opencl: add swiglu_oai and add_id (#15121 )

* opencl: add `swiglu-oai` * opencl: add `add_id` * opencl: add missing `add_id.cl`
chat : support Granite model reasoning and tool call (#14864 )
2026-06-15 10:16:45 +02:00 · 2025-08-06 12:12:17 -07:00 · 2025-08-06 20:27:30 +02:00 · 2025-08-06 17:28:48 +01:00 · 2025-08-06 14:37:35 +02:00 · 2025-08-06 13:26:49 +02:00
124 changed files with 5548 additions and 1023 deletions
@@ -159,31 +159,15 @@ jobs:
      - name: Dawn Dependency
        id: dawn-depends
        run: |
-          ARTIFACTS_JSON=$(curl -s -L \
-            -H "Accept: application/vnd.github+json" \
-            -H "Authorization: Bearer ${{ secrets.GITHUB_TOKEN }}" \
-            -H "X-GitHub-Api-Version: 2022-11-28" \
-            "https://api.github.com/repos/google/dawn/actions/artifacts")
-          echo "Finding latest macos-latest-Release artifact..."
-          DOWNLOAD_URL=$(echo "$ARTIFACTS_JSON" | jq -r '.artifacts
-            | sort_by(.created_at)
-            | reverse
-            | map(select(.name | test("macos-latest-Release$")))
-            | .[0].archive_download_url')
-          if [ "$DOWNLOAD_URL" = "null" ] || [ -z "$DOWNLOAD_URL" ]; then
-            echo "No suitable Dawn artifact found!"
-            exit 1
-          fi
-          echo "Downloading from: $DOWNLOAD_URL"
-          curl -L \
-            -H "Accept: application/vnd.github+json" \
-            -H "Authorization: Bearer ${{ secrets.GITHUB_TOKEN }}" \
-            -o artifact.zip "$DOWNLOAD_URL"
-          unzip artifact.zip
+          DAWN_VERSION="v1.0.0"
+          DAWN_OWNER="reeselevine"
+          DAWN_REPO="dawn"
+          DAWN_ASSET_NAME="Dawn-a1a6b45cced25a3b7f4fb491e0ae70796cc7f22b-macos-latest-Release.tar.gz"
+          echo "Fetching release asset from https://github.com/${DAWN_OWNER}/${DAWN_REPO}/releases/download/${DAWN_VERSION}/${DAWN_ASSET_NAME}"
+          curl -L -o artifact.tar.gz \
+            "https://github.com/${DAWN_OWNER}/${DAWN_REPO}/releases/download/${DAWN_VERSION}/${DAWN_ASSET_NAME}"
          mkdir dawn
-          tar_file=$(find . -name '*.tar.gz' | head -n 1)
-          echo "Extracting: $tar_file"
-          tar -xvf "$tar_file" -C dawn --strip-components=1
+          tar -xvf artifact.tar.gz -C dawn --strip-components=1

      - name: Build
        id: cmake_build
@@ -195,6 +179,7 @@ jobs:
      - name: Test
        id: cmake_test
        run: |
+          export LLAMA_SET_ROWS=0
          cd build
          ctest -L main --verbose --timeout 900

@@ -433,31 +418,15 @@ jobs:
        id: dawn-depends
        run: |
          sudo apt-get install -y libxrandr-dev libxinerama-dev libxcursor-dev mesa-common-dev libx11-xcb-dev libxi-dev
-          ARTIFACTS_JSON=$(curl -s -L \
-            -H "Accept: application/vnd.github+json" \
-            -H "Authorization: Bearer ${{ secrets.GITHUB_TOKEN }}" \
-            -H "X-GitHub-Api-Version: 2022-11-28" \
-            "https://api.github.com/repos/google/dawn/actions/artifacts")
-          echo "Finding latest ubuntu-latest-Release artifact..."
-          DOWNLOAD_URL=$(echo "$ARTIFACTS_JSON" | jq -r '.artifacts
-            | sort_by(.created_at)
-            | reverse
-            | map(select(.name | test("ubuntu-latest-Release$")))
-            | .[0].archive_download_url')
-          if [ "$DOWNLOAD_URL" = "null" ] || [ -z "$DOWNLOAD_URL" ]; then
-            echo "No suitable Dawn artifact found!"
-            exit 1
-          fi
-          echo "Downloading from: $DOWNLOAD_URL"
-          curl -L \
-            -H "Accept: application/vnd.github+json" \
-            -H "Authorization: Bearer ${{ secrets.GITHUB_TOKEN }}" \
-            -o artifact.zip "$DOWNLOAD_URL"
-          unzip artifact.zip
+          DAWN_VERSION="v1.0.0"
+          DAWN_OWNER="reeselevine"
+          DAWN_REPO="dawn"
+          DAWN_ASSET_NAME="Dawn-a1a6b45cced25a3b7f4fb491e0ae70796cc7f22b-ubuntu-latest-Release.tar.gz"
+          echo "Fetching release asset from https://github.com/${DAWN_OWNER}/${DAWN_REPO}/releases/download/${DAWN_VERSION}/${DAWN_ASSET_NAME}"
+          curl -L -o artifact.tar.gz \
+            "https://github.com/${DAWN_OWNER}/${DAWN_REPO}/releases/download/${DAWN_VERSION}/${DAWN_ASSET_NAME}"
          mkdir dawn
-          tar_file=$(find . -name '*.tar.gz' | head -n 1)
-          echo "Extracting: $tar_file"
-          tar -xvf "$tar_file" -C dawn --strip-components=1
+          tar -xvf artifact.tar.gz -C dawn --strip-components=1

      - name: Build
        id: cmake_build
@@ -469,6 +438,7 @@ jobs:
      - name: Test
        id: cmake_test
        run: |
+          export LLAMA_SET_ROWS=0
          cd build
          # This is using llvmpipe and runs slower than other backends
          ctest -L main --verbose --timeout 3600
@@ -0,0 +1,45 @@
+name: Check Pre-Tokenizer Hashes
+
+on:
+    push:
+        paths:
+            - 'convert_hf_to_gguf.py'
+            - 'convert_hf_to_gguf_update.py'
+    pull_request:
+        paths:
+            - 'convert_hf_to_gguf.py'
+            - 'convert_hf_to_gguf_update.py'
+
+jobs:
+    pre-tokenizer-hashes:
+        runs-on: ubuntu-latest
+
+        steps:
+        - name: Checkout repository
+          uses: actions/checkout@v4
+
+        - name: Set up Python
+          uses: actions/setup-python@v5
+          with:
+              python-version: '3.11'
+
+        - name: Install Python dependencies
+          run: |
+              python3 -m venv .venv
+              .venv/bin/pip install -r requirements/requirements-convert_hf_to_gguf_update.txt
+
+        - name: Update pre-tokenizer hashes
+          run: |
+              cp convert_hf_to_gguf.py /tmp
+              .venv/bin/python convert_hf_to_gguf_update.py --check-missing
+
+        - name: Check if committed pre-tokenizer hashes matches generated version
+          run: |
+              if ! diff -q convert_hf_to_gguf.py /tmp/convert_hf_to_gguf.py; then
+                  echo "Model pre-tokenizer hashes (in convert_hf_to_gguf.py) do not match generated hashes (from convert_hf_to_gguf_update.py)."
+                  echo "To fix: run ./convert_hf_to_gguf_update.py and commit the updated convert_hf_to_gguf.py along with your changes"
+                  echo "Differences found:"
+                  diff convert_hf_to_gguf.py /tmp/convert_hf_to_gguf.py || true
+                  exit 1
+              fi
+              echo "Model pre-tokenizer hashes are up to date."
@@ -17,6 +17,7 @@ LLM inference in C/C++

 ## Hot topics

+- Support for the `gpt-oss` model with native MXFP4 format has been added | [PR](https://github.com/ggml-org/llama.cpp/pull/15091) | [Collaboration with NVIDIA](https://blogs.nvidia.com/blog/rtx-ai-garage-openai-oss) | [Comment](https://github.com/ggml-org/llama.cpp/discussions/15095)
 - Hot PRs: [All](https://github.com/ggml-org/llama.cpp/pulls?q=is%3Apr+label%3Ahot+) | [Open](https://github.com/ggml-org/llama.cpp/pulls?q=is%3Apr+label%3Ahot+is%3Aopen)
 - Multimodal support arrived in `llama-server`: [#12898](https://github.com/ggml-org/llama.cpp/pull/12898) | [documentation](./docs/multimodal.md)
 - VS Code extension for FIM completions: https://github.com/ggml-org/llama.vscode
@@ -24,6 +24,7 @@
 #include <cstdarg>
 #include <filesystem>
 #include <fstream>
+#include <list>
 #include <regex>
 #include <set>
 #include <string>
@@ -2375,20 +2376,35 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
                    }
                    throw std::invalid_argument("unknown buffer type");
                }
-                // FIXME: this leaks memory
-                params.tensor_buft_overrides.push_back({strdup(tensor_name.c_str()), buft_list.at(buffer_type)});
+                // keep strings alive and avoid leaking memory by storing them in a static vector
+                static std::list<std::string> buft_overrides;
+                buft_overrides.push_back(tensor_name);
+                params.tensor_buft_overrides.push_back({buft_overrides.back().c_str(), buft_list.at(buffer_type)});
            }
        }
    ));
    add_opt(common_arg(
-        {"--cpu-moe"},
-        "use CPU for Mixture of Experts (MoE) weights",
+        {"--cpu-moe", "-cmoe"},
+        "keep all Mixture of Experts (MoE) weights in the CPU",
        [](common_params & params) {
-            params.tensor_buft_overrides.push_back({"\\.ffn_up_exps\\.weight$",   ggml_backend_cpu_buffer_type()});
-            params.tensor_buft_overrides.push_back({"\\.ffn_down_exps\\.weight$", ggml_backend_cpu_buffer_type()});
-            params.tensor_buft_overrides.push_back({"\\.ffn_gate_exps\\.weight$", ggml_backend_cpu_buffer_type()});
+            params.tensor_buft_overrides.push_back({"\\.ffn_(up|down|gate)_exps", ggml_backend_cpu_buffer_type()});
        }
    ).set_env("LLAMA_ARG_CPU_MOE"));
+    add_opt(common_arg(
+        {"--n-cpu-moe", "-ncmoe"}, "N",
+        "keep the Mixture of Experts (MoE) weights of the first N layers in the CPU",
+        [](common_params & params, int value) {
+            if (value < 0) {
+                throw std::invalid_argument("invalid value");
+            }
+            for (int i = 0; i < value; ++i) {
+                // keep strings alive and avoid leaking memory by storing them in a static vector
+                static std::list<std::string> buft_overrides;
+                buft_overrides.push_back(string_format("blk\\.%d\\.ffn_(up|down|gate)_exps", i));
+                params.tensor_buft_overrides.push_back({buft_overrides.back().c_str(), ggml_backend_cpu_buffer_type()});
+            }
+        }
+    ).set_env("LLAMA_ARG_N_CPU_MOE"));
    add_opt(common_arg(
        {"-ngl", "--gpu-layers", "--n-gpu-layers"}, "N",
        "number of layers to store in VRAM",
@@ -2647,6 +2663,15 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
            params.n_out_freq = value;
        }
    ).set_examples({LLAMA_EXAMPLE_IMATRIX}));
+    add_opt(common_arg(
+        {"--output-format"}, "{gguf,dat}",
+        string_format("output format for imatrix file (default: %s)", params.imat_dat > 0 ? "dat" : "gguf"),
+        [](common_params & params, const std::string & value) {
+            /**/ if (value == "gguf") { params.imat_dat = -1; }
+            else if (value == "dat")  { params.imat_dat = 1;  }
+            else { throw std::invalid_argument("invalid output format"); }
+        }
+    ).set_examples({LLAMA_EXAMPLE_IMATRIX}));
    add_opt(common_arg(
        {"--save-frequency"}, "N",
        string_format("save an imatrix copy every N iterations (default: %d)", params.n_save_freq),
@@ -2922,11 +2947,12 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
        "controls whether thought tags are allowed and/or extracted from the response, and in which format they're returned; one of:\n"
        "- none: leaves thoughts unparsed in `message.content`\n"
        "- deepseek: puts thoughts in `message.reasoning_content` (except in streaming mode, which behaves as `none`)\n"
-        "(default: deepseek)",
+        "(default: auto)",
        [](common_params & params, const std::string & value) {
            /**/ if (value == "deepseek") { params.reasoning_format = COMMON_REASONING_FORMAT_DEEPSEEK; }
            else if (value == "deepseek-legacy") { params.reasoning_format = COMMON_REASONING_FORMAT_DEEPSEEK_LEGACY; }
            else if (value == "none") {     params.reasoning_format = COMMON_REASONING_FORMAT_NONE; }
+            else if (value == "auto") {     params.reasoning_format = COMMON_REASONING_FORMAT_AUTO; }
            else { throw std::invalid_argument("invalid value"); }
        }
    ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_MAIN}).set_env("LLAMA_ARG_THINK"));
@@ -55,7 +55,15 @@ bool common_chat_msg_parser::add_tool_call(const std::string & name, const std::
 bool common_chat_msg_parser::add_tool_call(const json & tool_call) {
    std::string name = tool_call.contains("name") ? tool_call.at("name") : "";
    std::string id = tool_call.contains("id") ? tool_call.at("id") : "";
-    std::string arguments = tool_call.contains("arguments") ? tool_call.at("arguments") : "";
+    std::string arguments = "";
+    if (tool_call.contains("arguments")) {
+        if (tool_call.at("arguments").is_object()) {
+            arguments = tool_call.at("arguments").dump();
+        } else {
+            arguments = tool_call.at("arguments");
+        }
+    }
+
    return add_tool_call(name, id, arguments);
 }

@@ -126,6 +126,8 @@ std::vector<common_chat_msg_diff> common_chat_msg_diff::compute_diffs(const comm
 typedef minja::chat_template common_chat_template;

 struct common_chat_templates {
+    bool add_bos;
+    bool add_eos;
    bool has_explicit_template; // Model had builtin template or template overridde was specified.
    std::unique_ptr<common_chat_template> template_default; // always set (defaults to chatml)
    std::unique_ptr<common_chat_template> template_tool_use;
@@ -143,6 +145,8 @@ struct templates_params {
    bool enable_thinking = true;
    std::chrono::system_clock::time_point now = std::chrono::system_clock::now();
    json extra_context;
+    bool add_bos;
+    bool add_eos;
 };

 common_chat_tool_choice common_chat_tool_choice_parse_oaicompat(const std::string & tool_choice) {
@@ -445,6 +449,8 @@ std::string common_chat_format_single(

    common_chat_templates_inputs inputs;
    inputs.use_jinja = use_jinja;
+    inputs.add_bos = tmpls->add_bos;
+    inputs.add_eos = tmpls->add_eos;

    std::string fmt_past_msg;
    if (!past_msg.empty()) {
@@ -469,6 +475,8 @@ std::string common_chat_format_single(
 std::string common_chat_format_example(const struct common_chat_templates * tmpls, bool use_jinja) {
    common_chat_templates_inputs inputs;
    inputs.use_jinja = use_jinja;
+    inputs.add_bos = tmpls->add_bos;
+    inputs.add_eos = tmpls->add_eos;
    auto add_simple_msg = [&](auto role, auto content) {
        common_chat_msg msg;
        msg.role = role;
@@ -546,6 +554,8 @@ common_chat_templates_ptr common_chat_templates_init(
    }
    std::string token_bos = bos_token_override;
    std::string token_eos = eos_token_override;
+    bool add_bos = false;
+    bool add_eos = false;
    if (model) {
        const auto * vocab = llama_model_get_vocab(model);
        const auto get_token = [&](llama_token token, const char * name, const char * jinja_variable_name) {
@@ -560,9 +570,13 @@ common_chat_templates_ptr common_chat_templates_init(
        };
        token_bos = get_token(llama_vocab_bos(vocab), "BOS", "bos_token");
        token_eos = get_token(llama_vocab_eos(vocab), "EOS", "eos_token");
+        add_bos = llama_vocab_get_add_bos(vocab);
+        add_eos = llama_vocab_get_add_eos(vocab);
    }
    common_chat_templates_ptr tmpls(new common_chat_templates());
    tmpls->has_explicit_template = has_explicit_template;
+    tmpls->add_bos = add_bos;
+    tmpls->add_eos = add_eos;
    try {
        tmpls->template_default = std::make_unique<minja::chat_template>(default_template_src, token_bos, token_eos);
    } catch (const std::exception & e) {
@@ -592,6 +606,8 @@ const char * common_chat_format_name(common_chat_format format) {
        case COMMON_CHAT_FORMAT_FUNCTIONARY_V3_1_LLAMA_3_1: return "Functionary v3.1 Llama 3.1";
        case COMMON_CHAT_FORMAT_HERMES_2_PRO: return "Hermes 2 Pro";
        case COMMON_CHAT_FORMAT_COMMAND_R7B: return "Command R7B";
+        case COMMON_CHAT_FORMAT_GRANITE: return "Granite";
+        case COMMON_CHAT_FORMAT_GPT_OSS: return "GPT-OSS";
        default:
            throw std::runtime_error("Unknown chat format");
    }
@@ -600,8 +616,10 @@ const char * common_chat_format_name(common_chat_format format) {
 const char * common_reasoning_format_name(common_reasoning_format format) {
    switch (format) {
        case COMMON_REASONING_FORMAT_NONE:     return "none";
+        case COMMON_REASONING_FORMAT_AUTO:     return "auto";
        case COMMON_REASONING_FORMAT_DEEPSEEK: return "deepseek";
        case COMMON_REASONING_FORMAT_DEEPSEEK_LEGACY: return "deepseek-legacy";
+        case COMMON_REASONING_FORMAT_GRANITE: return "granite";
        default:
            throw std::runtime_error("Unknown reasoning format");
    }
@@ -748,10 +766,10 @@ static std::string apply(
    // instead of using `chat_template_options.use_bos_token = false`, since these tokens
    // may be needed inside the template / between messages too.
    auto result = tmpl.apply(tmpl_inputs, tmpl_opts);
-    if (string_starts_with(result, tmpl.bos_token())) {
+    if (inputs.add_bos && string_starts_with(result, tmpl.bos_token())) {
        result = result.substr(tmpl.bos_token().size());
    }
-    if (string_ends_with(result, tmpl.eos_token())) {
+    if (inputs.add_eos && string_ends_with(result, tmpl.eos_token())) {
        result = result.substr(0, result.size() - tmpl.eos_token().size());
    }
    return result;
@@ -1289,6 +1307,26 @@ static void common_chat_parse_deepseek_r1(common_chat_msg_parser & builder) {
        tool_calls_end);
 }

+static common_chat_params common_chat_params_init_gpt_oss(const common_chat_template & tmpl, const struct templates_params & inputs) {
+    common_chat_params data;
+    auto prompt = apply(tmpl, inputs);
+
+    data.prompt = prompt;
+    data.format = COMMON_CHAT_FORMAT_GPT_OSS;
+
+    // TODO: support tool calls in GPT-OSS?
+
+    return data;
+}
+static void common_chat_parse_gpt_oss(common_chat_msg_parser & builder) {
+    // TODO @ngxson : this won't work with --special enabled, we should fix that
+    builder.try_parse_reasoning("<|channel|>analysis<|message|>", "<|start|>assistant<|channel|>final<|message|>");
+    if (!builder.syntax().parse_tool_calls) {
+        builder.add_content(builder.consume_rest());
+        return;
+    }
+}
+
 static common_chat_params common_chat_params_init_firefunction_v2(const common_chat_template & tmpl, const struct templates_params & inputs) {
    LOG_DBG("%s\n", __func__);
    common_chat_params data;
@@ -1646,7 +1684,7 @@ static void common_chat_parse_hermes_2_pro(common_chat_msg_parser & builder) {
        "|<function name=\"([^\"]+)\">"  // match 5 (function name again)
    );

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

@@ -1668,7 +1706,6 @@ static void common_chat_parse_hermes_2_pro(common_chat_msg_parser & builder) {
                    builder.consume_literal(block_end);
                    builder.consume_spaces();
                }
-                builder.add_content(builder.consume_rest());
            } else {
                throw common_chat_msg_partial_exception("failed to parse tool call");
            }
@@ -1693,7 +1730,124 @@ static void common_chat_parse_hermes_2_pro(common_chat_msg_parser & builder) {
                    builder.consume_spaces();
                }
            }
-            builder.add_content(builder.consume_rest());
+        }
+    }
+
+    builder.add_content(builder.consume_rest());
+}
+
+static common_chat_params common_chat_params_init_granite(const common_chat_template & tmpl, const struct templates_params & inputs) {
+    common_chat_params data;
+
+    // Pass thinking context for Granite template
+    json additional_context = {
+        {"thinking", inputs.enable_thinking},
+    };
+
+    data.prompt = apply(tmpl, inputs, /* messages_override= */ std::nullopt, /* tools_override= */ std::nullopt, additional_context);
+    data.format = COMMON_CHAT_FORMAT_GRANITE;
+
+    if (string_ends_with(data.prompt, "<think>\n") || string_ends_with(data.prompt, "<think>")) {
+        if (!inputs.enable_thinking) {
+            data.prompt += "</think>";
+        } else {
+            data.thinking_forced_open = true;
+        }
+    }
+
+    if (!inputs.tools.is_null()) {
+        // Granite uses <|tool_call|> followed by JSON list
+        data.grammar_lazy = inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_REQUIRED;
+        data.grammar = build_grammar([&](const common_grammar_builder & builder) {
+            std::vector<std::string> tool_rules;
+            foreach_function(inputs.tools, [&](const json & tool) {
+                const auto & function = tool.at("function");
+                std::string name = function.at("name");
+                auto parameters = function.at("parameters");
+                builder.resolve_refs(parameters);
+                tool_rules.push_back(builder.add_rule(name + "-call", builder.add_schema(name +
+"-args", {
+                    {"type", "object"},
+                    {"properties", {
+                        {"name", {{"const", name}}},
+                        {"arguments", parameters},
+                    }},
+                    {"required", json::array({"name", "arguments"})},
+                })));
+            });
+
+            auto tool_call = builder.add_rule("tool_call", string_join(tool_rules, " | "));
+            auto tool_list = builder.add_rule("tool_list", "\"[\" space " + tool_call + " (\",\" space " + tool_call + ")* space \"]\"");
+
+            if (data.thinking_forced_open) {
+                builder.add_rule("root", "\"</think>\" space \"<response>\" space [^<]* \"</response>\" space \"<|tool_call|>\" space " + tool_list);
+            } else {
+                builder.add_rule("root", "\"<|tool_call|>\" space " + tool_list);
+            }
+
+            data.grammar_triggers.push_back({
+                COMMON_GRAMMAR_TRIGGER_TYPE_WORD,
+                "<|tool_call|>"
+            });
+
+            data.preserved_tokens = {
+                "<think>",
+                "</think>",
+                "<response>",
+                "</response>",
+                "<|tool_call|>",
+            };
+        });
+    } else {
+        // Handle thinking tags for non-tool responses
+        if (data.thinking_forced_open && inputs.enable_thinking) {
+            data.grammar_lazy = false;
+            data.grammar = build_grammar([&](const common_grammar_builder & builder) {
+                builder.add_rule("root", "\"</think>\" space \"<response>\" space .* \"</response>\" space");
+            });
+            data.preserved_tokens = {
+                "<think>",
+                "</think>",
+                "<response>",
+                "</response>",
+            };
+        }
+    }
+
+    return data;
+}
+
+static void common_chat_parse_granite(common_chat_msg_parser & builder) {
+    // Parse thinking tags
+    builder.try_parse_reasoning("<think>", "</think>");
+
+    // Parse response tags using regex
+    static const common_regex response_regex("<response>([\\s\\S]*?)</response>");
+    if (auto res = builder.try_find_regex(response_regex)) {
+        // Extract the content between the tags (capture group 1)
+        auto content = builder.str(res->groups[1]);
+        builder.add_content(content);
+        builder.move_to(res->groups[0].end);
+    }
+
+    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
+        auto tool_calls_data = builder.consume_json();
+        if (tool_calls_data.json.is_array()) {
+            if (!builder.add_tool_calls(tool_calls_data.json)) {
+                builder.add_content("<|tool_call|>" + tool_calls_data.json.dump());
+            }
+        } else {
+            builder.add_content("<|tool_call|>" + tool_calls_data.json.dump());
        }
    } else {
        builder.add_content(builder.consume_rest());
@@ -1733,6 +1887,8 @@ static common_chat_params common_chat_templates_apply_jinja(
    params.enable_thinking = inputs.enable_thinking;
    params.grammar = inputs.grammar;
    params.now = inputs.now;
+    params.add_bos = inputs.add_bos;
+    params.add_eos = inputs.add_eos;

    params.extra_context = json::object();
    for (auto el : inputs.chat_template_kwargs) {
@@ -1769,11 +1925,21 @@ static common_chat_params common_chat_templates_apply_jinja(
        return common_chat_params_init_command_r7b(tmpl, params);
    }

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

+    // GPT-OSS
+    if (src.find("<|channel|>") != std::string::npos && params.json_schema.is_null()) {
+        return common_chat_params_init_gpt_oss(tmpl, params);
+    }
+
    // Use generic handler when mixing tools + JSON schema.
    // TODO: support that mix in handlers below.
    if ((params.tools.is_array() && params.json_schema.is_object())) {
@@ -1824,6 +1990,7 @@ static common_chat_params common_chat_templates_apply_legacy(
    int alloc_size = 0;
    std::vector<llama_chat_message> chat;
    std::vector<std::string> contents;
+
    for (const auto & msg : inputs.messages) {
        auto content = msg.content;
        for (const auto & part : msg.content_parts) {
@@ -1925,6 +2092,12 @@ static void common_chat_parse(common_chat_msg_parser & builder) {
        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;
        default:
            throw std::runtime_error(std::string("Unsupported format: ") + common_chat_format_name(builder.syntax().format));
    }
@@ -109,6 +109,8 @@ enum common_chat_format {
    COMMON_CHAT_FORMAT_FUNCTIONARY_V3_1_LLAMA_3_1,
    COMMON_CHAT_FORMAT_HERMES_2_PRO,
    COMMON_CHAT_FORMAT_COMMAND_R7B,
+    COMMON_CHAT_FORMAT_GRANITE,
+    COMMON_CHAT_FORMAT_GPT_OSS,

    COMMON_CHAT_FORMAT_COUNT, // Not a format, just the # formats
 };
@@ -127,6 +129,8 @@ struct common_chat_templates_inputs {
    bool enable_thinking = true;
    std::chrono::system_clock::time_point now = std::chrono::system_clock::now();
    std::map<std::string, std::string> chat_template_kwargs;
+    bool add_bos = false;
+    bool add_eos = false;
 };

 struct common_chat_params {
@@ -236,8 +236,10 @@ struct common_params_diffusion {

 enum common_reasoning_format {
    COMMON_REASONING_FORMAT_NONE,
+    COMMON_REASONING_FORMAT_AUTO,
    COMMON_REASONING_FORMAT_DEEPSEEK_LEGACY, // Extract thinking tag contents and return as `message.reasoning_content`, or leave inline in <think> tags in stream mode
    COMMON_REASONING_FORMAT_DEEPSEEK,        // Extract thinking tag contents and return as `message.reasoning_content`, including in streaming deltas.
+    COMMON_REASONING_FORMAT_GRANITE,         // Extract thinking tag contents and return as `message.reasoning_content`, including in streaming deltas.
 };

 struct common_params {
@@ -394,7 +396,7 @@ struct common_params {
    std::string chat_template = "";                                                                         // NOLINT
    bool use_jinja = false;                                                                                 // NOLINT
    bool enable_chat_template = true;
-    common_reasoning_format reasoning_format = COMMON_REASONING_FORMAT_DEEPSEEK;
+    common_reasoning_format reasoning_format = COMMON_REASONING_FORMAT_AUTO;
    int reasoning_budget = -1;
    bool prefill_assistant = true;                                                                          // if true, any trailing assistant message will be prefilled into the response

@@ -439,6 +441,7 @@ struct common_params {
    int32_t n_out_freq  = 10; // output the imatrix every n_out_freq iterations
    int32_t n_save_freq =  0; // save the imatrix every n_save_freq iterations
    int32_t i_chunk     =  0; // start processing from this chunk
+    int8_t  imat_dat    =  0; // whether the legacy imatrix.dat format should be output (gguf <= 0 < dat)

    bool process_output  = false; // collect data for the output tensor
    bool compute_ppl     = true;  // whether to compute perplexity
@@ -678,6 +678,9 @@ class TextModel(ModelBase):
        if chkhsh == "a1336059768a55c99a734006ffb02203cd450fed003e9a71886c88acf24fdbc2":
            # ref: https://huggingface.co/THUDM/glm-4-9b-hf
            res = "glm4"
+        if chkhsh == "9ca2dd618e8afaf09731a7cf6e2105b373ba6a1821559f258b272fe83e6eb902":
+            # ref: https://huggingface.co/zai-org/GLM-4.5-Air
+            res = "glm4"
        if chkhsh == "1431a23e583c97432bc230bff598d103ddb5a1f89960c8f1d1051aaa944d0b35":
            # ref: https://huggingface.co/sapienzanlp/Minerva-7B-base-v1.0
            res = "minerva-7b"
@@ -702,6 +705,9 @@ class TextModel(ModelBase):
        if chkhsh == "81212dc7cdb7e0c1074ca62c5aeab0d43c9f52b8a737be7b12a777c953027890":
            # ref: https://huggingface.co/moonshotai/Kimi-K2-Base
            res = "kimi-k2"
+        if chkhsh == "d4540891389ea895b53b399da6ac824becc30f2fba0e9ddbb98f92e55ca0e97c":
+            # ref: https://huggingface.co/Qwen/Qwen3-Embedding-0.6B
+            res = "qwen2"
        if chkhsh == "0ef9807a4087ebef797fc749390439009c3b9eda9ad1a097abbe738f486c01e5":
            # ref: https://huggingface.co/meta-llama/Meta-Llama-3-8B
            res = "llama-bpe"
@@ -849,6 +855,9 @@ class TextModel(ModelBase):
        if chkhsh == "2085e1638f6c377a0aa4ead21b27bb4cb941bf800df86ed391011769c1758dfb":
            # ref: https://huggingface.co/LGAI-EXAONE/EXAONE-4.0-32B
            res = "exaone4"
+        if chkhsh == "a1e163ecab2e718a4c829d1148b6e86824ec36163bb71941c3dca9cd5ac25756":
+            # ref: https://huggingface.co/JetBrains/Mellum-4b-base
+            res = "mellum"

        if res is None:
            logger.warning("\n")
@@ -6056,6 +6065,7 @@ class DeepseekModel(TextModel):

@ModelBase.register("DeepseekV2ForCausalLM")
@ModelBase.register("DeepseekV3ForCausalLM")
+@ModelBase.register("KimiVLForConditionalGeneration")
 class DeepseekV2Model(TextModel):
    model_arch = gguf.MODEL_ARCH.DEEPSEEK2

@@ -6158,6 +6168,13 @@ class DeepseekV2Model(TextModel):
    _experts: list[dict[str, Tensor]] | None = None

    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        # skip vision tensors and remove "language_model." for Kimi-VL
+        if "vision_tower" in name or "multi_modal_projector" in name:
+            return []
+
+        if name.startswith("language_model."):
+            name = name.replace("language_model.", "")
+
        # rename e_score_correction_bias tensors
        if name.endswith("e_score_correction_bias"):
            name = name.replace("e_score_correction_bias", "e_score_correction.bias")
@@ -6682,6 +6699,139 @@ class Glm4Model(TextModel):
        return super().modify_tensors(data_torch, name, bid)


+@ModelBase.register("Glm4MoeForCausalLM")
+class Glm4MoeModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.GLM4_MOE
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        # GLM4_MOE has num_hidden_layers + 1 actual layers (including NextN layer)
+        self.block_count = self.hparams["num_hidden_layers"] + self.hparams.get("num_nextn_predict_layers", 0)
+        self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
+
+    def set_vocab(self):
+        from transformers import AutoTokenizer
+
+        tokenizer = AutoTokenizer.from_pretrained(self.dir_model)
+        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
+        tokens, toktypes, tokpre = self.get_vocab_base()
+        self.gguf_writer.add_tokenizer_model("gpt2")
+        self.gguf_writer.add_tokenizer_pre(tokpre)
+        self.gguf_writer.add_token_list(tokens)
+        self.gguf_writer.add_token_types(toktypes)
+
+        # Special tokens
+        # Note: Using <|endoftext|> (151329) for eot causes endless generation
+        special_vocab._set_special_token("bos", tokenizer.get_added_vocab()["[gMASK]"])  # 151331
+        special_vocab._set_special_token("eot", tokenizer.get_added_vocab()["<|user|>"])  # 151336
+        special_vocab._set_special_token("unk", tokenizer.get_added_vocab()["<|endoftext|>"]) # 151329
+        special_vocab._set_special_token("eom", tokenizer.get_added_vocab()["<|observation|>"])  # 151338
+
+        # Patch broken chat template
+        if isinstance(special_vocab.chat_template, str) and "visible_text(m.content).endswith" in special_vocab.chat_template:
+            special_vocab.chat_template = special_vocab.chat_template.replace(
+                """{{ visible_text(m.content) }}\n{{- '/nothink' if (enable_thinking is defined and not enable_thinking and not visible_text(m.content).endswith("/nothink")) else '' -}}""",
+                """{% set content = visible_text(m.content) %}{{ content }}\n{{- '/nothink' if (enable_thinking is defined and not enable_thinking and not content.endswith("/nothink")) else '' -}}""")
+
+        special_vocab.add_to_gguf(self.gguf_writer)
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        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", 0.5))
+        )
+
+        # MoE parameters - Use only routed expert count (shared experts handled separately)
+        if (n_routed_experts := self.hparams.get("n_routed_experts")) is not None:
+            self.gguf_writer.add_expert_count(n_routed_experts)
+        if (moe_intermediate_size := self.hparams.get("moe_intermediate_size")) is not None:
+            self.gguf_writer.add_expert_feed_forward_length(moe_intermediate_size)
+        if (n_shared_experts := self.hparams.get("n_shared_experts")) is not None:
+            self.gguf_writer.add_expert_shared_count(n_shared_experts)
+        if (first_k_dense_replace := self.hparams.get("first_k_dense_replace")) is not None:
+            self.gguf_writer.add_leading_dense_block_count(first_k_dense_replace)
+
+        # Expert gating function (sigmoid for GLM4_MOE)
+        self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SIGMOID)
+
+        # Routed scaling factor
+        if (routed_scaling_factor := self.hparams.get("routed_scaling_factor")) is not None:
+            self.gguf_writer.add_expert_weights_scale(routed_scaling_factor)
+
+        # Normalise topk probabilities
+        if (norm_topk_prob := self.hparams.get("norm_topk_prob")) is not None:
+            self.gguf_writer.add_expert_weights_norm(norm_topk_prob)
+
+        # NextN/MTP prediction layers
+        if (num_nextn_predict_layers := self.hparams.get("num_nextn_predict_layers")) is not None:
+            self.gguf_writer.add_nextn_predict_layers(num_nextn_predict_layers)
+
+    _experts: list[dict[str, Tensor]] | None = None
+
+    def modify_tensors(
+        self, data_torch: Tensor, name: str, bid: int | None
+    ) -> Iterable[tuple[str, Tensor]]:
+        if name.startswith("model.visual."):  # ignore visual part
+            return []
+        elif name.startswith("model.language_model."):
+            name = name.replace("language_model.", "")  # for multimodal variants
+
+        # Handle main token embedding (but not layer-specific NextN embeddings)
+        if name == "model.embed_tokens.weight" and ".layers." not in name:
+            return [(self.map_tensor_name("token_embd.weight"), data_torch)]
+
+        # Handle routed experts
+        if name.find("mlp.experts") != -1:
+            n_experts = self.hparams["n_routed_experts"]
+            assert bid is not None
+
+            if self._experts is None:
+                self._experts = [{} for _ in range(self.block_count)]
+
+            self._experts[bid][name] = data_torch
+
+            if len(self._experts[bid]) >= n_experts * 3:
+                tensors: list[tuple[str, Tensor]] = []
+
+                # merge the experts into a single 3d tensor
+                for w_name in ["down_proj", "gate_proj", "up_proj"]:
+                    datas: list[Tensor] = []
+
+                    for xid in range(n_experts):
+                        ename = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight"
+                        datas.append(self._experts[bid][ename])
+                        del self._experts[bid][ename]
+
+                    data_torch = torch.stack(datas, dim=0)
+
+                    merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight"
+
+                    new_name = self.map_tensor_name(merged_name)
+                    tensors.append((new_name, data_torch))
+                return tensors
+            else:
+                return []
+
+        if name.endswith("e_score_correction_bias"):
+            name = name.replace("e_score_correction_bias", "e_score_correction.bias")
+
+        new_name = self.map_tensor_name(name)
+
+        return [(new_name, data_torch)]
+
+    def prepare_tensors(self):
+        super().prepare_tensors()
+        if self._experts is not None:
+            # flatten `list[dict[str, Tensor]]` into `list[str]`
+            experts = [k for d in self._experts for k in d.keys()]
+            if len(experts) > 0:
+                raise ValueError(f"Unprocessed experts: {experts}")
+
+
@ModelBase.register("GlmForCausalLM", "ChatGLMModel", "ChatGLMForConditionalGeneration")
 class ChatGLMModel(TextModel):
    model_arch = gguf.MODEL_ARCH.CHATGLM
@@ -7800,6 +7950,119 @@ class SmolLM3Model(LlamaModel):
            self.gguf_writer.add_chat_template(chat_template)


+@ModelBase.register("GptOssForCausalLM")
+class GptOssModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.GPT_OSS
+
+    def transform_nibble_layout(self, tensor):
+        assert tensor.dtype == torch.uint8
+        assert tensor.shape[-1] == 16
+        # swap nibbles
+        t_lo = tensor & 0x0F
+        t_hi = tensor & 0xF0
+        t_swapped = (t_lo << 4) | (t_hi >> 4)
+        tensor = t_swapped
+        # transform aaaa...bbbb... to abababab...
+        blk_a, blk_b = tensor.chunk(2, dim=-1)
+        # get a_
+        blk_a0 = (blk_a & 0xF0).view(-1, 1)
+        blk_a1 = (blk_a << 4).view(-1, 1)
+        blk_a = torch.stack((blk_a0, blk_a1), dim=2).view(tensor.shape)
+        # get _b
+        blk_b0 = (blk_b >> 4).view(-1, 1)
+        blk_b1 = (blk_b & 0x0F).view(-1, 1)
+        blk_b = torch.stack((blk_b0, blk_b1), dim=2).view(tensor.shape)
+        # swap once more
+        out = blk_a | blk_b
+        out_h = out & 0xF0
+        out_l = out & 0x0F
+        out = (out_h >> 4) | (out_l << 4)
+        return out
+
+    def repack_mxfp4(self, new_name: str, blocks: Tensor, scales: Tensor):
+        assert blocks.dtype == torch.uint8
+        assert scales.dtype == torch.uint8
+        scales = scales.unsqueeze(-1)
+        assert len(blocks.shape) == 4
+        assert len(scales.shape) == 4
+        blocks = self.transform_nibble_layout(blocks)
+        new_data = torch.concat((scales, blocks), dim=-1)
+        new_shape = [new_data.shape[0], new_data.shape[1], new_data.shape[2] * 32]
+        logger.info(f"Repacked {new_name} with shape {new_shape} and quantization MXFP4")
+        # flatten last dim
+        new_data = new_data.view(new_data.shape[0], new_data.shape[1], new_data.shape[2] * new_data.shape[3])
+        new_data = new_data.numpy()
+        self.gguf_writer.add_tensor(new_name, new_data, raw_dtype=gguf.GGMLQuantizationType.MXFP4)
+
+    def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
+        blocks0: Tensor = torch.zeros(1)
+        blocks1: Tensor = torch.zeros(1)
+        found_mxfp4_tensors = False
+        # we assume that tensors are loaded in the correct order
+        for name, data_torch in self.get_tensors():
+            if "mlp.experts.down_proj_blocks" in name:
+                blocks0 = data_torch
+            elif "mlp.experts.down_proj_scales" in name:
+                new_name = self.map_tensor_name(name.replace("_scales", ".weight"))
+                self.repack_mxfp4(new_name, blocks0, data_torch)
+                found_mxfp4_tensors = True
+            elif "mlp.experts.gate_up_proj_blocks" in name:
+                blocks0, blocks1 = data_torch[:, ::2, :, :], data_torch[:, 1::2, :, :]
+            elif "mlp.experts.gate_up_proj_scales" in name:
+                scales0, scales1 = data_torch[:, ::2, :], data_torch[:, 1::2, :]
+                new_name_gate = self.map_tensor_name(name.replace("gate_up_proj_scales", "gate_proj.weight"))
+                new_name_up = self.map_tensor_name(name.replace("gate_up_proj_scales", "up_proj.weight"))
+                self.repack_mxfp4(new_name_gate, blocks0, scales0)
+                self.repack_mxfp4(new_name_up, blocks1, scales1)
+                found_mxfp4_tensors = True
+        if not found_mxfp4_tensors:
+            raise ValueError("No MXFP4 tensors found in the model. Please make sure you are using MXFP4 model.")
+        return []
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        del bid  # unused
+
+        if "sinks" in name:
+            name += ".weight"
+
+        # correct naming for down_proj
+        if "down_proj" in name:
+            if name.endswith("_bias"):
+                name = name.replace("down_proj_bias", "down_proj.bias")
+            else:
+                return []
+
+        # split the gate_up into gate and up
+        if "gate_up_proj" in name:
+            if name.endswith("_bias"):
+                name_up = name.replace("gate_up_proj_bias", "up_proj.bias")
+                name_gate = name.replace("gate_up_proj_bias", "gate_proj.bias")
+                gate_proj_bias, up_proj_bias = data_torch[..., ::2], data_torch[..., 1::2]
+                return [
+                    (self.map_tensor_name(name_gate), gate_proj_bias),
+                    (self.map_tensor_name(name_up), up_proj_bias)
+                ]
+            else:
+                return []
+
+        return [(self.map_tensor_name(name), data_torch)]
+
+    def set_vocab(self):
+        self._set_vocab_gpt2()
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self.gguf_writer.add_sliding_window(self.hparams["sliding_window"])
+        self.gguf_writer.add_expert_feed_forward_length(self.hparams["intermediate_size"])
+
+        rope_scaling = self.hparams.get("rope_scaling") or {}
+        rope_type = rope_scaling.get("rope_type", rope_scaling.get("type"))
+        assert rope_type == "yarn", f"GPT-OSS only supports yarn rope scaling, got {rope_type}"
+        self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN)
+        self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"])
+        self.gguf_writer.add_rope_scaling_orig_ctx_len(rope_scaling.get("original_max_position_embeddings", 4096))
+
+
@ModelBase.register("Lfm2ForCausalLM")
@ModelBase.register("LFM2ForCausalLM")
 class LFM2Model(TextModel):
@@ -7939,6 +8202,7 @@ class LazyTorchTensor(gguf.LazyBase):
    _dtype_map: dict[torch.dtype, type] = {
        torch.float16: np.float16,
        torch.float32: np.float32,
+        torch.uint8: np.uint8,
    }

    # used for safetensors slices
@@ -59,6 +59,10 @@ parser.add_argument(
    "--full", action="store_true",
    help="download full list of models - make sure you have access to all of them",
 )
+parser.add_argument(
+    "--check-missing", action="store_true",
+    help="only check for missing pre-tokenizer hashes",
+)
 parser.add_argument(
    "hf_token",
    help="optional HF token",
@@ -70,6 +74,10 @@ hf_token = args.hf_token if args.hf_token is not None else hf_token
 if hf_token is None:
    logger.warning("HF token not found. You can provide it as an argument or set it in ~/.cache/huggingface/token")

+if args.check_missing and args.full:
+    logger.warning("Downloading full list of models requested, ignoring --check-missing!")
+    args.check_missing = False
+
 # TODO: this string has to exercise as much pre-tokenizer functionality as possible
 #       will be updated with time - contributions welcome
 CHK_TXT = '\n \n\n \n\n\n \t \t\t \t\n  \n   \n    \n     \n🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ 🦙🦙 3 33 333 3333 33333 333333 3333333 33333333 3.3 3..3 3...3 កាន់តែពិសេសអាច😁 ?我想在apple工作1314151天～ ------======= нещо на Български \'\'\'\'\'\'```````\"\"\"\"......!!!!!!?????? I\'ve been \'told he\'s there, \'RE you sure? \'M not sure I\'ll make it, \'D you like some tea? We\'Ve a\'lL'
@@ -130,6 +138,7 @@ models = [
    {"name": "midm-2.0",         "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/K-intelligence/Midm-2.0-Base-Instruct", },
    {"name": "lfm2",             "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LiquidAI/LFM2-Tokenizer"},
    {"name": "exaone4",          "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LGAI-EXAONE/EXAONE-4.0-32B", },
+    {"name": "mellum",           "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/JetBrains/Mellum-4b-base", },
 ]

 # some models are known to be broken upstream, so we will skip them as exceptions
@@ -138,6 +147,7 @@ pre_computed_hashes = [
    {"name": "chatglm-bpe", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/THUDM/glm-4-9b-chat", "chkhsh": "b6e8e1518dc4305be2fe39c313ed643381c4da5db34a98f6a04c093f8afbe99b"},
    {"name": "chatglm-bpe", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/THUDM/glm-4-9b-chat", "chkhsh": "81d72c7348a9f0ebe86f23298d37debe0a5e71149e29bd283904c02262b27516"},
    {"name": "glm4", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/THUDM/glm-4-9b-hf", "chkhsh": "a1336059768a55c99a734006ffb02203cd450fed003e9a71886c88acf24fdbc2"},
+    {"name": "glm4", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/zai-org/GLM-4.5-Air", "chkhsh": "9ca2dd618e8afaf09731a7cf6e2105b373ba6a1821559f258b272fe83e6eb902"},
    {"name": "minerva-7b", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/sapienzanlp/Minerva-7B-base-v1.0", "chkhsh": "1431a23e583c97432bc230bff598d103ddb5a1f89960c8f1d1051aaa944d0b35"},
    {"name": "hunyuan", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tencent/Hunyuan-A13B-Instruct", "chkhsh": "7e57df22b1fe23a7b1e1c7f3dc4e3f96d43a4eb0836d0c6bdc3436d7b2f1c664"},
    {"name": "hunyuan-dense", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tencent/Hunyuan-4B-Instruct", "chkhsh": "bba3b3366b646dbdded5dbc42d59598b849371afc42f7beafa914afaa5b70aa6"},
@@ -147,6 +157,7 @@ pre_computed_hashes = [
    {"name": "falcon-h1", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/Falcon-H1-7B-Base", "chkhsh": "3eda48b4c4dc7de733d1a8b3e3b4a85243dbbf704da2ee9d42c6beced8897896"},
    {"name": "falcon-h1", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/Falcon-H1-34B-Base", "chkhsh": "48f8e02c0359c0bbdd82f26909171fac1c18a457bb47573ed1fe3bbb2c1cfd4b"},
    {"name": "kimi-k2",   "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/moonshotai/Kimi-K2-Base",   "chkhsh": "81212dc7cdb7e0c1074ca62c5aeab0d43c9f52b8a737be7b12a777c953027890"},
+    {"name": "qwen2",     "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/Qwen/Qwen3-Embedding-0.6B", "chkhsh": "d4540891389ea895b53b399da6ac824becc30f2fba0e9ddbb98f92e55ca0e97c"},
 ]


@@ -221,12 +232,13 @@ if not args.full:
    all_models = models.copy()
    models = [model for model in all_models if model["name"] not in existing_models]

-logging.info(f"Downloading {len(models)} models...")
-for model in models:
-    try:
-        download_model(model)
-    except Exception as e:
-        logger.error(f"Failed to download model {model['name']}. Error: {e}")
+if not args.check_missing:
+    logging.info(f"Downloading {len(models)} models...")
+    for model in models:
+        try:
+            download_model(model)
+        except Exception as e:
+            logger.error(f"Failed to download model {model['name']}. Error: {e}")


 # generate the source code for the convert_hf_to_gguf.py:get_vocab_base_pre() function:
@@ -39,8 +39,9 @@ if (WIN32)
    set(CMAKE_SHARED_MODULE_PREFIX  "")
 endif()

-option(BUILD_SHARED_LIBS "ggml: build shared libraries" ${BUILD_SHARED_LIBS_DEFAULT})
-option(GGML_BACKEND_DL   "ggml: build backends as dynamic libraries (requires BUILD_SHARED_LIBS)" OFF)
+option(BUILD_SHARED_LIBS           "ggml: build shared libraries" ${BUILD_SHARED_LIBS_DEFAULT})
+option(GGML_BACKEND_DL             "ggml: build backends as dynamic libraries (requires BUILD_SHARED_LIBS)" OFF)
+set(GGML_BACKEND_DIR "" CACHE PATH "ggml: directory to load dynamic backends from (requires GGML_BACKEND_DL")

 #
 # option list
@@ -106,7 +106,7 @@ if(NOT TARGET ggml::ggml)

    find_library(GGML_LIBRARY ggml
        REQUIRED
-        HINTS ${GGML_LIB_DIR}
+        HINTS ${GGML_LIB_DIR} ${GGML_BACKEND_DIR}
        NO_CMAKE_FIND_ROOT_PATH)

    add_library(ggml::ggml UNKNOWN IMPORTED)
@@ -304,6 +304,16 @@
    GGML_TENSOR_LOCALS(int64_t, ne,  dst,  ne) \
    GGML_TENSOR_LOCALS(size_t,  nb,  dst,  nb)

+#define GGML_TENSOR_TERNARY_OP_LOCALS \
+    GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne) \
+    GGML_TENSOR_LOCALS(size_t,  nb0, src0, nb) \
+    GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne) \
+    GGML_TENSOR_LOCALS(size_t,  nb1, src1, nb) \
+    GGML_TENSOR_LOCALS(int64_t, ne2, src2, ne) \
+    GGML_TENSOR_LOCALS(size_t,  nb2, src2, nb) \
+    GGML_TENSOR_LOCALS(int64_t, ne,  dst,  ne) \
+    GGML_TENSOR_LOCALS(size_t,  nb,  dst,  nb)
+
 #define GGML_TENSOR_BINARY_OP_LOCALS01 \
    GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne) \
    GGML_TENSOR_LOCALS(size_t,  nb0, src0, nb) \
@@ -395,7 +405,8 @@ extern "C" {
        // GGML_TYPE_IQ4_NL_4_4 = 36,
        // GGML_TYPE_IQ4_NL_4_8 = 37,
        // GGML_TYPE_IQ4_NL_8_8 = 38,
-        GGML_TYPE_COUNT   = 39,
+        GGML_TYPE_MXFP4   = 39, // MXFP4 (1 block)
+        GGML_TYPE_COUNT   = 40,
    };

    // precision
@@ -430,6 +441,7 @@ extern "C" {
        GGML_FTYPE_MOSTLY_IQ4_XS  = 22, // except 1d tensors
        GGML_FTYPE_MOSTLY_IQ1_M   = 23, // except 1d tensors
        GGML_FTYPE_MOSTLY_BF16    = 24, // except 1d tensors
+        GGML_FTYPE_MOSTLY_MXFP4   = 25, // except 1d tensors
    };

    // available tensor operations:
@@ -438,6 +450,7 @@ extern "C" {

        GGML_OP_DUP,
        GGML_OP_ADD,
+        GGML_OP_ADD_ID,
        GGML_OP_ADD1,
        GGML_OP_ACC,
        GGML_OP_SUB,
@@ -557,6 +570,7 @@ extern "C" {
        GGML_GLU_OP_REGLU,
        GGML_GLU_OP_GEGLU,
        GGML_GLU_OP_SWIGLU,
+        GGML_GLU_OP_SWIGLU_OAI,
        GGML_GLU_OP_GEGLU_ERF,
        GGML_GLU_OP_GEGLU_QUICK,

@@ -831,6 +845,13 @@ extern "C" {
            struct ggml_tensor  * b,
            enum   ggml_type      type);

+    // dst[i0, i1, i2] = a[i0, i1, i2] + b[i0, ids[i1, i2]]
+    GGML_API struct ggml_tensor * ggml_add_id(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b,
+            struct ggml_tensor  * ids);
+
    GGML_API struct ggml_tensor * ggml_add1(
            struct ggml_context * ctx,
            struct ggml_tensor  * a,
@@ -1198,6 +1219,13 @@ extern "C" {
            struct ggml_tensor  * a,
            struct ggml_tensor  * b);

+    GGML_API struct ggml_tensor * ggml_swiglu_oai(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b,
+            float                 alpha,
+            float                 limit);
+
    // normalize along rows
    GGML_API struct ggml_tensor * ggml_norm(
            struct ggml_context * ctx,
@@ -1570,6 +1598,10 @@ extern "C" {
            float                 scale,
            float                 max_bias);

+    GGML_API void ggml_soft_max_add_sinks(
+            struct ggml_tensor * a,
+            struct ggml_tensor * sinks);
+
    GGML_API struct ggml_tensor * ggml_soft_max_ext_back(
            struct ggml_context * ctx,
            struct ggml_tensor  * a,
@@ -2052,6 +2084,10 @@ extern "C" {
    GGML_API enum ggml_prec ggml_flash_attn_ext_get_prec(
            const struct ggml_tensor * a);

+    GGML_API void ggml_flash_attn_ext_add_sinks(
+            struct ggml_tensor * a,
+            struct ggml_tensor * sinks);
+
    // TODO: needs to be adapted to ggml_flash_attn_ext
    GGML_API struct ggml_tensor * ggml_flash_attn_back(
           struct ggml_context * ctx,
@@ -214,6 +214,13 @@ add_library(ggml
            ggml-backend-reg.cpp)
 add_library(ggml::ggml ALIAS ggml)

