vulkan: change graph_compute to be async and enable get_tensor_async (#17158)

* vulkan: change graph_compute to be async and enable get_tensor_async

This allows some additional CPU/GPU overlap for large pp workloads. Also seems
to help a bit for token gen, maybe getting rid of a small bubble between
graph_compute and get_tensor.

Async set and copy functions seem to be very rarely used, so I didn't enable
them because I didn't have a good way to test them.

The async commands need to be ordered against each other, so put them all on
the compute queue. The non-async commands still use the transfer queue.

The fence for graph_compute/get_tensor_async is submitted and waited on in
ggml_vk_synchronize.

* fix thread safety errors

* teardown context cleanly

* Handle async read to non-pinned dst

.devops/cann.Dockerfile

@@ -49,7 +49,7 @@ RUN source /usr/local/Ascend/ascend-toolkit/set_env.sh --force \
 # -- Organize build artifacts for copying in later stages --
 # Create a lib directory to store all .so files
 RUN mkdir -p /app/lib && \
-    find build -name "*.so" -exec cp {} /app/lib \;
+    find build -name "*.so*" -exec cp -P {} /app/lib \;
 
 # Create a full directory to store all executables and Python scripts
 RUN mkdir -p /app/full && \

.devops/cpu.Dockerfile

@@ -20,7 +20,7 @@ RUN if [ "$TARGETARCH" = "amd64" ] || [ "$TARGETARCH" = "arm64" ]; then \
     cmake --build build -j $(nproc)
 
 RUN mkdir -p /app/lib && \
-    find build -name "*.so" -exec cp {} /app/lib \;
+    find build -name "*.so*" -exec cp -P {} /app/lib \;
 
 RUN mkdir -p /app/full \
     && cp build/bin/* /app/full \

.devops/cuda.Dockerfile

@@ -25,7 +25,7 @@ RUN if [ "${CUDA_DOCKER_ARCH}" != "default" ]; then \
     cmake --build build --config Release -j$(nproc)
 
 RUN mkdir -p /app/lib && \
-    find build -name "*.so" -exec cp {} /app/lib \;
+    find build -name "*.so*" -exec cp -P {} /app/lib \;
 
 RUN mkdir -p /app/full \
     && cp build/bin/* /app/full \

.devops/intel.Dockerfile

@@ -21,7 +21,7 @@ RUN if [ "${GGML_SYCL_F16}" = "ON" ]; then \
     cmake --build build --config Release -j$(nproc)
 
 RUN mkdir -p /app/lib && \
-    find build -name "*.so" -exec cp {} /app/lib \;
+    find build -name "*.so*" -exec cp -P {} /app/lib \;
 
 RUN mkdir -p /app/full \
     && cp build/bin/* /app/full \

.devops/musa.Dockerfile

@@ -32,7 +32,7 @@ RUN if [ "${MUSA_DOCKER_ARCH}" != "default" ]; then \
     cmake --build build --config Release -j$(nproc)
 
 RUN mkdir -p /app/lib && \
-    find build -name "*.so" -exec cp {} /app/lib \;
+    find build -name "*.so*" -exec cp -P {} /app/lib \;
 
 RUN mkdir -p /app/full \
     && cp build/bin/* /app/full \

.devops/rocm.Dockerfile

@@ -45,7 +45,7 @@ RUN HIPCXX="$(hipconfig -l)/clang" HIP_PATH="$(hipconfig -R)" \
     && cmake --build build --config Release -j$(nproc)
 
 RUN mkdir -p /app/lib \
-    && find build -name "*.so" -exec cp {} /app/lib \;
+    && find build -name "*.so*" -exec cp -P {} /app/lib \;
 
 RUN mkdir -p /app/full \
     && cp build/bin/* /app/full \

.devops/vulkan.Dockerfile

@@ -20,7 +20,7 @@ RUN cmake -B build -DGGML_NATIVE=OFF -DGGML_VULKAN=ON -DLLAMA_BUILD_TESTS=OFF -D
     cmake --build build --config Release -j$(nproc)
 
 RUN mkdir -p /app/lib && \
-    find build -name "*.so" -exec cp {} /app/lib \;
+    find build -name "*.so*" -exec cp -P {} /app/lib \;
 
 RUN mkdir -p /app/full \
     && cp build/bin/* /app/full \

.github/copilot-instructions.md

@@ -9,7 +9,7 @@ llama.cpp is a large-scale C/C++ project for efficient LLM (Large Language Model
 - **Size**: ~200k+ lines of code across 1000+ files
 - **Architecture**: Modular design with main library (`libllama`) and 40+ executable tools/examples
 - **Core dependency**: ggml tensor library (vendored in `ggml/` directory)
-- **Backends supported**: CPU (AVX/NEON optimized), CUDA, Metal, Vulkan, SYCL, ROCm, MUSA
+- **Backends supported**: CPU (AVX/NEON/RVV optimized), CUDA, Metal, Vulkan, SYCL, ROCm, MUSA
 - **License**: MIT
 
 ## Build Instructions

.github/workflows/build.yml

@@ -1651,3 +1651,50 @@ jobs:
          run: |
            GG_BUILD_KLEIDIAI=1 GG_BUILD_EXTRA_TESTS_0=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt
 
+  ggml-ci-arm64-graviton4-kleidiai:
+     runs-on: ah-ubuntu_22_04-c8g_8x
+
+     steps:
+       - name: Clone
+         id: checkout
+         uses: actions/checkout@v4
+
+       - name: Dependencies
+         id: depends
+         run: |
+           set -euxo pipefail
+           sudo apt-get update
+           sudo DEBIAN_FRONTEND=noninteractive NEEDRESTART_MODE=a \
+           apt-get install -y \
+            build-essential \
+            libcurl4-openssl-dev \
+            python3-venv \
+            gpg \
+            wget \
+            time \
+            git-lfs
+
+           git lfs install
+
+           # install the latest cmake
+           sudo install -d /usr/share/keyrings
+           wget -O - https://apt.kitware.com/keys/kitware-archive-latest.asc \
+            | gpg --dearmor \
+            | sudo tee /usr/share/keyrings/kitware-archive-keyring.gpg >/dev/null
+           echo 'deb [signed-by=/usr/share/keyrings/kitware-archive-keyring.gpg] https://apt.kitware.com/ubuntu/ jammy main' \
+            | sudo tee /etc/apt/sources.list.d/kitware.list
+           sudo apt-get update
+           sudo apt-get install -y cmake
+
+       - name: ccache
+         uses: ggml-org/ccache-action@v1.2.16
+         with:
+           key: ggml-ci-arm64-graviton4-kleidiai
+           evict-old-files: 1d
+
+       - name: Test
+         id: ggml-ci
+         run: |
+           GG_BUILD_KLEIDIAI=1 \
+           GG_BUILD_EXTRA_TESTS_0=1 \
+           bash ./ci/run.sh ./tmp/results ./tmp/mnt

.github/workflows/check-vendor.yml

@@ -0,0 +1,52 @@
+name: Check vendor
+
+on:
+  workflow_dispatch: # allows manual triggering
+  push:
+    branches:
+      - master
+    paths: [
+      'vendor/**',
+      'scripts/sync_vendor.py'
+    ]
+
+  pull_request:
+    types: [opened, synchronize, reopened]
+    paths: [
+      'vendor/**',
+      'scripts/sync_vendor.py'
+    ]
+
+jobs:
+  check-vendor:
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+
+      - name: Setup Python
+        uses: actions/setup-python@v4
+        with:
+          python-version: '3.x'
+
+      - name: Run vendor sync
+        run: |
+          set -euo pipefail
+          python3 scripts/sync_vendor.py
+
+      - name: Check for changes
+        run: |
+          set -euo pipefail
+          # detect modified or untracked files
+          changed=$(git status --porcelain --untracked-files=all || true)
+          if [ -n "$changed" ]; then
+            echo "Vendor sync modified files:"
+            echo "$changed" | awk '{ print $2 }' | sed '/^$/d'
+            echo "Failing because vendor files mismatch. Please update scripts/sync_vendor.py"
+            exit 1
+          else
+            echo "Vendor files are up-to-date."
+          fi

.github/workflows/server.yml

@@ -209,7 +209,7 @@ jobs:
         working-directory: tools/server/webui
 
       - name: Run UI tests
-        run: npm run test:ui
+        run: npm run test:ui -- --testTimeout=60000
         working-directory: tools/server/webui
 
       - name: Run E2E tests

CMakeLists.txt

@@ -92,6 +92,7 @@ option(LLAMA_TOOLS_INSTALL  "llama: install tools"        ${LLAMA_TOOLS_INSTALL_
 
 # 3rd party libs
 option(LLAMA_CURL       "llama: use libcurl to download model from an URL" ON)
+option(LLAMA_HTTPLIB    "llama: if libcurl is disabled, use httplib to download model from an URL" ON)
 option(LLAMA_OPENSSL    "llama: use openssl to support HTTPS" OFF)
 option(LLAMA_LLGUIDANCE "llama-common: include LLGuidance library for structured output in common utils" OFF)
 
@@ -200,6 +201,9 @@ endif()
 
 if (LLAMA_BUILD_COMMON)
     add_subdirectory(common)
+    if (LLAMA_HTTPLIB)
+        add_subdirectory(vendor/cpp-httplib)
+    endif()
 endif()
 
 if (LLAMA_BUILD_COMMON AND LLAMA_BUILD_TESTS AND NOT CMAKE_JS_VERSION)

README.md

@@ -61,6 +61,7 @@ range of hardware - locally and in the cloud.
 - Plain C/C++ implementation without any dependencies
 - Apple silicon is a first-class citizen - optimized via ARM NEON, Accelerate and Metal frameworks
 - AVX, AVX2, AVX512 and AMX support for x86 architectures
+- RVV, ZVFH, ZFH and ZICBOP support for RISC-V architectures
 - 1.5-bit, 2-bit, 3-bit, 4-bit, 5-bit, 6-bit, and 8-bit integer quantization for faster inference and reduced memory use
 - Custom CUDA kernels for running LLMs on NVIDIA GPUs (support for AMD GPUs via HIP and Moore Threads GPUs via MUSA)
 - Vulkan and SYCL backend support

build-xcframework.sh

@@ -454,6 +454,8 @@ cmake -B build-visionos -G Xcode \
     -DCMAKE_C_FLAGS="-D_XOPEN_SOURCE=700 ${COMMON_C_FLAGS}" \
     -DCMAKE_CXX_FLAGS="-D_XOPEN_SOURCE=700 ${COMMON_CXX_FLAGS}" \
     -DLLAMA_CURL=OFF \
+    -DLLAMA_HTTPLIB=OFF \
+    -DLLAMA_BUILD_SERVER=OFF \
     -S .
 cmake --build build-visionos --config Release -- -quiet
 
@@ -468,6 +470,8 @@ cmake -B build-visionos-sim -G Xcode \
     -DCMAKE_C_FLAGS="-D_XOPEN_SOURCE=700 ${COMMON_C_FLAGS}" \
     -DCMAKE_CXX_FLAGS="-D_XOPEN_SOURCE=700 ${COMMON_CXX_FLAGS}" \
     -DLLAMA_CURL=OFF \
+    -DLLAMA_HTTPLIB=OFF \
+    -DLLAMA_BUILD_SERVER=OFF \
     -S .
 cmake --build build-visionos-sim --config Release -- -quiet
 

ci/run.sh

@@ -121,7 +121,12 @@ fi
 if [ -n "${GG_BUILD_KLEIDIAI}" ]; then
     echo ">>===== Enabling KleidiAI support"
 
-    CANDIDATES=("armv9-a+dotprod+i8mm" "armv8.6-a+dotprod+i8mm" "armv8.2-a+dotprod")
+    CANDIDATES=(
+        "armv9-a+dotprod+i8mm+sve2"
+        "armv9-a+dotprod+i8mm"
+        "armv8.6-a+dotprod+i8mm"
+        "armv8.2-a+dotprod"
+    )
     CPU=""
 
     for cpu in "${CANDIDATES[@]}"; do

common/CMakeLists.txt

@@ -79,10 +79,11 @@ if (BUILD_SHARED_LIBS)
     set_target_properties(${TARGET} PROPERTIES POSITION_INDEPENDENT_CODE ON)
 endif()
 
+# TODO: use list(APPEND LLAMA_COMMON_EXTRA_LIBS ...)
 set(LLAMA_COMMON_EXTRA_LIBS build_info)
 
-# Use curl to download model url
 if (LLAMA_CURL)
+    # Use curl to download model url
     find_package(CURL)
     if (NOT CURL_FOUND)
         message(FATAL_ERROR "Could NOT find CURL. Hint: to disable this feature, set -DLLAMA_CURL=OFF")
@@ -90,42 +91,10 @@ if (LLAMA_CURL)
     target_compile_definitions(${TARGET} PUBLIC LLAMA_USE_CURL)
     include_directories(${CURL_INCLUDE_DIRS})
     set(LLAMA_COMMON_EXTRA_LIBS ${LLAMA_COMMON_EXTRA_LIBS} ${CURL_LIBRARIES})
-endif()
-
-if (LLAMA_OPENSSL)
-    find_package(OpenSSL)
-    if (OpenSSL_FOUND)
-        include(CheckCSourceCompiles)
-        set(SAVED_CMAKE_REQUIRED_INCLUDES ${CMAKE_REQUIRED_INCLUDES})
-        set(CMAKE_REQUIRED_INCLUDES ${OPENSSL_INCLUDE_DIR})
-        check_c_source_compiles("
-        #include <openssl/opensslv.h>
-        #if defined(OPENSSL_IS_BORINGSSL) || defined(LIBRESSL_VERSION_NUMBER)
-        #    if OPENSSL_VERSION_NUMBER < 0x1010107f
-        #        error bad version
-        #    endif
-        #else
-        #    if OPENSSL_VERSION_NUMBER < 0x30000000L
-        #        error bad version
-        #    endif
-        #endif
-        int main() { return 0; }
-        " OPENSSL_VERSION_SUPPORTED)
-        set(CMAKE_REQUIRED_INCLUDES ${SAVED_CMAKE_REQUIRED_INCLUDES})
-        if (OPENSSL_VERSION_SUPPORTED)
-            message(STATUS "OpenSSL found: ${OPENSSL_VERSION}")
-            target_compile_definitions(${TARGET} PUBLIC CPPHTTPLIB_OPENSSL_SUPPORT)
-            target_link_libraries(${TARGET} PUBLIC OpenSSL::SSL OpenSSL::Crypto)
-            if (APPLE AND CMAKE_SYSTEM_NAME STREQUAL "Darwin")
-                target_compile_definitions(${TARGET} PUBLIC CPPHTTPLIB_USE_CERTS_FROM_MACOSX_KEYCHAIN)
-                find_library(CORE_FOUNDATION_FRAMEWORK CoreFoundation REQUIRED)
-                find_library(SECURITY_FRAMEWORK Security REQUIRED)
-                target_link_libraries(${TARGET} PUBLIC ${CORE_FOUNDATION_FRAMEWORK} ${SECURITY_FRAMEWORK})
-            endif()
-        endif()
-    else()
-        message(STATUS "OpenSSL not found, SSL support disabled")
-    endif()
+elseif (LLAMA_HTTPLIB)
+    # otherwise, use cpp-httplib
+    target_compile_definitions(${TARGET} PUBLIC LLAMA_USE_HTTPLIB)
+    set(LLAMA_COMMON_EXTRA_LIBS ${LLAMA_COMMON_EXTRA_LIBS} cpp-httplib)
 endif()
 
 if (LLAMA_LLGUIDANCE)

common/common.cpp

@@ -355,11 +355,7 @@ bool parse_cpu_mask(const std::string & mask, bool (&boolmask)[GGML_MAX_N_THREAD
 }
 
 void common_init() {
-    llama_log_set([](ggml_log_level level, const char * text, void * /*user_data*/) {
-        if (LOG_DEFAULT_LLAMA <= common_log_verbosity_thold) {
-            common_log_add(common_log_main(), level, "%s", text);
-        }
-    }, NULL);
+    llama_log_set(common_log_default_callback, NULL);
 
 #ifdef NDEBUG
     const char * build_type = "";

common/download.cpp

@@ -20,7 +20,7 @@
 #if defined(LLAMA_USE_CURL)
 #include <curl/curl.h>
 #include <curl/easy.h>
-#else
+#elif defined(LLAMA_USE_HTTPLIB)
 #include "http.h"
 #endif
 
@@ -467,7 +467,7 @@ std::pair<long, std::vector<char>> common_remote_get_content(const std::string &
     return { res_code, std::move(res_buffer) };
 }
 
-#else
+#elif defined(LLAMA_USE_HTTPLIB)
 
 static bool is_output_a_tty() {
 #if defined(_WIN32)
@@ -713,6 +713,8 @@ std::pair<long, std::vector<char>> common_remote_get_content(const std::string
 
 #endif // LLAMA_USE_CURL
 
+#if defined(LLAMA_USE_CURL) || defined(LLAMA_USE_HTTPLIB)
+
 static bool common_download_file_single(const std::string & url,
                                         const std::string & path,
                                         const std::string & bearer_token,
@@ -907,33 +909,6 @@ common_hf_file_res common_get_hf_file(const std::string & hf_repo_with_tag, cons
     return { hf_repo, ggufFile, mmprojFile };
 }
 
-std::vector<common_cached_model_info> common_list_cached_models() {
-    std::vector<common_cached_model_info> models;
-    const std::string cache_dir = fs_get_cache_directory();
-    const std::vector<common_file_info> files = fs_list_files(cache_dir);
-    for (const auto & file : files) {
-        if (string_starts_with(file.name, "manifest=") && string_ends_with(file.name, ".json")) {
-            common_cached_model_info model_info;
-            model_info.manifest_path = file.path;
-            std::string fname = file.name;
-            string_replace_all(fname, ".json", ""); // remove extension
-            auto parts = string_split<std::string>(fname, '=');
-            if (parts.size() == 4) {
-                // expect format: manifest=<user>=<model>=<tag>=<other>
-                model_info.user  = parts[1];
-                model_info.model = parts[2];
-                model_info.tag   = parts[3];
-            } else {
-                // invalid format
-                continue;
-            }
-            model_info.size = 0; // TODO: get GGUF size, not manifest size
-            models.push_back(model_info);
-        }
-    }
-    return models;
-}
-
 //
 // Docker registry functions
 //
@@ -1052,3 +1027,46 @@ std::string common_docker_resolve_model(const std::string & docker) {
         throw;
     }
 }
+
+#else
+
+common_hf_file_res common_get_hf_file(const std::string &, const std::string &, bool) {
+    throw std::runtime_error("download functionality is not enabled in this build");
+}
+
+bool common_download_model(const common_params_model &, const std::string &, bool) {
+    throw std::runtime_error("download functionality is not enabled in this build");
+}
+
+std::string common_docker_resolve_model(const std::string &) {
+    throw std::runtime_error("download functionality is not enabled in this build");
+}
+
+#endif // LLAMA_USE_CURL || LLAMA_USE_HTTPLIB
+
+std::vector<common_cached_model_info> common_list_cached_models() {
+    std::vector<common_cached_model_info> models;
+    const std::string cache_dir = fs_get_cache_directory();
+    const std::vector<common_file_info> files = fs_list_files(cache_dir);
+    for (const auto & file : files) {
+        if (string_starts_with(file.name, "manifest=") && string_ends_with(file.name, ".json")) {
+            common_cached_model_info model_info;
+            model_info.manifest_path = file.path;
+            std::string fname = file.name;
+            string_replace_all(fname, ".json", ""); // remove extension
+            auto parts = string_split<std::string>(fname, '=');
+            if (parts.size() == 4) {
+                // expect format: manifest=<user>=<model>=<tag>=<other>
+                model_info.user  = parts[1];
+                model_info.model = parts[2];
+                model_info.tag   = parts[3];
+            } else {
+                // invalid format
+                continue;
+            }
+            model_info.size = 0; // TODO: get GGUF size, not manifest size
+            models.push_back(model_info);
+        }
+    }
+    return models;
+}

common/log.cpp

@@ -442,3 +442,9 @@ void common_log_set_prefix(struct common_log * log, bool prefix) {
 void common_log_set_timestamps(struct common_log * log, bool timestamps) {
     log->set_timestamps(timestamps);
 }
+
+void common_log_default_callback(enum ggml_log_level level, const char * text, void * /*user_data*/) {
+    if (LOG_DEFAULT_LLAMA <= common_log_verbosity_thold) {
+        common_log_add(common_log_main(), level, "%s", text);
+    }
+}

common/log.h

@@ -36,6 +36,8 @@ extern int common_log_verbosity_thold;
 
 void common_log_set_verbosity_thold(int verbosity); // not thread-safe
 
+void common_log_default_callback(enum ggml_log_level level, const char * text, void * user_data);
+
 // the common_log uses an internal worker thread to print/write log messages
 // when the worker thread is paused, incoming log messages are discarded
 struct common_log;

convert_hf_to_gguf.py

@@ -1124,6 +1124,9 @@ class TextModel(ModelBase):
         if chkhsh == "a1e163ecab2e718a4c829d1148b6e86824ec36163bb71941c3dca9cd5ac25756":
             # ref: https://huggingface.co/JetBrains/Mellum-4b-base
             res = "mellum"
+        if chkhsh == "49fc0303c9e0d2c2c565c510f64b2d9b271276acdcdadff733249eda9f7d59df":
+            # ref: https://huggingface.co/arcee-ai/Trinity-Tokenizer
+            res = "afmoe"
         if chkhsh == "9b1be57e70d20d9501b2b3186e792d81181ae36ada3903c26f9fea418cf87206":
             # ref: https://huggingface.co/inclusionAI/Ling-mini-base-2.0
             res = "bailingmoe2"
@@ -2533,6 +2536,81 @@ class ArceeModel(LlamaModel):
             self.gguf_writer.add_rope_scaling_orig_ctx_len(rope_scaling["original_max_position_embeddings"])
 
 
+@ModelBase.register("AfmoeForCausalLM")
+class AfmoeModel(LlamaModel):
+    model_arch = gguf.MODEL_ARCH.AFMOE
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+
+        # MoE parameters
+        if (n_experts := self.hparams.get("num_experts")) is not None:
+            self.gguf_writer.add_expert_count(n_experts)
+        if (n_shared_experts := self.hparams.get("num_shared_experts")) is not None:
+            self.gguf_writer.add_expert_shared_count(n_shared_experts)
+        if (moe_intermediate_size := self.hparams.get("moe_intermediate_size")) is not None:
+            self.gguf_writer.add_expert_feed_forward_length(moe_intermediate_size)
+        if (n_dense_layers := self.hparams.get("num_dense_layers")) is not None:
+            self.gguf_writer.add_leading_dense_block_count(n_dense_layers)
+
+        # Expert Gating Function
+        score_func = self.hparams.get("score_func")
+        if score_func == "sigmoid":
+            self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SIGMOID)
+        elif score_func == "softmax":
+            self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SOFTMAX)
+        elif score_func is not None:
+            raise ValueError(f"Unsupported score_function value: {score_func}")
+
+        # Route normalization and scaling
+        if (route_norm := self.hparams.get("route_norm")) is not None:
+            self.gguf_writer.add_expert_weights_norm(route_norm)
+        if (route_scale := self.hparams.get("route_scale")) is not None:
+            self.gguf_writer.add_expert_weights_scale(route_scale)
+
+        # Sliding window attention
+        if (sliding_window := self.hparams.get("sliding_window")) is not None:
+            self.gguf_writer.add_sliding_window(sliding_window)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        # Handle expert weights - they're already merged in the HF format
+        # process the experts separately
+        if name.find("mlp.experts") != -1:
+            n_experts = self.hparams["num_experts"]
+            assert bid is not None
+
+            if self._experts is None:
+                self._experts = [{} for _ in range(self.block_count)]
+
+            self._experts[bid][name] = data_torch
+
+            if len(self._experts[bid]) >= n_experts * 3:
+                tensors: list[tuple[str, Tensor]] = []
+
+                # merge the experts into a single 3d tensor
+                for w_name in ["gate_proj", "up_proj", "down_proj"]:
+                    datas: list[Tensor] = []
+
+                    for xid in range(n_experts):
+                        ename_to_retrieve = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight"
+                        datas.append(self._experts[bid][ename_to_retrieve])
+                        del self._experts[bid][ename_to_retrieve]
+
+                    data_torch = torch.stack(datas, dim=0)
+                    merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight"
+                    new_name = self.map_tensor_name(merged_name)
+                    tensors.append((new_name, data_torch))
+
+                return tensors
+            else:
+                return []
+
+        if name.endswith(".expert_bias"):
+            name = name.replace(".expert_bias", ".expert_bias.bias")
+
+        return [(self.map_tensor_name(name), data_torch)]
+
+
 @ModelBase.register(
     "LlavaForConditionalGeneration", # pixtral
     "Mistral3ForConditionalGeneration", # mistral small 3.1

convert_hf_to_gguf_update.py

@@ -139,6 +139,7 @@ models = [
     {"name": "lfm2",             "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LiquidAI/LFM2-Tokenizer"},
     {"name": "exaone4",          "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LGAI-EXAONE/EXAONE-4.0-32B", },
     {"name": "mellum",           "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/JetBrains/Mellum-4b-base", },
+    {"name": "afmoe",            "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/arcee-ai/Trinity-Tokenizer", },
     {"name": "bailingmoe2",      "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/inclusionAI/Ling-mini-base-2.0", },
     {"name": "granite-docling",  "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/ibm-granite/granite-docling-258M", },
     {"name": "minimax-m2",       "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/MiniMaxAI/MiniMax-M2", },

docs/backend/CANN.md

@@ -313,7 +313,12 @@ Converting the matmul weight format from ND to NZ to improve performance. Enable
 
 ### GGML_CANN_ACL_GRAPH
 
-Operators are executed using ACL graph execution, rather than in op-by-op (eager) mode. Enabled by default.
+Operators are executed using ACL graph execution, rather than in op-by-op (eager) mode. Enabled by default. This option is only effective if `USE_ACL_GRAPH` was enabled at compilation time. To enable it, recompile using:
+
+```sh
+cmake -B build -DGGML_CANN=on -DCMAKE_BUILD_TYPE=release -DUSE_ACL_GRAPH=ON
+cmake --build build --config release
+```
 
 ### GGML_CANN_GRAPH_CACHE_CAPACITY
 

docs/ops.md

@@ -18,17 +18,17 @@ Legend:
 |                              ACC | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ |
 |                              ADD | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ❌ |
 |                             ADD1 | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ |
-|                           ADD_ID | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
-|                           ARANGE | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ |
+|                           ADD_ID | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ |
+|                           ARANGE | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ❌ |
 |                           ARGMAX | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ |
 |                          ARGSORT | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ |
-|                             CEIL | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | ✅ | ❌ | ❌ |
+|                             CEIL | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | ❌ | ❌ |
 |                            CLAMP | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ❌ |
 |                           CONCAT | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | ✅ | ❌ |
 |                             CONT | ❌ | 🟡 | ✅ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ❌ |
-|                          CONV_2D | ❌ | ❌ | ✅ | 🟡 | ❌ | ✅ | ❌ | ✅ | ❌ |
+|                          CONV_2D | ❌ | ❌ | ✅ | ✅ | ❌ | ✅ | ❌ | ✅ | ❌ |
 |                       CONV_2D_DW | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ |
-|                          CONV_3D | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
+|                          CONV_3D | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
 |                CONV_TRANSPOSE_1D | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ |
 |                CONV_TRANSPOSE_2D | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ |
 |                              COS | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | 🟡 | ❌ |
@@ -36,13 +36,16 @@ Legend:
 |                              CPY | ❌ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ |
 |               CROSS_ENTROPY_LOSS | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ |
 |          CROSS_ENTROPY_LOSS_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ |
+|                           CUMSUM | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
 |                    DIAG_MASK_INF | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ❌ |
 |                              DIV | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ❌ |
 |                              DUP | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ❌ |
 |                              ELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ | ❌ |
 |                              EXP | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ | ❌ |
+|                            EXPM1 | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | ❌ | ❌ | ❌ |
+|                             FILL | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
 |                   FLASH_ATTN_EXT | ❌ | 🟡 | ✅ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ❌ |
-|                            FLOOR | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | ✅ | ❌ | ❌ |
+|                            FLOOR | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | ❌ | ❌ |
 |                GATED_LINEAR_ATTN | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ |
 |                            GEGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ❌ |
 |                        GEGLU_ERF | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ❌ |
@@ -57,11 +60,11 @@ Legend:
 |                      HARDSIGMOID | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ | ❌ |
 |                        HARDSWISH | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ | ❌ |
 |                           IM2COL | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ |
-|                        IM2COL_3D | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
+|                        IM2COL_3D | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ |
 |                          L2_NORM | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ |
 |                       LEAKY_RELU | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ |
 |                              LOG | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ |
-|                             MEAN | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ |
+|                             MEAN | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ❌ |
 |                              MUL | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ❌ |
 |                          MUL_MAT | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 |
 |                       MUL_MAT_ID | ❌ | 🟡 | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ❌ |
@@ -69,26 +72,26 @@ Legend:
 |                             NORM | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ❌ |
 |                     NORM_MUL_ADD | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ |
 |                   OPT_STEP_ADAMW | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ |
-|                     OPT_STEP_SGD | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
+|                     OPT_STEP_SGD | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ |
 |                         OUT_PROD | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ❌ | ❌ |
-|                              PAD | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | 🟡 | ✅ | ❌ |
-|                   PAD_REFLECT_1D | ❌ | ✅ | ✅ | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ |
+|                              PAD | ❌ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ |
+|                   PAD_REFLECT_1D | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ❌ |
 |                          POOL_2D | ❌ | 🟡 | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ |
 |                            REGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ❌ |
 |                             RELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ |
 |                           REPEAT | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | 🟡 | ❌ |
-|                      REPEAT_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ |
+|                      REPEAT_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ |
 |                         RMS_NORM | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ |
 |                    RMS_NORM_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ |
-|                 RMS_NORM_MUL_ADD | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ |
-|                             ROLL | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ |
+|                 RMS_NORM_MUL_ADD | ❌ | ✅ | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ |
+|                             ROLL | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ |
 |                             ROPE | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ |
 |                        ROPE_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ |
-|                            ROUND | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | ✅ | ❌ | ❌ |
+|                            ROUND | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | ❌ | ❌ |
 |                        RWKV_WKV6 | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ |
 |                        RWKV_WKV7 | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ |
 |                            SCALE | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ |
-|                              SET | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ |
+|                              SET | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | 🟡 | ❌ | ❌ |
 |                         SET_ROWS | ❌ | ❌ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ |
 |                              SGN | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ | ❌ |
 |                          SIGMOID | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ |
@@ -96,21 +99,24 @@ Legend:
 |                        SILU_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ |
 |                              SIN | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | 🟡 | ❌ |
 |                          SOFTCAP | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ |
+|                         SOFTPLUS | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | ❌ | ❌ | ❌ |
 |                         SOFT_MAX | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ |
 |                    SOFT_MAX_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ✅ | ❌ |
+|                        SOLVE_TRI | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
 |                              SQR | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | 🟡 | ❌ |
 |                             SQRT | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | ❌ | ❌ |
-|                         SSM_CONV | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ |
+|                         SSM_CONV | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ |
 |                         SSM_SCAN | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ |
 |                             STEP | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ | ❌ |
 |                              SUB | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ❌ |
-|                              SUM | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ |
-|                         SUM_ROWS | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | 🟡 | ✅ | ❌ |
+|                              SUM | ❌ | ✅ | ✅ | 🟡 | ❌ | ❌ | 🟡 | ✅ | ❌ |
+|                         SUM_ROWS | ❌ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ |
 |                           SWIGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ❌ |
-|                       SWIGLU_OAI | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
+|                       SWIGLU_OAI | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ |
 |                             TANH | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | 🟡 | ❌ |
 |               TIMESTEP_EMBEDDING | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ |
 |                         TOPK_MOE | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ |
-|                            TRUNC | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | ✅ | ❌ | ❌ |
+|                              TRI | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
+|                            TRUNC | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | ❌ | ❌ |
 |                          UPSCALE | ❌ | 🟡 | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | ❌ |
-|                            XIELU | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
+|                            XIELU | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |

ggml/include/ggml.h

@@ -475,6 +475,7 @@ extern "C" {
         GGML_OP_COS,
         GGML_OP_SUM,
         GGML_OP_SUM_ROWS,
+        GGML_OP_CUMSUM,
         GGML_OP_MEAN,
         GGML_OP_ARGMAX,
         GGML_OP_COUNT_EQUAL,
@@ -530,6 +531,8 @@ extern "C" {
         GGML_OP_TIMESTEP_EMBEDDING,
         GGML_OP_ARGSORT,
         GGML_OP_LEAKY_RELU,
+        GGML_OP_TRI,
+        GGML_OP_FILL,
 
         GGML_OP_FLASH_ATTN_EXT,
         GGML_OP_FLASH_ATTN_BACK,
@@ -542,6 +545,7 @@ extern "C" {
         GGML_OP_RWKV_WKV6,
         GGML_OP_GATED_LINEAR_ATTN,
         GGML_OP_RWKV_WKV7,
+        GGML_OP_SOLVE_TRI,
 
         GGML_OP_UNARY,
 
@@ -576,6 +580,8 @@ extern "C" {
         GGML_UNARY_OP_HARDSWISH,
         GGML_UNARY_OP_HARDSIGMOID,
         GGML_UNARY_OP_EXP,
+        GGML_UNARY_OP_EXPM1,
+        GGML_UNARY_OP_SOFTPLUS,
         GGML_UNARY_OP_GELU_ERF,
         GGML_UNARY_OP_XIELU,
         GGML_UNARY_OP_FLOOR,
@@ -620,6 +626,13 @@ extern "C" {
         GGML_TENSOR_FLAG_LOSS   =  8, // ...defines loss for numerical optimization (multiple loss tensors add up)
     };
 
+    enum ggml_tri_type {
+        GGML_TRI_TYPE_UPPER_DIAG = 0,
+        GGML_TRI_TYPE_UPPER      = 1,
+        GGML_TRI_TYPE_LOWER_DIAG = 2,
+        GGML_TRI_TYPE_LOWER      = 3
+    };
+
     struct ggml_init_params {
         // memory pool
         size_t mem_size;   // bytes
@@ -957,6 +970,22 @@ extern "C" {
             struct ggml_context * ctx,
             struct ggml_tensor  * a);
 
+    GGML_API struct ggml_tensor * ggml_expm1(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_expm1_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_softplus(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
+    GGML_API struct ggml_tensor * ggml_softplus_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a);
+
     GGML_API struct ggml_tensor * ggml_sin(
             struct ggml_context * ctx,
             struct ggml_tensor  * a);
@@ -983,6 +1012,10 @@ extern "C" {
             struct ggml_context * ctx,
             struct ggml_tensor  * a);
 
+    GGML_API struct ggml_tensor * ggml_cumsum(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a);
+
     // mean along rows
     GGML_API struct ggml_tensor * ggml_mean(
             struct ggml_context * ctx,
@@ -2187,6 +2220,23 @@ extern "C" {
             int                   shift2,
             int                   shift3);
 
+    // Convert matrix into a triangular one (upper, strict upper, lower or strict lower) by writing
+    // zeroes everywhere outside the masked area
+    GGML_API struct ggml_tensor * ggml_tri(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            enum ggml_tri_type    type);
+
+    // Fill tensor a with constant c
+    GGML_API struct ggml_tensor * ggml_fill(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            float                 c);
+
+    GGML_API struct ggml_tensor * ggml_fill_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            float                 c);
 
     // Ref: https://github.com/CompVis/stable-diffusion/blob/main/ldm/modules/diffusionmodules/util.py#L151
     // timesteps: [N,]
@@ -2356,6 +2406,27 @@ extern "C" {
             struct ggml_tensor  * b,
             struct ggml_tensor  * state);
 
+    /* Solves a specific equation of the form Ax=B, where A is a triangular matrix
+    *  without zeroes on the diagonal (i.e. invertible).
+    *  B can have any number of columns, but must have the same number of rows as A
+    *  If A is [n, n] and B is [n, m], then the result will be [n, m] as well
+    *  Has O(n^3) complexity (unlike most matrix ops out there), so use on cases
+    *  where n > 100 sparingly, pre-chunk if necessary.
+    *
+    *  If left = false, solves xA=B instead
+    *  If lower = false, assumes upper triangular instead
+    *  If uni = true, assumes diagonal of A to be all ones (will override actual values)
+    *
+    *  TODO: currently only lower, right, non-unitriangular variant is implemented
+    */
+    GGML_API struct ggml_tensor * ggml_solve_tri(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        struct ggml_tensor  * b,
+        bool                  left,
+        bool                  lower,
+        bool                  uni);
+
     // custom operators
 
     typedef void (*ggml_custom1_op_t)(struct ggml_tensor * dst , const struct ggml_tensor * a, int ith, int nth, void * userdata);

ggml/src/ggml-backend.cpp

@@ -1698,8 +1698,6 @@ bool ggml_backend_sched_reserve(ggml_backend_sched_t sched, struct ggml_cgraph *
     GGML_ASSERT(sched);
     GGML_ASSERT((int)sched->hash_set.size >= measure_graph->n_nodes + measure_graph->n_leafs);
 
-    ggml_backend_sched_reset(sched);
-
     ggml_backend_sched_synchronize(sched);
 
     ggml_backend_sched_split_graph(sched, measure_graph);

ggml/src/ggml-cann/aclnn_ops.cpp

@@ -448,6 +448,121 @@ void ggml_cann_norm(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
     ggml_cann_release_resources(ctx, norm, acl_src, acl_dst);
 }
 
+void ggml_cann_l2_norm(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
+    ggml_tensor * src = dst->src[0];
+
+    aclTensor * acl_src = ggml_cann_create_tensor(src);
+    aclTensor * acl_dst = ggml_cann_create_tensor(dst);
+
+    size_t  type_size = ggml_type_size(src->type);
+    int64_t n_bytes   = src->ne[3]* src->ne[2]* src->ne[1]* type_size;
+    ggml_cann_pool_alloc temp_buffer_allocator(ctx.pool(), n_bytes);
+    void *               buffer       = temp_buffer_allocator.get();
+
+    int64_t div_ne[] = {1, src->ne[1], src->ne[2], src->ne[3]};
+    size_t  div_nb[GGML_MAX_DIMS];
+    div_nb[0] = sizeof(float);
+    for (int i = 1; i < GGML_MAX_DIMS; ++i) {
+        div_nb[i] = div_nb[i - 1] * div_ne[i - 1];
+    }
+    aclTensor *          acl_div      = ggml_cann_create_tensor(buffer, ACL_FLOAT, type_size, div_ne, div_nb, GGML_MAX_DIMS);
+
+    std::vector<int64_t> norm_dims = { 3 };
+    aclIntArray * dims_array = aclCreateIntArray(norm_dims.data(), norm_dims.size());
+
+    float p_value = 2.0f;
+    aclScalar * p_scalar = aclCreateScalar(&p_value, aclDataType::ACL_FLOAT);
+    GGML_CANN_CALL_ACLNN_OP(ctx, Norm, acl_src, p_scalar, dims_array, true, acl_div);
+    GGML_CANN_CALL_ACLNN_OP(ctx, Div, acl_src, acl_div, acl_dst);
+    ggml_cann_release_resources(ctx, dims_array, p_scalar, acl_src, acl_dst, acl_div);
+}
+
+void ggml_cann_cross_entropy_loss(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
+    ggml_tensor * src0 = dst->src[0];
+    ggml_tensor * src1 = dst->src[1];
+
+    const int64_t nc = src0->ne[0];
+    const int64_t nr = ggml_nrows(src0);
+
+    int64_t logits_ne[] = {nc, nr};
+    size_t logits_nb[2];
+    logits_nb[0] = ggml_type_size(src0->type);
+    logits_nb[1] = logits_nb[0] * logits_ne[0];
+    aclTensor * acl_logits = ggml_cann_create_tensor(src0->data, ACL_FLOAT, sizeof(float), logits_ne, logits_nb, 2);
+
+    size_t log_softmax_type_size = sizeof(float);
+    int64_t log_softmax_n_bytes = nr * nc * log_softmax_type_size;
+    ggml_cann_pool_alloc log_softmax_allocator(ctx.pool(), log_softmax_n_bytes);
+    void * log_softmax_buffer = log_softmax_allocator.get();
+
+    int64_t log_softmax_ne[] = {nc, nr};
+    size_t log_softmax_nb[2];
+    log_softmax_nb[0] = log_softmax_type_size;
+    log_softmax_nb[1] = log_softmax_nb[0] * log_softmax_ne[0];
+    aclTensor * acl_log_softmax = ggml_cann_create_tensor(log_softmax_buffer, ACL_FLOAT, log_softmax_type_size, log_softmax_ne, log_softmax_nb, 2);
+
+    GGML_CANN_CALL_ACLNN_OP(ctx, LogSoftmax, acl_logits, 1, acl_log_softmax);
+
+    int64_t labels_ne[] = {nc, nr};
+    size_t labels_nb[2];
+    labels_nb[0] = ggml_type_size(src1->type);
+    labels_nb[1] = labels_nb[0] * labels_ne[0];
+    aclTensor * acl_labels = ggml_cann_create_tensor(src1->data, ACL_FLOAT, sizeof(float), labels_ne, labels_nb, 2);
+
+    size_t mul_type_size = sizeof(float);
+    int64_t mul_n_bytes = nr * nc * mul_type_size;
+    ggml_cann_pool_alloc mul_allocator(ctx.pool(), mul_n_bytes);
+    void * mul_buffer = mul_allocator.get();
+
+    int64_t mul_ne[] = {nc, nr};
+    size_t mul_nb[2];
+    mul_nb[0] = mul_type_size;
+    mul_nb[1] = mul_nb[0] * mul_ne[0];
+    aclTensor * acl_mul_result = ggml_cann_create_tensor(mul_buffer, ACL_FLOAT, mul_type_size, mul_ne, mul_nb, 2);
+
+    GGML_CANN_CALL_ACLNN_OP(ctx, Mul, acl_log_softmax, acl_labels, acl_mul_result);
+
+    size_t sum_per_sample_type_size = sizeof(float);
+    int64_t sum_per_sample_n_bytes = nr * sum_per_sample_type_size;
+    ggml_cann_pool_alloc sum_per_sample_allocator(ctx.pool(), sum_per_sample_n_bytes);
+    void * sum_per_sample_buffer = sum_per_sample_allocator.get();
+
+    int64_t sum_per_sample_ne[] = {nr};
+    size_t sum_per_sample_nb[1];
+    sum_per_sample_nb[0] = sum_per_sample_type_size;
+    aclTensor * acl_sum_per_sample = ggml_cann_create_tensor(sum_per_sample_buffer, ACL_FLOAT, sum_per_sample_type_size, sum_per_sample_ne, sum_per_sample_nb, 1);
+
+    std::vector<int64_t> sum_dims = {1};
+    aclIntArray * dims_array = aclCreateIntArray(sum_dims.data(), sum_dims.size());
+    bool keep_dims = false;
+
+    GGML_CANN_CALL_ACLNN_OP(ctx, ReduceSum, acl_mul_result, dims_array, keep_dims, ACL_FLOAT, acl_sum_per_sample);
+
+    size_t total_sum_type_size = sizeof(float);
+    int64_t total_sum_n_bytes = 1 * total_sum_type_size;
+    ggml_cann_pool_alloc total_sum_allocator(ctx.pool(), total_sum_n_bytes);
+    void * total_sum_buffer = total_sum_allocator.get();
+
+    int64_t total_sum_ne[] = {1};
+    size_t total_sum_nb[1];
+    total_sum_nb[0] = total_sum_type_size;
+
+    aclTensor * acl_total_sum = ggml_cann_create_tensor(total_sum_buffer, ACL_FLOAT, total_sum_type_size, total_sum_ne, total_sum_nb, 1);
+
+    std::vector<int64_t> total_sum_dims = {0};
+    aclIntArray * total_sum_dims_array = aclCreateIntArray(total_sum_dims.data(), total_sum_dims.size());
+
+    GGML_CANN_CALL_ACLNN_OP(ctx, ReduceSum, acl_sum_per_sample, total_sum_dims_array, keep_dims, ACL_FLOAT, acl_total_sum);
+
+    float value = -1.0f / static_cast<float>(nr);
+    aclScalar * scale_factor = aclCreateScalar(&value, aclDataType::ACL_FLOAT);
+    aclTensor * acl_dst = ggml_cann_create_tensor(dst->data, ACL_FLOAT, sizeof(float), total_sum_ne, total_sum_nb, 1);
+
+    GGML_CANN_CALL_ACLNN_OP(ctx, Muls, acl_total_sum, scale_factor, acl_dst);
+
+    ggml_cann_release_resources(ctx, acl_logits, acl_log_softmax, acl_labels, acl_mul_result, acl_sum_per_sample, acl_total_sum, acl_dst, scale_factor, dims_array, total_sum_dims_array);
+}
+
 void ggml_cann_group_norm(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
     ggml_tensor * src = dst->src[0];
 

ggml/src/ggml-cann/aclnn_ops.h

@@ -46,6 +46,8 @@
 #include <aclnnop/aclnn_cos.h>
 #include <aclnnop/aclnn_log.h>
 #include <aclnnop/aclnn_sign.h>
+#include <aclnnop/aclnn_norm.h>
+#include <aclnnop/aclnn_logsoftmax.h>
 #include "acl_tensor.h"
 #include "common.h"
 
@@ -187,6 +189,66 @@ void ggml_cann_argsort(ggml_backend_cann_context & ctx, ggml_tensor * dst);
  */
 void ggml_cann_norm(ggml_backend_cann_context & ctx, ggml_tensor * dst);
 
+/**
+ * @brief   Computes the L2 Normalization for a ggml tensor using the CANN
+ *          backend.
+ *
+ * @details This function applies the L2 Normalization operation on the
+ *          input tensor `src` and stores the result in the destination tensor
+ *          `dst`. L2 Normalization scales the input tensor such that the
+ *          L2 norm along the specified dimension equals 1. This operation
+ *          is commonly used in neural networks for feature normalization
+ *          and vector scaling.
+ *          The operation is defined as:
+ *          \f[
+ *              \text{out} = \frac{x}{\sqrt{\sum{x^2}}}
+ *          \f]
+ *          The normalization is performed along the last dimension by default.
+ *
+ * @param ctx The CANN context used for operations.
+ * @param dst The destination tensor where the normalized values will be stored.
+ * @attention The normalization is performed along the last dimension of the
+ *            input tensor by default.
+ */
+void ggml_cann_l2_norm(ggml_backend_cann_context & ctx, ggml_tensor * dst);
+
+/**
+ * @brief   Computes the Cross Entropy Loss for a ggml tensor using the CANN
+ *          backend.
+ *
+ * @details This function computes the cross entropy loss between the predicted
+ *          logits and target probability distributions. The operation follows
+ *          the same computation pattern as the CPU implementation:
+ *          1. Applies log_softmax to the logits along the class dimension
+ *          2. Element-wise multiplication with target distributions
+ *          3. Summation along the class dimension to get per-sample losses
+ *          4. Global summation and scaling by -1/nr to get final loss
+ *
+ *          The computation can be expressed as:
+ *          \f[
+ *              \text{loss} = -\frac{1}{N} \sum_{i=1}^{N} \sum_{j=1}^{C} y_{ij} \cdot \log(\text{softmax}(x_{ij}))
+ *          \f]
+ *          where \f$N\f$ is the total number of samples, \f$C\f$ is the number
+ *          of classes, \f$x\f$ are the logits, and \f$y\f$ are the target
+ *          probability distributions.
+ *
+ * @param ctx The CANN context used for operations.
+ * @param dst The destination tensor where the computed loss will be stored.
+ *            This should be a scalar tensor containing the final loss value.
+ *
+ * @note This implementation computes cross entropy between probability
+ *       distributions, not the typical classification cross entropy that
+ *       expects class indices as targets. Both input tensors (src0 and src1)
+ *       should have the same shape and represent probability distributions
+ *       over the class dimension.
+ * @note The function expects two source tensors:
+ *       - dst->src[0]: Logits tensor (before softmax)
+ *       - dst->src[1]: Target probability distributions tensor
+ * @note The computation is performed using CANN backend operators including
+ *       LogSoftmax, Mul, ReduceSum, and Muls for the final scaling.
+ */
+void ggml_cann_cross_entropy_loss(ggml_backend_cann_context & ctx, ggml_tensor * dst);
+
 /**
  * @brief  Computes the Group Normalization for a ggml tensor using the CANN
  *         backend.

