server: (router) do not evict busy models (#26567)

The reference implementation has two mutually exclusive prompt layouts.
In non streaming mode the prefill carries the whole utterance text plus
tts_eos summed with codec_pad, and the trailing text hidden collapses to
a single tts_pad row. In streaming mode the prefill carries only the
first text token and the trailing rows stream the rest of the text
followed by tts_eos.

The pipeline built the non streaming prefill but the streaming overlay,
so the talker saw the utterance a second time during generation and read
it twice before emitting codec_eos.

The overlay is now the single tts_pad row that matches the prefill.

.github/workflows/build-apple.yml

@@ -60,10 +60,10 @@ jobs:
             -DCMAKE_BUILD_RPATH="@loader_path" \
             -DLLAMA_FATAL_WARNINGS=ON \
             -DLLAMA_BUILD_BORINGSSL=ON \
-            -DGGML_METAL_USE_BF16=ON \
             -DGGML_METAL_EMBED_LIBRARY=OFF \
             -DGGML_METAL_SHADER_DEBUG=ON \
-            -DGGML_RPC=ON
+            -DGGML_RPC=ON \
+            -DCMAKE_OSX_DEPLOYMENT_TARGET=13.3
           time cmake --build build --config Release -j $(sysctl -n hw.logicalcpu)
           leaks -atExit -- ./build/bin/test-thread-safety -hf ggml-org/gemma-3-270m-qat-GGUF -ngl 99 -p "$(printf 'hello %.0s' {1..128})" -n 16 -c 512 -ub 32 -np 2 -t 2 -lv 1
 
@@ -126,7 +126,6 @@ jobs:
         run: |
           sysctl -a
           cmake -B build -G Xcode \
-            -DGGML_METAL_USE_BF16=ON \
             -DGGML_METAL_EMBED_LIBRARY=ON \
             -DLLAMA_OPENSSL=OFF \
             -DLLAMA_BUILD_APP=OFF \
@@ -177,7 +176,6 @@ jobs:
         run: |
           sysctl -a
           cmake -B build -G Xcode \
-            -DGGML_METAL_USE_BF16=ON \
             -DGGML_METAL_EMBED_LIBRARY=ON \
             -DLLAMA_BUILD_COMMON=OFF \
             -DLLAMA_BUILD_APP=OFF \
@@ -211,7 +209,6 @@ jobs:
         run: |
           sysctl -a
           cmake -B build -G Xcode \
-            -DGGML_METAL_USE_BF16=ON \
             -DGGML_METAL_EMBED_LIBRARY=ON \
             -DLLAMA_BUILD_COMMON=OFF \
             -DLLAMA_BUILD_APP=OFF \
@@ -256,7 +253,6 @@ jobs:
         run: |
           sysctl -a
           cmake -B build -G Xcode \
-            -DGGML_METAL_USE_BF16=ON \
             -DGGML_METAL_EMBED_LIBRARY=ON \
             -DLLAMA_OPENSSL=OFF \
             -DLLAMA_BUILD_APP=OFF \

.github/workflows/build-self-hosted.yml

@@ -71,6 +71,26 @@ jobs:
           nvidia-smi
           GG_BUILD_CUDA=1 bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp
 
+  gpu-rocm:
+    runs-on: [self-hosted, Linux, AMD]
+
+    steps:
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v6
+
+      - name: Test
+        id: ggml-ci
+        # HIP_LAUNCH_BLOCKING=1: workaround for an async-execution correctness
+        # issue on integrated RDNA3.5 (gfx1151) where batched inference returns
+        # incorrect output (perplexity ~88 vs ~9.4). Serializing kernel launches
+        # restores correctness. Remove once the underlying ROCm/HIP issue is fixed.
+        env:
+          HIP_LAUNCH_BLOCKING: "1"
+        run: |
+          rocminfo
+          GG_BUILD_ROCM=1 GG_BUILD_AMDGPU_TARGETS=gfx1151 bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp
+
   gpu-vulkan-nvidia-cm:
     runs-on: [self-hosted, Linux, NVIDIA]
 

.github/workflows/build-vulkan.yml

@@ -119,6 +119,7 @@ jobs:
         run: |
           source ./vulkan_sdk/setup-env.sh
           cmake -B build \
+            -DGGML_NATIVE=OFF \
             -DGGML_VULKAN=ON
           cmake --build build --config Release -j $(nproc)
 

.github/workflows/release.yml

@@ -93,13 +93,13 @@ jobs:
           - build: 'arm64'
             arch: 'arm64'
             os: macos-26
-            defines: "-DGGML_METAL_USE_BF16=ON -DGGML_METAL_EMBED_LIBRARY=ON"
+            defines: "-DGGML_METAL_EMBED_LIBRARY=ON -DCMAKE_OSX_DEPLOYMENT_TARGET=13.3"
           # TODO: this build is disabled to save Github Actions resources (https://github.com/ggml-org/llama.cpp/pull/23780)
           #       in order to enable it again, we have to provision dedicated runners  to run it
           #- build: 'arm64-kleidiai'
           #  arch: 'arm64'
           #  os: macos-14
-          #  defines: "-DGGML_METAL_USE_BF16=ON -DGGML_METAL_EMBED_LIBRARY=ON -DGGML_CPU_KLEIDIAI=ON"
+          #  defines: "-DGGML_METAL_EMBED_LIBRARY=ON -DCMAKE_OSX_DEPLOYMENT_TARGET=13.3 -DGGML_CPU_KLEIDIAI=ON"
           - build: 'x64'
             arch: 'x64'
             os: macos-15-intel
@@ -1402,7 +1402,6 @@ jobs:
         run: |
           sysctl -a
           cmake -B build -G Xcode \
-            -DGGML_METAL_USE_BF16=ON \
             -DGGML_METAL_EMBED_LIBRARY=ON \
             -DLLAMA_OPENSSL=OFF \
             -DLLAMA_BUILD_APP=OFF \

SECURITY.md

@@ -21,11 +21,18 @@ Please disclose it as a private [security advisory](https://github.com/ggml-org/
 
 A team of volunteers on a reasonable-effort basis maintains this project. As such, please give us at least 90 days to work on a fix before public exposure.
 
+### AI-powered code scan
+
+llama.cpp has an AI security scanner that scans the code periodically. The full prompts and tool set can be found in [ggml-org/security-scan-prompt](https://github.com/ggml-org/security-scan-prompt).
+
+We greatly appreciate reports that reflect genuine research effort, and we are happy to spend our time reviewing them. Findings that an autonomous AI agent can surface on its own add little on top of the scans we already run.
+
 ### Requirements
 
 Before submitting your report, ensure you meet the following requirements:
 
 - You have read this policy and fully understand it.
+- You have searched for existing discussions of the issue. If it has already been reported, your report will likely be rejected as a duplicate.
 - AI is only permitted in an assistive capacity as stated in [AGENTS.md](AGENTS.md). We do not accept reports that are written exclusively by AI.
 - Your report must include a working Proof-of-Concept in the form of a script and/or attached files.
 
@@ -46,6 +53,8 @@ Only vulnerabilities that fall within these parts of the project are considered
 
 Note that none of the topics under [Using llama.cpp securely](#using-llamacpp-securely) are considered vulnerabilities in LLaMA C++.
 
+Denial-of-Service (DoS) bugs are generally not treated as vulnerabilities. We don't reject them outright, but we look at them case-by-case and only accept those that are genuinely worth fixing.
+
 For vulnerabilities that fall within the `vendor` directory, please report them directly to the third-party project.
 
 ## Using llama.cpp securely

build-xcframework.sh

@@ -17,7 +17,6 @@ LLAMA_BUILD_MTMD=ON
 GGML_METAL=ON
 GGML_METAL_EMBED_LIBRARY=ON
 GGML_BLAS_DEFAULT=ON
-GGML_METAL_USE_BF16=ON
 GGML_OPENMP=OFF
 
 COMMON_C_FLAGS="-Wno-macro-redefined -Wno-shorten-64-to-32 -Wno-unused-command-line-argument -g"
@@ -44,7 +43,6 @@ COMMON_CMAKE_ARGS=(
     -DGGML_METAL_EMBED_LIBRARY=${GGML_METAL_EMBED_LIBRARY}
     -DGGML_BLAS_DEFAULT=${GGML_BLAS_DEFAULT}
     -DGGML_METAL=${GGML_METAL}
-    -DGGML_METAL_USE_BF16=${GGML_METAL_USE_BF16}
     -DGGML_NATIVE=OFF
     -DGGML_OPENMP=${GGML_OPENMP}
 )

ci/run.sh

@@ -10,6 +10,9 @@
 # # with CUDA support
 # GG_BUILD_CUDA=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt
 #
+# # with ROCm support
+# GG_BUILD_ROCM=1 GG_BUILD_AMDGPU_TARGETS=gfx1151 bash ./ci/run.sh ./tmp/results ./tmp/mnt
+#
 # # with SYCL support
 # GG_BUILD_SYCL=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt
 #
@@ -89,7 +92,7 @@ if [ ! -z ${GG_BUILD_CUDA} ]; then
 fi
 
 if [ ! -z ${GG_BUILD_ROCM} ]; then
-    CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_HIP=ON"
+    CMAKE_EXTRA="${CMAKE_EXTRA} -DCMAKE_HIP_COMPILER=$(hipconfig -l)/clang -DGGML_HIP=ON -DGGML_HIP_ROCWMMA_FATTN=ON"
     if [ -z ${GG_BUILD_AMDGPU_TARGETS} ]; then
         echo "Missing GG_BUILD_AMDGPU_TARGETS, please set it to your GPU architecture (e.g. gfx90a, gfx1100, etc.)"
         exit 1
@@ -640,39 +643,52 @@ function gg_sum_rerank_tiny {
 
 function gg_check_build_requirements {
     if ! command -v git &> /dev/null; then
-        gg_printf 'git not found, please install'
+        gg_printf 'git not found, please install\n'
+        exit 1
     fi
 
     if ! command -v git-lfs &> /dev/null; then
-        gg_printf 'git-lfs not found, please install'
+        gg_printf 'git-lfs not found, please install\n'
+        exit 1
+    fi
+
+    if ! git config --get filter.lfs.clean &> /dev/null; then
+        gg_printf 'git-lfs not initialized, please run `git lfs install`\n'
+        exit 1
     fi
 
     if ! command -v wget &> /dev/null; then
-        gg_printf 'wget not found, please install'
+        gg_printf 'wget not found, please install\n'
+        exit 1
     fi
 
     if ! command -v python3 &> /dev/null; then
-        gg_printf 'python3 not found, please install'
+        gg_printf 'python3 not found, please install\n'
+        exit 1
     fi
 
     if ! command -v pip3 &> /dev/null; then
-        gg_printf 'pip3 not found, please install'
+        gg_printf 'pip3 not found, please install\n'
+        exit 1
     fi
 
     if ! python3 -m ensurepip --help &> /dev/null; then
-        gg_printf 'ensurepip not found, please install python3-venv package'
+        gg_printf 'ensurepip not found, please install python3-venv package\n'
+        exit 1
     fi
 
     if ! command -v cmake &> /dev/null; then
-        gg_printf 'cmake not found, please install'
+        gg_printf 'cmake not found, please install\n'
+        exit 1
     fi
 
     if ! command -v ccache &> /dev/null; then
-        gg_printf 'ccache not found, please consider installing for faster builds'
+        gg_printf 'ccache not found, please consider installing for faster builds\n'
     fi
 
     if ! command -v ctest &> /dev/null; then
-        gg_printf 'ctest not found, please install'
+        gg_printf 'ctest not found, please install\n'
+        exit 1
     fi
 }
 

common/arg.cpp

@@ -61,6 +61,7 @@ static std::initializer_list<enum llama_example> mmproj_examples = {
     LLAMA_EXAMPLE_MTMD,
     LLAMA_EXAMPLE_SERVER,
     LLAMA_EXAMPLE_CLI,
+    LLAMA_EXAMPLE_TTS,
 };
 
 static std::string read_file(const std::string & fname) {
@@ -360,7 +361,6 @@ static bool spec_types_is_default(const common_params & params) {
 common_models_handler common_models_handler_init(const common_params & params, llama_example curr_ex) {
     common_download_hf_plan plan;
     common_download_hf_plan plan_spec;
-    common_download_hf_plan plan_voc;
     common_download_opts opts;
 
     const bool spec_type_draft_mtp = std::find(params.speculative.types.begin(),
@@ -413,11 +413,7 @@ common_models_handler common_models_handler_init(const common_params & params, l
         plan_spec = common_download_get_hf_plan(params.speculative.draft.mparams, opts_spec);
     }
 
-    if (!params.vocoder.model.hf_repo.empty()) {
-        plan_voc = common_download_get_hf_plan(params.vocoder.model, opts);
-    }
-
-    return common_models_handler{plan, plan_spec, plan_voc, opts};
+    return common_models_handler{plan, plan_spec, opts};
 }
 
 bool common_models_handler_is_preset_repo(const common_models_handler & handler) {
@@ -467,7 +463,6 @@ void common_models_handler_apply(common_models_handler & handler, common_params
 
     auto & plan      = handler.plan;
     auto & plan_spec = handler.plan_spec;
-    auto & plan_voc  = handler.plan_voc;
 
     auto opts = handler.opts; // copy
     opts.callback = callback;
@@ -482,7 +477,6 @@ void common_models_handler_apply(common_models_handler & handler, common_params
     };
     handle_url(params.model);
     handle_url(params.mmproj);
-    handle_url(params.vocoder.model);
     handle_url(params.speculative.draft.mparams);
 
     // optionally, if docker repo is set, resolve it
@@ -510,14 +504,6 @@ void common_models_handler_apply(common_models_handler & handler, common_params
         task.opts       = opts;
         tasks.push_back(task);
     }
-    if (!params.vocoder.model.url.empty()) {
-        common_download_task task;
-        task.url        = params.vocoder.model.url;
-        task.local_path = params.vocoder.model.path;
-        task.opts       = opts;
-        tasks.push_back(task);
-    }
-
     bool had_spec_url = false;
     if (!params.speculative.draft.mparams.url.empty()) {
         common_download_task task;
@@ -631,11 +617,6 @@ void common_models_handler_apply(common_models_handler & handler, common_params
         had_spec_url = true;
     }
 
-    // handle vocoder plan (e.g. --hf-repo-v)
-    if (!plan_voc.model_files.empty()) {
-        add_tasks(plan_voc.model_files, plan_voc.primary, params.vocoder.model);
-    }
-
     if (!plan.model_files.empty()) {
         add_tasks(plan.model_files, plan.primary, params.model);
     }
@@ -1361,6 +1342,10 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         params.n_parallel = -1;     // auto by default
     } else if (ex == LLAMA_EXAMPLE_TOKENIZE) {
         params.parse_special = true; // parse special tokens by default, like the old tokenize tool
+    } else if (ex == LLAMA_EXAMPLE_TTS) {
+        params.out_file = "output.wav";
+        params.sampling.penalty_repeat = 1.05f;
+        params.sampling.penalty_last_n = -1;
     }
 
     params.use_color = tty_can_use_colors();
@@ -2023,9 +2008,9 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ).set_sampling());
     add_opt(common_arg(
         {"--repeat-last-n"}, "N",
-        string_format("last n tokens to consider for penalize (default: %d, 0 = disabled, -1 = ctx_size)", params.sampling.penalty_last_n),
+        string_format("last n tokens to consider for penalize (default: %d, 0 = disabled)", params.sampling.penalty_last_n),
         [](common_params & params, int value) {
-            if (value < -1) {
+            if (value < 0) {
                 throw std::runtime_error(string_format("error: invalid repeat-last-n = %d\n", value));
             }
             params.sampling.penalty_last_n = value;
@@ -2096,9 +2081,9 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ).set_sampling());
     add_opt(common_arg(
         {"--dry-penalty-last-n"}, "N",
-        string_format("set DRY penalty for the last n tokens (default: %d, 0 = disable, -1 = context size)", params.sampling.dry_penalty_last_n),
+        string_format("set DRY penalty for the last n tokens (default: %d, 0 = disable)", params.sampling.dry_penalty_last_n),
         [](common_params & params, int value) {
-            if (value < -1) {
+            if (value < 0) {
                 throw std::runtime_error(string_format("error: invalid dry-penalty-last-n = %d\n", value));
             }
             params.sampling.dry_penalty_last_n = value;
@@ -2983,20 +2968,6 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.model.hf_file = value;
         }
     ).set_examples({LLAMA_EXAMPLE_COMMON, LLAMA_EXAMPLE_DOWNLOAD, LLAMA_EXAMPLE_TOKENIZE}).set_env("LLAMA_ARG_HF_FILE"));
-    add_opt(common_arg(
-        {"-hfv", "-hfrv", "--hf-repo-v"}, "<user>/<model>[:quant]",
-        "Hugging Face model repository for the vocoder model (default: unused)",
-        [](common_params & params, const std::string & value) {
-            params.vocoder.model.hf_repo = value;
-        }
-    ).set_env("LLAMA_ARG_HF_REPO_V"));
-    add_opt(common_arg(
-        {"-hffv", "--hf-file-v"}, "FILE",
-        "Hugging Face model file for the vocoder model (default: unused)",
-        [](common_params & params, const std::string & value) {
-            params.vocoder.model.hf_file = value;
-        }
-    ).set_env("LLAMA_ARG_HF_FILE_V"));
     add_opt(common_arg(
         {"-hft", "--hf-token"}, "TOKEN",
         "Hugging Face access token (default: value from HF_TOKEN environment variable)",
@@ -4272,24 +4243,18 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     //
 
     add_opt(common_arg(
-        {"-mv", "--model-vocoder"}, "FNAME",
-        "vocoder model for audio generation (default: unused)",
+        {"--tts-lang"}, "FNAME",
+        "language (ISO 639-1) for audio generation\n"
+        "see tts/README.md for per-model usage notes",
         [](common_params & params, const std::string & value) {
-            params.vocoder.model.path = value;
+            params.tts_lang = value;
         }
-    ).set_examples({LLAMA_EXAMPLE_TTS, LLAMA_EXAMPLE_SERVER}));
-     add_opt(common_arg(
-        {"--tts-use-guide-tokens"},
-        "Use guide tokens to improve TTS word recall",
-        [](common_params & params) {
-            params.vocoder.use_guide_tokens = true;
-        }
-    ).set_examples({LLAMA_EXAMPLE_TTS, LLAMA_EXAMPLE_SERVER}));
+    ).set_examples({LLAMA_EXAMPLE_TTS}));
     add_opt(common_arg(
         {"--tts-speaker-file"}, "FNAME",
         "speaker file path for audio generation",
         [](common_params & params, const std::string & value) {
-            params.vocoder.speaker_file = value;
+            params.tts_speaker_file = value;
         }
     ).set_examples({LLAMA_EXAMPLE_TTS}));
 
@@ -4409,16 +4374,6 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ).set_examples({LLAMA_EXAMPLE_DEBUG}));
 
     // presets
-    add_opt(common_arg(
-        {"--tts-oute-default"},
-        string_format("use default OuteTTS models (note: can download weights from the internet)"),
-        [](common_params & params) {
-            params.model.hf_repo = "OuteAI/OuteTTS-0.2-500M-GGUF";
-            params.model.hf_file = "OuteTTS-0.2-500M-Q8_0.gguf";
-            params.vocoder.model.hf_repo = "ggml-org/WavTokenizer";
-            params.vocoder.model.hf_file = "WavTokenizer-Large-75-F16.gguf";
-        }
-    ).set_examples({LLAMA_EXAMPLE_TTS}));
 
     add_opt(common_arg(
         {"--embd-gemma-default"},

common/arg.h

@@ -137,7 +137,6 @@ void common_params_add_preset_options(std::vector<common_arg> & args);
 struct common_models_handler {
     common_download_hf_plan plan;
     common_download_hf_plan plan_spec;
-    common_download_hf_plan plan_voc;
     common_download_opts opts;
 };
 

common/common.cpp

@@ -1302,23 +1302,12 @@ common_init_result::common_init_result(common_params & params, bool model_only)
                 params.sampling.logit_bias_eog.begin(), params.sampling.logit_bias_eog.end());
     }
 
-    //if (params.sampling.penalty_last_n == -1) {
-    //    LOG_TRC("%s: setting penalty_last_n to ctx_size = %d\n", __func__, llama_n_ctx(lctx));
-    //    params.sampling.penalty_last_n = llama_n_ctx(lctx);
-    //}
-
-    //if (params.sampling.dry_penalty_last_n == -1) {
-    //    LOG_TRC("%s: setting dry_penalty_last_n to ctx_size = %d\n", __func__, llama_n_ctx(lctx));
-    //    params.sampling.dry_penalty_last_n = llama_n_ctx(lctx);
-    //}
-
     // init the backend samplers as part of the context creation
     pimpl->samplers.resize(cparams.n_seq_max);
     pimpl->samplers_seq_config.resize(cparams.n_seq_max);
 
-    const int32_t n_ctx = cparams.n_ctx > 0 ? (int32_t) cparams.n_ctx : llama_model_n_ctx_train(model);
     for (int i = 0; i < (int) cparams.n_seq_max; ++i) {
-        pimpl->samplers[i].reset(common_sampler_init(model, params.sampling, n_ctx));
+        pimpl->samplers[i].reset(common_sampler_init(model, params.sampling));
         pimpl->samplers_seq_config[i] = { i, common_sampler_get(pimpl->samplers[i].get()) };
     }
 

common/common.h

@@ -235,14 +235,14 @@ struct common_params_sampling {
     float   temp               = 0.80f;  // <= 0.0 to sample greedily, 0.0 to not output probabilities
     float   dynatemp_range     = 0.00f;  // 0.0 = disabled
     float   dynatemp_exponent  = 1.00f;  // controls how entropy maps to temperature in dynamic temperature sampler
-    int32_t penalty_last_n     = 64;     // last n tokens to penalize (0 = disable penalty, -1 = context size)
+    int32_t penalty_last_n     = 64;     // last n tokens to penalize (0 = disable penalty)
     float   penalty_repeat     = 1.00f;  // 1.0 = disabled
     float   penalty_freq       = 0.00f;  // 0.0 = disabled
     float   penalty_present    = 0.00f;  // 0.0 = disabled
     float   dry_multiplier     = 0.0f;   // 0.0 = disabled;      DRY repetition penalty for tokens extending repetition:
     float   dry_base           = 1.75f;  // 0.0 = disabled;      multiplier * base ^ (length of sequence before token - allowed length)
     int32_t dry_allowed_length = 2;      // tokens extending repetitions beyond this receive penalty
-    int32_t dry_penalty_last_n = -1;     // how many tokens to scan for repetitions (0 = disable penalty, -1 = context size)
+    int32_t dry_penalty_last_n = 64;     // how many tokens to scan for repetitions (0 = disable penalty)
     float   adaptive_target    = -1.0f;  // select tokens near this probability (valid range 0.0 to 1.0; negative = disabled)
     float   adaptive_decay     = 0.90f;  // EMA decay for adaptation; history ≈ 1/(1-decay) tokens (0.0 - 0.99)
     int32_t mirostat           = 0;      // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0
@@ -392,14 +392,6 @@ struct common_params_speculative {
     }
 };
 
-struct common_params_vocoder {
-    struct common_params_model model;
-
-    std::string speaker_file; // speaker file path
-
-    bool use_guide_tokens = false; // enable guide tokens to improve TTS accuracy
-};
-
 struct common_params_diffusion {
     int32_t steps         = 128;
     bool    visual_mode   = false;
@@ -497,7 +489,6 @@ struct common_params {
 
     struct common_params_sampling    sampling;
     struct common_params_speculative speculative;
-    struct common_params_vocoder     vocoder;
     struct common_params_diffusion   diffusion;
 
     struct common_params_model model;
@@ -740,6 +731,10 @@ struct common_params {
     void *                  load_progress_callback_user_data = NULL;
     bool no_alloc = false; // Don't allocate model buffers
 
+    // TTS params
+    std::string tts_lang = "";
+    std::string tts_speaker_file = "";
+
     bool is_gen_docs = false; // whether we are running inside llama-gen-docs
 };
 

common/fit.cpp

@@ -136,7 +136,10 @@ static std::vector<llama_device_memory_data> common_get_device_memory_data_impl(
         devs.push_back(llama_model_get_device(model, i));
     }
 
-    hp_ngl         = llama_model_n_layer(model) + llama_model_n_layer_nextn(model);
+    hp_ngl         = llama_model_n_layer(model);
+    if (mparams->load_mtp) {
+        hp_ngl    += llama_model_n_layer_nextn(model);
+    }
     hp_n_ctx_train = llama_model_n_ctx_train(model);
     hp_n_expert    = llama_model_n_expert(model);
 

common/sampling.cpp

@@ -186,8 +186,7 @@ std::string common_params_sampling::print() const {
 
 struct common_sampler * common_sampler_init(
         const struct llama_model * model,
-        struct common_params_sampling & params,
-        int32_t n_ctx) {
+        struct common_params_sampling & params) {
     if (!std::isfinite(params.penalty_repeat) ||
         params.penalty_repeat <= 0.0f ||
         !std::isfinite(1.0f/params.penalty_repeat)) {
@@ -199,10 +198,6 @@ struct common_sampler * common_sampler_init(
     if (!std::isfinite(params.penalty_present)) {
         throw std::invalid_argument("penalty_present must be finite");
     }
-    if (params.penalty_last_n == -1) {
-        params.penalty_last_n = n_ctx > 0 ? n_ctx : llama_model_n_ctx_train(model);
-    }
-
     const llama_vocab * vocab = llama_model_get_vocab(model);
     llama_sampler_chain_params lparams = llama_sampler_chain_default_params();
 
@@ -355,7 +350,7 @@ struct common_sampler * common_sampler_init(
                         for (const auto & str : params.dry_sequence_breakers) {
                             c_breakers.push_back(str.c_str());
                         }
-                        samplers.push_back(llama_sampler_init_dry(vocab, llama_model_n_ctx_train(model), params.dry_multiplier, params.dry_base, params.dry_allowed_length, params.dry_penalty_last_n, c_breakers.data(), c_breakers.size()));
+                        samplers.push_back(llama_sampler_init_dry(vocab, params.dry_multiplier, params.dry_base, params.dry_allowed_length, params.dry_penalty_last_n, c_breakers.data(), c_breakers.size()));
                     }
                     break;
                 case COMMON_SAMPLER_TYPE_TOP_K:
@@ -383,7 +378,7 @@ struct common_sampler * common_sampler_init(
                     samplers.push_back(llama_sampler_init_infill(vocab));
                     break;
                 case COMMON_SAMPLER_TYPE_PENALTIES:
-                    samplers.push_back(llama_sampler_init_penalties(params.penalty_last_n, params.penalty_repeat, params.penalty_freq, params.penalty_present));
+                    samplers.push_back(llama_sampler_init_penalties(llama_vocab_n_tokens(vocab), params.penalty_last_n, params.penalty_repeat, params.penalty_freq, params.penalty_present));
                     break;
                 case COMMON_SAMPLER_TYPE_ADAPTIVE_P:
                     // the `adaptive-p` sampler is like `dist` and `mirostat` in that it selects

common/sampling.h

@@ -39,8 +39,7 @@ struct common_sampler;
 // note: can mutate params in some cases
 struct common_sampler * common_sampler_init(
         const struct llama_model * model,
-        struct common_params_sampling & params,
-        int32_t n_ctx = 0);
+        struct common_params_sampling & params);
 
 void common_sampler_free(struct common_sampler * gsmpl);
 

common/speculative.cpp

@@ -2385,57 +2385,28 @@ common_speculative * common_speculative_init(common_params_speculative & params,
     {
         uint32_t enabled_configs = common_get_enabled_speculative_configs(params.types);
 
-        bool has_draft_simple = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_DRAFT_SIMPLE));
-        bool has_draft_eagle3 = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3)) && params.draft.ctx_dft != nullptr;
-        bool has_draft_mtp    = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_DRAFT_MTP))    && params.draft.ctx_dft != nullptr;
-        bool has_draft_dflash = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_DRAFT_DFLASH)) && params.draft.ctx_dft != nullptr;
-        bool has_draft_dspark = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_DRAFT_DSPARK)) && params.draft.ctx_dft != nullptr;
-
-
-
-        bool has_ngram_cache   = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_NGRAM_CACHE));
-        bool has_ngram_simple  = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_NGRAM_SIMPLE));
-        bool has_ngram_map_k   = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K));
-        bool has_ngram_map_k4v = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K4V));
-        bool has_ngram_mod     = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_NGRAM_MOD));
+        auto add_config_if_enabled = [&](common_speculative_type type, bool available = true) {
+            if (available && (enabled_configs & (1u << type))) {
+                configs.emplace_back(type, params);
+            }
+        };
 
         // when adding a new type - update here the logic above
         static_assert(COMMON_SPECULATIVE_TYPE_COUNT == 11);
 
         // this list here defines the priority of the speculators
         // the one with highest priority are listed first
-        if (has_ngram_simple) {
-            // This implementation can guess a lot of tokens without any draft model.
-            configs.push_back(common_speculative_config(COMMON_SPECULATIVE_TYPE_NGRAM_SIMPLE, params));
-        }
-        if (has_ngram_map_k) {
-            configs.push_back(common_speculative_config(COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K, params));
-        }
-        if (has_ngram_map_k4v) {
-            // This implementation can guess tokens with high acceptance rate but is more expensive.
-            configs.push_back(common_speculative_config(COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K4V, params));
-        }
-        if (has_ngram_mod) {
-            configs.push_back(common_speculative_config(COMMON_SPECULATIVE_TYPE_NGRAM_MOD, params));
-        }
-        if (has_ngram_cache) {
-            configs.push_back(common_speculative_config(COMMON_SPECULATIVE_TYPE_NGRAM_CACHE, params));
-        }
-        if (has_draft_simple) {
-            configs.push_back(common_speculative_config(COMMON_SPECULATIVE_TYPE_DRAFT_SIMPLE, params));
-        }
-        if (has_draft_eagle3) {
-            configs.push_back(common_speculative_config(COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3, params));
-        }
-        if (has_draft_mtp) {
-            configs.push_back(common_speculative_config(COMMON_SPECULATIVE_TYPE_DRAFT_MTP, params));
-        }
-        if (has_draft_dflash) {
-            configs.push_back(common_speculative_config(COMMON_SPECULATIVE_TYPE_DRAFT_DFLASH, params));
-        }
-        if (has_draft_dspark) {
-            configs.push_back(common_speculative_config(COMMON_SPECULATIVE_TYPE_DRAFT_DSPARK, params));
-        }
+        add_config_if_enabled(COMMON_SPECULATIVE_TYPE_NGRAM_SIMPLE);
+        add_config_if_enabled(COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K);
+        add_config_if_enabled(COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K4V);
+        add_config_if_enabled(COMMON_SPECULATIVE_TYPE_NGRAM_MOD);
+        add_config_if_enabled(COMMON_SPECULATIVE_TYPE_NGRAM_CACHE);
+
+        add_config_if_enabled(COMMON_SPECULATIVE_TYPE_DRAFT_SIMPLE);
+        add_config_if_enabled(COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3, params.draft.ctx_dft != nullptr);
+        add_config_if_enabled(COMMON_SPECULATIVE_TYPE_DRAFT_MTP,    params.draft.ctx_dft != nullptr);
+        add_config_if_enabled(COMMON_SPECULATIVE_TYPE_DRAFT_DFLASH, params.draft.ctx_dft != nullptr);
+        add_config_if_enabled(COMMON_SPECULATIVE_TYPE_DRAFT_DSPARK, params.draft.ctx_dft != nullptr);
     }
 
     std::vector<std::unique_ptr<common_speculative_impl>> impls = {};

conversion/__init__.py

@@ -70,6 +70,7 @@ TEXT_MODEL_MAP: dict[str, str] = {
     "Exaone4ForCausalLM": "exaone",
     "ExaoneForCausalLM": "exaone",
     "ExaoneMoEForCausalLM": "exaone",
+    "ExaoneMoeForCausalLM": "exaone",
     "FalconForCausalLM": "falcon",
     "FalconH1ForCausalLM": "falcon_h1",
     "FalconMambaForCausalLM": "mamba",
@@ -210,6 +211,7 @@ TEXT_MODEL_MAP: dict[str, str] = {
     "Qwen3MoeForCausalLM": "qwen",
     "Qwen3NextForCausalLM": "qwen",
     "Qwen3OmniMoeForConditionalGeneration": "qwen3vl",
+    "Qwen3TTSForConditionalGeneration": "qwen3tts",
     "Qwen3VLForConditionalGeneration": "qwen3vl",
     "Qwen3VLMoeForConditionalGeneration": "qwen3vl",
     "Qwen3_5ForCausalLM": "qwen",
@@ -304,6 +306,7 @@ MMPROJ_MODEL_MAP: dict[str, str] = {
     "Qwen2_5_VLForConditionalGeneration": "qwenvl",
     "Qwen3ASRForConditionalGeneration": "qwen3vl",
     "Qwen3OmniMoeForConditionalGeneration": "qwen3vl",
+    "Qwen3TTSForConditionalGeneration": "qwen3tts",
     "Qwen3VLForConditionalGeneration": "qwen3vl",
     "Qwen3VLMoeForConditionalGeneration": "qwen3vl",
     "Qwen3_5ForConditionalGeneration": "qwen3vl",

conversion/chatglm.py

@@ -81,7 +81,7 @@ class ChatGLMModel(TextModel):
 
     @staticmethod
     def token_bytes_to_string(b):
-        from transformers.models.gpt2.tokenization_gpt2 import bytes_to_unicode  # ty: ignore[unresolved-import]
+        from transformers.convert_slow_tokenizer import bytes_to_unicode
         byte_encoder = bytes_to_unicode()
         return ''.join([byte_encoder[ord(char)] for char in b.decode('latin-1')])
 

conversion/deepseek.py

@@ -17,8 +17,11 @@ from .base import LazyTorchTensor, MmprojModel, ModelBase, TextModel, gguf, logg
 from .qwen import QwenModel
 
 
-@ModelBase.register("DeepseekOCRForCausalLM", "UnlimitedOCRForCausalLM")
+@ModelBase.register("DeepseekOCRForCausalLM")
 class DeepseekOCRVisionModel(MmprojModel):
+    # HF dynamic_preprocess() max_num, which differs per model
+    preproc_max_tiles = 9
+
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
         self.clip_projector_type = gguf.VisionProjectorType.DEEPSEEKOCR
@@ -43,6 +46,9 @@ class DeepseekOCRVisionModel(MmprojModel):
         # @bluebread: there's no window_size in config but just add it here anyway
         self.gguf_writer.add_vision_window_size(self.hparams.get("window_size", 14))
 
+        self.gguf_writer.add_vision_preproc_min_tiles(2)
+        self.gguf_writer.add_vision_preproc_max_tiles(self.preproc_max_tiles)
+
         # SAM configuration
         sam_hparams = hparams['sam']
         self.gguf_writer.add_vision_sam_layers_count(sam_hparams['layers'])
@@ -93,8 +99,15 @@ class DeepseekOCRVisionModel(MmprojModel):
         return super().filter_tensors((name, gen))
 
 
+@ModelBase.register("UnlimitedOCRForCausalLM")
+class UnlimitedOCRVisionModel(DeepseekOCRVisionModel):
+    preproc_max_tiles = 32
+
+
 @ModelBase.register("DeepseekOCR2ForCausalLM")
 class DeepseekOCR2VisionModel(DeepseekOCRVisionModel):
+    preproc_max_tiles = 6
+
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
         self.clip_projector_type = gguf.VisionProjectorType.DEEPSEEKOCR2
@@ -520,6 +533,13 @@ class DeepseekV4Model(TextModel):
         for key, value in raw_hparams.items():
             self.hparams.setdefault(key, value)
 
+        # workaround for special rope_parameters (main/compress) in transformers 5.x
+        if self.rope_parameters.get("full_attention", self.rope_parameters).get("rope_type") is None:
+            if (rope_scaling := raw_hparams.get("rope_scaling")) is not None:
+                if "rope_type" not in rope_scaling and (rope_type := rope_scaling.get("type")) is not None:
+                    rope_scaling["rope_type"] = rope_type
+                self.rope_parameters.update(**rope_scaling)
+
         self.block_count = self.hparams["num_hidden_layers"]
         if self.mtp_only:
             self.block_count += self.hparams.get("num_nextn_predict_layers", 0)

conversion/exaone.py

@@ -123,7 +123,9 @@ class Exaone4Model(TextModel):
                 yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FREQS), torch.tensor(rope_factors, dtype=torch.float32))
 
 
-@ModelBase.register("ExaoneMoEForCausalLM")
+# note: transformers >= 5.1 renamed the class to "ExaoneMoeForCausalLM" (lowercase 'e'),
+#       so accept both spellings - LG AI have updated the configs of already-released models
+@ModelBase.register("ExaoneMoEForCausalLM", "ExaoneMoeForCausalLM")
 class ExaoneMoEModel(Exaone4Model):
     model_arch = gguf.MODEL_ARCH.EXAONE_MOE
 

conversion/llama.py

@@ -119,7 +119,7 @@ class LlamaModel(TextModel):
         path_tekken_json = self.dir_model / "tekken.json"
         path_tokenizer_json = self.dir_model / "tokenizer.json"
         if path_tekken_json.is_file() and not path_tokenizer_json.is_file():
-            self._set_vocab_mistral()
+            return self._set_vocab_mistral()
 
         tokenizer_config_file = self.dir_model / 'tokenizer_config.json'
         if tokenizer_config_file.is_file():

conversion/qwen.py

@@ -18,7 +18,7 @@ class QwenModel(TextModel):
 
