SYCL: implement memset ggml backend buffer interface (#12580)

* SYCL: implement memset ggml backend buffer interface

* use GGML_ABORT macro

* Do not wait for all queues to finish for memset operation

ci/README.md

@@ -26,4 +26,43 @@ GG_BUILD_CUDA=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt
 # with SYCL support
 source /opt/intel/oneapi/setvars.sh
 GG_BUILD_SYCL=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt
+
+# with MUSA support
+GG_BUILD_MUSA=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt
 ```
+
+## Running MUSA CI in a Docker Container
+
+Assuming `$PWD` is the root of the `llama.cpp` repository, follow these steps to set up and run MUSA CI in a Docker container:
+
+### 1. Create a local directory to store cached models, configuration files and venv:
+
+```bash
+mkdir -p $HOME/llama.cpp/ci-cache
+```
+
+### 2. Create a local directory to store CI run results:
+
+```bash
+mkdir -p $HOME/llama.cpp/ci-results
+```
+
+### 3. Start a Docker container and run the CI:
+
+```bash
+docker run --privileged -it \
+    -v $HOME/llama.cpp/ci-cache:/ci-cache \
+    -v $HOME/llama.cpp/ci-results:/ci-results \
+    -v $PWD:/ws -w /ws \
+    mthreads/musa:rc3.1.1-devel-ubuntu22.04
+```
+
+Inside the container, execute the following commands:
+
+```bash
+apt update -y && apt install -y bc cmake git python3.10-venv time unzip wget
+git config --global --add safe.directory /ws
+GG_BUILD_MUSA=1 bash ./ci/run.sh /ci-results /ci-cache
+```
+
+This setup ensures that the CI runs within an isolated Docker environment while maintaining cached files and results across runs.

ci/run.sh

@@ -16,6 +16,9 @@
 # # with VULKAN support
 # GG_BUILD_VULKAN=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt
 #
+# # with MUSA support
+# GG_BUILD_MUSA=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt
+#
 
 if [ -z "$2" ]; then
     echo "usage: $0 <output-dir> <mnt-dir>"
@@ -52,13 +55,22 @@ if [ ! -z ${GG_BUILD_SYCL} ]; then
         echo "source /opt/intel/oneapi/setvars.sh"
         exit 1
     fi
-
+    # Use only main GPU
+    export ONEAPI_DEVICE_SELECTOR="level_zero:0"
+    # Enable sysman for correct memory reporting
+    export ZES_ENABLE_SYSMAN=1
     CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_SYCL=1 -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DGGML_SYCL_F16=ON"
 fi
 
 if [ ! -z ${GG_BUILD_VULKAN} ]; then
     CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_VULKAN=1"
 fi
+
+if [ ! -z ${GG_BUILD_MUSA} ]; then
+    # Use qy1 by default (MTT S80)
+    MUSA_ARCH=${MUSA_ARCH:-21}
+    CMAKE_EXTRA="-DGGML_MUSA=ON -DMUSA_ARCHITECTURES=${MUSA_ARCH}"
+fi
 ## helpers
 
 # download a file if it does not exist or if it is outdated
@@ -808,7 +820,7 @@ export LLAMA_LOG_PREFIX=1
 export LLAMA_LOG_TIMESTAMPS=1
 
 if [ -z ${GG_BUILD_LOW_PERF} ]; then
-    # Create symlink: ./llama.cpp/models-mnt -> $MNT/models/models-mnt
+    # Create symlink: ./llama.cpp/models-mnt -> $MNT/models
     rm -rf ${SRC}/models-mnt
     mnt_models=${MNT}/models
     mkdir -p ${mnt_models}
@@ -826,8 +838,10 @@ if [ -z ${GG_BUILD_LOW_PERF} ]; then
 fi
 
 ret=0
-
-test $ret -eq 0 && gg_run ctest_debug
+if [ -z ${GG_BUILD_SYCL} ]; then
+    # SYCL build breaks with debug build flags
+    test $ret -eq 0 && gg_run ctest_debug
+fi
 test $ret -eq 0 && gg_run ctest_release
 
 if [ -z ${GG_BUILD_LOW_PERF} ]; then
@@ -835,7 +849,9 @@ if [ -z ${GG_BUILD_LOW_PERF} ]; then
     test $ret -eq 0 && gg_run rerank_tiny
 
     if [ -z ${GG_BUILD_CLOUD} ] || [ ${GG_BUILD_EXTRA_TESTS_0} ]; then
-        test $ret -eq 0 && gg_run test_scripts_debug
+        if [ -z ${GG_BUILD_SYCL} ]; then
+            test $ret -eq 0 && gg_run test_scripts_debug
+        fi
         test $ret -eq 0 && gg_run test_scripts_release
     fi
 
@@ -846,7 +862,9 @@ if [ -z ${GG_BUILD_LOW_PERF} ]; then
             test $ret -eq 0 && gg_run pythia_2_8b
             #test $ret -eq 0 && gg_run open_llama_7b_v2
         fi
-        test $ret -eq 0 && gg_run ctest_with_model_debug
+        if [ -z ${GG_BUILD_SYCL} ]; then
+            test $ret -eq 0 && gg_run ctest_with_model_debug
+        fi
         test $ret -eq 0 && gg_run ctest_with_model_release
     fi
 fi

common/CMakeLists.txt

@@ -114,8 +114,8 @@ if (LLAMA_LLGUIDANCE)
 
     ExternalProject_Add(llguidance_ext
         GIT_REPOSITORY https://github.com/guidance-ai/llguidance
-        # v0.6.12:
-        GIT_TAG ced1c9023d47ec194fa977932d35ce65c2ebfc09
+        # v0.7.10:
+        GIT_TAG 0309d2a6bf40abda35344a362edc71e06d5009f8
         PREFIX ${CMAKE_BINARY_DIR}/llguidance
         SOURCE_DIR ${LLGUIDANCE_SRC}
         BUILD_IN_SOURCE TRUE

common/llguidance.cpp

@@ -11,25 +11,24 @@ struct llama_sampler_llg {
     std::string         grammar_kind;
     std::string         grammar_data;
     LlgTokenizer *      tokenizer;
-    LlgConstraint *     grammar;
-    LlgMaskResult       llg_res;
-    bool                has_llg_res;
+    LlgMatcher *        grammar;
 };
 
-static LlgConstraint * llama_sampler_llg_new(LlgTokenizer * tokenizer, const char * grammar_kind,
-                                             const char * grammar_data) {
+static LlgMatcher * llama_sampler_llg_new(LlgTokenizer * tokenizer, const char * grammar_kind,
+                                          const char * grammar_data) {
     LlgConstraintInit cinit;
     llg_constraint_init_set_defaults(&cinit, tokenizer);
     const char * log_level = getenv("LLGUIDANCE_LOG_LEVEL");
     if (log_level && *log_level) {
         cinit.log_stderr_level = atoi(log_level);
     }
-    auto c = llg_new_constraint_any(&cinit, grammar_kind, grammar_data);
-    if (llg_get_error(c)) {
-        LOG_ERR("llg error: %s\n", llg_get_error(c));
-        llg_free_constraint(c);
+    auto c = llg_new_matcher(&cinit, grammar_kind, grammar_data);
+    if (llg_matcher_get_error(c)) {
+        LOG_ERR("llg error: %s\n", llg_matcher_get_error(c));
+        llg_free_matcher(c);
         return nullptr;
     }
+
     return c;
 }
 
@@ -40,39 +39,29 @@ static const char * llama_sampler_llg_name(const llama_sampler * /*smpl*/) {
 static void llama_sampler_llg_accept_impl(llama_sampler * smpl, llama_token token) {
     auto * ctx = (llama_sampler_llg *) smpl->ctx;
     if (ctx->grammar) {
-        LlgCommitResult res;
-        llg_commit_token(ctx->grammar, token, &res);
-        ctx->has_llg_res = false;
+        llg_matcher_consume_token(ctx->grammar, token);
     }
 }
 
 static void llama_sampler_llg_apply(llama_sampler * smpl, llama_token_data_array * cur_p) {
     auto * ctx = (llama_sampler_llg *) smpl->ctx;
     if (ctx->grammar) {
-        if (!ctx->has_llg_res) {
-            if (llg_compute_mask(ctx->grammar, &ctx->llg_res) == 0) {
-                ctx->has_llg_res = true;
+        const uint32_t * mask = llg_matcher_get_mask(ctx->grammar);
+        if (mask == nullptr) {
+            if (llg_matcher_compute_mask(ctx->grammar) == 0) {
+                mask = llg_matcher_get_mask(ctx->grammar);
             } else {
-                LOG_ERR("llg error: %s\n", llg_get_error(ctx->grammar));
-                llg_free_constraint(ctx->grammar);
+                LOG_ERR("llg error: %s\n", llg_matcher_get_error(ctx->grammar));
+                llg_free_matcher(ctx->grammar);
                 ctx->grammar = nullptr;
+                return;
             }
         }
-        if (ctx->has_llg_res) {
-            if (ctx->llg_res.is_stop) {
-                for (size_t i = 0; i < cur_p->size; ++i) {
-                    if (!llama_vocab_is_eog(ctx->vocab, cur_p->data[i].id)) {
-                        cur_p->data[i].logit = -INFINITY;
-                    }
-                }
-            } else {
-                const uint32_t * mask = ctx->llg_res.sample_mask;
-                for (size_t i = 0; i < cur_p->size; ++i) {
-                    auto token = cur_p->data[i].id;
-                    if ((mask[token / 32] & (1 << (token % 32))) == 0) {
-                        cur_p->data[i].logit = -INFINITY;
-                    }
-                }
+
+        for (size_t i = 0; i < cur_p->size; ++i) {
+            auto token = cur_p->data[i].id;
+            if ((mask[token / 32] & (1 << (token % 32))) == 0) {
+                cur_p->data[i].logit = -INFINITY;
             }
         }
     }
@@ -80,14 +69,9 @@ static void llama_sampler_llg_apply(llama_sampler * smpl, llama_token_data_array
 
 static void llama_sampler_llg_reset(llama_sampler * smpl) {
     auto * ctx = (llama_sampler_llg *) smpl->ctx;
-    if (!ctx->grammar) {
-        return;
+    if (ctx->grammar) {
+        llg_matcher_reset(ctx->grammar);
     }
-
-    auto * grammar_new = llama_sampler_llg_new(ctx->tokenizer, ctx->grammar_kind.c_str(), ctx->grammar_data.c_str());
-    llg_free_constraint(ctx->grammar);
-    ctx->grammar     = grammar_new;
-    ctx->has_llg_res = false;
 }
 
 static llama_sampler * llama_sampler_llg_clone(const llama_sampler * smpl) {
@@ -102,7 +86,7 @@ static llama_sampler * llama_sampler_llg_clone(const llama_sampler * smpl) {
         if (ctx->grammar) {
             result_ctx->grammar_kind = ctx->grammar_kind;
             result_ctx->grammar_data = ctx->grammar_data;
-            result_ctx->grammar      = llg_clone_constraint(ctx->grammar);
+            result_ctx->grammar      = llg_clone_matcher(ctx->grammar);
             result_ctx->tokenizer    = llg_clone_tokenizer(ctx->tokenizer);
         }
     }
@@ -114,7 +98,7 @@ static void llama_sampler_llg_free(llama_sampler * smpl) {
     const auto * ctx = (llama_sampler_llg *) smpl->ctx;
 
     if (ctx->grammar) {
-        llg_free_constraint(ctx->grammar);
+        llg_free_matcher(ctx->grammar);
         llg_free_tokenizer(ctx->tokenizer);
     }
 
@@ -239,9 +223,11 @@ llama_sampler * llama_sampler_init_llg(const llama_vocab * vocab, const char * g
             /* .grammar_data = */ grammar_data,
             /* .tokenizer    = */ tokenizer,
             /* .grammar      = */ llama_sampler_llg_new(tokenizer, grammar_kind, grammar_data),
-            /* .llg_res      = */ {},
-            /* .has_llg_res  = */ false,
         };
+        if (ctx->grammar) {
+            GGML_ASSERT(((size_t) llama_vocab_n_tokens(vocab) + 31) / 32 * 4 ==
+                        llg_matcher_get_mask_byte_size(ctx->grammar));
+        }
     } else {
         *ctx = {
             /* .vocab        = */ vocab,
@@ -249,15 +235,12 @@ llama_sampler * llama_sampler_init_llg(const llama_vocab * vocab, const char * g
             /* .grammar_data = */ {},
             /* .tokenizer    = */ nullptr,
             /* .grammar      = */ nullptr,
-            /* .llg_res      = */ {},
-            /* .has_llg_res  = */ false,
         };
     }
 
     return llama_sampler_init(
         /* .iface = */ &llama_sampler_llg_i,
-        /* .ctx   = */ ctx
-    );
+        /* .ctx   = */ ctx);
 }
 
 #else

convert_hf_to_gguf.py

@@ -705,6 +705,9 @@ class Model:
         if chkhsh == "ccc2ef013c104be7bae2965776d611e1d7a8a2a9c547dd93a682c9a9fc80352e":
             # ref: https://huggingface.co/Xenova/gpt-4o
             res = "gpt-4o"
+        if chkhsh == "7dec86086fcc38b66b7bc1575a160ae21cf705be7718b9d5598190d7c12db76f":
+            # ref: https://huggingface.co/UW/OLMo2-8B-SuperBPE-t180k
+            res = "superbpe"
 
         if res is None:
             logger.warning("\n")
@@ -1749,7 +1752,7 @@ class Mistral3Model(LlamaModel):
 
     # we need to merge the text_config into the root level of hparams
     def __init__(self, *args, **kwargs):
-        hparams = Model.load_hparams(kwargs["dir_model"])
+        hparams = kwargs["hparams"] if "hparams" in kwargs else Model.load_hparams(args[0])
         if "text_config" in hparams:
             hparams = {**hparams, **hparams["text_config"]}
             kwargs["hparams"] = hparams
@@ -3382,7 +3385,7 @@ class Gemma3Model(Model):
 
     # we need to merge the text_config into the root level of hparams
     def __init__(self, *args, **kwargs):
-        hparams = Model.load_hparams(kwargs["dir_model"])
+        hparams = kwargs["hparams"] if "hparams" in kwargs else Model.load_hparams(args[0])
         if "text_config" in hparams:
             hparams = {**hparams, **hparams["text_config"]}
             kwargs["hparams"] = hparams
@@ -3800,8 +3803,6 @@ class MambaModel(Model):
     _tok_embd = None
 
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        del bid  # unused
-
         output_name = self.format_tensor_name(gguf.MODEL_TENSOR.OUTPUT)
         tok_embd_name = self.format_tensor_name(gguf.MODEL_TENSOR.TOKEN_EMBD)
 
@@ -3811,6 +3812,10 @@ class MambaModel(Model):
             logger.debug("A_log --> A ==> " + new_name)
             data_torch = -torch.exp(data_torch)
 
+        # [4 1 8192 1] -> [4 8192 1 1]
+        if self.match_model_tensor_name(new_name, gguf.MODEL_TENSOR.SSM_CONV1D, bid):
+            data_torch = data_torch.squeeze()
+
         # assuming token_embd.weight is seen before output.weight
         if self._tok_embd is not None and new_name == output_name:
             if torch.equal(self._tok_embd, data_torch):
@@ -5355,7 +5360,7 @@ def main() -> None:
             logger.error(f"Model {model_architecture} is not supported")
             sys.exit(1)
 
-        model_instance = model_class(dir_model=dir_model, ftype=output_type, fname_out=fname_out,
+        model_instance = model_class(dir_model, output_type, fname_out,
                                      is_big_endian=args.bigendian, use_temp_file=args.use_temp_file,
                                      eager=args.no_lazy,
                                      metadata_override=args.metadata, model_name=args.model_name,

convert_hf_to_gguf_update.py

@@ -110,6 +110,7 @@ models = [
     {"name": "deepseek-v3",      "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/deepseek-ai/DeepSeek-V3"},
     {"name": "deepseek-r1-qwen", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B"},
     {"name": "gpt-4o",           "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/Xenova/gpt-4o", },
+    {"name": "superbpe",         "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/UW/OLMo2-8B-SuperBPE-t180k", },
 ]
 
 

docs/backend/CUDA-FEDORA.md

@@ -14,9 +14,7 @@ In this guide we setup [Nvidia CUDA](https://docs.nvidia.com/cuda/) in a toolbox
 - [Creating a Fedora Toolbox Environment](#creating-a-fedora-toolbox-environment)
 - [Installing Essential Development Tools](#installing-essential-development-tools)
 - [Adding the CUDA Repository](#adding-the-cuda-repository)
-- [Installing `nvidia-driver-libs`](#installing-nvidia-driver-libs)
-- [Manually Resolving Package Conflicts](#manually-resolving-package-conflicts)
-- [Finalizing the Installation of `nvidia-driver-libs`](#finalizing-the-installation-of-nvidia-driver-libs)
+- [Installing Nvidia Driver Libraries](#installing-nvidia-driver-libraries)
 - [Installing the CUDA Meta-Package](#installing-the-cuda-meta-package)
 - [Configuring the Environment](#configuring-the-environment)
 - [Verifying the Installation](#verifying-the-installation)
@@ -67,7 +65,7 @@ This guide focuses on Fedora hosts, but with small adjustments, it can work for
    sudo dnf distro-sync
    ```
 
-2. **Install the Default Text Editor (Optional):**
+2. **Install **Vim** the default text editor (Optional):**
 
    ```bash
    sudo dnf install vim-default-editor --allowerasing
@@ -97,36 +95,48 @@ After adding the repository, synchronize the package manager again:
 sudo dnf distro-sync
 ```
 
-## Installing `nvidia-driver-libs` and `nvidia-driver-cuda-libs`
+## Installing Nvidia Driver Libraries
 
-We need to detect if the host is supplying the [NVIDIA driver libraries into the toolbox](https://github.com/containers/toolbox/blob/main/src/pkg/nvidia/nvidia.go).
+First, we need to detect if the host is supplying the [NVIDIA driver libraries into the toolbox](https://github.com/containers/toolbox/blob/main/src/pkg/nvidia/nvidia.go):
 
 ```bash
 ls -la /usr/lib64/libcuda.so.1
 ```
 
