[SYCL] Fix Q8_0 reorder: garbage on 2nd prompt + crash on full VRAM (#21638)

* [SYCL] Fix Q8_0 reorder: add missing dequantize path for GEMM

The Q8_0 reorder optimization (#21527) was missing a reorder-aware
dequantizer for the GEMM code path used during prompt processing.
After token generation reordered Q8_0 weights (via DMMV/MMVQ), the
next prompt processing pass would read them with the standard
dequantizer, producing garbage output.

Add dequantize_block_q8_0_reorder() and wire it into both
ggml_get_to_fp16_sycl() and ggml_get_to_fp32_sycl(), matching the
pattern already used by Q4_0, Q4_K, and Q6_K.

Fixes #21589

AI (Claude) was used to assist with root cause investigation and
writing the kernel code. All code was human-reviewed and tested
on real hardware.

* SYCL: fix reorder crash when device memory is full

The reorder optimization allocates a temporary buffer the full size of
the weight tensor on the device. When VRAM is nearly full (large models
on a single GPU), this allocation fails and the subsequent memcpy crashes
on a NULL pointer.

Fix: try device allocation first, fall back to host memory if device
memory is full. The reorder kernel still works correctly reading from
host memory over PCIe. This is slower for the one-time reorder (~21 t/s
vs ~38 t/s on Intel Arc Pro B70), but the optimization is preserved for
all subsequent inference. If both device and host allocation fail, skip
the reorder and fall back to the unoptimized kernel path.

Also fixes a bug where opt_for_reorder() marked tensors as reordered
even when the reorder was skipped due to allocation failure. This caused
DMMV/MMVQ kernels to read the original AoS data as if it were SoA,
producing garbage output or NaN results.

Tested on Intel Arc Pro B70 (32GB) with Q8_0, Q4_K_M models. Coding was
AI-assisted (Claude), reviewed and tested on hardware by a human.

Fixes #20478

* SYCL: add RAII temp buffer class + macro guard for host fallback

Replace sycl_ext_malloc_with_fallback/sycl_ext_free_fallback free
functions with sycl_reorder_temp_buffer RAII class. The host_fallback
bool is now a private member, and cleanup happens automatically at
scope exit.

Add GGML_SYCL_HOST_MEM_FALLBACK cmake option (default ON) to guard
the host memory fallback code path. Device access to host memory
requires Linux kernel 6.8+ (Ubuntu 26.04+); users on older kernels
can set -DGGML_SYCL_HOST_MEM_FALLBACK=OFF to disable it.

Addresses arthw's review on PR #21638.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* SYCL: document GGML_SYCL_HOST_MEM_FALLBACK build option in SYCL.md

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* SYCL: add reorder-aware DMMV dequantizers for Q4_K and Q6_K

Q4_K and Q6_K had reorder support for MMVQ and GEMM paths but not
DMMV. When the DMMV path encountered reordered data it would abort.

Add DMMV kernels that read from the SOA reorder layout for both
types. Same math as the non-reorder versions, different memory
access pattern.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

---------

Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

.devops/vulkan.Dockerfile

@@ -7,7 +7,7 @@ RUN apt update && apt install -y git build-essential cmake wget xz-utils
 
 # Install SSL and Vulkan SDK dependencies
 RUN apt install -y libssl-dev curl \
-    libxcb-xinput0 libxcb-xinerama0 libxcb-cursor-dev libvulkan-dev glslc
+    libxcb-xinput0 libxcb-xinerama0 libxcb-cursor-dev libvulkan-dev glslc spirv-headers
 
 # Build it
 WORKDIR /app

.github/labeler.yml

@@ -73,10 +73,18 @@ android:
     - changed-files:
         - any-glob-to-any-file:
             - examples/llama.android/**
+server/webui:
+    - changed-files:
+        - any-glob-to-any-file:
+            - tools/server/webui/**
+            - tools/server/public/**
 server:
     - changed-files:
         - any-glob-to-any-file:
             - tools/server/**
+
+
+
 ggml:
     - changed-files:
         - any-glob-to-any-file:

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

@@ -141,61 +141,59 @@ jobs:
   #         amd-smi static
   #         GG_BUILD_ROCM=1 GG_BUILD_AMDGPU_TARGETS="gfx1101" bash ./ci/run.sh ~/results/llama.cpp /mnt/llama.cpp
 
-  # TODO: sandbox Mac runners
-  #  ggml-ci-mac-metal:
-  #    runs-on: [self-hosted, macOS, ARM64]
-  #
-  #    steps:
-  #      - name: Clone
-  #        id: checkout
-  #        uses: actions/checkout@v6
-  #
-  #      - name: Test
-  #        id: ggml-ci
-  #        run: |
-  #          GG_BUILD_METAL=1 bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp
-  #
-  #  ggml-ci-mac-webgpu:
-  #    runs-on: [self-hosted, macOS, ARM64]
-  #
-  #    steps:
-  #      - name: Clone
-  #        id: checkout
-  #        uses: actions/checkout@v6
-  #
-  #      - name: Dawn Dependency
-  #        id: dawn-depends
-  #        run: |
-  #          DAWN_VERSION="v2.0.0"
-  #          DAWN_OWNER="reeselevine"
-  #          DAWN_REPO="dawn"
-  #          DAWN_ASSET_NAME="Dawn-5e9a4865b1635796ccc77dd30057f2b4002a1355-macos-latest-Release"
-  #          echo "Fetching release asset from https://github.com/${DAWN_OWNER}/${DAWN_REPO}/releases/download/${DAWN_VERSION}/${DAWN_ASSET_NAME}.zip"
-  #          curl -L -o artifact.zip \
-  #            "https://github.com/${DAWN_OWNER}/${DAWN_REPO}/releases/download/${DAWN_VERSION}/${DAWN_ASSET_NAME}.zip"
-  #          mkdir dawn
-  #          unzip artifact.zip
-  #          tar -xvf ${DAWN_ASSET_NAME}.tar.gz -C dawn --strip-components=1
-  #
-  #      - name: Test
-  #        id: ggml-ci
-  #        run: |
-  #          GG_BUILD_WEBGPU=1 GG_BUILD_WEBGPU_DAWN_PREFIX="$GITHUB_WORKSPACE/dawn" \
-  #            bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp
-  #
-  #  ggml-ci-mac-vulkan:
-  #    runs-on: [self-hosted, macOS, ARM64]
-  #
-  #    steps:
-  #      - name: Clone
-  #        id: checkout
-  #        uses: actions/checkout@v6
-  #
-  #      - name: Test
-  #        id: ggml-ci
-  #        run: |
-  #          vulkaninfo --summary
-  #          GG_BUILD_VULKAN=1 bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp
+  ggml-ci-mac-metal:
+    runs-on: [self-hosted, macOS, ARM64]
+
+    steps:
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v6
+
+      - name: Test
+        id: ggml-ci
+        run: |
+          GG_BUILD_METAL=1 bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp
+
+  ggml-ci-mac-webgpu:
+    runs-on: [self-hosted, macOS, ARM64]
+
+    steps:
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v6
+
+      - name: Dawn Dependency
+        id: dawn-depends
+        run: |
+          DAWN_VERSION="v20260317.182325"
+          DAWN_OWNER="google"
+          DAWN_REPO="dawn"
+          DAWN_ASSET_NAME="Dawn-18eb229ef5f707c1464cc581252e7603c73a3ef0-macos-latest-Release"
+          echo "Fetching release asset from https://github.com/google/dawn/releases/download/${DAWN_VERSION}/${DAWN_ASSET_NAME}.tar.gz"
+          curl -L -o artifact.tar.gz \
+            "https://github.com/google/dawn/releases/download/${DAWN_VERSION}/${DAWN_ASSET_NAME}.tar.gz"
+          mkdir dawn
+          tar -xvf artifact.tar.gz -C dawn --strip-components=1
+
+      - name: Test
+        id: ggml-ci
+        run: |
+          GG_BUILD_WEBGPU=1 GG_BUILD_WEBGPU_DAWN_PREFIX="$GITHUB_WORKSPACE/dawn" \
+            bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp
+
+  ggml-ci-mac-vulkan:
+    runs-on: [self-hosted, macOS, ARM64]
+
+    steps:
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v6
+
+      - name: Test
+        id: ggml-ci
+        run: |
+          vulkaninfo --summary
+          GG_BUILD_VULKAN=1 bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp
 
   ggml-ci-linux-intel-vulkan:
     runs-on: [self-hosted, Linux, Intel]

.github/workflows/build-vulkan.yml

@@ -93,4 +93,5 @@ jobs:
           export GGML_VK_DISABLE_F16=1
           export GGML_VK_DISABLE_COOPMAT=1
           # This is using llvmpipe and runs slower than other backends
-          ctest -L main --verbose --timeout 4800
+          # test-backend-ops is too slow on llvmpipe, skip it
+          ctest -L main -E test-backend-ops --verbose --timeout 900

.github/workflows/build.yml

@@ -318,7 +318,7 @@ jobs:
         id: depends
         run: |
           sudo apt-get update
-          sudo apt-get install -y gcc-14 g++-14 build-essential glslc libvulkan-dev libssl-dev ninja-build
+          sudo apt-get install -y gcc-14 g++-14 build-essential glslc libvulkan-dev spirv-headers libssl-dev ninja-build
           echo "CC=gcc-14" >> "$GITHUB_ENV"
           echo "CXX=g++-14" >> "$GITHUB_ENV"
 

.github/workflows/close-issue.yml

@@ -17,7 +17,7 @@ jobs:
     steps:
       - uses: actions/stale@v10
         with:
-          exempt-issue-labels: "refactoring,help wanted,good first issue,research 🔬,bug,roadmap"
+          exempt-issue-labels: "refactoring,help wanted,good first issue,research 🔬,bug,roadmap,security"
           days-before-issue-stale: 30
           days-before-issue-close: 14
           stale-issue-label: "stale"

.github/workflows/release.yml

@@ -202,7 +202,7 @@ jobs:
             sudo apt-get install -y build-essential mesa-vulkan-drivers vulkan-sdk libssl-dev
           else
             sudo apt-get update -y
-            sudo apt-get install -y gcc-14 g++-14 build-essential glslc libvulkan-dev libssl-dev ninja-build
+            sudo apt-get install -y gcc-14 g++-14 build-essential glslc libvulkan-dev spirv-headers libssl-dev ninja-build
             echo "CC=gcc-14" >> "$GITHUB_ENV"
             echo "CXX=g++-14" >> "$GITHUB_ENV"
           fi

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

@@ -84,41 +84,42 @@ jobs:
           export ${{ matrix.extra_args }}
           pytest -v -x -m "not slow"
 
-  server-cuda:
-    runs-on: [self-hosted, llama-server, Linux, NVIDIA]
-
-    name: server-cuda (${{ matrix.wf_name }})
-    strategy:
-      matrix:
-        build_type: [Release]
-        wf_name: ["GPUx1"]
-        include:
-          - build_type: Release
-            extra_args: "LLAMA_ARG_BACKEND_SAMPLING=1"
-            wf_name:    "GPUx1, backend-sampling"
-      fail-fast: false
-
-    steps:
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v6
-        with:
-          fetch-depth: 0
-          ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }}
-
-      - name: Build
-        id: cmake_build
-        run: |
-          cmake -B build -DGGML_SCHED_NO_REALLOC=ON
-          cmake --build build --config ${{ matrix.build_type }} -j $(sysctl -n hw.logicalcpu) --target llama-server
-
-      - name: Tests
-        id: server_integration_tests
-        if: ${{ (!matrix.disabled_on_pr || !github.event.pull_request) }}
-        run: |
-          cd tools/server/tests
-          python3 -m venv venv
-          source venv/bin/activate
-          pip install -r requirements.txt
-          export ${{ matrix.extra_args }}
-          pytest -v -x -m "not slow"
+  # TODO: provision CUDA runner
+  #  server-cuda:
+  #    runs-on: [self-hosted, llama-server, Linux, NVIDIA]
+  #
+  #    name: server-cuda (${{ matrix.wf_name }})
+  #    strategy:
+  #      matrix:
+  #        build_type: [Release]
+  #        wf_name: ["GPUx1"]
+  #        include:
+  #          - build_type: Release
+  #            extra_args: "LLAMA_ARG_BACKEND_SAMPLING=1"
+  #            wf_name:    "GPUx1, backend-sampling"
+  #      fail-fast: false
+  #
+  #    steps:
+  #      - name: Clone
+  #        id: checkout
+  #        uses: actions/checkout@v6
+  #        with:
+  #          fetch-depth: 0
+  #          ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }}
+  #
+  #      - name: Build
+  #        id: cmake_build
+  #        run: |
+  #          cmake -B build -DGGML_SCHED_NO_REALLOC=ON
+  #          cmake --build build --config ${{ matrix.build_type }} -j $(sysctl -n hw.logicalcpu) --target llama-server
+  #
+  #      - name: Tests
+  #        id: server_integration_tests
+  #        if: ${{ (!matrix.disabled_on_pr || !github.event.pull_request) }}
+  #        run: |
+  #          cd tools/server/tests
+  #          python3 -m venv venv
+  #          source venv/bin/activate
+  #          pip install -r requirements.txt
+  #          export ${{ matrix.extra_args }}
+  #          pytest -v -x -m "not slow"

cmake/arm64-linux-clang.cmake

@@ -0,0 +1,17 @@
+set( CMAKE_SYSTEM_NAME Linux )
+set( CMAKE_SYSTEM_PROCESSOR arm64 )
+
+set( target aarch64-linux-gnu )
+
+set( CMAKE_C_COMPILER    clang )
+set( CMAKE_CXX_COMPILER  clang++ )
+
+set( CMAKE_C_COMPILER_TARGET   ${target} )
+set( CMAKE_CXX_COMPILER_TARGET ${target} )
+
+set( arch_c_flags "-march=armv8.7-a -fvectorize -ffp-model=fast -fno-finite-math-only" )
+set( warn_c_flags "-Wno-format -Wno-unused-variable -Wno-unused-function -Wno-gnu-zero-variadic-macro-arguments" )
+
+set( CMAKE_C_FLAGS_INIT   "${arch_c_flags} ${warn_c_flags}" )
+set( CMAKE_CXX_FLAGS_INIT "${arch_c_flags} ${warn_c_flags}" )
+

common/arg.cpp

@@ -291,14 +291,16 @@ static bool common_params_handle_remote_preset(common_params & params, llama_exa
         hf_tag = "default";
     }
 
-    const bool offline = params.offline;
     std::string model_endpoint = get_model_endpoint();
     auto preset_url = model_endpoint + hf_repo + "/resolve/main/preset.ini";
 
     // prepare local path for caching
     auto preset_fname = clean_file_name(hf_repo + "_preset.ini");
     auto preset_path = fs_get_cache_file(preset_fname);
-    const int status = common_download_file_single(preset_url, preset_path, params.hf_token, offline);
+    common_download_opts opts;
+    opts.bearer_token = params.hf_token;
+    opts.offline = params.offline;
+    const int status = common_download_file_single(preset_url, preset_path, opts);
     const bool has_preset = status >= 200 && status < 400;
 
     // remote preset is optional, so we don't error out if not found
@@ -341,10 +343,10 @@ static handle_model_result common_params_handle_model(struct common_params_model
             model.hf_file = model.path;
             model.path = "";
         }
-        common_download_model_opts opts;
-        opts.download_mmproj = true;
+        common_download_opts opts;
+        opts.bearer_token = bearer_token;
         opts.offline = offline;
-        auto download_result = common_download_model(model, bearer_token, opts);
+        auto download_result = common_download_model(model, opts, true);
 
         if (download_result.model_path.empty()) {
             LOG_ERR("error: failed to download model from Hugging Face\n");
@@ -365,9 +367,10 @@ static handle_model_result common_params_handle_model(struct common_params_model
             model.path = fs_get_cache_file(string_split<std::string>(f, '/').back());
         }
 
-        common_download_model_opts opts;
+        common_download_opts opts;
+        opts.bearer_token = bearer_token;
         opts.offline = offline;
-        auto download_result = common_download_model(model, bearer_token, opts);
+        auto download_result = common_download_model(model, opts);
         if (download_result.model_path.empty()) {
             LOG_ERR("error: failed to download model from %s\n", model.url.c_str());
             exit(1);
@@ -2348,19 +2351,21 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         }
     ).set_env("LLAMA_ARG_N_GPU_LAYERS"));
     add_opt(common_arg(
-        {"-sm", "--split-mode"}, "{none,layer,row}",
+        {"-sm", "--split-mode"}, "{none,layer,row,tensor}",
         "how to split the model across multiple GPUs, one of:\n"
         "- none: use one GPU only\n"
-        "- layer (default): split layers and KV across GPUs\n"
-        "- row: split rows across GPUs",
+        "- layer (default): split layers and KV across GPUs (pipelined)\n"
+        "- row: split weight across GPUs by rows (parallelized)\n"
+        "- tensor: split weights and KV across GPUs (parallelized, EXPERIMENTAL)",
         [](common_params & params, const std::string & value) {
-            std::string arg_next = value;
-            if (arg_next == "none") {
+            if (value == "none") {
                 params.split_mode = LLAMA_SPLIT_MODE_NONE;
-            } else if (arg_next == "layer") {
+            } else if (value == "layer") {
                 params.split_mode = LLAMA_SPLIT_MODE_LAYER;
-            } else if (arg_next == "row") {
+            } else if (value == "row") {
                 params.split_mode = LLAMA_SPLIT_MODE_ROW;
+            } else if (value == "tensor") {
+                params.split_mode = LLAMA_SPLIT_MODE_TENSOR;
             } else {
                 throw std::invalid_argument("invalid value");
             }

common/chat-auto-parser-generator.cpp

@@ -69,6 +69,10 @@ common_chat_params peg_generator::generate_parser(const common_chat_template &
                 auto         schema   = function.contains("parameters") ? function.at("parameters") : json::object();
                 builder.resolve_refs(schema);
             });
+            if (has_response_format) {
+                auto schema = inputs.json_schema;
+                builder.resolve_refs(schema);
+            }
             parser.build_grammar(builder, data.grammar_lazy);
         });
 
