 blk0 and blk1
+                const int16x4_t   sc_4567_lo = vget_high_s16(q4sb_scales[0]);
+                const int16x4_t   sc_4567_hi = vget_high_s16(q4sb_scales[1]);
+                const float32x4_t sumf_4567  = vcvtq_f32_s32(vaddq_s32(vmulq_s32(vmovl_s16(sc_4567_lo), acc_lo[1]),
+                                                                       vmulq_s32(vmovl_s16(sc_4567_hi), acc_hi[1])));
+                acc_f32[1]                   = vfmaq_f32(acc_f32[1], sb_scale_4567, sumf_4567);
+
+                // Bias Correction
+                const int16x4_t bsums_vec_lo = vdup_n_s16(bsums_arr[2 * sb + 0]);
+                const int16x4_t bsums_vec_hi = vdup_n_s16(bsums_arr[2 * sb + 1]);
+
+                bias_acc[0] = vmlal_s16(bias_acc[0], bsums_vec_lo, vget_low_s16(q4sb_mins[0]));
+                bias_acc[0] = vmlal_s16(bias_acc[0], bsums_vec_hi, vget_low_s16(q4sb_mins[1]));
+                bias_acc[1] = vmlal_s16(bias_acc[1], bsums_vec_lo, vget_high_s16(q4sb_mins[0]));
+                bias_acc[1] = vmlal_s16(bias_acc[1], bsums_vec_hi, vget_high_s16(q4sb_mins[1]));
+            }  // for sb
+
+            acc_f32[0] = vmlsq_f32(acc_f32[0], vcvtq_f32_s32(bias_acc[0]), sb_min_0123);
+            acc_f32[1] = vmlsq_f32(acc_f32[1], vcvtq_f32_s32(bias_acc[1]), sb_min_4567);
+        }  // for b
+
+        int base = x * ncols_interleaved;
+        vst1q_f32(s + base, acc_f32[0]);
+        vst1q_f32(s + base + 4, acc_f32[1]);
+    }  // for x
+    return;
+#endif  // #if defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_DOTPROD)
+    ggml_gemv_q4_K_8x4_q8_K_generic(n, s, bs, vx, vy, nr, nc);
+}
+
 void ggml_gemv_q4_K_8x8_q8_K(int                        n,
                              float * GGML_RESTRICT      s,
                              size_t                     bs,
@@ -518,7 +652,7 @@ void ggml_gemv_q4_K_8x8_q8_K(int                        n,
     UNUSED(ncols_interleaved);
     UNUSED(blocklen);
 
-#if defined(__aarch64__) && defined(__ARM_NEON)
+#if defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_DOTPROD)
     constexpr int    col_pairs = ncols_interleaved / 2;
     const uint8x16_t m4b       = vdupq_n_u8(0x0f);
 
@@ -615,7 +749,6 @@ void ggml_gemv_q4_K_8x8_q8_K(int                        n,
                     float32x4_t sb_scale        = p == 0 ? sb_scale_0 : sb_scale_1;
 
                     // 0123 or 4567
-                    // TODO: Single superblock mul at the end of the superblock
                     float32x4_t sumf_0 =
                         vcvtq_f32_s32(vmulq_s32(vmovl_s16(group_scales_lo), vpaddq_s32(acc_lo[p], acc_lo[p + 1])));
                     acc_f32[i] = vfmaq_f32(acc_f32[i], sb_scale, sumf_0);
@@ -649,7 +782,7 @@ void ggml_gemv_q4_K_8x8_q8_K(int                        n,
         vst1q_f32(s + base + 4, acc_f32[1]);
     }  // for x
     return;
-#endif  // defined(__aarch64__) && defined(__ARM_NEON)
+#endif  // defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_DOTPROD)
     ggml_gemv_q4_K_8x8_q8_K_generic(n, s, bs, vx, vy, nr, nc);
 }
 
@@ -2069,6 +2202,206 @@ void ggml_gemm_iq4_nl_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const
     ggml_gemm_iq4_nl_4x4_q8_0_generic(n, s, bs, vx, vy, nr, nc);
 }
 
+void ggml_gemm_q4_K_8x4_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
+    constexpr int qk = QK_K;
+    const int     nb = n / qk;
+
+    constexpr int ncols_interleaved = 8;
+    constexpr int blocklen          = 4;
+
+    assert(n % qk == 0);
+    assert(nr % 4 == 0);
+    assert(nc % ncols_interleaved == 0);
+
+    UNUSED(nb);
+    UNUSED(ncols_interleaved);
+    UNUSED(blocklen);
+
+#if defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_DOTPROD)
+    constexpr int    q8_k_blocklen = 4;
+    constexpr int    acc_size  = 2 * 4;  // 2 row pairs × 4 col pairs
+    const uint8x16_t m4b       = vdupq_n_u8(0x0f);
+
+    // 8 accumulators: 2 row pairs × 4 col pairs
+    float32x4_t acc_f32[acc_size];
+
+    for (int y = 0; y < nr / q8_k_blocklen; y++) {
+        const block_q8_Kx4 * GGML_RESTRICT q8_ptr = (const block_q8_Kx4 *) vy + (y * nb);
+
+        for (int x = 0; x < nc / ncols_interleaved; x++) {
+            const block_q4_Kx8 * GGML_RESTRICT q4_ptr = (const block_q4_Kx8 *) vx + (x * nb);
+
+            for (int i = 0; i < acc_size; i++) {
+                acc_f32[i] = vdupq_n_f32(0);
+            }
+
+            for (int b = 0; b < nb; b++) {
+                // d4 0 1 2 3, 4 5 6 7
+                float32x4_t q4_d_0123    = vcvt_f32_f16(vld1_f16((const __fp16 *) q4_ptr[b].d));
+                float32x4_t q4_d_4567    = vcvt_f32_f16(vld1_f16((const __fp16 *) q4_ptr[b].d + 4));
+                // d8 0 1 2 3
+                float32x4_t q8_d_0123    = vld1q_f32(q8_ptr[b].d);
+                // mins
+                float32x4_t q4_dmin_0123 = vcvt_f32_f16(vld1_f16((const __fp16 *) q4_ptr[b].dmin));
+                float32x4_t q4_dmin_4567 = vcvt_f32_f16(vld1_f16((const __fp16 *) q4_ptr[b].dmin + 4));
+
+                // Precomputation of scales and mins
+                float32x4_t sbd_scale_0123[q8_k_blocklen];
+                float32x4_t sbd_scale_4567[q8_k_blocklen];
+                float32x4_t sbd_min_0123[q8_k_blocklen];
+                float32x4_t sbd_min_4567[q8_k_blocklen];
+
+                sbd_scale_0123[0] = vmulq_laneq_f32(q4_d_0123, q8_d_0123, 0);
+                sbd_scale_4567[0] = vmulq_laneq_f32(q4_d_4567, q8_d_0123, 0);
+                sbd_min_0123[0]   = vmulq_laneq_f32(q4_dmin_0123, q8_d_0123, 0);
+                sbd_min_4567[0]   = vmulq_laneq_f32(q4_dmin_4567, q8_d_0123, 0);
+
+                sbd_scale_0123[1] = vmulq_laneq_f32(q4_d_0123, q8_d_0123, 1);
+                sbd_scale_4567[1] = vmulq_laneq_f32(q4_d_4567, q8_d_0123, 1);
+                sbd_min_0123[1]   = vmulq_laneq_f32(q4_dmin_0123, q8_d_0123, 1);
+                sbd_min_4567[1]   = vmulq_laneq_f32(q4_dmin_4567, q8_d_0123, 1);
+
+                sbd_scale_0123[2] = vmulq_laneq_f32(q4_d_0123, q8_d_0123, 2);
+                sbd_scale_4567[2] = vmulq_laneq_f32(q4_d_4567, q8_d_0123, 2);
+                sbd_min_0123[2]   = vmulq_laneq_f32(q4_dmin_0123, q8_d_0123, 2);
+                sbd_min_4567[2]   = vmulq_laneq_f32(q4_dmin_4567, q8_d_0123, 2);
+
+                sbd_scale_0123[3] = vmulq_laneq_f32(q4_d_0123, q8_d_0123, 3);
+                sbd_scale_4567[3] = vmulq_laneq_f32(q4_d_4567, q8_d_0123, 3);
+                sbd_min_0123[3]   = vmulq_laneq_f32(q4_dmin_0123, q8_d_0123, 3);
+                sbd_min_4567[3]   = vmulq_laneq_f32(q4_dmin_4567, q8_d_0123, 3);
+
+                // Precomputation of bsums, each vpaddq calcs all the bsums for each row
+                const int16x8_t bsums[q8_k_blocklen] = {
+                    vpaddq_s16(vld1q_s16(q8_ptr[b].bsums + 16 * 0), vld1q_s16(q8_ptr[b].bsums + 16 * 0 + 8)),
+                    vpaddq_s16(vld1q_s16(q8_ptr[b].bsums + 16 * 1), vld1q_s16(q8_ptr[b].bsums + 16 * 1 + 8)),
+                    vpaddq_s16(vld1q_s16(q8_ptr[b].bsums + 16 * 2), vld1q_s16(q8_ptr[b].bsums + 16 * 2 + 8)),
+                    vpaddq_s16(vld1q_s16(q8_ptr[b].bsums + 16 * 3), vld1q_s16(q8_ptr[b].bsums + 16 * 3 + 8)),
+                };
+                int16_t bsums_arr[QK_K / 64][8];
+                for (int q8_row = 0; q8_row < 4; q8_row++) {
+                    vst1q_s16(bsums_arr[q8_row], bsums[q8_row]);
+                }
+
+                // interleaved bias_acc: [0]->r0 0123, [1]->r1 0123, .., [4]->r0 4567, [5]->r1 4567 ..
+                int32x4_t bias_acc[acc_size];
+                for (int i = 0; i < acc_size; i++) {
+                    bias_acc[i] = vdupq_n_s32(0);
+                }
+
+                for (int sb = 0; sb < QK_K / 64; sb++) {
+                    // Int accumulators for qs vecdot (4 row x 2 col quartets)
+                    int32x4_t acc_lo[acc_size];
+                    int32x4_t acc_hi[acc_size];
+                    for (int i = 0; i < acc_size; i++) {
+                        acc_lo[i] = vdupq_n_s32(0);
+                        acc_hi[i] = vdupq_n_s32(0);
+                    }
+                    // Need scales for the low and high nibbles
+                    // 2 * 12 = 24 bytes per subblock, 4 sbs -> 4 * 24 = 96 bytes total
+                    int16x8_t q4sb_scales[2];
+                    int16x8_t q4sb_mins[2];
+                    for (int i = 0; i < 2; i++) {
+                        int8_t    aux_q4sb[8];
+                        const int offset = sb * 24 + i * 12;
+                        decode_q4_Kx8_scales_mins(&q4_ptr[b].scales[offset], &q4sb_mins[i], aux_q4sb);
+                        q4sb_scales[i] = vmovl_s8(vld1_s8(aux_q4sb));
+                    }
+
+                    constexpr int reads_per_sb = 8;  // 8 * 16 bytes each => 32 qs * 4 rows
+                    for (int k = 0; k < reads_per_sb; k++) {
+                        const int8x16_t q8_blk0 = vld1q_s8(q8_ptr[b].qs + sb * 256 + 16 * k);
+                        const int8x16_t q8_blk1 = vld1q_s8(q8_ptr[b].qs + sb * 256 + 16 * k + 128);
+
+                        // 0..3 & 32..35
+                        const uint8x16_t q4_0123 = vld1q_u8(q4_ptr[b].qs + sb * QK_K + 32 * k);
+                        const uint8x16_t q4_4567 = vld1q_u8(q4_ptr[b].qs + sb * QK_K + 32 * k + 16);
+
+                        const int8x16_t q4_0123_lo = vreinterpretq_s8_u8(vandq_u8(q4_0123, m4b));
+                        const int8x16_t q4_0123_hi = vreinterpretq_s8_u8(vshrq_n_u8(q4_0123, 4));
+
+                        acc_lo[0] = vdotq_laneq_s32(acc_lo[0], q4_0123_lo, q8_blk0, 0);  //  0..3  r0 c0123
+                        acc_lo[1] = vdotq_laneq_s32(acc_lo[1], q4_0123_lo, q8_blk0, 1);  //  0..3  r1 c0123
+                        acc_lo[2] = vdotq_laneq_s32(acc_lo[2], q4_0123_lo, q8_blk0, 2);  //  0..3  r2 c0123
+                        acc_lo[3] = vdotq_laneq_s32(acc_lo[3], q4_0123_lo, q8_blk0, 3);  //  0..3  r3 c0123
+
+                        acc_hi[0] = vdotq_laneq_s32(acc_hi[0], q4_0123_hi, q8_blk1, 0);  // 32..35 r0 c0123
+                        acc_hi[1] = vdotq_laneq_s32(acc_hi[1], q4_0123_hi, q8_blk1, 1);  // 32..35 r1 c0123
+                        acc_hi[2] = vdotq_laneq_s32(acc_hi[2], q4_0123_hi, q8_blk1, 2);  // 32..35 r2 c0123
+                        acc_hi[3] = vdotq_laneq_s32(acc_hi[3], q4_0123_hi, q8_blk1, 3);  // 32..35 r3 c0123
+
+                        const int8x16_t q4_4567_lo = vreinterpretq_s8_u8(vandq_u8(q4_4567, m4b));
+                        const int8x16_t q4_4567_hi = vreinterpretq_s8_u8(vshrq_n_u8(q4_4567, 4));
+
+                        acc_lo[4] = vdotq_laneq_s32(acc_lo[4], q4_4567_lo, q8_blk0, 0);  //  0..3  r0 c4567
+                        acc_lo[5] = vdotq_laneq_s32(acc_lo[5], q4_4567_lo, q8_blk0, 1);  //  0..3  r1 c4567
+                        acc_lo[6] = vdotq_laneq_s32(acc_lo[6], q4_4567_lo, q8_blk0, 2);  //  0..3  r2 c4567
+                        acc_lo[7] = vdotq_laneq_s32(acc_lo[7], q4_4567_lo, q8_blk0, 3);  //  0..3  r3 c4567
+
+                        acc_hi[4] = vdotq_laneq_s32(acc_hi[4], q4_4567_hi, q8_blk1, 0);  // 32..35 r0 c4567
+                        acc_hi[5] = vdotq_laneq_s32(acc_hi[5], q4_4567_hi, q8_blk1, 1);  // 32..35 r1 c4567
+                        acc_hi[6] = vdotq_laneq_s32(acc_hi[6], q4_4567_hi, q8_blk1, 2);  // 32..35 r2 c4567
+                        acc_hi[7] = vdotq_laneq_s32(acc_hi[7], q4_4567_hi, q8_blk1, 3);  // 32..35 r3 c4567
+                    }
+
+                    // Scale and bias application
+                    // acc is stored interleaved to match output layout
+                    const int16x4_t sc_0123_lo = vget_low_s16(q4sb_scales[0]);
+                    const int16x4_t sc_4567_lo = vget_high_s16(q4sb_scales[0]);
+                    const int16x4_t sc_0123_hi = vget_low_s16(q4sb_scales[1]);
+                    const int16x4_t sc_4567_hi = vget_high_s16(q4sb_scales[1]);
+                    for (int row = 0; row < q8_k_blocklen; row++) {
+                        // Bias correction
+                        // row c0123 blk0 and blk1
+                        const float32x4_t sumf_0123 =
+                            vcvtq_f32_s32(vaddq_s32(vmulq_s32(vmovl_s16(sc_0123_lo), acc_lo[row]),
+                                                    vmulq_s32(vmovl_s16(sc_0123_hi), acc_hi[row])));
+                        acc_f32[2 * row] = vfmaq_f32(acc_f32[2 * row], sbd_scale_0123[row], sumf_0123);
+
+                        // row c4567 blk0 and blk1
+                        const float32x4_t sumf_4567 =
+                            vcvtq_f32_s32(vaddq_s32(vmulq_s32(vmovl_s16(sc_4567_lo), acc_lo[row + 4]),
+                                                    vmulq_s32(vmovl_s16(sc_4567_hi), acc_hi[row + 4])));
+                        acc_f32[2 * row + 1] = vfmaq_f32(acc_f32[2 * row + 1], sbd_scale_4567[row], sumf_4567);
+
+                        // Bias
+                        const int16x4_t bsums_vec_lo = vdup_n_s16(bsums_arr[sb][row * 2]);
+                        const int16x4_t bsums_vec_hi = vdup_n_s16(bsums_arr[sb][row * 2 + 1]);
+
+                        // row c0123 blk0 and blk1
+                        bias_acc[2 * row] = vmlal_s16(bias_acc[2 * row], bsums_vec_lo, vget_low_s16(q4sb_mins[0]));
+                        bias_acc[2 * row] = vmlal_s16(bias_acc[2 * row], bsums_vec_hi, vget_low_s16(q4sb_mins[1]));
+
+                        // row c4567 blk0 and blk1
+                        bias_acc[2 * row + 1] =
+                            vmlal_s16(bias_acc[2 * row + 1], bsums_vec_lo, vget_high_s16(q4sb_mins[0]));
+                        bias_acc[2 * row + 1] =
+                            vmlal_s16(bias_acc[2 * row + 1], bsums_vec_hi, vget_high_s16(q4sb_mins[1]));
+                    }
+                }  // for sb
+
+                for (int row = 0; row < q8_k_blocklen; row++) {
+                    acc_f32[2 * row] = vmlsq_f32(acc_f32[2 * row], vcvtq_f32_s32(bias_acc[2 * row]), sbd_min_0123[row]);
+                    acc_f32[2 * row + 1] =
+                        vmlsq_f32(acc_f32[2 * row + 1], vcvtq_f32_s32(bias_acc[2 * row + 1]), sbd_min_4567[row]);
+                }
+            }  // for b
+
+            for (int i = 0; i < q8_k_blocklen; i++) {
+                int row = y * q8_k_blocklen + i;
+                for (int j = 0; j < 2; j++) {
+                    int col    = x * ncols_interleaved + j * 4;
+                    int offset = row * bs + col;
+                    vst1q_f32(s + offset, acc_f32[2 * i + j]);
+                }
+            }
+        }  // for x
+    }  // for y
+    return;
+#endif  // defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_DOTPROD)
+    ggml_gemm_q4_K_8x4_q8_K_generic(n, s, bs, vx, vy, nr, nc);
+}
+
 void ggml_gemm_q4_K_8x8_q8_K(int                        n,
                              float * GGML_RESTRICT      s,
                              size_t                     bs,

ggml/src/ggml-cpu/arch/riscv/cpu-feats.cpp

@@ -1,20 +1,23 @@
 #include "ggml-backend-impl.h"
 
 #if defined(__riscv) && __riscv_xlen == 64
-#include <sys/auxv.h>
-
-//https://github.com/torvalds/linux/blob/master/arch/riscv/include/uapi/asm/hwcap.h#L24
-#ifndef COMPAT_HWCAP_ISA_V
-#define COMPAT_HWCAP_ISA_V (1 << ('V' - 'A'))
-#endif
+#include <asm/hwprobe.h>
+#include <asm/unistd.h>
+#include <unistd.h>
 
 struct riscv64_features {
     bool has_rvv = false;
 
     riscv64_features() {
-        uint32_t hwcap = getauxval(AT_HWCAP);
+        struct riscv_hwprobe probe;
+        probe.key = RISCV_HWPROBE_KEY_IMA_EXT_0;
+        probe.value = 0;
 
-        has_rvv = !!(hwcap & COMPAT_HWCAP_ISA_V);
+        int ret = syscall(__NR_riscv_hwprobe, &probe, 1, 0, NULL, 0);
+
+        if (0 == ret) {
+            has_rvv = !!(probe.value & RISCV_HWPROBE_IMA_V);
+        }
     }
 };
 

ggml/src/ggml-cpu/ops.cpp

@@ -7420,6 +7420,65 @@ static void ggml_compute_forward_upscale_f32(
                 }
             }
         }
+    } else if (mode == GGML_SCALE_MODE_BILINEAR && (mode_flags & GGML_SCALE_FLAG_ANTIALIAS)) {
+        // Similar to F.interpolate(..., mode="bilinear", align_corners=False, antialias=True)
+        // https://github.com/pytorch/pytorch/blob/8871ff29b743948d1225389d5b7068f37b22750b/aten/src/ATen/native/cpu/UpSampleKernel.cpp
+        auto triangle_filter = [](float x) -> float {
+            return std::max(1.0f - fabsf(x), 0.0f);
+        };
+
+        // support and invscale, minimum 1 pixel for bilinear
+        const float support1  = std::max(1.0f, 1.0f / sf1);
+        const float invscale1 = 1.0f / support1;
+        const float support0  = std::max(1.0f, 1.0f / sf0);
+        const float invscale0 = 1.0f / support0;
+
+        for (int64_t i3 = 0; i3 < ne3; i3++) {
+            const int64_t i03 = i3 / sf3;
+            for (int64_t i2 = ith; i2 < ne2; i2 += nth) {
+                const int64_t i02 = i2 / sf2;
+                for (int64_t i1 = 0; i1 < ne1; i1++) {
+                    const float y = ((float) i1 + pixel_offset) / sf1;
+                    for (int64_t i0 = 0; i0 < ne0; i0++) {
+                        const float x = ((float) i0 + pixel_offset) / sf0;
+
+                        // the range of source pixels that contribute
+                        const int64_t x_min = std::max<int64_t>(x - support0 + pixel_offset, 0);
+                        const int64_t x_max = std::min<int64_t>(x + support0 + pixel_offset, ne00);
+                        const int64_t y_min = std::max<int64_t>(y - support1 + pixel_offset, 0);
+                        const int64_t y_max = std::min<int64_t>(y + support1 + pixel_offset, ne01);
+
+                        // bilinear filter with antialiasing
+                        float val = 0.0f;
+                        float total_weight = 0.0f;
+
+                        for (int64_t sy = y_min; sy < y_max; sy++) {
+                            const float weight_y = triangle_filter((sy - y + pixel_offset) * invscale1);
+
+                            for (int64_t sx = x_min; sx < x_max; sx++) {
+                                const float weight_x = triangle_filter((sx - x + pixel_offset) * invscale0);
+                                const float weight = weight_x * weight_y;
+
+                                if (weight <= 0.0f) {
+                                    continue;
+                                }
+
+                                const float pixel = *(const float *)((const char *)src0->data + sx*nb00 + sy*nb01 + i02*nb02 + i03*nb03);
+                                val += pixel * weight;
+                                total_weight += weight;
+                            }
+                        }
+
+                        if (total_weight > 0.0f) {
+                            val /= total_weight;
+                        }
+
+                        float * dst_ptr = (float *)((char *)dst->data + i0*nb0 + i1*nb1 + i2*nb2 + i3*nb3);
+                        *dst_ptr = val;
+                    }
+                }
+            }
+        }
     } else if (mode == GGML_SCALE_MODE_BILINEAR) {
         for (int64_t i3 = 0; i3 < ne3; i3++) {
             const int64_t i03 = i3 / sf3;
@@ -9766,7 +9825,8 @@ static void ggml_compute_forward_solve_tri_f32(const struct ggml_compute_params
             }
 
             const float diag = A_batch[i00 * n + i00];
-            GGML_ASSERT(diag != 0.0f && "Zero diagonal in triangular matrix");
+            assert(diag != 0.0f && "Zero diagonal in triangular matrix");
+
             X_batch[i00 * k + i01] = (B_batch[i00 * k + i01] - sum) / diag;
         }
     }

ggml/src/ggml-cpu/repack.cpp

@@ -124,6 +124,58 @@ void ggml_quantize_mat_q8_0_4x8_generic(const float * GGML_RESTRICT x, void * GG
     }
 }
 
+
+void ggml_quantize_mat_q8_K_4x4_generic(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t k) {
+    assert(QK_K == 256);
+    assert(k % QK_K == 0);
+    const int nb = k / QK_K;
+
+    block_q8_Kx4 * GGML_RESTRICT y = (block_q8_Kx4 *) vy;
+
+    // scalar
+    const int blck_size_interleave = 4;
+    float srcv[4][QK_K];
+    float iscale[4];
+
+    for (int i = 0; i < nb; i++) {
+        for (int row_iter = 0; row_iter < 4; row_iter++) {
+            float amax = 0.0f; // absolute max
+            float max = 0;
+
+            for (int j = 0; j < QK_K; j++) {
+                srcv[row_iter][j] = x[row_iter * k + i * QK_K + j];
+                // Update the maximum value of the corresponding super block
+                if(amax < fabsf(srcv[row_iter][j])) {
+                    amax = fabsf(srcv[row_iter][j]);
+                    max = srcv[row_iter][j];
+                }
+            }
+
+            iscale[row_iter] = amax ? -127.f/max : 0;
+
+            y[i].d[row_iter] = amax ? 1/iscale[row_iter] : 0;
+        }
+
+        for (int j = 0; j < QK_K / 4; j++) {
+            y[i].bsums[j] = 0;
+        }
+
+        // Quants values are interleaved in sequence of four bytes from corresponding super blocks
+        // Bsums values are interleaved in sequence of four bsums from each super block taken for interleaving
+        // i.e first four bsums from the first super block, followed by first four bsums from second super block and so on
+        for (int j = 0; j < QK_K * 4; j++) {
+            int src_offset = (j / (4 * blck_size_interleave)) * blck_size_interleave;
+            int src_id     = (j % (4 * blck_size_interleave)) / blck_size_interleave;
+            src_offset += (j % blck_size_interleave);
+            int index = (((j & 15) >> 2) << 2) + ((j >> 8) << 4) + ((j >> 6) & 3);
+
+            float x0 = srcv[src_id][src_offset] * iscale[src_id];
+            y[i].qs[j] = nearest_int(x0);
+            y[i].bsums[index] += y[i].qs[j];
+        }
+    }
+}
+
 void ggml_quantize_mat_q8_K_4x8_generic(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t k) {
     assert(QK_K == 256);
     assert(k % QK_K == 0);
@@ -192,6 +244,12 @@ template <> void ggml_quantize_mat_t<8, GGML_TYPE_Q8_0>(const float * GGML_RESTR
     ggml_quantize_mat_q8_0_4x8(x, vy, n_per_row);
 }
 
+template <> void ggml_quantize_mat_t<4, GGML_TYPE_Q8_K>(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t nrow, int64_t n_per_row) {
+    assert(nrow == 4);
+    UNUSED(nrow);
+    ggml_quantize_mat_q8_K_4x4(x, vy, n_per_row);
+}
+
 template <> void ggml_quantize_mat_t<8, GGML_TYPE_Q8_K>(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t nrow, int64_t n_per_row) {
     assert(nrow == 4);
     UNUSED(nrow);
@@ -333,6 +391,77 @@ void ggml_gemv_q4_0_8x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs,
     }
 }
 
+void ggml_gemv_q4_K_8x4_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
+    const int qk = QK_K;
+    const int nb = n / qk;
+    const int ncols_interleaved = 8;
+    const int blocklen = 4;
+    static const uint32_t kmask1 = 0x3f3f3f3f;
+    static const uint32_t kmask2 = 0x0f0f0f0f;
+    static const uint32_t kmask3 = 0x03030303;
+
+    assert (n % qk == 0);
+    assert (nc % ncols_interleaved == 0);
+
+    UNUSED(bs);
+    UNUSED(nr);
+
+    float sumf[8];
+    float sum_minf[8];
+    uint32_t utmp[32];
+    int sumi1;
+    int sumi2;
+    int sumi;
+
+    const block_q8_K * a_ptr = (const block_q8_K *) vy;
+    for (int x = 0; x < nc / ncols_interleaved; x++) {
+        const block_q4_Kx8 * b_ptr = (const block_q4_Kx8 *) vx + (x * nb);
+
+        for (int j = 0; j < ncols_interleaved; j++) {
+            sumf[j] = 0.0;
+            sum_minf[j] = 0.0;
+        }
+        for (int l = 0; l < nb; l++) {
+            for (int sb = 0; sb < 8; sb++) {
+                memcpy(utmp + sb * 4, b_ptr[l].scales + sb * 12, 12);
+                utmp[sb * 4 + 3] = ((utmp[sb * 4 + 2] >> 4) & kmask2) | (((utmp[sb * 4 + 1] >> 6) & kmask3) << 4);
+                const uint32_t uaux_0 = utmp[sb * 4 + 1] & kmask1;
+                utmp[sb * 4 + 1] = (utmp[sb * 4 + 2] & kmask2) | (((utmp[sb * 4 + 0] >> 6) & kmask3) << 4);
+                utmp[sb * 4 + 2] = uaux_0;
+                utmp[sb * 4 + 0] &= kmask1;
+            }
+            for (int k = 0; k < (qk / (2 * blocklen)); k++) {
+                uint8_t * scales_0 = (uint8_t *) utmp + (k / 8) * 32;
+                uint8_t * scales_1 = (uint8_t *) utmp + (k / 8) * 32 + 16;
+                for (int j = 0; j < ncols_interleaved; j++) {
+                    sumi1 = 0;
+                    sumi2 = 0;
+                    sumi = 0;
+                    for (int i = 0; i < blocklen; ++i) {
+                        const int v0 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0xF);
+                        const int v1 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] >> 4);
+                        sumi1 = (v0 * a_ptr[l].qs[(k / 8) * 64 + (k % 8) * blocklen + i]);
+                        sumi2 = (v1 * a_ptr[l].qs[(k / 8) * 64 + (k % 8) * blocklen + i + 32]);
+                        sumi1 = sumi1 * scales_0[j];
+                        sumi2 = sumi2 * scales_1[j];
+                        sumi += sumi1 + sumi2;
+                    }
+                    sumf[j] += sumi * GGML_CPU_FP16_TO_FP32(b_ptr[l].d[j]) * a_ptr[l].d;
+                }
+            }
+            for (int sb = 0; sb < 8; sb++) {
+                uint8_t * mins = (uint8_t *) utmp + 8 + sb * 16;
+                for (int j = 0; j < ncols_interleaved; j++) {
+                    sum_minf[j] += mins[j] * (a_ptr[l].bsums[sb * 2] + a_ptr[l].bsums[sb * 2 + 1]) * GGML_CPU_FP16_TO_FP32(b_ptr[l].dmin[j]) * a_ptr[l].d;
+                }
+            }
+        }
+        for (int j = 0; j < ncols_interleaved; j++) {
+            s[x * ncols_interleaved + j] = sumf[j] - sum_minf[j];
+        }
+    }
+}
+
 void ggml_gemv_q4_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
     const int qk = QK_K;
     const int nb = n / qk;
@@ -727,6 +856,89 @@ void ggml_gemm_q4_0_8x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs,
     }
 }
 
+void ggml_gemm_q4_K_8x4_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
+    const int qk = QK_K;
+    const int nb = n / qk;
+    const int ncols_interleaved = 8;
+    const int blocklen = 4;
+    static const uint32_t kmask1 = 0x3f3f3f3f;
+    static const uint32_t kmask2 = 0x0f0f0f0f;
+    static const uint32_t kmask3 = 0x03030303;
+
+    assert (n % qk == 0);
+    assert (nr % 4 == 0);
+    assert (nc % ncols_interleaved == 0);
+
+    UNUSED(nb);
+    UNUSED(ncols_interleaved);
+    UNUSED(blocklen);
+
+    float sumf[4][8];
+    float sum_minf[4][8];
+    uint32_t utmp[32];
+    int sumi1;
+    int sumi2;
+    int sumi;
+
+    for (int y = 0; y < nr / 4; y++) {
+        const block_q8_Kx4 * a_ptr = (const block_q8_Kx4 *) vy + (y * nb);
+        for (int x = 0; x < nc / ncols_interleaved; x++) {
+            const block_q4_Kx8 * b_ptr = (const block_q4_Kx8 *) vx + (x * nb);
+            for (int m = 0; m < 4; m++) {
+                for (int j = 0; j < ncols_interleaved; j++) {
+                    sumf[m][j] = 0.0;
+                    sum_minf[m][j] = 0.0;
+                }
+            }
+            for (int l = 0; l < nb; l++) {
+                for (int sb = 0; sb < 8; sb++) {
+                    memcpy(utmp + sb * 4, b_ptr[l].scales + sb * 12, 12);
+                    utmp[sb * 4 + 3] = ((utmp[sb * 4 + 2] >> 4) & kmask2) | (((utmp[sb * 4 + 1] >> 6) & kmask3) << 4);
+                    const uint32_t uaux_0 = utmp[sb * 4 + 1] & kmask1;
+                    utmp[sb * 4 + 1] = (utmp[sb * 4 + 2] & kmask2) | (((utmp[sb * 4 + 0] >> 6) & kmask3) << 4);
+                    utmp[sb * 4 + 2] = uaux_0;
+                    utmp[sb * 4 + 0] &= kmask1;
+                }
+                for (int k = 0; k < (qk / (2 * blocklen)); k++) {
+                    uint8_t * scales_0 = (uint8_t *) utmp + (k / 8) * 32;
+                    uint8_t * scales_1 = (uint8_t *) utmp + (k / 8) * 32 + 16;
+                    for (int m = 0; m < 4; m++) {
+                        for (int j = 0; j < ncols_interleaved; j++) {
+                            sumi1 = 0;
+                            sumi2 = 0;
+                            sumi = 0;
+                            for (int i = 0; i < blocklen; ++i) {
+                                const int v0 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0xF);
+                                const int v1 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] >> 4);
+                                sumi1 = (v0 * a_ptr[l].qs[(k / 8) * 256 + (k % 8) * 4 * blocklen + m * blocklen + i]);
+                                sumi2 = (v1 * a_ptr[l].qs[(k / 8) * 256 + (k % 8) * 4 * blocklen + m * blocklen + i + 128]);
+                                sumi1 = sumi1 * scales_0[j];
+                                sumi2 = sumi2 * scales_1[j];
+                                sumi += sumi1 + sumi2;
+                            }
+                            sumf[m][j] += sumi * GGML_CPU_FP16_TO_FP32(b_ptr[l].d[j]) * a_ptr[l].d[m];
+                        }
+                    }
+                }
+                for (int sb = 0; sb < 8; sb++) {
+                    uint8_t * mins = (uint8_t *) utmp + 8 + sb * 16;
+                    for(int m = 0; m < 4; m++) {
+                        const int16_t * bsums = a_ptr[l].bsums + (sb * 8) + (m * 4) - ((sb % 2) * 6);
+                        for(int j = 0; j < ncols_interleaved; j++) {
+                            sum_minf[m][j] += mins[j] * (bsums[0] + bsums[1]) * GGML_CPU_FP16_TO_FP32(b_ptr[l].dmin[j]) * a_ptr[l].d[m];
+                        }
+                    }
+                }
+            }
+            for (int m = 0; m < 4; m++) {
+                for (int j = 0; j < ncols_interleaved; j++) {
+                    s[(y * 4 + m) * bs + x * ncols_interleaved + j] = sumf[m][j] - sum_minf[m][j];
+                }
+            }
+        }
+    }
+}
+
 void ggml_gemm_q4_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
     const int qk = QK_K;
     const int nb = n / qk;
@@ -1228,9 +1440,10 @@ static int repack_q4_0_to_q4_0_4_bl(struct ggml_tensor * t, int interleave_block
 
     GGML_UNUSED(data_size);
 }
+
 static int repack_q4_K_to_q4_K_8_bl(struct ggml_tensor * t, int interleave_block, const void * GGML_RESTRICT data, size_t data_size) {
     GGML_ASSERT(t->type == GGML_TYPE_Q4_K);
-    GGML_ASSERT(interleave_block == 8);
+    GGML_ASSERT(interleave_block == 8 || interleave_block == 4);
     constexpr int nrows_interleaved = 8;
 
     block_q4_Kx8 * dst = (block_q4_Kx8*)t->data;
@@ -1468,6 +1681,10 @@ template <> int repack<block_q4_K, 8, 8>(struct ggml_tensor * t, const void * da
     return repack_q4_K_to_q4_K_8_bl(t, 8, data, data_size);
 }
 
+template <> int repack<block_q4_K, 4, 8>(struct ggml_tensor * t, const void * data, size_t data_size) {
+    return repack_q4_K_to_q4_K_8_bl(t, 4, data, data_size);
+}
+
 template <> int repack<block_q2_K, 8, 8>(struct ggml_tensor * t, const void * data, size_t data_size) {
     return repack_q2_K_to_q2_K_8_bl(t, 8, data, data_size);
 }
@@ -1501,6 +1718,10 @@ template <> void gemv<block_q4_0, 8, 8, GGML_TYPE_Q8_0>(int n, float * s, size_t
     ggml_gemv_q4_0_8x8_q8_0(n, s, bs, vx, vy, nr, nc);
 }
 
+template <> void gemv<block_q4_K, 4, 8, GGML_TYPE_Q8_K>(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) {
+    ggml_gemv_q4_K_8x4_q8_K(n, s, bs, vx, vy, nr, nc);
+}
+
 template <> void gemv<block_q4_K, 8, 8, GGML_TYPE_Q8_K>(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) {
     ggml_gemv_q4_K_8x8_q8_K(n, s, bs, vx, vy, nr, nc);
 }
@@ -1529,6 +1750,10 @@ template <> void gemm<block_q4_0, 8, 4, GGML_TYPE_Q8_0>(int n, float * s, size_t
     ggml_gemm_q4_0_4x8_q8_0(n, s, bs, vx, vy, nr, nc);
 }
 
+template <> void gemm<block_q4_K, 4, 8, GGML_TYPE_Q8_K>(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) {
+    ggml_gemm_q4_K_8x4_q8_K(n, s, bs, vx, vy, nr, nc);
+}
+
 template <> void gemm<block_q4_0, 8, 8, GGML_TYPE_Q8_0>(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) {
     ggml_gemm_q4_0_8x8_q8_0(n, s, bs, vx, vy, nr, nc);
 }
@@ -1931,6 +2156,9 @@ static const ggml::cpu::tensor_traits * ggml_repack_get_optimal_repack_type(cons
     static const ggml::cpu::repack::tensor_traits<block_q4_0, 4, 4, GGML_TYPE_Q8_0> q4_0_4x4_q8_0;
     static const ggml::cpu::repack::tensor_traits<block_q4_0, 8, 4, GGML_TYPE_Q8_0> q4_0_4x8_q8_0;
     static const ggml::cpu::repack::tensor_traits<block_q4_0, 8, 8, GGML_TYPE_Q8_0> q4_0_8x8_q8_0;
+
+    // instance for Q4_K
+    static const ggml::cpu::repack::tensor_traits<block_q4_K, 4, 8, GGML_TYPE_Q8_K> q4_K_8x4_q8_K;
     static const ggml::cpu::repack::tensor_traits<block_q4_K, 8, 8, GGML_TYPE_Q8_K> q4_K_8x8_q8_K;
 