+if (GGML_BACKEND_DIR)
+    if (NOT GGML_BACKEND_DL)
+        message(FATAL_ERROR "GGML_BACKEND_DIR requires GGML_BACKEND_DL")
+    endif()
+    target_compile_definitions(ggml PUBLIC GGML_BACKEND_DIR="${GGML_BACKEND_DIR}")
+endif()
+
 target_link_libraries(ggml PUBLIC ggml-base)

 if (CMAKE_SYSTEM_NAME MATCHES "Linux")
@@ -227,7 +234,11 @@ function(ggml_add_backend_library backend)
        set_target_properties(${backend} PROPERTIES LIBRARY_OUTPUT_DIRECTORY ${CMAKE_RUNTIME_OUTPUT_DIRECTORY})
        target_compile_definitions(${backend} PRIVATE GGML_BACKEND_DL)
        add_dependencies(ggml ${backend})
-        install(TARGETS ${backend} LIBRARY DESTINATION ${CMAKE_INSTALL_BINDIR})
+        if (GGML_BACKEND_DIR)
+            install(TARGETS ${backend} LIBRARY DESTINATION ${GGML_BACKEND_DIR})
+        else()
+            install(TARGETS ${backend} LIBRARY DESTINATION ${CMAKE_INSTALL_BINDIR})
+        endif()
    else()
        add_library(${backend} ${ARGN})
        target_link_libraries(ggml PUBLIC ${backend})
@@ -29,6 +29,7 @@ static bool ggml_op_can_inplace(enum ggml_op op) {
        case GGML_OP_DIAG_MASK_ZERO:
        case GGML_OP_DIAG_MASK_INF:
        case GGML_OP_ADD:
+        case GGML_OP_ADD_ID:
        case GGML_OP_ADD1:
        case GGML_OP_SUB:
        case GGML_OP_MUL:
@@ -498,6 +498,9 @@ static ggml_backend_reg_t ggml_backend_load_best(const char * name, bool silent,

    std::vector<fs::path> search_paths;
    if (user_search_path == nullptr) {
+#ifdef GGML_BACKEND_DIR
+        search_paths.push_back(fs::u8path(GGML_BACKEND_DIR));
+#endif
        // default search paths: executable directory, current directory
        search_paths.push_back(get_executable_path());
        search_paths.push_back(fs::current_path());
@@ -1071,6 +1071,11 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg
                }
            }
        }
+        // if the node is still unassigned, assign it to the first backend that supports it
+        for (int b = 0; b < sched->n_backends && *cur_backend_id == -1; b++) {
+            ggml_backend_sched_set_if_supported(sched, node, b, cur_backend_id);
+        }
+        GGML_ASSERT(*cur_backend_id != -1);
    }

    // pass 5: split graph, find tensors that need to be copied
@@ -1098,7 +1103,7 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg

            const int node_backend_id = tensor_backend_id(node);

-            assert(node_backend_id != -1); // all nodes should be assigned by now, this can happen if there is no CPU fallback
+            GGML_ASSERT(node_backend_id != -1); // all nodes should be assigned by now, this can happen if there is no CPU fallback

            // check if we should start a new split based on the sources of the current node
            bool need_new_split = false;
@@ -1156,7 +1161,7 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg

                size_t src_id = hash_id(src);
                const int src_backend_id = sched->hv_tensor_backend_ids[src_id];
-                assert(src_backend_id != -1); // all inputs should be assigned by now
+                GGML_ASSERT(src_backend_id != -1); // all inputs should be assigned by now

                if (src->flags & GGML_TENSOR_FLAG_INPUT && sched->n_copies > 1) {
                    if (tensor_id_copy(src_id, src_backend_id, 0) == NULL) {
@@ -31,6 +31,13 @@ string(REGEX MATCH "[0-9]+[a-zA-Z]" SOC_TYPE_MAJOR_SN "${SOC_VERSION}")
 set(SOC_TYPE_COMPILE_OPTION "ASCEND_${SOC_TYPE_MAJOR_SN}")
 string(TOUPPER ${SOC_TYPE_COMPILE_OPTION} SOC_TYPE_COMPILE_OPTION)
 message(STATUS "CANN: SOC_VERSION =  ${SOC_VERSION}")
+option(USE_ACL_GRAPH "Enable CANN graph execution (ACL graph mode)" OFF)
+
+if(USE_ACL_GRAPH AND (SOC_TYPE_MAJOR_SN STREQUAL "310P" OR SOC_TYPE_COMPILE_OPTION STREQUAL "ASCEND_310P"))
+    message(FATAL_ERROR
+        "CANN Graph (ACL graph mode) is not supported on 310P devices. "
+        "Please build with -DUSE_ACL_GRAPH=OFF or use a supported SOC.")
+endif()

 if (CANN_INSTALL_DIR)
    # Only Support Linux.
@@ -68,6 +75,13 @@ if (CANN_INSTALL_DIR)

    target_compile_definitions(ggml-cann PRIVATE "-D${SOC_TYPE_COMPILE_OPTION}")

+    if (USE_ACL_GRAPH)
+        target_compile_definitions(ggml-cann PRIVATE USE_ACL_GRAPH)
+        message(STATUS "CANN: USE_ACL_GRAPH is enabled.")
+    else()
+        message(STATUS "CANN: USE_ACL_GRAPH is disabled.")
+    endif()
+
    message(STATUS "CANN: CANN_INCLUDE_DIRS =  ${CANN_INCLUDE_DIRS}")
    message(STATUS "CANN: CANN_LIBRARIES =  ${CANN_LIBRARIES}")
 else()
@@ -337,6 +337,29 @@ private:
    int32_t device_;
 };

+#ifdef USE_ACL_GRAPH
+struct ggml_graph_node_properties {
+    void * node_address;
+    ggml_op node_op;
+    int64_t ne[GGML_MAX_DIMS];
+    size_t nb[GGML_MAX_DIMS];
+    void * src_address[GGML_MAX_SRC];
+    int32_t op_params[GGML_MAX_OP_PARAMS / sizeof(int32_t)];
+};
+
+struct ggml_cann_graph {
+    ~ggml_cann_graph() {
+        if (graph != nullptr) {
+            aclmdlRIDestroy(graph);
+        }
+    }
+
+    aclmdlRI graph = nullptr;
+
+    std::vector<ggml_graph_node_properties> ggml_graph_properties;
+};
+#endif  // USE_ACL_GRAPH
+
 /**
 * @brief Context for managing CANN backend operations.
 */
@@ -345,8 +368,13 @@ struct ggml_backend_cann_context {
    std::string name;                /**< Name of the device. */
    std::string description;         /**< Description of the device. */
    aclrtEvent copy_event = nullptr; /**< Event for managing copy operations. */
+#ifdef USE_ACL_GRAPH
+    /// Cached CANN ACL graph used for executing the current ggml computation graph.
+    std::unique_ptr<ggml_cann_graph> cann_graph;
+#endif
    cann_task_queue task_queue;
    bool async_mode;
+    bool support_set_rows;

    aclrtStream streams[GGML_CANN_MAX_STREAMS] = {nullptr}; /**< Array of streams for the device. */

@@ -362,6 +390,14 @@ struct ggml_backend_cann_context {
        async_mode = parse_bool(get_env("GGML_CANN_ASYNC_MODE").value_or(""));
        GGML_LOG_INFO("%s: device %d async operator submission is %s\n", __func__,
            device, async_mode ? "ON" : "OFF");
+
+        support_set_rows = parse_bool(get_env("LLAMA_SET_ROWS").value_or(""));
+        GGML_LOG_INFO("%s: LLAMA_SET_ROWS is %s\n", __func__, support_set_rows ? "ON" : "OFF");
+
+        if (!support_set_rows) {
+            GGML_LOG_INFO("%s: CANN Graph currently only supports execution when LLAMA_SET_ROWS is ON. "
+                    "Falling back to eager mode.\n", __func__);
+        }
    }

    /**
@@ -2075,6 +2075,160 @@ static void ggml_backend_cann_synchronize(ggml_backend_t backend) {
    ACL_CHECK(aclrtSynchronizeStream(cann_ctx->stream()));
 }

+#ifdef USE_ACL_GRAPH
+/**
+ * @brief Populate the internal CANN graph node properties from the ggml computation graph.
+ *
+ * This function copies all node attributes (operation type, dimensions, strides, input sources,
+ * and operation parameters) into the cached CANN graph structure for later reuse or comparison.
+ *
+ * @param cann_ctx  The CANN backend context.
+ * @param cgraph    The ggml computational graph.
+ */
+static void set_ggml_graph_node_properties(ggml_backend_cann_context * cann_ctx, ggml_cgraph * cgraph) {
+    for (int node_idx = 0; node_idx < cgraph->n_nodes; node_idx++) {
+        ggml_tensor * node = cgraph->nodes[node_idx];
+        cann_ctx->cann_graph->ggml_graph_properties[node_idx].node_address = node->data;
+        cann_ctx->cann_graph->ggml_graph_properties[node_idx].node_op = node->op;
+
+        for (int dim = 0; dim < GGML_MAX_DIMS; dim++) {
+            cann_ctx->cann_graph->ggml_graph_properties[node_idx].ne[dim] = node->ne[dim];
+            cann_ctx->cann_graph->ggml_graph_properties[node_idx].nb[dim] = node->nb[dim];
+        }
+        for (int src = 0; src < GGML_MAX_SRC; src++) {
+            cann_ctx->cann_graph->ggml_graph_properties[node_idx].src_address[src] =
+                node->src[src] ? node->src[src]->data : nullptr;
+        }
+        memcpy(cann_ctx->cann_graph->ggml_graph_properties[node_idx].op_params, node->op_params, GGML_MAX_OP_PARAMS);
+    }
+}
+
+/**
+ * @brief Check if a ggml tensor node matches a previously captured CANN graph node.
+ *
+ * This function compares all relevant fields (address, op type, shape, source inputs, op params)
+ * to determine whether the current node matches a previously recorded version.
+ *
+ * @param node                  The current ggml tensor node.
+ * @param graph_node_properties The stored properties of a CANN graph node.
+ * @return true if all fields match (excluding GGML_OP_VIEW); false otherwise.
+ */
+static bool ggml_graph_node_has_matching_properties(ggml_tensor * node, ggml_graph_node_properties * graph_node_properties) {
+    if (node->data != graph_node_properties->node_address &&
+           node->op != GGML_OP_VIEW) {
+        return false;
+    }
+    if (node->op != graph_node_properties->node_op) {
+        return false;
+    }
+    for (int i = 0; i < GGML_MAX_DIMS; i++) {
+        if (node->ne[i] != graph_node_properties->ne[i]) {
+            return false;
+        }
+        if (node->nb[i] != graph_node_properties->nb[i]) {
+            return false;
+        }
+    }
+    for (int i = 0; i < GGML_MAX_SRC; i++) {
+        if (node->src[i] &&
+            node->src[i]->data != graph_node_properties->src_address[i] &&
+            node->op != GGML_OP_VIEW
+        ) {
+            return false;
+        }
+    }
+    if (node->op == GGML_OP_SCALE &&
+        memcmp(graph_node_properties->op_params, node->op_params, GGML_MAX_OP_PARAMS) != 0) {
+        return false;
+    }
+    return true;
+}
+
+/**
+ * @brief Determine if the CANN graph needs to be rebuilt due to graph changes.
+ *
+ * This checks whether the number or properties of ggml graph nodes have changed
+ * compared to the last captured CANN graph. If so, the CANN graph must be re-captured.
+ *
+ * @param cann_ctx  The CANN backend context.
+ * @param cgraph    The current ggml computation graph.
+ * @return true if an update is required; false otherwise.
+ */
+static bool is_cann_graph_update_required(ggml_backend_cann_context * cann_ctx, ggml_cgraph * cgraph) {
+    // The number of nodes is different, so the graph needs to be reconstructed.
+    if (cann_ctx->cann_graph->ggml_graph_properties.size() != (size_t)cgraph->n_nodes) {
+        cann_ctx->cann_graph->ggml_graph_properties.resize(cgraph->n_nodes);
+        return true;
+    }
+
+    // The number of nodes is the same; iterate over each node to check whether they match.
+    for (int i = 0; i < cgraph->n_nodes; i++) {
+        bool has_matching_properties = ggml_graph_node_has_matching_properties(
+            cgraph->nodes[i], &cann_ctx->cann_graph->ggml_graph_properties[i]);
+        if(!has_matching_properties) {
+            return true;
+        }
+    }
+    return false;
+}
+#endif  // USE_ACL_GRAPH
+
+/**
+ * @brief Evaluate the computation graph and optionally capture or execute it using CANN graph API.
+ *
+ * If CANN graph execution is enabled and graph capture is required, this function begins
+ * graph capture, runs the graph, ends capture, and stores the captured graph.
+ *
+ * Otherwise, it falls back to op-by-op execution using the CANN compute kernel dispatcher.
+ *
+ * @param cann_ctx                 The CANN backend context.
+ * @param cgraph                   The ggml computation graph.
+ * @param use_cann_graph           Whether to use CANN graph execution.
+ * @param cann_graph_update_required Whether graph capture is needed due to graph changes.
+ */
+static void evaluate_and_capture_cann_graph(ggml_backend_cann_context * cann_ctx, ggml_cgraph * cgraph,
+    bool & use_cann_graph, bool & cann_graph_update_required) {
+#ifdef USE_ACL_GRAPH
+    if (use_cann_graph && cann_graph_update_required) {
+        if (cann_ctx->cann_graph->graph != nullptr) {
+            ACL_CHECK(aclmdlRIDestroy(cann_ctx->cann_graph->graph));
+            cann_ctx->cann_graph->graph = nullptr;
+        }
+        ACL_CHECK(aclmdlRICaptureBegin(cann_ctx->stream(), ACL_MODEL_RI_CAPTURE_MODE_GLOBAL));
+    }
+#endif // USE_ACL_GRAPH
+
+    // Only perform the graph execution if CANN graphs are not enabled, or we are capturing the graph.
+    // With the use of CANN graphs, the execution will be performed by the graph launch.
+    if (!use_cann_graph || cann_graph_update_required) {
+        for (int i = 0; i < cgraph->n_nodes; i++) {
+            ggml_tensor * node = cgraph->nodes[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;
+            }
+
+            bool ok = ggml_cann_compute_forward(*cann_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);
+        }
+    }
+
+#ifdef USE_ACL_GRAPH
+    if (use_cann_graph && cann_graph_update_required) { // End CANN graph capture
+        ACL_CHECK(aclmdlRICaptureEnd(cann_ctx->stream(), &cann_ctx->cann_graph->graph));
+    }
+
+    if (use_cann_graph) {
+        // Execute graph
+        ACL_CHECK(aclmdlRIExecuteAsync(cann_ctx->cann_graph->graph, cann_ctx->stream()));
+    }
+#endif // USE_ACL_GRAPH
+}
+
+
 /**
 * @brief Computes a computational graph using a CANN backend.
 *
@@ -2091,27 +2245,38 @@ static enum ggml_status ggml_backend_cann_graph_compute(
    ggml_backend_t backend, ggml_cgraph* cgraph) {
    ggml_backend_cann_context* cann_ctx =
        (ggml_backend_cann_context*)backend->context;
-
    ggml_cann_set_device(cann_ctx->device);
-    //release temp buffer create by set tensor.
    release_nz_workspace();
+#ifdef USE_ACL_GRAPH
+    bool use_cann_graph = true;
+    bool cann_graph_update_required = false;

-    for (int i = 0; i < cgraph->n_nodes; i++) {
-        ggml_tensor* node = cgraph->nodes[i];
-
-        if (ggml_is_empty(node) || node->op == GGML_OP_NONE) {
-            continue;
-        }
-
-        bool ok = ggml_cann_compute_forward(*cann_ctx, node);
-
-        if (!ok) {
-            GGML_LOG_ERROR("%s: error: op not supported %s (%s)\n", __func__,
-                    node->name, ggml_op_name(node->op));
-        }
-        GGML_ASSERT(ok);
+    // check environment LLAMA_SET_ROWS
+    if (!cann_ctx->support_set_rows) {
+        use_cann_graph = false;
    }

+    if (use_cann_graph) {
+        if (cann_ctx->cann_graph == nullptr) {
+            cann_ctx->cann_graph.reset(new ggml_cann_graph());
+            cann_graph_update_required = true;
+        }
+
+        cann_graph_update_required = is_cann_graph_update_required(cann_ctx, cgraph);
+        set_ggml_graph_node_properties(cann_ctx, cgraph);
+    }
+#else
+    bool use_cann_graph = false;
+    bool cann_graph_update_required = false;
+#endif  // USE_ACL_GRAPH
+
+    evaluate_and_capture_cann_graph(
+        cann_ctx,
+        cgraph,
+        use_cann_graph,
+        cann_graph_update_required
+    );
+
    return GGML_STATUS_SUCCESS;
 }

@@ -2226,12 +2391,6 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev,
                // only support F32 and F16.
                return false;
            }
-
-            if (!ggml_are_same_shape(op, src) && !ggml_is_contiguous(op)) {
-                // unsupport dst is not contiguous.
-                return false;
-            }
-
            return true;
        } break;
        case GGML_OP_CONT: {
@@ -2340,6 +2499,10 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev,
            memcpy(&bias, (float*)op->op_params + 1, sizeof(float));
            return bias == 0.0f; // TODO: support bias != 0.0f
        case GGML_OP_SOFT_MAX:
+            // TODO: support attention sinks [TAG_ATTN_SINKS]
+            if (op->src[2]) {
+                return false;
+            }
            // TODO: support broadcast
            // ref: https://github.com/ggml-org/llama.cpp/pull/14435
            return !op->src[1] || (op->src[1]->ne[2] == 1 && op->src[1]->ne[3] == 1);
@@ -2354,6 +2517,10 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev,
            if(op->type != GGML_TYPE_F16 && op->type != GGML_TYPE_F32 && op->type != GGML_TYPE_BF16){
                return false;
            }
+            // TODO: support attention sinks [TAG_ATTN_SINKS]
+            if (op->src[4]) {
+                return false;
+            }
            if (op->src[1]->ne[0] != op->src[2]->ne[0]) {
                // different head sizes of K and V are not supported yet
                return false;
@@ -99,6 +99,9 @@ typedef sycl::half2 ggml_half2;
 #define QI4_1 (QK4_1 / (4 * QR4_1))
 #define QR4_1 2

+#define QI_MXFP4 (QK_MXFP4 / (4 * QR_MXFP4))
+#define QR_MXFP4 2
+
 #define QI5_0 (QK5_0 / (4 * QR5_0))
 #define QR5_0 2

@@ -184,6 +187,13 @@ typedef struct {
 } block_q4_1;
 static_assert(sizeof(block_q4_1) == 2 * sizeof(ggml_half) + QK4_1 / 2, "wrong q4_1 block size/padding");

+#define QK_MXFP4 32
+typedef struct {
+    uint8_t e; // E8M0
+    uint8_t qs[QK_MXFP4/2];
+} block_mxfp4;
+static_assert(sizeof(block_mxfp4) == sizeof(uint8_t) + QK_MXFP4/2, "wrong mxfp4 block size/padding");
+
 #define QK5_0 32
 typedef struct {
    ggml_half d;           // delta
@@ -1074,10 +1084,17 @@ GGML_TABLE_BEGIN(uint32_t, iq3s_grid, 512)
    0x0f090307, 0x0f090501, 0x0f090b01, 0x0f0b0505, 0x0f0b0905, 0x0f0d0105, 0x0f0d0703, 0x0f0f0101,
 GGML_TABLE_END()

+// TODO: fix name to kvalues_iq4_nl
 GGML_TABLE_BEGIN(int8_t, kvalues_iq4nl, 16)
    -127, -104, -83, -65, -49, -35, -22, -10, 1, 13, 25, 38, 53, 69, 89, 113,
 GGML_TABLE_END()

+// e2m1 values (doubled)
+// ref: https://www.opencompute.org/documents/ocp-microscaling-formats-mx-v1-0-spec-final-pdf
+GGML_TABLE_BEGIN(int8_t, kvalues_mxfp4, 16)
+    0, 1, 2, 3, 4, 6, 8, 12, 0, -1, -2, -3, -4, -6, -8, -12,
+GGML_TABLE_END()
+
 #define NGRID_IQ1S 2048
 #define IQ1S_DELTA 0.125f
 #define IQ1M_DELTA 0.125f
@@ -13,6 +13,7 @@
 #define ggml_vec_dot_q5_0_q8_0_generic ggml_vec_dot_q5_0_q8_0
 #define ggml_vec_dot_q5_1_q8_1_generic ggml_vec_dot_q5_1_q8_1
 #define ggml_vec_dot_q8_0_q8_0_generic ggml_vec_dot_q8_0_q8_0
+#define ggml_vec_dot_mxfp4_q8_0_generic ggml_vec_dot_mxfp4_q8_0
 #define ggml_vec_dot_tq1_0_q8_K_generic ggml_vec_dot_tq1_0_q8_K
 #define ggml_vec_dot_tq2_0_q8_K_generic ggml_vec_dot_tq2_0_q8_K
 #define ggml_vec_dot_q2_K_q8_K_generic ggml_vec_dot_q2_K_q8_K
@@ -68,6 +69,7 @@
 #define ggml_vec_dot_tq1_0_q8_K_generic ggml_vec_dot_tq1_0_q8_K
 #define ggml_vec_dot_tq2_0_q8_K_generic ggml_vec_dot_tq2_0_q8_K
 #define ggml_vec_dot_iq1_m_q8_K_generic ggml_vec_dot_iq1_m_q8_K
+#define ggml_vec_dot_mxfp4_q8_0_generic ggml_vec_dot_mxfp4_q8_0
 // 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
@@ -90,6 +92,7 @@
 #define ggml_vec_dot_tq1_0_q8_K_generic ggml_vec_dot_tq1_0_q8_K
 #define ggml_vec_dot_tq2_0_q8_K_generic ggml_vec_dot_tq2_0_q8_K
 #define ggml_vec_dot_iq1_m_q8_K_generic ggml_vec_dot_iq1_m_q8_K
+#define ggml_vec_dot_mxfp4_q8_0_generic ggml_vec_dot_mxfp4_q8_0
 // 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
@@ -120,6 +123,7 @@
 #define ggml_vec_dot_iq1_m_q8_K_generic ggml_vec_dot_iq1_m_q8_K
 #define ggml_vec_dot_iq4_nl_q8_0_generic ggml_vec_dot_iq4_nl_q8_0
 #define ggml_vec_dot_iq4_xs_q8_K_generic ggml_vec_dot_iq4_xs_q8_K
+#define ggml_vec_dot_mxfp4_q8_0_generic ggml_vec_dot_mxfp4_q8_0
 // 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
@@ -149,6 +153,7 @@
 #define ggml_vec_dot_iq3_s_q8_K_generic ggml_vec_dot_iq3_s_q8_K
 #define ggml_vec_dot_iq1_s_q8_K_generic ggml_vec_dot_iq1_s_q8_K
 #define ggml_vec_dot_iq1_m_q8_K_generic ggml_vec_dot_iq1_m_q8_K
+#define ggml_vec_dot_mxfp4_q8_0_generic ggml_vec_dot_mxfp4_q8_0
 // 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
@@ -179,6 +184,7 @@
 #define ggml_vec_dot_iq1_m_q8_K_generic ggml_vec_dot_iq1_m_q8_K
 #define ggml_vec_dot_iq4_nl_q8_0_generic ggml_vec_dot_iq4_nl_q8_0
 #define ggml_vec_dot_iq4_xs_q8_K_generic ggml_vec_dot_iq4_xs_q8_K
+#define ggml_vec_dot_mxfp4_q8_0_generic ggml_vec_dot_mxfp4_q8_0
 // 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
@@ -589,6 +589,67 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * GGML_RESTRICT s, size_t bs, const voi
    *s = sumf;
 }

+void ggml_vec_dot_mxfp4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
+    assert(nrc == 1);
+    UNUSED(nrc);
+    UNUSED(bx);
+    UNUSED(by);
+    UNUSED(bs);
+    assert(n % QK_MXFP4 == 0);
+    static_assert(QK_MXFP4 == QK8_0, "QK_MXFP4 and QK8_0 must be the same");
+
+    const block_mxfp4 * GGML_RESTRICT x = vx;
+    const block_q8_0 * GGML_RESTRICT y = vy;
+
+    const int nb = n / QK_MXFP4;
+
+    int ib = 0;
+    float sumf = 0;
+
+#if defined __ARM_NEON
+    const int8x16_t values = vld1q_s8(kvalues_mxfp4);
+    const uint8x16_t m4b = vdupq_n_u8(0x0f);
+    uint8x16x2_t q4bits;
+    int8x16x4_t q4b;
+    int8x16x4_t q8b;
+    int32x4_t prod_1;
+    int32x4_t prod_2;
+
+    for (; ib + 1 < nb; ib += 2) {
+        q4bits.val[0] = vld1q_u8(x[ib + 0].qs);
+        q4bits.val[1] = vld1q_u8(x[ib + 1].qs);
+        q8b.val[0]    = vld1q_s8(y[ib + 0].qs);
+        q8b.val[1]    = vld1q_s8(y[ib + 0].qs + 16);
+        q8b.val[2]    = vld1q_s8(y[ib + 1].qs);
+        q8b.val[3]    = vld1q_s8(y[ib + 1].qs + 16);
+
+        q4b.val[0] = ggml_vqtbl1q_s8(values, vandq_u8  (q4bits.val[0], m4b));
+        q4b.val[1] = ggml_vqtbl1q_s8(values, vshrq_n_u8(q4bits.val[0], 4));
+        q4b.val[2] = ggml_vqtbl1q_s8(values, vandq_u8  (q4bits.val[1], m4b));
+        q4b.val[3] = ggml_vqtbl1q_s8(values, vshrq_n_u8(q4bits.val[1], 4));
+
+        prod_1 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q4b.val[0], q8b.val[0]), q4b.val[1], q8b.val[1]);
+        prod_2 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q4b.val[2], q8b.val[2]), q4b.val[3], q8b.val[3]);
+
+        sumf +=
+            GGML_E8M0_TO_FP32_HALF(x[ib + 0].e) * GGML_CPU_FP16_TO_FP32(y[ib + 0].d) * vaddvq_s32(prod_1) +
+            GGML_E8M0_TO_FP32_HALF(x[ib + 1].e) * GGML_CPU_FP16_TO_FP32(y[ib + 1].d) * vaddvq_s32(prod_2);
+    }
+
+#endif
+    for (; ib < nb; ++ib) {
+        const float d = GGML_CPU_FP16_TO_FP32(y[ib].d)*GGML_E8M0_TO_FP32_HALF(x[ib].e);
+        int sumi1 = 0;
+        int sumi2 = 0;
+        for (int j = 0; j < QK_MXFP4/2; ++j) {
+            sumi1 += y[ib].qs[j +          0] * kvalues_mxfp4[x[ib].qs[j] & 0xf];
+            sumi2 += y[ib].qs[j + QK_MXFP4/2] * kvalues_mxfp4[x[ib].qs[j] >>  4];
+        }
+        sumf += d * (sumi1 + sumi2);
+    }
+    *s = sumf;
+}
+
 void ggml_vec_dot_q5_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
    const int qk = QK8_0;
    const int nb = n / qk;
@@ -66,6 +66,12 @@ static inline int hsum_i32_4(const __m128i a) {
 }

 #if defined(__AVX2__) || defined(__AVX512F__)
+static inline __m256i mul_add_epi8(const __m256i x, const __m256i y) {
+    const __m256i ax = _mm256_sign_epi8(x, x);
+    const __m256i sy = _mm256_sign_epi8(y, x);
+    return _mm256_maddubs_epi16(ax, sy);
+}
+
 // spread 32 bits to 32 bytes { 0x00, 0xFF }
 static inline __m256i bytes_from_bits_32(const uint8_t * x) {
    uint32_t x32;
@@ -261,6 +267,11 @@ static inline __m256 quad_fp16_delta_float(const float x0, const float y0, const
    return _mm256_set_m128(_mm_set1_ps(GGML_CPU_FP16_TO_FP32(x1) * GGML_CPU_FP16_TO_FP32(y1)),
                           _mm_set1_ps(GGML_CPU_FP16_TO_FP32(x0) * GGML_CPU_FP16_TO_FP32(y0)));
 }
+
+static inline __m256 quad_mx_delta_float(const int8_t x0, const float y0, const int8_t x1, const float y1) {
+    return _mm256_set_m128(_mm_set1_ps(GGML_E8M0_TO_FP32_HALF(x1) * GGML_CPU_FP16_TO_FP32(y1)),
+                           _mm_set1_ps(GGML_E8M0_TO_FP32_HALF(x0) * GGML_CPU_FP16_TO_FP32(y0)));
+}
 #endif
 #elif defined(__SSSE3__)
 // horizontally add 4x4 floats
@@ -746,6 +757,91 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * GGML_RESTRICT s, size_t bs, const voi
 #endif
 }

+void ggml_vec_dot_mxfp4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
+    assert(nrc == 1);
+    UNUSED(nrc);
+    UNUSED(bx);
+    UNUSED(by);
+    UNUSED(bs);
+    assert(n % QK_MXFP4 == 0);
+    static_assert(QK_MXFP4 == QK8_0, "QK_MXFP4 and QK8_0 must be the same");
+
+    const block_mxfp4 * GGML_RESTRICT x = vx;
+    const block_q8_0 * GGML_RESTRICT y = vy;
+
+    const int nb = n / QK_MXFP4;
+
+    int ib = 0;
+    float sumf = 0;
+
+#if defined __AVX2__
+
+    const __m128i values128 = _mm_loadu_si128((const __m128i*)kvalues_mxfp4);
+    const __m128i m4b  = _mm_set1_epi8(0x0f);
+    const __m256i mone = _mm256_set1_epi16(1);
+
+    __m256 accum1 = _mm256_setzero_ps();
+    __m256 accum2 = _mm256_setzero_ps();
+    for (; ib + 1 < nb; ib += 2) {
+        const __m128i q4bits_1 = _mm_loadu_si128((const __m128i*)x[ib + 0].qs);
+        const __m128i q4bits_2 = _mm_loadu_si128((const __m128i*)x[ib + 1].qs);
+        const __m256i q8b_1 = _mm256_loadu_si256((const __m256i *)y[ib + 0].qs);
+        const __m256i q8b_2 = _mm256_loadu_si256((const __m256i *)y[ib + 1].qs);
+        const __m256i q4b_1 = MM256_SET_M128I(_mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_1, 4), m4b)),
+                                              _mm_shuffle_epi8(values128, _mm_and_si128(q4bits_1, m4b)));
+        const __m256i q4b_2 = MM256_SET_M128I(_mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_2, 4), m4b)),
+                                              _mm_shuffle_epi8(values128, _mm_and_si128(q4bits_2, m4b)));
+        const __m256i p16_1 = mul_add_epi8(q4b_1, q8b_1);
+        const __m256i p16_2 = mul_add_epi8(q4b_2, q8b_2);
+        const __m256i p_1 = _mm256_madd_epi16(p16_1, mone);
+        const __m256i p_2 = _mm256_madd_epi16(p16_2, mone);
+        accum1 = _mm256_fmadd_ps(_mm256_set1_ps(GGML_CPU_FP16_TO_FP32(y[ib + 0].d)*GGML_E8M0_TO_FP32_HALF(x[ib + 0].e)),
+                _mm256_cvtepi32_ps(p_1), accum1);
+        accum2 = _mm256_fmadd_ps(_mm256_set1_ps(GGML_CPU_FP16_TO_FP32(y[ib + 1].d)*GGML_E8M0_TO_FP32_HALF(x[ib + 1].e)),
+                _mm256_cvtepi32_ps(p_2), accum2);
+    }
+
+    sumf = hsum_float_8(_mm256_add_ps(accum1, accum2));
+
+#elif defined __AVX__
+    const __m128i values128 = _mm_loadu_si128((const __m128i*)kvalues_mxfp4);
+    const __m128i m4b  = _mm_set1_epi8(0x0f);
+
+    __m256 accum = _mm256_setzero_ps();
+    for (; ib + 1 < nb; ib += 2) {
+        const __m128i q4bits_1 = _mm_loadu_si128((const __m128i *)x[ib + 0].qs);
+        const __m128i q4bits_2 = _mm_loadu_si128((const __m128i *)x[ib + 1].qs);
+        const __m128i q8b_1_0 = _mm_loadu_si128((const __m128i *)y[ib + 0].qs);
+        const __m128i q8b_1_1 = _mm_loadu_si128((const __m128i *)y[ib + 0].qs + 1);
+        const __m128i q8b_2_0 = _mm_loadu_si128((const __m128i *)y[ib + 1].qs);
+        const __m128i q8b_2_1 = _mm_loadu_si128((const __m128i *)y[ib + 1].qs + 1);
+
+        const __m128i q4b_1_0 = _mm_shuffle_epi8(values128, _mm_and_si128(q4bits_1, m4b));
+        const __m128i q4b_1_1 = _mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_1, 4), m4b));
+        const __m128i q4b_2_0 = _mm_shuffle_epi8(values128, _mm_and_si128(q4bits_2, m4b));
+        const __m128i q4b_2_1 = _mm_shuffle_epi8(values128, _mm_and_si128(_mm_srli_epi16(q4bits_2, 4), m4b));
+
+        const __m256 p = mul_sum_i8_quad_float(q4b_1_0, q4b_1_1, q4b_2_0, q4b_2_1, q8b_1_0, q8b_1_1, q8b_2_0, q8b_2_1);
+        const __m256 deltas = quad_mx_delta_float(x[ib].e, y[ib].d, x[ib + 1].e, y[ib + 1].d);
+        accum = _mm256_add_ps(_mm256_mul_ps(deltas, p), accum);
+    }
+
+    sumf = hsum_float_8(accum);
+
+#endif
+    for (; ib < nb; ++ib) {
+        const float d = GGML_CPU_FP16_TO_FP32(y[ib].d)*GGML_E8M0_TO_FP32_HALF(x[ib].e);
+        int sumi1 = 0;
+        int sumi2 = 0;
+        for (int j = 0; j < QK_MXFP4/2; ++j) {
+            sumi1 += y[ib].qs[j +          0] * kvalues_mxfp4[x[ib].qs[j] & 0xf];
+            sumi2 += y[ib].qs[j + QK_MXFP4/2] * kvalues_mxfp4[x[ib].qs[j] >>  4];
+        }
+        sumf += d * (sumi1 + sumi2);
+    }
+    *s = sumf;
+}
+
 void ggml_vec_dot_q5_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
    const int qk = QK8_0;
    const int nb = n / qk;
@@ -3206,14 +3302,6 @@ void ggml_vec_dot_iq3_s_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const vo
 #endif
 }

-#if defined(__AVX2__)
-static inline __m256i mul_add_epi8(const __m256i x, const __m256i y) {
-    const __m256i ax = _mm256_sign_epi8(x, x);
-    const __m256i sy = _mm256_sign_epi8(y, x);
-    return _mm256_maddubs_epi16(ax, sy);
-}
-#endif
-
 void ggml_vec_dot_iq1_s_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
    assert(n % QK_K == 0);
    assert(nrc == 1);
@@ -253,6 +253,12 @@ static const struct ggml_type_traits_cpu type_traits_cpu[GGML_TYPE_COUNT] = {
        .vec_dot_type             = GGML_TYPE_Q8_1,
        .nrows                    = 1,
    },
+    [GGML_TYPE_MXFP4] = {
+        .from_float               = quantize_row_mxfp4,
+        .vec_dot                  = ggml_vec_dot_mxfp4_q8_0,
+        .vec_dot_type             = GGML_TYPE_Q8_0,
+        .nrows                    = 1,
+    },
    [GGML_TYPE_Q2_K] = {
        .from_float               = quantize_row_q2_K,
        .vec_dot                  = ggml_vec_dot_q2_K_q8_K,
@@ -1670,6 +1676,10 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
            {
                ggml_compute_forward_add(params, tensor);
            } break;
+        case GGML_OP_ADD_ID:
+            {
+                ggml_compute_forward_add_id(params, tensor);
+            } break;
        case GGML_OP_ADD1:
            {
                ggml_compute_forward_add1(params, tensor);
@@ -1924,7 +1934,7 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
            } break;
        case GGML_OP_FLASH_ATTN_EXT:
            {
-                ggml_compute_forward_flash_attn_ext(params, tensor->src[0], tensor->src[1], tensor->src[2], tensor->src[3], tensor);
+                ggml_compute_forward_flash_attn_ext(params, tensor);
            } break;
        case GGML_OP_FLASH_ATTN_BACK:
            {
@@ -2111,6 +2121,7 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
        case GGML_OP_DUP:
        case GGML_OP_CONT:
        case GGML_OP_ADD:
+        case GGML_OP_ADD_ID:
        case GGML_OP_ADD1:
        case GGML_OP_ACC:
            {
@@ -2172,6 +2183,7 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
                case GGML_GLU_OP_REGLU:
                case GGML_GLU_OP_GEGLU:
                case GGML_GLU_OP_SWIGLU:
+                case GGML_GLU_OP_SWIGLU_OAI:
                case GGML_GLU_OP_GEGLU_ERF:
                case GGML_GLU_OP_GEGLU_QUICK:
                    {
@@ -2673,6 +2685,7 @@ struct ggml_cplan ggml_graph_plan(
                        }
                    } break;
                case GGML_OP_ADD:
+                case GGML_OP_ADD_ID:
                case GGML_OP_ADD1:
                    {
                        if (ggml_is_quantized(node->src[0]->type)) {
@@ -35,7 +35,7 @@

 // ggml-backend interface

-std::vector<ggml_backend_buffer_type_t>& ggml_backend_cpu_get_extra_buffers_type() {
+std::vector<ggml_backend_buffer_type_t> & ggml_backend_cpu_get_extra_buffer_types() {
    static std::vector<ggml_backend_buffer_type_t> bufts = []() {
        std::vector<ggml_backend_buffer_type_t> bufts;

@@ -57,8 +57,6 @@ std::vector<ggml_backend_buffer_type_t>& ggml_backend_cpu_get_extra_buffers_type
        }
 #endif

-        bufts.push_back(NULL);
-
        return bufts;
    }();

@@ -66,14 +64,20 @@ std::vector<ggml_backend_buffer_type_t>& ggml_backend_cpu_get_extra_buffers_type
 }

 static ggml_backend_buffer_type_t * ggml_backend_cpu_device_get_extra_buffers_type(ggml_backend_dev_t device) {
-    return ggml_backend_cpu_get_extra_buffers_type().data();
+    static std::vector<ggml_backend_buffer_type_t> extra_bufts = [] {
+        std::vector<ggml_backend_buffer_type_t> bufts = ggml_backend_cpu_get_extra_buffer_types();
+        bufts.push_back(nullptr);
+        return bufts;
+    }();
+
+    return extra_bufts.data();

    GGML_UNUSED(device);
 }

 static bool ggml_backend_cpu_is_extra_buffer_type(ggml_backend_buffer_type_t buft) {
-    for (auto * extra : ggml_backend_cpu_get_extra_buffers_type()) {
-        if (extra && extra == buft) {
+    for (auto * extra : ggml_backend_cpu_get_extra_buffer_types()) {
+        if (extra == buft) {
            return true;
        }
    }
@@ -397,20 +401,13 @@ static bool ggml_backend_cpu_device_supports_op(ggml_backend_dev_t dev, const st
        return true;
    }

-    // extra_buffer_op?
-    for (auto extra : ggml_backend_cpu_get_extra_buffers_type()) {
-        if (extra) {
-            auto buf_extra = (ggml::cpu::extra_buffer_type*) extra->context;
-            if (buf_extra && buf_extra->supports_op(dev, op)) {
-                return true;
-            }
-        }
-    }
-
-    // the other case need host buffer.
-    for (int i = 0; i < GGML_MAX_SRC; i++) {
-        if (op->src[i] && op->src[i]->buffer && !ggml_backend_buft_is_host(op->src[i]->buffer->buft)) {
-            return false;
+    // check extra buffer types
+    // note: only the first sources are checked for extra buffer types to reduce overhead, increase if necessary
+    for (int i = 0; i < 4; i++) {
+        if (op->src[i] && op->src[i]->buffer &&
+            ggml_backend_cpu_is_extra_buffer_type(op->src[i]->buffer->buft)) {
+            auto * buf_extra = (ggml::cpu::extra_buffer_type *) op->src[i]->buffer->buft->context;
+            return buf_extra->supports_op(dev, op);
        }
    }

@@ -8,6 +8,7 @@
 #include "vec.h"

 #include <float.h>
+#include <algorithm>

 // ggml_compute_forward_dup

@@ -1283,6 +1284,7 @@ void ggml_compute_forward_add(
        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:
@@ -1309,6 +1311,77 @@ void ggml_compute_forward_add(
    }
 }

+// ggml_compute_forward_add_id
+
+static void ggml_compute_forward_add_id_f32(
+        const ggml_compute_params * params,
+        ggml_tensor * dst) {
+
+    const ggml_tensor * src0 = dst->src[0];
+    const ggml_tensor * src1 = dst->src[1];
+    const ggml_tensor * src2 = dst->src[2];
+
+    GGML_ASSERT(dst->type  == GGML_TYPE_F32);
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT(src1->type == GGML_TYPE_F32);
+    GGML_ASSERT(src2->type == GGML_TYPE_I32);
+
+    GGML_ASSERT(src0->nb[0] == sizeof(float));
+    GGML_ASSERT(src1->nb[0] == sizeof(float));
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    const int nr  = ggml_nrows(src0);
+
+    GGML_TENSOR_TERNARY_OP_LOCALS
+
+    GGML_ASSERT( nb0 == sizeof(float));
+    GGML_ASSERT(nb10 == sizeof(float));
+
+    // rows per thread
+    const int dr = (nr + nth - 1)/nth;
+
+    // row range for this thread
+    const int ir0 = dr*ith;
+    const int ir1 = MIN(ir0 + dr, nr);
+
+    for (int ir = ir0; ir < ir1; ++ir) {
+        // src0 indices
+        const int i3 = ir/(ne2*ne1);
+        const int i2 = (ir - i3*ne2*ne1)/ne1;
+        const int i1 = (ir - i3*ne2*ne1 - i2*ne1);
+
+        // src1 indices
+        const int i11 = *(int32_t *) ((char *) src2->data + i1*nb20 + i2*nb21);
+
+        GGML_ASSERT(i11 >= 0 && i11 < ne11);
+
+        ggml_vec_add_f32(ne0,
+                (float *) ((char *) dst->data  + i3*nb3  + i2*nb2  + i1*nb1 ),
+                (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01),
+                (float *) ((char *) src1->data + i11*nb11));
+    }
+}
+
+void ggml_compute_forward_add_id(
+        const ggml_compute_params * params,
+        ggml_tensor * dst) {
+
+    const ggml_tensor * src0 = dst->src[0];
+
+    switch (src0->type) {
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_add_id_f32(params, dst);
+            } break;
+        default:
+            {
+                GGML_ABORT("unsupported type for ggml_compute_forward_add_id: %s", ggml_type_name(src0->type));
+            }
+    }
+}
+
 // ggml_compute_forward_add1

 static void ggml_compute_forward_add1_f32(
@@ -1660,6 +1733,7 @@ void ggml_compute_forward_add1(
        case GGML_TYPE_Q5_1:
        case GGML_TYPE_Q8_0:
        case GGML_TYPE_Q8_1:
+        case GGML_TYPE_MXFP4:
        case GGML_TYPE_Q2_K:
        case GGML_TYPE_Q3_K:
        case GGML_TYPE_Q4_K:
@@ -1787,6 +1861,7 @@ void ggml_compute_forward_acc(
        case GGML_TYPE_Q5_1:
        case GGML_TYPE_Q8_0:
        case GGML_TYPE_Q8_1:
+        case GGML_TYPE_MXFP4:
        case GGML_TYPE_Q2_K:
        case GGML_TYPE_Q3_K:
        case GGML_TYPE_Q4_K:
@@ -3614,6 +3689,93 @@ static void ggml_compute_forward_swiglu(
    }
 }

+// ggml_compute_forward_swiglu_oai
+
+static void ggml_compute_forward_swiglu_oai_f32(
+        const ggml_compute_params * params,
+        ggml_tensor * dst) {
+
+    const ggml_tensor * src0 = dst->src[0];
+    const ggml_tensor * src1 = dst->src[1];
+    char * src0_d = (char *) src0->data;
+    char * src1_d = (char *) (src1 ? src1->data : src0->data);
+    const size_t src0_o = src0->nb[1];
+    const size_t src1_o = src1 ? src1->nb[1] : src0->nb[1];
+
+    GGML_ASSERT(ggml_is_contiguous_1(src0));
+    GGML_ASSERT(ggml_is_contiguous_1(dst));
+
+    if (src1) {
+        GGML_ASSERT(ggml_is_contiguous_1(src1));
+        GGML_ASSERT(src0->type == src1->type);
+    }
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    const int nc = src1 ? src0->ne[0] : src0->ne[0] / 2;
+    const int nr = ggml_nrows(src0);
+
+    GGML_ASSERT(dst->ne[0] == nc);
+    GGML_ASSERT(ggml_nrows(dst) == nr);
+
+    const int32_t swapped = ggml_get_op_params_i32(dst, 1);
+    const float alpha = ggml_get_op_params_f32(dst, 2);
+    const float limit = ggml_get_op_params_f32(dst, 3);
+
+    // rows per thread
+    const int dr = (nr + nth - 1)/nth;
+
+    // row range for this thread
+    const int ir0 = dr*ith;
+    const int ir1 = MIN(ir0 + dr, nr);
+
+    for (int i1 = ir0; i1 < ir1; i1++) {
+        float * src0_p = (float *) (src0_d + i1*src0_o);
+        float * src1_p = (float *) (src1_d + i1*src1_o);
+        float * dst_p  = (float *) ((char *) dst->data + i1*(dst->nb[1]));
+
+        if (!src1) {
+            src0_p += swapped ? nc : 0;
+            src1_p += swapped ? 0 : nc;
+        }
+
+        for (int k = 0; k < nc; k++) {
+            const float x = std::min(src0_p[k], limit);
+            const float y = std::clamp(src1_p[k], -limit, limit);
+            const float out_glu = x / (1.f + expf(alpha * (-x)));
+            dst_p[k] = out_glu * (y + 1.f);
+        }
+
+#ifndef NDEBUG
+        for (int k = 0; k < nc; k++) {
+            const float x = dst_p[k];
+            GGML_UNUSED(x);
+            assert(!isnan(x));
+            assert(!isinf(x));
+        }
+#endif
+    }
+}
+
+static void ggml_compute_forward_swiglu_oai(
+        const ggml_compute_params * params,
+        ggml_tensor * dst) {
+
+    const ggml_tensor * src0 = dst->src[0];
+
+    switch (src0->type) {
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_swiglu_oai_f32(params, dst);
+            } break;
+        default:
+            {
+                GGML_ABORT("fatal error");
+            }
+    }
+}
+
 // ggml_compute_forward_geglu_erf

 static void ggml_compute_forward_geglu_erf_f32(
@@ -4599,6 +4761,7 @@ void ggml_compute_forward_out_prod(
        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:
@@ -4873,6 +5036,7 @@ void ggml_compute_forward_set(
        case GGML_TYPE_Q5_1:
        case GGML_TYPE_Q8_0:
        case GGML_TYPE_Q8_1:
+        case GGML_TYPE_MXFP4:
        case GGML_TYPE_Q2_K:
        case GGML_TYPE_Q3_K:
        case GGML_TYPE_Q4_K:
@@ -5134,6 +5298,7 @@ void ggml_compute_forward_get_rows(
        case GGML_TYPE_Q5_1:
        case GGML_TYPE_Q8_0:
        case GGML_TYPE_Q8_1:
+        case GGML_TYPE_MXFP4:
        case GGML_TYPE_Q2_K:
        case GGML_TYPE_Q3_K:
        case GGML_TYPE_Q4_K:
@@ -5523,6 +5688,7 @@ static void ggml_compute_forward_soft_max_f32(

    const ggml_tensor * src0 = dst->src[0];
    const ggml_tensor * src1 = dst->src[1];
+    const ggml_tensor * src2 = dst->src[2];

    assert(ggml_is_contiguous(dst));
    assert(ggml_are_same_shape(src0, dst));
@@ -5557,6 +5723,9 @@ static void ggml_compute_forward_soft_max_f32(

    const bool use_f16 = (src1 && src1->type == GGML_TYPE_F16);

+    // sinks
+    const float * sk = src2 ? (float *)((char *) src2->data) : nullptr;
+
    for (int64_t i03 = 0; i03 < ne03; i03++) {
        for (int64_t i02 = 0; i02 < ne02; i02++) {
            for (int64_t i01 = ith; i01 < ne01; i01 += nth) {
@@ -5599,9 +5768,18 @@ static void ggml_compute_forward_soft_max_f32(
                float max = -INFINITY;
                ggml_vec_max_f32(ne00, &max, wp);

+                // if we have sinks, make a correction as if they were included in the softmax
+                if (sk) {
+                    max = MAX(max, sk[i02]);
+                }
+
                ggml_float sum = ggml_vec_soft_max_f32(ne00, dp, wp, max);
                assert(sum > 0.0);

+                if (sk) {
+                    sum += (ggml_float) expf(sk[i02] - max);
+                }
+
                sum = 1.0/sum;
                ggml_vec_scale_f32(ne00, dp, sum);

@@ -5836,6 +6014,7 @@ void ggml_compute_forward_clamp(
        case GGML_TYPE_Q5_1:
        case GGML_TYPE_Q8_0:
        case GGML_TYPE_Q8_1:
+        case GGML_TYPE_MXFP4:
        case GGML_TYPE_Q2_K:
        case GGML_TYPE_Q3_K:
        case GGML_TYPE_Q4_K:
@@ -7989,12 +8168,14 @@ void ggml_compute_forward_argsort(

 static void ggml_compute_forward_flash_attn_ext_f16(
        const ggml_compute_params * params,
-        const ggml_tensor * q,
-        const ggml_tensor * k,
-        const ggml_tensor * v,
-        const ggml_tensor * mask,
        ggml_tensor * dst) {

+    const ggml_tensor * q     = dst->src[0];
+    const ggml_tensor * k     = dst->src[1];
+    const ggml_tensor * v     = dst->src[2];
+    const ggml_tensor * mask  = dst->src[3];
+    const ggml_tensor * sinks = dst->src[4];
+
    GGML_TENSOR_LOCALS(int64_t, neq, q,   ne)
    GGML_TENSOR_LOCALS(size_t,  nbq, q,   nb)
    GGML_TENSOR_LOCALS(int64_t, nek, k,   ne)
@@ -8189,6 +8370,23 @@ static void ggml_compute_forward_flash_attn_ext_f16(
            }
        }

+        // sinks
+        if (sinks) {
+            const float s = ((float *)((char *) sinks->data))[h];
+
+            float ms = 1.0f;
+            float vs = 1.0f;
+
+            if (s > M) {
+                ms = expf(M - s);
+                ggml_vec_scale_f32(DV, VKQ32, ms);
+            } else {
+                vs = expf(s - M);
+            }
+
+            S = S*ms + vs;
+        }
+
        // V /= S
        const float S_inv = 1.0f/S;
        ggml_vec_scale_f32(DV, VKQ32, S_inv);
@@ -8208,17 +8406,13 @@ static void ggml_compute_forward_flash_attn_ext_f16(

 void ggml_compute_forward_flash_attn_ext(
        const ggml_compute_params * params,
-        const ggml_tensor * q,
-        const ggml_tensor * k,
-        const ggml_tensor * v,
-        const ggml_tensor * mask,
        ggml_tensor * dst) {
    switch (dst->op_params[3]) {
        case GGML_PREC_DEFAULT:
        case GGML_PREC_F32:
            {
                // uses F32 accumulators
-                ggml_compute_forward_flash_attn_ext_f16(params, q, k, v, mask, dst);
+                ggml_compute_forward_flash_attn_ext_f16(params, dst);
            } break;
        default:
            {
@@ -9080,6 +9274,10 @@ void ggml_compute_forward_glu(
            {
                ggml_compute_forward_swiglu(params, dst);
            } break;
+        case GGML_GLU_OP_SWIGLU_OAI:
+            {
+                ggml_compute_forward_swiglu_oai(params, dst);
+            } break;
        case GGML_GLU_OP_GEGLU_ERF:
            {
                ggml_compute_forward_geglu_erf(params, dst);
@@ -29,6 +29,7 @@ extern "C" {

 void ggml_compute_forward_dup(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_add(const struct ggml_compute_params * params, struct ggml_tensor * dst);
+void ggml_compute_forward_add_id(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_add1(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_acc(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_sum(const struct ggml_compute_params * params, struct ggml_tensor * dst);
@@ -82,13 +83,7 @@ void ggml_compute_forward_arange(const struct ggml_compute_params * params, stru
 void ggml_compute_forward_timestep_embedding(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_argsort(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_leaky_relu(const struct ggml_compute_params * params, struct ggml_tensor * dst);
-void ggml_compute_forward_flash_attn_ext(
-    const struct ggml_compute_params * params,
-    const struct ggml_tensor * q,
-    const struct ggml_tensor * k,
-    const struct ggml_tensor * v,
-    const struct ggml_tensor * mask,
-    struct ggml_tensor * dst);
+void ggml_compute_forward_flash_attn_ext(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_flash_attn_back(
        const struct ggml_compute_params * params,
        const bool masked,
@@ -46,6 +46,10 @@ void quantize_row_q8_1_generic(const float * GGML_RESTRICT x, void * GGML_RESTRI
    quantize_row_q8_1_ref(x, y, k);
 }

+void quantize_row_mxfp4(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k) {
+    quantize_row_mxfp4_ref(x, y, k);
+}
+
 //
 // 2-6 bit quantization in super-blocks
 //
@@ -181,6 +185,37 @@ void ggml_vec_dot_q4_1_q8_1_generic(int n, float * GGML_RESTRICT s, size_t bs, c
    *s = sumf;
 }