@@ -194,10 +198,19 @@ common_peg_parser analyze_tools::build_tool_parser_json_native(parser_build_cont
         args_field = format.function_field + "." + args_field;
     }
 
-    auto tools_parser = p.standard_json_tools(
-        format.section_start, format.section_end, inputs.tools, inputs.parallel_tool_calls,
-        inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED, name_field, args_field, format.tools_array_wrapped,
-        format.fun_name_is_key, format.id_field, format.gen_id_field, format.parameter_order);
+    auto tools_parser = p.eps();
+    if (format.section_start.empty() && !format.per_call_start.empty()) {
+        auto single_tool_parser = p.standard_json_tools(
+            format.per_call_start, format.per_call_end, inputs.tools, inputs.parallel_tool_calls,
+            inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED, name_field, args_field, format.tools_array_wrapped,
+            format.fun_name_is_key, format.id_field, format.gen_id_field, format.parameter_order);
+        tools_parser = p.trigger_rule("tool-calls", p.one_or_more(single_tool_parser + p.space()));
+    } else {
+        tools_parser = p.standard_json_tools(
+            format.section_start, format.section_end, inputs.tools, inputs.parallel_tool_calls,
+            inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED, name_field, args_field, format.tools_array_wrapped,
+            format.fun_name_is_key, format.id_field, format.gen_id_field, format.parameter_order);
+    }
 
     // Handle content wrappers if present
     if (ctx.content && ctx.content->is_always_wrapped()) {
@@ -332,58 +345,36 @@ common_peg_parser analyze_tools::build_tool_parser_tag_tagged(parser_build_conte
     const auto & inputs      = ctx.inputs;
     bool         force_tools = inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED;
 
+    auto until_suffix = p.rule("until-suffix", p.until(arguments.value_suffix));
+
     common_peg_parser tool_choice = p.choice();
 
     foreach_function(inputs.tools, [&](const json & tool) {
         const auto &          func       = tool.at("function");
         std::string           name       = func.at("name");
-        const auto &          params     = func.contains("parameters") ? func.at("parameters") : json::object();
+        auto                  params     = func.contains("parameters") ? func.at("parameters") : json::object();
         const auto &          properties = params.contains("properties") ? params.at("properties") : json::object();
+
         std::set<std::string> required;
+        if (params.contains("required")) {
+            params.at("required").get_to(required);
+        }
+
+        auto schema_info = common_schema_info();
+        schema_info.resolve_refs(params);
 
         // Build parser for each argument, separating required and optional
         std::vector<common_peg_parser> required_parsers;
         std::vector<common_peg_parser> optional_parsers;
         for (const auto & [param_name, param_schema] : properties.items()) {
-            bool        is_required = required.find(param_name) != required.end();
-            std::string type        = "object";
-            if (param_schema.contains("type")) {
-                const auto & type_obj = param_schema.at("type");
-                if (type_obj.is_string()) {
-                    type_obj.get_to(type);
-                } else if (type_obj.is_array()) {
-                    // Handle nullable types like ["string", "null"]
-                    for (const auto & t : type_obj) {
-                        if (t.is_string() && t.get<std::string>() != "null") {
-                            type = t.get<std::string>();
-                            break;
-                        }
-                    }
-                } else if (type_obj.is_object()) {
-                    if (type_obj.contains("type") && type_obj.at("type").is_string()) {
-                        type_obj.at("type").get_to(type);
-                    }
-                }
-            }
-            // Infer string type from enum values when type is unspecified
-            if (type == "object" && param_schema.contains("enum")) {
-                const auto & enum_vals = param_schema.at("enum");
-                if (enum_vals.is_array()) {
-                    for (const auto & v : enum_vals) {
-                        if (v.is_string()) {
-                            type = "string";
-                            break;
-                        }
-                    }
-                }
-            }
+            bool is_required = required.find(param_name) != required.end();
 
             auto arg =
                 p.tool_arg(p.tool_arg_open(arguments.name_prefix + p.tool_arg_name(p.literal(param_name)) +
                                            arguments.name_suffix) +
                            arguments.value_prefix +
-                           (type == "string" ?
-                                p.tool_arg_string_value(p.schema(p.until(arguments.value_suffix),
+                           (schema_info.resolves_to_string(param_schema) ?
+                                p.tool_arg_string_value(p.schema(until_suffix,
                                                                  "tool-" + name + "-arg-" + param_name + "-schema",
                                                                  param_schema, true)) :
                                 p.tool_arg_json_value(p.schema(
@@ -414,7 +405,7 @@ common_peg_parser analyze_tools::build_tool_parser_tag_tagged(parser_build_conte
             for (const auto & opt : optional_parsers) {
                 any_opt |= opt;
             }
-            args_seq = args_seq + p.repeat(p.space() + any_opt, 0, (int) optional_parsers.size());
+            args_seq = args_seq + p.repeat(p.space() + any_opt, 0, -1);
         }
 
         if (!arguments.start.empty()) {

common/chat-auto-parser.h

@@ -308,19 +308,23 @@ struct analyze_tools : analyze_base {
 
   private:
     // Extract tool calling 'haystack' for further analysis and delegate further analysis based on format
-    void analyze_tool_calls(const analyze_reasoning & reasoning);
+    void analyze_tool_calls(const analyze_reasoning & reasoning, bool supports_parallel_tool_calls);
 
     // Analyze format based on position of function and argument name in needle
     void analyze_tool_call_format(const std::string &       haystack,
                                   const std::string &       fun_name_needle,
                                   const std::string &       arg_name_needle,
-                                  const analyze_reasoning & reasoning);
+                                  const analyze_reasoning & reasoning,
+                                  bool                      supports_parallel_tool_calls);
 
     // Analyze specifics of JSON native format (entire tool call is a JSON object)
     void analyze_tool_call_format_json_native(const std::string & clean_haystack,
                                               const std::string & fun_name_needle,
                                               const std::string & arg_name_needle);
 
+    // Check if parallel calls in JSON native format array wrapped or tag wrapped
+    void analyze_json_native_parallel_calls();
+
     // Analyze specifics of non-JSON native format (tags for function name or for function name and arguments)
     void analyze_tool_call_format_non_json(const std::string & clean_haystack,
                                            const std::string & fun_name_needle);

common/chat-diff-analyzer.cpp

@@ -558,7 +558,7 @@ analyze_tools::analyze_tools(const common_chat_template & tmpl,
     : analyze_base(tmpl) {
     LOG_DBG(ANSI_ORANGE "Phase 3: Tool call analysis\n" ANSI_RESET);
 
-    analyze_tool_calls(reasoning);
+    analyze_tool_calls(reasoning, caps.supports_parallel_tool_calls);
 
     if (format.mode != tool_format::NONE && format.mode != tool_format::JSON_NATIVE) {
         if (caps.supports_parallel_tool_calls) {
@@ -577,7 +577,7 @@ analyze_tools::analyze_tools(const common_chat_template & tmpl,
     }
 }
 
-void analyze_tools::analyze_tool_calls(const analyze_reasoning & reasoning) {
+void analyze_tools::analyze_tool_calls(const analyze_reasoning & reasoning, bool supports_parallel_tool_calls) {
     json assistant_no_tools = json{
         { "role",    "assistant"   },
         { "content", ASSISTANT_MSG }
@@ -611,13 +611,14 @@ void analyze_tools::analyze_tool_calls(const analyze_reasoning & reasoning) {
         return;
     }
 
-    analyze_tool_call_format(tool_section, FUN_FIRST, ARG_FIRST, reasoning);
+    analyze_tool_call_format(tool_section, FUN_FIRST, ARG_FIRST, reasoning, supports_parallel_tool_calls);
 }
 
 void analyze_tools::analyze_tool_call_format(const std::string &       haystack,
                                              const std::string &       fun_name_needle,
                                              const std::string &       arg_name_needle,
-                                             const analyze_reasoning & reasoning) {
+                                             const analyze_reasoning & reasoning,
+                                             bool                      supports_parallel_tool_calls) {
     if (fun_name_needle.empty() || arg_name_needle.empty() || haystack.empty()) {
         return;
     }
@@ -660,6 +661,9 @@ void analyze_tools::analyze_tool_call_format(const std::string &       haystack,
 
     if (format.mode == tool_format::JSON_NATIVE) {
         analyze_tool_call_format_json_native(clean_haystack, fun_name_needle, arg_name_needle);
+        if (supports_parallel_tool_calls) {
+            analyze_json_native_parallel_calls();
+        }
     } else {
         analyze_tool_call_format_non_json(clean_haystack, fun_name_needle);
     }
@@ -668,6 +672,42 @@ void analyze_tools::analyze_tool_call_format(const std::string &       haystack,
     format.per_call_end = trim_whitespace(format.per_call_end);
 }
 
+void analyze_tools::analyze_json_native_parallel_calls() {
+    json assistant_one_tool = json{
+        { "role",       "assistant" },
+        { "content",    ""          },
+        { "tool_calls", json::array({ first_tool_call }) }
+    };
+
+    json assistant_two_tools = json{
+        { "role",       "assistant" },
+        { "content",    ""          },
+        { "tool_calls", json::array({ first_tool_call, second_tool_call }) }
+    };
+
+    template_params params;
+    params.messages              = json::array({ user_msg, assistant_one_tool });
+    params.tools                 = tools;
+    params.add_generation_prompt = false;
+    params.enable_thinking       = true;
+
+    auto comparison = compare_variants(
+        *tmpl, params, [&](template_params & p) { p.messages = json::array({ user_msg, assistant_two_tools }); });
+
+    if (!comparison) {
+        LOG_DBG(ANSI_ORANGE "%s: Template application failed\n" ANSI_RESET, __func__);
+        return;
+    }
+
+    std::string & second_call = comparison->diff.right;
+    if (!format.section_start.empty() && second_call.find(format.section_start) != std::string::npos) {
+        format.per_call_start = format.section_start;
+        format.per_call_end = format.section_end;
+        format.section_start.clear();
+        format.section_end.clear();
+    }
+}
+
 void analyze_tools::analyze_tool_call_format_json_native(const std::string & clean_haystack,
                                                          const std::string & fun_name_needle,
                                                          const std::string & arg_name_needle) {

common/chat-peg-parser.cpp

@@ -676,7 +676,7 @@ common_peg_parser common_chat_peg_builder::build_json_tools_nested_keys(
         ordered_json   params   = function.contains("parameters") ? function.at("parameters") : ordered_json::object();
 
         auto nested_name = literal("\"" + nested_name_field + "\"") + space() + literal(":") + space() +
-                          literal("\"") + tool_name(literal(name)) + literal("\"");
+                          atomic(literal("\"") + tool_name(literal(name)) + literal("\""));
         auto nested_args = literal("\"" + nested_args_field + "\"") + space() + literal(":") + space() +
                           tool_args(schema(json(), "tool-" + name + "-schema", params));
 
@@ -744,7 +744,7 @@ common_peg_parser common_chat_peg_builder::build_json_tools_flat_keys(
         ordered_json   params   = function.contains("parameters") ? function.at("parameters") : ordered_json::object();
 
         auto tool_name_ = name_key_parser + space() + literal(":") + space() +
-                         literal("\"") + tool_name(literal(name)) + literal("\"");
+                         atomic(literal("\"") + tool_name(literal(name)) + literal("\""));
         auto tool_args_ = args_key_parser + space() + literal(":") + space() +
                          tool_args(schema(json(), "tool-" + name + "-schema", params));
 

common/chat.cpp

@@ -865,9 +865,10 @@ static common_chat_params common_chat_params_init_ministral_3(const common_chat_
         adjusted_messages.push_back(adjusted);
     }
 
-    auto has_tools         = inputs.tools.is_array() && !inputs.tools.empty();
-    auto extract_reasoning = inputs.reasoning_format != COMMON_REASONING_FORMAT_NONE;
-    auto include_grammar   = true;
+    auto has_tools            = inputs.tools.is_array() && !inputs.tools.empty();
+    auto has_response_format  = inputs.json_schema.is_object() && !inputs.json_schema.empty();
+    auto extract_reasoning    = inputs.reasoning_format != COMMON_REASONING_FORMAT_NONE;
+    auto include_grammar      = true;
 
     data.supports_thinking  = true;
     data.thinking_start_tag = "[THINK]";
@@ -887,7 +888,7 @@ static common_chat_params common_chat_params_init_ministral_3(const common_chat_
             extract_reasoning ? p.optional("[THINK]" + p.reasoning(p.until("[/THINK]")) + "[/THINK]") : p.eps();
 
         // Response format parser
-        if (inputs.json_schema.is_object() && !inputs.json_schema.empty()) {
+        if (has_response_format) {
             // Ministral wants to emit json surrounded by code fences
             return generation_prompt + (reasoning << "```json" << p.content(p.schema(p.json(), "response-format", inputs.json_schema)) << "```");
         }
@@ -928,6 +929,10 @@ static common_chat_params common_chat_params_init_ministral_3(const common_chat_
                 auto         schema   = function.at("parameters");
                 builder.resolve_refs(schema);
             });
+            if (has_response_format) {
+                auto schema = inputs.json_schema;
+                builder.resolve_refs(schema);
+            }
             parser.build_grammar(builder, data.grammar_lazy);
         });
 
@@ -1063,6 +1068,10 @@ static common_chat_params common_chat_params_init_gpt_oss(const common_chat_temp
                 auto         schema   = function.at("parameters");
                 builder.resolve_refs(schema);
             });
+            if (has_response_format) {
+                auto schema = inputs.json_schema;
+                builder.resolve_refs(schema);
+            }
             parser.build_grammar(builder, data.grammar_lazy);
         });
 
@@ -1082,8 +1091,18 @@ static common_chat_params common_chat_params_init_gemma4(const common_chat_templ
     common_chat_params data;
 
     data.prompt            = common_chat_template_direct_apply_impl(tmpl, inputs);
+
+    if (inputs.add_generation_prompt && string_ends_with(data.prompt, "<turn|>\n")) {
+        // This may happen if the model generates content + tool_call, the
+        // template does not add the model's next turn and confuses the model
+        // from emitting its proper reasoning token sequence.
+        data.prompt += "<|turn>model\n";
+    }
+
     data.format            = COMMON_CHAT_FORMAT_PEG_GEMMA4;
-    data.supports_thinking = true;
+    data.supports_thinking  = true;
+    data.thinking_start_tag = "<|channel>thought";
+    data.thinking_end_tag   = "<channel|>";
 
     data.preserved_tokens = {
         "<|channel>",
@@ -1102,12 +1121,13 @@ static common_chat_params common_chat_params_init_gemma4(const common_chat_templ
         auto start = p.rule("start", p.prefix(inputs.generation_prompt, "<|channel>"));
 
         if (extract_reasoning) {
-            p.rule("thought", p.literal("<|channel>thought\n") + p.reasoning(p.until("<channel|>")) + p.literal("<channel|>"));
+            p.rule("thought", p.literal("<|channel>thought") + p.space() + p.reasoning(p.until("<channel|>")) + p.literal("<channel|>"));
         } else {
-            p.rule("thought", p.content(p.literal("<|channel>thought\n") + p.until("<channel|>") + p.literal("<channel|>")));
+            p.rule("thought", p.content(p.literal("<|channel>thought") + p.space() + p.until("<channel|>") + p.literal("<channel|>")));
         }
 
-        auto thought = (p.peek(p.literal("<|channel>")) + p.ref("thought")) | p.negate(p.literal("<|channel>"));
+        auto consume_empty_channels = p.gbnf(p.zero_or_more(p.literal("<|channel>") + p.negate(p.literal("thought"))), "");
+        auto thought = (p.peek(p.literal("<|channel>")) + consume_empty_channels + p.ref("thought")) | p.negate(p.literal("<|channel>"));
 
         if (has_response_format) {
             auto response_format = p.literal("```json") <<
@@ -1124,7 +1144,7 @@ static common_chat_params common_chat_params_init_gemma4(const common_chat_templ
             p.rule("gemma4-bool", p.json_bool());
             p.rule("gemma4-null", p.json_null());
             p.rule("gemma4-number", p.json_number());
-            p.rule("gemma4-dict-key", p.rule("gemma4-dict-key-name", p.until(":")) + p.literal(":"));
+            p.rule("gemma4-dict-key", p.rule("gemma4-dict-key-name", p.chars("[^:}]", 1, -1)) + p.literal(":"));
             p.rule("gemma4-dict-kv", p.ref("gemma4-dict-key") + p.space() + p.ref("gemma4-value"));
             p.rule("gemma4-dict", [&]() {
                 auto ws = p.space();
@@ -1171,12 +1191,16 @@ static common_chat_params common_chat_params_init_gemma4(const common_chat_templ
                 /* max = */ inputs.parallel_tool_calls ? -1 : 1
             ));
 
-            auto content = p.rule("content", p.content(p.until_one_of({"<|channel>", "<|tool_call>"})));
+            auto scan_to_toolcall = p.rule("scan-to-toolcall", p.until("<|tool_call>"));
+            auto content = p.rule("content", p.content(p.until_one_of({"<|channel>", "<channel|>", "<|tool_call>"})));
             auto message = p.rule("message", thought + content);
-            return start + p.zero_or_more(message) + tool_call;
+            return start + p.zero_or_more(message) + scan_to_toolcall + tool_call;
         }
 