     // instance for Q2
@@ -1967,6 +2195,11 @@ static const ggml::cpu::tensor_traits * ggml_repack_get_optimal_repack_type(cons
                 return &q4_K_8x8_q8_K;
             }
         }
+        if (ggml_cpu_has_neon() && ggml_cpu_has_dotprod()) {
+            if (cur->ne[1] % 8 == 0) {
+                return &q4_K_8x4_q8_K;
+            }
+        }
     } else if (cur->type == GGML_TYPE_Q2_K) {
         if (ggml_cpu_has_avx512()) {
             if (cur->ne[1] % 8 == 0) {

ggml/src/ggml-cpu/repack.h

@@ -80,10 +80,12 @@ extern "C" {
 
 void ggml_quantize_mat_q8_0_4x4(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t k);
 void ggml_quantize_mat_q8_0_4x8(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t k);
+void ggml_quantize_mat_q8_K_4x4(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t k);
 void ggml_quantize_mat_q8_K_4x8(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t k);
 void ggml_gemv_q4_0_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemv_q4_0_4x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemv_q4_0_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
+void ggml_gemv_q4_K_8x4_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemv_q4_K_8x8_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemv_q2_K_8x8_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemv_iq4_nl_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
@@ -91,6 +93,7 @@ void ggml_gemv_iq4_nl_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const
 void ggml_gemm_q4_0_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_q4_0_4x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_q4_0_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
+void ggml_gemm_q4_K_8x4_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_q4_K_8x8_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_q2_K_8x8_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_iq4_nl_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
@@ -99,10 +102,12 @@ void ggml_gemm_iq4_nl_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const
 // Native implementations
 void ggml_quantize_mat_q8_0_4x4_generic(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t k);
 void ggml_quantize_mat_q8_0_4x8_generic(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t k);
+void ggml_quantize_mat_q8_K_4x4_generic(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t k);
 void ggml_quantize_mat_q8_K_4x8_generic(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t k);
 void ggml_gemv_q4_0_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemv_q4_0_4x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemv_q4_0_8x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
+void ggml_gemv_q4_K_8x4_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemv_q4_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemv_q2_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemv_iq4_nl_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
@@ -110,6 +115,7 @@ void ggml_gemv_iq4_nl_8x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs
 void ggml_gemm_q4_0_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_q4_0_4x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_q4_0_8x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
+void ggml_gemm_q4_K_8x4_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_q4_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_q2_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_iq4_nl_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);

ggml/src/ggml-cuda/argsort.cu

@@ -44,7 +44,7 @@ static void argsort_f32_i32_cuda_cub(ggml_cuda_pool & pool,
     const dim3 offset_grid((nrows + block_size - 1) / block_size);
     init_offsets<<<offset_grid, block_size, 0, stream>>>(d_offsets, ncols, nrows);
 
-    cudaMemcpyAsync(temp_keys, x, ncols * nrows * sizeof(float), cudaMemcpyDeviceToDevice, stream);
+    CUDA_CHECK(cudaMemcpyAsync(temp_keys, x, ncols * nrows * sizeof(float), cudaMemcpyDeviceToDevice, stream));
 
     size_t temp_storage_bytes = 0;
 

ggml/src/ggml-cuda/common.cuh

@@ -21,10 +21,12 @@
 #include "ggml-common.h"
 
 #include <array>
+#include <algorithm>
 #include <cassert>
 #include <cfloat>
 #include <cstdio>
 #include <string>
+#include <unordered_map>
 #include <vector>
 
 #if defined(GGML_USE_HIP)
@@ -84,12 +86,12 @@
 
 #define GGML_CUDA_CC_QY1 (GGML_CUDA_CC_OFFSET_MTHREADS + 0x210) // MTT S80, MTT S3000
 #define GGML_CUDA_CC_QY2 (GGML_CUDA_CC_OFFSET_MTHREADS + 0x220) // MTT S4000
-#define GGML_CUDA_CC_NG  (GGML_CUDA_CC_OFFSET_MTHREADS + 0x310) // TBD
+#define GGML_CUDA_CC_PH1 (GGML_CUDA_CC_OFFSET_MTHREADS + 0x310) // MTT S5000
 
 #define GGML_CUDA_CC_IS_MTHREADS(cc) (cc >= GGML_CUDA_CC_OFFSET_MTHREADS && cc < GGML_CUDA_CC_OFFSET_AMD)
 #define GGML_CUDA_CC_IS_QY1(cc)      (cc >= GGML_CUDA_CC_QY1 && cc < GGML_CUDA_CC_QY2)
-#define GGML_CUDA_CC_IS_QY2(cc)      (cc >= GGML_CUDA_CC_QY2 && cc < GGML_CUDA_CC_NG)
-#define GGML_CUDA_CC_IS_NG(cc)       (cc >= GGML_CUDA_CC_NG)
+#define GGML_CUDA_CC_IS_QY2(cc)      (cc >= GGML_CUDA_CC_QY2 && cc < GGML_CUDA_CC_PH1)
+#define GGML_CUDA_CC_IS_PH1(cc)      (cc >= GGML_CUDA_CC_PH1)
 
 #if !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA) && CUDART_VERSION >= 11070
 #    define GGML_CUDA_USE_CUB
@@ -212,9 +214,9 @@ static const char * cu_get_error_str(CUresult err) {
 #define GGML_USE_VMM
 #endif // (!defined(GGML_USE_HIP) && !defined(GGML_CUDA_NO_VMM)) || (defined(GGML_USE_HIP) && !defined(GGML_HIP_NO_VMM))
 
-#if defined(GGML_USE_HIP) || __CUDA_ARCH__ >= GGML_CUDA_CC_PASCAL
+#if defined(GGML_USE_HIP) || defined(GGML_USE_MUSA) || __CUDA_ARCH__ >= GGML_CUDA_CC_PASCAL
 #define FP16_AVAILABLE
-#endif // defined(GGML_USE_HIP) || __CUDA_ARCH__ >= GGML_CUDA_CC_PASCAL
+#endif // defined(GGML_USE_HIP) || defined(GGML_USE_MUSA) || __CUDA_ARCH__ >= GGML_CUDA_CC_PASCAL
 
 #if defined(FP16_AVAILABLE) && __CUDA_ARCH__ != 610
 #define FAST_FP16_AVAILABLE
@@ -250,12 +252,14 @@ static const char * cu_get_error_str(CUresult err) {
 #endif // !defined(GGML_CUDA_NO_FA) && !(defined(GGML_USE_MUSA) && __MUSA_ARCH__ < 220)
 
 static bool fp16_available(const int cc) {
-    return ggml_cuda_highest_compiled_arch(cc) >= GGML_CUDA_CC_PASCAL;
+    return ggml_cuda_highest_compiled_arch(cc) >= GGML_CUDA_CC_PASCAL ||
+        (GGML_CUDA_CC_IS_MTHREADS(cc) && cc >= GGML_CUDA_CC_PH1);
 }
 
 static bool fast_fp16_available(const int cc) {
     return GGML_CUDA_CC_IS_AMD(cc) ||
-        (GGML_CUDA_CC_IS_NVIDIA(cc) && fp16_available(cc) && ggml_cuda_highest_compiled_arch(cc) != 610);
+        (GGML_CUDA_CC_IS_NVIDIA(cc) && fp16_available(cc) && ggml_cuda_highest_compiled_arch(cc) != 610) ||
+        (GGML_CUDA_CC_IS_MTHREADS(cc) && fp16_available(cc));
 }
 
 // To be used for feature selection of external libraries, e.g. cuBLAS.
@@ -272,7 +276,9 @@ static bool fp16_mma_hardware_available(const int cc) {
 }
 
 static bool bf16_mma_hardware_available(const int cc) {
-    return (GGML_CUDA_CC_IS_NVIDIA(cc) && cc >= GGML_CUDA_CC_AMPERE) || GGML_CUDA_CC_IS_CDNA(cc) || cc >= GGML_CUDA_CC_RDNA3;
+    return (GGML_CUDA_CC_IS_NVIDIA(cc) && cc >= GGML_CUDA_CC_AMPERE) ||
+        GGML_CUDA_CC_IS_CDNA(cc) || cc >= GGML_CUDA_CC_RDNA3 ||
+        (GGML_CUDA_CC_IS_MTHREADS(cc) && cc >= GGML_CUDA_CC_PH1);
 }
 
 static bool fp32_mma_hardware_available(const int cc) {
@@ -558,8 +564,12 @@ static __device__ __forceinline__ void ggml_cuda_mad(float & acc, const float2 v
     acc += v.y*u.y;
 }
 
-static __device__ __forceinline__ void ggml_cuda_mad(float & acc, const half2 v, const half2 u) {
 #if defined(GGML_USE_HIP) && (defined(RDNA2) || defined(RDNA3) || defined(RDNA4) || defined(__gfx906__) || defined(CDNA))
+#define V_DOT2_F32_F16_AVAILABLE
+#endif // defined(GGML_USE_HIP) && (defined(RDNA2) || defined(RDNA3) || defined(RDNA4) || defined(__gfx906__) || defined(CDNA))
+
+static __device__ __forceinline__ void ggml_cuda_mad(float & acc, const half2 v, const half2 u) {
+#ifdef V_DOT2_F32_F16_AVAILABLE
     asm volatile("v_dot2_f32_f16 %0, %1, %2, %0" : "+v"(acc) : "v"(v), "v"(u));
 #else
 #ifdef FAST_FP16_AVAILABLE
@@ -571,7 +581,7 @@ static __device__ __forceinline__ void ggml_cuda_mad(float & acc, const half2 v,
     acc += tmpv.x * tmpu.x;
     acc += tmpv.y * tmpu.y;
 #endif // FAST_FP16_AVAILABLE
-#endif // defined(GGML_USE_HIP) && (defined(RDNA2)  || defined(RDNA3) || defined(RDNA4) || defined(GCN5) || defined(CDNA))
+#endif // V_DOT2_F32_F16_AVAILABLE
 }
 
 static __device__ __forceinline__ void ggml_cuda_mad(half2 & acc, const half2 v, const half2 u) {
@@ -972,6 +982,154 @@ struct ggml_cuda_graph {
 #endif
 };
 
+struct ggml_cuda_concurrent_event {
+    std::vector<cudaEvent_t> join_events;
+    cudaEvent_t              fork_event = nullptr;
+
+    int                                          n_streams = 0;
+    std::unordered_map<const ggml_tensor *, int> stream_mapping;
+
+    const ggml_tensor * join_node;
+
+    ggml_cuda_concurrent_event() = default;
+
+    ggml_cuda_concurrent_event(const ggml_cuda_concurrent_event &) = delete;
+    ggml_cuda_concurrent_event & operator=(const ggml_cuda_concurrent_event &) = delete;
+
+    explicit ggml_cuda_concurrent_event(int n_streams) : n_streams(n_streams) {
+        join_events.resize(n_streams);
+
+        for (size_t i = 0; i < join_events.size(); ++i) {
+            CUDA_CHECK(cudaEventCreateWithFlags(&join_events[i], cudaEventDisableTiming));
+        }
+
+        CUDA_CHECK(cudaEventCreateWithFlags(&fork_event, cudaEventDisableTiming));
+    }
+
+    ggml_cuda_concurrent_event(ggml_cuda_concurrent_event && other) noexcept
+    : join_events(std::move(other.join_events))
+    , fork_event(other.fork_event)
+    , n_streams(other.n_streams)
+    , stream_mapping(std::move(other.stream_mapping))
+    , join_node(other.join_node) {
+        other.fork_event = nullptr;
+    }
+
+    // 1. check if any branches write to overlapping memory ranges (except the join node)
+    // 2. check whether all srcs are either within the branch or outside the nodes covered by ggml_cuda_concurrent_event
+    // we assume all nodes have the same buffer
+    bool is_valid() const {
+        std::vector<std::vector<std::pair<int64_t, int64_t>>> write_ranges;
+        write_ranges.resize(n_streams);
+
+        // get join_node's memory range to exclude from overlap checking.
+        // multiple nodes can use join_node's buffer; we synchronize on the join node.
+        const ggml_tensor * join_t     = join_node->view_src ? join_node->view_src : join_node;
+        const int64_t       join_start = (int64_t) join_t->data;
+        const int64_t       join_end   = join_start + ggml_nbytes(join_t);
+
+        for (const auto & [tensor, stream] : stream_mapping) {
+            const ggml_tensor * t = tensor->view_src ? tensor->view_src : tensor;
+            const int64_t       t_start = (int64_t) t->data;
+            const int64_t       t_end   = t_start + ggml_nbytes(t);
+
+            // skip tensors that overlap with join_node's buffer.
+            if ((t_start <= join_start && join_start < t_end) || (join_start <= t_start && t_start < join_end)) {
+                continue;
+            }
+
+            // concurrent streams begin from 1
+            write_ranges[stream - 1].emplace_back(t_start, t_end);
+        }
+
+        for (int i = 0; i < n_streams; ++i) {
+            // sorts first by start then by end of write range
+            std::sort(write_ranges[i].begin(), write_ranges[i].end());
+        }
+
+        bool writes_overlap = false;
+        bool dependent_srcs = false;
+        for (const auto & [tensor, stream] : stream_mapping) {
+            const ggml_tensor * t = tensor->view_src ? tensor->view_src : tensor;
+            const int64_t       t_start = (int64_t) t->data;
+            const int64_t       t_end   = t_start + ggml_nbytes(t);
+
+            // skip tensors that overlap with join_node's buffer
+            if ((t_start <= join_start && join_start < t_end) || (join_start <= t_start && t_start < join_end)) {
+                continue;
+            }
+
+            // check if this buffer's write data overlaps with another stream's
+            std::pair<int64_t, int64_t> data_range = std::make_pair(t_start, t_end);
+            for (int i = 0; i < n_streams; ++i) {
+                if (i == stream - 1) {
+                    continue;
+                }
+                auto it = std::lower_bound(write_ranges[i].begin(), write_ranges[i].end(), data_range);
+
+                if (it != write_ranges[i].end()) {
+                    const std::pair<int64_t, int64_t> & other = *it;
+
+                    // std::lower_bound returns the first element where other >= data_range (lexicographically).
+                    // This guarantees other.first >= data_range.first.
+                    // Therefore, overlap occurs iff other.first < data_range.second
+                    // (i.e., the other range starts before this range ends).
+                    if (other.first < data_range.second) {
+                        GGML_LOG_DEBUG("Writes overlap for %s", tensor->name);
+                        writes_overlap = true;
+                        break;
+                    }
+                }
+            }
+
+            //check if all srcs are either in branch or don't have a branch
+            for (int i = 0; i < GGML_MAX_SRC; ++i) {
+                if (!tensor->src[i]) {
+                    continue;
+                }
+
+                auto it = stream_mapping.find(tensor->src[i]);
+
+                if (it == stream_mapping.end()) {
+                    continue;
+                }
+
+                if (it->second != stream) {
+                    dependent_srcs = true;
+                    break;
+                }
+            }
+
+            if (dependent_srcs || writes_overlap) {
+                break;
+            }
+        }
+
+        return !writes_overlap && !dependent_srcs;
+    }
+
+    ~ggml_cuda_concurrent_event() {
+        if (fork_event != nullptr) {
+            CUDA_CHECK(cudaEventDestroy(fork_event));
+        }
+        for (cudaEvent_t e : join_events) {
+            if (e != nullptr) {
+                CUDA_CHECK(cudaEventDestroy(e));
+            }
+        }
+    }
+};
+
+struct ggml_cuda_stream_context {
+    std::vector<const ggml_tensor *>                                    original_nodes;
+    std::unordered_map<const ggml_tensor *, ggml_cuda_concurrent_event> concurrent_events;
+
+    void reset() {
+        original_nodes.clear();
+        concurrent_events.clear();
+    }
+};
+
 struct ggml_backend_cuda_context {
     int device;
     std::string name;
@@ -982,11 +1140,15 @@ struct ggml_backend_cuda_context {
 
     std::unique_ptr<ggml_cuda_graph> cuda_graph;
 
+    int curr_stream_no = 0;
+
     explicit ggml_backend_cuda_context(int device) :
         device(device),
         name(GGML_CUDA_NAME + std::to_string(device)) {
     }
 
+    ggml_cuda_stream_context concurrent_stream_context;
+
     ~ggml_backend_cuda_context();
 
     cudaStream_t stream(int device, int stream) {
@@ -997,9 +1159,9 @@ struct ggml_backend_cuda_context {
         return streams[device][stream];
     }
 
-    cudaStream_t stream() {
-        return stream(device, 0);
-    }
+    cudaStream_t stream() { return stream(device, curr_stream_no); }
+
+    ggml_cuda_stream_context & stream_context() { return concurrent_stream_context; }
 
     cublasHandle_t cublas_handle(int device) {
         if (cublas_handles[device] == nullptr) {
@@ -1015,15 +1177,15 @@ struct ggml_backend_cuda_context {
     }
 
     // pool
-    std::unique_ptr<ggml_cuda_pool> pools[GGML_CUDA_MAX_DEVICES];
+    std::unique_ptr<ggml_cuda_pool> pools[GGML_CUDA_MAX_DEVICES][GGML_CUDA_MAX_STREAMS];
 
-    static std::unique_ptr<ggml_cuda_pool> new_pool_for_device(int device);
+    static std::unique_ptr<ggml_cuda_pool> new_pool_for_device(int device, int stream_no);
 
     ggml_cuda_pool & pool(int device) {
-        if (pools[device] == nullptr) {
-            pools[device] = new_pool_for_device(device);
+        if (pools[device][curr_stream_no] == nullptr) {
+            pools[device][curr_stream_no] = new_pool_for_device(device, curr_stream_no);
         }
-        return *pools[device];
+        return *pools[device][curr_stream_no];
     }
 
     ggml_cuda_pool & pool() {

ggml/src/ggml-cuda/cpy.cu

@@ -86,6 +86,9 @@ static __global__ void cpy_scalar_transpose(const char * cx, char * cdst, const
             }
         }
     }
+
+    GGML_UNUSED_VARS(ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11,
+        nb12, nb13);
 }
 
 static __device__ void cpy_blck_q8_0_f32(const char * cxi, char * cdsti) {
@@ -202,7 +205,7 @@ static void ggml_cpy_scalar_cuda(
             ne00n = ne00;
             ne01n = ne01;
             ne02n = ne02;
-        } else if (nb00 > nb02) {
+        } else {
             ne00n = ne00;
             ne01n = ne01*ne02;
             ne02n = 1;

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

@@ -55,11 +55,11 @@ static __device__ __forceinline__ float vec_dot_fattn_vec_KQ_f16(
         ggml_cuda_memcpy_1<sizeof(tmp)>(tmp, K_h2 + k_KQ_0 + (threadIdx.x % nthreads)*cpy_ne);
 #pragma unroll
         for (int k_KQ_1 = 0; k_KQ_1 < cpy_ne; ++k_KQ_1) {
-#ifdef FAST_FP16_AVAILABLE
+#ifdef V_DOT2_F32_F16_AVAILABLE
             ggml_cuda_mad(sum,                tmp[k_KQ_1] , ((const half2  *) Q_v)[k_KQ_0/nthreads + k_KQ_1]);
 #else
             ggml_cuda_mad(sum, __half22float2(tmp[k_KQ_1]), ((const float2 *) Q_v)[k_KQ_0/nthreads + k_KQ_1]);
-#endif // FP16_AVAILABLE
+#endif // V_DOT2_F32_F16_AVAILABLE
         }
     }
 

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

@@ -609,7 +609,7 @@ static __device__ __forceinline__ void flash_attn_tile_iter(
             float KQ_sum_add = 0.0f;
 #pragma unroll
             for (int i0 = 0; i0 < nbatch_fa; i0 += np*warp_size) {
-                const float val = !oob_check || i0 + (threadIdx.y % np)*warp_size + threadIdx.x < k_VKQ_sup ?
+                const float val = !oob_check || i0 + (threadIdx.y % np)*warp_size + threadIdx.x < static_cast<uint32_t>(k_VKQ_sup) ?
                     expf(KQ_acc[(i0/(np*warp_size))*cpw + jc] - KQ_max[jc]) : 0.0f;
                 KQ_sum_add += val;
                 tmp[i0/(np*warp_size)][jc1] = val;

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

@@ -86,11 +86,11 @@ static __global__ void flash_attn_ext_vec(
 
     constexpr vec_dot_KQ_t vec_dot_KQ = get_vec_dot_KQ<type_K, D, nthreads_KQ>();
     constexpr bool Q_q8_1 = type_K != GGML_TYPE_F16;
-#ifdef FAST_FP16_AVAILABLE
+#ifdef V_DOT2_F32_F16_AVAILABLE
     constexpr dequantize_V_t dequantize_V = get_dequantize_V<type_V, half,  V_rows_per_thread>();
 #else
     constexpr dequantize_V_t dequantize_V = get_dequantize_V<type_V, float, V_rows_per_thread>();
-#endif // FAST_FP16_AVAILABLE
+#endif // V_DOT2_F32_F16_AVAILABLE
 
     const int ic0 = blockIdx.x * ncols; // Index of the Q/QKV column to work on.
 
@@ -112,13 +112,13 @@ static __global__ void flash_attn_ext_vec(
 
     constexpr int ne_KQ      = ncols*D;
     constexpr int ne_combine = nwarps*V_cols_per_iter*D;
-#ifdef FAST_FP16_AVAILABLE
+#ifdef V_DOT2_F32_F16_AVAILABLE
     half2            VKQ[ncols][(D/2)/nthreads_V] = {{{0.0f, 0.0f}}};
     __shared__ half   KQ[ne_KQ > ne_combine ? ne_KQ : ne_combine];
 #else
     float2           VKQ[ncols][(D/2)/nthreads_V] = {{{0.0f, 0.0f}}};
     __shared__ float  KQ[ne_KQ > ne_combine ? ne_KQ : ne_combine];
-#endif // FAST_FP16_AVAILABLE
+#endif // V_DOT2_F32_F16_AVAILABLE
 
     float KQ_max[ncols];
     float KQ_sum[ncols];
@@ -129,11 +129,11 @@ static __global__ void flash_attn_ext_vec(
     }
 
     // Convert Q to float2 (f16 K) or q8_1 (quantized K) and store in registers:
-#ifdef FAST_FP16_AVAILABLE
+#ifdef V_DOT2_F32_F16_AVAILABLE
     half2  Q_reg[ncols][(D/2)/nthreads_KQ]; // Will be initialized completely.
 #else
     float2 Q_reg[ncols][(D/2)/nthreads_KQ] = {{{0.0f, 0.0f}}}; // May be only partially initialized.
-#endif // FAST_FP16_AVAILABLE
+#endif // V_DOT2_F32_F16_AVAILABLE
     int    Q_i32[ncols][1 > D/(sizeof(int)*nthreads_KQ) ? 1 : D/(sizeof(int)*nthreads_KQ)];
     float2  Q_ds[ncols][1 > D/(sizeof(int)*nthreads_KQ) ? 1 : D/(sizeof(int)*nthreads_KQ)];
     if constexpr (Q_q8_1) {
@@ -155,7 +155,7 @@ static __global__ void flash_attn_ext_vec(
                 for (int i0 = 0; i0 < int(D/sizeof(int)); i0 += WARP_SIZE) {
                     const int i = i0 + threadIdx.x;
 
-                    if (i0 + WARP_SIZE <= D/sizeof(int) || i < D/sizeof(int)) {
+                    if (i0 + WARP_SIZE <= int(D/sizeof(int)) || i < int(D/sizeof(int))) {
                         tmp_q_i32[i] = 0;
                     }
                 }
@@ -191,7 +191,7 @@ static __global__ void flash_attn_ext_vec(
 
         __syncthreads();
     } else {
-#ifdef FAST_FP16_AVAILABLE
+#ifdef V_DOT2_F32_F16_AVAILABLE
         const half2 scale_h2 = make_half2(scale, scale);
 #pragma unroll
         for (int j = 0; j < ncols; ++j) {
@@ -233,7 +233,7 @@ static __global__ void flash_attn_ext_vec(
                 Q_reg[j][k].y *= scale;
             }
         }
-#endif // FAST_FP16_AVAILABLE
+#endif // V_DOT2_F32_F16_AVAILABLE
     }
 
     const int k_VKQ_max = KV_max ? KV_max[sequence*gridDim.x + blockIdx.x] : ne11;
@@ -272,7 +272,7 @@ static __global__ void flash_attn_ext_vec(
 
                 KQ_max_new[j] = fmaxf(KQ_max_new[j], sum);
 
-                if ((nthreads_KQ == WARP_SIZE ? threadIdx.x : threadIdx.x % nthreads_KQ) == i_KQ_0) {
+                if ((nthreads_KQ == WARP_SIZE ? threadIdx.x : threadIdx.x % nthreads_KQ) == uint32_t(i_KQ_0)) {
                     KQ_reg[j] = sum;
                 }
             }
@@ -291,7 +291,7 @@ static __global__ void flash_attn_ext_vec(
             KQ_sum[j] = KQ_sum[j]*KQ_max_scale + KQ_reg[j];
             KQ[j*nthreads + tid] = KQ_reg[j];
 
-#ifdef FAST_FP16_AVAILABLE
+#ifdef V_DOT2_F32_F16_AVAILABLE
             const half2 KQ_max_scale_h2 = make_half2(KQ_max_scale, KQ_max_scale);
 #pragma unroll
             for (int i_VKQ_0 = 0; i_VKQ_0 < D/2; i_VKQ_0 += nthreads_V) {
@@ -303,7 +303,7 @@ static __global__ void flash_attn_ext_vec(
                 VKQ[j][i_VKQ_0/nthreads_V].x *= KQ_max_scale;
                 VKQ[j][i_VKQ_0/nthreads_V].y *= KQ_max_scale;
             }
-#endif // FAST_FP16_AVAILABLE
+#endif // V_DOT2_F32_F16_AVAILABLE
         }
 
 #ifndef GGML_USE_HIP
@@ -314,7 +314,7 @@ static __global__ void flash_attn_ext_vec(
         for (int k0 = 0; k0 < WARP_SIZE; k0 += V_cols_per_iter) {
             const int k = threadIdx.y*WARP_SIZE + k0 + (nthreads_V == WARP_SIZE ? 0 : threadIdx.x / nthreads_V);
 
-#ifdef FAST_FP16_AVAILABLE
+#ifdef V_DOT2_F32_F16_AVAILABLE
             half2 KQ_k[ncols];
 #pragma unroll
             for (int j = 0; j < ncols; ++j) {
@@ -353,7 +353,7 @@ static __global__ void flash_attn_ext_vec(
                     }
                 }
             }
-#endif // FAST_FP16_AVAILABLE
+#endif // V_DOT2_F32_F16_AVAILABLE
         }
     }
 
@@ -374,7 +374,7 @@ static __global__ void flash_attn_ext_vec(
 
             KQ_sum[j] = KQ_sum[j]*KQ_max_scale + (threadIdx.x == 0 ? expf(sink - KQ_max[j]) : 0.0f);
 
-#ifdef FAST_FP16_AVAILABLE
+#ifdef V_DOT2_F32_F16_AVAILABLE
             const half2 KQ_max_scale_h2 = make_half2(KQ_max_scale, KQ_max_scale);
 #pragma unroll
             for (int i_VKQ_0 = 0; i_VKQ_0 < D/2; i_VKQ_0 += nthreads_V) {
@@ -386,7 +386,7 @@ static __global__ void flash_attn_ext_vec(
                 VKQ[j][i_VKQ_0/nthreads_V].x *= KQ_max_scale;
                 VKQ[j][i_VKQ_0/nthreads_V].y *= KQ_max_scale;
             }
-#endif // FAST_FP16_AVAILABLE
+#endif // V_DOT2_F32_F16_AVAILABLE
         }
     }
 
@@ -421,7 +421,7 @@ static __global__ void flash_attn_ext_vec(
         const float kqmax_scale = expf(KQ_max[j_VKQ] - kqmax_new);
         KQ_max[j_VKQ] = kqmax_new;
 
-#ifdef FAST_FP16_AVAILABLE
+#ifdef V_DOT2_F32_F16_AVAILABLE
         half2 * VKQ_tmp = (half2 *) KQ + threadIdx.y*(V_cols_per_iter*D/2)
             + (nthreads_V == WARP_SIZE ? 0 : threadIdx.x / nthreads_V)*(D/2);
 
@@ -452,7 +452,7 @@ static __global__ void flash_attn_ext_vec(
             ggml_cuda_memcpy_1<V_rows_per_thread/2*sizeof(float)>(VKQ_tmp + i_VKQ,                       &VKQ[j_VKQ][i_VKQ_0/nthreads_V]);
             ggml_cuda_memcpy_1<V_rows_per_thread/2*sizeof(float)>(VKQ_tmp + i_VKQ + V_rows_per_thread/4, &VKQ[j_VKQ][i_VKQ_0/nthreads_V + V_rows_per_thread/4]);
         }
-#endif // FAST_FP16_AVAILABLE
+#endif // V_DOT2_F32_F16_AVAILABLE
 
         KQ_sum[j_VKQ] *= kqmax_scale;
         KQ_sum[j_VKQ] = warp_reduce_sum(KQ_sum[j_VKQ]);

ggml/src/ggml-cuda/ggml-cuda.cu

@@ -53,6 +53,7 @@
 #include "ggml-cuda/set.cuh"
 #include "ggml-cuda/set-rows.cuh"
 #include "ggml-cuda/pad_reflect_1d.cuh"
+#include "ggml-cuda/solve_tri.cuh"
 #include "ggml.h"
 
 #include <algorithm>
@@ -521,7 +522,8 @@ struct ggml_cuda_pool_vmm : public ggml_cuda_pool {
 };
 #endif // defined(GGML_USE_VMM)
 
-std::unique_ptr<ggml_cuda_pool> ggml_backend_cuda_context::new_pool_for_device(int device) {
+std::unique_ptr<ggml_cuda_pool> ggml_backend_cuda_context::new_pool_for_device(int                  device,
+                                                                               [[maybe_unused]] int stream_no) {
 #if defined(GGML_USE_VMM)
     if (ggml_cuda_info().devices[device].vmm) {
         return std::unique_ptr<ggml_cuda_pool>(new ggml_cuda_pool_vmm(device));
@@ -2717,6 +2719,9 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg
         case GGML_OP_OPT_STEP_SGD:
             ggml_cuda_opt_step_sgd(ctx, dst);
             break;
+        case GGML_OP_SOLVE_TRI:
+            ggml_cuda_op_solve_tri(ctx, dst);
+            break;
         default:
             return false;
     }
@@ -3046,7 +3051,12 @@ static bool ggml_cuda_can_fuse(const struct ggml_cgraph * cgraph, int node_idx,
     std::initializer_list<enum ggml_op> topk_moe_ops_delayed_softmax =
         ggml_cuda_topk_moe_ops(/*with_norm=*/false, /*delayed_softmax=*/true);
 
-    if (ops.size() == topk_moe_ops_with_norm.size() &&
+    const auto is_equal = [](const std::initializer_list<enum ggml_op> & list1,
+                             const std::initializer_list<enum ggml_op> & list2) {
+        return std::equal(list1.begin(), list1.end(), list2.begin(), list2.end());
+    };
+
+    if (is_equal(topk_moe_ops_with_norm, ops) &&
         ggml_can_fuse_subgraph(cgraph, node_idx, ops, { node_idx + 3, node_idx + 9 })) {
         ggml_tensor * softmax = cgraph->nodes[node_idx];
         ggml_tensor * weights = cgraph->nodes[node_idx + 9];
@@ -3056,8 +3066,7 @@ static bool ggml_cuda_can_fuse(const struct ggml_cgraph * cgraph, int node_idx,
         }
     }
 
-    if (ops.size() == topk_moe_ops.size() &&
-        ggml_can_fuse_subgraph(cgraph, node_idx, ops, { node_idx + 3, node_idx + 4 })) {
+    if (is_equal(topk_moe_ops, ops) && ggml_can_fuse_subgraph(cgraph, node_idx, ops, { node_idx + 3, node_idx + 4 })) {
         ggml_tensor * softmax = cgraph->nodes[node_idx];
         ggml_tensor * weights = cgraph->nodes[node_idx + 4];
         if (ggml_cuda_should_use_topk_moe(softmax, weights)) {
@@ -3065,7 +3074,7 @@ static bool ggml_cuda_can_fuse(const struct ggml_cgraph * cgraph, int node_idx,
         }
     }
 
-    if (ops.size() == topk_moe_ops_delayed_softmax.size() &&
+    if (is_equal(topk_moe_ops_delayed_softmax, ops) &&
         ggml_can_fuse_subgraph(cgraph, node_idx, ops, { node_idx + 1, node_idx + 5 })) {
         ggml_tensor * softmax = cgraph->nodes[node_idx + 4];
         ggml_tensor * weights = cgraph->nodes[node_idx + 5];
@@ -3081,9 +3090,8 @@ static bool ggml_cuda_can_fuse(const struct ggml_cgraph * cgraph, int node_idx,
     std::initializer_list<enum ggml_op> mul_mat_id_glu_ops = { GGML_OP_MUL_MAT_ID, GGML_OP_MUL_MAT_ID, GGML_OP_GLU };
     std::initializer_list<enum ggml_op> mul_mat_glu_ops    = { GGML_OP_MUL_MAT,    GGML_OP_MUL_MAT,    GGML_OP_GLU };
 
-    if (ops.size() == 5 && (ggml_can_fuse_subgraph(cgraph, node_idx, ops, {node_idx + 4}) ||
-                            ggml_can_fuse_subgraph(cgraph, node_idx, ops, {node_idx + 4}))) {
-
+    if ((is_equal(mul_mat_bias_glu_ops, ops) || is_equal(mul_mat_id_bias_glu_ops, ops)) &&
+        ggml_can_fuse_subgraph(cgraph, node_idx, ops, { node_idx + 4 })) {
         const ggml_tensor * ffn_gate      = cgraph->nodes[node_idx];
         const ggml_tensor * ffn_gate_bias = cgraph->nodes[node_idx + 1];
         const ggml_tensor * ffn_up        = cgraph->nodes[node_idx + 2];
@@ -3095,9 +3103,8 @@ static bool ggml_cuda_can_fuse(const struct ggml_cgraph * cgraph, int node_idx,
         }
     }
 
-    if (ops.size() == 3 && (ggml_can_fuse_subgraph(cgraph, node_idx, ops, {node_idx + 2}) ||
-                            ggml_can_fuse_subgraph(cgraph, node_idx, ops, {node_idx + 2}))) {
-
+    if ((is_equal(mul_mat_id_glu_ops, ops) || is_equal(mul_mat_glu_ops, ops)) &&
+        ggml_can_fuse_subgraph(cgraph, node_idx, ops, { node_idx + 2 })) {
         const ggml_tensor * ffn_gate = cgraph->nodes[node_idx];
         const ggml_tensor * ffn_up   = cgraph->nodes[node_idx + 1];
         const ggml_tensor * glu      = cgraph->nodes[node_idx + 2];
@@ -3107,7 +3114,9 @@ static bool ggml_cuda_can_fuse(const struct ggml_cgraph * cgraph, int node_idx,
         }
     }
 
-    if (ops.size() == 3 && ggml_can_fuse_subgraph(cgraph, node_idx, ops, { node_idx + 2 })) {
+    std::initializer_list<enum ggml_op> rope_set_rows_ops = { GGML_OP_ROPE, GGML_OP_VIEW, GGML_OP_SET_ROWS };
+
+    if (is_equal(rope_set_rows_ops, ops) && ggml_can_fuse_subgraph(cgraph, node_idx, ops, { node_idx + 2 })) {
         const ggml_tensor * rope     = cgraph->nodes[node_idx];
         const ggml_tensor * view     = cgraph->nodes[node_idx + 1];
         const ggml_tensor * set_rows = cgraph->nodes[node_idx + 2];
@@ -3192,27 +3201,94 @@ static void evaluate_and_capture_cuda_graph(ggml_backend_cuda_context * cuda_ctx
     // flag used to determine whether it is an integrated_gpu
     const bool integrated = ggml_cuda_info().devices[cuda_ctx->device].integrated;
 
+    ggml_cuda_stream_context & stream_ctx = cuda_ctx->stream_context();
+    bool                         is_concurrent_event_active = false;
+    ggml_cuda_concurrent_event * concurrent_event           = nullptr;
+    bool                         should_launch_concurrent_events = false;
+
+    const auto try_launch_concurrent_event = [&](const ggml_tensor * node) {
+        if (stream_ctx.concurrent_events.find(node) != stream_ctx.concurrent_events.end()) {
+            concurrent_event = &stream_ctx.concurrent_events[node];
+
+            is_concurrent_event_active = true;
+
+            GGML_LOG_DEBUG("Launching %d streams at %s\n", concurrent_event->n_streams, node->name);
+
+            cudaStream_t main_stream = cuda_ctx->stream();  // this should be stream 0
+            GGML_ASSERT(cuda_ctx->curr_stream_no == 0);
+            CUDA_CHECK(cudaEventRecord(concurrent_event->fork_event, main_stream));
+
+            for (int i = 1; i <= concurrent_event->n_streams; ++i) {
+                cudaStream_t stream = cuda_ctx->stream(cuda_ctx->device, i);
+                CUDA_CHECK(cudaStreamWaitEvent(stream, concurrent_event->fork_event));
+            }
+        }
+    };
+
     while (!graph_evaluated_or_captured) {
         // Only perform the graph execution if CUDA graphs are not enabled, or we are capturing the graph.
         // With the use of CUDA graphs, the execution will be performed by the graph launch.
         if (!use_cuda_graph || cuda_graph_update_required) {
-
             [[maybe_unused]] int prev_i = 0;
 
+            if (stream_ctx.concurrent_events.size() > 0) {
+                should_launch_concurrent_events = true;
+                for (const auto & [tensor, event] : stream_ctx.concurrent_events) {
+                    should_launch_concurrent_events = should_launch_concurrent_events && event.is_valid();
+                }
+            }
+            if (should_launch_concurrent_events) {
+                //Restore the original graph to enable fusion within the streams
+                cgraph->nodes   = const_cast<ggml_tensor **>(stream_ctx.original_nodes.data());
+                cgraph->n_nodes = (int) stream_ctx.original_nodes.size();
+            }
+
             for (int i = 0; i < cgraph->n_nodes; i++) {
                 ggml_tensor * node = cgraph->nodes[i];
+                if (is_concurrent_event_active) {
+                    GGML_ASSERT(concurrent_event);
+
+                    if (node == concurrent_event->join_node) {
+                        cuda_ctx->curr_stream_no = 0;
+                        for (int i = 1; i <= concurrent_event->n_streams; ++i) {
+                            // Wait on join events of forked streams in the main stream
+                            CUDA_CHECK(cudaEventRecord(concurrent_event->join_events[i - 1],
+                                                       cuda_ctx->stream(cuda_ctx->device, i)));
+                            CUDA_CHECK(cudaStreamWaitEvent(cuda_ctx->stream(), concurrent_event->join_events[i - 1]));
+                        }
+
+                        is_concurrent_event_active = false;
+                        concurrent_event           = nullptr;
+                    } else {
+                        GGML_ASSERT (concurrent_event->stream_mapping.find(node) != concurrent_event->stream_mapping.end());
+                        cuda_ctx->curr_stream_no = concurrent_event->stream_mapping[node];
+                        GGML_LOG_DEBUG("Setting stream no to %d for node %s\n", cuda_ctx->curr_stream_no, node->name);
+                    }
+                } else if (i - prev_i > 1) {
+                    //the previous node was fused
+                    const ggml_tensor * prev_node = cgraph->nodes[i - 1];
+                    try_launch_concurrent_event(prev_node);
+
+                    if (is_concurrent_event_active) {
+                        cuda_ctx->curr_stream_no = concurrent_event->stream_mapping[node];
+                        GGML_LOG_DEBUG("Setting stream no to %d for node %s\n", cuda_ctx->curr_stream_no, node->name);
+                    }
+                }
+
 #ifdef GGML_CUDA_DEBUG
                 const int nodes_fused = i - prev_i - 1;
-                prev_i = i;
                 if (nodes_fused > 0) {
                     GGML_LOG_INFO("nodes_fused: %d\n", nodes_fused);
                 }
 #endif
+                prev_i = i;
 
                 if (ggml_is_empty(node) || node->op == GGML_OP_RESHAPE || node->op == GGML_OP_TRANSPOSE || node->op == GGML_OP_VIEW || node->op == GGML_OP_PERMUTE || node->op == GGML_OP_NONE) {
                     continue;
                 }
 
+
+                // start of fusion operations
                 static bool disable_fusion = (getenv("GGML_CUDA_DISABLE_FUSION") != nullptr);
                 if (!disable_fusion) {
 