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

@@ -1777,6 +1777,12 @@ static bool ggml_cann_compute_forward(ggml_backend_cann_context & ctx, struct gg
         case GGML_OP_GROUP_NORM:
             ggml_cann_group_norm(ctx, dst);
             break;
+        case GGML_OP_L2_NORM:
+            ggml_cann_l2_norm(ctx, dst);
+            break;
+        case GGML_OP_CROSS_ENTROPY_LOSS:
+            ggml_cann_cross_entropy_loss(ctx, dst);
+            break;
         case GGML_OP_CONCAT:
             ggml_cann_concat(ctx, dst);
             break;
@@ -2515,6 +2521,8 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev, const ggml_ten
                 // value of paddingW should be at most half of kernelW
                 return (p0 <= (k0 / 2)) && (p1 <= (k1 / 2));
             }
+        case GGML_OP_L2_NORM:
+        case GGML_OP_CROSS_ENTROPY_LOSS:
         case GGML_OP_DUP:
         case GGML_OP_SUM:
         case GGML_OP_IM2COL:

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

@@ -1731,6 +1731,10 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
             {
                 ggml_compute_forward_sum_rows(params, tensor);
             } break;
+        case GGML_OP_CUMSUM:
+            {
+                ggml_compute_forward_cumsum(params, tensor);
+            } break;
         case GGML_OP_MEAN:
             {
                 ggml_compute_forward_mean(params, tensor);
@@ -1927,6 +1931,14 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
             {
                 ggml_compute_forward_leaky_relu(params, tensor);
             } break;
+        case GGML_OP_TRI:
+            {
+                ggml_compute_forward_tri(params, tensor);
+            } break;
+        case GGML_OP_FILL:
+            {
+                ggml_compute_forward_fill(params, tensor);
+            } break;
         case GGML_OP_FLASH_ATTN_EXT:
             {
                 ggml_compute_forward_flash_attn_ext(params, tensor);
@@ -1982,6 +1994,10 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
             {
                 ggml_compute_forward_rwkv_wkv7(params, tensor);
             } break;
+        case GGML_OP_SOLVE_TRI:
+            {
+                ggml_compute_forward_solve_tri(params, tensor);
+            } break;
         case GGML_OP_MAP_CUSTOM1:
             {
                 ggml_compute_forward_map_custom1(params, tensor);
@@ -2140,6 +2156,9 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
         case GGML_OP_ADD_ID:
         case GGML_OP_ADD1:
         case GGML_OP_ACC:
+        case GGML_OP_CUMSUM:
+        case GGML_OP_TRI:
+        case GGML_OP_FILL:
             {
                 n_tasks = n_threads;
             } break;
@@ -2157,6 +2176,7 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
                 n_tasks = 1;
             } break;
         case GGML_OP_COUNT_EQUAL:
+        case GGML_OP_SOLVE_TRI:
             {
                 n_tasks = n_threads;
             } break;
@@ -2179,6 +2199,8 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
                 case GGML_UNARY_OP_HARDSWISH:
                 case GGML_UNARY_OP_HARDSIGMOID:
                 case GGML_UNARY_OP_EXP:
+                case GGML_UNARY_OP_SOFTPLUS:
+                case GGML_UNARY_OP_EXPM1:
                 case GGML_UNARY_OP_FLOOR:
                 case GGML_UNARY_OP_CEIL:
                 case GGML_UNARY_OP_ROUND:

ggml/src/ggml-cpu/ops.cpp

@@ -7,8 +7,10 @@
 #include "unary-ops.h"
 #include "vec.h"
 
-#include <float.h>
+#include <cfloat>
 #include <algorithm>
+#include <cmath>
+#include <functional>
 
 // ggml_compute_forward_dup
 
@@ -1394,6 +1396,56 @@ void ggml_compute_forward_sum(
     }
 }
 
+// ggml_compute_forward_cumsum
+
+static void ggml_compute_forward_cumsum_f32(
+        const ggml_compute_params * params,
+        ggml_tensor * dst) {
+
+    const ggml_tensor * src0 = dst->src[0];
+
+    GGML_ASSERT(src0->nb[0] == sizeof(float));
+    GGML_ASSERT(dst->nb[0] == sizeof(float));
+
+    GGML_TENSOR_UNARY_OP_LOCALS
+
+    GGML_ASSERT(ne0 == ne00);
+    GGML_ASSERT(ne1 == ne01);
+    GGML_ASSERT(ne2 == ne02);
+    GGML_ASSERT(ne3 == ne03);
+
+    const auto [ir0, ir1] = get_thread_range(params, src0);
+
+    for (int64_t ir = ir0; ir < ir1; ++ir) {
+        const int64_t i03 = ir/(ne02*ne01);
+        const int64_t i02 = (ir - i03*ne02*ne01)/ne01;
+        const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01);
+
+        float * src_row = (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03);
+        float * dst_row = (float *) ((char *) dst->data  + i01*nb1  + i02*nb2  + i03*nb3);
+
+        ggml_vec_cumsum_f32(ne00, dst_row, src_row);
+    }
+}
+
+void ggml_compute_forward_cumsum(
+        const ggml_compute_params * params,
+        ggml_tensor * dst) {
+
+    const ggml_tensor * src0 = dst->src[0];
+
+    switch (src0->type) {
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_cumsum_f32(params, dst);
+            } break;
+        default:
+            {
+                GGML_ABORT("fatal error");
+            }
+    }
+}
+
 // ggml_compute_forward_sum_rows
 
 static void ggml_compute_forward_sum_rows_f32(
@@ -2140,6 +2192,83 @@ static void ggml_compute_forward_gelu(
     }
 }
 
+// ggml_compute_fill
+
+static void ggml_compute_forward_fill_f32(const ggml_compute_params * params, ggml_tensor * dst) {
+    const float c = ggml_get_op_params_f32(dst, 0);
+
+    GGML_TENSOR_LOCALS(int64_t, ne, dst, ne);
+    GGML_TENSOR_LOCALS(size_t,  nb, dst, nb);
+
+    const auto [ir0, ir1] = get_thread_range(params, dst);
+
+    for (int64_t ir = ir0; ir < ir1; ++ir) {
+        const int64_t i03 = ir/(ne2*ne1);
+        const int64_t i02 = (ir - i03*ne2*ne1)/ne1;
+        const int64_t i01 = (ir - i03*ne2*ne1 - i02*ne1);
+
+        float * dst_ptr  = (float *) ((char *) dst->data + i03*nb3 + i02*nb2 + i01*nb1);
+
+        ggml_vec_set_f32(ne0, dst_ptr, c);
+    }
+}
+
+void ggml_compute_forward_fill(const ggml_compute_params * params, ggml_tensor * dst) {
+    ggml_compute_forward_fill_f32(params, dst);
+}
+
+// ggml_compute_tri
+
+static void ggml_compute_forward_tri_f32(const ggml_compute_params * params, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+
+    const ggml_tri_type ttype = (ggml_tri_type) ggml_get_op_params_i32(dst, 0);
+
+    GGML_ASSERT(ggml_is_contiguous(src0));
+
+    GGML_TENSOR_UNARY_OP_LOCALS
+
+    const auto [ir0, ir1] = get_thread_range(params, src0);
+
+    bool (*bipred)(int, int);
+
+    switch (ttype) {
+        case GGML_TRI_TYPE_LOWER:      bipred = [](int i, int r) { return i <  r; }; break;
+        case GGML_TRI_TYPE_LOWER_DIAG: bipred = [](int i, int r) { return i <= r; }; break;
+        case GGML_TRI_TYPE_UPPER:      bipred = [](int i, int r) { return i >  r; }; break;
+        case GGML_TRI_TYPE_UPPER_DIAG: bipred = [](int i, int r) { return i >= r; }; break;
+        default: GGML_ABORT("invalid tri type");
+    }
+
+    for (int64_t ir = ir0; ir < ir1; ++ir) {
+        const int64_t i03 = ir/(ne02*ne01);
+        const int64_t i02 = (ir - i03*ne02*ne01)/ne01;
+        const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01);
+
+        const float * src_ptr = (const float  *) ((const char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01);
+              float * dst_ptr = (      float  *) ((      char *) dst->data  + i03*nb3  + i02*nb2  + i01*nb1);
+
+        for (int i0 = 0; i0 < ne0; ++i0) {
+            dst_ptr[i0] = bipred(i0, i01) ? src_ptr[i0] : 0.0f;
+        }
+    }
+}
+
+void ggml_compute_forward_tri(const ggml_compute_params * params, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+
+    switch (src0->type) {
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_tri_f32(params, dst);
+            } break;
+        default:
+            {
+                GGML_ABORT("fatal error");
+            }
+    }
+}
+
 // ggml_compute_forward_gelu_erf
 
 static void ggml_compute_forward_gelu_erf_f32(
@@ -7664,6 +7793,18 @@ void ggml_compute_forward_timestep_embedding(
 
 // ggml_compute_forward_argsort
 
+template<enum ggml_sort_order order>
+struct argsort_cmp {
+    const float * data;
+    bool operator()(int32_t a, int32_t b) const {
+        if constexpr (order == GGML_SORT_ORDER_ASC) {
+            return data[a] < data[b];
+        } else {
+            return data[a] > data[b];
+        }
+    }
+};
+
 static void ggml_compute_forward_argsort_f32(
     const ggml_compute_params * params,
     ggml_tensor * dst) {
@@ -7682,23 +7823,25 @@ static void ggml_compute_forward_argsort_f32(
     ggml_sort_order order = (ggml_sort_order) ggml_get_op_params_i32(dst, 0);
 
     for (int64_t i = ith; i < nr; i += nth) {
-        int32_t * dst_data = (int32_t *)((char *) dst->data + i*nb1);
         const float * src_data = (float *)((char *) src0->data + i*nb01);
 
+        int32_t * dst_data = (int32_t *)((char *) dst->data + i*nb1);
+
         for (int64_t j = 0; j < ne0; j++) {
             dst_data[j] = j;
         }
 
-        // C doesn't have a functional sort, so we do a bubble sort instead
-        for (int64_t j = 0; j < ne0; j++) {
-            for (int64_t k = j + 1; k < ne0; k++) {
-                if ((order == GGML_SORT_ORDER_ASC  && src_data[dst_data[j]] > src_data[dst_data[k]]) ||
-                    (order == GGML_SORT_ORDER_DESC && src_data[dst_data[j]] < src_data[dst_data[k]])) {
-                    int32_t tmp = dst_data[j];
-                    dst_data[j] = dst_data[k];
-                    dst_data[k] = tmp;
-                }
-            }
+        switch (order) {
+            case GGML_SORT_ORDER_ASC:
+                std::sort(dst_data, dst_data + ne0, argsort_cmp<GGML_SORT_ORDER_ASC>{src_data});
+                break;
+
+            case GGML_SORT_ORDER_DESC:
+                std::sort(dst_data, dst_data + ne0, argsort_cmp<GGML_SORT_ORDER_DESC>{src_data});
+                break;
+
+            default:
+                GGML_ABORT("invalid sort order");
         }
     }
 }
@@ -8521,7 +8664,7 @@ static void ggml_compute_forward_ssm_scan_f32(
                 // n_head
                 for (int h = ih0; h < ih1; ++h) {
                     // ref: https://github.com/state-spaces/mamba/blob/62db608da60f6fc790b8ed9f4b3225e95ca15fde/mamba_ssm/ops/triton/softplus.py#L16
-                    const float dt_soft_plus = ggml_softplus(dt[h]);
+                    const float dt_soft_plus = ggml_compute_softplus_f32(dt[h]);
                     const float dA = expf(dt_soft_plus * A[h]);
                     const int g = h / (nh / ng); // repeat_interleave
 
@@ -8618,7 +8761,7 @@ static void ggml_compute_forward_ssm_scan_f32(
                 // n_head
                 for (int h = ih0; h < ih1; ++h) {
                     // ref: https://github.com/state-spaces/mamba/blob/62db608da60f6fc790b8ed9f4b3225e95ca15fde/mamba_ssm/ops/triton/softplus.py#L16
-                    const float dt_soft_plus = ggml_softplus(dt[h]);
+                    const float dt_soft_plus = ggml_compute_softplus_f32(dt[h]);
                     const int g = h / (nh / ng); // repeat_interleave
 
                     // dim
@@ -8901,6 +9044,14 @@ void ggml_compute_forward_unary(
             {
                 ggml_compute_forward_xielu(params, dst);
             } break;
+        case GGML_UNARY_OP_EXPM1:
+            {
+                ggml_compute_forward_expm1(params, dst);
+            } break;
+        case GGML_UNARY_OP_SOFTPLUS:
+            {
+                ggml_compute_forward_softplus(params, dst);
+            } break;
         default:
             {
                 GGML_ABORT("fatal error");
@@ -9497,6 +9648,76 @@ void ggml_compute_forward_gla(
     }
 }
 
+static void ggml_compute_forward_solve_tri_f32(const struct ggml_compute_params * params, struct ggml_tensor * dst) {
+    const struct ggml_tensor * src0 = dst->src[0];  // A (lower triangular)
+    const struct ggml_tensor * src1 = dst->src[1];  // B (RHS)
+
+    GGML_TENSOR_BINARY_OP_LOCALS;
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT(src1->type == GGML_TYPE_F32);
+    GGML_ASSERT(dst->type  == GGML_TYPE_F32);
+
+    GGML_ASSERT(ne00 == ne01); // A must be square
+    GGML_ASSERT(ne0  == ne10); // solution cols == B cols
+    GGML_ASSERT(ne1  == ne11); // solution rows == B rows
+
+    GGML_ASSERT(ne02 == ne12 && ne12 == ne2);
+    GGML_ASSERT(ne03 == ne13 && ne13 == ne3);
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    const int64_t k = ne10;   // number of RHS columns
+    const int64_t n = ne11;   // A is n×n
+    const int64_t nr = ne02 * ne03 * k; // we're parallelizing on columns here, so seq x token x column will be the unit
+
+    // chunks per thread
+    const int64_t dr = (nr + nth - 1)/nth;
+
+    // chunk range for this thread
+    const int64_t ir0 = dr*ith;
+    const int64_t ir1 = MIN(ir0 + dr, nr);
+
+    const float * A = (const float *) src0->data;  // [n, n, B1, B2]
+    const float * B = (const float *) src1->data;  // [n, k, B1, B2]
+          float * X = (      float *) dst->data;   // [n, k, B1, B2]
+
+    for (int64_t ir = ir0; ir < ir1; ++ir) {
+        const int64_t i03 = ir/(ne02*k);
+        const int64_t i02 = (ir - i03*ne02*k)/k;
+        const int64_t i01 = (ir - i03*ne02*k - i02*k);
+
+        const float * A_batch = A + i02 * nb02 / sizeof(float) + i03 * nb03 / sizeof(float);
+        const float * B_batch = B + i02 * nb12 / sizeof(float) + i03 * nb13 / sizeof(float);
+
+        float * X_batch = X + i02 * nb2 / sizeof(float) + i03 * nb3 / sizeof(float);
+
+        for (int64_t i00 = 0; i00 < n; ++i00) {
+            float sum = 0.0f;
+            for (int64_t t = 0; t < i00; ++t) {
+                sum += A_batch[i00 * n + t] * X_batch[i01 * n + t];
+            }
+
+            const float diag = A_batch[i00 * n + i00];
+            GGML_ASSERT(diag != 0.0f && "Zero diagonal in triangular matrix");
+
+            X_batch[i01 * n + i00] = (B_batch[i00 * k + i01] - sum) / diag;
+        }
+    }
+}
+
+void ggml_compute_forward_solve_tri(const struct ggml_compute_params * params, struct ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    const ggml_tensor * src1 = dst->src[1];
+
+    if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32) {
+        ggml_compute_forward_solve_tri_f32(params, dst);
+    } else {
+        GGML_ABORT("fatal error");
+    }
+}
+
 // ggml_compute_forward_rwkv_wkv7
 
 static void ggml_compute_forward_rwkv_wkv7_f32(

ggml/src/ggml-cpu/ops.h

@@ -34,6 +34,7 @@ void ggml_compute_forward_add1(const struct ggml_compute_params * params, struct
 void ggml_compute_forward_acc(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_sum(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_sum_rows(const struct ggml_compute_params * params, struct ggml_tensor * dst);
+void ggml_compute_forward_cumsum(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_mean(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_argmax(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_count_equal(const struct ggml_compute_params * params, struct ggml_tensor * dst);
@@ -81,6 +82,8 @@ void ggml_compute_forward_arange(const struct ggml_compute_params * params, stru
 void ggml_compute_forward_timestep_embedding(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_argsort(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_leaky_relu(const struct ggml_compute_params * params, struct ggml_tensor * dst);
+void ggml_compute_forward_tri(const struct ggml_compute_params * params, struct ggml_tensor * dst);
+void ggml_compute_forward_fill(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_flash_attn_ext(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_flash_attn_back(
         const struct ggml_compute_params * params,
@@ -96,6 +99,7 @@ void ggml_compute_forward_get_rel_pos(const struct ggml_compute_params * params,
 void ggml_compute_forward_add_rel_pos(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_rwkv_wkv6(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_rwkv_wkv7(const struct ggml_compute_params * params, struct ggml_tensor * dst);
+void ggml_compute_forward_solve_tri(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_gla(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_map_custom1(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_map_custom2(const struct ggml_compute_params * params, struct ggml_tensor * dst);

ggml/src/ggml-cpu/repack.cpp

@@ -1600,29 +1600,52 @@ template <typename BLOC_TYPE, int64_t INTER_SIZE, int64_t NB_COLS, ggml_type PAR
         return false;
     }
 
-    void forward_mul_mat_one_chunk(ggml_compute_params * params, ggml_tensor * op, int64_t src0_start, int64_t src0_end) {
+    void forward_mul_mat_one_chunk(ggml_compute_params * params,
+                                   ggml_tensor *         op,
+                                   int64_t               src0_start,
+                                   int64_t               src0_end,
+                                   int64_t               src1_start,
+                                   int64_t               src1_end) {
         const ggml_tensor * src0 = op->src[0];
         const ggml_tensor * src1 = op->src[1];
         ggml_tensor *       dst  = op;
 
         GGML_TENSOR_BINARY_OP_LOCALS
 
-        const void * src1_wdata      = params->wdata;
         const size_t src1_col_stride = ggml_row_size(PARAM_TYPE, ne10);
 
+        GGML_ASSERT(ne03 == 1 && ne13 == 1);
+        GGML_ASSERT(ne12 % ne02 == 0);
+        const int64_t r2 = ne12 / ne02;
+
+        const int64_t i12 = src1_start / ne1;
+        const int64_t i11 = src1_start - i12 * ne1;
+
+        // Determine batch index
+        const int64_t i02 = i12 / r2;
+
+        const int64_t i1 = i11;
+        const int64_t i2 = i12;
+
+        const char * src0_ptr = (const char *) src0->data + i02 * nb02;
+        const char * src1_ptr = (const char *) params->wdata + (i11 + i12 * ne11) * src1_col_stride;
+        char *       dst_ptr  = ((char *) dst->data + (i1 * nb1 + i2 * nb2));
+
+        const int64_t nrows = src1_end - src1_start;
+        const int64_t ncols = src0_end - src0_start;
+
+        GGML_ASSERT(src1_ptr + src1_col_stride * nrows <= (const char *) params->wdata + params->wsize);
+
         // If there are more than three rows in src1, use gemm; otherwise, use gemv.
-        if (ne11 > 3) {
-            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);
+        if (nrows > 3) {
+            gemm<BLOC_TYPE, INTER_SIZE, NB_COLS, PARAM_TYPE>(ne00, (float *) (dst_ptr) + src0_start, nb1 / nb0,
+                                                             src0_ptr + src0_start * nb01, src1_ptr,
+                                                             nrows - (nrows % 4), ncols);
         }
-        for (int iter = ne11 - ne11 % 4; iter < ne11; iter++) {
-            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);
+        for (int iter = nrows - (nrows % 4); iter < nrows; iter++) {
+            gemv<BLOC_TYPE, INTER_SIZE, NB_COLS, PARAM_TYPE>(ne00, (float *) (dst_ptr + (iter * nb1)) + src0_start,
+                                                             ne01, src0_ptr + src0_start * nb01,
+                                                             src1_ptr + (src1_col_stride * iter), 1 /* nrows */, ncols);
         }
     }
 
@@ -1647,6 +1670,12 @@ template <typename BLOC_TYPE, int64_t INTER_SIZE, int64_t NB_COLS, ggml_type PAR
         GGML_ASSERT(nb1 <= nb2);
         GGML_ASSERT(nb2 <= nb3);
 
+        // TODO: General batched mul mat for 4D tensors
+        // Currently only supports 3D tensors
+        GGML_ASSERT(ne03 == 1);
+        GGML_ASSERT(ne13 == 1);
+        GGML_ASSERT(ne3 == 1);
+
         GGML_ASSERT(src1->type == GGML_TYPE_F32);
 
         GGML_ASSERT(ggml_n_dims(op->src[0]) == 2);
@@ -1654,47 +1683,64 @@ template <typename BLOC_TYPE, int64_t INTER_SIZE, int64_t NB_COLS, ggml_type PAR
 
         char *       wdata = static_cast<char *>(params->wdata);
         const size_t nbw1  = ggml_row_size(PARAM_TYPE, ne10);
+        const size_t nbw2  = nbw1 * ne11;
 
-        assert(params->wsize >= nbw1 * ne11);
+        assert(params->wsize >= nbw2 * ne12);
 
         const ggml_from_float_t from_float = ggml_get_type_traits_cpu(PARAM_TYPE)->from_float;
 
-        int64_t i11_processed = 0;
-        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);
-        }
+        // INFO: Quantization is done in planes to avoid extra complexity in chunking.
+        // Flattening dimensions not multiple of INTER_SIZE would require extra handling depending on how
+        // the planes are broadcast.
+        for (int64_t i12 = 0; i12 < ne12; i12++) {
+            char * data_ptr  = (char *) src1->data + i12 * nb12;
+            char * wdata_ptr = wdata + i12 * nbw2;
 
-        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);
+            for (int64_t i11 = ith * 4; i11 < ne11 - ne11 % 4; i11 += nth * 4) {
+                ggml_quantize_mat_t<INTER_SIZE, PARAM_TYPE>((float *) (data_ptr + i11 * nb11),
+                                                            (void *) (wdata_ptr + i11 * nbw1), 4, ne10);
+            }
+
+            const int64_t i11_processed = ne11 - ne11 % 4;
+            for (int64_t i11 = i11_processed + ith; i11 < ne11; i11 += nth) {
+                from_float((float *) (data_ptr + i11 * nb11), (void *) (wdata_ptr + i11 * nbw1), ne10);
+            }
         }
 
         // disable for NUMA
         const bool disable_chunking = ggml_is_numa();
 
         // 4x chunks per thread
-        int64_t nr = ggml_nrows(op->src[0]);
-        int nth_scaled = nth * 4;
-        int64_t chunk_size = (nr + nth_scaled - 1) / nth_scaled;
-        int64_t nchunk     = (nr + chunk_size - 1) / chunk_size;
+        const int64_t nr0 = ggml_nrows(op->src[0]);
+
+        int     nth_scaled  = nth * 4;
+        int64_t chunk_size0 = (nr0 + nth_scaled - 1) / nth_scaled;
+        int64_t nchunk0     = (nr0 + chunk_size0 - 1) / chunk_size0;
+
+        // src1 is chunked only by full planes.
+        // When we flatten we need to address dimensions not multiple of the q8 INTER_SIZE
+        // to route them thorugh GEMV.
+        // nchunk1 = ne12 also avoids messing the chunking for models with no 3d tensors
+        // to avoid affecting their performance
+        int64_t nchunk1 = ne12;
 
         // Ensure minimum chunk size to avoid alignment issues with high thread counts
         // Minimum chunk size should be at least NB_COLS to prevent overlapping chunks after alignment
         const int64_t min_chunk_size = NB_COLS;
-        if (nchunk > 0 && (nr / nchunk) < min_chunk_size && nr >= min_chunk_size) {
-            nchunk = (nr + min_chunk_size - 1) / min_chunk_size;
+        if (nchunk0 > 0 && (nr0 / nchunk0) < min_chunk_size && nr0 >= min_chunk_size) {
+            nchunk0 = (nr0 + min_chunk_size - 1) / min_chunk_size;
         }
 
-        if (nth == 1 || nchunk < nth || disable_chunking) {
-            nchunk = nth;
+        if (nth == 1 || nchunk0 < nth || disable_chunking) {
+            nchunk0 = nth;
         }
 
+        const int64_t dr0 = (nr0 + nchunk0 - 1) / nchunk0;
+
         // Ensure nchunk doesn't exceed the number of rows divided by minimum chunk size
         // This prevents creating too many tiny chunks that could overlap after alignment
-        const int64_t max_nchunk = (nr + min_chunk_size - 1) / min_chunk_size;
-        if (nchunk > max_nchunk) {
-            nchunk = max_nchunk;
-        }
+        const int64_t max_nchunk = (nr0 + min_chunk_size - 1) / min_chunk_size;
+        nchunk0                  = MIN(nchunk0, max_nchunk);
 
         if (ith == 0) {
             // Every thread starts at ith, so the first unprocessed chunk is nth.  This save a bit of coordination right at the start.
@@ -1706,23 +1752,30 @@ template <typename BLOC_TYPE, int64_t INTER_SIZE, int64_t NB_COLS, ggml_type PAR
         // The first chunk comes from our thread_id, the rest will get auto-assigned.
         int current_chunk = ith;
 
-        while (current_chunk < nchunk) {
-            int64_t src0_start = (current_chunk * ne01) / nchunk;
-            int64_t src0_end   = ((current_chunk + 1) * ne01) / nchunk;
+        while (current_chunk < nchunk0 * nchunk1) {
+            const int64_t ith0 = current_chunk % nchunk0;
+            const int64_t ith1 = current_chunk / nchunk0;
+
+            int64_t src0_start = dr0 * ith0;
+            int64_t src0_end   = MIN(src0_start + dr0, nr0);
+
+            // full-plane range for src1
+            int64_t src1_start = ith1 * ne11;
+            int64_t src1_end = (ith1 + 1) * ne11;
 
             // Align boundaries to NB_COLS - round up to ensure all data is included
             // The chunk size limiting above ensures chunks are large enough to prevent overlaps
             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;
-            if (src0_end > ne01) {
-                src0_end = ne01;
-            }
+            src0_end   = (src0_end % NB_COLS) ? src0_end + NB_COLS - (src0_end % NB_COLS) : src0_end;
+            src0_end   = MIN(src0_end, ne01);
 
+            // Make sure current plane is the last one before exiting
             if (src0_start >= src0_end) {
-                break;
+                current_chunk = ggml_threadpool_chunk_add(params->threadpool, 1);
+                continue;
             }
 
-            forward_mul_mat_one_chunk(params, dst, src0_start, src0_end);
+            forward_mul_mat_one_chunk(params, dst, src0_start, src0_end, src1_start, src1_end);
 
             current_chunk = ggml_threadpool_chunk_add(params->threadpool, 1);
         }

ggml/src/ggml-cpu/unary-ops.cpp

@@ -73,6 +73,14 @@ static inline float op_log(float x) {
     return logf(x);
 }
 
+static inline float op_expm1(float x) {
+    return expf(x) - 1.0f;
+}
+
+static inline float op_softplus(float x) {
+    return (x > 20.0f) ? x : logf(1.0f + expf(x));
+}
+
 static inline float op_floor(float x) {
     return floorf(x);
 }
@@ -290,6 +298,14 @@ void ggml_compute_forward_log(const ggml_compute_params * params, ggml_tensor *
     unary_op<op_log>(params, dst);
 }
 
+void ggml_compute_forward_expm1(const ggml_compute_params * params, ggml_tensor * dst) {
+    unary_op<op_expm1>(params, dst);
+}
+
+void ggml_compute_forward_softplus(const ggml_compute_params * params, ggml_tensor * dst) {
+    unary_op<op_softplus>(params, dst);
+}
+
 void ggml_compute_forward_floor(const ggml_compute_params * params, ggml_tensor * dst) {
     unary_op<op_floor>(params, dst);
 }

ggml/src/ggml-cpu/unary-ops.h

@@ -22,6 +22,8 @@ void ggml_compute_forward_sqrt(const struct ggml_compute_params * params, struct
 void ggml_compute_forward_sin(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_cos(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_log(const struct ggml_compute_params * params, struct ggml_tensor * dst);
+void ggml_compute_forward_expm1(const struct ggml_compute_params * params, struct ggml_tensor * dst);
+void ggml_compute_forward_softplus(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_floor(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_ceil(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_round(const struct ggml_compute_params * params, struct ggml_tensor * dst);

ggml/src/ggml-cpu/vec.cpp

@@ -360,6 +360,13 @@ void ggml_vec_silu_f32(const int n, float * y, const float * x) {
     for (; i + 3 < n; i += 4) {
         vst1q_f32(y + i, ggml_v_silu(vld1q_f32(x + i)));
     }
+#elif defined(__riscv_v_intrinsic)
+    for (int vl; i < n; i += vl) {
+        vl = __riscv_vsetvl_e32m2(n - i);
+        vfloat32m2_t vx = __riscv_vle32_v_f32m2(&x[i], vl);
+        vfloat32m2_t vy = ggml_v_silu_m2(vx, vl);
+        __riscv_vse32_v_f32m2(&y[i], vy, vl);
+    }
 #endif
     for (; i < n; ++i) {
         y[i] = ggml_silu_f32(x[i]);
@@ -460,6 +467,16 @@ ggml_float ggml_vec_cvar_f32(const int n, float * y, const float * x, const floa
         val = vec_mul(val, val);
         sum += (ggml_float)vec_hsum_f32x4(val);
     }
+#elif defined(__riscv_v_intrinsic)
+    vfloat64m1_t vsum = __riscv_vfmv_v_f_f64m1(0, 1);
+    for (int vl; i < n; i += vl) {
+        vl = __riscv_vsetvl_e32m2(n - i);
+        vfloat32m2_t val = __riscv_vfsub_vf_f32m2(__riscv_vle32_v_f32m2(&x[i], vl), mean, vl);
+        __riscv_vse32_v_f32m2(&y[i], val, vl);
+        val = __riscv_vfmul_vv_f32m2(val, val, vl);
+        vsum = __riscv_vfwredusum_vs_f32m2_f64m1(val, vsum, vl);
+    }
+    sum = (ggml_float)__riscv_vfmv_f_s_f64m1_f64(vsum);
 #endif
     for (; i < n; ++i) {
         float val = x[i] - mean;

ggml/src/ggml-cpu/vec.h

@@ -1416,6 +1416,16 @@ inline static void ggml_vec_sum_f32(const int n, float * s, const float * x) {
 #endif
 }
 
+inline static void ggml_vec_cumsum_f32(const int n, float * y, const float * x) {
+    for (int i = 0; i < n; ++i) {
+        if (i == 0) {
+            y[i] = x[i];
+        } else {
+            y[i] = y[i - 1] + x[i];
+        }
+    }
+}
+
 inline static void ggml_vec_sum_f32_ggf(const int n, ggml_float * s, const float * x) {
     ggml_float sum = 0.0;
     for (int i = 0; i < n; ++i) {

ggml/src/ggml-cuda/common.cuh

@@ -586,6 +586,12 @@ static __device__ __forceinline__ void ggml_cuda_mad(half2 & acc, const half2 v,
 //     If dst and src point at different address spaces then they are guaranteed to not be aliased.
 template <int nbytes, int alignment = 0>
 static __device__ __forceinline__ void ggml_cuda_memcpy_1(void * __restrict__ dst, const void * __restrict__ src) {
+    static_assert(
+        nbytes <= ggml_cuda_get_max_cpy_bytes() || alignment == 0,
+        "You are misusing the alignment parameter for ggml_cuda_memcpy_1. "
+        "The intent is for the parameter is only as a workaround if either one of the pointers is not properly aligned. "
+        "If you use it to do more bytes per copy than ggml_cuda_max_cpy_bytes() the reads and writes may not be coalesced. "
+        "Call ggml_cuda_memcpy_1 in a loop instead.");
     if constexpr (alignment != 0) {
         static_assert(nbytes % alignment == 0, "bad alignment");
     }

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

@@ -2527,6 +2527,12 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg
                 case GGML_UNARY_OP_TRUNC:
                     ggml_cuda_op_trunc(ctx, dst);
                     break;
+                case GGML_UNARY_OP_EXPM1:
+                    ggml_cuda_op_expm1(ctx, dst);
+                    break;
+                case GGML_UNARY_OP_SOFTPLUS:
+                    ggml_cuda_op_softplus(ctx, dst);
+                    break;
                 default:
                     return false;
             }
@@ -2992,6 +2998,36 @@ static void update_cuda_graph_executable(ggml_backend_cuda_context * cuda_ctx) {
 }
 #endif
 
+static bool ggml_cuda_should_fuse_rope_set_rows(const ggml_tensor * rope,
+                                                const ggml_tensor * view,
+                                                const ggml_tensor * set_rows) {
+    // ne3 not tested
+    if (rope->src[0]->ne[3] != 1) {
+        return false;
+    }
+
+    if (set_rows->type != GGML_TYPE_F32 && set_rows->type != GGML_TYPE_F16) {
+        return false;
+    }
+
+    if (set_rows->src[1]->type != GGML_TYPE_I64) {
+        return false;
+    }
+
+    // The view should flatten two dims of rope into one dim
+    if (!ggml_is_contiguous(view) || view->ne[0] != rope->ne[0] * rope->ne[1]) {
+        return false;
+    }
+
+    // Only norm/neox shaders have the fusion code
+    const int mode = ((const int32_t *) rope->op_params)[2];
+    if (mode != GGML_ROPE_TYPE_NORMAL && mode != GGML_ROPE_TYPE_NEOX) {
+        return false;
+    }
+
+    return true;
+}
+
 static bool ggml_cuda_can_fuse(const struct ggml_cgraph * cgraph, int node_idx, std::initializer_list<enum ggml_op> ops, std::initializer_list<enum ggml_unary_op> unary_ops) {
 #ifndef NDEBUG
     const size_t num_unary = std::count(ops.begin(), ops.end(), GGML_OP_UNARY);
@@ -3067,6 +3103,16 @@ static bool ggml_cuda_can_fuse(const struct ggml_cgraph * cgraph, int node_idx,
         }
     }
 
+    if (ops.size() == 3 && ggml_can_fuse_subgraph(cgraph, node_idx, ops, { node_idx + 2 })) {
+        const ggml_tensor * rope     = cgraph->nodes[node_idx];
+        const ggml_tensor * view     = cgraph->nodes[node_idx + 1];
+        const ggml_tensor * set_rows = cgraph->nodes[node_idx + 2];
+
+        if (ggml_cuda_should_fuse_rope_set_rows(rope, view, set_rows)) {
+            return true;
+        }
+    }
+
     if (!ggml_can_fuse(cgraph, node_idx, ops)) {
         return false;
     }
@@ -3196,6 +3242,15 @@ static void evaluate_and_capture_cuda_graph(ggml_backend_cuda_context * cuda_ctx
                         continue;
                     }
 
+                    if (ggml_cuda_can_fuse(cgraph, i, { GGML_OP_ROPE, GGML_OP_VIEW, GGML_OP_SET_ROWS }, {})) {
+                        ggml_tensor * rope = cgraph->nodes[i];
+                        ggml_tensor * set_rows = cgraph->nodes[i + 2];
+
+                        ggml_cuda_op_rope_fused(*cuda_ctx, rope, set_rows);
+                        i += 2;
+                        continue;
+                    }
+
                     if (node->op == GGML_OP_ADD) {
                         int n_fuse = 0;
                         ggml_op ops[8];
@@ -3780,6 +3835,8 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
                 case GGML_UNARY_OP_GELU_QUICK:
                 case GGML_UNARY_OP_TANH:
                 case GGML_UNARY_OP_EXP:
+                case GGML_UNARY_OP_EXPM1:
+                case GGML_UNARY_OP_SOFTPLUS:
                 case GGML_UNARY_OP_ELU:
                 case GGML_UNARY_OP_FLOOR:
                 case GGML_UNARY_OP_CEIL:

ggml/src/ggml-cuda/rope.cu

@@ -1,3 +1,6 @@
+#include "convert.cuh"
+#include "ggml-cuda/common.cuh"
+#include "ggml.h"
 #include "rope.cuh"
 
 struct rope_corr_dims {
@@ -37,11 +40,23 @@ static __device__ void rope_yarn(
     }
 }
 