-        auto content = p.rule("content", p.content(p.until("<|channel>")));
+        // Gemma 4 may emit an extra <|channel>thought\n<channel|> at the end of the content. It may
+        // also emit a single trailing <channel|> token. Consume all complete reasoning blocks and
+        // then stop at the first unmatched <channel|> token.
+        auto content = p.rule("content", p.content(p.until_one_of({"<|channel>", "<channel|>"})));
         auto message = p.rule("message", thought + content);
         return start + p.one_or_more(message);
     });
@@ -1191,6 +1215,10 @@ static common_chat_params common_chat_params_init_gemma4(const common_chat_templ
                 auto         schema   = function.at("parameters");
                 builder.resolve_refs(schema);
             });
+            if (has_response_format) {
+                auto schema = inputs.json_schema;
+                builder.resolve_refs(schema);
+            }
             parser.build_grammar(builder, data.grammar_lazy);
         });
 
@@ -1641,6 +1669,173 @@ static common_chat_params common_chat_params_init_gigachat_v3(
     return data;
 }
 
+static common_chat_params common_chat_params_init_deepseek_v3_2(const common_chat_template &    tmpl,
+                                                                 const autoparser::generation_params & inputs) {
+    common_chat_params data;
+
+    data.prompt            = common_chat_template_direct_apply_impl(tmpl, inputs);
+    data.format            = COMMON_CHAT_FORMAT_PEG_NATIVE;
+    data.supports_thinking = true;
+    data.thinking_start_tag = "<think>";
+    data.thinking_end_tag   = "</think>";
+    data.preserved_tokens  = {
+        "｜DSML｜",
+        "<think>",
+        "</think>",
+    };
+
+    auto has_tools           = inputs.tools.is_array() && !inputs.tools.empty();
+    auto has_response_format = !inputs.json_schema.is_null() && inputs.json_schema.is_object();
+    auto extract_reasoning   = inputs.reasoning_format != COMMON_REASONING_FORMAT_NONE;
+    auto include_grammar     = has_response_format || (has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE);
+
+    const std::string DSML         = "｜DSML｜";
+    const std::string THINK_START  = "<think>";
+    const std::string THINK_END    = "</think>";
+    const std::string FC_START     = "<" + DSML + "function_calls>";
+    const std::string FC_END       = "</" + DSML + "function_calls>";
+    const std::string INVOKE_START = "<" + DSML + "invoke";
+    const std::string INVOKE_END   = "</" + DSML + "invoke>";
+    const std::string PARAM_START  = "<" + DSML + "parameter";
+    const std::string PARAM_END    = "</" + DSML + "parameter>";
+
+    auto parser = build_chat_peg_parser([&](common_chat_peg_builder & p) {
+        auto generation_prompt = p.prefix(inputs.generation_prompt, THINK_START);
+        auto end = p.end();
+
+        auto reasoning = p.eps();
+        if (extract_reasoning && inputs.enable_thinking) {
+            reasoning = p.optional(THINK_START + p.reasoning(p.until(THINK_END)) + THINK_END);
+        } else if (extract_reasoning) {
+            // Thinking disabled but reasoning extraction requested: the generation prompt
+            // contains an empty <think></think> pair that must still be consumed.
+            reasoning = p.optional(p.literal(THINK_START) + p.until(THINK_END) + p.literal(THINK_END));
+        }
+
+        if (has_response_format) {
+            auto response_format = p.rule("response-format",
+                p.literal("```json") + p.space() +
+                p.content(p.schema(p.json(), "response-format-schema", inputs.json_schema)) +
+                p.space() + p.literal("```"));
+            return generation_prompt + reasoning + response_format + end;
+        }
+
+        if (!has_tools || inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_NONE) {
+            return generation_prompt + reasoning + p.content(p.rest()) + end;
+        }
+
+        auto tool_choice = p.choice();
+        foreach_function(inputs.tools, [&](const json & tool) {
+            const auto & function = tool.at("function");
+            std::string  name     = function.at("name");
+            auto params   = function.contains("parameters") ? function.at("parameters") : json::object();
+            const auto & props    = params.contains("properties") ? params.at("properties") : json::object();
+
+            std::set<std::string> required;
+            if (params.contains("required")) {
+                params.at("required").get_to(required);
+            }
+
+            auto schema_info = common_schema_info();
+            schema_info.resolve_refs(params);
+
+            std::vector<common_peg_parser> required_parsers;
+            std::vector<common_peg_parser> optional_parsers;
+            for (const auto & [param_name, param_schema] : props.items()) {
+                bool is_required = required.find(param_name) != required.end();
+                bool is_string   = schema_info.resolves_to_string(param_schema);
+
+                auto arg = p.tool_arg(
+                    p.tool_arg_open(
+                        p.literal(PARAM_START + " name=\"") +
+                        p.tool_arg_name(p.literal(param_name)) +
+                        p.literal("\" string=\"" + std::string(is_string ? "true" : "false") + "\">")) +
+                    (is_string
+                         ? p.tool_arg_string_value(p.until(PARAM_END))
+                         : p.tool_arg_json_value(p.schema(p.json(),
+                                                          "tool-" + name + "-arg-" + param_name + "-schema",
+                                                          param_schema, false))) +
+                    p.tool_arg_close(p.literal(PARAM_END)));
+
+                auto named_arg = p.rule("tool-" + name + "-arg-" + param_name, arg);
+                if (is_required) {
+                    required_parsers.push_back(named_arg);
+                } else {
+                    optional_parsers.push_back(named_arg);
+                }
+            }
+
+            common_peg_parser args_seq = p.eps();
+            for (size_t i = 0; i < required_parsers.size(); i++) {
+                if (i > 0) {
+                    args_seq = args_seq + p.space();
+                }
+                args_seq = args_seq + required_parsers[i];
+            }
+
+            if (!optional_parsers.empty()) {
+                common_peg_parser any_opt = p.choice();
+                for (const auto & opt : optional_parsers) {
+                    any_opt |= opt;
+                }
+                args_seq = args_seq + p.repeat(p.space() + any_opt, 0, -1);
+            }
+
+            common_peg_parser invoke_body = args_seq;
+            auto func_parser = p.tool(
+                p.tool_open(p.literal(INVOKE_START + " name=\"") +
+                            p.tool_name(p.literal(name)) + p.literal("\">\n")) +
+                invoke_body + p.space() +
+                p.tool_close(p.literal(INVOKE_END)));
+
+            tool_choice |= p.rule("tool-" + name, func_parser);
+        });
+
+        auto require_tools = inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED;
+
+        common_peg_parser tool_calls = p.eps();
+        if (inputs.parallel_tool_calls) {
+            tool_calls = p.trigger_rule("tool-call",
+                p.literal(FC_START) + p.space() + tool_choice +
+                p.zero_or_more(p.space() + tool_choice) + p.space() + p.literal(FC_END));
+        } else {
+            tool_calls = p.trigger_rule("tool-call",
+                p.literal(FC_START) + p.space() + tool_choice + p.space() + p.literal(FC_END));
+        }
+
+        if (!require_tools) {
+            tool_calls = p.optional(tool_calls);
+        }
+
+        auto content_before_tools = p.content(p.until(FC_START));
+        return generation_prompt + reasoning + content_before_tools + tool_calls + end;
+    });
+
+    data.parser = parser.save();
+
+    if (include_grammar) {
+        data.grammar_lazy = !(has_response_format || (has_tools && inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED));
+        data.grammar      = build_grammar([&](const common_grammar_builder & builder) {
+            foreach_function(inputs.tools, [&](const json & tool) {
+                const auto & function = tool.at("function");
+                auto         schema   = function.contains("parameters") ? function.at("parameters") : json::object();
+                builder.resolve_refs(schema);
+            });
+            if (has_response_format) {
+                auto schema = inputs.json_schema;
+                builder.resolve_refs(schema);
+            }
+            parser.build_grammar(builder, data.grammar_lazy);
+        });
+
+        data.grammar_triggers = {
+            { COMMON_GRAMMAR_TRIGGER_TYPE_WORD, FC_START },
+        };
+    }
+
+    return data;
+}
+
 namespace workaround {
 
 static void map_developer_role_to_system(json & messages) {
@@ -1912,9 +2107,23 @@ std::optional<common_chat_params> common_chat_try_specialized_template(
         return common_chat_params_init_gigachat_v3(tmpl, params);
     }
 
+    // DeepSeek V3.2 format detection: template defines dsml_token and uses it for tool calls.
+    // The template source contains the token as a variable assignment, not as a literal in markup.
+    if (src.find("dsml_token") != std::string::npos &&
+        src.find("function_calls") != std::string::npos &&
+        src.find("DSML") != std::string::npos) {
+        LOG_DBG("Using specialized template: DeepSeek V3.2\n");
+        return common_chat_params_init_deepseek_v3_2(tmpl, params);
+    }
+
     // Gemma4 format detection
     if (src.find("'<|tool_call>call:'") != std::string::npos) {
-        workaround::convert_tool_responses_gemma4(params.messages);
+        if (src.find("{#- OpenAI Chat Completions:") == std::string::npos) {
+            // apply workarounds if using the older gemma4 templates
+            LOG_WRN("%s: detected an outdated gemma4 chat template, applying compatibility workarounds. "
+                    "Consider updating to the official template.\n", __func__);
+            workaround::convert_tool_responses_gemma4(params.messages);
+        }
         return common_chat_params_init_gemma4(tmpl, params);
     }
 
@@ -1963,7 +2172,7 @@ static common_chat_params common_chat_templates_apply_jinja(const struct common_
     params.add_generation_prompt = true;
     std::string gen_prompt       = common_chat_template_direct_apply_impl(tmpl, params);
     auto        diff             = calculate_diff_split(no_gen_prompt, gen_prompt);
-    params.generation_prompt     = diff.right;
+    params.generation_prompt     = diff.right + diff.suffix;
 
     params.add_generation_prompt = inputs.add_generation_prompt;
 

common/download.cpp

@@ -114,7 +114,7 @@ std::pair<std::string, std::string> common_download_split_repo_tag(const std::st
     return {hf_repo, tag};
 }
 
-class ProgressBar {
+class ProgressBar : public common_download_callback {
     static inline std::mutex mutex;
     static inline std::map<const ProgressBar *, int> lines;
     static inline int max_line = 0;
@@ -138,7 +138,11 @@ class ProgressBar {
     }
 
 public:
-    ProgressBar(const std::string & url = "") : filename(url) {
+    ProgressBar() = default;
+
+    void on_start(const common_download_progress & p) override {
+        filename = p.url;
+
         if (auto pos = filename.rfind('/'); pos != std::string::npos) {
             filename = filename.substr(pos + 1);
         }
@@ -156,13 +160,13 @@ public:
         }
     }
 
-    ~ProgressBar() {
+    void on_done(const common_download_progress &, bool) override {
         std::lock_guard<std::mutex> lock(mutex);
         cleanup(this);
     }
 
-    void update(size_t current, size_t total) {
-        if (!total || !is_output_a_tty()) {
+    void on_update(const common_download_progress & p) override {
+        if (!p.total || !is_output_a_tty()) {
             return;
         }
 
@@ -174,17 +178,17 @@ public:
         }
         int lines_up = max_line - lines[this];
 
-        size_t bar = 55 - len;
-        size_t pct = (100 * current) / total;
-        size_t pos = (bar * current) / total;
+        size_t bar = (55 - len) * 2;
+        size_t pct = (100 * p.downloaded) / p.total;
+        size_t pos = (bar * p.downloaded) / p.total;
 
         if (lines_up > 0) {
             std::cout << "\033[" << lines_up << "A";
         }
         std::cout << '\r' << "Downloading " << filename << " ";
 
-        for (size_t i = 0; i < bar; ++i) {
-            std::cout << (i < pos ? "—" : " ");
+        for (size_t i = 0; i < bar; i += 2) {
+            std::cout << (i + 1 < pos ? "─" : (i < pos ? "╴" : " "));
         }
         std::cout << std::setw(4) << pct << "%\033[K";
 
@@ -193,7 +197,7 @@ public:
         }
         std::cout << '\r' << std::flush;
 
-        if (current == total) {
+        if (p.downloaded == p.total) {
             cleanup(this);
         }
     }
@@ -206,8 +210,8 @@ static bool common_pull_file(httplib::Client & cli,
                              const std::string & resolve_path,
                              const std::string & path_tmp,
                              bool supports_ranges,
-                             size_t existing_size,
-                             size_t & total_size) {
+                             common_download_progress & p,
+                             common_download_callback * callback) {
     std::ofstream ofs(path_tmp, std::ios::binary | std::ios::app);
     if (!ofs.is_open()) {
         LOG_ERR("%s: error opening local file for writing: %s\n", __func__, path_tmp.c_str());
@@ -215,29 +219,27 @@ static bool common_pull_file(httplib::Client & cli,
     }
 
     httplib::Headers headers;
-    if (supports_ranges && existing_size > 0) {
-        headers.emplace("Range", "bytes=" + std::to_string(existing_size) + "-");
+    if (supports_ranges && p.downloaded > 0) {
+        headers.emplace("Range", "bytes=" + std::to_string(p.downloaded) + "-");
     }
 
     const char * func = __func__; // avoid __func__ inside a lambda
-    size_t downloaded = existing_size;
     size_t progress_step = 0;
-    ProgressBar bar(resolve_path);
 
     auto res = cli.Get(resolve_path, headers,
         [&](const httplib::Response &response) {
-            if (existing_size > 0 && response.status != 206) {
+            if (p.downloaded > 0 && response.status != 206) {
                 LOG_WRN("%s: server did not respond with 206 Partial Content for a resume request. Status: %d\n", func, response.status);
                 return false;
             }
-            if (existing_size == 0 && response.status != 200) {
+            if (p.downloaded == 0 && response.status != 200) {
                 LOG_WRN("%s: download received non-successful status code: %d\n", func, response.status);
                 return false;
             }
-            if (total_size == 0 && response.has_header("Content-Length")) {
+            if (p.total == 0 && response.has_header("Content-Length")) {
                 try {
                     size_t content_length = std::stoull(response.get_header_value("Content-Length"));
-                    total_size = existing_size + content_length;
+                    p.total = p.downloaded + content_length;
                 } catch (const std::exception &e) {
                     LOG_WRN("%s: invalid Content-Length header: %s\n", func, e.what());
                 }
@@ -250,11 +252,16 @@ static bool common_pull_file(httplib::Client & cli,
                 LOG_ERR("%s: error writing to file: %s\n", func, path_tmp.c_str());
                 return false;
             }
-            downloaded += len;
+            p.downloaded += len;
             progress_step += len;
 
-            if (progress_step >= total_size / 1000 || downloaded == total_size) {
-                bar.update(downloaded, total_size);
+            if (progress_step >= p.total / 1000 || p.downloaded == p.total) {
+                if (callback) {
+                    callback->on_update(p);
+                    if (callback->is_cancelled()) {
+                        return false;
+                    }
+                }
                 progress_step = 0;
             }
             return true;
@@ -275,28 +282,13 @@ static bool common_pull_file(httplib::Client & cli,
 
 // download one single file from remote URL to local path
 // returns status code or -1 on error
-static int common_download_file_single_online(const std::string        & url,
-                                              const std::string        & path,
-                                              const std::string        & bearer_token,
-                                              const common_header_list & custom_headers,
-                                              bool                       skip_etag = false) {
+static int common_download_file_single_online(const std::string & url,
+                                              const std::string & path,
+                                              const common_download_opts & opts,
+                                              bool skip_etag) {
     static const int max_attempts        = 3;
     static const int retry_delay_seconds = 2;
 
-    auto [cli, parts] = common_http_client(url);
-
-    httplib::Headers headers;
-    for (const auto & h : custom_headers) {
-        headers.emplace(h.first, h.second);
-    }
-    if (headers.find("User-Agent") == headers.end()) {
-        headers.emplace("User-Agent", "llama-cpp/" + build_info);
-    }
-    if (!bearer_token.empty()) {
-        headers.emplace("Authorization", "Bearer " + bearer_token);
-    }
-    cli.set_default_headers(headers);
-
     const bool file_exists = std::filesystem::exists(path);
 
     if (file_exists && skip_etag) {
@@ -304,6 +296,20 @@ static int common_download_file_single_online(const std::string        & url,
         return 304; // 304 Not Modified - fake cached response
     }
 
+    auto [cli, parts] = common_http_client(url);
+
+    httplib::Headers headers;
+    for (const auto & h : opts.headers) {
+        headers.emplace(h.first, h.second);
+    }
+    if (headers.find("User-Agent") == headers.end()) {
+        headers.emplace("User-Agent", "llama-cpp/" + build_info);
+    }
+    if (!opts.bearer_token.empty()) {
+        headers.emplace("Authorization", "Bearer " + opts.bearer_token);
+    }
+    cli.set_default_headers(headers);
+
     std::string last_etag;
     if (file_exists) {
         last_etag = read_etag(path);
@@ -326,10 +332,11 @@ static int common_download_file_single_online(const std::string        & url,
         etag = head->get_header_value("ETag");
     }
 
-    size_t total_size = 0;
+    common_download_progress p;
+    p.url = url;
     if (head->has_header("Content-Length")) {
         try {
-            total_size = std::stoull(head->get_header_value("Content-Length"));
+            p.total = std::stoull(head->get_header_value("Content-Length"));
         } catch (const std::exception& e) {
             LOG_WRN("%s: invalid Content-Length in HEAD response: %s\n", __func__, e.what());
         }
@@ -357,14 +364,21 @@ static int common_download_file_single_online(const std::string        & url,
 