@@ -3505,13 +3581,17 @@ static void evaluate_and_capture_cuda_graph(ggml_backend_cuda_context * cuda_ctx
                 }
 #else
                 GGML_UNUSED(integrated);
-#endif // NDEBUG
+#endif  // NDEBUG
 
                 bool ok = ggml_cuda_compute_forward(*cuda_ctx, node);
                 if (!ok) {
                     GGML_LOG_ERROR("%s: op not supported %s (%s)\n", __func__, node->name, ggml_op_name(node->op));
                 }
                 GGML_ASSERT(ok);
+
+                if (!is_concurrent_event_active) {
+                    try_launch_concurrent_event(node);
+               }
             }
         }
 
@@ -3651,6 +3731,235 @@ static void ggml_backend_cuda_event_wait(ggml_backend_t backend, ggml_backend_ev
     }
 }
 
+static void ggml_backend_cuda_graph_optimize(ggml_backend_t backend, ggml_cgraph * cgraph) {
+    ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *) backend->context;
+
+    static bool enable_graph_optimization = [] {
+        const char * env = getenv("GGML_CUDA_GRAPH_OPT");
+        return env != nullptr && atoi(env) == 1;
+    }();
+
+    if (!enable_graph_optimization) {
+        return;
+    }
+
+    GGML_ASSERT(ggml_backend_cuda_get_device_count() == 1 && "compute graph optimization is only supported on single GPU in the CUDA backend");
+    GGML_LOG_DEBUG("Optimizing CUDA graph %p with %d nodes\n", cgraph->nodes, cgraph->n_nodes);
+
+    ggml_cuda_stream_context & stream_context = cuda_ctx->stream_context();
+    stream_context.reset();
+
+    // number of out-degrees for a particular node
+    std::unordered_map<const ggml_tensor *, int> fan_out;
+    // reverse mapping of node to index in the cgraph
+    std::unordered_map<const ggml_tensor *, int> node_indices;
+
+    const auto & is_noop = [](const ggml_tensor * node) -> bool {
+        return ggml_is_empty(node) || node->op == GGML_OP_NONE || node->op == GGML_OP_RESHAPE ||
+               node->op == GGML_OP_TRANSPOSE || node->op == GGML_OP_VIEW || node->op == GGML_OP_PERMUTE;
+    };
+
+    const auto & depends_on = [](const ggml_tensor * dst, const ggml_tensor * src) -> bool {
+        for (uint32_t s = 0; s < GGML_MAX_SRC; ++s) {
+            if (dst->src[s] == src) {
+                return true;
+            }
+        }
+        // implicit dependency if they view the same tensor
+        const ggml_tensor * dst2 = dst->view_src ? dst->view_src : dst;
+        const ggml_tensor * src2 = src->view_src ? src->view_src : src;
+        if (dst2 == src2) {
+            return true;
+        }
+        return false;
+    };
+
+    for (int node_idx = 0; node_idx < cgraph->n_nodes; node_idx++) {
+        const ggml_tensor * node = cgraph->nodes[node_idx];
+        node_indices[node]       = node_idx;
+
+        if (is_noop(node)) {
+            continue;
+        }
+        for (int src_idx = 0; src_idx < GGML_MAX_SRC; ++src_idx) {
+            const ggml_tensor * src = cgraph->nodes[node_idx]->src[src_idx];
+            //TODO: check why nrows > 1 fails
+            if (node && !is_noop(node) && ggml_nrows(node) <= 1) {
+                fan_out[src] += 1;
+            }
+        }
+    }
+
+    // Target Q, K, V for concurrency
+    // this is a more general way to find nodes which can be candidates for concurrency (although it has not been tested for anything else):
+    // 1. find fan-out (fork) nodes where the same input is used at least N times (in QKV, it would be "attn-norm")
+    // 2. find the join node, where 2 or more of the outputs are required (in QKV, this would "KQ" or "flash-attn")
+    // 3. account for all branches from the fork to the join
+    // 4. To extend lifetimes of the tensors, we interleave the branches (see below for more details)
+    // 5. save the original cgraph and restore it in graph_compute, to enable fusion within streams
+    // See discussion: https://github.com/ggml-org/llama.cpp/pull/16991#issuecomment-3522620030
+
+    const int min_fan_out = 3;
+    const int max_fan_out = 3;
+
+    // store {fork_idx, join_idx}
+    std::vector<std::pair<int, int>> concurrent_node_ranges;
+
+    // save the original nodes
+    std::vector<const ggml_tensor *> original_nodes;
+    original_nodes.reserve(cgraph->n_nodes);
+    for (int i = 0; i < cgraph->n_nodes; ++i) {
+        original_nodes.push_back(cgraph->nodes[i]);
+    }
+    cuda_ctx->stream_context().original_nodes = std::move(original_nodes);
+
+    for (const auto & [root_node, count] : fan_out) {
+        if (count >= min_fan_out && count <= max_fan_out) {
+            const int root_node_idx = node_indices[root_node];
+
+            bool is_part_of_event = false;
+            for (const auto & [start, end] : concurrent_node_ranges) {
+                if (root_node_idx >= start && root_node_idx <= end) {
+                    is_part_of_event = true;
+                }
+            }
+
+            if (is_part_of_event) {
+                continue;
+            }
+
+            std::vector<std::vector<const ggml_tensor *>> nodes_per_branch;
+            for (int i = root_node_idx + 1; i < cgraph->n_nodes; ++i) {
+                const ggml_tensor * node = cgraph->nodes[i];
+                if (!is_noop(node) && depends_on(node, root_node)) {
+                    nodes_per_branch.push_back({ node });
+                }
+            }
+
+            GGML_ASSERT(nodes_per_branch.size() == (size_t) count);
+
+            //find the join point
+            const ggml_tensor * join_node = nullptr;
+
+            const auto & belongs_to_branch = [&](const ggml_tensor *                      node,
+                                                 const std::vector<const ggml_tensor *> & branch) -> bool {
+                for (const ggml_tensor * n : branch) {
+                    if (depends_on(node, n)) {
+                        return true;
+                    }
+                }
+                return false;
+            };
+
+            for (int i = root_node_idx + 1; i < cgraph->n_nodes; ++i) {
+                const ggml_tensor * curr_node = cgraph->nodes[i];
+
+                int num_joins = 0;
+                for (size_t branch_idx = 0; branch_idx < nodes_per_branch.size(); branch_idx++) {
+                    if (belongs_to_branch(curr_node, nodes_per_branch[branch_idx])) {
+                        num_joins++;
+                    }
+                }
+
+                if (num_joins >= 2) {
+                    join_node = curr_node;
+                    break;
+                }
+
+                bool found_branch = false;
+                for (size_t branch_idx = 0; branch_idx < nodes_per_branch.size(); branch_idx++) {
+                    std::vector<const ggml_tensor *> & branch_vec = nodes_per_branch[branch_idx];
+                    if (belongs_to_branch(curr_node, branch_vec)) {
+                        //continue accumulating
+                        if (std::find(branch_vec.begin(), branch_vec.end(), curr_node) == branch_vec.end()) {
+                            branch_vec.push_back(curr_node);
+                        }
+                        found_branch = true;
+                    }
+                }
+
+                if (!found_branch && is_noop(curr_node)) {
+                    // we can put it in any branch because it will be ignored
+                    nodes_per_branch[0].push_back({ curr_node });
+                }
+            }
+
+            if (join_node) {
+                //Create ggml_cuda_concurrent_event
+                ggml_cuda_concurrent_event concurrent_event(nodes_per_branch.size());
+                concurrent_event.join_node = join_node;
+
+                for (size_t branch_idx = 0; branch_idx < nodes_per_branch.size(); branch_idx++) {
+                    for (const ggml_tensor * n : nodes_per_branch[branch_idx]) {
+                        concurrent_event.stream_mapping[n] = branch_idx + 1;
+                    }
+                }
+
+                int fork_node_idx = node_indices[root_node];
+                int join_node_idx = node_indices[join_node];
+
+                int       current_branch_idx = 0;
+                int       current_node_idx   = fork_node_idx + 1;
+                const int n_branches         = nodes_per_branch.size();
+
+                int total_branch_nodes = 0;
+                for (std::vector<const ggml_tensor *> branch_nodes : nodes_per_branch) {
+                    total_branch_nodes += branch_nodes.size();
+                }
+
+                // there are other nodes in the middle which are unaccounted for
+                // usually (cpy) nodes, then ignore this fork
+                if (join_node_idx - fork_node_idx - 1 != total_branch_nodes) {
+                    GGML_LOG_DEBUG(
+                        "Skipping %s because the number of nodes in the middle is not equal to the total number of "
+                        "branch nodes %d != %d\n",
+                        root_node->name, join_node_idx - fork_node_idx - 1, total_branch_nodes);
+                    continue;
+                }
+
+                std::unordered_map<const ggml_tensor *, ggml_cuda_concurrent_event> & concurrent_events = cuda_ctx->stream_context().concurrent_events;
+                GGML_ASSERT(concurrent_events.find(root_node) == concurrent_events.end());
+                concurrent_events.emplace(root_node, std::move(concurrent_event));
+                GGML_LOG_DEBUG("Adding stream at node %s %p\n", root_node->name, root_node);
+                concurrent_node_ranges.emplace_back(fork_node_idx, join_node_idx);
+
+                // interleave tensors to extend lifetimes so that ggml graph doesn't recycle them
+                // example transformation:
+                // [attn-norm, QMul, QNorm, QRope, KMul, KNorm, KRope, VMul, attn] ->
+                // [attn-norm, QMul, KMul, VMul, QNorm, VNorm, QRope, KRope, attn]
+                while (current_node_idx < join_node_idx) {
+                    std::vector<const ggml_tensor *> & branch_nodes = nodes_per_branch[current_branch_idx];
+
+                    bool has_node = false;
+                    for (std::vector<const ggml_tensor *> branch_node : nodes_per_branch) {
+                        has_node |= branch_node.size() > 0;
+                    }
+
+                    GGML_ASSERT(has_node);
+
+                    if (branch_nodes.empty()) {
+                        current_branch_idx = (current_branch_idx + 1) % n_branches;
+                        continue;
+                    }
+
+                    cgraph->nodes[current_node_idx] = const_cast<ggml_tensor *>(branch_nodes.front());
+                    current_node_idx++;
+                    branch_nodes.erase(branch_nodes.begin());
+
+                    // append all empty nodes
+                    while (!branch_nodes.empty() && is_noop(branch_nodes.front())) {
+                        cgraph->nodes[current_node_idx] = const_cast<ggml_tensor *>(branch_nodes.front());
+                        current_node_idx++;
+                        branch_nodes.erase(branch_nodes.begin());
+                    }
+
+                    current_branch_idx = (current_branch_idx + 1) % n_branches;
+                }
+            }
+        }
+    }
+}
+
 static const ggml_backend_i ggml_backend_cuda_interface = {
     /* .get_name                = */ ggml_backend_cuda_get_name,
     /* .free                    = */ ggml_backend_cuda_free,
@@ -3665,7 +3974,7 @@ static const ggml_backend_i ggml_backend_cuda_interface = {
     /* .graph_compute           = */ ggml_backend_cuda_graph_compute,
     /* .event_record            = */ ggml_backend_cuda_event_record,
     /* .event_wait              = */ ggml_backend_cuda_event_wait,
-    /* .graph_optimize          = */ NULL,
+    /* .graph_optimize          = */ ggml_backend_cuda_graph_optimize,
 };
 
 static ggml_guid_t ggml_backend_cuda_guid() {
@@ -3837,7 +4146,7 @@ static void ggml_backend_cuda_device_get_memory(ggml_backend_dev_t dev, size_t *
 
     // Check if UMA is explicitly enabled via environment variable
     bool uma_env = getenv("GGML_CUDA_ENABLE_UNIFIED_MEMORY") != nullptr;
-    bool is_uma = prop.unifiedAddressing > 0 || uma_env;
+    bool is_uma = prop.integrated > 0 || uma_env;
 
     if (is_uma) {
         // For UMA systems (like DGX Spark), use system memory info
@@ -4255,6 +4564,8 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
         case GGML_OP_OPT_STEP_ADAMW:
         case GGML_OP_OPT_STEP_SGD:
             return true;
+        case GGML_OP_SOLVE_TRI:
+            return op->src[0]->ne[0] <= 64 && op->src[1]->ne[0] <= 32;
         default:
             return false;
     }

ggml/src/ggml-cuda/mma.cuh

@@ -889,8 +889,8 @@ namespace ggml_cuda_mma {
             : "+r"(Dxi[0]), "+r"(Dxi[1]), "+r"(Dxi[2]), "+r"(Dxi[3]), "+r"(Dxi[4]), "+r"(Dxi[5]), "+r"(Dxi[6]), "+r"(Dxi[7])
             : "r"(Axi[6]), "r"(Axi[7]), "r"(Bxi[6]), "r"(Bxi[7]));
 #else
-        tile<16, 8, float> * D16 = (tile<16, 8, float> *) &D;
-        tile<16, 8, half2> * A16 = (tile<16, 8, half2> *) &A;
+        tile      <16, 8, float> * D16 = reinterpret_cast<tile      <16, 8, float> *>(&D);
+        const tile<16, 8, half2> * A16 = reinterpret_cast<const tile<16, 8, half2> *>(&A);
         mma(D16[0], A16[0], B);
         mma(D16[1], A16[1], B);
 #endif // __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE

ggml/src/ggml-cuda/mmf.cu

@@ -151,7 +151,7 @@ bool ggml_cuda_should_use_mmf(enum ggml_type type, int cc, int warp_size, const
             return false;
         }
     } else {
-        if (src1_ncols > 16 || GGML_CUDA_CC_IS_RDNA4(cc)) {
+        if (src1_ncols > 16) {
             return false;
         }
     }

ggml/src/ggml-cuda/solve_tri.cu

@@ -0,0 +1,203 @@
+#include "common.cuh"
+#include "ggml.h"
+#include "solve_tri.cuh"
+
+#define MAX_N_FAST 64
+#define MAX_K_FAST 32
+
+// ======================
+// Fast Kernel (n <= 64, k <= 32) - Warp-based parallel reduction
+// ======================
+// When ncols_template == 0 the bounds for the loops in this function are not
+// known and can't be unrolled. As we want to keep pragma unroll for all other
+// cases we supress the clang transformation warning here.
+#ifdef __clang__
+#    pragma clang diagnostic push
+#    pragma clang diagnostic ignored "-Wpass-failed"
+#endif  // __clang__
+template <int n_template, int k_template>
+static __global__ void solve_tri_f32_fast(const float * __restrict__ A,
+                                          const float * __restrict__ B,
+                                          float * __restrict__ X,
+                                          const uint3  ne02,
+                                          const size_t nb02,
+                                          const size_t nb03,
+                                          const size_t nb12,
+                                          const size_t nb13,
+                                          const size_t nb2,
+                                          const size_t nb3,
+                                          const int    n_arg,
+                                          const int    k_arg) {
+    const int n = n_template == 0 ? n_arg : n_template;
+    const int k = k_template == 0 ? k_arg : k_template;
+
+    const int batch_idx = blockIdx.x;
+    const int lane      = threadIdx.x;
+    const int col_idx   = threadIdx.y;
+
+    if (col_idx >= k) {
+        return;
+    }
+
+    const uint2   i02_i03 = fast_div_modulo(batch_idx, ne02);
+    const int64_t i02     = i02_i03.y;
+    const int64_t i03     = i02_i03.x;
+
+    const float * const A_batch = (const float *) (A + i02 * nb02 + i03 * nb03);
+    const float * const B_batch = (const float *) (B + i02 * nb12 + i03 * nb13);
+    float *             X_batch = (float *) (X + i02 * nb2 + i03 * nb3);
+
+    __shared__ float sA[MAX_N_FAST * MAX_N_FAST];
+    __shared__ float sXt[MAX_N_FAST * (MAX_K_FAST + 1)];
+
+    const int offset = threadIdx.x + threadIdx.y * blockDim.x;
+
+#pragma unroll
+    for (int i = 0; i < n * n; i += k * WARP_SIZE) {
+        int i0 = i + offset;
+        if (i0 < n * n) {
+            sA[i0] = A_batch[i0];
+        }
+    }
+
+    const int rows_per_warp = (n + WARP_SIZE - 1) / WARP_SIZE;
+
+#pragma unroll
+    for (int i = 0; i < rows_per_warp; i++) {
+        const int i0 = lane + i * WARP_SIZE;
+        if (i0 < n) {
+            sXt[col_idx * n + i0] = B_batch[i0 * k + col_idx];
+        }
+    }
+
+    __syncthreads();
+
+#pragma unroll
+    for (int row = 0; row < n; ++row) {
+        float sum = 0.0f;
+
+        {
+            int j = lane;
+            if (j < row) {
+                sum += sA[row * n + j] * sXt[col_idx * n + j];
+            }
+        }
+        if (row >= WARP_SIZE) {
+            int j = WARP_SIZE + lane;
+            if (j < row) {
+                sum += sA[row * n + j] * sXt[col_idx * n + j];
+            }
+        }
+
+        sum = warp_reduce_sum(sum);
+
+        if (lane == 0) {
+            const float b_val      = sXt[col_idx * n + row];
+            const float a_diag     = sA[row * n + row];
+            // no safeguards for division by zero because that indicates corrupt
+            // data anyway
+            sXt[col_idx * n + row] = (b_val - sum) / a_diag;
+        }
+    }
+
+    __syncthreads();
+
+#pragma unroll
+    for (int i = 0; i < rows_per_warp; i++) {
+        const int i0 = lane + i * WARP_SIZE;
+        if (i0 < n) {
+            X_batch[i0 * k + col_idx] = sXt[col_idx * n + i0];
+        }
+    }
+}
+#ifdef __clang__
+#    pragma clang diagnostic pop
+#endif  // __clang__
+
+static void solve_tri_f32_cuda(const float * A,
+                               const float * B,
+                               float *       X,
+                               int           n,
+                               int           k,
+                               int64_t       ne02,
+                               int64_t       ne03,
+                               size_t        nb02,
+                               size_t        nb03,
+                               size_t        nb12,
+                               size_t        nb13,
+                               size_t        nb2,
+                               size_t        nb3,
+                               cudaStream_t  stream) {
+    const uint3 ne02_fd = init_fastdiv_values((uint32_t) ne02);
+    dim3        threads(WARP_SIZE, k);
+    dim3        grid(ne02 * ne03);
+    if (n == 64) {
+        switch (k) {
+            case 32:
+                solve_tri_f32_fast<64, 32>
+                    <<<grid, threads, 0, stream>>>(A, B, X, ne02_fd, nb02, nb03, nb12, nb13, nb2, nb3, 0, 0);
+                break;
+            case 16:
+                solve_tri_f32_fast<64, 16>
+                    <<<grid, threads, 0, stream>>>(A, B, X, ne02_fd, nb02, nb03, nb12, nb13, nb2, nb3, 0, 0);
+                break;
+            case 14:
+                solve_tri_f32_fast<64, 14>
+                    <<<grid, threads, 0, stream>>>(A, B, X, ne02_fd, nb02, nb03, nb12, nb13, nb2, nb3, 0, 0);
+                break;
+            case 12:
+                solve_tri_f32_fast<64, 12>
+                    <<<grid, threads, 0, stream>>>(A, B, X, ne02_fd, nb02, nb03, nb12, nb13, nb2, nb3, 0, 0);
+                break;
+            case 10:
+                solve_tri_f32_fast<64, 10>
+                    <<<grid, threads, 0, stream>>>(A, B, X, ne02_fd, nb02, nb03, nb12, nb13, nb2, nb3, 0, 0);
+                break;
+            case 8:
+                solve_tri_f32_fast<64, 8>
+                    <<<grid, threads, 0, stream>>>(A, B, X, ne02_fd, nb02, nb03, nb12, nb13, nb2, nb3, 0, 0);
+                break;
+            case 6:
+                solve_tri_f32_fast<64, 6>
+                    <<<grid, threads, 0, stream>>>(A, B, X, ne02_fd, nb02, nb03, nb12, nb13, nb2, nb3, 0, 0);
+                break;
+            case 4:
+                solve_tri_f32_fast<64, 4>
+                    <<<grid, threads, 0, stream>>>(A, B, X, ne02_fd, nb02, nb03, nb12, nb13, nb2, nb3, 0, 0);
+                break;
+            case 2:
+                solve_tri_f32_fast<64, 2>
+                    <<<grid, threads, 0, stream>>>(A, B, X, ne02_fd, nb02, nb03, nb12, nb13, nb2, nb3, 0, 0);
+                break;
+            case 1:
+                solve_tri_f32_fast<64, 1>
+                    <<<grid, threads, 0, stream>>>(A, B, X, ne02_fd, nb02, nb03, nb12, nb13, nb2, nb3, 0, 0);
+                break;
+            default:
+                solve_tri_f32_fast<0, 0>
+                    <<<grid, threads, 0, stream>>>(A, B, X, ne02_fd, nb02, nb03, nb12, nb13, nb2, nb3, n, k);
+        }
+    } else {  // run general case
+        solve_tri_f32_fast<0, 0>
+            <<<grid, threads, 0, stream>>>(A, B, X, ne02_fd, nb02, nb03, nb12, nb13, nb2, nb3, n, k);
+    }
+}
+
+void ggml_cuda_op_solve_tri(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];  // A (triangular n x x matrix)
+    const ggml_tensor * src1 = dst->src[1];  // B (right hand side of n x k equation columns)
+
+    ggml_is_contiguous(src0);
+    ggml_is_contiguous(src1);
+
+    const int64_t n = src0->ne[0];
+    const int64_t k = src1->ne[0];
+
+    GGML_ASSERT(n <= 64);
+    GGML_ASSERT(k <= 32);
+
+    solve_tri_f32_cuda((const float *) src0->data, (const float *) src1->data, (float *) dst->data, n, k, src0->ne[2],
+                       src0->ne[3], src0->nb[2] / sizeof(float), src0->nb[3] / sizeof(float),
+                       src1->nb[2] / sizeof(float), src1->nb[3] / sizeof(float), dst->nb[2] / sizeof(float),
+                       dst->nb[3] / sizeof(float), ctx.stream());
+}

ggml/src/ggml-cuda/solve_tri.cuh

@@ -0,0 +1,3 @@
+#include "common.cuh"
+
+void ggml_cuda_op_solve_tri(ggml_backend_cuda_context & ctx, ggml_tensor * dst);

ggml/src/ggml-cuda/upscale.cu

@@ -81,6 +81,76 @@ static __global__ void upscale_f32_bilinear(const float * x, float * dst,
     dst[index] = result;
 }
 
+// Similar to F.interpolate(..., mode="bilinear", align_corners=False, antialias=True)
+// https://github.com/pytorch/pytorch/blob/8871ff29b743948d1225389d5b7068f37b22750b/aten/src/ATen/native/cpu/UpSampleKernel.cpp
+static __global__ void upscale_f32_bilinear_antialias(const float * src0, float * dst,
+        const int nb00, const int nb01, const int nb02, const int nb03,
+        const int ne00_src, const int ne01_src,
+        const int ne10_dst, const int ne11_dst, const int ne12_dst, const int ne13_dst,
+        const float sf0, const float sf1, const float sf2, const float sf3,
+        const float pixel_offset) {
+    const int64_t index              = threadIdx.x + blockIdx.x * blockDim.x;
+    const int64_t dst_total_elements = ne10_dst * ne11_dst * ne12_dst * ne13_dst;
+
+    if (index >= dst_total_elements) {
+        return;
+    }
+
+    const int i10_dst = index % ne10_dst;
+    const int i11_dst = (index / ne10_dst) % ne11_dst;
+    const int i12_dst = (index / (ne10_dst * ne11_dst)) % ne12_dst;
+    const int i13_dst = index / (ne10_dst * ne11_dst * ne12_dst);
+
+    const int i02_src = (int)(i12_dst / sf2);
+    const int i03_src = (int)(i13_dst / sf3);
+
+    const float y = ((float)i11_dst + pixel_offset) / sf1;
+    const float x = ((float)i10_dst + pixel_offset) / sf0;
+
+    // support and invscale, minimum 1 pixel for bilinear
+    const float support1  = max(1.0f / sf1, 1.0f);
+    const float invscale1 = 1.0f / support1;
+    const float support0  = max(1.0f / sf0, 1.0f);
+    const float invscale0 = 1.0f / support0;
+
+    // the range of source pixels that contribute
+    const int64_t x_min = max(int64_t(0), int64_t(x - support0 + pixel_offset));
+    const int64_t x_max = min(int64_t(ne00_src), int64_t(x + support0 + pixel_offset));
+    const int64_t y_min = max(int64_t(0), int64_t(y - support1 + pixel_offset));
+    const int64_t y_max = min(int64_t(ne01_src), int64_t(y + support1 + pixel_offset));
+
+    // bilinear filter with antialiasing
+    float val = 0.0f;
+    float total_weight = 0.0f;
+
+    auto triangle_filter = [](float x) -> float {
+        return max(1.0f - fabsf(x), 0.0f);
+    };
+
+    for (int64_t sy = y_min; sy < y_max; sy++) {
+        const float weight_y = triangle_filter((sy - y + pixel_offset) * invscale1);
+
+        for (int64_t sx = x_min; sx < x_max; sx++) {
+            const float weight_x = triangle_filter((sx - x + pixel_offset) * invscale0);
+            const float weight = weight_x * weight_y;
+
+            if (weight <= 0.0f) {
+                continue;
+            }
+
+            const float pixel = *(const float *)((const char *)src0 + sx*nb00 + sy*nb01 + i02_src*nb02 + i03_src*nb03);
+            val += pixel * weight;
+            total_weight += weight;
+        }
+    }
+
+    if (total_weight > 0.0f) {
+        val /= total_weight;
+    }
+
+    dst[index] = val;
+}
+
 namespace bicubic_interpolation {
 // https://en.wikipedia.org/wiki/Bicubic_interpolation#Bicubic_convolution_algorithm
 __device__ const float a = -0.75f; // use alpha = -0.75 (same as PyTorch)
@@ -161,11 +231,15 @@ static void upscale_f32_bilinear_cuda(const float * x, float * dst,
         const int ne00_src, const int ne01_src,
         const int ne10_dst, const int ne11_dst, const int ne12_dst, const int ne13_dst,
         const float sf0, const float sf1, const float sf2, const float sf3,
-        const float pixel_offset, cudaStream_t stream) {
+        const float pixel_offset, bool antialias, cudaStream_t stream) {
     const int64_t dst_size   = ne10_dst * ne11_dst * ne12_dst * ne13_dst;
     const int64_t num_blocks = (dst_size + CUDA_UPSCALE_BLOCK_SIZE - 1) / CUDA_UPSCALE_BLOCK_SIZE;
 
-    upscale_f32_bilinear<<<num_blocks, CUDA_UPSCALE_BLOCK_SIZE,0,stream>>>(x, dst, nb00, nb01, nb02, nb03, ne00_src, ne01_src, ne10_dst, ne11_dst, ne12_dst, ne13_dst, sf0, sf1, sf2, sf3, pixel_offset);
+    if (antialias) {
+        upscale_f32_bilinear_antialias<<<num_blocks, CUDA_UPSCALE_BLOCK_SIZE,0,stream>>>(x, dst, nb00, nb01, nb02, nb03, ne00_src, ne01_src, ne10_dst, ne11_dst, ne12_dst, ne13_dst, sf0, sf1, sf2, sf3, pixel_offset);
+    } else {
+        upscale_f32_bilinear<<<num_blocks, CUDA_UPSCALE_BLOCK_SIZE,0,stream>>>(x, dst, nb00, nb01, nb02, nb03, ne00_src, ne01_src, ne10_dst, ne11_dst, ne12_dst, ne13_dst, sf0, sf1, sf2, sf3, pixel_offset);
+    }
 }
 
 static void upscale_f32_bicubic_cuda(const float * x, float * dst,
@@ -207,9 +281,10 @@ void ggml_cuda_op_upscale(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
     if (mode == GGML_SCALE_MODE_NEAREST) {
         upscale_f32_cuda(src0_d, dst_d, src0->nb[0], src0->nb[1], src0->nb[2], src0->nb[3], dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3], sf0, sf1, sf2, sf3, stream);
     } else if (mode == GGML_SCALE_MODE_BILINEAR) {
+        const bool antialias = (mode_flags & GGML_SCALE_FLAG_ANTIALIAS);
         upscale_f32_bilinear_cuda(src0_d, dst_d, src0->nb[0], src0->nb[1], src0->nb[2], src0->nb[3],
                                  src0->ne[0], src0->ne[1], dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3],
-                                 sf0, sf1, sf2, sf3, pixel_offset, stream);
+                                 sf0, sf1, sf2, sf3, pixel_offset, antialias, stream);
     } else if (mode == GGML_SCALE_MODE_BICUBIC) {
         upscale_f32_bicubic_cuda(src0_d, dst_d, src0->nb[0], src0->nb[1], src0->nb[2], src0->nb[3],
                                  src0->ne[0], src0->ne[1], dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3],

ggml/src/ggml-cuda/vendors/hip.h

@@ -105,7 +105,7 @@
 #define cudaStreamNonBlocking hipStreamNonBlocking
 #define cudaStreamPerThread hipStreamPerThread
 #define cudaStreamSynchronize hipStreamSynchronize
-#define cudaStreamWaitEvent(stream, event, flags) hipStreamWaitEvent(stream, event, flags)
+#define cudaStreamWaitEvent hipStreamWaitEvent
 #define cudaGraphExec_t hipGraphExec_t
 #define cudaGraphNode_t hipGraphNode_t
 #define cudaKernelNodeParams hipKernelNodeParams

ggml/src/ggml-metal/ggml-metal-device.m

@@ -894,7 +894,7 @@ bool ggml_metal_device_supports_op(ggml_metal_device_t dev, const struct ggml_te
         case GGML_OP_POOL_1D:
             return false;
         case GGML_OP_UPSCALE:
-            return op->src[0]->type == GGML_TYPE_F32 && op->op_params[0] == GGML_SCALE_MODE_NEAREST;
+            return op->src[0]->type == GGML_TYPE_F32 && op->op_params[0] == GGML_SCALE_MODE_NEAREST && !(op->op_params[0] & GGML_SCALE_FLAG_ANTIALIAS);
         case GGML_OP_POOL_2D:
             return op->src[0]->type == GGML_TYPE_F32;
         case GGML_OP_PAD:

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

@@ -3086,8 +3086,9 @@ static bool ggml_opencl_supports_op(ggml_backend_dev_t dev, const struct ggml_te
             return op->src[0]->type == GGML_TYPE_F32 && op->type == GGML_TYPE_F32;
         case GGML_OP_UPSCALE: {
             ggml_scale_mode mode = (ggml_scale_mode)(ggml_get_op_params_i32(op, 0) & 0xFF);
+            const bool antialias = (ggml_scale_mode)(ggml_get_op_params_i32(op, 0) & GGML_SCALE_FLAG_ANTIALIAS);
             return op->src[0]->type == GGML_TYPE_F32 && op->type == GGML_TYPE_F32 &&
-                   (mode == GGML_SCALE_MODE_NEAREST || mode == GGML_SCALE_MODE_BILINEAR);
+                   (mode == GGML_SCALE_MODE_NEAREST || mode == GGML_SCALE_MODE_BILINEAR) && !antialias;
         }
         case GGML_OP_CONV_2D:
             return (op->src[0]->type == GGML_TYPE_F16 && op->src[1]->type == GGML_TYPE_F16 && op->type == GGML_TYPE_F16) ||

ggml/src/ggml-rpc/ggml-rpc.cpp

@@ -106,6 +106,7 @@ enum rpc_cmd {
     RPC_CMD_GET_ALLOC_SIZE,
     RPC_CMD_HELLO,
     RPC_CMD_DEVICE_COUNT,
+    RPC_CMD_GRAPH_RECOMPUTE,
     RPC_CMD_COUNT,
 };
 
@@ -205,10 +206,6 @@ struct rpc_msg_copy_tensor_rsp {
     uint8_t result;
 };
 
-struct rpc_msg_graph_compute_rsp {
-    uint8_t result;
-};
-
 struct rpc_msg_get_device_memory_req {
     uint32_t device;
 };
@@ -217,6 +214,11 @@ struct rpc_msg_get_device_memory_rsp {
     uint64_t free_mem;
     uint64_t total_mem;
 };
+
+struct rpc_msg_graph_recompute_req {
+    uint32_t device;
+};
+
 #pragma pack(pop)
 
 // RPC data structures
@@ -234,10 +236,35 @@ struct ggml_backend_rpc_buffer_type_context {
     size_t      max_size;
 };
 
+struct graph_cache {
+
+    bool is_cached(const ggml_cgraph * cgraph) {
+        if ((int)last_graph.size() != cgraph->n_nodes) {
+            return false;
+        }
+        for (int i = 0; i < cgraph->n_nodes; i++) {
+            if (memcmp(&last_graph[i], cgraph->nodes[i], sizeof(ggml_tensor)) != 0) {
+                return false;
+            }
+        }
+        return true;
+    }
+
+    void add(const ggml_cgraph * cgraph) {
+        last_graph.resize(cgraph->n_nodes);
+        for (int i = 0; i < cgraph->n_nodes; i++) {
+            memcpy(&last_graph[i], cgraph->nodes[i], sizeof(ggml_tensor));
+        }
+    }
+
+    std::vector<ggml_tensor> last_graph;
+};
+
 struct ggml_backend_rpc_context {
     std::string endpoint;
     uint32_t    device;
     std::string name;
+    graph_cache gc;
 };
 
 struct ggml_backend_rpc_buffer_context {
@@ -815,13 +842,24 @@ static void serialize_graph(uint32_t device, const ggml_cgraph * cgraph, std::ve
 
 static enum ggml_status ggml_backend_rpc_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) {
     ggml_backend_rpc_context * rpc_ctx = (ggml_backend_rpc_context *)backend->context;
-    std::vector<uint8_t> input;
-    serialize_graph(rpc_ctx->device, cgraph, input);
-    rpc_msg_graph_compute_rsp response;
-    auto sock = get_socket(rpc_ctx->endpoint);
-    bool status = send_rpc_cmd(sock, RPC_CMD_GRAPH_COMPUTE, input.data(), input.size(), &response, sizeof(response));
-    RPC_STATUS_ASSERT(status);
-    return (enum ggml_status)response.result;
+
+    GGML_ASSERT(cgraph->n_nodes > 0);
+    bool reuse = rpc_ctx->gc.is_cached(cgraph);
+    if (reuse) {
+        rpc_msg_graph_recompute_req request;
+        request.device = rpc_ctx->device;
+        auto sock = get_socket(rpc_ctx->endpoint);
+        bool status = send_rpc_cmd(sock, RPC_CMD_GRAPH_RECOMPUTE, &request, sizeof(request));
+        RPC_STATUS_ASSERT(status);
+    } else {
+        rpc_ctx->gc.add(cgraph);
+        std::vector<uint8_t> input;
+        serialize_graph(rpc_ctx->device, cgraph, input);
+        auto sock = get_socket(rpc_ctx->endpoint);
+        bool status = send_rpc_cmd(sock, RPC_CMD_GRAPH_COMPUTE, input.data(), input.size());
+        RPC_STATUS_ASSERT(status);
+    }
+    return GGML_STATUS_SUCCESS;
 }
 
 static ggml_backend_i ggml_backend_rpc_interface = {
@@ -880,7 +918,8 @@ ggml_backend_t ggml_backend_rpc_init(const char * endpoint, uint32_t device) {
     ggml_backend_rpc_context * ctx = new ggml_backend_rpc_context {
         /* .endpoint = */ endpoint,
         /* .device   = */ device,
-        /* .name     = */ dev_name
+        /* .name     = */ dev_name,
+        /* .gc       = */ {},
     };
     auto reg = ggml_backend_rpc_add_server(endpoint);
     ggml_backend_t backend = new ggml_backend {
@@ -920,8 +959,9 @@ void ggml_backend_rpc_get_device_memory(const char * endpoint, uint32_t device,
 
 class rpc_server {
 public:
-    rpc_server(std::vector<ggml_backend_t> backends, const char * cache_dir)
-        : backends(std::move(backends)), cache_dir(cache_dir) {
+    rpc_server(std::vector<ggml_backend_t> all_backends, const char * cache_dir)
+        : backends(std::move(all_backends)), cache_dir(cache_dir) {
+        stored_graphs.resize(backends.size());
     }
     ~rpc_server();
 
@@ -936,11 +976,17 @@ public:
     bool set_tensor_hash(const rpc_msg_set_tensor_hash_req & request, rpc_msg_set_tensor_hash_rsp & response);
     bool get_tensor(const rpc_msg_get_tensor_req & request, std::vector<uint8_t> & response);
     bool copy_tensor(const rpc_msg_copy_tensor_req & request, rpc_msg_copy_tensor_rsp & response);
-    bool graph_compute(const std::vector<uint8_t> & input, rpc_msg_graph_compute_rsp & response);
+    bool graph_compute(const std::vector<uint8_t> & input);
+    bool graph_recompute(const rpc_msg_graph_recompute_req & request);
     bool init_tensor(const rpc_msg_init_tensor_req & request);
     bool get_alloc_size(const rpc_msg_get_alloc_size_req & request, rpc_msg_get_alloc_size_rsp & response);
     bool get_device_memory(const rpc_msg_get_device_memory_req & request, rpc_msg_get_device_memory_rsp & response);
 
+    struct stored_graph {
+        ggml_context_ptr ctx_ptr;
+        ggml_cgraph *    graph;
+    };
+
 private:
     bool get_cached_file(uint64_t hash, std::vector<uint8_t> & data);
     ggml_tensor * deserialize_tensor(struct ggml_context * ctx, const rpc_tensor * tensor);
@@ -953,6 +999,8 @@ private:
     std::vector<ggml_backend_t> backends;
     const char * cache_dir;
     std::unordered_set<ggml_backend_buffer_t> buffers;
+    // store the last computed graph for each backend
+    std::vector<stored_graph> stored_graphs;
 };
 
 void rpc_server::hello(rpc_msg_hello_rsp & response) {
@@ -1394,7 +1442,7 @@ ggml_tensor * rpc_server::create_node(uint64_t id,
     return result;
 }
 
-bool rpc_server::graph_compute(const std::vector<uint8_t> & input, rpc_msg_graph_compute_rsp & response) {
+bool rpc_server::graph_compute(const std::vector<uint8_t> & input) {
     // serialization format:
     // | device (4 bytes) | n_nodes (4 bytes) | nodes (n_nodes * sizeof(uint64_t) | n_tensors (4 bytes) | tensors (n_tensors * sizeof(rpc_tensor)) |
     if (input.size() < 2*sizeof(uint32_t)) {
@@ -1455,7 +1503,24 @@ bool rpc_server::graph_compute(const std::vector<uint8_t> & input, rpc_msg_graph
         }
     }
     ggml_status status = ggml_backend_graph_compute(backends[device], graph);
-    response.result = status;
+    GGML_ASSERT(status == GGML_STATUS_SUCCESS && "Unsuccessful graph computations are not supported with RPC");
+    stored_graphs[device].ctx_ptr.swap(ctx_ptr);
+    stored_graphs[device].graph = graph;
+    return true;
+}
+
+bool rpc_server::graph_recompute(const rpc_msg_graph_recompute_req & request) {
+    uint32_t device = request.device;
+    if (device >= backends.size()) {
+        return false;
+    }
+    if (stored_graphs[device].graph == nullptr) {
+        return false;
+    }
+    ggml_cgraph * graph = stored_graphs[device].graph;
+    LOG_DBG("[%s] device: %u\n", __func__, device);
+    ggml_status status = ggml_backend_graph_compute(backends[device], graph);
+    GGML_ASSERT(status == GGML_STATUS_SUCCESS && "Unsuccessful graph computations are not supported with RPC");
     return true;
 }
 