+void ggml_vec_dot_mxfp4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
+    assert(nrc == 1);
+    UNUSED(nrc);
+    UNUSED(bx);
+    UNUSED(by);
+    UNUSED(bs);
+    assert(n % QK_MXFP4 == 0);
+    static_assert(QK_MXFP4 == QK8_0, "QK_MXFP4 and QK8_0 must be the same");
+
+    const block_mxfp4 * GGML_RESTRICT x = vx;
+    const block_q8_0 * GGML_RESTRICT y = vy;
+
+    const int nb = n / QK_MXFP4;
+
+    int ib = 0;
+    float sumf = 0;
+
+    for (; ib < nb; ++ib) {
+        const float d = GGML_CPU_FP16_TO_FP32(y[ib].d)*GGML_E8M0_TO_FP32_HALF(x[ib].e);
+
+        int sumi1 = 0;
+        int sumi2 = 0;
+        for (int j = 0; j < QK_MXFP4/2; ++j) {
+            sumi1 += y[ib].qs[j +          0] * kvalues_mxfp4[x[ib].qs[j] & 0xf];
+            sumi2 += y[ib].qs[j + QK_MXFP4/2] * kvalues_mxfp4[x[ib].qs[j] >>  4];
+        }
+        sumf += d * (sumi1 + sumi2);
+    }
+    *s = sumf;
+}
+
 void ggml_vec_dot_q5_0_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
    const int qk = QK8_0;
    const int nb = n / qk;
@@ -19,6 +19,8 @@ void quantize_row_q5_1(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, in
 void quantize_row_q8_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
 void quantize_row_q8_1(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);

+void quantize_row_mxfp4(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
+
 void quantize_row_q2_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
 void quantize_row_q3_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
 void quantize_row_q4_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
@@ -39,6 +41,8 @@ void ggml_vec_dot_q5_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const voi
 void ggml_vec_dot_q5_1_q8_1(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
 void ggml_vec_dot_q8_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);

+void ggml_vec_dot_mxfp4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
+
 void ggml_vec_dot_q2_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
 void ggml_vec_dot_q3_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
 void ggml_vec_dot_q4_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
@@ -67,8 +71,12 @@ void ggml_vec_dot_q4_1_q8_1_generic(int n, float * GGML_RESTRICT s, size_t bs, c
 void ggml_vec_dot_q5_0_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
 void ggml_vec_dot_q5_1_q8_1_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
 void ggml_vec_dot_q8_0_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
+
+void ggml_vec_dot_mxfp4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
+
 void ggml_vec_dot_tq1_0_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
 void ggml_vec_dot_tq2_0_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
+
 void ggml_vec_dot_q2_K_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
 void ggml_vec_dot_q3_K_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
 void ggml_vec_dot_q4_K_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
@@ -10,7 +10,7 @@ extra_buffer_type::~extra_buffer_type() {}
 }  // namespace ggml::cpu

 bool ggml_cpu_extra_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * op) {
-    for (auto extra : ggml_backend_cpu_get_extra_buffers_type()) {
+    for (auto extra : ggml_backend_cpu_get_extra_buffer_types()) {
        if (extra && extra->context) {
            auto buf_extra     = (ggml::cpu::extra_buffer_type *) extra->context;
            auto tensor_traits = buf_extra->get_tensor_traits(op);
@@ -23,7 +23,7 @@ bool ggml_cpu_extra_compute_forward(struct ggml_compute_params * params, struct
 }

 bool ggml_cpu_extra_work_size(int n_threads, const struct ggml_tensor * op, size_t * size) {
-    for (auto extra : ggml_backend_cpu_get_extra_buffers_type()) {
+    for (auto extra : ggml_backend_cpu_get_extra_buffer_types()) {
        if (extra && extra->context) {
            auto buf_extra     = (ggml::cpu::extra_buffer_type *) extra->context;
            auto tensor_traits = buf_extra->get_tensor_traits(op);
@@ -33,6 +33,6 @@ class extra_buffer_type {
 }  // namespace ggml::cpu

 // implemented in ggml-cpu.cpp.
-std::vector<ggml_backend_buffer_type_t> & ggml_backend_cpu_get_extra_buffers_type();
+std::vector<ggml_backend_buffer_type_t> & ggml_backend_cpu_get_extra_buffer_types();

 #endif
@@ -55,7 +55,22 @@ inline static void ggml_vec_cpy_i32(const int n, int32_t * y, const int32_t * x)

 inline static void ggml_vec_set_f16(const int n, ggml_fp16_t * x, const ggml_fp16_t v) { for (int i = 0; i < n; ++i) x[i] = v; }
 inline static void ggml_vec_set_bf16(const int n, ggml_bf16_t * x, const ggml_bf16_t v) { for (int i = 0; i < n; ++i) x[i] = v; }
-inline static void ggml_vec_add_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i]  = x[i] + y[i]; }
+
+inline static void ggml_vec_add_f32 (const int n, float * z, const float * x, const float * y) {
+    int i = 0;
+#if defined(__AVX2__)
+    for (; i + 7 < n; i += 8) {
+        __m256 vx = _mm256_loadu_ps(x + i);
+        __m256 vy = _mm256_loadu_ps(y + i);
+        __m256 vz = _mm256_add_ps(vx, vy);
+        _mm256_storeu_ps(z + i, vz);
+    }
+#endif
+    for (; i < n; ++i) {
+        z[i] = x[i] + y[i];
+    }
+}
+
 inline static void ggml_vec_add_f16 (const int n, ggml_fp16_t * z, const ggml_fp16_t * x, const ggml_fp16_t * y) {
    for (int i = 0; i < n; ++i) {
        z[i] = GGML_CPU_FP32_TO_FP16(GGML_CPU_FP16_TO_FP32(x[i]) + GGML_CPU_FP16_TO_FP32(y[i]));
@@ -992,9 +1007,9 @@ void ggml_vec_swiglu_f32(const int n, float * y, const float * x, const float *

 inline static void ggml_vec_swiglu_f16(const int n, ggml_fp16_t * y, const ggml_fp16_t * x, const ggml_fp16_t * g) {
    for (int i = 0; i < n; ++i) {
-        float v = GGML_CPU_FP16_TO_FP32(x[i]);
-        float w = GGML_CPU_FP16_TO_FP32(g[i]);
-        y[i] = GGML_CPU_FP32_TO_FP16((v/(1.0f + expf(-v))) * w);
+        float xi = GGML_CPU_FP16_TO_FP32(x[i]);
+        float gi = GGML_CPU_FP16_TO_FP32(g[i]);
+        y[i] = GGML_CPU_FP32_TO_FP16((xi/(1.0f + expf(-xi))) * gi);
    }
 }

@@ -0,0 +1,58 @@
+#include "add-id.cuh"
+
+static __global__ void add_id_kernel(
+        const float * src0, const float * src1, const int32_t * src2, float * dst,
+        int64_t ne0, int64_t ne1,
+        size_t nb01, size_t nb02,
+        size_t nb11,
+        size_t nb21
+    ) {
+
+    const int64_t i1 = blockIdx.x;
+    const int64_t i2 = blockIdx.y;
+
+    const int i11 = *(int32_t *) ((char *) src2 + i1*sizeof(int32_t) + i2*nb21);
+
+    const size_t nb1 = ne0 * sizeof(float);
+    const size_t nb2 = ne1 * nb1;
+
+    float * dst_row = (float *)((char *)dst + i1*nb1 + i2*nb2);
+    const float * src0_row = (const float *)((char *)src0 +  i1*nb01 + i2*nb02);
+    const float * src1_row = (const float *)((char *)src1 + i11*nb11);
+
+    for (int64_t i0 = threadIdx.x; i0 < ne0; i0 += blockDim.x) {
+        dst_row[i0] = src0_row[i0] + src1_row[i0];
+    }
+}
+
+void ggml_cuda_op_add_id(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    const ggml_tensor * src1 = dst->src[1];
+    const ggml_tensor * src2 = dst->src[2];
+
+    GGML_TENSOR_TERNARY_OP_LOCALS
+
+    GGML_ASSERT(dst->type  == GGML_TYPE_F32);
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT(src1->type == GGML_TYPE_F32);
+    GGML_ASSERT(src2->type == GGML_TYPE_I32);
+
+    GGML_ASSERT(nb00 == sizeof(float));
+    GGML_ASSERT(nb10 == sizeof(float));
+    GGML_ASSERT(nb20 == sizeof(int32_t));
+
+    const float * src0_d = (const float *)src0->data;
+    const float * src1_d = (const float *)src1->data;
+    const int32_t * src2_d = (const int32_t *)src2->data;
+    float * dst_d = (float *)dst->data;
+
+    int threads = std::min((int)ne00, 768); // cols
+    dim3 blocks(ne01, ne02); // n_experts_used, n_tokens
+    add_id_kernel<<<blocks, threads, 0, ctx.stream()>>>(
+        src0_d, src1_d, src2_d, dst_d,
+        ne0, ne1,
+        nb01, nb02,
+        nb11,
+        nb21
+    );
+}
@@ -0,0 +1,3 @@
+#include "common.cuh"
+
+void ggml_cuda_op_add_id(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
@@ -1,6 +1,7 @@
 #pragma once

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

 #include <cstdint>
@@ -549,6 +550,24 @@ static __device__ __forceinline__ int ggml_cuda_dp4a(const int a, const int b, i
 #endif // defined(GGML_USE_HIP)
 }

+static __device__ __forceinline__ float ggml_cuda_e8m0_to_fp32(uint8_t x) {
+#if CUDART_VERSION >= 12080
+    const nv_bfloat16 e = __nv_cvt_e8m0_to_bf16raw(x);
+    return (float) e;
+#else
+    uint32_t bits;
+    if (x == 0) {
+        bits = 0x00400000;
+    } else {
+        bits = (uint32_t) x << 23;
+    }
+
+    float result;
+    memcpy(&result, &bits, sizeof(float));
+    return result;
+#endif // CUDART_VERSION >= 12050
+}
+
 typedef void (*dequantize_kernel_t)(const void * vx, const int64_t ib, const int iqs, dfloat2 & v);

 static __device__ __forceinline__ float get_alibi_slope(
@@ -607,6 +626,13 @@ struct ggml_cuda_type_traits<GGML_TYPE_Q8_0> {
    static constexpr int qi = QI8_0;
 };

+template<>
+struct ggml_cuda_type_traits<GGML_TYPE_MXFP4> {
+    static constexpr int qk = QK_MXFP4;
+    static constexpr int qr = QR_MXFP4;
+    static constexpr int qi = QI_MXFP4;
+};
+
 template<>
 struct ggml_cuda_type_traits<GGML_TYPE_Q2_K> {
    static constexpr int qk = QK_K;
@@ -465,6 +465,24 @@ static __global__ void dequantize_block_iq4_xs(const void * __restrict__ vx, dst
    }
 }

+template<typename dst_t>
+static __global__ void dequantize_block_mxfp4(const void * __restrict__ vx, dst_t * __restrict__ yy) {
+
+    const int64_t i   = blockIdx.x;
+    const block_mxfp4 * x = (const block_mxfp4 *) vx + i*(QK_K/QK_MXFP4);
+
+    const int64_t tid = threadIdx.x;
+    const int64_t il = tid/8; // 0...3
+    const int64_t ib = tid%8; // 0...7
+    dst_t * y = yy + i*QK_K + 32*ib + 4*il;
+    const uint8_t  * q4 = x[ib].qs + 4*il;
+    const float d = ggml_cuda_e8m0_to_fp32(x[ib].e);
+    for (int j = 0; j < 4; ++j) {
+        y[j+ 0] = d * kvalues_mxfp4[q4[j] & 0xf]*0.5f;
+        y[j+16] = d * kvalues_mxfp4[q4[j] >>  4]*0.5f;
+    }
+}
+
 template <int qk, int qr, dequantize_kernel_t dequantize_kernel, typename dst_t>
 static void dequantize_block_cuda(const void * vx, dst_t * y,
        const int64_t ne00, const int64_t ne01, const int64_t ne02, const int64_t ne03,
@@ -588,6 +606,12 @@ static void dequantize_row_iq4_xs_cuda(const void * vx, dst_t * y, const int64_t
    dequantize_block_iq4_xs<<<nb, 32, 0, stream>>>(vx, y);
 }

+template<typename dst_t>
+static void dequantize_row_mxfp4_cuda(const void * vx, dst_t * y, const int64_t k, cudaStream_t stream) {
+    const int nb = (k + QK_K - 1) / QK_K;
+    dequantize_block_mxfp4<<<nb, 32, 0, stream>>>(vx, y);
+}
+
 template <typename src_t, typename dst_t>
 static __global__ void convert_unary(
        const void * __restrict__ vx, dst_t * __restrict__ y, const int64_t ne00, const int64_t ne01, const int64_t ne02,
@@ -677,6 +701,8 @@ to_fp16_cuda_t ggml_get_to_fp16_cuda(ggml_type type) {
            return dequantize_row_iq4_xs_cuda;
        case GGML_TYPE_IQ3_S:
            return dequantize_row_iq3_s_cuda;
+        case GGML_TYPE_MXFP4:
+            return dequantize_row_mxfp4_cuda;
        case GGML_TYPE_F32:
            return convert_unary_cont_cuda<float>;
        case GGML_TYPE_BF16:
@@ -726,6 +752,8 @@ to_fp32_cuda_t ggml_get_to_fp32_cuda(ggml_type type) {
            return dequantize_row_iq4_xs_cuda;
        case GGML_TYPE_IQ3_S:
            return dequantize_row_iq3_s_cuda;
+        case GGML_TYPE_MXFP4:
+            return dequantize_row_mxfp4_cuda;
        case GGML_TYPE_F16:
            return convert_unary_cont_cuda<half>;
        case GGML_TYPE_BF16:
@@ -15,6 +15,7 @@ typedef void (* fattn_kernel_t)(
        const char * __restrict__ K,
        const char * __restrict__ V,
        const char * __restrict__ mask,
+        const char * __restrict__ sinks,
        const int  * __restrict__ KV_max,
        float      * __restrict__ dst,
        float2     * __restrict__ dst_meta,
@@ -736,7 +737,8 @@ void launch_fattn(

    GGML_ASSERT(V || is_mla);

-    const ggml_tensor * mask = dst->src[3];
+    const ggml_tensor * mask  = dst->src[3];
+    const ggml_tensor * sinks = dst->src[4];

    ggml_tensor * KQV = dst;

@@ -940,6 +942,7 @@ void launch_fattn(
        K_data,
        V_data,
        mask ? ((const char *) mask->data) : nullptr,
+        sinks ? ((const char *) sinks->data) : nullptr,
        KV_max.ptr,
        !stream_k && parallel_blocks > 1 ? dst_tmp.ptr : (float *) KQV->data, dst_tmp_meta.ptr,
        scale, max_bias, m0, m1, n_head_log2, logit_softcap,
@@ -1206,6 +1206,7 @@ static __global__ void flash_attn_ext_f16(
        const char * __restrict__ K,
        const char * __restrict__ V,
        const char * __restrict__ mask,
+        const char * __restrict__ sinks,
        const int  * __restrict__ KV_max,
        float      * __restrict__ dst,
        float2     * __restrict__ dst_meta,
@@ -1267,6 +1268,7 @@ static __global__ void flash_attn_ext_f16(
    // kb0 == k start index when in the output tile.
    int kb0_start = kbc % iter_k;
    int kb0_stop  = min(iter_k, kb0_start + kbc_stop - kbc);
+
    while (kbc < kbc_stop && kb0_stop == iter_k) {
        const int sequence = kbc / (iter_k*iter_j*(ne02/ncols2));
        const int head = (kbc - iter_k*iter_j*(ne02/ncols2)*sequence) / (iter_k*iter_j);
@@ -1340,7 +1342,7 @@ static __global__ void flash_attn_ext_f16(
        (Q_f2, K_h2, V_h2, mask_h2, dstk, dst_meta, scale, slope, logit_softcap,
         ne01, ne02, stride_Q1, stride_Q2, stride_K, stride_V, stride_mask, jt, kb0_start_kernel, kb0_stop_kernel);
 #else
-    GGML_UNUSED(Q); GGML_UNUSED(K); GGML_UNUSED(V); GGML_UNUSED(mask);
+    GGML_UNUSED(Q); GGML_UNUSED(K); GGML_UNUSED(V); GGML_UNUSED(mask); GGML_UNUSED(sinks);
    GGML_UNUSED(dst); GGML_UNUSED(dst_meta);
    GGML_UNUSED(scale); GGML_UNUSED(max_bias); GGML_UNUSED(m0); GGML_UNUSED(m1);
    GGML_UNUSED(n_head_log2); GGML_UNUSED(logit_softcap);
@@ -13,6 +13,7 @@ static __global__ void flash_attn_tile_ext_f16(
        const char * __restrict__ K,
        const char * __restrict__ V,
        const char * __restrict__ mask,
+        const char * __restrict__ sinks,
        const int  * __restrict__ KV_max,
        float      * __restrict__ dst,
        float2     * __restrict__ dst_meta,
@@ -272,7 +273,7 @@ static __global__ void flash_attn_tile_ext_f16(
        }
    }
 #else
-    GGML_UNUSED(Q); GGML_UNUSED(K); GGML_UNUSED(V); GGML_UNUSED(mask);
+    GGML_UNUSED(Q); GGML_UNUSED(K); GGML_UNUSED(V); GGML_UNUSED(mask); GGML_UNUSED(sinks);
    GGML_UNUSED(dst); GGML_UNUSED(dst_meta); GGML_UNUSED(scale);
    GGML_UNUSED(max_bias); GGML_UNUSED(m0); GGML_UNUSED(m1);
    GGML_UNUSED(n_head_log2); GGML_UNUSED(logit_softcap);
@@ -13,6 +13,7 @@ static __global__ void flash_attn_tile_ext_f32(
        const char * __restrict__ K,
        const char * __restrict__ V,
        const char * __restrict__ mask,
+        const char * __restrict__ sinks,
        const int  * __restrict__ KV_max,
        float      * __restrict__ dst,
        float2     * __restrict__ dst_meta,
@@ -37,7 +38,7 @@ static __global__ void flash_attn_tile_ext_f32(
    return;
 #endif // FP16_MMA_AVAILABLE
    if (use_logit_softcap && !(D == 128 || D == 256)) {
-        GGML_UNUSED(Q); GGML_UNUSED(K); GGML_UNUSED(V); GGML_UNUSED(mask);
+        GGML_UNUSED(Q); GGML_UNUSED(K); GGML_UNUSED(V); GGML_UNUSED(mask); GGML_UNUSED(sinks);
        GGML_UNUSED(dst); GGML_UNUSED(dst_meta);
        GGML_UNUSED(scale); GGML_UNUSED(max_bias); GGML_UNUSED(m0); GGML_UNUSED(m1);
        GGML_UNUSED(n_head_log2); GGML_UNUSED(logit_softcap);
@@ -16,6 +16,7 @@ static __global__ void flash_attn_vec_ext_f16(
        const char * __restrict__ K,
        const char * __restrict__ V,
        const char * __restrict__ mask,
+        const char * __restrict__ sinks,
        const int  * __restrict__ KV_max,
        float      * __restrict__ dst,
        float2     * __restrict__ dst_meta,
@@ -61,7 +62,8 @@ static __global__ void flash_attn_vec_ext_f16(
    K += nb13*sequence + nb12*(head / gqa_ratio);
    V += nb23*sequence + nb22*(head / gqa_ratio);

-    const half * maskh = (const half *) (mask + nb33*(sequence % ne33) + nb31*ic0);
+    const half  * maskh  = (const half  *) (mask + nb33*(sequence % ne33) + nb31*ic0);
+    const float * sinksf = (const float *) (sinks);

    const float slopef = get_alibi_slope(max_bias, head, n_head_log2, m0, m1);
    const half  slopeh = __float2half(slopef);
@@ -75,11 +77,12 @@ static __global__ void flash_attn_vec_ext_f16(
    half2 * KQ2 = (half2 *) KQ;

    half kqmax[ncols];
+    half kqsum[ncols];
 #pragma unroll
    for (int j = 0; j < ncols; ++j) {
        kqmax[j] = -HALF_MAX_HALF;
+        kqsum[j] = 0.0f;
    }
-    half kqsum[ncols] = {0.0f};

    __shared__ half kqmax_shared[ncols][WARP_SIZE];
    __shared__ half kqsum_shared[ncols][WARP_SIZE];
@@ -283,6 +286,39 @@ static __global__ void flash_attn_vec_ext_f16(
        __syncthreads();
    }

+    if (sinksf && blockIdx.y == 0) {
+        const half sink = __float2half(sinksf[head]);
+
+#pragma unroll
+        for (int j = 0; j < ncols; ++j) {
+            if (threadIdx.x == 0) {
+                kqmax_shared[j][threadIdx.y] = fmaxf(kqmax[j], sink);
+            }
+        }
+
+        __syncthreads();
+
+#pragma unroll
+        for (int j = 0; j < ncols; ++j) {
+            half kqmax_new_j = kqmax_shared[j][threadIdx.x];
+            kqmax_new_j = warp_reduce_max(kqmax_new_j);
+
+            const half KQ_max_scale = hexp(kqmax[j] - kqmax_new_j);
+            kqmax[j] = kqmax_new_j;
+
+            const half val = hexp(sink - kqmax[j]);
+            kqsum[j] = kqsum[j]*KQ_max_scale;
+
+            if (tid == 0) {
+                kqsum[j] += val;
+            }
+
+            VKQ[j] *= __half2half2(KQ_max_scale);
+        }
+
+        __syncthreads();
+    }
+
 #pragma unroll
    for (int j = 0; j < ncols; ++j) {
        kqsum[j] = warp_reduce_sum((float)kqsum[j]);
@@ -313,7 +349,7 @@ static __global__ void flash_attn_vec_ext_f16(
        dst_meta[((sequence*ne01 + ic0 + tid)*ne02 + head)*gridDim.y + blockIdx.y] = make_float2(kqmax[tid], kqsum[tid]);
    }
 #else
-    GGML_UNUSED(Q); GGML_UNUSED(K); GGML_UNUSED(V); GGML_UNUSED(mask);
+    GGML_UNUSED(Q); GGML_UNUSED(K); GGML_UNUSED(V); GGML_UNUSED(mask); GGML_UNUSED(sinks);
    GGML_UNUSED(dst); GGML_UNUSED(dst_meta);
    GGML_UNUSED(scale); GGML_UNUSED(max_bias); GGML_UNUSED(m0); GGML_UNUSED(m1);
    GGML_UNUSED(n_head_log2); GGML_UNUSED(logit_softcap);
@@ -16,6 +16,7 @@ static __global__ void flash_attn_vec_ext_f32(
        const char * __restrict__ K,
        const char * __restrict__ V,
        const char * __restrict__ mask,
+        const char * __restrict__ sinks,
        const int  * __restrict__ KV_max,
        float      * __restrict__ dst,
        float2     * __restrict__ dst_meta,
@@ -72,7 +73,8 @@ static __global__ void flash_attn_vec_ext_f32(
    K += nb13*sequence + nb12*(head / gqa_ratio);
    V += nb23*sequence + nb22*(head / gqa_ratio);

-    const half * maskh = (const half *) (mask + nb33*(sequence % ne33) + nb31*ic0);
+    const half  * maskh  = (const half  *) (mask + nb33*(sequence % ne33) + nb31*ic0);
+    const float * sinksf = (const float *) (sinks);

    const float slope = get_alibi_slope(max_bias, head, n_head_log2, m0, m1);

@@ -88,11 +90,12 @@ static __global__ void flash_attn_vec_ext_f32(
    }

    float kqmax[ncols];
+    float kqsum[ncols];
 #pragma unroll
    for (int j = 0; j < ncols; ++j) {
        kqmax[j] = -FLT_MAX/2.0f;
+        kqsum[j] = 0.0f;
    }
-    float kqsum[ncols] = {0.0f};

    __shared__ float kqmax_shared[ncols][WARP_SIZE];
    __shared__ float kqsum_shared[ncols][WARP_SIZE];
@@ -279,6 +282,39 @@ static __global__ void flash_attn_vec_ext_f32(
        __syncthreads();
    }

+    if (sinksf && blockIdx.y == 0) {
+        const float sink = sinksf[head];
+
+#pragma unroll
+        for (int j = 0; j < ncols; ++j) {
+            if (threadIdx.x == 0) {
+                kqmax_shared[j][threadIdx.y] = fmaxf(kqmax[j], sink);
+            }
+        }
+
+        __syncthreads();
+
+#pragma unroll
+        for (int j = 0; j < ncols; ++j) {
+            float kqmax_new_j = kqmax_shared[j][threadIdx.x];
+            kqmax_new_j = warp_reduce_max(kqmax_new_j);
+
+            const float KQ_max_scale = expf(kqmax[j] - kqmax_new_j);
+            kqmax[j] = kqmax_new_j;
+
+            const float val = expf(sink - kqmax[j]);
+            kqsum[j] = kqsum[j]*KQ_max_scale;
+
+            if (tid == 0) {
+                kqsum[j] += val;
+            }
+
+            VKQ[j] *= KQ_max_scale;
+        }
+
+        __syncthreads();
+    }
+
 #pragma unroll
    for (int j = 0; j < ncols; ++j) {
        kqsum[j] = warp_reduce_sum(kqsum[j]);
@@ -29,6 +29,7 @@ static __global__ void flash_attn_ext_f16(
        const char * __restrict__ K,
        const char * __restrict__ V,
        const char * __restrict__ mask,
+        const char * __restrict__ sinks,
        const int  * __restrict__ KV_max,
        float      * __restrict__ dst,
        float2     * __restrict__ dst_meta,
@@ -423,7 +424,7 @@ static __global__ void flash_attn_ext_f16(
        dst_meta[j_dst_unrolled] = dst_meta_val;
    }
 #else
-    GGML_UNUSED(Q); GGML_UNUSED(K); GGML_UNUSED(V); GGML_UNUSED(mask);
+    GGML_UNUSED(Q); GGML_UNUSED(K); GGML_UNUSED(V); GGML_UNUSED(mask); GGML_UNUSED(sinks);
    GGML_UNUSED(dst); GGML_UNUSED(dst_meta); GGML_UNUSED(scale);
    GGML_UNUSED(max_bias); GGML_UNUSED(m0); GGML_UNUSED(m1);
    GGML_UNUSED(n_head_log2); GGML_UNUSED(logit_softcap);
@@ -269,17 +269,28 @@ static void ggml_cuda_flash_attn_ext_vec_f32(ggml_backend_cuda_context & ctx, gg
 }

 void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
-    const ggml_tensor * KQV  = dst;
-    const ggml_tensor * Q    = dst->src[0];
-    const ggml_tensor * K    = dst->src[1];
-    const ggml_tensor * V    = dst->src[2];
-    const ggml_tensor * mask = dst->src[3];
+    const ggml_tensor * KQV   = dst;
+    const ggml_tensor * Q     = dst->src[0];
+    const ggml_tensor * K     = dst->src[1];
+    const ggml_tensor * V     = dst->src[2];
+    const ggml_tensor * mask  = dst->src[3];
+    const ggml_tensor * sinks = dst->src[4];

    ggml_cuda_set_device(ctx.device);
    const int cc = ggml_cuda_info().devices[ggml_cuda_get_device()].cc;
    const int warp_size = ggml_cuda_info().devices[ggml_cuda_get_device()].warp_size;
    const enum ggml_prec prec = ggml_flash_attn_ext_get_prec(KQV);

+    // TODO: currently only vec implementation for sinks is supported [TAG_ATTN_SINKS]
+    if (sinks) {
+        if (prec == GGML_PREC_DEFAULT && fast_fp16_available(cc)) {
+            ggml_cuda_flash_attn_ext_vec_f16(ctx, dst);
+        } else {
+            ggml_cuda_flash_attn_ext_vec_f32(ctx, dst);
+        }
+        return;
+    }
+
 #if defined(GGML_HIP_ROCWMMA_FATTN)
    if (GGML_CUDA_CC_IS_AMD(cc) && fp16_mma_available(cc)) {
        ggml_cuda_flash_attn_ext_wmma_f16(ctx, dst);
@@ -315,8 +326,9 @@ void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst

    const bool gqa_opt_applies = ((Q->ne[2] / K->ne[2]) % 2 == 0) && mask; // The mma-based kernels have GQA-specific optimizations
    const bool mma_needs_data_conversion = K->type != GGML_TYPE_F16 || V->type != GGML_TYPE_F16;
-    const bool mma_faster_for_bs1 = new_mma_available(cc) && gqa_opt_applies &&
-        (Q->ne[3] > 1 || cc < GGML_CUDA_CC_ADA_LOVELACE) && !mma_needs_data_conversion;
+    const bool mma_faster_for_rtx4000 = Q->ne[3] > 1 || (Q->ne[2] > 4*K->ne[2] && K->ne[1] >= 8192);
+    const bool mma_faster_for_bs1 = new_mma_available(cc) && gqa_opt_applies && !mma_needs_data_conversion &&
+        (cc < GGML_CUDA_CC_ADA_LOVELACE || mma_faster_for_rtx4000);
    const bool can_use_vector_kernel = Q->ne[0] <= 256 && Q->ne[0] % (2*warp_size) == 0;
    if (Q->ne[1] == 1 && can_use_vector_kernel && !mma_faster_for_bs1) {
        if (prec == GGML_PREC_DEFAULT) {
@@ -4,6 +4,7 @@

 #include "ggml-cuda/common.cuh"
 #include "ggml-cuda/acc.cuh"
+#include "ggml-cuda/add-id.cuh"
 #include "ggml-cuda/arange.cuh"
 #include "ggml-cuda/argmax.cuh"
 #include "ggml-cuda/argsort.cuh"
@@ -1852,6 +1853,9 @@ static void ggml_cuda_mul_mat_batched_cublas_impl(ggml_backend_cuda_context & ct
    ggml_cuda_pool_alloc<cuda_t> src0_alloc(ctx.pool());
    ggml_cuda_pool_alloc<cuda_t> src1_alloc(ctx.pool());

+    bool is_src0_cont_2 = ggml_is_contiguous_2(src0);
+    bool is_src1_cont_2 = ggml_is_contiguous_2(src1);
+
    // Handle src0
    src0_ptr = (const cuda_t *) src0->data;

@@ -1870,6 +1874,8 @@ static void ggml_cuda_mul_mat_batched_cublas_impl(ggml_backend_cuda_context & ct
        s11 = ne10;
        s12 = ne11*s11;
        s13 = ne12*s12;
+
+        is_src1_cont_2 = true;
    }

    // Setup destination buffer
@@ -1918,15 +1924,19 @@ static void ggml_cuda_mul_mat_batched_cublas_impl(ggml_backend_cuda_context & ct
    const int64_t r2 = ne12/ne02;
    const int64_t r3 = ne13/ne03;

-    if (r2 == 1 && r3 == 1 && ggml_is_contiguous_2(src0) && ggml_is_contiguous_2(src1)) {
+    if (r2 == 1 && r3 == 1 && is_src0_cont_2 && is_src1_cont_2) {
+        // with a [0, 2, 1, 3] perm. and ne02==1 the matrix strides need to be determined from dim 3:
+        const int64_t sma = ne02 == 1 ? nb03/nb00 : nb02/nb00;
+        const int64_t smb = ne12 == 1 ? s13       : s12;
+
        // there is no broadcast and src0, src1 are contiguous across dims 2, 3
        // use cublasGemmStridedBatchedEx
        CUBLAS_CHECK(
        cublasGemmStridedBatchedEx(ctx.cublas_handle(), CUBLAS_OP_T, CUBLAS_OP_N,
                ne01, ne11, ne10,
-                alpha, src0_ptr, cu_data_type_a, nb01/nb00, nb02/nb00, // strideA
-                       src1_ptr, cu_data_type_b, s11,       s12,       // strideB
-                beta,     dst_t, cu_data_type,   ne0,       ne1*ne0,   // strideC
+                alpha, src0_ptr, cu_data_type_a, nb01/nb00, sma,     // strideA
+                       src1_ptr, cu_data_type_b, s11,       smb,     // strideB
+                beta,     dst_t, cu_data_type,   ne0,       ne1*ne0, // strideC
                ne12*ne13,
                cu_compute_type,
                CUBLAS_GEMM_DEFAULT_TENSOR_OP));
@@ -2250,6 +2260,9 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg
        case GGML_OP_ADD1: // TODO: more efficient implementation
            ggml_cuda_op_add(ctx, dst);
            break;
+        case GGML_OP_ADD_ID:
+            ggml_cuda_op_add_id(ctx, dst);
+            break;
        case GGML_OP_SUB:
            ggml_cuda_op_sub(ctx, dst);
            break;
@@ -2324,6 +2337,9 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg
                case GGML_GLU_OP_SWIGLU:
                    ggml_cuda_op_swiglu(ctx, dst);
                    break;
+                case GGML_GLU_OP_SWIGLU_OAI:
+                    ggml_cuda_op_swiglu_oai(ctx, dst);
+                    break;
                case GGML_GLU_OP_GEGLU_ERF:
                    ggml_cuda_op_geglu_erf(ctx, dst);
                    break;
@@ -2598,6 +2614,9 @@ static bool check_node_graph_compatibility_and_refresh_copy_ops(ggml_backend_cud

    const std::string gemma3n_per_layer_proj_src0_name = "inp_per_layer_selected";
    const std::string gemma3n_per_layer_proj_src1_name = "per_layer_proj";
+    const std::string ffn_moe_gate_bias_prefix = "ffn_moe_gate_biased";
+    const std::string ffn_moe_up_bias_prefix = "ffn_moe_up_biased";
+    const std::string ffn_moe_down_bias_prefix = "ffn_moe_down_biased";

    for (int i = 0; i < cgraph->n_nodes; i++) {
        ggml_tensor * node = cgraph->nodes[i];
@@ -2620,7 +2639,13 @@ static bool check_node_graph_compatibility_and_refresh_copy_ops(ggml_backend_cud
 #endif
        }

-        if (node->op == GGML_OP_ADD && node->src[1] && node->src[1]->ne[1] > 1 && (node->src[0] ? node->src[0]->name != gemma3n_per_layer_proj_src0_name : true) && (node->src[1] ? node->src[1]->name != gemma3n_per_layer_proj_src1_name : true)) {
+        if (node->op == GGML_OP_ADD &&
+            node->src[1] && node->src[1]->ne[1] > 1 &&
+            (node->src[0] ? node->src[0]->name != gemma3n_per_layer_proj_src0_name : true) &&
+            (node->src[1] ? node->src[1]->name != gemma3n_per_layer_proj_src1_name : true) &&
+            strncmp(node->name, ffn_moe_gate_bias_prefix.c_str(), ffn_moe_gate_bias_prefix.size()) != 0 &&
+            strncmp(node->name, ffn_moe_up_bias_prefix.c_str(), ffn_moe_up_bias_prefix.size()) != 0 &&
+            strncmp(node->name, ffn_moe_down_bias_prefix.c_str(), ffn_moe_down_bias_prefix.size()) != 0) {
            // disable CUDA graphs for batch size > 1 for now while excluding the matrix-matrix addition as part of Gemma3n's `project_per_layer_input` operation
            // by means of matching node names. See
            // https://github.com/ggml-org/llama.cpp/blob/f9a31eea06a859e34cecb88b4d020c7f03d86cc4/src/llama-model.cpp#L10199-L10241 and
@@ -3218,6 +3243,7 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
                case GGML_GLU_OP_REGLU:
                case GGML_GLU_OP_GEGLU:
                case GGML_GLU_OP_SWIGLU:
+                case GGML_GLU_OP_SWIGLU_OAI:
                case GGML_GLU_OP_GEGLU_ERF:
                case GGML_GLU_OP_GEGLU_QUICK:
                    return ggml_is_contiguous_1(op->src[0]);
@@ -3268,6 +3294,7 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
                    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:
@@ -3414,6 +3441,7 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
        case GGML_OP_PERMUTE:
        case GGML_OP_TRANSPOSE:
        case GGML_OP_ADD:
+        case GGML_OP_ADD_ID:
        case GGML_OP_ADD1:
        case GGML_OP_SUB:
        case GGML_OP_MUL:
@@ -3494,6 +3522,10 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
                const int gqa_ratio = op->src[0]->ne[2] / op->src[1]->ne[2];
                return op->src[1]->ne[0] == 576 && op->src[2]->ne[0] == 512 && op->src[3] && gqa_ratio % 16 == 0;
            }
+            // TODO: more general-purpose attention sink support [TAG_ATTN_SINKS]
+            if (op->src[4] && op->src[0]->ne[0] != 64 && op->src[0]->ne[0] != 128) { // currently only sinks for head_size 64 and 128 are supported
+                return false;
+            }
            if (op->src[0]->ne[0] == 192) {
                return false;
            }
@@ -1,65 +1,76 @@
 #include "im2col.cuh"

+#define MAX_GRIDDIM_Z 65535
+
 template <typename T>
 static  __global__ void im2col_kernel(
-        const float * x, T * dst, int64_t batch_offset,
-        int64_t offset_delta, int64_t IC, int64_t IW, int64_t IH, int64_t OH, int64_t OW, int64_t KW, int64_t KH, int64_t pelements, int64_t CHW,
+        const float * x, T * dst,
+        int64_t IC, int64_t IW, int64_t IH, int64_t OH, int64_t OW, int64_t KW, int64_t KH,
+        int64_t IC_IH_IW, int64_t IH_IW, int64_t N_OH, int64_t KH_KW, int64_t IC_KH_KW,
        int s0, int s1, int p0, int p1, int d0, int d1) {
    const int64_t i = threadIdx.x + blockIdx.x * blockDim.x;
-    if (i >= pelements) {
+    if (i >= IC_KH_KW) {
        return;
    }

-    const int64_t  ksize = OW * KH;
-    const int64_t  kx = i / ksize;
-    const int64_t  kd = kx * ksize;
-    const int64_t  ky = (i - kd) / OW;
-    const int64_t  ix = i % OW;
+    const int64_t iic = i / (KH_KW);
+    const int64_t rem = i - iic * KH_KW;
+    const int64_t ikh = rem / KW;
+    const int64_t ikw = rem - ikh * KW;

-    const int64_t  oh = blockIdx.y;
-    const int64_t  batch = blockIdx.z / IC;
-    const int64_t  ic = blockIdx.z % IC;
+    const int64_t  iow = blockIdx.y;
+    for (int64_t iz = blockIdx.z; iz < N_OH; iz+=MAX_GRIDDIM_Z) {
+        const int64_t  in = iz / OH;
+        const int64_t  ioh = iz - in * OH;

-    const int64_t iiw = ix * s0 + kx * d0 - p0;
-    const int64_t iih = oh * s1 + ky * d1 - p1;
+        const int64_t iiw = iow * s0 + ikw * d0 - p0;
+        const int64_t iih = ioh * s1 + ikh * d1 - p1;

-    const int64_t offset_dst =
-        ((batch * OH + oh) * OW + ix) * CHW +
-        (ic * (KW * KH) + ky * KW + kx);
+        const int64_t offset_dst =
+            ((in * OH + ioh) * OW + iow) * IC_KH_KW + iic * KH_KW + ikh * KW + ikw;

-    if (iih < 0 || iih >= IH || iiw < 0 || iiw >= IW) {
-        dst[offset_dst] = 0.0f;
-    } else {
-        const int64_t offset_src = ic * offset_delta + batch * batch_offset;
-        dst[offset_dst] = x[offset_src + iih * IW + iiw];
+        if (iih < 0 || iih >= IH || iiw < 0 || iiw >= IW) {
+            dst[offset_dst] = 0.0f;
+        } else {
+            const int64_t offset_src = iic * IC_IH_IW + in * IH_IW;
+            dst[offset_dst] = x[offset_src + iih * IW + iiw];
+        }
    }
+
+    GGML_UNUSED(IC);
+    GGML_UNUSED(KH);
 }

+// im2col: [N, IC, IH, IW] => [N, OH, OW, IC*KH*KW]
 template <typename T>
 static void im2col_cuda(const float * x, T* dst,
    int64_t IW, int64_t IH, int64_t OW, int64_t OH, int64_t KW, int64_t KH, int64_t IC,
-    int64_t batch, int64_t batch_offset, int64_t offset_delta,
+    int64_t N, int64_t IC_IH_IW, int64_t IH_IW,
    int s0,int s1,int p0,int p1,int d0,int d1, cudaStream_t stream) {
-    const int parallel_elements = OW * KW * KH;
-    const int num_blocks = (parallel_elements + CUDA_IM2COL_BLOCK_SIZE - 1) / CUDA_IM2COL_BLOCK_SIZE;
-    dim3 block_nums(num_blocks, OH, batch * IC);
-    im2col_kernel<<<block_nums, CUDA_IM2COL_BLOCK_SIZE, 0, stream>>>(x, dst, batch_offset, offset_delta, IC, IW, IH, OH, OW, KW, KH, parallel_elements, (IC * KH * KW), s0, s1, p0, p1, d0, d1);
+    const int64_t IC_KH_KW = IC * KH * KW;
+    const int64_t num_blocks = (IC_KH_KW + CUDA_IM2COL_BLOCK_SIZE - 1) / CUDA_IM2COL_BLOCK_SIZE;
+    const int64_t N_OH = N * OH;
+    const int64_t KH_KW = KW*KH;
+    dim3 block_nums(num_blocks, OW, MIN(N_OH, MAX_GRIDDIM_Z));
+    im2col_kernel<<<block_nums, MIN(IC_KH_KW, CUDA_IM2COL_BLOCK_SIZE) , 0, stream>>>(x, dst, IC, IW, IH, OH, OW, KW, KH,
+                                                                                     IC_IH_IW, IH_IW, N_OH, KH_KW, IC_KH_KW,
+                                                                                     s0, s1, p0, p1, d0, d1);
 }

 static void im2col_cuda_f16(const float * x, half * dst,
    int64_t IW, int64_t IH, int64_t OW, int64_t OH, int64_t KW, int64_t KH, int64_t IC,
-    int64_t batch, int64_t batch_offset, int64_t offset_delta,
+    int64_t N, int64_t IC_IH_IW, int64_t IH_IW,
    int s0,int s1,int p0,int p1,int d0,int d1, cudaStream_t stream) {

-    im2col_cuda<half>(x, dst, IW, IH, OW, OH, KW, KH, IC, batch, batch_offset, offset_delta, s0, s1, p0, p1, d0, d1, stream);
+    im2col_cuda<half>(x, dst, IW, IH, OW, OH, KW, KH, IC, N, IC_IH_IW, IH_IW, s0, s1, p0, p1, d0, d1, stream);
 }

 static void im2col_cuda_f32(const float * x, float * dst,
    int64_t IW, int64_t IH, int64_t OW, int64_t OH, int64_t KW, int64_t KH, int64_t IC,
-    int64_t batch, int64_t batch_offset, int64_t offset_delta,
+    int64_t N, int64_t IC_IH_IW, int64_t IH_IW,
    int s0,int s1,int p0,int p1,int d0,int d1, cudaStream_t stream) {

-    im2col_cuda<float>(x, dst, IW, IH, OW, OH, KW, KH, IC, batch, batch_offset, offset_delta, s0, s1, p0, p1, d0, d1, stream);
+    im2col_cuda<float>(x, dst, IW, IH, OW, OH, KW, KH, IC, N, IC_IH_IW, IH_IW, s0, s1, p0, p1, d0, d1, stream);
 }

 void ggml_cuda_op_im2col(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
@@ -91,13 +102,13 @@ void ggml_cuda_op_im2col(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
    const int64_t OH = is_2D ? dst->ne[2] : 1;
    const int64_t OW =         dst->ne[1];

-    const size_t  delta_offset = src1->nb[is_2D ? 2 : 1] / 4; // nb is byte offset, src is type float32
-    const int64_t batch        = src1->ne[is_2D ? 3 : 2];
-    const size_t  batch_offset = src1->nb[is_2D ? 3 : 2] / 4; // nb is byte offset, src is type float32
+    const int64_t IC_IH_IW = src1->nb[is_2D ? 2 : 1] / 4; // nb is byte offset, src is type float32
+    const int64_t N        = src1->ne[is_2D ? 3 : 2];
+    const int64_t IH_IW    = src1->nb[is_2D ? 3 : 2] / 4; // nb is byte offset, src is type float32

    if(dst->type == GGML_TYPE_F16) {
-        im2col_cuda_f16(src1_d, (half *) dst_d, IW, IH, OW, OH, KW, KH, IC, batch, batch_offset, delta_offset, s0, s1, p0, p1, d0, d1, stream);
+        im2col_cuda_f16(src1_d, (half *) dst_d, IW, IH, OW, OH, KW, KH, IC, N, IC_IH_IW, IH_IW, s0, s1, p0, p1, d0, d1, stream);
    } else {
-        im2col_cuda_f32(src1_d, (float *) dst_d, IW, IH, OW, OH, KW, KH, IC, batch, batch_offset, delta_offset, s0, s1, p0, p1, d0, d1, stream);
+        im2col_cuda_f32(src1_d, (float *) dst_d, IW, IH, OW, OH, KW, KH, IC, N, IC_IH_IW, IH_IW, s0, s1, p0, p1, d0, d1, stream);
    }
 }
@@ -20,6 +20,9 @@ static void ggml_cuda_mul_mat_q_switch_type(ggml_backend_cuda_context & ctx, con
        case GGML_TYPE_Q8_0:
            mul_mat_q_case<GGML_TYPE_Q8_0>(ctx, args, stream);
            break;
+        case GGML_TYPE_MXFP4:
+            mul_mat_q_case<GGML_TYPE_MXFP4>(ctx, args, stream);
+            break;
        case GGML_TYPE_Q2_K:
            mul_mat_q_case<GGML_TYPE_Q2_K>(ctx, args, stream);
            break;
@@ -282,6 +285,7 @@ bool ggml_cuda_should_use_mmq(enum ggml_type type, int cc, int64_t ne11) {
        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:
@@ -58,6 +58,8 @@ static mmq_q8_1_ds_layout mmq_get_q8_1_ds_layout(const ggml_type type_x) {
            return MMQ_Q8_1_DS_LAYOUT_DS4;
        case GGML_TYPE_Q8_0:
            return MMQ_Q8_1_DS_LAYOUT_D4;
+        case GGML_TYPE_MXFP4:
+            return MMQ_Q8_1_DS_LAYOUT_D4;
        case GGML_TYPE_Q2_K:
            return MMQ_Q8_1_DS_LAYOUT_D2S6;
        case GGML_TYPE_Q3_K:
@@ -170,6 +172,7 @@ static constexpr __host__ __device__ tile_x_sizes mmq_get_dp4a_tile_x_sizes(ggml
        case GGML_TYPE_Q5_0:    return MMQ_DP4A_TXS_Q8_0;
        case GGML_TYPE_Q5_1:    return MMQ_DP4A_TXS_Q8_1;
        case GGML_TYPE_Q8_0:    return MMQ_DP4A_TXS_Q8_0;
+        case GGML_TYPE_MXFP4:   return MMQ_DP4A_TXS_Q8_1;
        case GGML_TYPE_Q2_K:    return MMQ_DP4A_TXS_Q2_K;
        case GGML_TYPE_Q3_K:    return MMQ_DP4A_TXS_Q3_K;
        case GGML_TYPE_Q4_K:    return MMQ_DP4A_TXS_Q4_K;
@@ -206,6 +209,7 @@ static constexpr __host__ __device__ int mmq_get_mma_tile_x_k(ggml_type type) {
        case GGML_TYPE_Q5_0:    return MMQ_MMA_TILE_X_K_Q8_0;
        case GGML_TYPE_Q5_1:    return MMQ_MMA_TILE_X_K_Q8_1;
        case GGML_TYPE_Q8_0:    return MMQ_MMA_TILE_X_K_Q8_0;
+        case GGML_TYPE_MXFP4:   return MMQ_MMA_TILE_X_K_Q8_1;
        case GGML_TYPE_Q2_K:    return MMQ_MMA_TILE_X_K_Q2_K;
        case GGML_TYPE_Q3_K:    return MMQ_MMA_TILE_X_K_Q3_K;
        case GGML_TYPE_Q4_K:    return MMQ_MMA_TILE_X_K_Q8_1;
@@ -692,6 +696,71 @@ template <int mmq_y, bool need_check> static __device__ __forceinline__ void loa
    }
 }

+template <int mmq_y, bool need_check> static __device__ __forceinline__ void load_tiles_mxfp4(
+    const char * __restrict__ x, int * __restrict__ x_tile, const int kbx0, const int i_max, const int stride) {
+    constexpr int nwarps = mmq_get_nwarps_device();
+    constexpr int warp_size = ggml_cuda_get_physical_warp_size();
+
+#if defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+    int   * x_qs = (int   *)  x_tile;
+    float * x_df = (float *) (x_qs + MMQ_TILE_NE_K*2);
+#else
+    constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_MXFP4, mmq_y);
+    int   * x_qs = (int   *)  x_tile;
+    float * x_df = (float *) (x_qs + txs.qs);
+#endif // defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+
+    constexpr int threads_per_row = MMQ_ITER_K / (4 * QR_MXFP4);
+    constexpr int nrows = warp_size / threads_per_row;
+    const int txi = warp_size > threads_per_row ? threadIdx.x % threads_per_row : threadIdx.x;
+    const int kbx  = txi / QI_MXFP4;
+    const int kqsx = txi % QI_MXFP4;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nrows*nwarps) {
+        int i = i0 + (nrows == 1 ? threadIdx.y : threadIdx.y*nrows + threadIdx.x/threads_per_row);
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_mxfp4 * bxi = (const block_mxfp4 *) x + kbx0 + i*stride + kbx;
+
+        const int aux_q4 = get_int_b1(bxi->qs, kqsx);
+        const int2 v = get_int_from_table_16(aux_q4, kvalues_mxfp4);
+        const int k0 = kbx * (2 * QI_MXFP4) + kqsx;
+
+#if defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+        x_qs[i*MMQ_MMA_TILE_X_K_Q8_1 + k0 + 0]        = v.x;
+        x_qs[i*MMQ_MMA_TILE_X_K_Q8_1 + k0 + QI_MXFP4] = v.y;
+#else
+        x_qs[i*(2*MMQ_TILE_NE_K + 1) + k0 + 0]        = v.x;
+        x_qs[i*(2*MMQ_TILE_NE_K + 1) + k0 + QI_MXFP4] = v.y;
+#endif // defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+    }
+
+    constexpr int blocks_per_tile_x_row = MMQ_TILE_NE_K / QI_MXFP4;
+    constexpr int rows_per_warp = warp_size / blocks_per_tile_x_row;
+    const int kbxd = threadIdx.x % blocks_per_tile_x_row;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps * rows_per_warp) {
+        int i = i0 + threadIdx.y * rows_per_warp + threadIdx.x / blocks_per_tile_x_row;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_mxfp4 * bxi = (const block_mxfp4 *) x + kbx0 + i*stride + kbxd;
+
+#if defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+        x_df[i*MMQ_MMA_TILE_X_K_Q8_1                 + kbxd] = ggml_cuda_e8m0_to_fp32(bxi->e)*0.5f;
+#else
+        x_df[i*(MMQ_TILE_NE_K/QI_MXFP4) + i/QI_MXFP4 + kbxd] = ggml_cuda_e8m0_to_fp32(bxi->e)*0.5f;
+#endif // defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
+    }
+}
+
 template <int mmq_x, int mmq_y>
 static __device__ __forceinline__ void vec_dot_q8_0_q8_1_dp4a(
    const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int k00) {
@@ -2268,7 +2337,7 @@ template <int mmq_y, bool need_check> static __device__ __forceinline__ void loa
        const block_iq4_nl * bxi = (const block_iq4_nl *) x + kbx0 + i*stride + kbx;

        const int aux_q4 = get_int_b2(bxi->qs, kqsx);
-        const int2 v = get_int_from_table_16(aux_q4);
+        const int2 v = get_int_from_table_16(aux_q4, kvalues_iq4nl);
        const int k0 = kbx * (2 * QI4_NL) + kqsx;

 #if defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
@@ -2707,7 +2776,7 @@ template <int mmq_y, bool need_check> static __device__ __forceinline__ void loa
        const block_iq4_xs * bxi = (const block_iq4_xs *) x + kbx0 + i*stride;

        const int aux_q4 = get_int_b4(bxi->qs, kqsx);
-        const int2 v = get_int_from_table_16(aux_q4);
+        const int2 v = get_int_from_table_16(aux_q4, kvalues_iq4nl);
        const int k0 = 8 * (kqsx / 4) + kqsx % 4;

 #if defined(AMD_MFMA_AVAILABLE) || defined(NEW_MMA_AVAILABLE)
@@ -2863,6 +2932,14 @@ struct mmq_type_traits<mmq_x, mmq_y, need_check, GGML_TYPE_Q8_0> {
    static constexpr vec_dot_mmq_t    vec_dot_dp4a = vec_dot_q8_0_q8_1_dp4a<mmq_x, mmq_y>;
 };