     { // silent
         std::error_code ec;
-        std::filesystem::path p(path);
-        std::filesystem::create_directories(p.parent_path(), ec);
+        std::filesystem::create_directories(std::filesystem::path(path).parent_path(), ec);
     }
 
+    bool success = false;
     const std::string path_temporary = path + ".downloadInProgress";
     int delay = retry_delay_seconds;
 
+    if (opts.callback) {
+        opts.callback->on_start(p);
+    }
+
     for (int i = 0; i < max_attempts; ++i) {
+        if (opts.callback && opts.callback->is_cancelled()) {
+            break;
+        }
         if (i) {
             LOG_WRN("%s: retrying after %d seconds...\n", __func__, delay);
             std::this_thread::sleep_for(std::chrono::seconds(delay));
@@ -378,28 +392,44 @@ static int common_download_file_single_online(const std::string        & url,
                 existing_size = std::filesystem::file_size(path_temporary);
             } else if (remove(path_temporary.c_str()) != 0) {
                 LOG_ERR("%s: unable to delete file: %s\n", __func__, path_temporary.c_str());
-                return -1;
+                break;
             }
         }
 
+        p.downloaded = existing_size;
+
         LOG_DBG("%s: downloading from %s to %s (etag:%s)...\n",
                 __func__, common_http_show_masked_url(parts).c_str(),
                 path_temporary.c_str(), etag.c_str());
 
-        if (common_pull_file(cli, parts.path, path_temporary, supports_ranges, existing_size, total_size)) {
+        if (common_pull_file(cli, parts.path, path_temporary, supports_ranges, p, opts.callback)) {
             if (std::rename(path_temporary.c_str(), path.c_str()) != 0) {
                 LOG_ERR("%s: unable to rename file: %s to %s\n", __func__, path_temporary.c_str(), path.c_str());
-                return -1;
+                break;
             }
             if (!etag.empty() && !skip_etag) {
                 write_etag(path, etag);
             }
-            return head->status;
+            success = true;
+            break;
         }
     }
 
-    LOG_ERR("%s: download failed after %d attempts\n", __func__, max_attempts);
-    return -1; // max attempts reached
+    if (opts.callback) {
+        opts.callback->on_done(p, success);
+    }
+    if (opts.callback && opts.callback->is_cancelled() &&
+        std::filesystem::exists(path_temporary)) {
+        if (remove(path_temporary.c_str()) != 0) {
+            LOG_ERR("%s: unable to delete temporary file: %s\n", __func__, path_temporary.c_str());
+        }
+    }
+    if (!success) {
+        LOG_ERR("%s: download failed after %d attempts\n", __func__, max_attempts);
+        return -1; // max attempts reached
+    }
+
+    return head->status;
 }
 
 std::pair<long, std::vector<char>> common_remote_get_content(const std::string          & url,
@@ -438,12 +468,15 @@ std::pair<long, std::vector<char>> common_remote_get_content(const std::string
 
 int common_download_file_single(const std::string & url,
                                 const std::string & path,
-                                const std::string & bearer_token,
-                                bool offline,
-                                const common_header_list & headers,
+                                const common_download_opts & opts,
                                 bool skip_etag) {
-    if (!offline) {
-        return common_download_file_single_online(url, path, bearer_token, headers, skip_etag);
+    if (!opts.offline) {
+        ProgressBar tty_cb;
+        common_download_opts online_opts = opts;
+        if (!online_opts.callback) {
+            online_opts.callback = &tty_cb;
+        }
+        return common_download_file_single_online(url, path, online_opts, skip_etag);
     }
 
     if (!std::filesystem::exists(path)) {
@@ -452,6 +485,16 @@ int common_download_file_single(const std::string & url,
     }
 
     LOG_DBG("%s: using cached file (offline mode): %s\n", __func__, path.c_str());
+
+    // notify the callback that the file was cached
+    if (opts.callback) {
+        common_download_progress p;
+        p.url = url;
+        p.cached = true;
+        opts.callback->on_start(p);
+        opts.callback->on_done(p, true);
+    }
+
     return 304; // Not Modified - fake cached response
 }
 
@@ -591,6 +634,10 @@ static hf_cache::hf_file find_best_model(const hf_cache::hf_files & files,
         for (const auto & f : files) {
             if (gguf_filename_is_model(f.path) &&
                 std::regex_search(f.path, pattern)) {
+                auto split = get_gguf_split_info(f.path);
+                if (split.count > 1 && split.index != 1) {
+                    continue;
+                }
                 return f;
             }
         }
@@ -600,6 +647,10 @@ static hf_cache::hf_file find_best_model(const hf_cache::hf_files & files,
     if (tag.empty()) {
         for (const auto & f : files) {
             if (gguf_filename_is_model(f.path)) {
+                auto split = get_gguf_split_info(f.path);
+                if (split.count > 1 && split.index != 1) {
+                    continue;
+                }
                 return f;
             }
         }
@@ -618,20 +669,21 @@ static void list_available_gguf_files(const hf_cache::hf_files & files) {
 }
 
 struct hf_plan {
+    hf_cache::hf_file primary;
     hf_cache::hf_files model_files;
     hf_cache::hf_file mmproj;
 };
 
-static hf_plan get_hf_plan(const common_params_model        & model,
-                           const std::string                & token,
-                           const common_download_model_opts & opts) {
+static hf_plan get_hf_plan(const common_params_model  & model,
+                           const common_download_opts & opts,
+                           bool download_mmproj) {
     hf_plan plan;
     hf_cache::hf_files all;
 
     auto [repo, tag] = common_download_split_repo_tag(model.hf_repo);
 
     if (!opts.offline) {
-        all = hf_cache::get_repo_files(repo, token);
+        all = hf_cache::get_repo_files(repo, opts.bearer_token);
     }
     if (all.empty()) {
         all = hf_cache::get_cached_files(repo);
@@ -663,9 +715,10 @@ static hf_plan get_hf_plan(const common_params_model        & model,
         }
     }
 
+    plan.primary = primary;
     plan.model_files = get_split_files(all, primary);
 
-    if (opts.download_mmproj) {
+    if (download_mmproj) {
         plan.mmproj = find_best_mmproj(all, primary.path);
     }
 
@@ -700,10 +753,9 @@ static std::vector<download_task> get_url_tasks(const common_params_model & mode
     return tasks;
 }
 
-common_download_model_result common_download_model(const common_params_model        & model,
-                                                   const std::string                & bearer_token,
-                                                   const common_download_model_opts & opts,
-                                                   const common_header_list         & headers) {
+common_download_model_result common_download_model(const common_params_model  & model,
+                                                   const common_download_opts & opts,
+                                                   bool download_mmproj) {
     common_download_model_result result;
     std::vector<download_task> tasks;
     hf_plan hf;
@@ -711,7 +763,7 @@ common_download_model_result common_download_model(const common_params_model
     bool is_hf = !model.hf_repo.empty();
 
     if (is_hf) {
-        hf = get_hf_plan(model, bearer_token, opts);
+        hf = get_hf_plan(model, opts, download_mmproj);
         for (const auto & f : hf.model_files) {
             tasks.push_back({f.url, f.local_path});
         }
@@ -732,8 +784,8 @@ common_download_model_result common_download_model(const common_params_model
     std::vector<std::future<bool>> futures;
     for (const auto & task : tasks) {
         futures.push_back(std::async(std::launch::async,
-            [&task, &bearer_token, offline = opts.offline, &headers, is_hf]() {
-                int status = common_download_file_single(task.url, task.path, bearer_token, offline, headers, is_hf);
+            [&task, &opts, is_hf]() {
+                int status = common_download_file_single(task.url, task.path, opts, is_hf);
                 return is_http_status_ok(status);
             }
         ));
@@ -749,7 +801,7 @@ common_download_model_result common_download_model(const common_params_model
         for (const auto & f : hf.model_files) {
             hf_cache::finalize_file(f);
         }
-        result.model_path = hf.model_files[0].final_path;
+        result.model_path = hf.primary.final_path;
 
         if (!hf.mmproj.path.empty()) {
             result.mmproj_path = hf_cache::finalize_file(hf.mmproj);
@@ -869,7 +921,9 @@ std::string common_docker_resolve_model(const std::string & docker) {
         std::string local_path = fs_get_cache_file(model_filename);
 
         const std::string blob_url = url_prefix + "/blobs/" + gguf_digest;
-        const int http_status = common_download_file_single(blob_url, local_path, token, false, {});
+        common_download_opts opts;
+        opts.bearer_token = token;
+        const int http_status = common_download_file_single(blob_url, local_path, opts);
         if (!is_http_status_ok(http_status)) {
             throw std::runtime_error("Failed to download Docker Model");
         }

common/download.h

@@ -8,6 +8,22 @@ struct common_params_model;
 using common_header      = std::pair<std::string, std::string>;
 using common_header_list = std::vector<common_header>;
 
+struct common_download_progress {
+    std::string url;
+    size_t downloaded = 0;
+    size_t total      = 0;
+    bool cached       = false;
+};
+
+class common_download_callback {
+public:
+    virtual ~common_download_callback() = default;
+    virtual void on_start(const common_download_progress & p) = 0;
+    virtual void on_update(const common_download_progress & p) = 0;
+    virtual void on_done(const common_download_progress & p, bool ok) = 0;
+    virtual bool is_cancelled() const { return false; }
+};
+
 struct common_remote_params {
     common_header_list headers;
     long timeout  = 0;           // in seconds, 0 means no timeout
@@ -31,10 +47,12 @@ struct common_cached_model_info {
     }
 };
 
-// Options for common_download_model
-struct common_download_model_opts {
-    bool download_mmproj = false;
-    bool offline         = false;
+// Options for common_download_model and common_download_file_single
+struct common_download_opts {
+    std::string bearer_token;
+    common_header_list headers;
+    bool offline = false;
+    common_download_callback * callback = nullptr;
 };
 
 // Result of common_download_model
@@ -69,9 +87,8 @@ struct common_download_model_result {
 // returns result with model_path and mmproj_path (empty on failure)
 common_download_model_result common_download_model(
     const common_params_model & model,
-    const std::string & bearer_token,
-    const common_download_model_opts & opts = {},
-    const common_header_list & headers = {}
+    const common_download_opts & opts = {},
+    bool download_mmproj = false
 );
 
 // returns list of cached models
@@ -82,9 +99,7 @@ std::vector<common_cached_model_info> common_list_cached_models();
 // skip_etag: if true, don't read/write .etag files (for HF cache where filename is the hash)
 int common_download_file_single(const std::string & url,
                                 const std::string & path,
-                                const std::string & bearer_token,
-                                bool offline,
-                                const common_header_list & headers = {},
+                                const common_download_opts & opts = {},
                                 bool skip_etag = false);
 
 // resolve and download model from Docker registry

common/jinja/runtime.cpp

@@ -251,6 +251,23 @@ value binary_expression::execute_impl(context & ctx) {
         return res;
     }
 
+    // Python-style string repetition
+    // TODO: support array/tuple repetition (e.g., [1, 2] * 3 → [1, 2, 1, 2, 1, 2])
+    if (op.value == "*" &&
+            ((is_val<value_string>(left_val) && is_val<value_int>(right_val)) ||
+             (is_val<value_int>(left_val) && is_val<value_string>(right_val)))) {
+        const auto & str = is_val<value_string>(left_val) ? left_val->as_string() : right_val->as_string();
+        const int64_t repeat = is_val<value_int>(right_val) ? right_val->as_int() : left_val->as_int();
+        auto res = mk_val<value_string>();
+        if (repeat <= 0) {
+            return res;
+        }
+        for (int64_t i = 0; i < repeat; ++i) {
+            res->val_str = res->val_str.append(str);
+        }
+        return res;
+    }
+
     // String membership
     if (is_val<value_string>(left_val) && is_val<value_string>(right_val)) {
         // case: "a" in "abc"

common/jinja/value.cpp

@@ -1,4 +1,5 @@
 #include "runtime.h"
+#include "unicode.h"
 #include "value.h"
 
 // for converting from JSON to jinja values
@@ -154,6 +155,83 @@ static value test_compare_fn(const func_args & args) {
     return mk_val<value_bool>(value_compare(args.get_pos(0), args.get_pos(1), op));
 }
 
+static void append_codepoint_as_ascii_json_escape(std::string & out, uint32_t codepoint) {
+    auto append_u16 = [&out](uint32_t value) {
+        char buf[8];
+        snprintf(buf, sizeof(buf), "\\u%04x", static_cast<unsigned int>(value));
+        out += buf;
+    };
+
+    if (codepoint <= 0xFFFF) {
+        append_u16(codepoint);
+        return;
+    }
+
+    codepoint -= 0x10000;
+    append_u16(0xD800 + ((codepoint >> 10) & 0x3FF));
+    append_u16(0xDC00 + (codepoint & 0x3FF));
+}
+
+static std::string json_ensure_ascii_preserving_format(const std::string & json_str) {
+    std::string output;
+    output.reserve(json_str.size());
+
+    bool in_string = false;
+    bool escaped = false;
+
+    for (size_t pos = 0; pos < json_str.size();) {
+        const char ch = json_str[pos];
+        if (!in_string) {
+            output.push_back(ch);
+            if (ch == '"') {
+                in_string = true;
+            }
+            ++pos;
+            continue;
+        }
+
+        if (escaped) {
+            output.push_back(ch);
+            escaped = false;
+            ++pos;
+            continue;
+        }
+
+        if (ch == '\\') {
+            output.push_back(ch);
+            escaped = true;
+            ++pos;
+            continue;
+        }
+
+        if (ch == '"') {
+            output.push_back(ch);
+            in_string = false;
+            ++pos;
+            continue;
+        }
+
+        const unsigned char uch = static_cast<unsigned char>(ch);
+        if (uch < 0x80) {
+            output.push_back(ch);
+            ++pos;
+            continue;
+        }
+
+        auto parsed = common_parse_utf8_codepoint(json_str, pos);
+        if (parsed.status != utf8_parse_result::SUCCESS) {
+            output += "\\ufffd";
+            ++pos;
+            continue;
+        }
+
+        append_codepoint_as_ascii_json_escape(output, parsed.codepoint);
+        pos += parsed.bytes_consumed;
+    }
+
+    return output;
+}
+
 static value tojson(const func_args & args) {
     args.ensure_count(1, 5);
     value val_ascii      = args.get_kwarg_or_pos("ensure_ascii", 1);
@@ -169,16 +247,17 @@ static value tojson(const func_args & args) {
     if (is_val<value_int>(val_indent)) {
         indent = static_cast<int>(val_indent->as_int());
     }
-    if (val_ascii->as_bool()) { // undefined == false
-        throw not_implemented_exception("tojson ensure_ascii=true not implemented");
-    }
     if (val_sort->as_bool()) { // undefined == false
         throw not_implemented_exception("tojson sort_keys=true not implemented");
     }
+    const bool ensure_ascii = val_ascii->as_bool(); // undefined == false
     auto separators = (is_val<value_array>(val_separators) ? val_separators : mk_val<value_array>())->as_array();
     std::string item_sep = separators.size() > 0 ? separators[0]->as_string().str() : (indent < 0 ? ", " : ",");
     std::string key_sep = separators.size() > 1 ? separators[1]->as_string().str() : ": ";
     std::string json_str = value_to_json(args.get_pos(0), indent, item_sep, key_sep);
+    if (ensure_ascii) {
+        json_str = json_ensure_ascii_preserving_format(json_str);
+    }
     return mk_val<value_string>(json_str);
 }
 