@@ -1690,11 +1755,17 @@ static void rpc_serve_client(const std::vector<ggml_backend_t> & backends, const
                 if (!recv_msg(sockfd, input)) {
                     return;
                 }
-                rpc_msg_graph_compute_rsp response;
-                if (!server.graph_compute(input, response)) {
+                if (!server.graph_compute(input)) {
                     return;
                 }
-                if (!send_msg(sockfd, &response, sizeof(response))) {
+                break;
+            }
+            case RPC_CMD_GRAPH_RECOMPUTE: {
+                rpc_msg_graph_recompute_req request;
+                if (!recv_msg(sockfd, &request, sizeof(request))) {
+                    return;
+                }
+                if (!server.graph_recompute(request)) {
                     return;
                 }
                 break;

ggml/src/ggml-sycl/CMakeLists.txt

@@ -91,7 +91,10 @@ if (GGML_SYCL_F16)
     add_compile_definitions(GGML_SYCL_F16)
 endif()
 
-if (GGML_SYCL_TARGET STREQUAL "NVIDIA")
+if (GGML_SYCL_TARGET STREQUAL "INTEL")
+    add_compile_definitions(GGML_SYCL_WARP_SIZE=16)
+    target_link_options(ggml-sycl PRIVATE  -Xs   -ze-intel-greater-than-4GB-buffer-required)
+elseif (GGML_SYCL_TARGET STREQUAL "NVIDIA")
     add_compile_definitions(GGML_SYCL_WARP_SIZE=32)
 elseif (GGML_SYCL_TARGET STREQUAL "AMD")
     # INFO: Allowed Sub_group_sizes are not consistent through all
@@ -100,7 +103,8 @@ elseif (GGML_SYCL_TARGET STREQUAL "AMD")
     # Target archs tested working: gfx1030, gfx1031, (Only tested sub_group_size = 32)
     add_compile_definitions(GGML_SYCL_WARP_SIZE=32)
 else()
-    add_compile_definitions(GGML_SYCL_WARP_SIZE=16)
+    # default for other target
+    add_compile_definitions(GGML_SYCL_WARP_SIZE=32)
 endif()
 
 if (GGML_SYCL_GRAPH)

ggml/src/ggml-sycl/common.hpp

@@ -617,4 +617,30 @@ static __dpct_inline__ float get_alibi_slope(const float    max_bias,
     return dpct::pow(base, exph);
 }
 
+static const sycl::uint3 init_fastdiv_values(uint32_t d) {
+    GGML_ASSERT(d != 0);
+
+    uint32_t L = 0;
+    while (L < 32 && (uint32_t{ 1 } << L) < d) {
+        L++;
+    }
+
+    uint32_t mp = (uint32_t) ((uint64_t{ 1 } << 32) * ((uint64_t{ 1 } << L) - d) / d + 1);
+    return sycl::uint3(mp, L, d);
+}
+
+
+static __dpct_inline__ uint32_t fastdiv(uint32_t n, const sycl::uint3 fastdiv_values) {
+    const uint32_t hi = sycl::mul_hi<unsigned>(n, fastdiv_values.x());
+    return (hi + n) >> fastdiv_values.y();
+}
+
+
+static __dpct_inline__ sycl::uint2 fast_div_modulo(uint32_t n, const sycl::uint3 fastdiv_values) {
+    const uint32_t div_val = fastdiv(n, fastdiv_values);
+    const uint32_t mod_val = n - div_val * fastdiv_values.z();
+    return sycl::uint2(div_val, mod_val);
+}
+
+
 #endif // GGML_SYCL_COMMON_HPP

ggml/src/ggml-sycl/cpy.cpp

@@ -515,9 +515,6 @@ void ggml_sycl_cpy(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, co
     const int64_t ne = ggml_nelements(src0);
     GGML_ASSERT(ne == ggml_nelements(src1));
 
-    GGML_ASSERT(ggml_nbytes(src0) <= INT_MAX);
-    GGML_ASSERT(ggml_nbytes(src1) <= INT_MAX);
-
     GGML_TENSOR_BINARY_OP_LOCALS01;
 
     SYCL_CHECK(ggml_sycl_set_device(ctx.device));

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

@@ -4597,7 +4597,7 @@ static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const g
         case GGML_OP_IM2COL:
             return true;
         case GGML_OP_UPSCALE:
-            return op->src[0]->type == GGML_TYPE_F32 && op->op_params[0] == GGML_SCALE_MODE_NEAREST;
+            return op->src[0]->type == GGML_TYPE_F32 && op->op_params[0] == GGML_SCALE_MODE_NEAREST && !(op->op_params[0] & GGML_SCALE_FLAG_ANTIALIAS);
         case GGML_OP_SUM:
         case GGML_OP_SUM_ROWS:
         case GGML_OP_MEAN:

ggml/src/ggml-sycl/pad_reflect_1d.cpp

@@ -1,72 +1,100 @@
 #include "pad_reflect_1d.hpp"
 
-void pad_reflect_1d_f32(const float* src,float* dst,
-    const int64_t ne0, const int64_t ne02, const int p0, const int p1,
-    const int64_t nb0, const int64_t nb1, const int64_t nb2, const int64_t nb3,
-    const int64_t nb00, const int64_t nb01, const int64_t nb02, const int64_t nb03,
-    const sycl::nd_item<3> &item_ct1){
+static void pad_reflect_1d_kernel_f32(
+    const void *__restrict__ src0, void *__restrict__ dst, const int64_t ne0,
+    const int64_t ne00, const sycl::uint3 ne01, const int64_t ne02,
+    const int64_t ne03, const int64_t nb00, const int64_t nb01,
+    const int64_t nb02, const int64_t nb03, const int64_t nb0,
+    const int64_t nb1, const int64_t nb2, const int64_t nb3, const int p0,
+    const int p1, sycl::nd_item<3> item_ct1) {
 
-    const int i0 = item_ct1.get_group(0) * SYCL_CONCAT_BLOCK_SIZE + item_ct1.get_local_id(0);
-    const int i1 = item_ct1.get_group(1);
-    const int g2 = item_ct1.get_group(2);
-    const int i2 = g2 % ne02;
-    const int i3 = g2 / ne02;
+    const int64_t i3 = item_ct1.get_group(0);
+    const int64_t i2 = item_ct1.get_group(1);
 
-    if (i0 >= p0 + ne0 + p1) return;
+    const sycl::uint2 div_mod_packed =
+        fast_div_modulo(item_ct1.get_group(2), ne01);
+    const int64_t tile1 = div_mod_packed.y();
+    const int64_t tile0 = div_mod_packed.x();
+    const int64_t i1 = tile1;
+    const int64_t i0 =
+        item_ct1.get_local_id(2) + tile0 * item_ct1.get_local_range(2);
 
-    int t = i0 - p0;
-    int period = 2 * ne0 -2;
-    int m = t % period;
-    m += (m < 0) * period;
-    int center = ne0 -1;
-    int srci0 = center - abs(center - m);
+    if (i0 >= ne0 || i1 >= ne01.z() || i2 >= ne02 || i3 >= ne03) {
+        return;
+    }
 
-    int offest_src = i3*nb3 + i2*nb2 + i1*nb1 + srci0*nb0;
-    int offest_dst =  i3*nb03 +  i2*nb02 +  i1*nb01 +  i0*nb00;
-    dst[offest_dst] = src[offest_src];
+    const char *src0_ptr =
+        (const char *)src0 + i3 * nb03 + i2 * nb02 + i1 * nb01;
+    char *dst_ptr = (char *)dst + i3 * nb3 + i2 * nb2 + i1 * nb1;
 
+    const int64_t rel_i0 = i0 - p0; // relative i0 in src0
+    int64_t src_idx;
+
+    if (rel_i0 < 0) {
+        // Left padding - reflect
+        src_idx = -rel_i0;
+    } else if (rel_i0 < ne00) {
+        // Middle - copy
+        src_idx = rel_i0;
+    } else {
+        // Right padding - reflect
+        src_idx = 2 * ne00 - 2 - rel_i0;
+    }
+    const float value = *(const float *)(src0_ptr + src_idx * nb00);
+    *(float *)(dst_ptr + i0 * nb0) = value;
+
+    GGML_UNUSED(p1);
 }
 
-void ggml_sycl_op_pad_reflect_1d(ggml_backend_sycl_context& ctx, ggml_tensor* dst){
+void ggml_sycl_op_pad_reflect_1d(ggml_backend_sycl_context &ctx,
+                                 ggml_tensor *dst) {
 
-    const ggml_tensor * src0 = dst->src[0];
-    queue_ptr           stream = ctx.stream();
+    const ggml_tensor *src0 = dst->src[0];
+    dpct::queue_ptr stream = ctx.stream();
 
     GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+    GGML_ASSERT(dst->type == GGML_TYPE_F32);
 
-    const int32_t * opts = (const int32_t *) dst->op_params;
+    const int32_t *opts = (const int32_t *)dst->op_params;
     const int p0 = opts[0];
     const int p1 = opts[1];
 
-    const int64_t ne0 = src0->ne[0];
+    const int64_t ne00 = src0->ne[0];
+    const int64_t ne01 = src0->ne[1];
+    const sycl::uint3 ne01_packed = init_fastdiv_values(ne01);
+    const int64_t ne02 = src0->ne[2];
+    const int64_t ne03 = src0->ne[3];
 
-    const int64_t ne00 = dst->ne[0];
-    const int64_t ne01 = dst->ne[1];
-    const int64_t ne02 = dst->ne[2];
-    const int64_t ne03 = dst->ne[3];
+    const int64_t ne0 = dst->ne[0];
 
-    const int64_t nb00 = dst->nb[0];
-    const int64_t nb01 = dst->nb[1];
-    const int64_t nb02 = dst->nb[2];
-    const int64_t nb03 = dst->nb[3];
-    const int64_t nb0 = src0->nb[0];
-    const int64_t nb1 = src0->nb[1];
-    const int64_t nb2 = src0->nb[2];
-    const int64_t nb3 = src0->nb[3];
+    GGML_ASSERT(ne0 == ne00 + p0 + p1);
 
-    int num_blocks = (ne00 + SYCL_CONCAT_BLOCK_SIZE - 1) / SYCL_CONCAT_BLOCK_SIZE;
-    sycl::range<3> global(num_blocks * SYCL_CONCAT_BLOCK_SIZE, ne01, ne02*ne03);
-    sycl::range<3> local(SYCL_CONCAT_BLOCK_SIZE, 1, 1);
+    constexpr int64_t bx = SYCL_PAD_REFLECT_1D_BLOCK_SIZE;
+    const int64_t tiles0 = (ne0 + bx - 1) / bx;
+    const dpct::dim3 grid_dims((unsigned)(ne01 * tiles0), (unsigned)ne02,
+                               (unsigned)ne03);
+    const dpct::dim3 block_dims((unsigned)bx, 1, 1);
 
-    stream->parallel_for(
-        sycl::nd_range<3>(global,
-                            local),
-        [=](sycl::nd_item<3> item_ct1) { pad_reflect_1d_f32(
-            (const float *) src0->data, (float *) dst->data,
-            ne0, ne02, p0, p1,
-            nb0, nb1, nb2, nb3,
-            nb00, nb01, nb02, nb03
-            , item_ct1);
-         });
+    stream->submit([&](sycl::handler &cgh) {
+        auto src0_data_ct0 = src0->data;
+        auto dst_data_ct1 = dst->data;
+        auto src0_nb_ct7 = src0->nb[0];
+        auto src0_nb_ct8 = src0->nb[1];
+        auto src0_nb_ct9 = src0->nb[2];
+        auto src0_nb_ct10 = src0->nb[3];
+        auto dst_nb_ct11 = dst->nb[0];
+        auto dst_nb_ct12 = dst->nb[1];
+        auto dst_nb_ct13 = dst->nb[2];
+        auto dst_nb_ct14 = dst->nb[3];
+
+        cgh.parallel_for(sycl::nd_range<3>(grid_dims * block_dims, block_dims),
+                         [=](sycl::nd_item<3> item_ct1) {
+                             pad_reflect_1d_kernel_f32(
+                                 src0_data_ct0, dst_data_ct1, ne0, ne00,
+                                 ne01_packed, ne02, ne03, src0_nb_ct7,
+                                 src0_nb_ct8, src0_nb_ct9, src0_nb_ct10,
+                                 dst_nb_ct11, dst_nb_ct12, dst_nb_ct13,
+                                 dst_nb_ct14, p0, p1, item_ct1);
+                         });
+    });
 }

ggml/src/ggml-sycl/pad_reflect_1d.hpp

@@ -3,6 +3,8 @@
 
 #include "common.hpp"
 
+#define SYCL_PAD_REFLECT_1D_BLOCK_SIZE 256
+
 void ggml_sycl_op_pad_reflect_1d(ggml_backend_sycl_context& ctx, ggml_tensor* dst);
 
 #endif // GGML_SYCL_PAD_REFLECT_1D_HPP

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

@@ -399,6 +399,18 @@ struct vk_conv2d_pipeline_state {
     }
 };
 
+struct vk_solve_tri_pipeline_state {
+    vk_solve_tri_pipeline_state(uint32_t N, uint32_t K)
+        : N(N), K(K) {}
+
+    uint32_t N, K;
+
+    bool operator<(const vk_solve_tri_pipeline_state &b) const {
+        return std::tie(N, K) <
+               std::tie(b.N, b.K);
+    }
+};
+
 enum shader_reduction_mode {
     SHADER_REDUCTION_MODE_SHMEM,
     SHADER_REDUCTION_MODE_HYBRID,
@@ -601,9 +613,10 @@ struct vk_device_struct {
     vk_pipeline pipeline_dequant[GGML_TYPE_COUNT];
     vk_pipeline pipeline_dequant_mul_mat_vec_f32_f32[DMMV_WG_SIZE_COUNT][GGML_TYPE_COUNT][mul_mat_vec_max_cols];
     vk_pipeline pipeline_dequant_mul_mat_vec_f16_f32[DMMV_WG_SIZE_COUNT][GGML_TYPE_COUNT][mul_mat_vec_max_cols];
-    vk_pipeline pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_COUNT];
+    vk_pipeline pipeline_dequant_mul_mat_vec_id_f32[DMMV_WG_SIZE_COUNT][GGML_TYPE_COUNT];
 
     vk_pipeline pipeline_dequant_mul_mat_vec_q8_1_f32[DMMV_WG_SIZE_COUNT][GGML_TYPE_COUNT][mul_mat_vec_max_cols];
+    vk_pipeline pipeline_dequant_mul_mat_vec_id_q8_1_f32[DMMV_WG_SIZE_COUNT][GGML_TYPE_COUNT];
 
     vk_pipeline pipeline_mul_mat_vec_p021_f16_f32[p021_max_gqa_ratio];
     vk_pipeline pipeline_mul_mat_vec_nc_f16_f32;
@@ -637,6 +650,7 @@ struct vk_device_struct {
     vk_pipeline pipeline_sin_f32;
     vk_pipeline pipeline_cos_f32;
     vk_pipeline pipeline_log[2];
+    vk_pipeline pipeline_tri[2];
     vk_pipeline pipeline_clamp_f32;
     vk_pipeline pipeline_pad_f32;
     vk_pipeline pipeline_roll_f32;
@@ -711,6 +725,7 @@ struct vk_device_struct {
     vk_pipeline pipeline_cumsum_f32;
     vk_pipeline pipeline_argmax_f32;
     vk_pipeline pipeline_count_equal_i32;
+    std::map<vk_solve_tri_pipeline_state, vk_pipeline> pipeline_solve_tri_f32;
     vk_pipeline pipeline_im2col_f32, pipeline_im2col_f32_f16;
     vk_pipeline pipeline_im2col_3d_f32, pipeline_im2col_3d_f32_f16;
     vk_pipeline pipeline_timestep_embedding_f32;
@@ -1597,7 +1612,7 @@ class vk_perf_logger {
         }
         if (node->op == GGML_OP_MUL_MAT || node->op == GGML_OP_MUL_MAT_ID) {
             const uint64_t m     = node->src[0]->ne[1];
-            const uint64_t n     = node->ne[1];
+            const uint64_t n     = (node->op == GGML_OP_MUL_MAT) ? node->ne[1] : node->ne[2];
             const uint64_t k     = node->src[1]->ne[0];
             const uint64_t batch = node->src[1]->ne[2] * node->src[1]->ne[3];
             std::string    name  = ggml_op_name(node->op);
@@ -3511,13 +3526,18 @@ static void ggml_vk_load_shaders(vk_device& device) {
     // the number of rows computed per shader depends on GPU model and quant
     uint32_t rm_stdq = 1;
     uint32_t rm_kq = 2;
+    uint32_t rm_stdq_int = 1;
+    uint32_t rm_kq_int = 1;
     if (device->vendor_id == VK_VENDOR_ID_AMD) {
         if (device->architecture == AMD_GCN) {
             rm_stdq = 2;
             rm_kq = 4;
+            rm_stdq_int = 4;
         }
-    } else if (device->vendor_id == VK_VENDOR_ID_INTEL)
+    } else if (device->vendor_id == VK_VENDOR_ID_INTEL) {
         rm_stdq = 2;
+        rm_stdq_int = 2;
+    }
     uint32_t rm_iq = 2 * rm_kq;
 
     const bool use_subgroups = device->subgroup_arithmetic && device->architecture != vk_device_architecture::AMD_GCN;
@@ -3598,39 +3618,73 @@ static void ggml_vk_load_shaders(vk_device& device) {
                 const uint32_t subgroup_size_int = (device->vendor_id == VK_VENDOR_ID_INTEL && device->subgroup_size_control) ? device->subgroup_min_size : device->subgroup_size;
                 const uint32_t wg_size_subgroup_int = (w == DMMV_WG_SIZE_SUBGROUP) ? subgroup_size_int : (subgroup_size_int * 4);
 
-                ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_q8_1_f32[w][GGML_TYPE_Q4_0][i], "mul_mat_vec_q4_0_q8_1_f32", arr_dmmv_q4_0_q8_1_f32_len[reduc], arr_dmmv_q4_0_q8_1_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup_int, 2*rm_stdq, i+1}, 1, true, use_subgroups, subgroup_size_int);
-                ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_q8_1_f32[w][GGML_TYPE_Q4_1][i], "mul_mat_vec_q4_1_q8_1_f32", arr_dmmv_q4_1_q8_1_f32_len[reduc], arr_dmmv_q4_1_q8_1_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup_int, 2*rm_stdq, i+1}, 1, true, use_subgroups, subgroup_size_int);
-                ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_q8_1_f32[w][GGML_TYPE_Q5_0][i], "mul_mat_vec_q5_0_q8_1_f32", arr_dmmv_q5_0_q8_1_f32_len[reduc], arr_dmmv_q5_0_q8_1_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup_int, 2*rm_stdq, i+1}, 1, true, use_subgroups, subgroup_size_int);
-                ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_q8_1_f32[w][GGML_TYPE_Q5_1][i], "mul_mat_vec_q5_1_q8_1_f32", arr_dmmv_q5_1_q8_1_f32_len[reduc], arr_dmmv_q5_1_q8_1_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup_int, 2*rm_stdq, i+1}, 1, true, use_subgroups, subgroup_size_int);
-                ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_q8_1_f32[w][GGML_TYPE_Q8_0][i], "mul_mat_vec_q8_0_q8_1_f32", arr_dmmv_q8_0_q8_1_f32_len[reduc], arr_dmmv_q8_0_q8_1_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {1*rm_stdq, 1, 1}, {wg_size_subgroup_int, 1*rm_stdq, i+1}, 1, true, use_subgroups, subgroup_size_int);
+                ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_q8_1_f32[w][GGML_TYPE_Q4_0][i], "mul_mat_vec_q4_0_q8_1_f32", arr_dmmv_q4_0_q8_1_f32_len[reduc], arr_dmmv_q4_0_q8_1_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {1*rm_stdq_int, 1, 1}, {wg_size_subgroup_int, 1*rm_stdq_int, i+1}, 1, true, use_subgroups, subgroup_size_int);
+                ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_q8_1_f32[w][GGML_TYPE_Q4_1][i], "mul_mat_vec_q4_1_q8_1_f32", arr_dmmv_q4_1_q8_1_f32_len[reduc], arr_dmmv_q4_1_q8_1_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {1*rm_stdq_int, 1, 1}, {wg_size_subgroup_int, 1*rm_stdq_int, i+1}, 1, true, use_subgroups, subgroup_size_int);
+                ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_q8_1_f32[w][GGML_TYPE_Q5_0][i], "mul_mat_vec_q5_0_q8_1_f32", arr_dmmv_q5_0_q8_1_f32_len[reduc], arr_dmmv_q5_0_q8_1_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {1*rm_stdq_int, 1, 1}, {wg_size_subgroup_int, 1*rm_stdq_int, i+1}, 1, true, use_subgroups, subgroup_size_int);
+                ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_q8_1_f32[w][GGML_TYPE_Q5_1][i], "mul_mat_vec_q5_1_q8_1_f32", arr_dmmv_q5_1_q8_1_f32_len[reduc], arr_dmmv_q5_1_q8_1_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {1*rm_stdq_int, 1, 1}, {wg_size_subgroup_int, 1*rm_stdq_int, i+1}, 1, true, use_subgroups, subgroup_size_int);
+                ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_q8_1_f32[w][GGML_TYPE_Q8_0][i], "mul_mat_vec_q8_0_q8_1_f32", arr_dmmv_q8_0_q8_1_f32_len[reduc], arr_dmmv_q8_0_q8_1_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {1*rm_stdq_int, 1, 1}, {wg_size_subgroup_int, 1*rm_stdq_int, i+1}, 1, true, use_subgroups, subgroup_size_int);
+
+                ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_q8_1_f32[w][GGML_TYPE_MXFP4][i], "mul_mat_vec_mxfp4_q8_1_f32", arr_dmmv_mxfp4_q8_1_f32_len[reduc], arr_dmmv_mxfp4_q8_1_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {2*rm_stdq_int, 1, 1}, {wg_size_subgroup_int, 2*rm_stdq_int, i+1}, 1, true, use_subgroups, subgroup_size_int);
+
+                ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_q8_1_f32[w][GGML_TYPE_Q2_K][i], "mul_mat_vec_q2_k_q8_1_f32", arr_dmmv_q2_k_q8_1_f32_len[reduc], arr_dmmv_q2_k_q8_1_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {2*rm_kq_int, 1, 1}, {wg_size_subgroup_int, 2*rm_kq_int, i+1}, 1, true, use_subgroups, subgroup_size_int);
+                ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_q8_1_f32[w][GGML_TYPE_Q3_K][i], "mul_mat_vec_q3_k_q8_1_f32", arr_dmmv_q3_k_q8_1_f32_len[reduc], arr_dmmv_q3_k_q8_1_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {1*rm_kq_int, 1, 1}, {wg_size_subgroup_int, 1*rm_kq_int, i+1}, 1, true, use_subgroups, subgroup_size_int);
+                ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_q8_1_f32[w][GGML_TYPE_Q4_K][i], "mul_mat_vec_q4_k_q8_1_f32", arr_dmmv_q4_k_q8_1_f32_len[reduc], arr_dmmv_q4_k_q8_1_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {1*rm_kq_int, 1, 1}, {wg_size_subgroup_int, 1*rm_kq_int, i+1}, 1, true, use_subgroups, subgroup_size_int);
+                ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_q8_1_f32[w][GGML_TYPE_Q5_K][i], "mul_mat_vec_q5_k_q8_1_f32", arr_dmmv_q5_k_q8_1_f32_len[reduc], arr_dmmv_q5_k_q8_1_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {1*rm_kq_int, 1, 1}, {wg_size_subgroup_int, 1*rm_kq_int, i+1}, 1, true, use_subgroups, subgroup_size_int);
+                ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_q8_1_f32[w][GGML_TYPE_Q6_K][i], "mul_mat_vec_q6_k_q8_1_f32", arr_dmmv_q6_k_q8_1_f32_len[reduc], arr_dmmv_q6_k_q8_1_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {1*rm_kq_int, 1, 1}, {wg_size_subgroup_int, 1*rm_kq_int, i+1}, 1, true, use_subgroups, subgroup_size_int);
             }
 #endif // GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT
         }
+
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_F32 ], "mul_mat_vec_id_f32_f32",        arr_dmmv_id_f32_f32_f32_len[reduc],     arr_dmmv_id_f32_f32_f32_data[reduc],     "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {2, 1, 1}, {wg_size_subgroup, 2}, 1, false, use_subgroups, force_subgroup_size);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_F16 ], "mul_mat_vec_id_f16_f32",        arr_dmmv_id_f16_f32_f32_len[reduc],     arr_dmmv_id_f16_f32_f32_data[reduc],     "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {2, 1, 1}, {wg_size_subgroup, 2}, 1, false, use_subgroups, force_subgroup_size);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_BF16], "mul_mat_vec_id_bf16_f32",       arr_dmmv_id_bf16_f32_f32_len[reduc],    arr_dmmv_id_bf16_f32_f32_data[reduc],    "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {2, 1, 1}, {wg_size_subgroup, 2}, 1, false, use_subgroups, force_subgroup_size);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_Q4_0], "mul_mat_vec_id_q4_0_f32",       arr_dmmv_id_q4_0_f32_f32_len[reduc],    arr_dmmv_id_q4_0_f32_f32_data[reduc],    "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq}, 1, true, use_subgroups, force_subgroup_size);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_Q4_1], "mul_mat_vec_id_q4_1_f32",       arr_dmmv_id_q4_1_f32_f32_len[reduc],    arr_dmmv_id_q4_1_f32_f32_data[reduc],    "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq}, 1, true, use_subgroups, force_subgroup_size);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_Q5_0], "mul_mat_vec_id_q5_0_f32",       arr_dmmv_id_q5_0_f32_f32_len[reduc],    arr_dmmv_id_q5_0_f32_f32_data[reduc],    "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq}, 1, true, use_subgroups, force_subgroup_size);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_Q5_1], "mul_mat_vec_id_q5_1_f32",       arr_dmmv_id_q5_1_f32_f32_len[reduc],    arr_dmmv_id_q5_1_f32_f32_data[reduc],    "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq}, 1, true, use_subgroups, force_subgroup_size);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_Q8_0], "mul_mat_vec_id_q8_0_f32",       arr_dmmv_id_q8_0_f32_f32_len[reduc],    arr_dmmv_id_q8_0_f32_f32_data[reduc],    "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {1*rm_stdq, 1, 1}, {wg_size_subgroup, 1*rm_stdq}, 1, true, use_subgroups, force_subgroup_size);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_Q2_K], "mul_mat_vec_id_q2_k_f32",       arr_dmmv_id_q2_k_f32_f32_len[reduc16],    arr_dmmv_id_q2_k_f32_f32_data[reduc16],    "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq}, 1, true, use_subgroups16, force_subgroup_size16);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_Q3_K], "mul_mat_vec_id_q3_k_f32",       arr_dmmv_id_q3_k_f32_f32_len[reduc16],    arr_dmmv_id_q3_k_f32_f32_data[reduc16],    "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq}, 1, true, use_subgroups16, force_subgroup_size16);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_Q4_K], "mul_mat_vec_id_q4_k_f32",       arr_dmmv_id_q4_k_f32_f32_len[reduc16],    arr_dmmv_id_q4_k_f32_f32_data[reduc16],    "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq}, 1, true, use_subgroups16, force_subgroup_size16);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_Q5_K], "mul_mat_vec_id_q5_k_f32",       arr_dmmv_id_q5_k_f32_f32_len[reduc16],    arr_dmmv_id_q5_k_f32_f32_data[reduc16],    "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq}, 1, true, use_subgroups16, force_subgroup_size16);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_Q6_K], "mul_mat_vec_id_q6_k_f32",       arr_dmmv_id_q6_k_f32_f32_len[reduc16],    arr_dmmv_id_q6_k_f32_f32_data[reduc16],    "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq}, 1, true, use_subgroups16, force_subgroup_size16);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_IQ1_S],   "mul_mat_vec_id_iq1_s_f32",   arr_dmmv_id_iq1_s_f32_f32_len[reduc16],   arr_dmmv_id_iq1_s_f32_f32_data[reduc16],   "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq}, 1, true, use_subgroups16, force_subgroup_size16);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_IQ1_M],   "mul_mat_vec_id_iq1_m_f32",   arr_dmmv_id_iq1_m_f32_f32_len[reduc16],   arr_dmmv_id_iq1_m_f32_f32_data[reduc16],   "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq}, 1, true, use_subgroups16, force_subgroup_size16);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_IQ2_XXS], "mul_mat_vec_id_iq2_xxs_f32", arr_dmmv_id_iq2_xxs_f32_f32_len[reduc16], arr_dmmv_id_iq2_xxs_f32_f32_data[reduc16], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq}, 1, true, use_subgroups16, force_subgroup_size16);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_IQ2_XS],  "mul_mat_vec_id_iq2_xs_f32",  arr_dmmv_id_iq2_xs_f32_f32_len[reduc16],  arr_dmmv_id_iq2_xs_f32_f32_data[reduc16],  "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq}, 1, true, use_subgroups16, force_subgroup_size16);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_IQ2_S],   "mul_mat_vec_id_iq2_s_f32",   arr_dmmv_id_iq2_s_f32_f32_len[reduc16],   arr_dmmv_id_iq2_s_f32_f32_data[reduc16],   "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq}, 1, true, use_subgroups16, force_subgroup_size16);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_IQ3_XXS], "mul_mat_vec_id_iq3_xxs_f32", arr_dmmv_id_iq3_xxs_f32_f32_len[reduc16], arr_dmmv_id_iq3_xxs_f32_f32_data[reduc16], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq}, 1, true, use_subgroups16, force_subgroup_size16);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_IQ3_S],   "mul_mat_vec_id_iq3_s_f32",   arr_dmmv_id_iq3_s_f32_f32_len[reduc16],   arr_dmmv_id_iq3_s_f32_f32_data[reduc16],   "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq}, 1, true, use_subgroups16, force_subgroup_size16);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_IQ4_XS],  "mul_mat_vec_id_iq4_xs_f32",  arr_dmmv_id_iq4_xs_f32_f32_len[reduc16],  arr_dmmv_id_iq4_xs_f32_f32_data[reduc16],  "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq}, 1, true, use_subgroups16, force_subgroup_size16);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_IQ4_NL],  "mul_mat_vec_id_iq4_nl_f32",  arr_dmmv_id_iq4_nl_f32_f32_len[reduc16],  arr_dmmv_id_iq4_nl_f32_f32_data[reduc16],  "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq}, 1, true, use_subgroups16, force_subgroup_size16);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_MXFP4],   "mul_mat_vec_id_mxfp4_f32",   arr_dmmv_id_mxfp4_f32_f32_len[reduc16],   arr_dmmv_id_mxfp4_f32_f32_data[reduc16],   "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq}, 1, true, use_subgroups16, force_subgroup_size16);
+
+#if defined(GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT)
+        if (device->integer_dot_product) {
+            const uint32_t subgroup_size_int = (device->vendor_id == VK_VENDOR_ID_INTEL && device->subgroup_size_control) ? device->subgroup_min_size : device->subgroup_size;
+            const uint32_t wg_size_subgroup_int = (w == DMMV_WG_SIZE_SUBGROUP) ? subgroup_size_int : (subgroup_size_int * 4);
+
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_q8_1_f32[w][GGML_TYPE_Q4_0], "mul_mat_vec_id_q4_0_q8_1_f32", arr_dmmv_id_q4_0_q8_1_f32_len[reduc], arr_dmmv_id_q4_0_q8_1_f32_data[reduc], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_push_constants), {1*rm_stdq_int, 1, 1}, {wg_size_subgroup_int, 1*rm_stdq_int}, 1, true, use_subgroups, subgroup_size_int);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_q8_1_f32[w][GGML_TYPE_Q4_1], "mul_mat_vec_id_q4_1_q8_1_f32", arr_dmmv_id_q4_1_q8_1_f32_len[reduc], arr_dmmv_id_q4_1_q8_1_f32_data[reduc], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_push_constants), {1*rm_stdq_int, 1, 1}, {wg_size_subgroup_int, 1*rm_stdq_int}, 1, true, use_subgroups, subgroup_size_int);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_q8_1_f32[w][GGML_TYPE_Q5_0], "mul_mat_vec_id_q5_0_q8_1_f32", arr_dmmv_id_q5_0_q8_1_f32_len[reduc], arr_dmmv_id_q5_0_q8_1_f32_data[reduc], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_push_constants), {1*rm_stdq_int, 1, 1}, {wg_size_subgroup_int, 1*rm_stdq_int}, 1, true, use_subgroups, subgroup_size_int);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_q8_1_f32[w][GGML_TYPE_Q5_1], "mul_mat_vec_id_q5_1_q8_1_f32", arr_dmmv_id_q5_1_q8_1_f32_len[reduc], arr_dmmv_id_q5_1_q8_1_f32_data[reduc], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_push_constants), {1*rm_stdq_int, 1, 1}, {wg_size_subgroup_int, 1*rm_stdq_int}, 1, true, use_subgroups, subgroup_size_int);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_q8_1_f32[w][GGML_TYPE_Q8_0], "mul_mat_vec_id_q8_0_q8_1_f32", arr_dmmv_id_q8_0_q8_1_f32_len[reduc], arr_dmmv_id_q8_0_q8_1_f32_data[reduc], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_push_constants), {1*rm_stdq_int, 1, 1}, {wg_size_subgroup_int, 1*rm_stdq_int}, 1, true, use_subgroups, subgroup_size_int);
+
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_q8_1_f32[w][GGML_TYPE_MXFP4], "mul_mat_vec_id_mxfp4_q8_1_f32", arr_dmmv_id_mxfp4_q8_1_f32_len[reduc], arr_dmmv_id_mxfp4_q8_1_f32_data[reduc], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_push_constants), {2*rm_stdq_int, 1, 1}, {wg_size_subgroup_int, 2*rm_stdq_int}, 1, true, use_subgroups, subgroup_size_int);
+
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_q8_1_f32[w][GGML_TYPE_Q2_K], "mul_mat_vec_id_q2_k_q8_1_f32", arr_dmmv_id_q2_k_q8_1_f32_len[reduc], arr_dmmv_id_q2_k_q8_1_f32_data[reduc], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_push_constants), {2*rm_kq_int, 1, 1}, {wg_size_subgroup_int, 2*rm_kq_int}, 1, true, use_subgroups, subgroup_size_int);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_q8_1_f32[w][GGML_TYPE_Q3_K], "mul_mat_vec_id_q3_k_q8_1_f32", arr_dmmv_id_q3_k_q8_1_f32_len[reduc], arr_dmmv_id_q3_k_q8_1_f32_data[reduc], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_push_constants), {1*rm_kq_int, 1, 1}, {wg_size_subgroup_int, 1*rm_kq_int}, 1, true, use_subgroups, subgroup_size_int);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_q8_1_f32[w][GGML_TYPE_Q4_K], "mul_mat_vec_id_q4_k_q8_1_f32", arr_dmmv_id_q4_k_q8_1_f32_len[reduc], arr_dmmv_id_q4_k_q8_1_f32_data[reduc], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_push_constants), {1*rm_kq_int, 1, 1}, {wg_size_subgroup_int, 1*rm_kq_int}, 1, true, use_subgroups, subgroup_size_int);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_q8_1_f32[w][GGML_TYPE_Q5_K], "mul_mat_vec_id_q5_k_q8_1_f32", arr_dmmv_id_q5_k_q8_1_f32_len[reduc], arr_dmmv_id_q5_k_q8_1_f32_data[reduc], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_push_constants), {1*rm_kq_int, 1, 1}, {wg_size_subgroup_int, 1*rm_kq_int}, 1, true, use_subgroups, subgroup_size_int);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_q8_1_f32[w][GGML_TYPE_Q6_K], "mul_mat_vec_id_q6_k_q8_1_f32", arr_dmmv_id_q6_k_q8_1_f32_len[reduc], arr_dmmv_id_q6_k_q8_1_f32_data[reduc], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_push_constants), {1*rm_kq_int, 1, 1}, {wg_size_subgroup_int, 1*rm_kq_int}, 1, true, use_subgroups, subgroup_size_int);
+        }
+#endif // GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT
     }
 
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_F32 ], "mul_mat_vec_id_f32_f32",  mul_mat_vec_id_f32_f32_len,  mul_mat_vec_id_f32_f32_data,  "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_F16 ], "mul_mat_vec_id_f16_f32",  mul_mat_vec_id_f16_f32_len,  mul_mat_vec_id_f16_f32_data,  "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_BF16], "mul_mat_vec_id_bf16_f32", mul_mat_vec_id_bf16_f32_len, mul_mat_vec_id_bf16_f32_data, "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q4_0], "mul_mat_vec_id_q4_0_f32", mul_mat_vec_id_q4_0_f32_len, mul_mat_vec_id_q4_0_f32_data, "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {2*rm_stdq, 1, 1}, {device->subgroup_size, 2*rm_stdq}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q4_1], "mul_mat_vec_id_q4_1_f32", mul_mat_vec_id_q4_1_f32_len, mul_mat_vec_id_q4_1_f32_data, "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {2*rm_stdq, 1, 1}, {device->subgroup_size, 2*rm_stdq}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q5_0], "mul_mat_vec_id_q5_0_f32", mul_mat_vec_id_q5_0_f32_len, mul_mat_vec_id_q5_0_f32_data, "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {2*rm_stdq, 1, 1}, {device->subgroup_size, 2*rm_stdq}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q5_1], "mul_mat_vec_id_q5_1_f32", mul_mat_vec_id_q5_1_f32_len, mul_mat_vec_id_q5_1_f32_data, "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {2*rm_stdq, 1, 1}, {device->subgroup_size, 2*rm_stdq}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q8_0], "mul_mat_vec_id_q8_0_f32", mul_mat_vec_id_q8_0_f32_len, mul_mat_vec_id_q8_0_f32_data, "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {1*rm_stdq, 1, 1}, {device->subgroup_size, 1*rm_stdq}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q2_K], "mul_mat_vec_id_q2_k_f32", mul_mat_vec_id_q2_k_f32_len, mul_mat_vec_id_q2_k_f32_data, "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q3_K], "mul_mat_vec_id_q3_k_f32", mul_mat_vec_id_q3_k_f32_len, mul_mat_vec_id_q3_k_f32_data, "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q4_K], "mul_mat_vec_id_q4_k_f32", mul_mat_vec_id_q4_k_f32_len, mul_mat_vec_id_q4_k_f32_data, "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q5_K], "mul_mat_vec_id_q5_k_f32", mul_mat_vec_id_q5_k_f32_len, mul_mat_vec_id_q5_k_f32_data, "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q6_K], "mul_mat_vec_id_q6_k_f32", mul_mat_vec_id_q6_k_f32_len, mul_mat_vec_id_q6_k_f32_data, "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_IQ1_S],   "mul_mat_vec_id_iq1_s_f32",   mul_mat_vec_id_iq1_s_f32_len,   mul_mat_vec_id_iq1_s_f32_data,   "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {rm_iq, 1, 1}, {subgroup_size_16, rm_iq}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_IQ1_M],   "mul_mat_vec_id_iq1_m_f32",   mul_mat_vec_id_iq1_m_f32_len,   mul_mat_vec_id_iq1_m_f32_data,   "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {rm_iq, 1, 1}, {subgroup_size_16, rm_iq}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_IQ2_XXS], "mul_mat_vec_id_iq2_xxs_f32", mul_mat_vec_id_iq2_xxs_f32_len, mul_mat_vec_id_iq2_xxs_f32_data, "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {rm_iq, 1, 1}, {subgroup_size_16, rm_iq}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_IQ2_XS],  "mul_mat_vec_id_iq2_xs_f32",  mul_mat_vec_id_iq2_xs_f32_len,  mul_mat_vec_id_iq2_xs_f32_data,  "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {rm_iq, 1, 1}, {subgroup_size_16, rm_iq}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_IQ2_S],   "mul_mat_vec_id_iq2_s_f32",   mul_mat_vec_id_iq2_s_f32_len,   mul_mat_vec_id_iq2_s_f32_data,   "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {rm_iq, 1, 1}, {subgroup_size_16, rm_iq}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_IQ3_XXS], "mul_mat_vec_id_iq3_xxs_f32", mul_mat_vec_id_iq3_xxs_f32_len, mul_mat_vec_id_iq3_xxs_f32_data, "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {rm_iq, 1, 1}, {subgroup_size_16, rm_iq}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_IQ3_S],   "mul_mat_vec_id_iq3_s_f32",   mul_mat_vec_id_iq3_s_f32_len,   mul_mat_vec_id_iq3_s_f32_data,   "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {rm_iq, 1, 1}, {subgroup_size_16, rm_iq}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_IQ4_XS],  "mul_mat_vec_id_iq4_xs_f32",  mul_mat_vec_id_iq4_xs_f32_len,  mul_mat_vec_id_iq4_xs_f32_data,  "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {rm_iq, 1, 1}, {subgroup_size_16, rm_iq}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_IQ4_NL],  "mul_mat_vec_id_iq4_nl_f32",  mul_mat_vec_id_iq4_nl_f32_len,  mul_mat_vec_id_iq4_nl_f32_data,  "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {rm_iq, 1, 1}, {subgroup_size_16, rm_iq}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_MXFP4],   "mul_mat_vec_id_mxfp4_f32",   mul_mat_vec_id_mxfp4_f32_len,   mul_mat_vec_id_mxfp4_f32_data,   "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {rm_iq, 1, 1}, {subgroup_size_16, rm_iq}, 1, true);
+#if !defined(GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT)
+    GGML_UNUSED(rm_stdq_int);
+    GGML_UNUSED(rm_kq_int);
+#endif
 