     @staticmethod
     def token_bytes_to_string(b):
-        from transformers.models.gpt2.tokenization_gpt2 import bytes_to_unicode  # ty: ignore[unresolved-import]
+        from transformers.convert_slow_tokenizer import bytes_to_unicode
         byte_encoder = bytes_to_unicode()
         return ''.join([byte_encoder[ord(char)] for char in b.decode('latin-1')])
 

conversion/qwen3tts.py

@@ -0,0 +1,471 @@
+from __future__ import annotations
+
+import json
+from pathlib import Path
+from typing import Any, Callable, Iterable, TYPE_CHECKING
+
+import torch
+import torch.nn.functional as F
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import ModelBase, MmprojModel, TextModel, gguf
+
+# Tricks being used to support this model via existing llama.cpp code paths:
+# - Text projection MLP is folded into the embedding table
+# - codec_embedding is concat to the text embedding table, vocab is extended
+#   example: codec_bos_id(2149) --> "<|codec_bos|>"
+#            codec_eos_token_id(2150) --> "<|codec_eos_token|>"
+#            codec_language_id.chinese(2055) --> "<|codec_language_chinese|>"
+#            other rows --> "<|codec_0|>", "<|codec_1|>", ..., "<|codec_1023|>"
+# - output tensor codec_head is smaller than vocab, so logits will be padded at inference time
+# - suppress_tokens is used to limit the backbone to only sample either semantic or EOS (stop) token
+
+# pipeline stage mapping:
+#   speaker reference encoder --> mapped to normal mtmd audio encoder
+#   backbone --> mapped to normal libllama text model (autoregressive)
+#   code_predictor --> MTMD_GEN_PROCESS_TYPE_GEN_CODE
+#   code2wav --> MTMD_GEN_PROCESS_TYPE_GEN_WAV
+
+# torch activation functions used by Qwen3TTSTalkerResizeMLP (config's hidden_act)
+_ACT2FN = {
+    "silu": F.silu,
+    "gelu": F.gelu,
+    "relu": F.relu,
+}
+
+
+@ModelBase.register("Qwen3TTSForConditionalGeneration")
+class Qwen3TTSTalkerModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.QWEN3TTS
+
+    _TEXT_PROJ_KEYS = (
+        "model.text_embedding.weight",
+        "text_projection.linear_fc1.weight",
+        "text_projection.linear_fc1.bias",
+        "text_projection.linear_fc2.weight",
+        "text_projection.linear_fc2.bias",
+    )
+
+    _text_proj_buffer: dict[str, Tensor]
+    _folded_text_embed: Tensor | None
+    _codec_embed: Tensor | None
+
+    def __init__(self, dir_model: Path, *args, **kwargs):
+        hparams = kwargs.pop("hparams", None)
+        if hparams is None:
+            hparams = ModelBase.load_hparams(dir_model, is_mistral_format=False)
+        raw_talker_config = dict(hparams["talker_config"])
+        self._talker_config = raw_talker_config
+        self.n_codec_vocab = raw_talker_config["vocab_size"]
+        talker_config = dict(raw_talker_config)
+        talker_config["vocab_size"] = talker_config["text_vocab_size"]
+        hparams["text_config"] = talker_config
+        super().__init__(dir_model, *args, hparams=hparams, **kwargs)
+        self._text_proj_buffer = {}
+        self._folded_text_embed = None
+        self._codec_embed = None
+
+    def _codec_token_names(self) -> list[str]:
+        # start every row with a generic name, then override the ones with a
+        # known meaning (bos/eos/language/etc, derived from the *_id fields
+        # of talker_config) with a more descriptive one
+        names = [f"<|codec_{i}|>" for i in range(self.n_codec_vocab)]
+        for key, val in self._talker_config.items():
+            if not key.endswith("_id"):
+                continue
+            prefix = key[:-len("_id")]
+            if isinstance(val, int):
+                names[val] = f"<|{prefix}|>"
+            elif isinstance(val, dict):
+                for subkey, subval in val.items():
+                    names[subval] = f"<|{prefix}_{subkey}|>"
+        return names
+
+    def set_vocab(self):
+        codec_tokens = self._codec_token_names()
+        codec_toktypes = [gguf.TokenType.CONTROL] * len(codec_tokens)
+
+        try:
+            tokens, scores, toktypes = self._create_vocab_sentencepiece()
+            self.gguf_writer.add_tokenizer_model("llama")
+            self.gguf_writer.add_tokenizer_pre("default")
+            tokens += [t.encode("utf-8") for t in codec_tokens]
+            scores += [0.0] * len(codec_tokens)
+            toktypes += codec_toktypes
+            self.gguf_writer.add_token_list(tokens)
+            self.gguf_writer.add_token_scores(scores)
+            self.gguf_writer.add_token_types(toktypes)
+            special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
+            special_vocab.add_to_gguf(self.gguf_writer)
+            return
+        except FileNotFoundError:
+            pass
+
+        tokens, toktypes, tokpre = self.get_vocab_base()
+        tokens += codec_tokens
+        toktypes += codec_toktypes
+        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_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
+        special_vocab.add_to_gguf(self.gguf_writer)
+
+        # make sure that the model has no chat template, so chat will be disabled
+        self.gguf_writer.add_chat_template(None)
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+
+        # note: final vocab layout is [text_vocab | codec_vocab], with text_vocab is actually padded with -inf in cgraph
+        # for codec_vocab, only first 2048 rows can be sampled for semantic code
+        # plus codec_eos_token_id that used for signaling end of generation
+        # ref: https://github.com/QwenLM/Qwen3-TTS/blob/022e286b98fbec7e1e916cb940cdf532cd9f488e/qwen_tts/core/models/modeling_qwen3_tts.py#L2059-L2063
+
+        vocab_size = self.hparams["vocab_size"] + self.n_codec_vocab
+        codec_eos_token_id = self.hparams["vocab_size"] + self._talker_config["codec_eos_token_id"]
+        self.gguf_writer.add_suppress_tokens([
+            i for i in range(vocab_size - 1024, vocab_size)
+            if i != codec_eos_token_id
+        ])
+        self.gguf_writer.add_eos_token_id(codec_eos_token_id)
+        self.gguf_writer.add_add_eos_token(False)
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+
+        if not name.startswith("talker.") or name.startswith("talker.code_predictor."):
+            return None
+
+        name = name[len("talker."):]
+        return super().filter_tensors((name, gen))
+
+    def _maybe_emit_token_embd(self) -> Iterable[tuple[str, Tensor]]:
+        if self._folded_text_embed is None or self._codec_embed is None:
+            return
+        combined = torch.cat([self._folded_text_embed, self._codec_embed], dim=0)
+        yield (self.format_tensor_name(gguf.MODEL_TENSOR.TOKEN_EMBD), combined)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        # codec_embedding rows are appended after the text vocab, extending the embedding table
+        if name == "model.codec_embedding.weight":
+            self._codec_embed = data_torch
+            yield from self._maybe_emit_token_embd()
+            return
+
+        # codec_head is the output head for the (smaller) codec vocab; logits get padded to
+        # the extended vocab size at inference time
+        if name == "codec_head.weight":
+            yield (self.format_tensor_name(gguf.MODEL_TENSOR.OUTPUT), data_torch)
+            return
+
+        if name in self._TEXT_PROJ_KEYS:
+            self._text_proj_buffer[name] = data_torch
+            if len(self._text_proj_buffer) < len(self._TEXT_PROJ_KEYS):
+                return
+
+            # fold MLP into the embedding table at conversion time, MLP won't be used at inference time anyway
+            act_fn = _ACT2FN[self.hparams["hidden_act"]]
+            embed = self._text_proj_buffer["model.text_embedding.weight"]
+            hidden = act_fn(F.linear(embed,
+                                     self._text_proj_buffer["text_projection.linear_fc1.weight"],
+                                     self._text_proj_buffer["text_projection.linear_fc1.bias"]))
+            folded = F.linear(hidden,
+                              self._text_proj_buffer["text_projection.linear_fc2.weight"],
+                              self._text_proj_buffer["text_projection.linear_fc2.bias"])
+            self._folded_text_embed = folded
+            yield from self._maybe_emit_token_embd()
+            return
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
+
+@ModelBase.register("Qwen3TTSForConditionalGeneration")
+class Qwen3TTSSpeakerEncoderModel(MmprojModel):
+    has_vision_encoder = False
+    has_audio_encoder = True
+
+    # talker.code_predictor.model.layers.{bid}.<key> -> A_GEN_CODE_*
+    # bypass tensor_mapping.py for now to make it simple
+    _CODE_LAYER_TENSOR_MAP = {
+        "input_layernorm": gguf.MODEL_TENSOR.A_GEN_CODE_ATTN_NORM,
+        "self_attn.q_proj": gguf.MODEL_TENSOR.A_GEN_CODE_ATTN_Q,
+        "self_attn.q_norm": gguf.MODEL_TENSOR.A_GEN_CODE_ATTN_Q_NORM,
+        "self_attn.k_proj": gguf.MODEL_TENSOR.A_GEN_CODE_ATTN_K,
+        "self_attn.k_norm": gguf.MODEL_TENSOR.A_GEN_CODE_ATTN_K_NORM,
+        "self_attn.v_proj": gguf.MODEL_TENSOR.A_GEN_CODE_ATTN_V,
+        "self_attn.o_proj": gguf.MODEL_TENSOR.A_GEN_CODE_ATTN_OUT,
+        "post_attention_layernorm": gguf.MODEL_TENSOR.A_GEN_CODE_FFN_NORM,
+        "mlp.gate_proj": gguf.MODEL_TENSOR.A_GEN_CODE_FFN_GATE,
+        "mlp.up_proj": gguf.MODEL_TENSOR.A_GEN_CODE_FFN_UP,
+        "mlp.down_proj": gguf.MODEL_TENSOR.A_GEN_CODE_FFN_DOWN,
+    }
+
+    # note: codebook pages will be stacked to 3D
+    _CODE_GEN_N_CODEBOOKS = 15
+    _code_embed_buffer: dict[int, Tensor] = {}
+    _code_head_buffer: dict[int, Tensor] = {}
+    _wav_config_cache: dict[str, Any] | None = None
+
+    def __init__(self, dir_model: Path, *args, **kwargs):
+        hparams = kwargs.pop("hparams", None)
+        if hparams is None:
+            hparams = ModelBase.load_hparams(dir_model, is_mistral_format=False)
+        hparams["text_config"] = {"hidden_size": hparams["talker_config"]["hidden_size"]}
+        # ECAPA-TDNN has a fixed 4-stage backbone, but MmprojModel.__init__ needs a n_block_keys
+        hparams["speaker_encoder_config"]["n_layers"] = 4
+        super().__init__(dir_model, *args, hparams=hparams, **kwargs)
+        self._wav_config_cache = None
+
+    def get_audio_config(self) -> dict[str, Any] | None:
+        return self.global_config.get("speaker_encoder_config")
+
+    def set_gguf_parameters(self):
+        self.gguf_writer.add_file_type(self.ftype)
+        self.gguf_writer.add_clip_has_audio_encoder(True)
+        self.gguf_writer.add_clip_audio_projector_type(gguf.VisionProjectorType.QWEN3TTS_SPKENC)
+
+        # handle speaker encoder config
+        self.gguf_writer.add_audio_projection_dim(self.n_embd_text)
+        # mel_spectrogram() front-end: sr=24000, n_fft=1024, hop=256, n_mels=128, fmin=0, fmax=12000 (=sr/2, the clip.cpp default)
+        self.gguf_writer.add_audio_num_mel_bins(128)
+        # 3 SE-Res2Net stages; the stem conv, mfa, asp and fc are not counted here
+        self.gguf_writer.add_audio_block_count(3)
+        # ECAPA-TDNN has no attention/FFN, these are dummy to allow clip.cpp to load it
+        self.gguf_writer.add_audio_embedding_length(1536)
+        self.gguf_writer.add_audio_head_count(1)
+        self.gguf_writer.add_audio_feed_forward_length(1536)
+        self.gguf_writer.add_audio_attention_layernorm_eps(1e-5)
+
+        # handle code predictor config
+        self.gguf_writer.add_clip_has_gen_audio_encoder(True)
+        self.gguf_writer.add_clip_gen_audio_projector_type(gguf.VisionProjectorType.QWEN3TTS_GEN)
+        code_predictor_config = self.global_config["talker_config"]["code_predictor_config"]
+        self.gguf_writer.add_gen_audio_projection_dim(self.n_embd_text)
+        self.gguf_writer.add_gen_audio_embedding_length(code_predictor_config["hidden_size"])
+        self.gguf_writer.add_gen_audio_feed_forward_length(code_predictor_config["intermediate_size"])
+        self.gguf_writer.add_gen_audio_block_count(code_predictor_config["num_hidden_layers"])
+        self.gguf_writer.add_gen_audio_head_count(code_predictor_config["num_attention_heads"])
+        self.gguf_writer.add_gen_audio_head_count_kv(code_predictor_config["num_key_value_heads"])
+        self.gguf_writer.add_gen_audio_attention_layernorm_eps(code_predictor_config["rms_norm_eps"])
+        # note: code2wav hparams are hardcoded on the mtmd/clip.cpp side for now, not written here
+
+    def _wav_decoder_config(self) -> dict[str, Any] | None:
+        # code2wav has its own config.json, inside the speech_tokenizer dir
+        if self._wav_config_cache is None:
+            path = self.dir_model / "speech_tokenizer" / "config.json"
+            with open(path, "r", encoding="utf-8") as f:
+                cfg = json.load(f)
+            self._wav_config_cache = cfg["decoder_config"]
+        return self._wav_config_cache
+
+    def tensor_force_quant(self, name, new_name, bid, n_dims):
+        # conv1d/conv1d_dw kernels must be F16, ggml_conv_1d(_dw) has no BF16 path
+        if new_name.endswith(".weight") and (
+            new_name in ("a.gen.wav.pre_conv.weight", "a.gen.wav.dac.entry.weight", "a.gen.wav.dac.post_conv.weight")
+            or (".up.blk." in new_name and new_name.endswith(".dwconv.weight"))
+            or (".dac.blk." in new_name and (new_name.endswith(".conv1.weight") or new_name.endswith(".conv2.weight")))
+        ):
+            return gguf.GGMLQuantizationType.F16
+        # ConvTranspose1d kernels: only F16/F32 are implemented, no BF16
+        if new_name.endswith(".conv.weight") and (".up.blk." in new_name or ".dac.blk." in new_name):
+            return gguf.GGMLQuantizationType.F32
+        return super().tensor_force_quant(name, new_name, bid, n_dims)
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+
+        if not (
+            name.startswith("speaker_encoder.")
+            or name.startswith("talker.code_predictor.")
+            or name == "talker.model.codec_embedding.weight"
+        ):
+            return None
+
+        return super().filter_tensors((name, gen))
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        # code2wav tensors are already named by generate_extra_tensors(), pass them through
+        if name.startswith("a.gen.wav."):
+            yield (name, data_torch)
+            return
+
+        # codebook-0 embedding, fed back to the talker backbone (codebooks 1-15 live in code_predictor)
+        if name == "talker.model.codec_embedding.weight":
+            yield (self.format_tensor_name(gguf.MODEL_TENSOR.A_GEN_CODE_OUT_EMBD), data_torch)
+            return
+
+        if name == "talker.code_predictor.model.norm.weight":
+            yield (self.format_tensor_name(gguf.MODEL_TENSOR.A_GEN_CODE_OUTPUT_NORM), data_torch)
+            return
+
+        if name.startswith("talker.code_predictor.small_to_mtp_projection."):
+            suffix = "." + name.rsplit(".", 1)[1]
+            yield (self.format_tensor_name(gguf.MODEL_TENSOR.A_GEN_CODE_PROJ_IN, suffix=suffix), data_torch)
+            return
+
+        if name.startswith("talker.code_predictor.model.codec_embedding."):
+            idx = int(name.split("codec_embedding.")[1].split(".")[0])
+            self._code_embed_buffer[idx] = data_torch
+            if len(self._code_embed_buffer) < self._CODE_GEN_N_CODEBOOKS:
+                return
+            stacked = torch.stack([self._code_embed_buffer.pop(i) for i in range(self._CODE_GEN_N_CODEBOOKS)], dim=0)
+            yield (self.format_tensor_name(gguf.MODEL_TENSOR.A_GEN_CODE_EMBD), stacked)
+            return
+
+        if name.startswith("talker.code_predictor.lm_head."):
+            idx = int(name.split("lm_head.")[1].split(".")[0])
+            self._code_head_buffer[idx] = data_torch
+            if len(self._code_head_buffer) < self._CODE_GEN_N_CODEBOOKS:
+                return
+            stacked = torch.stack([self._code_head_buffer.pop(i) for i in range(self._CODE_GEN_N_CODEBOOKS)], dim=0)
+            yield (self.format_tensor_name(gguf.MODEL_TENSOR.A_GEN_CODE_HEAD), stacked)
+            return
+
+        if name.startswith("talker.code_predictor.model.layers."):
+            rest = name.split("model.layers.")[1]        # "{bid}.<key>.weight"
+            _, key_with_suffix = rest.split(".", 1)       # "<key>.weight"
+            key = key_with_suffix.rsplit(".", 1)[0]        # "<key>"
+            tensor = self._CODE_LAYER_TENSOR_MAP.get(key)
+            if tensor is not None:
+                yield (self.format_tensor_name(tensor, bid), data_torch)
+                return
+
+        if "res2net_block.blocks." in name:
+            assert bid is not None  # the outer stage index, picked up from the tensor name automatically
+            xid = int(name.split("res2net_block.blocks.")[1].split(".")[0])
+            suffix = "." + name.rsplit(".", 1)[1]
+            new_name = gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.A_ENC_CONV_RES2].format(bid=bid, xid=xid) + suffix
+            yield (new_name, data_torch)
+            return
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
+    def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
+        yield from self._generate_code2wav_tensors()
+
+    def _generate_code2wav_tensors(self) -> Iterable[tuple[str, Tensor]]:
+        # code2wav weights live in speech_tokenizer/model.safetensors, not the main safetensors
+        from safetensors.torch import load_file
+
+        wav_config = self._wav_decoder_config()
+        state_dict = load_file(self.dir_model / "speech_tokenizer" / "model.safetensors")
+
+        def get(name: str) -> Tensor:
+            return state_dict[name]
+
+        def snake_fold(alpha: Tensor, beta: Tensor) -> tuple[Tensor, Tensor]:
+            # fold SnakeBeta's exp()/reciprocal here, so the graph is only mul/sin/sqr/mul/add
+            return torch.exp(alpha), 1.0 / (torch.exp(beta) + 1e-9)
+
+        def rvq_codebook(prefix: str, n_layers: int) -> Tensor:
+            # checkpoint has EMA accumulators, so codebook[i] = embedding_sum[i] / cluster_usage[i]
+            books = []
+            for i in range(n_layers):
+                embedding_sum = get(f"{prefix}.vq.layers.{i}._codebook.embedding_sum")
+                cluster_usage = get(f"{prefix}.vq.layers.{i}._codebook.cluster_usage")
+                books.append(embedding_sum / cluster_usage.clamp_min(1e-5).unsqueeze(-1))
+            return torch.stack(books, dim=0) if n_layers > 1 else books[0]
+
+        T = gguf.MODEL_TENSOR
+
+        # --- quantizer: RVQ codebook decode ---
+        yield (self.format_tensor_name(T.A_GEN_WAV_QUANT_FIRST_IN), get("decoder.quantizer.rvq_first.input_proj.weight").squeeze(-1))
+        yield (self.format_tensor_name(T.A_GEN_WAV_QUANT_FIRST_OUT), get("decoder.quantizer.rvq_first.output_proj.weight").squeeze(-1))
+        yield (self.format_tensor_name(T.A_GEN_WAV_QUANT_FIRST_CB), rvq_codebook("decoder.quantizer.rvq_first", 1))
+        yield (self.format_tensor_name(T.A_GEN_WAV_QUANT_REST_IN), get("decoder.quantizer.rvq_rest.input_proj.weight").squeeze(-1))
+        yield (self.format_tensor_name(T.A_GEN_WAV_QUANT_REST_OUT), get("decoder.quantizer.rvq_rest.output_proj.weight").squeeze(-1))
+        yield (self.format_tensor_name(T.A_GEN_WAV_QUANT_REST_CB), rvq_codebook("decoder.quantizer.rvq_rest", self._CODE_GEN_N_CODEBOOKS))
+
+        # --- pre_conv ---
+        yield (self.format_tensor_name(T.A_GEN_WAV_PRE_CONV, suffix=".weight"), get("decoder.pre_conv.conv.weight"))
+        yield (self.format_tensor_name(T.A_GEN_WAV_PRE_CONV, suffix=".bias"), get("decoder.pre_conv.conv.bias"))
+
+        # --- pre_transformer ---
+        yield (self.format_tensor_name(T.A_GEN_WAV_TFM_IN_PROJ, suffix=".weight"), get("decoder.pre_transformer.input_proj.weight"))
+        yield (self.format_tensor_name(T.A_GEN_WAV_TFM_IN_PROJ, suffix=".bias"), get("decoder.pre_transformer.input_proj.bias"))
+        yield (self.format_tensor_name(T.A_GEN_WAV_TFM_OUT_PROJ, suffix=".weight"), get("decoder.pre_transformer.output_proj.weight"))
+        yield (self.format_tensor_name(T.A_GEN_WAV_TFM_OUT_PROJ, suffix=".bias"), get("decoder.pre_transformer.output_proj.bias"))
+        yield (self.format_tensor_name(T.A_GEN_WAV_TFM_OUTPUT_NORM), get("decoder.pre_transformer.norm.weight"))
+
+        tfm_layer_map = {
+            "input_layernorm.weight":         T.A_GEN_WAV_TFM_ATTN_NORM,
+            "self_attn.q_proj.weight":        T.A_GEN_WAV_TFM_ATTN_Q,
+            "self_attn.k_proj.weight":        T.A_GEN_WAV_TFM_ATTN_K,
+            "self_attn.v_proj.weight":        T.A_GEN_WAV_TFM_ATTN_V,
+            "self_attn.o_proj.weight":        T.A_GEN_WAV_TFM_ATTN_OUT,
+            "self_attn_layer_scale.scale":    T.A_GEN_WAV_TFM_ATTN_SCALE,
+            "post_attention_layernorm.weight": T.A_GEN_WAV_TFM_FFN_NORM,
+            "mlp.gate_proj.weight":           T.A_GEN_WAV_TFM_FFN_GATE,
+            "mlp.up_proj.weight":             T.A_GEN_WAV_TFM_FFN_UP,
+            "mlp.down_proj.weight":           T.A_GEN_WAV_TFM_FFN_DOWN,
+            "mlp_layer_scale.scale":          T.A_GEN_WAV_TFM_FFN_SCALE,
+        }
+        assert wav_config is not None
+        for bid in range(wav_config["num_hidden_layers"]):
+            for key, tensor_id in tfm_layer_map.items():
+                yield (self.format_tensor_name(tensor_id, bid), get(f"decoder.pre_transformer.layers.{bid}.{key}"))
+
+        # --- upsample: 2x (causal ConvTranspose1d + ConvNeXt block) ---
+        up_map = {
+            "0.conv.weight":     (T.A_GEN_WAV_UP_CONV, ".weight"),
+            "0.conv.bias":       (T.A_GEN_WAV_UP_CONV, ".bias"),
+            "1.dwconv.conv.weight": (T.A_GEN_WAV_UP_DWCONV, ".weight"),
+            "1.dwconv.conv.bias":   (T.A_GEN_WAV_UP_DWCONV, ".bias"),
+            "1.norm.weight":     (T.A_GEN_WAV_UP_NORM, ".weight"),
+            "1.norm.bias":       (T.A_GEN_WAV_UP_NORM, ".bias"),
+            "1.pwconv1.weight":  (T.A_GEN_WAV_UP_PW1, ".weight"),
+            "1.pwconv1.bias":    (T.A_GEN_WAV_UP_PW1, ".bias"),
+            "1.pwconv2.weight":  (T.A_GEN_WAV_UP_PW2, ".weight"),
+            "1.pwconv2.bias":    (T.A_GEN_WAV_UP_PW2, ".bias"),
+            "1.gamma":           (T.A_GEN_WAV_UP_GAMMA, ""),
+        }
+        for bid in range(len(wav_config["upsampling_ratios"])):
+            for key, (tensor_id, suffix) in up_map.items():
+                yield (self.format_tensor_name(tensor_id, bid, suffix=suffix), get(f"decoder.upsample.{bid}.{key}"))
+
+        # --- DAC decoder ---
+        yield (self.format_tensor_name(T.A_GEN_WAV_DAC_ENTRY, suffix=".weight"), get("decoder.decoder.0.conv.weight"))
+        yield (self.format_tensor_name(T.A_GEN_WAV_DAC_ENTRY, suffix=".bias"), get("decoder.decoder.0.conv.bias"))
+
+        n_dac_blocks = len(wav_config["upsample_rates"])
+        for bid in range(n_dac_blocks):
+            py = bid + 1  # decoder.decoder.0 is the entry conv, blocks start at 1
+
+            a, b = snake_fold(get(f"decoder.decoder.{py}.block.0.alpha"), get(f"decoder.decoder.{py}.block.0.beta"))
+            yield (self.format_tensor_name(T.A_GEN_WAV_DAC_UP_SNAKE, bid, suffix=".alpha"), a)
+            yield (self.format_tensor_name(T.A_GEN_WAV_DAC_UP_SNAKE, bid, suffix=".beta"), b)
+            yield (self.format_tensor_name(T.A_GEN_WAV_DAC_UP_CONV, bid, suffix=".weight"), get(f"decoder.decoder.{py}.block.1.conv.weight"))
+            yield (self.format_tensor_name(T.A_GEN_WAV_DAC_UP_CONV, bid, suffix=".bias"), get(f"decoder.decoder.{py}.block.1.conv.bias"))
+
+            for xid in range(3):
+                ridx = xid + 2  # block.2/3/4 are the 3 residual units
+
+                a1, b1 = snake_fold(get(f"decoder.decoder.{py}.block.{ridx}.act1.alpha"), get(f"decoder.decoder.{py}.block.{ridx}.act1.beta"))
+                name1 = gguf.TENSOR_NAMES[T.A_GEN_WAV_DAC_RES_ACT1].format(bid=bid, xid=xid)
+                yield (name1 + ".alpha", a1)
+                yield (name1 + ".beta", b1)
+
+                name_conv1 = gguf.TENSOR_NAMES[T.A_GEN_WAV_DAC_RES_CONV1].format(bid=bid, xid=xid)
+                yield (name_conv1 + ".weight", get(f"decoder.decoder.{py}.block.{ridx}.conv1.conv.weight"))
+                yield (name_conv1 + ".bias", get(f"decoder.decoder.{py}.block.{ridx}.conv1.conv.bias"))
+
+                a2, b2 = snake_fold(get(f"decoder.decoder.{py}.block.{ridx}.act2.alpha"), get(f"decoder.decoder.{py}.block.{ridx}.act2.beta"))
+                name2 = gguf.TENSOR_NAMES[T.A_GEN_WAV_DAC_RES_ACT2].format(bid=bid, xid=xid)
+                yield (name2 + ".alpha", a2)
+                yield (name2 + ".beta", b2)
+
+                name_conv2 = gguf.TENSOR_NAMES[T.A_GEN_WAV_DAC_RES_CONV2].format(bid=bid, xid=xid)
+                yield (name_conv2 + ".weight", get(f"decoder.decoder.{py}.block.{ridx}.conv2.conv.weight"))
+                yield (name_conv2 + ".bias", get(f"decoder.decoder.{py}.block.{ridx}.conv2.conv.bias"))
+
+        a5, b5 = snake_fold(get("decoder.decoder.5.alpha"), get("decoder.decoder.5.beta"))
+        yield (self.format_tensor_name(T.A_GEN_WAV_DAC_POST_SNAKE, suffix=".alpha"), a5)
+        yield (self.format_tensor_name(T.A_GEN_WAV_DAC_POST_SNAKE, suffix=".beta"), b5)
+        yield (self.format_tensor_name(T.A_GEN_WAV_DAC_POST_CONV, suffix=".weight"), get("decoder.decoder.6.conv.weight"))
+        yield (self.format_tensor_name(T.A_GEN_WAV_DAC_POST_CONV, suffix=".bias"), get("decoder.decoder.6.conv.bias"))

docs/backend/SYCL.md

@@ -449,6 +449,8 @@ Or
 use 1 SYCL GPUs: [0] with Max compute units:512
 ```
 
+User can use the device management in [docs/multi-gpu.md](https://github.com/ggml-org/llama.cpp/blob/master/docs/multi-gpu.md), like parameter `--device SYCL0,SYCL1` to assign one or more devices.
+
 ## Windows
 
 ### Install GPU driver
@@ -763,6 +765,7 @@ Or
 use 1 SYCL GPUs: [0] with Max compute units:512
 ```
 
+User can use the device management in [docs/multi-gpu.md](https://github.com/ggml-org/llama.cpp/blob/master/docs/multi-gpu.md), like parameter `--device SYCL0,SYCL1` to assign one or more devices.
 