+### If *`libcuda.so.1`* is missing:
+
+```
+ls: cannot access '/usr/lib64/libcuda.so.1': No such file or directory
+```
+
 **Explanation:**
+The host dose not supply the CUDA drivers, **install them now:**
 
-- `nvidia-driver-libs` and `nvidia-driver-cuda-libs` contains necessary NVIDIA driver libraries required by CUDA,
-  on hosts with NVIDIA drivers installed the Fedora Container will supply the host libraries.
-
-### Install Nvidia Driver Libraries on Guest (if `libcuda.so.1` was NOT found).
+#### Install the Nvidia Driver Libraries on Guest:
 
 ```bash
-sudo dnf install nvidia-driver-libs nvidia-driver-cuda-libs
+sudo dnf install nvidia-driver-cuda nvidia-driver-libs nvidia-driver-cuda-libs nvidia-persistenced
 ```
 
-### Manually Updating the RPM database for host-supplied NVIDIA drivers (if `libcuda.so.1` was found).
+### If *`libcuda.so.1`* exists:
+```
+lrwxrwxrwx. 1 root root 21 Mar 24 11:26 /usr/lib64/libcuda.so.1 -> libcuda.so.570.133.07
+```
 
-If the installation fails due to conflicts, we'll manually download and install the required packages, excluding conflicting files.
+**Explanation:**
+The host is supply the CUDA drivers, **we need to update the guest RPM Database accordingly:**
 
-#### 1. Download `nvidia-driver-libs` and `nvidia-driver-cuda-libs` RPM's (with dependencies)
+#### Update the Toolbox RPM Database to include the Host-Supplied Libraries:
+
+Note: we do not actually install the libraries, we just update the DB so that the guest system knows they are supplied by the host.
+
+##### 1. Download `nvidia-` parts that are supplied by the host RPM's (with dependencies)
 
 ```bash
-sudo dnf download --destdir=/tmp/nvidia-driver-libs --resolve --arch x86_64 nvidia-driver-libs nvidia-driver-cuda-libs
+sudo dnf download --destdir=/tmp/nvidia-driver-libs --resolve --arch x86_64 nvidia-driver-cuda nvidia-driver-libs nvidia-driver-cuda-libs nvidia-persistenced
 ```
 
-#### 2. Update the RPM database to assume the installation of these packages.
+##### 2. Update the RPM database to assume the installation of these packages.
 
 ```bash
 sudo rpm --install --verbose --hash --justdb /tmp/nvidia-driver-libs/*
@@ -134,23 +144,26 @@ sudo rpm --install --verbose --hash --justdb /tmp/nvidia-driver-libs/*
 
 **Note:**
 
-- The `--justdb` option only updates the RPM database, without touching the filesystem.
+- The `--justdb` option only updates the RPM database, without touching the filesystem elsewhere.
 
-#### Finalizing the Installation of `nvidia-driver-libs` and `nvidia-driver-cuda-libs`
+##### Check that the RPM Database has been correctly updated:
+
+**Note:** This is the same command as in the *"Install the Nvidia Driver Libraries on Guest"* for if *`libcuda.so.1`* was missing.
 
-After manually installing the dependencies, run:
 
 ```bash
-sudo dnf install nvidia-driver-libs nvidia-driver-cuda-libs
+sudo dnf install nvidia-driver-cuda nvidia-driver-libs nvidia-driver-cuda-libs nvidia-persistenced
 ```
 
-You should receive a message indicating the package is already installed:
+*(this time it will not install anything, as the database things that these packages are already installed)*
 
 ```
 Updating and loading repositories:
 Repositories loaded.
-Package "nvidia-driver-libs-3:570.86.10-1.fc41.x86_64" is already installed.
-Package "nvidia-driver-cuda-libs-3:570.86.10-1.fc41.x86_64" is already installed.
+Package "nvidia-driver-cuda-3:570.124.06-1.fc41.x86_64" is already installed.
+Package "nvidia-driver-libs-3:570.124.06-1.fc41.x86_64" is already installed.
+Package "nvidia-driver-cuda-libs-3:570.124.06-1.fc41.x86_64" is already installed.
+Package "nvidia-persistenced-3:570.124.06-1.fc41.x86_64" is already installed.
 
 Nothing to do.
 ```
@@ -207,9 +220,9 @@ You should see output similar to:
 ```
 nvcc: NVIDIA (R) Cuda compiler driver
 Copyright (c) 2005-2025 NVIDIA Corporation
-Built on Wed_Jan_15_19:20:09_PST_2025
-Cuda compilation tools, release 12.8, V12.8.61
-Build cuda_12.8.r12.8/compiler.35404655_0
+Built on Fri_Feb_21_20:23:50_PST_2025
+Cuda compilation tools, release 12.8, V12.8.93
+Build cuda_12.8.r12.8/compiler.35583870_0
 ```
 
 This output confirms that the CUDA compiler is accessible and indicates the installed version.

docs/build.md

@@ -132,12 +132,14 @@ You may find the official downloads here: [NVIDIA developer site](https://develo
 
 
 #### Compile and run inside a Fedora Toolbox Container
-We also have a [guide](./cuda-fedora.md) for setting up CUDA toolkit in a Fedora [toolbox container](https://containertoolbx.org/).
+We also have a [guide](./backend/CUDA-FEDORA.md) for setting up CUDA toolkit in a Fedora [toolbox container](https://containertoolbx.org/).
 
 **Recommended for:**
-
-- ***Particularly*** *convenient* for users of [Atomic Desktops for Fedora](https://fedoraproject.org/atomic-desktops/); such as: [Silverblue](https://fedoraproject.org/atomic-desktops/silverblue/) and [Kinoite](https://fedoraproject.org/atomic-desktops/kinoite/).
-- Toolbox is installed by default: [Fedora Workstation](https://fedoraproject.org/workstation/) or [Fedora KDE Plasma Desktop](https://fedoraproject.org/spins/kde).
+- ***Necessary*** for users of [Atomic Desktops for Fedora](https://fedoraproject.org/atomic-desktops/); such as: [Silverblue](https://fedoraproject.org/atomic-desktops/silverblue/) and [Kinoite](https://fedoraproject.org/atomic-desktops/kinoite/).
+  - (there are no supported CUDA packages for these systems)
+- ***Necessary*** for users that have a host that is not a: [Supported Nvidia CUDA Release Platform](https://developer.nvidia.com/cuda-downloads).
+  - (for example, you may have [Fedora 42 Beta](https://fedoramagazine.org/announcing-fedora-linux-42-beta/) as your your host operating system)
+- ***Convenient*** For those running [Fedora Workstation](https://fedoraproject.org/workstation/) or [Fedora KDE Plasma Desktop](https://fedoraproject.org/spins/kde), and want to keep their host system clean.
 - *Optionally* toolbox packages are available: [Arch Linux](https://archlinux.org/), [Red Hat Enterprise Linux >= 8.5](https://www.redhat.com/en/technologies/linux-platforms/enterprise-linux), or [Ubuntu](https://ubuntu.com/download)
 
 
@@ -189,7 +191,7 @@ The following compilation options are also available to tweak performance:
 
 | Option                        | Legal values           | Default | Description                                                                                                                                                                                                                                                                             |
 |-------------------------------|------------------------|---------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| GGML_CUDA_FORCE_MMQ           | Boolean                | false   | Force the use of custom matrix multiplication kernels for quantized models instead of FP16 cuBLAS even if there is no int8 tensor core implementation available (affects V100, RDNA3). MMQ kernels are enabled by default on GPUs with int8 tensor core support. With MMQ force enabled, speed for large batch sizes will be worse but VRAM consumption will be lower.                       |
+| GGML_CUDA_FORCE_MMQ           | Boolean                | false   | Force the use of custom matrix multiplication kernels for quantized models instead of FP16 cuBLAS even if there is no int8 tensor core implementation available (affects V100, CDNA and RDNA3+). MMQ kernels are enabled by default on GPUs with int8 tensor core support. With MMQ force enabled, speed for large batch sizes will be worse but VRAM consumption will be lower.                       |
 | GGML_CUDA_FORCE_CUBLAS        | Boolean                | false   | Force the use of FP16 cuBLAS instead of custom matrix multiplication kernels for quantized models                                                                                                                                                                                       |
 | GGML_CUDA_F16                 | Boolean                | false   | If enabled, use half-precision floating point arithmetic for the CUDA dequantization + mul mat vec kernels and for the q4_1 and q5_1 matrix matrix multiplication kernels. Can improve performance on relatively recent GPUs.                                                           |
 | GGML_CUDA_PEER_MAX_BATCH_SIZE | Positive integer       | 128     | Maximum batch size for which to enable peer access between multiple GPUs. Peer access requires either Linux or NVLink. When using NVLink enabling peer access for larger batch sizes is potentially beneficial.                                                                         |
@@ -216,6 +218,7 @@ By default, all supported compute capabilities are enabled. To customize this be
 
 ```bash
 cmake -B build -DGGML_MUSA=ON -DMUSA_ARCHITECTURES="21"
+cmake --build build --config Release
 ```
 
 This configuration enables only compute capability `2.1` (MTT S80) during compilation, which can help reduce compilation time.
@@ -433,6 +436,26 @@ llama_new_context_with_model:       CANN compute buffer size =  1260.81 MiB
 
 For detailed info, such as model/device supports, CANN install, please refer to [llama.cpp for CANN](./backend/CANN.md).
 
+## Arm® KleidiAI™
+KleidiAI is a library of optimized microkernels for AI workloads, specifically designed for Arm CPUs. These microkernels enhance performance and can be enabled for use by the CPU backend.
+
+To enable KleidiAI, go to the llama.cpp directory and build using CMake
+```bash
+cmake -B build -DGGML_CPU_KLEIDIAI=ON
+cmake --build build --config Release
+```
+You can verify that KleidiAI is being used by running
+```bash
+./build/bin/llama-cli -m PATH_TO_MODEL -p "What is a car?"
+```
+If KleidiAI is enabled, the ouput will contain a line similar to:
+```
+load_tensors: CPU_KLEIDIAI model buffer size =  3474.00 MiB
+```
+KleidiAI's microkernels implement optimized tensor operations using Arm CPU features such as dotprod, int8mm and SME. llama.cpp selects the most efficient kernel based on runtime CPU feature detection. However, on platforms that support SME, you must manually enable SME microkernels by setting the environment variable `GGML_KLEIDIAI_SME=1`.
+
+Depending on your build target, other higher priority backends may be enabled by default. To ensure the CPU backend is used, you must disable the higher priority backends either at compile time, e.g. -DGGML_METAL=OFF, or during run-time using the command line option `--device none`.
+
 ## Android
 
 To read documentation for how to build on Android, [click here](./android.md)

docs/install.md

@@ -9,6 +9,13 @@ brew install llama.cpp
 ```
 The formula is automatically updated with new `llama.cpp` releases. More info: https://github.com/ggml-org/llama.cpp/discussions/7668
 
+## MacPorts
+
+```sh
+sudo port install llama.cpp
+```
+see also: https://ports.macports.org/port/llama.cpp/details/
+
 ## Nix
 
 On Mac and Linux, the Nix package manager can be used via

examples/llava/clip.cpp

@@ -2989,7 +2989,10 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i
     assert(itype < GGML_TYPE_COUNT);
     ggml_type type = static_cast<ggml_type>(itype);
 
-    auto * ctx_clip = clip_model_load(fname_inp, 2);
+    auto * ctx_clip = clip_init(fname_inp, clip_context_params{
+        /* use_gpu */   false,
+        /* verbosity */ 2,
+    });
 
     const auto & ctx_src = ctx_clip->ctx_gguf;
     const auto & ctx_data = ctx_clip->ctx_data;

examples/run/run.cpp

@@ -38,24 +38,6 @@
 }
 #endif
 
-GGML_ATTRIBUTE_FORMAT(1, 2)
-static std::string fmt(const char * fmt, ...) {
-    va_list ap;
-    va_list ap2;
-    va_start(ap, fmt);
-    va_copy(ap2, ap);
-    const int size = vsnprintf(NULL, 0, fmt, ap);
-    GGML_ASSERT(size >= 0 && size < INT_MAX); // NOLINT
-    std::string buf;
-    buf.resize(size);
-    const int size2 = vsnprintf(const_cast<char *>(buf.data()), buf.size() + 1, fmt, ap2);
-    GGML_ASSERT(size2 == size);
-    va_end(ap2);
-    va_end(ap);
-
-    return buf;
-}
-
 GGML_ATTRIBUTE_FORMAT(1, 2)
 static int printe(const char * fmt, ...) {
     va_list args;
@@ -525,11 +507,11 @@ class HttpClient {
         int secs = static_cast<int>(seconds) % 60;
 
         if (hrs > 0) {
-            return fmt("%dh %02dm %02ds", hrs, mins, secs);
+            return string_format("%dh %02dm %02ds", hrs, mins, secs);
         } else if (mins > 0) {
-            return fmt("%dm %02ds", mins, secs);
+            return string_format("%dm %02ds", mins, secs);
         } else {
-            return fmt("%ds", secs);
+            return string_format("%ds", secs);
         }
     }
 
@@ -544,7 +526,7 @@ class HttpClient {
             }
         }
 
-        return fmt("%.2f %s", dbl_size, suffix[i]);
+        return string_format("%.2f %s", dbl_size, suffix[i]);
     }
 
     static int update_progress(void * ptr, curl_off_t total_to_download, curl_off_t now_downloaded, curl_off_t,
@@ -578,7 +560,9 @@ class HttpClient {
         return (now_downloaded_plus_file_size * 100) / total_to_download;
     }
 
-    static std::string generate_progress_prefix(curl_off_t percentage) { return fmt("%3ld%% |", static_cast<long int>(percentage)); }
+    static std::string generate_progress_prefix(curl_off_t percentage) {
+        return string_format("%3ld%% |", static_cast<long int>(percentage));
+    }
 
     static double calculate_speed(curl_off_t now_downloaded, const std::chrono::steady_clock::time_point & start_time) {
         const auto                          now             = std::chrono::steady_clock::now();
@@ -589,9 +573,9 @@ class HttpClient {
     static std::string generate_progress_suffix(curl_off_t now_downloaded_plus_file_size, curl_off_t total_to_download,
                                                 double speed, double estimated_time) {
         const int width = 10;
-        return fmt("%*s/%*s%*s/s%*s", width, human_readable_size(now_downloaded_plus_file_size).c_str(), width,
-                   human_readable_size(total_to_download).c_str(), width, human_readable_size(speed).c_str(), width,
-                   human_readable_time(estimated_time).c_str());
+        return string_format("%*s/%*s%*s/s%*s", width, human_readable_size(now_downloaded_plus_file_size).c_str(),
+                             width, human_readable_size(total_to_download).c_str(), width,
+                             human_readable_size(speed).c_str(), width, human_readable_time(estimated_time).c_str());
     }
 
     static int calculate_progress_bar_width(const std::string & progress_prefix, const std::string & progress_suffix) {

examples/server/server.cpp

@@ -830,6 +830,11 @@ struct server_task_result_cmpl_final : server_task_result {
             ret.push_back({"timings", timings.to_json()});
         }
 
+        // extra fields for debugging purposes
+        if (verbose) {
+            ret["__verbose"] = to_json_non_oaicompat();
+        }
+
         return ret;
     }
 };

ggml/src/ggml-cpu/CMakeLists.txt

@@ -359,9 +359,9 @@ function(ggml_add_cpu_backend_variant_impl tag_name)
 
         # Fetch KleidiAI sources:
         include(FetchContent)
-        set(KLEIDIAI_COMMIT_TAG "v1.3.0")
+        set(KLEIDIAI_COMMIT_TAG "v1.5.0")
         set(KLEIDIAI_DOWNLOAD_URL "https://github.com/ARM-software/kleidiai/archive/refs/tags/${KLEIDIAI_COMMIT_TAG}.tar.gz")
-        set(KLEIDIAI_ARCHIVE_MD5  "060bd2dc64642b091f461cc8dd7426d9")
+        set(KLEIDIAI_ARCHIVE_MD5  "ea22e1aefb800e9bc8c74d91633cc58e")
 
         if (POLICY CMP0135)
             cmake_policy(SET CMP0135 NEW)

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

@@ -250,7 +250,7 @@ static inline __m256i mul_sum_i8_pairs_int32x8(const __m256i x, const __m256i y)
 
 static const int8_t kvalues_iq4nl[16] = {-127, -104, -83, -65, -49, -35, -22, -10, 1, 13, 25, 38, 53, 69, 89, 113};
 
-static void quantize_q8_0_4x4(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t k) {
+static void ggml_quantize_mat_q8_0_4x4(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t k) {
     assert(QK8_0 == 32);
     assert(k % QK8_0 == 0);
     const int nb = k / QK8_0;
@@ -344,7 +344,7 @@ static void quantize_q8_0_4x4(const float * GGML_RESTRICT x, void * GGML_RESTRIC
 #endif
 }
 
-static void quantize_q8_0_4x8(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t k) {
+static void ggml_quantize_mat_q8_0_4x8(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t k) {
     assert(QK8_0 == 32);
     assert(k % QK8_0 == 0);
     const int nb = k / QK8_0;
@@ -559,7 +559,7 @@ static void quantize_q8_0_4x8(const float * GGML_RESTRICT x, void * GGML_RESTRIC
 #endif
 }
 
-static void quantize_q8_K_4x8(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t k) {
+static void ggml_quantize_mat_q8_K_4x8(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t k) {
     assert(QK_K == 256);
     assert(k % QK_K == 0);
     const int nb = k / QK_K;
@@ -811,7 +811,7 @@ static void quantize_q8_K_4x8(const float * GGML_RESTRICT x, void * GGML_RESTRIC
         // i.e first four bsums from the first super block, followed by first four bsums from second super block and so on
         for (int j = 0; j < QK_K * 4; j++) {
             int src_offset = (j / (4 * blck_size_interleave)) * blck_size_interleave;
-            int src_id = (j % (4 * blck_size_interleave)) / blck_size_interleave;
+            int src_id     = (j % (4 * blck_size_interleave)) / blck_size_interleave;
             src_offset += (j % blck_size_interleave);
             int index = (((j & 31) >> 3) << 2) + ((j >> 8) << 4) + ((j >> 6) & 3);
 