     // dequant shaders
     ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_F32 ], "f32_to_f16",   dequant_f32_len,  dequant_f32_data,  "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1);
@@ -3863,6 +3917,9 @@ static void ggml_vk_load_shaders(vk_device& device) {
         ggml_vk_create_pipeline(device, device->pipeline_log[1], "log_f16", log_f16_len, log_f16_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
     }
 
+    ggml_vk_create_pipeline(device, device->pipeline_tri[0], "tri_f32", tri_f32_len, tri_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_tri[1], "tri_f16", tri_f16_len, tri_f16_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
+
     ggml_vk_create_pipeline(device, device->pipeline_clamp_f32, "clamp_f32", clamp_f32_len, clamp_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
 
     ggml_vk_create_pipeline(device, device->pipeline_pad_f32, "pad_f32", pad_f32_len, pad_f32_data, "main", 2, sizeof(vk_op_pad_push_constants), {512, 1, 1}, {}, 1);
@@ -4002,6 +4059,14 @@ static void ggml_vk_load_shaders(vk_device& device) {
 
     ggml_vk_create_pipeline(device, device->pipeline_count_equal_i32, "count_equal_i32", count_equal_i32_len, count_equal_i32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, { device->subgroup_size }, 1);
 
+    for (auto &s : device->pipeline_solve_tri_f32) {
+        const vk_solve_tri_pipeline_state &state = s.first;
+        ggml_vk_create_pipeline(
+            device, s.second, "solve_tri_f32",
+            solve_tri_f32_len, solve_tri_f32_data, "main", 3,
+            sizeof(vk_op_binary_push_constants), {1, 1, 1}, { 0, state.N, state.K }, 1, true);
+    }
+
 #define IM2COL(bda) \
     ggml_vk_create_pipeline(device, device->pipeline_im2col_f32, "im2col_f32", im2col_f32 ## bda ## _len, im2col_f32 ## bda ## _data, "main", 2, sizeof(vk_op_im2col_push_constants), {512, 1, 1}, { device->subgroup_size }, 1, true);   \
     ggml_vk_create_pipeline(device, device->pipeline_im2col_3d_f32, "im2col_3d_f32", im2col_3d_f32 ## bda ## _len, im2col_3d_f32 ## bda ## _data, "main", 2, sizeof(vk_op_im2col_3d_push_constants), {512, 1, 1}, { 512 }, 1, true);      \
@@ -5428,6 +5493,12 @@ static vk_pipeline ggml_vk_get_dequantize_mul_mat_vec(ggml_backend_vk_context *
             case GGML_TYPE_Q5_0:
             case GGML_TYPE_Q5_1:
             case GGML_TYPE_Q8_0:
+            case GGML_TYPE_MXFP4:
+            case GGML_TYPE_Q2_K:
+            case GGML_TYPE_Q3_K:
+            case GGML_TYPE_Q4_K:
+            case GGML_TYPE_Q5_K:
+            case GGML_TYPE_Q6_K:
                 break;
             default:
                 return nullptr;
@@ -5567,9 +5638,28 @@ static vk_matmul_pipeline ggml_vk_get_mul_mat_mat_id_pipeline(ggml_backend_vk_co
     }
 }
 
-static vk_pipeline ggml_vk_get_dequantize_mul_mat_vec_id(ggml_backend_vk_context * ctx, ggml_type a_type, ggml_type b_type) {
+static vk_pipeline ggml_vk_get_dequantize_mul_mat_vec_id(ggml_backend_vk_context * ctx, ggml_type a_type, ggml_type b_type, uint32_t m, uint32_t k) {
     VK_LOG_DEBUG("ggml_vk_get_dequantize_mul_mat_vec_id()");
-    GGML_ASSERT(b_type == GGML_TYPE_F32);
+    GGML_ASSERT(b_type == GGML_TYPE_F32 || b_type == GGML_TYPE_Q8_1);
+
+    if (b_type == GGML_TYPE_Q8_1) {
+        switch (a_type) {
+            case GGML_TYPE_Q4_0:
+            case GGML_TYPE_Q4_1:
+            case GGML_TYPE_Q5_0:
+            case GGML_TYPE_Q5_1:
+            case GGML_TYPE_Q8_0:
+            case GGML_TYPE_MXFP4:
+            case GGML_TYPE_Q2_K:
+            case GGML_TYPE_Q3_K:
+            case GGML_TYPE_Q4_K:
+            case GGML_TYPE_Q5_K:
+            case GGML_TYPE_Q6_K:
+                break;
+            default:
+                return nullptr;
+        }
+    }
 
     switch (a_type) {
         case GGML_TYPE_F32:
@@ -5600,7 +5690,31 @@ static vk_pipeline ggml_vk_get_dequantize_mul_mat_vec_id(ggml_backend_vk_context
             return nullptr;
     }
 
-    return ctx->device->pipeline_dequant_mul_mat_vec_id_f32[a_type];
+    // heuristic to choose workgroup size
+    uint32_t dmmv_wg = DMMV_WG_SIZE_SUBGROUP;
+    if ((ctx->device->vendor_id == VK_VENDOR_ID_NVIDIA && ctx->device->architecture != vk_device_architecture::NVIDIA_PRE_TURING) || ctx->device->vendor_id == VK_VENDOR_ID_INTEL) {
+        // Prefer larger workgroups when M is small, to spread the work out more
+        // and keep more SMs busy.
+        // q6_k seems to prefer small workgroup size even for "medium" values of M.
+        if (a_type == GGML_TYPE_Q6_K) {
+            if (m < 4096 && k >= 1024) {
+                dmmv_wg = DMMV_WG_SIZE_LARGE;
+            }
+        } else {
+            if (m <= 8192 && k >= 1024) {
+                dmmv_wg = DMMV_WG_SIZE_LARGE;
+            }
+        }
+    }
+
+    if (b_type == GGML_TYPE_Q8_1) {
+        if (ctx->device->vendor_id == VK_VENDOR_ID_INTEL) {
+            dmmv_wg = DMMV_WG_SIZE_SUBGROUP;
+        }
+        return ctx->device->pipeline_dequant_mul_mat_vec_id_q8_1_f32[dmmv_wg][a_type];
+    }
+
+    return ctx->device->pipeline_dequant_mul_mat_vec_id_f32[dmmv_wg][a_type];
 }
 
 static void * ggml_vk_host_malloc(vk_device& device, size_t size) {
@@ -6792,20 +6906,35 @@ static bool ggml_vk_should_use_mmvq(const vk_device& device, uint32_t m, uint32_
         return false;
     }
 
+    // General performance issue with q3_k and q6_k due to 2-byte alignment
+    if (src0_type == GGML_TYPE_Q3_K || src0_type == GGML_TYPE_Q6_K) {
+        return false;
+    }
+
     // MMVQ is generally good for batches
     if (n > 1) {
         return true;
     }
 
+    // Quantization overhead is not worth it for small k
     switch (device->vendor_id) {
     case VK_VENDOR_ID_NVIDIA:
+        if (k <= 4096) {
+            return false;
+        }
+
         switch (src0_type) {
+        case GGML_TYPE_MXFP4:
         case GGML_TYPE_Q8_0:
             return device->architecture == vk_device_architecture::NVIDIA_PRE_TURING;
         default:
             return true;
         }
     case VK_VENDOR_ID_AMD:
+        if (k < 2048) {
+            return false;
+        }
+
         switch (src0_type) {
         case GGML_TYPE_Q8_0:
             return device->architecture == vk_device_architecture::AMD_GCN;
@@ -6813,6 +6942,10 @@ static bool ggml_vk_should_use_mmvq(const vk_device& device, uint32_t m, uint32_
             return true;
         }
     case VK_VENDOR_ID_INTEL:
+        if (k < 2048) {
+            return false;
+        }
+
         switch (src0_type) {
         // From tests on A770 Linux, may need more tuning
         case GGML_TYPE_Q4_0:
@@ -6826,7 +6959,6 @@ static bool ggml_vk_should_use_mmvq(const vk_device& device, uint32_t m, uint32_
     }
 
     GGML_UNUSED(m);
-    GGML_UNUSED(k);
 }
 
 static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context& subctx, const struct ggml_cgraph * cgraph, int node_idx) {
@@ -7549,7 +7681,7 @@ static void ggml_vk_mul_mat_id_q_f16(ggml_backend_vk_context * ctx, vk_context&
     if (x_non_contig || qx_needs_dequant) {
         ctx->prealloc_x_need_sync = true;
     }
-    if (y_non_contig) {
+    if (y_non_contig || quantize_y) {
         ctx->prealloc_y_need_sync = true;
     }
 }
@@ -7575,7 +7707,7 @@ static void ggml_vk_mul_mat_vec_id_q_f16(ggml_backend_vk_context * ctx, vk_conte
 
     const uint64_t ne10 = src1->ne[0];
     const uint64_t ne11 = src1->ne[1];
-    // const uint64_t ne12 = src1->ne[2];
+    const uint64_t ne12 = src1->ne[2];
     // const uint64_t ne13 = src1->ne[3];
 
     const uint64_t nei0 = ids->ne[0];
@@ -7592,19 +7724,7 @@ static void ggml_vk_mul_mat_vec_id_q_f16(ggml_backend_vk_context * ctx, vk_conte
     const bool y_non_contig = !ggml_vk_dim01_contiguous(src1);
 
     const bool f16_f32_kernel = src1->type == GGML_TYPE_F32;
-
-    const bool qx_needs_dequant = x_non_contig;
-    const bool qy_needs_dequant = (src1->type != GGML_TYPE_F16 && !f16_f32_kernel) || y_non_contig;
-
-    // Not implemented
-    GGML_ASSERT(y_non_contig || !qy_needs_dequant);  // NOLINT
-
-    const uint64_t x_ne = ggml_nelements(src0);
-    const uint64_t y_ne = ggml_nelements(src1);
-
-    const uint64_t qx_sz = ggml_vk_align_size(ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type), ctx->device->properties.limits.minStorageBufferOffsetAlignment);
-    const uint64_t x_sz = x_non_contig ? ggml_vk_align_size(ggml_type_size(src0->type) * x_ne, ctx->device->properties.limits.minStorageBufferOffsetAlignment) : qx_sz;
-    const uint64_t y_sz = f16_f32_kernel ? sizeof(float) * y_ne : sizeof(ggml_fp16_t) * y_ne;
+    bool quantize_y = ctx->device->integer_dot_product && src1->type == GGML_TYPE_F32 && ggml_is_contiguous(src1) && !y_non_contig && (ne11 * ne10) % 4 == 0 && ggml_vk_should_use_mmvq(ctx->device, ne01, ne12, ne10, src0->type);
 
     vk_pipeline to_fp16_vk_0 = nullptr;
     vk_pipeline to_fp16_vk_1 = nullptr;
@@ -7616,11 +7736,38 @@ static void ggml_vk_mul_mat_vec_id_q_f16(ggml_backend_vk_context * ctx, vk_conte
     } else {
         to_fp16_vk_1 = ggml_vk_get_to_fp16(ctx, src1->type);
     }
-    vk_pipeline dmmv = ggml_vk_get_dequantize_mul_mat_vec_id(ctx, src0->type, src1->type);
+
+    // Check for mmq first
+    vk_pipeline dmmv = quantize_y ? ggml_vk_get_dequantize_mul_mat_vec_id(ctx, src0->type, GGML_TYPE_Q8_1, ne20, ne00) : nullptr;
+    vk_pipeline to_q8_1 = nullptr;
+
+    if (dmmv == nullptr) {
+        // Fall back to f16 dequant mul mat
+        dmmv = ggml_vk_get_dequantize_mul_mat_vec_id(ctx, src0->type, src1->type, ne20, ne00);
+        quantize_y = false;
+    }
+
+    if (quantize_y) {
+        to_q8_1 = ggml_vk_get_quantize_pipeline(ctx, GGML_TYPE_Q8_1);
+    }
+
+    const bool qx_needs_dequant = x_non_contig;
+    const bool qy_needs_dequant = !quantize_y && ((src1->type != GGML_TYPE_F16 && !f16_f32_kernel) || y_non_contig);
+
+    // Not implemented
+    GGML_ASSERT(y_non_contig || !qy_needs_dequant);  // NOLINT
     GGML_ASSERT(!qx_needs_dequant || to_fp16_vk_0 != nullptr);  // NOLINT
     GGML_ASSERT(!qy_needs_dequant || to_fp16_vk_1 != nullptr);  // NOLINT
     GGML_ASSERT(dmmv != nullptr);
 
+    const uint64_t x_ne = ggml_nelements(src0);
+    const uint64_t y_ne = ggml_nelements(src1);
+
+    const uint64_t qx_sz = ggml_vk_align_size(ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type), ctx->device->properties.limits.minStorageBufferOffsetAlignment);
+    const uint64_t x_sz = x_non_contig ? ggml_vk_align_size(ggml_type_size(src0->type) * x_ne, ctx->device->properties.limits.minStorageBufferOffsetAlignment) : qx_sz;
+    const uint64_t y_sz = quantize_y ? (ggml_vk_align_size(y_ne, 128) * ggml_type_size(GGML_TYPE_Q8_1) / ggml_blck_size(GGML_TYPE_Q8_1)) :
+                                       (f16_f32_kernel ? sizeof(float) * y_ne : sizeof(ggml_fp16_t) * y_ne);
+
     {
         if (
                 (qx_needs_dequant && x_sz > ctx->device->properties.limits.maxStorageBufferRange) ||
@@ -7631,7 +7778,7 @@ static void ggml_vk_mul_mat_vec_id_q_f16(ggml_backend_vk_context * ctx, vk_conte
             ctx->prealloc_size_x = x_sz;
             ggml_vk_preallocate_buffers(ctx, subctx);
         }
-        if (qy_needs_dequant && ctx->prealloc_size_y < y_sz) {
+        if ((qy_needs_dequant || quantize_y) && ctx->prealloc_size_y < y_sz) {
             ctx->prealloc_size_y = y_sz;
             ggml_vk_preallocate_buffers(ctx, subctx);
         }
@@ -7643,6 +7790,9 @@ static void ggml_vk_mul_mat_vec_id_q_f16(ggml_backend_vk_context * ctx, vk_conte
         if (qy_needs_dequant) {
             ggml_pipeline_request_descriptor_sets(ctx, to_fp16_vk_1, 1);
         }
+        if (quantize_y) {
+            ggml_pipeline_request_descriptor_sets(ctx, to_q8_1, 1);
+        }
         ggml_pipeline_request_descriptor_sets(ctx, dmmv, 1);
     }
 
@@ -7658,7 +7808,7 @@ static void ggml_vk_mul_mat_vec_id_q_f16(ggml_backend_vk_context * ctx, vk_conte
     } else {
         d_X = d_Qx;
     }
-    if (qy_needs_dequant) {
+    if (qy_needs_dequant || quantize_y) {
         d_Y = { ctx->prealloc_y, 0, ctx->prealloc_y->size };
     } else {
         d_Y = d_Qy;
@@ -7686,6 +7836,17 @@ static void ggml_vk_mul_mat_vec_id_q_f16(ggml_backend_vk_context * ctx, vk_conte
             ctx->prealloc_y_last_tensor_used = src1;
         }
     }
+    if (quantize_y) {
+        if (ctx->prealloc_y_last_pipeline_used != to_q8_1.get() ||
+            ctx->prealloc_y_last_tensor_used != src1) {
+            if (ctx->prealloc_y_need_sync) {
+                ggml_vk_sync_buffers(ctx, subctx);
+            }
+            ggml_vk_quantize_q8_1(ctx, subctx, d_Qy, d_Y, y_ne);
+            ctx->prealloc_y_last_pipeline_used = to_q8_1.get();
+            ctx->prealloc_y_last_tensor_used = src1;
+        }
+    }
 
     uint32_t stride_batch_y = ne10*ne11;
 
@@ -7747,7 +7908,7 @@ static void ggml_vk_mul_mat_vec_id_q_f16(ggml_backend_vk_context * ctx, vk_conte
     if (x_non_contig) {
         ctx->prealloc_x_need_sync = true;
     }
-    if (y_non_contig) {
+    if (y_non_contig || quantize_y) {
         ctx->prealloc_y_need_sync = true;
     }
 }
@@ -8269,6 +8430,12 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const
             return ctx->device->pipeline_log[dst->type == GGML_TYPE_F16];
         }
         return nullptr;
+    case GGML_OP_TRI:
+        if (src0->type == dst->type &&
+            (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16)) {
+            return ctx->device->pipeline_tri[dst->type == GGML_TYPE_F16];
+        }
+        return nullptr;
     case GGML_OP_CLAMP:
         if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
             return ctx->device->pipeline_clamp_f32;
@@ -8496,6 +8663,26 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const
             return ctx->device->pipeline_cumsum_f32;
         }
         return nullptr;
+    case GGML_OP_SOLVE_TRI:
+        if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
+
+            vk_solve_tri_pipeline_state solve_tri_pipeline_state(src0->ne[0], src1->ne[0]);
+
+            vk_pipeline pipeline = nullptr;
+
+            {
+                std::lock_guard<std::recursive_mutex> guard(ctx->device->mutex);
+                auto it = ctx->device->pipeline_solve_tri_f32.find(solve_tri_pipeline_state);
+                if (it != ctx->device->pipeline_solve_tri_f32.end()) {
+                    pipeline = it->second;
+                } else {
+                    ctx->device->pipeline_solve_tri_f32[solve_tri_pipeline_state] = pipeline = std::make_shared<vk_pipeline_struct>();
+                }
+            }
+
+            return pipeline;
+        }
+        return nullptr;
     case GGML_OP_ARGMAX:
         if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_I32) {
             return ctx->device->pipeline_argmax_f32;
@@ -8832,6 +9019,18 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context& subctx, co
                 elements = { nr, 1, 1 };
             }
         } break;
+    case GGML_OP_SOLVE_TRI:
+        {
+            uint32_t nr = (uint32_t)(ne02 * ne03);
+            if (nr > 262144) {
+                elements = { 512, 512, CEIL_DIV(nr, 262144) };
+            } else if (nr > 512) {
+                elements = { 512, CEIL_DIV(nr, 512), 1 };
+            } else {
+                elements = { nr, 1, 1 };
+            }
+        }
+        break;
     case GGML_OP_RMS_NORM:
         if (ctx->do_add_rms_partials) {
             // Run one element per thread, 128 threads per workgroup
@@ -8938,6 +9137,7 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context& subctx, co
     case GGML_OP_SIN:
     case GGML_OP_COS:
     case GGML_OP_LOG:
+    case GGML_OP_TRI:
     case GGML_OP_CLAMP:
     case GGML_OP_PAD:
     case GGML_OP_ROLL:
@@ -9618,6 +9818,13 @@ static void ggml_vk_log(ggml_backend_vk_context * ctx, vk_context& subctx, const
     ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_LOG, vk_op_unary_push_constants_init(src0, dst));
 }
 
+static void ggml_vk_tri(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) {
+    vk_op_unary_push_constants p = vk_op_unary_push_constants_init(src0, dst);
+    p.param1 = ggml_get_op_params_f32(dst, 0);
+
+    ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_TRI, std::move(p));
+}
+
 static void ggml_vk_clamp(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) {
     vk_op_unary_push_constants p = vk_op_unary_push_constants_init(src0, dst);
     p.param1 = ggml_get_op_params_f32(dst, 0);
@@ -10168,7 +10375,9 @@ static void ggml_vk_topk(ggml_backend_vk_context * ctx, vk_context& subctx, cons
 
         // Prefer going as small as num_topk_pipelines - 3 for perf reasons.
         // But if K is larger, then we need a larger workgroup
-        uint32_t max_pipeline = num_topk_pipelines - 3;
+        uint32_t max_pipeline = num_topk_pipelines - 1;
+        uint32_t preferred_pipeline = std::max(num_topk_pipelines - 3, (uint32_t)log2f(float(k)) + 2);
+        max_pipeline = std::min(preferred_pipeline, max_pipeline);
         uint32_t min_pipeline = (uint32_t)log2f(float(k)) + 1;
         // require full subgroup
         min_pipeline = std::max(min_pipeline, ctx->device->subgroup_size_log2);
@@ -10260,6 +10469,21 @@ static void ggml_vk_count_equal(ggml_backend_vk_context * ctx, vk_context& subct
     ggml_vk_op_f32<vk_op_push_constants>(ctx, subctx, src0, src1, nullptr, nullptr, dst, GGML_OP_COUNT_EQUAL, { (uint32_t)ggml_nelements(src0), 0, 0.0f, 0.0f });
 }
 
+static void ggml_vk_solve_tri(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    const uint32_t src0_type_size = ggml_type_size(src0->type);
+    const uint32_t src1_type_size = ggml_type_size(src1->type);
+    const uint32_t dst_type_size = ggml_type_size(dst->type);
+
+    ggml_vk_op_f32<vk_op_binary_push_constants>(ctx, subctx, src0, src1, nullptr, nullptr, dst, GGML_OP_SOLVE_TRI, {
+        (uint32_t)ggml_nelements(src0),
+        (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2],(uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size,
+        (uint32_t)src1->ne[0], (uint32_t)src1->ne[1], (uint32_t)src1->ne[2],(uint32_t)src1->ne[3], (uint32_t)src1->nb[0] / src1_type_size, (uint32_t)src1->nb[1] / src1_type_size, (uint32_t)src1->nb[2] / src1_type_size, (uint32_t)src1->nb[3] / src1_type_size,
+        (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2],(uint32_t) dst->ne[3], (uint32_t) dst->nb[0] /  dst_type_size, (uint32_t) dst->nb[1] /  dst_type_size, (uint32_t) dst->nb[2] /  dst_type_size, (uint32_t) dst->nb[3] /  dst_type_size,
+        0,
+        0.0f, 0.0f, 0,
+    });
+}
+
 static void ggml_vk_im2col(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
     const int32_t s0 = dst->op_params[0];
     const int32_t s1 = dst->op_params[1];
@@ -11726,6 +11950,10 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgr
     case GGML_OP_LOG:
         ggml_vk_log(ctx, compute_ctx, src0, node);
 
+        break;
+    case GGML_OP_TRI:
+        ggml_vk_tri(ctx, compute_ctx, src0, node);
+
         break;
     case GGML_OP_CLAMP:
         ggml_vk_clamp(ctx, compute_ctx, src0, node);
@@ -11871,6 +12099,10 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgr
     case GGML_OP_COUNT_EQUAL:
         ggml_vk_count_equal(ctx, compute_ctx, src0, src1, node);
 
+        break;
+    case GGML_OP_SOLVE_TRI:
+        ggml_vk_solve_tri(ctx, compute_ctx, src0, src1, node);
+
         break;
     case GGML_OP_IM2COL:
         ggml_vk_im2col(ctx, compute_ctx, src0, src1, node);
@@ -13847,7 +14079,9 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
         case GGML_OP_OPT_STEP_SGD:
             return ggml_is_contiguous(op->src[0]) && op->src[0]->type == GGML_TYPE_F32;
         case GGML_OP_LOG:
-            return op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16;
+        case GGML_OP_TRI:
+            return (op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16) &&
+                   op->type == op->src[0]->type;
         case GGML_OP_ARGSORT:
             {
                 if (!ggml_is_contiguous(op) || !ggml_is_contiguous(op->src[0])) {
@@ -13879,6 +14113,7 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
             }
             return true;
         case GGML_OP_UPSCALE:
+            return op->src[0]->type == GGML_TYPE_F32 && !(op->op_params[0] & GGML_SCALE_FLAG_ANTIALIAS);
         case GGML_OP_ACC:
             return op->src[0]->type == GGML_TYPE_F32;
         case GGML_OP_CONCAT:
@@ -13916,6 +14151,25 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
                 }
                 return false;
             }
+        case GGML_OP_SOLVE_TRI:
+            {
+                ggml_backend_vk_device_context * ctx = (ggml_backend_vk_device_context *)dev->context;
+                const vk_device& device = ggml_vk_get_device(ctx->device);
+
+                if (op->type != GGML_TYPE_F32 || op->src[0]->type != GGML_TYPE_F32) {
+                    return false;
+                }
+                const uint32_t N = op->src[0]->ne[0];
+                const uint32_t K = op->src[1]->ne[0];
+                // K dimension limited to workgroup size
+                if (K > 128) {
+                    return false;
+                }
+                if (N * N * sizeof(float) + N * K * sizeof(float) > device->properties.limits.maxComputeSharedMemorySize) {
+                    return false;
+                }
+                return true;
+            }
         case GGML_OP_ARGMAX:
             return ggml_is_contiguous(op->src[0]) && op->src[0]->type == GGML_TYPE_F32;
         case GGML_OP_COUNT_EQUAL:
@@ -14419,6 +14673,8 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_cgraph *
             tensor_clone = ggml_cos(ggml_ctx, src_clone[0]);
         } else if (tensor->op == GGML_OP_LOG) {
             tensor_clone = ggml_log(ggml_ctx, src_clone[0]);
+        } else if (tensor->op == GGML_OP_TRI) {
+            tensor_clone = ggml_tri(ggml_ctx, src_clone[0], ggml_get_op_params_i32(tensor, 0));
         } else if (tensor->op == GGML_OP_CLAMP) {
             const float * params = (const float *)tensor->op_params;
             tensor_clone = ggml_clamp(ggml_ctx, src_clone[0], params[0], params[1]);
@@ -14588,6 +14844,8 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_cgraph *
             tensor_clone = ggml_argmax(ggml_ctx, src_clone[0]);
         } else if (tensor->op == GGML_OP_COUNT_EQUAL) {
             tensor_clone = ggml_count_equal(ggml_ctx, src_clone[0], src_clone[1]);
+        } else if (tensor->op == GGML_OP_SOLVE_TRI) {
+            tensor_clone = ggml_solve_tri(ggml_ctx, src_clone[0], src_clone[1], true, true, false);
         } else if (tensor->op == GGML_OP_IM2COL) {
             const int32_t s0 = tensor->op_params[0];
             const int32_t s1 = tensor->op_params[1];

ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs.glsl

@@ -4,13 +4,6 @@
 
 #include "types.glsl"
 
-#if defined(A_TYPE_PACKED16)
-layout (binding = 0) readonly buffer A_PACKED16 {A_TYPE_PACKED16 data_a_packed16[];};
-#endif
-#if defined(A_TYPE_PACKED32)
-layout (binding = 0) readonly buffer A_PACKED32 {A_TYPE_PACKED32 data_a_packed32[];};
-#endif
-
 #if defined(DATA_A_F32)
 vec2 dequantize(uint ib, uint iqs, uint a_offset) {
     return vec2(data_a[a_offset + ib], data_a[a_offset + ib + 1]);

ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm2.comp

@@ -156,7 +156,7 @@ void main() {
                 tensorLayoutM = setTensorLayoutStrideNV(tensorLayoutM, m_stride, 1);
                 tensorLayoutM = setTensorLayoutClampValueNV(tensorLayoutM, 0xfc00); // -inf in float16_t
 
-                coopmat<float16_t, gl_ScopeWorkgroup, Br, Bc, gl_MatrixUseAccumulator> mv, mvmax;
+                coopmat<float16_t, gl_ScopeWorkgroup, Br, Bc, gl_MatrixUseAccumulator> mvmax;
 
                 coopMatLoadTensorNV(mv, data_m, m_offset, sliceTensorLayoutNV(tensorLayoutM, i * Br, Br, j * Bc, Bc));
 

ggml/src/ggml-vulkan/vulkan-shaders/generic_binary_head.glsl

@@ -22,6 +22,13 @@ layout (push_constant) uniform parameter
 
 #if !RMS_NORM_ROPE_FUSION
 layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
+#if defined(A_TYPE_PACKED16)
+layout (binding = 0) readonly buffer A_PACKED16 {A_TYPE_PACKED16 data_a_packed16[];};
+#endif
+#if defined(A_TYPE_PACKED32)
+layout (binding = 0) readonly buffer A_PACKED32 {A_TYPE_PACKED32 data_a_packed32[];};
+#endif
+
 layout (binding = 1) readonly buffer B {B_TYPE data_b[];};
 layout (binding = 2) writeonly buffer D {D_TYPE data_d[];};
 #endif

ggml/src/ggml-vulkan/vulkan-shaders/generic_unary_head.glsl

@@ -18,6 +18,13 @@ layout (push_constant) uniform parameter
 } p;
 
 layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
+#if defined(A_TYPE_PACKED16)
+layout (binding = 0) readonly buffer A_PACKED16 {A_TYPE_PACKED16 data_a_packed16[];};
+#endif
+#if defined(A_TYPE_PACKED32)
+layout (binding = 0) readonly buffer A_PACKED32 {A_TYPE_PACKED32 data_a_packed32[];};
+#endif
+
 layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
 
 uint get_idx() {

ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec.comp

@@ -3,6 +3,7 @@
 #extension GL_EXT_shader_explicit_arithmetic_types_int32 : require
 
 #include "mul_mat_vec_base.glsl"
+#include "dequant_funcs.glsl"
 
 layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in;
 

ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_base.glsl

@@ -13,8 +13,6 @@
 
 #include "mul_mat_vec_iface.glsl"
 
-#include "dequant_funcs.glsl"
-
 layout (push_constant) uniform parameter
 {
     uint ncols;

ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_iface.glsl

@@ -5,13 +5,15 @@
 #define MAT_VEC_FUSION_FLAGS_SCALE0 0x4
 #define MAT_VEC_FUSION_FLAGS_SCALE1 0x8
 
-#ifndef MMQ
 layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
 #if defined(A_TYPE_VEC4)
 layout (binding = 0) readonly buffer AV4 {A_TYPE_VEC4 data_a_v4[];};
 #endif
-#else
-layout (binding = 0) readonly buffer A {A_TYPE_PACKED16 data_a[];};
+#if defined(A_TYPE_PACKED16)
+layout (binding = 0) readonly buffer A_PACKED16 {A_TYPE_PACKED16 data_a_packed16[];};
+#endif
+#if defined(A_TYPE_PACKED32)
+layout (binding = 0) readonly buffer A_PACKED32 {A_TYPE_PACKED32 data_a_packed32[];};
 #endif
 
 layout (binding = 1) readonly buffer B {B_TYPE data_b[];};

ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vecq.comp

@@ -10,60 +10,56 @@
 
 layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in;
 
+#if defined(DATA_A_QUANT_LEGACY) || defined(DATA_A_MXFP4)
 #define K_PER_ITER 8
-
-#include "mul_mmq_funcs.glsl"
+#elif defined(DATA_A_QUANT_K)
+#define K_PER_ITER 16
+#else
+#error unimplemented
+#endif
 
 uint a_offset, b_offset, d_offset;
 
-int32_t cache_b_qs[2];
+int32_t cache_b_qs[K_PER_ITER / 4];
 vec2 cache_b_ds;
 
+#include "mul_mat_vecq_funcs.glsl"
+
 void iter(inout FLOAT_TYPE temp[NUM_COLS][NUM_ROWS], const uint first_row, const uint num_rows, const uint tid, const uint i) {
     [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) {
         const uint col = i*BLOCK_SIZE + tid*K_PER_ITER;
 
         // Preload data_b block
         const uint b_block_idx = (j*p.batch_stride_b + col) / QUANT_K_Q8_1 + b_offset;
-        const uint b_qs_idx = tid % 4;
+        const uint b_qs_idx = tid % (32 / K_PER_ITER);
         const uint b_block_idx_outer = b_block_idx / 4;
         const uint b_block_idx_inner = b_block_idx % 4;
         cache_b_ds = vec2(data_b[b_block_idx_outer].ds[b_block_idx_inner]);
 
 #if QUANT_R == 2
+        // Assumes K_PER_ITER == 8
         cache_b_qs[0] = data_b[b_block_idx_outer].qs[b_block_idx_inner * 8 + b_qs_idx];
         cache_b_qs[1] = data_b[b_block_idx_outer].qs[b_block_idx_inner * 8 + b_qs_idx + 4];
 #else
+#if K_PER_ITER == 8
         cache_b_qs[0] = data_b[b_block_idx_outer].qs[b_block_idx_inner * 8 + b_qs_idx * 2];
         cache_b_qs[1] = data_b[b_block_idx_outer].qs[b_block_idx_inner * 8 + b_qs_idx * 2 + 1];
+#elif K_PER_ITER == 16
+        cache_b_qs[0] = data_b[b_block_idx_outer].qs[b_block_idx_inner * 8 + b_qs_idx * 4    ];
+        cache_b_qs[1] = data_b[b_block_idx_outer].qs[b_block_idx_inner * 8 + b_qs_idx * 4 + 1];
+        cache_b_qs[2] = data_b[b_block_idx_outer].qs[b_block_idx_inner * 8 + b_qs_idx * 4 + 2];
+        cache_b_qs[3] = data_b[b_block_idx_outer].qs[b_block_idx_inner * 8 + b_qs_idx * 4 + 3];
+#else
+#error unimplemented
+#endif
 #endif
 
         uint ibi = first_row*p.ncols;
         [[unroll]] for (uint n = 0; n < num_rows; ++n) {
-            const uint a_block_idx = (ibi + col)/QUANT_K + a_offset;
+            const uint a_block_idx = (ibi + col)/QUANT_K_Q8_1 + a_offset;
             ibi += p.ncols;
 
-            int32_t q_sum = 0;
-#if QUANT_R == 2
-            const i32vec2 data_a_qs = repack(a_block_idx, b_qs_idx);
-            q_sum += dotPacked4x8EXT(data_a_qs.x,
-                                     cache_b_qs[0]);
-            q_sum += dotPacked4x8EXT(data_a_qs.y,
-                                     cache_b_qs[1]);
-#else
-            int32_t data_a_qs = repack(a_block_idx, b_qs_idx * 2);
-            q_sum += dotPacked4x8EXT(data_a_qs,
-                                     cache_b_qs[0]);
-            data_a_qs = repack(a_block_idx, b_qs_idx * 2 + 1);
-            q_sum += dotPacked4x8EXT(data_a_qs,
-                                     cache_b_qs[1]);
-#endif
-
-#if QUANT_AUXF == 1
-            temp[j][n] += mul_q8_1(q_sum,  get_d(a_block_idx), cache_b_ds, 4);
-#else
-            temp[j][n] += mul_q8_1(q_sum, get_dm(a_block_idx), cache_b_ds, 4);
-#endif
+            temp[j][n] += mmvq_dot_product(a_block_idx, b_qs_idx);
         }
     }
 }
@@ -72,7 +68,7 @@ void compute_outputs(const uint32_t first_row, const uint32_t num_rows) {
     const uint tid = gl_LocalInvocationID.x;
 
     get_offsets(a_offset, b_offset, d_offset);
-    a_offset /= QUANT_K;
+    a_offset /= QUANT_K_Q8_1;
     b_offset /= QUANT_K_Q8_1;
 
     FLOAT_TYPE temp[NUM_COLS][NUM_ROWS];
@@ -102,14 +98,6 @@ void compute_outputs(const uint32_t first_row, const uint32_t num_rows) {
     unroll_count = 2;
     unrolled_iters = num_iters & ~(unroll_count - 1);
 
-#if K_PER_ITER == 2
-    if ((p.ncols & 1) != 0 &&
-        unrolled_iters == num_iters &&
-        unrolled_iters > 0) {
-        unrolled_iters -= unroll_count;
-    }
-#endif
-
     while (i < unrolled_iters) {
         // Manually partially unroll the loop
         [[unroll]] for (uint k = 0; k < unroll_count; ++k) {
@@ -128,6 +116,10 @@ void compute_outputs(const uint32_t first_row, const uint32_t num_rows) {
 void main() {
     const uint first_row = NUM_ROWS * (gl_WorkGroupID.x + gl_NumWorkGroups.x * gl_WorkGroupID.z);
 
+#ifdef NEEDS_INIT_IQ_SHMEM
+    init_iq_shmem(gl_WorkGroupSize);
+#endif
+
     // do NUM_ROWS at a time, unless there aren't enough remaining rows
     if (first_row + NUM_ROWS <= p.stride_d) {
         compute_outputs(first_row, NUM_ROWS);

ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vecq_funcs.glsl

@@ -0,0 +1,379 @@
+#extension GL_EXT_shader_explicit_arithmetic_types_int32 : require
+#extension GL_EXT_shader_explicit_arithmetic_types_int16 : require
+#extension GL_EXT_shader_explicit_arithmetic_types_int8 : require
+
+#include "types.glsl"
+
+#if defined(DATA_A_Q4_0) || defined(DATA_A_Q5_0) || defined(DATA_A_Q8_0) || defined(DATA_A_IQ1_S) || defined(DATA_A_IQ2_XXS) || defined(DATA_A_IQ2_XS) || defined(DATA_A_IQ2_S) || defined(DATA_A_IQ3_XXS) || defined(DATA_A_IQ3_S) || defined(DATA_A_IQ4_XS) || defined(DATA_A_IQ4_NL)
+FLOAT_TYPE get_dm(uint ib) {
+    return FLOAT_TYPE(data_a[ib].d);
+}
+#endif
+
+#if defined(DATA_A_Q4_1) || defined(DATA_A_Q5_1)
+FLOAT_TYPE_VEC2 get_dm(uint ib) {
+    return FLOAT_TYPE_VEC2(data_a_packed32[ib].dm);
+}
+#endif
+
+#if defined(DATA_A_MXFP4)
+FLOAT_TYPE get_dm(uint ib) {
+    return FLOAT_TYPE(e8m0_to_fp32(data_a[ib].e));
+}
+#endif
+
+#if defined(DATA_A_Q2_K)
+FLOAT_TYPE_VEC2 get_dm(uint ib) {
+    const uint ib_k = ib / 8;
+    return FLOAT_TYPE_VEC2(data_a_packed32[ib_k].dm);
+}
+#endif
+
+// Each iqs value maps to a 32-bit integer
+#if defined(DATA_A_Q4_0)
+// 2-byte loads for Q4_0 blocks (18 bytes)
+i32vec2 repack(uint ib, uint iqs) {
+    const u16vec2 quants = u16vec2(data_a_packed16[ib].qs[iqs * 2    ],
+                                   data_a_packed16[ib].qs[iqs * 2 + 1]);
+    const uint32_t vui = pack32(quants);
+    return i32vec2( vui       & 0x0F0F0F0F,
+                   (vui >> 4) & 0x0F0F0F0F);
+}
+
+FLOAT_TYPE mul_q8_1(const int32_t q_sum, const float da, const vec2 dsb, const int32_t sum_divisor) {
+    return FLOAT_TYPE(da * (float(q_sum) * dsb.x - (8 / sum_divisor) * dsb.y));
+}
+#endif
+
+#if defined(DATA_A_Q4_1)
+// 4-byte loads for Q4_1 blocks (20 bytes)
+i32vec2 repack(uint ib, uint iqs) {
+    const uint32_t vui = data_a_packed32[ib].qs[iqs];
+    return i32vec2( vui       & 0x0F0F0F0F,
+                   (vui >> 4) & 0x0F0F0F0F);
+}
+
+FLOAT_TYPE mul_q8_1(const int32_t q_sum, const vec2 dma, const vec2 dsb, const int32_t sum_divisor) {
+    return FLOAT_TYPE(float(q_sum) * dma.x * dsb.x + dma.y * dsb.y / sum_divisor);
+}
+#endif
+
+#if defined(DATA_A_Q5_0)
+// 2-byte loads for Q5_0 blocks (22 bytes)
+i32vec2 repack(uint ib, uint iqs) {
+    const u16vec2 quants = u16vec2(data_a_packed16[ib].qs[iqs * 2    ],
+                                   data_a_packed16[ib].qs[iqs * 2 + 1]);
+    const uint32_t vui = pack32(quants);
+    const int32_t qh = int32_t((uint32_t(data_a_packed16[ib].qh[1]) << 16 | data_a_packed16[ib].qh[0]) >> (4 * iqs));
+    const int32_t v0 = int32_t(vui & 0x0F0F0F0F)
+                     | ((qh & 0xF) * 0x02040810) & 0x10101010; // (0,1,2,3) -> (4,12,20,28)
+
+    const int32_t v1 = int32_t((vui >> 4) & 0x0F0F0F0F)
+                     | (((qh >> 16) & 0xF) * 0x02040810) & 0x10101010; // (16,17,18,19) -> (4,12,20,28)
+
+    return i32vec2(v0, v1);
+}
+
+FLOAT_TYPE mul_q8_1(const int32_t q_sum, const float da, const vec2 dsb, const int32_t sum_divisor) {
+    return FLOAT_TYPE(da * (float(q_sum) * dsb.x - (16 / sum_divisor) * dsb.y));
+}
+#endif
+
+#if defined(DATA_A_Q5_1)
+// 4-byte loads for Q5_1 blocks (24 bytes)
+i32vec2 repack(uint ib, uint iqs) {
+    const u16vec2 quants = u16vec2(data_a_packed16[ib].qs[iqs * 2    ],
+                                   data_a_packed16[ib].qs[iqs * 2 + 1]);
+    const uint32_t vui = pack32(quants);
+    const int32_t qh = int32_t(data_a_packed32[ib].qh >> (4 * iqs));
+    const int32_t v0 = int32_t(vui & 0x0F0F0F0F)
+                     | ((qh & 0xF) * 0x02040810) & 0x10101010; // (0,1,2,3) -> (4,12,20,28)
+
+    const int32_t v1 = int32_t((vui >> 4) & 0x0F0F0F0F)
+                     | (((qh >> 16) & 0xF) * 0x02040810) & 0x10101010; // (16,17,18,19) -> (4,12,20,28)
+
+    return i32vec2(v0, v1);
+}
+
+FLOAT_TYPE mul_q8_1(const int32_t q_sum, const vec2 dma, const vec2 dsb, const int32_t sum_divisor) {
+    return FLOAT_TYPE(float(q_sum) * dma.x * dsb.x + dma.y * dsb.y / sum_divisor);
+}
+#endif
+
+#if defined(DATA_A_Q8_0)
+// 2-byte loads for Q8_0 blocks (34 bytes)
+int32_t repack(uint ib, uint iqs) {
+    return pack32(i16vec2(data_a_packed16[ib].qs[iqs * 2    ],
+                          data_a_packed16[ib].qs[iqs * 2 + 1]));
+}
+
+FLOAT_TYPE mul_q8_1(const int32_t q_sum, const float da, const vec2 dsb, const int32_t sum_divisor) {
+    return FLOAT_TYPE(float(q_sum) * da * dsb.x);
+}
+#endif
+
+#if defined(DATA_A_MXFP4)
+// 1-byte loads for mxfp4 blocks (17 bytes)
+i32vec2 repack(uint ib, uint iqs) {
+    const uint32_t qs = pack32(u8vec4(data_a[ib].qs[iqs * 4    ],
+                                      data_a[ib].qs[iqs * 4 + 1],
+                                      data_a[ib].qs[iqs * 4 + 2],
+                                      data_a[ib].qs[iqs * 4 + 3]));
+
+    const u8vec4 i_a0 = unpack8( qs       & 0x0F0F0F0F);
+    const u8vec4 i_a1 = unpack8((qs >> 4) & 0x0F0F0F0F);
+
+    return i32vec2(pack32(i8vec4(kvalues_mxfp4[i_a0.x], kvalues_mxfp4[i_a0.y], kvalues_mxfp4[i_a0.z], kvalues_mxfp4[i_a0.w])),
+                   pack32(i8vec4(kvalues_mxfp4[i_a1.x], kvalues_mxfp4[i_a1.y], kvalues_mxfp4[i_a1.z], kvalues_mxfp4[i_a1.w])));
+}
+
+FLOAT_TYPE mul_q8_1(const int32_t q_sum, const float da, const vec2 dsb, const int32_t sum_divisor) {
+    return FLOAT_TYPE(da * dsb.x * float(q_sum) * 0.5);
+}
+#endif
+
+#if defined(DATA_A_QUANT_LEGACY) || defined(DATA_A_MXFP4)
+FLOAT_TYPE mmvq_dot_product(const uint ib_a, const uint iqs) {
+    int32_t q_sum = 0;
+#if QUANT_R == 2
+    const i32vec2 data_a_qs = repack(ib_a, iqs);
+    q_sum += dotPacked4x8EXT(data_a_qs.x,
+                             cache_b_qs[0]);
+    q_sum += dotPacked4x8EXT(data_a_qs.y,
+                             cache_b_qs[1]);
+#else
+    int32_t data_a_qs = repack(ib_a, iqs * 2);
+    q_sum += dotPacked4x8EXT(data_a_qs,
+                             cache_b_qs[0]);
+    data_a_qs = repack(ib_a, iqs * 2 + 1);
+    q_sum += dotPacked4x8EXT(data_a_qs,
+                             cache_b_qs[1]);
+#endif
+
+    // 2 quants per call => divide sums by 8/2 = 4
+    return mul_q8_1(q_sum, get_dm(ib_a), cache_b_ds, 4);
+}
+#endif
+
+#if defined(DATA_A_Q2_K)
+// 4-byte loads for Q2_K blocks (84 bytes)
+i32vec4 repack4(uint ib, uint iqs) {
+    const uint ib_k = ib / 8;
+    const uint iqs_k = (ib % 8) * 8 + iqs;
+
+    const uint qs_idx = (iqs_k / 32) * 8 + (iqs_k % 8);
+    const uint qs_shift = ((iqs_k % 32) / 8) * 2;
+
+    return i32vec4((data_a_packed32[ib_k].qs[qs_idx    ] >> qs_shift) & 0x03030303,
+                   (data_a_packed32[ib_k].qs[qs_idx + 1] >> qs_shift) & 0x03030303,
+                   (data_a_packed32[ib_k].qs[qs_idx + 2] >> qs_shift) & 0x03030303,
+                   (data_a_packed32[ib_k].qs[qs_idx + 3] >> qs_shift) & 0x03030303);
+}
+
+uint8_t get_scale(uint ib, uint iqs) {
+    const uint ib_k = ib / 8;
+    const uint iqs_k = (ib % 8) * 8 + iqs;
+
+    return data_a[ib_k].scales[iqs_k / 4];
+}
+
+FLOAT_TYPE mmvq_dot_product(const uint ib_a, const uint iqs) {
+    int32_t sum_d = 0;
+    int32_t sum_m = 0;
+
+    const i32vec4 qs_a = repack4(ib_a, iqs * 4);
+    const uint8_t scale = get_scale(ib_a, iqs * 4);
+    const vec2 dm = vec2(get_dm(ib_a));
+    const int32_t scale_m = int32_t(scale >> 4) * 0x01010101; // Duplicate 8-bit value across 32-bits.
+
+    sum_d += dotPacked4x8EXT(qs_a.x, cache_b_qs[0]) * (scale & 0xF);
+    sum_m += dotPacked4x8EXT(scale_m, cache_b_qs[0]);
+
+    sum_d += dotPacked4x8EXT(qs_a.y, cache_b_qs[1]) * (scale & 0xF);
+    sum_m += dotPacked4x8EXT(scale_m, cache_b_qs[1]);
+
+    sum_d += dotPacked4x8EXT(qs_a.z, cache_b_qs[2]) * (scale & 0xF);
+    sum_m += dotPacked4x8EXT(scale_m, cache_b_qs[2]);
+
+    sum_d += dotPacked4x8EXT(qs_a.w, cache_b_qs[3]) * (scale & 0xF);
+    sum_m += dotPacked4x8EXT(scale_m, cache_b_qs[3]);
+
+    return FLOAT_TYPE(float(cache_b_ds.x) * (float(dm.x) * float(sum_d) - float(dm.y) * float(sum_m)));
+}
+#endif
+
+#if defined(DATA_A_Q3_K)
+// 2-byte loads for Q3_K blocks (110 bytes)
+i32vec4 repack4(uint ib, uint iqs) {
+    const uint ib_k = ib / 8;
+    const uint iqs_k = (ib % 8) * 8 + iqs;
+
+    const uint qs_idx = (iqs_k / 32) * 8 + (iqs_k % 8);
+    const uint qs_shift = ((iqs_k % 32) / 8) * 2;
+    const uint hm_shift = iqs_k / 8;
+
+    // bitwise OR to add 4 if hmask is set, subtract later
+    const i8vec2 vals00 = unpack8(int16_t((data_a_packed16[ib_k].qs[qs_idx  * 2    ] >> qs_shift) & uint16_t(0x0303))) |
+                          unpack8(int16_t(((data_a_packed16[ib_k].hmask[iqs * 2    ] >> hm_shift) & uint16_t(0x0101)) << 2));
+    const i8vec2 vals01 = unpack8(int16_t((data_a_packed16[ib_k].qs[qs_idx  * 2 + 1] >> qs_shift) & uint16_t(0x0303))) |
+                          unpack8(int16_t(((data_a_packed16[ib_k].hmask[iqs * 2 + 1] >> hm_shift) & uint16_t(0x0101)) << 2));
+    const i8vec2 vals10 = unpack8(int16_t((data_a_packed16[ib_k].qs[qs_idx  * 2 + 2] >> qs_shift) & uint16_t(0x0303))) |
+                          unpack8(int16_t(((data_a_packed16[ib_k].hmask[iqs * 2 + 2] >> hm_shift) & uint16_t(0x0101)) << 2));
+    const i8vec2 vals11 = unpack8(int16_t((data_a_packed16[ib_k].qs[qs_idx  * 2 + 3] >> qs_shift) & uint16_t(0x0303))) |
+                          unpack8(int16_t(((data_a_packed16[ib_k].hmask[iqs * 2 + 3] >> hm_shift) & uint16_t(0x0101)) << 2));
+    const i8vec2 vals20 = unpack8(int16_t((data_a_packed16[ib_k].qs[qs_idx  * 2 + 4] >> qs_shift) & uint16_t(0x0303))) |
+                          unpack8(int16_t(((data_a_packed16[ib_k].hmask[iqs * 2 + 4] >> hm_shift) & uint16_t(0x0101)) << 2));
+    const i8vec2 vals21 = unpack8(int16_t((data_a_packed16[ib_k].qs[qs_idx  * 2 + 5] >> qs_shift) & uint16_t(0x0303))) |
+                          unpack8(int16_t(((data_a_packed16[ib_k].hmask[iqs * 2 + 5] >> hm_shift) & uint16_t(0x0101)) << 2));
+    const i8vec2 vals30 = unpack8(int16_t((data_a_packed16[ib_k].qs[qs_idx  * 2 + 6] >> qs_shift) & uint16_t(0x0303))) |
+                          unpack8(int16_t(((data_a_packed16[ib_k].hmask[iqs * 2 + 6] >> hm_shift) & uint16_t(0x0101)) << 2));
+    const i8vec2 vals31 = unpack8(int16_t((data_a_packed16[ib_k].qs[qs_idx  * 2 + 7] >> qs_shift) & uint16_t(0x0303))) |
+                          unpack8(int16_t(((data_a_packed16[ib_k].hmask[iqs * 2 + 7] >> hm_shift) & uint16_t(0x0101)) << 2));
+
+    return i32vec4(pack32(i8vec4(vals00.x, vals00.y, vals01.x, vals01.y) - int8_t(4)),
+                   pack32(i8vec4(vals10.x, vals10.y, vals11.x, vals11.y) - int8_t(4)),
+                   pack32(i8vec4(vals20.x, vals20.y, vals21.x, vals21.y) - int8_t(4)),
+                   pack32(i8vec4(vals30.x, vals30.y, vals31.x, vals31.y) - int8_t(4)));
+}
+
+float get_d_scale(uint ib, uint iqs) {
+    const uint ib_k = ib / 8;
+    const uint iqs_k = (ib % 8) * 8 + iqs;
+    const uint is = iqs_k / 4;
+
+    const int8_t scale = int8_t(((data_a[ib_k].scales[is % 8      ] >> (4 * (is / 8))) & 0x0F0F) |
+                               (((data_a[ib_k].scales[8 + (is % 4)] >> (2 * (is / 4))) & 0x0303) << 4));
+    return float(data_a[ib_k].d) * float(scale - 32);
+}
+
+FLOAT_TYPE mmvq_dot_product(const uint ib_a, const uint iqs) {
+    int32_t q_sum = 0;
+
+    const i32vec4 qs_a = repack4(ib_a, iqs * 4);
+    const float d_scale = get_d_scale(ib_a, iqs * 4);
+
+    q_sum += dotPacked4x8EXT(qs_a.x, cache_b_qs[0]);
+    q_sum += dotPacked4x8EXT(qs_a.y, cache_b_qs[1]);
+    q_sum += dotPacked4x8EXT(qs_a.z, cache_b_qs[2]);
+    q_sum += dotPacked4x8EXT(qs_a.w, cache_b_qs[3]);
+
+    return FLOAT_TYPE(float(cache_b_ds.x) * d_scale * float(q_sum));
+}
+#endif
+
+#if defined(DATA_A_Q4_K) || defined(DATA_A_Q5_K)
+// 4-byte loads for Q4_K blocks (144 bytes) and Q5_K blocks (176 bytes)
+i32vec4 repack4(uint ib, uint iqs) {
+    const uint ib_k = ib / 8;
+    const uint iqs_k = (ib % 8) * 8 + iqs;
+
+    const uint qs_idx = (iqs_k / 16) * 8 + (iqs_k % 8);
+    const uint qs_shift = ((iqs_k % 16) / 8) * 4;
+
+#if defined(DATA_A_Q4_K)
+    const uint32_t vals0 = (data_a_packed32[ib_k].qs[qs_idx    ] >> qs_shift) & 0x0F0F0F0F;
+    const uint32_t vals1 = (data_a_packed32[ib_k].qs[qs_idx + 1] >> qs_shift) & 0x0F0F0F0F;
+    const uint32_t vals2 = (data_a_packed32[ib_k].qs[qs_idx + 2] >> qs_shift) & 0x0F0F0F0F;
+    const uint32_t vals3 = (data_a_packed32[ib_k].qs[qs_idx + 3] >> qs_shift) & 0x0F0F0F0F;
+
+    return i32vec4(vals0, vals1, vals2, vals3);
+#else // defined(DATA_A_Q5_K)
+    const uint qh_idx = iqs;
+    const uint qh_shift = iqs_k / 8;
+
+    return i32vec4(((data_a_packed32[ib_k].qs[qs_idx    ] >> qs_shift) & 0x0F0F0F0F) |
+                  (((data_a_packed32[ib_k].qh[qh_idx    ] >> qh_shift) & 0x01010101) << 4),
+                   ((data_a_packed32[ib_k].qs[qs_idx + 1] >> qs_shift) & 0x0F0F0F0F) |
+                  (((data_a_packed32[ib_k].qh[qh_idx + 1] >> qh_shift) & 0x01010101) << 4),
+                   ((data_a_packed32[ib_k].qs[qs_idx + 2] >> qs_shift) & 0x0F0F0F0F) |
+                  (((data_a_packed32[ib_k].qh[qh_idx + 2] >> qh_shift) & 0x01010101) << 4),
+                   ((data_a_packed32[ib_k].qs[qs_idx + 3] >> qs_shift) & 0x0F0F0F0F) |
+                  (((data_a_packed32[ib_k].qh[qh_idx + 3] >> qh_shift) & 0x01010101) << 4));
+#endif
+}
+
+vec2 get_dm_scale(uint ib, uint iqs) {
+    const uint ib_k = ib / 8;
+    const uint iqs_k = (ib % 8) * 8 + iqs;
+    const uint is = iqs_k / 8;
+    u8vec2 scale_dm;
+    if (is < 4) {
+        scale_dm = u8vec2(data_a[ib_k].scales[is] & 0x3F, data_a[ib_k].scales[is + 4] & 0x3F);
+    } else {
+        scale_dm = u8vec2((data_a[ib_k].scales[is+4] & 0xF) | ((data_a[ib_k].scales[is-4] & 0xC0) >> 2),
+                          (data_a[ib_k].scales[is+4] >>  4) | ((data_a[ib_k].scales[is  ] & 0xC0) >> 2));
+    }
+
+    return FLOAT_TYPE_VEC2(data_a_packed32[ib_k].dm) * FLOAT_TYPE_VEC2(scale_dm);
+}
+
+FLOAT_TYPE mmvq_dot_product(const uint ib_a, const uint iqs) {
+    int32_t q_sum = 0;
+
+    const i32vec4 qs_a = repack4(ib_a, iqs * 4);
+    const vec2 dm_scale = get_dm_scale(ib_a, iqs * 4);
+
+    q_sum += dotPacked4x8EXT(qs_a.x, cache_b_qs[0]);
+    q_sum += dotPacked4x8EXT(qs_a.y, cache_b_qs[1]);
+    q_sum += dotPacked4x8EXT(qs_a.z, cache_b_qs[2]);
+    q_sum += dotPacked4x8EXT(qs_a.w, cache_b_qs[3]);
+
+    return FLOAT_TYPE(float(cache_b_ds.x) * float(dm_scale.x) * float(q_sum) - float(dm_scale.y) * float(cache_b_ds.y / 2));
+}
+#endif
+
+#if defined(DATA_A_Q6_K)
+// 2-byte loads for Q6_K blocks (210 bytes)
+i32vec4 repack4(uint ib, uint iqs) {
+    const uint ib_k = ib / 8;
+    const uint iqs_k = (ib % 8) * 8 + iqs;
+
+    const uint ql_idx = (iqs_k / 32) * 16 + iqs_k % 16;
+    const uint ql_shift = ((iqs_k % 32) / 16) * 4;
+
+    const uint qh_idx = (iqs_k / 32) * 8 + iqs;
+    const uint qh_shift = ((iqs_k % 32) / 8) * 2;
+
+    const i8vec2 vals00 = (unpack8(int16_t((data_a_packed16[ib_k].ql[ql_idx * 2    ] >> ql_shift) & uint16_t(0x0F0F))) |
+                          unpack8(int16_t(((data_a_packed16[ib_k].qh[qh_idx * 2    ] >> qh_shift) & uint16_t(0x0303)) << 4))) - int8_t(32);
+    const i8vec2 vals01 = (unpack8(int16_t((data_a_packed16[ib_k].ql[ql_idx * 2 + 1] >> ql_shift) & uint16_t(0x0F0F))) |
+                          unpack8(int16_t(((data_a_packed16[ib_k].qh[qh_idx * 2 + 1] >> qh_shift) & uint16_t(0x0303)) << 4))) - int8_t(32);
+    const i8vec2 vals10 = (unpack8(int16_t((data_a_packed16[ib_k].ql[ql_idx * 2 + 2] >> ql_shift) & uint16_t(0x0F0F))) |
+                          unpack8(int16_t(((data_a_packed16[ib_k].qh[qh_idx * 2 + 2] >> qh_shift) & uint16_t(0x0303)) << 4))) - int8_t(32);
+    const i8vec2 vals11 = (unpack8(int16_t((data_a_packed16[ib_k].ql[ql_idx * 2 + 3] >> ql_shift) & uint16_t(0x0F0F))) |
+                          unpack8(int16_t(((data_a_packed16[ib_k].qh[qh_idx * 2 + 3] >> qh_shift) & uint16_t(0x0303)) << 4))) - int8_t(32);
+    const i8vec2 vals20 = (unpack8(int16_t((data_a_packed16[ib_k].ql[ql_idx * 2 + 4] >> ql_shift) & uint16_t(0x0F0F))) |
+                          unpack8(int16_t(((data_a_packed16[ib_k].qh[qh_idx * 2 + 4] >> qh_shift) & uint16_t(0x0303)) << 4))) - int8_t(32);
+    const i8vec2 vals21 = (unpack8(int16_t((data_a_packed16[ib_k].ql[ql_idx * 2 + 5] >> ql_shift) & uint16_t(0x0F0F))) |
+                          unpack8(int16_t(((data_a_packed16[ib_k].qh[qh_idx * 2 + 5] >> qh_shift) & uint16_t(0x0303)) << 4))) - int8_t(32);
+    const i8vec2 vals30 = (unpack8(int16_t((data_a_packed16[ib_k].ql[ql_idx * 2 + 6] >> ql_shift) & uint16_t(0x0F0F))) |
+                          unpack8(int16_t(((data_a_packed16[ib_k].qh[qh_idx * 2 + 6] >> qh_shift) & uint16_t(0x0303)) << 4))) - int8_t(32);
+    const i8vec2 vals31 = (unpack8(int16_t((data_a_packed16[ib_k].ql[ql_idx * 2 + 7] >> ql_shift) & uint16_t(0x0F0F))) |
+                          unpack8(int16_t(((data_a_packed16[ib_k].qh[qh_idx * 2 + 7] >> qh_shift) & uint16_t(0x0303)) << 4))) - int8_t(32);
+
+    return i32vec4(pack32(i8vec4(vals00.x, vals00.y, vals01.x, vals01.y)),
+                   pack32(i8vec4(vals10.x, vals10.y, vals11.x, vals11.y)),
+                   pack32(i8vec4(vals20.x, vals20.y, vals21.x, vals21.y)),
+                   pack32(i8vec4(vals30.x, vals30.y, vals31.x, vals31.y)));
+}
+
+float get_d_scale(uint ib, uint iqs) {
+    const uint ib_k = ib / 8;
+    const uint iqs_k = (ib % 8) * 8 + iqs;
+    return float(data_a[ib_k].d) * float(data_a[ib_k].scales[iqs_k / 4]);
+}
+
+FLOAT_TYPE mmvq_dot_product(const uint ib_a, const uint iqs) {
+    int32_t q_sum = 0;
+
+    const i32vec4 qs_a = repack4(ib_a, iqs * 4);
+    const float d_scale = get_d_scale(ib_a, iqs * 4);
+
+    q_sum += dotPacked4x8EXT(qs_a.x, cache_b_qs[0]);
+    q_sum += dotPacked4x8EXT(qs_a.y, cache_b_qs[1]);
+    q_sum += dotPacked4x8EXT(qs_a.z, cache_b_qs[2]);
+    q_sum += dotPacked4x8EXT(qs_a.w, cache_b_qs[3]);
+
+    return FLOAT_TYPE(float(cache_b_ds.x) * float(d_scale) * float(q_sum));
+}
+#endif

ggml/src/ggml-vulkan/vulkan-shaders/mul_mmq.comp

@@ -78,8 +78,6 @@ layout (constant_id = 10) const uint WARP = 32;
 
 #define BK 32
 
-#define MMQ_SHMEM
-
 #include "mul_mmq_shmem_types.glsl"
 
 #ifdef MUL_MAT_ID

ggml/src/ggml-vulkan/vulkan-shaders/mul_mmq_funcs.glsl

@@ -9,31 +9,6 @@
 #if defined(DATA_A_Q4_0) || defined(DATA_A_Q4_1)
 // 2-byte loads for Q4_0 blocks (18 bytes)
 // 4-byte loads for Q4_1 blocks (20 bytes)
-i32vec2 repack(uint ib, uint iqs) {
-#ifdef DATA_A_Q4_0
-    const u16vec2 quants = u16vec2(data_a_packed16[ib].qs[iqs * 2    ],
-                                   data_a_packed16[ib].qs[iqs * 2 + 1]);
-    const uint32_t vui = pack32(quants);
-    return i32vec2( vui       & 0x0F0F0F0F,
-                   (vui >> 4) & 0x0F0F0F0F);
-#else // DATA_A_Q4_1
-    const uint32_t vui = data_a_packed32[ib].qs[iqs];
-    return i32vec2( vui       & 0x0F0F0F0F,
-                   (vui >> 4) & 0x0F0F0F0F);
-#endif
-}
-
-#ifdef DATA_A_Q4_0
-ACC_TYPE mul_q8_1(const int32_t q_sum, const float da, const vec2 dsb, const int32_t sum_divisor) {
-    return ACC_TYPE(da * (float(q_sum) * dsb.x - (8 / sum_divisor) * dsb.y));
-}
-#else // DATA_A_Q4_1
-ACC_TYPE mul_q8_1(const int32_t q_sum, const vec2 dma, const vec2 dsb, const int32_t sum_divisor) {
-    return ACC_TYPE(float(q_sum) * dma.x * dsb.x + dma.y * dsb.y / sum_divisor);
-}
-#endif
-
-#ifdef MMQ_SHMEM
 void block_a_to_shmem(const uint buf_ib, const uint ib, const uint iqs) {
 #ifdef DATA_A_Q4_0
     buf_a[buf_ib].qs[iqs] = pack32(u16vec2(data_a_packed16[ib].qs[iqs * 2],
@@ -73,42 +48,17 @@ ACC_TYPE mmq_dot_product(const uint ib_a) {
         q_sum += dotPacked4x8EXT(qs_a.y, qs_b1);
     }
 
-    return mul_q8_1(q_sum, cache_a[ib_a].dm, cache_b.ds, 1);
+#ifdef DATA_A_Q4_0
+    return ACC_TYPE(float(cache_a[ib_a].dm) * (float(q_sum) * float(cache_b.ds.x) - 8.0 * float(cache_b.ds.y)));
+#else // DATA_A_Q4_1
+    return ACC_TYPE(float(q_sum) * float(cache_a[ib_a].dm.x) * float(cache_b.ds.x) + float(cache_a[ib_a].dm.y) * float(cache_b.ds.y));
+#endif
 }
-#endif // MMQ_SHMEM
+#endif
 
-#elif defined(DATA_A_Q5_0) || defined(DATA_A_Q5_1)
+#if defined(DATA_A_Q5_0) || defined(DATA_A_Q5_1)
 // 2-byte loads for Q5_0 blocks (22 bytes)
 // 4-byte loads for Q5_1 blocks (24 bytes)
-i32vec2 repack(uint ib, uint iqs) {
-    const u16vec2 quants = u16vec2(data_a_packed16[ib].qs[iqs * 2    ],
-                                   data_a_packed16[ib].qs[iqs * 2 + 1]);
-    const uint32_t vui = pack32(quants);
-#ifdef DATA_A_Q5_0
-    const int32_t qh = int32_t((uint32_t(data_a_packed16[ib].qh[1]) << 16 | data_a_packed16[ib].qh[0]) >> (4 * iqs));
-#else // DATA_A_Q5_1
-    const int32_t qh = int32_t(data_a_packed32[ib].qh >> (4 * iqs));
-#endif
-    const int32_t v0 = int32_t(vui & 0x0F0F0F0F)
-                     | ((qh & 0xF) * 0x02040810) & 0x10101010; // (0,1,2,3) -> (4,12,20,28)
-
-    const int32_t v1 = int32_t((vui >> 4) & 0x0F0F0F0F)
-                     | (((qh >> 16) & 0xF) * 0x02040810) & 0x10101010; // (16,17,18,19) -> (4,12,20,28)
-
-    return i32vec2(v0, v1);
-}
-
-#ifdef DATA_A_Q5_0
-ACC_TYPE mul_q8_1(const int32_t q_sum, const float da, const vec2 dsb, const int32_t sum_divisor) {
-    return ACC_TYPE(da * (float(q_sum) * dsb.x - (16 / sum_divisor) * dsb.y));
-}
-#else // DATA_A_Q5_1
-ACC_TYPE mul_q8_1(const int32_t q_sum, const vec2 dma, const vec2 dsb, const int32_t sum_divisor) {
-    return ACC_TYPE(float(q_sum) * dma.x * dsb.x + dma.y * dsb.y / sum_divisor);
-}
-#endif
-
-#ifdef MMQ_SHMEM
 void block_a_to_shmem(const uint buf_ib, const uint ib, const uint iqs) {
 #ifdef DATA_A_Q5_0
     buf_a[buf_ib].qs[iqs] = pack32(u16vec2(data_a_packed16[ib].qs[iqs * 2],
@@ -154,23 +104,16 @@ ACC_TYPE mmq_dot_product(const uint ib_a) {
         q_sum += dotPacked4x8EXT(qs_a1, qs_b1);
     }
 
-    return mul_q8_1(q_sum, cache_a[ib_a].dm, cache_b.ds, 1);
+#ifdef DATA_A_Q5_0
+    return ACC_TYPE(float(cache_a[ib_a].dm) * (float(q_sum) * float(cache_b.ds.x) - 16.0 * float(cache_b.ds.y)));
+#else // DATA_A_Q5_1
+    return ACC_TYPE(float(q_sum) * float(cache_a[ib_a].dm.x) * float(cache_b.ds.x) + float(cache_a[ib_a].dm.y) * float(cache_b.ds.y));
+#endif
 }
-#endif // MMQ_SHMEM
 #endif
 
 #if defined(DATA_A_Q8_0)
 // 2-byte loads for Q8_0 blocks (34 bytes)
-int32_t repack(uint ib, uint iqs) {
-    return pack32(i16vec2(data_a_packed16[ib].qs[iqs * 2    ],
-                          data_a_packed16[ib].qs[iqs * 2 + 1]));
-}
-
-ACC_TYPE mul_q8_1(const int32_t q_sum, const float da, const vec2 dsb, const int32_t sum_divisor) {
-    return ACC_TYPE(float(q_sum) * da * dsb.x);
-}
-
-#ifdef MMQ_SHMEM
 void block_a_to_shmem(const uint buf_ib, const uint ib, const uint iqs) {
     buf_a[buf_ib].qs[iqs] = pack32(i16vec2(data_a_packed16[ib].qs[iqs * 2],
                                            data_a_packed16[ib].qs[iqs * 2 + 1]));
@@ -197,28 +140,12 @@ ACC_TYPE mmq_dot_product(const uint ib_a) {
         q_sum += dotPacked4x8EXT(qs_a, qs_b);
     }
 
-    return mul_q8_1(q_sum, cache_a[ib_a].dm, cache_b.ds, 1);
+    return ACC_TYPE(float(q_sum) * float(cache_a[ib_a].dm) * float(cache_b.ds.x));
 }
-#endif // MMQ_SHMEM
 #endif
 
 #if defined(DATA_A_MXFP4)
 // 1-byte loads for mxfp4 blocks (17 bytes)
-i32vec2 repack(uint ib, uint iqs) {
-    const uint32_t quants = pack32(u8vec4(data_a[ib].qs[iqs * 4    ],
-                                          data_a[ib].qs[iqs * 4 + 1],
-                                          data_a[ib].qs[iqs * 4 + 2],
-                                          data_a[ib].qs[iqs * 4 + 3]));
-
-    return i32vec2( quants       & 0x0F0F0F0F,
-                   (quants >> 4) & 0x0F0F0F0F);
-}
-
-ACC_TYPE mul_q8_1(const int32_t q_sum, const float da, const vec2 dsb, const int32_t sum_divisor) {
-    return ACC_TYPE(da * dsb.x * float(q_sum));
-}
-
-#ifdef MMQ_SHMEM
 void block_a_to_shmem(const uint buf_ib, const uint ib, const uint iqs) {
     const uint32_t qs = pack32(u8vec4(data_a[ib].qs[iqs * 4    ],
                                       data_a[ib].qs[iqs * 4 + 1],
@@ -252,37 +179,14 @@ ACC_TYPE mmq_dot_product(const uint ib_a) {
         q_sum += dotPacked4x8EXT(qs_a, cache_b.qs[iqs]);
     }
 
-    return mul_q8_1(q_sum, cache_a[ib_a].d, cache_b.ds, 1);
+    return ACC_TYPE(float(cache_a[ib_a].d) * float(cache_b.ds.x) * float(q_sum));
 }
-#endif // MMQ_SHMEM
 #endif
 
 // For k-quants, ib and iqs still assume 32-wide blocks, but k-quants are 256-wide
 // iqs still refers to a 32-bit integer, meaning 0..7 for 32-wide quants
 #if defined(DATA_A_Q2_K)
 // 4-byte loads for Q2_K blocks (84 bytes)
-int32_t repack(uint ib, uint iqs) {
-    const uint ib_k = ib / 8;
-    const uint iqs_k = (ib % 8) * 8 + iqs;
-
-    const uint qs_idx = (iqs_k / 32) * 8 + (iqs_k % 8);
-    const uint qs_shift = ((iqs_k % 32) / 8) * 2;
-
-    return int32_t((data_a_packed32[ib_k].qs[qs_idx] >> qs_shift) & 0x03030303);
-}
-
-uint8_t get_scale(uint ib, uint iqs) {
-    const uint ib_k = ib / 8;
-    const uint iqs_k = (ib % 8) * 8 + iqs;
-
-    return data_a[ib_k].scales[iqs_k / 4];
-}
-
-ACC_TYPE mul_q8_1(const int32_t sum_d, const int32_t sum_m, const vec2 dma, const vec2 dsb, const int32_t sum_divisor) {
-    return ACC_TYPE(dsb.x * (dma.x * float(sum_d) - dma.y * float(sum_m)));
-}
-
-#ifdef MMQ_SHMEM
 void block_a_to_shmem(const uint buf_ib, const uint ib, const uint iqs) {
     const uint ib_k = ib / 8;
     const uint iqs_k = (ib % 8) * 8 + iqs * QUANT_R_MMQ;
@@ -326,14 +230,12 @@ ACC_TYPE mmq_dot_product(const uint ib_a) {
         sum_m += dotPacked4x8EXT(scale_m, cache_b.qs[iqs]);
     }
 
-    return mul_q8_1(sum_d, sum_m, cache_a[ib_a].dm, cache_b.ds, 1);
+    return ACC_TYPE(float(cache_b.ds.x) * (float(cache_a[ib_a].dm.x) * float(sum_d) - float(cache_a[ib_a].dm.y) * float(sum_m)));
 }
-#endif // MMQ_SHMEM
 #endif
 
 #if defined(DATA_A_Q3_K)
 // 2-byte loads for Q3_K blocks (110 bytes)
-#ifdef MMQ_SHMEM
 void block_a_to_shmem(const uint buf_ib, const uint ib, const uint iqs) {
     const uint ib_k = ib / 8;
     const uint hm_idx = iqs * QUANT_R_MMQ;
@@ -394,18 +296,12 @@ ACC_TYPE mmq_dot_product(const uint ib_a) {
     }
     result += float(cache_a[ib_a].d_scales[1]) * float(q_sum);
 
-    return ACC_TYPE(cache_b.ds.x * result);
+    return ACC_TYPE(float(cache_b.ds.x) * result);
 }
-#endif // MMQ_SHMEM
 #endif
 
 #if defined(DATA_A_Q4_K) || defined(DATA_A_Q5_K)
 // 4-byte loads for Q4_K blocks (144 bytes) and Q5_K blocks (176 bytes)
-ACC_TYPE mul_q8_1(const int32_t q_sum, const vec2 dma, const vec2 dsb, const int32_t sum_divisor) {
-    return ACC_TYPE(dsb.x * dma.x * float(q_sum) - dma.y * dsb.y);
-}
-
-#ifdef MMQ_SHMEM
 void block_a_to_shmem(const uint buf_ib, const uint ib, const uint iqs) {
     const uint ib_k = ib / 8;
     const uint iqs_k = (ib % 8) * 8 + iqs * QUANT_R_MMQ;
@@ -427,7 +323,6 @@ void block_a_to_shmem(const uint buf_ib, const uint ib, const uint iqs) {
                                    (((data_a_packed32[ib_k].qh[qh_idx] >> qh_shift) & 0x01010101) << 4));
 #endif
 
-
     if (iqs == 0) {
         // Scale index
         const uint is = iqs_k / 8;
@@ -464,49 +359,12 @@ ACC_TYPE mmq_dot_product(const uint ib_a) {
         q_sum += dotPacked4x8EXT(qs_a, cache_b.qs[iqs]);
     }
 
-    return mul_q8_1(q_sum, cache_a[ib_a].dm, cache_b.ds, 1);
-}
-#endif // MMQ_SHMEM
-#endif
-
-#ifdef MMQ_SHMEM
-void block_b_to_shmem(const uint buf_ib, const uint ib, const uint iqs, const bool is_in_bounds) {
-    if (is_in_bounds) {
-        const uint ib_outer = ib / 4;
-        const uint ib_inner = ib % 4;
-
-        if (iqs == 0) {
-            buf_b[buf_ib].ds = FLOAT_TYPE_VEC2(data_b[ib_outer].ds[ib_inner]);
-        }
-
-        const ivec4 values = data_b[ib_outer].qs[ib_inner * 2 + iqs];
-        buf_b[buf_ib].qs[iqs * 4    ] = values.x;
-        buf_b[buf_ib].qs[iqs * 4 + 1] = values.y;
-        buf_b[buf_ib].qs[iqs * 4 + 2] = values.z;
-        buf_b[buf_ib].qs[iqs * 4 + 3] = values.w;
-    } else {
-        if (iqs == 0) {
-            buf_b[buf_ib].ds = FLOAT_TYPE_VEC2(0.0f);
-        }
-
-        buf_b[buf_ib].qs[iqs * 4    ] = 0;
-        buf_b[buf_ib].qs[iqs * 4 + 1] = 0;
-        buf_b[buf_ib].qs[iqs * 4 + 2] = 0;
-        buf_b[buf_ib].qs[iqs * 4 + 3] = 0;
-    }
-}
-
-void block_b_to_registers(const uint ib) {
-    cache_b.ds = buf_b[ib].ds;
-    [[unroll]] for (uint iqs = 0; iqs < BK / 4; iqs++) {
-        cache_b.qs[iqs] = buf_b[ib].qs[iqs];
-    }
+    return ACC_TYPE(float(cache_b.ds.x) * float(cache_a[ib_a].dm.x) * float(q_sum) - float(cache_a[ib_a].dm.y) * float(cache_b.ds.y));
 }
 #endif
 
 #if defined(DATA_A_Q6_K)
 // 2-byte loads for Q6_K blocks (210 bytes)
-#ifdef MMQ_SHMEM
 void block_a_to_shmem(const uint buf_ib, const uint ib, const uint iqs) {
     const uint ib_k = ib / 8;
     const uint iqs_k = (ib % 8) * 8 + iqs;
@@ -558,32 +416,39 @@ ACC_TYPE mmq_dot_product(const uint ib_a) {
     }
     result += float(cache_a[ib_a].d_scales[1]) * float(q_sum);
 