 ## Environment Variable
 
@@ -895,6 +898,45 @@ Pass these via `CXXFLAGS` or add a one-off `#define` to enable a flag on the spo
     set UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS=1
   ```
 
+- When I set `SYCL_CACHE_PERSISTENT=1` in running time, I meet crash.
+
+  `SYCL_CACHE_PERSISTENT=1` is not recommended by llama.cpp SYCL backend.
+  When cache is enabled, SYCL runtime will try to cache and reuse JIT-compiled binaries.
+
+  We find some AI will tell user this cmd to speed up SYCL backend. It only speeds up the startup to skip the JIT process, instead of running speed.
+
+  It will bring negative impact when the SYCL binary file is changed frequently in your running environment. The new & old codes mix will lead to crash.
+
+  Compare to the benefit, it has brought more failed cases.
+  If you are not familiar with the SYCL compiler principle of JIT and AOT, please don't use it.
+
+  To restore, you need to remove the local cache: `~/.cache/libsycl_cache/` and execute `unset SYCL_CACHE_PERSISTENT` in running time.
+
+- How to use iGPU and dGPU in same time?
+
+  1. Detect the devices in your running time.
+  ```
+  source /opt/intel/oneapi/setvars.sh
+  ./build/bin/llama-server --list-devices
+
+  or
+  ./build/bin/llama-cli --list-devices
+  ./build/bin/llama-bench --list-devices
+  ./build/bin/llama-completion --list-devices
+
+  Available devices:
+    SYCL0: Intel(R) Arc(TM) A770 Graphics (15473 MiB, 15473 MiB free)
+    SYCL1: Intel(R) UHD Graphics 770 (59675 MiB, 44986 MiB free)
+  ```
+
+  The dGPU will be in the head of this list and iGPU will be the end.
+  If not all GPUs are listed, please check the env var: ONEAPI_DEVICE_SELECTOR and unset it.
+
+  2. Set the iGPU and dGPU
+
+  Set the iGPU and dGPU by `./build/bin/llama-server --device SYCL0,SYCL1,SYCLxxx`.
+
+
 ### **GitHub contribution**:
 Please add the `[SYCL]` prefix/tag in issues/PRs titles to help the SYCL contributors to check/address them without delay.
 

docs/development/HOWTO-add-model.md

@@ -133,6 +133,7 @@ Note:
 - To debug the multimodal preprocessor and encoder, you can use [llama-mtmd-debug](tools/mtmd/debug/mtmd-debug.cpp).
 - Adding a model-specific API or CLI is an anti-pattern in `libmtmd`. The goal of `libmtmd` is to provide an easy-to-use, model-agnostic library for multimodal pipeline.
 - In most cases, `llama-mtmd-cli` should not be modified. If a model requires a specific prompt, either let the user provide it or bake it into the Jinja chat template.
+- For audio generation models, see `tools/mtmd/README-dev.md`
 
 ## Tips and tricks
 

docs/ops.md

@@ -15,7 +15,7 @@ Legend:
 | Operation | BLAS | CANN | CPU | CUDA | ET | MTL | OpenCL | SYCL | Vulkan | WebGPU | ZenDNN | zDNN |
 |-----------|------|------|------|------|------|------|------|------|------|------|------|------|
 |                              ABS | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                              ACC | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | 🟡 | ✅ | ❌ | ❌ | ❌ |
+|                              ACC | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                              ADD | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                             ADD1 | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
 |                           ADD_ID | ❌ | ❌ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
@@ -23,16 +23,16 @@ Legend:
 |                           ARGMAX | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                          ARGSORT | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                             CEIL | ❌ | ❌ | ✅ | 🟡 | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                            CLAMP | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ |
-|                        COL2IM_1D | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ |
+|                            CLAMP | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                        COL2IM_1D | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                           CONCAT | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ✅ | 🟡 | ❌ | ❌ |
 |                             CONT | ❌ | 🟡 | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | ✅ | 🟡 | ❌ | ❌ |
-|                          CONV_2D | ❌ | ❌ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                          CONV_2D | ❌ | ❌ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                       CONV_2D_DW | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                          CONV_3D | ❌ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ |
+|                          CONV_3D | ❌ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                CONV_TRANSPOSE_1D | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                CONV_TRANSPOSE_2D | ❌ | ❌ | ✅ | ✅ | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                              COS | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
+|                              COS | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                      COUNT_EQUAL | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                              CPY | ❌ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ |
 |               CROSS_ENTROPY_LOSS | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ |
@@ -41,9 +41,9 @@ Legend:
 |                             DIAG | ❌ | ❌ | ✅ | ✅ | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                    DIAG_MASK_INF | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | 🟡 | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                              DIV | ❌ | ✅ | ✅ | ✅ | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                     DSV4_HC_COMB | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
-|                     DSV4_HC_POST | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
-|                      DSV4_HC_PRE | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
+|                     DSV4_HC_COMB | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ |
+|                     DSV4_HC_POST | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ |
+|                      DSV4_HC_PRE | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ |
 |                              DUP | ❌ | ✅ | ✅ | 🟡 | ❌ | 🟡 | 🟡 | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                              ELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                              EXP | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
@@ -51,24 +51,24 @@ Legend:
 |                             FILL | ❌ | ❌ | ✅ | ✅ | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                   FLASH_ATTN_EXT | ❌ | 🟡 | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ |
 |                            FLOOR | ❌ | ❌ | ✅ | 🟡 | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                  GATED_DELTA_NET | ❌ | ❌ | ✅ | ❌ | ✅ | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
-|                GATED_LINEAR_ATTN | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ |
+|                  GATED_DELTA_NET | ❌ | ❌ | ✅ | ❌ | ✅ | 🟡 | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                GATED_LINEAR_ATTN | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                            GEGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                        GEGLU_ERF | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                      GEGLU_QUICK | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                             GELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                         GELU_ERF | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                       GELU_QUICK | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                         GET_ROWS | ❌ | 🟡 | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | ✅ | ✅ | 🟡 | ❌ | ❌ |
-|                    GET_ROWS_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
+|                         GET_ROWS | ❌ | 🟡 | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ❌ | ❌ |
+|                    GET_ROWS_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ |
 |                       GROUP_NORM | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                      HARDSIGMOID | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                        HARDSWISH | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                           IM2COL | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                        IM2COL_3D | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                          L2_NORM | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ❌ | ✅ | ✅ | 🟡 | ❌ | ❌ |
-|                       LEAKY_RELU | ❌ | ✅ | ✅ | ✅ | ❌ | 🟡 | ❌ | ✅ | 🟡 | ❌ | ❌ | ❌ |
-|                LIGHTNING_INDEXER | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
+|                       LEAKY_RELU | ❌ | ✅ | ✅ | ✅ | ❌ | 🟡 | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
+|                LIGHTNING_INDEXER | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ |
 |                              LOG | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                             MEAN | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                              MUL | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
@@ -76,13 +76,13 @@ Legend:
 |                 MUL_MAT_HADAMARD | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                       MUL_MAT_ID | ❌ | 🟡 | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | ✅ | 🟡 | 🟡 | ❌ |
 |                              NEG | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                             NORM | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ❌ |
+|                             NORM | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ✅ | 🟡 | ❌ | ❌ |
 |                   OPT_STEP_ADAMW | ❌ | ❌ | ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
 |                     OPT_STEP_SGD | ❌ | ❌ | ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
-|                         OUT_PROD | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ | 🟡 |
+|                         OUT_PROD | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | 🟡 |
 |                              PAD | ❌ | 🟡 | ✅ | 🟡 | ❌ | 🟡 | 🟡 | 🟡 | ✅ | ✅ | ❌ | ❌ |
 |                   PAD_REFLECT_1D | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ |
-|                          POOL_1D | ❌ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ |
+|                          POOL_1D | ❌ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                          POOL_2D | ❌ | 🟡 | ✅ | ✅ | ❌ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                            REGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                             RELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
@@ -103,13 +103,13 @@ Legend:
 |                          SIGMOID | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                             SILU | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                        SILU_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
-|                              SIN | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
+|                              SIN | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                         SOFTPLUS | ❌ | ❌ | ✅ | 🟡 | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                         SOFT_MAX | ❌ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                    SOFT_MAX_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ | 🟡 | ✅ | ❌ | ❌ | ❌ |
 |                        SOLVE_TRI | ❌ | ❌ | ✅ | 🟡 | 🟡 | ✅ | ❌ | 🟡 | ✅ | ✅ | ❌ | ❌ |
-|                              SQR | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ |
-|                             SQRT | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ |
+|                              SQR | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                             SQRT | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                         SSM_CONV | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                         SSM_SCAN | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | 🟡 | 🟡 | ✅ | ❌ | ❌ |
 |                             STEP | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |

examples/model-conversion/scripts/causal/convert-model.sh

@@ -47,6 +47,7 @@ CMD_ARGS+=("../../convert_hf_to_gguf.py" "--verbose")
 CMD_ARGS+=("${MODEL_PATH}")
 CMD_ARGS+=("--outfile" "${CONVERTED_MODEL}")
 CMD_ARGS+=("--outtype" "${TYPE}")
+CMD_ARGS+=("--model-name" "${MODEL_NAME}")
 [[ -n "$METADATA_OVERRIDE" ]] && CMD_ARGS+=("--metadata" "${METADATA_OVERRIDE}")
 [[ -n "$MMPROJ" ]] && CMD_ARGS+=("${MMPROJ}")
 

examples/model-conversion/scripts/embedding/convert-model.sh

@@ -31,6 +31,7 @@ python ../../convert_hf_to_gguf.py --verbose \
     ${EMBEDDING_MODEL_PATH} \
     --outfile ${CONVERTED_MODEL} \
     --outtype ${TYPE} \
+    --model-name ${MODEL_NAME} \
     ${SENTENCE_TRANSFORMERS}
 
 echo ""

examples/sycl/start-svr.sh

@@ -12,6 +12,7 @@ This script processes files with specified options.
 
 Options:
   -h, --help    Display this help message and exit.
+  -d, --device  <value>    Set SYCL devices (default: SYCL0).
   -c, --context <value>    Set context length. Bigger need more memory.
   -p, --promote <value>    Prompt to start generation with.
   -m, --model   <value>    Full model file path.
@@ -41,10 +42,16 @@ MODEL_FILE=../models/Qwen3.5-4B-Q4_0.gguf
 NGL=99
 CONTEXT=4096
 GGML_SYCL_DEVICE=-1
+SYCL_DEVICES="SYCL0"
 SPLIT_MODE=layer
 LOG_VERBOSE=3
 while [[ $# -gt 0 ]]; do
     case "$1" in
+        -d|--device)
+            SYCL_DEVICES="$2"
+            shift
+            shift
+            ;;
         -c|--context)
             CONTEXT=$2
             # Shift twice to consume both the option flag and its value
@@ -95,8 +102,6 @@ while [[ $# -gt 0 ]]; do
     esac
 done
 
-
-
 source /opt/intel/oneapi/setvars.sh
 
 #export GGML_SYCL_DEBUG=1
@@ -107,17 +112,19 @@ source /opt/intel/oneapi/setvars.sh
 export UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS=1
 echo "UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS=${UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS}"
 
+echo "ONEAPI_DEVICE_SELECTOR=${ONEAPI_DEVICE_SELECTOR}"
+
+
 if [ $GGML_SYCL_DEVICE -ne -1 ]; then
     echo "Use $GGML_SYCL_DEVICE as main GPU"
     #use signle GPU only
     GPUS_SETTING="-mg $GGML_SYCL_DEVICE -sm ${SPLIT_MODE}"
-    echo "ONEAPI_DEVICE_SELECTOR=${ONEAPI_DEVICE_SELECTOR}"
 else
-    echo "Use all Intel GPUs, including iGPU & dGPU"
+    echo "Use Intel GPUs: ${SYCL_DEVICES}"
     GPUS_SETTING="-sm ${SPLIT_MODE}"
- fi
+fi
 
-echo "run cmd: ZES_ENABLE_SYSMAN=1 ${BIN_FILE} -m ${MODEL_FILE} -ngl ${NGL} -s ${SEED} -c ${CONTEXT} ${GPUS_SETTING} -lv ${LOG_VERBOSE}  --mmap --host 0.0.0.0 --port 8000"
-ZES_ENABLE_SYSMAN=1 ${BIN_FILE} -m ${MODEL_FILE} -ngl ${NGL} -s ${SEED} -c ${CONTEXT} ${GPUS_SETTING} -lv ${LOG_VERBOSE} --mmap --host 0.0.0.0 --port 8000
+echo "run cmd: ZES_ENABLE_SYSMAN=1 ${BIN_FILE} -m ${MODEL_FILE} -ngl ${NGL} -s ${SEED} -c ${CONTEXT} ${GPUS_SETTING} -lv ${LOG_VERBOSE} --device ${SYCL_DEVICES} --mmap --host 0.0.0.0 --port 8000"
+ZES_ENABLE_SYSMAN=1 ${BIN_FILE} -m ${MODEL_FILE} -ngl ${NGL} -s ${SEED} -c ${CONTEXT} ${GPUS_SETTING} -lv ${LOG_VERBOSE} --device ${SYCL_DEVICES} --mmap --host 0.0.0.0 --port 8000
 
 

examples/sycl/test.sh

@@ -12,6 +12,7 @@ This script processes files with specified options.
 
 Options:
   -h, --help    Display this help message and exit.
+  -d, --device  <value>    Set SYCL devices (default: SYCL0).
   -c, --context <value>    Set context length. Bigger need more memory.
   -p, --promote <value>    Prompt to start generation with.
   -m, --model   <value>    Full model file path.
@@ -42,10 +43,16 @@ MODEL_FILE=../models/llama-2-7b.Q4_0.gguf
 NGL=99
 CONTEXT=4096
 GGML_SYCL_DEVICE=-1
+SYCL_DEVICES="SYCL0"
 SPLIT_MODE=layer
 LOG_VERBOSE=3
 while [[ $# -gt 0 ]]; do
     case "$1" in
+        -d|--device)
+            SYCL_DEVICES="$2"
+            shift
+            shift
+            ;;
         -c|--context)
             CONTEXT=$2
             # Shift twice to consume both the option flag and its value
@@ -115,16 +122,17 @@ source /opt/intel/oneapi/setvars.sh
 export UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS=1
 echo "UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS=${UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS}"
 
+echo "ONEAPI_DEVICE_SELECTOR=${ONEAPI_DEVICE_SELECTOR}"
+
 if [ $GGML_SYCL_DEVICE -ne -1 ]; then
     echo "Use $GGML_SYCL_DEVICE as main GPU"
     #use signle GPU only
     GPUS_SETTING="-mg $GGML_SYCL_DEVICE -sm ${SPLIT_MODE}"
-    echo "ONEAPI_DEVICE_SELECTOR=${ONEAPI_DEVICE_SELECTOR}"
 else
-    echo "Use all Intel GPUs, including iGPU & dGPU"
+    echo "Use Intel GPUs: ${SYCL_DEVICES}"
     GPUS_SETTING="-sm ${SPLIT_MODE}"
  fi
 
-echo "run cmd: ZES_ENABLE_SYSMAN=1 ${BIN_FILE} -m ${MODEL_FILE} -no-cnv -p "${INPUT_PROMPT}" -n 200 -e -ngl ${NGL} -s ${SEED} -c ${CONTEXT} ${GPUS_SETTING} -lv ${LOG_VERBOSE}  --mmap "
-ZES_ENABLE_SYSMAN=1 ${BIN_FILE} -m ${MODEL_FILE} -no-cnv -p "${INPUT_PROMPT}" -n 200 -e -ngl ${NGL} -s ${SEED} -c ${CONTEXT} ${GPUS_SETTING} -lv ${LOG_VERBOSE} --mmap
+echo "run cmd: ZES_ENABLE_SYSMAN=1 ${BIN_FILE} -m ${MODEL_FILE} -no-cnv -p "${INPUT_PROMPT}" -n 200 -e -ngl ${NGL} -s ${SEED} -c ${CONTEXT} ${GPUS_SETTING} -lv ${LOG_VERBOSE} --device ${SYCL_DEVICES} --mmap "
+ZES_ENABLE_SYSMAN=1 ${BIN_FILE} -m ${MODEL_FILE} -no-cnv -p "${INPUT_PROMPT}" -n 200 -e -ngl ${NGL} -s ${SEED} -c ${CONTEXT} ${GPUS_SETTING} -lv ${LOG_VERBOSE} --device ${SYCL_DEVICES} --mmap
 

examples/sycl/win-start-svr.bat

@@ -13,6 +13,7 @@ set "MODEL_FILE=..\models\Qwen3.5-4B-Q4_0.gguf"
 set "NGL=99"
 set "CONTEXT=4096"
 set "GGML_SYCL_DEVICE=-1"
+set "SYCL_DEVICES=SYCL0"
 set "SPLIT_MODE=layer"
 set "LOG_VERBOSE=3"
 
@@ -36,6 +37,21 @@ if /I "%~1"=="--context" (
   goto parse_args
 )
 
+if /I "%~1"=="-d" (
+  if "%~2"=="" goto missing_value
+  set "SYCL_DEVICES=%~2"
+  shift
+  shift
+  goto parse_args
+)
+if /I "%~1"=="--device" (
+  if "%~2"=="" goto missing_value
+  set "SYCL_DEVICES=%~2"
+  shift
+  shift
+  goto parse_args
+)
+
 if /I "%~1"=="-m" (
   if "%~2"=="" goto missing_value
   set "MODEL_FILE=%~2"
@@ -130,6 +146,7 @@ echo This script processes files with specified options.
 echo.
 echo Options:
 echo   -h, --help    Display this help message and exit.
+echo   -d, --device ^<value^>    Set SYCL devices (default: SYCL0).
 echo   -c, --context ^<value^>    Set context length. Bigger need more memory.
 echo   -m, --model   ^<value^>    Full model file path.
 echo   -mg,--main-gpu ^<value^>   Set main GPU ID (0 - n) for single GPU mode.
@@ -160,19 +177,20 @@ REM Support malloc device memory more than 4GB.
 set "UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS=1"
 echo UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS=%UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS%
 
+echo ONEAPI_DEVICE_SELECTOR=%ONEAPI_DEVICE_SELECTOR%
+
 if not "%GGML_SYCL_DEVICE%"=="-1" (
   echo Use %GGML_SYCL_DEVICE% as main GPU
   REM Use single GPU only.
   set "GPUS_SETTING=-mg %GGML_SYCL_DEVICE% -sm %SPLIT_MODE%"
-  echo ONEAPI_DEVICE_SELECTOR=%ONEAPI_DEVICE_SELECTOR%
-) else (
-  echo Use all Intel GPUs, including iGPU ^& dGPU
+  ) else (
+  echo Use Intel GPUs: %SYCL_DEVICES%
   set "GPUS_SETTING=-sm %SPLIT_MODE%"
 )
 
-echo run cmd: ZES_ENABLE_SYSMAN=1 %BIN_FILE% -m "%MODEL_FILE%" -ngl %NGL% -s %SEED% -c %CONTEXT% %GPUS_SETTING% -lv %LOG_VERBOSE% --mmap --host 0.0.0.0 --port 8000
+echo run cmd: ZES_ENABLE_SYSMAN=1 %BIN_FILE% -m "%MODEL_FILE%" -ngl %NGL% -s %SEED% -c %CONTEXT% %GPUS_SETTING% -lv %LOG_VERBOSE% --device %SYCL_DEVICES% --mmap --host 0.0.0.0 --port 8000
 set "ZES_ENABLE_SYSMAN=1"
-%BIN_FILE% -m "%MODEL_FILE%" -ngl %NGL% -s %SEED% -c %CONTEXT% %GPUS_SETTING% -lv %LOG_VERBOSE% --mmap --host 0.0.0.0 --port 8000
+%BIN_FILE% -m "%MODEL_FILE%" -ngl %NGL% -s %SEED% -c %CONTEXT% %GPUS_SETTING% -lv %LOG_VERBOSE% --device "%SYCL_DEVICES%" --mmap --host 0.0.0.0 --port 8000
 
 endlocal
 

examples/sycl/win-test.bat

@@ -19,6 +19,7 @@ set "MODEL_FILE=..\models\llama-2-7b.Q4_0.gguf"
 set "NGL=99"
 set "CONTEXT=4096"
 set "GGML_SYCL_DEVICE=-1"
+set "SYCL_DEVICES=SYCL0"
 set "SPLIT_MODE=layer"
 set "LOG_VERBOSE=3"
 
@@ -42,6 +43,21 @@ if /I "%~1"=="--context" (
   goto parse_args
 )
 
+if /I "%~1"=="-d" (
+  if "%~2"=="" goto missing_value
+  set "SYCL_DEVICES=%~2"
+  shift
+  shift
+  goto parse_args
+)
+if /I "%~1"=="--device" (
+  if "%~2"=="" goto missing_value
+  set "SYCL_DEVICES=%~2"
+  shift
+  shift
+  goto parse_args
+)
+
 if /I "%~1"=="-p" (
   if "%~2"=="" goto missing_value
   set "INPUT_PROMPT=%~2"
@@ -151,6 +167,7 @@ echo This script processes files with specified options.
 echo.
 echo Options:
 echo   -h, --help    Display this help message and exit.
+echo   -d, --device ^<value^>    Set SYCL devices (default: SYCL0).
 echo   -c, --context ^<value^>    Set context length. Bigger need more memory.
 echo   -p, --promote ^<value^>    Prompt to start generation with.
 echo   -m, --model   ^<value^>    Full model file path.
@@ -182,19 +199,21 @@ REM Support malloc device memory more than 4GB.
 set "UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS=1"
 echo UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS=%UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS%
 
+echo ONEAPI_DEVICE_SELECTOR=%ONEAPI_DEVICE_SELECTOR%
+
 if not "%GGML_SYCL_DEVICE%"=="-1" (
   echo Use %GGML_SYCL_DEVICE% as main GPU
   REM Use single GPU only.
   set "GPUS_SETTING=-mg %GGML_SYCL_DEVICE% -sm %SPLIT_MODE%"
-  echo ONEAPI_DEVICE_SELECTOR=%ONEAPI_DEVICE_SELECTOR%
-) else (
-  echo Use all Intel GPUs, including iGPU ^& dGPU
+  )
+else (
+  echo Use Intel GPUs: %SYCL_DEVICES%
   set "GPUS_SETTING=-sm %SPLIT_MODE%"
 )
 
-echo run cmd: ZES_ENABLE_SYSMAN=1 %BIN_FILE% -m %MODEL_FILE% -no-cnv -p "%INPUT_PROMPT%" -n 200 -e -ngl %NGL% -s %SEED% -c %CONTEXT% %GPUS_SETTING% -lv %LOG_VERBOSE% --mmap
+echo run cmd: ZES_ENABLE_SYSMAN=1 %BIN_FILE% -m %MODEL_FILE% -no-cnv -p "%INPUT_PROMPT%" -n 200 -e -ngl %NGL% -s %SEED% -c %CONTEXT% %GPUS_SETTING% -lv %LOG_VERBOSE% --device %SYCL_DEVICES% --mmap
 set "ZES_ENABLE_SYSMAN=1"
-%BIN_FILE% -m "%MODEL_FILE%" -no-cnv -p "%INPUT_PROMPT%" -n 200 -e -ngl %NGL% -s %SEED% -c %CONTEXT% %GPUS_SETTING% -lv %LOG_VERBOSE% --mmap
+%BIN_FILE% -m "%MODEL_FILE%" -no-cnv -p "%INPUT_PROMPT%" -n 200 -e -ngl %NGL% -s %SEED% -c %CONTEXT% %GPUS_SETTING% -lv %LOG_VERBOSE% --device "%SYCL_DEVICES%" --mmap
 
 endlocal
 

ggml/CMakeLists.txt

@@ -5,7 +5,7 @@ project("ggml" C CXX ASM)
 ### GGML Version
 set(GGML_VERSION_MAJOR 0)
 set(GGML_VERSION_MINOR 18)
-set(GGML_VERSION_PATCH 0)
+set(GGML_VERSION_PATCH 1)
 set(GGML_VERSION_BASE "${GGML_VERSION_MAJOR}.${GGML_VERSION_MINOR}.${GGML_VERSION_PATCH}")
 
 list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/cmake/")

ggml/include/ggml.h

@@ -2788,6 +2788,12 @@ extern "C" {
             struct ggml_cgraph * cgraph,
             struct ggml_tensor * tensor);
 
+    // add the tensor and its parents to the graph without marking them for compute
+    // the flag is set later, when the tensor is reached from a node that computes
+    GGML_API void ggml_build_forward_order(
+            struct ggml_cgraph * cgraph,
+            struct ggml_tensor * tensor);
+
     GGML_API void ggml_build_backward_expand(
         struct ggml_context *  ctx,        // context for gradient computation
         struct ggml_cgraph  *  cgraph,

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

@@ -8,6 +8,22 @@
 #include <sys/sysctl.h>
 #endif
 
+#if !defined(HWCAP_FPHP)
+#define HWCAP_FPHP (1 << 9)
+#endif
+
+#if !defined(HWCAP_ASIMDHP)
+#define HWCAP_ASIMDHP (1 << 10)
+#endif
+
+#if !defined(HWCAP_ASIMDDP)
+#define HWCAP_ASIMDDP (1 << 20)
+#endif
+
+#if !defined(HWCAP_SVE)
+#define HWCAP_SVE (1 << 22)
+#endif
+
 #if !defined(HWCAP2_SVE2)
 #define HWCAP2_SVE2 (1 << 1)
 #endif
@@ -23,7 +39,7 @@
 struct aarch64_features {
     // has_neon not needed, aarch64 has NEON guaranteed
     bool has_dotprod     = false;
-    bool has_fp16_va     = false;
+    bool has_fp16        = false;
     bool has_sve         = false;
     bool has_sve2        = false;
     bool has_i8mm        = false;
@@ -36,7 +52,7 @@ struct aarch64_features {
         uint32_t hwcap2 = getauxval(AT_HWCAP2);
 
         has_dotprod = !!(hwcap & HWCAP_ASIMDDP);
-        has_fp16_va = !!(hwcap & HWCAP_FPHP);
+        has_fp16    = !!(hwcap & HWCAP_FPHP) && !!(hwcap & HWCAP_ASIMDHP);
         has_sve     = !!(hwcap & HWCAP_SVE);
         has_sve2    = !!(hwcap2 & HWCAP2_SVE2);
         has_i8mm    = !!(hwcap2 & HWCAP2_I8MM);
@@ -75,7 +91,7 @@ static int ggml_backend_cpu_aarch64_score() {
     score += 1<<1;
 #endif
 #ifdef GGML_USE_FP16_VECTOR_ARITHMETIC
-    if (!af.has_fp16_va) { return 0; }
+    if (!af.has_fp16) { return 0; }
     score += 1<<2;
 #endif
 #ifdef GGML_USE_SVE

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

@@ -4033,7 +4033,11 @@ static void ggml_cuda_graph_evaluate_and_capture(ggml_backend_cuda_context * cud
                     continue;
                 }
 #ifndef NDEBUG
-                assert(node->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device));
+                // On integrated GPUs (APUs, e.g. RDNA3.5) the scheduler may place a
+                // node's output on the host-visible buffer, which the compute path
+                // handles. Allow that here, mirroring the src-tensor check below.
+                assert(node->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) ||
+                       (integrated && ggml_backend_buft_is_cuda_host(node->buffer->buft)));
                 for (int j = 0; j < GGML_MAX_SRC; j++) {
                     if (node->src[j] != nullptr) {
                         assert(node->src[j]->buffer);
@@ -5205,6 +5209,7 @@ static bool ggml_backend_cuda_device_offload_op(ggml_backend_dev_t dev, const gg
 
 static ggml_backend_event_t ggml_backend_cuda_device_event_new(ggml_backend_dev_t dev) {
 #ifdef GGML_CUDA_NO_PEER_COPY
+    GGML_UNUSED(dev);
     return nullptr;
 #else
     ggml_backend_cuda_device_context * dev_ctx = (ggml_backend_cuda_device_context *)dev->context;

ggml/src/ggml-cuda/quantize.cu

@@ -8,7 +8,6 @@ struct __builtin_align__(32) float8 {
     float x; float y; float z; float w;
     float p; float q; float r; float s;
 };
-#endif
 
 #if CUDART_VERSION >= 12080
 static __device__ __forceinline__ float nvfp4_native_scale_error(
@@ -49,6 +48,7 @@ static __device__ __forceinline__ float nvfp4_native_scale_error(
     return err;
 }
 #endif // CUDART_VERSION >= 12080
+#endif // defined(BLACKWELL_MMA_AVAILABLE)
 
 __launch_bounds__(CUDA_QUANTIZE_BLOCK_SIZE, 1)
 static __global__ void quantize_q8_1(

ggml/src/ggml-metal/ggml-metal.metal

@@ -11328,8 +11328,8 @@ kernel void kernel_lightning_indexer(
     const int i_kv_0   = tgpig.x*NK;            // first key of this threadgroup
     const int i_kv     = i_kv_0 + sgitg*NKPSG;  // first key of this simdgroup
 
-    threadgroup half4x4   sk4x4[NK*DK16];
-    threadgroup half    * sk = (threadgroup half *) sk4x4;
+    threadgroup half sk[NK * DK16 * 16];
+    threadgroup half4x4 * sk4x4 = (threadgroup half4x4 *) sk;
 
     for (short i = tiitg; i < NK*DK16; i += NTG) {
         const short ik  = i/DK16;

ggml/src/ggml-sycl/common.hpp

@@ -1022,9 +1022,20 @@ static T block_reduce(T val, T * shared_vals, int block_size_template) {
 }
 
 static __dpct_inline__ float ggml_sycl_ue4m3_to_fp32(uint8_t x) {
-    const uint32_t bits = x * (x != 0x7F && x != 0xFF);
-    const __nv_fp8_e4m3 xf = *reinterpret_cast<const __nv_fp8_e4m3 *>(&bits);
-    return static_cast<float>(xf) / 2;
+    // UE4M3 is unsigned: 4 exp bits (bias 7), 3 mantissa bits, no sign, no NaN.
+    // exp == 0xF is a valid exponent (256-448 range), not NaN.
+    if (x == 0 || x == 0x7F) {
+        return 0.0f;
+    }
+    const int exp = (x >> 3) & 0xF;
+    const int man = x & 0x7;
+    float raw;
+    if (exp == 0) {
+        raw = man * (1.0f / 8.0f) * sycl::pow(2.0f, -6.0f);
+    } else {
+        raw = (1.0f + man / 8.0f) * sycl::pow(2.0f, (float) exp - 7.0f);
+    }
+    return raw * 0.5f;
 }
 
 #endif // GGML_SYCL_COMMON_HPP

ggml/src/ggml-sycl/concat.cpp

@@ -127,7 +127,15 @@ static void concat_T_sycl_non_cont(
     int64_t ne2, int64_t ne3, uint64_t nb0, uint64_t nb1, uint64_t nb2,
     uint64_t nb3, int32_t dim) {
   sycl::range<3> gridDim(ne3, ne2, ne1);
-  stream->parallel_for(sycl::nd_range<3>(gridDim, sycl::range<3>(1, 1, 1)), [=](sycl::nd_item<3> item_ct1) {
+
+  // Avoid oversubscribing device when there is not enough elements along the innermost dim to
+  // fill a full SYCL_CONCAT_BLOCK_SIZE. For larger # of elements, the full SYCL_CONCAT_BLOCK_SIZE
+  // is used.
+  const int64_t ne0_pad   = GGML_PAD(ne0, WARP_SIZE);
+  const int64_t block_ne0 = ne0_pad < SYCL_CONCAT_BLOCK_SIZE ? ne0_pad : (int64_t) SYCL_CONCAT_BLOCK_SIZE;
+  sycl::range<3> blockDim(1, 1, block_ne0);
+
+  stream->parallel_for(sycl::nd_range<3>(gridDim * blockDim, blockDim), [=](sycl::nd_item<3> item_ct1) {
       int64_t i3 = item_ct1.get_group(0);
       int64_t i2 = item_ct1.get_group(1);
       int64_t i1 = item_ct1.get_group(2);

ggml/src/ggml-sycl/dsv4-hc.cpp

@@ -0,0 +1,280 @@
+#include "ggml-impl.h"
+#include "dsv4-hc.hpp"
+
+#include <cmath>
+
+static constexpr int DSV4_HC = 4;
+
+static void dsv4_hc_pre_f32_sycl(
+        const float * x, const float * weights, float * dst,
+        int64_t n_embd, int64_t hc, int64_t n_tokens,
+        int64_t sx0, int64_t sx1, int64_t sx2,
+        int64_t sw0, int64_t sw1,
+        int64_t sd0, int64_t sd1,
+        queue_ptr stream) {
+    const int64_t nr = n_embd * n_tokens;
+    const int64_t block_size = 256;
+    const int64_t num_blocks = (nr + block_size - 1) / block_size;
+
+    stream->parallel_for(
+        sycl::nd_range<1>(sycl::range<1>(num_blocks * block_size), sycl::range<1>(block_size)),
+        [=](sycl::nd_item<1> item) {
+            const int64_t ir = item.get_global_id(0);
+            if (ir >= nr) {
+                return;
+            }
+
+            const int64_t i0 = ir % n_embd;
+            const int64_t it = ir / n_embd;
+
+            float sum = x[i0*sx0 + it*sx2] * weights[it*sw1];
+            for (int64_t ih = 1; ih < hc; ++ih) {
+                const float xv = x[i0*sx0 + ih*sx1 + it*sx2];
+                const float wv = weights[ih*sw0 + it*sw1];
+                sum += xv * wv;
+            }
+
+            dst[i0*sd0 + it*sd1] = sum;
+        });
+}
+
+static void dsv4_hc_comb_norm_cols(float * comb, float eps) {
+    for (int idst = 0; idst < DSV4_HC; ++idst) {
+        float sum = eps;
+        for (int isrc = 0; isrc < DSV4_HC; ++isrc) {
+            sum += comb[idst + DSV4_HC*isrc];
+        }
+
+        const float inv_sum = 1.0f / sum;
+        for (int isrc = 0; isrc < DSV4_HC; ++isrc) {
+            comb[idst + DSV4_HC*isrc] *= inv_sum;
+        }
+    }
+}
+
+static void dsv4_hc_comb_norm_rows(float * comb, float eps) {
+    for (int isrc = 0; isrc < DSV4_HC; ++isrc) {
+        float sum = eps;
+        for (int idst = 0; idst < DSV4_HC; ++idst) {
+            sum += comb[idst + DSV4_HC*isrc];
+        }
+
+        const float inv_sum = 1.0f / sum;
+        for (int idst = 0; idst < DSV4_HC; ++idst) {
+            comb[idst + DSV4_HC*isrc] *= inv_sum;
+        }
+    }
+}
+
+static void dsv4_hc_comb_f32_sycl(
+        const float * mixes,
+        const float * scale,
+        const float * base,
+        float * dst,
+        int64_t n_tokens,
+        int64_t sm0,
+        int64_t sm1,
+        int64_t ss0,
+        int64_t sb0,
+        int64_t sd0,
+        int64_t sd1,
+        int64_t sd2,
+        float eps,
+        int32_t n_iter,
+        queue_ptr stream) {
+    constexpr int comb_offset = 2*DSV4_HC;
+
+    const int64_t block_size = 256;
+    const int64_t num_blocks = (n_tokens + block_size - 1) / block_size;
+
+    stream->parallel_for(
+        sycl::nd_range<1>(sycl::range<1>(num_blocks * block_size), sycl::range<1>(block_size)),
+        [=](sycl::nd_item<1> item_ct1) {
+            const int64_t it = item_ct1.get_global_id(0);
+
+            if (it >= n_tokens) {
+                return;
+            }
+
+            const float scale_comb = scale[2*ss0];
+            float comb[DSV4_HC*DSV4_HC];
+
+            for (int isrc = 0; isrc < DSV4_HC; ++isrc) {
+                float max = -INFINITY;
+                for (int idst = 0; idst < DSV4_HC; ++idst) {
+                    const int idx = idst + DSV4_HC*isrc;
+                    const float v = mixes[(comb_offset + idx)*sm0 + it*sm1] * scale_comb + base[(comb_offset + idx)*sb0];
+                    comb[idx] = v;
+                    max = fmaxf(max, v);
+                }
+
+                float sum = 0.0f;
+                for (int idst = 0; idst < DSV4_HC; ++idst) {
+                    const int idx = idst + DSV4_HC*isrc;
+                    const float v = expf(comb[idx] - max);
+                    comb[idx] = v;
+                    sum += v;
+                }
+
+                const float inv_sum = 1.0f / sum;
+                for (int idst = 0; idst < DSV4_HC; ++idst) {
+                    const int idx = idst + DSV4_HC*isrc;
+                    comb[idx] = comb[idx] * inv_sum + eps;
+                }
+            }
+
+            dsv4_hc_comb_norm_cols(comb, eps);
+            for (int32_t i = 1; i < n_iter; ++i) {
+                dsv4_hc_comb_norm_rows(comb, eps);
+                dsv4_hc_comb_norm_cols(comb, eps);
+            }
+
+            for (int isrc = 0; isrc < DSV4_HC; ++isrc) {
+                for (int idst = 0; idst < DSV4_HC; ++idst) {
+                    const int idx = idst + DSV4_HC*isrc;
+                    dst[idst*sd0 + isrc*sd1 + it*sd2] = comb[idx];
+                }
+            }
+        });
+}
+
+static void dsv4_hc_post_f32_sycl(
+        const float * x, const float * residual, const float * post, const float * comb, float * dst,
+        int64_t n_embd, int64_t hc, int64_t n_tokens,
+        int64_t sx0, int64_t sx1,
+        int64_t sr0, int64_t sr1, int64_t sr2,
+        int64_t sp0, int64_t sp1,
+        int64_t sc0, int64_t sc1, int64_t sc2,
+        int64_t sd0, int64_t sd1, int64_t sd2,
+        queue_ptr stream) {
+    const int64_t nr = n_embd * hc * n_tokens;
+    const int64_t block_size = 256;
+    const int64_t num_blocks = (nr + block_size - 1) / block_size;
+
+    stream->parallel_for(
+        sycl::nd_range<1>(sycl::range<1>(num_blocks * block_size), sycl::range<1>(block_size)),
+        [=](sycl::nd_item<1> item) {
+            const int64_t ir = item.get_global_id(0);
+            if (ir >= nr) {
+                return;
+            }
+
+            const int64_t i0   = ir % n_embd;
+            const int64_t idst = (ir / n_embd) % hc;
+            const int64_t it   = ir / (n_embd * hc);
+
+            float sum = x[i0*sx0 + it*sx1] * post[idst*sp0 + it*sp1];
+            for (int64_t isrc = 0; isrc < hc; ++isrc) {
+                sum += residual[i0*sr0 + isrc*sr1 + it*sr2] * comb[idst*sc0 + isrc*sc1 + it*sc2];
+            }
+
+            dst[i0*sd0 + idst*sd1 + it*sd2] = sum;
+        });
+}
+
+void ggml_sycl_op_dsv4_hc_pre(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/2);
+    const ggml_tensor * x       = dst->src[0];
+    const ggml_tensor * weights = dst->src[1];
+
+    GGML_ASSERT(x->type == GGML_TYPE_F32);
+    GGML_ASSERT(weights->type == GGML_TYPE_F32);
+    GGML_ASSERT(dst->type == GGML_TYPE_F32);
+
+    GGML_TENSOR_LOCALS(size_t, nbx, x,       nb);
+    GGML_TENSOR_LOCALS(size_t, nbw, weights, nb);
+    GGML_TENSOR_LOCALS(size_t, nbd, dst,     nb);
+
+    const int64_t n_embd   = x->ne[0];
+    const int64_t hc       = x->ne[1];
+    const int64_t n_tokens = x->ne[2];
+
+    queue_ptr stream = ctx.stream();
+
+    dsv4_hc_pre_f32_sycl(
+            (const float *) x->data, (const float *) weights->data, (float *) dst->data,
+            n_embd, hc, n_tokens,
+            nbx0 / sizeof(float), nbx1 / sizeof(float), nbx2 / sizeof(float),
+            nbw0 / sizeof(float), nbw1 / sizeof(float),
+            nbd0 / sizeof(float), nbd1 / sizeof(float),
+            stream);
+}
+
+void ggml_sycl_op_dsv4_hc_comb(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/3);
+
+    const ggml_tensor * mixes = dst->src[0];
+    const ggml_tensor * scale = dst->src[1];
+    const ggml_tensor * base  = dst->src[2];
+
+    GGML_ASSERT(mixes->type == GGML_TYPE_F32);
+    GGML_ASSERT(scale->type == GGML_TYPE_F32);
+    GGML_ASSERT(base->type == GGML_TYPE_F32);
+    GGML_ASSERT(dst->type == GGML_TYPE_F32);
+
+    constexpr int64_t hc_mix_dim = (2 + DSV4_HC)*DSV4_HC;
+
+    GGML_ASSERT(mixes->ne[0] == hc_mix_dim);
+    GGML_ASSERT(dst->ne[0] == DSV4_HC);
+    GGML_ASSERT(dst->ne[1] == DSV4_HC);
+    GGML_ASSERT(dst->ne[2] == mixes->ne[1]);
+    GGML_ASSERT(scale->ne[0] >= 3);
+    GGML_ASSERT(base->ne[0] == hc_mix_dim);
+
+    GGML_TENSOR_LOCALS(size_t, nbm, mixes, nb);
+    GGML_TENSOR_LOCALS(size_t, nbs, scale, nb);
+    GGML_TENSOR_LOCALS(size_t, nbb, base,  nb);
+    GGML_TENSOR_LOCALS(size_t, nbd, dst,   nb);
+
+    const int64_t n_tokens = mixes->ne[1];
+    const float eps = ggml_get_op_params_f32(dst, 0);
+    const int32_t n_iter = ggml_get_op_params_i32(dst, 1);
+
+    queue_ptr stream = ctx.stream();
+
+    dsv4_hc_comb_f32_sycl(