@@ -823,26 +823,25 @@ static void quantize_q8_K_4x8(const float * GGML_RESTRICT x, void * GGML_RESTRIC
 #endif
 }
 
-static void quantize_mat_q8_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t nrow, int64_t n_per_row, int64_t blck_size_interleave) {
+template <int64_t INTER_SIZE, ggml_type PARAM_TYPE>
+void ggml_quantize_mat_t(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t nrow, int64_t n_per_row);
+
+template <> void ggml_quantize_mat_t<4, GGML_TYPE_Q8_0>(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t nrow, int64_t n_per_row) {
     assert(nrow == 4);
     UNUSED(nrow);
-    if (blck_size_interleave == 4) {
-        quantize_q8_0_4x4(x, vy, n_per_row);
-    } else if (blck_size_interleave == 8) {
-        quantize_q8_0_4x8(x, vy, n_per_row);
-    } else {
-        assert(false);
-    }
+    ggml_quantize_mat_q8_0_4x4(x, vy, n_per_row);
 }
 
-static void quantize_mat_q8_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t nrow, int64_t n_per_row, int64_t blck_size_interleave) {
+template <> void ggml_quantize_mat_t<8, GGML_TYPE_Q8_0>(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t nrow, int64_t n_per_row) {
     assert(nrow == 4);
     UNUSED(nrow);
-    if (blck_size_interleave == 8) {
-        quantize_q8_K_4x8(x, vy, n_per_row);
-    } else {
-        assert(false);
-    }
+    ggml_quantize_mat_q8_0_4x8(x, vy, n_per_row);
+}
+
+template <> void ggml_quantize_mat_t<8, GGML_TYPE_Q8_K>(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t nrow, int64_t n_per_row) {
+    assert(nrow == 4);
+    UNUSED(nrow);
+    ggml_quantize_mat_q8_K_4x8(x, vy, n_per_row);
 }
 
 static void ggml_gemv_q4_0_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
@@ -5276,52 +5275,50 @@ template <> int repack<block_iq4_nl, 4, 4>(struct ggml_tensor * t, const void *
 //}
 
 // gemv
-template <typename BLOC_TYPE, int64_t INTER_SIZE, int64_t NB_COLS>
+template <typename BLOC_TYPE, int64_t INTER_SIZE, int64_t NB_COLS, ggml_type PARAM_TYPE>
 void gemv(int, float *, size_t, const void *, const void *, int, int);
 
-template <> void gemv<block_q4_0, 4, 4>(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) {
+template <> void gemv<block_q4_0, 4, 4, GGML_TYPE_Q8_0>(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) {
     ggml_gemv_q4_0_4x4_q8_0(n, s, bs, vx, vy, nr, nc);
 }
 
-template <> void gemv<block_q4_0, 8, 4>(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) {
+template <> void gemv<block_q4_0, 8, 4, GGML_TYPE_Q8_0>(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) {
     ggml_gemv_q4_0_4x8_q8_0(n, s, bs, vx, vy, nr, nc);
 }
 
-template <> void gemv<block_q4_0, 8, 8>(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) {
+template <> void gemv<block_q4_0, 8, 8, GGML_TYPE_Q8_0>(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) {
     ggml_gemv_q4_0_8x8_q8_0(n, s, bs, vx, vy, nr, nc);
 }
 
-template <> void gemv<block_q4_K, 8, 8>(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) {
+template <> void gemv<block_q4_K, 8, 8, GGML_TYPE_Q8_K>(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) {
     ggml_gemv_q4_K_8x8_q8_K(n, s, bs, vx, vy, nr, nc);
 }
 
-template <>
-void gemv<block_iq4_nl, 4, 4>(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) {
+template <> void gemv<block_iq4_nl, 4, 4, GGML_TYPE_Q8_0>(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) {
     ggml_gemv_iq4_nl_4x4_q8_0(n, s, bs, vx, vy, nr, nc);
 }
 
 // gemm
-template <typename BLOC_TYPE, int64_t INTER_SIZE, int64_t NB_COLS>
+template <typename BLOC_TYPE, int64_t INTER_SIZE, int64_t NB_COLS, ggml_type PARAM_TYPE>
 void gemm(int, float *, size_t, const void *, const void *, int, int);
 
-template <> void gemm<block_q4_0, 4, 4>(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) {
+template <> void gemm<block_q4_0, 4, 4, GGML_TYPE_Q8_0>(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) {
     ggml_gemm_q4_0_4x4_q8_0(n, s, bs, vx, vy, nr, nc);
 }
 
-template <> void gemm<block_q4_0, 8, 4>(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) {
+template <> void gemm<block_q4_0, 8, 4, GGML_TYPE_Q8_0>(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) {
     ggml_gemm_q4_0_4x8_q8_0(n, s, bs, vx, vy, nr, nc);
 }
 
-template <> void gemm<block_q4_0, 8, 8>(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) {
+template <> void gemm<block_q4_0, 8, 8, GGML_TYPE_Q8_0>(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) {
     ggml_gemm_q4_0_8x8_q8_0(n, s, bs, vx, vy, nr, nc);
 }
 
-template <> void gemm<block_q4_K, 8, 8>(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) {
+template <> void gemm<block_q4_K, 8, 8, GGML_TYPE_Q8_K>(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) {
     ggml_gemm_q4_K_8x8_q8_K(n, s, bs, vx, vy, nr, nc);
 }
 
-template <>
-void gemm<block_iq4_nl, 4, 4>(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) {
+template <> void gemm<block_iq4_nl, 4, 4, GGML_TYPE_Q8_0>(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) {
     ggml_gemm_iq4_nl_4x4_q8_0(n, s, bs, vx, vy, nr, nc);
 }
 
@@ -5335,32 +5332,32 @@ template <typename BLOC_TYPE, int64_t INTER_SIZE, int64_t NB_COLS, ggml_type PAR
     bool work_size(int /* n_threads */, const struct ggml_tensor * op, size_t & size) override {
         // not realy a GGML_TYPE_Q8_0 but same size.
         switch (op->op) {
-        case GGML_OP_MUL_MAT:
-            size = ggml_row_size(PARAM_TYPE, ggml_nelements(op->src[1]));
-            return true;
-        case GGML_OP_MUL_MAT_ID:
-            size = ggml_row_size(PARAM_TYPE, ggml_nelements(op->src[1]));
-            size = GGML_PAD(size, sizeof(int64_t));  // + padding for next bloc.
-            size += sizeof(int64_t) * (1+op->src[0]->ne[2]) * op->src[1]->ne[2];
-            return true;
-        default:
-            // GGML_ABORT("fatal error");
-            break;
+            case GGML_OP_MUL_MAT:
+                size = ggml_row_size(PARAM_TYPE, ggml_nelements(op->src[1]));
+                return true;
+            case GGML_OP_MUL_MAT_ID:
+                size = ggml_row_size(PARAM_TYPE, ggml_nelements(op->src[1]));
+                size = GGML_PAD(size, sizeof(int64_t));  // + padding for next bloc.
+                size += sizeof(int64_t) * (1+op->src[0]->ne[2]) * op->src[1]->ne[2];
+                return true;
+            default:
+                // GGML_ABORT("fatal error");
+                break;
         }
         return false;
     }
 
     bool compute_forward(struct ggml_compute_params * params, struct ggml_tensor * op) override {
         switch (op->op) {
-        case GGML_OP_MUL_MAT:
-            forward_mul_mat(params, op);
-            return true;
-        case GGML_OP_MUL_MAT_ID:
-            forward_mul_mat_id(params, op);
-            return true;
-        default:
-            // GGML_ABORT("fatal error");
-            break;
+            case GGML_OP_MUL_MAT:
+                forward_mul_mat(params, op);
+                return true;
+            case GGML_OP_MUL_MAT_ID:
+                forward_mul_mat_id(params, op);
+                return true;
+            default:
+                // GGML_ABORT("fatal error");
+                break;
         }
         return false;
     }
@@ -5399,17 +5396,10 @@ template <typename BLOC_TYPE, int64_t INTER_SIZE, int64_t NB_COLS, ggml_type PAR
         const ggml_from_float_t from_float = ggml_get_type_traits_cpu(PARAM_TYPE)->from_float;
 
         int64_t i11_processed = 0;
-        if(PARAM_TYPE == GGML_TYPE_Q8_K) {
-            for (int64_t i11 = ith * 4; i11 < ne11 - ne11 % 4; i11 += nth * 4) {
-                quantize_mat_q8_K((float *) ((char *) src1->data + i11 * nb11), (void *) (wdata + i11 * nbw1), 4, ne10,
-                              INTER_SIZE);
-            }
-        } else {
-            for (int64_t i11 = ith * 4; i11 < ne11 - ne11 % 4; i11 += nth * 4) {
-                quantize_mat_q8_0((float *) ((char *) src1->data + i11 * nb11), (void *) (wdata + i11 * nbw1), 4, ne10,
-                                INTER_SIZE);
-            }
+        for (int64_t i11 = ith * 4; i11 < ne11 - ne11 % 4; i11 += nth * 4) {
+            ggml_quantize_mat_t<INTER_SIZE, PARAM_TYPE>((float *) ((char *) src1->data + i11 * nb11), (void *) (wdata + i11 * nbw1), 4, ne10);
         }
+
         i11_processed = ne11 - ne11 % 4;
         for (int64_t i11 = i11_processed + ith; i11 < ne11; i11 += nth) {
             from_float((float *) ((char *) src1->data + i11 * nb11), (void *) (wdata + i11 * nbw1), ne10);
@@ -5422,22 +5412,24 @@ template <typename BLOC_TYPE, int64_t INTER_SIZE, int64_t NB_COLS, ggml_type PAR
         int64_t      src0_start      = (ith * ne01) / nth;
         int64_t      src0_end        = ((ith + 1) * ne01) / nth;
         src0_start = (src0_start % NB_COLS) ? src0_start + NB_COLS - (src0_start % NB_COLS) : src0_start;
-        src0_end   = (src0_end % NB_COLS) ? src0_end + NB_COLS - (src0_end % NB_COLS) : src0_end;
+        src0_end   = (src0_end   % NB_COLS) ? src0_end   + NB_COLS - (src0_end   % NB_COLS) : src0_end;
         if (src0_start >= src0_end) {
             return;
         }
 
         // If there are more than three rows in src1, use gemm; otherwise, use gemv.
         if (ne11 > 3) {
-            gemm<BLOC_TYPE, INTER_SIZE, NB_COLS>(ne00, (float *) ((char *) dst->data) + src0_start, ne01,
-                                                 (const char *) src0->data + src0_start * nb01,
-                                                 (const char *) src1_wdata, ne11 - ne11 % 4, src0_end - src0_start);
+            gemm<BLOC_TYPE, INTER_SIZE, NB_COLS, PARAM_TYPE>(ne00,
+                    (float *) ((char *) dst->data) + src0_start, ne01,
+                    (const char *) src0->data + src0_start * nb01,
+                    (const char *) src1_wdata, ne11 - ne11 % 4, src0_end - src0_start);
         }
         for (int iter = ne11 - ne11 % 4; iter < ne11; iter++) {
-            gemv<BLOC_TYPE, INTER_SIZE, NB_COLS>(ne00, (float *) ((char *) dst->data + (iter * nb1)) + src0_start, ne01,
-                                                 (const char *) src0->data + src0_start * nb01,
-                                                 (const char *) src1_wdata + (src1_col_stride * iter), 1,
-                                                 src0_end - src0_start);
+            gemv<BLOC_TYPE, INTER_SIZE, NB_COLS, PARAM_TYPE>(ne00,
+                    (float *) ((char *) dst->data + (iter * nb1)) + src0_start, ne01,
+                    (const char *) src0->data + src0_start * nb01,
+                    (const char *) src1_wdata + (src1_col_stride * iter), 1,
+                    src0_end - src0_start);
         }
     }
 
@@ -5452,7 +5444,7 @@ template <typename BLOC_TYPE, int64_t INTER_SIZE, int64_t NB_COLS, ggml_type PAR
         const int ith = params->ith;
         const int nth = params->nth;
 
-        const ggml_from_float_t from_float = ggml_get_type_traits_cpu(GGML_TYPE_Q8_0)->from_float;
+        const ggml_from_float_t from_float = ggml_get_type_traits_cpu(PARAM_TYPE)->from_float;
 
         // we don't support permuted src0 or src1
         GGML_ASSERT(nb00 == ggml_type_size(src0->type));
@@ -5474,7 +5466,7 @@ template <typename BLOC_TYPE, int64_t INTER_SIZE, int64_t NB_COLS, ggml_type PAR
         const int n_ids = ids->ne[0]; // n_expert_used
         const int n_as  = ne02;       // n_expert
 
-        const size_t nbw1 = ggml_row_size(GGML_TYPE_Q8_0, ne10);
+        const size_t nbw1 = ggml_row_size(PARAM_TYPE, ne10);
         const size_t nbw2 = nbw1*ne11;
         const size_t nbw3 = nbw2*ne12;
 
@@ -5486,12 +5478,13 @@ template <typename BLOC_TYPE, int64_t INTER_SIZE, int64_t NB_COLS, ggml_type PAR
         GGML_ASSERT(params->wsize >= (GGML_PAD(nbw3, sizeof(int64_t)) + n_as * sizeof(int64_t) +
                                       n_as * ne12 * sizeof(mmid_row_mapping)));
 
-        auto                      wdata             = (char *) params->wdata;
-        auto                      wdata_src1_end    = (char *) wdata + GGML_PAD(nbw3, sizeof(int64_t));
-        int64_t *                 matrix_row_counts = (int64_t *) (wdata_src1_end);                      // [n_as]
+        auto * wdata             = (char *)     params->wdata;
+        auto * wdata_src1_end    = (char *)     wdata + GGML_PAD(nbw3, sizeof(int64_t));
+        auto * matrix_row_counts = (int64_t *) (wdata_src1_end); // [n_as]
+
         struct mmid_row_mapping * matrix_rows = (struct mmid_row_mapping *) (matrix_row_counts + n_as);  // [n_as][ne12]
 
-        // src1: float32 => block_q8_0
+        // src1: float32 => param type
         for (int64_t i12 = 0; i12 < ne12; ++i12) {
             for (int64_t i11 = ith; i11 < ne11; i11 += nth) {
                 from_float((float *)((char *) src1->data + i12 * nb12 + i11 * nb11),
@@ -5530,34 +5523,37 @@ template <typename BLOC_TYPE, int64_t INTER_SIZE, int64_t NB_COLS, ggml_type PAR
                 continue;
             }
 
-            auto src0_cur = (const char *) src0->data + cur_a*nb02;
+            const auto * src0_cur = (const char *) src0->data + cur_a*nb02;
 
             //const int64_t nr0 = ne01; // src0 rows
             const int64_t nr1 = cne1; // src1 rows
 
             int64_t src0_cur_start = (ith * ne01) / nth;
             int64_t src0_cur_end   = ((ith + 1) * ne01) / nth;
-            src0_cur_start =
-                (src0_cur_start % NB_COLS) ? src0_cur_start + NB_COLS - (src0_cur_start % NB_COLS) : src0_cur_start;
-            src0_cur_end = (src0_cur_end % NB_COLS) ? src0_cur_end + NB_COLS - (src0_cur_end % NB_COLS) : src0_cur_end;
 
-            if (src0_cur_start >= src0_cur_end) return;
+            src0_cur_start = (src0_cur_start % NB_COLS) ? src0_cur_start + NB_COLS - (src0_cur_start % NB_COLS) : src0_cur_start;
+            src0_cur_end   = (src0_cur_end   % NB_COLS) ? src0_cur_end   + NB_COLS - (src0_cur_end   % NB_COLS) : src0_cur_end;
+
+            if (src0_cur_start >= src0_cur_end) {
+                return;
+            }
 
             for (int ir1 = 0; ir1 < nr1; ir1++) {
                 struct mmid_row_mapping row_mapping = MMID_MATRIX_ROW(cur_a, ir1);
-                const int id       = row_mapping.i1; // selected expert index
 
-                const int64_t  i11 = id % ne11;
-                const int64_t  i12 = row_mapping.i2; // row index in src1
+                const int id = row_mapping.i1; // selected expert index
 
-                const int64_t  i1 = id;  // selected expert index
-                const int64_t  i2 = i12; // row
+                const int64_t i11 = id % ne11;
+                const int64_t i12 = row_mapping.i2; // row index in src1
 
-                auto src1_col = (const char *) wdata + (i11 * nbw1 + i12 * nbw2);
+                const int64_t i1 = id;  // selected expert index
+                const int64_t i2 = i12; // row
 
-                gemv<BLOC_TYPE, INTER_SIZE, NB_COLS>(
-                        ne00, (float *)((char *) dst->data + (i1 * nb1 + i2 * nb2)) + src0_cur_start,
-                        ne01,                    src0_cur + src0_cur_start * nb01,
+                const auto * src1_col = (const char *) wdata + (i11 * nbw1 + i12 * nbw2);
+
+                gemv<BLOC_TYPE, INTER_SIZE, NB_COLS, PARAM_TYPE>(ne00,
+                        (float *)((char *) dst->data + (i1 * nb1 + i2 * nb2)) + src0_cur_start, ne01,
+                        src0_cur + src0_cur_start * nb01,
                         src1_col, 1, src0_cur_end - src0_cur_start);
             }
         }
@@ -5578,7 +5574,7 @@ static const tensor_traits<block_q4_0, 8, 8, GGML_TYPE_Q8_0> q4_0_8x8_q8_0;
 static const tensor_traits<block_q4_K, 8, 8, GGML_TYPE_Q8_K> q4_K_8x8_q8_K;
 
 // instance for IQ4
-static const tensor_traits<block_iq4_nl, 4, 4, GGML_TYPE_IQ4_NL> iq4_nl_4x4_q8_0;
+static const tensor_traits<block_iq4_nl, 4, 4, GGML_TYPE_Q8_0> iq4_nl_4x4_q8_0;
 
 }  // namespace ggml::cpu::aarch64
 

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

@@ -3110,17 +3110,17 @@ static void ggml_compute_forward_dup_same_cont(
     const int ith = params->ith; // thread index
     const int nth = params->nth; // number of threads
 