-template<bool forward, bool has_ff, typename T>
-static __global__ void rope_norm(
-        const T * x, T * dst, const int ne0, const int ne1, const int s1, const int s2, const int n_dims,
-        const int32_t * pos, const float freq_scale, const float ext_factor, const float attn_factor,
-        const rope_corr_dims corr_dims, const float theta_scale, const float * freq_factors) {
+template <bool forward, bool has_ff, typename T, typename D>
+static __global__ void rope_norm(const T *            x,
+                                 D *                  dst,
+                                 const int            ne0,
+                                 const int            ne1,
+                                 const int            s1,
+                                 const int            s2,
+                                 const int            n_dims,
+                                 const int32_t *      pos,
+                                 const float          freq_scale,
+                                 const float          ext_factor,
+                                 const float          attn_factor,
+                                 const rope_corr_dims corr_dims,
+                                 const float          theta_scale,
+                                 const float *        freq_factors,
+                                 const int64_t *      row_indices,
+                                 const int            set_rows_stride) {
     const int i0 = 2*(blockDim.y*blockIdx.y + threadIdx.y);
 
     if (i0 >= ne0) {
@@ -53,13 +68,27 @@ static __global__ void rope_norm(
     const int row_x     = row_dst % ne1;
     const int channel_x = row_dst / ne1;
 
-    const int idst = row_dst*ne0 + i0;
+    int       idst = row_dst * ne0 + i0;
     const int ix   = channel_x*s2 + row_x*s1 + i0;
 
-    if (i0 >= n_dims) {
-        dst[idst + 0] = x[ix + 0];
-        dst[idst + 1] = x[ix + 1];
+    // Fusion optimization: ROPE + VIEW + SET_ROWS.
+    // The rope output is viewed as a 1D tensor and offset based on a row index in row_indices.
+    if (set_rows_stride != 0) {
+        idst = row_x * ne0 + i0;
+        idst += row_indices[channel_x] * set_rows_stride;
+    }
 
+    const auto & store_coaelsced = [&](float x0, float x1) {
+        if constexpr (std::is_same_v<float, D>) {
+            float2 v = make_float2(x0, x1);
+            ggml_cuda_memcpy_1<8>(dst + idst, &v);
+        } else if constexpr (std::is_same_v<half, D>) {
+            half2 v = make_half2(x0, x1);
+            ggml_cuda_memcpy_1<4>(dst + idst, &v);
+        }
+    };
+    if (i0 >= n_dims) {
+        store_coaelsced(x[ix + 0], x[ix + 1]);
         return;
     }
 
@@ -75,15 +104,26 @@ static __global__ void rope_norm(
     const float x0 = x[ix + 0];
     const float x1 = x[ix + 1];
 
-    dst[idst + 0] = x0*cos_theta - x1*sin_theta;
-    dst[idst + 1] = x0*sin_theta + x1*cos_theta;
+    store_coaelsced(x0 * cos_theta - x1 * sin_theta, x0 * sin_theta + x1 * cos_theta);
 }
 
-template<bool forward, bool has_ff, typename T>
-static __global__ void rope_neox(
-        const T * x, T * dst, const int ne0, const int ne1, const int s1, const int s2, const int n_dims,
-        const int32_t * pos, const float freq_scale, const float ext_factor, const float attn_factor,
-        const rope_corr_dims corr_dims, const float theta_scale, const float * freq_factors) {
+template <bool forward, bool has_ff, typename T, typename D>
+static __global__ void rope_neox(const T *            x,
+                                 D *                  dst,
+                                 const int            ne0,
+                                 const int            ne1,
+                                 const int            s1,
+                                 const int            s2,
+                                 const int            n_dims,
+                                 const int32_t *      pos,
+                                 const float          freq_scale,
+                                 const float          ext_factor,
+                                 const float          attn_factor,
+                                 const rope_corr_dims corr_dims,
+                                 const float          theta_scale,
+                                 const float *        freq_factors,
+                                 const int64_t *      row_indices,
+                                 const int            set_rows_stride) {
     const int i0 = 2*(blockDim.y*blockIdx.y + threadIdx.y);
 
     if (i0 >= ne0) {
@@ -95,12 +135,19 @@ static __global__ void rope_neox(
     const int row_x     = row_dst % ne1;
     const int channel_x = row_dst / ne1;
 
-    const int idst = row_dst*ne0 + i0/2;
+    int       idst = row_dst * ne0 + i0 / 2;
     const int ix   = channel_x*s2 + row_x*s1 + i0/2;
 
+    // Fusion optimization: ROPE + VIEW + SET_ROWS.
+    // The rope output is viewed as a 1D tensor and offset based on a row index in row_indices.
+    if (set_rows_stride != 0) {
+        idst = row_x * ne0 + i0 / 2;
+        idst += row_indices[channel_x] * set_rows_stride;
+    }
+
     if (i0 >= n_dims) {
-        dst[idst + i0/2 + 0] = x[ix + i0/2 + 0];
-        dst[idst + i0/2 + 1] = x[ix + i0/2 + 1];
+        dst[idst + i0 / 2 + 0] = ggml_cuda_cast<D>(x[ix + i0 / 2 + 0]);
+        dst[idst + i0 / 2 + 1] = ggml_cuda_cast<D>(x[ix + i0 / 2 + 1]);
 
         return;
     }
@@ -117,8 +164,8 @@ static __global__ void rope_neox(
     const float x0 = x[ix + 0];
     const float x1 = x[ix + n_dims/2];
 
-    dst[idst + 0]        = x0*cos_theta - x1*sin_theta;
-    dst[idst + n_dims/2] = x0*sin_theta + x1*cos_theta;
+    dst[idst + 0]          = ggml_cuda_cast<D>(x0 * cos_theta - x1 * sin_theta);
+    dst[idst + n_dims / 2] = ggml_cuda_cast<D>(x0 * sin_theta + x1 * cos_theta);
 }
 
 template<bool forward, bool has_ff, typename T>
@@ -238,11 +285,25 @@ static __global__ void rope_vision(
     dst[idst + n_dims] = x0*sin_theta + x1*cos_theta;
 }
 
-template<bool forward, typename T>
-static void rope_norm_cuda(
-        const T * x, T * dst, const int ne0, const int ne1, const int s1, const int s2, const int n_dims, const int nr,
-        const int32_t * pos, const float freq_scale, const float freq_base, const float ext_factor, const float attn_factor,
-        const rope_corr_dims corr_dims, const float * freq_factors, cudaStream_t stream) {
+template <bool forward, typename T, typename D>
+static void rope_norm_cuda(const T *            x,
+                           D *                  dst,
+                           const int            ne0,
+                           const int            ne1,
+                           const int            s1,
+                           const int            s2,
+                           const int            n_dims,
+                           const int            nr,
+                           const int32_t *      pos,
+                           const float          freq_scale,
+                           const float          freq_base,
+                           const float          ext_factor,
+                           const float          attn_factor,
+                           const rope_corr_dims corr_dims,
+                           const float *        freq_factors,
+                           const int64_t *      row_indices,
+                           const int            set_rows_stride,
+                           cudaStream_t         stream) {
     GGML_ASSERT(ne0 % 2 == 0);
     const dim3 block_dims(1, CUDA_ROPE_BLOCK_SIZE, 1);
     const int n_blocks_x = (ne0 + 2*CUDA_ROPE_BLOCK_SIZE - 1) / (2*CUDA_ROPE_BLOCK_SIZE);
@@ -252,20 +313,34 @@ static void rope_norm_cuda(
 
     if (freq_factors == nullptr) {
         rope_norm<forward, false><<<block_nums, block_dims, 0, stream>>>(
-            x, dst, ne0, ne1, s1, s2, n_dims, pos, freq_scale, ext_factor,
-            attn_factor, corr_dims, theta_scale, freq_factors);
+            x, dst, ne0, ne1, s1, s2, n_dims, pos, freq_scale, ext_factor, attn_factor, corr_dims, theta_scale,
+            freq_factors, row_indices, set_rows_stride);
     } else {
         rope_norm<forward, true><<<block_nums, block_dims, 0, stream>>>(
-            x, dst, ne0, ne1, s1, s2, n_dims, pos, freq_scale, ext_factor,
-            attn_factor, corr_dims, theta_scale, freq_factors);
+            x, dst, ne0, ne1, s1, s2, n_dims, pos, freq_scale, ext_factor, attn_factor, corr_dims, theta_scale,
+            freq_factors, row_indices, set_rows_stride);
     }
 }
 
-template<bool forward, typename T>
-static void rope_neox_cuda(
-        const T * x, T * dst, const int ne0, const int ne1, const int s1, const int s2, const int n_dims, const int nr,
-        const int32_t * pos, const float freq_scale, const float freq_base, const float ext_factor, const float attn_factor,
-        const rope_corr_dims corr_dims, const float * freq_factors, cudaStream_t stream) {
+template <bool forward, typename T, typename D>
+static void rope_neox_cuda(const T *            x,
+                           D *                  dst,
+                           const int            ne0,
+                           const int            ne1,
+                           const int            s1,
+                           const int            s2,
+                           const int            n_dims,
+                           const int            nr,
+                           const int32_t *      pos,
+                           const float          freq_scale,
+                           const float          freq_base,
+                           const float          ext_factor,
+                           const float          attn_factor,
+                           const rope_corr_dims corr_dims,
+                           const float *        freq_factors,
+                           const int64_t *      row_indices,
+                           const int            set_rows_stride,
+                           cudaStream_t         stream) {
     GGML_ASSERT(ne0 % 2 == 0);
     const dim3 block_dims(1, CUDA_ROPE_BLOCK_SIZE, 1);
     const int n_blocks_x = (ne0 + 2*CUDA_ROPE_BLOCK_SIZE - 1) / (2*CUDA_ROPE_BLOCK_SIZE);
@@ -274,13 +349,13 @@ static void rope_neox_cuda(
     const float theta_scale = powf(freq_base, -2.0f/n_dims);
 
     if (freq_factors == nullptr) {
-        rope_neox<forward, false, T><<<block_nums, block_dims, 0, stream>>>(
-            x, dst, ne0, ne1, s1, s2, n_dims, pos, freq_scale, ext_factor,
-            attn_factor, corr_dims, theta_scale, freq_factors);
+        rope_neox<forward, false><<<block_nums, block_dims, 0, stream>>>(
+            x, dst, ne0, ne1, s1, s2, n_dims, pos, freq_scale, ext_factor, attn_factor, corr_dims, theta_scale,
+            freq_factors, row_indices, set_rows_stride);
     } else {
-        rope_neox<forward, true, T><<<block_nums, block_dims, 0, stream>>>(
-            x, dst, ne0, ne1, s1, s2, n_dims, pos, freq_scale, ext_factor,
-            attn_factor, corr_dims, theta_scale, freq_factors);
+        rope_neox<forward, true><<<block_nums, block_dims, 0, stream>>>(
+            x, dst, ne0, ne1, s1, s2, n_dims, pos, freq_scale, ext_factor, attn_factor, corr_dims, theta_scale,
+            freq_factors, row_indices, set_rows_stride);
     }
 }
 
@@ -333,7 +408,9 @@ static void rope_vision_cuda(
 }
 
 template <bool forward>
-void ggml_cuda_op_rope_impl(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+void ggml_cuda_op_rope_impl(ggml_backend_cuda_context & ctx,
+                            ggml_tensor *               dst,
+                            const ggml_tensor *         set_rows = nullptr) {
     const ggml_tensor * src0 = dst->src[0];
     const ggml_tensor * src1 = dst->src[1];
     const ggml_tensor * src2 = dst->src[2];
@@ -341,12 +418,25 @@ void ggml_cuda_op_rope_impl(ggml_backend_cuda_context & ctx, ggml_tensor * dst)
     const float * src0_d = (const float *)src0->data;
     const float * src1_d = (const float *)src1->data;
 
-    float * dst_d = (float *)dst->data;
+    void *          dst_d           = dst->data;
+    const int64_t * row_indices     = nullptr;
+    ggml_type       dst_type        = dst->type;
+    int             set_rows_stride = 0;
+
+    if (set_rows != nullptr) {
+        GGML_ASSERT(forward);
+        dst_d           = set_rows->data;
+        row_indices     = (const int64_t *) set_rows->src[1]->data;
+        dst_type        = set_rows->type;
+        set_rows_stride = set_rows->nb[1] / ggml_type_size(set_rows->type);
+    }
     cudaStream_t stream = ctx.stream();
 
     GGML_ASSERT(src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16);
     GGML_ASSERT( dst->type == GGML_TYPE_F32 ||  dst->type == GGML_TYPE_F16);
-    GGML_ASSERT(src0->type == dst->type);
+    // When not fused, src0 and dst types must match
+    // When fused (ROPE+VIEW+SET_ROWS), src0 may be F32 and dst may be F16
+    GGML_ASSERT(src0->type == dst->type || (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F16));
 
     const int64_t ne00 = src0->ne[0]; // head dims
     const int64_t ne01 = src0->ne[1]; // num heads
@@ -404,14 +494,18 @@ void ggml_cuda_op_rope_impl(ggml_backend_cuda_context & ctx, ggml_tensor * dst)
 
     // compute
     if (is_neox) {
-        if (src0->type == GGML_TYPE_F32) {
-            rope_neox_cuda<forward>(
-                (const float *) src0_d, (float *) dst_d, ne00, ne01, s01, s02, n_dims, nr, pos, freq_scale,
-                freq_base, ext_factor, attn_factor, corr_dims, freq_factors, stream);
-        } else if (src0->type == GGML_TYPE_F16) {
-            rope_neox_cuda<forward>(
-                (const half *) src0_d, (half *) dst_d, ne00, ne01, s01, s02, n_dims, nr, pos, freq_scale,
-                freq_base, ext_factor, attn_factor, corr_dims, freq_factors, stream);
+        if (src0->type == GGML_TYPE_F32 && dst_type == GGML_TYPE_F32) {
+            rope_neox_cuda<forward, float, float>((const float *) src0_d, (float *) dst_d, ne00, ne01, s01, s02, n_dims,
+                                                  nr, pos, freq_scale, freq_base, ext_factor, attn_factor, corr_dims,
+                                                  freq_factors, row_indices, set_rows_stride, stream);
+        } else if (src0->type == GGML_TYPE_F32 && dst_type == GGML_TYPE_F16) {
+            rope_neox_cuda<forward, float, half>((const float *) src0_d, (half *) dst_d, ne00, ne01, s01, s02, n_dims,
+                                                 nr, pos, freq_scale, freq_base, ext_factor, attn_factor, corr_dims,
+                                                 freq_factors, row_indices, set_rows_stride, stream);
+        } else if (src0->type == GGML_TYPE_F16 && dst_type == GGML_TYPE_F16) {
+            rope_neox_cuda<forward, half, half>((const half *) src0_d, (half *) dst_d, ne00, ne01, s01, s02, n_dims, nr,
+                                                pos, freq_scale, freq_base, ext_factor, attn_factor, corr_dims,
+                                                freq_factors, row_indices, set_rows_stride, stream);
         } else {
             GGML_ABORT("fatal error");
         }
@@ -440,14 +534,18 @@ void ggml_cuda_op_rope_impl(ggml_backend_cuda_context & ctx, ggml_tensor * dst)
             GGML_ABORT("fatal error");
         }
     } else {
-        if (src0->type == GGML_TYPE_F32) {
-            rope_norm_cuda<forward>(
-                (const float *) src0_d, (float *) dst_d, ne00, ne01, s01, s02, n_dims, nr, pos, freq_scale,
-                freq_base, ext_factor, attn_factor, corr_dims, freq_factors, stream);
-        } else if (src0->type == GGML_TYPE_F16) {
-            rope_norm_cuda<forward>(
-                (const half *) src0_d, (half *) dst_d, ne00, ne01, s01, s02, n_dims, nr, pos, freq_scale,
-                freq_base, ext_factor, attn_factor, corr_dims, freq_factors, stream);
+        if (src0->type == GGML_TYPE_F32 && dst_type == GGML_TYPE_F32) {
+            rope_norm_cuda<forward, float, float>((const float *) src0_d, (float *) dst_d, ne00, ne01, s01, s02, n_dims,
+                                                  nr, pos, freq_scale, freq_base, ext_factor, attn_factor, corr_dims,
+                                                  freq_factors, row_indices, set_rows_stride, stream);
+        } else if (src0->type == GGML_TYPE_F32 && dst_type == GGML_TYPE_F16) {
+            rope_norm_cuda<forward, float, half>((const float *) src0_d, (half *) dst_d, ne00, ne01, s01, s02, n_dims,
+                                                 nr, pos, freq_scale, freq_base, ext_factor, attn_factor, corr_dims,
+                                                 freq_factors, row_indices, set_rows_stride, stream);
+        } else if (src0->type == GGML_TYPE_F16 && dst_type == GGML_TYPE_F16) {
+            rope_norm_cuda<forward, half, half>((const half *) src0_d, (half *) dst_d, ne00, ne01, s01, s02, n_dims, nr,
+                                                pos, freq_scale, freq_base, ext_factor, attn_factor, corr_dims,
+                                                freq_factors, row_indices, set_rows_stride, stream);
         } else {
             GGML_ABORT("fatal error");
         }
@@ -461,3 +559,7 @@ void ggml_cuda_op_rope(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
 void ggml_cuda_op_rope_back(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
     ggml_cuda_op_rope_impl<false>(ctx, dst);
 }
+
+void ggml_cuda_op_rope_fused(ggml_backend_cuda_context & ctx, ggml_tensor * rope, ggml_tensor * set_rows) {
+    ggml_cuda_op_rope_impl<true>(ctx, rope, set_rows);
+}

ggml/src/ggml-cuda/rope.cuh

@@ -5,3 +5,5 @@
 void ggml_cuda_op_rope(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
 
 void ggml_cuda_op_rope_back(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
+
+void ggml_cuda_op_rope_fused(ggml_backend_cuda_context & ctx, ggml_tensor * dst, ggml_tensor * set_rows);

ggml/src/ggml-cuda/unary.cu

@@ -81,6 +81,14 @@ static __device__ __forceinline__ float op_log(float x) {
     return logf(x);
 }
 
+static __device__ __forceinline__ float op_expm1(float x) {
+    return expm1f(x);
+}
+
+static __device__ __forceinline__ float op_softplus(float x) {
+    return (x > 20.0f) ? x : logf(1.0f + expf(x));
+}
+
 static __device__ __forceinline__ float op_elu(float x) {
     return (x > 0.f) ? x : expm1f(x);
 }
@@ -233,6 +241,14 @@ void ggml_cuda_op_round(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
 void ggml_cuda_op_trunc(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
     ggml_cuda_op_unary<op_trunc>(ctx, dst);
 }
+
+void ggml_cuda_op_expm1(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    ggml_cuda_op_unary<op_expm1>(ctx, dst);
+}
+
+void ggml_cuda_op_softplus(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    ggml_cuda_op_unary<op_softplus>(ctx, dst);
+}
 /* gated ops */
 
 template <float (*op)(float), typename T>

ggml/src/ggml-cuda/unary.cuh

@@ -61,6 +61,10 @@ void ggml_cuda_op_cos(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
 
 void ggml_cuda_op_log(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
 
+void ggml_cuda_op_expm1(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
+
+void ggml_cuda_op_softplus(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
+
 void ggml_cuda_op_elu(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
 
 void ggml_cuda_op_floor(ggml_backend_cuda_context & ctx, ggml_tensor * dst);

ggml/src/ggml-hexagon/ggml-hexagon.cpp

@@ -3156,26 +3156,17 @@ static inline bool op_reuse_src1(const ggml_tensor * op1, const ggml_tensor * op
     return (op0 && op0->src[1] == op1->src[1]);
 }
 
+static inline bool is_compute_op(ggml_tensor *node)
+{
+    return !(ggml_op_is_empty(node->op) || ggml_is_empty(node));
+}
+
 // scan the graph and figure out last compute op index
 static inline int last_compute_op(ggml_cgraph * graph) {
-    int last;
+    int last = 0;
     for (int i = 0; i < graph->n_nodes; ++i) {
-        ggml_tensor * node = graph->nodes[i];
-
-        switch (node->op) {
-            case GGML_OP_MUL_MAT:
-            case GGML_OP_MUL_MAT_ID:
-            case GGML_OP_MUL:
-            case GGML_OP_ADD:
-            case GGML_OP_SUB:
-            case GGML_OP_RMS_NORM:
-            case GGML_OP_GLU:
-            case GGML_OP_ADD_ID:
-                last = i;
-                break;
-
-            default:
-                break;
+        if (is_compute_op(graph->nodes[i])) {
+            last = i;
         }
     }
 
@@ -3194,6 +3185,10 @@ static ggml_status ggml_backend_hexagon_graph_compute(ggml_backend_t backend, gg
     for (int i = 0; i < graph->n_nodes; ++i) {
         ggml_tensor * node = graph->nodes[i];
 
+        if (!is_compute_op(node)) {
+            continue;
+        }
+
         uint32_t flags = 0;
 
         // skip quantizer if src1 is reused
@@ -3245,14 +3240,6 @@ static ggml_status ggml_backend_hexagon_graph_compute(ggml_backend_t backend, gg
                 ggml_hexagon_rope(node, flags);
                 break;
 
-            // non-compute ops
-            case GGML_OP_NONE:
-            case GGML_OP_RESHAPE:
-            case GGML_OP_VIEW:
-            case GGML_OP_PERMUTE:
-            case GGML_OP_TRANSPOSE:
-                break;
-
             default:
                 GGML_ABORT("\nggml-hex: graph-compute %s is not supported\n", ggml_op_desc(node));
         }

ggml/src/ggml-hexagon/htp/binary-ops.c

@@ -34,6 +34,11 @@ static hvx_elemwise_f32_func func_table_HVX[]     = { hvx_mul_f32, hvx_add_f32,
 static hvx_elemwise_f32_func func_table_HVX_opt[] = { hvx_mul_f32_opt, hvx_add_f32_opt, hvx_sub_f32_opt };
 
 #define htp_binary_preamble            \
+    const struct htp_tensor * src0 = &octx->src0; \
+    const struct htp_tensor * src1 = &octx->src1; \
+    const struct htp_tensor * src2 = &octx->src2; \
+    struct htp_tensor *       dst  = &octx->dst;  \
+                                       \
     const uint32_t ne00 = src0->ne[0]; \
     const uint32_t ne01 = src0->ne[1]; \
     const uint32_t ne02 = src0->ne[2]; \
@@ -62,16 +67,15 @@ static hvx_elemwise_f32_func func_table_HVX_opt[] = { hvx_mul_f32_opt, hvx_add_f
     const uint32_t nb0 = dst->nb[0];   \
     const uint32_t nb1 = dst->nb[1];   \
     const uint32_t nb2 = dst->nb[2];   \
-    const uint32_t nb3 = dst->nb[3];
+    const uint32_t nb3 = dst->nb[3];   \
+                                       \
+    const uint32_t src0_nrows_per_thread = octx->src0_nrows_per_thread;
 
-static void binary_job_f32_per_thread(const struct htp_tensor * src0,
-                                      const struct htp_tensor * src1,
-                                      struct htp_tensor *       dst,
-                                      uint8_t *                 spad_data,
-                                      uint32_t                  nth,
-                                      uint32_t                  ith,
-                                      uint32_t                  src0_nrows_per_thread,
-                                      enum htp_op               op) {
+static void binary_job_f32_per_thread(struct htp_ops_context * octx,
+                                      uint8_t *                spad_data,
+                                      uint32_t                 nth,
+                                      uint32_t                 ith,
+                                      enum htp_op              op) {
     htp_binary_preamble;
 
     const size_t src0_row_size = nb01;
@@ -107,16 +111,23 @@ static void binary_job_f32_per_thread(const struct htp_tensor * src0,
 
     uint8_t * restrict spad_data_th = spad_data + (ith * src0_row_size);
 
-    const uint32_t nr0 = ne00 / ne10;
-
     const uint8_t * restrict src0_ptr = (const uint8_t *) src0->data + (src0_start_row * src0_row_size);
     uint8_t * restrict dst_ptr        = (uint8_t *) dst->data + (src0_start_row * dst_row_size);
 
     const uint8_t * restrict data_src1 = (const uint8_t *) src1->data;
-    const uint8_t * restrict src1_ptr  = NULL;
+
+    const uint32_t ne02_ne01 = ne02 * ne01;
 
     for (uint32_t ir = src0_start_row; ir < src0_end_row; ir++) {
-        src1_ptr = data_src1 + (ir % src1_nrows) * src1_row_size;
+        const uint32_t i03 = fastdiv(ir, &octx->src0_div21);
+        const uint32_t i02 = fastdiv(ir - i03 * ne02_ne01, &octx->src0_div1);
+        const uint32_t i01 = (ir - i03 * ne02_ne01 - i02 * ne01);
+
+        const uint32_t i13 = fastmodulo(i03, ne13, &octx->src1_div3);
+        const uint32_t i12 = fastmodulo(i02, ne12, &octx->src1_div2);
+        const uint32_t i11 = fastmodulo(i01, ne11, &octx->src1_div1);
+
+        const uint8_t * restrict src1_ptr = data_src1 + i13 * nb13 + i12 * nb12 + i11 * src1_row_size;
 
         if (ir + 1 < src0_end_row) {
             htp_l2fetch(src0_ptr + ne00, 1, src0_row_size, src0_row_size);
@@ -125,6 +136,7 @@ static void binary_job_f32_per_thread(const struct htp_tensor * src0,
             }
         }
 
+        const uint32_t nr0 = ne00 / ne10;
         if (nr0 > 1) {
             if ((1 == is_aligned) && (nr0 == ne00)) {
                 hvx_bcast_fp32_a(spad_data_th, *(float *) src1_ptr, nr0);
@@ -149,22 +161,17 @@ static void binary_job_f32_per_thread(const struct htp_tensor * src0,
          (unsigned) HAP_perf_qtimer_count_to_us(t2 - t1));
 }
 
-static void binary_add_id_job_f32_per_thread(const struct htp_tensor * src0,
-                                             const struct htp_tensor * src1,
-                                             const struct htp_tensor * src2,
-                                             struct htp_tensor *       dst,
-                                             uint8_t *                 spad_data,
-                                             uint32_t                  nth,
-                                             uint32_t                  ith,
-                                             uint32_t                  src0_nrows_per_thread,
-                                             hvx_elemwise_f32_func     func_HVX) {
+static void binary_add_id_job_f32_per_thread(struct htp_ops_context * octx,
+                                             uint8_t *                spad_data,
+                                             uint32_t                 nth,
+                                             uint32_t                 ith,
+                                             hvx_elemwise_f32_func    func_HVX) {
     htp_binary_preamble;
 
     const size_t src0_row_size = nb01;
     const size_t src1_row_size = nb11;
     const size_t dst_row_size  = nb1;
 
-    const uint32_t ne02_ne01  = ne02 * ne01;
     const uint32_t src0_nrows = ne01 * ne02 * ne03;  // src0 rows
 
     const uint32_t src0_start_row = src0_nrows_per_thread * ith;
@@ -187,10 +194,11 @@ static void binary_add_id_job_f32_per_thread(const struct htp_tensor * src0,
     const uint8_t * restrict data_src1 = (const uint8_t *) src1->data;
     uint8_t * restrict data_dst        = (uint8_t *) dst->data;
 
+    const uint32_t ne02_ne01  = ne02 * ne01;
     for (uint32_t ir = src0_start_row; ir < src0_end_row; ir++) {
         // src0 indices
-        const uint32_t i03 = ir / ne02_ne01;
-        const uint32_t i02 = (ir - i03 * ne02_ne01) / ne01;
+        const uint32_t i03 = fastdiv(ir, &octx->src0_div21);
+        const uint32_t i02 = fastdiv(ir - i03 * ne02_ne01, &octx->src0_div1);
         const uint32_t i01 = (ir - i03 * ne02_ne01 - i02 * ne01);
 
         // src1 indices
@@ -234,13 +242,11 @@ static void binary_job_dispatcher_f32(unsigned int n, unsigned int i, void * dat
         case HTP_OP_MUL:
         case HTP_OP_ADD:
         case HTP_OP_SUB:
-            binary_job_f32_per_thread(&octx->src0, &octx->src1, &octx->dst, octx->src1_spad.data, n, i,
-                                      octx->src0_nrows_per_thread, octx->op);
+            binary_job_f32_per_thread(octx, octx->src1_spad.data, n, i, octx->op);
             break;
 
         case HTP_OP_ADD_ID:
-            binary_add_id_job_f32_per_thread(&octx->src0, &octx->src1, &octx->src2, &octx->dst, octx->src0_spad.data, n,
-                                             i, octx->src0_nrows_per_thread, hvx_add_f32);
+            binary_add_id_job_f32_per_thread(octx, octx->src0_spad.data, n, i, hvx_add_f32);
             break;
 
         default:
@@ -321,6 +327,16 @@ static int execute_op_binary_f32(struct htp_ops_context * octx) {
 
         octx->src0_nrows_per_thread = (src0_nrows + n_jobs - 1) / n_jobs;
 
+        octx->src0_div21 = init_fastdiv_values(src0->ne[2] * src0->ne[1]);
+        octx->src0_div3  = init_fastdiv_values(src0->ne[3]);
+        octx->src0_div2  = init_fastdiv_values(src0->ne[2]);
+        octx->src0_div1  = init_fastdiv_values(src0->ne[1]);
+
+        octx->src1_div21 = init_fastdiv_values(src1->ne[2] * src1->ne[1]);
+        octx->src1_div3  = init_fastdiv_values(src1->ne[3]);
+        octx->src1_div2  = init_fastdiv_values(src1->ne[2]);
+        octx->src1_div1  = init_fastdiv_values(src1->ne[1]);
+
         worker_pool_run_func(octx->ctx->worker_pool, binary_op_func, octx, n_jobs);
     }
 

ggml/src/ggml-hexagon/htp/htp-msg.h

@@ -119,10 +119,10 @@ static const char * htp_type_name(uint32_t t) {
 #define HTP_MAX_DIMS 4
 
 struct htp_tensor {
-    uint32_t data;              // Buffer offset in the messages, and data pointer on the NSP
-    uint32_t type;              // Data type
-    uint32_t ne[HTP_MAX_DIMS];  // Number of elements
-    uint32_t nb[HTP_MAX_DIMS];  // Stride in bytes (see ggml.h ggml_tensor)
+    uint32_t data;                // Buffer offset in the messages, and data pointer on the NSP
+    uint32_t type;                // Data type
+    uint32_t ne[HTP_MAX_DIMS];    // Number of elements
+    uint32_t nb[HTP_MAX_DIMS];    // Stride in bytes (see ggml.h ggml_tensor)
 };
 
 #define HTP_MAX_OP_PARAMS 64

ggml/src/ggml-hexagon/htp/htp-ops.h

@@ -4,6 +4,7 @@
 #include "htp-ctx.h"
 #include "htp-msg.h"
 #include "worker-pool.h"
+#include "ops-utils.h"
 
 #include <assert.h>
 #include <stdint.h>
@@ -38,6 +39,16 @@ struct htp_ops_context {
     uint32_t src0_nrows_per_thread;
     uint32_t src1_nrows_per_thread;
 
+    struct fastdiv_values src0_div1;  // fastdiv values for ne1
+    struct fastdiv_values src0_div2;  // fastdiv values for ne2
+    struct fastdiv_values src0_div3;  // fastdiv values for ne3
+    struct fastdiv_values src0_div21; // fastdiv values for ne2 * ne1
+
+    struct fastdiv_values src1_div1;  // fastdiv values for ne1
+    struct fastdiv_values src1_div2;  // fastdiv values for ne2
+    struct fastdiv_values src1_div3;  // fastdiv values for ne3
+    struct fastdiv_values src1_div21; // fastdiv values for ne2 * ne1
+
     uint32_t flags;
 };
 

ggml/src/ggml-hexagon/htp/ops-utils.h

@@ -31,6 +31,39 @@ static inline uint32_t htp_round_up(uint32_t n, uint32_t m) {
     return m * ((n + m - 1) / m);
 }
 
+// See https://gmplib.org/~tege/divcnst-pldi94.pdf figure 4.1.
+// Precompute mp (m' in the paper) and L such that division
+// can be computed using a multiply (high 32b of 64b result)
+// and a shift:
+//
+// n/d = (mulhi(n, mp) + n) >> L;
+struct fastdiv_values {
+    uint32_t mp;
+    uint32_t l;
+};
+
+static inline struct fastdiv_values init_fastdiv_values(uint32_t d) {
+    struct fastdiv_values result = { 0, 0 };
+    // compute L = ceil(log2(d));
+    while (result.l < 32 && ((uint32_t) 1 << result.l) < d) {
+        ++(result.l);
+    }
+
+    result.mp = (uint32_t) (((uint64_t) 1 << 32) * (((uint64_t) 1 << result.l) - d) / d + 1);
+    return result;
+}
+
+static inline uint32_t fastdiv(uint32_t n, const struct fastdiv_values * vals) {
+    // Compute high 32 bits of n * mp
+    const uint32_t hi = (uint32_t) (((uint64_t) n * vals->mp) >> 32);  // mulhi(n, mp)
+    // add n, apply bit shift
+    return (hi + n) >> vals->l;
+}
+
+static inline uint32_t fastmodulo(uint32_t n, uint32_t d, const struct fastdiv_values * vals) {
+    return n - fastdiv(n, vals) * d;
+}
+
 static inline void htp_l2fetch(const void * p, uint32_t height, uint32_t width, uint32_t stride) {
     const uint64_t control = Q6_P_combine_RR(stride, Q6_R_combine_RlRl(width, height));
     asm volatile(" l2fetch(%0,%1) " : : "r"(p), "r"(control));

ggml/src/ggml-impl.h

@@ -102,7 +102,7 @@ static bool ggml_op_is_empty(enum ggml_op op) {
     }
 }
 
-static inline float ggml_softplus(float input) {
+static inline float ggml_compute_softplus_f32(float input) {
     return (input > 20.0f) ? input : logf(1 + expf(input));
 }
 //

ggml/src/ggml-metal/ggml-metal-device.cpp

@@ -943,6 +943,34 @@ ggml_metal_pipeline_t ggml_metal_library_get_pipeline_argsort(ggml_metal_library
     return res;
 }
 
+ggml_metal_pipeline_t ggml_metal_library_get_pipeline_argsort_merge(ggml_metal_library_t lib, const ggml_tensor * op) {
+    assert(op->op == GGML_OP_ARGSORT);
+
+    char base[256];
+    char name[256];
+
+    ggml_sort_order order = (ggml_sort_order) op->op_params[0];
+
+    const char * order_str = "undefined";
+    switch (order) {
+        case GGML_SORT_ORDER_ASC:  order_str = "asc";  break;
+        case GGML_SORT_ORDER_DESC: order_str = "desc"; break;
+        default: GGML_ABORT("fatal error");
+    };
+
+    snprintf(base, 256, "kernel_argsort_merge_%s_%s_%s", ggml_type_name(op->src[0]->type), ggml_type_name(op->type), order_str);
+    snprintf(name, 256, "%s", base);
+
+    ggml_metal_pipeline_t res = ggml_metal_library_get_pipeline(lib, name);
+    if (res) {
+        return res;
+    }
+
+    res = ggml_metal_library_compile_pipeline(lib, base, name, nullptr);
+
+    return res;
+}
+
 ggml_metal_pipeline_t ggml_metal_library_get_pipeline_flash_attn_ext_pad(
         ggml_metal_library_t lib,
         const struct ggml_tensor * op,
@@ -1438,6 +1466,30 @@ ggml_metal_pipeline_t ggml_metal_library_get_pipeline_conv_transpose_2d(ggml_met
     return res;
 }
 
+ggml_metal_pipeline_t ggml_metal_library_get_pipeline_conv_2d(ggml_metal_library_t lib, const ggml_tensor * op) {
+    assert(op->op == GGML_OP_CONV_2D);
+
+    GGML_ASSERT(ggml_is_contiguous(op->src[0]));
+    GGML_ASSERT(op->src[0]->type == GGML_TYPE_F16 || op->src[0]->type == GGML_TYPE_F32);
+    GGML_ASSERT(op->src[1]->type == GGML_TYPE_F32);
+    GGML_ASSERT(op->type         == GGML_TYPE_F32);
+
+    char base[256];
+    char name[256];
+
+    snprintf(base, 256, "kernel_conv_2d_%s_%s", ggml_type_name(op->src[0]->type), ggml_type_name(op->src[1]->type));
+    snprintf(name, 256, "%s", base);
+
+    ggml_metal_pipeline_t res = ggml_metal_library_get_pipeline(lib, name);
+    if (res) {
+        return res;
+    }
+
+    res = ggml_metal_library_compile_pipeline(lib, base, name, nullptr);
+
+    return res;
+}
+
 ggml_metal_pipeline_t ggml_metal_library_get_pipeline_upscale(ggml_metal_library_t lib, const ggml_tensor * op) {
     assert(op->op == GGML_OP_UPSCALE);
 

ggml/src/ggml-metal/ggml-metal-device.h

@@ -125,6 +125,7 @@ ggml_metal_pipeline_t ggml_metal_library_get_pipeline_mul_mm_id         (ggml_me
 ggml_metal_pipeline_t ggml_metal_library_get_pipeline_mul_mv_id         (ggml_metal_library_t lib, const struct ggml_tensor * op);
 ggml_metal_pipeline_t ggml_metal_library_get_pipeline_argmax            (ggml_metal_library_t lib, const struct ggml_tensor * op);
 ggml_metal_pipeline_t ggml_metal_library_get_pipeline_argsort           (ggml_metal_library_t lib, const struct ggml_tensor * op);
+ggml_metal_pipeline_t ggml_metal_library_get_pipeline_argsort_merge     (ggml_metal_library_t lib, const struct ggml_tensor * op);
 ggml_metal_pipeline_t ggml_metal_library_get_pipeline_bin               (ggml_metal_library_t lib, enum ggml_op op, int32_t n_fuse, bool row);
 ggml_metal_pipeline_t ggml_metal_library_get_pipeline_l2_norm           (ggml_metal_library_t lib, const struct ggml_tensor * op);
 ggml_metal_pipeline_t ggml_metal_library_get_pipeline_group_norm        (ggml_metal_library_t lib, const struct ggml_tensor * op);
@@ -133,6 +134,7 @@ ggml_metal_pipeline_t ggml_metal_library_get_pipeline_rope              (ggml_me
 ggml_metal_pipeline_t ggml_metal_library_get_pipeline_im2col            (ggml_metal_library_t lib, const struct ggml_tensor * op);
 ggml_metal_pipeline_t ggml_metal_library_get_pipeline_conv_transpose_1d (ggml_metal_library_t lib, const struct ggml_tensor * op);
 ggml_metal_pipeline_t ggml_metal_library_get_pipeline_conv_transpose_2d (ggml_metal_library_t lib, const struct ggml_tensor * op);
+ggml_metal_pipeline_t ggml_metal_library_get_pipeline_conv_2d           (ggml_metal_library_t lib, const struct ggml_tensor * op);
 ggml_metal_pipeline_t ggml_metal_library_get_pipeline_upscale           (ggml_metal_library_t lib, const struct ggml_tensor * op);
 ggml_metal_pipeline_t ggml_metal_library_get_pipeline_pad               (ggml_metal_library_t lib, const struct ggml_tensor * op);
 ggml_metal_pipeline_t ggml_metal_library_get_pipeline_pad_reflect_1d    (ggml_metal_library_t lib, const struct ggml_tensor * op);

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

@@ -885,6 +885,11 @@ bool ggml_metal_device_supports_op(ggml_metal_device_t dev, const struct ggml_te
             return true;
         case GGML_OP_IM2COL:
             return ggml_is_contiguous(op->src[1]) && op->src[1]->type == GGML_TYPE_F32 && (op->type == GGML_TYPE_F16 || op->type == GGML_TYPE_F32);
+        case GGML_OP_CONV_2D:
+            return ggml_is_contiguous(op->src[0]) &&
+                   op->src[1]->type == GGML_TYPE_F32 &&
+                   op->type == GGML_TYPE_F32 &&
+                   (op->src[0]->type == GGML_TYPE_F16 || op->src[0]->type == GGML_TYPE_F32);
         case GGML_OP_POOL_1D:
             return false;
         case GGML_OP_UPSCALE:
@@ -899,8 +904,6 @@ bool ggml_metal_device_supports_op(ggml_metal_device_t dev, const struct ggml_te
         case GGML_OP_LEAKY_RELU:
             return op->src[0]->type == GGML_TYPE_F32;
         case GGML_OP_ARGSORT:
-            // TODO: Support arbitrary column width
-            return op->src[0]->ne[0] <= 1024;
         case GGML_OP_ARANGE:
             return true;
         case GGML_OP_FLASH_ATTN_EXT:

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

@@ -528,6 +528,36 @@ typedef struct {
     uint64_t nb2;
 } ggml_metal_kargs_conv_transpose_2d;
 
+typedef struct {
+    uint64_t nb00;
+    uint64_t nb01;
+    uint64_t nb02;
+    uint64_t nb03;
+    uint64_t nb10;
+    uint64_t nb11;
+    uint64_t nb12;
+    uint64_t nb13;
+    uint64_t nb0;
+    uint64_t nb1;
+    uint64_t nb2;
+    uint64_t nb3;
+    int32_t  IW;
+    int32_t  IH;
+    int32_t  KW;
+    int32_t  KH;
+    int32_t  IC;
+    int32_t  OC;
+    int32_t  OW;
+    int32_t  OH;
+    int32_t  N;
+    int32_t  s0;
+    int32_t  s1;
+    int32_t  p0;
+    int32_t  p1;
+    int32_t  d0;
+    int32_t  d1;
+} ggml_metal_kargs_conv_2d;
+
 typedef struct {
     uint64_t  ofs0;
     uint64_t  ofs1;
@@ -763,10 +793,28 @@ typedef struct {
 } ggml_metal_kargs_leaky_relu;
 
 typedef struct {
-    int64_t  ncols;
-    int64_t  ncols_pad;
+    int64_t  ne00;
+    int64_t  ne01;
+    int64_t  ne02;
+    int64_t  ne03;
+    uint64_t nb00;
+    uint64_t nb01;
+    uint64_t nb02;
+    uint64_t nb03;
 } ggml_metal_kargs_argsort;
 