-    return ACC_TYPE(cache_b.ds.x * result);
-}
-#endif // MMQ_SHMEM
-#endif
-
-#if defined(DATA_A_Q4_0) || defined(DATA_A_Q5_0) || defined(DATA_A_Q8_0) || defined(DATA_A_IQ1_S) || defined(DATA_A_IQ2_XXS) || defined(DATA_A_IQ2_XS) || defined(DATA_A_IQ2_S) || defined(DATA_A_IQ3_XXS) || defined(DATA_A_IQ3_S) || defined(DATA_A_IQ4_XS) || defined(DATA_A_IQ4_NL)
-FLOAT_TYPE get_d(uint ib) {
-    return FLOAT_TYPE(data_a[ib].d);
+    return ACC_TYPE(float(cache_b.ds.x) * result);
 }
 #endif
 
-#if defined(DATA_A_MXFP4)
-FLOAT_TYPE get_d(uint ib) {
-    return FLOAT_TYPE(e8m0_to_fp32(data_a[ib].e));
-}
-#endif
+void block_b_to_shmem(const uint buf_ib, const uint ib, const uint iqs, const bool is_in_bounds) {
+    if (is_in_bounds) {
+        const uint ib_outer = ib / 4;
+        const uint ib_inner = ib % 4;
 
-#if defined(DATA_A_Q4_1) || defined(DATA_A_Q5_1)
-FLOAT_TYPE_VEC2 get_dm(uint ib) {
-    return FLOAT_TYPE_VEC2(data_a_packed32[ib].dm);
-}
-#endif
+        if (iqs == 0) {
+            buf_b[buf_ib].ds = FLOAT_TYPE_VEC2(data_b[ib_outer].ds[ib_inner]);
+        }
 
-#if defined(DATA_A_Q2_K)
-FLOAT_TYPE_VEC2 get_dm(uint ib) {
-    const uint ib_k = ib / 8;
-    return FLOAT_TYPE_VEC2(data_a_packed32[ib_k].dm);
+        const ivec4 values = data_b[ib_outer].qs[ib_inner * 2 + iqs];
+        buf_b[buf_ib].qs[iqs * 4    ] = values.x;
+        buf_b[buf_ib].qs[iqs * 4 + 1] = values.y;
+        buf_b[buf_ib].qs[iqs * 4 + 2] = values.z;
+        buf_b[buf_ib].qs[iqs * 4 + 3] = values.w;
+    } else {
+        if (iqs == 0) {
+            buf_b[buf_ib].ds = FLOAT_TYPE_VEC2(0.0f);
+        }
+
+        buf_b[buf_ib].qs[iqs * 4    ] = 0;
+        buf_b[buf_ib].qs[iqs * 4 + 1] = 0;
+        buf_b[buf_ib].qs[iqs * 4 + 2] = 0;
+        buf_b[buf_ib].qs[iqs * 4 + 3] = 0;
+    }
+}
+
+void block_b_to_registers(const uint ib) {
+    cache_b.ds = buf_b[ib].ds;
+    [[unroll]] for (uint iqs = 0; iqs < BK / 4; iqs++) {
+        cache_b.qs[iqs] = buf_b[ib].qs[iqs];
+    }
 }
-#endif

ggml/src/ggml-vulkan/vulkan-shaders/solve_tri.comp

@@ -0,0 +1,72 @@
+#version 450
+
+#include "types.glsl"
+#include "generic_binary_head.glsl"
+
+layout (constant_id = 1) const uint N = 64;
+layout (constant_id = 2) const uint K = 32;
+
+layout(local_size_x = 128, local_size_y = 1, local_size_z = 1) in;
+
+uint a_base, b_base, x_base;
+
+FLOAT_TYPE get_a(uint r, uint c) {
+    return FLOAT_TYPE(data_a[a_base + r * p.nb01 + c * p.nb00]);
+}
+
+FLOAT_TYPE get_b(uint r, uint c) {
+    return FLOAT_TYPE(data_b[b_base + r * p.nb11 + c * p.nb10]);
+}
+
+void store_x(uint r, uint c, FLOAT_TYPE v) {
+    data_d[x_base + r * p.nb21 + c * p.nb20] = D_TYPE(v);
+}
+
+shared FLOAT_TYPE shA[N * N];
+shared FLOAT_TYPE shB[N * K];
+
+void main() {
+    const uint batch = gl_WorkGroupID.z * 262144 + gl_WorkGroupID.y * 512 + gl_WorkGroupID.x;
+    const uint tid = gl_LocalInvocationID.x;
+
+    if (batch >= p.ne02 * p.ne03) {
+        return;
+    }
+
+    const uint i3 = batch / p.ne22;
+    const uint i2 = batch % p.ne22;
+    a_base = get_aoffset() + i2 * p.nb02 + i3 * p.nb03;
+    b_base = get_boffset() + i2 * p.nb12 + i3 * p.nb13;
+    x_base = get_doffset() + i2 * p.nb22 + i3 * p.nb23;
+
+    // Load the A matrix into shA
+    [[unroll]] for (uint i = 0; i < N * N; i += gl_WorkGroupSize.x) {
+        uint idx = i + tid;
+        if (((N * N) % gl_WorkGroupSize.x == 0) || idx < N * N) {
+            shA[idx] = get_a(idx / N, idx % N);
+        }
+    }
+    // Load the B matrix into shB
+    [[unroll]] for (uint i = 0; i < N * K; i += gl_WorkGroupSize.x) {
+        uint idx = i + tid;
+        if (((N * K) % gl_WorkGroupSize.x == 0) || idx < N * K) {
+            shB[idx] = get_b(idx / K, idx % K);
+        }
+    }
+    barrier();
+
+    FLOAT_TYPE X[N];
+    // Each thread solves one column
+    if (tid < K) {
+        [[unroll]] for (int r = 0; r < N; ++r) {
+            FLOAT_TYPE b = shB[r * K + tid];
+            // Compute x[r,c] = (b[r,c] - sum(a[r,c]*x[c])) / a[r,r]
+            [[unroll]] for (int c = 0; c < r; ++c) {
+                b -= shA[r * N + c] * X[c];
+            }
+            FLOAT_TYPE x = b / shA[r * N + r];
+            X[r] = x;
+            store_x(r, tid, x);
+        }
+    }
+}

ggml/src/ggml-vulkan/vulkan-shaders/tri.comp

@@ -0,0 +1,43 @@
+#version 450
+
+#include "rte.glsl"
+#include "types.glsl"
+#include "generic_unary_head.glsl"
+
+#define GGML_TRI_TYPE_UPPER_DIAG 0
+#define GGML_TRI_TYPE_UPPER      1
+#define GGML_TRI_TYPE_LOWER_DIAG 2
+#define GGML_TRI_TYPE_LOWER      3
+
+layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
+
+void main() {
+    const uint idx = get_idx();
+
+    if (idx >= p.ne) {
+        return;
+    }
+
+    const uint i03 = fastdiv(idx, p.ne0_012mp, p.ne0_012L);
+    const uint i03_offset = i03 * p.ne02*p.ne01*p.ne00;
+    const uint i02 = fastdiv(idx - i03_offset, p.ne0_01mp, p.ne0_01L);
+    const uint i02_offset = i02*p.ne01*p.ne00;
+    const uint i01 = fastdiv(idx - i03_offset - i02_offset, p.ne0_0mp, p.ne0_0L);
+    const uint i00 = idx - i03_offset - i02_offset - i01*p.ne00;
+
+    int param = floatBitsToInt(p.param1);
+    bool pass = false;
+    switch (param) {
+    case GGML_TRI_TYPE_UPPER_DIAG: pass = i00 >= i01; break;
+    case GGML_TRI_TYPE_UPPER:      pass = i00 >  i01; break;
+    case GGML_TRI_TYPE_LOWER_DIAG: pass = i00 <= i01; break;
+    case GGML_TRI_TYPE_LOWER:      pass = i00 <  i01; break;
+    }
+
+    if (pass) {
+        const float val = float(data_a[get_aoffset() + src0_idx(idx)]);
+        data_d[get_doffset() + dst_idx(idx)] = D_TYPE(val);
+    } else {
+        data_d[get_doffset() + dst_idx(idx)] = D_TYPE(0);
+    }
+}

ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp

@@ -679,14 +679,20 @@ void process_shaders() {
         string_to_spv("mul_mat_vec_" + tname + "_f32_f32_subgroup_no_shmem", shader, merge_maps(base_dict, {{data_a_key, "1"}, {"B_TYPE", "float"}, {"B_TYPE_VEC2", "vec2"}, {"B_TYPE_VEC4", "vec4"}, {"D_TYPE", "float"}, {"USE_SUBGROUP_ADD_NO_SHMEM", "1"}}));
         string_to_spv("mul_mat_vec_" + tname + "_f16_f32_subgroup_no_shmem", shader, merge_maps(base_dict, {{data_a_key, "1"}, {"B_TYPE", "float16_t"}, {"B_TYPE_VEC2", "f16vec2"}, {"B_TYPE_VEC4", "f16vec4"}, {"D_TYPE", "float"}, {"USE_SUBGROUP_ADD_NO_SHMEM", "1"}}));
 
-        string_to_spv("mul_mat_vec_id_" + tname + "_f32", shader, merge_maps(base_dict, {{"MUL_MAT_ID", "1"}, {data_a_key, "1"}, {"B_TYPE", "float"}, {"B_TYPE_VEC2", "vec2"}, {"B_TYPE_VEC4", "vec4"}, {"D_TYPE", "float"}}));
+        string_to_spv("mul_mat_vec_id_" + tname + "_f32_f32", shader, merge_maps(base_dict, {{"MUL_MAT_ID", "1"}, {data_a_key, "1"}, {"B_TYPE", "float"}, {"B_TYPE_VEC2", "vec2"}, {"B_TYPE_VEC4", "vec4"}, {"D_TYPE", "float"}}));
+        string_to_spv("mul_mat_vec_id_" + tname + "_f32_f32_subgroup", shader, merge_maps(base_dict, {{"MUL_MAT_ID", "1"}, {data_a_key, "1"}, {"B_TYPE", "float"}, {"B_TYPE_VEC2", "vec2"}, {"B_TYPE_VEC4", "vec4"}, {"D_TYPE", "float"}, {"USE_SUBGROUP_ADD", "1"}}));
+        string_to_spv("mul_mat_vec_id_" + tname + "_f32_f32_subgroup_no_shmem", shader, merge_maps(base_dict, {{"MUL_MAT_ID", "1"}, {data_a_key, "1"}, {"B_TYPE", "float"}, {"B_TYPE_VEC2", "vec2"}, {"B_TYPE_VEC4", "vec4"}, {"D_TYPE", "float"}, {"USE_SUBGROUP_ADD_NO_SHMEM", "1"}}));
 
         // mul mat vec with integer dot product
 #if defined(GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT)
-        if (is_legacy_quant(tname)) {
+        if (is_legacy_quant(tname) || tname == "mxfp4" || is_k_quant(tname)) {
             string_to_spv("mul_mat_vec_" + tname + "_q8_1_f32", "mul_mat_vecq.comp", merge_maps(base_dict, {{data_a_key, "1"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}, {"FLOAT_TYPE_VEC2", "vec2"}, {"ACC_TYPE", "float"}}));
             string_to_spv("mul_mat_vec_" + tname + "_q8_1_f32_subgroup", "mul_mat_vecq.comp", merge_maps(base_dict, {{data_a_key, "1"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}, {"FLOAT_TYPE_VEC2", "vec2"}, {"ACC_TYPE", "float"}, {"USE_SUBGROUP_ADD", "1"}}));
             string_to_spv("mul_mat_vec_" + tname + "_q8_1_f32_subgroup_no_shmem", "mul_mat_vecq.comp", merge_maps(base_dict, {{data_a_key, "1"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}, {"FLOAT_TYPE_VEC2", "vec2"}, {"ACC_TYPE", "float"}, {"USE_SUBGROUP_ADD_NO_SHMEM", "1"}}));
+
+            string_to_spv("mul_mat_vec_id_" + tname + "_q8_1_f32", "mul_mat_vecq.comp", merge_maps(base_dict, {{"MUL_MAT_ID", "1"}, {data_a_key, "1"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}, {"FLOAT_TYPE_VEC2", "vec2"}, {"ACC_TYPE", "float"}}));
+            string_to_spv("mul_mat_vec_id_" + tname + "_q8_1_f32_subgroup", "mul_mat_vecq.comp", merge_maps(base_dict, {{"MUL_MAT_ID", "1"}, {data_a_key, "1"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}, {"FLOAT_TYPE_VEC2", "vec2"}, {"ACC_TYPE", "float"}, {"USE_SUBGROUP_ADD", "1"}}));
+            string_to_spv("mul_mat_vec_id_" + tname + "_q8_1_f32_subgroup_no_shmem", "mul_mat_vecq.comp", merge_maps(base_dict, {{"MUL_MAT_ID", "1"}, {data_a_key, "1"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}, {"FLOAT_TYPE_VEC2", "vec2"}, {"ACC_TYPE", "float"}, {"USE_SUBGROUP_ADD_NO_SHMEM", "1"}}));
         }
 #endif
 
@@ -846,6 +852,9 @@ void process_shaders() {
     string_to_spv("abs_f16",        "abs.comp",         {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}});
     string_to_spv("abs_f32",        "abs.comp",         {{"A_TYPE", "float"},       {"D_TYPE", "float"}});
 
+    string_to_spv("tri_f16",        "tri.comp",         {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}});
+    string_to_spv("tri_f32",        "tri.comp",         {{"A_TYPE", "float"},       {"D_TYPE", "float"}});
+
     string_to_spv("softplus_f16",   "softplus.comp",    {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}});
     string_to_spv("softplus_f32",   "softplus.comp",    {{"A_TYPE", "float"},       {"D_TYPE", "float"}});
 
@@ -944,6 +953,8 @@ void process_shaders() {
     string_to_spv("opt_step_adamw_f32", "opt_step_adamw.comp", merge_maps(base_dict, {{"A_TYPE", "float"}}));
     string_to_spv("opt_step_sgd_f32", "opt_step_sgd.comp", merge_maps(base_dict, {{"A_TYPE", "float"}}));
 
+    string_to_spv("solve_tri_f32", "solve_tri.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}}));
+
     for (auto transpose : {false, true}) {
         for (auto unroll : {false, true}) {
             for (auto a_f16 : {false, true}) {
@@ -1095,7 +1106,7 @@ void write_output_files() {
 
     for (const std::string& btype : btypes) {
     for (const auto& tname : type_names) {
-        if (btype == "q8_1" && !is_legacy_quant(tname)) {
+        if (btype == "q8_1" && !is_legacy_quant(tname) && tname != "mxfp4" && !is_k_quant(tname)) {
             continue;
         }
         hdr << "extern const void * arr_dmmv_"   << tname << "_" << btype << "_f32_data[3];\n";
@@ -1104,6 +1115,16 @@ void write_output_files() {
             src << "const void * arr_dmmv_"   << tname << "_" << btype << "_f32_data[3] = {mul_mat_vec_" << tname << "_" << btype << "_f32_data, mul_mat_vec_" << tname << "_" << btype << "_f32_subgroup_data, mul_mat_vec_" << tname << "_" << btype << "_f32_subgroup_no_shmem_data};\n";
             src << "const uint64_t arr_dmmv_" << tname << "_" << btype << "_f32_len[3] =  {mul_mat_vec_" << tname << "_" << btype << "_f32_len,  mul_mat_vec_" << tname << "_" << btype << "_f32_subgroup_len, mul_mat_vec_"  << tname << "_" << btype << "_f32_subgroup_no_shmem_len};\n";
         }
+
+        if (btype == "f16") {
+            continue;
+        }
+        hdr << "extern const void * arr_dmmv_id_"   << tname << "_" << btype << "_f32_data[3];\n";
+        hdr << "extern const uint64_t arr_dmmv_id_" << tname << "_" << btype << "_f32_len[3];\n";
+        if (basename(input_filepath) == "mul_mat_vec.comp") {
+            src << "const void * arr_dmmv_id_"   << tname << "_" << btype << "_f32_data[3] = {mul_mat_vec_id_" << tname << "_" << btype << "_f32_data, mul_mat_vec_id_" << tname << "_" << btype << "_f32_subgroup_data, mul_mat_vec_id_" << tname << "_" << btype << "_f32_subgroup_no_shmem_data};\n";
+            src << "const uint64_t arr_dmmv_id_" << tname << "_" << btype << "_f32_len[3] =  {mul_mat_vec_id_" << tname << "_" << btype << "_f32_len,  mul_mat_vec_id_" << tname << "_" << btype << "_f32_subgroup_len, mul_mat_vec_id_"  << tname << "_" << btype << "_f32_subgroup_no_shmem_len};\n";
+        }
     }
     }
 

ggml/src/ggml.c

@@ -4891,6 +4891,8 @@ static struct ggml_tensor * ggml_interpolate_impl(
         int64_t               ne3,
         uint32_t              mode) {
     GGML_ASSERT((mode & 0xFF) < GGML_SCALE_MODE_COUNT);
+    // TODO: implement antialias for modes other than bilinear
+    GGML_ASSERT(!(mode & GGML_SCALE_FLAG_ANTIALIAS) || (mode & 0xFF) == GGML_SCALE_MODE_BILINEAR);
 
     struct ggml_tensor * result = ggml_new_tensor_4d(ctx, a->type, ne0, ne1, ne2, ne3);
 

gguf-py/gguf/constants.py

@@ -366,6 +366,7 @@ class MODEL_ARCH(IntEnum):
     QWEN2VL          = auto()
     QWEN3            = auto()
     QWEN3MOE         = auto()
+    QWEN3NEXT        = auto()
     QWEN3VL          = auto()
     QWEN3VLMOE       = auto()
     PHI2             = auto()
@@ -531,6 +532,7 @@ class MODEL_TENSOR(IntEnum):
     SSM_D                = auto()
     SSM_NORM             = auto()
     SSM_OUT              = auto()
+    SSM_BETA_ALPHA       = auto() # qwen3next
     TIME_MIX_W0          = auto()
     TIME_MIX_W1          = auto()
     TIME_MIX_W2          = auto()
@@ -736,6 +738,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
     MODEL_ARCH.QWEN2VL:          "qwen2vl",
     MODEL_ARCH.QWEN3:            "qwen3",
     MODEL_ARCH.QWEN3MOE:         "qwen3moe",
+    MODEL_ARCH.QWEN3NEXT:        "qwen3next",
     MODEL_ARCH.QWEN3VL:          "qwen3vl",
     MODEL_ARCH.QWEN3VLMOE:       "qwen3vlmoe",
     MODEL_ARCH.PHI2:             "phi2",
@@ -900,6 +903,7 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
     MODEL_TENSOR.SSM_D:                     "blk.{bid}.ssm_d",
     MODEL_TENSOR.SSM_NORM:                  "blk.{bid}.ssm_norm",
     MODEL_TENSOR.SSM_OUT:                   "blk.{bid}.ssm_out",
+    MODEL_TENSOR.SSM_BETA_ALPHA:            "blk.{bid}.ssm_ba",
     MODEL_TENSOR.TIME_MIX_W0:               "blk.{bid}.time_mix_w0",
     MODEL_TENSOR.TIME_MIX_W1:               "blk.{bid}.time_mix_w1",
     MODEL_TENSOR.TIME_MIX_W2:               "blk.{bid}.time_mix_w2",
@@ -1569,6 +1573,35 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.FFN_DOWN_EXP,
         MODEL_TENSOR.FFN_UP_EXP,
     ],
+    MODEL_ARCH.QWEN3NEXT: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ATTN_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.ATTN_POST_NORM,
+        MODEL_TENSOR.ATTN_GATE,
+        MODEL_TENSOR.FFN_GATE_INP,
+        MODEL_TENSOR.FFN_GATE_INP_SHEXP,
+        MODEL_TENSOR.FFN_UP_SHEXP,
+        MODEL_TENSOR.FFN_DOWN_SHEXP,
+        MODEL_TENSOR.FFN_GATE_SHEXP,
+        MODEL_TENSOR.FFN_DOWN_EXP,
+        MODEL_TENSOR.FFN_UP_EXP,
+        MODEL_TENSOR.FFN_GATE_EXP,
+        MODEL_TENSOR.SSM_A,
+        MODEL_TENSOR.SSM_CONV1D,
+        MODEL_TENSOR.SSM_DT,
+        MODEL_TENSOR.SSM_NORM,
+        MODEL_TENSOR.SSM_IN,
+        MODEL_TENSOR.SSM_BETA_ALPHA,
+        MODEL_TENSOR.SSM_OUT
+    ],
     MODEL_ARCH.QWEN3VL: [
         MODEL_TENSOR.TOKEN_EMBD,
         MODEL_TENSOR.OUTPUT_NORM,

gguf-py/gguf/gguf_writer.py

@@ -371,10 +371,13 @@ class GGUFWriter:
 
     def add_tensor(
         self, name: str, tensor: np.ndarray[Any, Any], raw_shape: Sequence[int] | None = None,
-        raw_dtype: GGMLQuantizationType | None = None,
+        raw_dtype: GGMLQuantizationType | None = None, tensor_endianess: GGUFEndian | None = None
     ) -> None:
-        if (self.endianess == GGUFEndian.BIG and sys.byteorder != 'big') or \
-                (self.endianess == GGUFEndian.LITTLE and sys.byteorder != 'little'):
+        # if tensor endianness is not passed, assume it's native to system
+        if tensor_endianess is None:
+            tensor_endianess = GGUFEndian.BIG if sys.byteorder == 'big' else GGUFEndian.LITTLE
+
+        if tensor_endianess != self.endianess:
             # Don't byteswap inplace since lazy copies cannot handle it
             tensor = tensor.byteswap(inplace=False)
         if self.use_temp_file and self.temp_file is None:
@@ -397,13 +400,16 @@ class GGUFWriter:
         if pad != 0:
             fp.write(bytes([0] * pad))
 
-    def write_tensor_data(self, tensor: np.ndarray[Any, Any]) -> None:
+    def write_tensor_data(self, tensor: np.ndarray[Any, Any], tensor_endianess: GGUFEndian | None = None) -> None:
         if self.state is not WriterState.TI_DATA and self.state is not WriterState.WEIGHTS:
             raise ValueError(f'Expected output file to contain tensor info or weights, got {self.state}')
         assert self.fout is not None
 
-        if (self.endianess == GGUFEndian.BIG and sys.byteorder != 'big') or \
-                (self.endianess == GGUFEndian.LITTLE and sys.byteorder != 'little'):
+        # if tensor endianness is not passed, assume it's native to system
+        if tensor_endianess is None:
+            tensor_endianess = GGUFEndian.BIG if sys.byteorder == 'big' else GGUFEndian.LITTLE
+
+        if tensor_endianess != self.endianess:
             # Don't byteswap inplace since lazy copies cannot handle it
             tensor = tensor.byteswap(inplace=False)
 

gguf-py/gguf/scripts/gguf_convert_endian.py

@@ -19,6 +19,11 @@ import gguf
 logger = logging.getLogger("gguf-convert-endian")
 
 
+def byteswap_noop(tensor, block_offs):
+    # this function is used when byteswapping is not needed
+    pass
+
+
 def byteswap_q4_0(tensor, block_offs):
     # Each block_q4_0 consists of an f16 delta (scaling factor) followed by 16 int8 quantizations.
 
@@ -55,22 +60,11 @@ def byteswap_q6_k(tensor, block_offs):
 
 
 byteswap_tensors = {
-    gguf.GGMLQuantizationType.Q4_0: {
-        "block_size": 18, # 18 bytes = <f16 delta scaling factor> + 16 * <int8 quant>
-        "byteswap_func": byteswap_q4_0,
-    },
-    gguf.GGMLQuantizationType.Q8_0: {
-        "block_size": 34, # 34 bytes = <f16 delta scaling factor> + 32 * <int8 quant>
-        "byteswap_func": byteswap_q8_0,
-    },
-    gguf.GGMLQuantizationType.Q4_K: {
-        "block_size": 144, # 144 bytes = 2 * <f16 delta scaling factor> + 140 * <int8 quant>
-        "byteswap_func": byteswap_q4_k,
-    },
-    gguf.GGMLQuantizationType.Q6_K: {
-        "block_size": 210, # 210 bytes = <f16 delta scaling factor> + 208 * <int8 quant>
-        "byteswap_func": byteswap_q6_k,
-    },
+    gguf.GGMLQuantizationType.Q4_0:  byteswap_q4_0,
+    gguf.GGMLQuantizationType.Q8_0:  byteswap_q8_0,
+    gguf.GGMLQuantizationType.Q4_K:  byteswap_q4_k,
+    gguf.GGMLQuantizationType.Q6_K:  byteswap_q6_k,
+    gguf.GGMLQuantizationType.MXFP4: byteswap_noop,
 }
 
 
@@ -135,8 +129,8 @@ def convert_byteorder(reader: gguf.GGUFReader, args: argparse.Namespace) -> None
 
             tensor.data.resize(newshape)
 
-            block_size    = byteswap_tensors[tensor.tensor_type]["block_size"]
-            byteswap_func = byteswap_tensors[tensor.tensor_type]["byteswap_func"]
+            block_size    = gguf.constants.GGML_QUANT_SIZES[tensor.tensor_type][1]
+            byteswap_func = byteswap_tensors[tensor.tensor_type]
 
             n_blocks = len(tensor.data) // block_size
             for block_num in (inner_pbar := tqdm(range(n_blocks), desc="Byte-swapping Blocks", leave=False)):

gguf-py/gguf/scripts/gguf_editor_gui.py

@@ -1552,7 +1552,7 @@ class GGUFEditorWindow(QMainWindow):
 
             # Add tensors (including data)
             for tensor in self.reader.tensors:
-                writer.add_tensor(tensor.name, tensor.data, raw_shape=tensor.data.shape, raw_dtype=tensor.tensor_type)
+                writer.add_tensor(tensor.name, tensor.data, raw_shape=tensor.data.shape, raw_dtype=tensor.tensor_type, tensor_endianess=self.reader.endianess)
 
             # Write header and metadata
             writer.open_output_file(Path(file_path))

gguf-py/gguf/scripts/gguf_new_metadata.py

@@ -94,7 +94,7 @@ def copy_with_new_metadata(reader: gguf.GGUFReader, writer: gguf.GGUFWriter, new
     writer.write_ti_data_to_file()
 
     for tensor in reader.tensors:
-        writer.write_tensor_data(tensor.data)
+        writer.write_tensor_data(tensor.data, tensor_endianess=reader.endianess)
         bar.update(tensor.n_bytes)
 
     writer.close()

gguf-py/gguf/tensor_mapping.py

@@ -672,10 +672,11 @@ class TensorNameMap:
         ),
 
         MODEL_TENSOR.SSM_IN: (
-            "model.layers.{bid}.in_proj",               # mamba-hf
-            "backbone.layers.{bid}.mixer.in_proj",      # mamba
-            "model.layers.{bid}.mamba.in_proj",         # jamba falcon-h1 granite-hybrid
-            "model.layers.layers.{bid}.mixer.in_proj",  # plamo2
+            "model.layers.{bid}.in_proj",                   # mamba-hf
+            "backbone.layers.{bid}.mixer.in_proj",          # mamba
+            "model.layers.{bid}.mamba.in_proj",             # jamba falcon-h1 granite-hybrid
+            "model.layers.layers.{bid}.mixer.in_proj",      # plamo2
+            "model.layers.{bid}.linear_attn.in_proj_qkvz",  # qwen3next
         ),
 
         MODEL_TENSOR.SSM_CONV1D: (
@@ -683,6 +684,7 @@ class TensorNameMap:
             "backbone.layers.{bid}.mixer.conv1d",      # mamba
             "model.layers.{bid}.mamba.conv1d",         # jamba falcon-h1 granite-hybrid
             "model.layers.layers.{bid}.mixer.conv1d",  # plamo2
+            "model.layers.{bid}.linear_attn.conv1d",   # qwen3next
         ),
 
         MODEL_TENSOR.SSM_X: (
@@ -697,6 +699,7 @@ class TensorNameMap:
             "backbone.layers.{bid}.mixer.dt_proj",      # mamba
             "model.layers.{bid}.mamba.dt_proj",         # jamba falcon-h1 granite-hybrid
             "model.layers.layers.{bid}.mixer.dt_proj",  # plamo2
+            "model.layers.{bid}.linear_attn.dt_proj",   # qwen3next
         ),
 
         MODEL_TENSOR.SSM_DT_NORM: (
@@ -709,6 +712,7 @@ class TensorNameMap:
             "backbone.layers.{bid}.mixer.A_log",      # mamba
             "model.layers.{bid}.mamba.A_log",         # jamba falcon-h1 granite-hybrid
             "model.layers.layers.{bid}.mixer.A_log",  # plamo2
+            "model.layers.{bid}.linear_attn.A_log",   # qwen3next
         ),
 
         MODEL_TENSOR.SSM_B_NORM: (
@@ -731,17 +735,23 @@ class TensorNameMap:
         ),
 
         MODEL_TENSOR.SSM_NORM: (
-            "model.layers.{bid}.mamba.norm", # falcon-h1 granite-hybrid
-            "backbone.layers.{bid}.mixer.norm",  # mamba2
+            "model.layers.{bid}.mamba.norm",        # falcon-h1 granite-hybrid
+            "model.layers.{bid}.linear_attn.norm",  # qwen3next
+            "backbone.layers.{bid}.mixer.norm",     # mamba2
         ),
 
         MODEL_TENSOR.SSM_OUT: (
             "model.layers.{bid}.out_proj",               # mamba-hf
             "backbone.layers.{bid}.mixer.out_proj",      # mamba
             "model.layers.{bid}.mamba.out_proj",         # jamba falcon-h1 granite-hybrid
+            "model.layers.{bid}.linear_attn.out_proj",   # qwen3next
             "model.layers.layers.{bid}.mixer.out_proj",  # plamo2
         ),
 
+        MODEL_TENSOR.SSM_BETA_ALPHA: (
+            "model.layers.{bid}.linear_attn.in_proj_ba",  # qwen3next
+        ),
+
         MODEL_TENSOR.TIME_MIX_W0: (
             "model.layers.{bid}.attention.w0",            # rwkv7
         ),

src/CMakeLists.txt

@@ -114,6 +114,7 @@ add_library(llama
             models/qwen3vl.cpp
             models/qwen3vl-moe.cpp
             models/qwen3moe.cpp
+            models/qwen3next.cpp
             models/refact.cpp
             models/rnd1.cpp
             models/rwkv6-base.cpp

src/llama-arch.cpp

@@ -32,6 +32,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
     { LLM_ARCH_QWEN2VL,          "qwen2vl"          },
     { LLM_ARCH_QWEN3,            "qwen3"            },
     { LLM_ARCH_QWEN3MOE,         "qwen3moe"         },
+    { LLM_ARCH_QWEN3NEXT,        "qwen3next"        },
     { LLM_ARCH_QWEN3VL,          "qwen3vl"          },
     { LLM_ARCH_QWEN3VLMOE,       "qwen3vlmoe"       },
     { LLM_ARCH_PHI2,             "phi2"             },
@@ -829,6 +830,38 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
             { LLM_TENSOR_FFN_UP_EXPS,        "blk.%d.ffn_up_exps" },
         },
     },
+    {
+        LLM_ARCH_QWEN3NEXT,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,         "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,        "output_norm" },
+            { LLM_TENSOR_OUTPUT,             "output" },
+            { LLM_TENSOR_ATTN_NORM,          "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_POST_NORM,     "blk.%d.post_attention_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_FFN_NORM,           "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_GATE_INP,       "blk.%d.ffn_gate_inp" },
+            { LLM_TENSOR_FFN_GATE_EXPS,      "blk.%d.ffn_gate_exps" },
+            { LLM_TENSOR_FFN_DOWN_EXPS,      "blk.%d.ffn_down_exps" },
+            { LLM_TENSOR_FFN_UP_EXPS,        "blk.%d.ffn_up_exps" },
+            { LLM_TENSOR_FFN_GATE_INP_SHEXP, "blk.%d.ffn_gate_inp_shexp" },
+            { LLM_TENSOR_FFN_GATE_SHEXP,     "blk.%d.ffn_gate_shexp" },
+            { LLM_TENSOR_FFN_DOWN_SHEXP,     "blk.%d.ffn_down_shexp" },
+            { LLM_TENSOR_FFN_UP_SHEXP,       "blk.%d.ffn_up_shexp" },
+            { LLM_TENSOR_SSM_A,              "blk.%d.ssm_a" },
+            { LLM_TENSOR_SSM_CONV1D,         "blk.%d.ssm_conv1d" },
+            { LLM_TENSOR_SSM_DT,             "blk.%d.ssm_dt" },
+            { LLM_TENSOR_SSM_BETA_ALPHA,     "blk.%d.ssm_ba" },
+            { LLM_TENSOR_SSM_IN,             "blk.%d.ssm_in" },
+            { LLM_TENSOR_SSM_NORM,           "blk.%d.ssm_norm" },
+            { LLM_TENSOR_SSM_OUT,            "blk.%d.ssm_out" },
+        },
+    },
     {
         LLM_ARCH_QWEN3VL,
         {
@@ -2237,7 +2270,7 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
             { LLM_TENSOR_SHORTCONV_INPROJ,  "blk.%d.shortconv.in_proj" },
             { LLM_TENSOR_SHORTCONV_OUTPROJ, "blk.%d.shortconv.out_proj" },
             { LLM_TENSOR_TOKEN_EMBD,        "token_embd" },
-            { LLM_TENSOR_TOKEN_EMBD_NORM,   "token_embd_norm" },
+            { LLM_TENSOR_OUTPUT_NORM,       "token_embd_norm" }, // note: wrong tensor name
             { LLM_TENSOR_OUTPUT,            "output" },
         }
     },
@@ -2259,7 +2292,7 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
             { LLM_TENSOR_SHORTCONV_INPROJ,  "blk.%d.shortconv.in_proj" },
             { LLM_TENSOR_SHORTCONV_OUTPROJ, "blk.%d.shortconv.out_proj" },
             { LLM_TENSOR_TOKEN_EMBD,        "token_embd" },
-            { LLM_TENSOR_TOKEN_EMBD_NORM,   "token_embd_norm" },
+            { LLM_TENSOR_OUTPUT_NORM,       "token_embd_norm" }, // note: wrong tensor name
             { LLM_TENSOR_FFN_GATE_INP,      "blk.%d.ffn_gate_inp" },
             { LLM_TENSOR_FFN_GATE_EXPS,     "blk.%d.ffn_gate_exps" },
             { LLM_TENSOR_FFN_DOWN_EXPS,     "blk.%d.ffn_down_exps" },
@@ -2487,11 +2520,21 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
     },
 };
 
+// declare information about the model weight tensors:
+// - the layer in which the tensor is going to be used. this is needed in order to assign the correct buffer type for the weight
+// - the operator which is going to use the weight. this is needed to determine if the respective backend supports the operator
+//
+// for example, input layers are usually assigned to CPU/host buffer types
+//
+// a mismatch between the declared information and the actual layer/op in which the tensor is used can lead to sub-optimal
+//   assignment of the buffer types and extra overhead during computation
+// example: https://github.com/ggml-org/llama.cpp/pull/17548
+//
 static const std::map<llm_tensor, llm_tensor_info> LLM_TENSOR_INFOS = {
     {LLM_TENSOR_TOKEN_EMBD,                 {LLM_TENSOR_LAYER_INPUT, GGML_OP_GET_ROWS}},
     {LLM_TENSOR_POS_EMBD,                   {LLM_TENSOR_LAYER_INPUT, GGML_OP_GET_ROWS}},
-    {LLM_TENSOR_TOKEN_EMBD_NORM,            {LLM_TENSOR_LAYER_INPUT, GGML_OP_GET_ROWS}},
     {LLM_TENSOR_TOKEN_TYPES,                {LLM_TENSOR_LAYER_INPUT, GGML_OP_GET_ROWS}},
+    {LLM_TENSOR_TOKEN_EMBD_NORM,            {LLM_TENSOR_LAYER_INPUT, GGML_OP_MUL}},
     {LLM_TENSOR_OUTPUT,                     {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}},
     {LLM_TENSOR_CLS,                        {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}},
     {LLM_TENSOR_CLS_OUT,                    {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}},
@@ -2546,6 +2589,7 @@ static const std::map<llm_tensor, llm_tensor_info> LLM_TENSOR_INFOS = {
     {LLM_TENSOR_SSM_X,                      {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
     {LLM_TENSOR_SSM_DT,                     {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
     {LLM_TENSOR_SSM_OUT,                    {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_SSM_BETA_ALPHA,             {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
     {LLM_TENSOR_TIME_MIX_W1,                {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
     {LLM_TENSOR_TIME_MIX_W2,                {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
     {LLM_TENSOR_TIME_MIX_A1,                {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
@@ -2744,6 +2788,7 @@ bool llm_arch_is_hybrid(const llm_arch & arch) {
         case LLM_ARCH_LFM2:
         case LLM_ARCH_LFM2MOE:
         case LLM_ARCH_NEMOTRON_H:
+        case LLM_ARCH_QWEN3NEXT:
             return true;
         default:
             return false;

src/llama-arch.h

@@ -36,6 +36,7 @@ enum llm_arch {
     LLM_ARCH_QWEN2VL,
     LLM_ARCH_QWEN3,
     LLM_ARCH_QWEN3MOE,
+    LLM_ARCH_QWEN3NEXT,
     LLM_ARCH_QWEN3VL,
     LLM_ARCH_QWEN3VLMOE,
     LLM_ARCH_PHI2,
@@ -381,6 +382,7 @@ enum llm_tensor {
     LLM_TENSOR_SSM_D,
     LLM_TENSOR_SSM_NORM,
     LLM_TENSOR_SSM_OUT,
+    LLM_TENSOR_SSM_BETA_ALPHA,      // qwen3next
     LLM_TENSOR_TIME_MIX_W0,
     LLM_TENSOR_TIME_MIX_W1,
     LLM_TENSOR_TIME_MIX_W2,

src/llama-context.cpp

@@ -1,5 +1,6 @@
 #include "llama-context.h"
 
+#include "llama-arch.h"
 #include "llama-impl.h"
 #include "llama-batch.h"
 #include "llama-io.h"
@@ -299,7 +300,7 @@ llama_context::llama_context(
 
         cross.v_embd.clear();
 
-        const uint32_t n_seqs = cparams.kv_unified ? 1 : cparams.n_seq_max;
+        const uint32_t n_seqs = cparams.n_seq_max;
         const uint32_t n_tokens = std::min(cparams.n_ctx, cparams.n_ubatch);
 
         // avoid reserving graphs with zero outputs - assume one output per sequence
@@ -542,7 +543,7 @@ bool llama_context::memory_update(bool optimize) {
             throw std::runtime_error("failed to initialize memory context");
         }
 
-        const uint32_t n_seqs = cparams.kv_unified ? 1 : cparams.n_seq_max;
+        const uint32_t n_seqs = cparams.n_seq_max;
         const uint32_t n_tokens = std::min(cparams.n_ctx, cparams.n_ubatch);
 
         auto * gf = graph_reserve(n_tokens, n_seqs, n_tokens, mctx.get());
@@ -1386,6 +1387,9 @@ void llama_context::output_reorder() {
 //
 
 uint32_t llama_context::graph_max_nodes() const {
+    if (model.arch == LLM_ARCH_QWEN3NEXT) {
+        return std::max<uint32_t>(8192u, 32u*model.n_tensors());
+    }
     return std::max<uint32_t>(1024u, 8u*model.n_tensors());
 }
 

src/llama-graph.cpp

@@ -810,9 +810,6 @@ ggml_tensor * llm_graph_context::build_ffn(
             GGML_ABORT("fatal error");
     }
 