-    // parallelize by elements
-    const int ne = ggml_nelements(dst);
-    const int dr = (ne + nth - 1) / nth;
-    const int ie0 = dr * ith;
-    const int ie1 = MIN(ie0 + dr, ne);
+    // parallelize by blocks
+    const int nk = ggml_nelements(src0)/ggml_blck_size(src0->type);
+    const int dr = (nk + nth - 1) / nth;
+    const int k0 = dr * ith;
+    const int k1 = MIN(k0 + dr, nk);
 
-    if (ie0 < ie1) {
+    if (k0 < k1) {
         memcpy(
-            ((char *)  dst->data + ie0*nb0),
-            ((char *) src0->data + ie0*nb0),
-            (ie1 - ie0) * nb0);
+            ((char *)  dst->data + k0*nb0),
+            ((char *) src0->data + k0*nb0),
+            (k1 - k0) * nb0);
     }
 }
 
@@ -4055,7 +4055,6 @@ static void ggml_compute_forward_dup_f32(
 static void ggml_compute_forward_dup_bytes(
         const struct ggml_compute_params * params,
         struct ggml_tensor * dst) {
-
     const struct ggml_tensor * src0 = dst->src[0];
 
     GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0));
@@ -4069,10 +4068,10 @@ static void ggml_compute_forward_dup_bytes(
     }
 
     const size_t type_size = ggml_type_size(src0->type);
+
     const int ith = params->ith; // thread index
     const int nth = params->nth; // number of threads
 
-
     // parallelize by rows
     const int nr = ne01;
     // number of rows per thread
@@ -4082,10 +4081,10 @@ static void ggml_compute_forward_dup_bytes(
     const int ir1 = MIN(ir0 + dr, nr);
 
     if (src0->type == dst->type &&
-        ne00 == ne0 &&
+        ggml_are_same_shape(src0, dst) &&
         nb00 == type_size && nb0 == type_size) {
         // copy by rows
-        const size_t rs = ne00 * type_size;
+        const size_t rs = ggml_row_size(src0->type, ne00);
         for (int64_t i03 = 0; i03 < ne03; i03++) {
             for (int64_t i02 = 0; i02 < ne02; i02++) {
                 for (int64_t i01 = ir0; i01 < ir1; i01++) {
@@ -4140,17 +4139,20 @@ static void ggml_compute_forward_dup_bytes(
     }
 
     // dst counters
-
-    int64_t i10 = 0;
+    int64_t k10 = 0;
     int64_t i11 = 0;
     int64_t i12 = 0;
     int64_t i13 = 0;
 
+    // number of blocks in a row
+    const int64_t nk00 = ne00 / ggml_blck_size(src0->type);
+    const int64_t nk0  = ne0  / ggml_blck_size(dst->type);
+
     for (int64_t i03 = 0; i03 < ne03; i03++) {
         for (int64_t i02 = 0; i02 < ne02; i02++) {
-            i10 += ne00 * ir0;
-            while (i10 >= ne0) {
-                i10 -= ne0;
+            k10 += nk00 * ir0;
+            while (k10 >= nk0) {
+                k10 -= nk0;
                 if (++i11 == ne1) {
                     i11 = 0;
                     if (++i12 == ne2) {
@@ -4162,14 +4164,14 @@ static void ggml_compute_forward_dup_bytes(
                 }
             }
             for (int64_t i01 = ir0; i01 < ir1; i01++) {
-                for (int64_t i00 = 0; i00 < ne00; i00++) {
-                    const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03);
-                          char * dst_ptr  = ((char *)  dst->data + i10*nb0  + i11*nb1  + i12*nb2  + i13*nb3);
+                for (int64_t k00 = 0; k00 < nk00; k00++) {
+                    const char * src0_ptr = ((char *) src0->data + k00*nb00 + i01*nb01 + i02*nb02 + i03*nb03);
+                          char * dst_ptr  = ((char *)  dst->data + k10*nb0  + i11*nb1  + i12*nb2  + i13*nb3);
 
                     memcpy(dst_ptr, src0_ptr, type_size);
 
-                    if (++i10 == ne0) {
-                        i10 = 0;
+                    if (++k10 == nk0) {
+                        k10 = 0;
                         if (++i11 == ne1) {
                             i11 = 0;
                             if (++i12 == ne2) {
@@ -4182,9 +4184,9 @@ static void ggml_compute_forward_dup_bytes(
                     }
                 }
             }
-            i10 += ne00 * (ne01 - ir1);
-            while (i10 >= ne0) {
-                i10 -= ne0;
+            k10 += nk00 * (ne01 - ir1);
+            while (k10 >= nk0) {
+                k10 -= nk0;
                 if (++i11 == ne1) {
                     i11 = 0;
                     if (++i12 == ne2) {
@@ -14308,7 +14310,9 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
     }
 
     // extra_buffer op?
-    if (ggml_cpu_extra_compute_forward(params, tensor)) return;
+    if (ggml_cpu_extra_compute_forward(params, tensor)) {
+        return;
+    }
 
     switch (tensor->op) {
         case GGML_OP_DUP:

ggml/src/ggml-cpu/kleidiai/kernels.cpp

@@ -51,11 +51,10 @@ static ggml_kleidiai_kernels gemm_gemv_kernels[] = {
             /* .run_kernel            = */ kai_run_matmul_clamp_f32_qsi8d32p1x4_qsi4c32p4vlx4_1x4vl_sme2_sdot,
         },
         /* .lhs_info = */ {
-            /* .get_offset            = */ kai_get_lhs_offset_lhs_quant_pack_qsi8d32p_f32,
-            /* .get_packed_offset     = */ kai_get_lhs_packed_offset_lhs_quant_pack_qsi8d32p_f32,
+            /* .get_offset            = */ kai_get_lhs_offset_lhs_quant_pack_qsi8d32p_f32_neon,
+            /* .get_packed_offset     = */ kai_get_lhs_packed_offset_lhs_quant_pack_qsi8d32p_f32_neon,
             /* .packed_size           = */ kai_get_lhs_packed_size_lhs_quant_pack_qsi8d32p_f32_neon,
             /* .pack_func             = */ kai_run_lhs_quant_pack_qsi8d32p_f32_neon,
-            /* .require_aligned_m_idx = */ true,
         },
         /* .rhs_info = */ {
             /* .packed_size = */ kai_get_rhs_packed_size_rhs_pack_nxk_qsi4c32ps1s0scalef16_qsu4c32s16s0_neon,
@@ -100,7 +99,6 @@ static ggml_kleidiai_kernels gemm_gemv_kernels[] = {
             /* .get_packed_offset     = */ kai_get_lhs_packed_offset_lhs_quant_pack_qsi8d32p_f32,
             /* .packed_size           = */ kai_get_lhs_packed_size_lhs_quant_pack_qsi8d32p_f32,
             /* .pack_func             = */ kai_run_lhs_quant_pack_qsi8d32p_f32,
-            /* .require_aligned_m_idx = */ false,
         },
         /* .rhs_info = */ {
             /* .packed_size = */ kai_get_rhs_packed_size_rhs_pack_nxk_qsi4c32pscalef16_qsu4c32s16s0,
@@ -144,7 +142,6 @@ static ggml_kleidiai_kernels gemm_gemv_kernels[] = {
             /* .get_packed_offset     = */ kai_get_lhs_packed_offset_lhs_quant_pack_qsi8d32p_f32,
             /* .packed_size           = */ kai_get_lhs_packed_size_lhs_quant_pack_qsi8d32p_f32,
             /* .pack_func             = */ kai_run_lhs_quant_pack_qsi8d32p_f32,
-            /* .require_aligned_m_idx = */ false,
         },
         /* .rhs_info = */ {
             /* .packed_size = */ kai_get_rhs_packed_size_rhs_pack_nxk_qsi4c32pscalef16_qsu4c32s16s0,
@@ -189,7 +186,6 @@ static ggml_kleidiai_kernels gemm_gemv_kernels[] = {
             /* .get_packed_offset     = */ kai_get_lhs_packed_offset_lhs_quant_pack_qsi8d32p_f32,
             /* .packed_size           = */ kai_get_lhs_packed_size_lhs_quant_pack_qsi8d32p_f32,
             /* .pack_func             = */ kai_run_lhs_quant_pack_qsi8d32p_f32,
-            /* .require_aligned_m_idx = */ false,
         },
         /* .rhs_info = */ {
             /* .packed_size = */ kai_get_rhs_packed_size_rhs_pack_nxk_qsi4c32pscalef16_qsu4c32s16s0,
@@ -233,7 +229,6 @@ static ggml_kleidiai_kernels gemm_gemv_kernels[] = {
             /* .get_packed_offset     = */ kai_get_lhs_packed_offset_lhs_quant_pack_qsi8d32p_f32,
             /* .packed_size           = */ kai_get_lhs_packed_size_lhs_quant_pack_qsi8d32p_f32,
             /* .pack_func             = */ kai_run_lhs_quant_pack_qsi8d32p_f32,
-            /* .require_aligned_m_idx = */ false,
         },
         /* .rhs_info = */ {
             /* .packed_size = */ kai_get_rhs_packed_size_rhs_pack_nxk_qsi4c32pscalef16_qsu4c32s16s0,

ggml/src/ggml-cpu/kleidiai/kernels.h

@@ -40,7 +40,6 @@ struct lhs_packing_info {
     size_t (*packed_size)(size_t m, size_t k, size_t bl, size_t mr, size_t kr, size_t sr);
     void (*pack_func)(size_t m, size_t k, size_t bl, size_t mr, size_t kr, size_t sr, size_t m_idx_start, const float* lhs,
                       size_t lhs_stride, void* lhs_packed);
-    bool require_aligned_m_idx;
 };
 
 struct rhs_packing_info {

ggml/src/ggml-cpu/kleidiai/kleidiai.cpp

@@ -124,8 +124,7 @@ class tensor_traits : public ggml::cpu::tensor_traits {
             size_t sr = kernel->get_sr();
 
             // Calculate number of columns to be processed per thread
-            const bool use_multithread = lhs_info->require_aligned_m_idx && m <= mr ? false : true;
-            const size_t num_m_per_thread = use_multithread ? kai_roundup(m, nth) / nth : m;
+            const size_t num_m_per_thread = kai_roundup(m, mr * nth) / nth;
             const size_t m_start = ith * num_m_per_thread;
             size_t m_to_process = num_m_per_thread;
             if ((m_start + m_to_process) > m) {
@@ -135,11 +134,11 @@ class tensor_traits : public ggml::cpu::tensor_traits {
             if(m_start < m) {
                 // Transform LHS
                 const size_t src_stride        = src1->nb[1];
-                const float * src_ptr          = reinterpret_cast<const float *>(lhs + lhs_info->get_offset(0, dst->src[1]->nb[1]));
+                const float * src_ptr          = reinterpret_cast<const float *>(lhs + lhs_info->get_offset(m_start, dst->src[1]->nb[1]));
                 const size_t lhs_packed_offset = lhs_info->get_packed_offset(m_start, k, QK4_0, mr, kr, sr);
                 void * lhs_packed_ptr          = static_cast<void *>(lhs_packed + lhs_packed_offset);
 
-                lhs_info->pack_func(m_to_process, k, QK4_0, mr, kr, sr, m_start, src_ptr, src_stride, lhs_packed_ptr);
+                lhs_info->pack_func(m_to_process, k, QK4_0, mr, kr, sr, 0, src_ptr, src_stride, lhs_packed_ptr);
             }
 
             ggml_barrier(params->threadpool);

ggml/src/ggml-cuda/common.cuh

@@ -41,15 +41,18 @@
 #define CUDART_HMAX   11070 // CUDA 11.7, min. ver. for which __hmax and __hmax2 are known to work (may be higher than needed)
 #define CUDART_HMASK  12000 // CUDA 12.0, min. ver. for half2 -> uint mask comparisons
 
-#define GGML_CUDA_CC_PASCAL       600
-#define GGML_CUDA_CC_DP4A         610 // minimum compute capability for __dp4a, an intrinsic for byte-wise dot products
-#define GGML_CUDA_CC_VOLTA        700
-#define GGML_CUDA_CC_TURING       750
-#define GGML_CUDA_CC_AMPERE       800
-#define GGML_CUDA_CC_ADA_LOVELACE 890
-#define GGML_CUDA_CC_OFFSET_AMD   0x1000000
+#define GGML_CUDA_CC_PASCAL          600
+#define GGML_CUDA_CC_DP4A            610 // minimum compute capability for __dp4a, an intrinsic for byte-wise dot products
+#define GGML_CUDA_CC_VOLTA           700
+#define GGML_CUDA_CC_TURING          750
+#define GGML_CUDA_CC_AMPERE          800
+#define GGML_CUDA_CC_ADA_LOVELACE    890
+#define GGML_CUDA_CC_OFFSET_AMD      0x1000000
+#define GGML_CUDA_CC_OFFSET_MTHREADS 0x0100000
+#define GGML_CUDA_CC_IS_NVIDIA(cc)   (cc < GGML_CUDA_CC_OFFSET_MTHREADS)
 
-// GCN/CNDA, wave size is 64
+// AMD
+// GCN/CDNA, wave size is 64
 #define GGML_CUDA_CC_GCN4       (GGML_CUDA_CC_OFFSET_AMD + 0x803)  // Tonga, Fiji, Polaris, minimum for fast fp16
 #define GGML_CUDA_CC_VEGA       (GGML_CUDA_CC_OFFSET_AMD + 0x900)  // Vega56/64, minimum for fp16 dual issue
 #define GGML_CUDA_CC_VEGA20     (GGML_CUDA_CC_OFFSET_AMD + 0x906)  // MI50/Radeon VII, minimum for dp4a
@@ -57,21 +60,32 @@
 #define GGML_CUDA_CC_CDNA2      (GGML_CUDA_CC_OFFSET_AMD + 0x910)  // MI210, minimum acc register renameing
 #define GGML_CUDA_CC_CDNA3      (GGML_CUDA_CC_OFFSET_AMD + 0x942)  // MI300
 
-// RNDA removes MFMA, dp4a, xnack, acc registers, wave size is 32
+// RDNA removes MFMA, dp4a, xnack, acc registers, wave size is 32
 #define GGML_CUDA_CC_RDNA1      (GGML_CUDA_CC_OFFSET_AMD + 0x1010) // RX 5000
 #define GGML_CUDA_CC_RDNA2      (GGML_CUDA_CC_OFFSET_AMD + 0x1030) // RX 6000, minimum for dp4a
 #define GGML_CUDA_CC_RDNA3      (GGML_CUDA_CC_OFFSET_AMD + 0x1100) // RX 7000, minimum for WMMA
+#define GGML_CUDA_CC_RDNA4      (GGML_CUDA_CC_OFFSET_AMD + 0x1200) // RX 9000
 
 #define GGML_CUDA_CC_IS_AMD(cc)   (cc >= GGML_CUDA_CC_OFFSET_AMD)
 #define GGML_CUDA_CC_IS_RDNA(cc)  (cc >= GGML_CUDA_CC_RDNA1)
 #define GGML_CUDA_CC_IS_RDNA1(cc) (cc >= GGML_CUDA_CC_RDNA1 && cc < GGML_CUDA_CC_RDNA2)
 #define GGML_CUDA_CC_IS_RDNA2(cc) (cc >= GGML_CUDA_CC_RDNA2 && cc < GGML_CUDA_CC_RDNA3)
-#define GGML_CUDA_CC_IS_RDNA3(cc) (cc >= GGML_CUDA_CC_RDNA3)
+#define GGML_CUDA_CC_IS_RDNA3(cc) (cc >= GGML_CUDA_CC_RDNA3 && cc < GGML_CUDA_CC_RDNA4)
+#define GGML_CUDA_CC_IS_RDNA4(cc) (cc >= GGML_CUDA_CC_RDNA4)
 #define GGML_CUDA_CC_IS_GCN(cc)   (cc > GGML_CUDA_CC_OFFSET_AMD && cc < GGML_CUDA_CC_CDNA)
 #define GGML_CUDA_CC_IS_CDNA(cc)  (cc >= GGML_CUDA_CC_CDNA && cc < GGML_CUDA_CC_RDNA1)
 
-#define GGML_CUDA_CC_QY1        210
-#define GGML_CUDA_CC_QY2        220
+// Moore Threads
+#define GGML_CUDA_MUSA_ARCH_IS_QY1 (__MUSA_ARCH__ <= 210)
+
+#define GGML_CUDA_CC_QY1  (GGML_MUSA_CC_OFFSET_MTHREADS + 0x210) // MTT S80, MTT S3000
+#define GGML_CUDA_CC_QY2  (GGML_MUSA_CC_OFFSET_MTHREADS + 0x220) // MTT S4000
+#define GGML_CUDA_CC_NG   (GGML_MUSA_CC_OFFSET_MTHREADS + 0x310) // TBD
+
+#define GGML_CUDA_CC_IS_MTHREADS(cc) (cc >= GGML_CUDA_CC_OFFSET_MTHREADS && cc < GGML_CUDA_CC_OFFSET_AMD)
+#define GGML_CUDA_CC_IS_QY1(cc)      (cc >= GGML_CUDA_CC_QY1 && cc < GGML_CUDA_CC_QY2)
+#define GGML_CUDA_CC_IS_QY2(cc)      (cc >= GGML_CUDA_CC_QY2 && cc < GGML_CUDA_CC_NEXT)
+#define GGML_CUDA_CC_IS_NG(cc)       (cc >= GGML_CUDA_CC_NG)
 
 #ifdef __CUDA_ARCH_LIST__
 constexpr bool ggml_cuda_has_arch_impl(int) {
@@ -197,9 +211,9 @@ typedef float2 dfloat2;
 #define FP16_MMA_AVAILABLE
 #endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= GGML_CUDA_CC_VOLTA
 
-#if defined(GGML_HIP_ROCWMMA_FATTN) && (defined(CDNA) || defined(RDNA3))
+#if defined(GGML_HIP_ROCWMMA_FATTN) && (defined(CDNA) || defined(RDNA3) || defined(RDNA4))
 #define FP16_MMA_AVAILABLE
-#endif // defined(GGML_HIP_ROCWMMA_FATTN) && (defined(CDNA) || defined(RDNA3))
+#endif // defined(GGML_HIP_ROCWMMA_FATTN) && (defined(CDNA) || defined(RDNA3) || defined(RDNA4))
 