@@ -460,6 +539,10 @@ const func_builtins & value_int_t::get_builtins() const {
             int64_t val = args.get_pos(0)->as_int();
             return mk_val<value_int>(val < 0 ? -val : val);
         }},
+        {"int", [](const func_args & args) -> value {
+            args.ensure_vals<value_int>();
+            return mk_val<value_int>(args.get_pos(0)->as_int());
+        }},
         {"float", [](const func_args & args) -> value {
             args.ensure_vals<value_int>();
             double val = static_cast<double>(args.get_pos(0)->as_int());
@@ -486,6 +569,10 @@ const func_builtins & value_float_t::get_builtins() const {
             int64_t val = static_cast<int64_t>(args.get_pos(0)->as_float());
             return mk_val<value_int>(val);
         }},
+        {"float", [](const func_args & args) -> value {
+            args.ensure_vals<value_float>();
+            return mk_val<value_float>(args.get_pos(0)->as_float());
+        }},
         {"safe", tojson},
         {"string", tojson},
         {"tojson", tojson},

common/peg-parser.cpp

@@ -890,6 +890,10 @@ struct parser_executor {
         }
         return result;
     }
+
+    common_peg_parse_result operator()(const common_peg_gbnf_parser & p) {
+        return arena.parse(p.child, ctx, start_pos);
+    }
 };
 
 common_peg_parse_result common_peg_arena::parse(common_peg_parse_context & ctx, size_t start) const {
@@ -957,7 +961,8 @@ void common_peg_arena::resolve_refs() {
                                  std::is_same_v<T, common_peg_and_parser> ||
                                  std::is_same_v<T, common_peg_not_parser> ||
                                  std::is_same_v<T, common_peg_tag_parser> ||
-                                 std::is_same_v<T, common_peg_atomic_parser>) {
+                                 std::is_same_v<T, common_peg_atomic_parser> ||
+                                 std::is_same_v<T, common_peg_gbnf_parser>) {
                 p.child = resolve_ref(p.child);
             } else if constexpr (std::is_same_v<T, common_peg_rule_parser>) {
                 p.child = resolve_ref(p.child);
@@ -1036,6 +1041,8 @@ std::string common_peg_arena::dump_impl(common_peg_parser_id
             return "Not(" + dump_impl(p.child, visited) + ")";
         } else if constexpr (std::is_same_v<T, common_peg_atomic_parser>) {
             return "Atomic(" + dump_impl(p.child, visited) + ")";
+        } else if constexpr (std::is_same_v<T, common_peg_gbnf_parser>) {
+            return "Gbnf(" + p.grammar + ", " + dump_impl(p.child, visited) + ")";
         } else if constexpr (std::is_same_v<T, common_peg_any_parser>) {
             return "Any";
         } else if constexpr (std::is_same_v<T, common_peg_space_parser>) {
@@ -1565,6 +1572,7 @@ static std::unordered_set<std::string> collect_reachable_rules(
                                  std::is_same_v<T, common_peg_not_parser> ||
                                  std::is_same_v<T, common_peg_tag_parser> ||
                                  std::is_same_v<T, common_peg_atomic_parser> ||
+                                 std::is_same_v<T, common_peg_gbnf_parser> ||
                                  std::is_same_v<T, common_peg_schema_parser>) {
                 visit(p.child);
             } else if constexpr (std::is_same_v<T, common_peg_rule_parser>) {
@@ -1651,10 +1659,13 @@ void common_peg_arena::build_grammar(const common_grammar_builder & builder, boo
             } else if constexpr (std::is_same_v<T, common_peg_sequence_parser>) {
                 std::string s;
                 for (const auto & child : p.children) {
+                    auto child_gbnf = to_gbnf(child);
+                    if (child_gbnf.empty()) {
+                        continue;
+                    }
                     if (!s.empty()) {
                         s += " ";
                     }
-                    auto child_gbnf = to_gbnf(child);
                     const auto & child_parser = effective_parser(child);
                     if (std::holds_alternative<common_peg_choice_parser>(child_parser) ||
                         std::holds_alternative<common_peg_sequence_parser>(child_parser)) {
@@ -1754,6 +1765,8 @@ void common_peg_arena::build_grammar(const common_grammar_builder & builder, boo
                 return to_gbnf(p.child);
             } else if constexpr (std::is_same_v<T, common_peg_atomic_parser>) {
                 return to_gbnf(p.child);
+            } else if constexpr (std::is_same_v<T, common_peg_gbnf_parser>) {
+                return p.grammar;
             } else {
                 static_assert(is_always_false_v<T>);
             }
@@ -1888,6 +1901,8 @@ static nlohmann::json serialize_parser_variant(const common_peg_parser_variant &
                 {"child", p.child},
                 {"tag", p.tag}
             };
+        } else if constexpr (std::is_same_v<T, common_peg_gbnf_parser>) {
+            return json{{"type", "gbnf"}, {"child", p.child}, {"grammar", p.grammar}};
         }
     }, variant);
 }
@@ -2050,6 +2065,16 @@ static common_peg_parser_variant deserialize_parser_variant(const nlohmann::json
         };
     }
 
+    if (type == "gbnf") {
+        if (!j.contains("child") || !j.contains("grammar")) {
+            throw std::runtime_error("gbnf parser missing required fields");
+        }
+        return common_peg_gbnf_parser{
+            j["child"].get<common_peg_parser_id>(),
+            j["grammar"].get<std::string>(),
+        };
+    }
+
     throw std::runtime_error("Unknown parser type: " + type);
 }
 

common/peg-parser.h

@@ -270,6 +270,11 @@ struct common_peg_tag_parser {
     std::string tag;
 };
 
+struct common_peg_gbnf_parser {
+    common_peg_parser_id child;
+    std::string grammar;
+};
+
 // Variant holding all parser types
 using common_peg_parser_variant = std::variant<
     common_peg_epsilon_parser,
@@ -290,7 +295,8 @@ using common_peg_parser_variant = std::variant<
     common_peg_rule_parser,
     common_peg_ref_parser,
     common_peg_atomic_parser,
-    common_peg_tag_parser
+    common_peg_tag_parser,
+    common_peg_gbnf_parser
 >;
 
 class common_peg_arena {
@@ -504,6 +510,10 @@ class common_peg_parser_builder {
     // Unlike rules, you can tag multiple nodes with the same tag.
     common_peg_parser tag(const std::string & tag, const common_peg_parser & p) { return add(common_peg_tag_parser{p.id(), tag}); }
 
+    // Wraps a child parser but emits a custom GBNF grammar string instead of
+    // the child's grammar. Parsing delegates entirely to the child.
+    common_peg_parser gbnf(const common_peg_parser & p, const std::string & grammar) { return add(common_peg_gbnf_parser{p, grammar}); }
+
     void set_root(const common_peg_parser & p);
 
     common_peg_arena build();

common/sampling.cpp

@@ -287,8 +287,8 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, st
         }
     }
 
-    // reasoning budget sampler
-    if (!params.reasoning_budget_start.empty() && !params.reasoning_budget_end.empty()) {
+    // reasoning budget sampler (skip when budget is unlimited unless a lazy grammar is active, which needs rbudget for thinking-block suppression)
+    if (!params.reasoning_budget_start.empty() && !params.reasoning_budget_end.empty() && (params.grammar_lazy || params.reasoning_budget_tokens >= 0)) {
         rbudget = common_reasoning_budget_init(
             vocab,
             params.reasoning_budget_start,

convert_hf_to_gguf.py

@@ -1229,15 +1229,15 @@ class TextModel(ModelBase):
 
         from transformers import AutoTokenizer
         tokenizer = AutoTokenizer.from_pretrained(self.dir_model)
-        vocab_size = self.hparams.get("vocab_size", len(tokenizer.vocab))
-        assert max(tokenizer.vocab.values()) < vocab_size
+        vocab_size = self.hparams.get("vocab_size", len(tokenizer.vocab))  # ty: ignore[unresolved-attribute]
+        assert max(tokenizer.vocab.values()) < vocab_size  # ty: ignore[unresolved-attribute]
 
         tokpre = self.get_vocab_base_pre(tokenizer)
 
-        reverse_vocab = {id_: encoded_tok for encoded_tok, id_ in tokenizer.vocab.items()}
-        added_vocab = tokenizer.get_added_vocab()
+        reverse_vocab = {id_: encoded_tok for encoded_tok, id_ in tokenizer.vocab.items()}  # ty: ignore[unresolved-attribute]
+        added_vocab = tokenizer.get_added_vocab()  # ty: ignore[unresolved-attribute]
 
-        added_tokens_decoder = tokenizer.added_tokens_decoder
+        added_tokens_decoder = tokenizer.added_tokens_decoder  # ty: ignore[unresolved-attribute]
 
         for i in range(vocab_size):
             if i not in reverse_vocab:
@@ -1250,7 +1250,7 @@ class TextModel(ModelBase):
                     # To avoid unexpected issues - we make sure to normalize non-normalized tokens
                     if not added_tokens_decoder[i].normalized:
                         previous_token = token
-                        token = tokenizer.decode(tokenizer.encode(token, add_special_tokens=False))
+                        token = tokenizer.decode(tokenizer.encode(token, add_special_tokens=False))  # ty: ignore[unresolved-attribute, invalid-assignment]
                         if previous_token != token:
                             logger.info(f"{repr(previous_token)} is encoded and decoded back to {repr(token)} using AutoTokenizer")
 
@@ -1583,13 +1583,13 @@ class TextModel(ModelBase):
         from transformers import AutoTokenizer
         tokenizer = AutoTokenizer.from_pretrained(dir_model, trust_remote_code=True)
         vocab_size = hparams["vocab_size"]
-        assert max(tokenizer.get_vocab().values()) < vocab_size
+        assert max(tokenizer.get_vocab().values()) < vocab_size  # ty: ignore[unresolved-attribute]
 
         tokpre = self.get_vocab_base_pre(tokenizer)
 
         merges = []
         vocab = {}
-        mergeable_ranks = tokenizer.mergeable_ranks
+        mergeable_ranks = tokenizer.mergeable_ranks  # ty: ignore[unresolved-attribute]
         for token, rank in mergeable_ranks.items():
             vocab[QwenModel.token_bytes_to_string(token)] = rank
             if len(token) == 1:
@@ -1599,7 +1599,7 @@ class TextModel(ModelBase):
             merges.append(' '.join(map(QwenModel.token_bytes_to_string, merged)))
 
         # for this kind of tokenizer, added_vocab is not a subset of vocab, so they need to be combined
-        added_vocab = tokenizer.special_tokens
+        added_vocab = tokenizer.special_tokens  # ty: ignore[unresolved-attribute]
         reverse_vocab = {id_ : encoded_tok for encoded_tok, id_ in {**vocab, **added_vocab}.items()}
 
         for i in range(vocab_size):
@@ -1622,10 +1622,10 @@ class TextModel(ModelBase):
         special_vocab.merges = merges
         # only add special tokens when they were not already loaded from config.json
         if len(special_vocab.special_token_ids) == 0:
-            special_vocab._set_special_token("bos", tokenizer.special_tokens["<|endoftext|>"])
-            special_vocab._set_special_token("eos", tokenizer.special_tokens["<|endoftext|>"])
+            special_vocab._set_special_token("bos", tokenizer.special_tokens["<|endoftext|>"])  # ty: ignore[unresolved-attribute]
+            special_vocab._set_special_token("eos", tokenizer.special_tokens["<|endoftext|>"])  # ty: ignore[unresolved-attribute]
         # this one is usually not in config.json anyway
-        special_vocab._set_special_token("unk", tokenizer.special_tokens["<|endoftext|>"])
+        special_vocab._set_special_token("unk", tokenizer.special_tokens["<|endoftext|>"])  # ty: ignore[unresolved-attribute]
         special_vocab.add_to_gguf(self.gguf_writer)
 
     def _set_vocab_sentencepiece(self, add_to_gguf=True):
@@ -1877,10 +1877,10 @@ class TextModel(ModelBase):
         self.gguf_writer.add_tokenizer_pre(tokpre)
         self.gguf_writer.add_token_list(tokens)
         self.gguf_writer.add_token_types(toktypes)
-        special_vocab._set_special_token("eos", tokenizer.get_added_vocab()["<|endoftext|>"])
-        special_vocab._set_special_token("eot", tokenizer.get_added_vocab()["<|user|>"])
-        special_vocab._set_special_token("unk", tokenizer.get_added_vocab()["<|endoftext|>"])
-        special_vocab._set_special_token("bos", tokenizer.get_added_vocab()["<|endoftext|>"])
+        special_vocab._set_special_token("eos", tokenizer.get_added_vocab()["<|endoftext|>"])  # ty: ignore[unresolved-attribute]
+        special_vocab._set_special_token("eot", tokenizer.get_added_vocab()["<|user|>"])  # ty: ignore[unresolved-attribute]
+        special_vocab._set_special_token("unk", tokenizer.get_added_vocab()["<|endoftext|>"])  # ty: ignore[unresolved-attribute]
+        special_vocab._set_special_token("bos", tokenizer.get_added_vocab()["<|endoftext|>"])  # ty: ignore[unresolved-attribute]
         special_vocab.add_to_gguf(self.gguf_writer)
 
     def _set_vocab_glm(self):
@@ -1894,10 +1894,10 @@ class TextModel(ModelBase):
         self.gguf_writer.add_token_types(toktypes)
         # Special tokens
         # Note: Using <|endoftext|> (151329) for eot causes endless generation
-        special_vocab._set_special_token("bos", tokenizer.get_added_vocab()["[gMASK]"])  # 151331
-        special_vocab._set_special_token("eot", tokenizer.get_added_vocab()["<|user|>"])  # 151336
-        special_vocab._set_special_token("unk", tokenizer.get_added_vocab()["<|endoftext|>"]) # 151329
-        special_vocab._set_special_token("eom", tokenizer.get_added_vocab()["<|observation|>"])  # 151338
+        special_vocab._set_special_token("bos", tokenizer.get_added_vocab()["[gMASK]"])  # ty: ignore[unresolved-attribute]  # 151331
+        special_vocab._set_special_token("eot", tokenizer.get_added_vocab()["<|user|>"])  # ty: ignore[unresolved-attribute]  # 151336
+        special_vocab._set_special_token("unk", tokenizer.get_added_vocab()["<|endoftext|>"])  # ty: ignore[unresolved-attribute]  # 151329
+        special_vocab._set_special_token("eom", tokenizer.get_added_vocab()["<|observation|>"])  # ty: ignore[unresolved-attribute]  # 151338
         special_vocab.add_to_gguf(self.gguf_writer)
 
     def _set_vocab_interns1(self):
@@ -1906,16 +1906,16 @@ class TextModel(ModelBase):
 
         from transformers import AutoTokenizer
         tokenizer = AutoTokenizer.from_pretrained(self.dir_model, trust_remote_code=True)
-        vocab = getattr(tokenizer, 'vocab', tokenizer.get_vocab())
+        vocab = getattr(tokenizer, 'vocab', tokenizer.get_vocab())  # ty: ignore[unresolved-attribute]
         vocab_size = self.hparams.get("vocab_size", len(vocab))
         assert max(vocab.values()) < vocab_size
 
         tokpre = self.get_vocab_base_pre(tokenizer)
 
         reverse_vocab = {id_: encoded_tok for encoded_tok, id_ in vocab.items()}
-        added_vocab = tokenizer.get_added_vocab()
+        added_vocab = tokenizer.get_added_vocab()  # ty: ignore[unresolved-attribute]
 
-        added_tokens_decoder = tokenizer.added_tokens_decoder
+        added_tokens_decoder = tokenizer.added_tokens_decoder  # ty: ignore[unresolved-attribute]
 
         for i in range(vocab_size):
             if i not in reverse_vocab:
@@ -1928,7 +1928,7 @@ class TextModel(ModelBase):
                     # To avoid unexpected issues - we make sure to normalize non-normalized tokens
                     if not added_tokens_decoder[i].normalized:
                         previous_token = token
-                        token = tokenizer.decode(tokenizer.encode(token, add_special_tokens=False))
+                        token = tokenizer.decode(tokenizer.encode(token, add_special_tokens=False))  # ty: ignore[unresolved-attribute, invalid-assignment]
                         if previous_token != token:
                             logger.info(f"{repr(previous_token)} is encoded and decoded back to {repr(token)} using AutoTokenizer")
 
@@ -2219,10 +2219,10 @@ class MmprojModel(ModelBase):
             self.image_size = self.find_vparam(["image_size"])
             self.gguf_writer.add_vision_image_size(self.image_size)
             self.gguf_writer.add_vision_patch_size(self.find_vparam(["patch_size"]))
-            self.gguf_writer.add_vision_embedding_length(self.find_vparam(["hidden_size", "vt_hidden_size"]))
+            self.gguf_writer.add_vision_embedding_length(self.find_vparam(["hidden_size", "width", "vt_hidden_size"]))
             self.gguf_writer.add_vision_feed_forward_length(self.find_vparam(["intermediate_size", "vt_intermediate_size"]))
             self.gguf_writer.add_vision_block_count(self.find_vparam(self.n_block_keys))
-            self.gguf_writer.add_vision_head_count(self.find_vparam(["num_attention_heads", "num_heads", "vt_num_attention_heads"]))
+            self.gguf_writer.add_vision_head_count(self.find_vparam(["num_attention_heads", "num_heads", "heads", "vt_num_attention_heads"]))
 
             # preprocessor config
             image_mean = _MISTRAL_COMMON_DATASET_MEAN if self.is_mistral_format else self.preprocessor_config["image_mean"]
@@ -2516,15 +2516,15 @@ class XverseModel(TextModel):
 
         from transformers import AutoTokenizer
         tokenizer = AutoTokenizer.from_pretrained(dir_model)
-        vocab_size = hparams.get("vocab_size", len(tokenizer.vocab))
+        vocab_size = hparams.get("vocab_size", len(tokenizer.vocab))  # ty: ignore[unresolved-attribute]
         # Since we are checking the maximum index, we need to ensure it's strictly less than vocab_size,
         # because vocab_size is the count of items, and indexes start at 0.
-        max_vocab_index = max(tokenizer.get_vocab().values())
+        max_vocab_index = max(tokenizer.get_vocab().values())  # ty: ignore[unresolved-attribute]
         if max_vocab_index >= vocab_size:
             raise ValueError("Vocabulary size exceeds expected maximum size.")
 