+template <int mmq_x, int mmq_y, bool need_check>
+struct mmq_type_traits<mmq_x, mmq_y, need_check, GGML_TYPE_MXFP4> {
+    static constexpr int              vdr          = VDR_MXFP4_Q8_1_MMQ;
+    static constexpr load_tiles_mmq_t load_tiles   = load_tiles_mxfp4<mmq_y, need_check>;
+    static constexpr vec_dot_mmq_t    vec_dot_mma  = vec_dot_q8_0_q8_1_mma<mmq_x, mmq_y, MMQ_Q8_1_DS_LAYOUT_D4>;
+    static constexpr vec_dot_mmq_t    vec_dot_dp4a = vec_dot_q8_0_q8_1_dp4a<mmq_x, mmq_y>;
+};
+
 template <int mmq_x, int mmq_y, bool need_check>
 struct mmq_type_traits<mmq_x, mmq_y, need_check, GGML_TYPE_Q2_K> {
    static constexpr int              vdr          = VDR_Q2_K_Q8_1_MMQ;
@@ -3642,6 +3719,7 @@ extern DECL_MMQ_CASE(GGML_TYPE_Q4_1);
 extern DECL_MMQ_CASE(GGML_TYPE_Q5_0);
 extern DECL_MMQ_CASE(GGML_TYPE_Q5_1);
 extern DECL_MMQ_CASE(GGML_TYPE_Q8_0);
+extern DECL_MMQ_CASE(GGML_TYPE_MXFP4);
 extern DECL_MMQ_CASE(GGML_TYPE_Q2_K);
 extern DECL_MMQ_CASE(GGML_TYPE_Q3_K);
 extern DECL_MMQ_CASE(GGML_TYPE_Q4_K);
@@ -13,6 +13,7 @@ static constexpr __device__ vec_dot_q_cuda_t get_vec_dot_q_cuda(ggml_type type)
        case GGML_TYPE_Q5_0:    return vec_dot_q5_0_q8_1;
        case GGML_TYPE_Q5_1:    return vec_dot_q5_1_q8_1;
        case GGML_TYPE_Q8_0:    return vec_dot_q8_0_q8_1;
+        case GGML_TYPE_MXFP4:   return vec_dot_mxfp4_q8_1;
        case GGML_TYPE_Q2_K:    return vec_dot_q2_K_q8_1;
        case GGML_TYPE_Q3_K:    return vec_dot_q3_K_q8_1;
        case GGML_TYPE_Q4_K:    return vec_dot_q4_K_q8_1;
@@ -38,6 +39,7 @@ static constexpr __device__ int get_vdr_mmvq(ggml_type type) {
        case GGML_TYPE_Q5_0:    return VDR_Q5_0_Q8_1_MMVQ;
        case GGML_TYPE_Q5_1:    return VDR_Q5_1_Q8_1_MMVQ;
        case GGML_TYPE_Q8_0:    return VDR_Q8_0_Q8_1_MMVQ;
+        case GGML_TYPE_MXFP4:   return VDR_MXFP4_Q8_1_MMVQ;
        case GGML_TYPE_Q2_K:    return VDR_Q2_K_Q8_1_MMVQ;
        case GGML_TYPE_Q3_K:    return VDR_Q3_K_Q8_1_MMVQ;
        case GGML_TYPE_Q4_K:    return VDR_Q4_K_Q8_1_MMVQ;
@@ -384,6 +386,13 @@ static void mul_mat_vec_q_switch_type(
                 nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst,
                 stream);
            break;
+        case GGML_TYPE_MXFP4:
+            mul_mat_vec_q_switch_ncols_dst<GGML_TYPE_MXFP4>
+                (vx, vy, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, stride_col_y, stride_col_dst,
+                 nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
+                 nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst,
+                 stream);
+            break;
        case GGML_TYPE_Q2_K:
            mul_mat_vec_q_switch_ncols_dst<GGML_TYPE_Q2_K>
                (vx, vy, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, stride_col_y, stride_col_dst,
@@ -45,7 +45,7 @@ struct soft_max_params {
 #endif // __clang__
 template <bool use_shared, int ncols_template, int block_size_template, typename T>
 static __global__ void soft_max_f32(
-        const float * x, const T * mask, float * dst, const soft_max_params p) {
+        const float * x, const T * mask, const float * sinks, float * dst, const soft_max_params p) {
    const int ncols = ncols_template == 0 ? p.ncols : ncols_template;

    const int tid  = threadIdx.x;
@@ -77,7 +77,7 @@ static __global__ void soft_max_f32(
    // shared memory buffer to cache values between iterations:
    float * vals = use_shared ? buf_iw + WARP_SIZE : dst;

-    float max_val = -INFINITY;
+    float max_val = sinks ? sinks[i02] : -INFINITY;

 #pragma unroll
    for (int col0 = 0; col0 < ncols; col0 += block_size) {
@@ -143,6 +143,10 @@ static __global__ void soft_max_f32(
        tmp = warp_reduce_sum(tmp);
    }

+    if (sinks) {
+        tmp += expf(sinks[i02] - max_val);
+    }
+
    const float inv_sum = 1.0f / tmp;

 #pragma unroll
@@ -183,7 +187,7 @@ static __global__ void soft_max_back_f32(
 }

 template<int... Ns, typename T>
-static void launch_soft_max_kernels(const float * x, const T * mask, float * dst,
+static void launch_soft_max_kernels(const float * x, const T * mask, const float * sinks, float * dst,
                             const soft_max_params & p, cudaStream_t stream, dim3 block_dims, dim3 block_nums, size_t nbytes_shared)
 {
    const int id       = ggml_cuda_get_device();
@@ -196,7 +200,7 @@ static void launch_soft_max_kernels(const float * x, const T * mask, float * dst
        if (p.ncols == ncols) {
            CUDA_SET_SHARED_MEMORY_LIMIT((soft_max_f32<true, ncols, block, T>), smpbo);
            soft_max_f32<true, ncols, block><<<block_nums, block_dims, nbytes_shared, stream>>>
-                (x, mask, dst, p);
+                (x, mask, sinks, dst, p);
            return true;
        }
        return false;
@@ -209,12 +213,12 @@ static void launch_soft_max_kernels(const float * x, const T * mask, float * dst

    //default case
    CUDA_SET_SHARED_MEMORY_LIMIT((soft_max_f32<true, 0, 0, T>), smpbo);
-    soft_max_f32<true, 0, 0><<<block_nums, block_dims, nbytes_shared, stream>>>(x, mask, dst, p);
+    soft_max_f32<true, 0, 0><<<block_nums, block_dims, nbytes_shared, stream>>>(x, mask, sinks, dst, p);
 }


 template<typename T>
-static void soft_max_f32_cuda(const float * x, const T * mask, float * dst, const soft_max_params & params, cudaStream_t stream) {
+static void soft_max_f32_cuda(const float * x, const T * mask, const float * sinks, float * dst, const soft_max_params & params, cudaStream_t stream) {
    int nth = WARP_SIZE;
    const int64_t ncols_x = params.ncols;

@@ -230,10 +234,10 @@ static void soft_max_f32_cuda(const float * x, const T * mask, float * dst, cons


    if (nbytes_shared <= smpbo) {
-        launch_soft_max_kernels<32, 64, 128, 256, 512, 1024, 2048, 4096>(x, mask, dst, params, stream, block_dims, block_nums, nbytes_shared);
+        launch_soft_max_kernels<32, 64, 128, 256, 512, 1024, 2048, 4096>(x, mask, sinks, dst, params, stream, block_dims, block_nums, nbytes_shared);
    } else {
        const size_t nbytes_shared_low = WARP_SIZE*sizeof(float);
-        soft_max_f32<false, 0, 0><<<block_nums, block_dims, nbytes_shared_low, stream>>>(x, mask, dst, params);
+        soft_max_f32<false, 0, 0><<<block_nums, block_dims, nbytes_shared_low, stream>>>(x, mask, sinks, dst, params);
    }
 }

@@ -249,9 +253,11 @@ static void soft_max_back_f32_cuda(
 void ggml_cuda_op_soft_max(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
    const ggml_tensor * src0 = dst->src[0];
    const ggml_tensor * src1 = dst->src[1];
+    const ggml_tensor * src2 = dst->src[2];

    const float * src0_d = (const float *) src0->data;
    const void  * src1_d = src1 ? (const void *) src1->data : nullptr;
+    const void  * src2_d = src2 ? (const void *) src2->data : nullptr;
    float       *  dst_d = (float *) dst->data;

    cudaStream_t stream = ctx.stream();
@@ -309,9 +315,9 @@ void ggml_cuda_op_soft_max(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
    params.m1 = m1;

    if (use_f16) {
-        soft_max_f32_cuda(src0_d, (const half  *) src1_d, dst_d, params, stream);
+        soft_max_f32_cuda(src0_d, (const half  *) src1_d, (const float *) src2_d, dst_d, params, stream);
    } else {
-        soft_max_f32_cuda(src0_d, (const float *) src1_d, dst_d, params, stream);
+        soft_max_f32_cuda(src0_d, (const float *) src1_d, (const float *) src2_d, dst_d, params, stream);
    }
 }

@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../mmq.cuh"
+
+DECL_MMQ_CASE(GGML_TYPE_MXFP4);
@@ -300,6 +300,81 @@ void ggml_cuda_op_geglu_quick(ggml_backend_cuda_context & ctx, ggml_tensor * dst
    ggml_cuda_op_unary_gated<op_gelu_quick>(ctx, dst);
 }

+// swiglu_oai
+
+template <typename T>
+static __global__ void swiglu_oai_kernel(const T * x, const T * g, T * dst, const int64_t k, const int64_t n, const int64_t o0, const int64_t o1, float alpha, float limit) {
+    const int64_t i = int64_t(blockDim.x)*blockIdx.x + threadIdx.x;
+
+    if (i >= k) {
+        return;
+    }
+
+    // perform base op and multiply with gate (either offset in same tensor or a separate one)
+    const int64_t j0 = (i / n) * o0 + (i % n);
+    const int64_t j1 = o0 == o1 ? j0 : (i / n) * o1 + (i % n);
+
+    float xi = x[j0];
+    float gi = g[j1];
+    xi = fminf(xi, limit);
+    gi = fmaxf(fminf(gi, limit), -limit);
+
+    float out_glu = xi / (1.0f + expf(-xi * alpha));
+    out_glu = out_glu * (1.0f + gi);
+
+    dst[i] = out_glu;
+}
+
+template <typename T>
+static void swiglu_oai_cuda(const T * x, const T * g, T * dst, const int64_t k, const int64_t n, const int64_t o0, const int64_t o1, const float alpha, const float limit, cudaStream_t stream) {
+    const int64_t num_blocks = (k + CUDA_GLU_BLOCK_SIZE - 1) / CUDA_GLU_BLOCK_SIZE;
+    swiglu_oai_kernel<<<num_blocks, CUDA_GLU_BLOCK_SIZE, 0, stream>>>(x, g, dst, k, n, o0, o1, alpha, limit);
+}
+
+void ggml_cuda_op_swiglu_oai(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    const ggml_tensor * src1 = dst->src[1];
+    void * src0_d = src0->data;
+    void * src1_d = src1 ? src1->data : src0->data;
+    const int64_t src0_o = src0->nb[1];
+    const int64_t src1_o = src1 ? src1->nb[1] : src0->nb[1];
+    void * dst_d = dst->data;
+    const int64_t nc = src1 ? src0->ne[0] : src0->ne[0] / 2;
+    cudaStream_t stream = ctx.stream();
+
+    GGML_ASSERT(ggml_is_contiguous_1(src0));
+    GGML_ASSERT(src0->nb[0] == ggml_element_size(src0));
+    GGML_ASSERT(ggml_is_contiguous(dst));
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+    GGML_ASSERT(src0->type == dst->type);
+    GGML_ASSERT(dst->ne[0] == nc);
+    GGML_ASSERT(ggml_nrows(dst) == ggml_nrows(src0));
+
+    if (src1) {
+        GGML_ASSERT(ggml_is_contiguous_1(src1));
+        GGML_ASSERT(src1->nb[0] == ggml_element_size(src1));
+        GGML_ASSERT(src1->ne[0] == nc);
+        GGML_ASSERT(src0->type == src1->type);
+    }
+
+    //const int32_t swapped = ((const int32_t *) dst->op_params)[1];
+    const int32_t swapped = ggml_get_op_params_i32(dst, 1);
+    const float alpha = ggml_get_op_params_f32(dst, 2);
+    const float limit = ggml_get_op_params_f32(dst, 3);
+
+    float * src0_p = (float *) src0_d;
+    float * src1_p = (float *) src1_d;
+
+    if (!src1) {
+        src0_p += swapped ? nc : 0;
+        src1_p += swapped ? 0 : nc;
+    }
+
+    swiglu_oai_cuda(src0_p, src1_p, (float *)dst_d, ggml_nelements(dst), nc, src0_o / sizeof(float), src1_o / sizeof(float), alpha, limit, stream);
+}
+
 /* silu_back */

 static __device__ __forceinline__ float op_silu_back(float grad, float x) {
@@ -67,6 +67,8 @@ void ggml_cuda_op_geglu(ggml_backend_cuda_context & ctx, ggml_tensor * dst);

 void ggml_cuda_op_swiglu(ggml_backend_cuda_context & ctx, ggml_tensor * dst);

+void ggml_cuda_op_swiglu_oai(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
+
 void ggml_cuda_op_geglu_erf(ggml_backend_cuda_context & ctx, ggml_tensor * dst);

 void ggml_cuda_op_geglu_quick(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
@@ -1,8 +1,20 @@
 #pragma once

 #include "common.cuh"
+
 #include <cstdint>

+static __device__ __forceinline__ int get_int_b1(const void * x, const int & i32) {
+    const uint8_t * x8 = (const uint8_t *) x;
+
+    int x32  = x8[4*i32 + 0] <<  0;
+    x32     |= x8[4*i32 + 1] <<  8;
+    x32     |= x8[4*i32 + 2] << 16;
+    x32     |= x8[4*i32 + 3] << 24;
+
+    return x32;
+}
+
 static __device__ __forceinline__ int get_int_b2(const void * x, const int & i32) {
    const uint16_t * x16 = (const uint16_t *) x; // assume at least 2 byte alignment

@@ -16,6 +28,20 @@ static __device__ __forceinline__ int get_int_b4(const void * x, const int & i32
    return ((const int *) x)[i32]; // assume at least 4 byte alignment
 }

+static __device__ __forceinline__ int2 get_int_from_table_16(const int & q4, const int8_t * table) {
+    const int      q0_32  = (q4 >> 0) & 0x0F0F0F0F;
+    const int8_t * q0_8   = (const int8_t *) &q0_32;
+    const char4    val0_8 = make_char4(
+        table[q0_8[0]], table[q0_8[1]], table[q0_8[2]], table[q0_8[3]]);
+
+    const int      q1_32  = (q4 >> 4) & 0x0F0F0F0F;
+    const int8_t * q1_8   = (const int8_t *) &q1_32;
+    const char4    val1_8 = make_char4(
+        table[q1_8[0]], table[q1_8[1]], table[q1_8[2]], table[q1_8[3]]);
+
+    return make_int2(*((const int *) &val0_8), *((const int *) &val1_8));
+}
+
 // VDR = vec dot ratio, how many contiguous integers each thread processes when the vec dot kernel is called
 // MMVQ = mul_mat_vec_q, MMQ = mul_mat_q

@@ -211,6 +237,30 @@ template <int vdr> static __device__ __forceinline__ float vec_dot_q8_0_16_q8_1_
    return d8_1*sumf;
 }

+#define VDR_MXFP4_Q8_1_MMVQ 2
+#define VDR_MXFP4_Q8_1_MMQ  4
+
+static __device__ __forceinline__ float vec_dot_mxfp4_q8_1(
+    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs) {
+
+    const block_mxfp4 * bq4 = (const block_mxfp4 *) vbq + kbx;
+
+    const int * q8 = (const int *) bq8_1->qs + iqs;
+
+    int sumi = 0;
+#pragma unroll
+    for (int l = 0; l < VDR_MXFP4_Q8_1_MMVQ; ++l) {
+        const int aux_q4 = get_int_b1(bq4->qs, iqs + l);
+        const int2 v = get_int_from_table_16(aux_q4, kvalues_mxfp4);
+
+        sumi = ggml_cuda_dp4a(v.x, q8[l + 0], sumi);
+        sumi = ggml_cuda_dp4a(v.y, q8[l + 4], sumi);
+    }
+
+    const float d = ggml_cuda_e8m0_to_fp32(bq4->e) * 0.5f * __low2float(bq8_1->ds);
+    return d * sumi;
+}
+
 #define VDR_Q2_K_Q8_1_MMVQ 1
 #define VDR_Q2_K_Q8_1_MMQ  4

@@ -1068,20 +1118,6 @@ static __device__ __forceinline__ float vec_dot_iq1_m_q8_1(
    return d * ((sumi[0] + sumf[0]) * sc0 + (sumi[1] + sumf[1]) * sc1);
 }

-static __device__ __forceinline__ int2 get_int_from_table_16(const int & q4) {
-    const int      q0_32  = (q4 >> 0) & 0x0F0F0F0F;
-    const int8_t * q0_8   = (const int8_t *) &q0_32;
-    const char4    val0_8 = make_char4(
-        kvalues_iq4nl[q0_8[0]], kvalues_iq4nl[q0_8[1]], kvalues_iq4nl[q0_8[2]], kvalues_iq4nl[q0_8[3]]);
-
-    const int      q1_32  = (q4 >> 4) & 0x0F0F0F0F;
-    const int8_t * q1_8   = (const int8_t *) &q1_32;
-    const char4    val1_8 = make_char4(
-        kvalues_iq4nl[q1_8[0]], kvalues_iq4nl[q1_8[1]], kvalues_iq4nl[q1_8[2]], kvalues_iq4nl[q1_8[3]]);
-
-    return make_int2(*((const int *) &val0_8), *((const int *) &val1_8));
-}
-
 #define VDR_IQ4_NL_Q8_1_MMVQ 2
 #define VDR_IQ4_NL_Q8_1_MMQ  4

@@ -1096,7 +1132,7 @@ static __device__ __forceinline__ float vec_dot_iq4_nl_q8_1(
 #pragma unroll
    for (int l = 0; l < VDR_Q4_0_Q8_1_MMVQ; ++l) {
        const int aux_q4 = get_int_b2(bq4->qs, iqs + l);
-        const int2 v = get_int_from_table_16(aux_q4);
+        const int2 v = get_int_from_table_16(aux_q4, kvalues_iq4nl);

        sumi = ggml_cuda_dp4a(v.x, q8[l + 0], sumi);
        sumi = ggml_cuda_dp4a(v.y, q8[l + 4], sumi);
@@ -1118,7 +1154,7 @@ static __device__ __forceinline__ float vec_dot_iq4_xs_q8_1(
 #pragma unroll
    for (int j = 0; j < 4; ++j) {
        const int aux_q4 = get_int_b4(bq4->qs, iqs + j);
-        const int2 v = get_int_from_table_16(aux_q4);
+        const int2 v = get_int_from_table_16(aux_q4, kvalues_iq4nl);

        const int u0 = get_int_b4(bq8_1[iqs/4].qs, j + 0);
        const int u1 = get_int_b4(bq8_1[iqs/4].qs, j + 4);
@@ -6,6 +6,10 @@
 #include <cuda_bf16.h>
 #include <cuda_fp16.h>

+#if CUDART_VERSION >= 12050
+#include <cuda_fp8.h>
+#endif // CUDART_VERSION >= 12050
+
 #if CUDART_VERSION < 11020
 #define CU_DEVICE_ATTRIBUTE_VIRTUAL_MEMORY_MANAGEMENT_SUPPORTED CU_DEVICE_ATTRIBUTE_VIRTUAL_ADDRESS_MANAGEMENT_SUPPORTED
 #define CUBLAS_TF32_TENSOR_OP_MATH CUBLAS_TENSOR_OP_MATH
@@ -410,6 +410,67 @@ static inline ggml_fp16_t ggml_compute_fp32_to_fp16(float f) {
 #define GGML_FP16_TO_FP32(x) GGML_COMPUTE_FP16_TO_FP32(x)
 #define GGML_FP32_TO_FP16(x) GGML_COMPUTE_FP32_TO_FP16(x)

+static inline float ggml_e8m0_to_fp32(uint8_t x) {
+    uint32_t bits;  // Stores the raw bit representation of the float
+
+    // Handle special case for minimum exponent (denormalized float)
+    if (x == 0) {
+        // Bit pattern for 2^(-127):
+        // - Sign bit: 0 (positive)
+        // - Exponent: 0 (denormalized number)
+        // - Mantissa: 0x400000 (0.5 in fractional form)
+        // Value = 0.5 * 2^(-126) = 2^(-127)
+        bits = 0x00400000;
+    }
+    // note: disabled as we don't need to handle NaNs
+    //// Handle special case for NaN (all bits set)
+    //else if (x == 0xFF) {
+    //    // Standard quiet NaN pattern:
+    //    // - Sign bit: 0
+    //    // - Exponent: all 1s (0xFF)
+    //    // - Mantissa: 0x400000 (quiet NaN flag)
+    //    bits = 0x7FC00000;
+    //}
+    // Normalized values (most common case)
+    else {
+        // Construct normalized float by shifting exponent into position:
+        // - Exponent field: 8 bits (positions 30-23)
+        // - Mantissa: 0 (implicit leading 1)
+        // Value = 2^(x - 127)
+        bits = (uint32_t) x << 23;
+    }
+
+    float result;  // Final float value
+                   // Safely reinterpret bit pattern as float without type-punning issues
+    memcpy(&result, &bits, sizeof(float));
+    return result;
+}
+
+// Equal to ggml_e8m0_to_fp32/2
+// Useful with MXFP4 quantization since the E0M2 values are doubled
+static inline float ggml_e8m0_to_fp32_half(uint8_t x) {
+    uint32_t bits;
+
+    // For x < 2: use precomputed denormal patterns
+    if (x < 2) {
+        // 0x00200000 = 2^(-128), 0x00400000 = 2^(-127)
+        bits = 0x00200000 << x;
+    }
+    // For x >= 2: normalized exponent adjustment
+    else {
+        // 0.5 * 2^(x-127) = 2^(x-128) = normalized with exponent (x-1)
+        bits = (uint32_t)(x - 1) << 23;
+    }
+    // Note: NaNs are not handled here
+
+    float result;
+    memcpy(&result, &bits, sizeof(float));
+    return result;
+}
+
+#define GGML_E8M0_TO_FP32(x) ggml_e8m0_to_fp32(x)
+#define GGML_E8M0_TO_FP32_HALF(x) ggml_e8m0_to_fp32_half(x)
+
 /**
 * Converts brain16 to float32.
 *
@@ -23,6 +23,9 @@
 #define N_R0_Q8_0 4
 #define N_SG_Q8_0 2

+#define N_R0_MXFP4 2
+#define N_SG_MXFP4 2
+
 #define N_R0_Q2_K 4
 #define N_SG_Q2_K 2

@@ -129,6 +132,15 @@ typedef struct {
    uint64_t o1[8];
 } ggml_metal_kargs_bin;

+typedef struct {
+    int64_t ne0;
+    int64_t ne1;
+    size_t nb01;
+    size_t nb02;
+    size_t nb11;
+    size_t nb21;
+} ggml_metal_kargs_add_id;
+
 typedef struct {
    int32_t  ne00;
    int32_t  ne01;
@@ -444,6 +456,8 @@ typedef struct{
    uint64_t nb1;
    int32_t  i00;
    int32_t  i10;
+    float    alpha;
+    float    limit;
 } ggml_metal_kargs_glu;

 typedef struct {
@@ -195,6 +195,7 @@ enum ggml_metal_kernel_type {
    GGML_METAL_KERNEL_TYPE_MUL_ROW_C4,
    GGML_METAL_KERNEL_TYPE_DIV,
    GGML_METAL_KERNEL_TYPE_DIV_ROW_C4,
+    GGML_METAL_KERNEL_TYPE_ADD_ID,
    GGML_METAL_KERNEL_TYPE_REPEAT_F32,
    GGML_METAL_KERNEL_TYPE_REPEAT_F16,
    GGML_METAL_KERNEL_TYPE_REPEAT_I32,
@@ -234,6 +235,7 @@ enum ggml_metal_kernel_type {
    GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_0,
    GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_1,
    GGML_METAL_KERNEL_TYPE_GET_ROWS_Q8_0,
+    GGML_METAL_KERNEL_TYPE_GET_ROWS_MXFP4,
    GGML_METAL_KERNEL_TYPE_GET_ROWS_Q2_K,
    GGML_METAL_KERNEL_TYPE_GET_ROWS_Q3_K,
    GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_K,
@@ -286,6 +288,7 @@ enum ggml_metal_kernel_type {
    GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_0_F32,
    GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_1_F32,
    GGML_METAL_KERNEL_TYPE_MUL_MV_Q8_0_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MV_MXFP4_F32,
    GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_F16_F32_R1_2,
    GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_F16_F32_R1_3,
    GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_F16_F32_R1_4,
@@ -310,6 +313,10 @@ enum ggml_metal_kernel_type {
    GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q8_0_F32_R1_3,
    GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q8_0_F32_R1_4,
    GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q8_0_F32_R1_5,
+    GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_MXFP4_F32_R1_2,
+    GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_MXFP4_F32_R1_3,
+    GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_MXFP4_F32_R1_4,
+    GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_MXFP4_F32_R1_5,
    GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_K_F32_R1_2,
    GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_K_F32_R1_3,
    GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_K_F32_R1_4,
@@ -351,6 +358,7 @@ enum ggml_metal_kernel_type {
    GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_0_F32,
    GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_1_F32,
    GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q8_0_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MV_ID_MXFP4_F32,
    GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q2_K_F32,
    GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q3_K_F32,
    GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_K_F32,
@@ -373,6 +381,7 @@ enum ggml_metal_kernel_type {
    GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_0_F32,
    GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_1_F32,
    GGML_METAL_KERNEL_TYPE_MUL_MM_Q8_0_F32,
+    GGML_METAL_KERNEL_TYPE_MUL_MM_MXFP4_F32,
    GGML_METAL_KERNEL_TYPE_MUL_MM_Q2_K_F32,
    GGML_METAL_KERNEL_TYPE_MUL_MM_Q3_K_F32,
    GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_K_F32,
@@ -397,6 +406,7 @@ enum ggml_metal_kernel_type {
    GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_0_F16,
    GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_1_F16,
    GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q8_0_F16,
+    GGML_METAL_KERNEL_TYPE_MUL_MM_ID_MXFP4_F16,
    GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q2_K_F16,
    GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q3_K_F16,
    GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_K_F16,
@@ -579,6 +589,7 @@ enum ggml_metal_kernel_type {
    GGML_METAL_KERNEL_TYPE_REGLU,
    GGML_METAL_KERNEL_TYPE_GEGLU,
    GGML_METAL_KERNEL_TYPE_SWIGLU,
+    GGML_METAL_KERNEL_TYPE_SWIGLU_OAI,
    GGML_METAL_KERNEL_TYPE_GEGLU_ERF,
    GGML_METAL_KERNEL_TYPE_GEGLU_QUICK,
    GGML_METAL_KERNEL_TYPE_SUM_ROWS,
@@ -1199,6 +1210,7 @@ static struct ggml_backend_metal_context * ggml_metal_init(ggml_backend_dev_t de
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_ROW_C4,                      mul_row_c4,                      true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIV,                             div,                             true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIV_ROW_C4,                      div_row_c4,                      true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ADD_ID,                          add_id,                          true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_REPEAT_F32,                      repeat_f32,                      true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_REPEAT_F16,                      repeat_f16,                      true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_REPEAT_I32,                      repeat_i32,                      true);
@@ -1238,6 +1250,7 @@ static struct ggml_backend_metal_context * ggml_metal_init(ggml_backend_dev_t de
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_0,                   get_rows_q5_0,                   true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_1,                   get_rows_q5_1,                   true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q8_0,                   get_rows_q8_0,                   true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_MXFP4,                  get_rows_mxfp4,                   true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q2_K,                   get_rows_q2_K,                   true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q3_K,                   get_rows_q3_K,                   true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_K,                   get_rows_q4_K,                   true);
@@ -1290,6 +1303,7 @@ static struct ggml_backend_metal_context * ggml_metal_init(ggml_backend_dev_t de
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_0_F32,                 mul_mv_q5_0_f32,                 has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_1_F32,                 mul_mv_q5_1_f32,                 has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q8_0_F32,                 mul_mv_q8_0_f32,                 has_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_MXFP4_F32,                mul_mv_mxfp4_f32,                has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_F16_F32_R1_2,         mul_mv_ext_f16_f32_r1_2,         has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_F16_F32_R1_3,         mul_mv_ext_f16_f32_r1_3,         has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_F16_F32_R1_4,         mul_mv_ext_f16_f32_r1_4,         has_simdgroup_reduction);
@@ -1314,6 +1328,10 @@ static struct ggml_backend_metal_context * ggml_metal_init(ggml_backend_dev_t de
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q8_0_F32_R1_3,        mul_mv_ext_q8_0_f32_r1_3,        has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q8_0_F32_R1_4,        mul_mv_ext_q8_0_f32_r1_4,        has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q8_0_F32_R1_5,        mul_mv_ext_q8_0_f32_r1_5,        has_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_MXFP4_F32_R1_2,       mul_mv_ext_mxfp4_f32_r1_2,       has_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_MXFP4_F32_R1_3,       mul_mv_ext_mxfp4_f32_r1_3,       has_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_MXFP4_F32_R1_4,       mul_mv_ext_mxfp4_f32_r1_4,       has_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_MXFP4_F32_R1_5,       mul_mv_ext_mxfp4_f32_r1_5,       has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_K_F32_R1_2,        mul_mv_ext_q4_K_f32_r1_2,        has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_K_F32_R1_3,        mul_mv_ext_q4_K_f32_r1_3,        has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_K_F32_R1_4,        mul_mv_ext_q4_K_f32_r1_4,        has_simdgroup_reduction);
@@ -1355,6 +1373,7 @@ static struct ggml_backend_metal_context * ggml_metal_init(ggml_backend_dev_t de
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_0_F32,              mul_mv_id_q5_0_f32,              has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_1_F32,              mul_mv_id_q5_1_f32,              has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q8_0_F32,              mul_mv_id_q8_0_f32,              has_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_MXFP4_F32,             mul_mv_id_mxfp4_f32,             has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q2_K_F32,              mul_mv_id_q2_K_f32,              has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q3_K_F32,              mul_mv_id_q3_K_f32,              has_simdgroup_reduction);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_K_F32,              mul_mv_id_q4_K_f32,              has_simdgroup_reduction);
@@ -1377,6 +1396,8 @@ static struct ggml_backend_metal_context * ggml_metal_init(ggml_backend_dev_t de
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_0_F32,                 mul_mm_q5_0_f32,                 has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_1_F32,                 mul_mm_q5_1_f32,                 has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q8_0_F32,                 mul_mm_q8_0_f32,                 has_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_MXFP4_F32,                mul_mm_mxfp4_f32,                has_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_MXFP4_F32,                mul_mm_mxfp4_f32,                has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q2_K_F32,                 mul_mm_q2_K_f32,                 has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q3_K_F32,                 mul_mm_q3_K_f32,                 has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_K_F32,                 mul_mm_q4_K_f32,                 has_simdgroup_mm);
@@ -1401,6 +1422,7 @@ static struct ggml_backend_metal_context * ggml_metal_init(ggml_backend_dev_t de
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_0_F16,              mul_mm_id_q5_0_f16,              has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_1_F16,              mul_mm_id_q5_1_f16,              has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q8_0_F16,              mul_mm_id_q8_0_f16,              has_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_MXFP4_F16,             mul_mm_id_mxfp4_f16,             has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q2_K_F16,              mul_mm_id_q2_K_f16,              has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q3_K_F16,              mul_mm_id_q3_K_f16,              has_simdgroup_mm);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_K_F16,              mul_mm_id_q4_K_f16,              has_simdgroup_mm);
@@ -1583,6 +1605,7 @@ static struct ggml_backend_metal_context * ggml_metal_init(ggml_backend_dev_t de
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_REGLU,                           reglu,                           true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GEGLU,                           geglu,                           true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SWIGLU,                          swiglu,                          true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SWIGLU_OAI,                      swiglu_oai,                      true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GEGLU_ERF,                       geglu_erf,                       true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GEGLU_QUICK,                     geglu_quick,                     true);
        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SUM_ROWS,                        sum_rows,                        true);
@@ -1774,6 +1797,7 @@ static bool ggml_metal_supports_op(const struct ggml_backend_metal_device_contex
                case GGML_GLU_OP_REGLU:
                case GGML_GLU_OP_GEGLU:
                case GGML_GLU_OP_SWIGLU:
+                case GGML_GLU_OP_SWIGLU_OAI:
                case GGML_GLU_OP_GEGLU_ERF:
                case GGML_GLU_OP_GEGLU_QUICK:
                    return ggml_is_contiguous_1(op->src[0]) && op->src[0]->type == GGML_TYPE_F32;
@@ -1791,6 +1815,7 @@ static bool ggml_metal_supports_op(const struct ggml_backend_metal_device_contex
        case GGML_OP_SUB:
        case GGML_OP_MUL:
        case GGML_OP_DIV:
+        case GGML_OP_ADD_ID:
            return op->src[0]->type == GGML_TYPE_F32;
        case GGML_OP_ACC:
        case GGML_OP_REPEAT:
@@ -2042,6 +2067,7 @@ static int ggml_metal_encode_node(

    const enum ggml_type src0t = src0 ? src0->type : GGML_TYPE_COUNT;
    const enum ggml_type src1t = src1 ? src1->type : GGML_TYPE_COUNT;
+    const enum ggml_type src2t = src2 ? src2->type : GGML_TYPE_COUNT;
    const enum ggml_type dstt  = dst  ? dst->type  : GGML_TYPE_COUNT;

    size_t offs_src0 = 0;
@@ -2291,6 +2317,38 @@ static int ggml_metal_encode_node(
                    [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
                }
            } break;
+        case GGML_OP_ADD_ID:
+            {
+                GGML_ASSERT(src0t == GGML_TYPE_F32);
+                GGML_ASSERT(src1t == GGML_TYPE_F32);
+                GGML_ASSERT(src2t == GGML_TYPE_I32);
+                GGML_ASSERT(dstt  == GGML_TYPE_F32);
+
+                GGML_ASSERT(ggml_is_contiguous_rows(src0));
+
+                id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ADD_ID].pipeline;
+
+                ggml_metal_kargs_add_id args = {
+                    /*.ne0  =*/ ne0,
+                    /*.ne1  =*/ ne1,
+                    /*.nb01 =*/ nb01,
+                    /*.nb02 =*/ nb02,
+                    /*.nb11 =*/ nb11,
+                    /*.nb21 =*/ nb21,
+
+                };
+
+                [encoder setComputePipelineState:pipeline];
+                [encoder setBytes:&args length:sizeof(args) atIndex:0];
+                [encoder setBuffer:id_src0 offset:offs_src0 atIndex:1];
+                [encoder setBuffer:id_src1 offset:offs_src1 atIndex:2];
+                [encoder setBuffer:id_src2 offset:offs_src2 atIndex:3];
+                [encoder setBuffer:id_dst  offset:offs_dst  atIndex:4];
+
+                const int nth = MIN((int) pipeline.maxTotalThreadsPerThreadgroup, ne00);
+
+                [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
+            } break;
        case GGML_OP_REPEAT:
            {
                id<MTLComputePipelineState> pipeline;
@@ -2710,6 +2768,9 @@ static int ggml_metal_encode_node(
                    case GGML_GLU_OP_SWIGLU:
                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SWIGLU].pipeline;
                        break;
+                    case GGML_GLU_OP_SWIGLU_OAI:
+                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SWIGLU_OAI].pipeline;
+                        break;
                    case GGML_GLU_OP_GEGLU_ERF:
                        pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GEGLU_ERF].pipeline;
                        break;
@@ -2720,7 +2781,9 @@ static int ggml_metal_encode_node(
                        GGML_ABORT("fatal error");
                }

-                const int32_t swp = ((const int32_t *) dst->op_params)[1];
+                const int32_t swp = ggml_get_op_params_i32(dst, 1);
+                const float alpha = ggml_get_op_params_f32(dst, 2);
+                const float limit = ggml_get_op_params_f32(dst, 3);

                const int32_t i00 = swp ? ne0 : 0;
                const int32_t i10 = swp ? 0 : ne0;
@@ -2734,6 +2797,8 @@ static int ggml_metal_encode_node(
                    /*.nb1  =*/ nb1,
                    /*.i00  =*/ src1 ? 0 : i00,
                    /*.i10  =*/ src1 ? 0 : i10,
+                    /*.alpha=*/ alpha,
+                    /*.limit=*/ limit
                };

                [encoder setComputePipelineState:pipeline];
@@ -2992,8 +3057,13 @@ static int ggml_metal_encode_node(
                } else {
                    [encoder setBuffer:h_src0 offset:offs_src0  atIndex:1];
                }
-                [encoder setBuffer:id_dst offset:offs_dst       atIndex:2];
-                [encoder setBytes:&args   length:sizeof(args)   atIndex:3];
+                if (id_src2) {
+                    [encoder setBuffer:id_src2 offset:offs_src2 atIndex:2];
+                } else {
+                    [encoder setBuffer:h_src0 offset:offs_src0  atIndex:2];
+                }
+                [encoder setBuffer:id_dst offset:offs_dst       atIndex:3];
+                [encoder setBytes:&args   length:sizeof(args)   atIndex:4];

                [encoder setThreadgroupMemoryLength:32*sizeof(float) atIndex:0];

@@ -3291,6 +3361,7 @@ static int ggml_metal_encode_node(
                       src0t == GGML_TYPE_Q5_0 ||
                       src0t == GGML_TYPE_Q5_1 ||
                       src0t == GGML_TYPE_Q8_0 ||
+                       src0t == GGML_TYPE_MXFP4 ||
                       src0t == GGML_TYPE_IQ4_NL ||
                       false) && (ne11 >= 2 && ne11 <= 8)
                     ) ||
@@ -3383,6 +3454,14 @@ static int ggml_metal_encode_node(
                                case 5: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q8_0_F32_R1_5].pipeline; break;
                                default: GGML_ABORT("not implemented");
                            } break;
+                        case GGML_TYPE_MXFP4:
+                            switch (r1ptg) {
+                                case 2: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_MXFP4_F32_R1_2].pipeline; break;
+                                case 3: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_MXFP4_F32_R1_3].pipeline; break;
+                                case 4: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_MXFP4_F32_R1_4].pipeline; break;
+                                case 5: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_MXFP4_F32_R1_5].pipeline; break;
+                                default: GGML_ABORT("not implemented");
+                            } break;
                        case GGML_TYPE_Q4_K:
                            switch (r1ptg) {
                                case 2: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_EXT_Q4_K_F32_R1_2].pipeline; break;
@@ -3481,6 +3560,7 @@ static int ggml_metal_encode_node(
                        case GGML_TYPE_Q5_0:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_0_F32   ].pipeline; break;
                        case GGML_TYPE_Q5_1:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_1_F32   ].pipeline; break;
                        case GGML_TYPE_Q8_0:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q8_0_F32   ].pipeline; break;
+                        case GGML_TYPE_MXFP4:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_MXFP4_F32   ].pipeline; break;
                        case GGML_TYPE_Q2_K:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q2_K_F32   ].pipeline; break;
                        case GGML_TYPE_Q3_K:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q3_K_F32   ].pipeline; break;
                        case GGML_TYPE_Q4_K:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_K_F32   ].pipeline; break;
@@ -3623,6 +3703,13 @@ static int ggml_metal_encode_node(
                                nr0 = N_R0_Q8_0;
                                pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q8_0_F32].pipeline;
                            } break;
+                        case GGML_TYPE_MXFP4:
+                            {
+                                nsg = N_SG_MXFP4;
+                                nr0 = N_R0_MXFP4;
+                                smem = 32*sizeof(float);
+                                pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_MXFP4_F32].pipeline;
+                            } break;
                        case GGML_TYPE_Q2_K:
                            {
                                nsg = N_SG_Q2_K;
@@ -3756,8 +3843,6 @@ static int ggml_metal_encode_node(
        case GGML_OP_MUL_MAT_ID:
            {
                // src2 = ids
-                const enum ggml_type src2t = src2->type; GGML_UNUSED(src2t);
-
                GGML_ASSERT(src2t == GGML_TYPE_I32);

                GGML_ASSERT(!ggml_is_transposed(src0));
@@ -3883,6 +3968,7 @@ static int ggml_metal_encode_node(
                            case GGML_TYPE_Q5_0:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_0_F16   ].pipeline; break;
                            case GGML_TYPE_Q5_1:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_1_F16   ].pipeline; break;
                            case GGML_TYPE_Q8_0:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q8_0_F16   ].pipeline; break;
+                            case GGML_TYPE_MXFP4:   pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_MXFP4_F16  ].pipeline; break;
                            case GGML_TYPE_Q2_K:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q2_K_F16   ].pipeline; break;
                            case GGML_TYPE_Q3_K:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q3_K_F16   ].pipeline; break;
                            case GGML_TYPE_Q4_K:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_K_F16   ].pipeline; break;
@@ -4018,6 +4104,13 @@ static int ggml_metal_encode_node(
                                nr0 = N_R0_Q8_0;
                                pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q8_0_F32].pipeline;
                            } break;
+                        case GGML_TYPE_MXFP4:
+                            {
+                                nsg = N_SG_MXFP4;
+                                nr0 = N_R0_MXFP4;
+                                smem = 32*sizeof(float);
+                                pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_MXFP4_F32].pipeline;
+                            } break;
                        case GGML_TYPE_Q2_K:
                            {
                                nsg = N_SG_Q2_K;
@@ -4170,6 +4263,7 @@ static int ggml_metal_encode_node(
                    case GGML_TYPE_Q5_0:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_0   ].pipeline; break;
                    case GGML_TYPE_Q5_1:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_1   ].pipeline; break;
                    case GGML_TYPE_Q8_0:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q8_0   ].pipeline; break;
+                    case GGML_TYPE_MXFP4:   pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_MXFP4  ].pipeline; break;
                    case GGML_TYPE_Q2_K:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q2_K   ].pipeline; break;
                    case GGML_TYPE_Q3_K:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q3_K   ].pipeline; break;
                    case GGML_TYPE_Q4_K:    pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_K   ].pipeline; break;
@@ -4980,11 +5074,14 @@ static int ggml_metal_encode_node(
                GGML_ASSERT(ne11 == ne21);
                GGML_ASSERT(ne12 == ne22);

-                struct ggml_tensor * src3 = node->src[3];
+                struct ggml_tensor * src3 = node->src[3]; // mask
+                struct ggml_tensor * src4 = node->src[4]; // sinks

                size_t offs_src3 = 0;
+                size_t offs_src4 = 0;

                id<MTLBuffer> id_src3 = src3 ? ggml_metal_get_buffer(src3, &offs_src3) : nil;
+                id<MTLBuffer> id_src4 = src4 ? ggml_metal_get_buffer(src4, &offs_src4) : nil;

                GGML_ASSERT(!src3 || src3->type == GGML_TYPE_F16);
                GGML_ASSERT(!src3 || src3->ne[1] >= GGML_PAD(src0->ne[1], 8) &&
@@ -5000,8 +5097,6 @@ static int ggml_metal_encode_node(
                const uint64_t nb32 = src3 ? src3->nb[2] : 0; GGML_UNUSED(nb32);
                const uint64_t nb33 = src3 ? src3->nb[3] : 0; GGML_UNUSED(nb33);

-                const enum ggml_type src2t = src2 ? src2->type : GGML_TYPE_COUNT; GGML_UNUSED(src2t);
-
                float scale;
                float max_bias;
                float logit_softcap;
@@ -5389,7 +5484,12 @@ static int ggml_metal_encode_node(
                } else {
                    [encoder setBuffer:id_src0 offset:offs_src0 atIndex:4];
                }
-                [encoder setBuffer:id_dst offset:offs_dst       atIndex:5];
+                if (id_src4) {
+                    [encoder setBuffer:id_src4 offset:offs_src4 atIndex:5];
+                } else {
+                    [encoder setBuffer:id_src0 offset:offs_src0 atIndex:5];
+                }
+                [encoder setBuffer:id_dst offset:offs_dst       atIndex:6];

                if (!use_vec_kernel) {
                    // half8x8 kernel
@@ -35,6 +35,10 @@ constexpr constant static float kvalues_iq4nl_f[16] = {
    -127.f, -104.f, -83.f, -65.f, -49.f, -35.f, -22.f, -10.f, 1.f, 13.f, 25.f, 38.f, 53.f, 69.f, 89.f, 113.f
 };

+constexpr constant static float kvalues_mxfp4_f[16] = {
+    0, .5f, 1.f, 1.5f, 2.f, 3.f, 4.f, 6.f, -0, -.5f, -1.f, -1.5f, -2.f, -3.f, -4.f, -6.f
+};
+
 static inline int best_index_int8(int n, constant float * val, float x) {
    if (x <= val[0]) return 0;
    if (x >= val[n-1]) return n-1;
@@ -46,6 +50,18 @@ static inline int best_index_int8(int n, constant float * val, float x) {
    return x - val[mu-1] < val[mu] - x ? mu-1 : mu;
 }

+static inline float e8m0_to_fp32(uint8_t x) {
+    uint32_t bits;
+
+    if (x == 0) {
+        bits = 0x00400000;
+    } else {
+        bits = (uint32_t) x << 23;
+    }
+
+    return as_type<float>(bits);
+}
+
 // NOTE: this is not dequantizing - we are simply fitting the template
 template <typename type4x4>
 void dequantize_f32(device const float4x4 * src, short il, thread type4x4 & reg) {
@@ -242,6 +258,27 @@ void quantize_q5_1(device const float * src, device block_q5_1 & dst) {
    }
 }

+void quantize_q8_0(device const float * src, device block_q8_0 & dst) {
+#pragma METAL fp math_mode(safe)
+    float amax = 0.0f; // absolute max
+
+    for (int j = 0; j < QK8_0; j++) {
+        const float v = src[j];
+        amax = MAX(amax, fabs(v));
+    }
+
+    const float d = amax / ((1 << 7) - 1);
+    const float id = d ? 1.0f/d : 0.0f;
+
+    dst.d = d;
+
+    for (int j = 0; j < QK8_0; ++j) {
+        const float x0 = src[j]*id;
+
+        dst.qs[j] = round(x0);
+    }
+}
+
 void quantize_iq4_nl(device const float * src, device block_iq4_nl & dst) {
 #pragma METAL fp math_mode(safe)
    float amax = 0.0f; // absolute max
@@ -462,25 +499,34 @@ void dequantize_q8_0_t4(device const block_q8_0 *xb, short il, thread type4 & re
    }
 }

-void quantize_q8_0(device const float * src, device block_q8_0 & dst) {
-#pragma METAL fp math_mode(safe)
-    float amax = 0.0f; // absolute max
+template <typename type4x4>
+void dequantize_mxfp4(device const block_mxfp4 * xb, short il, thread type4x4 & reg) {
+    device const uint8_t * q2 = (device const uint8_t *)xb->qs;

-    for (int j = 0; j < QK8_0; j++) {
-        const float v = src[j];
-        amax = MAX(amax, fabs(v));
+    const float d = e8m0_to_fp32(xb->e);
+    const uint8_t shr = il >= 1 ? 4 : 0;
+
+    for (int i = 0; i < 4; ++i) {
+        reg[i][0] = d * kvalues_mxfp4_f[(q2[4*i + 0] >> shr) & 0x0F];
+        reg[i][1] = d * kvalues_mxfp4_f[(q2[4*i + 1] >> shr) & 0x0F];
+        reg[i][2] = d * kvalues_mxfp4_f[(q2[4*i + 2] >> shr) & 0x0F];
+        reg[i][3] = d * kvalues_mxfp4_f[(q2[4*i + 3] >> shr) & 0x0F];
    }
+}

-    const float d = amax / ((1 << 7) - 1);
-    const float id = d ? 1.0f/d : 0.0f;
+template <typename type4>
+void dequantize_mxfp4_t4(device const block_mxfp4 * xb, short il, thread type4 & reg) {
+    device const uint8_t * q2 = (device const uint8_t *)xb->qs;

-    dst.d = d;
+    const float d = e8m0_to_fp32(xb->e);
+    const short il4 = il%4;

-    for (int j = 0; j < QK8_0; ++j) {
-        const float x0 = src[j]*id;
+    const uint8_t shr = il >= 4 ? 4 : 0;

-        dst.qs[j] = round(x0);
-    }
+    reg[0] = d * kvalues_mxfp4_f[(q2[4*il4 + 0] >> shr) & 0x0F];
+    reg[1] = d * kvalues_mxfp4_f[(q2[4*il4 + 1] >> shr) & 0x0F];
+    reg[2] = d * kvalues_mxfp4_f[(q2[4*il4 + 2] >> shr) & 0x0F];
+    reg[3] = d * kvalues_mxfp4_f[(q2[4*il4 + 3] >> shr) & 0x0F];
 }

 template <typename type4x4>
@@ -960,6 +1006,32 @@ kernel void kernel_div(
    }
 }

+kernel void kernel_add_id(
+        constant ggml_metal_kargs_add_id & args,
+        device const char * src0,
+        device const char * src1,
+        device const char * src2,
+        device       char * dst,
+        uint3   tgpig[[threadgroup_position_in_grid]],
+        ushort3 tpitg[[thread_position_in_threadgroup]],
+        ushort3   ntg[[threads_per_threadgroup]]) {
+    const int i1 = tgpig.x;
+    const int i2 = tgpig.y;
+
+    const int i11 = *((device const int32_t *) (src2 + i1*sizeof(int32_t) + i2*args.nb21));
+
+    const size_t nb1 = args.ne0 * sizeof(float);
+    const size_t nb2 = args.ne1 * nb1;
+
+    device       float * dst_row  = (device       float *)((device char *)dst + i1*nb1 + i2*nb2);
+    device const float * src0_row = (device const float *)((device char *)src0 +  i1*args.nb01 + i2*args.nb02);
+    device const float * src1_row = (device const float *)((device char *)src1 + i11*args.nb11);
+
+    for (int i0 = tpitg.x; i0 < args.ne0; i0 += ntg.x) {
+        dst_row[i0] = src0_row[i0] + src1_row[i0];
+    }
+}
+
 template<typename T>
 kernel void kernel_repeat(
        constant ggml_metal_kargs_repeat & args,
@@ -1431,6 +1503,32 @@ kernel void kernel_swiglu(
    }
 }

+kernel void kernel_swiglu_oai(
+        device const char * src0,
+        device const char * src1,
+        device       char * dst,
+        constant ggml_metal_kargs_glu & args,
+        uint tgpig[[threadgroup_position_in_grid]],
+        uint tpitg[[thread_position_in_threadgroup]],
+        uint   ntg[[threads_per_threadgroup]]) {
+    device const float * src0_row = (device const float *) ((device const char *) src0 + tgpig*args.nb01) + args.i00;
+    device const float * src1_row = (device const float *) ((device const char *) src1 + tgpig*args.nb11) + args.i10;
+    device       float * dst_row  = (device       float *) ((device       char *) dst  + tgpig*args.nb1);
+
+    for (int i0 = tpitg; i0 < args.ne0; i0 += ntg) {
+        float x0 = src0_row[i0];
+        float x1 = src1_row[i0];
+
+        x0 = min(x0, args.limit);
+        x1 = max(min(x1, args.limit), -args.limit);
+
+        float out_glu = x0 / (1.0f + exp(-x0 * args.alpha));
+        out_glu = out_glu * (1.0f + x1);
+
+        dst_row[i0] = out_glu;
+    }
+}
+
 kernel void kernel_geglu_erf(
        device const char * src0,
        device const char * src1,
@@ -1534,6 +1632,7 @@ template<typename T>
 kernel void kernel_soft_max(
        device const  char * src0,
        device const  char * src1,
+        device const  char * src2,
        device        char * dst,
        constant ggml_metal_kargs_soft_max & args,
        threadgroup  float * buf [[threadgroup(0)]],
@@ -1552,6 +1651,7 @@ kernel void kernel_soft_max(

    device const float * psrc0 =                (device const float *) (src0 + i01*args.nb01 + i02*args.nb02 + i03*args.nb03);
    device const     T * pmask = src1 != src0 ? (device const T *    ) (src1 + i11*args.nb11 + i12*args.nb12 + i13*args.nb13) : nullptr;
+    device const float * psrc2 = src2 != src0 ? (device const float *) (src2)                                                 : nullptr;
    device       float * pdst  =                (device       float *) (dst  + i01*args.nb1  + i02*args.nb2  + i03*args.nb3);

    float slope = 1.0f;
@@ -1567,7 +1667,7 @@ kernel void kernel_soft_max(
    }

    // parallel max
-    float lmax = -INFINITY;
+    float lmax = psrc2 ? psrc2[i02] : -INFINITY;

    for (int i00 = tpitg.x; i00 < args.ne00; i00 += tptg.x) {
        lmax = MAX(lmax, psrc0[i00]*args.scale + (pmask ? slope*pmask[i00] : 0.0f));
@@ -1623,6 +1723,10 @@ kernel void kernel_soft_max(
        sum = simd_sum(sum);
    }

+    if (psrc2) {
+        sum += exp(psrc2[i02] - max_val);
+    }
+
    const float inv_sum = 1.0f/sum;

    for (int i00 = tpitg.x; i00 < args.ne00; i00 += tptg.x) {
@@ -1634,6 +1738,7 @@ template<typename T>
 kernel void kernel_soft_max_4(
        device const  char * src0,
        device const  char * src1,
+        device const  char * src2,
        device        char * dst,
        constant ggml_metal_kargs_soft_max & args,
        threadgroup  float * buf [[threadgroup(0)]],
@@ -1652,6 +1757,7 @@ kernel void kernel_soft_max_4(

    device const float4 * psrc4 =                (device const float4 *) (src0 + i01*args.nb01 + i02*args.nb02 + i03*args.nb03);
    device const      T * pmask = src1 != src0 ? (device const T *     ) (src1 + i11*args.nb11 + i12*args.nb12 + i13*args.nb13) : nullptr;
+    device const float *  psrc2 = src2 != src0 ? (device const float * ) (src2)                                                 : nullptr;
    device       float4 * pdst4 =                (device       float4 *) (dst  + i01*args.nb1  + i02*args.nb2  + i03*args.nb3);

    float slope = 1.0f;
@@ -1666,7 +1772,7 @@ kernel void kernel_soft_max_4(
    }

    // parallel max
-    float4 lmax4 = -INFINITY;
+    float4 lmax4 = psrc2 ? psrc2[i02] : -INFINITY;

    for (int i00 = tpitg.x; i00 < args.ne00/4; i00 += tptg.x) {
        lmax4 = fmax(lmax4, psrc4[i00]*args.scale + (float4)((pmask ? slope*pmask[i00] : 0.0f)));
@@ -1725,6 +1831,10 @@ kernel void kernel_soft_max_4(
        sum = simd_sum(sum);
    }