 #if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= GGML_CUDA_CC_TURING
 #define NEW_MMA_AVAILABLE
@@ -209,21 +223,21 @@ typedef float2 dfloat2;
 #define CP_ASYNC_AVAILABLE
 #endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE
 
-#if !defined(GGML_CUDA_NO_FA) && !(defined(GGML_USE_MUSA) && __MUSA_ARCH__ <= GGML_CUDA_CC_QY1)
+#if !defined(GGML_CUDA_NO_FA) && !(defined(GGML_USE_MUSA) && GGML_CUDA_MUSA_ARCH_IS_QY1)
 #define FLASH_ATTN_AVAILABLE
-#endif // !defined(GGML_CUDA_NO_FA) && !(defined(GGML_USE_MUSA) && __MUSA_ARCH__ <= GGML_CUDA_CC_QY1)
+#endif // !defined(GGML_CUDA_NO_FA) && !(defined(GGML_USE_MUSA) && GGML_CUDA_MUSA_ARCH_IS_QY1)
 
 static bool fp16_available(const int cc) {
     return ggml_cuda_highest_compiled_arch(cc) >= GGML_CUDA_CC_PASCAL;
 }
 
 static bool fast_fp16_available(const int cc) {
-    return fp16_available(cc) && cc != 610;
+    return (GGML_CUDA_CC_IS_NVIDIA(cc) && fp16_available(cc) && cc != 610) || GGML_CUDA_CC_IS_AMD(cc);
 }
 
 // To be used for feature selection of external libraries, e.g. cuBLAS.
 static bool fast_fp16_hardware_available(const int cc) {
-    return cc >= GGML_CUDA_CC_PASCAL && cc != 610;
+    return (GGML_CUDA_CC_IS_NVIDIA(cc) && cc >= GGML_CUDA_CC_PASCAL && cc != 610) || GGML_CUDA_CC_IS_AMD(cc);
 }
 
 // Any FP16 tensor core instructions are available for ggml code.
@@ -231,20 +245,20 @@ static bool fp16_mma_available(const int cc) {
 #if defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__) && !defined(GGML_HIP_ROCWMMA_FATTN)
     return false;
 #else
-    return cc < GGML_CUDA_CC_OFFSET_AMD && ggml_cuda_highest_compiled_arch(cc) >= GGML_CUDA_CC_VOLTA ||
-        GGML_CUDA_CC_IS_CDNA(cc) || cc >= GGML_CUDA_CC_RDNA3;
+    return (GGML_CUDA_CC_IS_NVIDIA(cc) && ggml_cuda_highest_compiled_arch(cc) >= GGML_CUDA_CC_VOLTA) ||
+        GGML_CUDA_CC_IS_CDNA(cc) || GGML_CUDA_CC_IS_RDNA3(cc) || GGML_CUDA_CC_IS_RDNA4(cc);
 #endif // defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__) && !defined(GGML_HIP_ROCWMMA_FATTN)
 }
 
 // To be used for feature selection of external libraries, e.g. cuBLAS.
 static bool fp16_mma_hardware_available(const int cc) {
-    return cc < GGML_CUDA_CC_OFFSET_AMD && cc >= GGML_CUDA_CC_VOLTA ||
-        GGML_CUDA_CC_IS_CDNA(cc) || cc >= GGML_CUDA_CC_RDNA3;
+    return (GGML_CUDA_CC_IS_NVIDIA(cc) && cc >= GGML_CUDA_CC_VOLTA) ||
+        GGML_CUDA_CC_IS_CDNA(cc) || GGML_CUDA_CC_IS_RDNA3(cc) || GGML_CUDA_CC_IS_RDNA4(cc);
 }
 
 // Volta technically had FP16 tensor cores but they work very differently compared to Turing and later.
 static bool new_mma_available(const int cc) {
-    return cc < GGML_CUDA_CC_OFFSET_AMD && ggml_cuda_highest_compiled_arch(cc) >= GGML_CUDA_CC_TURING;
+    return GGML_CUDA_CC_IS_NVIDIA(cc) && ggml_cuda_highest_compiled_arch(cc) >= GGML_CUDA_CC_TURING;
 }
 
 static bool cp_async_available(const int cc) {
@@ -397,7 +411,7 @@ static __device__ __forceinline__ int ggml_cuda_dp4a(const int a, const int b, i
 #if defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)
 #if defined(CDNA) || defined(RDNA2) || defined(__gfx906__)
     c = __builtin_amdgcn_sdot4(a, b, c, false);
-#elif defined(RDNA3)
+#elif defined(RDNA3) || defined(RDNA4)
     c = __builtin_amdgcn_sudot4( true, a, true, b, c, false);
 #elif defined(RDNA1) || defined(__gfx900__)
     int tmp1;

ggml/src/ggml-cuda/fattn.cu

@@ -253,7 +253,7 @@ void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst
     const int warp_size = ggml_cuda_info().devices[ggml_cuda_get_device()].warp_size;
     const enum ggml_prec prec = ggml_flash_attn_ext_get_prec(KQV);
 
-    if (cc >= GGML_CUDA_CC_OFFSET_AMD) {
+    if (GGML_CUDA_CC_IS_AMD(cc)) {
 #if defined(GGML_HIP_ROCWMMA_FATTN)
         if (fp16_mma_available(cc)) {
             ggml_cuda_flash_attn_ext_wmma_f16(ctx, dst);

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

@@ -264,9 +264,9 @@ static ggml_cuda_device_info ggml_cuda_init() {
 #elif defined(GGML_USE_MUSA)
         // FIXME: Ensure compatibility with varying warp sizes across different MUSA archs.
         info.devices[id].warp_size = 32;
-        // TODO: refine the .cc to reflect MUSA's actual CC capabilities
         info.devices[id].smpbo = prop.sharedMemPerBlockOptin;
-        info.devices[id].cc = 100*prop.major + 10*prop.minor;
+        info.devices[id].cc = GGML_CUDA_CC_OFFSET_MTHREADS + prop.major * 0x100;
+        info.devices[id].cc += prop.minor * 0x10;
         GGML_LOG_INFO("  Device %d: %s, compute capability %d.%d, VMM: %s\n",
                         id, prop.name, prop.major, prop.minor, device_vmm ? "yes" : "no");
 #else
@@ -1188,11 +1188,11 @@ static void ggml_cuda_op_mul_mat_cublas(
     // ldc == nrows of the matrix that cuBLAS writes into
     int64_t ldc = id == ctx.device ? ne0 : row_diff;
 
-    const int compute_capability = ggml_cuda_info().devices[id].cc;
+    const int cc = ggml_cuda_info().devices[id].cc;
 
     const bool use_fp16 = (src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type)) && ggml_is_contiguous(src0) && row_diff == src0->ne[1] && dst->op_params[0] == GGML_PREC_DEFAULT;
 
-    if (compute_capability >= GGML_CUDA_CC_VOLTA && use_fp16) {
+    if (((GGML_CUDA_CC_IS_NVIDIA(cc) && cc >= GGML_CUDA_CC_VOLTA) || GGML_CUDA_CC_IS_AMD(cc)) && use_fp16) {
         // convert src0 and src1 to fp16, multiply as fp16, convert dst to fp32
         ggml_cuda_pool_alloc<half> src0_as_f16(ctx.pool(id));
         if (src0->type != GGML_TYPE_F16) {
@@ -1216,7 +1216,7 @@ static void ggml_cuda_op_mul_mat_cublas(
 
         CUBLAS_CHECK(cublasSetStream(ctx.cublas_handle(id), stream));
 
-        if (GGML_CUDA_CC_IS_CDNA(compute_capability)) {
+        if (GGML_CUDA_CC_IS_CDNA(cc) || GGML_CUDA_CC_IS_RDNA4(cc)) {
             const float alpha = 1.0f;
             const float beta = 0.0f;
             CUBLAS_CHECK(
@@ -1759,7 +1759,9 @@ static void ggml_cuda_mul_mat_batched_cublas(ggml_backend_cuda_context & ctx, co
         beta  = &beta_f32;
     }
 
-    if (GGML_CUDA_CC_IS_CDNA(ggml_cuda_info().devices[ctx.device].cc)) {
+    int id = ggml_cuda_get_device();
+    const int cc = ggml_cuda_info().devices[id].cc;
+    if (GGML_CUDA_CC_IS_CDNA(cc) || GGML_CUDA_CC_IS_RDNA4(cc)) {
         cu_compute_type = CUBLAS_COMPUTE_32F;
         alpha = &alpha_f32;
         beta  = &beta_f32;
@@ -1836,7 +1838,7 @@ static void ggml_cuda_mul_mat_batched_cublas(ggml_backend_cuda_context & ctx, co
     }
 #endif
 
-    if (dst->op_params[0] == GGML_PREC_DEFAULT) {
+    if (dst->op_params[0] == GGML_PREC_DEFAULT && cu_data_type == CUDA_R_16F) {
         const to_fp32_cuda_t to_fp32_cuda = ggml_get_to_fp32_cuda(GGML_TYPE_F16);
         to_fp32_cuda(dst_f16.get(), dst_ddf, ne_dst, main_stream);
     }

ggml/src/ggml-cuda/mmq.cu

@@ -27,8 +27,8 @@ void ggml_cuda_op_mul_mat_q(
     // The stream-k decomposition is only faster for recent NVIDIA GPUs.
     // Also its fixup needs to allocate a temporary buffer in the memory pool.
     // There are multiple parallel CUDA streams for src1_ncols != ne11 which would introduce a race condition for this buffer.
-    const bool use_stream_k = ggml_cuda_highest_compiled_arch(cc) >= GGML_CUDA_CC_VOLTA &&
-        cc < GGML_CUDA_CC_OFFSET_AMD && src1_ncols == ne11;
+    const bool use_stream_k = GGML_CUDA_CC_IS_NVIDIA(cc) &&
+        ggml_cuda_highest_compiled_arch(cc) >= GGML_CUDA_CC_VOLTA && src1_ncols == ne11;
     const mmq_args args = {src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stride00, src1_padded_row_size, src1_ncols, ne11, nrows_dst, use_stream_k};
 
     switch (src0->type) {
@@ -145,9 +145,9 @@ bool ggml_cuda_should_use_mmq(enum ggml_type type, int cc, int64_t ne11) {
     return true;
 #endif //GGML_CUDA_FORCE_MMQ
 
-    if (cc < GGML_CUDA_CC_OFFSET_AMD) {
+    if (GGML_CUDA_CC_IS_NVIDIA(cc)) {
         return !fp16_mma_hardware_available(cc) || ne11 < MMQ_DP4A_MAX_BATCH_SIZE;
     }
 
-    return (!GGML_CUDA_CC_IS_RDNA3(cc) && !GGML_CUDA_CC_IS_CDNA(cc)) || ne11 < MMQ_DP4A_MAX_BATCH_SIZE;
+    return (!GGML_CUDA_CC_IS_RDNA4(cc) && !GGML_CUDA_CC_IS_RDNA3(cc) && !GGML_CUDA_CC_IS_CDNA(cc)) || ne11 < MMQ_DP4A_MAX_BATCH_SIZE;
 }

ggml/src/ggml-cuda/mmq.cuh

@@ -90,7 +90,7 @@ struct tile_x_sizes {
 
 static int get_mmq_x_max_host(const int cc) {
     return new_mma_available(cc) ? 128 :
-        ggml_cuda_highest_compiled_arch(cc) >= GGML_CUDA_CC_VOLTA && cc < GGML_CUDA_CC_OFFSET_AMD ?
+        GGML_CUDA_CC_IS_NVIDIA(cc) && ggml_cuda_highest_compiled_arch(cc) >= GGML_CUDA_CC_VOLTA ?
 #ifdef GGML_CUDA_FORCE_MMQ
             128                     : 64;
 #else
@@ -123,8 +123,8 @@ static constexpr __device__ int get_mmq_x_max_device() {
 }
 
 static int get_mmq_y_host(const int cc) {
-    return cc >= GGML_CUDA_CC_OFFSET_AMD ? (GGML_CUDA_CC_IS_RDNA1(cc) ? 64 : 128) :
-        (ggml_cuda_highest_compiled_arch(cc) >= GGML_CUDA_CC_VOLTA ? 128 : 64);
+    return GGML_CUDA_CC_IS_AMD(cc) ? (GGML_CUDA_CC_IS_RDNA1(cc) ? 64 : 128) :
+        ((GGML_CUDA_CC_IS_NVIDIA(cc) && ggml_cuda_highest_compiled_arch(cc) >= GGML_CUDA_CC_VOLTA) ? 128 : 64);
 }
 
 static constexpr __device__ int get_mmq_y_device() {
@@ -2577,9 +2577,9 @@ static __device__ void mul_mat_q_process_tile(
 
 template <ggml_type type, int mmq_x, int nwarps, bool need_check>
 #if defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)
-#if defined(RDNA3) || defined(RDNA2) || defined(CDNA) || defined(GCN)
+#if defined(RDNA4) || defined(RDNA3) || defined(RDNA2) || defined(CDNA) || defined(GCN)
     __launch_bounds__(WARP_SIZE*nwarps, 2)
-#endif // defined(RDNA3) || defined(RDNA2) || defined(CDNA) || defined(GCN)
+#endif // defined(RDNA4) || defined(RDNA3) || defined(RDNA2) || defined(CDNA) || defined(GCN)
 #else
 #if __CUDA_ARCH__ >= GGML_CUDA_CC_VOLTA
     __launch_bounds__(WARP_SIZE*nwarps, 1)
@@ -2772,14 +2772,14 @@ static void launch_mul_mat_q(ggml_backend_cuda_context & ctx, const mmq_args & a
 
     const int shmem = mmq_get_shmem<type>(mmq_x, mmq_y, cc);
 
-#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__))
+#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && !defined(GGML_USE_MUSA)
     static bool shmem_limit_raised[GGML_CUDA_MAX_DEVICES] = {false};
     if (!shmem_limit_raised[id]) {
         CUDA_CHECK(cudaFuncSetAttribute(mul_mat_q<type, mmq_x, MMQ_NWARPS, false>, cudaFuncAttributeMaxDynamicSharedMemorySize, shmem));
         CUDA_CHECK(cudaFuncSetAttribute(mul_mat_q<type, mmq_x, MMQ_NWARPS, true>,  cudaFuncAttributeMaxDynamicSharedMemorySize, shmem));
         shmem_limit_raised[id] = true;
     }
-#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__))
+#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && !defined(GGML_USE_MUSA)
 
     const int nty = (args.ne01 + mmq_y - 1) / mmq_y;
     const int ntx = (args.ne11 + mmq_x - 1) / mmq_x;
@@ -2832,7 +2832,7 @@ void mul_mat_q_case(ggml_backend_cuda_context & ctx, const mmq_args & args, cuda
     const int mmq_x_max = get_mmq_x_max_host(cc);
     const int mmq_y = get_mmq_y_host(cc);
     const int block_num_y = (args.ne01 + mmq_y - 1) / mmq_y;
-    const bool use_stream_k = ggml_cuda_highest_compiled_arch(cc) >= GGML_CUDA_CC_VOLTA && cc < GGML_CUDA_CC_OFFSET_AMD;
+    const bool use_stream_k = GGML_CUDA_CC_IS_NVIDIA(cc) && ggml_cuda_highest_compiled_arch(cc) >= GGML_CUDA_CC_VOLTA;
 
     int mmq_x_best  = 0;
     int nparts_best = INT_MAX;

ggml/src/ggml-cuda/mmvq.cu

@@ -54,7 +54,7 @@ enum mmvq_parameter_table_id {
 };
 
 static constexpr __device__ mmvq_parameter_table_id get_device_table_id() {
-#if defined(RDNA2) || defined(RDNA3)
+#if defined(RDNA2) || defined(RDNA3) || defined(RDNA4)
     return MMVQ_PARAMETERS_RDNA2;
 #elif defined(GCN) || defined(CDNA)
     return MMVQ_PARAMETERS_GCN;
@@ -64,7 +64,7 @@ static constexpr __device__ mmvq_parameter_table_id get_device_table_id() {
 }
 
 static __host__ mmvq_parameter_table_id get_device_table_id(int cc) {
-    if (GGML_CUDA_CC_IS_RDNA2(cc) || GGML_CUDA_CC_IS_RDNA3(cc)) {
+    if (GGML_CUDA_CC_IS_RDNA2(cc) || GGML_CUDA_CC_IS_RDNA3(cc) || GGML_CUDA_CC_IS_RDNA4(cc)) {
         return MMVQ_PARAMETERS_RDNA2;
     }
     if (GGML_CUDA_CC_IS_GCN(cc) || GGML_CUDA_CC_IS_CDNA(cc)) {

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

@@ -151,6 +151,10 @@
 #define CDNA
 #endif
 
+#if defined(__GFX12__)
+#define RDNA4
+#endif
+
 #if defined(__gfx1100__) || defined(__gfx1101__) || defined(__gfx1102__) || defined(__gfx1103__) || \
     defined(__gfx1150__) || defined(__gfx1151__)
 #define RDNA3

ggml/src/ggml-metal/ggml-metal-impl.h

@@ -1,6 +1,70 @@
 #ifndef GGML_METAL_IMPL
 #define GGML_METAL_IMPL
 
+// kernel parameters for mat-vec threadgroups
+//
+// N_R0: number of src0 rows to process per simdgroup
+// N_SG: number of simdgroups per threadgroup
+//
+// TODO: for optimal performance, become function of the device and work size
+
+#define N_R0_Q4_0 4
+#define N_SG_Q4_0 2
+
+#define N_R0_Q4_1 4
+#define N_SG_Q4_1 2
+
+#define N_R0_Q5_0 4
+#define N_SG_Q5_0 2
+
+#define N_R0_Q5_1 4
+#define N_SG_Q5_1 2
+
+#define N_R0_Q8_0 4
+#define N_SG_Q8_0 2
+
+#define N_R0_Q2_K 4
+#define N_SG_Q2_K 2
+
+#define N_R0_Q3_K 2
+#define N_SG_Q3_K 2
+
+#define N_R0_Q4_K 4
+#define N_SG_Q4_K 2
+
+#define N_R0_Q5_K 2
+#define N_SG_Q5_K 2
+
+#define N_R0_Q6_K 1
+#define N_SG_Q6_K 2
+
+#define N_R0_IQ1_S 4
+#define N_SG_IQ1_S 2
+
+#define N_R0_IQ1_M 4
+#define N_SG_IQ1_M 2
+
+#define N_R0_IQ2_XXS 4
+#define N_SG_IQ2_XXS 2
+
+#define N_R0_IQ2_XS 4
+#define N_SG_IQ2_XS 2
+
+#define N_R0_IQ2_S 4
+#define N_SG_IQ2_S 2
+
+#define N_R0_IQ3_XXS 4
+#define N_SG_IQ3_XXS 2
+
+#define N_R0_IQ3_S 4
+#define N_SG_IQ3_S 2
+
+#define N_R0_IQ4_NL 2
+#define N_SG_IQ4_NL 2
+
+#define N_R0_IQ4_XS 2
+#define N_SG_IQ4_XS 2
+
 // kernel argument structs
 //
 // - element counters (e.g. ne00) typically use int32_t to reduce register usage