+typedef struct {
+    int64_t  ne00;
+    int64_t  ne01;
+    int64_t  ne02;
+    int64_t  ne03;
+    uint64_t nb00;
+    uint64_t nb01;
+    uint64_t nb02;
+    uint64_t nb03;
+    int32_t  len;
+} ggml_metal_kargs_argsort_merge;
+
 typedef struct {
     int64_t  ne0;
     float    start;

ggml/src/ggml-metal/ggml-metal-ops.cpp

@@ -10,6 +10,7 @@
 
 #include <cassert>
 #include <algorithm>
+#include <limits>
 
 static ggml_metal_buffer_id ggml_metal_get_buffer_id(const ggml_tensor * t) {
     if (!t) {
@@ -364,6 +365,10 @@ static int ggml_metal_op_encode_impl(ggml_metal_op_t ctx, int idx) {
             {
                 n_fuse = ggml_metal_op_im2col(ctx, idx);
             } break;
+        case GGML_OP_CONV_2D:
+            {
+                n_fuse = ggml_metal_op_conv_2d(ctx, idx);
+            } break;
         case GGML_OP_CONV_TRANSPOSE_1D:
             {
                 n_fuse = ggml_metal_op_conv_transpose_1d(ctx, idx);
@@ -1036,11 +1041,6 @@ int ggml_metal_op_set_rows(ggml_metal_op_t ctx, int idx) {
 
     nth = std::min(nth, nk0);
 
-    if (nth*nrptg > ggml_metal_pipeline_max_theads_per_threadgroup(pipeline)) {
-        nth = ggml_metal_pipeline_max_theads_per_threadgroup(pipeline);
-        nrptg = 1;
-    }
-
     ggml_metal_kargs_set_rows args = {
         /*.nk0  =*/ nk0,
         /*.ne01 =*/ ne01,
@@ -1975,7 +1975,9 @@ size_t ggml_metal_op_flash_attn_ext_extra_pad(const ggml_tensor * op) {
     const bool has_mask = op->src[3] != nullptr;
 
     if (ggml_metal_op_flash_attn_ext_use_vec(op)) {
-        const bool has_kvpad = ne11 % OP_FLASH_ATTN_EXT_VEC_NCPSG != 0;
+        // note: always reserve the padding space to avoid graph reallocations
+        //const bool has_kvpad = ne11 % OP_FLASH_ATTN_EXT_VEC_NCPSG != 0;
+        const bool has_kvpad = true;
 
         if (has_kvpad) {
             res += OP_FLASH_ATTN_EXT_VEC_NCPSG*(
@@ -1984,7 +1986,8 @@ size_t ggml_metal_op_flash_attn_ext_extra_pad(const ggml_tensor * op) {
                 (has_mask ? ggml_type_size(GGML_TYPE_F16)*ne31*ne32*ne33 : 0));
         }
     } else {
-        const bool has_kvpad = ne11 % OP_FLASH_ATTN_EXT_NCPSG != 0;
+        //const bool has_kvpad = ne11 % OP_FLASH_ATTN_EXT_NCPSG != 0;
+        const bool has_kvpad = true;
 
         if (has_kvpad) {
             res += OP_FLASH_ATTN_EXT_NCPSG*(
@@ -2020,9 +2023,10 @@ size_t ggml_metal_op_flash_attn_ext_extra_blk(const ggml_tensor * op) {
     const bool is_vec = ggml_metal_op_flash_attn_ext_use_vec(op);
 
     // this optimization is not useful for the vector kernels
-    if (is_vec) {
-        return res;
-    }
+    // note: always reserve the blk buffer to avoid graph reallocations
+    //if (is_vec) {
+    //    return res;
+    //}
 
     const int nqptg = is_vec ? OP_FLASH_ATTN_EXT_VEC_NQPTG : OP_FLASH_ATTN_EXT_NQPTG;
     const int ncpsg = is_vec ? OP_FLASH_ATTN_EXT_VEC_NCPSG : OP_FLASH_ATTN_EXT_NCPSG;
@@ -2049,13 +2053,16 @@ size_t ggml_metal_op_flash_attn_ext_extra_tmp(const ggml_tensor * op) {
 
     size_t res = 0;
 
-    if (ggml_metal_op_flash_attn_ext_use_vec(op)) {
+    // note: always reserve the temp buffer to avoid graph reallocations
+    //if (ggml_metal_op_flash_attn_ext_use_vec(op)) {
+    if (true) {
         const int64_t nwg = 32;
+        const int64_t ne01_max = std::min(ne01, 32);
 
         // temp buffer for writing the results from each workgroup
         // - ne20: the size of the Value head
         // -  + 2: the S and M values for each intermediate result
-        res += ggml_type_size(GGML_TYPE_F32)*(ne01*ne02*ne03*nwg*(ne20 + 2));
+        res += ggml_type_size(GGML_TYPE_F32)*(ne01_max*ne02*ne03*nwg*(ne20 + 2));
     }
 
     return res;
@@ -3082,6 +3089,84 @@ int ggml_metal_op_im2col(ggml_metal_op_t ctx, int idx) {
     return 1;
 }
 
+int ggml_metal_op_conv_2d(ggml_metal_op_t ctx, int idx) {
+    ggml_tensor * op = ctx->node(idx);
+
+    ggml_metal_library_t lib = ctx->lib;
+    ggml_metal_encoder_t enc = ctx->enc;
+
+    GGML_TENSOR_LOCALS( int32_t, ne0, op->src[0], ne);
+    GGML_TENSOR_LOCALS(uint64_t, nb0, op->src[0], nb);
+    GGML_TENSOR_LOCALS( int32_t, ne1, op->src[1], ne);
+    GGML_TENSOR_LOCALS(uint64_t, nb1, op->src[1], nb);
+    GGML_TENSOR_LOCALS( int32_t, ne,  op,         ne);
+    GGML_TENSOR_LOCALS(uint64_t, nb,  op,         nb);
+
+    GGML_ASSERT(ggml_is_contiguous(op->src[0]));
+    GGML_ASSERT(op->src[1]->type == GGML_TYPE_F32);
+    GGML_ASSERT(op->type == GGML_TYPE_F32);
+    GGML_ASSERT(op->src[0]->type == GGML_TYPE_F16 || op->src[0]->type == GGML_TYPE_F32);
+
+    const int32_t s0 = ((const int32_t *) op->op_params)[0];
+    const int32_t s1 = ((const int32_t *) op->op_params)[1];
+    const int32_t p0 = ((const int32_t *) op->op_params)[2];
+    const int32_t p1 = ((const int32_t *) op->op_params)[3];
+    const int32_t d0 = ((const int32_t *) op->op_params)[4];
+    const int32_t d1 = ((const int32_t *) op->op_params)[5];
+
+    ggml_metal_kargs_conv_2d args = {
+        /*.nb00 =*/ nb00,
+        /*.nb01 =*/ nb01,
+        /*.nb02 =*/ nb02,
+        /*.nb03 =*/ nb03,
+        /*.nb10 =*/ nb10,
+        /*.nb11 =*/ nb11,
+        /*.nb12 =*/ nb12,
+        /*.nb13 =*/ nb13,
+        /*.nb0  =*/ nb0,
+        /*.nb1  =*/ nb1,
+        /*.nb2  =*/ nb2,
+        /*.nb3  =*/ nb3,
+        /*.IW   =*/ ne10,
+        /*.IH   =*/ ne11,
+        /*.KW   =*/ ne00,
+        /*.KH   =*/ ne01,
+        /*.IC   =*/ ne02,
+        /*.OC   =*/ ne03,
+        /*.OW   =*/ ne0,
+        /*.OH   =*/ ne1,
+        /*.N    =*/ ne3,
+        /*.s0   =*/ s0,
+        /*.s1   =*/ s1,
+        /*.p0   =*/ p0,
+        /*.p1   =*/ p1,
+        /*.d0   =*/ d0,
+        /*.d1   =*/ d1,
+    };
+
+    ggml_metal_pipeline_t pipeline = ggml_metal_library_get_pipeline_conv_2d(lib, op);
+
+    int nth = ggml_metal_pipeline_max_theads_per_threadgroup(pipeline);
+    nth = std::min(nth, 256);
+    nth = std::max(nth, 1);
+
+    const uint64_t n_out = ggml_nelements(op);
+
+    uint64_t tg = (n_out + nth - 1)/nth;
+    tg = std::max<uint64_t>(tg, 1);
+    tg = std::min<uint64_t>(tg, (uint64_t) std::numeric_limits<int>::max());
+
+    ggml_metal_encoder_set_pipeline(enc, pipeline);
+    ggml_metal_encoder_set_bytes   (enc, &args, sizeof(args), 0);
+    ggml_metal_encoder_set_buffer  (enc, ggml_metal_get_buffer_id(op->src[0]), 1);
+    ggml_metal_encoder_set_buffer  (enc, ggml_metal_get_buffer_id(op->src[1]), 2);
+    ggml_metal_encoder_set_buffer  (enc, ggml_metal_get_buffer_id(op),         3);
+
+    ggml_metal_encoder_dispatch_threadgroups(enc, tg, 1, 1, nth, 1, 1);
+
+    return 1;
+}
+
 int ggml_metal_op_conv_transpose_1d(ggml_metal_op_t ctx, int idx) {
     ggml_tensor * op = ctx->node(idx);
 
@@ -3445,38 +3530,95 @@ int ggml_metal_op_argsort(ggml_metal_op_t ctx, int idx) {
     ggml_metal_library_t lib = ctx->lib;
     ggml_metal_encoder_t enc = ctx->enc;
 
+    GGML_ASSERT(ggml_is_contiguous_rows(op->src[0]));
+
     GGML_TENSOR_LOCALS( int32_t, ne0, op->src[0], ne);
     GGML_TENSOR_LOCALS(uint64_t, nb0, op->src[0], nb);
     GGML_TENSOR_LOCALS( int32_t, ne,  op,         ne);
     GGML_TENSOR_LOCALS(uint32_t, nb,  op,         nb);
 
-    // bitonic sort requires the number of elements to be power of 2
-    int64_t ne00_padded = 1;
-    while (ne00_padded < ne00) {
-        ne00_padded *= 2;
-    }
-
     ggml_metal_pipeline_t pipeline = ggml_metal_library_get_pipeline_argsort(lib, op);
 
-    const int64_t nrows = ggml_nrows(op->src[0]);
+    // bitonic sort requires the number of elements to be power of 2
+    int nth = 1;
+    while (nth < ne00 && 2*nth <= ggml_metal_pipeline_max_theads_per_threadgroup(pipeline)) {
+        nth *= 2;
+    }
+
+    const int nptg = (ne00 + nth - 1)/nth;
 
     // Metal kernels require the buffer size to be multiple of 16 bytes
     // https://developer.apple.com/documentation/metal/mtlcomputecommandencoder/1443142-setthreadgroupmemorylength
-    const size_t smem = GGML_PAD(ne00_padded*sizeof(int32_t), 16);
+    const size_t smem = GGML_PAD(nth*sizeof(int32_t), 16);
+
+    ggml_metal_buffer_id bid_src0 = ggml_metal_get_buffer_id(op->src[0]);
+    ggml_metal_buffer_id bid_dst  = ggml_metal_get_buffer_id(op);
+
+    ggml_metal_buffer_id bid_tmp = bid_dst;
+    bid_tmp.offs += ggml_nbytes(op);
+
+    if ((int) ceil(std::log(nptg) / std::log(2)) % 2 == 1) {
+        std::swap(bid_dst, bid_tmp);
+    }
 
     ggml_metal_kargs_argsort args = {
-        /*.ncols =*/ ne00,
-        /*.ncols_pad =*/ ne00_padded
+        /*.ne00 =*/ ne00,
+        /*.ne01 =*/ ne01,
+        /*.ne02 =*/ ne02,
+        /*.ne03 =*/ ne03,
+        /*.nb00 =*/ nb00,
+        /*.nb01 =*/ nb01,
+        /*.nb02 =*/ nb02,
+        /*.nb03 =*/ nb03,
     };
 
     ggml_metal_encoder_set_pipeline(enc, pipeline);
     ggml_metal_encoder_set_bytes   (enc, &args, sizeof(args), 0);
-    ggml_metal_encoder_set_buffer  (enc, ggml_metal_get_buffer_id(op->src[0]), 1);
-    ggml_metal_encoder_set_buffer  (enc, ggml_metal_get_buffer_id(op),         2);
+    ggml_metal_encoder_set_buffer  (enc, bid_src0, 1);
+    ggml_metal_encoder_set_buffer  (enc, bid_dst,  2);
 
     ggml_metal_encoder_set_threadgroup_memory_size(enc, smem, 0);
 
-    ggml_metal_encoder_dispatch_threadgroups(enc, 1, nrows, 1, ne00_padded, 1, 1);
+    ggml_metal_encoder_dispatch_threadgroups(enc, nptg*ne01, ne02, ne03, nth, 1, 1);
+
+    ggml_metal_pipeline_t pipeline_merge = ggml_metal_library_get_pipeline_argsort_merge(lib, op);
+
+    int len = nth;
+
+    while (len < ne00) {
+        ggml_metal_op_concurrency_reset(ctx);
+
+        ggml_metal_kargs_argsort_merge args_merge = {
+            .ne00 = ne00,
+            .ne01 = ne01,
+            .ne02 = ne02,
+            .ne03 = ne03,
+            .nb00 = nb00,
+            .nb01 = nb01,
+            .nb02 = nb02,
+            .nb03 = nb03,
+            .len  = len,
+        };
+
+        // merges per row
+        const int nm = (ne00 + 2*len - 1) / (2*len);
+
+        const int nth = std::min(512, ggml_metal_pipeline_max_theads_per_threadgroup(pipeline_merge));
+
+        ggml_metal_encoder_set_pipeline(enc, pipeline_merge);
+        ggml_metal_encoder_set_bytes   (enc, &args_merge, sizeof(args_merge), 0);
+        ggml_metal_encoder_set_buffer  (enc, bid_src0, 1);
+        ggml_metal_encoder_set_buffer  (enc, bid_dst,  2);
+        ggml_metal_encoder_set_buffer  (enc, bid_tmp,  3);
+
+        ggml_metal_encoder_set_threadgroup_memory_size(enc, 0, 0);
+
+        ggml_metal_encoder_dispatch_threadgroups(enc, nm*ne01, ne02, ne03, nth, 1, 1);
+
+        std::swap(bid_dst, bid_tmp);
+
+        len <<= 1;
+    }
 
     return 1;
 }

ggml/src/ggml-metal/ggml-metal-ops.h

@@ -70,6 +70,7 @@ int ggml_metal_op_group_norm        (ggml_metal_op_t ctx, int idx);
 int ggml_metal_op_norm              (ggml_metal_op_t ctx, int idx);
 int ggml_metal_op_rope              (ggml_metal_op_t ctx, int idx);
 int ggml_metal_op_im2col            (ggml_metal_op_t ctx, int idx);
+int ggml_metal_op_conv_2d           (ggml_metal_op_t ctx, int idx);
 int ggml_metal_op_conv_transpose_1d (ggml_metal_op_t ctx, int idx);
 int ggml_metal_op_conv_transpose_2d (ggml_metal_op_t ctx, int idx);
 int ggml_metal_op_upscale           (ggml_metal_op_t ctx, int idx);

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

@@ -197,6 +197,10 @@ static size_t ggml_backend_metal_buffer_type_get_alloc_size(ggml_backend_buffer_
                 res += ggml_metal_op_flash_attn_ext_extra_blk(tensor);
                 res += ggml_metal_op_flash_attn_ext_extra_tmp(tensor);
             } break;
+        case GGML_OP_ARGSORT:
+            {
+                res *= 2;
+            } break;
         default:
             break;
     }

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

@@ -4146,6 +4146,120 @@ template [[host_name("kernel_im2col_f16")]] kernel im2col_t kernel_im2col<half>;
 //template [[host_name("kernel_im2col_ext_f32")]] kernel im2col_ext_t kernel_im2col_ext<float>;
 //template [[host_name("kernel_im2col_ext_f16")]] kernel im2col_ext_t kernel_im2col_ext<half>;
 
+template <typename TK>
+kernel void kernel_conv_2d(
+        constant ggml_metal_kargs_conv_2d & args,
+        device const char * weights,
+        device const char * src,
+        device       char * dst,
+        uint3   tgpig[[threadgroup_position_in_grid]],
+        uint3    tgpg[[threadgroups_per_grid]],
+        uint3   tpitg[[thread_position_in_threadgroup]],
+        uint3     ntg[[threads_per_threadgroup]]) {
+
+    const uint threads_per_tg = ntg.x * ntg.y * ntg.z;
+    const uint tg_index = (tgpig.z * tgpg.y + tgpig.y) * tgpg.x + tgpig.x;
+    const uint local_thread = tpitg.z * (ntg.x * ntg.y) + tpitg.y * ntg.x + tpitg.x;
+    const uint thread_index = tg_index * threads_per_tg + local_thread;
+    const uint64_t total_threads = (uint64_t) threads_per_tg * tgpg.x * tgpg.y * tgpg.z;
+    const uint64_t total_outputs = (uint64_t) args.N * args.OC * args.OH * args.OW;
+
+    for (uint64_t index = thread_index; index < total_outputs; index += total_threads) {
+        uint64_t tmp = index;
+
+        const int32_t ow = tmp % args.OW; tmp /= args.OW;
+        const int32_t oh = tmp % args.OH; tmp /= args.OH;
+        const int32_t oc = tmp % args.OC; tmp /= args.OC;
+        const int32_t  n = tmp;
+
+        float acc = 0.0f;
+
+        const int32_t base_x = ow*args.s0 - args.p0;
+        const int32_t base_y = oh*args.s1 - args.p1;
+
+        int32_t ky_start = 0;
+        if (base_y < 0) {
+            ky_start = (-base_y + args.d1 - 1)/args.d1;
+        }
+        int32_t ky_end = args.KH;
+        const int32_t y_max = args.IH - 1 - base_y;
+        if (y_max < 0) {
+            ky_end = ky_start;
+        } else if (base_y + (args.KH - 1)*args.d1 >= args.IH) {
+            ky_end = min(ky_end, y_max/args.d1 + 1);
+        }
+
+        int32_t kx_start = 0;
+        if (base_x < 0) {
+            kx_start = (-base_x + args.d0 - 1)/args.d0;
+        }
+        int32_t kx_end = args.KW;
+        const int32_t x_max = args.IW - 1 - base_x;
+        if (x_max < 0) {
+            kx_end = kx_start;
+        } else if (base_x + (args.KW - 1)*args.d0 >= args.IW) {
+            kx_end = min(kx_end, x_max/args.d0 + 1);
+        }
+
+        if (ky_start < ky_end && kx_start < kx_end) {
+            const uint64_t src_base_n = (uint64_t) n  * args.nb13;
+            const uint64_t w_base_oc  = (uint64_t) oc * args.nb03;
+
+            for (int32_t ic = 0; ic < args.IC; ++ic) {
+                const uint64_t src_base_nc = src_base_n + (uint64_t) ic * args.nb12;
+                const uint64_t w_base_ocic = w_base_oc  + (uint64_t) ic * args.nb02;
+
+                for (int32_t ky = ky_start; ky < ky_end; ++ky) {
+                    const int32_t iy = base_y + ky*args.d1;
+                    const uint64_t src_base_row = src_base_nc + (uint64_t) iy * args.nb11;
+                    const uint64_t w_base_row   = w_base_ocic + (uint64_t) ky * args.nb01;
+
+                    for (int32_t kx = kx_start; kx < kx_end; ++kx) {
+                        const int32_t ix = base_x + kx*args.d0;
+                        const uint64_t src_offs = src_base_row + (uint64_t) ix * args.nb10;
+                        const uint64_t w_offs   = w_base_row   + (uint64_t) kx * args.nb00;
+
+                        const float x = *(device const float *)(src + src_offs);
+                        const float w = (float) (*(device const TK *)(weights + w_offs));
+
+                        acc += x * w;
+                    }
+                }
+            }
+        }
+
+        const uint64_t dst_offs =
+            (uint64_t) n  * args.nb3 +
+            (uint64_t) oc * args.nb2 +
+            (uint64_t) oh * args.nb1 +
+            (uint64_t) ow * args.nb0;
+
+        *(device float *)(dst + dst_offs) = acc;
+    }
+}
+
+template [[host_name("kernel_conv_2d_f32_f32")]]
+kernel void kernel_conv_2d<float>(
+        constant ggml_metal_kargs_conv_2d & args,
+        device const char * weights,
+        device const char * src,
+        device       char * dst,
+        uint3   tgpig[[threadgroup_position_in_grid]],
+        uint3    tgpg[[threadgroups_per_grid]],
+        uint3   tpitg[[thread_position_in_threadgroup]],
+        uint3     ntg[[threads_per_threadgroup]]);
+
+template [[host_name("kernel_conv_2d_f16_f32")]]
+kernel void kernel_conv_2d<half>(
+        constant ggml_metal_kargs_conv_2d & args,
+        device const char * weights,
+        device const char * src,
+        device       char * dst,
+        uint3   tgpig[[threadgroup_position_in_grid]],
+        uint3    tgpg[[threadgroups_per_grid]],
+        uint3   tpitg[[thread_position_in_threadgroup]],
+        uint3     ntg[[threads_per_threadgroup]]);
+
 typedef void (conv_transpose_1d_t)(
         constant ggml_metal_kargs_conv_transpose_1d & args,
         device const float * src0,
@@ -4427,69 +4541,179 @@ kernel void kernel_timestep_embedding_f32(
 // bitonic sort implementation following the CUDA kernels as reference
 typedef void (argsort_t)(
         constant   ggml_metal_kargs_argsort & args,
-        device  const float * x,
+        device   const char * src0,
         device      int32_t * dst,
-        threadgroup int32_t * shared_values [[threadgroup(0)]],
-        uint3 tgpig[[threadgroup_position_in_grid]],
-        uint3 tpitg[[thread_position_in_threadgroup]]);
+        threadgroup int32_t * smem_i32 [[threadgroup(0)]],
+        uint3   tgpig[[threadgroup_position_in_grid]],
+        ushort3 tpitg[[thread_position_in_threadgroup]],
+        ushort3   ntg[[threads_per_threadgroup]]);
 
 template<ggml_sort_order order>
 kernel void kernel_argsort_f32_i32(
         constant   ggml_metal_kargs_argsort & args,
-        device const float  * x,
+        device   const char * src0,
         device      int32_t * dst,
-        threadgroup int32_t * shared_values [[threadgroup(0)]],
-        uint3 tgpig[[threadgroup_position_in_grid]],
-        uint3 tpitg[[thread_position_in_threadgroup]]) {
+        threadgroup int32_t * smem_i32 [[threadgroup(0)]],
+        uint3   tgpig[[threadgroup_position_in_grid]],
+        ushort3 tpitg[[thread_position_in_threadgroup]],
+        ushort3   ntg[[threads_per_threadgroup]]) {
     // bitonic sort
-    int col = tpitg[0];
-    int row = tgpig[1];
+    const int col = tpitg[0];
 
-    if (col >= args.ncols_pad) return;
+    const int i00 = (tgpig[0]/args.ne01)*ntg.x;
+    const int i01 =  tgpig[0]%args.ne01;
+    const int i02 =  tgpig[1];
+    const int i03 =  tgpig[2];
 
-    device const float   * x_row   = x + row * args.ncols;
-    threadgroup int32_t  * dst_row = shared_values;
+    device const float * x_row = (device const float *) (src0 + args.nb01*i01 + args.nb02*i02 + args.nb03*i03);
 
     // initialize indices
-    dst_row[col] = col;
+    smem_i32[col] = i00 + col;
 
     threadgroup_barrier(mem_flags::mem_threadgroup);
 
-    for (int k = 2; k <= args.ncols_pad; k *= 2) {
+    for (int k = 2; k <= ntg.x; k *= 2) {
         for (int j = k / 2; j > 0; j /= 2) {
             int ixj = col ^ j;
             if (ixj > col) {
                 if ((col & k) == 0) {
-                    if (dst_row[col] >= args.ncols ||
-                        (dst_row[ixj] < args.ncols && (order == GGML_SORT_ORDER_ASC ?
-                            x_row[dst_row[col]] > x_row[dst_row[ixj]] :
-                            x_row[dst_row[col]] < x_row[dst_row[ixj]]))
+                    if (smem_i32[col] >= args.ne00 ||
+                       (smem_i32[ixj] <  args.ne00 && (order == GGML_SORT_ORDER_ASC ?
+                            x_row[smem_i32[col]] > x_row[smem_i32[ixj]] :
+                            x_row[smem_i32[col]] < x_row[smem_i32[ixj]]))
                     ) {
-                        SWAP(dst_row[col], dst_row[ixj]);
+                        SWAP(smem_i32[col], smem_i32[ixj]);
                     }
                 } else {
-                    if (dst_row[ixj] >= args.ncols ||
-                        (dst_row[col] < args.ncols && (order == GGML_SORT_ORDER_ASC ?
-                            x_row[dst_row[col]] < x_row[dst_row[ixj]] :
-                            x_row[dst_row[col]] > x_row[dst_row[ixj]]))
+                    if (smem_i32[ixj] >= args.ne00 ||
+                       (smem_i32[col] <  args.ne00 && (order == GGML_SORT_ORDER_ASC ?
+                            x_row[smem_i32[col]] < x_row[smem_i32[ixj]] :
+                            x_row[smem_i32[col]] > x_row[smem_i32[ixj]]))
                     ) {
-                        SWAP(dst_row[col], dst_row[ixj]);
+                        SWAP(smem_i32[col], smem_i32[ixj]);
                     }
                 }
             }
+
             threadgroup_barrier(mem_flags::mem_threadgroup);
         }
     }
 
     // copy the result to dst without the padding
-    if (col < args.ncols) {
-        dst[row * args.ncols + col] = dst_row[col];
+    if (i00 + col < args.ne00) {
+        dst += i00 + args.ne00*i01 + args.ne00*args.ne01*i02 + args.ne00*args.ne01*args.ne02*i03;
+
+        dst[col] = smem_i32[col];
     }
 }
 
 template [[host_name("kernel_argsort_f32_i32_asc")]]  kernel argsort_t kernel_argsort_f32_i32<GGML_SORT_ORDER_ASC>;
 template [[host_name("kernel_argsort_f32_i32_desc")]] kernel argsort_t kernel_argsort_f32_i32<GGML_SORT_ORDER_DESC>;
 
+typedef void (argsort_merge_t)(
+        constant   ggml_metal_kargs_argsort_merge & args,
+        device const char    * src0,
+        device const int32_t * tmp,
+        device       int32_t * dst,
+        uint3   tgpig[[threadgroup_position_in_grid]],
+        ushort3 tpitg[[thread_position_in_threadgroup]],
+        ushort3   ntg[[threads_per_threadgroup]]);
+
+template<ggml_sort_order order>
+kernel void kernel_argsort_merge_f32_i32(
+        constant   ggml_metal_kargs_argsort_merge & args,
+        device const char    * src0,
+        device const int32_t * tmp,
+        device       int32_t * dst,
+        uint3   tgpig[[threadgroup_position_in_grid]],
+        ushort3 tpitg[[thread_position_in_threadgroup]],
+        ushort3   ntg[[threads_per_threadgroup]]) {
+    int im  = tgpig[0] / args.ne01;
+    int i01 = tgpig[0] % args.ne01;
+    int i02 = tgpig[1];
+    int i03 = tgpig[2];
+
+    const int start = im * (2*args.len);
+
+    const int len0 = MIN(args.len, MAX(0, args.ne00 - (int)(start)));
+    const int len1 = MIN(args.len, MAX(0, args.ne00 - (int)(start + args.len)));
+
+    const int total = len0 + len1;
+
+    device const int32_t * tmp0 = tmp + start
+        + i01*args.ne00
+        + i02*args.ne00*args.ne01
+        + i03*args.ne00*args.ne01*args.ne02;
+
+    device const int32_t * tmp1 = tmp0 + args.len;
+
+    dst += start
+        + i01*args.ne00
+        + i02*args.ne00*args.ne01
+        + i03*args.ne00*args.ne01*args.ne02;
+
+    device const float * src0_row = (device const float *)(src0
+        + args.nb01*i01
+        + args.nb02*i02
+        + args.nb03*i03);
+
+    for (int k = tpitg.x; k < (int) total; k += ntg.x) {
+        // find partition (i,j) such that i+j = k
+        int low  = k > len1 ? k - len1 : 0;
+        int high = MIN(k, len0);
+
+        while (low < high) {
+            const int mid = (low + high) >> 1;
+
+            const int32_t idx0 = tmp0[mid];
+            const int32_t idx1 = tmp1[k - mid - 1];
+
+            const float val0 = src0_row[idx0];
+            const float val1 = src0_row[idx1];
+
+            if (order == GGML_SORT_ORDER_ASC) {
+                if (val0 <= val1) {
+                    low = mid + 1;
+                } else {
+                    high = mid;
+                }
+            } else {
+                if (val0 >= val1) {
+                    low = mid + 1;
+                } else {
+                    high = mid;
+                }
+            }
+        }
+
+        const int i = low;
+        const int j = k - i;
+
+        int32_t out_idx;
+
+        if (i >= len0) {
+            out_idx = tmp1[j];
+        } else if (j >= len1) {
+            out_idx = tmp0[i];
+        } else {
+            const int32_t idx0 = tmp0[i];
+            const int32_t idx1 = tmp1[j];
+
+            const float val0 = src0_row[idx0];
+            const float val1 = src0_row[idx1];
+
+            out_idx = (order == GGML_SORT_ORDER_ASC)
+                ? (val0 <= val1 ? idx0 : idx1)
+                : (val0 >= val1 ? idx0 : idx1);
+        }
+
+        dst[k] = out_idx;
+    }
+}
+
+template [[host_name("kernel_argsort_merge_f32_i32_asc")]]  kernel argsort_merge_t kernel_argsort_merge_f32_i32<GGML_SORT_ORDER_ASC>;
+template [[host_name("kernel_argsort_merge_f32_i32_desc")]] kernel argsort_merge_t kernel_argsort_merge_f32_i32<GGML_SORT_ORDER_DESC>;
+
 kernel void kernel_leaky_relu_f32(
         constant     ggml_metal_kargs_leaky_relu & args,
         device const float * src0,

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

@@ -3933,6 +3933,7 @@ static bool ggml_sycl_compute_forward(ggml_backend_sycl_context & ctx, struct gg
             break;
         case GGML_OP_SSM_CONV:
             ggml_sycl_ssm_conv(ctx, dst);
+            break;
         case GGML_OP_ROLL:
             ggml_sycl_roll(ctx, dst);
             break;

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

@@ -234,6 +234,7 @@ class vk_memory_logger;
 #endif
 class vk_perf_logger;
 static void ggml_vk_destroy_buffer(vk_buffer& buf);
+static void ggml_vk_synchronize(ggml_backend_vk_context * ctx);
 
 static constexpr uint32_t mul_mat_vec_max_cols = 8;
 static constexpr uint32_t p021_max_gqa_ratio = 8;
@@ -1581,8 +1582,9 @@ struct ggml_backend_vk_context {
     size_t semaphore_idx, event_idx;
     ggml_vk_garbage_collector gc;
     size_t prealloc_size_x, prealloc_size_y, prealloc_size_split_k, prealloc_size_add_rms_partials, prealloc_size_add_rms_partials_offset;
-    vk_buffer prealloc_x, prealloc_y, prealloc_split_k, prealloc_add_rms_partials;
+    vk_buffer prealloc_x, prealloc_y, prealloc_split_k, prealloc_add_rms_partials, sync_staging;
     vk::Fence fence, almost_ready_fence;
+    bool submit_pending {};
     bool almost_ready_fence_pending {};
     // Set before op_add and unset after op_rms_norm to indicate that the add should
     // write partial sums to accumulate the square of the vector components
@@ -5602,6 +5604,16 @@ static void ggml_vk_ensure_sync_staging_buffer(vk_device& device, size_t size) {
     }
 }
 
+static void ggml_vk_ensure_sync_staging_buffer(ggml_backend_vk_context * ctx, size_t size) {
+    if (ctx->sync_staging == nullptr || ctx->sync_staging->size < size) {
+        VK_LOG_MEMORY("ggml_vk_ensure_sync_staging_buffer(" << size << ")");
+        ggml_vk_destroy_buffer(ctx->sync_staging);
+        ctx->sync_staging = ggml_vk_create_buffer_check(ctx->device, size,
+            vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached,
+            vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent);
+    }
+}
+
 static void ggml_vk_buffer_write_nc_async(ggml_backend_vk_context * ctx, vk_context& subctx, vk_buffer& dst, size_t offset, const ggml_tensor * tensor, bool sync_staging = false) {
     VK_LOG_DEBUG("ggml_vk_buffer_write_nc_async(" << tensor << ")");
     GGML_ASSERT(!ggml_is_contiguous(tensor));
@@ -5803,7 +5815,7 @@ static void ggml_vk_buffer_write(vk_buffer& dst, size_t offset, const void * src
     ggml_vk_buffer_write_2d(dst, offset, src, 0, size, 1);
 }
 
-static void ggml_vk_buffer_read_2d_async(vk_context subctx, vk_buffer& src, size_t offset, void * dst, size_t spitch, size_t dpitch, size_t width, size_t height, bool sync_staging = false) {
+static bool ggml_vk_buffer_read_2d_async(vk_context subctx, vk_buffer& src, size_t offset, void * dst, size_t spitch, size_t dpitch, size_t width, size_t height, bool sync_staging = false) {
     VK_LOG_DEBUG("ggml_vk_buffer_read_2d_async(offset=" << offset << ", width=" << width << ", height=" << height << ")");
     GGML_ASSERT(width > 0);
     GGML_ASSERT(height > 0);
@@ -5836,12 +5848,13 @@ static void ggml_vk_buffer_read_2d_async(vk_context subctx, vk_buffer& src, size
         ggml_vk_sync_buffers(nullptr, subctx);
         subctx->s->buffer.copyBuffer(src->buffer, buf->buffer, slices);
 
-        return;
+        return true;
     }
     VK_LOG_DEBUG("STAGING");
 
     if (!sync_staging) {
-        GGML_ABORT("Asynchronous read from non-pinned memory not supported");
+        // copy was not handled caller needs to fall back
+        return false;
     }
 
     // Fall back to staging buffer
@@ -5854,9 +5867,10 @@ static void ggml_vk_buffer_read_2d_async(vk_context subctx, vk_buffer& src, size
     subctx->s->buffer.copyBuffer(src->buffer, staging_buffer->buffer, slices);
 
     deferred_memcpy(dst, staging_buffer->ptr, copy_size, &subctx->out_memcpys);
+    return true;
 }
 
-static void ggml_vk_buffer_read_async(vk_context subctx, vk_buffer& src, size_t offset, void * dst, size_t size, bool sync_staging = false) {
+static bool ggml_vk_buffer_read_async(vk_context subctx, vk_buffer& src, size_t offset, void * dst, size_t size, bool sync_staging = false) {
     return ggml_vk_buffer_read_2d_async(subctx, src, offset, dst, size, size, size, 1, sync_staging);
 }
 
@@ -5875,7 +5889,8 @@ static void ggml_vk_buffer_read(vk_buffer& src, size_t offset, void * dst, size_
 
         vk_context subctx = ggml_vk_create_temporary_context(src->device->transfer_queue.cmd_pool);
         ggml_vk_ctx_begin(src->device, subctx);
-        ggml_vk_buffer_read_async(subctx, src, offset, dst, size, true);
+        bool ret = ggml_vk_buffer_read_async(subctx, src, offset, dst, size, true);
+        GGML_ASSERT(ret);
         ggml_vk_ctx_end(subctx);
 
         ggml_vk_submit(subctx, src->device->fence);
@@ -11204,8 +11219,9 @@ static void ggml_vk_preallocate_buffers(ggml_backend_vk_context * ctx, vk_contex
     if (subctx) {
         // Submit and wait for any pending work before reallocating the buffers
         ggml_vk_ctx_end(subctx);
-        ggml_vk_submit(subctx, ctx->fence);
-        ggml_vk_wait_for_fence(ctx);
+        ggml_vk_submit(subctx, {});
+        ctx->submit_pending = true;
+        ggml_vk_synchronize(ctx);
         ggml_vk_ctx_begin(ctx->device, subctx);
     }
 
@@ -11243,7 +11259,7 @@ static void ggml_vk_preallocate_buffers(ggml_backend_vk_context * ctx, vk_contex
     }
 }
 
-static bool ggml_vk_compute_forward(ggml_backend_vk_context* ctx, ggml_cgraph * cgraph, ggml_tensor* tensor, int tensor_idx, bool use_fence, bool almost_ready);
+static bool ggml_vk_compute_forward(ggml_backend_vk_context* ctx, ggml_cgraph * cgraph, ggml_tensor* tensor, int tensor_idx, bool almost_ready);
 
 // Returns true if node has enqueued work into the queue, false otherwise
 // If submit is true the current all operations queued so far are being submitted to Vulkan to overlap cmdlist creation and GPU execution.
@@ -11787,7 +11803,7 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgr
 
         ctx->compute_ctx.reset();
 
-        bool ok = ggml_vk_compute_forward(ctx, cgraph, node_begin, node_idx_begin, false, almost_ready);
+        bool ok = ggml_vk_compute_forward(ctx, cgraph, node_begin, node_idx_begin, almost_ready);
         if (!ok) {
             if (node->op == GGML_OP_UNARY) {
                 std::cerr << __func__ << ": error: op not supported UNARY " << node->name << " (" << ggml_unary_op_name(static_cast<ggml_unary_op>(node->op_params[0])) << ")" << std::endl;
@@ -11802,7 +11818,7 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgr
     return true;
 }
 
-static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_cgraph * cgraph, ggml_tensor * tensor, int tensor_idx, bool use_fence = true, bool almost_ready = false) {
+static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_cgraph * cgraph, ggml_tensor * tensor, int tensor_idx, bool almost_ready = false) {
     GGML_UNUSED(cgraph);
     ggml_backend_buffer * buf = nullptr;
 
@@ -11919,16 +11935,10 @@ static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_cgraph *
 
     vk_context subctx = ctx->tensor_ctxs[tensor_idx].lock();
 
-    // always wait for the GPU work to be done for the last submit
-    if (tensor_idx == subctx->exit_tensor_idx) {
-        use_fence = true;
-    }
-
     // Only run if ctx hasn't been submitted yet
     if (!subctx->seqs.empty()) {
 #ifdef GGML_VULKAN_CHECK_RESULTS
         ggml_vk_check_results_0(ctx, cgraph, tensor_idx);
-        use_fence = true;
 #endif
 
         // Do staging buffer copies
@@ -11940,17 +11950,16 @@ static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_cgraph *
             memset(mset.dst, mset.val, mset.n);
         }
 
-        if (almost_ready && !ctx->almost_ready_fence_pending && !use_fence) {
+        if (almost_ready && !ctx->almost_ready_fence_pending) {
             ggml_vk_submit(subctx, ctx->almost_ready_fence);
             ctx->almost_ready_fence_pending = true;
         } else {
-            ggml_vk_submit(subctx, use_fence ? ctx->fence : vk::Fence{});
+            ggml_vk_submit(subctx, {});
         }
+        ctx->submit_pending = true;
 
-        if (use_fence) {
-            ggml_vk_wait_for_fence(ctx);
-        }
 #ifdef GGML_VULKAN_CHECK_RESULTS
+        ggml_vk_synchronize(ctx);
         ggml_vk_check_results_1(ctx, cgraph, tensor_idx);
 #endif
     }
@@ -12006,11 +12015,19 @@ static void ggml_vk_graph_cleanup(ggml_backend_vk_context * ctx) {
 // Clean up on backend free
 static void ggml_vk_cleanup(ggml_backend_vk_context * ctx) {
     VK_LOG_DEBUG("ggml_vk_cleanup(" << ctx->name << ")");
+    // discard any unsubmitted command buffers
+    ctx->transfer_ctx.reset();
+    // wait for any pending command buffers to finish
+    ggml_vk_synchronize(ctx);
+
     ggml_vk_graph_cleanup(ctx);
 
     ggml_vk_destroy_buffer(ctx->prealloc_x);
     ggml_vk_destroy_buffer(ctx->prealloc_y);
     ggml_vk_destroy_buffer(ctx->prealloc_split_k);
+    ggml_vk_destroy_buffer(ctx->prealloc_add_rms_partials);
+    ggml_vk_destroy_buffer(ctx->sync_staging);
+
     ctx->prealloc_y_last_pipeline_used = nullptr;
 
     ctx->prealloc_size_x = 0;
@@ -12305,7 +12322,7 @@ static void ggml_backend_vk_set_tensor_async(ggml_backend_t backend, ggml_tensor
 
     if (ctx->transfer_ctx.expired()) {
         // Initialize new transfer context
-        transfer_ctx = ggml_vk_create_context(ctx, ctx->transfer_cmd_pool);
+        transfer_ctx = ggml_vk_create_context(ctx, ctx->compute_cmd_pool);
         ctx->transfer_ctx = transfer_ctx;
         ggml_vk_ctx_begin(ctx->device, transfer_ctx);
     } else {
@@ -12328,7 +12345,7 @@ static void ggml_backend_vk_get_tensor_async(ggml_backend_t backend, const ggml_
 
     if (ctx->transfer_ctx.expired()) {
         // Initialize new transfer context
-        transfer_ctx = ggml_vk_create_context(ctx, ctx->transfer_cmd_pool);
+        transfer_ctx = ggml_vk_create_context(ctx, ctx->compute_cmd_pool);
         ctx->transfer_ctx = transfer_ctx;
         ggml_vk_ctx_begin(ctx->device, transfer_ctx);
     } else {
@@ -12337,7 +12354,23 @@ static void ggml_backend_vk_get_tensor_async(ggml_backend_t backend, const ggml_
 
     vk_buffer buf = buf_ctx->dev_buffer;
 