+    if (psrc2) {
+        sum += exp(psrc2[i02] - max_val);
+    }
+
    const float inv_sum = 1.0f/sum;

    for (int i00 = tpitg.x; i00 < args.ne00/4; i00 += tptg.x) {
@@ -3106,6 +3216,11 @@ template [[host_name("kernel_mul_mv_ext_q8_0_f32_r1_3")]]   kernel mul_mv_ext_q4
 template [[host_name("kernel_mul_mv_ext_q8_0_f32_r1_4")]]   kernel mul_mv_ext_q4_f32_t kernel_mul_mv_ext_q4_f32_disp<4, block_q8_0,   32, dequantize_q8_0_t4>;
 template [[host_name("kernel_mul_mv_ext_q8_0_f32_r1_5")]]   kernel mul_mv_ext_q4_f32_t kernel_mul_mv_ext_q4_f32_disp<5, block_q8_0,   32, dequantize_q8_0_t4>;

+template [[host_name("kernel_mul_mv_ext_mxfp4_f32_r1_2")]]  kernel mul_mv_ext_q4_f32_t kernel_mul_mv_ext_q4_f32_disp<2, block_mxfp4,  32, dequantize_mxfp4_t4>;
+template [[host_name("kernel_mul_mv_ext_mxfp4_f32_r1_3")]]  kernel mul_mv_ext_q4_f32_t kernel_mul_mv_ext_q4_f32_disp<3, block_mxfp4,  32, dequantize_mxfp4_t4>;
+template [[host_name("kernel_mul_mv_ext_mxfp4_f32_r1_4")]]  kernel mul_mv_ext_q4_f32_t kernel_mul_mv_ext_q4_f32_disp<4, block_mxfp4,  32, dequantize_mxfp4_t4>;
+template [[host_name("kernel_mul_mv_ext_mxfp4_f32_r1_5")]]  kernel mul_mv_ext_q4_f32_t kernel_mul_mv_ext_q4_f32_disp<5, block_mxfp4,  32, dequantize_mxfp4_t4>;
+
 template [[host_name("kernel_mul_mv_ext_iq4_nl_f32_r1_2")]] kernel mul_mv_ext_q4_f32_t kernel_mul_mv_ext_q4_f32_disp<2, block_iq4_nl, 32, dequantize_iq4_nl_t4>;
 template [[host_name("kernel_mul_mv_ext_iq4_nl_f32_r1_3")]] kernel mul_mv_ext_q4_f32_t kernel_mul_mv_ext_q4_f32_disp<3, block_iq4_nl, 32, dequantize_iq4_nl_t4>;
 template [[host_name("kernel_mul_mv_ext_iq4_nl_f32_r1_4")]] kernel mul_mv_ext_q4_f32_t kernel_mul_mv_ext_q4_f32_disp<4, block_iq4_nl, 32, dequantize_iq4_nl_t4>;
@@ -4092,6 +4207,7 @@ kernel void kernel_flash_attn_ext(
        device const char * k,
        device const char * v,
        device const char * mask,
+        device const char * sinks,
        device       char * dst,
        threadgroup  half * shmem_f16 [[threadgroup(0)]],
        uint3   tgpig[[threadgroup_position_in_grid]],
@@ -4407,6 +4523,35 @@ kernel void kernel_flash_attn_ext(
            }
        }

+        if (sinks != q && sgitg == 0) {
+            for (ushort j = 0; j < Q; ++j) {
+                const float m = M[j];
+                const float s = tiisg == 0 ? ((device const float *) sinks)[iq2] : -FLT_MAX/2;
+
+                M[j] = simd_max(max(M[j], s));
+
+                const float ms = exp(m - M[j]);
+                const float vs = exp(s - M[j]);
+
+                S[j] = S[j]*ms + simd_sum(vs);
+
+                if (tiisg == j) {
+                    ss[j*TS + 2*C + j] = ms;
+                }
+            }
+
+            // O = diag(ms)*O
+            {
+                s8x8_t ms;
+                simdgroup_load(ms, ss + 2*C, TS, 0, false);
+
+                #pragma unroll(DV8)
+                for (short i = 0; i < DV8; ++i) {
+                    simdgroup_multiply(lo[i], ms, lo[i]);
+                }
+            }
+        }
+
        // these are needed for reducing the results from the simdgroups (reuse the ss buffer)
        for (short j = tiisg; j < Q; j += NW) {
            ss[j*TS + 0] = S[j];
@@ -4618,6 +4763,7 @@ kernel void kernel_flash_attn_ext_vec(
        device const char * k,
        device const char * v,
        device const char * mask,
+        device const char * sinks,
        device       char * dst,
        threadgroup  half * shmem_f16 [[threadgroup(0)]],
        uint3   tgpig[[threadgroup_position_in_grid]],
@@ -4835,6 +4981,23 @@ kernel void kernel_flash_attn_ext_vec(
            }
        }

+        if (sinks != q && sgitg == 0) {
+            const float m = M;
+            const float s = tiisg == 0 ? ((device const float *) sinks)[iq2] : -FLT_MAX/2;
+
+            M = simd_max(max(M, s));
+
+            const float ms = exp(m - M);
+            const float vs = exp(s - M);
+
+            S = S*ms + simd_sum(vs);
+
+#pragma unroll(DV4/NL)
+            for (short ii = 0; ii < DV4; ii += NL) {
+                lo[ii/NL] *= ms;
+            }
+        }
+
        // these are needed for reducing the results from the simdgroups (reuse the ss buffer)
        if (tiisg == 0) {
            ss[0] = (s_t) S;
@@ -6940,6 +7103,95 @@ kernel void kernel_mul_mv_iq4_xs_f32(
    kernel_mul_mv_iq4_xs_f32_impl<N_R0_IQ4_XS, N_SG_IQ4_XS, N_SIMDWIDTH, constant ggml_metal_kargs_mul_mv &>(args, src0, src1, dst, shmem, tgpig, tiisg, sgitg);
 }

+template<int nr0, int nsg, int nw, typename args_t>
+void kernel_mul_mv_mxfp4_f32_impl(
+        args_t args,
+        device const char * src0,
+        device const char * src1,
+        device       char * dst,
+        threadgroup  char * shmem,
+        uint3  tgpig,
+        ushort tiisg,
+        ushort sgitg) {
+
+    threadgroup float * shmem_f32 = (threadgroup float *) shmem;
+    const int nb = args.ne00/QK_MXFP4;
+
+    const int r0 = tgpig.x;
+    const int r1 = tgpig.y;
+    const int im = tgpig.z;
+
+    const int first_row = (r0 * nsg + sgitg) * nr0;
+
+    const uint i12 = im%args.ne12;
+    const uint i13 = im/args.ne12;
+
+    const uint64_t offset0 = first_row*args.nb01 + (i12/args.r2)*args.nb02 + (i13/args.r3)*args.nb03;
+    const uint64_t offset1 =        r1*args.nb11 + (i12        )*args.nb12 + (i13        )*args.nb13;
+
+    device const block_mxfp4 * x = (device const block_mxfp4 *) (src0 + offset0);
+    device const float       * y = (device const float       *) (src1 + offset1);
+
+    const short ix = tiisg/2;  // 0...15
+    const short it = tiisg%2;  // 0 or 1
+
+    shmem_f32[tiisg] = kvalues_mxfp4_f[tiisg%16];
+    threadgroup_barrier(mem_flags::mem_threadgroup);
+
+    float4 yl[4];
+    float sumf[nr0]={0.f};
+
+    device const float * yb = y + ix * QK_MXFP4 + it * 8;
+
+    for (int ib = ix; ib < nb; ib += 16) {
+        device const float4 * y4 = (device const float4 *)yb;
+        yl[0] = y4[0];
+        yl[1] = y4[4];
+        yl[2] = y4[1];
+        yl[3] = y4[5];
+
+#pragma unroll(nr0)
+        for (short row = 0; row < nr0; row++) {
+            device const block_mxfp4 & xb = x[row*nb + ib];
+            device const uint8_t     * q2 = (device const uint8_t *)(xb.qs + 8*it);
+
+            float4 acc1 = yl[0]*float4(shmem_f32[q2[0] &  0x0F], shmem_f32[q2[1] &  0x0F], shmem_f32[q2[2] &  0x0F], shmem_f32[q2[3] &  0x0F]);
+            float4 acc2 = yl[1]*float4(shmem_f32[q2[0] >> 4   ], shmem_f32[q2[1] >> 4   ], shmem_f32[q2[2] >> 4   ], shmem_f32[q2[3] >> 4   ]);
+            float4 acc3 = yl[2]*float4(shmem_f32[q2[4] &  0x0F], shmem_f32[q2[5] &  0x0F], shmem_f32[q2[6] &  0x0F], shmem_f32[q2[7] &  0x0F]);
+            float4 acc4 = yl[3]*float4(shmem_f32[q2[4] >> 4   ], shmem_f32[q2[5] >> 4   ], shmem_f32[q2[6] >> 4   ], shmem_f32[q2[7] >> 4   ]);
+
+            acc1 = (acc1 + acc3) + (acc2 + acc4);
+
+            sumf[row] += e8m0_to_fp32(xb.e) * ((acc1[0] + acc1[1]) + (acc1[2] + acc1[3]));
+        }
+
+        yb += 16 * QK_MXFP4;
+    }
+
+    device float * dst_f32 = (device float *) dst + (uint64_t)im*args.ne0*args.ne1 + (uint64_t)r1*args.ne0;
+
+    for (int row = 0; row < nr0 && first_row + row < args.ne0; ++row) {
+        float sum_all = simd_sum(sumf[row]);
+        if (tiisg == 0) {
+            dst_f32[first_row + row] = sum_all;
+        }
+    }
+}
+
+[[host_name("kernel_mul_mv_mxfp4_f32")]]
+kernel void kernel_mul_mv_mxfp4_f32(
+        constant ggml_metal_kargs_mul_mv & args,
+        device const char * src0,
+        device const char * src1,
+        device       char * dst,
+        threadgroup  char * shmem [[threadgroup(0)]],
+        uint3  tgpig[[threadgroup_position_in_grid]],
+        ushort tiisg[[thread_index_in_simdgroup]],
+        ushort sgitg[[simdgroup_index_in_threadgroup]]) {
+
+    kernel_mul_mv_mxfp4_f32_impl<N_R0_MXFP4, N_SG_MXFP4, N_SIMDWIDTH, constant ggml_metal_kargs_mul_mv &>(args, src0, src1, dst, shmem, tgpig, tiisg, sgitg);
+}
+
 template<typename block_q, short nl, void (*dequantize_func)(device const block_q *, short, thread float4x4 &)>
 kernel void kernel_get_rows_q(
        constant ggml_metal_kargs_get_rows & args,
@@ -7475,6 +7727,7 @@ template [[host_name("kernel_get_rows_q4_1")]]    kernel get_rows_q_t kernel_get
 template [[host_name("kernel_get_rows_q5_0")]]    kernel get_rows_q_t kernel_get_rows_q<block_q5_0,    2, dequantize_q5_0>;
 template [[host_name("kernel_get_rows_q5_1")]]    kernel get_rows_q_t kernel_get_rows_q<block_q5_1,    2, dequantize_q5_1>;
 template [[host_name("kernel_get_rows_q8_0")]]    kernel get_rows_q_t kernel_get_rows_q<block_q8_0,    2, dequantize_q8_0>;
+template [[host_name("kernel_get_rows_mxfp4")]]   kernel get_rows_q_t kernel_get_rows_q<block_mxfp4,   2, dequantize_mxfp4>;
 template [[host_name("kernel_get_rows_q2_K")]]    kernel get_rows_q_t kernel_get_rows_q<block_q2_K,    QK_NL, dequantize_q2_K>;
 template [[host_name("kernel_get_rows_q3_K")]]    kernel get_rows_q_t kernel_get_rows_q<block_q3_K,    QK_NL, dequantize_q3_K>;
 template [[host_name("kernel_get_rows_q4_K")]]    kernel get_rows_q_t kernel_get_rows_q<block_q4_K,    QK_NL, dequantize_q4_K>;
@@ -7527,6 +7780,7 @@ template [[host_name("kernel_mul_mm_q4_1_f32")]]    kernel mul_mm_t kernel_mul_m
 template [[host_name("kernel_mul_mm_q5_0_f32")]]    kernel mul_mm_t kernel_mul_mm<half,   half4x4,   simdgroup_half8x8,   block_q5_0,    2,     dequantize_q5_0>;
 template [[host_name("kernel_mul_mm_q5_1_f32")]]    kernel mul_mm_t kernel_mul_mm<half,   half4x4,   simdgroup_half8x8,   block_q5_1,    2,     dequantize_q5_1>;
 template [[host_name("kernel_mul_mm_q8_0_f32")]]    kernel mul_mm_t kernel_mul_mm<half,   half4x4,   simdgroup_half8x8,   block_q8_0,    2,     dequantize_q8_0>;
+template [[host_name("kernel_mul_mm_mxfp4_f32")]]   kernel mul_mm_t kernel_mul_mm<half,   half4x4,   simdgroup_half8x8,   block_mxfp4,   2,     dequantize_mxfp4>;
 template [[host_name("kernel_mul_mm_q2_K_f32")]]    kernel mul_mm_t kernel_mul_mm<half,   half4x4,   simdgroup_half8x8,   block_q2_K,    QK_NL, dequantize_q2_K>;
 template [[host_name("kernel_mul_mm_q3_K_f32")]]    kernel mul_mm_t kernel_mul_mm<half,   half4x4,   simdgroup_half8x8,   block_q3_K,    QK_NL, dequantize_q3_K>;
 template [[host_name("kernel_mul_mm_q4_K_f32")]]    kernel mul_mm_t kernel_mul_mm<half,   half4x4,   simdgroup_half8x8,   block_q4_K,    QK_NL, dequantize_q4_K>;
@@ -7558,6 +7812,7 @@ template [[host_name("kernel_mul_mm_id_q4_1_f16")]]    kernel mul_mm_id kernel_m
 template [[host_name("kernel_mul_mm_id_q5_0_f16")]]    kernel mul_mm_id kernel_mul_mm_id<half,   half4x4,   simdgroup_half8x8,   block_q5_0,    2,     dequantize_q5_0>;
 template [[host_name("kernel_mul_mm_id_q5_1_f16")]]    kernel mul_mm_id kernel_mul_mm_id<half,   half4x4,   simdgroup_half8x8,   block_q5_1,    2,     dequantize_q5_1>;
 template [[host_name("kernel_mul_mm_id_q8_0_f16")]]    kernel mul_mm_id kernel_mul_mm_id<half,   half4x4,   simdgroup_half8x8,   block_q8_0,    2,     dequantize_q8_0>;
+template [[host_name("kernel_mul_mm_id_mxfp4_f16")]]   kernel mul_mm_id kernel_mul_mm_id<half,   half4x4,   simdgroup_half8x8,   block_mxfp4,   2,     dequantize_mxfp4>;
 template [[host_name("kernel_mul_mm_id_q2_K_f16")]]    kernel mul_mm_id kernel_mul_mm_id<half,   half4x4,   simdgroup_half8x8,   block_q2_K,    QK_NL, dequantize_q2_K>;
 template [[host_name("kernel_mul_mm_id_q3_K_f16")]]    kernel mul_mm_id kernel_mul_mm_id<half,   half4x4,   simdgroup_half8x8,   block_q3_K,    QK_NL, dequantize_q3_K>;
 template [[host_name("kernel_mul_mm_id_q4_K_f16")]]    kernel mul_mm_id kernel_mul_mm_id<half,   half4x4,   simdgroup_half8x8,   block_q4_K,    QK_NL, dequantize_q4_K>;
@@ -7703,6 +7958,8 @@ template [[host_name("kernel_mul_mv_id_q4_1_f32")]]    kernel kernel_mul_mv_id_t
 template [[host_name("kernel_mul_mv_id_q5_0_f32")]]    kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<mul_vec_q_n_f32_impl<block_q5_0, N_R0_Q5_0, N_SG_Q5_0, N_SIMDWIDTH>>>;
 template [[host_name("kernel_mul_mv_id_q5_1_f32")]]    kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<mul_vec_q_n_f32_impl<block_q5_1, N_R0_Q5_1, N_SG_Q5_1, N_SIMDWIDTH>>>;

+template [[host_name("kernel_mul_mv_id_mxfp4_f32")]]   kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_mxfp4_f32_impl<N_R0_MXFP4, N_SG_MXFP4, N_SIMDWIDTH>>>;
+
 template [[host_name("kernel_mul_mv_id_q2_K_f32")]]    kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_q2_K_f32_impl   <N_R0_Q2_K,    N_SG_Q2_K,    N_SIMDWIDTH>>>;
 template [[host_name("kernel_mul_mv_id_q3_K_f32")]]    kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_q3_K_f32_impl   <N_R0_Q3_K,    N_SG_Q3_K,    N_SIMDWIDTH>>>;
 template [[host_name("kernel_mul_mv_id_q4_K_f32")]]    kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_q4_K_f32_impl   <N_R0_Q4_K,    N_SG_Q4_K,    N_SIMDWIDTH>>>;
@@ -55,6 +55,7 @@ endfunction()

 set(GGML_OPENCL_KERNELS
    add
+    add_id
    argsort
    clamp
    cpy
@@ -345,6 +345,7 @@ struct ggml_backend_opencl_context {
    cl_command_queue queue;

    cl_program program_add;
+    cl_program program_add_id;
    cl_program program_clamp;
    cl_program program_cpy;
    cl_program program_cvt;
@@ -404,6 +405,7 @@ struct ggml_backend_opencl_context {
    cl_kernel kernel_mul, kernel_mul_row, kernel_mul_f16, kernel_mul_row_f16;
    cl_kernel kernel_div, kernel_div_row, kernel_div_f16, kernel_div_row_f16;
    cl_kernel kernel_sub, kernel_sub_row, kernel_sub_f16, kernel_sub_row_f16;
+    cl_kernel kernel_add_id;
    cl_kernel kernel_scale;
    cl_kernel kernel_silu, kernel_silu_4;
    cl_kernel kernel_gelu, kernel_gelu_4;
@@ -412,7 +414,7 @@ struct ggml_backend_opencl_context {
    cl_kernel kernel_relu;
    cl_kernel kernel_sigmoid_f32, kernel_sigmoid_f16;
    cl_kernel kernel_clamp;
-    cl_kernel kernel_geglu, kernel_reglu, kernel_swiglu, kernel_geglu_erf, kernel_geglu_quick,
+    cl_kernel kernel_geglu, kernel_reglu, kernel_swiglu, kernel_swiglu_oai, kernel_geglu_erf, kernel_geglu_quick,
              kernel_geglu_f16, kernel_reglu_f16, kernel_swiglu_f16, kernel_geglu_erf_f16, kernel_geglu_quick_f16;
    cl_kernel kernel_norm;
    cl_kernel kernel_rms_norm, kernel_rms_norm_mul;
@@ -681,6 +683,22 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve
        GGML_LOG_CONT(".");
    }

+    // add_id
+    {
+#ifdef GGML_OPENCL_EMBED_KERNELS
+        const std::string kernel_src {
+            #include "add_id.cl.h"
+        };
+#else
+        const std::string kernel_src = read_file("add_id.cl");
+#endif
+        backend_ctx->program_add_id =
+            build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts);
+
+        CL_CHECK((backend_ctx->kernel_add_id = clCreateKernel(backend_ctx->program_add_id, "kernel_add_id", &err), err));
+        GGML_LOG_CONT(".");
+    }
+
    // clamp
    {
 #ifdef GGML_OPENCL_EMBED_KERNELS
@@ -787,6 +805,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve
        CL_CHECK((backend_ctx->kernel_geglu           = clCreateKernel(backend_ctx->program_glu, "kernel_geglu", &err), err));
        CL_CHECK((backend_ctx->kernel_reglu           = clCreateKernel(backend_ctx->program_glu, "kernel_reglu", &err), err));
        CL_CHECK((backend_ctx->kernel_swiglu          = clCreateKernel(backend_ctx->program_glu, "kernel_swiglu", &err), err));
+        CL_CHECK((backend_ctx->kernel_swiglu_oai      = clCreateKernel(backend_ctx->program_glu, "kernel_swiglu_oai", &err), err));
        CL_CHECK((backend_ctx->kernel_geglu_erf       = clCreateKernel(backend_ctx->program_glu, "kernel_geglu_erf", &err), err));
        CL_CHECK((backend_ctx->kernel_geglu_quick     = clCreateKernel(backend_ctx->program_glu, "kernel_geglu_quick", &err), err));
        CL_CHECK((backend_ctx->kernel_geglu_f16       = clCreateKernel(backend_ctx->program_glu, "kernel_geglu_f16", &err), err));
@@ -2046,8 +2065,8 @@ static ggml_backend_opencl_context * ggml_cl2_init(ggml_backend_dev_t dev) {

    backend_ctx->adreno_cl_compiler_version = get_adreno_cl_compiler_version(driver_version);
    backend_ctx->has_vector_subgroup_broadcast =
-        backend_ctx->adreno_cl_compiler_version.major >= 47 ||
-        backend_ctx->adreno_cl_compiler_version.major == 17;
+        (backend_ctx->adreno_cl_compiler_version.type == E031 && backend_ctx->adreno_cl_compiler_version.major >= 47) ||
+        (backend_ctx->adreno_cl_compiler_version.type == DX   && backend_ctx->adreno_cl_compiler_version.major >= 17);
    GGML_LOG_INFO("ggml_opencl: vector subgroup broadcast support: %s\n",
        backend_ctx->has_vector_subgroup_broadcast ? "true" : "false");

@@ -2467,6 +2486,8 @@ static bool ggml_opencl_supports_op(ggml_backend_dev_t dev, const struct ggml_te
            return (op->src[0]->type == op->src[1]->type) &&
                   (op->src[0]->type == op->type) &&
                   (op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16);
+        case GGML_OP_ADD_ID:
+            return op->src[0]->type == GGML_TYPE_F32;
        case GGML_OP_UNARY:
            switch (ggml_get_unary_op(op)) {
                case GGML_UNARY_OP_GELU:
@@ -2488,6 +2509,7 @@ static bool ggml_opencl_supports_op(ggml_backend_dev_t dev, const struct ggml_te
                case GGML_GLU_OP_GEGLU:
                case GGML_GLU_OP_REGLU:
                case GGML_GLU_OP_SWIGLU:
+                case GGML_GLU_OP_SWIGLU_OAI:
                case GGML_GLU_OP_GEGLU_ERF:
                case GGML_GLU_OP_GEGLU_QUICK:
                    return ggml_is_contiguous_1(op->src[0]) && (op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16);
@@ -2497,6 +2519,8 @@ static bool ggml_opencl_supports_op(ggml_backend_dev_t dev, const struct ggml_te
        case GGML_OP_CLAMP:
            return op->src[0]->type == GGML_TYPE_F32;
        case GGML_OP_SOFT_MAX:
+            // TODO: support attention sinks [TAG_ATTN_SINKS]
+            return op->src[2] == nullptr;
        case GGML_OP_NORM:
        case GGML_OP_RMS_NORM:
            return true;
@@ -3822,6 +3846,75 @@ static void ggml_cl_add(ggml_backend_t backend, const ggml_tensor * src0, const
    }
 }

+static void ggml_cl_add_id(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_ASSERT(src0);
+    GGML_ASSERT(src0->extra);
+    GGML_ASSERT(src1);
+    GGML_ASSERT(src1->extra);
+    GGML_ASSERT(dst);
+    GGML_ASSERT(dst->extra);
+
+    const ggml_tensor * src2 = dst->src[2];
+    GGML_ASSERT(src2);
+    GGML_ASSERT(src2->extra);
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT(src1->type == GGML_TYPE_F32);
+    GGML_ASSERT(src2->type == GGML_TYPE_I32);
+    GGML_ASSERT(dst->type  == GGML_TYPE_F32);
+
+    GGML_ASSERT(ggml_is_contiguous_rows(src0));
+
+    const int ne00 = src0->ne[0];
+    const int ne01 = src0->ne[1];
+    const int ne02 = src0->ne[2];
+
+    const cl_ulong nb01 = src0->nb[1];
+    const cl_ulong nb02 = src0->nb[2];
+
+    const cl_ulong nb11 = src1->nb[1];
+
+    const cl_ulong nb21 = src2->nb[1];
+
+    const int ne0 = dst->ne[0];
+    const int ne1 = dst->ne[1];
+
+    ggml_backend_opencl_context *backend_ctx = (ggml_backend_opencl_context *)backend->context;
+
+    ggml_tensor_extra_cl * extra0 = (ggml_tensor_extra_cl *)src0->extra;
+    ggml_tensor_extra_cl * extra1 = (ggml_tensor_extra_cl *)src1->extra;
+    ggml_tensor_extra_cl * extra2 = (ggml_tensor_extra_cl *)src2->extra;
+    ggml_tensor_extra_cl * extrad = (ggml_tensor_extra_cl *)dst->extra;
+
+    cl_ulong offset0 = extra0->offset + src0->view_offs;
+    cl_ulong offset1 = extra1->offset + src1->view_offs;
+    cl_ulong offset2 = extra2->offset + src2->view_offs;
+    cl_ulong offsetd = extrad->offset + dst->view_offs;
+
+    cl_kernel kernel = backend_ctx->kernel_add_id;
+
+    CL_CHECK(clSetKernelArg(kernel,  0, sizeof(cl_mem),   &extra0->data_device));
+    CL_CHECK(clSetKernelArg(kernel,  1, sizeof(cl_ulong), &offset0));
+    CL_CHECK(clSetKernelArg(kernel,  2, sizeof(cl_mem),   &extra1->data_device));
+    CL_CHECK(clSetKernelArg(kernel,  3, sizeof(cl_ulong), &offset1));
+    CL_CHECK(clSetKernelArg(kernel,  4, sizeof(cl_mem),   &extra2->data_device));
+    CL_CHECK(clSetKernelArg(kernel,  5, sizeof(cl_ulong), &offset2));
+    CL_CHECK(clSetKernelArg(kernel,  6, sizeof(cl_mem),   &extrad->data_device));
+    CL_CHECK(clSetKernelArg(kernel,  7, sizeof(cl_ulong), &offsetd));
+    CL_CHECK(clSetKernelArg(kernel,  8, sizeof(cl_ulong), &nb01));
+    CL_CHECK(clSetKernelArg(kernel,  9, sizeof(cl_ulong), &nb02));
+    CL_CHECK(clSetKernelArg(kernel, 10, sizeof(cl_ulong), &nb11));
+    CL_CHECK(clSetKernelArg(kernel, 11, sizeof(cl_ulong), &nb21));
+    CL_CHECK(clSetKernelArg(kernel, 12, sizeof(int),      &ne0));
+    CL_CHECK(clSetKernelArg(kernel, 13, sizeof(int),      &ne1));
+
+    int nth = MIN(ne00, (int) backend_ctx->get_kernel_workgroup_size(kernel));
+    size_t global_work_size[] = { (size_t)ne01*nth, (size_t)ne02, 1 };
+    size_t local_work_size[] = { (size_t)nth, 1, 1 };
+
+    backend_ctx->enqueue_ndrange_kernel(kernel, 3, global_work_size, local_work_size, dst);
+}
+
 static void ggml_cl_mul(ggml_backend_t backend, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
    GGML_ASSERT(src0);
    GGML_ASSERT(src0->extra);
@@ -7003,6 +7096,9 @@ static void ggml_cl_glu(ggml_backend_t backend, const ggml_tensor * src0, const
                kernel = backend_ctx->kernel_swiglu_f16;
            }
            break;
+        case GGML_GLU_OP_SWIGLU_OAI:
+            kernel = backend_ctx->kernel_swiglu_oai;
+            break;
        case GGML_GLU_OP_GEGLU_ERF:
            if (dst->type == GGML_TYPE_F32) {
                kernel = backend_ctx->kernel_geglu_erf;
@@ -7038,7 +7134,10 @@ static void ggml_cl_glu(ggml_backend_t backend, const ggml_tensor * src0, const

    const cl_ulong nb1  = dst->nb[1];

-    const int swp = ((const int32_t *) dst->op_params)[1];
+    const int   swp   = ggml_get_op_params_i32(dst, 1);
+    const float alpha = ggml_get_op_params_f32(dst, 2);
+    const float limit = ggml_get_op_params_f32(dst, 3);
+
    const int ne00_off = src1 ? 0 : (swp ? ne0 : 0);
    const int ne10_off = src1 ? 0 : (swp ? 0 : ne0);

@@ -7055,6 +7154,11 @@ static void ggml_cl_glu(ggml_backend_t backend, const ggml_tensor * src0, const
    CL_CHECK(clSetKernelArg(kernel, 10, sizeof(int),      &ne00_off));
    CL_CHECK(clSetKernelArg(kernel, 11, sizeof(int),      &ne10_off));

+    if (ggml_get_glu_op(dst) == GGML_GLU_OP_SWIGLU_OAI) {
+        CL_CHECK(clSetKernelArg(kernel, 12, sizeof(float), &limit));
+        CL_CHECK(clSetKernelArg(kernel, 13, sizeof(float), &alpha));
+    }
+
    const size_t nrows = ggml_nrows(src0);
    size_t nth = 512;
    size_t global_work_size[] = {nrows*nth, 1, 1};
@@ -7111,6 +7215,12 @@ bool ggml_cl_compute_forward(ggml_backend_t backend, struct ggml_tensor * tensor
            }
            func = ggml_cl_add;
            break;
+        case GGML_OP_ADD_ID:
+            if (!any_on_device) {
+                return false;
+            }
+            func = ggml_cl_add_id;
+            break;
        case GGML_OP_MUL:
            if (!any_on_device) {
                return false;
@@ -0,0 +1,42 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+
+//------------------------------------------------------------------------------
+// add_id
+//------------------------------------------------------------------------------
+kernel void kernel_add_id(
+    global char * src0,
+    ulong         offset0,
+    global char * src1,
+    ulong         offset1,
+    global char * src2,
+    ulong         offset2,
+    global char * dst,
+    ulong         offsetd,
+    ulong         nb01,
+    ulong         nb02,
+    ulong         nb11,
+    ulong         nb21,
+    int           ne0,
+    int           ne1
+) {
+    src0 = (global char*)((global char*)src0 + offset0);
+    src1 = (global char*)((global char*)src1 + offset1);
+    src2 = (global char*)((global char*)src2 + offset2);
+    dst  = (global char*)((global char*)dst  + offsetd);
+
+    int i1 = get_group_id(0);
+    int i2 = get_group_id(1);
+
+    const int i11 = *((global const int *) (src2 + i1*sizeof(int) + i2*nb21));
+
+    const size_t nb1 = ne0 * sizeof(float);
+    const size_t nb2 = ne1 * nb1;
+
+    global float * dst_row  = (global float *)((global char *)dst  + i1*nb1 + i2*nb2);
+    global float * src0_row = (global float *)((global char *)src0 + i1*nb01 + i2*nb02);
+    global float * src1_row = (global float *)((global char *)src1 + i11*nb11);
+
+    for (int i0 = get_local_id(0); i0 < ne0; i0 += get_local_size(0)) {
+        dst_row[i0] = src0_row[i0] + src1_row[i0];
+    }
+}
@@ -202,6 +202,47 @@ kernel void kernel_swiglu_f16(
    }
 }

+//------------------------------------------------------------------------------
+// swiglu_oai
+//------------------------------------------------------------------------------
+kernel void kernel_swiglu_oai(
+    global char * src0,
+    ulong         offset0,
+    global char * src1,
+    ulong         offset1,
+    global char * dst,
+    ulong         offsetd,
+    ulong         nb01,
+    ulong         nb11,
+    int           ne0,
+    ulong         nb1,
+    int           ne00_off,
+    int           ne10_off,
+    float         limit,
+    float         alpha
+) {
+    src0 = (global char*)((global char*)src0 + offset0);
+    src1 = (global char*)((global char*)src1 + offset1);
+    dst  = (global char*)((global char*)dst  + offsetd);
+
+    global float * src0_row = (global float *) ((global char *) src0 + get_group_id(0)*nb01) + ne00_off;
+    global float * src1_row = (global float *) ((global char *) src1 + get_group_id(0)*nb11) + ne10_off;
+    global float * dst_row  = (global float *) ((global char *) dst  + get_group_id(0)*nb1);
+
+    for (int i0 = get_local_id(0); i0 < ne0; i0 += get_local_size(0)) {
+        float x0 = src0_row[i0];
+        float x1 = src1_row[i0];
+
+        x0 = min(x0, limit);
+        x1 = max(min(x1, limit), -limit);
+
+        float out_glu = x0 / (1.0f + exp(-x0 * alpha));
+        out_glu = out_glu * (1.0f + x1);
+
+        dst_row[i0] = out_glu;
+    }
+}
+
 //------------------------------------------------------------------------------
 // geglu_erf
 //------------------------------------------------------------------------------
@@ -21,6 +21,17 @@

 #define UNUSED GGML_UNUSED

+static inline int best_index_int8(int n, const int8_t * val, float x) {
+    if (x <= val[0]) return 0;
+    if (x >= val[n-1]) return n-1;
+    int ml = 0, mu = n-1;
+    while (mu-ml > 1) {
+        int mav = (ml+mu)/2;
+        if (x < val[mav]) mu = mav; else ml = mav;
+    }
+    return x - val[mu-1] < val[mu] - x ? mu-1 : mu;
+}
+
 // reference implementation for deterministic creation of model files
 void quantize_row_q4_0_ref(const float * GGML_RESTRICT x, block_q4_0 * GGML_RESTRICT y, int64_t k) {
    static const int qk = QK4_0;
@@ -246,6 +257,53 @@ void quantize_row_q8_1_ref(const float * GGML_RESTRICT x, block_q8_1 * GGML_REST
    }
 }

+static inline int best_index_mxfp4(float x, float e) {
+    int best_index = 0;
+    float best_err = fabsf(kvalues_mxfp4[0]*e - x);
+    for (int i = 1; i < 16; i++) {
+        float err = fabsf(kvalues_mxfp4[i]*e - x);
+        if (err < best_err) {
+            best_index = i;
+            best_err = err;
+        }
+    }
+    return best_index;
+}
+
+void quantize_row_mxfp4_ref(const float * GGML_RESTRICT x, block_mxfp4 * GGML_RESTRICT y, int64_t k) {
+    static const int qk = QK_MXFP4;
+
+    assert(k % qk == 0);
+
+    const int nb = k / qk;
+
+    for (int i = 0; i < nb; i++) {
+        float amax = 0.0f; // absolute max
+
+        for (int j = 0; j < qk; j++) {
+            const float v = x[i*qk + j];
+
+            if (amax < fabsf(v)) {
+                amax = fabsf(v);
+            }
+        }
+
+        const uint8_t e = (uint8_t) (floorf(log2f(amax)) - 2 + 127);
+
+        const float d = GGML_E8M0_TO_FP32_HALF(e);
+
+        y[i].e = e;
+
+        for (int j = 0; j < qk/2; ++j) {
+            const uint8_t x0 = best_index_mxfp4(x[i*qk + 0    + j], d);
+            const uint8_t x1 = best_index_mxfp4(x[i*qk + qk/2 + j], d);
+
+            y[i].qs[j]  = x0;
+            y[i].qs[j] |= x1 << 4;
+        }
+    }
+}
+
 void dequantize_row_q4_0(const block_q4_0 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k) {
    static const int qk = QK4_0;

@@ -356,6 +414,26 @@ void dequantize_row_q8_0(const block_q8_0 * GGML_RESTRICT x, float * GGML_RESTRI
    }
 }

+void dequantize_row_mxfp4(const block_mxfp4 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k) {
+    static const int qk = QK_MXFP4;
+
+    assert(k % qk == 0);
+
+    const int nb = k / qk;
+
+    for (int i = 0; i < nb; i++) {
+        const float d = GGML_E8M0_TO_FP32_HALF(x[i].e);
+
+        for (int j = 0; j < qk/2; ++j) {
+            const int8_t x0 = kvalues_mxfp4[x[i].qs[j] & 0x0F];
+            const int8_t x1 = kvalues_mxfp4[x[i].qs[j] >>   4];
+
+            y[i*qk + j + 0   ] = x0*d;
+            y[i*qk + j + qk/2] = x1*d;
+        }
+    }
+}
+
 //
 // 2-6 bit quantization in super-blocks
 //
@@ -2014,6 +2092,12 @@ size_t quantize_q8_0(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst,
    return nrow * row_size;
 }

+size_t quantize_mxfp4(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) {
+    GGML_UNUSED(quant_weights);
+    quantize_row_mxfp4_ref(src, dst, (int64_t)nrow*n_per_row);
+    return nrow * ggml_row_size(GGML_TYPE_MXFP4, n_per_row);
+}
+
 // ====================== Ternary (de)-quantization (BitNet b1.58 and TriLMs)

 void quantize_row_tq1_0_ref(const float * GGML_RESTRICT x, block_tq1_0 * GGML_RESTRICT y, int64_t k) {
@@ -4551,17 +4635,6 @@ size_t quantize_iq1_m(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst,

 // ============================ 4-bit non-linear quants

-static inline int best_index_int8(int n, const int8_t * val, float x) {
-    if (x <= val[0]) return 0;
-    if (x >= val[n-1]) return n-1;
-    int ml = 0, mu = n-1;
-    while (mu-ml > 1) {
-        int mav = (ml+mu)/2;
-        if (x < val[mav]) mu = mav; else ml = mav;
-    }
-    return x - val[mu-1] < val[mu] - x ? mu-1 : mu;
-}
-
 static void quantize_row_iq4_nl_impl(const int super_block_size, const int block_size, const float * GGML_RESTRICT x,
        ggml_fp16_t * dh, uint8_t * q4, uint16_t * scales_h, uint8_t * scales_l,
        float * scales, float * weight, uint8_t * L,
@@ -4961,6 +5034,15 @@ static bool validate_fp16(ggml_fp16_t f, size_t i) {
    return true;
 }

+static bool validate_e_e8m0(uint8_t e, size_t i) {
+    if (e == 0xff) {
+        fprintf(stderr, "ggml_validate_row_data: found invalid e value %d at block %zu\n", e, i);
+        return false;
+    }
+
+    return true;
+}
+
 #define VALIDATE_ROW_DATA_D_F16_IMPL(type, data, nb) \
    const type * q = (const type *) (data); \
    for (size_t i = 0; i < (nb); ++i) { \
@@ -4977,6 +5059,14 @@ static bool validate_fp16(ggml_fp16_t f, size_t i) {
        } \
    }

+#define VALIDATE_ROW_DATA_E_E8M0_IMPL(type, data, nb) \
+    const type * q = (const type *) (data); \
+    for (size_t i = 0; i < (nb); ++i) { \
+        if (!validate_e_e8m0(q[i].e, i)) { \
+            return false; \
+        } \
+    }
+
 #define VALIDATE_ROW_DATA_DVEC_F16_IMPL(type, data, nb, nr) \
    const type * q = (const type *) (data); \
    for (size_t i = 0; i < (nb); ++i) { \
@@ -5130,6 +5220,10 @@ bool ggml_validate_row_data(enum ggml_type type, const void * data, size_t nbyte
            {
                VALIDATE_ROW_DATA_D_F16_IMPL(block_q8_0, data, nb);
            } break;
+        case GGML_TYPE_MXFP4:
+            {
+                VALIDATE_ROW_DATA_E_E8M0_IMPL(block_mxfp4, data, nb);
+            } break;
        case GGML_TYPE_Q2_K:
            {
                VALIDATE_ROW_DATA_DM_F16_IMPL(block_q2_K, data, nb, d, dmin);
@@ -21,6 +21,8 @@ GGML_API void quantize_row_q5_1_ref(const float * GGML_RESTRICT x, block_q5_1 *
 GGML_API void quantize_row_q8_0_ref(const float * GGML_RESTRICT x, block_q8_0 * GGML_RESTRICT y, int64_t k);
 GGML_API void quantize_row_q8_1_ref(const float * GGML_RESTRICT x, block_q8_1 * GGML_RESTRICT y, int64_t k);

+GGML_API void quantize_row_mxfp4_ref(const float * GGML_RESTRICT x, block_mxfp4 * GGML_RESTRICT y, int64_t k);
+
 GGML_API void quantize_row_q2_K_ref(const float * GGML_RESTRICT x, block_q2_K * GGML_RESTRICT y, int64_t k);
 GGML_API void quantize_row_q3_K_ref(const float * GGML_RESTRICT x, block_q3_K * GGML_RESTRICT y, int64_t k);
 GGML_API void quantize_row_q4_K_ref(const float * GGML_RESTRICT x, block_q4_K * GGML_RESTRICT y, int64_t k);
@@ -45,6 +47,8 @@ GGML_API void dequantize_row_q5_1(const block_q5_1 * GGML_RESTRICT x, float * GG
 GGML_API void dequantize_row_q8_0(const block_q8_0 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
 //GGML_API void dequantize_row_q8_1(const block_q8_1 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);

+GGML_API void dequantize_row_mxfp4(const block_mxfp4 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
+
 GGML_API void dequantize_row_q2_K(const block_q2_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
 GGML_API void dequantize_row_q3_K(const block_q3_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
 GGML_API void dequantize_row_q4_K(const block_q4_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
@@ -90,6 +94,8 @@ GGML_API size_t quantize_q5_0(const float * GGML_RESTRICT src, void * GGML_RESTR
 GGML_API size_t quantize_q5_1(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
 GGML_API size_t quantize_q8_0(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);

+GGML_API size_t quantize_mxfp4(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
+
 GGML_API void iq2xs_init_impl(enum ggml_type type);
 GGML_API void iq2xs_free_impl(enum ggml_type type);
 GGML_API void iq3xs_init_impl(int grid_size);
@@ -2609,6 +2609,8 @@ static void ggml_sycl_mul_mat_vec_nc(ggml_backend_sycl_context & ctx, const ggml
    GGML_ASSERT(!ggml_backend_buffer_is_sycl_split(src0->buffer));
    GGML_ASSERT(src0->type == GGML_TYPE_F16);
    GGML_ASSERT(src1->type == GGML_TYPE_F32);
+    GGML_ASSERT(src1->ne[1] == 1);
+    GGML_ASSERT(src1->ne[3] == 1);

    const int64_t ne00 = src0->ne[0];
    const int64_t ne01 = src0->ne[1];
@@ -2688,6 +2690,9 @@ static void ggml_sycl_mul_mat_batched_sycl(ggml_backend_sycl_context & ctx, cons
    const size_t       type_size_src0 = ggml_type_size(src0->type);
    const size_t       type_size_src1 = ggml_type_size(src1->type);

+    bool is_src0_cont_2 = ggml_is_contiguous_2(src0);
+    bool is_src1_cont_2 = ggml_is_contiguous_2(src1);
+
    // SRC1 strides
    int64_t                          s11 = nb11 / type_size_src1;
    int64_t                          s12 = nb12 / type_size_src1;
@@ -2737,6 +2742,8 @@ static void ggml_sycl_mul_mat_batched_sycl(ggml_backend_sycl_context & ctx, cons
        s11      = ne10;
        s12      = ne11 * s11;
        s13      = ne12 * s12;
+
+        is_src1_cont_2 = true;
    }

    ggml_sycl_pool_alloc<sycl::half> dst_f16(ctx.pool());
@@ -2852,12 +2859,16 @@ static void ggml_sycl_mul_mat_batched_sycl(ggml_backend_sycl_context & ctx, cons
    else
 #endif
    {
-        if (r2 == 1 && r3 == 1 && ggml_is_contiguous_2(src0) && ggml_is_contiguous_2(src1)) {
+        if (r2 == 1 && r3 == 1 && is_src0_cont_2 && is_src1_cont_2) {
+            // with a [0, 2, 1, 3] perm. and ne02==1 the matrix strides need to be determined from dim 3:
+            const int64_t sma = ne02 == 1 ? nb03/nb00 : nb02/nb00;
+            const int64_t smb = ne12 == 1 ? s13       : s12;
+
            // there is no broadcast and src0, src1 are contiguous across dims 2, 3
            SYCL_CHECK(CHECK_TRY_ERROR(dpct::gemm_batch(*queue, oneapi::math::transpose::trans,
                                                        oneapi::math::transpose::nontrans, ne01, ne11, ne10, alpha,
-                                                        src0_f16, dpct::library_data_t::real_half, nb01 / nb00, nb02 / nb00,
-                                                        src1_f16, dpct::library_data_t::real_half, s11, s12, beta, dst_ddf,
+                                                        src0_f16, dpct::library_data_t::real_half, nb01 / nb00, sma,
+                                                        src1_f16, dpct::library_data_t::real_half, s11, smb, beta, dst_ddf,
                                                        mkl_data_type, ne0, ne1 * ne0, ne12 * ne13, mkl_compute_type)));
        } else {
            const int ne23 = ne12 * ne13;
@@ -3187,7 +3198,7 @@ static void ggml_sycl_mul_mat(ggml_backend_sycl_context & ctx, const ggml_tensor
            // The kernel from the if path is faster for that specific case, but does not support all mul mats.
            ggml_sycl_mul_mat_batched_sycl(ctx, src0, src1, dst);
        }
-    } else if (!split && src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && src1->ne[1] == 1) {
+    } else if (!split && src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && src1->ne[1] == 1 && src1->ne[3] == 1) {
        // KQV single-batch
        ggml_sycl_mul_mat_vec_nc(ctx, src0, src1, dst);
    } else if (!split && src0->type == GGML_TYPE_F16 && !ggml_is_transposed(src0) && !ggml_is_transposed(src1) && src1->ne[2] * src1->ne[3] > 1) {
@@ -4182,15 +4193,9 @@ static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const g
        case GGML_OP_MUL_MAT:
        case GGML_OP_MUL_MAT_ID:
            {
-                struct ggml_tensor * a;
-                struct ggml_tensor * b;
-                if (op->op == GGML_OP_MUL_MAT) {
-                    a = op->src[0];
-                    b = op->src[1];
-                } else {
-                    a = op->src[2];
-                    b = op->src[1];
-                }
+                struct ggml_tensor * a = op->src[0];
+                struct ggml_tensor * b = op->src[1];
+
                if (a->ne[3] != b->ne[3]) {
                    return false;
                }
@@ -4205,7 +4210,9 @@ static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const g
                    }
                }
                ggml_type src0_type = op->src[0]->type;
-                if (src0_type == GGML_TYPE_BF16) {
+                if (src0_type == GGML_TYPE_BF16 || src0_type == GGML_TYPE_MXFP4) {
+                    // TODO: support MXFP4
+                    // FIXME: keep a list of supported types to avoid breaking the backend when a new type is added
                    return false;
                }
                return true;
@@ -4350,6 +4357,10 @@ static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const g
            if (op->src[0]->ne[3] != 1) {
                return false;
            }
+            // TODO: support attention sinks [TAG_ATTN_SINKS]
+            if (op->src[2]) {
+                return false;
+            }
            // TODO: support broadcast
            // ref: https://github.com/ggml-org/llama.cpp/pull/14435
            return !op->src[1] || (op->src[1]->ne[2] == 1 && op->src[1]->ne[3] == 1);
@@ -222,6 +222,7 @@ enum vk_device_architecture {
    AMD_RDNA2,
    AMD_RDNA3,
    INTEL_XE2,
+    NVIDIA_PRE_TURING,
 };

 // HSK x HSV
@@ -315,10 +316,33 @@ static vk_device_architecture get_device_architecture(const vk::PhysicalDevice&
            // https://www.intel.com/content/www/us/en/docs/oneapi/optimization-guide-gpu/2025-0/intel-xe-gpu-architecture.html
            return vk_device_architecture::INTEL_XE2;
        }
+    } else if (props.vendorID == VK_VENDOR_ID_NVIDIA) {
+        const std::vector<vk::ExtensionProperties> ext_props = device.enumerateDeviceExtensionProperties();
+
+        bool cooperative_matrix = false;
+
+        // Detect "pre-turing" based on lack of coopmat support.
+        for (const auto& properties : ext_props) {
+            if (strcmp("VK_KHR_cooperative_matrix", properties.extensionName) == 0) {
+                cooperative_matrix = true;
+                break;
+            }
+        }
+
+        if (!cooperative_matrix) {
+            return vk_device_architecture::NVIDIA_PRE_TURING;
+        }
    }
    return vk_device_architecture::OTHER;
 }

+enum vk_conv_shapes {
+    CONV_SHAPE_128x128,
+    CONV_SHAPE_64x32,
+    CONV_SHAPE_32x256,
+    CONV_SHAPE_COUNT,
+};
+
 struct vk_device_struct {
    std::recursive_mutex mutex;

@@ -425,6 +449,8 @@ struct vk_device_struct {
    vk_pipeline pipeline_div[2][2][2];
    vk_pipeline pipeline_div_norepeat[2][2][2];

+    vk_pipeline pipeline_add_id_f32;
+
    vk_pipeline pipeline_concat_f32, pipeline_concat_f16, pipeline_concat_i32;
    vk_pipeline pipeline_upscale_nearest_f32, pipeline_upscale_bilinear_f32, pipeline_upscale_bilinear_ac_f32;
    vk_pipeline pipeline_scale_f32;
@@ -459,6 +485,7 @@ struct vk_device_struct {
    vk_pipeline pipeline_geglu[2];
    vk_pipeline pipeline_reglu[2];
    vk_pipeline pipeline_swiglu[2];
+    vk_pipeline pipeline_swiglu_oai[2];
    vk_pipeline pipeline_geglu_erf[2];
    vk_pipeline pipeline_geglu_quick[2];

@@ -483,8 +510,8 @@ struct vk_device_struct {
    vk_pipeline pipeline_rwkv_wkv6_f32;
    vk_pipeline pipeline_rwkv_wkv7_f32;
    vk_pipeline pipeline_opt_step_adamw_f32;
-    vk_pipeline pipeline_conv2d_f32;
-    vk_pipeline pipeline_conv2d_f16_f32;
+    vk_pipeline pipeline_conv2d_f32[CONV_SHAPE_COUNT];
+    vk_pipeline pipeline_conv2d_f16_f32[CONV_SHAPE_COUNT];
    vk_pipeline pipeline_conv2d_dw_whcn_f32;
    vk_pipeline pipeline_conv2d_dw_cwhn_f32;

@@ -681,6 +708,8 @@ struct vk_op_glu_push_constants {
    uint32_t ne00;
    uint32_t ne20;
    uint32_t mode;  // 0: default, 1: swapped, 2: split
+    float alpha; // for swiglu_oai
+    float limit;
 };

 struct vk_op_unary_push_constants {
@@ -770,6 +799,15 @@ struct vk_op_binary_push_constants {
    float param1; float param2; int32_t param3;
 };

+struct vk_op_add_id_push_constants {
+    uint32_t ne0;
+    uint32_t ne1;
+    uint32_t s01;
+    uint32_t s02;
+    uint32_t s11;
+    uint32_t s21;
+};
+
 struct vk_op_diag_mask_push_constants {
    uint32_t ncols;
    uint32_t rows_per_channel;
@@ -811,6 +849,7 @@ struct vk_op_soft_max_push_constants {
    float m1;
    uint32_t n_head_log2;
    uint32_t nrows_x;
+    uint32_t has_sinks;
 };

 struct vk_op_argsort_push_constants {
@@ -908,8 +947,22 @@ struct vk_op_conv2d_push_constants {
    uint32_t nb1;
    uint32_t nb2;
    uint32_t nb3;
+
+    // init_fastdiv_values constants for dividing by KW, KW*KH, OW, OW*OH
+    uint32_t KWmp;   uint32_t KWL;
+    uint32_t KWKHmp; uint32_t KWKHL;
+    uint32_t OWmp;   uint32_t OWL;
+    uint32_t OWOHmp; uint32_t OWOHL;
 };

+template <> void init_pushconst_fastdiv(vk_op_conv2d_push_constants &p) {
+    // Compute magic values to divide by KW, KW*KH, OW, OW*OH
+    init_fastdiv_values(p.KW,       p.KWmp,    p.KWL);
+    init_fastdiv_values(p.KW*p.KH,  p.KWKHmp,  p.KWKHL);
+    init_fastdiv_values(p.OW,       p.OWmp,    p.OWL);
+    init_fastdiv_values(p.OW*p.OH,  p.OWOHmp,  p.OWOHL);
+}
+
 struct vk_op_conv2d_dw_push_constants {
    uint32_t ne;
    uint32_t batches;
@@ -1939,6 +1992,7 @@ static bool ggml_vk_matmul_shmem_support(const vk_device& device, const std::vec
        break;
    case GGML_TYPE_IQ4_NL:
    case GGML_TYPE_IQ4_XS:
+    case GGML_TYPE_MXFP4:
        lut_size = 4*16;
        break;
    default:
@@ -2068,12 +2122,12 @@ static void ggml_vk_load_shaders(vk_device& device) {
        s_mmq_wg_denoms = { 32,  64,  1 };

        // spec constants and tile sizes for quant matmul (Qi_K)
-        l_warptile_mmq_k = { 256, 64, 128, 64,  1 };
-        m_warptile_mmq_k = { 256, 32,  64, 64,  0 };
-        s_warptile_mmq_k = { 256, 32,  32, 128, 0 };
-        l_mmq_wg_denoms_k = { 64, 128, 1 };
-        m_mmq_wg_denoms_k = { 32,  64, 1 };
-        s_mmq_wg_denoms_k = { 32,  32, 1 };
+        l_warptile_mmq_k = { 256, 128, 256, 64, 1 };
+        m_warptile_mmq_k = { 256, 128, 128, 64, 1 };
+        s_warptile_mmq_k = { 256, 32,  64, 128, 0 };
+        l_mmq_wg_denoms_k = { 128, 256, 1 };
+        m_mmq_wg_denoms_k = { 128, 128, 1 };
+        s_mmq_wg_denoms_k = { 32,  64,  1 };