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

@@ -2561,171 +2561,180 @@ static void ggml_metal_encode_node(
                     [encoder setThreadgroupMemoryLength:8192 atIndex:0];
                     [encoder dispatchThreadgroups:MTLSizeMake( (ne11 + 31)/32, (ne01 + 63)/64, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(128, 1, 1)];
                 } else {
-                    int nth0 = 32;
-                    int nth1 = 1;
-                    int nrows = 1;
-                    //printf("vector: ne00 = %6d, ne01 = %6d, ne02 = %6d, ne11 = %6d, ne12 = %6d\n", ne00, ne01, ne02, ne11, ne12);
-
                     id<MTLComputePipelineState> pipeline = nil;
 
+                    int nsg = 0; // number of simdgroups
+                    int nr0 = 0; // number of src0 rows per simdgroup
+                    int nr1 = 1; // number of src1 rows per threadgroup
+
+                    size_t smem = 0; // shared memory
+
                     // use custom matrix x vector kernel
                     switch (src0t) {
                         case GGML_TYPE_F32:
                             {
                                 GGML_ASSERT(src1t == GGML_TYPE_F32);
+                                nsg = 1;
+                                nr0 = 1;
+                                nr1 = 4;
                                 pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_F32_F32].pipeline;
-                                nrows = 4;
                             } break;
                         case GGML_TYPE_F16:
                             {
-                                nth0 = 32;
-                                nth1 = 1;
+                                nsg = 1;
+                                nr0 = 1;
                                 if (src1t == GGML_TYPE_F32) {
                                     if (ne11 * ne12 < 4) {
                                         pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_1ROW].pipeline;
                                     } else if (ne00 >= 128 && ne01 >= 8 && ne00%4 == 0) {
                                         pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_L4].pipeline;
-                                        nrows = ne11;
+                                        nr1 = ne11;
                                     } else {
                                         pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32].pipeline;
-                                        nrows = 4;
+                                        nr1 = 4;
                                     }
                                 } else {
                                     pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F16].pipeline;
-                                    nrows = 4;
+                                    nr1 = 4;
                                 }
                             } break;
                         case GGML_TYPE_BF16:
                             {
-                                nth0 = 32;
-                                nth1 = 1;
+                                nsg = 1;
+                                nr0 = 1;
                                 if (src1t == GGML_TYPE_F32) {
                                     if (ne11 * ne12 < 4) {
                                         pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_BF16_F32_1ROW].pipeline;
                                     } else if (ne00 >= 128 && ne01 >= 8 && ne00%4 == 0) {
                                         pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_BF16_F32_L4].pipeline;
-                                        nrows = ne11;
+                                        nr1 = ne11;
                                     } else {
                                         pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_BF16_F32].pipeline;
-                                        nrows = 4;
+                                        nr1 = 4;
                                     }
                                 } else {
                                     pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_BF16_BF16].pipeline;
-                                    nrows = 4;
+                                    nr1 = 4;
                                 }
                             } break;
                         case GGML_TYPE_Q4_0:
                             {
-                                nth0 = 8;
-                                nth1 = 8;
+                                nsg = N_SG_Q4_0;
+                                nr0 = N_R0_Q4_0;
                                 pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_0_F32].pipeline;
                             } break;
                         case GGML_TYPE_Q4_1:
                             {
-                                nth0 = 8;
-                                nth1 = 8;
+                                nsg = N_SG_Q4_1;
+                                nr0 = N_R0_Q4_1;
                                 pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_1_F32].pipeline;
                             } break;
                         case GGML_TYPE_Q5_0:
                             {
-                                nth0 = 8;
-                                nth1 = 8;
+                                nsg = N_SG_Q5_0;
+                                nr0 = N_R0_Q5_0;
                                 pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_0_F32].pipeline;
                             } break;
                         case GGML_TYPE_Q5_1:
                             {
-                                nth0 = 8;
-                                nth1 = 8;
+                                nsg = N_SG_Q5_1;
+                                nr0 = N_R0_Q5_1;
                                 pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_1_F32].pipeline;
                             } break;
                         case GGML_TYPE_Q8_0:
                             {
-                                nth0 = 8;
-                                nth1 = 8;
+                                nsg = N_SG_Q8_0;
+                                nr0 = N_R0_Q8_0;
                                 pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q8_0_F32].pipeline;
                             } break;
                         case GGML_TYPE_Q2_K:
                             {
-                                nth0 = 2;
-                                nth1 = 32;
+                                nsg = N_SG_Q2_K;
+                                nr0 = N_R0_Q2_K;
                                 pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q2_K_F32].pipeline;
                             } break;
                         case GGML_TYPE_Q3_K:
                             {
-                                nth0 = 2;
-                                nth1 = 32;
+                                nsg = N_SG_Q3_K;
+                                nr0 = N_R0_Q3_K;
                                 pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q3_K_F32].pipeline;
                             } break;
                         case GGML_TYPE_Q4_K:
                             {
-                                nth0 = 4; //1;
-                                nth1 = 8; //32;
+                                nsg = N_SG_Q4_K;
+                                nr0 = N_R0_Q4_K;
                                 pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_K_F32].pipeline;
                             } break;
                         case GGML_TYPE_Q5_K:
                             {
-                                nth0 = 2;
-                                nth1 = 32;
+                                nsg = N_SG_Q5_K;
+                                nr0 = N_R0_Q5_K;
                                 pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_K_F32].pipeline;
                             } break;
                         case GGML_TYPE_Q6_K:
                             {
-                                nth0 = 2;
-                                nth1 = 32;
+                                nsg = N_SG_Q6_K;
+                                nr0 = N_R0_Q6_K;
                                 pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_Q6_K_F32].pipeline;
                             } break;
                         case GGML_TYPE_IQ2_XXS:
                             {
-                                nth0 = 4;
-                                nth1 = 16;
+                                nsg = N_SG_IQ2_XXS;
+                                nr0 = N_R0_IQ2_XXS;
+                                smem = 256*8+128;
                                 pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XXS_F32].pipeline;
                             } break;
                         case GGML_TYPE_IQ2_XS:
                             {
-                                nth0 = 4;
-                                nth1 = 16;
+                                nsg = N_SG_IQ2_XS;
+                                nr0 = N_R0_IQ2_XS;
+                                smem = 512*8+128;
                                 pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XS_F32].pipeline;
                             } break;
                         case GGML_TYPE_IQ3_XXS:
                             {
-                                nth0 = 4;
-                                nth1 = 16;
+                                nsg = N_SG_IQ3_XXS;
+                                nr0 = N_R0_IQ3_XXS;
+                                smem = 256*4+128;
                                 pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_XXS_F32].pipeline;
                             } break;
                         case GGML_TYPE_IQ3_S:
                             {
-                                nth0 = 4;
-                                nth1 = 16;
+                                nsg = N_SG_IQ3_S;
+                                nr0 = N_R0_IQ3_S;
+                                smem = 512*4;
                                 pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_S_F32].pipeline;
                             } break;
                         case GGML_TYPE_IQ2_S:
                             {
-                                nth0 = 4;
-                                nth1 = 16;
+                                nsg = N_SG_IQ2_S;
+                                nr0 = N_R0_IQ2_S;
                                 pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_S_F32].pipeline;
                             } break;
                         case GGML_TYPE_IQ1_S:
                             {
-                                nth0 = 4;
-                                nth1 = 16;
+                                nsg = N_SG_IQ1_S;
+                                nr0 = N_R0_IQ1_S;
                                 pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_S_F32].pipeline;
                             } break;
                         case GGML_TYPE_IQ1_M:
                             {
-                                nth0 = 4;
-                                nth1 = 16;
+                                nsg = N_SG_IQ1_M;
+                                nr0 = N_R0_IQ1_M;
                                 pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_M_F32].pipeline;
                             } break;
                         case GGML_TYPE_IQ4_NL:
                             {
-                                nth0 = 4;
-                                nth1 = 16;
+                                nsg = N_SG_IQ4_NL;
+                                nr0 = N_R0_IQ4_NL;
+                                smem = 32*sizeof(float);
                                 pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_NL_F32].pipeline;
                             } break;
                         case GGML_TYPE_IQ4_XS:
                             {
-                                nth0 = 4;
-                                nth1 = 16;
+                                nsg = N_SG_IQ4_XS;
+                                nr0 = N_R0_IQ4_XS;
+                                smem = 32*sizeof(float);
                                 pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_XS_F32].pipeline;
                             } break;
                         default:
@@ -2762,41 +2771,10 @@ static void ggml_metal_encode_node(
                     [encoder setBuffer:id_src1 offset:offs_src1 atIndex:2];
                     [encoder setBuffer:id_dst  offset:offs_dst  atIndex:3];
 
-                    if (src0t == GGML_TYPE_Q4_0  || src0t == GGML_TYPE_Q4_1  || src0t == GGML_TYPE_Q5_0 ||
-                        src0t == GGML_TYPE_Q5_1  || src0t == GGML_TYPE_Q8_0  || src0t == GGML_TYPE_Q2_K ||
-                        src0t == GGML_TYPE_IQ1_S || src0t == GGML_TYPE_IQ1_M || src0t == GGML_TYPE_IQ2_S) {
-                        [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
-                    }
-                    else if (src0t == GGML_TYPE_IQ2_XXS || src0t == GGML_TYPE_IQ2_XS) {
-                        const int mem_size = src0t == GGML_TYPE_IQ2_XXS ? 256*8+128 : 512*8+128;
-                        [encoder setThreadgroupMemoryLength:mem_size atIndex:0];
-                        [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
-                    }
-                    else if (src0t == GGML_TYPE_IQ3_XXS || src0t == GGML_TYPE_IQ3_S) {
-                        const int mem_size = src0t == GGML_TYPE_IQ3_XXS ? 256*4+128 : 512*4;
-                        [encoder setThreadgroupMemoryLength:mem_size atIndex:0];
-                        [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
-                    }
-                    else if (src0t == GGML_TYPE_IQ4_NL || src0t == GGML_TYPE_IQ4_XS) {
-                        const int mem_size = 32*sizeof(float);
-                        [encoder setThreadgroupMemoryLength:mem_size atIndex:0];
-                        [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
-                    }
-                    else if (src0t == GGML_TYPE_Q4_K) {
-                        [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
-                    }
-                    else if (src0t == GGML_TYPE_Q3_K) {
-                        [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
-                    }
-                    else if (src0t == GGML_TYPE_Q5_K) {
-                        [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
-                    }
-                    else if (src0t == GGML_TYPE_Q6_K) {
-                        [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 1)/2, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
-                    } else {
-                        const int64_t ny = (ne11 + nrows - 1)/nrows;
-                        [encoder dispatchThreadgroups:MTLSizeMake(ne01, ny, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
+                    if (smem > 0) {
+                        [encoder setThreadgroupMemoryLength:smem atIndex:0];
                     }
+                    [encoder dispatchThreadgroups:MTLSizeMake((ne01 + nr0*nsg - 1)/(nr0*nsg), (ne11 + nr1 - 1)/nr1, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(32, nsg, 1)];
                 }
             } break;
         case GGML_OP_MUL_MAT_ID:
@@ -2902,146 +2880,155 @@ static void ggml_metal_encode_node(
 
                     [encoder dispatchThreadgroups:MTLSizeMake((ne21 + 31)/32, (ne01 + 63)/64, n_as) threadsPerThreadgroup:MTLSizeMake(128, 1, 1)];
                 } else {
-                    int nth0 = 32;
-                    int nth1 = 1;
-                    int nrows = 1;
-                    //printf("vector: ne00 = %6d, ne01 = %6d, ne02 = %6d, ne11 = %6d, ne12 = %6d\n", ne00, ne01, ne02, ne11, ne12);
-
                     id<MTLComputePipelineState> pipeline = nil;
 
+                    int nsg = 0; // number of simdgroups
+                    int nr0 = 0; // number of src0 rows per simdgroup
+                    int nr1 = 1; // number of src1 rows per threadgroup
+
+                    size_t smem = 0; // shared memory
+
                     // use custom matrix x vector kernel
                     switch (src0t) {
                         case GGML_TYPE_F32:
                             {
                                 GGML_ASSERT(src1t == GGML_TYPE_F32);
+                                nsg = 1;
+                                nr0 = 1;
                                 pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F32_F32].pipeline;
                             } break;
                         case GGML_TYPE_F16:
                             {
                                 GGML_ASSERT(src1t == GGML_TYPE_F32);
-                                nth0 = 32;
-                                nth1 = 1;
+                                nsg = 1;
+                                nr0 = 1;
                                 pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32].pipeline;
                             } break;
                         case GGML_TYPE_BF16:
                             {
                                 GGML_ASSERT(src1t == GGML_TYPE_F32);
-                                nth0 = 32;
-                                nth1 = 1;
+                                nsg = 1;
+                                nr0 = 1;
                                 pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_BF16_F32].pipeline;
                             } break;
                         case GGML_TYPE_Q4_0:
                             {
-                                nth0 = 8;
-                                nth1 = 8;
+                                nsg = N_SG_Q4_0;
+                                nr0 = N_R0_Q4_0;
                                 pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_0_F32].pipeline;
                             } break;
                         case GGML_TYPE_Q4_1:
                             {
-                                nth0 = 8;
-                                nth1 = 8;
+                                nsg = N_SG_Q4_1;
+                                nr0 = N_R0_Q4_1;
                                 pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_1_F32].pipeline;
                             } break;
                         case GGML_TYPE_Q5_0:
                             {
-                                nth0 = 8;
-                                nth1 = 8;
+                                nsg = N_SG_Q5_0;
+                                nr0 = N_R0_Q5_0;
                                 pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_0_F32].pipeline;
                             } break;
                         case GGML_TYPE_Q5_1:
                             {
-                                nth0 = 8;
-                                nth1 = 8;
+                                nsg = N_SG_Q5_1;
+                                nr0 = N_R0_Q5_1;
                                 pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_1_F32].pipeline;
                             } break;
                         case GGML_TYPE_Q8_0:
                             {
-                                nth0 = 8;
-                                nth1 = 8;
+                                nsg = N_SG_Q8_0;
+                                nr0 = N_R0_Q8_0;
                                 pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q8_0_F32].pipeline;
                             } break;
                         case GGML_TYPE_Q2_K:
                             {
-                                nth0 = 2;
-                                nth1 = 32;
+                                nsg = N_SG_Q2_K;
+                                nr0 = N_R0_Q2_K;
                                 pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q2_K_F32].pipeline;
                             } break;
                         case GGML_TYPE_Q3_K:
                             {
-                                nth0 = 2;
-                                nth1 = 32;
+                                nsg = N_SG_Q3_K;
+                                nr0 = N_R0_Q3_K;
                                 pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q3_K_F32].pipeline;
                             } break;
                         case GGML_TYPE_Q4_K:
                             {
-                                nth0 = 4; //1;
-                                nth1 = 8; //32;
+                                nsg = N_SG_Q4_K;
+                                nr0 = N_R0_Q4_K;
                                 pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_K_F32].pipeline;
                             } break;
                         case GGML_TYPE_Q5_K:
                             {
-                                nth0 = 2;
-                                nth1 = 32;
+                                nsg = N_SG_Q5_K;
+                                nr0 = N_R0_Q5_K;
                                 pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_K_F32].pipeline;
                             } break;
                         case GGML_TYPE_Q6_K:
                             {
-                                nth0 = 2;
-                                nth1 = 32;
+                                nsg = N_SG_Q6_K;
+                                nr0 = N_R0_Q6_K;
                                 pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q6_K_F32].pipeline;
                             } break;
                         case GGML_TYPE_IQ2_XXS:
                             {
-                                nth0 = 4;
-                                nth1 = 16;
+                                nsg = N_SG_IQ2_XXS;
+                                nr0 = N_R0_IQ2_XXS;
+                                smem = 256*8+128;
                                 pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XXS_F32].pipeline;
                             } break;
                         case GGML_TYPE_IQ2_XS:
                             {
-                                nth0 = 4;
-                                nth1 = 16;
+                                nsg = N_SG_IQ2_XS;
+                                nr0 = N_R0_IQ2_XS;
+                                smem = 512*8+128;
                                 pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XS_F32].pipeline;
                             } break;
                         case GGML_TYPE_IQ3_XXS:
                             {
-                                nth0 = 4;
-                                nth1 = 16;
+                                nsg = N_SG_IQ3_XXS;
+                                nr0 = N_R0_IQ3_XXS;
+                                smem = 256*4+128;
                                 pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_XXS_F32].pipeline;
                             } break;
                         case GGML_TYPE_IQ3_S:
                             {
-                                nth0 = 4;
-                                nth1 = 16;
+                                nsg = N_SG_IQ3_S;
+                                nr0 = N_R0_IQ3_S;
+                                smem = 512*4;
                                 pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_S_F32].pipeline;
                             } break;
                         case GGML_TYPE_IQ2_S:
                             {
-                                nth0 = 4;
-                                nth1 = 16;
+                                nsg = N_SG_IQ2_S;
+                                nr0 = N_R0_IQ2_S;
                                 pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_S_F32].pipeline;
                             } break;
                         case GGML_TYPE_IQ1_S:
                             {
-                                nth0 = 4;
-                                nth1 = 16;
+                                nsg = N_SG_IQ1_S;
+                                nr0 = N_R0_IQ1_S;
                                 pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_S_F32].pipeline;
                             } break;
                         case GGML_TYPE_IQ1_M:
                             {
-                                nth0 = 4;
-                                nth1 = 16;
+                                nsg = N_SG_IQ1_M;
+                                nr0 = N_R0_IQ1_M;
                                 pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_M_F32].pipeline;
                             } break;
                         case GGML_TYPE_IQ4_NL:
                             {
-                                nth0 = 4;
-                                nth1 = 16;
+                                nsg = N_SG_IQ4_NL;
+                                nr0 = N_R0_IQ4_NL;
+                                smem = 32*sizeof(float);
                                 pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_NL_F32].pipeline;
                             } break;
                         case GGML_TYPE_IQ4_XS:
                             {
-                                nth0 = 4;
-                                nth1 = 16;
+                                nsg = N_SG_IQ4_XS;
+                                nr0 = N_R0_IQ4_XS;
+                                smem = 32*sizeof(float);
                                 pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_XS_F32].pipeline;
                             } break;
                         default:
@@ -3052,7 +3039,7 @@ static void ggml_metal_encode_node(
                     };
 
                     if (ggml_is_quantized(src0t)) {
-                        GGML_ASSERT(ne00 >= nth0*nth1);
+                        GGML_ASSERT(ne00 >= nsg*nr0);
                     }
 
                     ggml_metal_kargs_mul_mv_id args = {
@@ -3085,43 +3072,12 @@ static void ggml_metal_encode_node(
                     [encoder setBuffer:id_src2 offset:offs_src2 atIndex:4];
 
                     const int64_t _ne1 = 1;
-                    const int tgz = dst_rows;
+                    const int64_t ne123 = dst_rows;
 