-    ggml_vk_buffer_read_async(transfer_ctx, buf, vk_tensor_offset(tensor) + tensor->view_offs + offset, data, size);
+    auto src_offset = vk_tensor_offset(tensor) + tensor->view_offs + offset;
+    bool ret = ggml_vk_buffer_read_async(transfer_ctx, buf, src_offset, data, size);
+
+    // If that failed, copy synchronously through a staging buffer
+    if (!ret) {
+        ggml_vk_ensure_sync_staging_buffer(ctx, size);
+        ggml_vk_sync_buffers(nullptr, transfer_ctx);
+
+        vk::BufferCopy buffer_cpy;
+        buffer_cpy.srcOffset = src_offset;
+        buffer_cpy.dstOffset = 0;
+        buffer_cpy.size = size;
+
+        transfer_ctx->s->buffer.copyBuffer(buf->buffer, ctx->sync_staging->buffer, { buffer_cpy });
+        deferred_memcpy(data, ctx->sync_staging->ptr, size, &transfer_ctx->out_memcpys);
+        ggml_vk_synchronize(ctx);
+    }
 }
 
 static bool ggml_backend_vk_cpy_tensor_async(ggml_backend_t backend, const ggml_tensor * src, ggml_tensor * dst) {
@@ -12351,7 +12384,7 @@ static bool ggml_backend_vk_cpy_tensor_async(ggml_backend_t backend, const ggml_
 
         if (ctx->transfer_ctx.expired()) {
             // Initialize new transfer context
-            transfer_ctx = ggml_vk_create_context(ctx, ctx->transfer_cmd_pool);
+            transfer_ctx = ggml_vk_create_context(ctx, ctx->compute_cmd_pool);
             ctx->transfer_ctx = transfer_ctx;
             ggml_vk_ctx_begin(ctx->device, transfer_ctx);
         } else {
@@ -12368,29 +12401,49 @@ static bool ggml_backend_vk_cpy_tensor_async(ggml_backend_t backend, const ggml_
     return false;
 }
 
+static void ggml_vk_synchronize(ggml_backend_vk_context * ctx) {
+    VK_LOG_DEBUG("ggml_vk_synchronize()");
+
+    bool do_transfer = !ctx->transfer_ctx.expired();
+
+    vk_context transfer_ctx;
+    if (do_transfer) {
+        transfer_ctx = ctx->transfer_ctx.lock();
+
+        ggml_vk_ctx_end(transfer_ctx);
+
+        for (auto& cpy : transfer_ctx->in_memcpys) {
+            memcpy(cpy.dst, cpy.src, cpy.n);
+        }
+
+        ggml_vk_submit(transfer_ctx, {});
+        ctx->submit_pending = true;
+    }
+
+    if (ctx->submit_pending) {
+        {
+            std::lock_guard<std::mutex> guard(queue_mutex);
+            ctx->device->compute_queue.queue.submit({}, ctx->fence);
+        }
+        ggml_vk_wait_for_fence(ctx);
+        ctx->submit_pending = false;
+    }
+
+    if (do_transfer) {
+        for (auto& cpy : transfer_ctx->out_memcpys) {
+            memcpy(cpy.dst, cpy.src, cpy.n);
+        }
+        ctx->transfer_ctx.reset();
+    }
+}
+
 static void ggml_backend_vk_synchronize(ggml_backend_t backend) {
     VK_LOG_DEBUG("ggml_backend_vk_synchronize()");
     ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context;
-    if(ctx->transfer_ctx.expired()) {
-        return;
-    }
 
-    vk_context transfer_ctx = ctx->transfer_ctx.lock();
+    ggml_vk_synchronize(ctx);
 
-    ggml_vk_ctx_end(transfer_ctx);
-
-    for (auto& cpy : transfer_ctx->in_memcpys) {
-        memcpy(cpy.dst, cpy.src, cpy.n);
-    }
-
-    ggml_vk_submit(transfer_ctx, ctx->fence);
-    ggml_vk_wait_for_fence(ctx);
-
-    for (auto& cpy : transfer_ctx->out_memcpys) {
-        memcpy(cpy.dst, cpy.src, cpy.n);
-    }
-
-    ctx->transfer_ctx.reset();
+    ggml_vk_graph_cleanup(ctx);
 }
 
 static bool ggml_vk_is_empty(ggml_tensor * node) {
@@ -12670,6 +12723,12 @@ static bool ggml_vk_can_fuse_rms_norm_mul_rope(ggml_backend_vk_context * ctx, co
         return false;
     }
 
+    // conditions for pipeline creation
+    if (!(ctx->device->float_controls_rte_fp16 &&
+        sizeof(vk_op_rms_norm_mul_rope_push_constants) <= ctx->device->properties.limits.maxPushConstantsSize)) {
+        return false;
+    }
+
     return true;
 }
 
@@ -12932,8 +12991,6 @@ static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cg
         ctx->device->perf_logger->print_timings();
     }
 
-    ggml_vk_graph_cleanup(ctx);
-
     return GGML_STATUS_SUCCESS;
 
     UNUSED(backend);
@@ -13162,9 +13219,9 @@ static ggml_backend_i ggml_backend_vk_interface = {
     /* .get_name                = */ ggml_backend_vk_name,
     /* .free                    = */ ggml_backend_vk_free,
     /* .set_tensor_async        = */ NULL,  // ggml_backend_vk_set_tensor_async,
-    /* .get_tensor_async        = */ NULL,  // ggml_backend_vk_get_tensor_async,
+    /* .get_tensor_async        = */ ggml_backend_vk_get_tensor_async,
     /* .cpy_tensor_async        = */ NULL,  // ggml_backend_vk_cpy_tensor_async,
-    /* .synchronize             = */ NULL,  // ggml_backend_vk_synchronize,
+    /* .synchronize             = */ ggml_backend_vk_synchronize,
     /* .graph_plan_create       = */ NULL,
     /* .graph_plan_free         = */ NULL,
     /* .graph_plan_update       = */ NULL,

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

@@ -76,7 +76,7 @@ enum MatMulIdType {
 
 namespace {
 
-void execute_command(const std::string& command, std::string& stdout_str, std::string& stderr_str) {
+void execute_command(std::vector<std::string>& command, std::string& stdout_str, std::string& stderr_str) {
 #ifdef _WIN32
     HANDLE stdout_read, stdout_write;
     HANDLE stderr_read, stderr_write;
@@ -99,8 +99,10 @@ void execute_command(const std::string& command, std::string& stdout_str, std::s
     si.hStdOutput = stdout_write;
     si.hStdError = stderr_write;
 
-    std::vector<char> cmd(command.begin(), command.end());
-    cmd.push_back('\0');
+    std::string cmd;
+    for (const auto& part : command) {
+        cmd += part + " ";
+    }
 
     if (!CreateProcessA(NULL, cmd.data(), NULL, NULL, TRUE, 0, NULL, NULL, &si, &pi)) {
         throw std::runtime_error("Failed to create process");
@@ -138,6 +140,12 @@ void execute_command(const std::string& command, std::string& stdout_str, std::s
         throw std::runtime_error("Failed to fork process");
     }
 
+    std::vector<char*> argv;
+    for (std::string& part : command) {
+        argv.push_back(part.data());
+    }
+    argv.push_back(nullptr);
+
     if (pid == 0) {
         close(stdout_pipe[0]);
         close(stderr_pipe[0]);
@@ -145,7 +153,7 @@ void execute_command(const std::string& command, std::string& stdout_str, std::s
         dup2(stderr_pipe[1], STDERR_FILENO);
         close(stdout_pipe[1]);
         close(stderr_pipe[1]);
-        execl("/bin/sh", "sh", "-c", command.c_str(), (char*) nullptr);
+        execvp(argv[0], argv.data());
         _exit(EXIT_FAILURE);
     } else {
         close(stdout_pipe[1]);
@@ -316,21 +324,27 @@ compile_count_guard acquire_compile_slot() {
 void string_to_spv_func(std::string name, std::string in_path, std::string out_path, std::map<std::string, std::string> defines, bool coopmat, bool dep_file, compile_count_guard slot) {
     std::string target_env = (name.find("_cm2") != std::string::npos) ? "--target-env=vulkan1.3" : "--target-env=vulkan1.2";
 
+    #ifdef _WIN32
+        std::vector<std::string> cmd = {GLSLC, "-fshader-stage=compute", target_env, "\"" + in_path + "\"", "-o", "\"" + out_path + "\""};
+    #else
+        std::vector<std::string> cmd = {GLSLC, "-fshader-stage=compute", target_env, in_path, "-o", out_path};
+    #endif
+
     // disable spirv-opt for coopmat shaders for https://github.com/ggerganov/llama.cpp/issues/10734
     // disable spirv-opt for bf16 shaders for https://github.com/ggml-org/llama.cpp/issues/15344
     // disable spirv-opt for rope shaders for https://github.com/ggml-org/llama.cpp/issues/16860
-    std::string opt_level = (coopmat || name.find("bf16") != std::string::npos || name.find("rope") != std::string::npos) ? "" : "-O";
-
-    #ifdef _WIN32
-        std::vector<std::string> cmd = {GLSLC, "-fshader-stage=compute", target_env, opt_level, "\"" + in_path + "\"", "-o", "\"" + out_path + "\""};
-    #else
-        std::vector<std::string> cmd = {GLSLC, "-fshader-stage=compute", target_env, opt_level, in_path, "-o",  out_path};
-    #endif
+    if (!coopmat && name.find("bf16") == std::string::npos && name.find("rope") == std::string::npos) {
+        cmd.push_back("-O");
+    }
 
     if (dep_file) {
         cmd.push_back("-MD");
         cmd.push_back("-MF");
+#ifdef _WIN32
         cmd.push_back("\"" + target_cpp + ".d\"");
+#else
+        cmd.push_back(target_cpp + ".d");
+#endif
     }
 
     #ifdef GGML_VULKAN_SHADER_DEBUG_INFO
@@ -354,9 +368,13 @@ void string_to_spv_func(std::string name, std::string in_path, std::string out_p
         // }
         // std::cout << std::endl;
 
-        execute_command(command, stdout_str, stderr_str);
+        execute_command(cmd, stdout_str, stderr_str);
         if (!stderr_str.empty()) {
-            std::cerr << "cannot compile " << name << "\n\n" << command << "\n\n" << stderr_str << std::endl;
+            std::cerr << "cannot compile " << name << "\n\n";
+            for (const auto& part : cmd) {
+                std::cerr << part << " ";
+            }
+            std::cerr << "\n\n" << stderr_str << std::endl;
             return;
         }
 
@@ -430,7 +448,7 @@ void matmul_shaders(bool fp16, MatMulIdType matmul_id_type, bool coopmat, bool c
     base_dict["ACC_TYPE"     ] = f16acc ? "float16_t" : "float";
     base_dict["ACC_TYPE_VEC2"] = f16acc ? "f16vec2"   : "vec2";
     if (f16acc) {
-        base_dict["ACC_TYPE_MAX"] = "\"float16_t(65504.0)\"";
+        base_dict["ACC_TYPE_MAX"] = "float16_t(65504.0)";
     }
 
     if (coopmat) {
@@ -610,7 +628,7 @@ void process_shaders() {
         fa_base_dict["ACC_TYPE"] = f16acc ? "float16_t" : "float";
         fa_base_dict["ACC_TYPEV4"] = f16acc ? "f16vec4" : "vec4";
         if (f16acc) {
-            fa_base_dict["ACC_TYPE_MAX"] = "\"float16_t(65504.0)\"";
+            fa_base_dict["ACC_TYPE_MAX"] = "float16_t(65504.0)";
         }
 
         for (const auto& tname : type_names) {
@@ -1081,11 +1099,6 @@ int main(int argc, char** argv) {
 
     if (args.find("--glslc") != args.end()) {
         GLSLC = args["--glslc"]; // Path to glslc
-
-        if (!std::filesystem::exists(GLSLC) || !std::filesystem::is_regular_file(GLSLC)) {
-            std::cerr << "Error: glslc not found at " << GLSLC << std::endl;
-            return EXIT_FAILURE;
-        }
     }
     if (args.find("--source") != args.end()) {
         input_filepath = args["--source"]; // The shader source file to compile

ggml/src/ggml.c

@@ -935,6 +935,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
     "COS",
     "SUM",
     "SUM_ROWS",
+    "CUMSUM",
     "MEAN",
     "ARGMAX",
     "COUNT_EQUAL",
@@ -990,6 +991,8 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
     "TIMESTEP_EMBEDDING",
     "ARGSORT",
     "LEAKY_RELU",
+    "TRI",
+    "FILL",
 
     "FLASH_ATTN_EXT",
     "FLASH_ATTN_BACK",
@@ -1002,6 +1005,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
     "RWKV_WKV6",
     "GATED_LINEAR_ATTN",
     "RWKV_WKV7",
+    "SOLVE_TRI",
 
     "UNARY",
 
@@ -1019,7 +1023,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
     "GLU",
 };
 
-static_assert(GGML_OP_COUNT == 90, "GGML_OP_COUNT != 90");
+static_assert(GGML_OP_COUNT == 94, "GGML_OP_COUNT != 94");
 
 static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
     "none",
@@ -1039,6 +1043,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
     "cos(x)",
     "Σx",
     "Σx_k",
+    "cumsum(x)",
     "Σx/n",
     "argmax(x)",
     "count_equal(x)",
@@ -1094,6 +1099,8 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
     "timestep_embedding(timesteps, dim, max_period)",
     "argsort(x)",
     "leaky_relu(x)",
+    "tri(x)",
+    "fill(x, c)",
 
     "flash_attn_ext(x)",
     "flash_attn_back(x)",
@@ -1106,6 +1113,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
     "rwkv_wkv6(k, v, r, tf, td, s)",
     "gated_linear_attn(k, v, q, gate, s)",
     "rwkv_wkv7(r, w, k, v, a, b, s)",
+    "A X = B, A triangular, solve X",
 
     "unary(x)",
 
@@ -1123,7 +1131,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
     "glu(x)",
 };
 
-static_assert(GGML_OP_COUNT == 90, "GGML_OP_COUNT != 90");
+static_assert(GGML_OP_COUNT == 94, "GGML_OP_COUNT != 94");
 
 static_assert(GGML_OP_POOL_COUNT == 2, "GGML_OP_POOL_COUNT != 2");
 
@@ -1142,6 +1150,8 @@ static const char * GGML_UNARY_OP_NAME[GGML_UNARY_OP_COUNT] = {
     "HARDSWISH",
     "HARDSIGMOID",
     "EXP",
+    "EXPM1",
+    "SOFTPLUS",
     "GELU_ERF",
     "XIELU",
     "FLOOR",
@@ -1150,7 +1160,7 @@ static const char * GGML_UNARY_OP_NAME[GGML_UNARY_OP_COUNT] = {
     "TRUNC",
 };
 
-static_assert(GGML_UNARY_OP_COUNT == 20, "GGML_UNARY_OP_COUNT != 20");
+static_assert(GGML_UNARY_OP_COUNT == 22, "GGML_UNARY_OP_COUNT != 22");
 
 static const char * GGML_GLU_OP_NAME[GGML_GLU_OP_COUNT] = {
     "REGLU",
@@ -2258,6 +2268,30 @@ struct ggml_tensor * ggml_log_inplace(
     return ggml_log_impl(ctx, a, true);
 }
 
+struct ggml_tensor * ggml_expm1(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a) {
+    return ggml_unary(ctx, a, GGML_UNARY_OP_EXPM1);
+}
+
+struct ggml_tensor * ggml_expm1_inplace(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a) {
+    return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_EXPM1);
+}
+
+struct ggml_tensor * ggml_softplus(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a) {
+    return ggml_unary(ctx, a, GGML_UNARY_OP_SOFTPLUS);
+}
+
+struct ggml_tensor * ggml_softplus_inplace(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a) {
+    return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_SOFTPLUS);
+}
+
 // ggml_sin
 
 static struct ggml_tensor * ggml_sin_impl(
@@ -2341,6 +2375,21 @@ struct ggml_tensor * ggml_sum_rows(
     return result;
 }
 
+// ggml_cumsum
+
+struct ggml_tensor * ggml_cumsum(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a) {
+    GGML_ASSERT(a->type == GGML_TYPE_F32);
+
+    struct ggml_tensor * result = ggml_dup_tensor(ctx, a);
+
+    result->op     = GGML_OP_CUMSUM;
+    result->src[0] = a;
+
+    return result;
+}
+
 // ggml_mean
 
 struct ggml_tensor * ggml_mean(
@@ -2668,8 +2717,8 @@ struct ggml_tensor * ggml_xielu(
     struct ggml_tensor * result = ggml_dup_tensor(ctx, a);
 
     ggml_set_op_params_i32(result, 0, (int32_t) GGML_UNARY_OP_XIELU);
-    ggml_set_op_params_f32(result, 1, beta + ggml_softplus(alpha_n));
-    ggml_set_op_params_f32(result, 2, ggml_softplus(alpha_p));
+    ggml_set_op_params_f32(result, 1, beta + ggml_compute_softplus_f32(alpha_n));
+    ggml_set_op_params_f32(result, 2, ggml_compute_softplus_f32(alpha_p));
     ggml_set_op_params_f32(result, 3, beta);
     ggml_set_op_params_f32(result, 4, eps);
 
@@ -5028,6 +5077,61 @@ struct ggml_tensor * ggml_timestep_embedding(
     return result;
 }
 
+// ggml_tri
+
+struct ggml_tensor * ggml_tri(
+    struct ggml_context * ctx,
+    struct ggml_tensor  * a,
+    enum ggml_tri_type    type) {
+    GGML_ASSERT(a->type == GGML_TYPE_F32);
+
+    GGML_ASSERT(ggml_is_contiguous(a));
+    GGML_ASSERT(a->ne[0] == a->ne[1]);
+
+    struct ggml_tensor * result = ggml_dup_tensor(ctx, a);
+
+    ggml_set_op_params_i32(result, 0, type);
+
+    result->op = GGML_OP_TRI;
+    result->src[0] = a;
+
+    return result;
+}
+
+// ggml_fill
+
+static struct ggml_tensor * ggml_fill_impl(
+    struct ggml_context * ctx,
+    struct ggml_tensor  * a,
+    float                 c,
+    bool                  inplace) {
+    GGML_ASSERT(a->type == GGML_TYPE_F32);
+    GGML_ASSERT(ggml_is_contiguous(a));
+
+    struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
+
+    ggml_set_op_params_f32(result, 0, c);
+
+    result->op = GGML_OP_FILL;
+    result->src[0] = a;
+
+    return result;
+}
+
+struct ggml_tensor * ggml_fill(
+    struct ggml_context * ctx,
+    struct ggml_tensor  * a,
+    float                 c) {
+    return ggml_fill_impl(ctx, a, c, false);
+}
+
+struct ggml_tensor * ggml_fill_inplace(
+    struct ggml_context * ctx,
+    struct ggml_tensor  * a,
+    float                 c) {
+    return ggml_fill_impl(ctx, a, c, true);
+}
+
 // ggml_argsort
 
 struct ggml_tensor * ggml_argsort(
@@ -5882,6 +5986,41 @@ struct ggml_tensor * ggml_opt_step_sgd(
     return result;
 }
 
+// solve_tri
+
+struct ggml_tensor * ggml_solve_tri(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        struct ggml_tensor  * b,
+        bool                  left,
+        bool                  lower,
+        bool                  uni) {
+    GGML_ASSERT(a->type == GGML_TYPE_F32);
+    GGML_ASSERT(b->type == GGML_TYPE_F32);
+
+    // A must be square and lower diagonal
+    GGML_ASSERT(a->ne[0] == a->ne[1]);
+    // B must have same outer dimension as A
+    GGML_ASSERT(a->ne[1] == b->ne[1]);
+
+    // batch dimensions must be equal
+    GGML_ASSERT(a->ne[2] == b->ne[2]);
+    GGML_ASSERT(a->ne[3] == b->ne[3]);
+
+    GGML_ASSERT(ggml_is_contiguous(a));
+    GGML_ASSERT(ggml_is_contiguous(b));
+
+    GGML_ASSERT(lower && left && !uni); // TODO: support other variants
+
+    struct ggml_tensor * result = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, b->ne[0], b->ne[1], b->ne[2], b->ne[3]);
+
+    result->op     = GGML_OP_SOLVE_TRI;
+    result->src[0] = a;
+    result->src[1] = b;
+
+    return result;
+}
+
 ////////////////////////////////////////////////////////////////////////////////
 
 struct ggml_hash_set ggml_hash_set_new(size_t size) {
@@ -6454,6 +6593,16 @@ static void ggml_compute_backward(
                         ggml_add_or_set(ctx, cgraph, isrc0, ggml_mul(ctx, tensor, grad));
                     }
                 } break;
+                case GGML_UNARY_OP_EXPM1: {
+                    if (src0_needs_grads) {
+                        ggml_add_or_set(ctx, cgraph, isrc0, ggml_mul(ctx, grad, ggml_exp(ctx, src0)));
+                    }
+                } break;
+                case GGML_UNARY_OP_SOFTPLUS: {
+                    if (src0_needs_grads) {
+                        ggml_add_or_set(ctx, cgraph, isrc0, ggml_mul(ctx, grad, ggml_sigmoid(ctx, src0)));
+                    }
+                } break;
                 default: {
                     fprintf(stderr, "%s: unsupported unary op for backward pass: %s\n",
                         __func__, ggml_unary_op_name(ggml_get_unary_op(tensor)));

gguf-py/gguf/constants.py

@@ -409,6 +409,7 @@ class MODEL_ARCH(IntEnum):
     BAILINGMOE2      = auto()
     DOTS1            = auto()
     ARCEE            = auto()
+    AFMOE            = auto()
     ERNIE4_5         = auto()
     ERNIE4_5_MOE     = auto()
     HUNYUAN_MOE      = auto()
@@ -464,6 +465,7 @@ class MODEL_TENSOR(IntEnum):
     ATTN_POST_NORM       = auto()
     ATTN_ROT_EMBD        = auto()
     ATTN_SINKS           = auto()
+    ATTN_GATE            = auto()
     FFN_GATE_INP         = auto()
     FFN_GATE_INP_SHEXP   = auto()
     FFN_NORM             = auto()
@@ -776,6 +778,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
     MODEL_ARCH.BAILINGMOE2:      "bailingmoe2",
     MODEL_ARCH.DOTS1:            "dots1",
     MODEL_ARCH.ARCEE:            "arcee",
+    MODEL_ARCH.AFMOE:            "afmoe",
     MODEL_ARCH.ERNIE4_5:         "ernie4_5",
     MODEL_ARCH.ERNIE4_5_MOE:     "ernie4_5-moe",
     MODEL_ARCH.FALCON_H1:        "falcon-h1",
@@ -828,6 +831,7 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
     MODEL_TENSOR.ATTN_OUT:                  "blk.{bid}.attn_output",
     MODEL_TENSOR.ATTN_ROT_EMBD:             "blk.{bid}.attn_rot_embd",
     MODEL_TENSOR.ATTN_SINKS:                "blk.{bid}.attn_sinks",
+    MODEL_TENSOR.ATTN_GATE:                 "blk.{bid}.attn_gate",
     MODEL_TENSOR.ATTN_Q_NORM:               "blk.{bid}.attn_q_norm",
     MODEL_TENSOR.ATTN_K_NORM:               "blk.{bid}.attn_k_norm",
     MODEL_TENSOR.ATTN_OUT_NORM:             "blk.{bid}.attn_output_norm",
@@ -2693,6 +2697,33 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.FFN_DOWN,
         MODEL_TENSOR.FFN_UP,
     ],
+    MODEL_ARCH.AFMOE: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_POST_NORM,
+        MODEL_TENSOR.ATTN_Q,
+        MODEL_TENSOR.ATTN_K,
+        MODEL_TENSOR.ATTN_V,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.ATTN_Q_NORM,
+        MODEL_TENSOR.ATTN_K_NORM,
+        MODEL_TENSOR.ATTN_GATE,
+        MODEL_TENSOR.FFN_GATE,
+        MODEL_TENSOR.FFN_DOWN,
+        MODEL_TENSOR.FFN_UP,
+        MODEL_TENSOR.FFN_GATE_INP,
+        MODEL_TENSOR.FFN_GATE_EXP,
+        MODEL_TENSOR.FFN_DOWN_EXP,
+        MODEL_TENSOR.FFN_UP_EXP,
+        MODEL_TENSOR.FFN_GATE_SHEXP,
+        MODEL_TENSOR.FFN_UP_SHEXP,
+        MODEL_TENSOR.FFN_DOWN_SHEXP,
+        MODEL_TENSOR.FFN_PRE_NORM,
+        MODEL_TENSOR.FFN_POST_NORM,
+        MODEL_TENSOR.FFN_EXP_PROBS_B,
+    ],
     MODEL_ARCH.ERNIE4_5: [
         MODEL_TENSOR.TOKEN_EMBD,
         MODEL_TENSOR.OUTPUT_NORM,

gguf-py/gguf/tensor_mapping.py

@@ -314,6 +314,10 @@ class TensorNameMap:
             "model.layers.{bid}.self_attn.sinks", # openai-moe
         ),
 
+        MODEL_TENSOR.ATTN_GATE: (
+            "model.layers.{bid}.self_attn.gate_proj", # afmoe
+        ),
+
         # Feed-forward norm
         MODEL_TENSOR.FFN_NORM: (
             "gpt_neox.layers.{bid}.post_attention_layernorm",                # gptneox
@@ -340,11 +344,12 @@ class TensorNameMap:
             "model.layers.{bid}.feedforward_layernorm",                      # apertus
         ),
 
-        # Post feed-forward norm
+        # Pre feed-forward norm
         MODEL_TENSOR.FFN_PRE_NORM: (
             "model.layers.{bid}.pre_feedforward_layernorm", # gemma2
             "layers.{bid}.pre_feedforward_layernorm",       # embeddinggemma
             "model.layers.{bid}.pre_ff_layernorm.weight",
+            "model.layers.{bid}.pre_mlp_layernorm",        # afmoe
         ),
 
         # Post feed-forward norm
@@ -370,6 +375,7 @@ class TensorNameMap:
             "model.layers.{bid}.mlp.gate.wg",                   # hunyuan
             "model.layers.{bid}.block_sparse_moe.primary_router", # smallthinker
             "model.layers.{bid}.feed_forward.gate",               # lfm2moe
+            "model.layers.{bid}.mlp.router.gate",               # afmoe
         ),
 
         MODEL_TENSOR.FFN_GATE_INP_SHEXP: (
@@ -380,6 +386,7 @@ class TensorNameMap:
             "model.layers.{bid}.mlp.gate.e_score_correction",               # deepseek-v3 dots1
             "model.layers.{bid}.mlp.moe_statics.e_score_correction",        # ernie4.5-moe
             "model.layers.{bid}.mlp.gate.expert_bias",                      # bailingmoe2
+            "model.layers.{bid}.mlp.expert_bias",                           # afmoe
             "model.layers.{bid}.feed_forward.expert_bias",                  # lfm2moe
             "model.layers.{bid}.block_sparse_moe.e_score_correction",       # minimax-m2
         ),

scripts/sync_vendor.py

@@ -12,11 +12,30 @@ vendor = {
 
     "https://raw.githubusercontent.com/nothings/stb/refs/heads/master/stb_image.h": "vendor/stb/stb_image.h",
 
-    "https://github.com/mackron/miniaudio/raw/refs/tags/0.11.22/miniaudio.h": "vendor/miniaudio/miniaudio.h",
+    # not using latest tag to avoid this issue: https://github.com/ggml-org/llama.cpp/pull/17179#discussion_r2515877926
+    # "https://github.com/mackron/miniaudio/raw/refs/tags/0.11.23/miniaudio.h": "vendor/miniaudio/miniaudio.h",
+    "https://github.com/mackron/miniaudio/raw/669ed3e844524fcd883231b13095baee9f6de304/miniaudio.h": "vendor/miniaudio/miniaudio.h",
 
-    "https://raw.githubusercontent.com/yhirose/cpp-httplib/refs/tags/v0.20.1/httplib.h": "vendor/cpp-httplib/httplib.h",
+    "https://raw.githubusercontent.com/yhirose/cpp-httplib/refs/tags/v0.27.0/httplib.h": "vendor/cpp-httplib/httplib.h",
 }
 
 for url, filename in vendor.items():
     print(f"downloading {url} to {filename}") # noqa: NP100
     urllib.request.urlretrieve(url, filename)
+
+    # split cpp/h files for httplib
+    # see: https://github.com/yhirose/cpp-httplib/blob/master/split.py
+    if 'httplib.h' in filename:
+        border = '// ----------------------------------------------------------------------------'
+        with open(filename, 'r') as f:
+            content = f.read()
+        header, implementation, footer = content.split(border, 2)
+        fname_cpp = filename.replace('.h', '.cpp')
+        with open(filename, 'w') as fh:
+            fh.write(header)
+            fh.write(footer)
+        with open(fname_cpp, 'w') as fc:
+            fc.write('#include "httplib.h"\n')
+            fc.write('namespace httplib {\n')
+            fc.write(implementation.replace('\ninline ', '\n'))
+            fc.write('} // namespace httplib\n')

src/CMakeLists.txt

@@ -35,6 +35,7 @@ add_library(llama
             unicode-data.cpp
             unicode.cpp
             unicode.h
+            models/afmoe.cpp
             models/apertus.cpp
             models/arcee.cpp
             models/arctic.cpp

src/llama-arch.cpp

@@ -90,6 +90,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
     { LLM_ARCH_BAILINGMOE2,      "bailingmoe2"      },
     { LLM_ARCH_DOTS1,            "dots1"            },
     { LLM_ARCH_ARCEE,            "arcee"            },
+    { LLM_ARCH_AFMOE,            "afmoe"            },
     { LLM_ARCH_ERNIE4_5,         "ernie4_5"         },
     { LLM_ARCH_ERNIE4_5_MOE,     "ernie4_5-moe"     },
     { LLM_ARCH_HUNYUAN_MOE,      "hunyuan-moe"      },
@@ -333,6 +334,36 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
             { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
         },
     },
+    {
+        LLM_ARCH_AFMOE,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_OUTPUT,          "output" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_POST_NORM,  "blk.%d.post_attention_norm" },
+            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_ATTN_Q_NORM,     "blk.%d.attn_q_norm" },
+            { LLM_TENSOR_ATTN_K_NORM,     "blk.%d.attn_k_norm" },
+            { LLM_TENSOR_ATTN_GATE,       "blk.%d.attn_gate" },
+            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_POST_NORM,   "blk.%d.post_ffw_norm" },
+            { LLM_TENSOR_FFN_GATE_INP,    "blk.%d.ffn_gate_inp" },
+            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+            { LLM_TENSOR_FFN_GATE_EXPS,   "blk.%d.ffn_gate_exps" },
+            { LLM_TENSOR_FFN_DOWN_EXPS,   "blk.%d.ffn_down_exps" },
+            { LLM_TENSOR_FFN_UP_EXPS,     "blk.%d.ffn_up_exps" },
+            { LLM_TENSOR_FFN_GATE_SHEXP,  "blk.%d.ffn_gate_shexp" },
+            { LLM_TENSOR_FFN_UP_SHEXP,    "blk.%d.ffn_up_shexp" },
+            { LLM_TENSOR_FFN_DOWN_SHEXP,  "blk.%d.ffn_down_shexp" },
+            { LLM_TENSOR_FFN_EXP_PROBS_B, "blk.%d.exp_probs_b" },
+        },
+    },
     {
         LLM_ARCH_LLAMA4,
         {
@@ -2444,6 +2475,7 @@ static const std::map<llm_tensor, llm_tensor_info> LLM_TENSOR_INFOS = {
     {LLM_TENSOR_ATTN_V,                     {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
     {LLM_TENSOR_ATTN_QKV,                   {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
     {LLM_TENSOR_ATTN_OUT,                   {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_ATTN_GATE,                  {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
     {LLM_TENSOR_FFN_GATE,                   {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
     {LLM_TENSOR_FFN_DOWN,                   {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
     {LLM_TENSOR_FFN_UP,                     {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},

src/llama-arch.h

@@ -94,6 +94,7 @@ enum llm_arch {
     LLM_ARCH_BAILINGMOE2,
     LLM_ARCH_DOTS1,
     LLM_ARCH_ARCEE,
+    LLM_ARCH_AFMOE,
     LLM_ARCH_ERNIE4_5,
     LLM_ARCH_ERNIE4_5_MOE,
     LLM_ARCH_HUNYUAN_MOE,
@@ -312,6 +313,7 @@ enum llm_tensor {
     LLM_TENSOR_ATTN_POST_NORM,
     LLM_TENSOR_ATTN_ROT_EMBD,
     LLM_TENSOR_ATTN_SINKS,
+    LLM_TENSOR_ATTN_GATE,
     LLM_TENSOR_FFN_GATE_INP,
     LLM_TENSOR_FFN_GATE_INP_SHEXP,
     LLM_TENSOR_FFN_NORM,

src/llama-graph.cpp

@@ -1592,9 +1592,10 @@ ggml_tensor * llm_graph_context::build_attn(
             int       il) const {
     // these nodes are added to the graph together so that they are not reordered
     // by doing so, the number of splits in the graph is reduced
+    // expand k later to enable rope fusion which directly writes into k-v cache
     ggml_build_forward_expand(gf, q_cur);
-    ggml_build_forward_expand(gf, k_cur);
     ggml_build_forward_expand(gf, v_cur);
+    ggml_build_forward_expand(gf, k_cur);
 
     const auto * mctx_cur = inp->mctx;
 

src/llama-model.cpp

@@ -84,6 +84,7 @@ const char * llm_type_name(llm_type type) {
         case LLM_TYPE_15B:           return "15B";
         case LLM_TYPE_16B:           return "16B";
         case LLM_TYPE_20B:           return "20B";
+        case LLM_TYPE_26B:           return "26B";
         case LLM_TYPE_27B:           return "27B";
         case LLM_TYPE_30B:           return "30B";
         case LLM_TYPE_32B:           return "32B";
@@ -695,6 +696,37 @@ void llama_model::load_hparams(llama_model_loader & ml) {
                     default: type = LLM_TYPE_UNKNOWN;
                 }
             } break;
+        case LLM_ARCH_AFMOE:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+                ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT,   hparams.n_layer_dense_lead);
+                ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,  hparams.n_ff_exp);
+                ml.get_key(LLM_KV_EXPERT_SHARED_COUNT,         hparams.n_expert_shared);
+                ml.get_key(LLM_KV_EXPERT_GATING_FUNC,          hparams.expert_gating_func, false);
+                ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE,        hparams.expert_weights_scale, false);
+                ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM,         hparams.expert_weights_norm, false);
+                ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW,    hparams.n_swa, false);
+
+                // Set up interleaved sliding window attention (ISWA)
+                // Pattern: 3 sliding - 1 full (global_attn_every_n_layers = 4)
+                if (hparams.n_swa > 0) {
+                    hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
+                    hparams.set_swa_pattern(4);
+                } else {
+                    hparams.swa_type = LLAMA_SWA_TYPE_NONE;
+                }
+
+                // Default to sigmoid if not set
+                if (hparams.expert_gating_func == LLAMA_EXPERT_GATING_FUNC_TYPE_NONE) {
+                    hparams.expert_gating_func = LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID;
+                }
+
+                switch (hparams.n_layer) {
+                    case 56: type = LLM_TYPE_6B; break;
+                    case 32: type = LLM_TYPE_26B; break;
+                    default: type = LLM_TYPE_UNKNOWN;
+                }
+            } break;
         case LLM_ARCH_DECI:
             {
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
@@ -5749,6 +5781,71 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                         layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
                     }
                 } break;
+            case LLM_ARCH_AFMOE:
+                {
+                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+                    // output
+                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
+
+                    // if output is NULL, init from the input tok embed
+                    if (output == NULL) {
+                        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
+                    }
+
+                    const int64_t n_ff_exp = hparams.n_ff_exp;
+                    const int64_t n_expert_shared = hparams.n_expert_shared;
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        auto & layer = layers[i];
+
+                        // dual attention normalization
+                        layer.attn_norm      = create_tensor(tn(LLM_TENSOR_ATTN_NORM,      "weight", i), {n_embd}, 0);
+                        layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), {n_embd}, 0);
+
+                        // attention projections
+                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd_head_k * n_head}, 0);
+                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_k_gqa}, 0);
+                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_v_gqa}, 0);
+                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
+
+                        // Q/K normalization
+                        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, 0);
+                        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0);
+
+                        // attention gating
+                        layer.wqkv_gate = create_tensor(tn(LLM_TENSOR_ATTN_GATE, "weight", i), {n_embd, n_embd_head_k * n_head}, 0);
+
+                        // dual ffn normalization
+                        layer.ffn_norm      = create_tensor(tn(LLM_TENSOR_FFN_NORM,      "weight", i), {n_embd}, 0);
+                        layer.ffn_post_norm = create_tensor(tn(LLM_TENSOR_FFN_POST_NORM, "weight", i), {n_embd}, 0);
+
+                        if (static_cast<uint32_t>(i) >= hparams.n_layer_dense_lead) {
+                            // MoE layers
+                            layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
+                            layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, 0);
+
+                            // grouped expert weights
+                            layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert}, 0);
+                            layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}, 0);
+                            layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd, n_ff_exp, n_expert}, 0);
+
+                            // shared expert
+                            if (n_expert_shared > 0) {
+                                const int64_t n_ff_shexp = n_ff_exp * n_expert_shared;
+                                layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_shexp}, 0);
+                                layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {n_ff_shexp, n_embd}, 0);
+                                layer.ffn_up_shexp   = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {n_embd, n_ff_shexp}, 0);
+                            }
+                        } else {
+                            // Dense layers
+                            layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
+                            layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
+                            layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff}, 0);
+                        }
+                    }
+                } break;
             case LLM_ARCH_ERNIE4_5:
             case LLM_ARCH_ERNIE4_5_MOE:
                 {
@@ -7243,6 +7340,10 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const {
             {
                 llm = std::make_unique<llm_build_arcee>(*this, params);
             } break;
+        case LLM_ARCH_AFMOE:
+            {
+                llm = std::make_unique<llm_build_afmoe>(*this, params);
+            } break;
         case LLM_ARCH_ERNIE4_5:
             {
                 llm = std::make_unique<llm_build_ernie4_5>(*this, params);
@@ -7528,6 +7629,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
         case LLM_ARCH_MINIMAX_M2:
         case LLM_ARCH_COGVLM:
         case LLM_ARCH_PANGU_EMBED:
+        case LLM_ARCH_AFMOE:
             return LLAMA_ROPE_TYPE_NEOX;
 
         case LLM_ARCH_QWEN2VL:

src/llama-model.h

@@ -76,6 +76,7 @@ enum llm_type {
     LLM_TYPE_15B,
     LLM_TYPE_16B,
     LLM_TYPE_20B,
+    LLM_TYPE_26B,
     LLM_TYPE_27B,
     LLM_TYPE_30B,
     LLM_TYPE_32B,
@@ -234,6 +235,7 @@ struct llama_layer {
     struct ggml_tensor * wk_enc    = nullptr;
     struct ggml_tensor * wv_enc    = nullptr;
     struct ggml_tensor * wo_enc    = nullptr;
+    struct ggml_tensor * wqkv_gate = nullptr;
 
     // attention bias
     struct ggml_tensor * bq   = nullptr;

src/llama-sampling.cpp

@@ -4,6 +4,7 @@
 #include "llama-vocab.h"
 #include "llama-grammar.h"
 