-        reverse_vocab: dict[int, str] = {id_: encoded_tok for encoded_tok, id_ in tokenizer.vocab.items()}
-        added_vocab = tokenizer.get_added_vocab()
+        reverse_vocab: dict[int, str] = {id_: encoded_tok for encoded_tok, id_ in tokenizer.vocab.items()}  # ty: ignore[unresolved-attribute]
+        added_vocab = tokenizer.get_added_vocab()  # ty: ignore[unresolved-attribute]
 
         for token_id in range(vocab_size):
             token_text = reverse_vocab[token_id].encode('utf-8')
@@ -2535,7 +2535,7 @@ class XverseModel(TextModel):
             elif re.fullmatch(br"<0x[0-9A-Fa-f]{2}>", token_text):
                 toktype = gguf.TokenType.BYTE  # special
             elif reverse_vocab[token_id] in added_vocab:
-                if tokenizer.added_tokens_decoder[token_id].special:
+                if tokenizer.added_tokens_decoder[token_id].special:  # ty: ignore[unresolved-attribute]
                     toktype = gguf.TokenType.CONTROL
                 else:
                     toktype = gguf.TokenType.USER_DEFINED
@@ -3752,7 +3752,7 @@ class QwenModel(TextModel):
 
     @staticmethod
     def token_bytes_to_string(b):
-        from transformers.models.gpt2.tokenization_gpt2 import bytes_to_unicode
+        from transformers.models.gpt2.tokenization_gpt2 import bytes_to_unicode  # ty: ignore[unresolved-import]
         byte_encoder = bytes_to_unicode()
         return ''.join([byte_encoder[ord(char)] for char in b.decode('latin-1')])
 
@@ -3777,7 +3777,14 @@ class QwenModel(TextModel):
         self._set_vocab_qwen()
 
 
-@ModelBase.register("Qwen2Model", "Qwen2ForCausalLM", "Qwen2AudioForConditionalGeneration", "KORMoForCausalLM", "AudioFlamingo3ForConditionalGeneration")
+@ModelBase.register(
+    "Qwen2Model",
+    "Qwen2ForCausalLM",
+    "Qwen2AudioForConditionalGeneration",
+    "KORMoForCausalLM",
+    "AudioFlamingo3ForConditionalGeneration",
+    "DotsOCRForCausalLM",
+)
 class Qwen2Model(TextModel):
     model_arch = gguf.MODEL_ARCH.QWEN2
 
@@ -3798,7 +3805,8 @@ class Qwen2Model(TextModel):
             name = name.replace("language_model.", "") # for InternVL
         if name.startswith("mlp") or name.startswith("multi_modal_projector") \
                 or name.startswith("vision_model") or name.startswith("audio_tower") \
-                or name.startswith("model.vision_tower") or name.startswith("model.multi_modal_projector"):
+                or name.startswith("model.vision_tower") or name.startswith("model.multi_modal_projector") \
+                or name.startswith("vision_tower."):
             # skip vision and audio tensors
             return
         yield from super().modify_tensors(data_torch, name, bid)
@@ -3815,14 +3823,14 @@ class DreamModel(TextModel):
         from transformers import AutoTokenizer
         tokenizer = AutoTokenizer.from_pretrained(self.dir_model, trust_remote_code=True)
 
-        vocab_dict = tokenizer.get_vocab()
+        vocab_dict = tokenizer.get_vocab()  # ty: ignore[unresolved-attribute]
         vocab_size = self.hparams.get("vocab_size", len(vocab_dict))
         assert max(vocab_dict.values()) < vocab_size
 
         tokpre = self.get_vocab_base_pre(tokenizer)
 
         reverse_vocab = {id_: encoded_tok for encoded_tok, id_ in vocab_dict.items()}
-        added_vocab = tokenizer.get_added_vocab()
+        added_vocab = tokenizer.get_added_vocab()  # ty: ignore[unresolved-attribute]
 
         for i in range(vocab_size):
             if i not in reverse_vocab:
@@ -3880,14 +3888,14 @@ class LLaDAModel(TextModel):
         from transformers import AutoTokenizer
         tokenizer = AutoTokenizer.from_pretrained(self.dir_model, trust_remote_code=True)
 
-        vocab_dict = tokenizer.get_vocab()
+        vocab_dict = tokenizer.get_vocab()  # ty: ignore[unresolved-attribute]
         vocab_size = self.hparams.get("vocab_size", len(vocab_dict))
         assert max(vocab_dict.values()) < vocab_size
 
         tokpre = self.get_vocab_base_pre(tokenizer)
 
         reverse_vocab = {id_: encoded_tok for encoded_tok, id_ in vocab_dict.items()}
-        added_vocab = tokenizer.get_added_vocab()
+        added_vocab = tokenizer.get_added_vocab()  # ty: ignore[unresolved-attribute]
 
         for i in range(vocab_size):
             if i not in reverse_vocab:
@@ -4250,9 +4258,7 @@ class Qwen2VLVisionModel(MmprojModel):
                 yield from super().modify_tensors(data_torch, name, bid)
 
 
-@ModelBase.register("Qwen2_5OmniModel")
-class Qwen25OmniModel(Qwen2VLVisionModel):
-    has_vision_encoder = True
+class Qwen25AudioModel(MmprojModel):
     has_audio_encoder = True
 
     def __init__(self, *args, **kwargs):
@@ -4268,12 +4274,6 @@ class Qwen25OmniModel(Qwen2VLVisionModel):
         self.gguf_writer.add_audio_num_mel_bins(self.hparams_audio["num_mel_bins"])
         self.gguf_writer.add_audio_attention_layernorm_eps(self.hparams_audio.get("layer_norm_eps", 1e-5))
 
-    def get_vision_config(self) -> dict[str, Any] | None:
-        return self.global_config["thinker_config"].get("vision_config")
-
-    def get_audio_config(self) -> dict[str, Any] | None:
-        return self.global_config["thinker_config"].get("audio_config")
-
     def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
         # SinusoidsPositionEmbedding
         assert self.hparams_audio is not None
@@ -4304,7 +4304,32 @@ class Qwen25OmniModel(Qwen2VLVisionModel):
                 # this tensor is left unused in transformers code
                 # https://github.com/huggingface/transformers/blob/6e3063422c4b1c014aa60c32b9254fd2902f0f28/src/transformers/models/qwen2_5_omni/modular_qwen2_5_omni.py#L1809
                 return
-        yield from super().modify_tensors(data_torch, name, bid)
+            yield from MmprojModel.modify_tensors(self, data_torch, name, bid)
+
+        return  # skip other tensors
+
+
+@ModelBase.register("Qwen2_5OmniModel")
+class Qwen25OmniModel(Qwen2VLVisionModel, Qwen25AudioModel):
+    has_audio_encoder = True
+    has_vision_encoder = True
+
+    def get_vision_config(self) -> dict[str, Any] | None:
+        return self.global_config["thinker_config"].get("vision_config")
+
+    def get_audio_config(self) -> dict[str, Any] | None:
+        return self.global_config["thinker_config"].get("audio_config")
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.QWEN25O)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        if "visual." in name:
+            yield from Qwen2VLVisionModel.modify_tensors(self, data_torch, name, bid)
+        elif "audio_tower." in name:
+            yield from Qwen25AudioModel.modify_tensors(self, data_torch, name, bid)
+        return  # skip other tensors
 
 
 @ModelBase.register("InternVisionModel")
@@ -4665,9 +4690,9 @@ class Qwen3Model(Qwen2Model):
 
         self.is_rerank = True
         self.is_tied_embeddings = self.hparams.get("tie_word_embeddings", False)
-        self.token_false_id = tokenizer.convert_tokens_to_ids("no")
-        self.token_true_id = tokenizer.convert_tokens_to_ids("yes")
-        self.sep_token_id = tokenizer.convert_tokens_to_ids("|")
+        self.token_false_id = tokenizer.convert_tokens_to_ids("no")  # ty: ignore[unresolved-attribute, invalid-assignment]
+        self.token_true_id = tokenizer.convert_tokens_to_ids("yes")  # ty: ignore[unresolved-attribute, invalid-assignment]
+        self.sep_token_id = tokenizer.convert_tokens_to_ids("|")  # ty: ignore[unresolved-attribute]
 
         assert self.token_false_id is not None and self.token_true_id is not None
 
@@ -4808,7 +4833,10 @@ class RND1Model(Qwen2MoeModel):
 class Qwen3VLVisionModel(MmprojModel):
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
-        assert self.hparams_vision is not None
+        if self.hparams_vision is None:
+            logger.info("No vision config found, skipping vision tensor processing")
+            return
+
         # Compute image_size if not present
         if "image_size" not in self.hparams_vision:
             # For Qwen3VL/Qwen3VLMoe, compute from num_position_embeddings
@@ -4829,7 +4857,9 @@ class Qwen3VLVisionModel(MmprojModel):
 
     def set_gguf_parameters(self):
         super().set_gguf_parameters()
-        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.QWEN3VL)
+        # in case mixed modalities, the arch will be handled by subclass
+        if not self.has_audio_encoder:
+            self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.QWEN3VL)
         self.gguf_writer.add_vision_use_gelu(True)
 
         if self.hparams_vision is not None:
@@ -4917,11 +4947,64 @@ class Qwen3VLVisionModel(MmprojModel):
             return
 
         if name.startswith("visual."):
-            yield from super().modify_tensors(data_torch, name, bid)
-            return
+            yield from MmprojModel.modify_tensors(self, data_torch, name, bid)
+        return  # skip other tensors
 
-        # Fall back to parent class for other tensors
-        yield from super().modify_tensors(data_torch, name, bid)
+
+@ModelBase.register("Qwen3OmniMoeForConditionalGeneration")
+class Qwen3OmniMmprojModel(Qwen3VLVisionModel, Qwen25AudioModel):
+    has_audio_encoder = True
+    has_vision_encoder = True
+
+    def get_vision_config(self) -> dict[str, Any] | None:
+        if self.has_vision_encoder:
+            return self.global_config["thinker_config"].get("vision_config")
+        else:
+            return None
+
+    def get_audio_config(self) -> dict[str, Any] | None:
+        if self.has_audio_encoder:
+            return self.global_config["thinker_config"].get("audio_config")
+        else:
+            return None
+
+    def set_gguf_parameters(self):
+        if self.has_vision_encoder:
+            Qwen3VLVisionModel.set_gguf_parameters(self)
+            self.gguf_writer.add_clip_vision_projector_type(gguf.VisionProjectorType.QWEN3VL)
+        if self.has_audio_encoder:
+            Qwen25AudioModel.set_gguf_parameters(self)
+            self.gguf_writer.add_clip_audio_projector_type(gguf.VisionProjectorType.QWEN3A)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        if "visual." in name:
+            if not self.has_vision_encoder:
+                raise ValueError(f"Model does not have vision encoder, but found tensor {name}")
+            # need to transform vision tensor naming, so that modify_tensors() logic can be used correctly
+            name = name.replace("thinker.visual.", "model.visual.")
+            if ".merger_list." in name:
+                name = name.replace(".merger_list.", ".deepstack_merger_list.")
+                name = name.replace(".ln_q", ".norm")
+                name = name.replace(".mlp.0", ".linear_fc1")
+                name = name.replace(".mlp.2", ".linear_fc2")
+            elif ".merger." in name:
+                name = name.replace(".ln_q", ".norm")
+                name = name.replace(".mlp.0", ".linear_fc1")
+                name = name.replace(".mlp.2", ".linear_fc2")
+            yield from Qwen3VLVisionModel.modify_tensors(self, data_torch, name, bid)
+        elif "audio_tower." in name:
+            if not self.has_audio_encoder:
+                raise ValueError(f"Model does not have audio encoder, but found tensor {name}")
+            if "conv2d" in name and name.endswith(".bias"):
+                # transform conv2d bias [n_embd] --> [1, 1, n_embd]
+                data_torch = data_torch.unsqueeze(-1).unsqueeze(-1)
+            yield from Qwen25AudioModel.modify_tensors(self, data_torch, name, bid)
+
+
+@ModelBase.register("Qwen3ASRForConditionalGeneration")
+class Qwen3ASRMmprojModel(Qwen3OmniMmprojModel):
+    has_audio_encoder = True
+    has_vision_encoder = False
 
 
 @ModelBase.register("Glm4vForConditionalGeneration", "Glm4vMoeForConditionalGeneration", "GlmOcrForConditionalGeneration")
@@ -4949,15 +5032,85 @@ class Glm4VVisionModel(Qwen3VLVisionModel):
         yield from super().modify_tensors(data_torch, name, bid)
 
 
+@ModelBase.register("StepVLForConditionalGeneration")
+class Step3VLVisionModel(MmprojModel):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        assert self.hparams_vision is not None
+
+        if not self.hparams_vision.get("intermediate_size"):
+            hidden_size = self.hparams_vision.get("hidden_size") or self.hparams_vision.get("width") or 0
+            assert hidden_size > 0
+            mlp_ratio = float(self.hparams_vision.get("mlp_ratio", 8960 / 1536))
+            self.hparams_vision["intermediate_size"] = int(round(hidden_size * mlp_ratio))
+
+        self.preprocessor_config.setdefault("image_mean", list(_MISTRAL_COMMON_DATASET_MEAN))
+        self.preprocessor_config.setdefault("image_std", list(_MISTRAL_COMMON_DATASET_STD))
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        assert self.hparams_vision is not None
+
+        projector_stride = int(self.global_config.get("understand_projector_stride", -1))
+        hidden_size = int(self.hparams_vision.get("hidden_size", self.hparams_vision.get("width", -1)))
+        num_layers = int(self.hparams_vision.get("num_hidden_layers", self.hparams_vision.get("layers", -1)))
+        assert (projector_stride, int(self.hparams_vision.get("image_size", -1)), hidden_size, num_layers) == (2, 728, 1536, 47), (
+            "current Step3-VL conversion path is only validated for Step3-VL-10B"
+        )
+
+        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.STEP3VL)
+        self.gguf_writer.add_vision_attention_layernorm_eps(float(self.hparams_vision.get("layer_norm_eps", 1e-5)))
+        self.gguf_writer.add_vision_projector_scale_factor(projector_stride ** 2)
+        # 3024 max resize comes from step3-vl-10b processing_step3.py.
+        self.gguf_writer.add_vision_preproc_image_size(3024)
+
+    def tensor_force_quant(self, name, new_name, bid, n_dims):
+        if ".position_embd." in new_name:
+            return gguf.GGMLQuantizationType.F32
+        if ("mm.0." in new_name or "mm.1." in new_name) and new_name.endswith(".weight"):
+            return gguf.GGMLQuantizationType.F16 if self.ftype == gguf.LlamaFileType.MOSTLY_F16 else gguf.GGMLQuantizationType.F32
+        return super().tensor_force_quant(name, new_name, bid, n_dims)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        if name.startswith("model.") or name.startswith("lm_head."):
+            return
+
+        if name.startswith("vision_model.vit_downsampler"):
+            match = re.match(r"vision_model\.vit_downsampler(\d+)\.(weight|bias)", name)
+            if match is None:
+                raise ValueError(f"Unexpected Step3-VL projector tensor {name!r}")
+
+            proj_id = int(match.group(1)) - 1
+            suffix = f".{match.group(2)}"
+            yield (self.format_tensor_name(gguf.MODEL_TENSOR.V_MMPROJ, proj_id, suffix=suffix), data_torch)
+            return
+
+        if name == "vit_large_projector.weight":
+            yield (self.format_tensor_name(gguf.MODEL_TENSOR.V_MMPROJ_FC), data_torch)
+            return
+
+        if name.startswith("vision_model."):
+            if name == "vision_model.positional_embedding":
+                name += ".weight"
+            elif name.endswith(".gamma") and ".ls_" in name:
+                name = name.removesuffix(".gamma") + ".weight"
+
+            name = name.replace("attn.in_proj_weight", "attn.in_proj.weight")
+            name = name.replace("attn.in_proj_bias", "attn.in_proj.bias")
+
+            yield from super().modify_tensors(data_torch, name, bid)
+
+
 @ModelBase.register("Qwen3VLForConditionalGeneration")
 class Qwen3VLTextModel(Qwen3Model):
     model_arch = gguf.MODEL_ARCH.QWEN3VL
 
     def set_gguf_parameters(self):
         super().set_gguf_parameters()
-
-        # Handle MRoPE (Multi-axis Rotary Position Embedding) for Qwen3-VL
-        vision_config = self.hparams.get("vision_config", {})
+        if "thinker_config" in self.hparams:
+            vision_config = self.hparams["thinker_config"].get("vision_config", {})
+        else:
+            vision_config = self.hparams.get("vision_config", {})
         deepstack_layer_num = len(vision_config.get("deepstack_visual_indexes", []))
         self.gguf_writer.add_num_deepstack_layers(deepstack_layer_num)
 
@@ -4969,6 +5122,16 @@ class Qwen3VLTextModel(Qwen3Model):
         yield from super().modify_tensors(data_torch, name, bid)
 
 
+@ModelBase.register("StepVLForConditionalGeneration")
+class Step3VLTextModel(Qwen3Model):
+    model_arch = gguf.MODEL_ARCH.QWEN3
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        if name.startswith("vision_model.") or name.startswith("model.vision_model.") or name.startswith("vit_large_projector."):
+            return
+        yield from super().modify_tensors(data_torch, name, bid)
+
+
 @ModelBase.register("Qwen3VLMoeForConditionalGeneration")
 class Qwen3VLMoeTextModel(Qwen3MoeModel):
     model_arch = gguf.MODEL_ARCH.QWEN3VLMOE
@@ -5016,6 +5179,70 @@ class Qwen3VLMoeTextModel(Qwen3MoeModel):
         yield from super().modify_tensors(data_torch, name, bid)
 