        // spec constants and tile sizes for quant matmul_id
        l_warptile_mmqid = { 256, 128, 128, 16, 0 };
@@ -2315,6 +2369,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
        CREATE_MM2(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_IQ3_S],   matmul_iq3_s_f16,   mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3)
        CREATE_MM2(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_IQ4_XS],  matmul_iq4_xs_f16,  mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3)
        CREATE_MM2(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_IQ4_NL],  matmul_iq4_nl_f16,  mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3)
+        CREATE_MM2(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_MXFP4],   matmul_mxfp4_f16,   mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3)

        CREATE_MM2(pipeline_matmul_id_f16, matmul_id_f16, wg_denoms, warptile, vk_mat_mat_id_push_constants, 4)
 #if defined(GGML_VULKAN_BFLOAT16_GLSLC_SUPPORT)
@@ -2341,6 +2396,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
        CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_S].f16acc,   matmul_id_iq3_s_f16,   , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
        CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_XS].f16acc,  matmul_id_iq4_xs_f16,  , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
        CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL].f16acc,  matmul_id_iq4_nl_f16,  , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
+        CREATE_MM(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_MXFP4].f16acc,   matmul_id_mxfp4_f16,   , mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 4)
 #undef CREATE_MM
 #undef CREATE_MM2
    } else
@@ -2402,6 +2458,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
            CREATE_MM2(GGML_TYPE_IQ3_S,   pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ3_S],   matmul_iq3_s_f32,   mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
            CREATE_MM2(GGML_TYPE_IQ4_XS,  pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_XS],  matmul_iq4_xs_f32,  mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
            CREATE_MM2(GGML_TYPE_IQ4_NL,  pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL],  matmul_iq4_nl_f32,  mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM2(GGML_TYPE_MXFP4,   pipeline_dequant_mul_mat_mat[GGML_TYPE_MXFP4],   matmul_mxfp4_f32,   mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
        } else {
            CREATE_MM(GGML_TYPE_Q4_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0].f32acc, matmul_q4_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
            CREATE_MM(GGML_TYPE_Q4_1, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1].f32acc, matmul_q4_1_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
@@ -2423,6 +2480,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
            CREATE_MM(GGML_TYPE_IQ3_S,   pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ3_S].f32acc,   matmul_iq3_s_f32,   , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
            CREATE_MM(GGML_TYPE_IQ4_XS,  pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_XS].f32acc,  matmul_iq4_xs_f32,  , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
            CREATE_MM(GGML_TYPE_IQ4_NL,  pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL].f32acc,  matmul_iq4_nl_f32,  , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+            CREATE_MM(GGML_TYPE_MXFP4,   pipeline_dequant_mul_mat_mat[GGML_TYPE_MXFP4].f32acc,   matmul_mxfp4_f32,   , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
        }

        CREATE_MM(GGML_TYPE_F32, pipeline_matmul_id_f32, matmul_id_f32_f32, , wg_denoms, warptile, vk_mat_mat_push_constants, 4, _id);
@@ -2455,6 +2513,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
            CREATE_MM(GGML_TYPE_IQ3_S,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_S].f16acc,   matmul_id_iq3_s_f32,   _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
            CREATE_MM(GGML_TYPE_IQ4_XS,  pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_XS].f16acc,  matmul_id_iq4_xs_f32,  _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
            CREATE_MM(GGML_TYPE_IQ4_NL,  pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL].f16acc,  matmul_id_iq4_nl_f32,  _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+            CREATE_MM(GGML_TYPE_MXFP4,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_MXFP4].f16acc,   matmul_id_mxfp4_f32,   _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
        } else {
            CREATE_MM(GGML_TYPE_Q4_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0].f16acc, matmul_id_q4_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
            CREATE_MM(GGML_TYPE_Q4_1, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1].f16acc, matmul_id_q4_1_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
@@ -2476,6 +2535,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
            CREATE_MM(GGML_TYPE_IQ3_S,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_S].f16acc,   matmul_id_iq3_s_f32,   , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
            CREATE_MM(GGML_TYPE_IQ4_XS,  pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_XS].f16acc,  matmul_id_iq4_xs_f32,  , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
            CREATE_MM(GGML_TYPE_IQ4_NL,  pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL].f16acc,  matmul_id_iq4_nl_f32,  , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+            CREATE_MM(GGML_TYPE_MXFP4,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_MXFP4].f16acc,   matmul_id_mxfp4_f32,   , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
        }
 #undef CREATE_MM2
 #undef CREATE_MM
@@ -2543,6 +2603,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
        CREATE_MM2(GGML_TYPE_IQ3_S,   pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ3_S],   matmul_iq3_s_f32,   mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
        CREATE_MM2(GGML_TYPE_IQ4_XS,  pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_XS],  matmul_iq4_xs_f32,  mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
        CREATE_MM2(GGML_TYPE_IQ4_NL,  pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL],  matmul_iq4_nl_f32,  mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+        CREATE_MM2(GGML_TYPE_MXFP4,   pipeline_dequant_mul_mat_mat[GGML_TYPE_MXFP4],   matmul_mxfp4_f32,   mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );

 #if defined(GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT)
        if (device->integer_dot_product) {
@@ -2580,6 +2641,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
        CREATE_MM(GGML_TYPE_IQ3_S,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_S].f16acc,   matmul_id_iq3_s_f32,   _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
        CREATE_MM(GGML_TYPE_IQ4_XS,  pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_XS].f16acc,  matmul_id_iq4_xs_f32,  _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
        CREATE_MM(GGML_TYPE_IQ4_NL,  pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL].f16acc,  matmul_id_iq4_nl_f32,  _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM(GGML_TYPE_MXFP4,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_MXFP4].f16acc,   matmul_id_mxfp4_f32,   _f16acc, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
 #undef CREATE_MM2
 #undef CREATE_MMQ
 #undef CREATE_MM
@@ -2634,6 +2696,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
        CREATE_MM(GGML_TYPE_IQ3_S,   pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ3_S].f32acc,   matmul_iq3_s_f32,   , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
        CREATE_MM(GGML_TYPE_IQ4_XS,  pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_XS].f32acc,  matmul_iq4_xs_f32,  , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
        CREATE_MM(GGML_TYPE_IQ4_NL,  pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL].f32acc,  matmul_iq4_nl_f32,  , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );
+        CREATE_MM(GGML_TYPE_MXFP4,   pipeline_dequant_mul_mat_mat[GGML_TYPE_MXFP4].f32acc,   matmul_mxfp4_f32,   , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, );

 #if defined(GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT)
        if (device->integer_dot_product) {
@@ -2671,6 +2734,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
        CREATE_MM(GGML_TYPE_IQ3_S,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_S].f32acc,   matmul_id_iq3_s_f32,   , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
        CREATE_MM(GGML_TYPE_IQ4_XS,  pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_XS].f32acc,  matmul_id_iq4_xs_f32,  , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
        CREATE_MM(GGML_TYPE_IQ4_NL,  pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL].f32acc,  matmul_id_iq4_nl_f32,  , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
+        CREATE_MM(GGML_TYPE_MXFP4,   pipeline_dequant_mul_mat_mat_id[GGML_TYPE_MXFP4].f32acc,   matmul_id_mxfp4_f32,   , mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, 4, _id);
    }
    // reusing CREATE_MM from the fp32 path
    if ((device->coopmat2 || device->coopmat_support)
@@ -2729,6 +2793,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_IQ3_S][i],   "mul_mat_vec_iq3_s_f32_f32_"+std::to_string(i+1),   mul_mat_vec_iq3_s_f32_f32_len,   mul_mat_vec_iq3_s_f32_f32_data,   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {subgroup_size_16, rm_iq, i+1}, 1, true);
        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_IQ4_XS][i],  "mul_mat_vec_iq4_xs_f32_f32_"+std::to_string(i+1),  mul_mat_vec_iq4_xs_f32_f32_len,  mul_mat_vec_iq4_xs_f32_f32_data,  "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {subgroup_size_16, rm_iq, i+1}, 1, true);
        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_IQ4_NL][i],  "mul_mat_vec_iq4_nl_f32_f32_"+std::to_string(i+1),  mul_mat_vec_iq4_nl_f32_f32_len,  mul_mat_vec_iq4_nl_f32_f32_data,  "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {subgroup_size_16, rm_iq, i+1}, 1, true);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_MXFP4][i],   "mul_mat_vec_mxfp4_f32_f32_"+std::to_string(i+1),   mul_mat_vec_mxfp4_f32_f32_len,   mul_mat_vec_mxfp4_f32_f32_data,   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {subgroup_size_16, rm_iq, i+1}, 1, true);

        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_F32 ][i], "mul_mat_vec_f32_f16_f32_"+std::to_string(i+1),  mul_mat_vec_f32_f16_f32_len,  mul_mat_vec_f32_f16_f32_data,  "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2, i+1}, 1);
        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_F16 ][i], "mul_mat_vec_f16_f16_f32_"+std::to_string(i+1),  mul_mat_vec_f16_f16_f32_len,  mul_mat_vec_f16_f16_f32_data,  "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {device->subgroup_size, 2, i+1}, 1);
@@ -2752,6 +2817,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_IQ3_S][i],   "mul_mat_vec_iq3_s_f16_f32_"+std::to_string(i+1),   mul_mat_vec_iq3_s_f16_f32_len,   mul_mat_vec_iq3_s_f16_f32_data,   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {subgroup_size_16, rm_iq, i+1}, 1, true);
        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_IQ4_XS][i],  "mul_mat_vec_iq4_xs_f16_f32_"+std::to_string(i+1),  mul_mat_vec_iq4_xs_f16_f32_len,  mul_mat_vec_iq4_xs_f16_f32_data,  "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {subgroup_size_16, rm_iq, i+1}, 1, true);
        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_IQ4_NL][i],  "mul_mat_vec_iq4_nl_f16_f32_"+std::to_string(i+1),  mul_mat_vec_iq4_nl_f16_f32_len,  mul_mat_vec_iq4_nl_f16_f32_data,  "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {subgroup_size_16, rm_iq, i+1}, 1, true);
+        ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_MXFP4][i],   "mul_mat_vec_mxfp4_f16_f32_"+std::to_string(i+1),   mul_mat_vec_mxfp4_f16_f32_len,   mul_mat_vec_mxfp4_f16_f32_data,   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {subgroup_size_16, rm_iq, i+1}, 1, true);
    }

    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", 4, sizeof(vk_mat_vec_id_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1);
@@ -2776,6 +2842,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
    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", 4, 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", 4, 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", 4, 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", 4, sizeof(vk_mat_vec_id_push_constants), {rm_iq, 1, 1}, {subgroup_size_16, rm_iq}, 1, true);

    // 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);
@@ -2798,6 +2865,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
    ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_IQ3_S],   "dequant_iq3_s",   dequant_iq3_s_len,   dequant_iq3_s_data,   "main", 2, 5 * sizeof(uint32_t), {256 * 32, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_IQ4_XS],  "dequant_iq4_xs",  dequant_iq4_xs_len,  dequant_iq4_xs_data,  "main", 2, 5 * sizeof(uint32_t), {256 * 32, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_IQ4_NL],  "dequant_iq4_nl",  dequant_iq4_nl_len,  dequant_iq4_nl_data,  "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_MXFP4],   "dequant_mxfp4",   dequant_mxfp4_len,   dequant_mxfp4_data,   "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1);

    // get_rows
    ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_F32 ], "get_rows_f32",  get_rows_f32_len,  get_rows_f32_data,  "main", 3, sizeof(vk_op_binary_push_constants), { 512, 1, 1}, {}, 1);
@@ -2817,6 +2885,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
    ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_IQ3_S],   "get_rows_iq3_s",   get_rows_iq3_s_len,   get_rows_iq3_s_data,   "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_IQ4_XS],  "get_rows_iq4_xs",  get_rows_iq4_xs_len,  get_rows_iq4_xs_data,  "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_IQ4_NL],  "get_rows_iq4_nl",  get_rows_iq4_nl_len,  get_rows_iq4_nl_data,  "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_MXFP4],   "get_rows_mxfp4",   get_rows_mxfp4_len,   get_rows_mxfp4_data,   "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);

    ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_F32 ], "get_rows_f32_f32",  get_rows_f32_f32_len,  get_rows_f32_f32_data,  "main", 3, sizeof(vk_op_binary_push_constants), { 512, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_F16 ], "get_rows_f16_f32",  get_rows_f16_f32_len,  get_rows_f16_f32_data,  "main", 3, sizeof(vk_op_binary_push_constants), { 512, 1, 1}, {}, 1);
@@ -2835,6 +2904,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
    ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_IQ3_S],   "get_rows_iq3_s_f32",   get_rows_iq3_s_f32_len,   get_rows_iq3_s_f32_data,   "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_IQ4_XS],  "get_rows_iq4_xs_f32",  get_rows_iq4_xs_f32_len,  get_rows_iq4_xs_f32_data,  "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_IQ4_NL],  "get_rows_iq4_nl_f32",  get_rows_iq4_nl_f32_len,  get_rows_iq4_nl_f32_data,  "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_MXFP4],   "get_rows_mxfp4_f32",   get_rows_mxfp4_f32_len,   get_rows_mxfp4_f32_data,   "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);

    ggml_vk_create_pipeline(device, device->pipeline_matmul_split_k_reduce, "split_k_reduce", split_k_reduce_len, split_k_reduce_data, "main", 2, 2 * sizeof(uint32_t), {256 * 4, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_flash_attn_split_k_reduce, "fa_split_k_reduce", fa_split_k_reduce_len, fa_split_k_reduce_data, "main", 2, 4 * sizeof(uint32_t), {1, device->subgroup_size, 1}, {device->subgroup_size}, 1, true);
@@ -2847,7 +2917,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
            ggml_vk_create_pipeline(device, device->pipeline_mul_mat_vec_p021_f16_f32[i], "mul_mat_vec_p021_f16_f32"+std::to_string(i+1), mul_mat_vec_p021_f16_f32_len,              mul_mat_vec_p021_f16_f32_data,              "main", 3, 6 * sizeof(uint32_t), {1, 1, 1}, {device->subgroup_size, i + 1}, 1, true);
        }
    }
-    ggml_vk_create_pipeline(device, device->pipeline_mul_mat_vec_nc_f16_f32, "mul_mat_vec_nc_f16_f32", mul_mat_vec_nc_f16_f32_len, mul_mat_vec_nc_f16_f32_data, "main", 3, 9 * sizeof(uint32_t), {1, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_mul_mat_vec_nc_f16_f32, "mul_mat_vec_nc_f16_f32", mul_mat_vec_nc_f16_f32_len, mul_mat_vec_nc_f16_f32_data, "main", 3, 12 * sizeof(uint32_t), {1, 1, 1}, {}, 1);

    ggml_vk_create_pipeline(device, device->pipeline_norm_f32, "norm_f32", norm_f32_len, norm_f32_data, "main", 2, sizeof(vk_op_push_constants), {1, 1, 1}, {}, 1);
    ggml_vk_create_pipeline(device, device->pipeline_group_norm_f32, "group_norm_f32", group_norm_f32_len, group_norm_f32_data, "main", 2, sizeof(vk_op_push_constants), {1, 1, 1}, {}, 1);
@@ -2938,6 +3008,8 @@ static void ggml_vk_load_shaders(vk_device& device) {
    CREATE_BINARY(div, _norepeat, {1})
 #undef CREATE_BINARY

+    ggml_vk_create_pipeline(device, device->pipeline_add_id_f32, "add_id_f32", add_id_f32_len, add_id_f32_data, "main", 4, sizeof(vk_op_add_id_push_constants), {1, 1, 1}, {}, 1);
+
    ggml_vk_create_pipeline(device, device->pipeline_acc_f32, "acc_f32", acc_f32_len, acc_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {}, 1);

    ggml_vk_create_pipeline(device, device->pipeline_concat_f32, "concat_f32", concat_f32_len, concat_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {}, 1);
@@ -2988,6 +3060,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
    CREATE_GLU(geglu)
    CREATE_GLU(reglu)
    CREATE_GLU(swiglu)
+    CREATE_GLU(swiglu_oai)
    CREATE_GLU(geglu_erf)
    CREATE_GLU(geglu_quick)
 #undef CREATE_GLU
@@ -2997,10 +3070,10 @@ static void ggml_vk_load_shaders(vk_device& device) {

    ggml_vk_create_pipeline(device, device->pipeline_diag_mask_inf_f32, "diag_mask_inf_f32", diag_mask_inf_f32_len, diag_mask_inf_f32_data, "main", 2, sizeof(vk_op_diag_mask_push_constants), {1, 512, 1}, {}, 1, true);

-    ggml_vk_create_pipeline(device, device->pipeline_soft_max_f32, "soft_max_f32", soft_max_f32_len, soft_max_f32_data, "main", 3, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
-    ggml_vk_create_pipeline(device, device->pipeline_soft_max_f32_wg512, "soft_max_f32_wg512", soft_max_f32_len, soft_max_f32_data, "main", 3, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, { 512 }, 1);
-    ggml_vk_create_pipeline(device, device->pipeline_soft_max_f32_f16, "soft_max_f32_f16", soft_max_f32_f16_len, soft_max_f32_f16_data, "main", 3, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
-    ggml_vk_create_pipeline(device, device->pipeline_soft_max_f32_f16_wg512, "soft_max_f32_f16_wg512", soft_max_f32_f16_len, soft_max_f32_f16_data, "main", 3, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, { 512 }, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_soft_max_f32, "soft_max_f32", soft_max_f32_len, soft_max_f32_data, "main", 4, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_soft_max_f32_wg512, "soft_max_f32_wg512", soft_max_f32_len, soft_max_f32_data, "main", 4, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, { 512 }, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_soft_max_f32_f16, "soft_max_f32_f16", soft_max_f32_f16_len, soft_max_f32_f16_data, "main", 4, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_soft_max_f32_f16_wg512, "soft_max_f32_f16_wg512", soft_max_f32_f16_len, soft_max_f32_f16_data, "main", 4, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, { 512 }, 1);
    ggml_vk_create_pipeline(device, device->pipeline_soft_max_back_f32, "soft_max_back_f32", soft_max_back_f32_len, soft_max_back_f32_data, "main", 3, sizeof(vk_op_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);

    ggml_vk_create_pipeline(device, device->pipeline_rope_norm_f32, "rope_norm_f32", rope_norm_f32_len, rope_norm_f32_data, "main", 4, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1);
@@ -3048,48 +3121,105 @@ static void ggml_vk_load_shaders(vk_device& device) {
    ggml_vk_create_pipeline(device, device->pipeline_opt_step_adamw_f32, "opt_step_adamw_f32", opt_step_adamw_f32_len, opt_step_adamw_f32_data, "main", 5, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1);

    // conv2d
-    uint32_t conv2d_WG_SIZE  = 256;
-    uint32_t conv2d_BS_K     = 128;
-    uint32_t conv2d_BS_CRS   = 16;
-    uint32_t use_collectives = 0;  // Enables subgroup ops for preventing the re-calculation of indices.
-    if (device->subgroup_shuffle &&
-        device->vendor_id != VK_VENDOR_ID_INTEL) {  // Do not enable collectives on Intel, see PR 14316
-        use_collectives = 1;
-        conv2d_BS_CRS   = std::min(
-            device->subgroup_size,
-            conv2d_BS_CRS);  // CRS block size should be capped at sugroup size for correctness when shuffle is used.
-    }
-    uint32_t conv2d_BS_NPQ = 128;
-    uint32_t conv2d_TS_K   = 8;
-    uint32_t conv2d_shmem_req =
-        (conv2d_BS_K * (conv2d_BS_CRS + 1) + conv2d_BS_CRS * (conv2d_BS_NPQ + 1)) * sizeof(float);
-    if (device->properties.limits.maxComputeSharedMemorySize < conv2d_shmem_req) {
-        conv2d_BS_CRS = 8;
-        if (use_collectives) {
-            conv2d_BS_CRS = std::min(device->subgroup_size, conv2d_BS_CRS);
-        }
-    }
+    for (uint32_t s = 0; s < CONV_SHAPE_COUNT; ++s) {
+        uint32_t conv2d_WG_SIZE  = 256;
+        uint32_t conv2d_BS_K     = 128;
+        uint32_t conv2d_BS_CRS   = 16;
+        uint32_t use_collectives = 0;  // Enables subgroup ops for preventing the re-calculation of indices.
+        uint32_t conv2d_BS_NPQ = 128;
+        uint32_t conv2d_TS_K   = 8;
+        uint32_t conv2d_SHMEM_PAD = 4;
+        bool conv2d_UNROLL = true;

-    if (use_collectives) {
-        ggml_vk_create_pipeline(
-            device, device->pipeline_conv2d_f32, "conv2d_f32", conv2d_f32_len, conv2d_f32_data, "main", 3,
-            sizeof(vk_op_conv2d_push_constants), { conv2d_BS_K, conv2d_BS_NPQ, 1 },
-            { conv2d_WG_SIZE, conv2d_BS_K, conv2d_BS_CRS, conv2d_BS_NPQ, conv2d_TS_K, use_collectives }, 1, true, true);
-        ggml_vk_create_pipeline(
-            device, device->pipeline_conv2d_f16_f32, "conv2d_f16_f32", conv2d_f16_f32_len, conv2d_f16_f32_data, "main", 3,
-            sizeof(vk_op_conv2d_push_constants), { conv2d_BS_K, conv2d_BS_NPQ, 1 },
-            { conv2d_WG_SIZE, conv2d_BS_K, conv2d_BS_CRS, conv2d_BS_NPQ, conv2d_TS_K, use_collectives }, 1, true, true);
-    } else {
-        ggml_vk_create_pipeline(
-            device, device->pipeline_conv2d_f32, "conv2d_f32", conv2d_f32_len, conv2d_f32_data, "main", 3,
-            sizeof(vk_op_conv2d_push_constants), { conv2d_BS_K, conv2d_BS_NPQ, 1 },
-            { conv2d_WG_SIZE, conv2d_BS_K, conv2d_BS_CRS, conv2d_BS_NPQ, conv2d_TS_K, use_collectives }, 1, true,
-            false);
-        ggml_vk_create_pipeline(
-            device, device->pipeline_conv2d_f16_f32, "conv2d_f16_f32", conv2d_f16_f32_len, conv2d_f16_f32_data, "main", 3,
-            sizeof(vk_op_conv2d_push_constants), { conv2d_BS_K, conv2d_BS_NPQ, 1 },
-            { conv2d_WG_SIZE, conv2d_BS_K, conv2d_BS_CRS, conv2d_BS_NPQ, conv2d_TS_K, use_collectives }, 1, true,
-            false);
+#if defined(GGML_VULKAN_COOPMAT2_GLSLC_SUPPORT)
+        if (device->coopmat2) {
+            conv2d_SHMEM_PAD = 8; // 8 float16_t
+        }
+#endif
+
+        if (device->vendor_id == VK_VENDOR_ID_INTEL) {
+            conv2d_SHMEM_PAD = 0;
+            conv2d_UNROLL = false;
+        } else if (device->vendor_id == VK_VENDOR_ID_AMD) {
+            conv2d_SHMEM_PAD = device->architecture == vk_device_architecture::AMD_GCN ? 1 : 4;
+        }
+
+        switch (s) {
+        default:
+        case CONV_SHAPE_128x128:
+            conv2d_BS_K = 128;
+            conv2d_BS_NPQ = 128;
+            conv2d_BS_CRS = 16;
+            if (device->vendor_id == VK_VENDOR_ID_AMD && device->architecture != vk_device_architecture::AMD_GCN) {
+                conv2d_UNROLL = false;
+            }
+            break;
+        case CONV_SHAPE_64x32:
+            conv2d_BS_K = 64;
+            conv2d_BS_NPQ = 32;
+            conv2d_BS_CRS = 32;
+            conv2d_TS_K   = 4;
+            break;
+        case CONV_SHAPE_32x256:
+            conv2d_BS_K = 32;
+            conv2d_BS_NPQ = 256;
+            conv2d_BS_CRS = 16;
+            break;
+        }
+
+        // Use collectives on pre-Turing NVIDIA GPUs and GCN AMD cards, which had slower integer math.
+        bool allow_collectives_nv = device->vendor_id != VK_VENDOR_ID_NVIDIA ||
+                                    device->architecture == vk_device_architecture::NVIDIA_PRE_TURING;
+        bool allow_collectives_amd = device->vendor_id != VK_VENDOR_ID_AMD ||
+                                     device->architecture == vk_device_architecture::AMD_GCN;
+
+        if (device->subgroup_shuffle &&
+            device->vendor_id != VK_VENDOR_ID_INTEL &&   // Do not enable collectives on Intel, see PR 14316.
+            allow_collectives_nv &&
+            allow_collectives_amd) {
+            use_collectives = 1;
+            conv2d_BS_CRS   = std::min(
+                device->subgroup_size,
+                conv2d_BS_CRS);  // CRS block size should be capped at subgroup size for correctness when shuffle is used.
+        }
+
+        uint32_t conv2d_shmem_req =
+            (conv2d_BS_K * (conv2d_BS_CRS + conv2d_SHMEM_PAD) + conv2d_BS_CRS * (conv2d_BS_NPQ + conv2d_SHMEM_PAD)) * sizeof(float);
+        if (device->properties.limits.maxComputeSharedMemorySize < conv2d_shmem_req) {
+            conv2d_BS_CRS = 8;
+            if (use_collectives) {
+                conv2d_BS_CRS = std::min(device->subgroup_size, conv2d_BS_CRS);
+            }
+        }
+
+        std::array<uint32_t, 3> wg_denoms = { conv2d_BS_K, conv2d_BS_NPQ, 1 };
+        std::vector<uint32_t> spec_constants = { conv2d_WG_SIZE, conv2d_BS_K, conv2d_BS_CRS, conv2d_BS_NPQ, conv2d_TS_K, use_collectives, conv2d_SHMEM_PAD };
+
+#if defined(GGML_VULKAN_COOPMAT2_GLSLC_SUPPORT)
+        if (device->coopmat2) {
+            ggml_vk_create_pipeline(
+                device, device->pipeline_conv2d_f32[s], "conv2d_f32", conv2d_f32_cm2_len, conv2d_f32_cm2_data, "main", 3,
+                sizeof(vk_op_conv2d_push_constants), wg_denoms, spec_constants, 1, true, use_collectives);
+            ggml_vk_create_pipeline(
+                device, device->pipeline_conv2d_f16_f32[s], "conv2d_f16_f32", conv2d_f16_f32_cm2_len, conv2d_f16_f32_cm2_data, "main", 3,
+                sizeof(vk_op_conv2d_push_constants), wg_denoms, spec_constants, 1, true, use_collectives);
+        } else
+#endif
+        if (conv2d_UNROLL) {
+            ggml_vk_create_pipeline(
+                device, device->pipeline_conv2d_f32[s], "conv2d_f32", conv2d_f32_unroll_len, conv2d_f32_unroll_data, "main", 3,
+                sizeof(vk_op_conv2d_push_constants), wg_denoms, spec_constants, 1, true, use_collectives);
+            ggml_vk_create_pipeline(
+                device, device->pipeline_conv2d_f16_f32[s], "conv2d_f16_f32", conv2d_f16_f32_unroll_len, conv2d_f16_f32_unroll_data, "main", 3,
+                sizeof(vk_op_conv2d_push_constants), wg_denoms, spec_constants, 1, true, use_collectives);
+        } else {
+            ggml_vk_create_pipeline(
+                device, device->pipeline_conv2d_f32[s], "conv2d_f32", conv2d_f32_len, conv2d_f32_data, "main", 3,
+                sizeof(vk_op_conv2d_push_constants), wg_denoms, spec_constants, 1, true, use_collectives);
+            ggml_vk_create_pipeline(
+                device, device->pipeline_conv2d_f16_f32[s], "conv2d_f16_f32", conv2d_f16_f32_len, conv2d_f16_f32_data, "main", 3,
+                sizeof(vk_op_conv2d_push_constants), wg_denoms, spec_constants, 1, true, use_collectives);
+        }
    }

    ggml_vk_create_pipeline(device, device->pipeline_conv2d_dw_whcn_f32, "conv2d_dw_whcn_f32", conv2d_dw_whcn_f32_len, conv2d_dw_whcn_f32_data, "main", 3, sizeof(vk_op_conv2d_dw_push_constants), {512, 1, 1}, {}, 1);
@@ -4149,6 +4279,7 @@ static vk_pipeline ggml_vk_get_to_fp16(ggml_backend_vk_context * ctx, ggml_type
        case GGML_TYPE_IQ3_S:
        case GGML_TYPE_IQ4_XS:
        case GGML_TYPE_IQ4_NL:
+        case GGML_TYPE_MXFP4:
            break;
        default:
            return nullptr;
@@ -4219,6 +4350,7 @@ static vk_matmul_pipeline ggml_vk_get_mul_mat_mat_pipeline(ggml_backend_vk_conte
        case GGML_TYPE_IQ3_S:
        case GGML_TYPE_IQ4_XS:
        case GGML_TYPE_IQ4_NL:
+        case GGML_TYPE_MXFP4:
            break;
        default:
            return nullptr;
@@ -4262,6 +4394,7 @@ static vk_pipeline ggml_vk_get_dequantize_mul_mat_vec(ggml_backend_vk_context *
        case GGML_TYPE_IQ3_S:
        case GGML_TYPE_IQ4_XS:
        case GGML_TYPE_IQ4_NL:
+        case GGML_TYPE_MXFP4:
            break;
        default:
            return nullptr;
@@ -4316,6 +4449,7 @@ static vk_matmul_pipeline ggml_vk_get_mul_mat_mat_id_pipeline(ggml_backend_vk_co
        case GGML_TYPE_IQ3_S:
        case GGML_TYPE_IQ4_XS:
        case GGML_TYPE_IQ4_NL:
+        case GGML_TYPE_MXFP4:
            break;
        default:
            return nullptr;
@@ -4351,6 +4485,7 @@ static vk_pipeline ggml_vk_get_dequantize_mul_mat_vec_id(ggml_backend_vk_context
        case GGML_TYPE_IQ3_S:
        case GGML_TYPE_IQ4_XS:
        case GGML_TYPE_IQ4_NL:
+        case GGML_TYPE_MXFP4:
            break;
        default:
            return nullptr;
@@ -4536,6 +4671,7 @@ static void ggml_vk_dispatch_pipeline(ggml_backend_vk_context* ctx, vk_context&
    std::cerr << "}, (" << wg0 << "," << wg1 << "," << wg2 << "))");
    GGML_ASSERT(ctx->descriptor_set_idx < ctx->descriptor_sets.size());
    GGML_ASSERT(descriptor_buffer_infos.size() <= MAX_PARAMETER_COUNT);
+    GGML_ASSERT(pipeline->parameter_count == descriptor_buffer_infos.size());

    vk::DescriptorSet& descriptor_set = ctx->descriptor_sets[ctx->descriptor_set_idx++];
    vk::WriteDescriptorSet write_descriptor_set{ descriptor_set, 0, 0, pipeline->parameter_count, vk::DescriptorType::eStorageBuffer, nullptr, descriptor_buffer_infos.begin() };
@@ -4943,26 +5079,37 @@ static void ggml_vk_buffer_memset(vk_buffer& dst, size_t offset, uint32_t c, siz
    ggml_vk_queue_command_pools_cleanup(dst->device);
 }

-static uint32_t ggml_vk_guess_split_k(ggml_backend_vk_context * ctx, int m, int n, int k, const vk_pipeline& pipeline) {
+static uint32_t ggml_vk_guess_split_k(ggml_backend_vk_context * ctx, uint32_t m, uint32_t n, uint32_t k, const vk_pipeline& pipeline) {
    VK_LOG_DEBUG("ggml_vk_guess_split_k(" << m << ", " << n << ", " << k << ")");

    uint32_t split_k = 1;
-    if (ctx->device->shader_core_count != 0 && m >= (int)pipeline->wg_denoms[0] && n >= (int)pipeline->wg_denoms[1]) {
+    if (ctx->device->shader_core_count != 0 && m >= pipeline->wg_denoms[0] && n >= pipeline->wg_denoms[1]) {
        // If k is 'large' and the SMs will fill less than halfway, use split_k.
        uint32_t m_tiles = CEIL_DIV(m, pipeline->wg_denoms[0]);
        uint32_t n_tiles = CEIL_DIV(n, pipeline->wg_denoms[1]);
-        if (k >= 2048 && m_tiles * n_tiles < ctx->device->shader_core_count / 2) {
-            split_k = ctx->device->shader_core_count / (m_tiles * n_tiles);
-            // Clamp to 2 or 4
-            split_k = std::min(split_k, 4u);
-            if (split_k == 3) {
-                split_k = 2;
+
+        if (k >= 2048) {
+            if (m_tiles * n_tiles <= ctx->device->shader_core_count / 2) {
+                split_k = ctx->device->shader_core_count / (m_tiles * n_tiles);
+            } else if (m_tiles * n_tiles <= ctx->device->shader_core_count * 2 / 3) {
+                split_k = 3;
            }
-            if (ctx->device->coopmat2) {
-                // coopmat2 shader expects splits to be aligned to 256
-                while (split_k > 1 && ((k / split_k) % 256) != 0) {
-                    split_k /= 2;
+            // Cap the split at 8x. Unless k is huge this is a lot of overhead.
+            split_k = std::min(split_k, 8u);
+
+            // ggml_vk_matmul will align the splits to be a multiple of 256.
+            // If this rounded up size would cause the last split to be empty,
+            // then reduce the split count.
+            while (true) {
+                if (split_k == 1) {
+                    break;
                }
+                uint32_t k_split = CEIL_DIV(k, split_k);
+                k_split = ROUNDUP_POW2(k_split, 256);
+                if (k_split * (split_k - 1) < k) {
+                    break;
+                }
+                split_k--;
            }
        }
    }
@@ -4974,9 +5121,22 @@ static vk_pipeline ggml_vk_guess_matmul_pipeline(ggml_backend_vk_context * ctx,
    VK_LOG_DEBUG("ggml_vk_guess_matmul_pipeline(" << m << ", " << n << ", " << aligned << ", " << ggml_type_name(src0_type) << ", " << ggml_type_name(src1_type) << ")");

    if (ctx->device->coopmat2) {
+        const uint32_t shader_core_count = ctx->device->shader_core_count;
+        const uint32_t tiles_l = CEIL_DIV(m, mmp->a_l->wg_denoms[0]) * CEIL_DIV(n, mmp->a_l->wg_denoms[1]);
+        const uint32_t tiles_m = CEIL_DIV(m, mmp->a_m->wg_denoms[0]) * CEIL_DIV(n, mmp->a_m->wg_denoms[1]);
+
        // Use large shader when the N dimension is greater than the medium shader's tile size
        uint32_t crossover_large = mmp->m->wg_denoms[1];
-        if ((ctx->device->mul_mat_l[src0_type] && (n > crossover_large)) || (!ctx->device->mul_mat_m[src0_type] && !ctx->device->mul_mat_s[src0_type])) {
+
+        // Prefer large over medium if either:
+        // - medium or large tiles would overfill the GPU
+        // - large tiles with a split_k==3 fits in the GPU and medium tiles with split_k==2 does not
+        //   (medium with split_k==2 is probably better if it fits - more workgroups running and less split_k overhead)
+        bool prefer_large = tiles_m > shader_core_count || tiles_l > shader_core_count ||
+                            // split_k==3 with large tiles likely better than medium tiles with no split_k.
+                            (tiles_l <= shader_core_count / 3 && tiles_m > shader_core_count / 2);
+
+        if ((ctx->device->mul_mat_l[src0_type] && (n > crossover_large && prefer_large)) || (!ctx->device->mul_mat_m[src0_type] && !ctx->device->mul_mat_s[src0_type])) {
            return aligned ? mmp->a_l : mmp->l;
        }
        // Use medium shader when the N dimension is greater than the small shader's tile size
@@ -5020,7 +5180,11 @@ static void ggml_vk_matmul(

    GGML_ASSERT(batch_stride_d == m * n);

-    const vk_mat_mat_push_constants pc1 = { m, n, k, stride_a, stride_b, stride_d, batch_stride_a, batch_stride_b, batch_stride_d, CEIL_DIV(k, split_k), ne02, ne12, broadcast2, broadcast3, padded_n };
+    // Round the split size up to a multiple of 256 (k-quant alignment)
+    uint32_t k_split = CEIL_DIV(k, split_k);
+    k_split = ROUNDUP_POW2(k_split, 256);
+
+    const vk_mat_mat_push_constants pc1 = { m, n, k, stride_a, stride_b, stride_d, batch_stride_a, batch_stride_b, batch_stride_d, k_split, ne02, ne12, broadcast2, broadcast3, padded_n };
    // Make sure enough workgroups get assigned for split k to work
    ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { a, b, split_k_buffer }, pc1, { (CEIL_DIV(m, pipeline->wg_denoms[0]) * pipeline->wg_denoms[0]) * split_k, n, batch });
    ggml_vk_sync_buffers(subctx);
@@ -5742,7 +5906,7 @@ static void ggml_vk_mul_mat_vec_nc_f16_f32(ggml_backend_vk_context * ctx, vk_con
    const uint64_t ne00 = src0->ne[0];
    const uint64_t ne01 = src0->ne[1];
    const uint64_t ne02 = src0->ne[2];
-    // const uint64_t ne03 = src0->ne[3];
+    const uint64_t ne03 = src0->ne[3];

    const uint64_t nb01 = src0->nb[1];
    const uint64_t nb02 = src0->nb[2];
@@ -5754,7 +5918,12 @@ static void ggml_vk_mul_mat_vec_nc_f16_f32(ggml_backend_vk_context * ctx, vk_con
    const uint64_t ne12 = src1->ne[2];
    // const uint64_t ne13 = src1->ne[3];

+    const uint32_t nb03 = (uint32_t)(src0->nb[3] / sizeof(ggml_fp16_t));
+    const uint32_t nb13 = (uint32_t)(src1->nb[3] / sizeof(float));
+    const uint32_t nb23 = (uint32_t)(dst->nb[3] / sizeof(float));
+
    GGML_ASSERT(ne11 == 1);
+    GGML_ASSERT(src0->ne[3] == src1->ne[3]); // checked in supports_op

    ggml_backend_vk_buffer_context * dst_buf_ctx = (ggml_backend_vk_buffer_context *)dst->buffer->context;
    ggml_backend_vk_buffer_context * src0_buf_ctx = (ggml_backend_vk_buffer_context *)src0->buffer->context;
@@ -5770,7 +5939,7 @@ static void ggml_vk_mul_mat_vec_nc_f16_f32(ggml_backend_vk_context * ctx, vk_con
        src1_uma = d_Qy != nullptr;
    }

-    const uint64_t d_ne = ne01 * ne11 * ne12;
+    const uint64_t d_ne = ne01 * ne11 * ne12 * ne03;

    const uint32_t row_stride_x = nb01 / sizeof(ggml_fp16_t);
    const uint32_t channel_stride_x = nb02 / sizeof(ggml_fp16_t);
@@ -5805,10 +5974,10 @@ static void ggml_vk_mul_mat_vec_nc_f16_f32(ggml_backend_vk_context * ctx, vk_con
    const uint64_t d_shader_offset = d_buf_offset - d_buffer_offset;

    // compute
-    const std::array<uint32_t, 9> pc = { (uint32_t)ne00, (uint32_t)ne01, row_stride_x, channel_stride_x, channel_stride_y, (uint32_t)(ne12 / ne02), (uint32_t)ne12, (uint32_t)(qy_shader_offset / ggml_type_size(src1->type)), (uint32_t)(d_shader_offset / ggml_type_size(dst->type)) };
+    const std::array<uint32_t, 12> pc = { (uint32_t)ne00, (uint32_t)ne01, row_stride_x, channel_stride_x, channel_stride_y, (uint32_t)(ne12 / ne02), (uint32_t)ne12, (uint32_t)(qy_shader_offset / ggml_type_size(src1->type)), (uint32_t)(d_shader_offset / ggml_type_size(dst->type)), nb03, nb13, nb23 };
    ggml_vk_sync_buffers(subctx);
    ggml_vk_dispatch_pipeline(ctx, subctx, ctx->device->pipeline_mul_mat_vec_nc_f16_f32,
-        { vk_subbuffer{ d_Qx, qx_buf_offset, qx_sz }, vk_subbuffer{ d_Qy, qy_buffer_offset, qy_sz + qy_shader_offset }, vk_subbuffer{ d_D, d_buffer_offset, d_sz + d_shader_offset } }, pc, { 1, (uint32_t)ne01, (uint32_t)ne12 });
+        { vk_subbuffer{ d_Qx, qx_buf_offset, qx_sz }, vk_subbuffer{ d_Qy, qy_buffer_offset, qy_sz + qy_shader_offset }, vk_subbuffer{ d_D, d_buffer_offset, d_sz + d_shader_offset } }, pc, { (uint32_t)ne03, (uint32_t)ne01, (uint32_t)ne12 });
 }

 static void ggml_vk_mul_mat(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, bool dryrun = false) {
@@ -6641,6 +6810,34 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx
    }
 }

+static std::array<uint32_t, 3> ggml_vk_get_conv_elements(const ggml_tensor *dst) {
+    const ggml_tensor *src0 = dst->src[0];
+    const ggml_tensor *src1 = dst->src[1];
+
+    // src0 - kernel:   [KW, KH, Cin, Cout]
+    // src1 - input:    [W, H, Cin, N]
+    // dst - result:    [OW, OH, Cout, N]
+
+    // Copied from ggml.c: int64_t ggml_calc_conv_output_size(int64_t ins, int64_t ks, int s, int p, int d)
+    auto calc_conv_output_size = [](int64_t ins, int64_t ks, int s, int p, int d) -> int64_t {
+        return (ins + 2 * p - d * (ks - 1) - 1) / s + 1;
+    };
+    // parallelize in {OW/BS_K, OH/BS_NPQ, 1}
+    int64_t W    = src1->ne[0];
+    int64_t H    = src1->ne[1];
+    int64_t KW   = src0->ne[0];
+    int64_t KH   = src0->ne[1];
+    int64_t Cout = src0->ne[3];
+    int64_t N    = src1->ne[3];
+    int64_t OH   = calc_conv_output_size(H, KH, dst->op_params[1], dst->op_params[3], dst->op_params[5]);
+    int64_t OW   = calc_conv_output_size(W, KW, dst->op_params[0], dst->op_params[2], dst->op_params[4]);
+    int64_t NPQ  = N * OW * OH;
+
+    // Tile output matrix to (K/NB_K, NPQ/NB_NPQ, 1) workgroups
+    std::array<uint32_t, 3> elements = { static_cast<uint32_t>(Cout), static_cast<uint32_t>(NPQ), 1 };
+    return elements;
+}
+
 static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, ggml_tensor * dst, ggml_op op) {
    switch (op) {
    case GGML_OP_GET_ROWS:
@@ -6691,6 +6888,11 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const
            break;
        }
        return nullptr;
+    case GGML_OP_ADD_ID:
+        if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && src2->type == GGML_TYPE_I32 && dst->type == GGML_TYPE_F32) {
+            return ctx->device->pipeline_add_id_f32;
+        }
+        return nullptr;
    case GGML_OP_CONCAT:
        if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
            return ctx->device->pipeline_concat_f32;
@@ -6836,6 +7038,8 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const
                return ctx->device->pipeline_reglu[dst->type == GGML_TYPE_F16];
            case GGML_GLU_OP_SWIGLU:
                return ctx->device->pipeline_swiglu[dst->type == GGML_TYPE_F16];
+            case GGML_GLU_OP_SWIGLU_OAI:
+                return ctx->device->pipeline_swiglu_oai[dst->type == GGML_TYPE_F16];
            case GGML_GLU_OP_GEGLU_ERF:
                return ctx->device->pipeline_geglu_erf[dst->type == GGML_TYPE_F16];
            case GGML_GLU_OP_GEGLU_QUICK:
@@ -6851,6 +7055,7 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const
        return nullptr;
    case GGML_OP_SOFT_MAX:
        GGML_ASSERT(!src1 || src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16);
+        GGML_ASSERT(!src2 || src2->type == GGML_TYPE_F32);

        if (src0->type == GGML_TYPE_F32 && (src1 == nullptr || src1->type == GGML_TYPE_F32) && dst->type == GGML_TYPE_F32) {
            return src0->ne[0] > 1024 ? ctx->device->pipeline_soft_max_f32_wg512 : ctx->device->pipeline_soft_max_f32;
@@ -6970,10 +7175,30 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const
    case GGML_OP_CONV_2D:
        if (src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32 &&
            ggml_is_contiguous(src0) && ggml_is_contiguous(src1) && ggml_is_contiguous(dst)) {
+            auto elements = ggml_vk_get_conv_elements(dst);
+            vk_conv_shapes shape;
+
+            uint32_t tiles[CONV_SHAPE_COUNT];
+            for (uint32_t i = 0; i < CONV_SHAPE_COUNT; ++i) {
+                tiles[i] = CEIL_DIV(elements[0], ctx->device->pipeline_conv2d_f32[i]->wg_denoms[0]) * CEIL_DIV(elements[1], ctx->device->pipeline_conv2d_f32[i]->wg_denoms[1]);
+            }
+
+            // We can't query number of shader cores on Intel, use 32 as a placeholder
+            // so small convolutions will still choose a smaller tile.
+            const uint32_t shader_core_count = ctx->device->shader_core_count > 0 ? ctx->device->shader_core_count : 32;
+
+            if (elements[0] > 64 && tiles[CONV_SHAPE_128x128] >= shader_core_count * 2) {
+                shape = CONV_SHAPE_128x128;
+            } else if (elements[0] <= 32 && tiles[CONV_SHAPE_32x256] >= shader_core_count * 2) {
+                shape = CONV_SHAPE_32x256;
+            } else {
+                shape = CONV_SHAPE_64x32;
+            }
+
            if (src0->type == GGML_TYPE_F32) {
-                return ctx->device->pipeline_conv2d_f32;
+                return ctx->device->pipeline_conv2d_f32[shape];
            } else if (src0->type == GGML_TYPE_F16) {
-                return ctx->device->pipeline_conv2d_f16_f32;
+                return ctx->device->pipeline_conv2d_f16_f32[shape];
            }
        }
        return nullptr;
@@ -7001,6 +7226,7 @@ static bool ggml_vk_op_supports_incontiguous(ggml_op op) {
    case GGML_OP_SUB:
    case GGML_OP_MUL:
    case GGML_OP_DIV:
+    case GGML_OP_ADD_ID:
    case GGML_OP_CONCAT:
    case GGML_OP_UPSCALE:
    case GGML_OP_SQR:
@@ -7301,29 +7527,8 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context& subctx, co
        } break;
    case GGML_OP_CONV_2D:
        {
-            // src0 - kernel:   [KW, KH, Cin, Cout]
-            // src1 - input:    [W, H, Cin, N]
-            // dst - result:    [OW, OH, Cout, N]
-
-            // Copied from ggml.c: int64_t ggml_calc_conv_output_size(int64_t ins, int64_t ks, int s, int p, int d)
-            auto calc_conv_output_size = [](int64_t ins, int64_t ks, int s, int p, int d) -> int64_t {
-                return (ins + 2 * p - d * (ks - 1) - 1) / s + 1;
-            };
-            // parallelize in {OW/BS_K, OH/BS_NPQ, 1}
-            int64_t W    = src1->ne[0];
-            int64_t H    = src1->ne[1];
-            int64_t KW   = src0->ne[0];
-            int64_t KH   = src0->ne[1];
-            int64_t Cout = src0->ne[3];
-            int64_t N    = src1->ne[3];
-            int64_t OH   = calc_conv_output_size(H, KH, dst->op_params[1], dst->op_params[3], dst->op_params[5]);
-            int64_t OW   = calc_conv_output_size(W, KW, dst->op_params[0], dst->op_params[2], dst->op_params[4]);
-            int64_t NPQ  = N * OW * OH;
-
-            // Tile output matrix to (K/NB_K, NPQ/NB_NPQ, 1) workgroups
-            elements = { static_cast<uint32_t>(Cout), static_cast<uint32_t>(NPQ), 1 };
-        }
-        break;
+            elements = ggml_vk_get_conv_elements(dst);
+        } break;
    case GGML_OP_ADD:
    case GGML_OP_SUB:
    case GGML_OP_DIV:
@@ -7368,6 +7573,10 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context& subctx, co
                elements = { ne, 1, 1 };
            }
        } break;
+    case GGML_OP_ADD_ID:
+        {
+            elements = { (uint32_t)ne01, (uint32_t)ne02, 1 };
+        } break;
    case GGML_OP_SET_ROWS:
        {
            uint32_t ne = ggml_nelements(src0);
@@ -7407,8 +7616,8 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context& subctx, co
        }
    }

-    if (op == GGML_OP_SOFT_MAX || op == GGML_OP_GLU) {
-        // Empty src1 is possible in soft_max, but the shader needs a buffer
+    if (op == GGML_OP_GLU) {
+        // Empty src1 is possible in glu, but the shader needs a buffer
        vk_subbuffer subbuf_y;
        if (use_src1) {
            subbuf_y = { d_Y, y_buf_offset, y_sz };
@@ -7418,6 +7627,24 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context& subctx, co

        ggml_vk_sync_buffers(subctx);
        ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { vk_subbuffer{ d_X, x_buf_offset, x_sz }, subbuf_y, vk_subbuffer{ d_D, d_buf_offset, d_sz } }, pc, elements);
+    } else if (op == GGML_OP_SOFT_MAX) {
+        // Empty src1 and src2 is possible in soft_max, but the shader needs a buffer
+        vk_subbuffer subbuf_y;
+        if (use_src1) {
+            subbuf_y = { d_Y, y_buf_offset, y_sz };
+        } else {
+            subbuf_y = { d_X, 0, x_sz };
+        }
+
+        vk_subbuffer subbuf_z;
+        if (use_src2) {
+            subbuf_z = { d_Z, z_buf_offset, z_sz };
+        } else {
+            subbuf_z = { d_X, 0, x_sz };
+        }
+
+        ggml_vk_sync_buffers(subctx);
+        ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { vk_subbuffer{ d_X, x_buf_offset, x_sz }, subbuf_y, subbuf_z, vk_subbuffer{ d_D, d_buf_offset, d_sz } }, pc, elements);
    } else if (op == GGML_OP_ROPE || op == GGML_OP_ROPE_BACK) {
        // Empty src2 is possible in rope, but the shader needs a buffer
        vk_subbuffer subbuf_z;
@@ -7546,6 +7773,21 @@ static void ggml_vk_div(ggml_backend_vk_context * ctx, vk_context& subctx, const
    }, dryrun);
 }

+static void ggml_vk_add_id(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, ggml_tensor * dst, bool dryrun = false) {
+    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 src2_type_size = ggml_type_size(src2->type);
+
+    ggml_vk_op_f32<vk_op_add_id_push_constants>(ctx, subctx, src0, src1, src2, dst, GGML_OP_ADD_ID, {
+        (uint32_t)dst->ne[0],
+        (uint32_t)dst->ne[1],
+        (uint32_t)src0->nb[1] / src0_type_size,
+        (uint32_t)src0->nb[2] / src0_type_size,
+        (uint32_t)src1->nb[1] / src1_type_size,
+        (uint32_t)src2->nb[1] / src2_type_size,
+    }, dryrun);
+}
+
 static void ggml_vk_op_f32_wkv(ggml_backend_vk_context * ctx, vk_context& subctx, ggml_tensor * dst, const vk_op_rwkv_wkv6_push_constants&& pc, int version, bool dryrun = false) {
    GGML_ASSERT(version == 6 || version == 7);
    int num_srcs = version == 6 ? 6 : 7;
@@ -7964,8 +8206,12 @@ static void ggml_vk_unary(ggml_backend_vk_context * ctx, vk_context& subctx, con
 }

 static void ggml_vk_glu(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, bool dryrun = false) {
+    const float * op_params_f = (const float *)dst->op_params;
+
    const bool swapped = (bool)dst->op_params[1];
    const bool split = src1 != nullptr;
+    const float alpha = op_params_f[2];
+    const float limit = op_params_f[3];

    GGML_ASSERT(ggml_is_contiguous(src0));

@@ -7979,7 +8225,15 @@ static void ggml_vk_glu(ggml_backend_vk_context * ctx, vk_context& subctx, const

    const uint32_t mode = split ? 2 : (swapped ? 1 : 0);

-    ggml_vk_op_f32<vk_op_glu_push_constants>(ctx, subctx, src0, src1, nullptr, dst, GGML_OP_GLU, { (uint32_t)ggml_nelements(dst), (uint32_t)src0->ne[0], (uint32_t)dst->ne[0], mode }, dryrun);
+    ggml_vk_op_f32<vk_op_glu_push_constants>(ctx, subctx, src0, src1, nullptr, dst, GGML_OP_GLU,
+        {
+            (uint32_t)ggml_nelements(dst),
+            (uint32_t)src0->ne[0],
+            (uint32_t)dst->ne[0],
+            mode,
+            alpha,
+            limit
+        }, dryrun);
 }

 static void ggml_vk_diag_mask_inf(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst, bool dryrun = false) {
@@ -7987,7 +8241,7 @@ static void ggml_vk_diag_mask_inf(ggml_backend_vk_context * ctx, vk_context& sub
    ggml_vk_op_f32<vk_op_diag_mask_push_constants>(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_DIAG_MASK_INF, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], op_params[0] }, dryrun);
 }

-static void ggml_vk_soft_max(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, bool dryrun = false) {
+static void ggml_vk_soft_max(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, ggml_tensor * dst, bool dryrun = false) {
    float * op_params = (float *)dst->op_params;

    float scale = op_params[0];
@@ -8009,7 +8263,7 @@ static void ggml_vk_soft_max(ggml_backend_vk_context * ctx, vk_context& subctx,
    const float m0 = powf(2.0f, -(max_bias       ) / n_head_log2);
    const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2);

-    ggml_vk_op_f32<vk_op_soft_max_push_constants>(ctx, subctx, src0, src1, nullptr, dst, GGML_OP_SOFT_MAX, {
+    ggml_vk_op_f32<vk_op_soft_max_push_constants>(ctx, subctx, src0, src1, src2, dst, GGML_OP_SOFT_MAX, {
        ncols,
        src1 != nullptr ? nrows_y : (uint32_t)0,
        (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2],
@@ -8019,6 +8273,7 @@ static void ggml_vk_soft_max(ggml_backend_vk_context * ctx, vk_context& subctx,
        m0, m1,
        n_head_log2,
        nrows_x,
+        src2 != nullptr
    }, dryrun);
 }