+#include <array>
 #include <algorithm>
 #include <cassert>
 #include <cfloat>
@@ -1625,10 +1626,12 @@ static struct llama_sampler * llama_sampler_init_grammar_impl(
     auto * ctx = new llama_sampler_grammar;
 
     if (grammar_str != nullptr && grammar_str[0] != '\0') {
+        std::string trigger_pattern;
+        llama_grammar * grammar = nullptr;
         // TODO: remove trigger_words support.
         if (trigger_words != nullptr && num_trigger_words > 0) {
             GGML_ASSERT(trigger_patterns == nullptr && num_trigger_patterns == 0);
-            std::string trigger_pattern("[\\s\\S]*?(");
+            trigger_pattern = "[\\s\\S]*?(";
             for (size_t i = 0; i < num_trigger_words; ++i) {
                 static const std::regex special_chars("[.^$|()*+?\\[\\]{}\\\\]");
                 if (i > 0) {
@@ -1637,15 +1640,17 @@ static struct llama_sampler * llama_sampler_init_grammar_impl(
                 trigger_pattern += std::regex_replace(trigger_words[i], special_chars, "\\$0");
             }
             trigger_pattern += ")[\\s\\S]*";
-            const auto * trigger_pattern_c = trigger_pattern.c_str();
-            trigger_patterns = &trigger_pattern_c;
-            num_trigger_patterns = 1;
+
+            std::array<const char *, 1> tmp_trigger_patterns = { trigger_pattern.c_str() };
+            grammar = llama_grammar_init_impl(vocab, grammar_str, grammar_root, lazy, tmp_trigger_patterns.data(), tmp_trigger_patterns.size(), trigger_tokens, num_trigger_tokens);
+        } else {
+            grammar = llama_grammar_init_impl(vocab, grammar_str, grammar_root, lazy, trigger_patterns, num_trigger_patterns, trigger_tokens, num_trigger_tokens);
         }
         *ctx = {
             /* .vocab        = */ vocab,
             /* .grammar_str  = */ grammar_str,
             /* .grammar_root = */ grammar_root,
-            /* .grammar      = */ llama_grammar_init_impl(vocab, grammar_str, grammar_root, lazy, trigger_patterns, num_trigger_patterns, trigger_tokens, num_trigger_tokens),
+            /* .grammar      = */ grammar,
         };
         if (!ctx->grammar) {
             delete ctx;

src/llama-vocab.cpp

@@ -443,6 +443,17 @@ struct llm_tokenizer_bpe : llm_tokenizer {
                     "(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
                 };
                 break;
+            case LLAMA_VOCAB_PRE_TYPE_AFMOE:
+                regex_exprs = {
+                    // Digit handling - uses custom implementation in unicode.cpp
+                    // Groups digits with leading 1-2 based on total length modulo 3
+                    "\\p{AFMoE_digits}",
+                    // CJK and Asian scripts (using direct Unicode literals)
+                    "[一-鿿㐀-䶿豈-﫿぀-ゟ゠-ヿ･-ﾟ⼀-⿟เ-๿຀-໿ក-៿က-႟ꩠ-ꩿꧠ-꧿가-힯ᄀ-ᇿ]+",
+                    // Main BPE pattern
+                    "[!\"#$%&'()*+,\\-./:;<=>?@\\[\\\\\\]^_`{|}~][A-Za-z]+|[^\\r\\n\\p{L}\\p{P}\\p{S}]?[\\p{L}\\p{M}]+| ?[\\p{P}\\p{S}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
+                };
+                break;
             default:
                 // default regex for BPE tokenization pre-processing
                 regex_exprs = {
@@ -1013,7 +1024,7 @@ private:
         }
     private:
         uint32_t get_node(size_t index) {
-            if (index > xcda_array_size) {
+            if (index >= xcda_array_size) {
                 throw std::runtime_error("Index out of array bounds in XCDA array!");
             }
             return xcda_array[index];
@@ -1993,6 +2004,10 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
                 tokenizer_pre == "grok-2") {
                 pre_type = LLAMA_VOCAB_PRE_TYPE_GROK_2;
                 clean_spaces = false;
+            } else if (
+                tokenizer_pre == "afmoe") {
+                pre_type = LLAMA_VOCAB_PRE_TYPE_AFMOE;
+                clean_spaces = false;
             } else if (
                 tokenizer_pre == "minimax-m2") {
                 pre_type = LLAMA_VOCAB_PRE_TYPE_MINIMAX_M2;

src/llama-vocab.h

@@ -50,6 +50,7 @@ enum llama_vocab_pre_type {
     LLAMA_VOCAB_PRE_TYPE_GROK_2          = 39,
     LLAMA_VOCAB_PRE_TYPE_GRANITE_DOCLING = 40,
     LLAMA_VOCAB_PRE_TYPE_MINIMAX_M2      = 41,
+    LLAMA_VOCAB_PRE_TYPE_AFMOE           = 42,
 };
 
 struct LLM_KV;

src/models/afmoe.cpp

@@ -0,0 +1,187 @@
+#include "models.h"
+
+llm_build_afmoe::llm_build_afmoe(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+    const int64_t n_embd_head = hparams.n_embd_head_v;
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+
+    ggml_tensor * cur;
+    ggml_tensor * inpL;
+
+    inpL = build_inp_embd(model.tok_embd);
+
+    // MuP scaling: embeddings * sqrt(hidden_size)
+    // mup_enabled = true, hidden_size = 1024, scale = 32.0
+    inpL = ggml_scale(ctx0, inpL, sqrtf(float(n_embd)));
+    cb(inpL, "inp_embd_scaled", -1);
+
+    // inp_pos - contains the positions
+    ggml_tensor * inp_pos = build_inp_pos();
+    auto * inp_attn = build_attn_inp_kv_iswa();
+    ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+    const float kq_scale = 1.0f/sqrtf(float(n_embd_head));
+
+    for (int il = 0; il < n_layer; ++il) {
+        ggml_tensor * inpSA = inpL;
+
+        // dual attention normalization (pre)
+        cur = build_norm(inpL,
+                model.layers[il].attn_norm, NULL,
+                LLM_NORM_RMS, il);
+        cb(cur, "attn_norm", il);
+
+        // self-attention
+        {
+            ggml_tensor * attn_inp = cur;  // save input for gate computation
+
+            ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
+            cb(Qcur, "Qcur", il);
+
+            ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
+            cb(Kcur, "Kcur", il);
+
+            ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
+            cb(Vcur, "Vcur", il);
+
+            // compute gate from input
+            ggml_tensor * gate = build_lora_mm(model.layers[il].wqkv_gate, attn_inp);
+            cb(gate, "attn_gate_proj", il);
+
+            Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
+            Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
+
+            // Q/K normalization
+            Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);
+            Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il);
+            cb(Qcur, "Qcur_normed", il);
+            cb(Kcur, "Kcur_normed", il);
+
+            // RoPE only for sliding_attention layers
+            const bool use_rope = hparams.n_no_rope_layer_step > 0 &&
+                                ((il + 1) % hparams.n_no_rope_layer_step) != 0;
+            if (use_rope) {
+                Qcur = ggml_rope_ext(
+                        ctx0, Qcur, inp_pos, nullptr,
+                        n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                        ext_factor, attn_factor, beta_fast, beta_slow);
+                cb(Qcur, "Qcur_rope", il);
+
+                Kcur = ggml_rope_ext(
+                        ctx0, Kcur, inp_pos, nullptr,
+                        n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                        ext_factor, attn_factor, beta_fast, beta_slow);
+                cb(Kcur, "Kcur_rope", il);
+            }
+
+            Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+            cur = build_attn(inp_attn,
+                    NULL, NULL,  // wo will be applied after gating
+                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
+            cb(cur, "attn_out", il);
+
+            // attention gating: attn_out * sigmoid(gate) BEFORE o_proj
+            gate = ggml_sigmoid(ctx0, gate);
+            cb(gate, "attn_gate_sig", il);
+            cur = ggml_mul(ctx0, cur, gate);
+            cb(cur, "attn_gated", il);
+
+            // now apply output projection
+            cur = build_lora_mm(model.layers[il].wo, cur);
+            cb(cur, "attn_o_proj", il);
+        }
+
+        // dual attention normalization (post)
+        cur = build_norm(cur,
+                model.layers[il].attn_post_norm, NULL,
+                LLM_NORM_RMS, il);
+        cb(cur, "attn_post_norm", il);
+
+        if (il == n_layer - 1 && inp_out_ids) {
+            cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+        }
+
+        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+        cb(ffn_inp, "ffn_inp", il);
+
+        // dual ffn normalization (pre)
+        cur = build_norm(ffn_inp,
+                model.layers[il].ffn_norm, NULL,
+                LLM_NORM_RMS, il);
+        cb(cur, "ffn_norm", il);
+
+        // MoE or dense FFN
+        if ((uint32_t)il >= hparams.n_layer_dense_lead) {
+            // MoE layer with sigmoid routing, normalization, and scaling
+            ggml_tensor * moe_out = build_moe_ffn(cur,
+                    model.layers[il].ffn_gate_inp,
+                    model.layers[il].ffn_up_exps,
+                    model.layers[il].ffn_gate_exps,
+                    model.layers[il].ffn_down_exps,
+                    model.layers[il].ffn_exp_probs_b,
+                    n_expert, n_expert_used,
+                    LLM_FFN_SILU,
+                    hparams.expert_weights_norm,           // norm_w (route_norm=True)
+                    hparams.expert_weights_scale,          // scale_w
+                    hparams.expert_weights_scale,          // w_scale (route_scale=2.826)
+                    (llama_expert_gating_func_type) hparams.expert_gating_func,
+                    il);
+            cb(moe_out, "ffn_moe_out", il);
+
+            // shared expert
+            if (hparams.n_expert_shared > 0) {
+                ggml_tensor * ffn_shexp = build_ffn(cur,
+                        model.layers[il].ffn_up_shexp,   NULL, NULL,
+                        model.layers[il].ffn_gate_shexp, NULL, NULL,
+                        model.layers[il].ffn_down_shexp, NULL, NULL,
+                        NULL,
+                        LLM_FFN_SILU, LLM_FFN_PAR, il);
+                cb(ffn_shexp, "ffn_shexp", il);
+
+                cur = ggml_add(ctx0, moe_out, ffn_shexp);
+                cb(cur, "ffn_out", il);
+            } else {
+                cur = moe_out;
+            }
+        } else {
+            // dense layer
+            cur = build_ffn(cur,
+                    model.layers[il].ffn_up,   NULL, NULL,
+                    model.layers[il].ffn_gate, NULL, NULL,
+                    model.layers[il].ffn_down, NULL, NULL,
+                    NULL,
+                    LLM_FFN_SILU, LLM_FFN_PAR, il);
+            cb(cur, "ffn_out", il);
+        }
+
+        // dual ffn normalization (post)
+        cur = build_norm(cur,
+                model.layers[il].ffn_post_norm, NULL,
+                LLM_NORM_RMS, il);
+        cb(cur, "ffn_post_norm", il);
+
+        cur = ggml_add(ctx0, cur, ffn_inp);
+        cur = build_cvec(cur, il);
+        cb(cur, "l_out", il);
+
+        // input for next layer
+        inpL = cur;
+    }
+
+    cur = inpL;
+
+    cur = build_norm(cur,
+            model.output_norm, NULL,
+            LLM_NORM_RMS, -1);
+    cb(cur, "result_norm", -1);
+
+    res->t_embd = cur;
+
+    // lm_head
+    cur = build_lora_mm(model.output, cur);
+    cb(cur, "result_output", -1);
+    res->t_logits = cur;
+
+    ggml_build_forward_expand(gf, cur);
+}

src/models/models.h

@@ -57,6 +57,10 @@ struct llm_build_rwkv7_base : public llm_graph_context {
                                        int                  il) const;
 };
 
+struct llm_build_afmoe : public llm_graph_context {
+    llm_build_afmoe(const llama_model & model, const llm_graph_params & params);
+};
+
 struct llm_build_apertus : public llm_graph_context {
     llm_build_apertus(const llama_model & model, const llm_graph_params & params);
 };

src/unicode.cpp

@@ -729,6 +729,80 @@ static std::vector<size_t> unicode_regex_split_custom_kimi_k2(const std::string
     return bpe_offsets;
 }
 
+// AFMOE digit handling: splits digits with leading 1-2 based on total length modulo 3
+static std::vector<size_t> unicode_regex_split_custom_afmoe(const std::string & text, const std::vector<size_t> & offsets) {
+    std::vector<size_t> bpe_offsets;
+    bpe_offsets.reserve(offsets.size());
+
+    const auto cpts = unicode_cpts_from_utf8(text);
+
+    size_t start = 0;
+    for (auto offset : offsets) {
+        const size_t offset_ini = start;
+        const size_t offset_end = start + offset;
+        assert(offset_end <= cpts.size());
+        start = offset_end;
+
+        auto _get_flags = [&] (const size_t pos) -> unicode_cpt_flags {
+            return (offset_ini <= pos && pos < offset_end) ? unicode_cpt_flags_from_cpt(cpts[pos]) : unicode_cpt_flags{};
+        };
+
+        size_t _prev_end = offset_ini;
+        auto _add_token = [&] (const size_t end) -> size_t {
+            assert(_prev_end <= end && end <= offset_end);
+            size_t len = end - _prev_end;
+            if (len > 0) {
+                bpe_offsets.push_back(len);
+            }
+            _prev_end = end;
+            return len;
+        };
+
+        for (size_t pos = offset_ini; pos < offset_end; ) {
+            const auto flags = _get_flags(pos);
+
+            // Handle digit sequences with special splitting logic
+            if (flags.is_number) {
+                size_t digit_start = pos;
+                size_t digit_count = 0;
+
+                // Count consecutive digits
+                while (_get_flags(pos).is_number && pos < offset_end) {
+                    digit_count++;
+                    pos++;
+                }
+
+                // Split based on total length modulo 3
+                size_t remainder = digit_count % 3;
+                size_t current = digit_start;
+
+                // Emit leading 1-2 digits if needed
+                if (remainder > 0) {
+                    _add_token(current + remainder);
+                    current += remainder;
+                }
+
+                // Emit groups of 3
+                while (current < digit_start + digit_count) {
+                    _add_token(current + 3);
+                    current += 3;
+                }
+                continue;
+            }
+
+            // For non-digits, just move forward
+            pos++;
+        }
+
+        // Add any remaining content
+        if (_prev_end < offset_end) {
+            _add_token(offset_end);
+        }
+    }
+
+    return bpe_offsets;
+}
+
 static std::vector<size_t> unicode_regex_split_custom(const std::string & text, const std::string & regex_expr, const std::vector<size_t> & offsets) {
     std::vector<size_t> bpe_offsets;
 
@@ -742,6 +816,9 @@ static std::vector<size_t> unicode_regex_split_custom(const std::string & text,
     } else if (regex_expr == "\\p{Han}+") {
         // K2's first pattern - handle all K2 patterns together
         bpe_offsets = unicode_regex_split_custom_kimi_k2(text, offsets);
+    } else if (regex_expr == "\\p{AFMoE_digits}") {
+        // AFMOE digit pattern - use custom implementation for proper splitting
+        bpe_offsets = unicode_regex_split_custom_afmoe(text, offsets);
     }
 
     return bpe_offsets;

tests/test-backend-ops.cpp

@@ -175,6 +175,38 @@ static void init_tensor_kq_mask(ggml_tensor * tensor, float min = -1.0f, float m
     ggml_backend_tensor_set(tensor, data_f16.data(), 0, data_f16.size()*sizeof(ggml_fp16_t));
 }
 
+// generate a lower triangular matrix
+static void init_tensor_tril(ggml_tensor * tensor, float min = -1.0f, float max = 1.0f) {
+    GGML_ASSERT(tensor->type == GGML_TYPE_F32);
+    GGML_ASSERT(tensor->ne[0] == tensor->ne[1]);
+
+    GGML_TENSOR_LOCALS(int32_t, ne, tensor, ne);
+    GGML_TENSOR_LOCALS(size_t, nb, tensor, nb);
+
+    std::vector<float> data_f32(ne0*ne1*ne2*ne3);
+
+    std::random_device rd;
+    std::mt19937 gen(rd());
+    std::uniform_real_distribution<float> dis(min, max);
+
+    for (int64_t i3 = 0; i3 < ne3; i3++) {
+        for (int64_t i2 = 0; i2 < ne2; i2++) {
+            for (int64_t i1 = 0; i1 < ne1; i1++) {
+                for (int64_t i0 = 0; i0 < ne0; i0++) {
+                    int64_t idx = (i0 * nb0 + i1 * nb1 + i2 * nb2 + i3 * nb3) / sizeof(float);
+                    if (i0 <= i1) {
+                        data_f32[idx] = dis(gen);
+                    } else {
+                        data_f32[idx] = 0.0f;
+                    }
+                }
+            }
+        }
+    }
+
+    ggml_backend_tensor_set(tensor, data_f32.data(), 0, ggml_nbytes(tensor));
+}
+
 static std::vector<float> tensor_to_float(const ggml_tensor * t) {
     std::vector<float> tv;
     tv.reserve(ggml_nelements(t));
@@ -1804,7 +1836,8 @@ struct test_unary : public test_case {
 
     ggml_tensor * build_graph(ggml_context * ctx) override {
         const bool grad_supported = op == GGML_UNARY_OP_ABS || op == GGML_UNARY_OP_SGN || op == GGML_UNARY_OP_NEG ||
-            op == GGML_UNARY_OP_STEP || op == GGML_UNARY_OP_RELU || op == GGML_UNARY_OP_SILU;
+            op == GGML_UNARY_OP_STEP || op == GGML_UNARY_OP_RELU || op == GGML_UNARY_OP_SILU ||
+            op == GGML_UNARY_OP_EXPM1 || op == GGML_UNARY_OP_SOFTPLUS;
 
         ggml_tensor * a;
         if (v & 1) {
@@ -2779,7 +2812,7 @@ struct test_bin_bcast : public test_case {
     const std::array<int, 4> nr;
     int nf; // number of fused ops, nf == 1 -> single op (no fusion)
 
-    bool run_whole_graph() override { return true; }
+    bool run_whole_graph() override { return nf > 1; }
 
     std::string vars() override {
         return VARS_TO_STR4(type, ne, nr, nf);
@@ -5395,6 +5428,7 @@ struct test_pad : public test_case {
     }
 };
 
+// GGML_OP_PAD (with extension)
 struct test_pad_ext : public test_case {
     const ggml_type type;
     const std::array<int64_t, 4> ne_a;
@@ -5802,6 +5836,7 @@ struct test_opt_step_adamw : public test_case {
     }
 };
 
+// GGML_OP_OPT_STEP_SGD
 struct test_opt_step_sgd : public test_case {
     const ggml_type              type;
     const std::array<int64_t, 4> ne;
@@ -5841,6 +5876,170 @@ struct test_opt_step_sgd : public test_case {
     }
 };
 
+// GGML_OP_CUMSUM
+struct test_cumsum : public test_case {
+    const ggml_type              type;
+    const std::array<int64_t, 4> ne;
+
+    std::string vars() override { return VARS_TO_STR2(type, ne); }
+
+    test_cumsum(ggml_type type = GGML_TYPE_F32,
+            std::array<int64_t, 4> ne = { 10, 5, 4, 3 })
+        : type(type), ne(ne) {}
+
+    ggml_tensor * build_graph(ggml_context * ctx) override {
+        ggml_tensor * a = ggml_new_tensor_4d(ctx, type, ne[0], ne[1], ne[2], ne[3]);
+        ggml_set_param(a);
+        ggml_set_name(a, "a");
+
+        ggml_tensor * out = ggml_cumsum(ctx, a);
+
+        ggml_set_name(out, "out");
+
+        return out;
+    }
+
+    void initialize_tensors(ggml_context * ctx) override {
+        for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) {
+            init_tensor_uniform(t, -1.0f, 1.0f);
+        }
+    }
+};
+
+// GGML_OP_XIELU
+struct test_xielu : public test_case {
+    const ggml_type              type;
+    const std::array<int64_t, 4> ne;
+
+    std::string vars() override { return VARS_TO_STR2(type, ne); }
+
+    test_xielu(ggml_type type = GGML_TYPE_F32,
+            std::array<int64_t, 4> ne = { 10, 5, 4, 3 })
+        : type(type), ne(ne) {}
+
+    ggml_tensor * build_graph(ggml_context * ctx) override {
+        ggml_tensor * a = ggml_new_tensor_4d(ctx, type, ne[0], ne[1], ne[2], ne[3]);
+        ggml_set_param(a);
+        ggml_set_name(a, "a");
+
+        float alpha_n = 4.0f;
+        float alpha_p = 20.0f;
+        float beta = 0.5f;
+        float eps = 0.0000001f;
+
+        ggml_tensor * out = ggml_xielu(ctx, a, alpha_n, alpha_p, beta, eps);
+
+        ggml_set_name(out, "out");
+
+        return out;
+    }
+
+    void initialize_tensors(ggml_context * ctx) override {
+        for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) {
+            init_tensor_uniform(t, -1.0f, 1.0f);
+        }
+    }
+};
+
+// GGML_OP_TRI
+struct test_tri : public test_case {
+    const ggml_type              type;
+    const std::array<int64_t, 4> ne;
+    const ggml_tri_type          tri_type;
+
+    std::string vars() override { return VARS_TO_STR3(type, ne, tri_type); }
+
+    test_tri(ggml_tri_type tri_type, ggml_type type = GGML_TYPE_F32,
+            std::array<int64_t, 4> ne = { 10, 10, 4, 3 })
+        : type(type), ne(ne), tri_type(tri_type) {
+            GGML_ASSERT(ne[0] == ne[1]);
+        }
+
+    ggml_tensor * build_graph(ggml_context * ctx) override {
+        ggml_tensor * a = ggml_new_tensor_4d(ctx, type, ne[0], ne[1], ne[2], ne[3]);
+        ggml_set_param(a);
+        ggml_set_name(a, "a");
+
+        ggml_tensor * out = ggml_tri(ctx, a, tri_type);
+
+        ggml_set_name(out, "out");
+
+        return out;
+    }
+
+    void initialize_tensors(ggml_context * ctx) override {
+        for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) {
+            init_tensor_uniform(t, -1.0f, 1.0f);
+        }
+    }
+};
+
+// GGML_OP_FILL
+struct test_fill : public test_case {
+    const ggml_type              type;
+    const std::array<int64_t, 4> ne;
+    float                        c;
+
+    std::string vars() override { return VARS_TO_STR3(type, ne, c); }
+
+    test_fill(float c, ggml_type type = GGML_TYPE_F32,
+            std::array<int64_t, 4> ne = { 10, 10, 4, 3 })
+        : type(type), ne(ne), c(c) {}
+
+    ggml_tensor * build_graph(ggml_context * ctx) override {
+        ggml_tensor * a = ggml_new_tensor_4d(ctx, type, ne[0], ne[1], ne[2], ne[3]);
+        ggml_set_param(a);
+        ggml_set_name(a, "a");
+
+        ggml_tensor * out = ggml_fill(ctx, a, c);
+
+        ggml_set_name(out, "out");
+
+        return out;
+    }
+};
+
+// GGML_OP_SOLVE_TRI
+struct test_solve_tri : public test_case {
+    const ggml_type              type;
+    const std::array<int64_t, 4> ne_lhs;
+    const std::array<int64_t, 4> ne_rhs;
+
+    std::string vars() override { return VARS_TO_STR3(type, ne_lhs, ne_rhs); }
+
+    test_solve_tri(ggml_type type = GGML_TYPE_F32,
+            std::array<int64_t, 4> ne_lhs = { 10, 10, 4, 3 },
+            std::array<int64_t, 4> ne_rhs = { 3, 10, 4, 3 }
+        )
+        : type(type), ne_lhs(ne_lhs), ne_rhs(ne_rhs) {}
+
+    ggml_tensor * build_graph(ggml_context * ctx) override {
+        ggml_tensor * a = ggml_new_tensor_4d(ctx, type, ne_lhs[0], ne_lhs[1], ne_lhs[2], ne_lhs[3]);
+        ggml_set_param(a);
+        ggml_set_name(a, "a");
+
+        ggml_tensor * b = ggml_new_tensor_4d(ctx, type, ne_rhs[0], ne_rhs[1], ne_rhs[2], ne_rhs[3]);
+        ggml_set_param(b);
+        ggml_set_name(b, "b");
+
+        ggml_tensor * out = ggml_solve_tri(ctx, a, b, true, true, false);
+        ggml_set_name(out, "out");
+
+        return out;
+    }
+
+    void initialize_tensors(ggml_context * ctx) override {
+        for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) {
+            if (strcmp(t->name, "a") == 0) {
+                // note: avoid zeros in the diagonal
+                init_tensor_tril(t, 0.1, 1.0f);
+            } else {
+                init_tensor_uniform(t, -1.0f, 1.0f);
+            }
+        }
+    }
+};
+
 enum llm_norm_type {
     LLM_NORM,
     LLM_NORM_RMS,
@@ -6282,6 +6481,9 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
     for (ggml_type type : {GGML_TYPE_F16, GGML_TYPE_F32}) {
         for (int v : {0, 1}) {
             for (int op = 0; op < GGML_UNARY_OP_COUNT; op++) {
+                if (op == GGML_UNARY_OP_XIELU) {
+                    continue; // need extra params, separate test
+                }
                 test_cases.emplace_back(new test_unary((ggml_unary_op) op, type, { 128, 2, 2, 2 }, v));
                 test_cases.emplace_back(new test_unary((ggml_unary_op) op, type, { 5, 7, 11, 13 }, v));
             }
@@ -7290,8 +7492,13 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
         test_cases.emplace_back(new test_argsort(GGML_TYPE_F32, {8, 1, 1, 1}, order));
         test_cases.emplace_back(new test_argsort(GGML_TYPE_F32, {16, 10, 10, 10}, order));
         test_cases.emplace_back(new test_argsort(GGML_TYPE_F32, {60, 10, 10, 10}, order)); // qwen
-        test_cases.emplace_back(new test_argsort(GGML_TYPE_F32, {1024, 1, 1, 1}, order));
+        test_cases.emplace_back(new test_argsort(GGML_TYPE_F32, {1023, 2, 1, 3}, order));
+        test_cases.emplace_back(new test_argsort(GGML_TYPE_F32, {1024, 2, 1, 3}, order));
+        test_cases.emplace_back(new test_argsort(GGML_TYPE_F32, {1025, 2, 1, 3}, order));
         test_cases.emplace_back(new test_argsort(GGML_TYPE_F32, {16384, 1, 1, 1}, order)); // many backends only handle up to 1024
+        test_cases.emplace_back(new test_argsort(GGML_TYPE_F32, {2047, 2, 1, 3}, order));
+        test_cases.emplace_back(new test_argsort(GGML_TYPE_F32, {2048, 2, 1, 3}, order));
+        test_cases.emplace_back(new test_argsort(GGML_TYPE_F32, {2049, 2, 1, 3}, order));
         test_cases.emplace_back(new test_argsort(GGML_TYPE_F32, {2, 8, 8192, 1}, order)); // bailingmoe2 (group selection)
     }
 
@@ -7339,6 +7546,26 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
     test_cases.emplace_back(new test_arange());
     test_cases.emplace_back(new test_timestep_embedding());
     test_cases.emplace_back(new test_leaky_relu());
+    test_cases.emplace_back(new test_cumsum());
+
+    test_cases.emplace_back(new test_xielu());
+
+    test_cases.emplace_back(new test_tri(GGML_TRI_TYPE_LOWER));
+    test_cases.emplace_back(new test_tri(GGML_TRI_TYPE_LOWER_DIAG));
+    test_cases.emplace_back(new test_tri(GGML_TRI_TYPE_UPPER));
+    test_cases.emplace_back(new test_tri(GGML_TRI_TYPE_UPPER_DIAG));
+
+    test_cases.emplace_back(new test_fill(0.0f));
+    test_cases.emplace_back(new test_fill(2.0f, GGML_TYPE_F32, { 303, 207, 11, 3 }));
+    test_cases.emplace_back(new test_fill(-152.0f, GGML_TYPE_F32, { 800, 600, 4, 4 }));
+
+    test_cases.emplace_back(new test_solve_tri());
+    test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 11, 11, 1, 1 }, { 5, 11, 1, 1 }));
+    test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 17, 17, 2, 4 }, { 9, 17, 2, 4 }));
+    test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 30, 30, 7, 1 }, { 8, 30, 7, 1 }));
+    test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 42, 42, 5, 2 }, { 10, 42, 5, 2 }));
+    test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 64, 64, 2, 2 }, { 10, 64, 2, 2 }));
+    test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 100, 100, 4, 4 }, { 41, 100, 4, 4 }));
 
     for (bool v : {false, true}) {
         test_cases.emplace_back(new test_pad_ext(GGML_TYPE_F32, {512, 512, 1, 1}, 0, 1, 0, 1, 0, 0, 0, 0, v));
@@ -7631,6 +7858,8 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_perf() {
         test_cases.emplace_back(new test_sum(GGML_TYPE_F32, it));
     }
 
+    test_cases.emplace_back(new test_argsort(GGML_TYPE_F32, {65000, 16, 1, 1}));
+
     return test_cases;
 }
 

tools/mtmd/clip-impl.h

@@ -224,7 +224,6 @@ static void clip_log_callback_default(enum ggml_log_level level, const char * te
 }
 
 struct clip_logger_state {
-    ggml_log_level verbosity_thold;
     ggml_log_callback log_callback;
     void * log_callback_user_data;
 };
@@ -258,17 +257,11 @@ static void clip_log_internal(enum ggml_log_level level, const char * format, ..
     va_end(args);
 }
 
-#define LOG_TMPL(level, ...) \
-    do { \
-        if ((level) >= g_logger_state.verbosity_thold) { \
-            clip_log_internal((level), __VA_ARGS__); \
-        } \
-    } while (0)
-#define LOG_INF(...) LOG_TMPL(GGML_LOG_LEVEL_INFO,  __VA_ARGS__)
-#define LOG_WRN(...) LOG_TMPL(GGML_LOG_LEVEL_WARN,  __VA_ARGS__)
-#define LOG_ERR(...) LOG_TMPL(GGML_LOG_LEVEL_ERROR, __VA_ARGS__)
-#define LOG_DBG(...) LOG_TMPL(GGML_LOG_LEVEL_DEBUG, __VA_ARGS__)
-#define LOG_CNT(...) LOG_TMPL(GGML_LOG_LEVEL_CONT,  __VA_ARGS__)
+#define LOG_INF(...) clip_log_internal(GGML_LOG_LEVEL_INFO,  __VA_ARGS__)
+#define LOG_WRN(...) clip_log_internal(GGML_LOG_LEVEL_WARN,  __VA_ARGS__)
+#define LOG_ERR(...) clip_log_internal(GGML_LOG_LEVEL_ERROR, __VA_ARGS__)
+#define LOG_DBG(...) clip_log_internal(GGML_LOG_LEVEL_DEBUG, __VA_ARGS__)
+#define LOG_CNT(...) clip_log_internal(GGML_LOG_LEVEL_CONT,  __VA_ARGS__)
 
 //
 // cpp wrappers

tools/mtmd/clip.cpp

@@ -24,8 +24,7 @@
 #include <array>
 #include <functional>
 
-// TODO: allow to pass callback from user code
-struct clip_logger_state g_logger_state = {GGML_LOG_LEVEL_CONT, clip_log_callback_default, NULL};
+struct clip_logger_state g_logger_state = {clip_log_callback_default, NULL};
 
 enum ffn_op_type {
     FFN_GELU,
@@ -3507,7 +3506,6 @@ struct clip_model_loader {
 };
 
 struct clip_init_result clip_init(const char * fname, struct clip_context_params ctx_params) {
-    g_logger_state.verbosity_thold = ctx_params.verbosity;
     clip_ctx * ctx_vision = nullptr;
     clip_ctx * ctx_audio = nullptr;
 

tools/mtmd/clip.h

@@ -31,7 +31,6 @@ enum clip_flash_attn_type {
 
 struct clip_context_params {
     bool use_gpu;
-    enum ggml_log_level verbosity;
     enum clip_flash_attn_type flash_attn_type;
     int image_min_tokens;
     int image_max_tokens;

tools/mtmd/mtmd-cli.cpp

@@ -135,7 +135,6 @@ struct mtmd_cli_context {
         mparams.use_gpu          = params.mmproj_use_gpu;
         mparams.print_timings    = true;
         mparams.n_threads        = params.cpuparams.n_threads;
-        mparams.verbosity        = params.verbosity > 0 ? GGML_LOG_LEVEL_DEBUG : GGML_LOG_LEVEL_INFO;
         mparams.flash_attn_type  = params.flash_attn_type;
         mparams.image_min_tokens = params.image_min_tokens;
         mparams.image_max_tokens = params.image_max_tokens;
@@ -277,6 +276,7 @@ int main(int argc, char ** argv) {
     }
 
     common_init();
+    mtmd_helper_log_set(common_log_default_callback, nullptr);
 
     if (params.mmproj.path.empty()) {
         show_additional_info(argc, argv);

tools/mtmd/mtmd-helper.cpp

@@ -32,8 +32,65 @@
 #define STB_IMAGE_IMPLEMENTATION
 #include "stb/stb_image.h"
 
-#define LOG_INF(...) fprintf(stdout, __VA_ARGS__)
-#define LOG_ERR(...) fprintf(stderr, __VA_ARGS__)
+//
+// internal logging functions
+//
+
+struct mtmd_helper_logger {
+    ggml_log_callback default_callback = [](ggml_log_level level, const char * text, void * user_data) {
+        (void) level;
+        (void) user_data;
+        fputs(text, stderr);
+        fflush(stderr);
+    };
+
+    ggml_log_callback log_callback = default_callback;
+    void * log_callback_user_data;
+
+    void log_v(enum ggml_log_level level, const char * format, va_list args) {
+        if (format == NULL) {
+            return;
+        }
+        va_list args_copy;
+        va_copy(args_copy, args);
+        char buffer[128];
+        int len = vsnprintf(buffer, 128, format, args);
+        if (len < 128) {
+            log_callback(level, buffer, log_callback_user_data);
+        } else {
+            char * buffer2 = (char *) calloc(len + 1, sizeof(char));
+            vsnprintf(buffer2, len + 1, format, args_copy);
+            buffer2[len] = 0;
+            log_callback(level, buffer2, log_callback_user_data);
+            free(buffer2);
+        }
+        va_end(args_copy);
+    }
+
+    void log(enum ggml_log_level level, const char * format, ...) {
+        va_list args;
+        va_start(args, format);
+        log_v(level, format, args);
+        va_end(args);
+    }
+} g_logger;
+
+#define LOG_INF(...) g_logger.log(GGML_LOG_LEVEL_INFO,  __VA_ARGS__)
+#define LOG_WRN(...) g_logger.log(GGML_LOG_LEVEL_WARN,  __VA_ARGS__)
+#define LOG_ERR(...) g_logger.log(GGML_LOG_LEVEL_ERROR, __VA_ARGS__)
+
+void mtmd_helper_log_set(ggml_log_callback log_callback, void * user_data) {
+    if (log_callback == nullptr) {
+        log_callback = g_logger.default_callback;
+    }
+    g_logger.log_callback = log_callback;
+    g_logger.log_callback_user_data = user_data;
+    mtmd_log_set(log_callback, user_data);
+}
+
+//
+// helper functions
+//
 
 size_t mtmd_helper_get_n_tokens(const mtmd_input_chunks * chunks) {
     size_t n_tokens = 0;
@@ -325,7 +382,7 @@ int32_t mtmd_helper_eval_chunks(mtmd_context * ctx,
                                 llama_pos * new_n_past) {
     size_t n_chunks = mtmd_input_chunks_size(chunks);
     if (n_chunks == 0) {
-        LOG_ERR("no chunks to eval\n");
+        LOG_WRN("no chunks to eval\n");
         return 0;
     }
 

tools/mtmd/mtmd-helper.h

@@ -20,6 +20,11 @@ extern "C" {
 // BREAKING CHANGES are expected.
 //
 
+// Set callback for all future logging events.
+// If this is not called, or NULL is supplied, everything is output on stderr.
+// Note: this also call mtmd_log_set() internally
+MTMD_API void mtmd_helper_log_set(ggml_log_callback log_callback, void * user_data);
+
 // helper function to construct a mtmd_bitmap from a file
 // it calls mtmd_helper_bitmap_init_from_buf() internally
 // returns nullptr on failure

tools/mtmd/mtmd.cpp

@@ -105,7 +105,6 @@ mtmd_context_params mtmd_context_params_default() {
         /* use_gpu           */ true,
         /* print_timings     */ true,
         /* n_threads         */ 4,
-        /* verbosity         */ GGML_LOG_LEVEL_INFO,
         /* image_marker      */ MTMD_DEFAULT_IMAGE_MARKER,
         /* media_marker      */ mtmd_default_marker(),
         /* flash_attn_type   */ LLAMA_FLASH_ATTN_TYPE_AUTO,
@@ -175,7 +174,6 @@ struct mtmd_context {
 
         clip_context_params ctx_clip_params {
             /* use_gpu           */ ctx_params.use_gpu,
-            /* verbosity         */ ctx_params.verbosity,
             /* flash_attn_type   */ CLIP_FLASH_ATTN_TYPE_AUTO,
             /* image_min_tokens  */ ctx_params.image_min_tokens,
             /* image_max_tokens  */ ctx_params.image_max_tokens,
@@ -1096,3 +1094,8 @@ mtmd_input_chunks * mtmd_test_create_input_chunks() {
 
     return chunks;
 }
+
+void mtmd_log_set(ggml_log_callback log_callback, void * user_data) {
+    g_logger_state.log_callback = log_callback ? log_callback : clip_log_callback_default;
+    g_logger_state.log_callback_user_data = user_data;
+}

tools/mtmd/mtmd.h

@@ -79,7 +79,6 @@ struct mtmd_context_params {
     bool use_gpu;
     bool print_timings;
     int n_threads;
-    enum ggml_log_level verbosity;
     const char * image_marker; // deprecated, use media_marker instead
     const char * media_marker;
     enum llama_flash_attn_type flash_attn_type;
@@ -215,6 +214,10 @@ MTMD_API int32_t mtmd_encode_chunk(mtmd_context * ctx,
 // llama_model_n_embd(model) * mtmd_input_chunk_get_n_tokens(chunk) * sizeof(float)
 MTMD_API float * mtmd_get_output_embd(mtmd_context * ctx);
 
+// Set callback for all future logging events.
+// If this is not called, or NULL is supplied, everything is output on stderr.
+MTMD_API void mtmd_log_set(ggml_log_callback log_callback, void * user_data);
+
 /////////////////////////////////////////
 
 // test function, to be used in test-mtmd-c-api.c

tools/server/CMakeLists.txt

@@ -7,6 +7,10 @@ if (MINGW)
     add_compile_definitions(_WIN32_WINNT=${GGML_WIN_VER})
 endif()
 
+if (NOT LLAMA_HTTPLIB)
+    message(FATAL_ERROR "LLAMA_HTTPLIB is OFF, cannot build llama-server. Hint: to skip building server, set -DLLAMA_BUILD_SERVER=OFF")
+endif()
+
 set(TARGET_SRCS
     server.cpp
     utils.hpp
@@ -33,7 +37,7 @@ install(TARGETS ${TARGET} RUNTIME)
 
 target_include_directories(${TARGET} PRIVATE ../mtmd)
 target_include_directories(${TARGET} PRIVATE ${CMAKE_SOURCE_DIR})
-target_link_libraries(${TARGET} PRIVATE common mtmd ${CMAKE_THREAD_LIBS_INIT})
+target_link_libraries(${TARGET} PRIVATE common mtmd cpp-httplib ${CMAKE_THREAD_LIBS_INIT})
 
 if (WIN32)
     TARGET_LINK_LIBRARIES(${TARGET} PRIVATE ws2_32)

tools/server/server.cpp

@@ -684,7 +684,7 @@ struct server_task_result {
     }
     virtual bool is_stop() {
         // only used by server_task_result_cmpl_*
-        return false;
+        return true;
     }
     virtual int get_index() {
         return -1;
@@ -2454,11 +2454,12 @@ struct server_context {
 
         std::string & mmproj_path = params_base.mmproj.path;
         if (!mmproj_path.empty()) {
+            mtmd_helper_log_set(common_log_default_callback, nullptr);
+
             mtmd_context_params mparams = mtmd_context_params_default();
             mparams.use_gpu          = params_base.mmproj_use_gpu;
             mparams.print_timings    = false;
             mparams.n_threads        = params_base.cpuparams.n_threads;
-            mparams.verbosity        = params_base.verbosity > 0 ? GGML_LOG_LEVEL_DEBUG : GGML_LOG_LEVEL_INFO;
             mparams.flash_attn_type  = params_base.flash_attn_type;
             mparams.image_min_tokens = params_base.image_min_tokens;
             mparams.image_max_tokens = params_base.image_max_tokens;
@@ -3238,105 +3239,6 @@ struct server_context {
         queue_results.send(std::move(res));
     }
 
-    //
-    // Functions to create new task(s) and receive result(s)
-    //
-
-    void cancel_tasks(const std::unordered_set<int> & id_tasks) {
-        std::vector<server_task> cancel_tasks;
-        cancel_tasks.reserve(id_tasks.size());
-        for (const auto & id_task : id_tasks) {
-            SRV_WRN("cancel task, id_task = %d\n", id_task);
-
-            server_task task(SERVER_TASK_TYPE_CANCEL);
-            task.id_target = id_task;
-            queue_results.remove_waiting_task_id(id_task);
-            cancel_tasks.push_back(std::move(task));
-        }
-        // push to beginning of the queue, so it has highest priority
-        queue_tasks.post(std::move(cancel_tasks), true);
-    }
-
-    // receive the results from task(s)
-    void receive_multi_results(
-            const std::unordered_set<int> & id_tasks,
-            const std::function<void(std::vector<server_task_result_ptr>&)> & result_handler,
-            const std::function<void(json)> & error_handler,
-            const std::function<bool()> & is_connection_closed) {
-        std::vector<server_task_result_ptr> results(id_tasks.size());
-        for (int i = 0; i < (int)id_tasks.size(); i++) {
-            server_task_result_ptr result = queue_results.recv_with_timeout(id_tasks, HTTP_POLLING_SECONDS);
-
-            if (is_connection_closed()) {
-                cancel_tasks(id_tasks);
-                return;
-            }
-
-            if (result == nullptr) {
-                i--; // retry
-                continue;
-            }
-
-            if (result->is_error()) {
-                error_handler(result->to_json());
-                cancel_tasks(id_tasks);
-                return;
-            }
-
-            GGML_ASSERT(
-                dynamic_cast<server_task_result_cmpl_final*>(result.get()) != nullptr
-                || dynamic_cast<server_task_result_embd*>(result.get()) != nullptr
-                || dynamic_cast<server_task_result_rerank*>(result.get()) != nullptr
-            );
-            const size_t idx = result->get_index();
-            GGML_ASSERT(idx < results.size() && "index out of range");
-            results[idx] = std::move(result);
-        }
-        result_handler(results);
-    }
-
-    // receive the results from task(s), in stream mode
-    void receive_cmpl_results_stream(
-            const std::unordered_set<int> & id_tasks,
-            const std::function<bool(server_task_result_ptr&)> & result_handler,
-            const std::function<void(json)> & error_handler,
-            const std::function<bool()> & is_connection_closed) {
-        size_t n_finished = 0;
-        while (true) {
-            server_task_result_ptr result = queue_results.recv_with_timeout(id_tasks, HTTP_POLLING_SECONDS);
-
-            if (is_connection_closed()) {
-                cancel_tasks(id_tasks);
-                return;
-            }
-
-            if (result == nullptr) {
-                continue; // retry
-            }
-
-            if (result->is_error()) {
-                error_handler(result->to_json());
-                cancel_tasks(id_tasks);
-                return;
-            }
-
-            GGML_ASSERT(
-                dynamic_cast<server_task_result_cmpl_partial*>(result.get()) != nullptr
-                || dynamic_cast<server_task_result_cmpl_final*>(result.get()) != nullptr
-            );
-            if (!result_handler(result)) {
-                cancel_tasks(id_tasks);
-                break;
-            }
-
-            if (result->is_stop()) {
-                if (++n_finished == id_tasks.size()) {
-                    break;
-                }
-            }
-        }
-    }
-
     //
     // Functions to process the task
     //
@@ -3690,13 +3592,13 @@ struct server_context {
         // next, batch any pending prompts without exceeding n_batch
         if (params_base.cont_batching || batch.n_tokens == 0) {
             for (auto & slot : slots) {
+                if (!slot.is_processing()) {
+                    continue;
+                }
+
                 // check if we can batch this slot with the previous one
-                if (slot.is_processing()) {
-                    if (!slot_batched) {
-                        slot_batched = &slot;
-                    } else if (!slot_batched->can_batch_with(slot)) {
-                        continue;
-                    }
+                if (slot_batched && !slot_batched->can_batch_with(slot)) {
+                    continue;
                 }
 
                 // this slot still has a prompt to be processed
@@ -4127,6 +4029,10 @@ struct server_context {
                     }
                 }
 
+                if (!slot_batched) {
+                    slot_batched = &slot;
+                }
+
                 if (batch.n_tokens >= n_batch) {
                     break;
                 }
@@ -4418,6 +4324,104 @@ struct server_context {
     }
 };
 