 
+@ModelBase.register("Qwen3OmniMoeForConditionalGeneration")
+class Qwen3OmniMoeTextModel(Qwen3VLMoeTextModel):
+    model_arch = gguf.MODEL_ARCH.QWEN3VLMOE
+
+    def set_vocab(self):
+        super().set_vocab()
+        # correct BOS/EOS tokens
+        with open(self.dir_model / "tokenizer_config.json", "r", encoding="utf-8") as f:
+            tokenizer_config = json.load(f)
+            added_tokens = tokenizer_config.get("added_tokens_decoder", {})
+            for token_id, data in added_tokens.items():
+                if data.get("content") == "<|im_end|>":
+                    self.gguf_writer.add_bos_token_id(int(token_id))
+                    self.gguf_writer.add_eos_token_id(int(token_id))
+                    break
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self.gguf_writer.add_num_deepstack_layers(0)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        # Skip vision and audio tensors - they go in the mmproj file
+        if "visual." in name or "audio_tower." in name \
+                or "talker." in name or "code2wav." in name:
+            return
+
+        name = name.replace("thinker.", "")
+        yield from super().modify_tensors(data_torch, name, bid)
+
+
+@ModelBase.register("Qwen3ASRForConditionalGeneration")
+class Qwen3ASRTextModel(Qwen3VLTextModel):
+    model_arch = gguf.MODEL_ARCH.QWEN3VL
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self.gguf_writer.add_num_deepstack_layers(0)
+
+    def set_vocab(self):
+        super().set_vocab()
+        # fix chat template, use correct chatml format
+        self.gguf_writer.add_chat_template("{% for message in messages %}{{'<|im_start|>' + message['role'] + '\\n' + message['content'] + '<|im_end|>' + '\\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\\n' }}{% endif %}")
+        # correct BOS/EOS tokens
+        with open(self.dir_model / "tokenizer_config.json", "r", encoding="utf-8") as f:
+            tokenizer_config = json.load(f)
+            added_tokens = tokenizer_config.get("added_tokens_decoder", {})
+            for token_id, data in added_tokens.items():
+                if data.get("content") == "<|im_end|>":
+                    self.gguf_writer.add_bos_token_id(int(token_id))
+                    self.gguf_writer.add_eos_token_id(int(token_id))
+                    break
+
+    def modify_tensors(self, data_torch, name, bid):
+        # qwen3-omni
+        name = name.replace("thinker.", "")
+
+        # Skip vision and audio tensors - they go in the mmproj file
+        if "visual." in name or "audio_tower." in name \
+                or "talker." in name or "code2wav." in name:
+            return
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
+
 class _LinearAttentionVReorderBase(Qwen3NextModel):
     model_arch = gguf.MODEL_ARCH.QWEN3NEXT  # overridden by subclasses
     """reorders V heads from grouped to tiled order for ggml broadcast
@@ -5859,7 +6086,7 @@ class KimiLinearModel(TextModel):
             # Build merges list using the approach similar to HunYuanMoE
             merges = []
             vocab = {}
-            mergeable_ranks = tokenizer.model._mergeable_ranks
+            mergeable_ranks = tokenizer.model._mergeable_ranks  # ty: ignore[unresolved-attribute]
             for token, rank in mergeable_ranks.items():
                 vocab[QwenModel.token_bytes_to_string(token)] = rank
                 if len(token) == 1:
@@ -5869,7 +6096,7 @@ class KimiLinearModel(TextModel):
                     merges.append(' '.join(map(QwenModel.token_bytes_to_string, merged)))
             # Build token list
             vocab_size = self.hparams["vocab_size"]
-            special_tokens = tokenizer.special_tokens
+            special_tokens = tokenizer.special_tokens  # ty: ignore[unresolved-attribute]
             reverse_vocab = {id_ : encoded_tok for encoded_tok, id_ in {**vocab, **special_tokens}.items()}
             tokens: list[str] = []
             toktypes: list[int] = []
@@ -5895,7 +6122,7 @@ class KimiLinearModel(TextModel):
             special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=False)
             special_vocab.add_to_gguf(self.gguf_writer)
             # override eos id in config.json with tiktoken eos id
-            self.gguf_writer.add_eos_token_id(tokenizer.eos_id)
+            self.gguf_writer.add_eos_token_id(tokenizer.eos_id)  # ty: ignore[unresolved-attribute]
         else:
             raise NotImplementedError(f"Deepseek pre-tokenizer {tokpre!r} is not supported yet!")
 
@@ -6389,11 +6616,11 @@ class BertModel(TextModel):
                 with open(tokenizer_config_path, "r", encoding="utf-8") as fp:
                     tokenizer_config_json = json.load(fp)
 
-            add_prefix = tokenizer.add_prefix_space
-            remove_whitespaces = tokenizer.clean_up_tokenization_spaces
+            add_prefix = tokenizer.add_prefix_space  # ty: ignore[unresolved-attribute]
+            remove_whitespaces = tokenizer.clean_up_tokenization_spaces  # ty: ignore[unresolved-attribute]
             precompiled_charsmap = b64decode(tokenizer_json["normalizer"]["precompiled_charsmap"])
 
-            vocab_size = max(self.hparams.get("vocab_size", 0), tokenizer.vocab_size)
+            vocab_size = max(self.hparams.get("vocab_size", 0), tokenizer.vocab_size)  # ty: ignore[unresolved-attribute]
         else:
             sentencepiece_model = model.ModelProto()  # pyright: ignore[reportAttributeAccessIssue] # ty: ignore[unresolved-attribute]
             sentencepiece_model.ParseFromString(open(tokenizer_path, "rb").read())
@@ -6410,7 +6637,7 @@ class BertModel(TextModel):
 
         tokens: list[bytes] = [f"[PAD{i}]".encode("utf-8") for i in range(vocab_size)]
         scores: list[float] = [-10000.0] * vocab_size
-        toktypes: list[int] = [SentencePieceTokenTypes.UNUSED] * vocab_size
+        toktypes: list[int] = [SentencePieceTokenTypes.UNUSED] * vocab_size  # ty: ignore[invalid-assignment]
 
         if isinstance(tokenizer, SentencePieceProcessor):
             for token_id in range(tokenizer.vocab_size()):
@@ -6432,20 +6659,20 @@ class BertModel(TextModel):
                 scores[token_id] = score
                 toktypes[token_id] = toktype
         else:
-            added_vocab = tokenizer.get_added_vocab()
+            added_vocab = tokenizer.get_added_vocab()  # ty: ignore[unresolved-attribute]
             unk_token = tokenizer_config_json.get("unk_token")
-            unk_token_id = added_vocab.get(unk_token, tokenizer_json["model"].get("unk_id", 3))
+            unk_token_id = added_vocab.get(unk_token, tokenizer_json["model"].get("unk_id", 3))  # ty: ignore[no-matching-overload]
 
-            for token_id in range(tokenizer.vocab_size):
-                piece = tokenizer._convert_id_to_token(token_id)
-                if (piece := tokenizer._convert_id_to_token(token_id)) is not None:
+            for token_id in range(tokenizer.vocab_size):  # ty: ignore[unresolved-attribute]
+                piece = tokenizer._convert_id_to_token(token_id)  # ty: ignore[unresolved-attribute]
+                if (piece := tokenizer._convert_id_to_token(token_id)) is not None:  # ty: ignore[unresolved-attribute]
                     text = piece.encode("utf-8")
                     score = tokenizer_json["model"]["vocab"][token_id][1]
 
                     toktype = SentencePieceTokenTypes.NORMAL
                     if token_id == unk_token_id:
                         toktype = SentencePieceTokenTypes.UNKNOWN
-                    elif token_id in tokenizer.all_special_ids:
+                    elif token_id in tokenizer.all_special_ids:  # ty: ignore[unresolved-attribute]
                         toktype = SentencePieceTokenTypes.CONTROL
                     elif token_id in added_vocab.values():
                         toktype = SentencePieceTokenTypes.USER_DEFINED
@@ -8754,7 +8981,7 @@ class DeepseekV2Model(TextModel):
             # Build merges list using the approach similar to HunYuanMoE
             merges = []
             vocab = {}
-            mergeable_ranks = tokenizer.model._mergeable_ranks
+            mergeable_ranks = tokenizer.model._mergeable_ranks  # ty: ignore[unresolved-attribute]
             for token, rank in mergeable_ranks.items():
                 vocab[QwenModel.token_bytes_to_string(token)] = rank
                 if len(token) == 1:
@@ -8765,7 +8992,7 @@ class DeepseekV2Model(TextModel):
 
             # Build token list
             vocab_size = self.hparams["vocab_size"]
-            special_tokens = tokenizer.special_tokens
+            special_tokens = tokenizer.special_tokens  # ty: ignore[unresolved-attribute]
             reverse_vocab = {id_ : encoded_tok for encoded_tok, id_ in {**vocab, **special_tokens}.items()}
             tokens: list[str] = []
             toktypes: list[int] = []
@@ -9736,10 +9963,10 @@ class Glm4Model(TextModel):
         self.gguf_writer.add_token_list(tokens)
         self.gguf_writer.add_token_types(toktypes)
         special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
-        special_vocab._set_special_token("eos", tokenizer.get_added_vocab()["<|endoftext|>"])
-        special_vocab._set_special_token("eot", tokenizer.get_added_vocab()["<|user|>"])
-        special_vocab._set_special_token("unk", tokenizer.get_added_vocab()["<|endoftext|>"])
-        special_vocab._set_special_token("bos", tokenizer.get_added_vocab()["<|endoftext|>"])
+        special_vocab._set_special_token("eos", tokenizer.get_added_vocab()["<|endoftext|>"])  # ty: ignore[unresolved-attribute]
+        special_vocab._set_special_token("eot", tokenizer.get_added_vocab()["<|user|>"])  # ty: ignore[unresolved-attribute]
+        special_vocab._set_special_token("unk", tokenizer.get_added_vocab()["<|endoftext|>"])  # ty: ignore[unresolved-attribute]
+        special_vocab._set_special_token("bos", tokenizer.get_added_vocab()["<|endoftext|>"])  # ty: ignore[unresolved-attribute]
         special_vocab.add_to_gguf(self.gguf_writer)
 
     def set_gguf_parameters(self):
@@ -9967,12 +10194,12 @@ class ChatGLMModel(TextModel):
 
         from transformers import AutoTokenizer
         tokenizer = AutoTokenizer.from_pretrained(dir_model, trust_remote_code=True)
-        vocab_size = hparams.get("padded_vocab_size", len(tokenizer.get_vocab()))
-        assert max(tokenizer.get_vocab().values()) < vocab_size
+        vocab_size = hparams.get("padded_vocab_size", len(tokenizer.get_vocab()))  # ty: ignore[unresolved-attribute]
+        assert max(tokenizer.get_vocab().values()) < vocab_size  # ty: ignore[unresolved-attribute]
         role_special_tokens = ["<|system|>", "<|user|>", "<|assistant|>", "<|observation|>"]
         special_tokens = ["[MASK]", "[gMASK]", "[sMASK]", "sop", "eop"] + role_special_tokens
         for token_id in range(vocab_size):
-            piece = tokenizer._convert_id_to_token(token_id)
+            piece = tokenizer._convert_id_to_token(token_id)  # ty: ignore[unresolved-attribute]
             if token_id == 0:
                 piece = "<unk>"
             elif token_id == 1:
@@ -9980,17 +10207,17 @@ class ChatGLMModel(TextModel):
             elif token_id == 2:
                 piece = "<eos>"
 
-            text = piece.encode("utf-8")
+            text = piece.encode("utf-8")  # ty: ignore[unresolved-attribute]
             score = 0.0
             # Referencing the tokenizer Python implementation(https://huggingface.co/THUDM/chatglm3-6b/blob/main/tokenization_chatglm.py),
             # it is only valid if it is less than tokenizer.tokenizer.sp_model.vocab_size()
-            if len(piece) != 0 and token_id < tokenizer.tokenizer.sp_model.vocab_size():
-                score = tokenizer.tokenizer.sp_model.get_score(token_id)
+            if len(piece) != 0 and token_id < tokenizer.tokenizer.sp_model.vocab_size():  # ty: ignore[unresolved-attribute, invalid-argument-type]
+                score = tokenizer.tokenizer.sp_model.get_score(token_id)  # ty: ignore[unresolved-attribute]
 
-            if token_id >= tokenizer.tokenizer.sp_model.vocab_size():
+            if token_id >= tokenizer.tokenizer.sp_model.vocab_size():  # ty: ignore[unresolved-attribute]
                 if piece in special_tokens:
                     toktype = SentencePieceTokenTypes.CONTROL
-                elif len(piece) == 0:
+                elif len(piece) == 0:  # ty: ignore[invalid-argument-type]
                     text = f"[PAD{token_id}]".encode("utf-8")
                     toktype = SentencePieceTokenTypes.UNUSED
                 else:
@@ -10001,13 +10228,13 @@ class ChatGLMModel(TextModel):
                 continue
 
             toktype = SentencePieceTokenTypes.NORMAL
-            if tokenizer.tokenizer.sp_model.is_unknown(token_id):
+            if tokenizer.tokenizer.sp_model.is_unknown(token_id):  # ty: ignore[unresolved-attribute]
                 toktype = SentencePieceTokenTypes.UNKNOWN
-            elif tokenizer.tokenizer.sp_model.is_control(token_id):
+            elif tokenizer.tokenizer.sp_model.is_control(token_id):  # ty: ignore[unresolved-attribute]
                 toktype = SentencePieceTokenTypes.CONTROL
-            elif tokenizer.tokenizer.sp_model.is_unused(token_id):
+            elif tokenizer.tokenizer.sp_model.is_unused(token_id):  # ty: ignore[unresolved-attribute]
                 toktype = SentencePieceTokenTypes.UNUSED
-            elif tokenizer.tokenizer.sp_model.is_byte(token_id):
+            elif tokenizer.tokenizer.sp_model.is_byte(token_id):  # ty: ignore[unresolved-attribute]
                 toktype = SentencePieceTokenTypes.BYTE
 
             tokens.append(text)
@@ -10027,7 +10254,7 @@ class ChatGLMModel(TextModel):
 
     @staticmethod
     def token_bytes_to_string(b):
-        from transformers.models.gpt2.tokenization_gpt2 import bytes_to_unicode
+        from transformers.models.gpt2.tokenization_gpt2 import bytes_to_unicode  # ty: ignore[unresolved-import]
         byte_encoder = bytes_to_unicode()
         return ''.join([byte_encoder[ord(char)] for char in b.decode('latin-1')])
 
@@ -10061,7 +10288,7 @@ class ChatGLMModel(TextModel):
         from transformers import AutoTokenizer
         tokenizer = AutoTokenizer.from_pretrained(dir_model, trust_remote_code=True)
         vocab_size = hparams.get("padded_vocab_size",hparams["vocab_size"])
-        assert max(tokenizer.get_vocab().values()) < vocab_size
+        assert max(tokenizer.get_vocab().values()) < vocab_size  # ty: ignore[unresolved-attribute]
 
         tokens, toktypes, tokpre = self.get_vocab_base()
         self.gguf_writer.add_tokenizer_model("gpt2")
@@ -10070,10 +10297,10 @@ class ChatGLMModel(TextModel):
         self.gguf_writer.add_token_types(toktypes)
         special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
         # only add special tokens when they were not already loaded from config.json
-        special_vocab._set_special_token("eos", tokenizer.get_added_vocab()["<|endoftext|>"])
-        special_vocab._set_special_token("eot", tokenizer.get_added_vocab()["<|user|>"])
+        special_vocab._set_special_token("eos", tokenizer.get_added_vocab()["<|endoftext|>"])  # ty: ignore[unresolved-attribute]
+        special_vocab._set_special_token("eot", tokenizer.get_added_vocab()["<|user|>"])  # ty: ignore[unresolved-attribute]
         # this one is usually not in config.json anyway
-        special_vocab._set_special_token("unk", tokenizer.get_added_vocab()["<|endoftext|>"])
+        special_vocab._set_special_token("unk", tokenizer.get_added_vocab()["<|endoftext|>"])  # ty: ignore[unresolved-attribute]
         special_vocab.add_to_gguf(self.gguf_writer)
 
     def set_gguf_parameters(self):
@@ -11194,6 +11421,48 @@ class UltravoxWhisperEncoderModel(WhisperEncoderModel):
         self.gguf_writer.add_audio_stack_factor(self.global_config["stack_factor"])
 
 
+@ModelBase.register("MERaLiON2ForConditionalGeneration")
+class MERaLiONWhisperEncoderModel(WhisperEncoderModel):
+    has_vision_encoder = False
+    has_audio_encoder = True
+
+    def get_audio_config(self) -> dict[str, Any] | None:
+        return self.global_config.get("speech_config")
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.MERALION)
+        self.gguf_writer.add_audio_stack_factor(self.global_config.get("speech_mlp_scale_factor", 15))
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        if name.startswith("text_decoder."):
+            return
+
+        if name.startswith("speech_encoder."):
+            name = name.replace("speech_encoder.", "audio_tower.")
+            yield from super().modify_tensors(data_torch, name, bid)
+            return
+
+        suffix = "." + name.rsplit(".", 1)[-1]
+
+        if name.startswith("ln_speech."):
+            yield (self.format_tensor_name(gguf.MODEL_TENSOR.A_MM_NORM_PRE, suffix=suffix), data_torch)
+            return
+
+        if name.startswith("speech_audio_adapter."):
+            if ".mlp_adapter.0." in name:
+                yield (self.format_tensor_name(gguf.MODEL_TENSOR.A_MMPROJ, 0, suffix=suffix), data_torch)
+            elif ".gate_proj." in name:
+                yield (self.format_tensor_name(gguf.MODEL_TENSOR.A_MMPROJ, 1, suffix=suffix), data_torch)
+            elif ".pool_proj." in name:
+                yield (self.format_tensor_name(gguf.MODEL_TENSOR.A_MMPROJ, 2, suffix=suffix), data_torch)
+            elif ".out_proj." in name:
+                yield (self.format_tensor_name(gguf.MODEL_TENSOR.A_MMPROJ, 3, suffix=suffix), data_torch)
+            return
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
+
 @ModelBase.register("VoxtralForConditionalGeneration")
 class VoxtralWhisperEncoderModel(WhisperEncoderModel):
     has_vision_encoder = False # no vision encoder
@@ -11339,7 +11608,7 @@ class HunYuanMoEModel(TextModel):
         # 2. Reverse-engineer the merges list from mergeable_ranks
         merges = []
         vocab = {}
-        mergeable_ranks = tokenizer.mergeable_ranks
+        mergeable_ranks = tokenizer.mergeable_ranks  # ty: ignore[unresolved-attribute]
         for token, rank in mergeable_ranks.items():
             vocab[QwenModel.token_bytes_to_string(token)] = rank
             if len(token) == 1:
@@ -11350,8 +11619,8 @@ class HunYuanMoEModel(TextModel):
 