-    //expand here so that we can fuse ffn gate
-    ggml_build_forward_expand(gf, cur);
-
     if (gate && type_gate == LLM_FFN_PAR) {
         cur = ggml_mul(ctx0, cur, tmp);
         cb(cur, "ffn_gate_par", il);
@@ -1093,9 +1090,6 @@ ggml_tensor * llm_graph_context::build_moe_ffn(
             GGML_ABORT("fatal error");
     }
 
-    //expand here so that we can fuse ffn gate
-    ggml_build_forward_expand(gf, cur);
-
     experts = build_lora_mm_id(down_exps, cur, selected_experts); // [n_embd, n_expert_used, n_tokens]
     cb(experts, "ffn_moe_down", il);
 

src/llama-hparams.h

@@ -6,7 +6,7 @@
 
 // bump if necessary
 #define LLAMA_MAX_LAYERS  512
-#define LLAMA_MAX_EXPERTS 384  // Kimi-K2
+#define LLAMA_MAX_EXPERTS 512 // Qwen3 Next
 
 enum llama_expert_gating_func_type {
     LLAMA_EXPERT_GATING_FUNC_TYPE_NONE           = 0,

src/llama-model.cpp

@@ -2,7 +2,6 @@
 
 #include "llama-impl.h"
 #include "llama-mmap.h"
-#include "llama-batch.h"
 #include "llama-cparams.h"
 #include "llama-model-loader.h"
 
@@ -2225,6 +2224,29 @@ void llama_model::load_hparams(llama_model_loader & ml) {
                     default: type = LLM_TYPE_UNKNOWN;
                 }
             } break;
+        case LLM_ARCH_QWEN3NEXT:
+            {
+                ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,        hparams.n_ff_exp, false);
+                ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, false);
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,       hparams.f_norm_rms_eps);
+
+                // Load linear attention (gated delta net) parameters
+                ml.get_key(LLM_KV_SSM_CONV_KERNEL,    hparams.ssm_d_conv);
+                ml.get_key(LLM_KV_SSM_INNER_SIZE,     hparams.ssm_d_inner);
+                ml.get_key(LLM_KV_SSM_STATE_SIZE,     hparams.ssm_d_state);
+                ml.get_key(LLM_KV_SSM_TIME_STEP_RANK, hparams.ssm_dt_rank);
+                ml.get_key(LLM_KV_SSM_GROUP_COUNT,    hparams.ssm_n_group);
+
+                // Mark recurrent layers (linear attention layers)
+                for (uint32_t i = 0; i < hparams.n_layer; ++i) {
+                    hparams.recurrent_layer_arr[i] = ((i + 1) % 4 != 0); // TODO: extract the magic 4 from "full_attention_interval"
+                }
+
+                switch (hparams.n_layer) {
+                    case 80: type = LLM_TYPE_80B_A3B; break;
+                    default: type = LLM_TYPE_UNKNOWN;
+                }
+            } break;
         default: throw std::runtime_error("unsupported model architecture");
     }
 
@@ -6133,9 +6155,10 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
             case LLM_ARCH_LFM2:
             case LLM_ARCH_LFM2MOE:
                 {
-                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD,      "weight"), {n_embd, n_vocab}, 0);
-                    tok_norm = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd}, 0);
-                    output   = create_tensor(tn(LLM_TENSOR_OUTPUT,          "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
 
                     if (output == NULL) {
                         output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
@@ -6414,6 +6437,74 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                         layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
                     }
                 } break;
+            case LLM_ARCH_QWEN3NEXT:
+                {
+                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, 0);
+
+                    // output
+                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), { n_embd }, 0);
+                    output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), { n_embd, n_vocab }, TENSOR_NOT_REQUIRED);
+
+                    // if output is NULL, init from the input tok embed
+                    if (output == NULL) {
+                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, TENSOR_DUPLICATED);
+                    }
+
+                    const int64_t n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff / n_expert_used;
+
+                    // Calculate dimensions from hyperparameters
+                    const int64_t head_k_dim = hparams.ssm_d_state;
+                    const int64_t head_v_dim = hparams.ssm_d_state;
+                    const int64_t n_k_heads  = hparams.ssm_n_group;
+                    const int64_t n_v_heads  = hparams.ssm_dt_rank;
+                    const int64_t key_dim    = head_k_dim * n_k_heads;
+                    const int64_t value_dim  = head_v_dim * n_v_heads;
+                    const int64_t conv_dim   = key_dim * 2 + value_dim;
+
+                    // Calculate projection sizes
+                    const int64_t qkvz_dim = key_dim * 2 + value_dim * 2;
+                    const int64_t ba_dim   = n_v_heads * 2;
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        auto & layer = layers[i];
+
+                        layer.attn_norm      = create_tensor(tn(LLM_TENSOR_ATTN_NORM,      "weight", i), { n_embd }, 0);
+                        layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), { n_embd }, 0);
+
+                        if (!hparams.is_recurrent(i)) {
+                            // Attention layers
+                            layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), { n_embd, n_embd_head_k * n_head * 2 }, 0);
+                            layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), { n_embd, n_embd_k_gqa }, 0);
+                            layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), { n_embd, n_embd_v_gqa }, 0);
+                            layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_k * n_head, n_embd }, 0);
+
+                            // Q/K normalization for attention layers
+                            layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), { n_embd_head_k }, 0);
+                            layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), { n_embd_head_k }, 0);
+                        } else {
+                            // Linear attention (gated delta net) specific tensors
+                            // Create tensors with calculated dimensions
+                            layer.ssm_in         = create_tensor(tn(LLM_TENSOR_SSM_IN,         "weight", i), { n_embd, qkvz_dim }, 0);
+                            layer.ssm_conv1d     = create_tensor(tn(LLM_TENSOR_SSM_CONV1D,     "weight", i), { hparams.ssm_d_conv, conv_dim }, 0);
+                            layer.ssm_dt         = create_tensor(tn(LLM_TENSOR_SSM_DT,         "bias",   i), { hparams.ssm_dt_rank }, 0);
+                            layer.ssm_a          = create_tensor(tn(LLM_TENSOR_SSM_A,                    i), { hparams.ssm_dt_rank }, 0);
+                            layer.ssm_beta_alpha = create_tensor(tn(LLM_TENSOR_SSM_BETA_ALPHA, "weight", i), { n_embd, ba_dim }, 0);
+                            layer.ssm_norm       = create_tensor(tn(LLM_TENSOR_SSM_NORM,       "weight", i), { head_v_dim }, 0);
+                            layer.ssm_out        = create_tensor(tn(LLM_TENSOR_SSM_OUT,        "weight", i), { value_dim, n_embd }, 0);
+                        }
+
+                        layer.ffn_gate_inp  = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "weight", i), { n_embd, n_expert }, 0);
+                        layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert }, 0);
+                        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff_exp, n_embd, n_expert }, 0);
+                        layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), { n_embd, n_ff_exp, n_expert }, 0);
+
+                        // Shared experts
+                        layer.ffn_gate_inp_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP_SHEXP, "weight", i), { n_embd }, 0);
+                        layer.ffn_gate_shexp     = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP,     "weight", i), { n_embd, hparams.n_ff_shexp }, 0);
+                        layer.ffn_up_shexp       = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,       "weight", i), { n_embd, hparams.n_ff_shexp }, 0);
+                        layer.ffn_down_shexp     = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP,     "weight", i), { hparams.n_ff_shexp, n_embd }, 0);
+                    }
+                } break;
             default:
                 throw std::runtime_error("unknown architecture");
         }
@@ -6684,6 +6775,7 @@ void llama_model::print_info() const {
         arch == LLM_ARCH_FALCON_H1 ||
         arch == LLM_ARCH_PLAMO2 ||
         arch == LLM_ARCH_GRANITE_HYBRID ||
+        arch == LLM_ARCH_QWEN3NEXT ||
         arch == LLM_ARCH_NEMOTRON_H) {
         LLAMA_LOG_INFO("%s: ssm_d_conv       = %u\n",     __func__, hparams.ssm_d_conv);
         LLAMA_LOG_INFO("%s: ssm_d_inner      = %u\n",     __func__, hparams.ssm_d_inner);
@@ -7425,7 +7517,11 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const {
         case LLM_ARCH_PANGU_EMBED:
             {
                 llm = std::make_unique<llm_build_pangu_embedded>(*this, params);
-            }break;
+            } break;
+        case LLM_ARCH_QWEN3NEXT:
+            {
+                llm = std::make_unique<llm_build_qwen3next>(*this, params);
+            } break;
         default:
             GGML_ABORT("fatal error");
     }
@@ -7652,6 +7748,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
         case LLM_ARCH_COGVLM:
         case LLM_ARCH_PANGU_EMBED:
         case LLM_ARCH_AFMOE:
+        case LLM_ARCH_QWEN3NEXT:
             return LLAMA_ROPE_TYPE_NEOX;
 
         case LLM_ARCH_QWEN2VL:

src/llama-model.h

@@ -113,6 +113,7 @@ enum llm_type {
     LLM_TYPE_16B_A1B,
     LLM_TYPE_21B_A3B, // Ernie MoE small
     LLM_TYPE_30B_A3B,
+    LLM_TYPE_80B_A3B, // Qwen3 Next
     LLM_TYPE_100B_A6B,
     LLM_TYPE_106B_A12B, // GLM-4.5-Air
     LLM_TYPE_230B_A10B, // Minimax M2
@@ -309,6 +310,9 @@ struct llama_layer {
     struct ggml_tensor * ssm_conv1d_b = nullptr;
     struct ggml_tensor * ssm_dt_b     = nullptr;
 
+    // qwen3next
+    struct ggml_tensor * ssm_beta_alpha = nullptr;
+
     // rwkv
     struct ggml_tensor * time_mix_w1         = nullptr;
     struct ggml_tensor * time_mix_w2         = nullptr;

src/llama-quant.cpp

@@ -681,7 +681,9 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
             }
             LLAMA_LOG_DEBUG("%s: pruning tensor %s\n", __func__, it.first.c_str());
             continue;
-        } else if (remapped_name != it.first) {
+        }
+
+        if (remapped_name != it.first) {
             ggml_set_name(it.second.tensor, remapped_name.c_str());
             LLAMA_LOG_DEBUG("%s: tensor %s remapped to %s\n", __func__, it.first.c_str(), ggml_get_name(it.second.tensor));
         }
@@ -726,13 +728,19 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
     {
         const auto & n_head_kv_iter = model.hparams.n_head_kv_arr.begin();
         // attention layers have a non-zero number of kv heads
-        int32_t n_attn_layer = model.hparams.n_layer - std::count(n_head_kv_iter, n_head_kv_iter + model.hparams.n_layer, 0);
+        int32_t n_layer_attn = model.hparams.n_layer - std::count(n_head_kv_iter, n_head_kv_iter + model.hparams.n_layer, 0);
         if (llama_model_has_encoder(&model)) {
-            // now n_attn_layer is the number of attention layers in the encoder
+            // now n_layer_attn is the number of attention layers in the encoder
             // for each decoder block, there are 2 attention layers
-            n_attn_layer += 2 * model.hparams.dec_n_layer;
+            n_layer_attn += 2 * model.hparams.dec_n_layer;
         }
-        GGML_ASSERT((qs.n_attention_wv == n_attn_layer - pruned_attention_w) && "n_attention_wv is unexpected");
+
+        // note: for linear-attention models (such as Qwen3 Next) this is the number of linear layers
+        const int32_t n_layer_recr = std::count(model.hparams.recurrent_layer_arr.begin(), model.hparams.recurrent_layer_arr.end(), true);
+
+        LLAMA_LOG_INFO("%s: n_layer_attn = %d, n_layer_recr = %d, pruned_attention_w = %d\n", __func__, n_layer_attn, n_layer_recr, pruned_attention_w);
+
+        GGML_ASSERT((qs.n_attention_wv == n_layer_attn - pruned_attention_w - n_layer_recr) && "n_attention_wv is unexpected");
     }
 
     size_t total_size_org = 0;

src/models/lfm2.cpp

@@ -9,6 +9,8 @@ llm_build_lfm2::llm_build_lfm2(const llama_model & model, const llm_graph_params
     ggml_tensor * cur = build_inp_embd(model.tok_embd);
     cb(cur, "model.embed_tokens", -1);
 
+    ggml_build_forward_expand(gf, cur);
+
     ggml_tensor * inp_pos     = build_inp_pos();
     auto *        inp_hybrid  = build_inp_mem_hybrid();
     ggml_tensor * inp_out_ids = build_inp_out_ids();
@@ -40,12 +42,12 @@ llm_build_lfm2::llm_build_lfm2(const llama_model & model, const llm_graph_params
         cur = ggml_add(ctx0, cur, ffn_out);
     }
 
-    cur = build_norm(cur, model.tok_norm, NULL, LLM_NORM_RMS, -1);
-    cb(cur, "model.embedding_norm", -1);
+    cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);
+    cb(cur, "result_norm", -1);
     res->t_embd = cur;
 
     cur = build_lora_mm(model.output, cur);
-    cb(cur, "lm_head", -1);
+    cb(cur, "result_output", -1);
 
     res->t_logits = cur;
 

src/models/models.h

@@ -2,8 +2,9 @@
 
 #include "../llama-model.h"
 #include "../llama-graph.h"
-#include "../llama-memory-recurrent.h"
 
+// TODO: remove in follow-up PR - move to .cpp files
+#include "../llama-memory-recurrent.h"
 #include <cmath>
 
 struct llm_graph_context_mamba : public llm_graph_context {
@@ -421,7 +422,56 @@ struct llm_build_qwen3vl : public llm_graph_context {
 struct llm_build_qwen3vlmoe : public llm_graph_context {
     llm_build_qwen3vlmoe(const llama_model & model, const llm_graph_params & params);
 };
+struct llm_build_qwen3next : public llm_graph_context_mamba {
+    llm_build_qwen3next(const llama_model & model, const llm_graph_params & params);
+private:
+    ggml_tensor * build_layer_attn(
+    llm_graph_input_attn_kv * inp_attn,
+                ggml_tensor * cur,
+                ggml_tensor * inp_pos,
+                        int   il);
 
+    ggml_tensor * build_layer_attn_linear(
+         llm_graph_input_rs * inp,
+                ggml_tensor * cur,
+                ggml_tensor * causal_mask,
+                ggml_tensor * identity,
+                        int   il);
+
+    ggml_tensor * build_layer_ffn(
+                ggml_tensor * cur,
+                        int   il);
+
+    ggml_tensor * build_delta_net_recurrent(
+                ggml_tensor * q,
+                ggml_tensor * k,
+                ggml_tensor * v,
+                ggml_tensor * g,
+                ggml_tensor * beta,
+                ggml_tensor * state,
+                ggml_tensor * causal_mask,
+                ggml_tensor * identity,
+                        int   il);
+
+    ggml_tensor * build_delta_net_chunking(
+                ggml_tensor * q,
+                ggml_tensor * k,
+                ggml_tensor * v,
+                ggml_tensor * g,
+                ggml_tensor * beta,
+                ggml_tensor * state,
+                ggml_tensor * causal_mask,
+                ggml_tensor * identity,
+                        int   il);
+
+    ggml_tensor * build_norm_gated(
+                ggml_tensor * input,
+                ggml_tensor * weights,
+                ggml_tensor * gate,
+                        int   layer);
+
+    const llama_model & model;
+};
 
 struct llm_build_qwen : public llm_graph_context {
     llm_build_qwen(const llama_model & model, const llm_graph_params & params);

tests/CMakeLists.txt

@@ -196,7 +196,7 @@ if (NOT WIN32)
     llama_build_and_test(test-arg-parser.cpp)
 endif()
 
-if (NOT LLAMA_SANITIZE_ADDRESS)
+if (NOT LLAMA_SANITIZE_ADDRESS AND NOT GGML_SCHED_NO_REALLOC)
   # TODO: repair known memory leaks
   llama_build_and_test(test-opt.cpp)
 endif()

tests/test-backend-ops.cpp

@@ -1446,14 +1446,14 @@ struct test_case {
             const uint64_t target_flops_cpu =   8ULL * GFLOP;
             const uint64_t target_flops_gpu = 100ULL * GFLOP;
             uint64_t target_flops = is_cpu ? target_flops_cpu : target_flops_gpu;
-            n_runs = std::min<int>(ggml_graph_size(gf) - ggml_graph_n_nodes(gf), target_flops / op_flops(out)) + 1;
+            n_runs = (int)std::min<int64_t>(ggml_graph_size(gf) - ggml_graph_n_nodes(gf), target_flops / op_flops(out)) + 1;
         } else {
             // based on memory size
             const size_t GB = 1ULL << 30;
             const size_t target_size_cpu =  8 * GB;
             const size_t target_size_gpu = 32 * GB;
             size_t target_size = is_cpu ? target_size_cpu : target_size_gpu;
-            n_runs = std::min<int>(ggml_graph_size(gf) - ggml_graph_n_nodes(gf), target_size / op_size(out)) + 1;
+            n_runs = (int)std::min<int64_t>(ggml_graph_size(gf) - ggml_graph_n_nodes(gf), target_size / op_size(out)) + 1;
         }
 
         // duplicate the op
@@ -7660,7 +7660,7 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
     //    test_cases.emplace_back(new test_top_k(GGML_TYPE_F32, {i, 2, 1, 3}, rand() % i + 1));
     //}
 
-    for (ggml_scale_mode mode : {GGML_SCALE_MODE_NEAREST, GGML_SCALE_MODE_BILINEAR, GGML_SCALE_MODE_BICUBIC}) {
+    for (ggml_scale_mode mode : {GGML_SCALE_MODE_NEAREST, GGML_SCALE_MODE_BILINEAR, GGML_SCALE_MODE_BICUBIC, ggml_scale_mode(GGML_SCALE_MODE_BILINEAR | GGML_SCALE_FLAG_ANTIALIAS)}) {
         test_cases.emplace_back(new test_upscale(GGML_TYPE_F32, {512, 512, 3, 2}, 2, mode));
         test_cases.emplace_back(new test_upscale(GGML_TYPE_F32, {512, 512, 3, 2}, 2, mode, true));
         test_cases.emplace_back(new test_interpolate(GGML_TYPE_F32, {2, 5,  7, 11}, {5, 7, 11, 13}, mode));
@@ -7935,6 +7935,9 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_perf() {
     test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32, 16416, 1, 128, {8,  1}, {4, 1}, {0, 2, 1, 3}));
     test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32, 128, 1, 16416, {8,  1}, {4, 1}, {0, 1, 2, 3}, 2*16416));
 
+    test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 64, 64, 4, 2 }, { 6, 64, 4, 2 }));
+    test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 128, 128, 4, 1 }, { 8, 128, 4, 1 }));
+
     for (int bs : {1, 2, 3, 4, 5, 8, 512}) {
         for (ggml_type type_a : all_types) {
             for (ggml_type type_b : {GGML_TYPE_F32}) {
@@ -8040,7 +8043,9 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_perf() {
     }
 
     test_cases.emplace_back(new test_argsort(GGML_TYPE_F32, {65000, 16, 1, 1}));
-    for (auto k : {1, 10, 40}) {
+
+    test_cases.emplace_back(new test_top_k(GGML_TYPE_F32, {2, 1, 1, 1}, 1));
+    for (auto k : {1, 10, 40, 400}) {
         for (auto nrows : {1, 16}) {
             for (auto cols : {k, 1000, 65000, 200000}) {
                 test_cases.emplace_back(new test_top_k(GGML_TYPE_F32, {cols, nrows, 1, 1}, k));

tests/test-json-schema-to-grammar.cpp

@@ -1339,6 +1339,32 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
             space ::= | " " | "\n"{1,2} [ \t]{0,20}
         )"""
     });
+
+    test({
+        SUCCESS,
+        "literal string with escapes",
+        R"""({
+            "properties": {
+                "code": {
+                    "const": " \r \n \" \\ ",
+                    "description": "Generated code",
+                    "title": "Code",
+                    "type": "string"
+                }
+            },
+            "required": [
+                "code"
+            ],
+            "title": "DecoderResponse",
+            "type": "object"
+        })""",
+        R"""(
+            code ::= "\" \\r \\n \\\" \\\\ \"" space
+            code-kv ::= "\"code\"" space ":" space code
+            root ::= "{" space code-kv "}" space
+            space ::= | " " | "\n"{1,2} [ \t]{0,20}
+        )"""
+    });
 }
 
 int main() {

tools/main/main.cpp

@@ -521,6 +521,12 @@ int main(int argc, char ** argv) {
         is_interacting = params.interactive_first;
     }
 
+    LOG_WRN("*****************************\n");
+    LOG_WRN("IMPORTANT: The current llama-cli will be moved to llama-completion in the near future\n");
+    LOG_WRN("  New llama-cli will have enhanced features and improved user experience\n");
+    LOG_WRN("  More info: https://github.com/ggml-org/llama.cpp/discussions/17618\n");
+    LOG_WRN("*****************************\n");
+
     bool is_antiprompt        = false;
     bool input_echo           = true;
     bool display              = true;

tools/mtmd/clip.cpp

@@ -987,12 +987,20 @@ struct clip_graph {
                 cur = ggml_mul_mat(ctx0, layer.qkv_w, cur);
                 cur = ggml_add(ctx0, cur, layer.qkv_b);
 
-                ggml_tensor * Qcur = ggml_view_3d(ctx0, cur, d_head, n_head, n_pos, d_head*sizeof(float),
-                    cur->nb[1], 0);
-                ggml_tensor * Kcur = ggml_view_3d(ctx0, cur, d_head, n_head, n_pos, d_head*sizeof(float),
-                    cur->nb[1], n_embd * sizeof(float));
-                ggml_tensor * Vcur = ggml_view_3d(ctx0, cur, d_head, n_head, n_pos, d_head*sizeof(float),
-                    cur->nb[1], 2 * n_embd * sizeof(float));
+                ggml_tensor * Qcur = ggml_view_3d(ctx0, cur, d_head, n_head, n_pos,
+                        /* nb1    */ ggml_row_size(cur->type, d_head),
+                        /* nb2    */ cur->nb[1],
+                        /* offset */ 0);
+
+                ggml_tensor * Kcur = ggml_view_3d(ctx0, cur, d_head, n_head, n_pos,
+                        /* nb1    */ ggml_row_size(cur->type, d_head),
+                        /* nb2    */ cur->nb[1],
+                        /* offset */ ggml_row_size(cur->type, n_embd));
+
+                ggml_tensor * Vcur = ggml_view_3d(ctx0, cur, d_head, n_head, n_pos,
+                        /* nb1    */ ggml_row_size(cur->type, d_head),
+                        /* nb2    */ cur->nb[1],
+                        /* offset */ ggml_row_size(cur->type, 2 * n_embd));
 
                 cb(Qcur, "Qcur", il);
                 cb(Kcur, "Kcur", il);
@@ -2012,7 +2020,7 @@ private:
         ggml_tensor * pos_embd = model.position_embeddings;
         const int height       = img.ny / patch_size;
         const int width        = img.nx / patch_size;
-        const uint32_t mode    = GGML_SCALE_MODE_BILINEAR;
+        const uint32_t mode    = GGML_SCALE_MODE_BILINEAR | GGML_SCALE_FLAG_ANTIALIAS;
         const int n_per_side   = (int)std::sqrt(pos_embd->ne[1]);
 
         GGML_ASSERT(pos_embd);
@@ -2787,7 +2795,8 @@ struct clip_model_loader {
                     {
                         get_u32(KEY_PROJ_SCALE_FACTOR, hparams.n_merge, false);
                         // ref: https://huggingface.co/LiquidAI/LFM2-VL-3B/blob/main/preprocessor_config.json
-                        hparams.set_limit_image_tokens(64, 256);
+                        // config above specifies number of tokens after downsampling, while here it is before, relax lowerbound to 64
+                        hparams.set_limit_image_tokens(64, 1024);
                     } break;
                 case PROJECTOR_TYPE_PIXTRAL:
                 case PROJECTOR_TYPE_LIGHTONOCR:
@@ -3737,12 +3746,13 @@ struct img_tool {
         const int width  = inp_size.width;
         const int height = inp_size.height;
 
+        auto round_by_factor = [f = align_size](float x) { return static_cast<int>(std::round(x / static_cast<float>(f))) * f; };
         auto ceil_by_factor  = [f = align_size](float x) { return static_cast<int>(std::ceil(x / static_cast<float>(f))) * f; };
         auto floor_by_factor = [f = align_size](float x) { return static_cast<int>(std::floor(x / static_cast<float>(f))) * f; };
 
         // always align up first
-        int h_bar = std::max(align_size, ceil_by_factor(height));
-        int w_bar = std::max(align_size, ceil_by_factor(width));
+        int h_bar = std::max(align_size, round_by_factor(height));
+        int w_bar = std::max(align_size, round_by_factor(width));
 
         if (h_bar * w_bar > max_pixels) {
             const auto beta = std::sqrt(static_cast<float>(height * width) / max_pixels);
@@ -4357,7 +4367,8 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, str
                 const std::array<uint8_t, 3> pad_color = {122, 116, 104};
 
                 clip_image_u8 resized_img;
-                img_tool::resize(*img, resized_img, target_size, img_tool::RESIZE_ALGO_BILINEAR, true, pad_color);
+                const bool pad = (ctx->proj_type() != PROJECTOR_TYPE_LFM2);
+                img_tool::resize(*img, resized_img, target_size, img_tool::RESIZE_ALGO_BILINEAR, pad, pad_color);
                 clip_image_f32_ptr res(clip_image_f32_init());
                 normalize_image_u8_to_f32(resized_img, *res, params.image_mean, params.image_std);
                 res_imgs->entries.push_back(std::move(res));

tools/mtmd/mtmd.cpp

@@ -304,6 +304,10 @@ struct mtmd_context {
             img_beg = "<|im_start|>";
             img_end = "<|im_end|>";
 
+        } else if (proj == PROJECTOR_TYPE_LFM2) {
+            img_beg = "<|image_start|>";
+            img_end = "<|image_end|>";
+
         }
     }
 

tools/server/CMakeLists.txt

@@ -21,6 +21,8 @@ set(TARGET_SRCS
     server-queue.h
     server-common.cpp
     server-common.h
+    server-context.cpp
+    server-context.h
 )
 set(PUBLIC_ASSETS
     index.html.gz

tools/server/README.md

@@ -7,6 +7,7 @@ Set of LLM REST APIs and a simple web front end to interact with llama.cpp.
 **Features:**
  * LLM inference of F16 and quantized models on GPU and CPU
  * [OpenAI API](https://github.com/openai/openai-openapi) compatible chat completions and embeddings routes
+ * [Anthropic Messages API](https://docs.anthropic.com/en/api/messages) compatible chat completions
  * Reranking endpoint (https://github.com/ggml-org/llama.cpp/pull/9510)
  * Parallel decoding with multi-user support
  * Continuous batching
@@ -1352,6 +1353,77 @@ See [OpenAI Embeddings API documentation](https://platform.openai.com/docs/api-r
   }'
   ```
 
+### POST `/v1/messages`: Anthropic-compatible Messages API
+
+Given a list of `messages`, returns the assistant's response. Streaming is supported via Server-Sent Events. While no strong claims of compatibility with the Anthropic API spec are made, in our experience it suffices to support many apps.
+
+*Options:*
+
+See [Anthropic Messages API documentation](https://docs.anthropic.com/en/api/messages). Tool use requires `--jinja` flag.
+
+`model`: Model identifier (required)
+
+`messages`: Array of message objects with `role` and `content` (required)
+
+`max_tokens`: Maximum tokens to generate (default: 4096)
+
+`system`: System prompt as string or array of content blocks
+
+`temperature`: Sampling temperature 0-1 (default: 1.0)
+
+`top_p`: Nucleus sampling (default: 1.0)
+
+`top_k`: Top-k sampling
+
+`stop_sequences`: Array of stop sequences
+
+`stream`: Enable streaming (default: false)
+
+`tools`: Array of tool definitions (requires `--jinja`)
+
+`tool_choice`: Tool selection mode (`{"type": "auto"}`, `{"type": "any"}`, or `{"type": "tool", "name": "..."}`)
+
+*Examples:*
+
+```shell
+curl http://localhost:8080/v1/messages \
+  -H "Content-Type: application/json" \
+  -H "x-api-key: your-api-key" \
+  -d '{
+    "model": "gpt-4",
+    "max_tokens": 1024,
+    "system": "You are a helpful assistant.",
+    "messages": [
+      {"role": "user", "content": "Hello!"}
+    ]
+  }'
+```
+
+### POST `/v1/messages/count_tokens`: Token Counting
+
+Counts the number of tokens in a request without generating a response.
+
+Accepts the same parameters as `/v1/messages`. The `max_tokens` parameter is not required.
+
+*Example:*
+
+```shell
+curl http://localhost:8080/v1/messages/count_tokens \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "gpt-4",
+    "messages": [
+      {"role": "user", "content": "Hello!"}
+    ]
+  }'
+```
+
+*Response:*
+
+```json
+{"input_tokens": 10}
+```
+
 ## More examples
 
 ### Interactive mode

tools/server/public_legacy/json-schema-to-grammar.mjs

@@ -257,9 +257,9 @@ const STRING_FORMAT_RULES = {
 const RESERVED_NAMES = {'root': true, ...PRIMITIVE_RULES, ...STRING_FORMAT_RULES};
 
 const INVALID_RULE_CHARS_RE = /[^\dA-Za-z-]+/g;
-const GRAMMAR_LITERAL_ESCAPE_RE = /[\n\r"]/g;
+const GRAMMAR_LITERAL_ESCAPE_RE = /[\n\r"\\]/g;
 const GRAMMAR_RANGE_LITERAL_ESCAPE_RE = /[\n\r"\]\-\\]/g;
-const GRAMMAR_LITERAL_ESCAPES = { '\r': '\\r', '\n': '\\n', '"': '\\"', '-': '\\-', ']': '\\]' };
+const GRAMMAR_LITERAL_ESCAPES = { '\r': '\\r', '\n': '\\n', '"': '\\"', '-': '\\-', ']': '\\]', '\\': '\\\\' };
 
 const NON_LITERAL_SET = new Set('|.()[]{}*+?');
 const ESCAPED_IN_REGEXPS_BUT_NOT_IN_LITERALS = new Set('^$.[]()|{}*+?');

tools/server/server-common.cpp

@@ -725,7 +725,6 @@ std::vector<server_tokens> tokenize_input_prompts(const llama_vocab * vocab, mtm
     return result;
 }
 
-
 //
 // OAI utils
 //
@@ -1048,6 +1047,222 @@ json oaicompat_chat_params_parse(
     return llama_params;
 }
 
+json convert_anthropic_to_oai(const json & body) {
+    json oai_body;
+
+    // Convert system prompt
+    json oai_messages = json::array();
+    auto system_param = json_value(body, "system", json());
+    if (!system_param.is_null()) {
+        std::string system_content;
+
+        if (system_param.is_string()) {
+            system_content = system_param.get<std::string>();
+        } else if (system_param.is_array()) {
+            for (const auto & block : system_param) {
+                if (json_value(block, "type", std::string()) == "text") {
+                    system_content += json_value(block, "text", std::string());
+                }
+            }
+        }
+
+        oai_messages.push_back({
+            {"role", "system"},
+            {"content", system_content}
+        });
+    }
+
+    // Convert messages
+    if (!body.contains("messages")) {
+        throw std::runtime_error("'messages' is required");
+    }
+    const json & messages = body.at("messages");
+    if (messages.is_array()) {
+        for (const auto & msg : messages) {
+            std::string role = json_value(msg, "role", std::string());
+
+            if (!msg.contains("content")) {
+                if (role == "assistant") {
+                    continue;
+                }
+                oai_messages.push_back(msg);
+                continue;
+            }
+
+            const json & content = msg.at("content");
+
+            if (content.is_string()) {
+                oai_messages.push_back(msg);
+                continue;
+            }
+
+            if (!content.is_array()) {
+                oai_messages.push_back(msg);
+                continue;
+            }
+
+            json tool_calls = json::array();
+            json converted_content = json::array();
+            json tool_results = json::array();
+            bool has_tool_calls = false;
+
+            for (const auto & block : content) {
+                std::string type = json_value(block, "type", std::string());
+
+                if (type == "text") {
+                    converted_content.push_back(block);
+                } else if (type == "image") {
+                    json source = json_value(block, "source", json::object());
+                    std::string source_type = json_value(source, "type", std::string());
+
+                    if (source_type == "base64") {
+                        std::string media_type = json_value(source, "media_type", std::string("image/jpeg"));
+                        std::string data = json_value(source, "data", std::string());
+                        std::ostringstream ss;
+                        ss << "data:" << media_type << ";base64," << data;
+
+                        converted_content.push_back({
+                            {"type", "image_url"},
+                            {"image_url", {
+                                {"url", ss.str()}
+                            }}
+                        });
+                    } else if (source_type == "url") {
+                        std::string url = json_value(source, "url", std::string());
+                        converted_content.push_back({
+                            {"type", "image_url"},
+                            {"image_url", {
+                                {"url", url}
+                            }}
+                        });
+                    }
+                } else if (type == "tool_use") {
+                    tool_calls.push_back({
+                        {"id", json_value(block, "id", std::string())},
+                        {"type", "function"},
+                        {"function", {
+                            {"name", json_value(block, "name", std::string())},
+                            {"arguments", json_value(block, "input", json::object()).dump()}
+                        }}
+                    });
+                    has_tool_calls = true;
+                } else if (type == "tool_result") {
+                    std::string tool_use_id = json_value(block, "tool_use_id", std::string());
+
+                    auto result_content = json_value(block, "content", json());
+                    std::string result_text;
+                    if (result_content.is_string()) {
+                        result_text = result_content.get<std::string>();
+                    } else if (result_content.is_array()) {
+                        for (const auto & c : result_content) {
+                            if (json_value(c, "type", std::string()) == "text") {
+                                result_text += json_value(c, "text", std::string());
+                            }
+                        }
+                    }
+
+                    tool_results.push_back({
+                        {"role", "tool"},
+                        {"tool_call_id", tool_use_id},
+                        {"content", result_text}
+                    });
+                }
+            }
+
+            if (!converted_content.empty() || has_tool_calls) {
+                json new_msg = {{"role", role}};
+                if (!converted_content.empty()) {
+                    new_msg["content"] = converted_content;
+                } else if (has_tool_calls) {
+                    new_msg["content"] = "";
+                }
+                if (!tool_calls.empty()) {
+                    new_msg["tool_calls"] = tool_calls;
+                }
+                oai_messages.push_back(new_msg);
+            }
+
+            for (const auto & tool_msg : tool_results) {
+                oai_messages.push_back(tool_msg);
+            }
+        }
+    }
+
+    oai_body["messages"] = oai_messages;
+
+    // Convert tools
+    if (body.contains("tools")) {
+        const json & tools = body.at("tools");
+        if (tools.is_array()) {
+            json oai_tools = json::array();
+            for (const auto & tool : tools) {
+                oai_tools.push_back({
+                    {"type", "function"},
+                    {"function", {
+                        {"name", json_value(tool, "name", std::string())},
+                        {"description", json_value(tool, "description", std::string())},
+                        {"parameters", tool.contains("input_schema") ? tool.at("input_schema") : json::object()}
+                    }}
+                });
+            }
+            oai_body["tools"] = oai_tools;
+        }
+    }
+
+    // Convert tool_choice
+    if (body.contains("tool_choice")) {
+        const json & tc = body.at("tool_choice");
+        if (tc.is_object()) {
+            std::string type = json_value(tc, "type", std::string());
+            if (type == "auto") {
+                oai_body["tool_choice"] = "auto";
+            } else if (type == "any" || type == "tool") {
+                oai_body["tool_choice"] = "required";
+            }
+        }
+    }
+
+    // Convert stop_sequences to stop
+    if (body.contains("stop_sequences")) {
+        oai_body["stop"] = body.at("stop_sequences");
+    }
+
+    // Handle max_tokens (required in Anthropic, but we're permissive)
+    if (body.contains("max_tokens")) {
+        oai_body["max_tokens"] = body.at("max_tokens");
+    } else {
+        oai_body["max_tokens"] = 4096;
+    }
+
+    // Pass through common params
+    for (const auto & key : {"temperature", "top_p", "top_k", "stream"}) {
+        if (body.contains(key)) {
+            oai_body[key] = body.at(key);
+        }
+    }
+
+    // Handle Anthropic-specific thinking param
+    if (body.contains("thinking")) {
+        json thinking = json_value(body, "thinking", json::object());
+        std::string thinking_type = json_value(thinking, "type", std::string());
+        if (thinking_type == "enabled") {
+            int budget_tokens = json_value(thinking, "budget_tokens", 10000);
+            oai_body["thinking_budget_tokens"] = budget_tokens;
+        }
+    }
+
+    // Handle Anthropic-specific metadata param
+    if (body.contains("metadata")) {
+        json metadata = json_value(body, "metadata", json::object());
+        std::string user_id = json_value(metadata, "user_id", std::string());
+        if (!user_id.empty()) {
+            oai_body["__metadata_user_id"] = user_id;
+        }
+    }
+
+    return oai_body;
+}
+
 json format_embeddings_response_oaicompat(const json & request, const json & embeddings, bool use_base64) {
     json data = json::array();
     int32_t n_tokens = 0;
@@ -1211,7 +1426,7 @@ std::string tokens_to_output_formatted_string(const llama_context * ctx, const l
 
 // format server-sent event (SSE), return the formatted string to send
 // note: if data is a json array, it will be sent as multiple events, one per item
-std::string format_sse(const json & data) {
+std::string format_oai_sse(const json & data) {
     std::ostringstream ss;
     auto send_single = [&ss](const json & data) {
         ss << "data: " <<
@@ -1230,6 +1445,29 @@ std::string format_sse(const json & data) {
     return ss.str();
 }
 
+std::string format_anthropic_sse(const json & data) {
+    std::ostringstream ss;
+
+    auto send_event = [&ss](const json & event_obj) {
+        if (event_obj.contains("event") && event_obj.contains("data")) {
+            ss << "event: " << event_obj.at("event").get<std::string>() << "\n";
+            ss << "data: " << safe_json_to_str(event_obj.at("data")) << "\n\n";
+        } else {
+            ss << "data: " << safe_json_to_str(event_obj) << "\n\n";
+        }
+    };
+
+    if (data.is_array()) {
+        for (const auto & event : data) {
+            send_event(event);
+        }
+    } else {
+        send_event(data);
+    }
+
+    return ss.str();
+}
+
 bool is_valid_utf8(const std::string & str) {
     const unsigned char* bytes = reinterpret_cast<const unsigned char*>(str.data());
     const unsigned char* end = bytes + str.length();

tools/server/server-common.h

@@ -294,6 +294,9 @@ json oaicompat_chat_params_parse(
     const oaicompat_parser_options & opt,
     std::vector<raw_buffer> & out_files);
 
+// convert Anthropic Messages API format to OpenAI Chat Completions API format
+json convert_anthropic_to_oai(const json & body);
+
 // TODO: move it to server-task.cpp
 json format_embeddings_response_oaicompat(const json & request, const json & embeddings, bool use_base64 = false);
 
@@ -320,7 +323,10 @@ std::string tokens_to_output_formatted_string(const llama_context * ctx, const l
 
 // format server-sent event (SSE), return the formatted string to send
 // note: if data is a json array, it will be sent as multiple events, one per item
-std::string format_sse(const json & data);
+std::string format_oai_sse(const json & data);
+
+// format Anthropic-style SSE with event types
+std::string format_anthropic_sse(const json & data);
 
 bool is_valid_utf8(const std::string & str);