@@ -9258,6 +9513,7 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgr
        case GGML_GLU_OP_GEGLU:
        case GGML_GLU_OP_REGLU:
        case GGML_GLU_OP_SWIGLU:
+        case GGML_GLU_OP_SWIGLU_OAI:
        case GGML_GLU_OP_GEGLU_ERF:
        case GGML_GLU_OP_GEGLU_QUICK:
            break;
@@ -9269,6 +9525,7 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgr
    case GGML_OP_REPEAT_BACK:
    case GGML_OP_GET_ROWS:
    case GGML_OP_ADD:
+    case GGML_OP_ADD_ID:
    case GGML_OP_ACC:
    case GGML_OP_SUB:
    case GGML_OP_MUL:
@@ -9423,6 +9680,10 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgr
    case GGML_OP_DIV:
        ggml_vk_div(ctx, compute_ctx, src0, src1, node, dryrun);

+        break;
+    case GGML_OP_ADD_ID:
+        ggml_vk_add_id(ctx, compute_ctx, src0, src1, src2, node, dryrun);
+
        break;
    case GGML_OP_CONCAT:
        ggml_vk_concat(ctx, compute_ctx, src0, src1, node, dryrun);
@@ -9520,6 +9781,7 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgr
        case GGML_GLU_OP_GEGLU:
        case GGML_GLU_OP_REGLU:
        case GGML_GLU_OP_SWIGLU:
+        case GGML_GLU_OP_SWIGLU_OAI:
        case GGML_GLU_OP_GEGLU_ERF:
        case GGML_GLU_OP_GEGLU_QUICK:
            ggml_vk_glu(ctx, compute_ctx, src0, src1, node, dryrun);
@@ -9533,7 +9795,7 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgr

        break;
    case GGML_OP_SOFT_MAX:
-        ggml_vk_soft_max(ctx, compute_ctx, src0, src1, node, dryrun);
+        ggml_vk_soft_max(ctx, compute_ctx, src0, src1, src2, node, dryrun);

        break;
    case GGML_OP_SOFT_MAX_BACK:
@@ -9679,6 +9941,7 @@ static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_cgraph *
    case GGML_OP_SUB:
    case GGML_OP_MUL:
    case GGML_OP_DIV:
+    case GGML_OP_ADD_ID:
    case GGML_OP_CONCAT:
    case GGML_OP_UPSCALE:
    case GGML_OP_SCALE:
@@ -9748,6 +10011,7 @@ static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_cgraph *
        case GGML_GLU_OP_GEGLU:
        case GGML_GLU_OP_REGLU:
        case GGML_GLU_OP_SWIGLU:
+        case GGML_GLU_OP_SWIGLU_OAI:
        case GGML_GLU_OP_GEGLU_ERF:
        case GGML_GLU_OP_GEGLU_QUICK:
            buf = tensor->buffer;
@@ -10597,6 +10861,7 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
                case GGML_GLU_OP_GEGLU:
                case GGML_GLU_OP_REGLU:
                case GGML_GLU_OP_SWIGLU:
+                case GGML_GLU_OP_SWIGLU_OAI:
                case GGML_GLU_OP_GEGLU_ERF:
                case GGML_GLU_OP_GEGLU_QUICK:
                    return ggml_is_contiguous(op->src[0]) &&
@@ -10642,6 +10907,7 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
                    case GGML_TYPE_IQ3_S:
                    case GGML_TYPE_IQ4_XS:
                    case GGML_TYPE_IQ4_NL:
+                    case GGML_TYPE_MXFP4:
                        break;
                    default:
                        return false;
@@ -10679,6 +10945,10 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
                if (head_sizes == FA_HEAD_SIZE_UNSUPPORTED) {
                    return false;
                }
+                // TODO: support attention sinks [TAG_ATTN_SINKS]
+                if (op->src[4]) {
+                    return false;
+                }
                if (op->src[0]->type != GGML_TYPE_F32) {
                    return false;
                }
@@ -10751,6 +11021,7 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
                    case GGML_TYPE_IQ3_S:
                    case GGML_TYPE_IQ4_XS:
                    case GGML_TYPE_IQ4_NL:
+                    case GGML_TYPE_MXFP4:
                        return true;
                    default:
                        return false;
@@ -10849,6 +11120,9 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
            return (op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16) &&
                   (op->src[1]->type == GGML_TYPE_F32 || op->src[1]->type == GGML_TYPE_F16) &&
                   (op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16);
+        case GGML_OP_ADD_ID:
+            return op->src[0]->type == GGML_TYPE_F32 && op->src[1]->type == GGML_TYPE_F32 && op->src[2]->type == GGML_TYPE_I32 &&
+                   op->type == GGML_TYPE_F32;
        case GGML_OP_SILU_BACK:
        case GGML_OP_RMS_NORM_BACK:
        case GGML_OP_SQR:
@@ -0,0 +1,42 @@
+#version 450
+
+#extension GL_EXT_control_flow_attributes : require
+
+#include "types.comp"
+
+layout (push_constant) uniform parameter
+{
+    uint ne0;
+    uint ne1;
+    uint s01;
+    uint s02;
+    uint s11;
+    uint s21;
+} p;
+
+#define BLOCK_SIZE 512
+
+layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
+layout (binding = 1) readonly buffer Y {B_TYPE data_b[];};
+layout (binding = 2) readonly buffer Z {int32_t data_c[];};
+layout (binding = 3) writeonly buffer D {D_TYPE data_d[];};
+
+void main() {
+    const uint i1 = gl_WorkGroupID.x;
+    const uint i2 = gl_WorkGroupID.y;
+
+    const uint i11 = data_c[i1 + i2 * p.s21];
+
+    const uint s1 = p.ne0;
+    const uint s2 = p.ne0 * p.ne1;
+
+    const uint d0 = i1 * s1 + i2 * s2;
+    const uint a0 = i1 * p.s01 + i2 * p.s02;
+    const uint b0 = i11 * p.s11;
+
+    for (uint i0 = gl_LocalInvocationID.x; i0 < p.ne0; i0 += BLOCK_SIZE) {
+        data_d[d0 + i0] = data_a[a0 + i0] + data_b[b0 + i0];
+    }
+}
@@ -1,14 +1,18 @@
 #version 450

+#extension GL_EXT_control_flow_attributes : enable
+#ifdef COOPMAT2
+#extension GL_NV_cooperative_matrix2 : enable
+#extension GL_EXT_shader_explicit_arithmetic_types_float16 : require
+#extension GL_KHR_memory_scope_semantics : enable
+#endif
+
 #ifdef USE_COLLECTIVES
 #    extension GL_KHR_shader_subgroup_shuffle : enable
 #endif

 #include "types.comp"

-// Make spec constant
-#define SHMEM_PAD 0
-
 // shape notation: [dim(N), ..., dim(0)] -- stride(dim(j)) >= stride(dim(i)) if i > j
 layout(binding = 0) readonly buffer A {
    A_TYPE knl_data[];
@@ -56,6 +60,12 @@ layout(push_constant) uniform parameter {
    uint32_t nb1;
    uint32_t nb2;
    uint32_t nb3;
+
+    // fastdiv helper values
+    uint32_t KWmp;   uint32_t KWL;
+    uint32_t KWKHmp; uint32_t KWKHL;
+    uint32_t OWmp;   uint32_t OWL;
+    uint32_t OWOHmp; uint32_t OWOHL;
 }

 p;
@@ -68,6 +78,7 @@ layout(constant_id = 3) const uint BS_NPQ          = 128;
 // Thread-tile sizes
 layout(constant_id = 4) const uint TS_K            = 8;
 layout(constant_id = 5) const uint use_collectives = 1;
+layout(constant_id = 6) const uint SHMEM_PAD       = 4;

 uint32_t       tid     = gl_LocalInvocationID.x;
 const uint32_t WG_SIZE = gl_WorkGroupSize.x;
@@ -85,6 +96,12 @@ uint32_t n_elems_out = K * NPQ;
 // Number of blocktiles per input
 uint32_t NB_CRS = splitWork(CRS, BS_CRS);

+#ifdef COOPMAT2
+#define SHMEM_TYPE float16_t
+#else
+#define SHMEM_TYPE float
+#endif
+
 const uint32_t Ash_stride = BS_CRS + SHMEM_PAD;
 const uint32_t Bsh_stride = BS_NPQ + SHMEM_PAD;

@@ -94,8 +111,8 @@ const uint32_t Bsh_numel = BS_CRS * BS_NPQ;
 const uint32_t Ash_len = BS_K * Ash_stride;
 const uint32_t Bsh_len = BS_CRS * Bsh_stride;

-shared float Ash[Ash_len];  // K x CRS
-shared float Bsh[Bsh_len];  // CRS x NPQ
+shared SHMEM_TYPE Ash[Ash_len];  // K x CRS
+shared SHMEM_TYPE Bsh[Bsh_len];  // CRS x NPQ

 // Threadtile sizes
 const uint32_t TS_NPQ = BS_K * BS_NPQ / WG_SIZE / TS_K;
@@ -104,10 +121,6 @@ const uint32_t TS_NPQ = BS_K * BS_NPQ / WG_SIZE / TS_K;
 const uint32_t NT_K   = BS_K / TS_K;
 const uint32_t NT_NPQ = BS_NPQ / TS_NPQ;

-float regA[TS_K];
-float regB[TS_NPQ];
-float regC[TS_K][TS_NPQ];
-
 /*
 Compute
 KxCRS @ CRSxNPQ = K x NPQ
@@ -131,12 +144,44 @@ uint32_t       Br    = tid / BS_NPQ;
 uint32_t       Bc    = tid % BS_NPQ;
 const uint32_t BrpWg = WG_SIZE / BS_NPQ;

+// see init_fastdiv_values in ggml-vulkan.cpp
+uint fastdiv(uint n, uint mp, uint L) {
+    uint msbs, lsbs;
+    // msbs = mulhi(n, mp)
+    umulExtended(n, mp, msbs, lsbs);
+    return (msbs + n) >> L;
+}
+
+#ifdef COOPMAT2
+#define ACC_TYPE float16_t
+
+ACC_TYPE perElemOpStore(const in uint32_t r, const in uint32_t c, const in ACC_TYPE elem)
+{
+    uint32_t K_idx   = B_idx_K * BS_K + r;
+    uint32_t NPQ_idx = B_idx_NPQ * BS_NPQ + c;
+    uint32_t N_idx   = fastdiv(NPQ_idx, p.OWOHmp, p.OWOHL); // divide by p.OH * p.OW;
+    uint32_t OH_idx  = fastdiv(NPQ_idx - N_idx * p.OH * p.OW, p.OWmp, p.OWL); // divide by p.OW;
+    uint32_t OW_idx  = NPQ_idx - N_idx * p.OH * p.OW - OH_idx * p.OW;
+    uint32_t dst_idx = OW_idx + OH_idx * p.nb1 + K_idx * p.nb2 + N_idx * p.nb3;
+    if (K_idx < K && NPQ_idx < NPQ) {
+        dst_data[dst_idx] = D_TYPE(elem);
+    }
+    return elem;
+}
+#endif
+
 void main() {
+#ifdef COOPMAT2
+    coopmat<ACC_TYPE, gl_ScopeWorkgroup, BS_K, BS_NPQ, gl_MatrixUseAccumulator> matC;
+    matC = coopmat<ACC_TYPE, gl_ScopeWorkgroup, BS_K, BS_NPQ, gl_MatrixUseAccumulator>(0.0);
+#else
+    float regC[TS_K][TS_NPQ];
    for (uint32_t T_ly = 0; T_ly < TS_K; T_ly++) {
        for (uint32_t T_lx = 0; T_lx < TS_NPQ; T_lx++) {
            regC[T_ly][T_lx] = 0.0;
        }
    }
+#endif
    /* Advance block in CRS dim */
    for (uint32_t B_idx_CRS = 0; B_idx_CRS < NB_CRS; B_idx_CRS++) {
        uint32_t CRS_idx_a;
@@ -151,9 +196,9 @@ void main() {
        uint32_t cached_KW_idx;
        if (use_collectives == 1) {
            cached_CRS_idx                = B_idx_CRS * BS_CRS + gl_SubgroupInvocationID;
-            cached_Cin_idx                = cached_CRS_idx / (p.KW * p.KH);
+            cached_Cin_idx                = fastdiv(cached_CRS_idx, p.KWKHmp, p.KWKHL); // divide by (p.KW * p.KH);
            uint32_t cached_CRS_remainder = (cached_CRS_idx - cached_Cin_idx * p.KW * p.KH);
-            cached_KH_idx                 = cached_CRS_remainder / p.KW;
+            cached_KH_idx                 = fastdiv(cached_CRS_remainder, p.KWmp, p.KWL); // divide by p.KW;
            cached_KW_idx                 = cached_CRS_remainder - cached_KH_idx * p.KW;

            CRS_idx_a = subgroupShuffle(cached_CRS_idx, Ac);
@@ -162,16 +207,16 @@ void main() {
            KW_idx_a  = subgroupShuffle(cached_KW_idx, Ac);
        } else {
            CRS_idx_a              = B_idx_CRS * BS_CRS + Ac;  // Global CRS_idx_a (column index of A)
-            Cin_idx_a              = CRS_idx_a / (p.KW * p.KH);
+            Cin_idx_a              = fastdiv(CRS_idx_a, p.KWKHmp, p.KWKHL); // divide by (p.KW * p.KH);
            uint32_t CRS_remainder = CRS_idx_a - Cin_idx_a * p.KW * p.KH;
-            KH_idx_a               = CRS_remainder / p.KW;
+            KH_idx_a               = fastdiv(CRS_remainder, p.KWmp, p.KWL); // divide by p.KW;
            KW_idx_a               = CRS_remainder - KH_idx_a * p.KW;
        }
 #else
        CRS_idx_a     = B_idx_CRS * BS_CRS + Ac;  // Global CRS_idx_a (column index of A)
-        Cin_idx_a     = CRS_idx_a / (p.KW * p.KH);
+        Cin_idx_a     = fastdiv(CRS_idx_a, p.KWKHmp, p.KWKHL); // divide by (p.KW * p.KH); / (p.KW * p.KH);
        CRS_remainder = CRS_idx_a - Cin_idx_a * p.KW * p.KH;
-        KH_idx_a      = CRS_remainder / p.KW;
+        KH_idx_a      = fastdiv(CRS_remainder, p.KWmp, p.KWL); // divide by p.KW;
        KW_idx_a      = CRS_remainder - KH_idx_a * p.KW;
 #endif

@@ -185,16 +230,16 @@ void main() {
            if (K_idx >= K || CRS_idx_a >= CRS) {
                val = 0.0;
            }
-            Ash[B_ly * Ash_stride + B_lx] = val;
+            Ash[B_ly * Ash_stride + B_lx] = SHMEM_TYPE(val);
        }
        /* Load input to B_block: (BS_CRS x BS_NPQ) */
-        for (uint32_t r_offset = 0; r_offset < BS_CRS; r_offset += BrpWg) {
+        UNROLL for (uint32_t r_offset = 0; r_offset < BS_CRS; r_offset += BrpWg) {
            uint32_t B_ly          = r_offset + Br;             /* Row index of B block */
            uint32_t B_lx          = Bc;
            uint32_t NPQ_idx       = B_idx_NPQ * BS_NPQ + B_lx; /* Global NPQ index (column index of B) */
-            uint32_t N_idx         = NPQ_idx / (p.OH * p.OW);
+            uint32_t N_idx         = fastdiv(NPQ_idx, p.OWOHmp, p.OWOHL); // divide by p.OH * p.OW;
            uint32_t NPQ_remainder = NPQ_idx - N_idx * p.OH * p.OW;
-            uint32_t OH_idx        = NPQ_remainder / p.OW;
+            uint32_t OH_idx        = fastdiv(NPQ_remainder, p.OWmp, p.OWL); // divide by p.OW;
            uint32_t OW_idx        = NPQ_remainder - OH_idx * p.OW;

            uint32_t CRS_idx_b;
@@ -209,16 +254,16 @@ void main() {
                KW_idx_b  = subgroupShuffle(cached_KW_idx, r_offset + Br);
            } else {
                CRS_idx_b              = B_idx_CRS * BS_CRS + B_ly; /* Global CRS index (row index of B) */
-                Cin_idx_b              = CRS_idx_b / (p.KW * p.KH);
+                Cin_idx_b              = fastdiv(CRS_idx_b, p.KWKHmp, p.KWKHL); // divide by (p.KW * p.KH);
                uint32_t CRS_remainder = CRS_idx_b - Cin_idx_b * p.KW * p.KH;
-                KH_idx_b               = CRS_remainder / p.KW;
+                KH_idx_b               = fastdiv(CRS_remainder, p.KWmp, p.KWL); // divide by p.KW;
                KW_idx_b               = CRS_remainder - KH_idx_b * p.KW;
            }
 #else
            CRS_idx_b              = B_idx_CRS * BS_CRS + B_ly; /* Global CRS index (row index of B) */
-            Cin_idx_b              = CRS_idx_b / (p.KW * p.KH);
+            Cin_idx_b              = fastdiv(CRS_idx_b, p.KWKHmp, p.KWKHL); // divide by (p.KW * p.KH);
            uint32_t CRS_remainder = CRS_idx_b - Cin_idx_b * p.KW * p.KH;
-            KH_idx_b               = CRS_remainder / p.KW;
+            KH_idx_b               = fastdiv(CRS_remainder, p.KWmp, p.KWL); // divide by p.KW;
            KW_idx_b               = CRS_remainder - KH_idx_b * p.KW;
 #endif

@@ -230,36 +275,55 @@ void main() {
            if (CRS_idx_b >= CRS || NPQ_idx >= NPQ || H_idx < 0 || H_idx >= p.H || W_idx < 0 || W_idx >= p.W) {
                val = 0.0;
            }
-            Bsh[B_ly * Bsh_stride + B_lx] = val;
+            Bsh[B_ly * Bsh_stride + B_lx] = SHMEM_TYPE(val);
        }
        barrier();
-        for (uint32_t CRS_lidx = 0; CRS_lidx < BS_CRS; CRS_lidx++) {
-            for (uint32_t T_ly = 0; T_ly < TS_K; T_ly++) {
-                regA[T_ly] = Ash[(T_y * TS_K + T_ly) * Ash_stride + CRS_lidx];
-            }
-            for (uint32_t T_lx = 0; T_lx < TS_NPQ; T_lx++) {
-                regB[T_lx] = Bsh[CRS_lidx * Bsh_stride + T_x * TS_NPQ + T_lx];
-            }
-            for (uint32_t T_ly = 0; T_ly < TS_K; T_ly++) {
+#ifdef COOPMAT2
+        coopmat<float16_t, gl_ScopeWorkgroup, BS_K, BS_CRS, gl_MatrixUseA> matA;
+        coopmat<float16_t, gl_ScopeWorkgroup, BS_CRS, BS_NPQ, gl_MatrixUseB> matB;
+
+        coopMatLoad(matA, Ash, 0, Ash_stride, gl_CooperativeMatrixLayoutRowMajor);
+        coopMatLoad(matB, Bsh, 0, Bsh_stride, gl_CooperativeMatrixLayoutRowMajor);
+        matC = coopMatMulAdd(matA, matB, matC);
+#else
+        if (T_y * TS_K < K) {
+            UNROLL for (uint32_t CRS_lidx = 0; CRS_lidx < BS_CRS; CRS_lidx++) {
+                float regA[TS_K];
+                float regB[TS_NPQ];
+                for (uint32_t T_ly = 0; T_ly < TS_K; T_ly++) {
+                    regA[T_ly] = Ash[(T_y * TS_K + T_ly) * Ash_stride + CRS_lidx];
+                }
                for (uint32_t T_lx = 0; T_lx < TS_NPQ; T_lx++) {
-                    regC[T_ly][T_lx] = fma(regA[T_ly], regB[T_lx], regC[T_ly][T_lx]);
+                    regB[T_lx] = Bsh[CRS_lidx * Bsh_stride + T_x * TS_NPQ + T_lx];
+                }
+                for (uint32_t T_ly = 0; T_ly < TS_K; T_ly++) {
+                    for (uint32_t T_lx = 0; T_lx < TS_NPQ; T_lx++) {
+                        regC[T_ly][T_lx] = fma(regA[T_ly], regB[T_lx], regC[T_ly][T_lx]);
+                    }
                }
            }
        }
+#endif
        barrier();
    }
    /* Save C* */
-    for (uint32_t T_ly = 0; T_ly < TS_K; T_ly++) {
-        for (uint32_t T_lx = 0; T_lx < TS_NPQ; T_lx++) {
-            uint32_t K_idx   = B_idx_K * BS_K + T_y * TS_K + T_ly;
-            uint32_t NPQ_idx = B_idx_NPQ * BS_NPQ + T_x * TS_NPQ + T_lx;
-            uint32_t N_idx   = NPQ_idx / (p.OH * p.OW);
-            uint32_t OH_idx  = (NPQ_idx - N_idx * p.OH * p.OW) / p.OW;
-            uint32_t OW_idx  = NPQ_idx - N_idx * p.OH * p.OW - OH_idx * p.OW;
-            uint32_t dst_idx = OW_idx + OH_idx * p.nb1 + K_idx * p.nb2 + N_idx * p.nb3;
-            if (K_idx < K && NPQ_idx < NPQ) {
-                dst_data[dst_idx] = regC[T_ly][T_lx];
+#ifdef COOPMAT2
+    coopMatPerElementNV(matC, matC, perElemOpStore);
+#else
+    if (T_y * TS_K < K) {
+        for (uint32_t T_ly = 0; T_ly < TS_K; T_ly++) {
+            for (uint32_t T_lx = 0; T_lx < TS_NPQ; T_lx++) {
+                uint32_t K_idx   = B_idx_K * BS_K + T_y * TS_K + T_ly;
+                uint32_t NPQ_idx = B_idx_NPQ * BS_NPQ + T_x * TS_NPQ + T_lx;
+                uint32_t N_idx   = fastdiv(NPQ_idx, p.OWOHmp, p.OWOHL); // divide by p.OH * p.OW;
+                uint32_t OH_idx  = fastdiv(NPQ_idx - N_idx * p.OH * p.OW, p.OWmp, p.OWL); // divide by p.OW;
+                uint32_t OW_idx  = NPQ_idx - N_idx * p.OH * p.OW - OH_idx * p.OW;
+                uint32_t dst_idx = OW_idx + OH_idx * p.nb1 + K_idx * p.nb2 + N_idx * p.nb3;
+                if (K_idx < K && NPQ_idx < NPQ) {
+                    dst_data[dst_idx] = regC[T_ly][T_lx];
+                }
            }
        }
    }
+#endif
 }
@@ -4,8 +4,8 @@
 #include "generic_unary_head.comp"
 #include "dequant_funcs.comp"

-#if defined(DATA_A_IQ4_NL)
-// 16 invocations needed for init_iq4nl_shmem
+#if defined(DATA_A_IQ4_NL) || defined(DATA_A_MXFP4)
+// 16 invocations needed for init_iq_shmem
 layout(local_size_x = 16, local_size_y = 1, local_size_z = 1) in;
 #else
 layout(local_size_x = 1, local_size_y = 1, local_size_z = 1) in;
@@ -434,6 +434,18 @@ vec4 dequantize4(uint ib, uint iqs, uint a_offset) {
 }
 #endif

+#if defined(DATA_A_MXFP4)
+vec2 dequantize(uint ib, uint iqs, uint a_offset) {
+    const uint vui = uint(data_a[a_offset + ib].qs[iqs]);
+    return vec2(kvalues_mxfp4[vui & 0xF], kvalues_mxfp4[vui >> 4]);
+}
+vec4 dequantize4(uint ib, uint iqs, uint a_offset) {
+    vec2 v0 = dequantize(ib, iqs, a_offset);
+    vec2 v1 = dequantize(ib, iqs + 1, a_offset);
+    return vec4(v0.x, v0.y, v1.x, v1.y);
+}
+#endif
+
 #if defined(DATA_A_F32) || defined(DATA_A_F16) || defined(DATA_A_BF16)
 vec2 get_dm(uint ib, uint a_offset) {
    return vec2(0, 0);
@@ -455,6 +467,12 @@ vec2 get_dm(uint ib, uint a_offset) {
 }
 #endif

+#if defined(DATA_A_MXFP4)
+vec2 get_dm(uint ib, uint a_offset) {
+    return vec2(e8m0_to_fp32(data_a[a_offset + ib].e), 0);
+}
+#endif
+
 #if defined(DATA_A_Q4_1) || defined(DATA_A_Q5_1)
 vec2 get_dm(uint ib, uint a_offset) {
    return vec2(float(data_a[a_offset + ib].d), float(data_a[a_offset + ib].m));
@@ -654,6 +654,25 @@ float16_t dequantFuncIQ4_NL(const in decodeBufIQ4_NL bl, const in uint blockCoor
 }
 #endif

+#if defined(DATA_A_MXFP4)
+layout(buffer_reference, std430, buffer_reference_align = 2) buffer decodeBufMXFP4 {
+   block_mxfp4 block;
+};
+
+float16_t dequantFuncMXFP4(const in decodeBufMXFP4 bl, const in uint blockCoords[2], const in uint coordInBlock[2])
+{
+    const float d = e8m0_to_fp32(bl.block.e);
+    const uint idx = coordInBlock[1];
+    const uint iqs = idx & 0xF;
+    const uint shift = (idx & 0x10) >> 2;
+    uint32_t qs = bl.block.qs[iqs];
+    qs >>= shift;
+    qs &= 0xF;
+    float16_t ret = float16_t(kvalues_mxfp4[qs] * d);
+    return ret;
+}
+#endif
+
 #if defined(DATA_A_Q4_0)
 #define dequantFuncA dequantFuncQ4_0
 #elif defined(DATA_A_Q4_1)
@@ -696,4 +715,6 @@ float16_t dequantFuncIQ4_NL(const in decodeBufIQ4_NL bl, const in uint blockCoor
 #define dequantFuncA dequantFuncIQ4_XS
 #elif defined(DATA_A_IQ4_NL)
 #define dequantFuncA dequantFuncIQ4_NL
+#elif defined(DATA_A_MXFP4)
+#define dequantFuncA dequantFuncMXFP4
 #endif
@@ -0,0 +1,32 @@
+#version 450
+
+#include "dequant_head.comp"
+
+layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer A {block_mxfp4 data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_b[];};
+
+void main() {
+    const uint i = gl_WorkGroupID.x * 4 + gl_LocalInvocationID.x / 64;
+
+    init_iq_shmem(gl_WorkGroupSize);
+
+    const uint tid = gl_LocalInvocationID.x % 64;
+    const uint il  = tid/32;
+    const uint ir  = tid%32;
+    const uint ib = 32*i + ir;
+    if (ib >= p.nel / 32) {
+        return;
+    }
+
+    const uint q_idx = 8*il;
+    const uint b_idx = 1024*i + 32*ir + q_idx;
+
+    const float d = e8m0_to_fp32(data_a[ib].e);
+
+    [[unroll]] for (uint l = 0; l < 8; ++l) {
+        data_b[b_idx + l +  0] = D_TYPE(d * kvalues_mxfp4[data_a[ib].qs[q_idx + l] & 0xF]);
+        data_b[b_idx + l + 16] = D_TYPE(d * kvalues_mxfp4[data_a[ib].qs[q_idx + l] >>  4]);
+    }
+}
@@ -14,4 +14,6 @@ layout (push_constant) uniform parameter
    uint ne00;
    uint ne20;
    uint mode;
+    float alpha;
+    float limit;
 } p;
@@ -26,6 +26,9 @@ layout (push_constant) uniform parameter
    uint ne12;
    uint b_offset;
    uint d_offset;
+    uint nb03;
+    uint nb13;
+    uint nb23;
 } p;

 shared FLOAT_TYPE tmp[BLOCK_SIZE];
@@ -34,6 +37,7 @@ void main() {
    const uint tid       = gl_LocalInvocationID.x;
    const uint row_x     = gl_GlobalInvocationID.y;
    const uint channel   = gl_GlobalInvocationID.z;
+    const uint i3        = gl_WorkGroupID.x;
    const uint channel_x = channel / p.channel_x_divisor;
    const uint channel_y = channel % p.ne12;

@@ -41,7 +45,7 @@ void main() {
    const uint nrows_dst = p.nrows_x;
    const uint row_dst   = row_x;

-    const uint idst = channel*nrows_dst + row_dst;
+    const uint idst = i3*p.nb23 + channel*nrows_dst + row_dst;

    FLOAT_TYPE temp = 0.0f;

@@ -58,8 +62,8 @@ void main() {

                const uint row_y = col_x;

-                const uint ix = channel_x*p.channel_stride_x + row_x*p.row_stride_x + col_x;
-                const uint iy = channel_y*p.channel_stride_y + row_y;
+                const uint ix = i3*p.nb03 + channel_x*p.channel_stride_x + row_x*p.row_stride_x + col_x;
+                const uint iy = i3*p.nb13 + channel_y*p.channel_stride_y + row_y;

                const vec4 av4 = vec4(data_a_v4[ix / 4]);
                const vec4 bv4 = vec4(data_b_v4[iy / 4]);
@@ -74,8 +78,8 @@ void main() {

            const uint row_y = col_x;

-            const uint ix = channel_x*p.channel_stride_x + row_x*p.row_stride_x + col_x;
-            const uint iy = channel_y*p.channel_stride_y + row_y;
+            const uint ix = i3*p.nb03 + channel_x*p.channel_stride_x + row_x*p.row_stride_x + col_x;
+            const uint iy = i3*p.nb13 + channel_y*p.channel_stride_y + row_y;

            const vec4 av4 = vec4(data_a_v4[ix / 4]);
            const vec4 bv4 = vec4(data_b_v4[iy / 4]);
@@ -91,8 +95,8 @@ void main() {

            const uint row_y = col_x;

-            const uint ix = channel_x*p.channel_stride_x + row_x*p.row_stride_x + col_x;
-            const uint iy = channel_y*p.channel_stride_y + row_y;
+            const uint ix = i3*p.nb03 + channel_x*p.channel_stride_x + row_x*p.row_stride_x + col_x;
+            const uint iy = i3*p.nb13 + channel_y*p.channel_stride_y + row_y;

            const FLOAT_TYPE xi = FLOAT_TYPE(data_a[ix]);

@@ -747,6 +747,21 @@ void main() {
            buf_a[buf_idx + 1 ] = FLOAT_TYPE(kvalues_iq4nl[bitfieldExtract(vui, 8, 4)]) * d;
            buf_a[buf_idx + 16] = FLOAT_TYPE(kvalues_iq4nl[bitfieldExtract(vui, 4, 4)]) * d;
            buf_a[buf_idx + 17] = FLOAT_TYPE(kvalues_iq4nl[vui >> 12]) * d;
+#elif defined(DATA_A_MXFP4)
+            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
+            const uint buf_idx = (loadc_a + l) * SHMEM_STRIDE + 2 * loadr_a;
+
+            const uint ib = idx / 8;
+            const uint iqs = (idx & 0x07) * 2;
+
+            const float d = e8m0_to_fp32(data_a[ib].e);
+            const uint vui = uint(data_a[ib].qs[iqs]);
+            const uint vui2 = uint(data_a[ib].qs[iqs+1]);
+
+            buf_a[buf_idx     ] = FLOAT_TYPE(kvalues_mxfp4[vui & 0xF] * d);
+            buf_a[buf_idx + 16] = FLOAT_TYPE(kvalues_mxfp4[vui >>  4] * d);
+            buf_a[buf_idx +  1] = FLOAT_TYPE(kvalues_mxfp4[vui2 & 0xF] * d);
+            buf_a[buf_idx + 17] = FLOAT_TYPE(kvalues_mxfp4[vui2 >>  4] * d);
 #endif
        }
        [[unroll]] for (uint l = 0; l < BN; l += loadstride_b) {
@@ -92,6 +92,12 @@ FLOAT_TYPE get_d(uint ib) {
 }
 #endif

+#if defined(DATA_A_MXFP4)
+FLOAT_TYPE get_d(uint ib) {
+    return FLOAT_TYPE(e8m0_to_fp32(data_a[ib].e));
+}
+#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);
@@ -20,6 +20,7 @@ layout (push_constant) uniform parameter
    float m1;
    uint n_head_log2;
    uint nrows_x;
+    uint has_sinks;
 } p;

 #include "types.comp"
@@ -29,7 +30,8 @@ layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in;

 layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
 layout (binding = 1) readonly buffer Y {B_TYPE data_b[];};
-layout (binding = 2) buffer D {D_TYPE data_d[];};
+layout (binding = 2) readonly buffer Z {float data_c[];};
+layout (binding = 3) buffer D {D_TYPE data_d[];};

 shared FLOAT_TYPE vals[BLOCK_SIZE];

@@ -60,13 +62,13 @@ void soft_max(uint num_iters) {
        const uint h = (rowx / p.ne01) % p.ne02; // head index

        const float base = h < p.n_head_log2 ? p.m0 : p.m1;
-        const uint   exp  = h < p.n_head_log2 ? h + 1 : 2*(h - p.n_head_log2) + 1;
+        const uint   exp = h < p.n_head_log2 ? h + 1 : 2*(h - p.n_head_log2) + 1;

        slope = pow(base, exp);
    }

    // Find max
-    FLOAT_TYPE max_val = uintBitsToFloat(0xFF800000);
+    FLOAT_TYPE max_val = p.has_sinks == 0 ? uintBitsToFloat(0xFF800000) : data_c[i02];

    // Cache values while we compute the max, so we don't need to read them
    // again when we're ready to compute exp(x-max).
@@ -148,6 +150,10 @@ void soft_max(uint num_iters) {
    }
    sum = vals[0];

+    if (p.has_sinks != 0) {
+        sum += FLOAT_TYPE(exp(FLOAT_TYPE(data_c[i02]) - max_val));
+    }
+
    FLOAT_TYPE rcpdivisor = 1.0/sum;

    [[unroll]] for (uint col0 = 0, idx = 0; idx < num_iters; col0 += BLOCK_SIZE, ++idx) {
@@ -0,0 +1,14 @@
+#version 450
+
+#include "glu_head.comp"
+
+float op(float a, float b) {
+    float xi = min(a, p.limit);
+    float gi = max(min(b, p.limit), -p.limit);
+
+    float out_glu = xi / (1.0f + exp(-xi * p.alpha));
+    out_glu = out_glu * (1.0f + gi);
+    return out_glu;
+}
+
+#include "glu_main.comp"
@@ -1337,6 +1337,29 @@ struct block_iq4_nl_packed16
 #define A_TYPE_PACKED16 block_iq4_nl_packed16
 #endif

+#define QUANT_K_MXFP4 32
+#define QUANT_R_MXFP4 2
+
+struct block_mxfp4
+{
+    uint8_t e;
+    uint8_t qs[QUANT_K_MXFP4/2];
+};
+
+//struct block_mxfp4_packed16
+//{
+//    uint8_t e;
+//    uint16_t qs[QUANT_K_MXFP4/2/2];
+//};
+
+#if defined(DATA_A_MXFP4)
+#define QUANT_K QUANT_K_MXFP4
+#define QUANT_R QUANT_R_MXFP4
+#define QUANT_AUXF 1
+#define A_TYPE block_mxfp4
+//#define A_TYPE_PACKED16 block_mxfp4_packed16
+#endif
+
 #if defined(DATA_A_IQ4_NL) || defined(DATA_A_IQ4_XS)
 const int8_t kvalues_iq4nl_const[16] = {
    int8_t(-127), int8_t(-104), int8_t(-83), int8_t(-65), int8_t(-49), int8_t(-35), int8_t(-22), int8_t(-10),
@@ -1356,6 +1379,25 @@ void init_iq_shmem(uvec3 wgsize)
 }
 #endif

+#if defined(DATA_A_MXFP4)
+const FLOAT_TYPE kvalues_mxfp4_const[16] = {
+    FLOAT_TYPE(0.0f), FLOAT_TYPE(0.5f), FLOAT_TYPE(1.0f), FLOAT_TYPE(1.5f), FLOAT_TYPE(2.0f), FLOAT_TYPE(3.0f), FLOAT_TYPE(4.0f), FLOAT_TYPE(6.0f),
+    FLOAT_TYPE(-0.0f), FLOAT_TYPE(-0.5f), FLOAT_TYPE(-1.0f), FLOAT_TYPE(-1.5f), FLOAT_TYPE(-2.0f), FLOAT_TYPE(-3.0f), FLOAT_TYPE(-4.0f), FLOAT_TYPE(-6.0f)
+};
+
+shared FLOAT_TYPE kvalues_mxfp4[16];
+
+#define NEEDS_INIT_IQ_SHMEM
+void init_iq_shmem(uvec3 wgsize)
+{
+    // copy the table into shared memory and sync
+    for (uint i = gl_LocalInvocationIndex.x; i < kvalues_mxfp4.length(); i += wgsize.x) {
+        kvalues_mxfp4[i] = kvalues_mxfp4_const[i];
+    }
+    barrier();
+}
+#endif
+
 // returns the bfloat value in the low 16b.
 // See ggml_compute_fp32_to_bf16
 uint32_t fp32_to_bf16(float f)
@@ -1370,4 +1412,17 @@ float bf16_to_fp32(uint32_t u)
    return uintBitsToFloat(u << 16);
 }

+float e8m0_to_fp32(uint8_t x) {
+    uint32_t bits;
+
+    if (x == 0) {
+        bits = 0x00400000;
+    } else {
+        bits = x;
+        bits = bits << 23;
+    }
+
+    return uintBitsToFloat(bits);
+}
+
 #endif // !defined(GGML_TYPES_COMP)
@@ -64,6 +64,7 @@ const std::vector<std::string> type_names = {
    "iq3_s",
    "iq4_xs",
    "iq4_nl",
+    "mxfp4",
    "bf16",
 };

@@ -118,7 +119,7 @@ void execute_command(const std::string& command, std::string& stdout_str, std::s
    CloseHandle(pi.hProcess);
    CloseHandle(pi.hThread);
 #else
-int stdout_pipe[2];
+    int stdout_pipe[2];
    int stderr_pipe[2];

    if (pipe(stdout_pipe) != 0 || pipe(stderr_pipe) != 0) {
@@ -362,7 +363,7 @@ void matmul_shaders(bool fp16, bool matmul_id, bool coopmat, bool coopmat2, bool
        std::string load_vec_quant = "2";
        if ((tname == "q4_0") || (tname == "q4_1") || (tname == "iq1_s") || (tname == "iq1_m") || (tname == "iq2_xxs") || (tname == "iq2_xs") || (tname == "iq2_s"))
            load_vec_quant = "8";
-        else if ((tname == "q5_0") || (tname == "q5_1") || (tname == "q8_0") || (tname == "iq3_xxs") || (tname == "iq3_s") || (tname == "iq4_nl"))
+        else if ((tname == "q5_0") || (tname == "q5_1") || (tname == "q8_0") || (tname == "iq3_xxs") || (tname == "iq3_s") || (tname == "iq4_nl") || (tname == "mxfp4"))
            load_vec_quant = "4";

        if (tname == "bf16") {
@@ -602,6 +603,8 @@ void process_shaders() {
        string_to_spv("reglu_f32" + suffix,      "reglu.comp",       {{"A_TYPE", "float"},       {"D_TYPE", "float"},       {"RTE16", rte ? "1" : "0"}});
        string_to_spv("swiglu_f16" + suffix,     "swiglu.comp",      {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"},   {"RTE16", rte ? "1" : "0"}});
        string_to_spv("swiglu_f32" + suffix,     "swiglu.comp",      {{"A_TYPE", "float"},       {"D_TYPE", "float"},       {"RTE16", rte ? "1" : "0"}});
+        string_to_spv("swiglu_oai_f16" + suffix, "swiglu_oai.comp",  {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"},   {"RTE16", rte ? "1" : "0"}});
+        string_to_spv("swiglu_oai_f32" + suffix, "swiglu_oai.comp",  {{"A_TYPE", "float"},       {"D_TYPE", "float"},       {"RTE16", rte ? "1" : "0"}});
        string_to_spv("geglu_erf_f16" + suffix,  "geglu_erf.comp",   {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"},   {"RTE16", rte ? "1" : "0"}});
        string_to_spv("geglu_erf_f32" + suffix,  "geglu_erf.comp",   {{"A_TYPE", "float"},       {"D_TYPE", "float"},       {"RTE16", rte ? "1" : "0"}});
        string_to_spv("geglu_quick_f16" + suffix,"geglu_quick.comp", {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"},   {"RTE16", rte ? "1" : "0"}});
@@ -655,14 +658,24 @@ void process_shaders() {

    string_to_spv("opt_step_adamw_f32", "opt_step_adamw.comp", merge_maps(base_dict, {{"A_TYPE", "float"}}));

-    string_to_spv("conv2d_f32", "conv2d_mm.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"USE_COLLECTIVES", "1"}});
-    string_to_spv("conv2d_f16_f32", "conv2d_mm.comp", {{"A_TYPE", "float16_t"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"USE_COLLECTIVES", "1"}});
+    string_to_spv("conv2d_f32_unroll", "conv2d_mm.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"USE_COLLECTIVES", "1"}, {"UNROLL", "[[unroll]]"}});
+    string_to_spv("conv2d_f16_f32_unroll", "conv2d_mm.comp", {{"A_TYPE", "float16_t"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"USE_COLLECTIVES", "1"}, {"UNROLL", "[[unroll]]"}});
+
+    string_to_spv("conv2d_f32", "conv2d_mm.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"USE_COLLECTIVES", "1"}, {"UNROLL", ""}});
+    string_to_spv("conv2d_f16_f32", "conv2d_mm.comp", {{"A_TYPE", "float16_t"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"USE_COLLECTIVES", "1"}, {"UNROLL", ""}});
+
+#if defined(GGML_VULKAN_COOPMAT2_GLSLC_SUPPORT)
+    string_to_spv("conv2d_f32", "conv2d_mm.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"USE_COLLECTIVES", "1"}, {"UNROLL", "[[unroll]]"}, {"COOPMAT2", "1"}}, true, false, true);
+    string_to_spv("conv2d_f16_f32", "conv2d_mm.comp", {{"A_TYPE", "float16_t"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"USE_COLLECTIVES", "1"}, {"UNROLL", "[[unroll]]"}, {"COOPMAT2", "1"}}, true, false, true);
+#endif

    string_to_spv("conv2d_dw_whcn_f32", "conv2d_dw.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"WHCN", "1"}}));
    string_to_spv("conv2d_dw_cwhn_f32", "conv2d_dw.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"CWHN", "1"}}));

    string_to_spv("roll_f32", "roll.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}}));

+    string_to_spv("add_id_f32", "add_id.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}}));
+
    for (auto &c : compiles) {
        c.wait();
    }
@@ -582,9 +582,6 @@ FILE * ggml_fopen(const char * fname, const char * mode) {
 #endif

 }
-static void ggml_vec_dot_f32(int n, float * GGML_RESTRICT s, size_t bs, const float * GGML_RESTRICT x, size_t bx, const float * GGML_RESTRICT y, size_t by, int nrc);
-static void ggml_vec_dot_f16(int n, float * GGML_RESTRICT s, size_t bs, ggml_fp16_t * GGML_RESTRICT x, size_t bx, ggml_fp16_t * GGML_RESTRICT y, size_t by, int nrc);
-static void ggml_vec_dot_bf16(int n, float * GGML_RESTRICT s, size_t bs, ggml_bf16_t * GGML_RESTRICT x, size_t bx, ggml_bf16_t * GGML_RESTRICT y, size_t by, int nrc);

 static const struct ggml_type_traits type_traits[GGML_TYPE_COUNT] = {
    [GGML_TYPE_I8] = {
@@ -690,6 +687,14 @@ static const struct ggml_type_traits type_traits[GGML_TYPE_COUNT] = {
        .is_quantized             = true,
        .from_float_ref           = (ggml_from_float_t) quantize_row_q8_1_ref,
    },
+    [GGML_TYPE_MXFP4] = {
+        .type_name                = "mxfp4",
+        .blck_size                = QK_MXFP4,
+        .type_size                = sizeof(block_mxfp4),
+        .is_quantized             = true,
+        .to_float                 = (ggml_to_float_t) dequantize_row_mxfp4,
+        .from_float_ref           = (ggml_from_float_t)quantize_row_mxfp4_ref,
+    },
    [GGML_TYPE_Q2_K] = {
        .type_name                = "q2_K",
        .blck_size                = QK_K,
@@ -917,6 +922,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {

    "DUP",
    "ADD",
+    "ADD_ID",
    "ADD1",
    "ACC",
    "SUB",
@@ -1010,13 +1016,14 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
    "GLU",
 };

-static_assert(GGML_OP_COUNT == 86, "GGML_OP_COUNT != 86");
+static_assert(GGML_OP_COUNT == 87, "GGML_OP_COUNT != 87");

 static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
    "none",

    "x",
    "x+y",
+    "x[i]+y",
    "x+y",
    "view(x,nb,offset)+=y->x",
    "x-y",
@@ -1110,7 +1117,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
    "glu(x)",
 };

-static_assert(GGML_OP_COUNT == 86, "GGML_OP_COUNT != 86");
+static_assert(GGML_OP_COUNT == 87, "GGML_OP_COUNT != 87");

 static_assert(GGML_OP_POOL_COUNT == 2, "GGML_OP_POOL_COUNT != 2");

@@ -1140,11 +1147,12 @@ static const char * GGML_GLU_OP_NAME[GGML_GLU_OP_COUNT] = {
    "REGLU",
    "GEGLU",
    "SWIGLU",
+    "SWIGLU_OAI",
    "GEGLU_ERF",
    "GEGLU_QUICK",
 };

-static_assert(GGML_GLU_OP_COUNT == 5, "GGML_GLU_OP_COUNT != 5");
+static_assert(GGML_GLU_OP_COUNT == 6, "GGML_GLU_OP_COUNT != 6");


 static_assert(sizeof(struct ggml_object)%GGML_MEM_ALIGN == 0, "ggml_object size must be a multiple of GGML_MEM_ALIGN");
@@ -1312,6 +1320,7 @@ enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype) {
        case GGML_FTYPE_MOSTLY_Q5_0:          wtype = GGML_TYPE_Q5_0;  break;
        case GGML_FTYPE_MOSTLY_Q5_1:          wtype = GGML_TYPE_Q5_1;  break;
        case GGML_FTYPE_MOSTLY_Q8_0:          wtype = GGML_TYPE_Q8_0;  break;
+        case GGML_FTYPE_MOSTLY_MXFP4:         wtype = GGML_TYPE_MXFP4; break;
        case GGML_FTYPE_MOSTLY_Q2_K:          wtype = GGML_TYPE_Q2_K;  break;
        case GGML_FTYPE_MOSTLY_Q3_K:          wtype = GGML_TYPE_Q3_K;  break;
        case GGML_FTYPE_MOSTLY_Q4_K:          wtype = GGML_TYPE_Q4_K;  break;
@@ -1962,6 +1971,27 @@ struct ggml_tensor * ggml_add_cast(
    return ggml_add_cast_impl(ctx, a, b, type);
 }

+struct ggml_tensor * ggml_add_id(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b,
+            struct ggml_tensor  * ids) {
+
+    GGML_ASSERT(a->ne[0] == b->ne[0]);
+    GGML_ASSERT(a->ne[1] == ids->ne[0]);
+    GGML_ASSERT(a->ne[2] == ids->ne[1]);
+    GGML_ASSERT(ids->type == GGML_TYPE_I32);
+
+    struct ggml_tensor * result = ggml_dup_tensor(ctx, a);
+
+    result->op     = GGML_OP_ADD_ID;
+    result->src[0] = a;
+    result->src[1] = b;
+    result->src[2] = ids;
+
+    return result;
+}
+
 // ggml_add1

 static struct ggml_tensor * ggml_add1_impl(
@@ -2812,6 +2842,19 @@ struct ggml_tensor * ggml_geglu_quick_split(
    return ggml_glu_impl(ctx, a, b, GGML_GLU_OP_GEGLU_QUICK, false);
 }

+struct ggml_tensor * ggml_swiglu_oai(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        struct ggml_tensor  * b,
+        float                 alpha,
+        float                 limit) {
+    struct ggml_tensor * result = ggml_glu_impl(ctx, a, b, GGML_GLU_OP_SWIGLU_OAI, false);
+    ggml_set_op_params_f32(result, 2, alpha);
+    ggml_set_op_params_f32(result, 3, limit);
+
+    return result;
+}
+
 // ggml_norm

 static struct ggml_tensor * ggml_norm_impl(
@@ -3779,6 +3822,22 @@ struct ggml_tensor * ggml_soft_max_ext(
    return ggml_soft_max_impl(ctx, a, mask, scale, max_bias, false);
 }

+void ggml_soft_max_add_sinks(
+        struct ggml_tensor * a,
+        struct ggml_tensor * sinks) {
+    if (!sinks) {
+        a->src[2] = NULL;
+        return;
+    }
+
+    GGML_ASSERT(a->op == GGML_OP_SOFT_MAX);
+    GGML_ASSERT(a->src[2] == NULL);
+    GGML_ASSERT(a->src[0]->ne[2] == sinks->ne[0]);
+    GGML_ASSERT(sinks->type == GGML_TYPE_F32);
+
+    a->src[2] = sinks;
+}
+
 // ggml_soft_max_ext_back

 static struct ggml_tensor * ggml_soft_max_ext_back_impl(
@@ -4812,6 +4871,22 @@ enum ggml_prec ggml_flash_attn_ext_get_prec(
    return (enum ggml_prec) prec_i32;
 }

+void ggml_flash_attn_ext_add_sinks(
+        struct ggml_tensor * a,
+        struct ggml_tensor * sinks) {
+    if (!sinks) {
+        a->src[4] = NULL;
+        return;
+    }
+
+    GGML_ASSERT(a->op == GGML_OP_FLASH_ATTN_EXT);
+    GGML_ASSERT(a->src[4] == NULL);
+    GGML_ASSERT(a->src[0]->ne[2] == sinks->ne[0]);
+    GGML_ASSERT(sinks->type == GGML_TYPE_F32);
+
+    a->src[4] = sinks;
+}
+
 // ggml_flash_attn_back

 struct ggml_tensor * ggml_flash_attn_back(
@@ -6872,6 +6947,7 @@ size_t ggml_quantize_chunk(
        case GGML_TYPE_Q5_0:    result = quantize_q5_0(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
        case GGML_TYPE_Q5_1:    result = quantize_q5_1(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
        case GGML_TYPE_Q8_0:    result = quantize_q8_0(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
+        case GGML_TYPE_MXFP4:   result = quantize_mxfp4(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
        case GGML_TYPE_Q2_K:    result = quantize_q2_K(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
        case GGML_TYPE_Q3_K:    result = quantize_q3_K(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
        case GGML_TYPE_Q4_K:    result = quantize_q4_K(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break;
@@ -105,6 +105,7 @@ class Keys:
        EXPERT_WEIGHTS_NORM               = "{arch}.expert_weights_norm"
        EXPERT_GATING_FUNC                = "{arch}.expert_gating_func"
        MOE_EVERY_N_LAYERS                = "{arch}.moe_every_n_layers"
+        NEXTN_PREDICT_LAYERS              = "{arch}.nextn_predict_layers"
        POOLING_TYPE                      = "{arch}.pooling_type"
        LOGIT_SCALE                       = "{arch}.logit_scale"
        DECODER_START_TOKEN_ID            = "{arch}.decoder_start_token_id"
@@ -357,6 +358,7 @@ class MODEL_ARCH(IntEnum):
    DEEPSEEK2        = auto()
    CHATGLM          = auto()
    GLM4             = auto()
+    GLM4_MOE         = auto()
    BITNET           = auto()
    T5               = auto()
    T5ENCODER        = auto()
@@ -378,6 +380,7 @@ class MODEL_ARCH(IntEnum):
    HUNYUAN_MOE      = auto()
    HUNYUAN_DENSE    = auto()
    SMOLLM3          = auto()
+    GPT_OSS          = auto()
    LFM2             = auto()
    DREAM            = auto()
    SMALLTHINKER     = auto()
@@ -414,6 +417,7 @@ class MODEL_TENSOR(IntEnum):
    ATTN_OUT_NORM        = auto()
    ATTN_POST_NORM       = auto()
    ATTN_ROT_EMBD        = auto()
+    ATTN_SINKS           = auto()
    FFN_GATE_INP         = auto()
    FFN_GATE_INP_SHEXP   = auto()
    FFN_NORM             = auto()
@@ -614,6 +618,13 @@ class MODEL_TENSOR(IntEnum):
    A_MMPROJ_FC          = auto()
    A_MM_NORM_PRE        = auto()
    A_MM_NORM_MID        = auto()
+    # nextn/mtp
+    NEXTN_EH_PROJ        = auto()
+    NEXTN_EMBED_TOKENS   = auto()
+    NEXTN_ENORM          = auto()
+    NEXTN_HNORM          = auto()
+    NEXTN_SHARED_HEAD_HEAD = auto()
+    NEXTN_SHARED_HEAD_NORM = auto()


 MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
@@ -678,6 +689,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
    MODEL_ARCH.DEEPSEEK2:        "deepseek2",
    MODEL_ARCH.CHATGLM:          "chatglm",
    MODEL_ARCH.GLM4:             "glm4",
+    MODEL_ARCH.GLM4_MOE:         "glm4moe",
    MODEL_ARCH.BITNET:           "bitnet",
    MODEL_ARCH.T5:               "t5",
    MODEL_ARCH.T5ENCODER:        "t5encoder",
@@ -700,6 +712,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
    MODEL_ARCH.HUNYUAN_MOE:      "hunyuan-moe",
    MODEL_ARCH.HUNYUAN_DENSE:    "hunyuan-dense",
    MODEL_ARCH.SMOLLM3:          "smollm3",
+    MODEL_ARCH.GPT_OSS:          "gpt-oss",
    MODEL_ARCH.LFM2:             "lfm2",
    MODEL_ARCH.DREAM:            "dream",
    MODEL_ARCH.SMALLTHINKER:     "smallthinker",
@@ -734,6 +747,7 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
    MODEL_TENSOR.ATTN_V:                    "blk.{bid}.attn_v",
    MODEL_TENSOR.ATTN_OUT:                  "blk.{bid}.attn_output",
    MODEL_TENSOR.ATTN_ROT_EMBD:             "blk.{bid}.attn_rot_embd",
+    MODEL_TENSOR.ATTN_SINKS:                "blk.{bid}.attn_sinks",
    MODEL_TENSOR.ATTN_Q_NORM:               "blk.{bid}.attn_q_norm",
    MODEL_TENSOR.ATTN_K_NORM:               "blk.{bid}.attn_k_norm",
    MODEL_TENSOR.ATTN_OUT_NORM:             "blk.{bid}.attn_output_norm",
@@ -936,6 +950,13 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
    MODEL_TENSOR.A_MMPROJ_FC:               "mm.a.fc",
    MODEL_TENSOR.A_MM_NORM_PRE:             "mm.a.norm_pre",
    MODEL_TENSOR.A_MM_NORM_MID:             "mm.a.norm_mid",
+    # NextN/MTP
+    MODEL_TENSOR.NEXTN_EH_PROJ:             "blk.{bid}.nextn.eh_proj",
+    MODEL_TENSOR.NEXTN_EMBED_TOKENS:        "blk.{bid}.nextn.embed_tokens",
+    MODEL_TENSOR.NEXTN_ENORM:               "blk.{bid}.nextn.enorm",
+    MODEL_TENSOR.NEXTN_HNORM:               "blk.{bid}.nextn.hnorm",
+    MODEL_TENSOR.NEXTN_SHARED_HEAD_HEAD:    "blk.{bid}.nextn.shared_head_head",
+    MODEL_TENSOR.NEXTN_SHARED_HEAD_NORM:    "blk.{bid}.nextn.shared_head_norm",
 }

 MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
@@ -2124,6 +2145,37 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
        MODEL_TENSOR.ATTN_POST_NORM,
        MODEL_TENSOR.FFN_POST_NORM,
    ],
+    MODEL_ARCH.GLM4_MOE: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_POST_NORM,
+        MODEL_TENSOR.ATTN_Q,
+        MODEL_TENSOR.ATTN_K,
+        MODEL_TENSOR.ATTN_V,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.ATTN_Q_NORM,
+        MODEL_TENSOR.ATTN_K_NORM,
+        MODEL_TENSOR.FFN_GATE,
+        MODEL_TENSOR.FFN_DOWN,
+        MODEL_TENSOR.FFN_UP,
+        MODEL_TENSOR.FFN_GATE_INP,
+        MODEL_TENSOR.FFN_GATE_EXP,
+        MODEL_TENSOR.FFN_DOWN_EXP,
+        MODEL_TENSOR.FFN_UP_EXP,
+        MODEL_TENSOR.FFN_GATE_SHEXP,
+        MODEL_TENSOR.FFN_DOWN_SHEXP,
+        MODEL_TENSOR.FFN_UP_SHEXP,
+        MODEL_TENSOR.FFN_EXP_PROBS_B,
+        # NextN/MTP tensors - preserved but unused
+        MODEL_TENSOR.NEXTN_EH_PROJ,
+        MODEL_TENSOR.NEXTN_EMBED_TOKENS,
+        MODEL_TENSOR.NEXTN_ENORM,
+        MODEL_TENSOR.NEXTN_HNORM,
+        MODEL_TENSOR.NEXTN_SHARED_HEAD_HEAD,
+        MODEL_TENSOR.NEXTN_SHARED_HEAD_NORM,
+    ],
    MODEL_ARCH.BITNET: [
        MODEL_TENSOR.ATTN_Q,
        MODEL_TENSOR.ATTN_K,
@@ -2505,6 +2557,22 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
        MODEL_TENSOR.FFN_DOWN,
        MODEL_TENSOR.FFN_UP,
    ],
+    MODEL_ARCH.GPT_OSS: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_POST_NORM,
+        MODEL_TENSOR.ATTN_Q,
+        MODEL_TENSOR.ATTN_K,
+        MODEL_TENSOR.ATTN_V,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.ATTN_SINKS,
+        MODEL_TENSOR.FFN_GATE_INP,
+        MODEL_TENSOR.FFN_GATE_EXP,
+        MODEL_TENSOR.FFN_DOWN_EXP,
+        MODEL_TENSOR.FFN_UP_EXP,
+    ],
    MODEL_ARCH.LFM2: [
        MODEL_TENSOR.TOKEN_EMBD,
        MODEL_TENSOR.TOKEN_EMBD_NORM,
@@ -2659,6 +2727,7 @@ class GGMLQuantizationType(IntEnum):
    BF16    = 30
    TQ1_0   = 34
    TQ2_0   = 35
+    MXFP4   = 39


 class ExpertGatingFuncType(IntEnum):
@@ -2799,6 +2868,7 @@ GGML_QUANT_SIZES: dict[GGMLQuantizationType, tuple[int, int]] = {
    GGMLQuantizationType.BF16:    (1, 2),
    GGMLQuantizationType.TQ1_0:   (256, 2 + 4 * 13),
    GGMLQuantizationType.TQ2_0:   (256, 2 + 64),
+    GGMLQuantizationType.MXFP4:   (32, 1 + 16),
 }


@@ -138,8 +138,9 @@ class GGUFWriter:
                size = prod(shape)

                if "_exps." in name:
-                    expert_params += (size // shape[-3])
-                    expert_sum += shape[-3]
+                    expert_count = shape[-2 if ".bias" in name else -3]
+                    expert_params += (size // expert_count)
+                    expert_sum += expert_count
                    n_expert_tensors += 1
                else:
                    shared_params += size
@@ -753,6 +754,9 @@ class GGUFWriter:
    def add_moe_every_n_layers(self, value: int) -> None:
        self.add_uint32(Keys.LLM.MOE_EVERY_N_LAYERS.format(arch=self.arch), value)

+    def add_nextn_predict_layers(self, count: int) -> None:
+        self.add_uint32(Keys.LLM.NEXTN_PREDICT_LAYERS.format(arch=self.arch), count)
+
    def add_swin_norm(self, value: bool) -> None:
        self.add_bool(Keys.LLM.SWIN_NORM.format(arch=self.arch), value)

@@ -111,6 +111,7 @@ def main() -> None:
    parser.add_argument("--general-description",                       type=str,  help="The models general.description", metavar='"Description ..."')
    parser.add_argument("--chat-template",                             type=str,  help="Chat template string (or JSON string containing templates)", metavar='"{% ... %} ..."')
    parser.add_argument("--chat-template-config",                      type=Path, help="Config file containing chat template(s)", metavar='tokenizer_config.json')
+    parser.add_argument("--chat-template-file",                        type=Path, help="Jinja file containing chat template", metavar='chat_template.jinja')
    parser.add_argument("--pre-tokenizer",                             type=str,  help="The models tokenizer.ggml.pre", metavar='"pre tokenizer"')
    parser.add_argument("--remove-metadata",      action="append",     type=str,  help="Remove metadata (by key name) from output model", metavar='general.url')
    parser.add_argument("--special-token",        action="append",     type=str,  help="Special token by value", nargs=2, metavar=(' | '.join(token_names.keys()), '"<token>"'))
@@ -134,12 +135,17 @@ def main() -> None:
        new_metadata[gguf.Keys.Tokenizer.CHAT_TEMPLATE] = MetadataDetails(gguf.GGUFValueType.STRING, json.loads(args.chat_template) if args.chat_template.startswith('[') else args.chat_template)

    if args.chat_template_config:
-        with open(args.chat_template_config, 'r') as fp:
+        with open(args.chat_template_config, 'r', encoding='utf-8') as fp:
            config = json.load(fp)
            template = config.get('chat_template')
            if template:
                new_metadata[gguf.Keys.Tokenizer.CHAT_TEMPLATE] = MetadataDetails(gguf.GGUFValueType.STRING, template)

+    if args.chat_template_file:
+        with open(args.chat_template_file, 'r', encoding='utf-8') as fp:
+            template = fp.read()
+            new_metadata[gguf.Keys.Tokenizer.CHAT_TEMPLATE] = MetadataDetails(gguf.GGUFValueType.STRING, template)
+
    if args.pre_tokenizer:
        new_metadata[gguf.Keys.Tokenizer.PRE] = MetadataDetails(gguf.GGUFValueType.STRING, args.pre_tokenizer)

@@ -33,6 +33,7 @@ class TensorNameMap:
            "language_model.model.embed_tokens",         # llama4
            "encoder",                                   # neobert
            "model.transformer.wte",                     # llada
+            "embed_tokens",                              # qwen3-embedding
        ),

        # Token type embeddings
@@ -143,6 +144,7 @@ class TensorNameMap:
            "transformer_encoder.{bid}.attention_norm",             # neobert
            "model.layers.{bid}.operator_norm",                     # lfm2
            "model.transformer.blocks.{bid}.attn_norm",             # llada
+            "layers.{bid}.input_layernorm",                         # qwen3-embedding
        ),

        # Attention norm 2
@@ -188,6 +190,7 @@ class TensorNameMap:
            "transformer.h.{bid}.attn.attention.q_proj",                 # exaone
            "model.layers.{bid}.self_attn.q_proj",                       # llama4
            "model.transformer.blocks.{bid}.q_proj",                     # llada
+            "layers.{bid}.self_attn.q_proj",                             # qwen3-embedding
        ),

        # Attention key
@@ -205,6 +208,7 @@ class TensorNameMap:
            "transformer.h.{bid}.attn.attention.k_proj",               # exaone
            "model.layers.{bid}.self_attn.k_proj",                     # llama4
            "model.transformer.blocks.{bid}.k_proj",                   # llada
+            "layers.{bid}.self_attn.k_proj",                           # qwen3-embedding
        ),

        # Attention value
@@ -221,6 +225,7 @@ class TensorNameMap:
            "transformer.h.{bid}.attn.attention.v_proj",                 # exaone
            "model.layers.{bid}.self_attn.v_proj",                       # llama4
            "model.transformer.blocks.{bid}.v_proj",                     # llada
+            "layers.{bid}.self_attn.v_proj",                             # qwen3-embedding
        ),

        # Attention output
@@ -254,6 +259,7 @@ class TensorNameMap:
            "model.layers.{bid}.self_attn.o_proj",                          # llama4
            "transformer_encoder.{bid}.wo",                                 # neobert
            "model.transformer.blocks.{bid}.attn_out",                      # llada
+            "layers.{bid}.self_attn.o_proj",                                # qwen3-embedding
        ),

        # Attention output norm
@@ -279,6 +285,10 @@ class TensorNameMap:
            "transformer.h.{bid}.attn.rotary_emb.inv_freq",            # codeshell
        ),

+        MODEL_TENSOR.ATTN_SINKS: (
+            "model.layers.{bid}.self_attn.sinks", # openai-moe
+        ),
+
        # Feed-forward norm
        MODEL_TENSOR.FFN_NORM: (
            "gpt_neox.layers.{bid}.post_attention_layernorm",                # gptneox
@@ -300,6 +310,7 @@ class TensorNameMap:
            "transformer_encoder.{bid}.ffn_norm",                            # neobert
            "model.layers.layers.{bid}.pre_mlp_norm",                        # plamo2
            "model.transformer.blocks.{bid}.ff_norm",                        # llada
+            "layers.{bid}.post_attention_layernorm",                         # qwen3-embedding
        ),

        # Post feed-forward norm
@@ -325,6 +336,7 @@ class TensorNameMap:
            "model.layers.{bid}.block_sparse_moe.router.layer", # granitemoe
            "model.layers.{bid}.feed_forward.router",           # llama4 jamba
            "encoder.layers.{bid}.mlp.router.layer",            # nomic-bert-moe
+            "model.layers.{bid}.mlp.router",                    # openai-moe
            "model.layers.{bid}.mlp.gate.wg",                   # hunyuan
            "model.layers.{bid}.block_sparse_moe.primary_router", # smallthinker
        ),
@@ -373,7 +385,8 @@ class TensorNameMap:
            "model.layers.{bid}.feed_forward.up_proj",                # llama4 jamba granite-hybrid
            "transformer_encoder.{bid}.ffn.w12",                      # neobert
            "model.layers.{bid}.block_sparse_moe.up",                 # smallthinker
-            "model.transformer.blocks.{bid}.up_proj",                  # llada
+            "model.transformer.blocks.{bid}.up_proj",                 # llada
+            "layers.{bid}.mlp.up_proj",                               # qwen3-embedding
        ),

        MODEL_TENSOR.FFN_UP_EXP: (
@@ -416,6 +429,7 @@ class TensorNameMap:
            "model.layers.{bid}.feed_forward.gate_proj",  # llama4 jamba granite-hybrid
            "model.layers.{bid}.block_sparse_moe.gate",   # smallthinker
            "model.transformer.blocks.{bid}.ff_proj",     # llada
+            "layers.{bid}.mlp.gate_proj",                 # qwen3-embedding
        ),

        MODEL_TENSOR.FFN_GATE_EXP: (
@@ -465,7 +479,8 @@ class TensorNameMap:
            "model.layers.{bid}.feed_forward.down_proj",              # llama4 jamba granite-hybrid
            "transformer_encoder.{bid}.ffn.w3",                       # neobert
            "model.layers.{bid}.block_sparse_moe.down",               # smallthinker
-            "model.transformer.blocks.{bid}.ff_out",                   # llada
+            "model.transformer.blocks.{bid}.ff_out",                  # llada
+            "layers.{bid}.mlp.down_proj",                             # qwen3-embedding
        ),

        MODEL_TENSOR.FFN_DOWN_EXP: (
@@ -497,6 +512,7 @@ class TensorNameMap:
            "encoder.layer.{bid}.attention.self.layer_norm_q",                # jina-bert-v2
            "transformer.layers.{bid}.attn.q_norm",                           # openelm
            "model.layers.layers.{bid}.mixer.q",                              # plamo2
+            "layers.{bid}.self_attn.q_norm",                                  # qwen3-embedding
        ),

        MODEL_TENSOR.ATTN_K_NORM: (
@@ -508,6 +524,7 @@ class TensorNameMap:
            "encoder.layer.{bid}.attention.self.layer_norm_k",                # jina-bert-v2
            "transformer.layers.{bid}.attn.k_norm",                           # openelm
            "model.layers.layers.{bid}.mixer.k",                              # plamo2
+            "layers.{bid}.self_attn.k_norm",                                  # qwen3-embedding
        ),

        MODEL_TENSOR.ROPE_FREQS: (
@@ -1357,6 +1374,31 @@ class TensorNameMap:
        MODEL_TENSOR.A_MM_NORM_MID: (
            "audio.multi_modal_projector.ln_mid", # ultravox
        ),
+
+        # NextN/MTP tensors for GLM4_MOE
+        MODEL_TENSOR.NEXTN_EH_PROJ: (
+            "model.layers.{bid}.eh_proj",
+        ),
+
+        MODEL_TENSOR.NEXTN_EMBED_TOKENS: (
+            "model.layers.{bid}.embed_tokens",
+        ),
+
+        MODEL_TENSOR.NEXTN_ENORM: (
+            "model.layers.{bid}.enorm",
+        ),
+
+        MODEL_TENSOR.NEXTN_HNORM: (
+            "model.layers.{bid}.hnorm",
+        ),
+
+        MODEL_TENSOR.NEXTN_SHARED_HEAD_HEAD: (
+            "model.layers.{bid}.shared_head.head",
+        ),
+
+        MODEL_TENSOR.NEXTN_SHARED_HEAD_NORM: (
+            "model.layers.{bid}.shared_head.norm",
+        ),
    }

    # architecture-specific block mappings
@@ -312,7 +312,11 @@ class SpecialVocab:
        with open(config_file, encoding = 'utf-8') as f:
            config = json.load(f)
        for typ in self.special_token_types:
-            self._set_special_token(typ, config.get(f'{typ}_token_id'))
+            token_id = config.get(f'{typ}_token_id')
+            # If not found at root, check in text_config (for multimodal models like Kimi-VL)
+            if token_id is None and 'text_config' in config:
+                token_id = config['text_config'].get(f'{typ}_token_id')
+            self._set_special_token(typ, token_id)
        return True


@@ -152,6 +152,7 @@ extern "C" {
        //LLAMA_FTYPE_MOSTLY_Q4_0_8_8      = 35, // removed from gguf files, use Q4_0 and runtime repack
        LLAMA_FTYPE_MOSTLY_TQ1_0         = 36, // except 1d tensors
        LLAMA_FTYPE_MOSTLY_TQ2_0         = 37, // except 1d tensors
+        LLAMA_FTYPE_MOSTLY_MXFP4_MOE     = 38, // except 1d tensors

        LLAMA_FTYPE_GUESSED = 1024, // not specified in the model file
    };
@@ -21,4 +21,5 @@ These templates can be updated with the following commands:
 ./scripts/get_chat_template.py Qwen/Qwen2.5-7B-Instruct                      > models/templates/Qwen-Qwen2.5-7B-Instruct.jinja
 ./scripts/get_chat_template.py Qwen/QwQ-32B                                  > models/templates/Qwen-QwQ-32B.jinja
 ./scripts/get_chat_template.py Qwen/Qwen3-0.6B                               > models/templates/Qwen-Qwen3-0.6B.jinja
-```
+./scripts/get_chat_template.py zai-org/GLM-4.5                               > models/templates/zai-org-GLM-4.5.jinja
+```
@@ -0,0 +1,59 @@
+{# Alias tools -> available_tools #}
+{%- if tools and not available_tools -%}
+    {%- set available_tools = tools -%}
+{%- endif -%}
+{%- if messages[0]['role'] == 'system' %}
+     {%- set system_message = messages[0]['content'] %}
+     {%- set loop_messages = messages[1:] %}
+ {%- else %}
+     {%- set system_message = "Knowledge Cutoff Date: April 2024. Today's Date: " + strftime_now('%B %d, %Y') + ". You are Granite, developed by IBM." %}
+     {%- if available_tools and documents %}
+         {%- set system_message = system_message + " You are a helpful assistant with access to the following tools. When a tool is required to answer the user's query, respond only with <|tool_call|> followed by a JSON list of tools used. If a tool does not exist in the provided list of tools, notify the user that you do not have the ability to fulfill the request. Write the response to the user's input by strictly aligning with the facts in the provided documents. If the information needed to answer the question is not available in the documents, inform the user that the question cannot be answered based on the available data." %}
+     {%- elif available_tools %}
+         {%- set system_message = system_message + " You are a helpful assistant with access to the following tools. When a tool is required to answer the user's query, respond only with <|tool_call|> followed by a JSON list of tools used. If a tool does not exist in the provided list of tools, notify the user that you do not have the ability to fulfill the request." %}
+     {%- elif documents %}
+         {%- set system_message = system_message + " Write the response to the user's input by strictly aligning with the facts in the provided documents. If the information needed to answer the question is not available in the documents, inform the user that the question cannot be answered based on the available data." %}
+    {%- elif thinking %}
+    {%- set system_message = system_message + " You are a helpful AI assistant.
+Respond to every user query in a comprehensive and detailed way. You can write down your thoughts and reasoning process before responding. In the thought process, engage in a comprehensive cycle of analysis, summarization, exploration, reassessment, reflection, backtracing, and iteration to develop well-considered thinking process. In the response section, based on various attempts, explorations, and reflections from the thoughts section, systematically present the final solution that you deem correct. The response should summarize the thought process. Write your thoughts between <think></think> and write your response between <response></response> for each user query." %}
+     {%- else %}
+         {%- set system_message = system_message + " You are a helpful AI assistant." %}
+     {%- endif %}
+     {%- if 'citations' in controls and documents %}
+         {%- set system_message = system_message + '
+Use the symbols <|start_of_cite|> and <|end_of_cite|> to indicate when a fact comes from a document in the search result, e.g <|start_of_cite|> {document_id: 1}my fact <|end_of_cite|> for a fact from document 1. Afterwards, list all the citations with their corresponding documents in an ordered list.' %}
+     {%- endif %}
+     {%- if 'hallucinations' in controls and documents %}
+         {%- set system_message = system_message + '
+Finally, after the response is written, include a numbered list of sentences from the response with a corresponding risk value that are hallucinated and not based in the documents.' %}
+     {%- endif %}
+     {%- set loop_messages = messages %}
+ {%- endif %}
+ {{- '<|start_of_role|>system<|end_of_role|>' + system_message + '<|end_of_text|>
+' }}
+ {%- if available_tools %}
+     {{- '<|start_of_role|>available_tools<|end_of_role|>' }}
+     {{- available_tools | tojson(indent=4) }}
+     {{- '<|end_of_text|>
+' }}
+ {%- endif %}
+ {%- if documents %}
+     {%- for document in documents %}
+         {{- '<|start_of_role|>document {"document_id": "' + document['doc_id'] | string + '"}<|end_of_role|>
+' }}
+         {{- document['text'] }}
+         {{- '<|end_of_text|>
+' }}
+              {%- endfor %}
+ {%- endif %}
+ {%- for message in loop_messages %}
+     {{- '<|start_of_role|>' + message['role'] + '<|end_of_role|>' + message['content'] + '<|end_of_text|>
+' }}
+     {%- if loop.last and add_generation_prompt %}
+         {{- '<|start_of_role|>assistant' }}
+             {%- if controls %}
+                 {{- ' ' + controls | tojson()}}
+             {%- endif %}
+         {{- '<|end_of_role|>' }}
+     {%- endif %}
+ {%- endfor %}
@@ -1,7 +1 @@
 -r ./requirements-convert_legacy_llama.txt
--extra-index-url https://download.pytorch.org/whl/cpu
-torch~=2.2.1; platform_machine != "s390x"
-
-# torch s390x packages can only be found from nightly builds
--extra-index-url https://download.pytorch.org/whl/nightly
-torch>=0.0.0.dev0; platform_machine == "s390x"
@@ -326,7 +326,7 @@ class LlamaBenchDataSQLite3(LlamaBenchData):

        # Set table name and schema based on tool
        if self.tool == "llama-bench":
-            self.table_name = "test"
+            self.table_name = "llama_bench"
            db_fields = LLAMA_BENCH_DB_FIELDS
            db_types = LLAMA_BENCH_DB_TYPES
        elif self.tool == "test-backend-ops":
@@ -409,8 +409,8 @@ class LlamaBenchDataSQLite3File(LlamaBenchDataSQLite3):

        # Tool selection logic
        if tool is None:
-            if "test" in table_names:
-                self.table_name = "test"
+            if "llama_bench" in table_names:
+                self.table_name = "llama_bench"
                self.tool = "llama-bench"
            elif "test_backend_ops" in table_names:
                self.table_name = "test_backend_ops"
@@ -418,8 +418,8 @@ class LlamaBenchDataSQLite3File(LlamaBenchDataSQLite3):
            else:
                raise RuntimeError(f"No suitable table found in database. Available tables: {table_names}")
        elif tool == "llama-bench":
-            if "test" in table_names:
-                self.table_name = "test"
+            if "llama_bench" in table_names:
+                self.table_name = "llama_bench"
                self.tool = "llama-bench"
            else:
                raise RuntimeError(f"Table 'test' not found for tool 'llama-bench'. Available tables: {table_names}")
@@ -62,6 +62,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
    { LLM_ARCH_DEEPSEEK2,        "deepseek2"        },
    { LLM_ARCH_CHATGLM,          "chatglm"          },
    { LLM_ARCH_GLM4,             "glm4"             },
+    { LLM_ARCH_GLM4_MOE,         "glm4moe"          },
    { LLM_ARCH_BITNET,           "bitnet"           },
    { LLM_ARCH_T5,               "t5"               },
    { LLM_ARCH_T5ENCODER,        "t5encoder"        },
@@ -87,6 +88,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
    { LLM_ARCH_HUNYUAN_MOE,      "hunyuan-moe"      },
    { LLM_ARCH_HUNYUAN_DENSE,    "hunyuan-dense"    },
    { LLM_ARCH_SMOLLM3,          "smollm3"          },
+    { LLM_ARCH_OPENAI_MOE,       "gpt-oss"          },
    { LLM_ARCH_LFM2,             "lfm2"             },
    { LLM_ARCH_DREAM,            "dream"            },
    { LLM_ARCH_SMALLTHINKER,     "smallthinker"     },
@@ -127,6 +129,7 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
    { LLM_KV_EXPERT_WEIGHTS_NORM,               "%s.expert_weights_norm"               },
    { LLM_KV_EXPERT_GATING_FUNC,                "%s.expert_gating_func"                },
    { LLM_KV_MOE_EVERY_N_LAYERS,                "%s.moe_every_n_layers"                },
+    { LLM_KV_NEXTN_PREDICT_LAYERS,              "%s.nextn_predict_layers"              },
    { LLM_KV_POOLING_TYPE,                      "%s.pooling_type"                      },
    { LLM_KV_LOGIT_SCALE,                       "%s.logit_scale"                       },
    { LLM_KV_DECODER_START_TOKEN_ID,            "%s.decoder_start_token_id"            },
@@ -1391,6 +1394,40 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
            { LLM_TENSOR_FFN_POST_NORM,   "blk.%d.post_ffw_norm" },
        },
    },
+    {
+        LLM_ARCH_GLM4_MOE,
+        {
+            { 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_K,             "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,             "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,           "blk.%d.attn_output" },
+            { LLM_TENSOR_ATTN_Q_NORM,        "blk.%d.attn_q_norm" },
+            { LLM_TENSOR_ATTN_K_NORM,        "blk.%d.attn_k_norm" },
+            { LLM_TENSOR_FFN_GATE,           "blk.%d.ffn_gate" },
+            { LLM_TENSOR_FFN_DOWN,           "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,             "blk.%d.ffn_up" },
+            { LLM_TENSOR_FFN_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_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_FFN_EXP_PROBS_B,    "blk.%d.exp_probs_b" },
+            // NextN/MTP tensors - preserved but unused (in final layer, dynamic layer number)
+            { LLM_TENSOR_NEXTN_EH_PROJ,      "blk.%d.nextn.eh_proj" },
+            { LLM_TENSOR_NEXTN_EMBED_TOKENS, "blk.%d.nextn.embed_tokens" },
+            { LLM_TENSOR_NEXTN_ENORM,        "blk.%d.nextn.enorm" },
+            { LLM_TENSOR_NEXTN_HNORM,        "blk.%d.nextn.hnorm" },
+            { LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD, "blk.%d.nextn.shared_head_head" },
+            { LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, "blk.%d.nextn.shared_head_norm" },
+        },
+    },
    {
        LLM_ARCH_BITNET,
        {
@@ -1935,6 +1972,25 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
            { LLM_TENSOR_FFN_UP,         "blk.%d.ffn_up" },
        },
    },
+    {
+        LLM_ARCH_OPENAI_MOE,
+        {
+            { 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_K,             "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,             "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,           "blk.%d.attn_output" },
+            { LLM_TENSOR_ATTN_SINKS,         "blk.%d.attn_sinks" },
+            { 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_ARCH_LFM2,
        {
@@ -2050,6 +2106,7 @@ static const std::map<llm_tensor, llm_tensor_info> LLM_TENSOR_INFOS = {
    {LLM_TENSOR_ATTN_KV_B,                  {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
    {LLM_TENSOR_ATTN_K_B,                   {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
    {LLM_TENSOR_ATTN_V_B,                   {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_ATTN_SINKS,                 {LLM_TENSOR_LAYER_REPEATING, GGML_OP_SCALE}},
    {LLM_TENSOR_DEC_ATTN_Q,                 {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
    {LLM_TENSOR_DEC_ATTN_K,                 {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
    {LLM_TENSOR_DEC_ATTN_V,                 {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
@@ -2181,6 +2238,14 @@ static const std::map<llm_tensor, llm_tensor_info> LLM_TENSOR_INFOS = {
    {LLM_TENSOR_SHORTCONV_CONV,             {LLM_TENSOR_LAYER_REPEATING, GGML_OP_SSM_CONV}},
    {LLM_TENSOR_SHORTCONV_INPROJ,           {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
    {LLM_TENSOR_SHORTCONV_OUTPROJ,          {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    // NextN/MTP tensors are currently ignored (reserved for future MTP support)
+    // These tensors only exist in the last layer(s) and are treated as output tensors
+    {LLM_TENSOR_NEXTN_EH_PROJ,              {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_NEXTN_EMBED_TOKENS,         {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_GET_ROWS}},
+    {LLM_TENSOR_NEXTN_ENORM,                {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_GET_ROWS}},
+    {LLM_TENSOR_NEXTN_HNORM,                {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL}},
+    {LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD,     {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_NEXTN_SHARED_HEAD_NORM,     {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL}},
 };

 LLM_KV::LLM_KV(llm_arch arch, const char * suffix) : arch(arch), suffix(suffix) {}
@@ -66,6 +66,7 @@ enum llm_arch {
    LLM_ARCH_DEEPSEEK2,
    LLM_ARCH_CHATGLM,
    LLM_ARCH_GLM4,
+    LLM_ARCH_GLM4_MOE,
    LLM_ARCH_BITNET,
    LLM_ARCH_T5,
    LLM_ARCH_T5ENCODER,
@@ -91,6 +92,7 @@ enum llm_arch {
    LLM_ARCH_HUNYUAN_MOE,
    LLM_ARCH_HUNYUAN_DENSE,
    LLM_ARCH_SMOLLM3,
+    LLM_ARCH_OPENAI_MOE,
    LLM_ARCH_LFM2,
    LLM_ARCH_DREAM,
    LLM_ARCH_SMALLTHINKER,
@@ -131,6 +133,7 @@ enum llm_kv {
    LLM_KV_EXPERT_WEIGHTS_NORM,
    LLM_KV_EXPERT_GATING_FUNC,
    LLM_KV_MOE_EVERY_N_LAYERS,
+    LLM_KV_NEXTN_PREDICT_LAYERS,
    LLM_KV_POOLING_TYPE,
    LLM_KV_LOGIT_SCALE,
    LLM_KV_DECODER_START_TOKEN_ID,
@@ -263,6 +266,7 @@ enum llm_tensor {
    LLM_TENSOR_ATTN_OUT_NORM,
    LLM_TENSOR_ATTN_POST_NORM,
    LLM_TENSOR_ATTN_ROT_EMBD,
+    LLM_TENSOR_ATTN_SINKS,
    LLM_TENSOR_FFN_GATE_INP,
    LLM_TENSOR_FFN_GATE_INP_SHEXP,
    LLM_TENSOR_FFN_NORM,
@@ -409,6 +413,12 @@ enum llm_tensor {
    LLM_TENSOR_SHORTCONV_CONV,
    LLM_TENSOR_SHORTCONV_INPROJ,
    LLM_TENSOR_SHORTCONV_OUTPROJ,
+    LLM_TENSOR_NEXTN_EH_PROJ,
+    LLM_TENSOR_NEXTN_EMBED_TOKENS,
+    LLM_TENSOR_NEXTN_ENORM,
+    LLM_TENSOR_NEXTN_HNORM,
+    LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD,
+    LLM_TENSOR_NEXTN_SHARED_HEAD_NORM,
 };

 enum llm_tensor_layer {
@@ -66,6 +66,7 @@ static const std::map<std::string, llm_chat_template> LLM_CHAT_TEMPLATES = {
    { "llama4",            LLM_CHAT_TEMPLATE_LLAMA4            },
    { "smolvlm",           LLM_CHAT_TEMPLATE_SMOLVLM           },
    { "hunyuan-moe",       LLM_CHAT_TEMPLATE_HUNYUAN_MOE       },
+    { "gpt-oss",           LLM_CHAT_TEMPLATE_OPENAI_MOE        },
    { "hunyuan-dense",     LLM_CHAT_TEMPLATE_HUNYUAN_DENSE     },
    { "kimi-k2",           LLM_CHAT_TEMPLATE_KIMI_K2           },
 };
@@ -192,9 +193,11 @@ llm_chat_template llm_chat_detect_template(const std::string & tmpl) {
        return LLM_CHAT_TEMPLATE_LLAMA4;
    } else if (tmpl_contains("<|endofuserprompt|>")) {
        return LLM_CHAT_TEMPLATE_DOTS1;
-    } else if (tmpl_contains("<|startoftext|>") && tmpl_contains("<|extra_4|>")) {
+    } else if (tmpl_contains("<|extra_0|>") && tmpl_contains("<|extra_4|>")) {
        return LLM_CHAT_TEMPLATE_HUNYUAN_MOE;
-    } else if (tmpl_contains("<｜hy_place▁holder▁no▁2｜>") && tmpl_contains("<｜hy_place▁holder▁no▁3｜>")) {
+    } else if (tmpl_contains("<|start|>") && tmpl_contains("<|channel|>")) {
+        return LLM_CHAT_TEMPLATE_OPENAI_MOE;
+    } else if (tmpl_contains("<｜hy_Assistant｜>") && tmpl_contains("<｜hy_place▁holder▁no▁3｜>")) {
        return LLM_CHAT_TEMPLATE_HUNYUAN_DENSE;
    } else if (tmpl_contains("<|im_assistant|>assistant<|im_middle|>")) {
        return LLM_CHAT_TEMPLATE_KIMI_K2;
@@ -622,8 +625,6 @@ int32_t llm_chat_apply_template(
    } else if (tmpl == LLM_CHAT_TEMPLATE_YANDEX) {
        // Yandex template ("\n\n" is defined as EOT token)

-        ss << "<s>";
-
        for (size_t i = 0; i < chat.size(); i++) {
            std::string role(chat[i]->role);
            if (role == "user") {
@@ -706,6 +707,16 @@ int32_t llm_chat_apply_template(
                ss << "<|startoftext|>" << message->content << "<|extra_0|>";
            }
        }
+    } else if (tmpl == LLM_CHAT_TEMPLATE_OPENAI_MOE) {
+        // OpenAI MoE (based on Harmony chat template)
+        for (auto message : chat) {
+            std::string role(message->role);
+            ss << "<|start|>" << role << "<|message|>" << message->content;
+            ss << (role == "assistant" ? "<|return|>" : "<|end|>");
+        }
+        if (add_ass) {
+            ss << "<|start|>assistant";
+        }
    } else if (tmpl == LLM_CHAT_TEMPLATE_HUNYUAN_DENSE) {
        // tencent/Hunyuan-4B-Instruct
        for (size_t i = 0; i < chat.size(); i++) {
@@ -46,6 +46,7 @@ enum llm_chat_template {
    LLM_CHAT_TEMPLATE_SMOLVLM,
    LLM_CHAT_TEMPLATE_DOTS1,
    LLM_CHAT_TEMPLATE_HUNYUAN_MOE,
+    LLM_CHAT_TEMPLATE_OPENAI_MOE,
    LLM_CHAT_TEMPLATE_HUNYUAN_DENSE,
    LLM_CHAT_TEMPLATE_KIMI_K2,
    LLM_CHAT_TEMPLATE_UNKNOWN,
@@ -105,7 +105,7 @@ llama_context::llama_context(

    {
        const char * LLAMA_SET_ROWS = getenv("LLAMA_SET_ROWS");
-        supports_set_rows = LLAMA_SET_ROWS ? (atoi(LLAMA_SET_ROWS) != 0) : false;
+        supports_set_rows = LLAMA_SET_ROWS ? (atoi(LLAMA_SET_ROWS) != 0) : supports_set_rows;

        if (!supports_set_rows && !cparams.kv_unified) {
            LLAMA_LOG_WARN("%s: non-unified KV cache requires ggml_set_rows() - forcing unified KV cache\n", __func__);
@@ -786,7 +786,7 @@ int llama_context::encode(const llama_batch & batch_inp) {
    const auto & hparams = model.hparams;

    const int64_t n_embd  = hparams.n_embd;
-    const int32_t n_vocab = model.vocab.n_tokens();
+    const int64_t n_vocab = model.vocab.n_tokens();

    // note: during encode, we always pass the full sequence starting from pos = 0
    if (!balloc->init(batch_inp, model.vocab, nullptr, n_embd, cparams.kv_unified ? LLAMA_MAX_SEQ : cparams.n_seq_max, true)) {
@@ -959,7 +959,7 @@ int llama_context::decode(const llama_batch & batch_inp) {
    const auto & vocab   = model.vocab;
    const auto & hparams = model.hparams;

-    const int32_t n_vocab = vocab.n_tokens();
+    const int64_t n_vocab = vocab.n_tokens();
    const int64_t n_embd  = hparams.n_embd;

    // when computing embeddings, all tokens are output
@@ -1328,21 +1328,21 @@ uint32_t llama_context::output_reserve(int32_t n_outputs) {
 }

 void llama_context::output_reorder() {
-    const uint32_t n_vocab = model.vocab.n_tokens();
+    const uint64_t n_vocab = model.vocab.n_tokens();
    const uint64_t n_embd  = model.hparams.n_embd;

-    for (uint32_t s = 0; s < output_swaps.size(); ++s) {
-        const uint32_t i0 = output_swaps[s].i0;
-        const uint32_t i1 = output_swaps[s].i1;
+    for (size_t s = 0; s < output_swaps.size(); ++s) {
+        const uint64_t i0 = output_swaps[s].i0;
+        const uint64_t i1 = output_swaps[s].i1;

        if (logits_size > 0) {
-            for (uint32_t k = 0; k < n_vocab; k++) {
+            for (uint64_t k = 0; k < n_vocab; k++) {
                std::swap(logits[i0*n_vocab + k], logits[i1*n_vocab + k]);
            }
        }

        if (embd_size > 0) {
-            for (uint32_t k = 0; k < n_embd; k++) {
+            for (uint64_t k = 0; k < n_embd; k++) {
                std::swap(embd[i0*n_embd + k], embd[i1*n_embd + k]);
            }
        }
@@ -289,7 +289,7 @@ private:

    // env: LLAMA_SET_ROWS (temporary)
    // ref: https://github.com/ggml-org/llama.cpp/pull/14285
-    bool supports_set_rows = false;
+    bool supports_set_rows = true;

    // env: LLAMA_GRAPH_REUSE_DISABLE
    bool graph_reuse_disable = false;
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
lhez	e725a1a982	opencl: add `swiglu_oai` and `add_id` (#15121 ) * opencl: add `swiglu-oai` * opencl: add `add_id` * opencl: add missing `add_id.cl`	2025-08-06 12:12:17 -07:00
Sachin Desai	3db4da56a5	chat : support Granite model reasoning and tool call (#14864 )	2025-08-06 20:27:30 +02:00
Juk Armstrong	476aa3fd57	Fixed name `-override-tensors` to `-override-tensor` (#15129 )	2025-08-06 17:28:48 +01:00
Diego Devesa	0d8831543c	ggml : fix fallback to CPU for ununsupported ops (#15118 )	2025-08-06 14:37:35 +02:00
Sigbjørn Skjæret	65c797c4fa	chat : fix yandex chat template (#15116 )	2025-08-06 13:26:49 +02:00
stevenkuang	25726898e8	chat : fix hunyuan auto-detection (#15114 ) Signed-off-by: stevenkuang <stevenkuang@tencent.com>	2025-08-06 11:48:30 +02:00
Chenguang Li	2241453252	CANN: add support for ACL Graph (#15065 ) * feat(cann): add optional support for ACL Graph execution This commit adds support for executing ggml computational graphs using Huawei's ACL graph mode via the USE_CANN_GRAPH flag. The support can be enabled at compile time using the CMake option: -DUSE_CANN_GRAPH=ON By default, ACL graph execution is disabled, and the fallback path uses node-by-node execution. Key additions: - CMake option to toggle graph mode - Graph capture and execution logic using - Tensor property matching to determine whether graph update is required - Safe fallback and logging if the environment variable LLAMA_SET_ROWS is unset or invalid This prepares the backend for performance improvements in repetitive graph execution scenarios on Ascend devices. Signed-off-by: noemotiovon <757486878@qq.com> * Fix review comments Signed-off-by: noemotiovon <757486878@qq.com> * remane USE_CANN_GRAPH to USE_ACL_GRAPH Signed-off-by: noemotiovon <757486878@qq.com> * fix typo Signed-off-by: noemotiovon <757486878@qq.com> --------- Signed-off-by: noemotiovon <757486878@qq.com>	2025-08-06 14:12:42 +08:00
Reese Levine	9515c6131a	ggml: WebGPU disable SET_ROWS for now (#15078 ) * Add paramater buffer pool, batching of submissions, refactor command building/submission * Add header for linux builds * Free staged parameter buffers at once * Format with clang-format * Fix thread-safe implementation * Use device implicit synchronization * Update workflow to use custom release * Remove testing branch workflow * Disable set_rows until it's implemented * Fix potential issue around empty queue submission * Try synchronous submission * Try waiting on all futures explicitly * Add debug * Add more debug messages * Work on getting ssh access for debugging * Debug on failure * Disable other tests * Remove extra if * Try more locking * maybe passes? * test * Some cleanups * Restore build file * Remove extra testing branch ci	2025-08-05 16:26:38 -07:00
Georgi Gerganov	fd1234cb46	llama : add gpt-oss (#15091 ) * oai moe * compat with new checkpoint * add attn sink impl * add rope scaling yarn * logits match with latest transformers code * wip chat template * rm trailing space * use ggml_scale_bias * rm redundant is_swa_all * convert interleaved gate_up * graph : fix activation function to match reference (#7) * vocab : handle o200k_harmony special tokens * ggml : add attention sinks support (#1) * llama : add attn sinks * ggml : add attn sinks * cuda : add attn sinks * vulkan : add support for sinks in softmax remove unnecessary return * ggml : add fused swiglu_oai op (#11) * ggml : add fused swiglu_oai op * Update ggml/src/ggml-cpu/ops.cpp Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * update CUDA impl * cont : metal impl * add vulkan impl * test-backend-ops : more test cases, clean up * llama : remove unfused impl * remove extra lines --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> --------- Co-authored-by: slaren <slarengh@gmail.com> * repack mxfp4 upon conversion * clean up a bit * enable thinking * add quick hack to render only some special tokens * fix bf16 conversion * remove vocab hack * webui ok * support chat parsing for gpt-oss * fix webui * direct mapping mxfp4, FINALLY * force using mxfp4 * properly use lazy tensor * ggml : add mxfp4 ggml : use e8m0 conversion instead of powf Co-authored-by: Diego Devesa <slarengh@gmail.com> change kvalues_mxfp4 table to match e2m1 (#6) metal : remove quantization for now (not used) cuda : fix disabled CUDA graphs due to ffn moe bias vulkan : add support for mxfp4 cont : add cm2 dequant * ggml : add ggml_add_id (#13) * ggml : add ggml_add_id * add cuda impl * llama : add weight support check for add_id * perf opt * add vulkan impl * rename cuda files * add metal impl * allow in-place ggml_add_id * llama : keep biases on CPU with --cpu-moe * llama : fix compile error ggml-ci * cuda : add fallback for __nv_cvt_e8m0_to_bf16raw ggml-ci * cleanup ggml-ci * sycl : fix supports_op for MXFP4 ggml-ci * fix Unknown reasoning format * ggml-cpu : fix AVX build ggml-ci * fix hip build ggml-ci * cuda : add mxfp4 dequantization support for cuBLAS ggml-ci * ggml-cpu : fix mxfp4 fallback definitions for some architectures ggml-ci * cuda : fix version required for __nv_cvt_e8m0_to_bf16raw --------- Co-authored-by: Xuan Son Nguyen <son@huggingface.co> Co-authored-by: slaren <slarengh@gmail.com>	2025-08-05 22:10:36 +03:00
Sigbjørn Skjæret	f324a3b715	chat : only remove double bos/eos if added (#15086 ) * only remove double bos/eos if added * fix tests	2025-08-05 20:43:36 +02:00
Georgi Gerganov	be42642581	readme : update hot topics (#15097 )	2025-08-05 20:19:33 +03:00
Romain Biessy	3306ceabf0	sycl: fix mul_mat selection (#15092 )	2025-08-05 18:39:55 +02:00
Juk Armstrong	c81de6e107	Fix `glm4moe` bug (#15088 )	2025-08-05 13:56:44 +01:00
Alex Wu	22f060c9c4	webui: fix markdown table (#15081 ) * webui: fix markdown table * webui: fix table display with themes	2025-08-05 13:56:44 +02:00
compilade	ee3a9fcf88	context : fix index overflow on huge outputs (#15080 ) * context : fix overflow when re-ordering huge outputs * context : fix logits size overflow for huge batches	2025-08-05 11:27:45 +02:00
Diego Devesa	ec428b02c3	llama : add --n-cpu-moe option (#15077 ) * llama : add --n-cpu-moe option Keeps the MoE weights of the first N layers in the CPU	2025-08-05 01:05:36 +02:00
compilade	19f68fa5a4	imatrix : warn when GGUF imatrix is saved without .gguf suffix (#15076 ) * imatrix : add warning when suffix is not .gguf for GGUF imatrix * imatrix : only warn about suffix when output format is unspecified	2025-08-04 23:26:52 +02:00
Christian Kastner	41613437ff	cmake: Add GGML_BACKEND_DIR option (#15074 ) * cmake: Add GGML_BACKEND_DIR option This can be used by distributions to specify where to look for backends when ggml is built with GGML_BACKEND_DL=ON. * Fix phrasing	2025-08-04 21:29:14 +02:00
Sigbjørn Skjæret	e5bebe5251	gguf-py : add --chat-template-file to gguf_new_metadata (#15075 )	2025-08-04 21:01:48 +02:00
Sam	ef0144c087	model: support GLM 4.5 family of models (#14939 ) * model: Add GLM 4.5 (#14921) Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * Merge in PR suggestions Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * model: Add GLM 4.5 family of models (#14921) 1. Updated tensor_mapping.py with NextN tensor mappings - Added proper tensor mappings for all NextN/MTP tensors in /Users/samm/git/llama.cpp/gguf-py/gguf/tensor_mapping.py - Added mappings for: eh_proj, embed_tokens, enorm, hnorm, shared_head.head, shared_head.norm 2. Added num_nextn_predict_layers configuration - Added LLM_KV_NUM_NEXTN_PREDICT_LAYERS constant to llama-arch.h and llama-arch.cpp - Added num_nextn_predict_layers field to llama_hparams struct - Updated GLM4_MOE parameter loading in llama-model.cpp to read this parameter - Modified tensor loading logic to conditionally load NextN tensors based on num_nextn_predict_layers - Added GGUF writer support in gguf_writer.py with add_num_nextn_predict_layers() method - Updated conversion script to extract and write this parameter from HuggingFace config 3. Added FIM tokens for GLM4_MOE - Added GLM-4.5's FIM tokens to llama-vocab.cpp: - <\|code_prefix\|> for FIM_PRE - <\|code_suffix\|> for FIM_SUF - <\|code_middle\|> for FIM_MID 4. Removed manual NextN tensor handling - Removed the special-case handling in convert_hf_to_gguf.py that manually mapped NextN tensors - NextN tensors are now handled automatically through the proper tensor mapping system * glm 4.5 update tensors names * model: glm 4.5 apply suggestions from code review Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * Update src/llama-model.cpp Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * model: glm 4.5 apply suggestions from code review Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * model: glm 4.5 apply suggestions from code review * Apply suggestions from code review * patch broken chat template * typings fix * add TENSOR_SKIP flag Co-authored-by: Diego Devesa <slarengh@gmail.com> * Update src/llama-model-loader.h Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> --------- Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> Co-authored-by: Diego Devesa <slarengh@gmail.com>	2025-08-04 20:29:25 +02:00
Sigbjørn Skjæret	2721257e3e	quantize : fix confusing error message if ftype is invalid (#15071 )	2025-08-04 18:11:02 +02:00
Reese Levine	587d0118f5	ggml: WebGPU backend host improvements and style fixing (#14978 ) * Add parameter buffer pool, batching of submissions, refactor command building/submission * Add header for linux builds * Free staged parameter buffers at once * Format with clang-format * Fix thread-safe implementation * Use device implicit synchronization * Update workflow to use custom release * Remove testing branch workflow	2025-08-04 08:52:43 -07:00
Jeff Bolz	5aa1105da2	vulkan: fix build when using glslang that does not support coopmat2 (#15062 )	2025-08-04 07:09:19 +02:00
compilade	d31192b4ee	imatrix : use GGUF by default (#14842 ) * imatrix : use GGUF by default * imatrix : use GGUF regardless of the output filename The legacy format can only be produced with --output-format dat	2025-08-03 22:00:05 +02:00
compilade	0a2f5496be	imatrix : fix 3d activation handling for hybrid and recurrent models (#14994 ) * imatrix : use a single count for dense 3d tensors * imatrix : fix 3d activations when model tensor is 2d * imatrix : fix 3d tensor counts	2025-08-03 21:49:13 +02:00
compilade	11a3811164	memory : handle kv_unified for hybrid models (#15050 )	2025-08-03 21:43:07 +02:00
Csaba Kecskemeti	97366dc6ab	vocab : JetBrains Mellum pre-tokenizer (#15045 )	2025-08-03 21:38:18 +02:00
Gabriel Larson	83bc2f288c	model : add text-only support for Kimi-VL (and find special tokens in text_config) (#15051 ) * basic kimi-vl textmodel conversion * check config["text_config"] for special tokens	2025-08-03 16:56:25 +02:00
Jeff Bolz	6c7a441161	vulkan: Use coopmat2 for conv2d (#14982 )	2025-08-03 14:23:57 +02:00
lhez	5c0eb5ef54	opencl: fix adreno compiler detection logic (#15029 )	2025-08-02 19:51:18 +02:00
Johannes Gäßler	03d4698218	CUDA: use mma FA kernel for gqa > 4 on RTX 4000 (#15035 )	2025-08-02 16:37:08 +02:00
leejet	3303c19b16	cuda: make im2col a little faster (#15025 )	2025-08-02 17:15:36 +03:00
Daniel Bevenius	4fdea540bd	kv-cache : skip alignment of n_stream in kv-cache log msg [no ci] (#15040 ) This commit removes the right alignment the `n_stream` value in the log message in the `llama_kv_cache_unified` constructor. The motivation for this change is to enhance the readability of log message. Currently the output looks like this: ```console llama_kv_cache_unified: size = 2048.00 MiB ( 4096 cells, 32 layers, 1/ 1 seqs), K (f16): 1024.00 MiB, V (f16): 1024.00 MiB ``` Notice that the `n_stream` value is right aligned, which makes it a little harder to read. With the change in this commit the output will look like ```console llama_kv_cache_unified: size = 2048.00 MiB ( 4096 cells, 32 layers, 1/1 seqs), K (f16): 1024.00 MiB, V (f16): 1024.00 MiB ```	2025-08-02 17:14:57 +03:00
Georgi Gerganov	a4569c41fd	llama : enable LLAMA_SET_ROWS=1 by default (#14959 ) ggml-ci	2025-08-02 17:14:21 +03:00
Georgi Gerganov	15e92fd337	cuda, sycl : fix batched gemm when ne02 == 1 && ne03 > 1 (#15038 ) * cuda, sycl : fix batched gemm when ne02 == 1 && ne03 > 1 ggml-ci * cont : fix cont types ggml-ci * cont : adopt variable names and comment from the other branch	2025-08-02 17:13:05 +03:00
Sigbjørn Skjæret	2bf3fbf0b5	ci : check that pre-tokenizer hashes are up-to-date (#15032 ) * torch is not required for convert_hf_to_gguf_update * add --check-missing parameter * check that pre-tokenizer hashes are up-to-date	2025-08-02 14:39:01 +02:00
Douglas Hanley	711d5e6fe6	convert : fix Qwen3-Embedding pre-tokenizer hash (#15030 )	2025-08-02 12:51:02 +02:00
Jhen-Jie Hong	f738989dcb	chat : fix multiple tool_calls on hermes-2-pro (#14962 )	2025-08-02 18:04:48 +08:00
Jeff Bolz	4cb208c93c	vulkan: coopmat2 mul_mat optimizations (#14934 ) - Increase tile size for k-quants, to match non-k-quants - Choose more carefully between large and medium tiles, considering how it interacts with split_k - Allow larger/non-power of two split_k, and make the splits a multiple of 256 - Use split_k==3 to when >1/2 and <=2/3 of the SMs would hae been used	2025-08-02 11:21:37 +02:00
R0CKSTAR	3025b621d1	llama-bench: rename DB table name from test to llama_bench (#15003 ) Signed-off-by: Xiaodong Ye <xiaodong.ye@mthreads.com>	2025-08-02 17:20:40 +08:00
Jeff Bolz	ec0b18802c	vulkan: Support ne[3]>1 in noncontig matrix-vector multiply (#15015 )	2025-08-02 10:48:30 +02:00
Douglas Hanley	339bd0268c	model : support Qwen3-Embedding (#15023 )	2025-08-02 10:44:50 +02:00
Johannes Gäßler	f906275537	server: enable token array inputs for OAI API (#15001 )	2025-08-02 10:12:41 +02:00
Jeff Bolz	a9f7541ec2	vulkan: optimizations for direct convolution (#14933 ) * vulkan: optimizations for direct convolution - Empirically choose a better tile size. Reducing BS_K/BS_NPQ helps fill the GPU. The new size should be amenable to using coopmat, too. - Fix shmem bank conflicts. 16B padding should work with coopmat. - Some explicit loop unrolling. - Skip math/stores work for parts of the tile that are OOB. - Apply fastdiv opt. - Disable shuffles for NV. * Three tiles sizes for CONV_2D, and a heuristic to choose * reallow collectives for pre-Turing * make SHMEM_PAD a spec constant * fixes for intel perf - no shmem padding, placeholder shader core count * shader variants with/without unrolling * 0cc4m's fixes for AMD perf Co-authored-by: 0cc4m <picard12@live.de> --------- Co-authored-by: 0cc4m <picard12@live.de>	2025-08-02 09:57:04 +02:00