+// generator-like API for server responses, support pooling connection state and aggregating results
+struct server_response_reader {
+    std::unordered_set<int> id_tasks;
+    server_context & ctx_server;
+    size_t received_count = 0;
+    bool cancelled = false;
+
+    server_response_reader(server_context & ctx_server) : ctx_server(ctx_server) {}
+    ~server_response_reader() {
+        stop();
+    }
+
+    void post_tasks(std::vector<server_task> && tasks) {
+        id_tasks = server_task::get_list_id(tasks);
+        ctx_server.queue_results.add_waiting_tasks(tasks);
+        ctx_server.queue_tasks.post(std::move(tasks));
+    }
+
+    bool has_next() {
+        return !cancelled && received_count < id_tasks.size();
+    }
+
+    // return nullptr if should_stop() is true before receiving a result
+    // note: if one error is received, it will stop further processing and return error result
+    server_task_result_ptr next(const std::function<bool()> & should_stop) {
+        while (true) {
+            server_task_result_ptr result = ctx_server.queue_results.recv_with_timeout(id_tasks, HTTP_POLLING_SECONDS);
+            if (result == nullptr) {
+                // timeout, check stop condition
+                if (should_stop()) {
+                    SRV_DBG("%s", "stopping wait for next result due to should_stop condition\n");
+                    return nullptr;
+                }
+            } else {
+                if (result->is_error()) {
+                    stop(); // cancel remaining tasks
+                    SRV_DBG("%s", "received error result, stopping further processing\n");
+                    return result;
+                }
+                if (result->is_stop()) {
+                    received_count++;
+                }
+                return result;
+            }
+        }
+
+        // should not reach here
+    }
+
+    struct batch_response {
+        bool is_terminated = false; // if true, indicates that processing was stopped before all results were received
+        std::vector<server_task_result_ptr> results;
+        server_task_result_ptr error; // nullptr if no error
+    };
+
+    batch_response wait_for_all(const std::function<bool()> & should_stop) {
+        batch_response batch_res;
+        batch_res.results.resize(id_tasks.size());
+        while (has_next()) {
+            auto res = next(should_stop);
+            if (res == nullptr) {
+                batch_res.is_terminated = true;
+                return batch_res;
+            }
+            if (res->is_error()) {
+                batch_res.error = std::move(res);
+                return batch_res;
+            }
+            const size_t idx = res->get_index();
+            GGML_ASSERT(idx < batch_res.results.size() && "index out of range");
+            GGML_ASSERT(batch_res.results[idx] == nullptr && "duplicate result received");
+            batch_res.results[idx] = std::move(res);
+        }
+        return batch_res;
+    }
+
+    void stop() {
+        ctx_server.queue_results.remove_waiting_task_ids(id_tasks);
+        if (has_next() && !cancelled) {
+            // if tasks is not finished yet, cancel them
+            cancelled = true;
+            std::vector<server_task> cancel_tasks;
+            cancel_tasks.reserve(id_tasks.size());
+            for (const auto & id_task : id_tasks) {
+                SRV_WRN("cancel task, id_task = %d\n", id_task);
+                server_task task(SERVER_TASK_TYPE_CANCEL);
+                task.id_target = id_task;
+                ctx_server.queue_results.remove_waiting_task_id(id_task);
+                cancel_tasks.push_back(std::move(task));
+            }
+            // push to beginning of the queue, so it has highest priority
+            ctx_server.queue_tasks.post(std::move(cancel_tasks), true);
+        } else {
+            SRV_DBG("%s", "all tasks already finished, no need to cancel\n");
+        }
+    }
+};
+
 static void log_server_request(const httplib::Request & req, const httplib::Response & res) {
     // skip GH copilot requests when using default port
     if (req.path == "/v1/health") {
@@ -4432,6 +4436,17 @@ static void log_server_request(const httplib::Request & req, const httplib::Resp
     SRV_DBG("response: %s\n", res.body.c_str());
 }
 
+static void res_err(httplib::Response & res, const json & error_data) {
+    json final_response {{"error", error_data}};
+    res.set_content(safe_json_to_str(final_response), MIMETYPE_JSON);
+    res.status = json_value(error_data, "code", 500);
+}
+
+static void res_ok(httplib::Response & res, const json & data) {
+    res.set_content(safe_json_to_str(data), MIMETYPE_JSON);
+    res.status = 200;
+}
+
 std::function<void(int)> shutdown_handler;
 std::atomic_flag is_terminating = ATOMIC_FLAG_INIT;
 
@@ -4501,19 +4516,7 @@ int main(int argc, char ** argv) {
 
     svr->set_default_headers({{"Server", "llama.cpp"}});
     svr->set_logger(log_server_request);
-
-    auto res_error = [](httplib::Response & res, const json & error_data) {
-        json final_response {{"error", error_data}};
-        res.set_content(safe_json_to_str(final_response), MIMETYPE_JSON);
-        res.status = json_value(error_data, "code", 500);
-    };
-
-    auto res_ok = [](httplib::Response & res, const json & data) {
-        res.set_content(safe_json_to_str(data), MIMETYPE_JSON);
-        res.status = 200;
-    };
-
-    svr->set_exception_handler([&res_error](const httplib::Request &, httplib::Response & res, const std::exception_ptr & ep) {
+    svr->set_exception_handler([](const httplib::Request &, httplib::Response & res, const std::exception_ptr & ep) {
         std::string message;
         try {
             std::rethrow_exception(ep);
@@ -4526,17 +4529,17 @@ int main(int argc, char ** argv) {
         try {
             json formatted_error = format_error_response(message, ERROR_TYPE_SERVER);
             LOG_WRN("got exception: %s\n", formatted_error.dump().c_str());
-            res_error(res, formatted_error);
+            res_err(res, formatted_error);
         } catch (const std::exception & e) {
             LOG_ERR("got another exception: %s | while hanlding exception: %s\n", e.what(), message.c_str());
         }
     });
 
-    svr->set_error_handler([&res_error](const httplib::Request &, httplib::Response & res) {
+    svr->set_error_handler([](const httplib::Request &, httplib::Response & res) {
         if (res.status == 404) {
-            res_error(res, format_error_response("File Not Found", ERROR_TYPE_NOT_FOUND));
+            res_err(res, format_error_response("File Not Found", ERROR_TYPE_NOT_FOUND));
         }
-        // for other error codes, we skip processing here because it's already done by res_error()
+        // for other error codes, we skip processing here because it's already done by res_err()
     });
 
     // set timeouts and change hostname and port
@@ -4562,7 +4565,7 @@ int main(int argc, char ** argv) {
     // Middlewares
     //
 
-    auto middleware_validate_api_key = [&params, &res_error](const httplib::Request & req, httplib::Response & res) {
+    auto middleware_validate_api_key = [&params](const httplib::Request & req, httplib::Response & res) {
         static const std::unordered_set<std::string> public_endpoints = {
             "/health",
             "/v1/health",
@@ -4593,14 +4596,14 @@ int main(int argc, char ** argv) {
         }
 
         // API key is invalid or not provided
-        res_error(res, format_error_response("Invalid API Key", ERROR_TYPE_AUTHENTICATION));
+        res_err(res, format_error_response("Invalid API Key", ERROR_TYPE_AUTHENTICATION));
 
         LOG_WRN("Unauthorized: Invalid API Key\n");
 
         return false;
     };
 
-    auto middleware_server_state = [&res_error, &state](const httplib::Request & req, httplib::Response & res) {
+    auto middleware_server_state = [&state](const httplib::Request & req, httplib::Response & res) {
         server_state current_state = state.load();
         if (current_state == SERVER_STATE_LOADING_MODEL) {
             auto tmp = string_split<std::string>(req.path, '.');
@@ -4611,7 +4614,7 @@ int main(int argc, char ** argv) {
                 // allow the models endpoint to be accessed during loading
                 return true;
             } else {
-                res_error(res, format_error_response("Loading model", ERROR_TYPE_UNAVAILABLE));
+                res_err(res, format_error_response("Loading model", ERROR_TYPE_UNAVAILABLE));
             }
             return false;
         }
@@ -4650,7 +4653,7 @@ int main(int argc, char ** argv) {
 
     const auto handle_slots = [&](const httplib::Request & req, httplib::Response & res) {
         if (!params.endpoint_slots) {
-            res_error(res, format_error_response("This server does not support slots endpoint. Start it with `--slots`", ERROR_TYPE_NOT_SUPPORTED));
+            res_err(res, format_error_response("This server does not support slots endpoint. Start it with `--slots`", ERROR_TYPE_NOT_SUPPORTED));
             return;
         }
 
@@ -4668,7 +4671,7 @@ int main(int argc, char ** argv) {
         ctx_server.queue_results.remove_waiting_task_id(task_id);
 
         if (result->is_error()) {
-            res_error(res, result->to_json());
+            res_err(res, result->to_json());
             return;
         }
 
@@ -4679,7 +4682,7 @@ int main(int argc, char ** argv) {
         // optionally return "fail_on_no_slot" error
         if (req.has_param("fail_on_no_slot")) {
             if (res_task->n_idle_slots == 0) {
-                res_error(res, format_error_response("no slot available", ERROR_TYPE_UNAVAILABLE));
+                res_err(res, format_error_response("no slot available", ERROR_TYPE_UNAVAILABLE));
                 return;
             }
         }
@@ -4689,7 +4692,7 @@ int main(int argc, char ** argv) {
 
     const auto handle_metrics = [&](const httplib::Request &, httplib::Response & res) {
         if (!params.endpoint_metrics) {
-            res_error(res, format_error_response("This server does not support metrics endpoint. Start it with `--metrics`", ERROR_TYPE_NOT_SUPPORTED));
+            res_err(res, format_error_response("This server does not support metrics endpoint. Start it with `--metrics`", ERROR_TYPE_NOT_SUPPORTED));
             return;
         }
 
@@ -4707,7 +4710,7 @@ int main(int argc, char ** argv) {
         ctx_server.queue_results.remove_waiting_task_id(task_id);
 
         if (result->is_error()) {
-            res_error(res, result->to_json());
+            res_err(res, result->to_json());
             return;
         }
 
@@ -4788,11 +4791,11 @@ int main(int argc, char ** argv) {
         res.status = 200; // HTTP OK
     };
 
-    const auto handle_slots_save = [&ctx_server, &res_error, &res_ok, &params](const httplib::Request & req, httplib::Response & res, int id_slot) {
+    const auto handle_slots_save = [&ctx_server, &params](const httplib::Request & req, httplib::Response & res, int id_slot) {
         json request_data = json::parse(req.body);
         std::string filename = request_data.at("filename");
         if (!fs_validate_filename(filename)) {
-            res_error(res, format_error_response("Invalid filename", ERROR_TYPE_INVALID_REQUEST));
+            res_err(res, format_error_response("Invalid filename", ERROR_TYPE_INVALID_REQUEST));
             return;
         }
         std::string filepath = params.slot_save_path + filename;
@@ -4813,18 +4816,18 @@ int main(int argc, char ** argv) {
         ctx_server.queue_results.remove_waiting_task_id(task_id);
 
         if (result->is_error()) {
-            res_error(res, result->to_json());
+            res_err(res, result->to_json());
             return;
         }
 
         res_ok(res, result->to_json());
     };
 
-    const auto handle_slots_restore = [&ctx_server, &res_error, &res_ok, &params](const httplib::Request & req, httplib::Response & res, int id_slot) {
+    const auto handle_slots_restore = [&ctx_server, &params](const httplib::Request & req, httplib::Response & res, int id_slot) {
         json request_data = json::parse(req.body);
         std::string filename = request_data.at("filename");
         if (!fs_validate_filename(filename)) {
-            res_error(res, format_error_response("Invalid filename", ERROR_TYPE_INVALID_REQUEST));
+            res_err(res, format_error_response("Invalid filename", ERROR_TYPE_INVALID_REQUEST));
             return;
         }
         std::string filepath = params.slot_save_path + filename;
@@ -4845,7 +4848,7 @@ int main(int argc, char ** argv) {
         ctx_server.queue_results.remove_waiting_task_id(task_id);
 
         if (result->is_error()) {
-            res_error(res, result->to_json());
+            res_err(res, result->to_json());
             return;
         }
 
@@ -4853,7 +4856,7 @@ int main(int argc, char ** argv) {
         res_ok(res, result->to_json());
     };
 
-    const auto handle_slots_erase = [&ctx_server, &res_error, &res_ok](const httplib::Request & /* req */, httplib::Response & res, int id_slot) {
+    const auto handle_slots_erase = [&ctx_server](const httplib::Request & /* req */, httplib::Response & res, int id_slot) {
         int task_id = ctx_server.queue_tasks.get_new_id();
         {
             server_task task(SERVER_TASK_TYPE_SLOT_ERASE);
@@ -4868,7 +4871,7 @@ int main(int argc, char ** argv) {
         ctx_server.queue_results.remove_waiting_task_id(task_id);
 
         if (result->is_error()) {
-            res_error(res, result->to_json());
+            res_err(res, result->to_json());
             return;
         }
 
@@ -4876,9 +4879,9 @@ int main(int argc, char ** argv) {
         res_ok(res, result->to_json());
     };
 
-    const auto handle_slots_action = [&params, &res_error, &handle_slots_save, &handle_slots_restore, &handle_slots_erase](const httplib::Request & req, httplib::Response & res) {
+    const auto handle_slots_action = [&params, &handle_slots_save, &handle_slots_restore, &handle_slots_erase](const httplib::Request & req, httplib::Response & res) {
         if (params.slot_save_path.empty()) {
-            res_error(res, format_error_response("This server does not support slots action. Start it with `--slot-save-path`", ERROR_TYPE_NOT_SUPPORTED));
+            res_err(res, format_error_response("This server does not support slots action. Start it with `--slot-save-path`", ERROR_TYPE_NOT_SUPPORTED));
             return;
         }
 
@@ -4888,7 +4891,7 @@ int main(int argc, char ** argv) {
         try {
             id_slot = std::stoi(id_slot_str);
         } catch (const std::exception &) {
-            res_error(res, format_error_response("Invalid slot ID", ERROR_TYPE_INVALID_REQUEST));
+            res_err(res, format_error_response("Invalid slot ID", ERROR_TYPE_INVALID_REQUEST));
             return;
         }
 
@@ -4901,11 +4904,11 @@ int main(int argc, char ** argv) {
         } else if (action == "erase") {
             handle_slots_erase(req, res, id_slot);
         } else {
-            res_error(res, format_error_response("Invalid action", ERROR_TYPE_INVALID_REQUEST));
+            res_err(res, format_error_response("Invalid action", ERROR_TYPE_INVALID_REQUEST));
         }
     };
 
-    const auto handle_props = [&params, &ctx_server, &res_ok](const httplib::Request &, httplib::Response & res) {
+    const auto handle_props = [&params, &ctx_server](const httplib::Request &, httplib::Response & res) {
         json default_generation_settings_for_props;
 
         {
@@ -4947,9 +4950,9 @@ int main(int argc, char ** argv) {
         res_ok(res, data);
     };
 
-    const auto handle_props_change = [&ctx_server, &res_error, &res_ok](const httplib::Request & req, httplib::Response & res) {
+    const auto handle_props_change = [&ctx_server](const httplib::Request & req, httplib::Response & res) {
         if (!ctx_server.params_base.endpoint_props) {
-            res_error(res, format_error_response("This server does not support changing global properties. Start it with `--props`", ERROR_TYPE_NOT_SUPPORTED));
+            res_err(res, format_error_response("This server does not support changing global properties. Start it with `--props`", ERROR_TYPE_NOT_SUPPORTED));
             return;
         }
 
@@ -4960,7 +4963,7 @@ int main(int argc, char ** argv) {
         res_ok(res, {{ "success", true }});
     };
 
-    const auto handle_api_show = [&ctx_server, &res_ok](const httplib::Request &, httplib::Response & res) {
+    const auto handle_api_show = [&ctx_server](const httplib::Request &, httplib::Response & res) {
         bool has_mtmd = ctx_server.mctx != nullptr;
         json data = {
             {
@@ -4991,7 +4994,7 @@ int main(int argc, char ** argv) {
 
     // handle completion-like requests (completion, chat, infill)
     // we can optionally provide a custom format for partial results and final results
-    const auto handle_completions_impl = [&ctx_server, &res_error, &res_ok](
+    const auto handle_completions_impl = [&ctx_server](
             server_task_type type,
             json & data,
             const std::vector<raw_buffer> & files,
@@ -5001,7 +5004,10 @@ int main(int argc, char ** argv) {
         GGML_ASSERT(type == SERVER_TASK_TYPE_COMPLETION || type == SERVER_TASK_TYPE_INFILL);
 
         auto completion_id = gen_chatcmplid();
-        std::unordered_set<int> task_ids;
+        // need to store the reader as a pointer, so that it won't be destroyed when the handle returns
+        // use shared_ptr as it's shared between the chunked_content_provider() and on_complete()
+        const auto rd = std::make_shared<server_response_reader>(ctx_server);
+
         try {
             std::vector<server_task> tasks;
 
@@ -5019,17 +5025,8 @@ int main(int argc, char ** argv) {
                 // Everything else, including multimodal completions.
                 inputs = tokenize_input_prompts(ctx_server.vocab, ctx_server.mctx, prompt, true, true);
             }
-            const size_t n_ctx_slot = ctx_server.slots.front().n_ctx;
             tasks.reserve(inputs.size());
             for (size_t i = 0; i < inputs.size(); i++) {
-                auto n_prompt_tokens = inputs[i].size();
-                if (n_prompt_tokens >= n_ctx_slot) {
-                    json error_data = format_error_response("the request exceeds the available context size, try increasing it", ERROR_TYPE_EXCEED_CONTEXT_SIZE);
-                    error_data["n_prompt_tokens"] = n_prompt_tokens;
-                    error_data["n_ctx"] = n_ctx_slot;
-                    res_error(res, error_data);
-                    return;
-                }
                 server_task task = server_task(type);
 
                 task.id    = ctx_server.queue_tasks.get_new_id();
@@ -5050,65 +5047,104 @@ int main(int argc, char ** argv) {
                 tasks.push_back(std::move(task));
             }
 
-            task_ids = server_task::get_list_id(tasks);
-            ctx_server.queue_results.add_waiting_tasks(tasks);
-            ctx_server.queue_tasks.post(std::move(tasks));
+            rd->post_tasks(std::move(tasks));
         } catch (const std::exception & e) {
-            res_error(res, format_error_response(e.what(), ERROR_TYPE_INVALID_REQUEST));
+            res_err(res, format_error_response(e.what(), ERROR_TYPE_INVALID_REQUEST));
             return;
         }
 
         bool stream = json_value(data, "stream", false);
 
         if (!stream) {
-            ctx_server.receive_multi_results(task_ids, [&](std::vector<server_task_result_ptr> & results) {
-                if (results.size() == 1) {
-                    // single result
-                    res_ok(res, results[0]->to_json());
-                } else {
-                    // multiple results (multitask)
-                    json arr = json::array();
-                    for (auto & res : results) {
-                        arr.push_back(res->to_json());
-                    }
-                    res_ok(res, arr);
+            // non-stream, wait for the results
+            auto all_results = rd->wait_for_all(is_connection_closed);
+            if (all_results.is_terminated) {
+                return; // connection is closed
+            } else if (all_results.error) {
+                res_err(res, all_results.error->to_json());
+                return;
+            } else {
+                json arr = json::array();
+                for (auto & res : all_results.results) {
+                    GGML_ASSERT(dynamic_cast<server_task_result_cmpl_final*>(res.get()) != nullptr);
+                    arr.push_back(res->to_json());
                 }
-            }, [&](const json & error_data) {
-                res_error(res, error_data);
-            }, is_connection_closed);
+                // if single request, return single object instead of array
+                res_ok(res, arr.size() == 1 ? arr[0] : arr);
+            }
 
-            ctx_server.queue_results.remove_waiting_task_ids(task_ids);
         } else {
-            const auto chunked_content_provider = [task_ids, &ctx_server, oaicompat](size_t, httplib::DataSink & sink) {
-                ctx_server.receive_cmpl_results_stream(task_ids, [&](server_task_result_ptr & result) -> bool {
-                    json res_json = result->to_json();
-                    if (res_json.is_array()) {
-                        for (const auto & res : res_json) {
-                            if (!server_sent_event(sink, res)) {
-                                // sending failed (HTTP connection closed), cancel the generation
-                                return false;
-                            }
-                        }
-                        return true;
-                    } else {
-                        return server_sent_event(sink, res_json);
+            // in streaming mode, the first error must be treated as non-stream response
+            // this is to match the OAI API behavior
+            // ref: https://github.com/ggml-org/llama.cpp/pull/16486#discussion_r2419657309
+            server_task_result_ptr first_result = rd->next(is_connection_closed);
+            if (first_result == nullptr) {
+                return; // connection is closed
+            } else if (first_result->is_error()) {
+                res_err(res, first_result->to_json());
+                return;
+            } else {
+                GGML_ASSERT(
+                    dynamic_cast<server_task_result_cmpl_partial*>(first_result.get()) != nullptr
+                    || dynamic_cast<server_task_result_cmpl_final*>(first_result.get()) != nullptr
+                );
+            }
+
+            // next responses are streamed
+            json first_result_json = first_result->to_json();
+            const auto chunked_content_provider = [first_result_json, rd, oaicompat](size_t, httplib::DataSink & sink) mutable -> bool {
+                // flush the first result as it's not an error
+                if (!first_result_json.empty()) {
+                    if (!server_sent_event(sink, first_result_json)) {
+                        sink.done();
+                        return false; // sending failed, go to on_complete()
                     }
-                }, [&](const json & error_data) {
-                    server_sent_event(sink, json{{"error", error_data}});
-                }, [&sink]() {
-                    // note: do not use req.is_connection_closed here because req is already destroyed
-                    return !sink.is_writable();
-                });
-                if (oaicompat != OAICOMPAT_TYPE_NONE) {
-                    static const std::string ev_done = "data: [DONE]\n\n";
-                    sink.write(ev_done.data(), ev_done.size());
+                    first_result_json.clear(); // mark as sent
                 }
-                sink.done();
-                return false;
+
+                // receive subsequent results
+                auto result = rd->next([&sink]{ return !sink.is_writable(); });
+                if (result == nullptr) {
+                    sink.done();
+                    return false; // connection is closed, go to on_complete()
+                }
+
+                // send the results
+                json res_json = result->to_json();
+                bool ok = false;
+                if (result->is_error()) {
+                    ok = server_sent_event(sink, json {{ "error", result->to_json() }});
+                    sink.done();
+                    return false; // go to on_complete()
+                } else {
+                    GGML_ASSERT(
+                        dynamic_cast<server_task_result_cmpl_partial*>(result.get()) != nullptr
+                        || dynamic_cast<server_task_result_cmpl_final*>(result.get()) != nullptr
+                    );
+                    ok = server_sent_event(sink, res_json);
+                }
+
+                if (!ok) {
+                    sink.done();
+                    return false; // sending failed, go to on_complete()
+                }
+
+                // check if there is more data
+                if (!rd->has_next()) {
+                    if (oaicompat != OAICOMPAT_TYPE_NONE) {
+                        static const std::string ev_done = "data: [DONE]\n\n";
+                        sink.write(ev_done.data(), ev_done.size());
+                    }
+                    sink.done();
+                    return false; // no more data, go to on_complete()
+                }
+
+                // has next data, continue
+                return true;
             };
 
-            auto on_complete = [task_ids, &ctx_server] (bool) {
-                ctx_server.queue_results.remove_waiting_task_ids(task_ids);
+            auto on_complete = [rd](bool) {
+                rd->stop();
             };
 
             res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete);
@@ -5139,7 +5175,7 @@ int main(int argc, char ** argv) {
             OAICOMPAT_TYPE_COMPLETION);
     };
 
-    const auto handle_infill = [&ctx_server, &res_error, &handle_completions_impl](const httplib::Request & req, httplib::Response & res) {
+    const auto handle_infill = [&ctx_server, &handle_completions_impl](const httplib::Request & req, httplib::Response & res) {
         // check model compatibility
         std::string err;
         if (llama_vocab_fim_pre(ctx_server.vocab) == LLAMA_TOKEN_NULL) {
@@ -5152,7 +5188,7 @@ int main(int argc, char ** argv) {
             err += "middle token is missing. ";
         }
         if (!err.empty()) {
-            res_error(res, format_error_response(string_format("Infill is not supported by this model: %s", err.c_str()), ERROR_TYPE_NOT_SUPPORTED));
+            res_err(res, format_error_response(string_format("Infill is not supported by this model: %s", err.c_str()), ERROR_TYPE_NOT_SUPPORTED));
             return;
         }
 
@@ -5161,20 +5197,20 @@ int main(int argc, char ** argv) {
         // validate input
         if (data.contains("prompt") && !data.at("prompt").is_string()) {
             // prompt is optional
-            res_error(res, format_error_response("\"prompt\" must be a string", ERROR_TYPE_INVALID_REQUEST));
+            res_err(res, format_error_response("\"prompt\" must be a string", ERROR_TYPE_INVALID_REQUEST));
         }
 
         if (!data.contains("input_prefix")) {
-            res_error(res, format_error_response("\"input_prefix\" is required", ERROR_TYPE_INVALID_REQUEST));
+            res_err(res, format_error_response("\"input_prefix\" is required", ERROR_TYPE_INVALID_REQUEST));
         }
 
         if (!data.contains("input_suffix")) {
-            res_error(res, format_error_response("\"input_suffix\" is required", ERROR_TYPE_INVALID_REQUEST));
+            res_err(res, format_error_response("\"input_suffix\" is required", ERROR_TYPE_INVALID_REQUEST));
         }
 
         if (data.contains("input_extra") && !data.at("input_extra").is_array()) {
             // input_extra is optional
-            res_error(res, format_error_response("\"input_extra\" must be an array of {\"filename\": string, \"text\": string}", ERROR_TYPE_INVALID_REQUEST));
+            res_err(res, format_error_response("\"input_extra\" must be an array of {\"filename\": string, \"text\": string}", ERROR_TYPE_INVALID_REQUEST));
             return;
         }
 
@@ -5182,12 +5218,12 @@ int main(int argc, char ** argv) {
         for (const auto & chunk : input_extra) {
             // { "text": string, "filename": string }
             if (!chunk.contains("text") || !chunk.at("text").is_string()) {
-                res_error(res, format_error_response("extra_context chunk must contain a \"text\" field with a string value", ERROR_TYPE_INVALID_REQUEST));
+                res_err(res, format_error_response("extra_context chunk must contain a \"text\" field with a string value", ERROR_TYPE_INVALID_REQUEST));
                 return;
             }
             // filename is optional
             if (chunk.contains("filename") && !chunk.at("filename").is_string()) {
-                res_error(res, format_error_response("extra_context chunk's \"filename\" field must be a string", ERROR_TYPE_INVALID_REQUEST));
+                res_err(res, format_error_response("extra_context chunk's \"filename\" field must be a string", ERROR_TYPE_INVALID_REQUEST));
                 return;
             }
         }
@@ -5238,7 +5274,7 @@ int main(int argc, char ** argv) {
     };
 
     // same with handle_chat_completions, but without inference part
-    const auto handle_apply_template = [&ctx_server, &res_ok](const httplib::Request & req, httplib::Response & res) {
+    const auto handle_apply_template = [&ctx_server](const httplib::Request & req, httplib::Response & res) {
         auto body = json::parse(req.body);
         std::vector<raw_buffer> files; // dummy, unused
         json data = oaicompat_chat_params_parse(
@@ -5248,7 +5284,7 @@ int main(int argc, char ** argv) {
         res_ok(res, {{ "prompt", std::move(data.at("prompt")) }});
     };
 
-    const auto handle_models = [&params, &ctx_server, &state, &res_ok](const httplib::Request &, httplib::Response & res) {
+    const auto handle_models = [&params, &ctx_server, &state](const httplib::Request &, httplib::Response & res) {
         server_state current_state = state.load();
         json model_meta = nullptr;
         if (current_state == SERVER_STATE_READY) {
@@ -5293,7 +5329,7 @@ int main(int argc, char ** argv) {
         res_ok(res, models);
     };
 
-    const auto handle_tokenize = [&ctx_server, &res_ok](const httplib::Request & req, httplib::Response & res) {
+    const auto handle_tokenize = [&ctx_server](const httplib::Request & req, httplib::Response & res) {
         const json body = json::parse(req.body);
 
         json tokens_response = json::array();
@@ -5334,7 +5370,7 @@ int main(int argc, char ** argv) {
         res_ok(res, data);
     };
 
-    const auto handle_detokenize = [&ctx_server, &res_ok](const httplib::Request & req, httplib::Response & res) {
+    const auto handle_detokenize = [&ctx_server](const httplib::Request & req, httplib::Response & res) {
         const json body = json::parse(req.body);
 
         std::string content;
@@ -5347,14 +5383,14 @@ int main(int argc, char ** argv) {
         res_ok(res, data);
     };
 
-    const auto handle_embeddings_impl = [&ctx_server, &res_error, &res_ok](const httplib::Request & req, httplib::Response & res, oaicompat_type oaicompat) {
+    const auto handle_embeddings_impl = [&ctx_server](const httplib::Request & req, httplib::Response & res, oaicompat_type oaicompat) {
         if (!ctx_server.params_base.embedding) {
-            res_error(res, format_error_response("This server does not support embeddings. Start it with `--embeddings`", ERROR_TYPE_NOT_SUPPORTED));
+            res_err(res, format_error_response("This server does not support embeddings. Start it with `--embeddings`", ERROR_TYPE_NOT_SUPPORTED));
             return;
         }
 
         if (oaicompat != OAICOMPAT_TYPE_NONE && llama_pooling_type(ctx_server.ctx) == LLAMA_POOLING_TYPE_NONE) {
-            res_error(res, format_error_response("Pooling type 'none' is not OAI compatible. Please use a different pooling type", ERROR_TYPE_INVALID_REQUEST));
+            res_err(res, format_error_response("Pooling type 'none' is not OAI compatible. Please use a different pooling type", ERROR_TYPE_INVALID_REQUEST));
             return;
         }
 
@@ -5368,7 +5404,7 @@ int main(int argc, char ** argv) {
             oaicompat = OAICOMPAT_TYPE_NONE; // "content" field is not OAI compatible
             prompt = body.at("content");
         } else {
-            res_error(res, format_error_response("\"input\" or \"content\" must be provided", ERROR_TYPE_INVALID_REQUEST));
+            res_err(res, format_error_response("\"input\" or \"content\" must be provided", ERROR_TYPE_INVALID_REQUEST));
             return;
         }
 
@@ -5378,7 +5414,7 @@ int main(int argc, char ** argv) {
             if (format == "base64") {
                 use_base64 = true;
             } else if (format != "float") {
-                res_error(res, format_error_response("The format to return the embeddings in. Can be either float or base64", ERROR_TYPE_INVALID_REQUEST));
+                res_err(res, format_error_response("The format to return the embeddings in. Can be either float or base64", ERROR_TYPE_INVALID_REQUEST));
                 return;
             }
         }
@@ -5387,7 +5423,7 @@ int main(int argc, char ** argv) {
         for (const auto & tokens : tokenized_prompts) {
             // this check is necessary for models that do not add BOS token to the input
             if (tokens.empty()) {
-                res_error(res, format_error_response("Input content cannot be empty", ERROR_TYPE_INVALID_REQUEST));
+                res_err(res, format_error_response("Input content cannot be empty", ERROR_TYPE_INVALID_REQUEST));
                 return;
             }
         }
@@ -5402,8 +5438,7 @@ int main(int argc, char ** argv) {
 
         // create and queue the task
         json responses = json::array();
-        bool error = false;
-        std::unordered_set<int> task_ids;
+        server_response_reader rd(ctx_server);
         {
             std::vector<server_task> tasks;
             for (size_t i = 0; i < tokenized_prompts.size(); i++) {
@@ -5419,27 +5454,23 @@ int main(int argc, char ** argv) {
 
                 tasks.push_back(std::move(task));
             }
-
-            task_ids = server_task::get_list_id(tasks);
-            ctx_server.queue_results.add_waiting_tasks(tasks);
-            ctx_server.queue_tasks.post(std::move(tasks));
+            rd.post_tasks(std::move(tasks));
         }
 
-        // get the result
-        ctx_server.receive_multi_results(task_ids, [&](std::vector<server_task_result_ptr> & results) {
-            for (auto & res : results) {
+        // wait for the results
+        auto all_results = rd.wait_for_all(req.is_connection_closed);
+
+        // collect results
+        if (all_results.is_terminated) {
+            return; // connection is closed
+        } else if (all_results.error) {
+            res_err(res, all_results.error->to_json());
+            return;
+        } else {
+            for (auto & res : all_results.results) {
                 GGML_ASSERT(dynamic_cast<server_task_result_embd*>(res.get()) != nullptr);
                 responses.push_back(res->to_json());
             }
-        }, [&](const json & error_data) {
-            res_error(res, error_data);
-            error = true;
-        }, req.is_connection_closed);
-
-        ctx_server.queue_results.remove_waiting_task_ids(task_ids);
-
-        if (error) {
-            return;
         }
 
         // write JSON response
@@ -5457,9 +5488,9 @@ int main(int argc, char ** argv) {
         handle_embeddings_impl(req, res, OAICOMPAT_TYPE_EMBEDDING);
     };
 
-    const auto handle_rerank = [&ctx_server, &res_error, &res_ok](const httplib::Request & req, httplib::Response & res) {
+    const auto handle_rerank = [&ctx_server](const httplib::Request & req, httplib::Response & res) {
         if (!ctx_server.params_base.embedding || ctx_server.params_base.pooling_type != LLAMA_POOLING_TYPE_RANK) {
-            res_error(res, format_error_response("This server does not support reranking. Start it with `--reranking`", ERROR_TYPE_NOT_SUPPORTED));
+            res_err(res, format_error_response("This server does not support reranking. Start it with `--reranking`", ERROR_TYPE_NOT_SUPPORTED));
             return;
         }
 
@@ -5474,18 +5505,18 @@ int main(int argc, char ** argv) {
         if (body.count("query") == 1) {
             query = body.at("query");
             if (!query.is_string()) {
-                res_error(res, format_error_response("\"query\" must be a string", ERROR_TYPE_INVALID_REQUEST));
+                res_err(res, format_error_response("\"query\" must be a string", ERROR_TYPE_INVALID_REQUEST));
                 return;
             }
         } else {
-            res_error(res, format_error_response("\"query\" must be provided", ERROR_TYPE_INVALID_REQUEST));
+            res_err(res, format_error_response("\"query\" must be provided", ERROR_TYPE_INVALID_REQUEST));
             return;
         }
 
         std::vector<std::string> documents = json_value(body, "documents",
                                              json_value(body, "texts", std::vector<std::string>()));
         if (documents.empty()) {
-            res_error(res, format_error_response("\"documents\" must be a non-empty string array", ERROR_TYPE_INVALID_REQUEST));
+            res_err(res, format_error_response("\"documents\" must be a non-empty string array", ERROR_TYPE_INVALID_REQUEST));
             return;
         }
 
@@ -5493,8 +5524,7 @@ int main(int argc, char ** argv) {
 
         // create and queue the task
         json responses = json::array();
-        bool error = false;
-        std::unordered_set<int> task_ids;
+        server_response_reader rd(ctx_server);
         {
             std::vector<server_task> tasks;
             tasks.reserve(documents.size());
@@ -5506,24 +5536,23 @@ int main(int argc, char ** argv) {
                 task.tokens = std::move(tmp);
                 tasks.push_back(std::move(task));
             }
-
-            task_ids = server_task::get_list_id(tasks);
-            ctx_server.queue_results.add_waiting_tasks(tasks);
-            ctx_server.queue_tasks.post(std::move(tasks));
+            rd.post_tasks(std::move(tasks));
         }
 
-        ctx_server.receive_multi_results(task_ids, [&](std::vector<server_task_result_ptr> & results) {
-            for (auto & res : results) {
+        // wait for the results
+        auto all_results = rd.wait_for_all(req.is_connection_closed);
+
+        // collect results
+        if (all_results.is_terminated) {
+            return; // connection is closed
+        } else if (all_results.error) {
+            res_err(res, all_results.error->to_json());
+            return;
+        } else {
+            for (auto & res : all_results.results) {
                 GGML_ASSERT(dynamic_cast<server_task_result_rerank*>(res.get()) != nullptr);
                 responses.push_back(res->to_json());
             }
-        }, [&](const json & error_data) {
-            res_error(res, error_data);
-            error = true;
-        }, req.is_connection_closed);
-
-        if (error) {
-            return;
         }
 
         // write JSON response
@@ -5570,7 +5599,7 @@ int main(int argc, char ** argv) {
     const auto handle_lora_adapters_apply = [&](const httplib::Request & req, httplib::Response & res) {
         const json body = json::parse(req.body);
         if (!body.is_array()) {
-            res_error(res, format_error_response("Request body must be an array", ERROR_TYPE_INVALID_REQUEST));
+            res_err(res, format_error_response("Request body must be an array", ERROR_TYPE_INVALID_REQUEST));
             return;
         }
 
@@ -5588,7 +5617,7 @@ int main(int argc, char ** argv) {
         ctx_server.queue_results.remove_waiting_task_id(task_id);
 
         if (result->is_error()) {
-            res_error(res, result->to_json());
+            res_err(res, result->to_json());
             return;
         }
 

tools/server/utils.hpp

@@ -9,14 +9,6 @@
 #include "mtmd-helper.h"
 #include "chat.h"
 
-// increase max payload length to allow use of larger context size
-#define CPPHTTPLIB_FORM_URL_ENCODED_PAYLOAD_MAX_LENGTH 1048576
-// increase backlog size to avoid connection resets for >> 1 slots
-#define CPPHTTPLIB_LISTEN_BACKLOG 512
-// increase max URI length to handle longer prompts in query string
-#define CPPHTTPLIB_REQUEST_URI_MAX_LENGTH 32768
-// disable Nagle's algorithm
-#define CPPHTTPLIB_TCP_NODELAY true
 #include <cpp-httplib/httplib.h>
 
 #define JSON_ASSERT GGML_ASSERT
@@ -461,15 +453,29 @@ static std::string tokens_to_output_formatted_string(const llama_context * ctx,
     return out;
 }
 
+// note: if data is a json array, it will be sent as multiple events, one per item
 static bool server_sent_event(httplib::DataSink & sink, const json & data) {
-    const std::string str =
-        "data: " +
-        data.dump(-1, ' ', false, json::error_handler_t::replace) +
-        "\n\n"; // required by RFC 8895 - A message is terminated by a blank line (two line terminators in a row).
+    static auto send_single = [](httplib::DataSink & sink, const json & data) -> bool {
+        const std::string str =
+            "data: " +
+            data.dump(-1, ' ', false, json::error_handler_t::replace) +
+            "\n\n"; // required by RFC 8895 - A message is terminated by a blank line (two line terminators in a row).
 