+            (const float *) mixes->data, (const float *) scale->data, (const float *) base->data, (float *) dst->data,
+            n_tokens,
+            nbm0 / sizeof(float), nbm1 / sizeof(float),
+            nbs0 / sizeof(float),
+            nbb0 / sizeof(float),
+            nbd0 / sizeof(float), nbd1 / sizeof(float), nbd2 / sizeof(float),
+            eps, n_iter, stream);
+}
+
+void ggml_sycl_op_dsv4_hc_post(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/4);
+    const ggml_tensor * x        = dst->src[0];
+    const ggml_tensor * residual = dst->src[1];
+    const ggml_tensor * post     = dst->src[2];
+    const ggml_tensor * comb     = dst->src[3];
+
+    GGML_ASSERT(x->type == GGML_TYPE_F32);
+    GGML_ASSERT(residual->type == GGML_TYPE_F32);
+    GGML_ASSERT(post->type == GGML_TYPE_F32);
+    GGML_ASSERT(comb->type == GGML_TYPE_F32);
+    GGML_ASSERT(dst->type == GGML_TYPE_F32);
+
+    GGML_TENSOR_LOCALS(size_t, nbx, x,        nb);
+    GGML_TENSOR_LOCALS(size_t, nbr, residual, nb);
+    GGML_TENSOR_LOCALS(size_t, nbp, post,     nb);
+    GGML_TENSOR_LOCALS(size_t, nbc, comb,     nb);
+    GGML_TENSOR_LOCALS(size_t, nbd, dst,      nb);
+
+    const int64_t n_embd   = x->ne[0];
+    const int64_t n_tokens = x->ne[1];
+    const int64_t hc       = residual->ne[1];
+
+    queue_ptr stream = ctx.stream();
+
+    dsv4_hc_post_f32_sycl(
+            (const float *) x->data, (const float *) residual->data,
+            (const float *) post->data, (const float *) comb->data, (float *) dst->data,
+            n_embd, hc, n_tokens,
+            nbx0 / sizeof(float), nbx1 / sizeof(float),
+            nbr0 / sizeof(float), nbr1 / sizeof(float), nbr2 / sizeof(float),
+            nbp0 / sizeof(float), nbp1 / sizeof(float),
+            nbc0 / sizeof(float), nbc1 / sizeof(float), nbc2 / sizeof(float),
+            nbd0 / sizeof(float), nbd1 / sizeof(float), nbd2 / sizeof(float),
+            stream);
+}

ggml/src/ggml-sycl/dsv4-hc.hpp

@@ -0,0 +1,10 @@
+#ifndef GGML_SYCL_DSV4_HC_HPP
+#define GGML_SYCL_DSV4_HC_HPP
+
+#include "common.hpp"
+
+void ggml_sycl_op_dsv4_hc_pre(ggml_backend_sycl_context & ctx, ggml_tensor * dst);
+void ggml_sycl_op_dsv4_hc_comb(ggml_backend_sycl_context & ctx, ggml_tensor * dst);
+void ggml_sycl_op_dsv4_hc_post(ggml_backend_sycl_context & ctx, ggml_tensor * dst);
+
+#endif // GGML_SYCL_DSV4_HC_HPP

ggml/src/ggml-sycl/element_wise.cpp

@@ -420,53 +420,31 @@ static void clamp(const T * x, T * dst, const float min, const float max, const
     }
 }
 
-template<typename T>
-static void gated_op_fused_geglu(const T * x, const T * g, T * dst, const uint64_t k, const sycl::uint3 n_fd, const uint64_t o0, const uint64_t o1, const sycl::nd_item<1> &item_ct1) {
+template<typename T, typename F>
+static void unary_gated_op_flat_kernel(const T * x, const T * g, T * dst, const uint64_t k, const sycl::nd_item<1> & item_ct1, F func) {
+    SYCL_GLOBAL_ID_LOOP(k, item_ct1) {
+        dst[i] = func(x[i]) * g[i];
+    }
+}
+
+template<typename T, typename F>
+static void unary_gated_op_generic_kernel(
+        const T * x,
+        const T * g,
+        T * dst,
+        const uint64_t k,
+        const sycl::uint3 n_fd,
+        const uint64_t o0,
+        const uint64_t o1,
+        const sycl::nd_item<1> & item_ct1,
+        F func) {
+
+    // rows of n columns at strides o0 and o1: two halves of one fused tensor, or two tensors
     SYCL_GLOBAL_ID_LOOP(k, item_ct1) {
         const sycl::uint2 rc = fast_div_modulo((uint32_t) i, n_fd);
         const int64_t j0 = rc.x() * o0 + rc.y();
         const int64_t j1 = o0 == o1 ? j0 : rc.x() * o1 + rc.y();
-        dst[i] = op_gelu(x[j0]) * g[j1];
-    }
-}
-
-template<typename T>
-static void gated_op_fused_reglu(const T * x, const T * g, T * dst, const uint64_t k, const sycl::uint3 n_fd, const uint64_t o0, const uint64_t o1, const sycl::nd_item<1> &item_ct1) {
-    SYCL_GLOBAL_ID_LOOP(k, item_ct1) {
-        const sycl::uint2 rc = fast_div_modulo((uint32_t) i, n_fd);
-        const int64_t j0 = rc.x() * o0 + rc.y();
-        const int64_t j1 = o0 == o1 ? j0 : rc.x() * o1 + rc.y();
-        dst[i] = op_relu(x[j0]) * g[j1];
-    }
-}
-
-template<typename T>
-static void gated_op_fused_swiglu(const T * x, const T * g, T * dst, const uint64_t k, const sycl::uint3 n_fd, const uint64_t o0, const uint64_t o1, const sycl::nd_item<1> &item_ct1) {
-    SYCL_GLOBAL_ID_LOOP(k, item_ct1)  {
-        const sycl::uint2 rc = fast_div_modulo((uint32_t) i, n_fd);
-        const int64_t j0 = rc.x() * o0 + rc.y();
-        const int64_t j1 = o0 == o1 ? j0 : rc.x() * o1 + rc.y();
-        dst[i] = op_silu(x[j0]) * g[j1];
-    }
-}
-
-template<typename T>
-static void gated_op_fused_geglu_erf(const T * x, const T * g, T * dst, const uint64_t k, const sycl::uint3 n_fd, const uint64_t o0, const uint64_t o1, const sycl::nd_item<1> &item_ct1) {
-    SYCL_GLOBAL_ID_LOOP(k, item_ct1) {
-        const sycl::uint2 rc = fast_div_modulo((uint32_t) i, n_fd);
-        const int64_t j0 = rc.x() * o0 + rc.y();
-        const int64_t j1 = o0 == o1 ? j0 : rc.x() * o1 + rc.y();
-        dst[i] = op_gelu_erf(x[j0]) * g[j1];
-    }
-}
-
-template<typename T>
-static void gated_op_fused_geglu_quick(const T * x, const T * g, T * dst, const uint64_t k, const sycl::uint3 n_fd, const uint64_t o0, const uint64_t o1, const sycl::nd_item<1> &item_ct1) {
-    SYCL_GLOBAL_ID_LOOP(k, item_ct1) {
-        const sycl::uint2 rc = fast_div_modulo((uint32_t) i, n_fd);
-        const int64_t j0 = rc.x() * o0 + rc.y();
-        const int64_t j1 = o0 == o1 ? j0 : rc.x() * o1 + rc.y();
-        dst[i] = op_gelu_quick(x[j0]) * g[j1];
+        dst[i] = func(x[j0]) * g[j1];
     }
 }
 
@@ -670,6 +648,35 @@ static inline void ggml_sycl_op_unary(
         });
 }
 
+template<typename F>
+static inline void ggml_sycl_op_unary_gated(
+        ggml_backend_sycl_context & ctx, ggml_tensor * dst, F func) {
+
+    dispatch_ggml_sycl_op_fused_glu(ctx, dst,
+        [func](const auto * x_ptr, const auto * g_ptr, auto * dst_ptr, uint64_t k, uint64_t n, uint64_t o0, uint64_t o1, queue_ptr main_stream) {
+
+            const uint32_t num_blocks = (uint32_t) ceil_div(k, SYCL_GLU_BLOCK_SIZE);
+            const sycl::nd_range<1> launch_range(num_blocks * sycl::range<1>(SYCL_GLU_BLOCK_SIZE),
+                                                 sycl::range<1>(SYCL_GLU_BLOCK_SIZE));
+
+            // o0 == n and o1 == n make the index math the identity, so index flat
+            // note: not ggml_is_contiguous - a fused [gate|up] src0 is contiguous with o0 == 2n
+            if (o0 == n && o1 == n) {
+                main_stream->parallel_for(launch_range,
+                    [=](sycl::nd_item<1> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
+                        unary_gated_op_flat_kernel(x_ptr, g_ptr, dst_ptr, k, item_ct1, func);
+                    });
+            } else {
+                // launch-invariant divisor, and only this path needs it
+                const sycl::uint3 n_fd = init_fastdiv_values((uint32_t) n);
+                main_stream->parallel_for(launch_range,
+                    [=](sycl::nd_item<1> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
+                        unary_gated_op_generic_kernel(x_ptr, g_ptr, dst_ptr, k, n_fd, o0, o1, item_ct1, func);
+                    });
+            }
+        });
+}
+
 
 static inline void ggml_sycl_op_arange(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
     GGML_ASSERT(dst->type == GGML_TYPE_F32);
@@ -967,42 +974,21 @@ static inline void ggml_sycl_op_acc(ggml_backend_sycl_context & ctx, ggml_tensor
 }
 
 static inline void ggml_sycl_op_geglu(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
-    ggml_sycl_detail::dispatch_ggml_sycl_op_fused_glu(ctx, dst,
-        [](const auto* x_ptr, const auto* g_ptr, auto* dst_ptr, uint64_t k, uint64_t n, uint64_t o0, uint64_t o1, queue_ptr main_stream) {
-            const uint32_t num_blocks = ceil_div(k, SYCL_GELU_BLOCK_SIZE);
-            const sycl::uint3 n_fd = init_fastdiv_values((uint32_t) n);
-            main_stream->parallel_for(
-                    sycl::nd_range<1>((num_blocks * sycl::range<1>(SYCL_GELU_BLOCK_SIZE)),
-                    sycl::range<1>(SYCL_GELU_BLOCK_SIZE)), [=](sycl::nd_item<1> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
-                gated_op_fused_geglu(x_ptr, g_ptr, dst_ptr, k, n_fd, o0, o1, item_ct1);
-            });
-        });
+    ggml_sycl_detail::ggml_sycl_op_unary_gated(ctx, dst, [](auto x) {
+        return op_gelu(x);
+    });
 }
 
 static inline void ggml_sycl_op_reglu(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
-    ggml_sycl_detail::dispatch_ggml_sycl_op_fused_glu(ctx, dst,
-        [](const auto* x_ptr, const auto* g_ptr, auto* dst_ptr, uint64_t k, uint64_t n, uint64_t o0, uint64_t o1, queue_ptr main_stream) {
-            const uint32_t num_blocks = ceil_div((uint32_t)k, SYCL_RELU_BLOCK_SIZE); // Using RELU block size for reglu
-            const sycl::uint3 n_fd = init_fastdiv_values((uint32_t) n);
-            main_stream->parallel_for(
-                    sycl::nd_range<1>((num_blocks * sycl::range<1>(SYCL_RELU_BLOCK_SIZE)),
-                    sycl::range<1>(SYCL_RELU_BLOCK_SIZE)), [=](sycl::nd_item<1> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
-                gated_op_fused_reglu(x_ptr, g_ptr, dst_ptr, k, n_fd, o0, o1, item_ct1);
-            });
-        });
+    ggml_sycl_detail::ggml_sycl_op_unary_gated(ctx, dst, [](auto x) {
+        return op_relu(x);
+    });
 }
 
 static inline void ggml_sycl_op_swiglu(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
-    ggml_sycl_detail::dispatch_ggml_sycl_op_fused_glu(ctx, dst,
-        [](const auto* x_ptr, const auto* g_ptr, auto* dst_ptr, uint64_t k, uint64_t n, uint64_t o0, uint64_t o1, queue_ptr main_stream) {
-            const uint32_t num_blocks = ceil_div((uint32_t)k, SYCL_SILU_BLOCK_SIZE); // Using SILU block size for swiglu
-            const sycl::uint3 n_fd = init_fastdiv_values((uint32_t) n);
-            main_stream->parallel_for(
-                    sycl::nd_range<1>((num_blocks * sycl::range<1>(SYCL_SILU_BLOCK_SIZE)),
-                    sycl::range<1>(SYCL_SILU_BLOCK_SIZE)), [=](sycl::nd_item<1> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
-                gated_op_fused_swiglu(x_ptr, g_ptr, dst_ptr, k, n_fd, o0, o1, item_ct1);
-            });
-        });
+    ggml_sycl_detail::ggml_sycl_op_unary_gated(ctx, dst, [](auto x) {
+        return op_silu(x);
+    });
 }
 
 __dpct_inline__ float ggml_sycl_op_swiglu_oai_single(float x, float g, float alpha = 1.702f, float limit = 7.0f) {
@@ -1097,29 +1083,15 @@ void ggml_sycl_op_swiglu_oai(ggml_backend_sycl_context & ctx, ggml_tensor * dst)
 }
 
 static inline void ggml_sycl_op_geglu_erf(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
-    ggml_sycl_detail::dispatch_ggml_sycl_op_fused_glu(ctx, dst,
-        [](const auto* x_ptr, const auto* g_ptr, auto* dst_ptr, uint64_t k, uint64_t n, uint64_t o0, uint64_t o1, queue_ptr main_stream) {
-            const uint32_t num_blocks = ceil_div(k, SYCL_GELU_BLOCK_SIZE);
-            const sycl::uint3 n_fd = init_fastdiv_values((uint32_t) n);
-            main_stream->parallel_for(
-                    sycl::nd_range<1>((num_blocks * sycl::range<1>(SYCL_GELU_BLOCK_SIZE)),
-                    sycl::range<1>(SYCL_GELU_BLOCK_SIZE)), [=](sycl::nd_item<1> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
-                gated_op_fused_geglu_erf(x_ptr, g_ptr, dst_ptr, k, n_fd, o0, o1, item_ct1);
-            });
-        });
+    ggml_sycl_detail::ggml_sycl_op_unary_gated(ctx, dst, [](auto x) {
+        return op_gelu_erf(x);
+    });
 }
 
 static inline void ggml_sycl_op_geglu_quick(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
-    ggml_sycl_detail::dispatch_ggml_sycl_op_fused_glu(ctx, dst,
-        [](const auto* x_ptr, const auto* g_ptr, auto* dst_ptr, uint64_t k, uint64_t n, uint64_t o0, uint64_t o1, queue_ptr main_stream) {
-            const uint32_t num_blocks = ceil_div(k, SYCL_GELU_BLOCK_SIZE);
-            const sycl::uint3 n_fd = init_fastdiv_values((uint32_t) n);
-            main_stream->parallel_for(
-                    sycl::nd_range<1>((num_blocks * sycl::range<1>(SYCL_GELU_BLOCK_SIZE)),
-                    sycl::range<1>(SYCL_GELU_BLOCK_SIZE)), [=](sycl::nd_item<1> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
-                gated_op_fused_geglu_quick(x_ptr, g_ptr, dst_ptr, k, n_fd, o0, o1, item_ct1);
-            });
-        });
+    ggml_sycl_detail::ggml_sycl_op_unary_gated(ctx, dst, [](auto x) {
+        return op_gelu_quick(x);
+    });
 }
 
 

ggml/src/ggml-sycl/fattn-onednn.cpp

@@ -2,11 +2,13 @@
 #include <cstdio>
 #include <cstring>
 #include <string>
+#include <optional>
 #include <unordered_map>
 #include <vector>
 
 #include "fattn-onednn.hpp"
 #include "fattn-tile.hpp"
+#include "convert.hpp"
 
 // set minimum query length to treat as prefill (32)
 #define GGML_SYCL_FA_ONEDNN_MIN_Q 32
@@ -33,10 +35,30 @@ bool ggml_sycl_flash_attn_ext_onednn_supported(const ggml_tensor * dst) {
     const ggml_tensor * mask  = dst->src[3];
     const ggml_tensor * sinks = dst->src[4];
 
-    // gate for f16 KV only for now
-    // need to implement quantized KV
+    // F16 KV: native SDPA at any KV length.
+    // Non-F16: dequant to F16 then SDPA at prefill lengths. Only the
+    // standard quantized KV cache types (Q4_0-Q8_0) and F32 are accepted
+    // because their to_fp16_sycl conversion is verified. BF16 and IQ*
+    // are excluded: BF16 needs a strided conversion kernel that does not
+    // exist yet; IQ types are model-weight-only quants with no dequant
+    // registration and are never used as KV caches.
     if (K->type != GGML_TYPE_F16 || V->type != GGML_TYPE_F16) {
-        return false;
+        auto kt = K->type, vt = V->type;
+        bool k_ok = kt == GGML_TYPE_F32 || kt == GGML_TYPE_Q4_0 || kt == GGML_TYPE_Q4_1 ||
+                    kt == GGML_TYPE_Q5_0 || kt == GGML_TYPE_Q5_1 || kt == GGML_TYPE_Q8_0;
+        bool v_ok = vt == GGML_TYPE_F32 || vt == GGML_TYPE_Q4_0 || vt == GGML_TYPE_Q4_1 ||
+                    vt == GGML_TYPE_Q5_0 || vt == GGML_TYPE_Q5_1 || vt == GGML_TYPE_Q8_0;
+        if (!k_ok || !v_ok) {
+            return false;
+        }
+        if (Q->ne[1] < 32 || K->ne[1] < 1024) {
+            return false;
+        }
+        for (const ggml_tensor * t : {K, V}) {
+            if (t->type == GGML_TYPE_F16 && t->nb[1] % (t->ne[0] * 2) != 0) {
+                return false;
+            }
+        }
     }
     // Optional KV-length ceiling (GGML_SYCL_FA_ONEDNN_MAX_KV, 0 = unlimited). Escape hatch:
     // very long sequences make the fused SDPA slow enough to risk the xe driver watchdog on
@@ -205,13 +227,101 @@ void ggml_sycl_flash_attn_ext_onednn(ggml_backend_sycl_context & ctx, ggml_tenso
     dnnl::engine    eng    = ctx.engine_dnnl(stream);
     dnnl::stream    strm   = ctx.stream_dnnl(stream);
 
-    // cont/cast inputs to contiguous f16 (head-major) -- the layout the fast systolic path wants.
-    ggml_sycl_pool_alloc<sycl::half> Qf(ctx.pool(), (size_t) H   * q   * d);
-    ggml_sycl_pool_alloc<sycl::half> Kf(ctx.pool(), (size_t) Hkv * seq * d);
-    ggml_sycl_pool_alloc<sycl::half> Vf(ctx.pool(), (size_t) Hkv * seq * d);
-    cont_to_f16_sycl<float>     ((const char *) Q->data, Qf.get(), d, q,   H,   mb, Q->nb[1], Q->nb[2], Q->nb[3], stream);
-    cont_to_f16_sycl<sycl::half>((const char *) K->data, Kf.get(), d, seq, Hkv, mb, K->nb[1], K->nb[2], K->nb[3], stream);
-    cont_to_f16_sycl<sycl::half>((const char *) V->data, Vf.get(), d, seq, Hkv, mb, V->nb[1], V->nb[2], V->nb[3], stream);
+    // Q: always f32 -- copy to dense f16.
+    ggml_sycl_pool_alloc<sycl::half> Qf(ctx.pool(), (size_t) H * q * d);
+    cont_to_f16_sycl<float>((const char *) Q->data, Qf.get(), d, q, H, mb, Q->nb[1], Q->nb[2], Q->nb[3], stream);
+
+    // K/V: use pool-alloc for both F16 and dequant paths.
+    sycl::half * K_ptr = nullptr;
+    sycl::half * V_ptr = nullptr;
+    std::optional<ggml_sycl_pool_alloc<sycl::half>> Kf_pool;
+    std::optional<ggml_sycl_pool_alloc<sycl::half>> Vf_pool;
+
+    if (K->type == GGML_TYPE_F16 && V->type == GGML_TYPE_F16) {
+        Kf_pool.emplace(ctx.pool(), (size_t) Hkv * seq * d);
+        Vf_pool.emplace(ctx.pool(), (size_t) Hkv * seq * d);
+        cont_to_f16_sycl<sycl::half>((const char *) K->data, Kf_pool->get(), d, seq, Hkv, mb, K->nb[1], K->nb[2], K->nb[3], stream);
+        cont_to_f16_sycl<sycl::half>((const char *) V->data, Vf_pool->get(), d, seq, Hkv, mb, V->nb[1], V->nb[2], V->nb[3], stream);
+        K_ptr = Kf_pool->get();
+        V_ptr = Vf_pool->get();
+    } else if (ggml_is_quantized(K->type)) {
+        // Quantized K/V: dequant to dense F16 using pool, same lifetime as F16 path.
+        Kf_pool.emplace(ctx.pool(), ggml_nelements(K));
+        K_ptr = Kf_pool->get();
+        {
+            const char * K_data = (const char *)K->data;
+            const bool k_non_dense = ((int64_t)K->ne[1] * K->nb[1] != K->nb[2]) && K->ne[2] > 1;
+            const bool k_gemma = k_non_dense &&
+                ((int64_t)K->nb[2] < (int64_t)K->ne[1] * (int64_t)K->nb[1]);
+            if (ggml_is_contiguously_allocated(K) && !k_non_dense) {
+                to_fp16_sycl_t to_fp16 = ggml_get_to_fp16_sycl(K->type, dst);
+                to_fp16(K_data, K_ptr, ggml_nelements(K), stream);
+            } else {
+                const size_t bs = ggml_blck_size(K->type);
+                const size_t ts = ggml_type_size(K->type);
+                to_fp16_nc_sycl_t to_fp16 = ggml_get_to_fp16_nc_sycl(K->type);
+                int64_t s01, s02, s03;
+                if (k_gemma) {
+                    const int64_t blk_per_row = (int64_t)K->ne[0] / bs;
+                    s01 = (int64_t)Hkv * blk_per_row;
+                    s02 = blk_per_row;
+                    s03 = (int64_t)K->ne[1] * s01;
+                } else {
+                    s01 = (int64_t)K->nb[1] / ts;
+                    s02 = (int64_t)K->nb[2] / ts;
+                    s03 = (int64_t)K->nb[3] / ts;
+                }
+                to_fp16(K_data, K_ptr,
+                        K->ne[0], K->ne[1], K->ne[2], K->ne[3],
+                        s01, s02, s03, stream);
+            }
+        }
+        // Quantized V: always dequant separately. Even when K and V share
+        // the same underlying allocation (V is a view of K with the same
+        // data pointer), their logical values differ because the quantized
+        // elements at different positions/offsets represent different K/V
+        // data. Master's F16 path also never aliases K and V.
+        Vf_pool.emplace(ctx.pool(), ggml_nelements(V));
+        V_ptr = Vf_pool->get();
+        {
+            const char * V_data = (const char *)V->data;
+            const bool v_non_dense = ((int64_t)V->ne[1] * V->nb[1] != V->nb[2]) && V->ne[2] > 1;
+            const bool v_gemma = v_non_dense &&
+                ((int64_t)V->nb[2] < (int64_t)V->ne[1] * (int64_t)V->nb[1]);
+            if (ggml_is_contiguously_allocated(V) && !v_non_dense) {
+                to_fp16_sycl_t to_fp16 = ggml_get_to_fp16_sycl(V->type, dst);
+                to_fp16(V_data, V_ptr, ggml_nelements(V), stream);
+            } else {
+                const size_t bs = ggml_blck_size(V->type);
+                const size_t ts = ggml_type_size(V->type);
+                to_fp16_nc_sycl_t to_fp16 = ggml_get_to_fp16_nc_sycl(V->type);
+                int64_t s01, s02, s03;
+                if (v_gemma) {
+                    const int64_t blk_per_row = (int64_t)V->ne[0] / bs;
+                    s01 = (int64_t)V->ne[2] * blk_per_row;
+                    s02 = blk_per_row;
+                    s03 = (int64_t)V->ne[1] * s01;
+                } else {
+                    s01 = (int64_t)V->nb[1] / ts;
+                    s02 = (int64_t)V->nb[2] / ts;
+                    s03 = (int64_t)V->nb[3] / ts;
+                }
+                to_fp16(V_data, V_ptr,
+                        V->ne[0], V->ne[1], V->ne[2], V->ne[3],
+                        s01, s02, s03, stream);
+            }
+        }
+    } else {
+        // F32: strided copy to dense F16 via cont_to_f16_sycl<float>.
+        Kf_pool.emplace(ctx.pool(), ggml_nelements(K));
+        K_ptr = Kf_pool->get();
+        cont_to_f16_sycl<float>((const char *) K->data, K_ptr, K->ne[0], K->ne[1], K->ne[2], K->ne[3],
+                                K->nb[1], K->nb[2], K->nb[3], stream);
+        Vf_pool.emplace(ctx.pool(), ggml_nelements(V));
+        V_ptr = Vf_pool->get();
+        cont_to_f16_sycl<float>((const char *) V->data, V_ptr, V->ne[0], V->ne[1], V->ne[2], V->ne[3],
+                                V->nb[1], V->nb[2], V->nb[3], stream);
+    }
 
     // divide-by-(1/scale) reproduces ggml's score *= kq_scale on the proven probe graph.
     //
@@ -244,8 +354,8 @@ void ggml_sycl_flash_attn_ext_onednn(ggml_backend_sycl_context & ctx, ggml_tenso
 
     auto id2ptr = [&](size_t r) -> void * {
         if (r == E.id_q)     return Qf.get();
-        if (r == E.id_k)     return Kf.get();
-        if (r == E.id_v)     return Vf.get();
+        if (r == E.id_k)     return K_ptr;
+        if (r == E.id_v)     return V_ptr;
         if (r == E.id_scale) return scale_dev;
         if (r == E.id_mask)  return (void *) mask->data;
         return nullptr;

ggml/src/ggml-sycl/fattn-vec.hpp

@@ -73,6 +73,7 @@ static void flash_attn_ext_vec(const char* __restrict__ Q,
                         const int32_t nb31,
                         const int32_t nb32,
                         const int64_t nb33) {
+
 #ifdef SYCL_FLASH_ATTN
     // Skip unused kernel variants for faster compilation:
 
@@ -469,7 +470,6 @@ static void flash_attn_ext_vec(const char* __restrict__ Q,
         }
     }
 
-
     item_ct1.barrier(sycl::access::fence_space::local_space);
 
 #pragma unroll
@@ -591,22 +591,24 @@ void ggml_sycl_flash_attn_ext_vec_case_impl(ggml_backend_sycl_context & ctx, ggm
 
     const auto arch = ggml_sycl_info().devices[ctx.device].hw_info.arch;
     const int nthreads = ggml_sycl_fattn_vec_get_nthreads_device(arch);
-    // 256 threads would overflow the 64 KB work-group local memory at D == 512, so keep 128 there.
-    if (D <= 256 && nthreads == 256) {
-        constexpr int nthreads_hw = 256;
-        constexpr int nwarps = nthreads_hw / warp_size;
-        launch_fattn<D, cols_per_block, 1,
-                     flash_attn_ext_vec<D, cols_per_block, type_K, type_V,
-                                        use_logit_softcap, warp_size, nthreads_hw>, warp_size>(
-            ctx, dst, nwarps, nbytes_shared, D, need_f16_K, need_f16_V, false);
-    } else {
-        constexpr int nthreads_hw = 128;
-        constexpr int nwarps = nthreads_hw / warp_size;
-        launch_fattn<D, cols_per_block, 1,
-                     flash_attn_ext_vec<D, cols_per_block, type_K, type_V,
-                                        use_logit_softcap, warp_size, nthreads_hw>, warp_size>(
-            ctx, dst, nwarps, nbytes_shared, D, need_f16_K, need_f16_V, false);
+    if constexpr (D <= 256) {
+        if (nthreads == 256) {
+            constexpr int nthreads_hw = 256;
+            constexpr int nwarps = nthreads_hw / warp_size;
+            launch_fattn<D, cols_per_block, 1,
+                         flash_attn_ext_vec<D, cols_per_block, type_K, type_V,
+                                            use_logit_softcap, warp_size, nthreads_hw>, warp_size>(
+                ctx, dst, nwarps, nbytes_shared, D, need_f16_K, need_f16_V, false);
+            return;
+        }
     }
+
+    constexpr int nthreads_hw = 128;
+    constexpr int nwarps = nthreads_hw / warp_size;
+    launch_fattn<D, cols_per_block, 1,
+                 flash_attn_ext_vec<D, cols_per_block, type_K, type_V,
+                                    use_logit_softcap, warp_size, nthreads_hw>, warp_size>(
+        ctx, dst, nwarps, nbytes_shared, D, need_f16_K, need_f16_V, false);
 }
 
 template <int D, int type_K, int type_V>

ggml/src/ggml-sycl/fattn.cpp

@@ -97,7 +97,7 @@ static void ggml_sycl_flash_attn_ext_vec(ggml_backend_sycl_context & ctx, ggml_t
 enum best_fattn_kernel {
     BEST_FATTN_KERNEL_NONE     =   0,
     BEST_FATTN_KERNEL_VEC      = 100,
-    BEST_FATTN_KERNEL_ONEDNN   = 150, // added enum for onednn==150
+    BEST_FATTN_KERNEL_ONEDNN   = 150, // oneDNN SDPA: native F16 (PR #25222)
     BEST_FATTN_KERNEL_TILE     = 200,
     BEST_FATTN_KERNEL_MKL      = 300,
 };
@@ -130,6 +130,14 @@ static best_fattn_kernel ggml_sycl_get_best_fattn_kernel(const int device, const
 
     bool gqa_opt_applies = gqa_ratio >= 2 && mask && max_bias == 0.0f && K->ne[1] % FATTN_KQ_STRIDE == 0;
 
+    // XMX-accelerated path: oneDNN SDPA (native F16 and dequant+non-F16).
+    // ONEDNN requires min 32 query tokens — short-circuit decode to avoid
+    // calling _supported() on every decode FA call.
+    if (Q->ne[1] >= 32
+        && ggml_sycl_flash_attn_ext_onednn_supported(dst)) {
+        return BEST_FATTN_KERNEL_ONEDNN;
+    }
+
     // MKL path: XMX-accelerated GEMM for prompt processing (all KV cache types).
     // The MKL kernel converts non-F16 K/V to F16 via to_fp16_sycl before GEMM,
     // so quantized, F16, BF16, and F32 caches all benefit from XMX acceleration.
@@ -167,7 +175,6 @@ static best_fattn_kernel ggml_sycl_get_best_fattn_kernel(const int device, const
             return BEST_FATTN_KERNEL_MKL;
         }
     }
-
     for (const ggml_tensor * t : {Q, K, V, mask}) {
         if (t == nullptr || ggml_is_quantized(t->type)) {
             continue;
@@ -215,6 +222,7 @@ static best_fattn_kernel ggml_sycl_get_best_fattn_kernel(const int device, const
     switch (K->type) {
         case GGML_TYPE_F32:
         case GGML_TYPE_F16:
+        case GGML_TYPE_BF16:
             break;
         case GGML_TYPE_Q4_1:
         case GGML_TYPE_Q5_0:
@@ -233,8 +241,11 @@ static best_fattn_kernel ggml_sycl_get_best_fattn_kernel(const int device, const
         return BEST_FATTN_KERNEL_NONE;
     }
 
-    // For small batch sizes the vector kernel may be preferable over the kernels optimized for large batch sizes:
-    const bool can_use_vector_kernel = Q->ne[0] <= 512 && Q->ne[0] % 64 == 0 && K->ne[1] % FATTN_KQ_STRIDE == 0;
+    // For small batch sizes the vector kernel may be preferable over the kernels optimized for large batch sizes.
+    // BF16 is excluded: the VEC kernel has no BF16 template (it needs GGML_SYCL_FA_ALL_QUANTS for non-F16/Q4_0/Q8_0).
+    const bool has_bf16 = (K->type == GGML_TYPE_BF16 || V->type == GGML_TYPE_BF16);
+    const bool can_use_vector_kernel = Q->ne[0] <= 512 && Q->ne[0] % 64 == 0 && K->ne[1] % FATTN_KQ_STRIDE == 0
+        && !has_bf16;
 
     // Fused-XMX path: oneDNN Graph SDPA (flash attention). Strictly
     // additive -- taken only when statically supported, otherwise falls through to VEC/TILE below.
@@ -276,6 +287,7 @@ void ggml_sycl_flash_attn_ext(ggml_backend_sycl_context & ctx, ggml_tensor * dst
         const char * kname = "TILE";
         best_fattn_kernel k = ggml_sycl_get_best_fattn_kernel(ctx.device, dst);
         if (k == BEST_FATTN_KERNEL_MKL)  kname = "MKL";
+        if (k == BEST_FATTN_KERNEL_ONEDNN)  kname = "ONEDNN";
         if (k == BEST_FATTN_KERNEL_VEC)  kname = "VEC";
         int64_t delta = 0;
         if (Dk == 256) {
@@ -292,7 +304,8 @@ void ggml_sycl_flash_attn_ext(ggml_backend_sycl_context & ctx, ggml_tensor * dst
                 (long long)V_dbg->ne[1]);
     }
 
-    switch (ggml_sycl_get_best_fattn_kernel(ggml_sycl_get_device(), dst)) {
+    const best_fattn_kernel fk = ggml_sycl_get_best_fattn_kernel(ggml_sycl_get_device(), dst);
+    switch (fk) {
         case BEST_FATTN_KERNEL_NONE:
             GGML_ABORT("Not support Flash-Attention");
         case BEST_FATTN_KERNEL_ONEDNN:
@@ -331,6 +344,7 @@ void ggml_sycl_flash_attn_ext(ggml_backend_sycl_context & ctx, ggml_tensor * dst
             q->wait();
             const char * kname = "???";
             best_fattn_kernel kb = ggml_sycl_get_best_fattn_kernel(ctx.device, dst);
+            if (kb == BEST_FATTN_KERNEL_ONEDNN) kname = "ONEDNN";
             if (kb == BEST_FATTN_KERNEL_MKL) kname = "MKL";
             if (kb == BEST_FATTN_KERNEL_TILE) kname = "TILE";
             if (kb == BEST_FATTN_KERNEL_VEC) kname = "VEC";
@@ -354,6 +368,7 @@ void ggml_sycl_flash_attn_ext(ggml_backend_sycl_context & ctx, ggml_tensor * dst
             }
         }
     }
+
 }
 
 bool ggml_sycl_flash_attn_ext_supported(int device, const ggml_tensor * dst) {

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

@@ -62,6 +62,8 @@
 #include "ggml-sycl/repeat_back.hpp"
 #include "ggml-sycl/set_rows.hpp"
 #include "ggml-sycl/set.hpp"
+#include "ggml-sycl/dsv4-hc.hpp"
+#include "ggml-sycl/lightning-indexer.hpp"
 #include "ggml-sycl/conv2d.hpp"
 #include "ggml-sycl/conv2d-dw.hpp"
 #include "ggml-sycl/conv2d-transpose.hpp"
@@ -4942,6 +4944,18 @@ static bool ggml_sycl_compute_forward(ggml_backend_sycl_context & ctx, struct gg
         case GGML_OP_SET_ROWS:
             ggml_sycl_op_set_rows(ctx, dst);
             break;
+        case GGML_OP_DSV4_HC_PRE:
+            ggml_sycl_op_dsv4_hc_pre(ctx, dst);
+            break;
+        case GGML_OP_DSV4_HC_COMB:
+            ggml_sycl_op_dsv4_hc_comb(ctx, dst);
+            break;
+        case GGML_OP_DSV4_HC_POST:
+            ggml_sycl_op_dsv4_hc_post(ctx, dst);
+            break;
+        case GGML_OP_LIGHTNING_INDEXER:
+            ggml_sycl_op_lightning_indexer(ctx, dst);
+            break;
         case GGML_OP_DUP:
             ggml_sycl_dup(ctx, dst);
             break;
@@ -5795,17 +5809,33 @@ static bool do_ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, cons
 
         case GGML_OP_SET_ROWS:
             {
-
-                auto res = ((op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16 || op->type == GGML_TYPE_BF16 ||
-                         op->type == GGML_TYPE_Q8_0 || op->type == GGML_TYPE_Q5_1 || op->type == GGML_TYPE_Q5_0 ||
-                         op->type == GGML_TYPE_Q1_0 ||
-                         op->type == GGML_TYPE_Q4_1 || op->type == GGML_TYPE_Q4_0 || op->type == GGML_TYPE_IQ4_NL ||
-                         op->type == GGML_TYPE_MXFP4 || op->type == GGML_TYPE_NVFP4) &&
-                        op->src[0]->type == GGML_TYPE_F32 &&
-                        (op->src[1]->type == GGML_TYPE_I64 || op->src[1]->type == GGML_TYPE_I32));
+                auto res = (op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16 ||
+                            op->src[0]->type == GGML_TYPE_BF16) &&
+                           (op->src[1]->type == GGML_TYPE_I64 || op->src[1]->type == GGML_TYPE_I32);
                 return res;
             }
             break;
+        case GGML_OP_DSV4_HC_PRE:
+            return op->src[0]->type == GGML_TYPE_F32 && op->src[1]->type == GGML_TYPE_F32 &&
+                op->type == GGML_TYPE_F32;
+        case GGML_OP_DSV4_HC_COMB:
+            return op->src[0]->type == GGML_TYPE_F32 && op->src[1]->type == GGML_TYPE_F32 &&
+                op->src[2]->type == GGML_TYPE_F32 && op->type == GGML_TYPE_F32;
+        case GGML_OP_DSV4_HC_POST:
+            return op->src[0]->type == GGML_TYPE_F32 && op->src[1]->type == GGML_TYPE_F32 &&
+                op->src[2]->type == GGML_TYPE_F32 && op->src[3]->type == GGML_TYPE_F32 &&
+                op->type == GGML_TYPE_F32;
+        case GGML_OP_LIGHTNING_INDEXER:
+            return op->src[0]->type == GGML_TYPE_F32 &&
+                (op->src[1]->type == GGML_TYPE_F16 || op->src[1]->type == GGML_TYPE_F32 ||
+                 op->src[1]->type == GGML_TYPE_BF16 || op->src[1]->type == GGML_TYPE_Q8_0 ||
+                 op->src[1]->type == GGML_TYPE_Q5_1 || op->src[1]->type == GGML_TYPE_Q5_0 ||
+                 op->src[1]->type == GGML_TYPE_Q4_1 || op->src[1]->type == GGML_TYPE_Q4_0 ||
+                 op->src[1]->type == GGML_TYPE_IQ4_NL) &&
+                op->src[2]->type == GGML_TYPE_F32 &&
+                op->src[3]->type == GGML_TYPE_F16 &&
+                op->type == GGML_TYPE_F32 &&
+                op->src[0]->ne[0] == WARP_SIZE * 8;
         case GGML_OP_CPY:
             {
                 ggml_type src0_type = op->src[0]->type;

ggml/src/ggml-sycl/lightning-indexer.cpp

@@ -0,0 +1,197 @@
+#include "lightning-indexer.hpp"
+#include "dequantize.hpp"
+
+static void lightning_indexer_f32_sycl(
+        const char * q, const char * k, const char * w, const char * m, float * dst,
+        int64_t n_embd, int64_t n_head, int64_t n_batch, int64_t n_stream, int64_t n_kv,
+        int64_t nem3,
+        int64_t nbq1, int64_t nbq2, int64_t nbq3,
+        int64_t nbk2, int64_t nbk3,
+        int64_t nbw1, int64_t nbw3,
+        int64_t nbm1, int64_t nbm3,
+        int64_t nb1, int64_t nb3,
+        ggml_type k_type,
+        queue_ptr stream) {
+
+    constexpr int64_t LANES = WARP_SIZE;
+    constexpr int64_t ELEMS_PER_LANE = 8;
+    constexpr int64_t ROWS_PER_BLOCK = 4;
+    constexpr int64_t BLOCK_SIZE = ROWS_PER_BLOCK * LANES;
+
+    const int64_t n_rows = n_batch * n_stream * n_kv;
+    const int64_t n_blocks = (n_rows + ROWS_PER_BLOCK - 1) / ROWS_PER_BLOCK;
+
+    stream->parallel_for(
+        sycl::nd_range<1>(
+            sycl::range<1>(n_blocks * BLOCK_SIZE),
+            sycl::range<1>(BLOCK_SIZE)),
+        [=](sycl::nd_item<1> item) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
+            const int64_t ir   = item.get_global_id(0);
+            const int64_t lane = ir % LANES;
+            const int64_t row  = ir / LANES;
+            if (row >= n_rows) {
+                return;
+            }
+
+            const int64_t i_bs     = row / n_kv;
+            const int64_t i_kv     = row % n_kv;
+            const int64_t i_batch  = i_bs / n_stream;
+            const int64_t i_stream = i_bs % n_stream;
+
+            // load K row slice into registers (row is contiguous, nbk0 == type size)
+            const char * k_base = k + i_kv*nbk2 + i_stream*nbk3;
+            float k_local[ELEMS_PER_LANE];
+            if (k_type == GGML_TYPE_F16) {
+                const sycl::half * k_row = (const sycl::half *) k_base;
+#pragma unroll
+                for (int64_t j = 0; j < ELEMS_PER_LANE; ++j) {
+                    k_local[j] = static_cast<float>(k_row[lane*ELEMS_PER_LANE + j]);
+                }
+            } else if (k_type == GGML_TYPE_F32) {
+                const float * k_row = (const float *) k_base;
+#pragma unroll
+                for (int64_t j = 0; j < ELEMS_PER_LANE; ++j) {
+                    k_local[j] = k_row[lane*ELEMS_PER_LANE + j];
+                }
+            } else {
+                const int64_t lane_base = lane * ELEMS_PER_LANE;
+                switch (k_type) {
+                    case GGML_TYPE_BF16: {
+                        const sycl::ext::oneapi::bfloat16 * k_row = (const sycl::ext::oneapi::bfloat16 *) k_base;
+#pragma unroll
+                        for (int64_t j = 0; j < ELEMS_PER_LANE; ++j) {
+                            k_local[j] = static_cast<float>(k_row[lane_base + j]);
+                        }
+                    } break;
+                    case GGML_TYPE_Q4_0:
+                    case GGML_TYPE_Q4_1:
+                    case GGML_TYPE_Q5_0:
+                    case GGML_TYPE_Q5_1: {
+#pragma unroll
+                        for (int64_t j = 0; j < ELEMS_PER_LANE; ++j) {
+                            const int64_t idx = lane_base + j;
+                            const int64_t ib  = idx / QK4_0;
+                            const int iqs     = idx % (QK4_0/2);
+                            dfloat2 kv;
+                            if (k_type == GGML_TYPE_Q4_0) {
+                                dequantize_q4_0(k_base, ib, iqs, kv);
+                            } else if (k_type == GGML_TYPE_Q4_1) {
+                                dequantize_q4_1(k_base, ib, iqs, kv);
+                            } else if (k_type == GGML_TYPE_Q5_0) {
+                                dequantize_q5_0(k_base, ib, iqs, kv);
+                            } else {
+                                dequantize_q5_1(k_base, ib, iqs, kv);
+                            }
+                            k_local[j] = (idx % QK4_0) < (QK4_0/2) ? static_cast<float>(kv.x()) : static_cast<float>(kv.y());
+                        }
+                    } break;
+                    case GGML_TYPE_Q8_0: {
+#pragma unroll
+                        for (int64_t pair = 0; pair < ELEMS_PER_LANE / 2; ++pair) {
+                            const int64_t elem0 = lane_base + 2 * pair;
+                            dfloat2 kv;
+                            dequantize_q8_0(k_base, elem0 / QK8_0, elem0 % QK8_0, kv);
+                            k_local[2 * pair + 0] = static_cast<float>(kv.x());
+                            k_local[2 * pair + 1] = static_cast<float>(kv.y());
+                        }
+                    } break;
+                    case GGML_TYPE_IQ4_NL: {
+#pragma unroll
+                        for (int64_t pair = 0; pair < ELEMS_PER_LANE / 2; ++pair) {
+                            const int64_t elem0 = lane_base + 2 * pair;
+                            dfloat2 kv;
+                            dequantize_iq4_nl(k_base, elem0 / QK4_NL, elem0 % QK4_NL, kv);
+                            k_local[2 * pair + 0] = static_cast<float>(kv.x());
+                            k_local[2 * pair + 1] = static_cast<float>(kv.y());
+                        }
+                    } break;
+                    default:
+#pragma unroll
+                        for (int64_t j = 0; j < ELEMS_PER_LANE; ++j) {
+                            k_local[j] = 0.0f;
+                        }
+                        break;