-                    if (src0t == GGML_TYPE_Q4_0  || src0t == GGML_TYPE_Q4_1  || src0t == GGML_TYPE_Q5_0 ||
-                            src0t == GGML_TYPE_Q5_1  || src0t == GGML_TYPE_Q8_0  || src0t == GGML_TYPE_Q2_K ||
-                            src0t == GGML_TYPE_IQ1_S || src0t == GGML_TYPE_IQ1_M || src0t == GGML_TYPE_IQ2_S) {
-                        [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
-                    }
-                    else if (src0t == GGML_TYPE_IQ2_XXS || src0t == GGML_TYPE_IQ2_XS) {
-                        const int mem_size = src0t == GGML_TYPE_IQ2_XXS ? 256*8+128 : 512*8+128;
-                        [encoder setThreadgroupMemoryLength:mem_size atIndex:0];
-                        [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
-                    }
-                    else if (src0t == GGML_TYPE_IQ3_XXS || src0t == GGML_TYPE_IQ3_S) {
-                        const int mem_size = src0t == GGML_TYPE_IQ3_XXS ? 256*4+128 : 512*4;
-                        [encoder setThreadgroupMemoryLength:mem_size atIndex:0];
-                        [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
-                    }
-                    else if (src0t == GGML_TYPE_IQ4_NL || src0t == GGML_TYPE_IQ4_XS) {
-                        const int mem_size = 32*sizeof(float);
-                        [encoder setThreadgroupMemoryLength:mem_size atIndex:0];
-                        [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
-                    }
-                    else if (src0t == GGML_TYPE_Q4_K) {
-                        [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
-                    }
-                    else if (src0t == GGML_TYPE_Q3_K) {
-                        [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
-                    }
-                    else if (src0t == GGML_TYPE_Q5_K) {
-                        [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
-                    }
-                    else if (src0t == GGML_TYPE_Q6_K) {
-                        [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 1)/2, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
-                    } else {
-                        const int64_t ny = (_ne1 + nrows - 1)/nrows; // = _ne1
-                        [encoder dispatchThreadgroups:MTLSizeMake(ne01, ny, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
+                    if (smem > 0) {
+                        [encoder setThreadgroupMemoryLength:smem atIndex:0];
                     }
+                    [encoder dispatchThreadgroups:MTLSizeMake((ne01 + nr0*nsg - 1)/(nr0*nsg), (_ne1 + nr1 - 1)/nr1, ne123) threadsPerThreadgroup:MTLSizeMake(32, nsg, 1)];
                 }
             } break;
         case GGML_OP_GET_ROWS:

ggml/src/ggml-opencl/CMakeLists.txt

@@ -25,124 +25,46 @@ endif ()
 if (GGML_OPENCL_EMBED_KERNELS)
     add_compile_definitions(GGML_OPENCL_EMBED_KERNELS)
 
-    set(OPENCL_CL_SOURCE_EMBED         "${CMAKE_BINARY_DIR}/autogenerated/ggml-opencl.cl.h")
-    set(OPENCL_MM_CL_SOURCE_EMBED      "${CMAKE_BINARY_DIR}/autogenerated/ggml-opencl_mm.cl.h")
-    set(OPENCL_CVT_CL_SOURCE_EMBED     "${CMAKE_BINARY_DIR}/autogenerated/ggml-opencl_cvt.cl.h")
+    set(EMBED_KERNEL_SCRIPT "${CMAKE_CURRENT_SOURCE_DIR}/kernels/embed_kernel.py")
+    file(MAKE_DIRECTORY     "${CMAKE_CURRENT_BINARY_DIR}/autogenerated")
 
-    set(OPENCL_GEMV_NOSHUFFLE_SOURCE_EMBED             "${CMAKE_BINARY_DIR}/autogenerated/ggml-opencl_gemv_noshuffle.cl.h")
-    set(OPENCL_GEMV_NOSHUFFLE_GENERAL_SOURCE_EMBED     "${CMAKE_BINARY_DIR}/autogenerated/ggml-opencl_gemv_noshuffle_general.cl.h")
-    set(OPENCL_MUL_MAT_Ab_Bi_8x4_SOURCE_EMBED          "${CMAKE_BINARY_DIR}/autogenerated/ggml-opencl_mul_mat_Ab_Bi_8x4.cl.h")
-    set(OPENCL_TRANSPOSE_16_SOURCE_EMBED               "${CMAKE_BINARY_DIR}/autogenerated/ggml-opencl_transpose_16.cl.h")
-    set(OPENCL_TRANSPOSE_32_SOURCE_EMBED               "${CMAKE_BINARY_DIR}/autogenerated/ggml-opencl_transpose_32.cl.h")
-    set(OPENCL_TRANSPOSE_32_16_SOURCE_EMBED            "${CMAKE_BINARY_DIR}/autogenerated/ggml-opencl_transpose_32_16.cl.h")
-
-    set(EMBED_KERNEL_SCRIPT             "${CMAKE_CURRENT_SOURCE_DIR}/kernels/embed_kernel.py")
-    file(MAKE_DIRECTORY                 "${CMAKE_BINARY_DIR}/autogenerated")
-
-    include_directories("${CMAKE_BINARY_DIR}/autogenerated")
-
-    # Python must be accessible from command line
-    add_custom_command(
-        OUTPUT ${OPENCL_CL_SOURCE_EMBED}
-        COMMAND ${Python3_EXECUTABLE} ${EMBED_KERNEL_SCRIPT}
-            ${CMAKE_CURRENT_SOURCE_DIR}/kernels/ggml-opencl.cl
-            ${OPENCL_CL_SOURCE_EMBED}
-        DEPENDS kernels/ggml-opencl.cl ${EMBED_KERNEL_SCRIPT}
-        COMMENT "Generate ggml-opencl.cl.h"
-    )
-
-    add_custom_command(
-        OUTPUT ${OPENCL_MM_CL_SOURCE_EMBED}
-        COMMAND ${Python3_EXECUTABLE} ${EMBED_KERNEL_SCRIPT}
-            ${CMAKE_CURRENT_SOURCE_DIR}/kernels/ggml-opencl_mm.cl
-            ${OPENCL_MM_CL_SOURCE_EMBED}
-        DEPENDS kernels/ggml-opencl_mm.cl ${EMBED_KERNEL_SCRIPT}
-        COMMENT "Generate ggml-opencl_mm.cl.h"
-    )
-
-    add_custom_command(
-        OUTPUT ${OPENCL_CVT_CL_SOURCE_EMBED}
-        COMMAND ${Python3_EXECUTABLE} ${EMBED_KERNEL_SCRIPT}
-            ${CMAKE_CURRENT_SOURCE_DIR}/kernels/ggml-opencl_cvt.cl
-            ${OPENCL_CVT_CL_SOURCE_EMBED}
-        DEPENDS kernels/ggml-opencl_cvt.cl ${EMBED_KERNEL_SCRIPT}
-        COMMENT "Generate ggml-opencl_cvt.cl.h"
-    )
-
-    add_custom_command(
-        OUTPUT ${OPENCL_GEMV_NOSHUFFLE_SOURCE_EMBED}
-        COMMAND ${Python3_EXECUTABLE} ${EMBED_KERNEL_SCRIPT}
-            ${CMAKE_CURRENT_SOURCE_DIR}/kernels/ggml-opencl_gemv_noshuffle.cl
-            ${OPENCL_GEMV_NOSHUFFLE_SOURCE_EMBED}
-        DEPENDS kernels/ggml-opencl_gemv_noshuffle.cl ${EMBED_KERNEL_SCRIPT}
-        COMMENT "Generate ggml-opencl_gemv_noshuffle.cl.h"
-    )
-
-    add_custom_command(
-        OUTPUT ${OPENCL_GEMV_NOSHUFFLE_GENERAL_SOURCE_EMBED}
-        COMMAND ${Python3_EXECUTABLE} ${EMBED_KERNEL_SCRIPT}
-            ${CMAKE_CURRENT_SOURCE_DIR}/kernels/ggml-opencl_gemv_noshuffle_general.cl
-            ${OPENCL_GEMV_NOSHUFFLE_GENERAL_SOURCE_EMBED}
-        DEPENDS kernels/ggml-opencl_gemv_noshuffle_general.cl ${EMBED_KERNEL_SCRIPT}
-        COMMENT "Generate ggml-opencl_gemv_noshuffle_general.cl.h"
-    )
-
-    add_custom_command(
-        OUTPUT ${OPENCL_MUL_MAT_Ab_Bi_8x4_SOURCE_EMBED}
-        COMMAND ${Python3_EXECUTABLE} ${EMBED_KERNEL_SCRIPT}
-            ${CMAKE_CURRENT_SOURCE_DIR}/kernels/ggml-opencl_mul_mat_Ab_Bi_8x4.cl
-            ${OPENCL_MUL_MAT_Ab_Bi_8x4_SOURCE_EMBED}
-        DEPENDS kernels/ggml-opencl_mul_mat_Ab_Bi_8x4.cl ${EMBED_KERNEL_SCRIPT}
-        COMMENT "Generate ggml-opencl_mul_mat_Ab_Bi_8x4.cl.cl.h"
-    )
-
-    add_custom_command(
-        OUTPUT ${OPENCL_TRANSPOSE_16_SOURCE_EMBED}
-        COMMAND ${Python3_EXECUTABLE} ${EMBED_KERNEL_SCRIPT}
-            ${CMAKE_CURRENT_SOURCE_DIR}/kernels/ggml-opencl_transpose_16.cl
-            ${OPENCL_TRANSPOSE_16_SOURCE_EMBED}
-        DEPENDS kernels/ggml-opencl_transpose_16.cl ${EMBED_KERNEL_SCRIPT}
-        COMMENT "Generate ggml-opencl_transpose_16.cl.h"
-    )
-
-    add_custom_command(
-        OUTPUT ${OPENCL_TRANSPOSE_32_SOURCE_EMBED}
-        COMMAND ${Python3_EXECUTABLE} ${EMBED_KERNEL_SCRIPT}
-            ${CMAKE_CURRENT_SOURCE_DIR}/kernels/ggml-opencl_transpose_32.cl
-            ${OPENCL_TRANSPOSE_32_SOURCE_EMBED}
-        DEPENDS kernels/ggml-opencl_transpose_32.cl ${EMBED_KERNEL_SCRIPT}
-        COMMENT "Generate ggml-opencl_transpose_32.cl.h"
-    )
-
-    add_custom_command(
-        OUTPUT ${OPENCL_TRANSPOSE_32_16_SOURCE_EMBED}
-        COMMAND ${Python3_EXECUTABLE} ${EMBED_KERNEL_SCRIPT}
-            ${CMAKE_CURRENT_SOURCE_DIR}/kernels/ggml-opencl_transpose_32_16.cl
-            ${OPENCL_TRANSPOSE_32_16_SOURCE_EMBED}
-        DEPENDS kernels/ggml-opencl_transpose_32_16.cl ${EMBED_KERNEL_SCRIPT}
-        COMMENT "Generate ggml-opencl_transpose_32_16.cl.h"
-    )
-
-    target_sources(${TARGET_NAME} PRIVATE
-                   ${OPENCL_CL_SOURCE_EMBED}
-                   ${OPENCL_MM_CL_SOURCE_EMBED}
-                   ${OPENCL_CVT_CL_SOURCE_EMBED}
-                   ${OPENCL_GEMV_NOSHUFFLE_SOURCE_EMBED}
-                   ${OPENCL_GEMV_NOSHUFFLE_GENERAL_SOURCE_EMBED}
-                   ${OPENCL_MUL_MAT_Ab_Bi_8x4_SOURCE_EMBED}
-                   ${OPENCL_TRANSPOSE_16_SOURCE_EMBED}
-                   ${OPENCL_TRANSPOSE_32_SOURCE_EMBED}
-                   ${OPENCL_TRANSPOSE_32_16_SOURCE_EMBED})
-else ()
-    # copy ggml-opencl.cl to bin directory
-    configure_file(kernels/ggml-opencl.cl ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-opencl.cl COPYONLY)
-    configure_file(kernels/ggml-opencl_mm.cl ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-opencl_mm.cl COPYONLY)
-    configure_file(kernels/ggml-opencl_cvt.cl ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-opencl_cvt.cl COPYONLY)
-
-    configure_file(kernels/ggml-opencl_gemv_noshuffle.cl ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-opencl_gemv_noshuffle.cl COPYONLY)
-    configure_file(kernels/ggml-opencl_gemv_noshuffle_general.cl ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-opencl_gemv_noshuffle_general.cl COPYONLY)
-    configure_file(kernels/ggml-opencl_mul_mat_Ab_Bi_8x4.cl ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-opencl_mul_mat_Ab_Bi_8x4.cl COPYONLY)
-    configure_file(kernels/ggml-opencl_transpose_16.cl ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-opencl_transpose_16.cl COPYONLY)
-    configure_file(kernels/ggml-opencl_transpose_32.cl ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-opencl_transpose_32.cl COPYONLY)
-    configure_file(kernels/ggml-opencl_transpose_32_16.cl ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-opencl_transpose_32_16.cl COPYONLY)
+    target_include_directories(${TARGET_NAME} PRIVATE "${CMAKE_CURRENT_BINARY_DIR}/autogenerated")
 endif ()
+
+function(ggml_opencl_add_kernel KNAME)
+    set(KERN_HDR ${CMAKE_CURRENT_BINARY_DIR}/autogenerated/${KNAME}.cl.h)
+    set(KERN_SRC ${CMAKE_CURRENT_SOURCE_DIR}/kernels/${KNAME}.cl)
+
+    if (GGML_OPENCL_EMBED_KERNELS)
+        message(STATUS "opencl: embedding kernel ${KNAME}")
+
+        # Python must be accessible from command line
+        add_custom_command(
+            OUTPUT ${KERN_HDR}
+            COMMAND ${Python3_EXECUTABLE} ${EMBED_KERNEL_SCRIPT} ${KERN_SRC} ${KERN_HDR}
+            DEPENDS ${KERN_SRC} ${EMBED_KERNEL_SCRIPT}
+            COMMENT "Generate ${KERN_HDR}"
+        )
+
+        target_sources(${TARGET_NAME} PRIVATE ${KERN_HDR})
+    else ()
+        message(STATUS "opencl: adding kernel ${KNAME}")
+        configure_file(${KERN_SRC} ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/${KNAME}.cl COPYONLY)
+    endif ()
+endfunction()
+
+set(GGML_OPENCL_KERNELS
+    ggml-opencl
+    ggml-opencl_mm
+    ggml-opencl_cvt
+    ggml-opencl_gemv_noshuffle
+    ggml-opencl_gemv_noshuffle_general
+    ggml-opencl_mul_mat_Ab_Bi_8x4
+    ggml-opencl_transpose_16
+    ggml-opencl_transpose_32
+    ggml-opencl_transpose_32_16
+)
+
+foreach (K ${GGML_OPENCL_KERNELS})
+    ggml_opencl_add_kernel(${K})
+endforeach()

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

@@ -37,6 +37,7 @@
 #include "ggml-backend-impl.h"
 
 #include "ggml-sycl/backend.hpp"
+#include "ggml-sycl/common.hpp"
 #include "ggml-sycl/presets.hpp"
 #include "ggml-sycl/gemm.hpp"
 #include "ggml-sycl/sycl_hw.hpp"
@@ -191,7 +192,7 @@ static void ggml_check_sycl() try {
 
     if (!initialized) {
         g_ggml_sycl_debug = get_sycl_env("GGML_SYCL_DEBUG", 0);
-        g_ggml_sycl_disable_optimize= get_sycl_env("GGML_SYCL_DISABLE_OPT", 0);
+        g_ggml_sycl_disable_optimize= get_sycl_env("GGML_SYCL_DISABLE_OPT", 1);
         g_ggml_sycl_disable_graph = get_sycl_env("GGML_SYCL_DISABLE_GRAPH", 1);
         GGML_SYCL_DEBUG("[SYCL] call ggml_check_sycl\n");
         GGML_LOG_INFO("Running with Environment Variables:\n");
@@ -490,6 +491,23 @@ catch (sycl::exception const &exc) {
   std::exit(1);
 }
 