-    LOG_DBG("data stream, to_send: %s", str.c_str());
+        LOG_DBG("data stream, to_send: %s", str.c_str());
+        return sink.write(str.c_str(), str.size());
+    };
 
-    return sink.write(str.c_str(), str.size());
+    if (data.is_array()) {
+        for (const auto & item : data) {
+            if (!send_single(sink, item)) {
+                return false;
+            }
+        }
+    } else {
+        return send_single(sink, data);
+    }
+
+    return true;
 }
 
 //

tools/server/webui/.storybook/preview.ts

@@ -11,8 +11,16 @@ const preview: Preview = {
 				date: /Date$/i
 			}
 		},
+
 		backgrounds: {
 			disable: true
+		},
+
+		a11y: {
+			// 'todo' - show a11y violations in the test UI only
+			// 'error' - fail CI on a11y violations
+			// 'off' - skip a11y checks entirely
+			test: 'todo'
 		}
 	},
 	decorators: [

tools/server/webui/.storybook/vitest.setup.ts

@@ -1,8 +1,9 @@
+import * as a11yAddonAnnotations from '@storybook/addon-a11y/preview';
 import { setProjectAnnotations } from '@storybook/sveltekit';
 import * as previewAnnotations from './preview';
 import { beforeAll } from 'vitest';
 
-const project = setProjectAnnotations([previewAnnotations]);
+const project = setProjectAnnotations([a11yAddonAnnotations, previewAnnotations]);
 
 beforeAll(async () => {
 	if (project.beforeAll) {

tools/server/webui/package-lock.json

@@ -22,20 +22,20 @@
 				"unist-util-visit": "^5.0.0"
 			},
 			"devDependencies": {
-				"@chromatic-com/storybook": "^4.0.1",
+				"@chromatic-com/storybook": "^4.1.2",
 				"@eslint/compat": "^1.2.5",
 				"@eslint/js": "^9.18.0",
 				"@internationalized/date": "^3.8.2",
 				"@lucide/svelte": "^0.515.0",
 				"@playwright/test": "^1.49.1",
-				"@storybook/addon-a11y": "^9.0.17",
-				"@storybook/addon-docs": "^9.0.17",
-				"@storybook/addon-svelte-csf": "^5.0.7",
-				"@storybook/addon-vitest": "^9.0.17",
-				"@storybook/sveltekit": "^9.0.17",
-				"@sveltejs/adapter-static": "^3.0.8",
-				"@sveltejs/kit": "^2.22.0",
-				"@sveltejs/vite-plugin-svelte": "^6.0.0",
+				"@storybook/addon-a11y": "^10.0.7",
+				"@storybook/addon-docs": "^10.0.7",
+				"@storybook/addon-svelte-csf": "^5.0.10",
+				"@storybook/addon-vitest": "^10.0.7",
+				"@storybook/sveltekit": "^10.0.7",
+				"@sveltejs/adapter-static": "^3.0.10",
+				"@sveltejs/kit": "^2.48.4",
+				"@sveltejs/vite-plugin-svelte": "^6.2.1",
 				"@tailwindcss/forms": "^0.5.9",
 				"@tailwindcss/typography": "^0.5.15",
 				"@tailwindcss/vite": "^4.0.0",
@@ -46,21 +46,21 @@
 				"dexie": "^4.0.11",
 				"eslint": "^9.18.0",
 				"eslint-config-prettier": "^10.0.1",
-				"eslint-plugin-storybook": "^9.0.17",
+				"eslint-plugin-storybook": "^10.0.7",
 				"eslint-plugin-svelte": "^3.0.0",
 				"fflate": "^0.8.2",
 				"globals": "^16.0.0",
 				"http-server": "^14.1.1",
 				"mdast": "^3.0.0",
 				"mdsvex": "^0.12.3",
-				"playwright": "^1.53.0",
+				"playwright": "^1.56.1",
 				"prettier": "^3.4.2",
 				"prettier-plugin-svelte": "^3.3.3",
 				"prettier-plugin-tailwindcss": "^0.6.11",
 				"rehype-katex": "^7.0.1",
 				"remark-math": "^6.0.0",
 				"sass": "^1.93.3",
-				"storybook": "^9.0.17",
+				"storybook": "^10.0.7",
 				"svelte": "^5.0.0",
 				"svelte-check": "^4.0.0",
 				"tailwind-merge": "^3.3.1",
@@ -71,7 +71,7 @@
 				"typescript-eslint": "^8.20.0",
 				"unified": "^11.0.5",
 				"uuid": "^13.0.0",
-				"vite": "^7.0.4",
+				"vite": "^7.2.2",
 				"vite-plugin-devtools-json": "^0.2.0",
 				"vitest": "^3.2.3",
 				"vitest-browser-svelte": "^0.1.0"
@@ -133,9 +133,9 @@
 			}
 		},
 		"node_modules/@chromatic-com/storybook": {
-			"version": "4.0.1",
-			"resolved": "https://registry.npmjs.org/@chromatic-com/storybook/-/storybook-4.0.1.tgz",
-			"integrity": "sha512-GQXe5lyZl3yLewLJQyFXEpOp2h+mfN2bPrzYaOFNCJjO4Js9deKbRHTOSaiP2FRwZqDLdQwy2+SEGeXPZ94yYw==",
+			"version": "4.1.2",
+			"resolved": "https://registry.npmjs.org/@chromatic-com/storybook/-/storybook-4.1.2.tgz",
+			"integrity": "sha512-QAWGtHwib0qsP5CcO64aJCF75zpFgpKK3jNpxILzQiPK3sVo4EmnVGJVdwcZWpWrGdH8E4YkncGoitw4EXzKMg==",
 			"dev": true,
 			"license": "MIT",
 			"dependencies": {
@@ -150,7 +150,7 @@
 				"yarn": ">=1.22.18"
 			},
 			"peerDependencies": {
-				"storybook": "^0.0.0-0 || ^9.0.0 || ^9.1.0-0"
+				"storybook": "^0.0.0-0 || ^9.0.0 || ^9.1.0-0 || ^9.2.0-0 || ^10.0.0-0 || ^10.1.0-0 || ^10.2.0-0 || ^10.3.0-0"
 			}
 		},
 		"node_modules/@esbuild/aix-ppc64": {
@@ -894,6 +894,17 @@
 				"@jridgewell/trace-mapping": "^0.3.24"
 			}
 		},
+		"node_modules/@jridgewell/remapping": {
+			"version": "2.3.5",
+			"resolved": "https://registry.npmjs.org/@jridgewell/remapping/-/remapping-2.3.5.tgz",
+			"integrity": "sha512-LI9u/+laYG4Ds1TDKSJW2YPrIlcVYOwi2fUC6xB43lueCjgxV4lffOCZCtYFiH6TNOX+tQKXx97T4IKHbhyHEQ==",
+			"dev": true,
+			"license": "MIT",
+			"dependencies": {
+				"@jridgewell/gen-mapping": "^0.3.5",
+				"@jridgewell/trace-mapping": "^0.3.24"
+			}
+		},
 		"node_modules/@jridgewell/resolve-uri": {
 			"version": "3.1.2",
 			"resolved": "https://registry.npmjs.org/@jridgewell/resolve-uri/-/resolve-uri-3.1.2.tgz",
@@ -1502,13 +1513,13 @@
 			}
 		},
 		"node_modules/@playwright/test": {
-			"version": "1.54.1",
-			"resolved": "https://registry.npmjs.org/@playwright/test/-/test-1.54.1.tgz",
-			"integrity": "sha512-FS8hQ12acieG2dYSksmLOF7BNxnVf2afRJdCuM1eMSxj6QTSE6G4InGF7oApGgDb65MX7AwMVlIkpru0yZA4Xw==",
+			"version": "1.56.1",
+			"resolved": "https://registry.npmjs.org/@playwright/test/-/test-1.56.1.tgz",
+			"integrity": "sha512-vSMYtL/zOcFpvJCW71Q/OEGQb7KYBPAdKh35WNSkaZA75JlAO8ED8UN6GUNTm3drWomcbcqRPFqQbLae8yBTdg==",
 			"dev": true,
 			"license": "Apache-2.0",
 			"dependencies": {
-				"playwright": "1.54.1"
+				"playwright": "1.56.1"
 			},
 			"bin": {
 				"playwright": "cli.js"
@@ -1812,9 +1823,9 @@
 			"license": "MIT"
 		},
 		"node_modules/@storybook/addon-a11y": {
-			"version": "9.0.17",
-			"resolved": "https://registry.npmjs.org/@storybook/addon-a11y/-/addon-a11y-9.0.17.tgz",
-			"integrity": "sha512-9cXNK3q/atx3hwJAt9HkJbd9vUxCXfKKiNNuSACbf8h9/j6u3jktulKOf6Xjc3B8lwn6ZpdK/x1HHZN2kTqsvg==",
+			"version": "10.0.7",
+			"resolved": "https://registry.npmjs.org/@storybook/addon-a11y/-/addon-a11y-10.0.7.tgz",
+			"integrity": "sha512-JsYPpZ/n67/2bI1XJeyrAWHHQkHemPkPHjCA0tAUnMz1Shlo/LV2q1Ahgpxoihx4strbHwZz71bcS4MqkHBduA==",
 			"dev": true,
 			"license": "MIT",
 			"dependencies": {
@@ -1826,20 +1837,20 @@
 				"url": "https://opencollective.com/storybook"
 			},
 			"peerDependencies": {
-				"storybook": "^9.0.17"
+				"storybook": "^10.0.7"
 			}
 		},
 		"node_modules/@storybook/addon-docs": {
-			"version": "9.0.17",
-			"resolved": "https://registry.npmjs.org/@storybook/addon-docs/-/addon-docs-9.0.17.tgz",
-			"integrity": "sha512-LOX/kKgQGnyulrqZHsvf77+ZoH/nSUaplGr5hvZglW/U6ak6fO9seJyXAzVKEnC6p+F8n02kFBZbi3s+znQhSg==",
+			"version": "10.0.7",
+			"resolved": "https://registry.npmjs.org/@storybook/addon-docs/-/addon-docs-10.0.7.tgz",
+			"integrity": "sha512-qQQMoeYZC4W+/8ubfOZiTrE8nYC/f4wWP1uq4peRyDy1N2nIN9SwhyxwMn0m3VpeGmRBga5dLvJY9ko6SnJekg==",
 			"dev": true,
 			"license": "MIT",
 			"dependencies": {
 				"@mdx-js/react": "^3.0.0",
-				"@storybook/csf-plugin": "9.0.17",
-				"@storybook/icons": "^1.2.12",
-				"@storybook/react-dom-shim": "9.0.17",
+				"@storybook/csf-plugin": "10.0.7",
+				"@storybook/icons": "^1.6.0",
+				"@storybook/react-dom-shim": "10.0.7",
 				"react": "^16.8.0 || ^17.0.0 || ^18.0.0 || ^19.0.0",
 				"react-dom": "^16.8.0 || ^17.0.0 || ^18.0.0 || ^19.0.0",
 				"ts-dedent": "^2.0.0"
@@ -1849,13 +1860,13 @@
 				"url": "https://opencollective.com/storybook"
 			},
 			"peerDependencies": {
-				"storybook": "^9.0.17"
+				"storybook": "^10.0.7"
 			}
 		},
 		"node_modules/@storybook/addon-svelte-csf": {
-			"version": "5.0.7",
-			"resolved": "https://registry.npmjs.org/@storybook/addon-svelte-csf/-/addon-svelte-csf-5.0.7.tgz",
-			"integrity": "sha512-6Zmy5HjOlrrG6OoKRTGDr9LR6zRK4/Sa7raFzQRKHGASgMlfKsMdNTNO0sxnMUWCu2JMS6HsuoLtB3Ma8SlYtg==",
+			"version": "5.0.10",
+			"resolved": "https://registry.npmjs.org/@storybook/addon-svelte-csf/-/addon-svelte-csf-5.0.10.tgz",
+			"integrity": "sha512-poSvTS7VdaQ42ZoqW5e4+2Hv1iLO0mekH9fwn/QuBNse48R4WlTyR8XFbHRTfatl9gdc9ZYC4uWzazrmV6zGIA==",
 			"dev": true,
 			"license": "MIT",
 			"dependencies": {
@@ -1868,22 +1879,22 @@
 				"zimmerframe": "^1.1.2"
 			},
 			"peerDependencies": {
-				"@storybook/svelte": "^0.0.0-0 || ^8.2.0 || ^9.0.0 || ^9.1.0-0",
+				"@storybook/svelte": "^0.0.0-0 || ^8.2.0 || ^9.0.0 || ^9.1.0-0 || ^10.0.0-0",
 				"@sveltejs/vite-plugin-svelte": "^4.0.0 || ^5.0.0 || ^6.0.0",
-				"storybook": "^0.0.0-0 || ^8.2.0 || ^9.0.0 || ^9.1.0-0",
+				"storybook": "^0.0.0-0 || ^8.2.0 || ^9.0.0 || ^9.1.0-0 || ^10.0.0-0",
 				"svelte": "^5.0.0",
 				"vite": "^5.0.0 || ^6.0.0 || ^7.0.0"
 			}
 		},
 		"node_modules/@storybook/addon-vitest": {
-			"version": "9.0.17",
-			"resolved": "https://registry.npmjs.org/@storybook/addon-vitest/-/addon-vitest-9.0.17.tgz",
-			"integrity": "sha512-eogqcGbACR1sTedBSE2SP/4QV1ruicHYEhYjBtoPIjvYgymN1g5KSuQNysLx4f0SvAzczrcNjX2WVVLX2DVyzA==",
+			"version": "10.0.7",
+			"resolved": "https://registry.npmjs.org/@storybook/addon-vitest/-/addon-vitest-10.0.7.tgz",
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 			"license": "MIT",
 			"dependencies": {
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-				"node": ">=20.0.0"
-			},
 			"peerDependencies": {
 				"eslint": ">=8",
-				"storybook": "^9.0.17"
+				"storybook": "^10.0.7"
 			}
 		},
 		"node_modules/eslint-plugin-svelte": {
@@ -4405,11 +4347,14 @@
 			}
 		},
 		"node_modules/fdir": {
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-			"resolved": "https://registry.npmjs.org/fdir/-/fdir-6.4.6.tgz",
-			"integrity": "sha512-hiFoqpyZcfNm1yc4u8oWCf9A2c4D3QjCrks3zmoVKVxpQRzmPNar1hUJcBG2RQHvEVGDN+Jm81ZheVLAQMK6+w==",
+			"version": "6.5.0",
+			"resolved": "https://registry.npmjs.org/fdir/-/fdir-6.5.0.tgz",
+			"integrity": "sha512-tIbYtZbucOs0BRGqPJkshJUYdL+SDH7dVM8gjy+ERp3WAUjLEFJE+02kanyHtwjWOnwrKYBiwAmM0p4kLJAnXg==",
 			"dev": true,
 			"license": "MIT",
+			"engines": {
+				"node": ">=12.0.0"
+			},
 			"peerDependencies": {
 				"picomatch": "^3 || ^4"
 			},
@@ -5072,22 +5017,6 @@
 			"integrity": "sha512-0aO8FkhNZlj/ZIbNi7Lxxr12obT7cL1moPfE4tg1LkX7LlLfC6DeX4l2ZEud1ukP9jNQyNnfzQVqwbwmAATY4Q==",
 			"license": "MIT"
 		},
-		"node_modules/is-docker": {
-			"version": "2.2.1",
-			"resolved": "https://registry.npmjs.org/is-docker/-/is-docker-2.2.1.tgz",
-			"integrity": "sha512-F+i2BKsFrH66iaUFc0woD8sLy8getkwTwtOBjvs56Cx4CgJDeKQeqfz8wAYiSb8JOprWhHH5p77PbmYCvvUuXQ==",
-			"dev": true,
-			"license": "MIT",
-			"bin": {
-				"is-docker": "cli.js"
-			},
-			"engines": {
-				"node": ">=8"
-			},
-			"funding": {
-				"url": "https://github.com/sponsors/sindresorhus"
-			}
-		},
 		"node_modules/is-extglob": {
 			"version": "2.1.1",
 			"resolved": "https://registry.npmjs.org/is-extglob/-/is-extglob-2.1.1.tgz",
@@ -5133,19 +5062,6 @@
 				"url": "https://github.com/sponsors/sindresorhus"
 			}
 		},
-		"node_modules/is-wsl": {
-			"version": "2.2.0",
-			"resolved": "https://registry.npmjs.org/is-wsl/-/is-wsl-2.2.0.tgz",
-			"integrity": "sha512-fKzAra0rGJUUBwGBgNkHZuToZcn+TtXHpeCgmkMJMMYx1sQDYaCSyjJBSCa2nH1DGm7s3n1oBnohoVTBaN7Lww==",
-			"dev": true,
-			"license": "MIT",
-			"dependencies": {
-				"is-docker": "^2.0.0"
-			},
-			"engines": {
-				"node": ">=8"
-			}
-		},
 		"node_modules/isexe": {
 			"version": "2.0.0",
 			"resolved": "https://registry.npmjs.org/isexe/-/isexe-2.0.0.tgz",
@@ -5591,16 +5507,6 @@
 			"dev": true,
 			"license": "MIT"
 		},
-		"node_modules/lower-case": {
-			"version": "2.0.2",
-			"resolved": "https://registry.npmjs.org/lower-case/-/lower-case-2.0.2.tgz",
-			"integrity": "sha512-7fm3l3NAF9WfN6W3JOmf5drwpVqX78JtoGJ3A6W0a6ZnldM41w2fV5D490psKFTpMds8TJse/eHLFFsNHHjHgg==",
-			"dev": true,
-			"license": "MIT",
-			"dependencies": {
-				"tslib": "^2.0.3"
-			}
-		},
 		"node_modules/lowlight": {
 			"version": "3.3.0",
 			"resolved": "https://registry.npmjs.org/lowlight/-/lowlight-3.3.0.tgz",
@@ -6783,17 +6689,6 @@
 			"dev": true,
 			"license": "MIT"
 		},
-		"node_modules/no-case": {
-			"version": "3.0.4",
-			"resolved": "https://registry.npmjs.org/no-case/-/no-case-3.0.4.tgz",
-			"integrity": "sha512-fgAN3jGAh+RoxUGZHTSOLJIqUc2wmoBwGR4tbpNAKmmovFoWq0OdRkb0VkldReO2a2iBT/OEulG9XSUc10r3zg==",
-			"dev": true,
-			"license": "MIT",
-			"dependencies": {
-				"lower-case": "^2.0.2",
-				"tslib": "^2.0.3"
-			}
-		},
 		"node_modules/node-addon-api": {
 			"version": "7.1.1",
 			"resolved": "https://registry.npmjs.org/node-addon-api/-/node-addon-api-7.1.1.tgz",
@@ -6815,24 +6710,6 @@
 				"url": "https://github.com/sponsors/ljharb"
 			}
 		},
-		"node_modules/open": {
-			"version": "8.4.2",
-			"resolved": "https://registry.npmjs.org/open/-/open-8.4.2.tgz",
-			"integrity": "sha512-7x81NCL719oNbsq/3mh+hVrAWmFuEYUqrq/Iw3kUzH8ReypT9QQ0BLoJS7/G9k6N81XjW4qHWtjWwe/9eLy1EQ==",
-			"dev": true,
-			"license": "MIT",
-			"dependencies": {
-				"define-lazy-prop": "^2.0.0",
-				"is-docker": "^2.1.1",
-				"is-wsl": "^2.2.0"
-			},
-			"engines": {
-				"node": ">=12"
-			},
-			"funding": {
-				"url": "https://github.com/sponsors/sindresorhus"
-			}
-		},
 		"node_modules/opener": {
 			"version": "1.5.2",
 			"resolved": "https://registry.npmjs.org/opener/-/opener-1.5.2.tgz",
@@ -6919,17 +6796,6 @@
 				"url": "https://github.com/inikulin/parse5?sponsor=1"
 			}
 		},
-		"node_modules/pascal-case": {
-			"version": "3.1.2",
-			"resolved": "https://registry.npmjs.org/pascal-case/-/pascal-case-3.1.2.tgz",
-			"integrity": "sha512-uWlGT3YSnK9x3BQJaOdcZwrnV6hPpd8jFH1/ucpiLRPh/2zCVJKS19E4GvYHvaCcACn3foXZ0cLB9Wrx1KGe5g==",
-			"dev": true,
-			"license": "MIT",
-			"dependencies": {
-				"no-case": "^3.0.4",
-				"tslib": "^2.0.3"
-			}
-		},
 		"node_modules/path-exists": {
 			"version": "4.0.0",
 			"resolved": "https://registry.npmjs.org/path-exists/-/path-exists-4.0.0.tgz",
@@ -7000,13 +6866,13 @@
 			}
 		},
 		"node_modules/playwright": {
-			"version": "1.54.1",
-			"resolved": "https://registry.npmjs.org/playwright/-/playwright-1.54.1.tgz",
-			"integrity": "sha512-peWpSwIBmSLi6aW2auvrUtf2DqY16YYcCMO8rTVx486jKmDTJg7UAhyrraP98GB8BoPURZP8+nxO7TSd4cPr5g==",
+			"version": "1.56.1",
+			"resolved": "https://registry.npmjs.org/playwright/-/playwright-1.56.1.tgz",
+			"integrity": "sha512-aFi5B0WovBHTEvpM3DzXTUaeN6eN0qWnTkKx4NQaH4Wvcmc153PdaY2UBdSYKaGYw+UyWXSVyxDUg5DoPEttjw==",
 			"dev": true,
 			"license": "Apache-2.0",
 			"dependencies": {
-				"playwright-core": "1.54.1"
+				"playwright-core": "1.56.1"
 			},
 			"bin": {
 				"playwright": "cli.js"
@@ -7019,9 +6885,9 @@
 			}
 		},
 		"node_modules/playwright-core": {
-			"version": "1.54.1",
-			"resolved": "https://registry.npmjs.org/playwright-core/-/playwright-core-1.54.1.tgz",
-			"integrity": "sha512-Nbjs2zjj0htNhzgiy5wu+3w09YetDx5pkrpI/kZotDlDUaYk0HVA5xrBVPdow4SAUIlhgKcJeJg4GRKW6xHusA==",
+			"version": "1.56.1",
+			"resolved": "https://registry.npmjs.org/playwright-core/-/playwright-core-1.56.1.tgz",
+			"integrity": "sha512-hutraynyn31F+Bifme+Ps9Vq59hKuUCz7H1kDOcBs+2oGguKkWTU50bBWrtz34OUWmIwpBTWDxaRPXrIXkgvmQ==",
 			"dev": true,
 			"license": "Apache-2.0",
 			"bin": {
@@ -7852,6 +7718,13 @@
 			"dev": true,
 			"license": "MIT"
 		},
+		"node_modules/scule": {
+			"version": "1.3.0",
+			"resolved": "https://registry.npmjs.org/scule/-/scule-1.3.0.tgz",
+			"integrity": "sha512-6FtHJEvt+pVMIB9IBY+IcCJ6Z5f1iQnytgyfKMhDKgmzYG+TeH/wx1y3l27rshSbLiSanrR9ffZDrEsmjlQF2g==",
+			"dev": true,
+			"license": "MIT"
+		},
 		"node_modules/secure-compare": {
 			"version": "3.0.1",
 			"resolved": "https://registry.npmjs.org/secure-compare/-/secure-compare-3.0.1.tgz",
@@ -8052,26 +7925,26 @@
 			"license": "MIT"
 		},
 		"node_modules/storybook": {
-			"version": "9.0.17",
-			"resolved": "https://registry.npmjs.org/storybook/-/storybook-9.0.17.tgz",
-			"integrity": "sha512-O+9jgJ+Trlq9VGD1uY4OBLKQWHHDKM/A/pA8vMW6PVehhGHNvpzcIC1bngr6mL5gGHZP2nBv+9XG8pTMcggMmg==",
+			"version": "10.0.7",
+			"resolved": "https://registry.npmjs.org/storybook/-/storybook-10.0.7.tgz",
+			"integrity": "sha512-7smAu0o+kdm378Q2uIddk32pn0UdIbrtTVU+rXRVtTVTCrK/P2cCui2y4JH+Bl3NgEq1bbBQpCAF/HKrDjk2Qw==",
 			"dev": true,
 			"license": "MIT",
 			"dependencies": {
 				"@storybook/global": "^5.0.0",
+				"@storybook/icons": "^1.6.0",
 				"@testing-library/jest-dom": "^6.6.3",
 				"@testing-library/user-event": "^14.6.1",
 				"@vitest/expect": "3.2.4",
+				"@vitest/mocker": "3.2.4",
 				"@vitest/spy": "3.2.4",
-				"better-opn": "^3.0.2",
 				"esbuild": "^0.18.0 || ^0.19.0 || ^0.20.0 || ^0.21.0 || ^0.22.0 || ^0.23.0 || ^0.24.0 || ^0.25.0",
-				"esbuild-register": "^3.5.0",
 				"recast": "^0.23.5",
 				"semver": "^7.6.2",
 				"ws": "^8.18.0"
 			},
 			"bin": {
-				"storybook": "bin/index.cjs"
+				"storybook": "dist/bin/dispatcher.js"
 			},
 			"funding": {
 				"type": "opencollective",
@@ -8418,14 +8291,14 @@
 			}
 		},
 		"node_modules/svelte2tsx": {
-			"version": "0.7.41",
-			"resolved": "https://registry.npmjs.org/svelte2tsx/-/svelte2tsx-0.7.41.tgz",
-			"integrity": "sha512-/TUwpyn/Qc1wcGuayf2GSwvZ7htdAOzpo0JFFm96srKnRXoTD0gy4n06g+XgH8w016S3lPtyFVtFAm+0yJ0BZw==",
+			"version": "0.7.45",
+			"resolved": "https://registry.npmjs.org/svelte2tsx/-/svelte2tsx-0.7.45.tgz",
+			"integrity": "sha512-cSci+mYGygYBHIZLHlm/jYlEc1acjAHqaQaDFHdEBpUueM9kSTnPpvPtSl5VkJOU1qSJ7h1K+6F/LIUYiqC8VA==",
 			"dev": true,
 			"license": "MIT",
 			"dependencies": {
 				"dedent-js": "^1.0.1",
-				"pascal-case": "^3.1.1"
+				"scule": "^1.3.0"
 			},
 			"peerDependencies": {
 				"svelte": "^3.55 || ^4.0.0-next.0 || ^4.0 || ^5.0.0-next.0",
@@ -8535,14 +8408,14 @@
 			"license": "MIT"
 		},
 		"node_modules/tinyglobby": {
-			"version": "0.2.14",
-			"resolved": "https://registry.npmjs.org/tinyglobby/-/tinyglobby-0.2.14.tgz",
-			"integrity": "sha512-tX5e7OM1HnYr2+a2C/4V0htOcSQcoSTH9KgJnVvNm5zm/cyEWKJ7j7YutsH9CxMdtOkkLFy2AHrMci9IM8IPZQ==",
+			"version": "0.2.15",
+			"resolved": "https://registry.npmjs.org/tinyglobby/-/tinyglobby-0.2.15.tgz",
+			"integrity": "sha512-j2Zq4NyQYG5XMST4cbs02Ak8iJUdxRM0XI5QyxXuZOzKOINmWurp3smXu3y5wDcJrptwpSjgXHzIQxR0omXljQ==",
 			"dev": true,
 			"license": "MIT",
 			"dependencies": {
-				"fdir": "^6.4.4",
-				"picomatch": "^4.0.2"
+				"fdir": "^6.5.0",
+				"picomatch": "^4.0.3"
 			},
 			"engines": {
 				"node": ">=12.0.0"
@@ -8918,17 +8791,19 @@
 			}
 		},
 		"node_modules/unplugin": {
-			"version": "1.16.1",
-			"resolved": "https://registry.npmjs.org/unplugin/-/unplugin-1.16.1.tgz",
-			"integrity": "sha512-4/u/j4FrCKdi17jaxuJA0jClGxB1AvU2hw/IuayPc4ay1XGaJs/rbb4v5WKwAjNifjmXK9PIFyuPiaK8azyR9w==",
+			"version": "2.3.10",
+			"resolved": "https://registry.npmjs.org/unplugin/-/unplugin-2.3.10.tgz",
+			"integrity": "sha512-6NCPkv1ClwH+/BGE9QeoTIl09nuiAt0gS28nn1PvYXsGKRwM2TCbFA2QiilmehPDTXIe684k4rZI1yl3A1PCUw==",
 			"dev": true,
 			"license": "MIT",
 			"dependencies": {
-				"acorn": "^8.14.0",
+				"@jridgewell/remapping": "^2.3.5",
+				"acorn": "^8.15.0",
+				"picomatch": "^4.0.3",
 				"webpack-virtual-modules": "^0.6.2"
 			},
 			"engines": {
-				"node": ">=14.0.0"
+				"node": ">=18.12.0"
 			}
 		},
 		"node_modules/uri-js": {
@@ -9054,18 +8929,18 @@
 			}
 		},
 		"node_modules/vite": {
-			"version": "7.0.5",
-			"resolved": "https://registry.npmjs.org/vite/-/vite-7.0.5.tgz",
-			"integrity": "sha512-1mncVwJxy2C9ThLwz0+2GKZyEXuC3MyWtAAlNftlZZXZDP3AJt5FmwcMit/IGGaNZ8ZOB2BNO/HFUB+CpN0NQw==",
+			"version": "7.2.2",
+			"resolved": "https://registry.npmjs.org/vite/-/vite-7.2.2.tgz",
+			"integrity": "sha512-BxAKBWmIbrDgrokdGZH1IgkIk/5mMHDreLDmCJ0qpyJaAteP8NvMhkwr/ZCQNqNH97bw/dANTE9PDzqwJghfMQ==",
 			"dev": true,
 			"license": "MIT",
 			"dependencies": {
 				"esbuild": "^0.25.0",
-				"fdir": "^6.4.6",
-				"picomatch": "^4.0.2",
+				"fdir": "^6.5.0",
+				"picomatch": "^4.0.3",
 				"postcss": "^8.5.6",
-				"rollup": "^4.40.0",
-				"tinyglobby": "^0.2.14"
+				"rollup": "^4.43.0",
+				"tinyglobby": "^0.2.15"
 			},
 			"bin": {
 				"vite": "bin/vite.js"

tools/server/webui/package.json

@@ -24,20 +24,20 @@
 		"cleanup": "rm -rf .svelte-kit build node_modules test-results"
 	},
 	"devDependencies": {
-		"@chromatic-com/storybook": "^4.0.1",
+		"@chromatic-com/storybook": "^4.1.2",
 		"@eslint/compat": "^1.2.5",
 		"@eslint/js": "^9.18.0",
 		"@internationalized/date": "^3.8.2",
 		"@lucide/svelte": "^0.515.0",
 		"@playwright/test": "^1.49.1",
-		"@storybook/addon-a11y": "^9.0.17",
-		"@storybook/addon-docs": "^9.0.17",
-		"@storybook/addon-svelte-csf": "^5.0.7",
-		"@storybook/addon-vitest": "^9.0.17",
-		"@storybook/sveltekit": "^9.0.17",
-		"@sveltejs/adapter-static": "^3.0.8",
-		"@sveltejs/kit": "^2.22.0",
-		"@sveltejs/vite-plugin-svelte": "^6.0.0",
+		"@storybook/addon-a11y": "^10.0.7",
+		"@storybook/addon-docs": "^10.0.7",
+		"@storybook/addon-svelte-csf": "^5.0.10",
+		"@storybook/addon-vitest": "^10.0.7",
+		"@storybook/sveltekit": "^10.0.7",
+		"@sveltejs/adapter-static": "^3.0.10",
+		"@sveltejs/kit": "^2.48.4",
+		"@sveltejs/vite-plugin-svelte": "^6.2.1",
 		"@tailwindcss/forms": "^0.5.9",
 		"@tailwindcss/typography": "^0.5.15",
 		"@tailwindcss/vite": "^4.0.0",
@@ -48,21 +48,21 @@
 		"dexie": "^4.0.11",
 		"eslint": "^9.18.0",
 		"eslint-config-prettier": "^10.0.1",
-		"eslint-plugin-storybook": "^9.0.17",
+		"eslint-plugin-storybook": "^10.0.7",
 		"eslint-plugin-svelte": "^3.0.0",
 		"fflate": "^0.8.2",
 		"globals": "^16.0.0",
 		"http-server": "^14.1.1",
 		"mdast": "^3.0.0",
 		"mdsvex": "^0.12.3",
-		"playwright": "^1.53.0",
+		"playwright": "^1.56.1",
 		"prettier": "^3.4.2",
 		"prettier-plugin-svelte": "^3.3.3",
 		"prettier-plugin-tailwindcss": "^0.6.11",
 		"rehype-katex": "^7.0.1",
 		"remark-math": "^6.0.0",
 		"sass": "^1.93.3",
-		"storybook": "^9.0.17",
+		"storybook": "^10.0.7",
 		"svelte": "^5.0.0",
 		"svelte-check": "^4.0.0",
 		"tailwind-merge": "^3.3.1",
@@ -73,7 +73,7 @@
 		"typescript-eslint": "^8.20.0",
 		"unified": "^11.0.5",
 		"uuid": "^13.0.0",
-		"vite": "^7.0.4",
+		"vite": "^7.2.2",
 		"vite-plugin-devtools-json": "^0.2.0",
 		"vitest": "^3.2.3",
 		"vitest-browser-svelte": "^0.1.0"

tools/server/webui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentFilePreview.svelte

@@ -1,6 +1,5 @@
 <script lang="ts">
-	import { X } from '@lucide/svelte';
-	import { Button } from '$lib/components/ui/button';
+	import { RemoveButton } from '$lib/components/app';
 	import { formatFileSize, getFileTypeLabel, getPreviewText } from '$lib/utils/file-preview';
 	import { FileTypeCategory, MimeTypeText } from '$lib/enums/files';
 
@@ -66,17 +65,15 @@
 		</button>
 	{:else}
 		<!-- Non-readonly mode (ChatForm) -->
-		<div class="relative rounded-lg border border-border bg-muted p-3 {className} w-64">
-			<Button
-				type="button"
-				variant="ghost"
-				size="sm"
-				class="absolute top-2 right-2 h-6 w-6 bg-white/20 p-0 hover:bg-white/30"
-				onclick={() => onRemove?.(id)}
-				aria-label="Remove file"
-			>
-				<X class="h-3 w-3" />
-			</Button>
+		<button
+			class="group relative rounded-lg border border-border bg-muted p-3 {className} {textContent
+				? 'max-h-24 max-w-72'
+				: 'max-w-36'} cursor-pointer text-left"
+			onclick={onClick}
+		>
+			<div class="absolute top-2 right-2 opacity-0 transition-opacity group-hover:opacity-100">
+				<RemoveButton {id} {onRemove} />
+			</div>
 
 			<div class="pr-8">
 				<span class="mb-3 block truncate text-sm font-medium text-foreground">{name}</span>
@@ -85,7 +82,7 @@
 					<div class="relative">
 						<div
 							class="overflow-hidden font-mono text-xs leading-relaxed break-words whitespace-pre-wrap text-muted-foreground"
-							style="max-height: 3.6em; line-height: 1.2em;"
+							style="max-height: 3rem; line-height: 1.2em;"
 						>
 							{getPreviewText(textContent)}
 						</div>
@@ -98,11 +95,11 @@
 					</div>
 				{/if}
 			</div>
-		</div>
+		</button>
 	{/if}
 {:else}
 	<button
-		class="flex items-center gap-2 gap-3 rounded-lg border border-border bg-muted p-3 {className}"
+		class="group flex items-center gap-3 rounded-lg border border-border bg-muted p-3 {className} relative"
 		onclick={onClick}
 	>
 		<div
@@ -112,7 +109,9 @@
 		</div>
 
 		<div class="flex flex-col gap-1">
-			<span class="max-w-36 truncate text-sm font-medium text-foreground md:max-w-72">
+			<span
+				class="max-w-24 truncate text-sm font-medium text-foreground group-hover:pr-6 md:max-w-32"
+			>
 				{name}
 			</span>
 
@@ -122,18 +121,9 @@
 		</div>
 
 		{#if !readonly}
-			<Button
-				type="button"
-				variant="ghost"
-				size="sm"
-				class="h-6 w-6 p-0"
-				onclick={(e) => {
-					e.stopPropagation();
-					onRemove?.(id);
-				}}
-			>
-				<X class="h-3 w-3" />
-			</Button>
+			<div class="absolute top-2 right-2 opacity-0 transition-opacity group-hover:opacity-100">
+				<RemoveButton {id} {onRemove} />
+			</div>
 		{/if}
 	</button>
 {/if}

tools/server/webui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentImagePreview.svelte

@@ -1,6 +1,5 @@
 <script lang="ts">
-	import { X } from '@lucide/svelte';
-	import { Button } from '$lib/components/ui/button';
+	import { RemoveButton } from '$lib/components/app';
 
 	interface Props {
 		id: string;
@@ -26,12 +25,12 @@
 		class: className = '',
 		// Default to small size for form previews
 		width = 'w-auto',
-		height = 'h-24',
+		height = 'h-16',
 		imageClass = ''
 	}: Props = $props();
 </script>
 
-<div class="relative overflow-hidden rounded-lg border border-border bg-muted {className}">
+<div class="group relative overflow-hidden rounded-lg border border-border bg-muted {className}">
 	{#if onClick}
 		<button
 			type="button"
@@ -55,17 +54,9 @@
 
 	{#if !readonly}
 		<div
-			class="absolute top-1 right-1 flex items-center justify-center opacity-0 transition-opacity hover:opacity-100"
+			class="absolute top-1 right-1 flex items-center justify-center opacity-0 transition-opacity group-hover:opacity-100"
 		>
-			<Button
-				type="button"
-				variant="ghost"
-				size="sm"
-				class="h-6 w-6 bg-white/20 p-0 text-white hover:bg-white/30"
-				onclick={() => onRemove?.(id)}
-			>
-				<X class="h-3 w-3" />
-			</Button>
+			<RemoveButton {id} {onRemove} class="text-white" />
 		</div>
 	{/if}
 </div>

tools/server/webui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentPreviewDialog.svelte

@@ -153,7 +153,7 @@
 <Dialog.Root bind:open>
 	<Dialog.Content class="grid max-h-[90vh] max-w-5xl overflow-hidden !p-10 sm:w-auto sm:max-w-6xl">
 		<Dialog.Header class="flex-shrink-0">
-			<div class="flex items-center justify-between">
+			<div class="flex items-center justify-between gap-6">
 				<div class="flex items-center gap-3">
 					{#if IconComponent}
 						<IconComponent class="h-5 w-5 text-muted-foreground" />

tools/server/webui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentsList.svelte

@@ -1,11 +1,16 @@
 <script lang="ts">
 	import { ChatAttachmentImagePreview, ChatAttachmentFilePreview } from '$lib/components/app';
+	import { Button } from '$lib/components/ui/button';
+	import { ChevronLeft, ChevronRight } from '@lucide/svelte';
 	import { FileTypeCategory } from '$lib/enums/files';
 	import { getFileTypeCategory } from '$lib/utils/file-type';
 	import ChatAttachmentPreviewDialog from './ChatAttachmentPreviewDialog.svelte';
+	import ChatAttachmentsViewAllDialog from './ChatAttachmentsViewAllDialog.svelte';
+	import type { ChatAttachmentDisplayItem, ChatAttachmentPreviewItem } from '$lib/types/chat';
 
 	interface Props {
 		class?: string;
+		style?: string;
 		// For ChatMessage - stored attachments
 		attachments?: DatabaseMessageExtra[];
 		readonly?: boolean;
@@ -16,10 +21,13 @@
 		imageClass?: string;
 		imageHeight?: string;
 		imageWidth?: string;
+		// Limit display to single row with "+ X more" button
+		limitToSingleRow?: boolean;
 	}
 
 	let {
 		class: className = '',
+		style = '',
 		attachments = [],
 		readonly = false,
 		onFileRemove,
@@ -27,36 +35,23 @@
 		// Default to small size for form previews
 		imageClass = '',
 		imageHeight = 'h-24',
-		imageWidth = 'w-auto'
+		imageWidth = 'w-auto',
+		limitToSingleRow = false
 	}: Props = $props();
 
 	let displayItems = $derived(getDisplayItems());
 
-	// Preview dialog state
+	let canScrollLeft = $state(false);
+	let canScrollRight = $state(false);
+	let isScrollable = $state(false);
 	let previewDialogOpen = $state(false);
-	let previewItem = $state<{
-		uploadedFile?: ChatUploadedFile;
-		attachment?: DatabaseMessageExtra;
-		preview?: string;
-		name?: string;
-		type?: string;
-		size?: number;
-		textContent?: string;
-	} | null>(null);
+	let previewItem = $state<ChatAttachmentPreviewItem | null>(null);
+	let scrollContainer: HTMLDivElement | undefined = $state();
+	let showViewAll = $derived(limitToSingleRow && displayItems.length > 0 && isScrollable);
+	let viewAllDialogOpen = $state(false);
 
-	function getDisplayItems() {
-		const items: Array<{
-			id: string;
-			name: string;
-			size?: number;
-			preview?: string;
-			type: string;
-			isImage: boolean;
-			uploadedFile?: ChatUploadedFile;
-			attachment?: DatabaseMessageExtra;
-			attachmentIndex?: number;
-			textContent?: string;
-		}> = [];
+	function getDisplayItems(): ChatAttachmentDisplayItem[] {
+		const items: ChatAttachmentDisplayItem[] = [];
 
 		// Add uploaded files (ChatForm)
 		for (const file of uploadedFiles) {
@@ -127,14 +122,12 @@
 			}
 		}
 
-		return items;
+		return items.reverse();
 	}
 
-	function openPreview(item: (typeof displayItems)[0], event?: Event) {
-		if (event) {
-			event.preventDefault();
-			event.stopPropagation();
-		}
+	function openPreview(item: ChatAttachmentDisplayItem, event?: MouseEvent) {
+		event?.stopPropagation();
+		event?.preventDefault();
 
 		previewItem = {
 			uploadedFile: item.uploadedFile,
@@ -147,38 +140,118 @@
 		};
 		previewDialogOpen = true;
 	}
+
+	function scrollLeft(event?: MouseEvent) {
+		event?.stopPropagation();
+		event?.preventDefault();
+
+		if (!scrollContainer) return;
+
+		scrollContainer.scrollBy({ left: scrollContainer.clientWidth * -0.67, behavior: 'smooth' });
+	}
+
+	function scrollRight(event?: MouseEvent) {
+		event?.stopPropagation();
+		event?.preventDefault();
+
+		if (!scrollContainer) return;
+
+		scrollContainer.scrollBy({ left: scrollContainer.clientWidth * 0.67, behavior: 'smooth' });
+	}
+
+	function updateScrollButtons() {
+		if (!scrollContainer) return;
+
+		const { scrollLeft, scrollWidth, clientWidth } = scrollContainer;
+
+		canScrollLeft = scrollLeft > 0;
+		canScrollRight = scrollLeft < scrollWidth - clientWidth - 1;
+		isScrollable = scrollWidth > clientWidth;
+	}
+
+	$effect(() => {
+		if (scrollContainer && displayItems.length) {
+			scrollContainer.scrollLeft = 0;
+
+			setTimeout(() => {
+				updateScrollButtons();
+			}, 0);
+		}
+	});
 </script>
 
 {#if displayItems.length > 0}
-	<div class="flex flex-wrap items-start {readonly ? 'justify-end' : ''} gap-3 {className}">
-		{#each displayItems as item (item.id)}
-			{#if item.isImage && item.preview}
-				<ChatAttachmentImagePreview
-					class="cursor-pointer"
-					id={item.id}
-					name={item.name}
-					preview={item.preview}
-					{readonly}
-					onRemove={onFileRemove}
-					height={imageHeight}
-					width={imageWidth}
-					{imageClass}
-					onClick={(event) => openPreview(item, event)}
-				/>
-			{:else}
-				<ChatAttachmentFilePreview
-					class="cursor-pointer"
-					id={item.id}
-					name={item.name}
-					type={item.type}
-					size={item.size}
-					{readonly}
-					onRemove={onFileRemove}
-					textContent={item.textContent}
-					onClick={(event) => openPreview(item, event)}
-				/>
-			{/if}
-		{/each}
+	<div class={className} {style}>
+		<div class="relative">
+			<button
+				class="absolute top-1/2 left-4 z-10 flex h-6 w-6 -translate-y-1/2 items-center justify-center rounded-full bg-foreground/15 shadow-md backdrop-blur-xs transition-opacity hover:bg-foreground/35 {canScrollLeft
+					? 'opacity-100'
+					: 'pointer-events-none opacity-0'}"
+				onclick={scrollLeft}
+				aria-label="Scroll left"
+			>
+				<ChevronLeft class="h-4 w-4" />
+			</button>
+
+			<div
+				class="scrollbar-hide flex items-start gap-3 overflow-x-auto"
+				bind:this={scrollContainer}
+				onscroll={updateScrollButtons}
+			>
+				{#each displayItems as item (item.id)}
+					{#if item.isImage && item.preview}
+						<ChatAttachmentImagePreview
+							class="flex-shrink-0 cursor-pointer {limitToSingleRow ? 'first:ml-4 last:mr-4' : ''}"
+							id={item.id}
+							name={item.name}
+							preview={item.preview}
+							{readonly}
+							onRemove={onFileRemove}
+							height={imageHeight}
+							width={imageWidth}
+							{imageClass}
+							onClick={(event) => openPreview(item, event)}
+						/>
+					{:else}
+						<ChatAttachmentFilePreview
+							class="flex-shrink-0 cursor-pointer {limitToSingleRow ? 'first:ml-4 last:mr-4' : ''}"
+							id={item.id}
+							name={item.name}
+							type={item.type}
+							size={item.size}
+							{readonly}
+							onRemove={onFileRemove}
+							textContent={item.textContent}
+							onClick={(event) => openPreview(item, event)}
+						/>
+					{/if}
+				{/each}
+			</div>
+
+			<button
+				class="absolute top-1/2 right-4 z-10 flex h-6 w-6 -translate-y-1/2 items-center justify-center rounded-full bg-foreground/15 shadow-md backdrop-blur-xs transition-opacity hover:bg-foreground/35 {canScrollRight
+					? 'opacity-100'
+					: 'pointer-events-none opacity-0'}"
+				onclick={scrollRight}
+				aria-label="Scroll right"
+			>
+				<ChevronRight class="h-4 w-4" />
+			</button>
+		</div>
+
+		{#if showViewAll}
+			<div class="mt-2 -mr-2 flex justify-end px-4">
+				<Button
+					type="button"
+					variant="ghost"
+					size="sm"
+					class="h-6 text-xs text-muted-foreground hover:text-foreground"
+					onclick={() => (viewAllDialogOpen = true)}
+				>
+					View all
+				</Button>
+			</div>
+		{/if}
 	</div>
 {/if}
 
@@ -194,3 +267,13 @@
 		textContent={previewItem.textContent}
 	/>
 {/if}
+
+<ChatAttachmentsViewAllDialog
+	bind:open={viewAllDialogOpen}
+	{uploadedFiles}
+	{attachments}
+	{readonly}
+	{onFileRemove}
+	imageHeight="h-64"
+	{imageClass}
+/>