+                }
+            }
+
+            const char  * q_base = q + i_batch*nbq2 + i_stream*nbq3;
+            const float * w_base = (const float *) (w + i_batch*nbw1 + i_stream*nbw3);
+
+            float score = 0.0f;
+            for (int64_t h = 0; h < n_head; ++h) {
+                const float * q_row = (const float *) (q_base + h*nbq1);
+                float dot = 0.0f;
+#pragma unroll
+                for (int64_t j = 0; j < ELEMS_PER_LANE; ++j) {
+                    const int64_t i = lane*ELEMS_PER_LANE + j;
+                    if (i < n_embd) {
+                        dot += q_row[i] * k_local[j];
+                    }
+                }
+                dot = sycl::reduce_over_group(item.get_sub_group(), dot, sycl::plus<float>());
+                if (lane == 0) {
+                    score += sycl::max(dot, 0.0f) * w_base[h];
+                }
+            }
+
+            if (lane == 0) {
+                const sycl::half * m_base = (const sycl::half *) (m + i_batch*nbm1 + (i_stream % nem3)*nbm3);
+                // flat-index store: storing through a strided base pointer
+                // hangs/misroutes writes on this stack when n_batch*n_stream > 1
+                const int64_t dst_idx = i_kv + i_batch*(nb1/sizeof(float)) + i_stream*(nb3/sizeof(float));
+                dst[dst_idx] = score + static_cast<float>(m_base[i_kv]);
+            }
+        });
+}
+
+void ggml_sycl_op_lightning_indexer(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/4);
+    const ggml_tensor * q = dst->src[0];
+    const ggml_tensor * k = dst->src[1];
+    const ggml_tensor * w = dst->src[2]; // weights
+    const ggml_tensor * m = dst->src[3]; // mask
+
+    GGML_ASSERT(dst->type == GGML_TYPE_F32);
+    GGML_ASSERT(  q->type == GGML_TYPE_F32);
+    GGML_ASSERT(  w->type == GGML_TYPE_F32);
+    GGML_ASSERT(  m->type == GGML_TYPE_F16);
+    GGML_ASSERT(k->type == GGML_TYPE_F16 || k->type == GGML_TYPE_F32 || k->type == GGML_TYPE_BF16 ||
+                k->type == GGML_TYPE_Q8_0 || k->type == GGML_TYPE_Q5_1 || k->type == GGML_TYPE_Q5_0 ||
+                k->type == GGML_TYPE_Q4_1 || k->type == GGML_TYPE_Q4_0 || k->type == GGML_TYPE_IQ4_NL);
+
+    GGML_TENSOR_LOCALS(int64_t, neq, q, ne);
+    GGML_TENSOR_LOCALS(size_t,  nbq, q, nb);
+    GGML_TENSOR_LOCALS(int64_t, nek, k, ne);
+    GGML_TENSOR_LOCALS(size_t,  nbk, k, nb);
+    GGML_TENSOR_LOCALS(size_t,  nbw, w, nb);
+    GGML_TENSOR_LOCALS(int64_t, nem, m, ne);
+    GGML_TENSOR_LOCALS(size_t,  nbm, m, nb);
+    GGML_TENSOR_LOCALS(int64_t, ne, dst, ne);
+    GGML_TENSOR_LOCALS(size_t,  nb, dst, nb);
+
+    // input rows must be contiguous
+    GGML_ASSERT(nbq0 == ggml_type_size(q->type));
+    GGML_ASSERT(nbk0 == ggml_type_size(k->type));
+    GGML_ASSERT(nbm0 == ggml_type_size(m->type));
+    GGML_ASSERT(nb0  == ggml_type_size(dst->type));
+
+    const int64_t n_embd   = neq0;
+    const int64_t n_head   = neq1;
+    const int64_t n_batch  = neq2;
+    const int64_t n_stream = neq3;
+    const int64_t n_kv     = nek2;
+
+    GGML_ASSERT(n_embd == WARP_SIZE * 8);
+
+    lightning_indexer_f32_sycl(
+            (const char *) q->data, (const char *) k->data,
+            (const char *) w->data, (const char *) m->data, (float *) dst->data,
+            n_embd, n_head, n_batch, n_stream, n_kv, nem3,
+            nbq1, nbq2, nbq3,
+            nbk2, nbk3,
+            nbw1, nbw3,
+            nbm1, nbm3,
+            nb1, nb3,
+            k->type,
+            ctx.stream());
+}

ggml/src/ggml-sycl/lightning-indexer.hpp

@@ -0,0 +1,8 @@
+#ifndef GGML_SYCL_LIGHTNING_INDEXER_HPP
+#define GGML_SYCL_LIGHTNING_INDEXER_HPP
+
+#include "common.hpp"
+
+void ggml_sycl_op_lightning_indexer(ggml_backend_sycl_context & ctx, ggml_tensor * dst);
+
+#endif // GGML_SYCL_LIGHTNING_INDEXER_HPP

ggml/src/ggml-sycl/presets.hpp

@@ -20,8 +20,6 @@
 #define MATRIX_ROW_PADDING 512 // last row of quant. matrices is a multiple of this to avoid out-of-bounds memory accesses
 
 #define SYCL_COL2IM_1D_BLOCK_SIZE 256
-#define SYCL_GELU_BLOCK_SIZE 256
-#define SYCL_SILU_BLOCK_SIZE 256
 #define SYCL_TANH_BLOCK_SIZE 256
 #define SYCL_RELU_BLOCK_SIZE 256
 #define SYCL_HARDSIGMOID_BLOCK_SIZE 256

ggml/src/ggml-sycl/set_rows.cpp

@@ -1,6 +1,10 @@
 #include "set_rows.hpp"
 #include "cpy.hpp"
 
+#include "ggml-quants.h"
+
+#include <vector>
+
 namespace utils {
 template<typename T>
 static constexpr bool is_arithmetic_v() {
@@ -20,7 +24,17 @@ convert (const char* src, char* dst) {
    *reinterpret_cast<TOut*>(dst) = dst_val;
 }
 
-template <typename TIdx, typename blockType, int qk, cpy_kernel_t cpyblck>
+#ifdef GGML_SYCL_HAS_BF16
+// sycl::vec::convert does not provide a half -> bfloat16 path, so route through float.
+template<>
+inline void convert<sycl::half, sycl::ext::oneapi::bfloat16>(const char* src, char* dst) {
+    const float tmp = sycl::vec<sycl::half, 1>(*reinterpret_cast<const sycl::half*>(src))
+                          .template convert<float, sycl::rounding_mode::automatic>()[0];
+    *reinterpret_cast<sycl::ext::oneapi::bfloat16*>(dst) = sycl::ext::oneapi::bfloat16(tmp);
+}
+#endif
+
+template <typename TIn, typename TIdx, typename blockType, int qk, cpy_kernel_t cpyblck>
 static void set_rows_sycl_q(const char * __restrict__ src0_d,
                             const TIdx * __restrict__ src1_d,
                             blockType * __restrict__ dst_d,
@@ -68,13 +82,22 @@ static void set_rows_sycl_q(const char * __restrict__ src0_d,
         const int64_t i11         = i02 % ne11;
         const int64_t i10         = i01;
         const size_t  src_offset  = calculate_offset<3>({ nb01, nb02, nb03 }, { i01, i02, i03 });
-        const char *  src_block   = src0_d + src_offset + i00 * sizeof(float);
+        const char *  src_block   = src0_d + src_offset + i00 * sizeof(TIn);
         const size_t  src1_offset = calculate_offset<3>({ nb10, nb11, nb12 }, { i10, i11, i12 });
         const int64_t dst_row     = src1_d[src1_offset / sizeof(TIdx)];
         const size_t  dst_offset =
             calculate_offset<3>({ nb1, nb2, nb3 }, { dst_row, i02, i03 }) + (i00 / qk) * sizeof(blockType);
         char * dst_block = reinterpret_cast<char *>(reinterpret_cast<char *>(dst_d) + dst_offset);
-        cpyblck(src_block, dst_block);
+        if constexpr (std::is_same_v<TIn, float>) {
+            cpyblck(src_block, dst_block);
+        } else {
+            float src_block_f32[qk];
+            const TIn * src_block_t = reinterpret_cast<const TIn *>(src_block);
+            for (int j = 0; j < qk; ++j) {
+                src_block_f32[j] = (float) src_block_t[j];
+            }
+            cpyblck(reinterpret_cast<const char *>(src_block_f32), dst_block);
+        }
     });
     GGML_UNUSED(ne10);
     GGML_UNUSED(ne13);
@@ -82,6 +105,139 @@ static void set_rows_sycl_q(const char * __restrict__ src0_d,
     GGML_UNUSED(nb13);
 }
 
+template<typename blockType>
+using quantize_row_qk_t = void (*)(const float *, blockType *, int64_t);
+
+using quantize_rows_f_t = size_t (*)(const float *, void *, int64_t, int64_t, const float *);
+
+template <typename TIn, typename TIdx, typename blockType, int qk, quantize_row_qk_t<blockType> quantize_row>
+static void set_rows_sycl_qk_host(
+        const ggml_tensor * src0,
+        const ggml_tensor * src1,
+        ggml_tensor * dst,
+        const int64_t ne00,
+        const int64_t ne01,
+        const int64_t ne02,
+        const int64_t ne03,
+        const int64_t ne11,
+        const int64_t ne12,
+        const size_t nb01,
+        const size_t nb02,
+        const size_t nb03,
+        const size_t nb10,
+        const size_t nb11,
+        const size_t nb12,
+        const size_t nb1,
+        const size_t nb2,
+        const size_t nb3,
+        queue_ptr stream) {
+    GGML_ASSERT(ne00 % qk == 0);
+
+    const size_t src0_bytes = ggml_nbytes(src0);
+    const size_t src1_bytes = ggml_nbytes(src1);
+
+    std::vector<char> src0_host(src0_bytes);
+    std::vector<char> src1_host(src1_bytes);
+
+    stream->memcpy(src0_host.data(), src0->data, src0_bytes);
+    stream->memcpy(src1_host.data(), src1->data, src1_bytes);
+    stream->wait();
+
+    std::vector<float> src_row_f32(ne00);
+    const int64_t nblocks = ne00 / qk;
+    std::vector<blockType> dst_row_q(nblocks);
+
+    for (int64_t i03 = 0; i03 < ne03; ++i03) {
+        for (int64_t i02 = 0; i02 < ne02; ++i02) {
+            for (int64_t i01 = 0; i01 < ne01; ++i01) {
+                const int64_t i12 = i03 % ne12;
+                const int64_t i11 = i02 % ne11;
+                const int64_t i10 = i01;
+
+                const size_t src1_offset = calculate_offset<3>({ nb10, nb11, nb12 }, { i10, i11, i12 });
+                const int64_t dst_row = *(const TIdx *) (src1_host.data() + src1_offset);
+
+                const size_t src0_row_offset = calculate_offset<3>({ nb01, nb02, nb03 }, { i01, i02, i03 });
+                const TIn * src_row = reinterpret_cast<const TIn *>(src0_host.data() + src0_row_offset);
+
+                for (int64_t i00 = 0; i00 < ne00; ++i00) {
+                    src_row_f32[i00] = (float) src_row[i00];
+                }
+
+                quantize_row(src_row_f32.data(), dst_row_q.data(), ne00);
+
+                const size_t dst_offset = calculate_offset<3>({ nb1, nb2, nb3 }, { dst_row, i02, i03 });
+                stream->memcpy((char *) dst->data + dst_offset, dst_row_q.data(), nblocks * sizeof(blockType));
+                stream->wait();
+            }
+        }
+    }
+}
+
+template <typename TIn, typename TIdx, typename blockType, int qk, quantize_rows_f_t quantize_rows>
+static void set_rows_sycl_iq_host(
+        const ggml_tensor * src0,
+        const ggml_tensor * src1,
+        ggml_tensor * dst,
+        const int64_t ne00,
+        const int64_t ne01,
+        const int64_t ne02,
+        const int64_t ne03,
+        const int64_t ne11,
+        const int64_t ne12,
+        const size_t nb01,
+        const size_t nb02,
+        const size_t nb03,
+        const size_t nb10,
+        const size_t nb11,
+        const size_t nb12,
+        const size_t nb1,
+        const size_t nb2,
+        const size_t nb3,
+        queue_ptr stream) {
+    GGML_ASSERT(ne00 % qk == 0);
+
+    const size_t src0_bytes = ggml_nbytes(src0);
+    const size_t src1_bytes = ggml_nbytes(src1);
+
+    std::vector<char> src0_host(src0_bytes);
+    std::vector<char> src1_host(src1_bytes);
+
+    stream->memcpy(src0_host.data(), src0->data, src0_bytes);
+    stream->memcpy(src1_host.data(), src1->data, src1_bytes);
+    stream->wait();
+
+    std::vector<float> src_row_f32(ne00);
+    const int64_t nblocks = ne00 / qk;
+    std::vector<blockType> dst_row_q(nblocks);
+
+    for (int64_t i03 = 0; i03 < ne03; ++i03) {
+        for (int64_t i02 = 0; i02 < ne02; ++i02) {
+            for (int64_t i01 = 0; i01 < ne01; ++i01) {
+                const int64_t i12 = i03 % ne12;
+                const int64_t i11 = i02 % ne11;
+                const int64_t i10 = i01;
+
+                const size_t src1_offset = calculate_offset<3>({ nb10, nb11, nb12 }, { i10, i11, i12 });
+                const int64_t dst_row = *(const TIdx *) (src1_host.data() + src1_offset);
+
+                const size_t src0_row_offset = calculate_offset<3>({ nb01, nb02, nb03 }, { i01, i02, i03 });
+                const TIn * src_row = reinterpret_cast<const TIn *>(src0_host.data() + src0_row_offset);
+
+                for (int64_t i00 = 0; i00 < ne00; ++i00) {
+                    src_row_f32[i00] = (float) src_row[i00];
+                }
+
+                quantize_rows(src_row_f32.data(), dst_row_q.data(), 1, ne00, nullptr);
+
+                const size_t dst_offset = calculate_offset<3>({ nb1, nb2, nb3 }, { dst_row, i02, i03 });
+                stream->memcpy((char *) dst->data + dst_offset, dst_row_q.data(), nblocks * sizeof(blockType));
+                stream->wait();
+            }
+        }
+    }
+}
+
 template<typename TIn, typename TIdx, typename TOut>
 static void k_set_rows(
         const char * __restrict__ src0, const TIdx * __restrict__ src1, char * __restrict__ dst,
@@ -200,31 +356,194 @@ static void set_rows_sycl(ggml_backend_sycl_context & ctx, const ggml_tensor * s
             break;
 #endif
         case GGML_TYPE_Q8_0:
-            set_rows_sycl_q<TIdx, block_q8_0, QK8_0, cpy_blck_f32_q8_0>(src0_d, src1_d, (block_q8_0 *)dst->data, ne00, ne01, ne02, ne03, ne10, ne11, ne12, ne13, nb00, nb01, nb02, nb03, nb10, nb11, nb12, nb13, nb1, nb2, nb3, stream);
+            set_rows_sycl_q<TIn, TIdx, block_q8_0, QK8_0, cpy_blck_f32_q8_0>(
+                src0_d, src1_d, (block_q8_0 *) dst->data, ne00, ne01, ne02, ne03,
+                ne10, ne11, ne12, ne13, nb00, nb01,
+                nb02, nb03, nb10, nb11, nb12, nb13, nb1, nb2, nb3, stream);
             break;
         case GGML_TYPE_Q1_0:
-            set_rows_sycl_q<TIdx, block_q1_0, QK1_0, cpy_blck_f32_q1_0>(src0_d, src1_d, (block_q1_0 *)dst->data, ne00, ne01, ne02, ne03, ne10, ne11, ne12, ne13, nb00, nb01, nb02, nb03, nb10, nb11, nb12, nb13, nb1, nb2, nb3, stream);
+            set_rows_sycl_q<TIn, TIdx, block_q1_0, QK1_0, cpy_blck_f32_q1_0>(
+                src0_d, src1_d, (block_q1_0 *) dst->data, ne00, ne01, ne02, ne03,
+                ne10, ne11, ne12, ne13, nb00, nb01,
+                nb02, nb03, nb10, nb11, nb12, nb13, nb1, nb2, nb3, stream);
+            break;
+        case GGML_TYPE_Q2_0:
+            set_rows_sycl_q<TIn, TIdx, block_q2_0, QK2_0, cpy_blck_f32_q2_0>(
+                src0_d, src1_d, (block_q2_0 *) dst->data, ne00, ne01, ne02, ne03,
+                ne10, ne11, ne12, ne13, nb00, nb01,
+                nb02, nb03, nb10, nb11, nb12, nb13, nb1, nb2, nb3, stream);
             break;
         case GGML_TYPE_Q5_1:
-            set_rows_sycl_q<TIdx, block_q5_1, QK5_1, cpy_blck_f32_q5_1>(src0_d, src1_d, (block_q5_1 *)dst->data, ne00, ne01, ne02, ne03, ne10, ne11, ne12, ne13, nb00, nb01, nb02, nb03, nb10, nb11, nb12, nb13, nb1, nb2, nb3, stream);
+            set_rows_sycl_q<TIn, TIdx, block_q5_1, QK5_1, cpy_blck_f32_q5_1>(
+                src0_d, src1_d, (block_q5_1 *) dst->data, ne00, ne01, ne02, ne03,
+                ne10, ne11, ne12, ne13, nb00, nb01,
+                nb02, nb03, nb10, nb11, nb12, nb13, nb1, nb2, nb3, stream);
             break;
         case GGML_TYPE_Q5_0:
-            set_rows_sycl_q<TIdx, block_q5_0, QK5_0, cpy_blck_f32_q5_0>(src0_d, src1_d, (block_q5_0 *)dst->data, ne00, ne01, ne02, ne03, ne10, ne11, ne12, ne13, nb00, nb01, nb02, nb03, nb10, nb11, nb12, nb13, nb1, nb2, nb3, stream);
+            set_rows_sycl_q<TIn, TIdx, block_q5_0, QK5_0, cpy_blck_f32_q5_0>(
+                src0_d, src1_d, (block_q5_0 *) dst->data, ne00, ne01, ne02, ne03,
+                ne10, ne11, ne12, ne13, nb00, nb01,
+                nb02, nb03, nb10, nb11, nb12, nb13, nb1, nb2, nb3, stream);
             break;
         case GGML_TYPE_Q4_1:
-            set_rows_sycl_q<TIdx, block_q4_1, QK4_1, cpy_blck_f32_q4_1>(src0_d, src1_d, (block_q4_1 *)dst->data, ne00, ne01, ne02, ne03, ne10, ne11, ne12, ne13, nb00, nb01, nb02, nb03, nb10, nb11, nb12, nb13, nb1, nb2, nb3, stream);
+            set_rows_sycl_q<TIn, TIdx, block_q4_1, QK4_1, cpy_blck_f32_q4_1>(
+                src0_d, src1_d, (block_q4_1 *) dst->data, ne00, ne01, ne02, ne03,
+                ne10, ne11, ne12, ne13, nb00, nb01,
+                nb02, nb03, nb10, nb11, nb12, nb13, nb1, nb2, nb3, stream);
             break;
         case GGML_TYPE_Q4_0:
-            set_rows_sycl_q<TIdx, block_q4_0, QK4_0, cpy_blck_f32_q4_0>(src0_d, src1_d, (block_q4_0 *)dst->data, ne00, ne01, ne02, ne03, ne10, ne11, ne12, ne13, nb00, nb01, nb02, nb03, nb10, nb11, nb12, nb13, nb1, nb2, nb3, stream);
+            set_rows_sycl_q<TIn, TIdx, block_q4_0, QK4_0, cpy_blck_f32_q4_0>(
+                src0_d, src1_d, (block_q4_0 *) dst->data, ne00, ne01, ne02, ne03,
+                ne10, ne11, ne12, ne13, nb00, nb01,
+                nb02, nb03, nb10, nb11, nb12, nb13, nb1, nb2, nb3, stream);
             break;
         case GGML_TYPE_IQ4_NL:
-            set_rows_sycl_q<TIdx, block_iq4_nl, QK4_NL, cpy_blck_f32_iq4_nl>(src0_d, src1_d, (block_iq4_nl *)dst->data, ne00, ne01, ne02, ne03, ne10, ne11, ne12, ne13, nb00, nb01, nb02, nb03, nb10, nb11, nb12, nb13, nb1, nb2, nb3, stream);
+            set_rows_sycl_q<TIn, TIdx, block_iq4_nl, QK4_NL, cpy_blck_f32_iq4_nl>(
+                src0_d, src1_d, (block_iq4_nl *) dst->data, ne00, ne01, ne02, ne03,
+                ne10, ne11, ne12, ne13, nb00, nb01,
+                nb02, nb03, nb10, nb11, nb12, nb13, nb1, nb2, nb3, stream);
             break;
         case GGML_TYPE_MXFP4:
-            set_rows_sycl_q<TIdx, block_mxfp4, QK_MXFP4, cpy_blck_f32_mxfp4>(src0_d, src1_d, (block_mxfp4 *)dst->data, ne00, ne01, ne02, ne03, ne10, ne11, ne12, ne13, nb00, nb01, nb02, nb03, nb10, nb11, nb12, nb13, nb1, nb2, nb3, stream);
+            set_rows_sycl_q<TIn, TIdx, block_mxfp4, QK_MXFP4, cpy_blck_f32_mxfp4>(
+                src0_d, src1_d, (block_mxfp4 *) dst->data, ne00, ne01, ne02, ne03,
+                ne10, ne11, ne12, ne13, nb00, nb01,
+                nb02, nb03, nb10, nb11, nb12, nb13, nb1, nb2, nb3, stream);
             break;
         case GGML_TYPE_NVFP4:
-            set_rows_sycl_q<TIdx, block_nvfp4, QK_NVFP4, cpy_blck_f32_nvfp4>(src0_d, src1_d, (block_nvfp4 *)dst->data, ne00, ne01, ne02, ne03, ne10, ne11, ne12, ne13, nb00, nb01, nb02, nb03, nb10, nb11, nb12, nb13, nb1, nb2, nb3, stream);
+            set_rows_sycl_q<TIn, TIdx, block_nvfp4, QK_NVFP4, cpy_blck_f32_nvfp4>(
+                src0_d, src1_d, (block_nvfp4 *) dst->data, ne00, ne01, ne02, ne03,
+                ne10, ne11, ne12, ne13, nb00, nb01,
+                nb02, nb03, nb10, nb11, nb12, nb13, nb1, nb2, nb3, stream);
+            break;
+        case GGML_TYPE_Q2_K:
+            set_rows_sycl_qk_host<TIn, TIdx, block_q2_K, QK_K, quantize_row_q2_K_ref>(
+                src0, src1, dst,
+                ne00, ne01, ne02, ne03,
+                ne11, ne12,
+                nb01, nb02, nb03,
+                nb10, nb11, nb12,
+                nb1, nb2, nb3,
+                stream);
+            break;
+        case GGML_TYPE_Q3_K:
+            set_rows_sycl_qk_host<TIn, TIdx, block_q3_K, QK_K, quantize_row_q3_K_ref>(
+                src0, src1, dst,
+                ne00, ne01, ne02, ne03,
+                ne11, ne12,
+                nb01, nb02, nb03,
+                nb10, nb11, nb12,
+                nb1, nb2, nb3,
+                stream);
+            break;
+        case GGML_TYPE_Q4_K:
+            set_rows_sycl_qk_host<TIn, TIdx, block_q4_K, QK_K, quantize_row_q4_K_ref>(
+                src0, src1, dst,
+                ne00, ne01, ne02, ne03,
+                ne11, ne12,
+                nb01, nb02, nb03,
+                nb10, nb11, nb12,
+                nb1, nb2, nb3,
+                stream);
+            break;
+        case GGML_TYPE_Q5_K:
+            set_rows_sycl_qk_host<TIn, TIdx, block_q5_K, QK_K, quantize_row_q5_K_ref>(
+                src0, src1, dst,
+                ne00, ne01, ne02, ne03,
+                ne11, ne12,
+                nb01, nb02, nb03,
+                nb10, nb11, nb12,
+                nb1, nb2, nb3,
+                stream);
+            break;
+        case GGML_TYPE_Q6_K:
+            set_rows_sycl_qk_host<TIn, TIdx, block_q6_K, QK_K, quantize_row_q6_K_ref>(
+                src0, src1, dst,
+                ne00, ne01, ne02, ne03,
+                ne11, ne12,
+                nb01, nb02, nb03,
+                nb10, nb11, nb12,
+                nb1, nb2, nb3,
+                stream);
+            break;
+        case GGML_TYPE_IQ2_XXS:
+            set_rows_sycl_iq_host<TIn, TIdx, block_iq2_xxs, QK_K, quantize_iq2_xxs>(
+                src0, src1, dst,
+                ne00, ne01, ne02, ne03,
+                ne11, ne12,
+                nb01, nb02, nb03,
+                nb10, nb11, nb12,
+                nb1, nb2, nb3,
+                stream);
+            break;
+        case GGML_TYPE_IQ2_XS:
+            set_rows_sycl_iq_host<TIn, TIdx, block_iq2_xs, QK_K, quantize_iq2_xs>(
+                src0, src1, dst,
+                ne00, ne01, ne02, ne03,
+                ne11, ne12,
+                nb01, nb02, nb03,
+                nb10, nb11, nb12,
+                nb1, nb2, nb3,
+                stream);
+            break;
+        case GGML_TYPE_IQ2_S:
+            set_rows_sycl_iq_host<TIn, TIdx, block_iq2_s, QK_K, quantize_iq2_s>(
+                src0, src1, dst,
+                ne00, ne01, ne02, ne03,
+                ne11, ne12,
+                nb01, nb02, nb03,
+                nb10, nb11, nb12,
+                nb1, nb2, nb3,
+                stream);
+            break;
+        case GGML_TYPE_IQ3_XXS:
+            set_rows_sycl_qk_host<TIn, TIdx, block_iq3_xxs, QK_K, quantize_row_iq3_xxs_ref>(
+                src0, src1, dst,
+                ne00, ne01, ne02, ne03,
+                ne11, ne12,
+                nb01, nb02, nb03,
+                nb10, nb11, nb12,
+                nb1, nb2, nb3,
+                stream);
+            break;
+        case GGML_TYPE_IQ3_S:
+            set_rows_sycl_qk_host<TIn, TIdx, block_iq3_s, QK_K, quantize_row_iq3_s_ref>(
+                src0, src1, dst,
+                ne00, ne01, ne02, ne03,
+                ne11, ne12,
+                nb01, nb02, nb03,
+                nb10, nb11, nb12,
+                nb1, nb2, nb3,
+                stream);
+            break;
+        case GGML_TYPE_IQ1_S:
+            set_rows_sycl_iq_host<TIn, TIdx, block_iq1_s, QK_K, quantize_iq1_s>(
+                src0, src1, dst,
+                ne00, ne01, ne02, ne03,
+                ne11, ne12,
+                nb01, nb02, nb03,
+                nb10, nb11, nb12,
+                nb1, nb2, nb3,
+                stream);
+            break;
+        case GGML_TYPE_IQ1_M:
+            set_rows_sycl_iq_host<TIn, TIdx, block_iq1_m, QK_K, quantize_iq1_m>(
+                src0, src1, dst,
+                ne00, ne01, ne02, ne03,
+                ne11, ne12,
+                nb01, nb02, nb03,
+                nb10, nb11, nb12,
+                nb1, nb2, nb3,
+                stream);
+            break;
+        case GGML_TYPE_IQ4_XS:
+            set_rows_sycl_qk_host<TIn, TIdx, block_iq4_xs, QK_K, quantize_row_iq4_xs_ref>(
+                src0, src1, dst,
+                ne00, ne01, ne02, ne03,
+                ne11, ne12,
+                nb01, nb02, nb03,
+                nb10, nb11, nb12,
+                nb1, nb2, nb3,
+                stream);
             break;
         default:
             GGML_ABORT("Unsupported tensor type!");
@@ -237,12 +556,21 @@ void ggml_sycl_op_set_rows(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
     const ggml_tensor * src0 = dst->src[0];
     const ggml_tensor * src1 = dst->src[1];
 
-    GGML_ASSERT(dst->src[0]->type == GGML_TYPE_F32);
+    GGML_ASSERT(dst->src[0]->type == GGML_TYPE_F32 || dst->src[0]->type == GGML_TYPE_F16);
     GGML_ASSERT(dst->src[1]->type == GGML_TYPE_I64 || dst->src[1]->type == GGML_TYPE_I32);
 
-    if (src1->type == GGML_TYPE_I64) {
-        set_rows_sycl<float, int64_t>(ctx, src0, src1, dst);
+    // dispatch on the index type (src1) and the source value type (src0)
+    if (src0->type == GGML_TYPE_F16) {
+        if (src1->type == GGML_TYPE_I64) {
+            set_rows_sycl<sycl::half, int64_t>(ctx, src0, src1, dst);
+        } else {
+            set_rows_sycl<sycl::half, int32_t>(ctx, src0, src1, dst);
+        }
     } else {
-        set_rows_sycl<float, int32_t>(ctx, src0, src1, dst);
+        if (src1->type == GGML_TYPE_I64) {
+            set_rows_sycl<float, int64_t>(ctx, src0, src1, dst);
+        } else {
+            set_rows_sycl<float, int32_t>(ctx, src0, src1, dst);
+        }
     }
 }

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

@@ -186,13 +186,22 @@ static bool is_pow2(uint32_t x) { return x > 1 && (x & (x-1)) == 0; }
 
 #define VK_DEVICE_DESCRIPTOR_POOL_SIZE 256
 
-#define VK_CHECK(err, msg)                                          \
+#define VK_CHECK(err, msg, dev)                                     \
     do {                                                            \
-        vk::Result err_ = (err);                                    \
+        vk::Result err_;                                            \
+        try {                                                       \
+            err_ = (err);                                           \
+        } catch (vk::DeviceLostError &) {                           \
+            ggml_vk_print_device_lost_info(dev);                    \
+            GGML_LOG_ERROR("ggml_vulkan: %s at %s:%d\n",            \
+                #err, __FILE__, __LINE__);                          \
+            throw;                                                  \
+        }                                                           \
         if (err_ != vk::Result::eSuccess) {                         \
-            fprintf(stderr, "ggml_vulkan: %s error %s at %s:%d\n",  \
+            GGML_LOG_ERROR("ggml_vulkan: %s error %s at %s:%d\n",   \
                 #err, to_string(err_).c_str(), __FILE__, __LINE__); \
-            exit(1);                                                \
+            throw vk::SystemError(vk::make_error_code(err_),        \
+                "ggml_vulkan: " msg);                               \
         }                                                           \
     } while (0)
 
@@ -302,9 +311,13 @@ struct vk_command_pool {
     }
 };
 
+static void ggml_vk_print_device_fault_info(const vk_device& device);
+static void ggml_vk_print_device_lost_info(const vk_device& device);
+
 // Prevent simultaneous submissions to the same queue.
 struct vk_queue_handle {
     vk::Queue queue;
+    vk_device_ref device;
     virtual void submit(vk::ArrayProxy<const vk::SubmitInfo> submits, vk::Fence fence) = 0;
     virtual void lock()   {}   // no-op by default (internally synchronized case)
     virtual void unlock() {}
@@ -315,7 +328,14 @@ struct vk_queue_handle_synchronized : vk_queue_handle {
     std::mutex mutex;
     void submit(vk::ArrayProxy<const vk::SubmitInfo> submits, vk::Fence fence) override {
         std::lock_guard<std::mutex> guard(mutex);
-        queue.submit(submits, fence);
+        try {
+            queue.submit(submits, fence);
+        } catch (vk::DeviceLostError &) {
+            if (auto dev = device.lock()) {
+                ggml_vk_print_device_lost_info(dev);
+            }
+            throw;
+        }
     }
     void lock()   override { mutex.lock(); }
     void unlock() override { mutex.unlock(); }
@@ -324,7 +344,14 @@ struct vk_queue_handle_synchronized : vk_queue_handle {
 struct vk_queue_handle_unsynchronized : vk_queue_handle {
     void submit(vk::ArrayProxy<const vk::SubmitInfo> submits, vk::Fence fence) override {
         // Driver guarantees internal synchronization via VK_KHR_internally_synchronized_queues
-        queue.submit(submits, fence);
+        try {
+            queue.submit(submits, fence);
+        } catch (vk::DeviceLostError &) {
+            if (auto dev = device.lock()) {
+                ggml_vk_print_device_lost_info(dev);
+            }
+            throw;
+        }
     }
     // lock()/unlock() inherited no-ops
 };
@@ -835,6 +862,15 @@ struct vk_device_struct {
 
     bool pipeline_executable_properties_support {};
 
+    bool device_fault {};
+    PFN_vkGetDeviceFaultInfoEXT pfn_vkGetDeviceFaultInfoEXT {};
+
+    bool serialize_submissions {};
+
+    const ggml_cgraph * diag_cgraph {};
+    int diag_prev_start = -1;
+    int diag_prev_end = -1;
+
     size_t idx;
 
     bool mul_mat_l[GGML_TYPE_COUNT];
@@ -1026,6 +1062,7 @@ struct vk_device_struct {
     vk_pipeline pipeline_pool2d_f32;
     vk_pipeline pipeline_rwkv_wkv6_f32;
     vk_pipeline pipeline_rwkv_wkv7_f32;
+    vk_pipeline pipeline_gated_linear_attn_f32;
     // [size_idx][kda] where size_idx: 0=d16, 1=d32, 2=d64, 3=d128
     vk_pipeline pipeline_gated_delta_net[4][2];
     vk_pipeline pipeline_ssm_scan_f32_d128;
@@ -1117,6 +1154,57 @@ void vk_command_pool::destroy(vk::Device& device) {
     cmd_buffers.clear();
 }
 
+static void ggml_vk_print_device_fault_info(const vk_device& device) {
+    if (!device->device_fault || !device->pfn_vkGetDeviceFaultInfoEXT) {
+        return;
+    }
+
+    VkDeviceFaultCountsEXT fault_counts {};
+    fault_counts.sType = VK_STRUCTURE_TYPE_DEVICE_FAULT_COUNTS_EXT;
+    VkResult res = device->pfn_vkGetDeviceFaultInfoEXT(device->device, &fault_counts, nullptr);
+    if (res != VK_SUCCESS) {
+        GGML_LOG_ERROR("ggml_vulkan: vkGetDeviceFaultInfoEXT (counts) failed: %d\n", res);
+        return;
+    }
+
+    std::vector<VkDeviceFaultAddressInfoEXT> address_infos(fault_counts.addressInfoCount);
+    std::vector<VkDeviceFaultVendorInfoEXT> vendor_infos(fault_counts.vendorInfoCount);
+
+    VkDeviceFaultInfoEXT fault_info {};
+    fault_info.sType = VK_STRUCTURE_TYPE_DEVICE_FAULT_INFO_EXT;
+    fault_info.pAddressInfos = address_infos.data();
+    fault_info.pVendorInfos = vendor_infos.data();
+
+    res = device->pfn_vkGetDeviceFaultInfoEXT(device->device, &fault_counts, &fault_info);
+    if (res != VK_SUCCESS) {
+        GGML_LOG_ERROR("ggml_vulkan: vkGetDeviceFaultInfoEXT (info) failed: %d\n", res);
+        return;
+    }
+
+    if (fault_counts.addressInfoCount == 0 && fault_counts.vendorInfoCount == 0 && fault_info.description[0] == '\0') {
+        return;
+    }
+
+    if (fault_info.description[0] != '\0') {
+        GGML_LOG_ERROR("ggml_vulkan: device fault on %s: %s\n", device->name.c_str(), fault_info.description);
+    }
+
+    for (uint32_t i = 0; i < fault_counts.addressInfoCount; i++) {
+        const auto& info = address_infos[i];
+        GGML_LOG_CONT("  address fault %u: type=%d address=0x%llx precision=0x%llx\n",
+                i, (int)info.addressType,
+                (unsigned long long)info.reportedAddress,
+                (unsigned long long)info.addressPrecision);
+    }
+    for (uint32_t i = 0; i < fault_counts.vendorInfoCount; i++) {
+        const auto& info = vendor_infos[i];
+        GGML_LOG_CONT("  vendor fault %u: %s (code=0x%llx data=0x%llx)\n",
+                i, info.description,
+                (unsigned long long)info.vendorFaultCode,
+                (unsigned long long)info.vendorFaultData);
+    }
+}
+
 struct vk_buffer_struct {
     vk::Buffer buffer = VK_NULL_HANDLE;
     vk::DeviceMemory device_memory = VK_NULL_HANDLE;
@@ -1747,6 +1835,13 @@ struct vk_op_rwkv_wkv7_push_constants {
     uint32_t C;
     uint32_t H;
 };
+struct vk_op_gated_linear_attn_push_constants {
+    uint32_t B;
+    uint32_t T;
+    uint32_t C;
+    uint32_t H;
+    float scale;
+};
 struct vk_op_gated_delta_net_push_constants {
     uint32_t H;
     uint32_t n_tokens;
@@ -2051,6 +2146,36 @@ static uint64_t ggml_vk_get_node_flops(const ggml_tensor * node) {
     return 0;
 }
 
+static void ggml_vk_print_node_list(const ggml_cgraph * cgraph, int start, int end) {
+    uint64_t total_flops = 0;
+    int n_ops = 0;
+    for (int j = start; j <= end && j < cgraph->n_nodes; j++) {
+        uint64_t flops = ggml_vk_get_node_flops(cgraph->nodes[j]);
+        total_flops += flops;
+        n_ops++;
+        if (flops > 0) {
+            GGML_LOG_CONT("  node %d: %s (%s) [%.2f GFLOP]\n",
+                    j, cgraph->nodes[j]->name, ggml_op_name(cgraph->nodes[j]->op),
+                    flops / 1e9);
+        } else {
+            GGML_LOG_CONT("  node %d: %s (%s)\n",
+                    j, cgraph->nodes[j]->name, ggml_op_name(cgraph->nodes[j]->op));
+        }
+    }
+    GGML_LOG_CONT("  total: %d ops, %.2f GFLOP\n", n_ops, total_flops / 1e9);
+}
+
+static void ggml_vk_print_device_lost_info(const vk_device& device) {
+    ggml_vk_print_device_fault_info(device);
+    if (device->serialize_submissions && device->diag_cgraph != nullptr && device->diag_prev_start >= 0) {
+        GGML_LOG_ERROR("ggml_vulkan: device lost on %s, likely caused by previous submission (nodes %d to %d):\n",
+                device->name.c_str(), device->diag_prev_start, device->diag_prev_end);
+        ggml_vk_print_node_list(device->diag_cgraph, device->diag_prev_start, device->diag_prev_end);
+    } else {
+        GGML_LOG_ERROR("ggml_vulkan: device lost on %s\n", device->name.c_str());
+    }
+}
+
 class vk_perf_logger {
   public:
     void print_timings(bool force = false) {
@@ -2463,17 +2588,27 @@ static void ggml_vk_wait_for_fence(ggml_backend_vk_context * ctx) {
     // Use waitForFences while most of the graph executes. Hopefully the CPU can sleep
     // during this wait.
     if (ctx->almost_ready_fence_pending) {
-        VK_CHECK(ctx->device->device.waitForFences({ ctx->almost_ready_fence }, true, UINT64_MAX), "almost_ready_fence");
+        VK_CHECK(ctx->device->device.waitForFences({ ctx->almost_ready_fence }, true, UINT64_MAX), "almost_ready_fence", ctx->device);
         ctx->device->device.resetFences({ ctx->almost_ready_fence });
         ctx->almost_ready_fence_pending = false;
     }
 
     // Spin (w/pause) waiting for the graph to finish executing.
     vk::Result result;
-    while ((result = ctx->device->device.getFenceStatus(ctx->fence)) != vk::Result::eSuccess) {
+    for (;;) {
+        try {
+            result = ctx->device->device.getFenceStatus(ctx->fence);
+        } catch (vk::DeviceLostError &) {
+            ggml_vk_print_device_lost_info(ctx->device);
+            GGML_LOG_ERROR("ggml_vulkan: getFenceStatus at %s:%d\n", __FILE__, __LINE__);
+            throw;
+        }
+        if (result == vk::Result::eSuccess) {
+            break;
+        }
         if (result != vk::Result::eNotReady) {
-            fprintf(stderr, "ggml_vulkan: error %s at %s:%d\n", to_string(result).c_str(), __FILE__, __LINE__);
-            exit(1);
+            GGML_LOG_ERROR("ggml_vulkan: error %s at %s:%d\n", to_string(result).c_str(), __FILE__, __LINE__);
+            throw vk::SystemError(vk::make_error_code(result), "ggml_vulkan: getFenceStatus");
         }
         for (uint32_t i = 0; i < 100; ++i) {
             YIELD();
@@ -3164,6 +3299,7 @@ static std::unique_ptr<vk_queue> ggml_vk_create_queue(vk_device& device, uint32_
     }
 
     h->queue = device->device.getQueue2(queue_info2);
+    h->device = device;
     q->handle = h;
 
     q->cmd_pool.init(device, q.get());
@@ -5665,6 +5801,8 @@ static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) {
 
     ggml_vk_create_pipeline(device, device->pipeline_rwkv_wkv7_f32, "rwkv_wkv7_f32", rwkv_wkv7_f32_len, rwkv_wkv7_f32_data, "main", 8, sizeof(vk_op_rwkv_wkv7_push_constants), {1, 1, 1}, {device->subgroup_size}, 1);
 
+    ggml_vk_create_pipeline(device, device->pipeline_gated_linear_attn_f32, "gated_linear_attn_f32", gated_linear_attn_f32_len, gated_linear_attn_f32_data, "main", 6, sizeof(vk_op_gated_linear_attn_push_constants), {1, 1, 1}, {}, 1);
+
     {
         const uint32_t gdn_sizes[] = {16, 32, 64, 128};
         const char * gdn_names[][2] = {
@@ -6107,6 +6245,8 @@ static vk_device ggml_vk_get_device(size_t idx) {
 #endif
             } else if (strcmp(VK_KHR_INTERNALLY_SYNCHRONIZED_QUEUES_EXTENSION_NAME, properties.extensionName) == 0) {
                 internally_sync_support = true;
+            } else if (strcmp("VK_EXT_device_fault", properties.extensionName) == 0) {
+                device->device_fault = true;
             }
         }
 
@@ -6461,8 +6601,18 @@ static vk_device ggml_vk_get_device(size_t idx) {
         }
 #endif
 
+        VkPhysicalDeviceFaultFeaturesEXT fault_features {};
+        fault_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_FAULT_FEATURES_EXT;
+        if (device->device_fault) {
+            last_struct->pNext = (VkBaseOutStructure *)&fault_features;
+            last_struct = (VkBaseOutStructure *)&fault_features;
+            device_extensions.push_back("VK_EXT_device_fault");
+        }
+
         vkGetPhysicalDeviceFeatures2(device->physical_device, &device_features2);
 
+        device->device_fault = device->device_fault && fault_features.deviceFault;
+
         device->has_internally_synchronized_queues = internally_synchronized_queues_features.internallySynchronizedQueues;
 
         // Build queue create infos only after querying whether internally synchronized queues are enabled.
@@ -6761,6 +6911,11 @@ static vk_device ggml_vk_get_device(size_t idx) {
         device_create_info.setPNext(&device_features2);
         device->device = device->physical_device.createDevice(device_create_info);
 
+        if (device->device_fault) {
+            device->pfn_vkGetDeviceFaultInfoEXT = (PFN_vkGetDeviceFaultInfoEXT)
+                vkGetDeviceProcAddr(device->device, "vkGetDeviceFaultInfoEXT");
+        }
+
         // Queues
         device->compute_queue = ggml_vk_create_queue(device, compute_queue_family_index, 0, { vk::PipelineStageFlagBits::eComputeShader | vk::PipelineStageFlagBits::eTransfer }, false);
 
@@ -6883,6 +7038,8 @@ static vk_device ggml_vk_get_device(size_t idx) {
 
         device->idx = idx;
 
+        device->serialize_submissions = getenv("GGML_VK_SERIALIZE_SUBMISSIONS") != nullptr;
+
         device->disable_fusion = getenv("GGML_VK_DISABLE_FUSION") != nullptr;
 
         device->add_rms_fusion = !device->disable_fusion &&
@@ -8309,7 +8466,7 @@ static void ggml_vk_buffer_write_2d(vk_buffer& dst, size_t offset, const void *
         }
 
         ggml_vk_submit(subctx, dst->device->fence);
-        VK_CHECK(dst->device->device.waitForFences({ dst->device->fence }, true, UINT64_MAX), "vk_buffer_write_2d waitForFences");