+static void ggml_backend_sycl_buffer_memset_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, uint8_t value,
+                                                   size_t offset, size_t size) {
+    GGML_SYCL_DEBUG(" [SYCL] call %s\n", __func__);
+    ggml_backend_sycl_buffer_context * ctx = (ggml_backend_sycl_buffer_context *) buffer->context;
+    SYCL_CHECK(ggml_sycl_set_device(ctx->device));
+    auto stream = &(dpct::dev_mgr::instance().get_device(ctx->device).default_queue());
+    if (size == 0) {
+        return;  // Nothing to do
+    }
+    if (tensor->data == nullptr) {
+        GGML_ABORT("Error: Tensor data pointer is null.\n");
+    }
+    void * target_ptr = static_cast<char *>(tensor->data) + offset;
+    SYCL_CHECK(CHECK_TRY_ERROR((*stream).memset(target_ptr, value, size)));
+    SYCL_CHECK(CHECK_TRY_ERROR((*stream).wait()));
+}
+
 static void ggml_backend_sycl_buffer_reset(ggml_backend_buffer_t buffer) {
     GGML_SYCL_DEBUG("[SYCL] call %s\n", __func__);
     if (buffer == nullptr) {
@@ -510,7 +528,7 @@ static const ggml_backend_buffer_i ggml_backend_sycl_buffer_interface = {
     /* .free_buffer     = */ ggml_backend_sycl_buffer_free_buffer,
     /* .get_base        = */ ggml_backend_sycl_buffer_get_base,
     /* .init_tensor     = */ ggml_backend_sycl_buffer_init_tensor,
-    /* .memset_tensor   = */ NULL,
+    /* .memset_tensor   = */ ggml_backend_sycl_buffer_memset_tensor,
     /* .set_tensor      = */ ggml_backend_sycl_buffer_set_tensor,
     /* .get_tensor      = */ ggml_backend_sycl_buffer_get_tensor,
     /* .cpy_tensor      = */ ggml_backend_sycl_buffer_cpy_tensor,

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

@@ -105,6 +105,16 @@ void compute_outputs(const uint32_t first_row, const uint32_t num_rows) {
     int unroll_count = 4;
     uint unrolled_iters = num_iters & ~(unroll_count - 1);
 
+#if K_PER_ITER == 2
+    // If the K dimension is odd, we need lastiter==true on the last iteration
+    // so OOB is computed correctly. Skip some unrolling to make that happen.
+    if ((p.ncols & 1) != 0 &&
+        unrolled_iters == num_iters &&
+        unrolled_iters > 0) {
+        unrolled_iters -= unroll_count;
+    }
+#endif
+
     uint i = 0;
     while (i < unrolled_iters) {
         // Manually partially unroll the loop
@@ -113,8 +123,18 @@ void compute_outputs(const uint32_t first_row, const uint32_t num_rows) {
             i++;
         }
     }
+
     unroll_count = 2;
     unrolled_iters = num_iters & ~(unroll_count - 1);
+
+#if K_PER_ITER == 2
+    if ((p.ncols & 1) != 0 &&
+        unrolled_iters == num_iters &&
+        unrolled_iters > 0) {
+        unrolled_iters -= unroll_count;
+    }
+#endif
+
     while (i < unrolled_iters) {
         // Manually partially unroll the loop
         [[unroll]] for (uint k = 0; k < unroll_count; ++k) {

gguf-py/gguf/constants.py

@@ -1113,6 +1113,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
     ],
     MODEL_ARCH.GEMMA3: [
         MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT,
         MODEL_TENSOR.OUTPUT_NORM,
         MODEL_TENSOR.ATTN_Q,
         MODEL_TENSOR.ATTN_Q_NORM,

include/llama.h

@@ -107,6 +107,7 @@ extern "C" {
         LLAMA_VOCAB_PRE_TYPE_MINERVA        = 27,
         LLAMA_VOCAB_PRE_TYPE_DEEPSEEK3_LLM  = 28,
         LLAMA_VOCAB_PRE_TYPE_GPT4O          = 29,
+        LLAMA_VOCAB_PRE_TYPE_SUPERBPE       = 30,
     };
 
     enum llama_rope_type {

src/llama-arch.cpp

@@ -778,6 +778,7 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
         {
             { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
             { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_OUTPUT,          "output" },
             { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
             { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
             { LLM_TENSOR_ATTN_Q_NORM,     "blk.%d.attn_q_norm" },

src/llama-context.cpp

@@ -294,10 +294,7 @@ llama_context::llama_context(
         // TODO: something cleaner
         const auto n_outputs_save = n_outputs;
 
-        // max number of outputs
-        n_outputs = n_tokens;
-
-        LLAMA_LOG_DEBUG("%s: n_tokens = %d, n_seqs = %d, n_outputs = %d\n", __func__, n_tokens, n_seqs, n_outputs);
+        LLAMA_LOG_DEBUG("%s: worst-case: n_tokens = %d, n_seqs = %d, n_outputs = %d\n", __func__, n_tokens, n_seqs, n_outputs);
 
         int n_splits_pp = -1;
         int n_nodes_pp  = -1;
@@ -313,8 +310,15 @@ llama_context::llama_context(
         // reserve pp graph first so that buffers are only allocated once
         {
             llama_ubatch ubatch_pp = { true, n_tokens, n_tokens / n_seqs, n_seqs, &token, nullptr, nullptr, nullptr, nullptr, nullptr};
+
+            // max number of outputs
+            n_outputs = ubatch_pp.n_tokens;
+
+            LLAMA_LOG_DEBUG("%s: reserving graph for n_tokens = %d, n_seqs = %d\n", __func__, ubatch_pp.n_tokens, ubatch_pp.n_seqs);
+
             auto * gf = graph_init();
             graph_build(ctx_compute.get(), gf, ubatch_pp, LLM_GRAPH_TYPE_DEFAULT);
+
             if (!ggml_backend_sched_reserve(sched.get(), gf)) {
                 throw std::runtime_error("failed to allocate compute pp buffers");
             }
@@ -326,11 +330,18 @@ llama_context::llama_context(
         // reserve with tg graph to get the number of splits and nodes
         {
             llama_ubatch ubatch_tg = { true, 1, 1, n_seqs, &token, nullptr, nullptr, nullptr, nullptr, nullptr};
+
+            n_outputs = ubatch_tg.n_tokens;
+
+            LLAMA_LOG_DEBUG("%s: reserving graph for n_tokens = %d, n_seqs = %d\n", __func__, ubatch_tg.n_tokens, ubatch_tg.n_seqs);
+
             auto * gf = graph_init();
             graph_build(ctx_compute.get(), gf, ubatch_tg, LLM_GRAPH_TYPE_DEFAULT);
+
             if (!ggml_backend_sched_reserve(sched.get(), gf)) {
                 throw std::runtime_error("failed to allocate compute tg buffers");
             }
+
             n_splits_tg = ggml_backend_sched_get_n_splits(sched.get());
             n_nodes_tg  = ggml_graph_n_nodes(gf);
         }
@@ -338,8 +349,14 @@ llama_context::llama_context(
         // reserve again with pp graph to avoid ggml-alloc reallocations during inference
         {
             llama_ubatch ubatch_pp = { true, n_tokens, n_tokens / n_seqs, n_seqs, &token, nullptr, nullptr, nullptr, nullptr, nullptr};
+
+            n_outputs = ubatch_pp.n_tokens;
+
+            LLAMA_LOG_DEBUG("%s: reserving graph for n_tokens = %d, n_seqs = %d\n", __func__, ubatch_pp.n_tokens, ubatch_pp.n_seqs);
+
             auto * gf = graph_init();
             graph_build(ctx_compute.get(), gf, ubatch_pp, LLM_GRAPH_TYPE_DEFAULT);
+
             if (!ggml_backend_sched_reserve(sched.get(), gf)) {
                 throw std::runtime_error("failed to allocate compute pp buffers");
             }

src/llama-mmap.cpp

@@ -476,7 +476,7 @@ struct llama_mlock::impl {
 
         char* errmsg = std::strerror(errno);
         bool suggest = (errno == ENOMEM);
-#if defined(TARGET_OS_VISION) || defined(TARGET_OS_TV)
+#if defined(TARGET_OS_VISION) || defined(TARGET_OS_TV) || defined(_AIX)
         // visionOS/tvOS dont't support RLIMIT_MEMLOCK
         // Skip resource limit checks on visionOS/tvOS
         suggest = false;

src/llama-model.cpp

@@ -2571,7 +2571,12 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
 
                     // output
                     output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
-                    output      = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD,  "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED); // same as tok_embd, duplicated to allow offloading
+                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+
+                    // if output is NULL, init from the input tok embed
+                    if (output == NULL) {
+                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD,   "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+                    }
 
                     for (int i = 0; i < n_layer; ++i) {
                         auto & layer = layers[i];

src/llama-vocab.cpp

@@ -400,6 +400,12 @@ struct llm_tokenizer_bpe : llm_tokenizer {
                     "[^\\r\\n\\p{L}\\p{N}]?((?=[\\p{L}])([^a-z]))*((?=[\\p{L}])([^A-Z]))+(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])?|[^\\r\\n\\p{L}\\p{N}]?((?=[\\p{L}])([^a-z]))+((?=[\\p{L}])([^A-Z]))*(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])?|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n/]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
                 };
                 break;
+            case LLAMA_VOCAB_PRE_TYPE_SUPERBPE:
+                regex_exprs = {
+                    "\\p{N}+",
+                    "(?=(\\d{3})+(?!\\d))",
+                };
+                break;
             default:
                 // default regex for BPE tokenization pre-processing
                 regex_exprs = {
@@ -1604,6 +1610,10 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
                 tokenizer_pre == "gpt-4o") {
                 pre_type = LLAMA_VOCAB_PRE_TYPE_GPT4O;
                 clean_spaces = false;
+            } else if (
+                tokenizer_pre == "superbpe") {
+                pre_type = LLAMA_VOCAB_PRE_TYPE_SUPERBPE;
+                clean_spaces = false;
             } else {
                 throw std::runtime_error(format("unknown pre-tokenizer type: '%s'", tokenizer_pre.c_str()));
             }

tests/test-backend-ops.cpp

@@ -1463,11 +1463,13 @@ struct test_cpy : public test_case {
     const ggml_type type_src;
     const ggml_type type_dst;
     const std::array<int64_t, 4> ne;
-    const std::array<int64_t, 4> permute;
+    const std::array<int64_t, 4> permute_src;
+    const std::array<int64_t, 4> permute_dst;
     bool _src_use_permute;
+    bool _dst_use_permute;
 
     std::string vars() override {
-        return VARS_TO_STR4(type_src, type_dst, ne, permute);
+        return VARS_TO_STR5(type_src, type_dst, ne, permute_src, permute_dst);
     }
 
     double max_nmse_err() override {
@@ -1480,9 +1482,11 @@ struct test_cpy : public test_case {
 
     test_cpy(ggml_type type_src = GGML_TYPE_F32, ggml_type type_dst = GGML_TYPE_F32,
             std::array<int64_t, 4> ne = {10, 10, 10, 1},
-            std::array<int64_t, 4> permute = {0, 0, 0, 0})
-        : type_src(type_src), type_dst(type_dst), ne(ne), permute(permute),
-          _src_use_permute(permute[0] + permute[1] + permute[2] + permute[3] > 0) {}
+            std::array<int64_t, 4> permute_src = {0, 0, 0, 0},
+            std::array<int64_t, 4> permute_dst = {0, 0, 0, 0})
+        : type_src(type_src), type_dst(type_dst), ne(ne), permute_src(permute_src), permute_dst(permute_dst),
+          _src_use_permute(permute_src[0] + permute_src[1] + permute_src[2] + permute_src[3] > 0),
+          _dst_use_permute(permute_dst[0] + permute_dst[1] + permute_dst[2] + permute_dst[3] > 0) {}
 
     ggml_tensor * build_graph(ggml_context * ctx) override {
         ggml_tensor * src = ggml_new_tensor(ctx, type_src, 4, ne.data());
@@ -1490,13 +1494,18 @@ struct test_cpy : public test_case {
         ggml_set_name(src, "src");
 
         if (_src_use_permute) {
-            src = ggml_permute(ctx, src, permute[0], permute[1], permute[2], permute[3]);
+            src = ggml_permute(ctx, src, permute_src[0], permute_src[1], permute_src[2], permute_src[3]);
             ggml_set_name(src, "src_permuted");
         }
 
-        ggml_tensor* dst = ggml_new_tensor(ctx, type_dst, 4, src->ne);
+        ggml_tensor * dst = ggml_new_tensor(ctx, type_dst, 4, src->ne);
         ggml_set_name(dst, "dst");
 
+        if (_dst_use_permute) {
+            dst = ggml_permute(ctx, dst, permute_dst[0], permute_dst[1], permute_dst[2], permute_dst[3]);
+            ggml_set_name(dst, "dst_permuted");
+        }
+
         ggml_tensor * out = ggml_cpy(ctx, src, dst);
         ggml_set_name(out, "out");
 
@@ -4004,14 +4013,25 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
         test_cases.emplace_back(new test_set(GGML_TYPE_I32, GGML_TYPE_I32, {6, 5, 4, 3}, dim));
     }
 
-    for (ggml_type type_src : {GGML_TYPE_F16, GGML_TYPE_F32}) {
+    // same-type copy
+    for (ggml_type type : all_types) {
+        const auto nk = ggml_blck_size(type);
+
+        for (int k = 1; k < 4; ++k) {
+            test_cases.emplace_back(new test_cpy(type, type, {k*nk, 2, 3, 4}));
+            test_cases.emplace_back(new test_cpy(type, type, {k*nk, 2, 3, 4}, {0, 2, 1, 3}));
+            test_cases.emplace_back(new test_cpy(type, type, {k*nk, 2, 3, 4}, {0, 3, 1, 2}, {0, 2, 1, 3}));
+        }
+    }
+
+    for (ggml_type type_src : {GGML_TYPE_F16, GGML_TYPE_BF16, GGML_TYPE_F32}) {
         for (ggml_type type_dst : all_types) {
             test_cases.emplace_back(new test_cpy(type_src, type_dst, {256, 4, 4, 4}));
             test_cases.emplace_back(new test_cpy(type_src, type_dst, {256, 2, 3, 4}, {0, 2, 1, 3})); // cpy by rows
         }
     }
-    for (ggml_type type_dst : {GGML_TYPE_F32}) {
-        for (ggml_type type_src : all_types) {
+    for (ggml_type type_src : all_types) {
+        for (ggml_type type_dst : {GGML_TYPE_F32}) {
             test_cases.emplace_back(new test_cpy(type_src, type_dst, {256, 4, 4, 4}));
             test_cases.emplace_back(new test_cpy(type_src, type_dst, {256, 2, 3, 4}, {0, 2, 1, 3})); // cpy by rows
         }
@@ -4184,6 +4204,8 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
     test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32,  83, 2,   64, { 8,  1}, {4, 1}));
     test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32,  64, 45, 128, { 8,  1}, {4, 1}));
     test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32, 128, 45,  64, { 8,  1}, {4, 1}));
+    test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32, 1056, 1, 193, {1,  1}, {4, 1}, {0, 2, 1, 3}));
+    test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32, 1056, 1, 67,  {1,  1}, {4, 1}, {0, 2, 1, 3}));
 
     for (auto bs : {1,2,4,8}) {
         for (auto nr : {1,4}) {

tests/test-grammar-llguidance.cpp

@@ -1086,6 +1086,65 @@ static void test_json_schema() {
         });
 }
 
+static void one_hot(llama_token_data_array & tok_arr, llama_token selected) {
+    auto n_vocab = tok_arr.size;
+
+    tok_arr.selected = -1;
+    tok_arr.sorted   = false;
+    for (llama_token token_id = 0; token_id < (llama_token) n_vocab; token_id++) {
+        tok_arr.data[token_id].id    = token_id;
+        tok_arr.data[token_id].logit = 0.0f;
+    }
+
+    tok_arr.data[selected].logit = 100.0f;
+}
+
+static void test_sampler_chain(void) {
+    auto sparams            = llama_sampler_chain_default_params();
+    sparams.no_perf         = false;
+    llama_sampler * sampler = llama_sampler_chain_init(sparams);
+
+    const auto grammar_data = R"(%llguidance {}
+start: /[A-Z ]*/)";
+
+    llama_sampler_chain_add(sampler, llama_sampler_init_llg(vocab, "lark", grammar_data));
+    llama_sampler_chain_add(sampler, llama_sampler_init_dist(42));
+
+    auto input  = "ALL YOUR BASE ARE BELONG TO US";
+    auto tokens = common_tokenize(vocab, input, false, false);
+
+    auto n_vocab = llama_vocab_n_tokens(vocab);
+
+    std::vector<llama_token_data> cur;
+    cur.reserve(n_vocab);
+    for (llama_token token_id = 0; token_id < (llama_token) n_vocab; token_id++) {
+        cur.emplace_back(llama_token_data{ token_id, 0.0f, 0.0f });
+    }
+    auto tok_arr = llama_token_data_array{ cur.data(), cur.size(), -1, false };
+
+    for (const auto token : tokens) {
+        one_hot(tok_arr, token);
+
+        fprintf(stderr, "applying token: %d\n", token);
+        llama_sampler_apply(sampler, &tok_arr);
+
+        auto idx = tok_arr.selected;
+        fprintf(stderr, " -> %d %f\n", cur[idx].id, cur[idx].logit);
+        assert(cur[tok_arr.selected].id == token);
+        llama_sampler_accept(sampler, token);
+    }
+
+    auto tok_eos = llama_vocab_eot(vocab);
+    if (tok_eos == LLAMA_TOKEN_NULL) {
+        tok_eos = llama_vocab_eos(vocab);
+    }
+
+    one_hot(tok_arr, tok_eos);
+
+    llama_sampler_apply(sampler, &tok_arr);
+    assert(cur[tok_arr.selected].id == tok_eos);
+}
+
 int main(int argc, const char ** argv) {
     fprintf(stdout, "Running llguidance integration tests...\n");
 
@@ -1135,6 +1194,9 @@ int main(int argc, const char ** argv) {
     test_special_chars();
     test_quantifiers();
     test_json_schema();
+
+    test_sampler_chain();
+
     fprintf(stdout, "All tests passed.\n");
     return 0;
 }