+        VK_CHECK(dst->device->device.waitForFences({ dst->device->fence }, true, UINT64_MAX), "vk_buffer_write_2d waitForFences", dst->device);
         dst->device->device.resetFences({ dst->device->fence });
         ggml_vk_queue_command_pools_cleanup(dst->device);
     }
@@ -8421,7 +8578,7 @@ static void ggml_vk_buffer_read_2d(vk_buffer& src, size_t offset, void * dst, si
         ggml_vk_ctx_end(subctx);
         ggml_vk_submit(subctx, src->device->fence);
         VK_CHECK(src->device->device.waitForFences({ src->device->fence }, true, UINT64_MAX),
-                 "vk_buffer_read_2d uma waitForFences");
+                 "vk_buffer_read_2d uma waitForFences", src->device);
         src->device->device.resetFences({ src->device->fence });
         ggml_vk_queue_command_pools_cleanup(src->device);
 
@@ -8442,7 +8599,7 @@ static void ggml_vk_buffer_read_2d(vk_buffer& src, size_t offset, void * dst, si
         ggml_vk_ctx_end(subctx);
 
         ggml_vk_submit(subctx, src->device->fence);
-        VK_CHECK(src->device->device.waitForFences({ src->device->fence }, true, UINT64_MAX), "vk_buffer_read_2d waitForFences");
+        VK_CHECK(src->device->device.waitForFences({ src->device->fence }, true, UINT64_MAX), "vk_buffer_read_2d waitForFences", src->device);
         src->device->device.resetFences({ src->device->fence });
         ggml_vk_queue_command_pools_cleanup(src->device);
 
@@ -8477,7 +8634,7 @@ static void ggml_vk_buffer_copy(vk_buffer& dst, size_t dst_offset, vk_buffer& sr
         ggml_vk_buffer_copy_async(subctx, dst, dst_offset, src, src_offset, size);
         ggml_vk_ctx_end(subctx);
         ggml_vk_submit(subctx, src->device->fence);
-        VK_CHECK(src->device->device.waitForFences({ src->device->fence }, true, UINT64_MAX), "vk_buffer_copy waitForFences");
+        VK_CHECK(src->device->device.waitForFences({ src->device->fence }, true, UINT64_MAX), "vk_buffer_copy waitForFences", src->device);
         src->device->device.resetFences({ src->device->fence });
         ggml_vk_queue_command_pools_cleanup(src->device);
     } else {
@@ -8521,7 +8678,7 @@ static void ggml_vk_buffer_memset(vk_buffer& dst, size_t offset, uint32_t c, siz
     ggml_vk_ctx_end(subctx);
 
     ggml_vk_submit(subctx, dst->device->fence);
-    VK_CHECK(dst->device->device.waitForFences({ dst->device->fence }, true, UINT64_MAX), "vk_memset waitForFences");
+    VK_CHECK(dst->device->device.waitForFences({ dst->device->fence }, true, UINT64_MAX), "vk_memset waitForFences", dst->device);
     dst->device->device.resetFences({ dst->device->fence });
     ggml_vk_queue_command_pools_cleanup(dst->device);
 }
@@ -11392,6 +11549,11 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const
             return ctx->device->pipeline_rwkv_wkv7_f32;
         }
         return nullptr;
+    case GGML_OP_GATED_LINEAR_ATTN:
+        if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
+            return ctx->device->pipeline_gated_linear_attn_f32;
+        }
+        return nullptr;
     case GGML_OP_GATED_DELTA_NET:
         if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
             const uint32_t S_v = dst->src[2]->ne[0];
@@ -12422,6 +12584,41 @@ static void ggml_vk_rwkv_wkv7(ggml_backend_vk_context * ctx, vk_context& subctx,
     );
 }
 
+static void ggml_vk_gated_linear_attn(ggml_backend_vk_context * ctx, vk_context& subctx, ggml_tensor * dst) {
+    const size_t seq_length = dst->src[0]->ne[2];
+    const size_t n_embed    = dst->ne[0];
+    const size_t n_heads    = dst->src[0]->ne[1];
+    const size_t n_seqs     = dst->src[4]->ne[1];
+
+    float scale;
+    memcpy(&scale, dst->op_params, sizeof(float));
+
+    GGML_ASSERT(dst->buffer != nullptr);
+
+    vk_pipeline pipeline = ggml_vk_op_get_pipeline(ctx, dst->src[0], dst->src[1], dst->src[2], dst, dst->op);
+    GGML_ASSERT(pipeline != nullptr);
+
+    ggml_pipeline_request_descriptor_sets(ctx, pipeline, 1);
+
+    vk_subbuffer dst_buf = ggml_vk_tensor_subbuffer(ctx, dst);
+    vk_subbuffer src_buf[5] = {};
+    for (int i = 0; i < 5; i++) {
+        src_buf[i] = ggml_vk_tensor_subbuffer(ctx, dst->src[i]);
+    }
+
+    const vk_op_gated_linear_attn_push_constants pc = {
+        (uint32_t)n_seqs,
+        (uint32_t)seq_length,
+        (uint32_t)n_embed,
+        (uint32_t)n_heads,
+        scale,
+    };
+
+    ggml_vk_dispatch_pipeline(ctx, subctx, pipeline,
+        {src_buf[0], src_buf[1], src_buf[2], src_buf[3], src_buf[4], dst_buf},
+        pc, { (uint32_t)(n_seqs * n_heads), 1, 1 });
+}
+
 static void ggml_vk_gated_delta_net(ggml_backend_vk_context * ctx, vk_context& subctx, ggml_tensor * dst) {
     const ggml_tensor * src_q     = dst->src[0];
     const ggml_tensor * src_v     = dst->src[2];
@@ -14216,7 +14413,7 @@ static void ggml_vk_test_matmul(ggml_backend_vk_context * ctx, size_t m, size_t
 
     auto begin = std::chrono::high_resolution_clock::now();
     ggml_vk_submit(subctx, ctx->fence);
-    VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_matmul waitForFences");
+    VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_matmul waitForFences", ctx->device);
     ctx->device->device.resetFences({ ctx->fence });
     ggml_vk_queue_command_pools_cleanup(ctx->device);
 
@@ -14418,7 +14615,7 @@ static void ggml_vk_test_dequant(ggml_backend_vk_context * ctx, size_t ne, ggml_
     auto begin = std::chrono::high_resolution_clock::now();
 
     ggml_vk_submit(subctx, ctx->fence);
-    VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_dequant waitForFences");
+    VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_dequant waitForFences", ctx->device);
     ctx->device->device.resetFences({ ctx->fence });
     ggml_vk_queue_command_pools_cleanup(ctx->device);
 
@@ -14704,7 +14901,7 @@ static void ggml_vk_test_dequant_matmul(ggml_backend_vk_context * ctx, size_t m,
     auto begin = std::chrono::high_resolution_clock::now();
 
     ggml_vk_submit(subctx, ctx->fence);
-    VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_dequant waitForFences");
+    VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_dequant waitForFences", ctx->device);
     ctx->device->device.resetFences({ ctx->fence });
     ggml_vk_queue_command_pools_cleanup(ctx->device);
 
@@ -15421,6 +15618,11 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgr
 
         break;
 
+    case GGML_OP_GATED_LINEAR_ATTN:
+        ggml_vk_gated_linear_attn(ctx, compute_ctx, node);
+
+        break;
+
     case GGML_OP_GATED_DELTA_NET:
         ggml_vk_gated_delta_net(ctx, compute_ctx, node);
 
@@ -15498,7 +15700,9 @@ static void ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_cgraph *
             memset(mset.dst, mset.val, mset.n);
         }
 
-        if (almost_ready && !ctx->almost_ready_fence_pending) {
+        if (ctx->device->serialize_submissions) {
+            ggml_vk_submit(subctx, ctx->fence);
+        } else if (almost_ready && !ctx->almost_ready_fence_pending) {
             ggml_vk_submit(subctx, ctx->almost_ready_fence);
             ctx->almost_ready_fence_pending = true;
         } else {
@@ -16109,12 +16313,20 @@ static void ggml_vk_synchronize(ggml_backend_vk_context * ctx) {
             memcpy(cpy.dst, cpy.src, cpy.n);
         }
 
-        ggml_vk_submit(compute_ctx, {});
+        if (ctx->device->serialize_submissions) {
+            ggml_vk_submit(compute_ctx, ctx->fence);
+            VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "synchronize waitForFences", ctx->device);
+            ctx->device->device.resetFences({ ctx->fence });
+        } else {
+            ggml_vk_submit(compute_ctx, {});
+        }
         ctx->submit_pending = true;
     }
 
     if (ctx->submit_pending) {
-        if (ctx->device->async_use_transfer_queue && ctx->transfer_semaphore_last_submitted < ctx->transfer_semaphore.value) {
+        if (ctx->device->serialize_submissions) {
+            ctx->submit_pending = false;
+        } else if (ctx->device->async_use_transfer_queue && ctx->transfer_semaphore_last_submitted < ctx->transfer_semaphore.value) {
             vk::TimelineSemaphoreSubmitInfo tl_info{
                 1, &ctx->transfer_semaphore.value,
                 0, nullptr,
@@ -16131,7 +16343,9 @@ static void ggml_vk_synchronize(ggml_backend_vk_context * ctx) {
         } else {
             ctx->device->compute_queue->handle->submit({}, ctx->fence);
         }
-        ggml_vk_wait_for_fence(ctx);
+        if (!ctx->device->serialize_submissions) {
+            ggml_vk_wait_for_fence(ctx);
+        }
         ctx->submit_pending = false;
         if (cmd_buf) {
             cmd_buf->in_use = false;
@@ -16703,6 +16917,10 @@ static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cg
     VK_LOG_DEBUG("ggml_backend_vk_graph_compute(" << cgraph->n_nodes << " nodes)");
     ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context;
 
+    ctx->device->diag_cgraph = nullptr;
+    ctx->device->diag_prev_start = -1;
+    ctx->device->diag_prev_end = -1;
+
     if (vk_instance.debug_utils_support) {
         vk::DebugUtilsLabelEXT dul = {};
         dul.pLabelName = "ggml_backend_vk_graph_compute";
@@ -16794,6 +17012,36 @@ static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cg
     }
     uint64_t flops_per_submit = std::min(flops_cap, ctx->last_total_flops / 40u);
 
+    auto const submit_after = [&](int start, int end) {
+        if (ctx->device->serialize_submissions) {
+            try {
+                auto res = ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX);
+                if (res != vk::Result::eSuccess) {
+                    GGML_LOG_ERROR("ggml_vulkan: waitForFences error during serialized submission\n");
+                    throw vk::SystemError(vk::make_error_code(res), "ggml_vulkan: waitForFences during serialized submission");
+                }
+            } catch (vk::DeviceLostError &) {
+                ggml_vk_print_device_fault_info(ctx->device);
+                GGML_LOG_ERROR("ggml_vulkan: device lost on %s waiting for submission (nodes %d to %d):\n",
+                        ctx->device->name.c_str(), start, end);
+                ggml_vk_print_node_list(cgraph, start, end);
+                throw;
+            }
+            ctx->device->device.resetFences({ ctx->fence });
+            ctx->submit_pending = false;
+            ctx->device->diag_cgraph = cgraph;
+            ctx->device->diag_prev_start = start;
+            ctx->device->diag_prev_end = end;
+        }
+        first_node_in_batch = true;
+        submitted_nodes = 0;
+        batch_flops = 0;
+        if (submit_count < 3) {
+            flops_per_submit *= 2;
+        }
+        submit_count++;
+    };
+
     for (int i = 0; i < cgraph->n_nodes; i++) {
         if (first_node_in_batch) {
             submit_node_idx = i;
@@ -16801,8 +17049,20 @@ static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cg
 
         {
             auto node_flops = ggml_vk_get_node_flops(cgraph->nodes[i]);
-            batch_flops += node_flops;
             total_flops += node_flops;
+
+            // Flush the current batch before recording a node that would push it over the flop threshold
+            if (flops_per_submit != 0 && submitted_nodes > 0 && batch_flops + node_flops >= flops_per_submit) {
+                vk_context flush_ctx = ggml_vk_get_compute_ctx(ctx);
+                ggml_vk_ctx_end(flush_ctx);
+                flush_ctx->exit_tensor_idx = -1;
+                ctx->compute_ctx.reset();
+                ggml_vk_compute_forward(ctx, cgraph, cgraph->nodes[submit_node_idx], submit_node_idx, false);
+                submit_after(submit_node_idx, i - 1);
+                submit_node_idx = i;
+            }
+
+            batch_flops += node_flops;
         }
 
         // op_srcs_fused_elementwise indicates whether an op's srcs all contribute to
@@ -17056,13 +17316,7 @@ static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cg
         }
 
         if (submit && enqueued) {
-            first_node_in_batch = true;
-            submitted_nodes = 0;
-            batch_flops = 0;
-            if (submit_count < 3) {
-                flops_per_submit *= 2;
-            }
-            submit_count++;
+            submit_after(submit_node_idx, i + (int)ctx->num_additional_fused_ops);
         }
         i += ctx->num_additional_fused_ops;
         ctx->num_additional_fused_ops = 0;
@@ -17078,13 +17332,13 @@ static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cg
         ggml_vk_ctx_end(compute_ctx);
 
         ggml_vk_submit(compute_ctx, ctx->device->fence);
-        VK_CHECK(ctx->device->device.waitForFences({ ctx->device->fence }, true, UINT64_MAX), "GGML_VULKAN_PERF waitForFences");
+        VK_CHECK(ctx->device->device.waitForFences({ ctx->device->fence }, true, UINT64_MAX), "GGML_VULKAN_PERF waitForFences", ctx->device);
         ctx->device->device.resetFences({ ctx->device->fence });
         ctx->compute_ctx.reset();
 
         // Get the results and pass them to the logger
         std::vector<uint64_t> timestamps(cgraph->n_nodes + 1);
-        VK_CHECK(ctx->device->device.getQueryPoolResults(ctx->query_pool, 0, ctx->query_idx, (cgraph->n_nodes + 1)*sizeof(uint64_t), timestamps.data(), sizeof(uint64_t), vk::QueryResultFlagBits::e64 | vk::QueryResultFlagBits::eWait), "get timestamp results");
+        VK_CHECK(ctx->device->device.getQueryPoolResults(ctx->query_pool, 0, ctx->query_idx, (cgraph->n_nodes + 1)*sizeof(uint64_t), timestamps.data(), sizeof(uint64_t), vk::QueryResultFlagBits::e64 | vk::QueryResultFlagBits::eWait), "get timestamp results", ctx->device);
         if (!vk_perf_logger_concurrent) {
             // Log each op separately
             for (int i = 1; i < ctx->query_idx; i++) {
@@ -18128,6 +18382,9 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
         case GGML_OP_RWKV_WKV6:
         case GGML_OP_RWKV_WKV7:
             return true; // all inputs are contiguous, see ggml.c
+        case GGML_OP_GATED_LINEAR_ATTN:
+            // the shader block size is hardcoded to head_size 64
+            return op->src[0]->type == GGML_TYPE_F32 && op->type == GGML_TYPE_F32 && op->src[0]->ne[0] == 64;
         case GGML_OP_GATED_DELTA_NET:
             {
                 const uint32_t S_v = op->src[2]->ne[0];
@@ -18308,7 +18565,7 @@ static void ggml_backend_vk_device_event_synchronize(ggml_backend_dev_t dev, ggm
         vk::Semaphore sem = vkev->tl_semaphore.s;
         uint64_t val = vkev->tl_semaphore.value;
         vk::SemaphoreWaitInfo swi{vk::SemaphoreWaitFlags{}, sem, val};
-        VK_CHECK(device->device.waitSemaphores(swi, UINT64_MAX), "event_synchronize");
+        VK_CHECK(device->device.waitSemaphores(swi, UINT64_MAX), "event_synchronize", device);
 
         // Reset and move submitted events
         for (auto& event : vkev->events_submitted) {
@@ -19117,6 +19374,10 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_cgraph *
         } else if (tensor->op == GGML_OP_RWKV_WKV7) {
             tensor_clone = ggml_rwkv_wkv7(ggml_ctx, src_clone[0], src_clone[1], src_clone[2], src_clone[3],
             src_clone[4], src_clone[5], src_clone[6]);
+        } else if (tensor->op == GGML_OP_GATED_LINEAR_ATTN) {
+            const float * op_params = (const float *)tensor->op_params;
+            tensor_clone = ggml_gated_linear_attn(ggml_ctx, src_clone[0], src_clone[1],
+            src_clone[2], src_clone[3], src_clone[4], op_params[0]);
         } else if (tensor->op == GGML_OP_GATED_DELTA_NET) {
             tensor_clone = ggml_gated_delta_net(ggml_ctx, src_clone[0], src_clone[1],
             src_clone[2], src_clone[3], src_clone[4], src_clone[5],

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

@@ -0,0 +1,82 @@
+#version 450
+
+#extension GL_EXT_control_flow_attributes : require
+
+#define BLOCK_SIZE 64
+layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in;
+
+layout(push_constant) uniform Parameters {
+    uint B;
+    uint T;
+    uint C;
+    uint H;
+    float scale;
+};
+
+layout(binding = 0) readonly buffer KBuf { A_TYPE k[]; };
+layout(binding = 1) readonly buffer VBuf { A_TYPE v[]; };
+layout(binding = 2) readonly buffer QBuf { A_TYPE q[]; };
+layout(binding = 3) readonly buffer GBuf { A_TYPE g[]; };
+layout(binding = 4) readonly buffer StateBuf { A_TYPE state_in[]; };
+layout(binding = 5) buffer DstBuf { A_TYPE dst[]; };
+
+shared A_TYPE _k[BLOCK_SIZE], _q[BLOCK_SIZE], _g[BLOCK_SIZE];
+
+void main() {
+    const uint head_size = BLOCK_SIZE;
+    const uint batch_id = gl_WorkGroupID.x / H;
+    const uint head_id = gl_WorkGroupID.x % H;
+    const uint tid = gl_LocalInvocationID.x;
+
+    const uint state_size = C * head_size;
+    const uint n_seq_tokens = T / B;
+
+    if (batch_id >= B || head_id >= H) {
+        return;
+    }
+
+    // state[i] holds column tid of this head's state matrix: S[i][tid]
+    A_TYPE state[BLOCK_SIZE];
+    [[unroll]] for (uint i = 0; i < head_size; i++) {
+        state[i] = state_in[batch_id * state_size + head_id * head_size * head_size
+                          + i * head_size + tid];
+    }
+
+    const uint start_t = batch_id * n_seq_tokens * C + head_id * head_size + tid;
+    const uint end_t = (batch_id + 1) * n_seq_tokens * C + head_id * head_size + tid;
+
+    for (uint t = start_t; t < end_t; t += C) {
+        barrier();
+        _k[tid] = k[t];
+        _q[tid] = q[t];
+        _g[tid] = g[t];
+        barrier();
+
+        const A_TYPE v_val = v[t];
+        A_TYPE y = 0.0;
+
+        [[unroll]] for (uint i = 0; i < head_size; i += 4) {
+            vec4 k_vec = vec4(_k[i], _k[i+1], _k[i+2], _k[i+3]);
+            vec4 q_vec = vec4(_q[i], _q[i+1], _q[i+2], _q[i+3]);
+            vec4 g_vec = vec4(_g[i], _g[i+1], _g[i+2], _g[i+3]);
+            vec4 s_vec = vec4(state[i], state[i+1], state[i+2], state[i+3]);
+
+            vec4 kv = k_vec * v_val;
+
+            s_vec = s_vec * g_vec + kv;
+            y += dot(q_vec, s_vec);
+
+            state[i]   = s_vec.x;
+            state[i+1] = s_vec.y;
+            state[i+2] = s_vec.z;
+            state[i+3] = s_vec.w;
+        }
+
+        dst[t] = y * scale;
+    }
+
+    [[unroll]] for (uint i = 0; i < head_size; i++) {
+        dst[T * C + batch_id * state_size + head_id * head_size * head_size
+            + i * head_size + tid] = state[i];
+    }
+}

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

@@ -1057,6 +1057,8 @@ void process_shaders() {
 
     string_to_spv("rwkv_wkv6_f32", "wkv6.comp", merge_maps(base_dict, {{"A_TYPE", "float"}}));
 
+    string_to_spv("gated_linear_attn_f32", "gla.comp", merge_maps(base_dict, {{"A_TYPE", "float"}}));
+
     string_to_spv("rwkv_wkv7_f32", "wkv7.comp", merge_maps(base_dict, {{"A_TYPE", "float"}}));
 
     string_to_spv("gated_delta_net_f32", "gated_delta_net.comp", merge_maps(base_dict, {{"FLOAT_TYPE", "float"}, {"USE_SUBGROUP_ADD", "1"}, {"USE_SUBGROUP_CLUSTERED", "1"}}));

ggml/src/ggml.c

@@ -7200,6 +7200,10 @@ void ggml_build_forward_expand(struct ggml_cgraph * cgraph, struct ggml_tensor *
     ggml_build_forward_impl(cgraph, tensor, true, true);
 }
 
+void ggml_build_forward_order(struct ggml_cgraph * cgraph, struct ggml_tensor * tensor) {
+    ggml_build_forward_impl(cgraph, tensor, true, false);
+}
+
 void ggml_build_backward_expand(
         struct ggml_context *  ctx,
         struct ggml_cgraph  *  cgraph,

gguf-py/gguf/constants.py

@@ -11,6 +11,7 @@ GGUF_MAGIC             = 0x46554747  # "GGUF"
 GGUF_VERSION           = 3
 GGUF_DEFAULT_ALIGNMENT = 32
 GGML_QUANT_VERSION     = 2  # GGML_QNT_VERSION from ggml.h
+GGML_MAX_DIMS          = 4  # GGML_MAX_DIMS from ggml.h
 
 #
 # metadata keys
@@ -322,6 +323,7 @@ class Keys:
         PROJECTOR_TYPE        = "clip.projector_type"
         HAS_VISION_ENCODER    = "clip.has_vision_encoder"
         HAS_AUDIO_ENCODER     = "clip.has_audio_encoder"
+        HAS_GEN_AUDIO_ENCODER = "clip.has_gen_audio_encoder"
         HAS_LLAVA_PROJECTOR   = "clip.has_llava_projector"
 
     class ClipVision:
@@ -396,6 +398,18 @@ class Keys:
             DOWNSAMPLE_RATE = "clip.audio.projector.downsample_rate"
             HEAD_COUNT      = "clip.audio.projector.head_count"
 
+    class ClipGenAudio:
+        PROJECTOR_TYPE      = "clip.gen.audio.projector_type" # for mixed modality models
+        EMBEDDING_LENGTH    = "clip.gen.audio.embedding_length"
+        FEED_FORWARD_LENGTH = "clip.gen.audio.feed_forward_length"
+        BLOCK_COUNT         = "clip.gen.audio.block_count"
+        PROJECTION_DIM      = "clip.gen.audio.projection_dim"
+
+        class Attention:
+            HEAD_COUNT      = "clip.gen.audio.attention.head_count"
+            HEAD_COUNT_KV   = "clip.gen.audio.attention.head_count_kv"
+            LAYERNORM_EPS   = "clip.gen.audio.attention.layer_norm_epsilon"
+
     class Diffusion:
         SHIFT_LOGITS        = "diffusion.shift_logits"
 
@@ -557,6 +571,7 @@ class MODEL_ARCH(IntEnum):
     TALKIE           = auto()
     MELLUM           = auto()
     NANBEIGE         = auto()
+    QWEN3TTS         = auto()
 
 
 class VISION_PROJECTOR_TYPE(IntEnum):
@@ -957,6 +972,65 @@ class MODEL_TENSOR(IntEnum):
     A_ENC_DOWNSAMPLE_CONV = auto() # mimo-audio-tokenizer: post-transformer downsample conv
     A_ENC_DOWNSAMPLE_NORM = auto() # mimo-audio-tokenizer: post-transformer downsample norm
     A_ENC_RVQ_CODEBOOK    = auto() # mimo-audio-tokenizer: residual vector quantizer codebook, per quantizer index
+    A_ENC_CONV_RES2       = auto() # qwen3tts
+    A_ENC_SE_CONV1        = auto() # qwen3tts
+    A_ENC_SE_CONV2        = auto() # qwen3tts
+    A_ENC_ASP_ATTN        = auto() # qwen3tts
+    A_ENC_ASP_TDNN        = auto() # qwen3tts
+    # qwen3tts code_predictor: predicts the remaining RVQ codebooks
+    A_GEN_CODE_PROJ_IN     = auto() # small_to_mtp_projection
+    A_GEN_CODE_EMBD        = auto() # per-codebook embedding table, merged 3D [n_codebooks, vocab, dim]
+    A_GEN_CODE_HEAD        = auto() # per-codebook output head, merged 3D [n_codebooks, vocab, dim]
+    A_GEN_CODE_OUT_EMBD    = auto() # codebook-0 embedding, re-fed into the talker backbone (talker.model.codec_embedding)
+    A_GEN_CODE_ATTN_NORM   = auto()
+    A_GEN_CODE_ATTN_Q      = auto()
+    A_GEN_CODE_ATTN_Q_NORM = auto()
+    A_GEN_CODE_ATTN_K      = auto()
+    A_GEN_CODE_ATTN_K_NORM = auto()
+    A_GEN_CODE_ATTN_V      = auto()
+    A_GEN_CODE_ATTN_OUT    = auto()
+    A_GEN_CODE_FFN_NORM    = auto()
+    A_GEN_CODE_FFN_GATE    = auto()
+    A_GEN_CODE_FFN_UP      = auto()
+    A_GEN_CODE_FFN_DOWN    = auto()
+    A_GEN_CODE_OUTPUT_NORM = auto()
+    # qwen3tts code2wav: RVQ codes -> raw PCM
+    A_GEN_WAV_QUANT_FIRST_IN       = auto() # semantic RVQ, in_proj (1x1 conv, loaded as 2D)
+    A_GEN_WAV_QUANT_FIRST_OUT      = auto() # semantic RVQ, out_proj
+    A_GEN_WAV_QUANT_FIRST_CB = auto() # semantic RVQ codebook (1 layer), folded from embedding_sum/cluster_usage
+    A_GEN_WAV_QUANT_REST_IN        = auto() # acoustic RVQ, in_proj
+    A_GEN_WAV_QUANT_REST_OUT       = auto() # acoustic RVQ, out_proj
+    A_GEN_WAV_QUANT_REST_CB  = auto() # acoustic RVQ codebooks, merged 3D [15, vocab, dim]
+    A_GEN_WAV_PRE_CONV             = auto()
+    A_GEN_WAV_TFM_IN_PROJ          = auto()
+    A_GEN_WAV_TFM_OUT_PROJ         = auto()
+    A_GEN_WAV_TFM_OUTPUT_NORM      = auto()
+    A_GEN_WAV_TFM_ATTN_NORM        = auto()
+    A_GEN_WAV_TFM_ATTN_Q           = auto()
+    A_GEN_WAV_TFM_ATTN_K           = auto()
+    A_GEN_WAV_TFM_ATTN_V           = auto()
+    A_GEN_WAV_TFM_ATTN_OUT         = auto()
+    A_GEN_WAV_TFM_ATTN_SCALE       = auto() # layer scale (gamma) on the attn output
+    A_GEN_WAV_TFM_FFN_NORM         = auto()
+    A_GEN_WAV_TFM_FFN_GATE         = auto()
+    A_GEN_WAV_TFM_FFN_UP           = auto()
+    A_GEN_WAV_TFM_FFN_DOWN         = auto()
+    A_GEN_WAV_TFM_FFN_SCALE        = auto() # layer scale (gamma) on the FFN output
+    A_GEN_WAV_UP_CONV              = auto() # causal ConvTranspose1d, 2x upsample
+    A_GEN_WAV_UP_DWCONV            = auto() # ConvNeXt depthwise conv
+    A_GEN_WAV_UP_NORM              = auto() # ConvNeXt LayerNorm
+    A_GEN_WAV_UP_PW1               = auto() # ConvNeXt pointwise conv 1 (expand)
+    A_GEN_WAV_UP_PW2               = auto() # ConvNeXt pointwise conv 2 (project)
+    A_GEN_WAV_UP_GAMMA             = auto() # ConvNeXt layer scale
+    A_GEN_WAV_DAC_ENTRY            = auto() # DAC conv_pre
+    A_GEN_WAV_DAC_UP_SNAKE         = auto() # DAC per-block SnakeBeta before the upsample conv
+    A_GEN_WAV_DAC_UP_CONV          = auto() # DAC per-block causal ConvTranspose1d
+    A_GEN_WAV_DAC_RES_ACT1         = auto() # DAC residual unit, SnakeBeta before conv1
+    A_GEN_WAV_DAC_RES_CONV1        = auto() # DAC residual unit, dilated causal conv
+    A_GEN_WAV_DAC_RES_ACT2         = auto() # DAC residual unit, SnakeBeta before conv2
+    A_GEN_WAV_DAC_RES_CONV2        = auto() # DAC residual unit, pointwise causal conv
+    A_GEN_WAV_DAC_POST_SNAKE       = auto() # DAC final SnakeBeta
+    A_GEN_WAV_DAC_POST_CONV        = auto() # DAC conv_post -> 1-channel PCM
     A_MMPROJ              = auto()
     A_MMPROJ_FC           = auto()
     A_MM_NORM_PRE         = auto()
@@ -1169,6 +1243,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
     MODEL_ARCH.TALKIE:           "talkie",
     MODEL_ARCH.MELLUM:           "mellum",
     MODEL_ARCH.NANBEIGE:         "nanbeige",
+    MODEL_ARCH.QWEN3TTS:         "qwen3tts",
 }
 
 VISION_PROJECTOR_TYPE_NAMES: dict[VISION_PROJECTOR_TYPE, str] = {
@@ -1566,6 +1641,63 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
     MODEL_TENSOR.A_ENC_DOWNSAMPLE_CONV:     "a.downsample.conv",
     MODEL_TENSOR.A_ENC_DOWNSAMPLE_NORM:     "a.downsample.norm",
     MODEL_TENSOR.A_ENC_RVQ_CODEBOOK:        "a.rvq.codebook",
+    MODEL_TENSOR.A_ENC_CONV_RES2:           "a.blk.{bid}.res2.{xid}",
+    MODEL_TENSOR.A_ENC_SE_CONV1:            "a.blk.{bid}.se_conv1",
+    MODEL_TENSOR.A_ENC_SE_CONV2:            "a.blk.{bid}.se_conv2",
+    MODEL_TENSOR.A_ENC_ASP_ATTN:            "a.asp_attn",
+    MODEL_TENSOR.A_ENC_ASP_TDNN:            "a.asp_tdnn",
+    MODEL_TENSOR.A_GEN_CODE_PROJ_IN:        "a.gen.code.proj_in",
+    MODEL_TENSOR.A_GEN_CODE_EMBD:           "a.gen.code.embd",
+    MODEL_TENSOR.A_GEN_CODE_HEAD:           "a.gen.code.head",
+    MODEL_TENSOR.A_GEN_CODE_OUT_EMBD:       "a.gen.code.out_embd",
+    MODEL_TENSOR.A_GEN_CODE_ATTN_NORM:      "a.gen.code.blk.{bid}.ln1", # reuses the generic clip.cpp block loader (TN_LN_1)
+    MODEL_TENSOR.A_GEN_CODE_ATTN_Q:         "a.gen.code.blk.{bid}.attn_q",
+    MODEL_TENSOR.A_GEN_CODE_ATTN_Q_NORM:    "a.gen.code.blk.{bid}.attn_q_norm",
+    MODEL_TENSOR.A_GEN_CODE_ATTN_K:         "a.gen.code.blk.{bid}.attn_k",
+    MODEL_TENSOR.A_GEN_CODE_ATTN_K_NORM:    "a.gen.code.blk.{bid}.attn_k_norm",
+    MODEL_TENSOR.A_GEN_CODE_ATTN_V:         "a.gen.code.blk.{bid}.attn_v",
+    MODEL_TENSOR.A_GEN_CODE_ATTN_OUT:       "a.gen.code.blk.{bid}.attn_out",
+    MODEL_TENSOR.A_GEN_CODE_FFN_NORM:       "a.gen.code.blk.{bid}.ln2", # reuses the generic clip.cpp block loader (TN_LN_2)
+    MODEL_TENSOR.A_GEN_CODE_FFN_GATE:       "a.gen.code.blk.{bid}.ffn_gate",
+    MODEL_TENSOR.A_GEN_CODE_FFN_UP:         "a.gen.code.blk.{bid}.ffn_up",
+    MODEL_TENSOR.A_GEN_CODE_FFN_DOWN:       "a.gen.code.blk.{bid}.ffn_down",
+    MODEL_TENSOR.A_GEN_CODE_OUTPUT_NORM:    "a.gen.code.output_norm",
+    MODEL_TENSOR.A_GEN_WAV_QUANT_FIRST_IN:  "a.gen.wav.quant.first.in_proj",
+    MODEL_TENSOR.A_GEN_WAV_QUANT_FIRST_OUT: "a.gen.wav.quant.first.out_proj",
+    MODEL_TENSOR.A_GEN_WAV_QUANT_FIRST_CB:  "a.gen.wav.quant.first.codebook",
+    MODEL_TENSOR.A_GEN_WAV_QUANT_REST_IN:   "a.gen.wav.quant.rest.in_proj",
+    MODEL_TENSOR.A_GEN_WAV_QUANT_REST_OUT:  "a.gen.wav.quant.rest.out_proj",
+    MODEL_TENSOR.A_GEN_WAV_QUANT_REST_CB:   "a.gen.wav.quant.rest.codebook",
+    MODEL_TENSOR.A_GEN_WAV_PRE_CONV:        "a.gen.wav.pre_conv",
+    MODEL_TENSOR.A_GEN_WAV_TFM_IN_PROJ:     "a.gen.wav.tfm.in_proj",
+    MODEL_TENSOR.A_GEN_WAV_TFM_OUT_PROJ:    "a.gen.wav.tfm.out_proj",
+    MODEL_TENSOR.A_GEN_WAV_TFM_OUTPUT_NORM: "a.gen.wav.tfm.output_norm",
+    MODEL_TENSOR.A_GEN_WAV_TFM_ATTN_NORM:   "a.gen.wav.tfm.blk.{bid}.ln1",
+    MODEL_TENSOR.A_GEN_WAV_TFM_ATTN_Q:      "a.gen.wav.tfm.blk.{bid}.attn_q",
+    MODEL_TENSOR.A_GEN_WAV_TFM_ATTN_K:      "a.gen.wav.tfm.blk.{bid}.attn_k",
+    MODEL_TENSOR.A_GEN_WAV_TFM_ATTN_V:      "a.gen.wav.tfm.blk.{bid}.attn_v",
+    MODEL_TENSOR.A_GEN_WAV_TFM_ATTN_OUT:    "a.gen.wav.tfm.blk.{bid}.attn_out",
+    MODEL_TENSOR.A_GEN_WAV_TFM_ATTN_SCALE:  "a.gen.wav.tfm.blk.{bid}.ls1",
+    MODEL_TENSOR.A_GEN_WAV_TFM_FFN_NORM:    "a.gen.wav.tfm.blk.{bid}.ln2",
+    MODEL_TENSOR.A_GEN_WAV_TFM_FFN_GATE:    "a.gen.wav.tfm.blk.{bid}.ffn_gate",
+    MODEL_TENSOR.A_GEN_WAV_TFM_FFN_UP:      "a.gen.wav.tfm.blk.{bid}.ffn_up",
+    MODEL_TENSOR.A_GEN_WAV_TFM_FFN_DOWN:    "a.gen.wav.tfm.blk.{bid}.ffn_down",
+    MODEL_TENSOR.A_GEN_WAV_TFM_FFN_SCALE:   "a.gen.wav.tfm.blk.{bid}.ls2",
+    MODEL_TENSOR.A_GEN_WAV_UP_CONV:         "a.gen.wav.up.blk.{bid}.conv",
+    MODEL_TENSOR.A_GEN_WAV_UP_DWCONV:       "a.gen.wav.up.blk.{bid}.dwconv",
+    MODEL_TENSOR.A_GEN_WAV_UP_NORM:         "a.gen.wav.up.blk.{bid}.norm",
+    MODEL_TENSOR.A_GEN_WAV_UP_PW1:          "a.gen.wav.up.blk.{bid}.pw1",
+    MODEL_TENSOR.A_GEN_WAV_UP_PW2:          "a.gen.wav.up.blk.{bid}.pw2",
+    MODEL_TENSOR.A_GEN_WAV_UP_GAMMA:        "a.gen.wav.up.blk.{bid}.gamma",
+    MODEL_TENSOR.A_GEN_WAV_DAC_ENTRY:       "a.gen.wav.dac.entry",
+    MODEL_TENSOR.A_GEN_WAV_DAC_UP_SNAKE:    "a.gen.wav.dac.blk.{bid}.snake",
+    MODEL_TENSOR.A_GEN_WAV_DAC_UP_CONV:     "a.gen.wav.dac.blk.{bid}.conv",
+    MODEL_TENSOR.A_GEN_WAV_DAC_RES_ACT1:    "a.gen.wav.dac.blk.{bid}.res.{xid}.act1",
+    MODEL_TENSOR.A_GEN_WAV_DAC_RES_CONV1:   "a.gen.wav.dac.blk.{bid}.res.{xid}.conv1",
+    MODEL_TENSOR.A_GEN_WAV_DAC_RES_ACT2:    "a.gen.wav.dac.blk.{bid}.res.{xid}.act2",
+    MODEL_TENSOR.A_GEN_WAV_DAC_RES_CONV2:   "a.gen.wav.dac.blk.{bid}.res.{xid}.conv2",
+    MODEL_TENSOR.A_GEN_WAV_DAC_POST_SNAKE:  "a.gen.wav.dac.post_snake",
+    MODEL_TENSOR.A_GEN_WAV_DAC_POST_CONV:   "a.gen.wav.dac.post_conv",
     MODEL_TENSOR.A_MMPROJ:                  "mm.a.mlp.{bid}",
     MODEL_TENSOR.A_MMPROJ_FC:               "mm.a.fc",
     MODEL_TENSOR.A_MM_NORM_PRE:             "mm.a.norm_pre",
@@ -1820,6 +1952,63 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.A_ENC_CONV_NORM,
         MODEL_TENSOR.A_ENC_CONV_PW1,
         MODEL_TENSOR.A_ENC_CONV_PW2,
+        MODEL_TENSOR.A_ENC_CONV_RES2,
+        MODEL_TENSOR.A_ENC_SE_CONV1,
+        MODEL_TENSOR.A_ENC_SE_CONV2,
+        MODEL_TENSOR.A_ENC_ASP_ATTN,
+        MODEL_TENSOR.A_ENC_ASP_TDNN,
+        MODEL_TENSOR.A_GEN_CODE_PROJ_IN,
+        MODEL_TENSOR.A_GEN_CODE_EMBD,
+        MODEL_TENSOR.A_GEN_CODE_HEAD,
+        MODEL_TENSOR.A_GEN_CODE_OUT_EMBD,
+        MODEL_TENSOR.A_GEN_CODE_ATTN_NORM,
+        MODEL_TENSOR.A_GEN_CODE_ATTN_Q,
+        MODEL_TENSOR.A_GEN_CODE_ATTN_Q_NORM,
+        MODEL_TENSOR.A_GEN_CODE_ATTN_K,
+        MODEL_TENSOR.A_GEN_CODE_ATTN_K_NORM,
+        MODEL_TENSOR.A_GEN_CODE_ATTN_V,
+        MODEL_TENSOR.A_GEN_CODE_ATTN_OUT,
+        MODEL_TENSOR.A_GEN_CODE_FFN_NORM,
+        MODEL_TENSOR.A_GEN_CODE_FFN_GATE,
+        MODEL_TENSOR.A_GEN_CODE_FFN_UP,
+        MODEL_TENSOR.A_GEN_CODE_FFN_DOWN,
+        MODEL_TENSOR.A_GEN_CODE_OUTPUT_NORM,
+        MODEL_TENSOR.A_GEN_WAV_QUANT_FIRST_IN,
+        MODEL_TENSOR.A_GEN_WAV_QUANT_FIRST_OUT,
+        MODEL_TENSOR.A_GEN_WAV_QUANT_FIRST_CB,
+        MODEL_TENSOR.A_GEN_WAV_QUANT_REST_IN,
+        MODEL_TENSOR.A_GEN_WAV_QUANT_REST_OUT,
+        MODEL_TENSOR.A_GEN_WAV_QUANT_REST_CB,
+        MODEL_TENSOR.A_GEN_WAV_PRE_CONV,
+        MODEL_TENSOR.A_GEN_WAV_TFM_IN_PROJ,
+        MODEL_TENSOR.A_GEN_WAV_TFM_OUT_PROJ,
+        MODEL_TENSOR.A_GEN_WAV_TFM_OUTPUT_NORM,
+        MODEL_TENSOR.A_GEN_WAV_TFM_ATTN_NORM,
+        MODEL_TENSOR.A_GEN_WAV_TFM_ATTN_Q,
+        MODEL_TENSOR.A_GEN_WAV_TFM_ATTN_K,
+        MODEL_TENSOR.A_GEN_WAV_TFM_ATTN_V,
+        MODEL_TENSOR.A_GEN_WAV_TFM_ATTN_OUT,
+        MODEL_TENSOR.A_GEN_WAV_TFM_ATTN_SCALE,
+        MODEL_TENSOR.A_GEN_WAV_TFM_FFN_NORM,
+        MODEL_TENSOR.A_GEN_WAV_TFM_FFN_GATE,
+        MODEL_TENSOR.A_GEN_WAV_TFM_FFN_UP,
+        MODEL_TENSOR.A_GEN_WAV_TFM_FFN_DOWN,
+        MODEL_TENSOR.A_GEN_WAV_TFM_FFN_SCALE,
+        MODEL_TENSOR.A_GEN_WAV_UP_CONV,
+        MODEL_TENSOR.A_GEN_WAV_UP_DWCONV,
+        MODEL_TENSOR.A_GEN_WAV_UP_NORM,
+        MODEL_TENSOR.A_GEN_WAV_UP_PW1,
+        MODEL_TENSOR.A_GEN_WAV_UP_PW2,
+        MODEL_TENSOR.A_GEN_WAV_UP_GAMMA,
+        MODEL_TENSOR.A_GEN_WAV_DAC_ENTRY,
+        MODEL_TENSOR.A_GEN_WAV_DAC_UP_SNAKE,
+        MODEL_TENSOR.A_GEN_WAV_DAC_UP_CONV,
+        MODEL_TENSOR.A_GEN_WAV_DAC_RES_ACT1,
+        MODEL_TENSOR.A_GEN_WAV_DAC_RES_CONV1,
+        MODEL_TENSOR.A_GEN_WAV_DAC_RES_ACT2,
+        MODEL_TENSOR.A_GEN_WAV_DAC_RES_CONV2,
+        MODEL_TENSOR.A_GEN_WAV_DAC_POST_SNAKE,
+        MODEL_TENSOR.A_GEN_WAV_DAC_POST_CONV,
         MODEL_TENSOR.A_ENC_CONV_NORM_MEAN,
         MODEL_TENSOR.A_ENC_CONV_NORM_VAR,
         MODEL_TENSOR.A_ENC_MEL_FILTERS,
@@ -4647,6 +4836,22 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.FFN_DOWN,
         MODEL_TENSOR.FFN_UP,
     ],
+    MODEL_ARCH.QWEN3TTS: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_Q,
+        MODEL_TENSOR.ATTN_Q_NORM,
+        MODEL_TENSOR.ATTN_K,
+        MODEL_TENSOR.ATTN_K_NORM,
+        MODEL_TENSOR.ATTN_V,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.FFN_NORM,
+        MODEL_TENSOR.FFN_GATE,
+        MODEL_TENSOR.FFN_DOWN,
+        MODEL_TENSOR.FFN_UP,
+    ],
 }
 
 # tensors that will not be serialized
@@ -4921,6 +5126,8 @@ class VisionProjectorType:
     GLM4V = "glm4v"
     YOUTUVL = "youtuvl"
     NEMOTRON_V2_VL = "nemotron_v2_vl"
+    QWEN3TTS_SPKENC = "qwen3tts_spkenc" # audio: ECAPA-TDNN speaker encoder
+    QWEN3TTS_GEN = "qwen3tts_gen" # audio generation: code_predictor
     HUNYUANVL      = "hunyuanvl"
     PARAKEET       = "parakeet"  # audio
     MINIMAXM3      = "minimax_m3"

gguf-py/gguf/gguf_reader.py

@@ -22,6 +22,7 @@ if __name__ == "__main__":
     sys.path.insert(0, str(Path(__file__).parent.parent))
 
 from gguf.constants import (
+    GGML_MAX_DIMS,
     GGML_QUANT_SIZES,
     GGUF_DEFAULT_ALIGNMENT,
     GGUF_MAGIC,
@@ -266,6 +267,8 @@ class GGUFReader:
         # Get Tensor Dimensions Count
         n_dims = self._get(offs, np.uint32)
         offs += int(n_dims.nbytes)
+        if n_dims[0] > GGML_MAX_DIMS:
+            raise ValueError(f'Tensor dimensions count {n_dims[0]} exceeds GGML_MAX_DIMS ({GGML_MAX_DIMS})')
 
         # Get Tensor Dimension Array
         dims = self._get(offs, np.uint64, n_dims[0])
@@ -326,7 +329,10 @@ class GGUFReader:
                 raise ValueError(f'Found duplicated tensor with name {tensor_name}')
             tensor_names.add(tensor_name)
             ggml_type = GGMLQuantizationType(raw_dtype[0])
-            n_elems = int(np.prod(dims))
+            # use Python ints: np.prod on uint64 wraps silently on overflow
+            n_elems = 1
+            for dim in dims.tolist():
+                n_elems *= int(dim)
             np_dims = tuple(reversed(dims.tolist()))
             block_size, type_size = GGML_QUANT_SIZES[ggml_type]
             n_bytes = n_elems * type_size // block_size

gguf-py/gguf/gguf_writer.py

@@ -280,6 +280,10 @@ class GGUFWriter:
 
         self.kv_data[0][key] = GGUFValue(value=val, type=vtype, sub_type=sub_type)
 
+    def remove_key(self, key: str) -> None:
+        for kv_data in self.kv_data:
+            kv_data.pop(key, None)
+
     def add_uint8(self, key: str, val: int) -> None:
         self.add_key_value(key,val, GGUFValueType.UINT8)
 
@@ -1144,7 +1148,11 @@ class GGUFWriter:
     def add_precompiled_charsmap(self, charsmap: bytes) -> None:
         self.add_array(Keys.Tokenizer.PRECOMPILED_CHARSMAP, charsmap)
 
-    def add_chat_template(self, value: str | Sequence[Mapping[str, str]]) -> None:
+    def add_chat_template(self, value: str | Sequence[Mapping[str, str]] | None) -> None:
+        if value is None:
+            self.remove_key(Keys.Tokenizer.CHAT_TEMPLATE)
+            return
+
         if not isinstance(value, str):
             template_default = None
             template_names = set()
@@ -1199,6 +1207,9 @@ class GGUFWriter:
     def add_clip_has_audio_encoder(self, value: bool) -> None:
         self.add_bool(Keys.Clip.HAS_AUDIO_ENCODER, value)
 
+    def add_clip_has_gen_audio_encoder(self, value: bool) -> None:
+        self.add_bool(Keys.Clip.HAS_GEN_AUDIO_ENCODER, value)
+
     def add_clip_projector_type(self, value: str) -> None:
         self.add_string(Keys.Clip.PROJECTOR_TYPE, value)
 
@@ -1401,6 +1412,32 @@ class GGUFWriter:
     def add_audio_projector_head_count(self, value: int) -> None:
         self.add_uint32(Keys.ClipAudio.Projector.HEAD_COUNT, value)
 
+    # audio generation (mmproj)
+
+    def add_clip_gen_audio_projector_type(self, value: str) -> None:
+        self.add_string(Keys.ClipGenAudio.PROJECTOR_TYPE, value)
+
+    def add_gen_audio_projection_dim(self, value: int) -> None:
+        self.add_uint32(Keys.ClipGenAudio.PROJECTION_DIM, value)
+
+    def add_gen_audio_embedding_length(self, value: int) -> None:
+        self.add_uint32(Keys.ClipGenAudio.EMBEDDING_LENGTH, value)
+
+    def add_gen_audio_feed_forward_length(self, value: int) -> None:
+        self.add_uint32(Keys.ClipGenAudio.FEED_FORWARD_LENGTH, value)
+
+    def add_gen_audio_block_count(self, value: int) -> None:
+        self.add_uint32(Keys.ClipGenAudio.BLOCK_COUNT, value)
+
+    def add_gen_audio_head_count(self, value: int) -> None:
+        self.add_uint32(Keys.ClipGenAudio.Attention.HEAD_COUNT, value)
+
+    def add_gen_audio_head_count_kv(self, value: int) -> None:
+        self.add_uint32(Keys.ClipGenAudio.Attention.HEAD_COUNT_KV, value)
+
+    def add_gen_audio_attention_layernorm_eps(self, value: float) -> None:
+        self.add_float32(Keys.ClipGenAudio.Attention.LAYERNORM_EPS, value)
+
     def add_xielu_alpha_p(self, values: Sequence[float]):
         self.add_array(Keys.xIELU.ALPHA_P, values)
 

gguf-py/gguf/scripts/gguf_convert_endian.py

@@ -59,11 +59,29 @@ def byteswap_q6_k(tensor, block_offs):
     delta.byteswap(inplace=True)
 
 
+def byteswap_q1_0(tensor, block_offs):
+    # Each block_q1_0 consists of an f16 delta followed by 16 int8 quantizations.
+
+    # Byte-Swap f16 sized delta field
+    delta = tensor.data[block_offs:block_offs + 2].view(dtype=np.uint16)
+    delta.byteswap(inplace=True)
+
+
+def byteswap_tq2_0(tensor, block_offs):
+    # Each block_tq2_0 consists of 64 int8 values followed by 1 f16 value.
+
+    # Byte-Swap f16 sized field
+    delta = tensor.data[block_offs + 64:block_offs + 66].view(dtype=np.uint16)
+    delta.byteswap(inplace=True)
+
+
 byteswap_tensors = {
+    gguf.GGMLQuantizationType.Q1_0:  byteswap_q1_0,
     gguf.GGMLQuantizationType.Q4_0:  byteswap_q4_0,
     gguf.GGMLQuantizationType.Q8_0:  byteswap_q8_0,
     gguf.GGMLQuantizationType.Q4_K:  byteswap_q4_k,
     gguf.GGMLQuantizationType.Q6_K:  byteswap_q6_k,
+    gguf.GGMLQuantizationType.TQ2_0: byteswap_tq2_0,
     gguf.GGMLQuantizationType.MXFP4: byteswap_noop,
     gguf.GGMLQuantizationType.NVFP4: byteswap_noop,
 }

gguf-py/gguf/tensor_mapping.py

@@ -2109,6 +2109,7 @@ class TensorNameMap:
             "conformer.subsample_conv_projection.layer{bid}.conv", # gemma4
             "sound_encoder.encoder.subsampling.layers.{bid}", # parakeet
             "encoder.conv{bid}", # mimo-audio-tokenizer
+            "speaker_encoder.blocks.{bid}.conv", # qwen3tts speaker encoder (only bid=0, the stem TDNN)
         ),
 
         MODEL_TENSOR.A_ENC_CONV1D_NORM: (
@@ -2126,6 +2127,7 @@ class TensorNameMap:
 
         MODEL_TENSOR.A_ENC_CONV_OUT: (
             "audio_tower.conv_out", # qwen3omni
+            "speaker_encoder.mfa.conv", # qwen3tts speaker encoder: multi-layer feature aggregation
         ),
 
         MODEL_TENSOR.A_PRE_NORM: (),
@@ -2336,7 +2338,8 @@ class TensorNameMap:
         MODEL_TENSOR.A_MMPROJ_FC: (
             "audio.multi_modal_projector.linear", # qwen2audio
             "audio_tower.proj", # qwen2omni
-            "model.audio_tower.output_proj" # gemma4
+            "model.audio_tower.output_proj", # gemma4
+            "speaker_encoder.fc", # qwen3tts speaker encoder: final speaker embedding projection
         ),
 
         MODEL_TENSOR.A_MM_NORM_PRE: (
@@ -2411,6 +2414,7 @@ class TensorNameMap:
             "conformer.layers.{bid}.lconv1d.linear_start", # gemma3n
             "sound_encoder.encoder.layers.{bid}.conv.pointwise_conv1", # parakeet
             "encoder.layers.{bid}.conv.up_conv", # granite_speech
+            "speaker_encoder.blocks.{bid}.tdnn1.conv", # qwen3tts speaker encoder
         ),
 
         MODEL_TENSOR.A_ENC_CONV_PW2: (
@@ -2418,6 +2422,23 @@ class TensorNameMap:
             "conformer.layers.{bid}.lconv1d.linear_end", # gemma3n
             "sound_encoder.encoder.layers.{bid}.conv.pointwise_conv2", # parakeet
             "encoder.layers.{bid}.conv.down_conv", # granite_speech
+            "speaker_encoder.blocks.{bid}.tdnn2.conv", # qwen3tts speaker encoder
+        ),
+
+        MODEL_TENSOR.A_ENC_SE_CONV1: (
+            "speaker_encoder.blocks.{bid}.se_block.conv1", # qwen3tts
+        ),
+
+        MODEL_TENSOR.A_ENC_SE_CONV2: (
+            "speaker_encoder.blocks.{bid}.se_block.conv2", # qwen3tts
+        ),
+
+        MODEL_TENSOR.A_ENC_ASP_ATTN: (
+            "speaker_encoder.asp.conv", # qwen3tts
+        ),
+
+        MODEL_TENSOR.A_ENC_ASP_TDNN: (
+            "speaker_encoder.asp.tdnn.conv", # qwen3tts
         ),
 
         MODEL_TENSOR.A_ENC_NORM_CONV: (

gguf-py/tests/test_gguf_reader_validation.py

@@ -0,0 +1,37 @@
+import struct
+import numpy as np
+import pytest
+
+from gguf.gguf_reader import GGUFReader
+
+
+def _write_gguf(path, n_dims_field, dims):
+    buf = b'GGUF' + struct.pack('<IQQ', 3, 1, 0)  # version 3, 1 tensor, 0 kv
+    name = b'bad_tensor'
+    buf += struct.pack('<Q', len(name)) + name
+    buf += struct.pack('<I', n_dims_field)
+    for d in dims:
+        buf += struct.pack('<Q', d)
+    buf += struct.pack('<I', 0)  # dtype F32
+    buf += struct.pack('<Q', 0)  # tensor offset
+    buf += b'\x00' * 64
+    path.write_bytes(buf)
+
+
+def test_n_dims_upper_bound(tmp_path):
+    # crafted file claims 1_000_000 dims; must be rejected, not read past EOF
+    p = tmp_path / 'evil_ndims.gguf'
+    _write_gguf(p, 1_000_000, [1] * 8)
+    with pytest.raises(ValueError, match='exceeds GGML_MAX_DIMS'):
+        GGUFReader(p)
+
+
+def test_dims_product_no_uint64_wraparound(tmp_path):
+    # dims whose true product overflows uint64; np.prod would wrap to 4 and
+    # silently pass an undersized read. The reader must not accept it.
+    dims = [4194305, 4194305, 211106198978564]
+    assert int(np.prod(np.array(dims, dtype=np.uint64))) == 4  # the wrap bug
+    p = tmp_path / 'evil_overflow.gguf'
+    _write_gguf(p, len(dims), dims)
+    with pytest.raises(ValueError):
+        GGUFReader(p)

include/llama.h

@@ -1256,7 +1256,6 @@ extern "C" {
         struct ggml_tensor * probs;
         struct ggml_tensor * sampled;
         struct ggml_tensor * candidates;
-        int64_t              n_vocab;
     };
 
     // user code can implement the interface below in order to create custom llama_sampler
@@ -1425,7 +1424,8 @@ extern "C" {
 
     /// NOTE: Avoid using on the full vocabulary as searching for repeated tokens can become slow. For example, apply top-k or top-p sampling first.
     LLAMA_API struct llama_sampler * llama_sampler_init_penalties(
-                             int32_t   penalty_last_n,   // last n tokens to penalize (0 = disable penalty, -1 = context size)
+                             int32_t   n_vocab,
+                             int32_t   penalty_last_n,   // last n tokens to penalize (0 = disable penalty)
                                float   penalty_repeat,   // must be > 0.0, 1.0 = disabled
                                float   penalty_freq,     // must be finite, 0.0 = disabled
                                float   penalty_present); // must be finite, 0.0 = disabled
@@ -1433,11 +1433,10 @@ extern "C" {
     ///  @details DRY sampler, designed by p-e-w, as described in: https://github.com/oobabooga/text-generation-webui/pull/5677, porting Koboldcpp implementation authored by pi6am: https://github.com/LostRuins/koboldcpp/pull/982
     LLAMA_API struct llama_sampler * llama_sampler_init_dry(
             const struct llama_vocab *  vocab,
-                             int32_t    n_ctx_train,
                                float    dry_multiplier,
                                float    dry_base,
                              int32_t    dry_allowed_length,
-                             int32_t    dry_penalty_last_n,
+                             int32_t    dry_penalty_last_n, // last n tokens to penalize (0 = disable penalty)
                           const char ** seq_breakers,
                               size_t    num_breakers);
 

scripts/sync-ggml.last

@@ -1 +1 @@
-06ca97616793248fadb410ea8d69c7511b2005e4
+90951f99af1fbebef3fbdd58ff5b8715b0bb9c43

scripts/sync_vendor.py

@@ -24,10 +24,31 @@ vendor = {
     "https://raw.githubusercontent.com/sheredom/subprocess.h/8671cee1fc09f11a70ce3782a0ee13177c3aa387/subprocess.h": "vendor/sheredom/subprocess.h",
 }
 
+# TODO @ngxson : this is temporary, to be removed in the future
+patches = [
+    # https://github.com/sheredom/subprocess.h/pull/102
+    "vendor/sheredom/patch-bsd.patch",
+    # https://github.com/sheredom/subprocess.h/pull/101
+    "vendor/sheredom/patch-windows-quote-backslash.patch",
+    # https://github.com/sheredom/subprocess.h/pull/104
+    # note: must be applied after patch-bsd.patch, they touch adjacent lines
+    "vendor/sheredom/patch-glibc-older-than-2.29.patch",
+]
+
 for url, filename in vendor.items():
     print(f"downloading {url} to {filename}") # noqa: NP100
     urllib.request.urlretrieve(url, filename)
 
+for patch in patches:
+    print(f"applying {patch}") # noqa: NP100
+    try:
+        subprocess.check_call([
+            "git", "apply", "--directory", os.path.dirname(patch), patch
+        ])
+    except Exception as e:
+        print(f"Error: {e}") # noqa: NP100
+        sys.exit(1)
+
 print("Splitting httplib.h...") # noqa: NP100
 try:
     subprocess.check_call([

scripts/ui-assets.cmake

@@ -123,15 +123,15 @@ function(npm_build out_var)
     endif()
 
     if(need_install)
-        message(STATUS "UI: running npm install")
+        message(STATUS "UI: running npm ci")
         execute_process(
-            COMMAND ${NPM_EXECUTABLE} install
+            COMMAND ${NPM_EXECUTABLE} ci
             WORKING_DIRECTORY "${WORK_DIR}"
             RESULT_VARIABLE rc
             ERROR_VARIABLE  err
         )
         if(NOT rc EQUAL 0)
-            message(STATUS "UI: npm install failed (${rc})")
+            message(STATUS "UI: npm ci failed (${rc})")
             message(STATUS "  stderr: ${err}")
             return()
         endif()

skills/code-review/SKILL.md

@@ -119,6 +119,7 @@ Public API changes carry a higher bar than internal ones (`CONTRIBUTING.md`). Re
 - In most cases, `build_vit` should be enough to build the transformer graph for vision models. Do not add a loop to build the transformer graph manually, unless you have a very good reason to do so. If you do, please explain why in the PR description.
 - If you need a dedicated preprocessor, there is a high chance that it can be a derived class from one of the existing preprocessors. Check carefully before adding a new preprocessor class.
 - If the model need a new public API in `mtmd.h`, open a discussion first.
+- For audio generation models, see `tools/mtmd/README-dev.md`
 
 ## General (always)
 

src/llama-arch.cpp

@@ -144,6 +144,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
     { LLM_ARCH_TALKIE,           "talkie"           },
     { LLM_ARCH_MELLUM,           "mellum"           },
     { LLM_ARCH_NANBEIGE,         "nanbeige"         },
+    { LLM_ARCH_QWEN3TTS,         "qwen3tts"         },
     { LLM_ARCH_UNKNOWN,          "(unknown)"        },
 };
 
@@ -1026,6 +1027,7 @@ bool llm_arch_supports_sm_tensor(const llm_arch & arch) {
         case LLM_ARCH_MINIMAX_M3:
         case LLM_ARCH_MISTRAL4:
         case LLM_ARCH_KIMI_LINEAR:
+        case LLM_ARCH_QWEN3TTS:
             return false;
         default:
             return true;

src/llama-arch.h

@@ -149,6 +149,7 @@ enum llm_arch {
     LLM_ARCH_MINIMAX_M3,
     LLM_ARCH_DFLASH,
     LLM_ARCH_NANBEIGE,
+    LLM_ARCH_QWEN3TTS,
     LLM_ARCH_UNKNOWN,
 };
 

src/llama-ext.h

@@ -124,3 +124,9 @@ LLAMA_API llama_context * llama_get_ctx_other(struct llama_context * ctx);
 LLAMA_API const int32_t * llama_model_target_layer_ids  (const struct llama_model * model);
 // returns the number of extracted layers from target model
 LLAMA_API uint32_t        llama_model_target_layer_ids_n(const struct llama_model * model);
+
+// retrieves the whole token embedding matrix in F32 format (n_embd * n_vocab)
+// returns total number of elements or 0 on error
+// if out is nullptr, returns the number of tokens without writing to out
+// caller must allocate enough memory for out before calling
+LLAMA_API uint32_t llama_model_get_tok_embd(const struct llama_model * model, float * out);

src/llama-grammar.cpp

@@ -648,10 +648,12 @@ const char * llama_grammar_parser::parse_sequence(
             } else {
                 throw std::runtime_error(std::string("expecting ',' at ") + pos);
             }
-            bool has_max = max_times != UINT64_MAX;
-            if (min_times > MAX_REPETITION_THRESHOLD || (has_max && max_times > MAX_REPETITION_THRESHOLD)) {
+            if (min_times > MAX_REPETITION_THRESHOLD) {
                 throw std::runtime_error(std::string("number of repetitions exceeds sane defaults, please reduce the number of repetitions"));
             }
+            if (max_times != UINT64_MAX && max_times > MAX_REPETITION_THRESHOLD) {
+                max_times = UINT64_MAX;
+            }
             handle_repetitions(min_times, max_times);
         } else {
             break;

src/llama-graph.cpp

@@ -3683,7 +3683,6 @@ void llm_graph_context::build_sampling() const {
             /*.probs       =*/ nullptr,
             /*.sampled     =*/ nullptr,
             /*.candidates  =*/ nullptr,
-            /*.n_vocab     =*/ logits_seq->ne[0],
         };
 
         assert(sampler->iface->backend_apply);

src/llama-model-loader.cpp

@@ -857,7 +857,11 @@ struct ggml_tensor * llama_model_loader::require_tensor_meta(const std::string &
     return tensor;
 }
 
-const struct ggml_tensor * llama_model_loader::check_tensor_dims(const std::string & name, const std::vector<int64_t> & ne, bool required) const {
+const struct ggml_tensor * llama_model_loader::check_tensor_dims(
+        const std::string & name,
+        const std::vector<int64_t> & ne,
+        bool required,
+        bool allow_reshape) const {
     const struct ggml_tensor * cur = get_tensor_meta(name.c_str());
 
     if (cur == NULL) {
@@ -867,21 +871,33 @@ const struct ggml_tensor * llama_model_loader::check_tensor_dims(const std::stri
         throw std::runtime_error(format("%s: tensor '%s' not found", __func__, name.c_str()));
     }
 
-    {
-        bool is_ok = true;
+    bool is_ok = true;
+
+    if (allow_reshape) {
+        // check total number of elements only
+        const int64_t ncur = ggml_nelements(cur);
+        int64_t nexp = 1;
+        for (size_t i = 0; i < ne.size(); ++i) {
+            nexp *= ne[i];
+        }
+        if (ncur != nexp) {
+            is_ok = false;
+        }
+    } else {
         for (size_t i = 0; i < GGML_MAX_DIMS; ++i) {
             if ((i < ne.size() && ne[i] != cur->ne[i]) || (i >= ne.size() && cur->ne[i] != 1)) {
                 is_ok = false;
                 break;
             }
         }
-        if (!is_ok) {
-            throw std::runtime_error(
-                    format("%s: tensor '%s' has wrong shape; expected %s, got %s",
-                        __func__, name.c_str(),
-                        llama_format_tensor_shape(ne).c_str(),
-                        llama_format_tensor_shape(cur).c_str()));
-        }
+    }
+
+    if (!is_ok) {
+        throw std::runtime_error(
+                format("%s: tensor '%s' has wrong shape; expected %s, got %s",
+                    __func__, name.c_str(),
+                    llama_format_tensor_shape(ne).c_str(),
+                    llama_format_tensor_shape(cur).c_str()));
     }
 
     return cur;
@@ -1233,7 +1249,13 @@ struct ggml_tensor * llama_model_loader::create_tensor(
         for (size_t dim = 0; dim < GGML_MAX_DIMS; dim++) {
             t_meta.ne[dim] = dim < ne.size() ? ne.begin()[dim] : 1;
             GGML_ASSERT(t_meta.ne[dim] >= 1);
-            t_meta.nb[dim] = dim == 0 ? ggml_type_size(type) : t_meta.ne[dim-1]*t_meta.nb[dim-1];
+            if (dim == 0) {
+                t_meta.nb[dim] = ggml_type_size(type);
+            } else if (dim == 1) {
+                t_meta.nb[dim] = ggml_row_size(type, t_meta.ne[dim-1]);
+            } else {
+                t_meta.nb[dim] = t_meta.nb[dim-1]*t_meta.ne[dim-1];
+            }
             GGML_ASSERT(t_meta.nb[dim] >= 1);
         }
         ggml_set_name(&t_meta, tn.str().c_str());
@@ -1246,11 +1268,33 @@ struct ggml_tensor * llama_model_loader::create_tensor(
         return ret;
     }
 
-    ggml_tensor * t_meta = get_tensor_meta(tn.str().c_str());
-    ggml_backend_buffer_type_t buft = buft_for_tensor(t_meta);
-    if (buft == nullptr) {
-        return nullptr; // return type is ggml_tensor *
+    LLAMA_LOG_DEBUG("%s: loading tensor %s\n", __func__, tn.str().c_str());
+    const struct ggml_tensor * cur = check_tensor_dims(tn.str(), ne, !(flags & TENSOR_NOT_REQUIRED), flags & TENSOR_ALLOW_RESHAPE);
+    if (cur == NULL) {
+        return NULL;
     }
+
+    ggml_tensor t_meta = *cur;
+    if (flags & TENSOR_ALLOW_RESHAPE) {
+        for (size_t dim = 0; dim < GGML_MAX_DIMS; dim++) {
+            t_meta.ne[dim] = dim < ne.size() ? ne.begin()[dim] : 1;
+            if (dim == 0) {
+                t_meta.nb[dim] = ggml_type_size(t_meta.type);
+            } else if (dim == 1) {
+                t_meta.nb[dim] = ggml_row_size(t_meta.type, t_meta.ne[dim-1]);
+            } else {
+                t_meta.nb[dim] = t_meta.ne[dim-1]*t_meta.nb[dim-1];
+            }
+        }
+    }
+
+    GGML_ASSERT(ggml_nbytes(&t_meta) == ggml_nbytes(cur));
+
+    ggml_backend_buffer_type_t buft = buft_for_tensor(&t_meta);
+    if (buft == nullptr) {
+        return nullptr;
+    }
+
     ggml_context * ctx = ctx_for_buft(buft);
 
     // if duplicated, check if the original tensor was allocated in the same buffer type context and avoid creating a new one
@@ -1261,20 +1305,13 @@ struct ggml_tensor * llama_model_loader::create_tensor(
         }
     }
 
-    LLAMA_LOG_DEBUG("%s: loading tensor %s\n", __func__, tn.str().c_str());
-    const struct ggml_tensor * cur = check_tensor_dims(tn.str(), ne, !(flags & TENSOR_NOT_REQUIRED));
-
-    if (cur == NULL) {
-        return NULL;
-    }
-
     const bool duplicated = flags & TENSOR_DUPLICATED;
 
-    struct ggml_tensor * tensor = ggml_dup_tensor(ctx, cur);
-    ggml_set_name(tensor, ggml_get_name(cur));
+    struct ggml_tensor * tensor = ggml_dup_tensor(ctx, &t_meta);
+    ggml_set_name(tensor, ggml_get_name(&t_meta));
 
     if (duplicated) {
-        size_data += ggml_nbytes(cur);
+        size_data += ggml_nbytes(&t_meta);
     } else {
         n_created++;
     }
@@ -1282,34 +1319,6 @@ struct ggml_tensor * llama_model_loader::create_tensor(
     return tensor;
 }
 
-struct ggml_tensor * llama_model_loader::create_tensor_as_view(struct ggml_context * ctx, struct ggml_tensor * base, const std::string & name, const std::initializer_list<int64_t> & ne, size_t offset, bool required) {
-    const struct ggml_tensor * cur = check_tensor_dims(name, ne, required);
-
-    if (cur == NULL) {
-        return NULL;
-    }
-
-    if (cur->type != base->type) {
-        throw std::runtime_error(format("%s: tensor '%s' has wrong type; expected %s, got %s", __func__, name.c_str(), ggml_type_name(base->type), ggml_type_name(cur->type)));
-    }
-
-    std::array<int64_t, GGML_MAX_DIMS> dims;
-    for (size_t i = 0; i < GGML_MAX_DIMS; ++i) {
-        dims[i] = i < ne.size() ? ne.begin()[i] : 1;
-    }
-
-    struct ggml_tensor * tensor = ggml_view_4d(ctx, base,
-                                    dims[0], dims[1], dims[2], dims[3],
-                                    cur->nb[1], cur->nb[2], cur->nb[3],
-                                    offset);
-
-    ggml_set_name(tensor, name.c_str());
-
-    n_created++;
-
-    return tensor;
-}
-
 void llama_model_loader::done_getting_tensors(bool partial) const {
     if (n_created > n_tensors) {
         throw std::runtime_error(format("%s: too many tensors created; expected %d, got %d", __func__, n_tensors, n_created));

src/llama-model-loader.h

@@ -67,6 +67,7 @@ struct llama_model_loader {
     static const int TENSOR_DUPLICATED      = 1 << 1;
     static const int TENSOR_SKIP            = 1 << 2;
     static const int TENSOR_SKIP_IF_VIRTUAL = 1 << 3;
+    static const int TENSOR_ALLOW_RESHAPE   = 1 << 4;
 
     int n_kv      = 0;
     int n_tensors = 0;
@@ -177,14 +178,16 @@ struct llama_model_loader {
 
     struct ggml_tensor * require_tensor_meta(const std::string & name) const;
 
-    const struct ggml_tensor * check_tensor_dims(const std::string & name, const std::vector<int64_t> & ne, bool required) const;
+    const struct ggml_tensor * check_tensor_dims(
+            const std::string & name,
+            const std::vector<int64_t> & ne,
+            bool required,
+            bool allow_reshape) const;
 
     struct ggml_tensor * create_tensor(
         const llama_hparams & hparams, const buft_list_t * buft_list_cpu, const buft_list_t * buft_list_input, const buft_list_t * buft_list_output,
         const buft_list_t * buft_list_layer, const LLM_TN_IMPL & tn, const std::initializer_list<int64_t> & ne, int flags);
 
-    struct ggml_tensor * create_tensor_as_view(struct ggml_context * ctx, struct ggml_tensor * base, const std::string & name, const std::initializer_list<int64_t> & ne, size_t offset, bool required = true);
-
     void done_getting_tensors(bool partial = false) const;
 
     void init_mappings(bool prefetch = true, llama_mlocks * mlock_mmaps = nullptr);

src/llama-model.cpp

@@ -112,6 +112,8 @@ static llama_model * llama_model_mapping(llm_arch arch, const llama_model_params
             return new llama_model_qwen3vl(params);
         case LLM_ARCH_QWEN3VLMOE:
             return new llama_model_qwen3vlmoe(params);
+        case LLM_ARCH_QWEN3TTS:
+            return new llama_model_qwen3tts(params);
         case LLM_ARCH_PHI2:
             return new llama_model_phi2(params);
         case LLM_ARCH_PHI3:
@@ -2693,6 +2695,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
         case LLM_ARCH_QWEN3VLMOE:
         case LLM_ARCH_QWEN35:
         case LLM_ARCH_QWEN35MOE:
+        case LLM_ARCH_QWEN3TTS:
             return LLAMA_ROPE_TYPE_IMROPE;
 
         case LLM_ARCH_GLM4:
@@ -2867,7 +2870,8 @@ llama_model_base::llama_model_base(const struct llama_model_params & params) : l
     TENSOR_DUPLICATED     (llama_model_loader::TENSOR_DUPLICATED),
     TENSOR_NOT_REQUIRED   (llama_model_loader::TENSOR_NOT_REQUIRED),
     TENSOR_SKIP           (llama_model_loader::TENSOR_SKIP),
-    TENSOR_SKIP_IF_VIRTUAL(llama_model_loader::TENSOR_SKIP_IF_VIRTUAL) {}
+    TENSOR_SKIP_IF_VIRTUAL(llama_model_loader::TENSOR_SKIP_IF_VIRTUAL),
+    TENSOR_ALLOW_RESHAPE  (llama_model_loader::TENSOR_ALLOW_RESHAPE) {}
 
 ggml_tensor * llama_model_base::create_tensor(const LLM_TN_IMPL & tn, const std::initializer_list<int64_t> & ne, int flags) {
     GGML_ASSERT(ml != nullptr);
@@ -2886,6 +2890,21 @@ void llama_model_base::create_tensor_qkv(llama_layer & layer, int bid,
         int64_t n_embd_, int64_t n_embd_q_, int64_t n_embd_k_, int64_t n_embd_v_,
         int flags) {
     const int64_t n_embd_qkv = n_embd_q_ + n_embd_k_ + n_embd_v_;
+
+    if (flags & TENSOR_SKIP) {
+        const int skip = TENSOR_NOT_REQUIRED | TENSOR_SKIP;
+
+        create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", bid), {n_embd_, n_embd_qkv}, skip | TENSOR_SKIP_IF_VIRTUAL);
+        create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias",   bid), {n_embd_qkv},          skip | TENSOR_SKIP_IF_VIRTUAL);
+        create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", bid), {n_embd_, n_embd_q_},  skip);
+        create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", bid), {n_embd_, n_embd_k_},  skip);
+        create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", bid), {n_embd_, n_embd_v_},  skip);
+        create_tensor(tn(LLM_TENSOR_ATTN_Q,   "bias",   bid), {n_embd_q_},           skip);
+        create_tensor(tn(LLM_TENSOR_ATTN_K,   "bias",   bid), {n_embd_k_},           skip);
+        create_tensor(tn(LLM_TENSOR_ATTN_V,   "bias",   bid), {n_embd_v_},           skip);
+        return;
+    }
+
     layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", bid), {n_embd_, n_embd_qkv}, TENSOR_NOT_REQUIRED | TENSOR_SKIP_IF_VIRTUAL);
     if (layer.wqkv) {
         layer.wqkv_b = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", bid), {n_embd_qkv}, TENSOR_NOT_REQUIRED | TENSOR_SKIP_IF_VIRTUAL);
@@ -2907,3 +2926,38 @@ const int32_t * llama_model_target_layer_ids(const struct llama_model * model) {
 uint32_t llama_model_target_layer_ids_n(const struct llama_model * model) {
     return (uint32_t) model->target_layer_ids.size();
 }
+
+uint32_t llama_model_get_tok_embd(const struct llama_model * model, float * out) {
+    if (model->vocab.n_tokens() == 0 || model->tok_embd == nullptr) {
+        return 0;
+    }
+
+    const ggml_tensor * tensor = model->tok_embd;
+    const size_t nelements = ggml_nelements(tensor);
+    GGML_ASSERT(nelements <= UINT32_MAX); // for the return type
+
+    if (out == nullptr) {
+        return (uint32_t) nelements;
+    }
+
+    if (tensor->type == GGML_TYPE_F32) {
+        ggml_backend_tensor_get(tensor, out, 0, nelements * sizeof(float));
+        return (uint32_t) nelements;
+    }
+
+    std::vector<uint8_t> buf(ggml_nbytes(tensor));
+    ggml_backend_tensor_get(tensor, buf.data(), 0, buf.size());
+
+    const ggml_type_traits * traits = ggml_get_type_traits(tensor->type);
+    if (tensor->type == GGML_TYPE_F16) {
+        ggml_fp16_to_fp32_row((const ggml_fp16_t *) buf.data(), out, nelements);
+    } else if (tensor->type == GGML_TYPE_BF16) {
+        ggml_bf16_to_fp32_row((const ggml_bf16_t *) buf.data(), out, nelements);
+    } else if (ggml_is_quantized(tensor->type) && traits->to_float != nullptr) {
+        traits->to_float(buf.data(), out, nelements);
+    } else {
+        GGML_ABORT("unsupported tensor type for dequantization: %s", ggml_type_name(tensor->type));
+    }
+
+    return (uint32_t) nelements;
+}

src/llama-model.h

@@ -719,6 +719,7 @@ struct llama_model_base : public llama_model {
     const int TENSOR_NOT_REQUIRED;
     const int TENSOR_SKIP;
     const int TENSOR_SKIP_IF_VIRTUAL;
+    const int TENSOR_ALLOW_RESHAPE;
 
     explicit llama_model_base(const llama_model_params & params);
     virtual ~llama_model_base() = default;

src/llama-sampler.cpp

@@ -589,7 +589,6 @@ static bool llama_sampler_backend_support(
         /*.probs      = */ nullptr,
         /*.sampled    = */ nullptr,
         /*.candidates = */ ggml_new_tensor_1d(ctx, GGML_TYPE_I32, n),
-        /*.n_vocab    = */ n,
     };
 
     ggml_cgraph * gf = ggml_new_graph(ctx);
@@ -2640,6 +2639,7 @@ struct llama_sampler * llama_sampler_init_grammar_lazy_patterns(
 // penalties
 
 struct llama_sampler_penalties : public llama_sampler_backend {
+    const int32_t n_vocab;
     const int32_t penalty_last_n;
     const float   penalty_repeat;
     const float   penalty_freq;
@@ -2655,7 +2655,6 @@ struct llama_sampler_penalties : public llama_sampler_backend {
     ggml_tensor * inp_counts    = nullptr;
 
     // backend helpers
-    int32_t n_vocab = 0;
     int32_t n_max   = 0;
     bool has_candidates = false;
 
@@ -2676,11 +2675,13 @@ struct llama_sampler_penalties : public llama_sampler_backend {
     }
 
     llama_sampler_penalties(
+            int32_t n_vocab,
             int32_t penalty_last_n,
             float   penalty_repeat,
             float   penalty_freq,
             float   penalty_present)
         : llama_sampler_backend("penalties")
+        , n_vocab         (n_vocab)
         , penalty_last_n  (penalty_last_n)
         , penalty_repeat  (penalty_repeat)
         , penalty_freq    (penalty_freq)
@@ -2766,6 +2767,7 @@ static void llama_sampler_penalties_reset(struct llama_sampler * smpl) {
 static struct llama_sampler * llama_sampler_penalties_clone(const struct llama_sampler * smpl) {
     const auto * ctx = (const llama_sampler_penalties *) smpl->ctx;
     auto * result = llama_sampler_init_penalties(
+            ctx->n_vocab,
             ctx->penalty_last_n,
             ctx->penalty_repeat,
             ctx->penalty_freq,
@@ -2811,10 +2813,9 @@ static void llama_sampler_penalties_backend_apply(
         return;
     }
 
-    GGML_ASSERT(data->n_vocab > 0 && data->n_vocab <= INT32_MAX);
+    GGML_ASSERT(sctx->n_vocab > 0);
 
     sctx->has_candidates = data->candidates != nullptr;
-    sctx->n_vocab = (int32_t) data->n_vocab;
     sctx->n_max   = std::min(sctx->penalty_last_n, sctx->n_vocab);
 
     sctx->inp_token_ids = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, sctx->n_max);
@@ -2965,6 +2966,7 @@ static struct llama_sampler_i llama_sampler_penalties_i = {
 };
 
 struct llama_sampler * llama_sampler_init_penalties(
+        int32_t n_vocab,
         int32_t penalty_last_n,
         float penalty_repeat,
         float penalty_freq,
@@ -2979,6 +2981,7 @@ struct llama_sampler * llama_sampler_init_penalties(
     return llama_sampler_init(
         /* .iface = */ &llama_sampler_penalties_i,
         /* .ctx   = */ new llama_sampler_penalties(
+            n_vocab,
             penalty_last_n,
             penalty_repeat,
             penalty_freq,
@@ -3075,8 +3078,6 @@ struct llama_sampler * llama_sampler_init_top_n_sigma(float n) {
 // DRY
 
 struct llama_sampler_dry {
-    int32_t total_context_size;
-
     const float   dry_multiplier;
     const float   dry_base;
     const int32_t dry_allowed_length;
@@ -3152,8 +3153,7 @@ static void llama_sampler_dry_apply(struct llama_sampler * smpl, llama_token_dat
         return;
     }
 
-    int32_t effective_dry_penalty_last_n = (ctx->dry_penalty_last_n == -1) ? ctx->total_context_size : std::max(ctx->dry_penalty_last_n, 0);
-    int last_n_repeat = std::min(std::min((int)ctx->last_tokens.size(), effective_dry_penalty_last_n), ctx->total_context_size);
+    int last_n_repeat = std::min((int) ctx->last_tokens.size(), ctx->dry_penalty_last_n);
 
     if (last_n_repeat <= ctx->dry_allowed_length) {
         return;
@@ -3366,7 +3366,7 @@ static struct llama_sampler * llama_sampler_dry_clone(const struct llama_sampler
     llama_vocab dummy_vocab;
 
     // dummy vocab is passed because it is only needed for raw sequence breaker processing, which we have already done and will simply be copying
-    auto * result = llama_sampler_init_dry(&dummy_vocab, ctx->total_context_size, ctx->dry_multiplier, ctx->dry_base, ctx->dry_allowed_length, ctx->dry_penalty_last_n, NULL, 0);
+    auto * result = llama_sampler_init_dry(&dummy_vocab, ctx->dry_multiplier, ctx->dry_base, ctx->dry_allowed_length, ctx->dry_penalty_last_n, NULL, 0);
 
     // Copy the state, including the processed breakers
     {
@@ -3397,8 +3397,8 @@ static struct llama_sampler_i llama_sampler_dry_i = {
     /* .backend_set_input = */ nullptr,
 };
 
-struct llama_sampler * llama_sampler_init_dry(const struct llama_vocab * vocab, int32_t n_ctx_train, float dry_multiplier, float dry_base, int32_t dry_allowed_length, int32_t dry_penalty_last_n, const char** seq_breakers, size_t num_breakers) {
-    int32_t effective_dry_penalty_last_n = (dry_penalty_last_n == -1) ? n_ctx_train : std::max(dry_penalty_last_n, 0);
+struct llama_sampler * llama_sampler_init_dry(const struct llama_vocab * vocab, float dry_multiplier, float dry_base, int32_t dry_allowed_length, int32_t dry_penalty_last_n, const char** seq_breakers, size_t num_breakers) {
+    dry_penalty_last_n = std::max(dry_penalty_last_n, 0);
     std::unordered_multimap<llama_token, std::vector<llama_token>> processed_breakers;
     const int MAX_CHAR_LEN = 40;
     const int MAX_SEQ_LEN = 20;
@@ -3435,23 +3435,22 @@ struct llama_sampler * llama_sampler_init_dry(const struct llama_vocab * vocab,
     return llama_sampler_init(
         /* .iface = */ &llama_sampler_dry_i,
         /* .ctx   = */ new llama_sampler_dry {
-            /* .total_context_size     = */ n_ctx_train,
             /* .dry_multiplier         = */ dry_multiplier,
             /* .dry_base               = */ dry_base,
             /* .dry_allowed_length     = */ dry_allowed_length,
             /* .dry_penalty_last_n     = */ dry_penalty_last_n,
             /* .dry_processed_breakers = */ std::move(processed_breakers),
-            /* .dry_repeat_count       = */ dry_enabled ? std::vector<int>(effective_dry_penalty_last_n, 0) : std::vector<int>{},
+            /* .dry_repeat_count       = */ dry_enabled ? std::vector<int>(dry_penalty_last_n, 0) : std::vector<int>{},
             /* .dry_max_token_repeat   = */ {},
-            /* .last_tokens            = */ dry_enabled ? ring_buffer<llama_token>(effective_dry_penalty_last_n) : ring_buffer<llama_token>(0),
+            /* .last_tokens            = */ dry_enabled ? ring_buffer<llama_token>(dry_penalty_last_n) : ring_buffer<llama_token>(0),
         }
     );
 }
 
 // wrapper for test-sampling.cpp
-struct llama_sampler * llama_sampler_init_dry_testing(int32_t context_size, float dry_multiplier, float dry_base, int32_t dry_allowed_length, int32_t dry_penalty_last_n, const std::vector<std::vector<llama_token>>& seq_breakers) {
+struct llama_sampler * llama_sampler_init_dry_testing(float dry_multiplier, float dry_base, int32_t dry_allowed_length, int32_t dry_penalty_last_n, const std::vector<std::vector<llama_token>>& seq_breakers) {
     llama_vocab dummy_vocab;
-    auto * result = llama_sampler_init_dry(&dummy_vocab, context_size, dry_multiplier, dry_base, dry_allowed_length, dry_penalty_last_n, NULL, 0);
+    auto * result = llama_sampler_init_dry(&dummy_vocab, dry_multiplier, dry_base, dry_allowed_length, dry_penalty_last_n, NULL, 0);
     auto * ctx = (llama_sampler_dry *) result->ctx;
 
     // Process the token-based sequence breakers

src/llama-sampler.h

@@ -34,7 +34,6 @@ struct llama_sampler_chain {
 };
 
 struct llama_sampler * llama_sampler_init_dry_testing(
-        int32_t context_size,
         float   dry_multiplier,
         float   dry_base,
         int32_t dry_allowed_length,

src/llama-vocab.cpp

@@ -1373,8 +1373,10 @@ struct llm_tokenizer_plamo2 : llm_tokenizer {
             if (vocab.is_byte(token_id)) {
                 if (entry.text.length() == 6 && entry.text.substr(0, 3) == "<0x" && entry.text.back() == '>') {
                     std::string hex_str = entry.text.substr(3, 2);
-                    int byte_val = std::stoi(hex_str, nullptr, 16);
-                    bytes_[byte_val] = static_cast<llama_token>(token_id);
+                    if (std::isxdigit(static_cast<unsigned char>(hex_str[0])) && std::isxdigit(static_cast<unsigned char>(hex_str[1]))) {
+                        int byte_val = std::stoi(hex_str, nullptr, 16);
+                        bytes_[byte_val] = static_cast<llama_token>(token_id);
+                    }
                 }
                 continue;
             }
@@ -3625,12 +3627,15 @@ int32_t llama_vocab::impl::token_to_piece(llama_token token, char * buf, int32_t
                 if (vocab.is_byte(token)) {
                     // Handle byte tokens like <0xXX>
                     if (token_text.length() == 6 && token_text.substr(0, 3) == "<0x" && token_text.back() == '>') {
-                        int hex_val = std::stoi(token_text.substr(3, 2), nullptr, 16);
-                        if (length < 1) {
-                            return -1;
+                        std::string hex_str = token_text.substr(3, 2);
+                        if (std::isxdigit(static_cast<unsigned char>(hex_str[0])) && std::isxdigit(static_cast<unsigned char>(hex_str[1]))) {
+                            int hex_val = std::stoi(hex_str, nullptr, 16);
+                            if (length < 1) {
+                                return -1;
+                            }
+                            buf[0] = static_cast<char>(hex_val);
+                            return 1;
                         }
-                        buf[0] = static_cast<char>(hex_val);
-                        return 1;
                     }
                 }
 

src/models/deepseek4.cpp

@@ -114,7 +114,9 @@ void llama_model_deepseek4::load_arch_tensors(llama_model_loader & ml) {
         layer.wq_b          = create_tensor(tn(LLM_TENSOR_ATTN_Q_B,      "weight", i), {q_lora_rank, n_head * n_embd_head}, flags);
         layer.wkv           = create_tensor(tn(LLM_TENSOR_ATTN_KV,       "weight", i), {n_embd, n_embd_head}, flags);
         layer.attn_kv_norm  = create_tensor(tn(LLM_TENSOR_ATTN_KV_NORM,  "weight", i), {n_embd_head}, flags);
-        layer.wo_a          = create_tensor(tn(LLM_TENSOR_ATTN_OUT_A,    "weight", i), {n_head * n_embd_head / o_groups, o_lora_rank * o_groups}, flags);
+        // for wo_a, the shape in the file is (n_head * n_embd_head / o_groups, o_lora_rank*o_groups)
+        // so we reshape here, to avoid reshaping the tensor in the graph
+        layer.wo_a          = create_tensor(tn(LLM_TENSOR_ATTN_OUT_A,    "weight", i), {n_head * n_embd_head / o_groups, o_lora_rank, o_groups}, flags | TENSOR_ALLOW_RESHAPE);
         layer.wo_b          = create_tensor(tn(LLM_TENSOR_ATTN_OUT_B,    "weight", i), {o_groups * o_lora_rank, n_embd}, flags);
 
         layer.hc_attn_fn    = create_tensor(tn(LLM_TENSOR_HC_ATTN_FN,    "weight", i), {hc_dim, hc_mix_dim}, flags);
@@ -1258,7 +1260,7 @@ ggml_tensor * llama_model_deepseek4::graph::build_attention_impl(
 
     out = ggml_reshape_3d(ctx0, out, o_group_dim, n_groups, nt);
     out = ggml_permute(ctx0, out, 0, 2, 1, 3);
-    ggml_tensor * oa = ggml_mul_mat(ctx0, ggml_reshape_3d(ctx0, layer.wo_a, layer.wo_a->ne[0], o_lora_rank, n_groups), out);
+    ggml_tensor * oa = ggml_mul_mat(ctx0, layer.wo_a, out);
     cb(oa, "attn_wo_a", il);
     oa = ggml_permute(ctx0, oa, 0, 2, 1, 3);
     oa = ggml_cont_2d(ctx0, oa, o_lora_rank*n_groups, nt);

src/models/dflash.cpp

@@ -125,7 +125,7 @@ void llama_model_dflash::load_arch_tensors(llama_model_loader &) {
             layer.wq_b          = create_tensor(tn(LLM_TENSOR_ATTN_Q_B,      "weight", i), {q_lora_rank, n_head * n_embd_head}, 0);
             layer.wkv           = create_tensor(tn(LLM_TENSOR_ATTN_KV,       "weight", i), {n_embd, n_embd_head}, 0);
             layer.attn_kv_norm  = create_tensor(tn(LLM_TENSOR_ATTN_KV_NORM,  "weight", i), {n_embd_head}, 0);
-            layer.wo_a          = create_tensor(tn(LLM_TENSOR_ATTN_OUT_A,    "weight", i), {n_head * n_embd_head / o_groups, o_lora_rank * o_groups}, 0);
+            layer.wo_a          = create_tensor(tn(LLM_TENSOR_ATTN_OUT_A,    "weight", i), {n_head * n_embd_head / o_groups, o_lora_rank, o_groups}, TENSOR_ALLOW_RESHAPE);
             layer.wo_b          = create_tensor(tn(LLM_TENSOR_ATTN_OUT_B,    "weight", i), {o_groups * o_lora_rank, n_embd}, 0);
 
             layer.hc_attn_fn    = create_tensor(tn(LLM_TENSOR_HC_ATTN_FN,    "weight", i), {hc_dim, hc_mix_dim}, 0);

src/models/models.h

@@ -596,6 +596,11 @@ struct llama_model_qwen3vlmoe : public llama_model_base {
 };
 
 
+struct llama_model_qwen3tts : public llama_model_qwen3vl {
+    llama_model_qwen3tts(const struct llama_model_params & params) : llama_model_qwen3vl(params) {}
+};
+
+
 struct llama_model_phi2 : public llama_model_base {
     llama_model_phi2(const struct llama_model_params & params) : llama_model_base(params) {}
     void load_arch_hparams(llama_model_loader & ml) override;

src/models/qwen3tts.cpp

@@ -0,0 +1,3 @@
+#include "models.h"
+
+// llama_model_qwen3tts reuses llama_model_qwen3vl's hparams/tensors/graph logic

src/models/qwen3vl.cpp

@@ -16,11 +16,16 @@ void llama_model_qwen3vl::load_arch_hparams(llama_model_loader & ml) {
 void llama_model_qwen3vl::load_arch_tensors(llama_model_loader &) {
     LLAMA_LOAD_LOCALS;
 
+    int64_t n_vocab_out = n_vocab;
+    if (arch == LLM_ARCH_QWEN3TTS) {
+        n_vocab_out = 3072;
+    }
+
     tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
 
     // output
     output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab_out}, TENSOR_NOT_REQUIRED);
     // if output is NULL, init from the input tok embed
     if (output == NULL) {
         output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
@@ -166,6 +171,24 @@ llama_model_qwen3vl::graph::graph(const llama_model & model, const llm_graph_par
     // lm_head
     cur = build_lora_mm(model.output, cur, model.output_s);
 
+    int64_t n_vocab_in  = model.tok_embd->ne[1];
+    int64_t n_vocab_out = model.output->ne[1];
+    if (n_vocab_in > n_vocab_out) {
+        // case: Qwen3TTS model with codec_head as output
+        GGML_ASSERT(model.output_norm);
+        int64_t pad = n_vocab_in - n_vocab_out;
+
+        // using this trick to get a scalar -inf tensor to pad the output
+        ggml_tensor * neg_inf = ggml_scale_bias(ctx0,
+                ggml_view_1d(ctx0, model.output_norm, 1, 0),
+                0.0f, -INFINITY);
+        neg_inf = ggml_repeat_4d(ctx0, neg_inf, pad, cur->ne[1], 1, 1);
+        cur = ggml_concat(ctx0, neg_inf, cur, 0); // [padded .. n_vocab_out, n_stream]
+
+    } else if (n_vocab_in < n_vocab_out) {
+        GGML_ABORT("invalid case");
+    }
+
     cb(cur, "result_output", -1);
     res->t_logits = cur;
 

tests/test-arg-parser.cpp

@@ -101,6 +101,14 @@ static void test(void) {
 
     {
         common_params penalty_params;
+        assert(penalty_params.sampling.penalty_last_n == 64);
+        assert(penalty_params.sampling.dry_penalty_last_n == 64);
+
+        argv = {"binary_name", "--repeat-last-n", "-1"};
+        assert(false == common_params_parse(argv.size(), list_str_to_char(argv).data(), penalty_params, LLAMA_EXAMPLE_COMMON));
+
+        argv = {"binary_name", "--dry-penalty-last-n", "-1"};
+        assert(false == common_params_parse(argv.size(), list_str_to_char(argv).data(), penalty_params, LLAMA_EXAMPLE_COMMON));
 
         argv = {"binary_name", "--repeat-penalty", "0"};
         assert(false == common_params_parse(argv.size(), list_str_to_char(argv).data(), penalty_params, LLAMA_EXAMPLE_COMMON));

tests/test-backend-ops.cpp

@@ -9747,6 +9747,15 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
 static std::vector<std::unique_ptr<test_case>> make_test_cases_perf() {
     std::vector<std::unique_ptr<test_case>> test_cases;
 
+    // SWIGLU at a 27B-class FFN width, fused [gate|up] vs split operands
+    // note: same bytes either way, so a backend that indexes them differently shows it here
+    for (ggml_type type : {GGML_TYPE_F16, GGML_TYPE_F32}) {
+        for (int64_t n_tokens : {512, 2048}) {
+            test_cases.emplace_back(new test_glu(GGML_GLU_OP_SWIGLU, type, { 2*17408, n_tokens, 1, 1 }, 0, false));
+            test_cases.emplace_back(new test_glu_split(GGML_GLU_OP_SWIGLU, type, { 17408, n_tokens, 1, 1 }, 0));
+        }
+    }
+
     // Conv2d: K=CRS=NPQ=4096 matmul performance
     uint32_t                        iwh_idx  = 0;
     uint32_t                        kwh_idx  = 1;

tests/test-backend-sampler.cpp

@@ -823,6 +823,7 @@ enum class penalties_position {
 static void add_filter_and_penalties(
         llama_sampler * chain,
         const sampler_init_fn & init_filter,
+        int32_t n_vocab,
         int32_t penalty_last_n,
         float penalty_repeat,
         float penalty_freq,
@@ -830,7 +831,7 @@ static void add_filter_and_penalties(
         penalties_position position) {
     const auto add_penalties = [&]() {
         llama_sampler_chain_add(chain, llama_sampler_init_penalties(
-                    penalty_last_n, penalty_repeat, penalty_freq, penalty_present));
+                    n_vocab, penalty_last_n, penalty_repeat, penalty_freq, penalty_present));
     };
 
     if (position == penalties_position::before_filter) {
@@ -1006,7 +1007,7 @@ static sampler_comparison_output run_penalties_comparison(
     const std::vector<float> raw_logits = decode_raw_logits(params, prompt);
     const auto add_samplers = [&](llama_sampler * chain) {
         llama_sampler_chain_add(chain, llama_sampler_init_penalties(
-                    penalty_last_n, penalty_repeat, penalty_freq, penalty_present));
+                    llama_vocab_n_tokens(vocab), penalty_last_n, penalty_repeat, penalty_freq, penalty_present));
     };
     const auto accept_history = [&](llama_sampler * chain) {
         accept_prompt(chain, vocab, prompt);
@@ -1105,7 +1106,7 @@ static void compare_top_k_penalties_logits(
     GGML_ASSERT(excluded_history_token != LLAMA_TOKEN_NULL);
 
     const auto add_samplers = [&](llama_sampler * chain) {
-        add_filter_and_penalties(chain, init_top_k,
+        add_filter_and_penalties(chain, init_top_k, n_vocab,
                 penalty_last_n, penalty_repeat, penalty_freq, penalty_present, position);
     };
 
@@ -1190,7 +1191,7 @@ static void compare_masking_penalties_logits(
     GGML_ASSERT(masked_token != LLAMA_TOKEN_NULL);
 
     const auto add_samplers = [&](llama_sampler * chain) {
-        add_filter_and_penalties(chain, init_filter,
+        add_filter_and_penalties(chain, init_filter, n_vocab,
                 penalty_last_n, penalty_repeat, penalty_freq, penalty_present, position);
     };
     auto accept_history = [&](llama_sampler * smpl) {
@@ -1218,7 +1219,7 @@ static void compare_masking_penalties_logits(
             GGML_ASSERT(fabsf(expected_logits.at(penalized_token) - raw_logits[penalized_token]) > 1e-6f);
         } else {
             llama_sampler_ptr penalties(llama_sampler_init_penalties(
-                        penalty_last_n, penalty_repeat, penalty_freq, penalty_present));
+                        n_vocab, penalty_last_n, penalty_repeat, penalty_freq, penalty_present));
             accept_history(penalties.get());
             const std::unordered_map<llama_token, float> penalized_logits =
                 map_logits(apply_cpu_sampler(raw_logits, penalties.get()));
@@ -1667,9 +1668,18 @@ static std::vector<const backend_test_case *> collect_tests_to_run(const std::st
         }
     } else {
         for (const auto & test : BACKEND_TESTS) {
-            if (test.enabled_by_default) {
-                selected.push_back(&test);
+            if (!test.enabled_by_default) {
+                continue;
             }
+#ifdef GGML_USE_HIP
+            // TODO: remove this when https://github.com/ggml-org/llama.cpp/pull/26592 is merged
+            if (test.name == "penalties" || test.name == "set_sampler" ||
+                test.name == "mixed"     || test.name == "top_p") {
+                fprintf(stderr, "Skipping test '%s' on HIP backend (no backend TOP_K support)\n", test.name.c_str());
+                continue;
+            }
+#endif // GGML_USE_HIP
+            selected.push_back(&test);
         }
     }
 

tests/test-grammar-parser.cpp

@@ -153,6 +153,53 @@ int main()
         root ::= "a"{,10}"
     )""");
 
+    verify_failure(R"""(
+        root ::= "a"{5000}
+    )""");
+
+    verify_failure(R"""(
+        root ::= "a"{5000,}
+    )""");
+
+    verify_failure(R"""(
+        root ::= "a"{5000,6000}
+    )""");
+
+    verify_parsing(R"""(
+        root ::= "a"{0,5000}
+    )""", {
+        {"root", 0},
+        {"root_1", 1},
+    }, {
+        // root (index 0)
+        {LLAMA_GRETYPE_RULE_REF, /* root_1 */ 1},
+        {LLAMA_GRETYPE_END, 0},
+        // root_1 (index 1)
+        {LLAMA_GRETYPE_CHAR, 'a'},
+        {LLAMA_GRETYPE_RULE_REF, /* root_1 */ 1},
+        {LLAMA_GRETYPE_ALT, 0},
+        {LLAMA_GRETYPE_END, 0},
+    });
+
+    verify_parsing(R"""(
+        root ::= "a"{3,5000}
+    )""", {
+        {"root", 0},
+        {"root_1", 1},
+    }, {
+        // root (index 0)
+        {LLAMA_GRETYPE_CHAR, 'a'},
+        {LLAMA_GRETYPE_CHAR, 'a'},
+        {LLAMA_GRETYPE_CHAR, 'a'},
+        {LLAMA_GRETYPE_RULE_REF, /* root_1 */ 1},
+        {LLAMA_GRETYPE_END, 0},
+        // root_1 (index 1)
+        {LLAMA_GRETYPE_CHAR, 'a'},
+        {LLAMA_GRETYPE_RULE_REF, /* root_1 */ 1},
+        {LLAMA_GRETYPE_ALT, 0},
+        {LLAMA_GRETYPE_END, 0},
+    });
+
     verify_parsing(R"""(
         root  ::= "a"
     )""", {

tests/test-llama-archs.cpp

@@ -113,6 +113,8 @@ static gguf_context_ptr get_gguf_ctx(const llm_arch arch, const bool moe) {
         n_layer = 3;
     } else if (arch == LLM_ARCH_CHAMELEON) {
         n_vocab = 10240;
+    } else if (arch == LLM_ARCH_QWEN3TTS) {
+        n_vocab = 4096; // must be >= the hard-coded codec head size (3072)
     }
 
     const uint32_t n_embd_head = n_embd / n_head;
@@ -430,11 +432,19 @@ static bool arch_supported(const llm_arch arch) {
 
     // FIXME: these hit scheduler/view-backed-output issues with WebGPU on CI.
 #ifdef GGML_USE_WEBGPU
-    if (arch == LLM_ARCH_DEEPSEEK32 || arch == LLM_ARCH_GLM_DSA || arch == LLM_ARCH_MINIMAX_M3) {
+    if (arch == LLM_ARCH_DEEPSEEK32 || arch == LLM_ARCH_GLM_DSA) {
         return false;
     }
 #endif // GGML_USE_WEBGPU
 
+    // FIXME: jamba produces incorrect output (~0.55 NMSE vs CPU) on the HIP
+    // backend on RDNA3.5 (gfx1151); the SSM kernels need investigation.
+#ifdef GGML_USE_HIP
+    if (arch == LLM_ARCH_JAMBA) {
+        return false;
+    }
+#endif // GGML_USE_HIP
+
     return true;
 }
 

tests/test-model-resolution.cpp

@@ -195,7 +195,7 @@ static const std::vector<std::string> dspark_dflash = {
 
 struct plan_case {
     const char * name;
-    const std::vector<std::string> & files;
+    const std::vector<std::string> files;
     const char * hf_repo;
     const char * hf_file;
     bool sidecars;        // request mmproj + mtp + dflash + eagle3 + dspark

tests/test-mtmd-c-api.c

@@ -1,4 +1,6 @@
 #include <stdio.h>
+#include <stdlib.h>
+#include <string.h>
 #include <assert.h>
 
 #include "mtmd.h"
@@ -62,6 +64,72 @@ int main(void) {
         }
     }
 
+    // test chunk save/load round-trip
+    for (size_t i = 0; i < n_chunks; i++) {
+        const mtmd_input_chunk * chunk = mtmd_input_chunks_get(chunks, i);
+        assert(chunk != NULL);
+        enum mtmd_input_chunk_type type = mtmd_input_chunk_get_type(chunk);
+
+        // query the required buffer size (out_buf == NULL)
+        size_t expected_len = 0;
+        int32_t rc = mtmd_input_chunk_save(chunk, NULL, 0, &expected_len);
+        printf("    Chunk %zu: save query rc = %d, expected_len = %zu\n", i, rc, expected_len);
+        assert(rc == 0);
+        assert(expected_len > 0);
+
+        // saving into a too-small buffer must fail, not crash
+        char tiny_buf[1];
+        rc = mtmd_input_chunk_save(chunk, tiny_buf, sizeof(tiny_buf), NULL);
+        printf("    Chunk %zu: save into too-small buffer rc = %d (expect non-zero)\n", i, rc);
+        assert(rc != 0);
+
+        // save into a properly-sized buffer
+        char * buf = (char *) malloc(expected_len);
+        assert(buf != NULL);
+        rc = mtmd_input_chunk_save(chunk, buf, expected_len, NULL);
+        assert(rc == 0);
+
+        // loading from a truncated buffer must fail gracefully, not crash
+        if (expected_len > 1) {
+            mtmd_input_chunk * bad = mtmd_input_chunk_load(buf, expected_len - 1);
+            printf("    Chunk %zu: load from truncated buffer = %p (expect NULL)\n", i, (void *) bad);
+            assert(bad == NULL);
+        }
+
+        // load it back
+        mtmd_input_chunk * loaded = mtmd_input_chunk_load(buf, expected_len);
+        assert(loaded != NULL);
+
+        // metadata must match the original chunk
+        assert(mtmd_input_chunk_get_type(loaded) == type);
+        assert(mtmd_input_chunk_get_n_tokens(loaded) == mtmd_input_chunk_get_n_tokens(chunk));
+        assert(mtmd_input_chunk_get_n_pos(loaded) == mtmd_input_chunk_get_n_pos(chunk));
+
+        if (type == MTMD_INPUT_CHUNK_TYPE_TEXT) {
+            size_t n_tok_orig, n_tok_loaded;
+            const llama_token * tok_orig   = mtmd_input_chunk_get_tokens_text(chunk, &n_tok_orig);
+            const llama_token * tok_loaded = mtmd_input_chunk_get_tokens_text(loaded, &n_tok_loaded);
+            printf("    Chunk %zu: loaded %zu text tokens (orig %zu), first token %d (orig %d)\n",
+                i, n_tok_loaded, n_tok_orig,
+                n_tok_loaded > 0 ? tok_loaded[0] : -1,
+                n_tok_orig   > 0 ? tok_orig[0]   : -1);
+            assert(n_tok_orig == n_tok_loaded);
+            for (size_t j = 0; j < n_tok_orig; j++) {
+                assert(tok_orig[j] == tok_loaded[j]);
+            }
+        } else if (type == MTMD_INPUT_CHUNK_TYPE_IMAGE || type == MTMD_INPUT_CHUNK_TYPE_AUDIO) {
+            const char * id_orig   = mtmd_input_chunk_get_id(chunk);
+            const char * id_loaded = mtmd_input_chunk_get_id(loaded);
+            printf("    Chunk %zu: loaded id '%s' (orig '%s')\n", i, id_loaded, id_orig);
+            assert(id_orig != NULL && id_loaded != NULL);
+            assert(strcmp(id_orig, id_loaded) == 0);
+        }
+
+        mtmd_input_chunk_free(loaded);
+        free(buf);
+    }
+    printf("Chunk save/load round-trip OK\n");
+
     // Free the chunks
     mtmd_input_chunks_free(chunks);
 

tests/test-sampling.cpp

@@ -10,7 +10,7 @@
 #include <string>
 #include <vector>
 
-extern struct llama_sampler * llama_sampler_init_dry_testing(int32_t context_size, float dry_multiplier, float dry_base, int32_t dry_allowed_length, int32_t dry_penalty_last_n, const std::vector<std::vector<llama_token>>& seq_breakers);
+extern struct llama_sampler * llama_sampler_init_dry_testing(float dry_multiplier, float dry_base, int32_t dry_allowed_length, int32_t dry_penalty_last_n, const std::vector<std::vector<llama_token>>& seq_breakers);
 
 static void dump(const llama_token_data_array * cur_p) {
     for (size_t i = 0; i < cur_p->size; i++) {
@@ -144,7 +144,7 @@ static void test_penalties(
 
     sampler_tester tester(probs, probs_expected);
 
-    auto * sampler = llama_sampler_init_penalties(last_tokens.size(), repeat_penalty, alpha_frequency, alpha_presence);
+    auto * sampler = llama_sampler_init_penalties((int32_t) probs.size(), (int32_t) last_tokens.size(), repeat_penalty, alpha_frequency, alpha_presence);
 
     for (size_t i = 0; i < last_tokens.size(); i++) {
         llama_sampler_accept(sampler, last_tokens[i]);
@@ -168,7 +168,7 @@ static void test_dry(
 
     sampler_tester tester(probs, expected_probs);
 
-    auto * sampler = llama_sampler_init_dry_testing(1024, dry_multiplier, dry_base, dry_allowed_length, dry_penalty_last_n, seq_breakers);
+    auto * sampler = llama_sampler_init_dry_testing(dry_multiplier, dry_base, dry_allowed_length, dry_penalty_last_n, seq_breakers);
 
     for (size_t i = 0; i < last_tokens.size(); i++) {
         llama_sampler_accept(sampler, last_tokens[i]);

tools/cli/README.md

@@ -116,14 +116,14 @@
 | `--xtc-probability N` | xtc probability (default: 0.00, 0.0 = disabled) |
 | `--xtc-threshold N` | xtc threshold (default: 0.10, 1.0 = disabled) |
 | `--typical, --typical-p N` | locally typical sampling, parameter p (default: 1.00, 1.0 = disabled) |
-| `--repeat-last-n N` | last n tokens to consider for penalize (default: 64, 0 = disabled, -1 = ctx_size) |
+| `--repeat-last-n N` | last n tokens to consider for penalize (default: 64, 0 = disabled) |
 | `--repeat-penalty N` | penalize repeat sequence of tokens (default: 1.00, 1.0 = disabled) |
 | `--presence-penalty N` | repeat alpha presence penalty (default: 0.00, 0.0 = disabled) |
 | `--frequency-penalty N` | repeat alpha frequency penalty (default: 0.00, 0.0 = disabled) |
 | `--dry-multiplier N` | set DRY sampling multiplier (default: 0.00, 0.0 = disabled) |
 | `--dry-base N` | set DRY sampling base value (default: 1.75) |
 | `--dry-allowed-length N` | set allowed length for DRY sampling (default: 2) |
-| `--dry-penalty-last-n N` | set DRY penalty for the last n tokens (default: -1, 0 = disable, -1 = context size) |
+| `--dry-penalty-last-n N` | set DRY penalty for the last n tokens (default: 64, 0 = disable) |
 | `--dry-sequence-breaker STRING` | add sequence breaker for DRY sampling, clearing out default breakers ('\n', ':', '"', '*') in the process; use "none" to not use any sequence breakers |
 | `--adaptive-target N` | adaptive-p: select tokens near this probability (valid range 0.0 to 1.0; negative = disabled) (default: -1.00)<br/>[(more info)](https://github.com/ggml-org/llama.cpp/pull/17927) |
 | `--adaptive-decay N` | adaptive-p: decay rate for target adaptation over time. lower values are more reactive, higher values are more stable.<br/>(valid range 0.0 to 0.99) (default: 0.90) |

tools/completion/README.md

@@ -199,14 +199,14 @@ llama-completion.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf --ignore-eos -n -1
 | `--xtc-probability N` | xtc probability (default: 0.00, 0.0 = disabled) |
 | `--xtc-threshold N` | xtc threshold (default: 0.10, 1.0 = disabled) |
 | `--typical, --typical-p N` | locally typical sampling, parameter p (default: 1.00, 1.0 = disabled) |
-| `--repeat-last-n N` | last n tokens to consider for penalize (default: 64, 0 = disabled, -1 = ctx_size) |
+| `--repeat-last-n N` | last n tokens to consider for penalize (default: 64, 0 = disabled) |
 | `--repeat-penalty N` | penalize repeat sequence of tokens (default: 1.00, 1.0 = disabled) |
 | `--presence-penalty N` | repeat alpha presence penalty (default: 0.00, 0.0 = disabled) |
 | `--frequency-penalty N` | repeat alpha frequency penalty (default: 0.00, 0.0 = disabled) |
 | `--dry-multiplier N` | set DRY sampling multiplier (default: 0.00, 0.0 = disabled) |
 | `--dry-base N` | set DRY sampling base value (default: 1.75) |
 | `--dry-allowed-length N` | set allowed length for DRY sampling (default: 2) |
-| `--dry-penalty-last-n N` | set DRY penalty for the last n tokens (default: -1, 0 = disable, -1 = context size) |
+| `--dry-penalty-last-n N` | set DRY penalty for the last n tokens (default: 64, 0 = disable) |
 | `--dry-sequence-breaker STRING` | add sequence breaker for DRY sampling, clearing out default breakers ('\n', ':', '"', '*') in the process; use "none" to not use any sequence breakers |
 | `--adaptive-target N` | adaptive-p: select tokens near this probability (valid range 0.0 to 1.0; negative = disabled) (default: -1.00)<br/>[(more info)](https://github.com/ggml-org/llama.cpp/pull/17927) |
 | `--adaptive-decay N` | adaptive-p: decay rate for target adaptation over time. lower values are more reactive, higher values are more stable.<br/>(valid range 0.0 to 0.99) (default: 0.90) |
@@ -388,11 +388,11 @@ Example usage: `--temp 0`
 ### Repeat Penalty
 
 -   `--repeat-penalty N`: Control the repetition of token sequences in the generated text default: 1.0, 1.0 = disabled).
--   `--repeat-last-n N`: Last n tokens to consider for penalizing repetition (default: 64, 0 = disabled, -1 = ctx-size).
+-   `--repeat-last-n N`: Last n tokens to consider for penalizing repetition (default: 64, 0 = disabled).
 
 The `repeat-penalty` option helps prevent the model from generating repetitive or monotonous text. A higher value (e.g., 1.5) will penalize repetitions more strongly, while a lower value (e.g., 0.9) will be more lenient. The default value is 1.
 
-The `repeat-last-n` option controls the number of tokens in the history to consider for penalizing repetition. A larger value will look further back in the generated text to prevent repetitions, while a smaller value will only consider recent tokens. A value of 0 disables the penalty, and a value of -1 sets the number of tokens considered equal to the context size (`ctx-size`).
+The `repeat-last-n` option controls the number of tokens in the history to consider for penalizing repetition. A larger value will look further back in the generated text to prevent repetitions, while a smaller value will only consider recent tokens. A value of 0 disables the penalty.
 
 ### DRY Repetition Penalty
 
@@ -401,7 +401,7 @@ DRY (Don't Repeat Yourself) sampling is an effective technique for reducing repe
 - `--dry-multiplier N`: Set the DRY sampling multiplier (default: 0.0, 0.0 = disabled).
 - `--dry-base N`: Set the DRY sampling base value (default: 1.75).
 - `--dry-allowed-length N`: Set the allowed length for DRY sampling (default: 2).
-- `--dry-penalty-last-n N`: Set DRY penalty for the last n tokens (default: -1, 0 = disable, -1 = context size).
+- `--dry-penalty-last-n N`: Set DRY penalty for the last n tokens (default: 64, 0 = disable).
 - `--dry-sequence-breaker STRING`: Add a sequence breaker for DRY sampling. Can be used more than once to add multiple sequence breakers. Using this clears out the default breakers, which consist of: `['\n', ':', '"', '*']`. If the string `"none"` is supplied, no sequence breakers are used.
 
 The `dry-multiplier` option controls the strength of the DRY sampling effect. A value of 0.0 disables DRY sampling, while higher values increase its influence. A typical recommended value is 0.8.
@@ -410,13 +410,13 @@ The `dry-base` option sets the base value for the exponential penalty calculatio
 
 The `dry-allowed-length` option sets the maximum length of repeated sequences that will not be penalized. Repetitions shorter than or equal to this length are not penalized, allowing for natural repetitions of short phrases or common words.
 
-The `dry-penalty-last-n` option controls how many recent tokens to consider when applying the DRY penalty. A value of -1 considers the entire context. Use a positive value to limit the consideration to a specific number of recent tokens.
+The `dry-penalty-last-n` option controls how many recent tokens to consider when applying the DRY penalty. A value of 0 disables the penalty. Use a positive value to limit the consideration to a specific number of recent tokens.
 
 The `dry-sequence-breaker` option adds a single sequence breaker and can be used more than once to specify multiple sequence breakers. Sequence breakers interrupt sequence matching and break the input into parts where matching can be applied.
 
 DRY sampling provides more nuanced control over text generation, particularly for reducing long-range repetitions and maintaining global coherence.
 
-Example usage: `--dry-multiplier 0.8 --dry-base 1.75 --dry-allowed-length 2 --dry-penalty-last-n -1 --dry-sequence-breaker "—" --dry-sequence-breaker "##"`
+Example usage: `--dry-multiplier 0.8 --dry-base 1.75 --dry-allowed-length 2 --dry-penalty-last-n 64 --dry-sequence-breaker "—" --dry-sequence-breaker "##"`
 
 ### Top-K Sampling
 

tools/gguf-split/gguf-split.cpp

@@ -47,6 +47,7 @@ struct split_params {
     std::string output;
     bool no_tensor_first_split = false;
     bool dry_run = false;
+    bool delete_splits = false;
 };
 
 static void split_print_usage(const char * executable) {
@@ -65,6 +66,7 @@ static void split_print_usage(const char * executable) {
     printf("  --split-max-size N(M|G) max size per split\n");
     printf("  --no-tensor-first-split do not add tensors to the first split (disabled by default)\n");
     printf("  --dry-run               only print out a split plan and exit, without writing any new files\n");
+    printf("  --delete-splits         delete the split files during merge to free up disk space WARNING: this option is unsafe and will leave you in an unrecoverable state if something fails during the merge\n");
     printf("\n");
 }
 
@@ -147,6 +149,9 @@ static void split_params_parse_ex(int argc, const char ** argv, split_params & p
             }
             params.mode = MODE_SIZE;
             params.n_bytes_split = split_str_to_n_bytes(argv[arg_idx]);
+        } else if (arg == "--delete-splits") {
+            arg_found = true;
+            params.delete_splits = true;
         }
 
         if (!arg_found) {
@@ -509,6 +514,7 @@ static void gguf_merge(const split_params & split_params) {
     }
 
     // Write tensors data
+    bool merge_error = false;
     for (int i_split = 0; i_split < n_split; i_split++) {
         llama_split_path(split_path, sizeof(split_path), split_prefix, i_split, n_split);
         std::ifstream f_input(split_path, std::ios::binary);
@@ -554,6 +560,16 @@ static void gguf_merge(const split_params & split_params) {
         ggml_free(ctx_meta);
         f_input.close();
         fprintf(stderr, "\033[3Ddone\n");
+
+        if (!split_params.dry_run && split_params.delete_splits) {
+            int delete_result = std::remove(split_path);
+            if (delete_result != 0) {
+                merge_error = true;
+                fprintf(stderr, "error: failed to delete %s\n", split_path);
+            } else {
+                fprintf(stderr, "%s: deleted file %s\n", __func__, split_path);
+            }
+        }
     }
 
     if (!split_params.dry_run) {
@@ -568,6 +584,10 @@ static void gguf_merge(const split_params & split_params) {
 
     fprintf(stderr, "%s: %s merged from %d split with %d tensors.\n",
             __func__, split_params.output.c_str(), n_split, total_tensors);
+
+    if (merge_error) {
+        exit(EXIT_FAILURE);
+    }
 }
 
 int main(int argc, const char ** argv) {

tools/gguf-split/tests.sh

@@ -66,12 +66,12 @@ echo PASS
 echo
 
 # 5. Merge
-#$SPLIT --merge $WORK_PATH/ggml-model-split-32-tensors-00001-of-00012.gguf $WORK_PATH/ggml-model-merge-2.gguf
+#$SPLIT --merge $WORK_PATH/ggml-model-split-32-tensors-00001-of-00011.gguf $WORK_PATH/ggml-model-merge-2.gguf
 #echo PASS
 #echo
 
 # 5b. Test the merged model is loading properly
-#$MAIN -no-cnv --model $WORK_PATH/ggml-model-merge-2.gguf --n-predict 32
+#$MAIN -no-cnv --model $WORK_PATH/ggml-model-merge-2.gguf -p "I believe the meaning of life is" --n-predict 32
 #echo PASS
 #echo
 
@@ -85,5 +85,25 @@ $MAIN -no-cnv --model $WORK_PATH/ggml-model-split-500M-00001-of-00002.gguf -p "I
 echo PASS
 echo
 
+# 7. Merge with delete splits
+#for i in $(seq -w 1 11); do
+#    cp "$WORK_PATH/ggml-model-split-32-tensors-000${i}-of-00011.gguf" "$WORK_PATH/ggml-model-split-32-tensors-copy-000${i}-of-00011.gguf"
+#done
+#$SPLIT --merge --delete-splits $WORK_PATH/ggml-model-split-32-tensors-copy-00001-of-00011.gguf $WORK_PATH/ggml-model-merge-3.gguf
+#echo PASS
+#echo
+
+# 7b. Test the merged model is loading properly
+#$MAIN -no-cnv --model $WORK_PATH/ggml-model-merge-3.gguf -p "I believe the meaning of life is" --n-predict 32
+#echo PASS
+#echo
+
+# 7c. Test the files were deleted
+#for i in $(seq -w 1 11); do
+#    test ! -f "$WORK_PATH/ggml-model-split-32-tensors-copy-000${i}-of-00011.gguf"
+#done
+#echo PASS
+#echo
+
 # Clean up
 rm -f $WORK_PATH/ggml-model-split*.gguf $WORK_PATH/ggml-model-merge*.gguf

tools/mtmd/CMakeLists.txt

@@ -18,6 +18,8 @@ add_library(mtmd
             mtmd-image.cpp
             mtmd.h
             mtmd-helper.cpp
+            mtmd-helper-gen.cpp
+            mtmd-helper-common.h
             mtmd-helper.h
             clip.cpp
             clip.h
@@ -52,6 +54,8 @@ add_library(mtmd
             models/mimovl.cpp
             models/qwen3a.cpp
             models/mimo-audio.cpp
+            models/qwen3tts-spkenc.cpp
+            models/qwen3tts-gen.cpp
             models/step3vl.cpp
             models/siglip.cpp
             models/whisper-enc.cpp

tools/mtmd/README-dev.md

@@ -33,3 +33,52 @@ A typical pipeline of the core libmtmd is as follows:
 We provide a set of helper functions via `mtmd_helper` to make using libmtmd easier. The helper provides:
 - Image, audio and video file decoding (for example, decode raw JPEG into RGB bitmap)
 - Manage `llama_batch` and calls to `llama_decode`
+
+## Audio generation support
+
+Audio generation is added to mtmd in PR [#26254](https://github.com/ggml-org/llama.cpp/pull/26254)
+
+Currently, we support the 3-stage pipeline below which should cover most TTS models:
+- Stage 1: Backbone / Semantic Stage: Backbone model accepts text prompt and reference voice as input
+- Stage 2: Acoustic Detail Generator: A model takes the hidden state from backbone and generate audio details (usually as audio codes or mel-spectrogram)
+- Stage 3: Waveform Reconstruction: Convert the semantic and acoustic data from previous stages to the final waveform
+
+For example, Qwen3-TTS:
+- Reference voice is encoded using ECAPA-TDNN speaker encoder (`speaker_encoder`)
+- Text prompt and reference voice are processed via a backbone (`talker.model`)
+- A model converts sampled semantic token and hidden state from stage 2 into a list of 15 acoustic codes (`talker.code_predictor`)
+- 16 generated codes are converted into waveform (`code2wav`)
+
+### API design constraints
+
+Due to wide variety of audio generation pipelines, the `mtmd_gen_audio` system is designed to be flexible and reusable by new models.
+
+`mtmd_gen_audio` is split into 2 main API:
+- Core API `mtmd.h`: handles main inference. Important: the API surface must be stateless; caller must handle state management and audio frame accumulation.
+- Helper API `mtmd-helper.h`: provides a model-agnostic stateful API. Usage example can be found in the `tools/tts` directory.
+
+### Checklist for porting new audio generation models to mtmd
+
+1. Establish a list of reusable and missing components from the current mtmd implementation.
+2. For GGUF conversion:
+    - Backbone model should be converted to a normal text model (loadable via `libllama`)
+        - If model used hard-coded embedding row ID, append them to token embeddings and assign token name for them (see `qwen3tts.py`)
+        - If model have a specific output logits head for audio codes (usually semantic code), keep the head as-is and pad the logits at inference time (see `src/models/qwen3vl.cpp`)
+    - Sidecar models (code2wav, bigvgan, etc) must live inside the mmproj GGUF (but can be in different `clip_context` if necessary)
+        - Note: it should use `ggml_build_forward_select` to select graphs if multiple graphs living in the same context
+    - Reuse existing GGUF metadata key name and tensor name whenever possible; think twice before adding extensive changes to GGUF writer. For example, Qwen3-TTS hard-code part of the hparams to `clip.cpp` as they won't likely to change.
+    - For tensor naming:
+        - Prefixed with `a.*` for tensors used by speaker encoder pipeline
+        - Prefixed with `a.gen.*` for generation stages (code / mel-spectrogram / PCM generation)
+3. Make sure most of the changes happen inside `mtmd-helper-gen.cpp`. A good PR looks like this:
+    - 10-20% changes is to add new backbone (text) model and conversion
+    - 60% changes inside `mtmd-helper-gen.cpp`
+    - 10% changes inside `libmtmd` and `clip.cpp` systems
+    - The rest downstream code (CLI, server) should have no changes at all
+4. Update usage documentation in `tools/tts/README.md`
+
+IMPORTANT: If your model needs changes that don't fit the existing infrastructure, **open an issue first for discussion**.
+
+No-go checklist (these will get the PR rejected and require discussion before proceeding):
+- Violating the API design constraints stated above
+- Adding a new model-specific binary: the API and binary surface must stay model-agnostic

tools/mtmd/clip-graph.h

@@ -54,6 +54,9 @@ struct clip_graph {
 
     clip_graph(clip_ctx * ctx, const clip_image_f32 & img);
 
+    // build sub-graph, reuse buf from parent
+    clip_graph(const clip_graph & parent);
+
     virtual ~clip_graph() = default;
     virtual ggml_cgraph * build() = 0;
 

tools/mtmd/clip-impl.h

@@ -32,6 +32,7 @@
 #define KEY_PROJ_TYPE           "clip.projector_type"
 #define KEY_HAS_AUDIO_ENC       "clip.has_audio_encoder"
 #define KEY_HAS_VISION_ENC      "clip.has_vision_encoder"
+#define KEY_HAS_GEN_AUDIO_ENC   "clip.has_gen_audio_encoder"
 #define KEY_USE_GELU            "clip.use_gelu"
 #define KEY_USE_SILU            "clip.use_silu"
 
@@ -89,6 +90,8 @@
 #define KEY_A_ATTN_WINDOW_SIZE     "clip.audio.window_size"          // mimo-audio-tokenizer: sliding-window radius
 #define KEY_A_LOCAL_BLOCK_COUNT    "clip.audio.local_block_count"    // mimo-v2.5: input_local_transformer layer count
 #define KEY_A_LOCAL_GROUP_SIZE     "clip.audio.local_group_size"     // mimo-v2.5: input_local_transformer grouping size
+// audio generation (gen-audio)-specific
+#define KEY_GEN_AUDIO_PROJ_TYPE    "clip.gen.audio.projector_type" // for models with mixed modalities
 #define KEY_AUDIO_SUBSAMPLING_FACTOR "clip.audio.subsampling_factor"
 
 //
@@ -201,6 +204,48 @@
 #define TN_MM_A_LOCAL_LN2      "mm.a.local_blk.%d.ln2.%s"
 #define TN_MM_A_LOCAL_NORM     "mm.a.local_norm.%s"
 
+// qwen3tts speaker encoder (ECAPA-TDNN)
+#define TN_A_SE_CONV1  "a.blk.%d.se_conv1.%s"
+#define TN_A_SE_CONV2  "a.blk.%d.se_conv2.%s"
+#define TN_A_CONV_RES2 "a.blk.%d.res2.%d.%s"
+#define TN_A_ASP_ATTN  "a.asp_attn.%s"
+#define TN_A_ASP_TDNN  "a.asp_tdnn.%s"
+
+// qwen3tts code_predictor
+#define TN_A_GEN_CODE_PROJ_IN  "a.gen.code.proj_in.%s"
+#define TN_A_GEN_CODE_EMBD     "a.gen.code.embd.%s"
+#define TN_A_GEN_CODE_HEAD     "a.gen.code.head.%s"
+#define TN_A_GEN_CODE_OUT_EMBD "a.gen.code.out_embd.%s"
+#define TN_A_GEN_CODE_NORM     "a.gen.code.output_norm.%s"
+
+// qwen3tts code2wav (RVQ codes -> raw PCM)
+// pre_transformer layers use the generic TN_ATTN_*/TN_FFN_*/TN_LN_*/TN_LS_* macros, prefix "a.gen.wav.tfm"
+#define TN_A_GEN_WAV_QUANT_FIRST_IN  "a.gen.wav.quant.first.in_proj.%s"
+#define TN_A_GEN_WAV_QUANT_FIRST_OUT "a.gen.wav.quant.first.out_proj.%s"
+#define TN_A_GEN_WAV_QUANT_FIRST_CB  "a.gen.wav.quant.first.codebook.%s"
+#define TN_A_GEN_WAV_QUANT_REST_IN   "a.gen.wav.quant.rest.in_proj.%s"
+#define TN_A_GEN_WAV_QUANT_REST_OUT  "a.gen.wav.quant.rest.out_proj.%s"
+#define TN_A_GEN_WAV_QUANT_REST_CB   "a.gen.wav.quant.rest.codebook.%s"
+#define TN_A_GEN_WAV_PRE_CONV        "a.gen.wav.pre_conv.%s"
+#define TN_A_GEN_WAV_TFM_IN_PROJ     "a.gen.wav.tfm.in_proj.%s"
+#define TN_A_GEN_WAV_TFM_OUT_PROJ    "a.gen.wav.tfm.out_proj.%s"
+#define TN_A_GEN_WAV_TFM_OUT_NORM    "a.gen.wav.tfm.output_norm.%s"
+#define TN_A_GEN_WAV_UP_CONV         "a.gen.wav.up.blk.%d.conv.%s"
+#define TN_A_GEN_WAV_UP_DWCONV       "a.gen.wav.up.blk.%d.dwconv.%s"
+#define TN_A_GEN_WAV_UP_NORM         "a.gen.wav.up.blk.%d.norm.%s"
+#define TN_A_GEN_WAV_UP_PW1          "a.gen.wav.up.blk.%d.pw1.%s"
+#define TN_A_GEN_WAV_UP_PW2          "a.gen.wav.up.blk.%d.pw2.%s"
+#define TN_A_GEN_WAV_UP_GAMMA        "a.gen.wav.up.blk.%d.gamma"
+#define TN_A_GEN_WAV_DAC_ENTRY       "a.gen.wav.dac.entry.%s"
+#define TN_A_GEN_WAV_DAC_SNAKE       "a.gen.wav.dac.blk.%d.snake.%s"
+#define TN_A_GEN_WAV_DAC_CONV        "a.gen.wav.dac.blk.%d.conv.%s"
+#define TN_A_GEN_WAV_DAC_RES_ACT1    "a.gen.wav.dac.blk.%d.res.%d.act1.%s"
+#define TN_A_GEN_WAV_DAC_RES_CONV1   "a.gen.wav.dac.blk.%d.res.%d.conv1.%s"
+#define TN_A_GEN_WAV_DAC_RES_ACT2    "a.gen.wav.dac.blk.%d.res.%d.act2.%s"
+#define TN_A_GEN_WAV_DAC_RES_CONV2   "a.gen.wav.dac.blk.%d.res.%d.conv2.%s"
+#define TN_A_GEN_WAV_DAC_POST_SNAKE  "a.gen.wav.dac.post_snake.%s"
+#define TN_A_GEN_WAV_DAC_POST_CONV   "a.gen.wav.dac.post_conv.%s"
+
 // cogvlm
 #define TN_MM_POST_FC_NORM "mm.post_fc_norm.%s"
 #define TN_MM_H_TO_4H      "mm.up.%s"
@@ -408,6 +453,8 @@ enum projector_type {
     PROJECTOR_TYPE_MINIMAX_M3,
     PROJECTOR_TYPE_GRANITE4_VISION,
     PROJECTOR_TYPE_MIMO_AUDIO,
+    PROJECTOR_TYPE_QWEN3TTS_SPKENC,
+    PROJECTOR_TYPE_QWEN3TTS_GEN,
     PROJECTOR_TYPE_UNKNOWN,
 };
 
@@ -465,6 +512,8 @@ static std::map<projector_type, std::string> PROJECTOR_TYPE_NAMES = {
     { PROJECTOR_TYPE_GRANITE4_VISION,   "granite4_vision"},
     { PROJECTOR_TYPE_MIMO_AUDIO,        "mimo_audio"},
     { PROJECTOR_TYPE_PARAKEET,          "parakeet"},
+    { PROJECTOR_TYPE_QWEN3TTS_SPKENC,   "qwen3tts_spkenc"},
+    { PROJECTOR_TYPE_QWEN3TTS_GEN,      "qwen3tts_gen"},
 };
 
 static projector_type clip_projector_type_from_string(const std::string & str) {
@@ -542,6 +591,8 @@ struct clip_image_u8 {
     }
 };
 
+struct mtmd_serialization; // forward declaration
+
 // For images, buf.size() == nx*ny*3
 //     Memory layout: RGBRGBRGB...
 // For seq, buf.size() == nx*ny*3*nt
@@ -622,6 +673,9 @@ struct clip_image_f32 {
         return buf.empty();
     }
 
+    void serialize(struct mtmd_serialization & ser) const;
+    void deserialize(struct mtmd_serialization & ser);
+
   private:
     std::vector<float> buf;
     int nx_ = 0;
@@ -703,6 +757,9 @@ struct clip_image_f32_batch {
         }
         return new_batch;
     }
+
+    void serialize(struct mtmd_serialization & ser) const;
+    void deserialize(struct mtmd_serialization & ser);
 };
 
 //