         # 3. Generate the tokens and toktypes lists
         vocab_size = self.hparams["vocab_size"]
-        assert tokenizer.vocab_size == vocab_size
-        special_tokens = tokenizer.special_tokens
+        assert tokenizer.vocab_size == vocab_size  # ty: ignore[unresolved-attribute]
+        special_tokens = tokenizer.special_tokens  # ty: ignore[unresolved-attribute]
         reverse_vocab = {id_ : encoded_tok for encoded_tok, id_ in {**vocab, **special_tokens}.items()}
         tokens: list[str] = []
         toktypes: list[int] = []
@@ -11575,7 +11844,7 @@ class HunYuanModel(TextModel):
             # 2. Reverse-engineer the merges list from mergeable_ranks
             merges = []
             vocab = {}
-            mergeable_ranks = tokenizer.mergeable_ranks
+            mergeable_ranks = tokenizer.mergeable_ranks  # ty: ignore[unresolved-attribute]
             for token, rank in mergeable_ranks.items():
                 vocab[QwenModel.token_bytes_to_string(token)] = rank
                 if len(token) == 1:
@@ -11586,8 +11855,8 @@ class HunYuanModel(TextModel):
 
             # 3. Generate the tokens and toktypes lists
             vocab_size = self.hparams["vocab_size"]
-            assert tokenizer.vocab_size == vocab_size
-            special_tokens = tokenizer.special_tokens
+            assert tokenizer.vocab_size == vocab_size  # ty: ignore[unresolved-attribute]
+            special_tokens = tokenizer.special_tokens  # ty: ignore[unresolved-attribute]
             reverse_vocab = {id_ : encoded_tok for encoded_tok, id_ in {**vocab, **special_tokens}.items()}
             tokens: list[str] = []
             toktypes: list[int] = []
@@ -12735,13 +13004,44 @@ class SolarOpenModel(Glm4MoeModel):
         self.gguf_writer.add_tokenizer_pre(tokpre)
         self.gguf_writer.add_token_list(tokens)
         self.gguf_writer.add_token_types(toktypes)
-        special_vocab._set_special_token("eos", tokenizer.get_added_vocab()["<|endoftext|>"])
-        special_vocab._set_special_token("eot", tokenizer.get_added_vocab()["<|endoftext|>"])
-        special_vocab._set_special_token("unk", tokenizer.get_added_vocab()["<unk>"])
-        special_vocab._set_special_token("bos", tokenizer.get_added_vocab()["<|startoftext|>"])
+        special_vocab._set_special_token("eos", tokenizer.get_added_vocab()["<|endoftext|>"])  # ty: ignore[unresolved-attribute]
+        special_vocab._set_special_token("eot", tokenizer.get_added_vocab()["<|endoftext|>"])  # ty: ignore[unresolved-attribute]
+        special_vocab._set_special_token("unk", tokenizer.get_added_vocab()["<unk>"])  # ty: ignore[unresolved-attribute]
+        special_vocab._set_special_token("bos", tokenizer.get_added_vocab()["<|startoftext|>"])  # ty: ignore[unresolved-attribute]
         special_vocab.add_to_gguf(self.gguf_writer)
 
 
+@ModelBase.register("DotsOCRForCausalLM")
+class DotsOCRVisionModel(MmprojModel):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        assert self.hparams_vision is not None
+        self.hparams_vision["image_size"] = 0 # dynamic resolution
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.DOTSOCR)
+        self.gguf_writer.add_vision_min_pixels(self.preprocessor_config["min_pixels"])
+        self.gguf_writer.add_vision_max_pixels(self.preprocessor_config["max_pixels"])
+        self.gguf_writer.add_vision_attention_layernorm_eps(self.find_vparam(["rms_norm_eps"]))
+        self.gguf_writer.add_vision_projector_scale_factor(self.find_vparam(["spatial_merge_size"]))
+        self.gguf_writer.add_vision_use_silu(True)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        if name.startswith("vision_tower."):
+            if "vision_tower.blocks." in name and ".mlp." in name:
+                # note: to avoid naming conflicts in tensor_mapping.py, we need to handle FFN renaming here
+                # x = F.silu(self.fc1(x)) * self.fc3(x)
+                # x = self.fc2(x)
+                # fc1 -> gate, fc2 -> down, fc3 -> up
+                # mapping original names to Qwen2.5 naming scheme
+                name = name.replace("vision_tower.blocks.", "visual.blocks.")
+                name = name.replace(".fc1", ".gate_proj")
+                name = name.replace(".fc2", ".down_proj")
+                name = name.replace(".fc3", ".up_proj")
+            yield from super().modify_tensors(data_torch, name, bid)
+
+
 ###### CONVERSION LOGIC ######
 
 
@@ -12994,6 +13294,12 @@ def get_model_architecture(hparams: dict[str, Any], model_type: ModelType) -> st
         # For non-hf Mamba and Mamba2 models
         arch = hparams["ssm_cfg"].get("layer", "Mamba") + "ForCausalLM"
 
+    # Step3-VL keeps text config under text_config but uses a custom top-level architecture.
+    # For text conversion we route to a dedicated text-only class.
+    # TODO: refactor this later to avoid adding exception here
+    if model_type == ModelType.TEXT and arch == "StepVLForConditionalGeneration":
+        return arch
+
     # if "architectures" is found in the sub-config, use that instead
     if model_type == ModelType.TEXT and text_config.get("architectures") is not None:
         arch = text_config["architectures"][0]

convert_hf_to_gguf_update.py

@@ -296,7 +296,7 @@ for model in [*pre_computed_hashes, *all_models]:
         except Exception as e:
             raise OSError(f"Error loading tokenizer for model {name}.") from e
 
-        chktok = tokenizer.encode(CHK_TXT)
+        chktok = tokenizer.encode(CHK_TXT)  # ty: ignore[unresolved-attribute]
         chkhsh = sha256(str(chktok).encode()).hexdigest()
 
         logger.info(f"model: {name}")
@@ -468,7 +468,7 @@ for model in models:
 
     with open(f"models/ggml-vocab-{name}.gguf.out", "w") as f:
         for text in tests:
-            res = tokenizer.encode(text, add_special_tokens=False)
+            res = tokenizer.encode(text, add_special_tokens=False)  # ty: ignore[unresolved-attribute]
             for r in res:
                 f.write(f" {r}")
             f.write("\n")

convert_lora_to_gguf.py

@@ -402,7 +402,7 @@ if __name__ == '__main__':
                     # the invocation string includes the "<|start_of_turn|>"
                     # token, but the adapters themselves were trained to
                     # activate _after_ that first token, so we drop it here.
-                    alora_invocation_tokens = tokenizer(invocation_string)["input_ids"][1:]
+                    alora_invocation_tokens = tokenizer(invocation_string)["input_ids"][1:]  # ty: ignore[call-non-callable]
                 if alora_invocation_tokens:
                     logger.debug("GGUF KV: %s = %s", gguf.Keys.Adapter.ALORA_INVOCATION_TOKENS, alora_invocation_tokens)
                     self.gguf_writer.add_key_value(

docs/backend/OPENVINO.md

@@ -3,7 +3,7 @@
 > [!NOTE]
 > Performance and memory optimizations, accuracy validation, broader quantization coverage, broader operator and model support are work in progress.
 
-[OpenVINO](https://docs.openvino.ai/) is an open-source toolkit for optimizing and deploying high-performance AI inference, specifically designed for Intel hardware, including CPUs, GPUs, and NPUs, in the cloud, on-premises, and on the edge. [OpenVINO backend for llama.cpp](../../src/ggml-openvino) enables hardware-accelerated inference on **Intel® CPUs, GPUs, and NPUs** while remaining compatible with the existing **GGUF model ecosystem**. The backend translates GGML compute graphs into OpenVINO graphs and leverages graph compilation, kernel fusion, and device-specific optimizations to improve inference performance on supported Intel hardware.
+[OpenVINO](https://docs.openvino.ai/) is an open-source toolkit for optimizing and deploying high-performance AI inference, specifically designed for Intel hardware, including CPUs, GPUs, and NPUs, in the cloud, on-premises, and on the edge. [OpenVINO backend for llama.cpp](../../ggml/src/ggml-openvino) enables hardware-accelerated inference on **Intel® CPUs, GPUs, and NPUs** while remaining compatible with the existing **GGUF model ecosystem**. The backend translates GGML compute graphs into OpenVINO graphs and leverages graph compilation, kernel fusion, and device-specific optimizations to improve inference performance on supported Intel hardware.
 
 The OpenVINO backend is implemented in `ggml/src/ggml-openvino` and provides a translation layer for core GGML operations. The OpenVINO backend replaces the standard GGML graph execution path with Intel's OpenVINO inference engine. This approach allows the same GGUF model file to run on Intel CPUs, Intel GPUs (integrated and discrete), and Intel NPUs without changes to the model or the rest of the llama.cpp stack. When a `ggml_cgraph` is dispatched to OpenVINO backend, it:
 

docs/backend/SYCL.md

@@ -689,6 +689,7 @@ use 1 SYCL GPUs: [0] with Max compute units:512
 | GGML_SYCL_F16      | OFF *(default)* \|ON *(optional)*     | Enable FP16 build with SYCL code path. (1.) |
 | GGML_SYCL_GRAPH    | OFF *(default)* \|ON *(Optional)*     | Enable build with [SYCL Graph extension](https://github.com/intel/llvm/blob/sycl/sycl/doc/extensions/experimental/sycl_ext_oneapi_graph.asciidoc). |
 | GGML_SYCL_DNN      | ON *(default)* \|OFF *(Optional)*     | Enable build with oneDNN.                   |
+| GGML_SYCL_HOST_MEM_FALLBACK | ON *(default)* \|OFF *(Optional)* | Allow host memory fallback when device memory is full during quantized weight reorder. Enables inference to continue at reduced speed (reading over PCIe) instead of failing. Requires Linux kernel 6.8+. |
 | CMAKE_C_COMPILER   | `icx` *(Linux)*, `icx/cl` *(Windows)* | Set `icx` compiler for SYCL code path.      |
 | CMAKE_CXX_COMPILER | `icpx` *(Linux)*, `icx` *(Windows)*   | Set `icpx/icx` compiler for SYCL code path. |
 

docs/backend/snapdragon/CMakeUserPresets.json

@@ -52,10 +52,39 @@
         }
     },
 
+    {
+        "name": "arm64-linux-snapdragon",
+        "hidden": true,
+        "architecture": { "value": "arm64",       "strategy": "external" },
+        "toolset":      { "value": "host=x86_64", "strategy": "external" },
+        "cacheVariables": {
+            "CMAKE_TOOLCHAIN_FILE": "cmake/arm64-linux-clang.cmake",
+            "CMAKE_C_FLAGS":   "-march=armv8 -fno-finite-math-only -flto -D_GNU_SOURCE",
+            "CMAKE_CXX_FLAGS": "-march=armv8 -fno-finite-math-only -flto -D_GNU_SOURCE",
+            "CMAKE_C_FLAGS_RELEASE":          "-O3 -DNDEBUG",
+            "CMAKE_CXX_FLAGS_RELEASE":        "-O3 -DNDEBUG",
+            "CMAKE_C_FLAGS_RELWITHDEBINFO":   "-O3 -DNDEBUG -g",
+            "CMAKE_CXX_FLAGS_RELWITHDEBINFO": "-O3 -DNDEBUG -g",
+            "CMAKE_PREFIX_PATH":  "$env{OPENCL_SDK_ROOT}",
+            "HEXAGON_SDK_ROOT":   "$env{HEXAGON_SDK_ROOT}",
+            "HEXAGON_TOOLS_ROOT": "$env{HEXAGON_TOOLS_ROOT}",
+            "PREBUILT_LIB_DIR": "linux_aarch64",
+            "GGML_OPENMP":      "OFF",
+            "GGML_LLAMAFILE":   "OFF",
+            "GGML_OPENCL":      "OFF",
+            "GGML_HEXAGON":     "ON",
+            "GGML_HEXAGON_FP32_QUANTIZE_GROUP_SIZE": "128",
+            "LLAMA_OPENSSL":    "OFF"
+        }
+    },
+
     { "name": "arm64-android-snapdragon-debug"  , "inherits": [ "base", "arm64-android-snapdragon", "debug" ] },
     { "name": "arm64-android-snapdragon-release", "inherits": [ "base", "arm64-android-snapdragon", "release" ] },
 
     { "name": "arm64-windows-snapdragon-debug"  , "inherits": [ "base", "arm64-windows-snapdragon", "debug" ] },
-    { "name": "arm64-windows-snapdragon-release", "inherits": [ "base", "arm64-windows-snapdragon", "release" ] }
+    { "name": "arm64-windows-snapdragon-release", "inherits": [ "base", "arm64-windows-snapdragon", "release" ] },
+
+    { "name": "arm64-linux-snapdragon-debug"  , "inherits": [ "base", "arm64-linux-snapdragon", "debug" ] },
+    { "name": "arm64-linux-snapdragon-release", "inherits": [ "base", "arm64-linux-snapdragon", "release" ] }
   ]
 }

docs/backend/snapdragon/README.md

@@ -236,10 +236,6 @@ build: 6a8cf8914 (6733)
   Controls whether the Hexagon backend allocates host buffers. By default, all buffers except for REPACK are host buffers.
   This option is required for testing Ops that require REPACK buffers (MUL_MAT and MUL_MAT_ID).
 
-- `GGML_HEXAGON_EXPERIMENTAL=1`
-  Controls whether the Hexagon backend enables experimental features.
-  This option is required for enabling/testing experimental Ops (FLASH_ATTN_EXT).
-
 - `GGML_HEXAGON_VERBOSE=1`
   Enables verbose logging of Ops from the backend. Example output:
 
@@ -259,11 +255,17 @@ build: 6a8cf8914 (6733)
   Allows enabling specific stages of the processing pipeline:
 
   - `0x1` Enable Op Queue (i.e., queuing Ops into NPU)
-  - `0x2` Enable Dynamic Quantizer (if needed for the Op)
-  - `0x4` Enable Op Compute (MUL_MAT, etc.)
+  - `0x2` Enable Op Compute (MUL_MAT, etc.)
 
   Examples:
 
       `GGML_HEXAGON_OPMASK=0x1 llama-completion ...` - Ops are enqueued but NPU-side processing is stubbed out
-      `GGML_HEXAGON_OPMASK=0x3 llama-completion ...` - NPU performs dynamic quantization and skips the rest
-      `GGML_HEXAGON_OPMASK=0x7 llama-completion ...` - Full queuing and processing of Ops (default)
+      `GGML_HEXAGON_OPMASK=0x3 llama-completion ...` - Full queuing and processing of Ops (default)
+
+- `GGML_HEXAGON_OPFILTER=regex`
+  Allows filtering (disabling) Ops that match the regex pattern:
+
+  Examples:
+
+      `GGML_HEXAGON_OPFILTER="FLASH_ATTN_EXT" llama-completion ...` - Disable Flash Attention on Hexagon (falls back to CPU or GPU)
+      `GGML_HEXAGON_OPFILTER="ADD\|SUB" llama-completion ...` - Disable ADD and SUB on Hexagon (fall back to CPU or GPU)

docs/backend/snapdragon/linux.md

@@ -0,0 +1,58 @@
+# Snapdragon-based Linux devices
+
+## Docker Setup
+
+The easiest way to build llama.cpp for a Snapdragon-based Linux device is using the toolchain Docker image (see [github.com/snapdragon-toolchain](https://github.com/snapdragon-toolchain)).
+This image includes OpenCL SDK, Hexagon SDK, CMake, and the ARM64 Linux cross-compilation toolchain.
+
+Cross-compilation is supported on **Linux X86** hosts. The resulting binaries are deployed to and run on the target **Qualcomm Snapdragon ARM64 Linux** device.
+
+```
+~/src/llama.cpp$ docker run -it -u $(id -u):$(id -g) --volume $(pwd):/workspace --platform linux/amd64 ghcr.io/snapdragon-toolchain/arm64-linux:v0.1
+[d]/> cd /workspace
+```
+
+Note: The rest of the **Linux** build process assumes that you're running inside the toolchain container.
+
+
+## How to Build
+
+Let's build llama.cpp with CPU, OpenCL, and Hexagon backends via CMake presets:
+
+```
+[d]/workspace> cp docs/backend/snapdragon/CMakeUserPresets.json .
+
+[d]/workspace> cmake --preset arm64-linux-snapdragon-release -B build-snapdragon
+
+[d]/workspace> cmake --build build-snapdragon -j $(nproc)
+```
+
+To generate an installable "package" simply use cmake --install, then zip it:
+
+```
+[d]/workspace> cmake --install build-snapdragon --prefix pkg-snapdragon
+[d]/workspace> zip -r pkg-snapdragon.zip pkg-snapdragon
+```
+
+## How to Install
+
+For this step, you will deploy the built binaries and libraries to the target Linux device. Transfer `pkg-snapdragon.zip` to the target device, then unzip it and set up the environment variables:
+
+```
+$ unzip pkg-snapdragon.zip
+$ cd pkg-snapdragon
+$ export LD_LIBRARY_PATH=./lib
+$ export ADSP_LIBRARY_PATH=./lib
+```
+
+At this point, you should also download some models onto the device:
+
+```
+$ wget https://huggingface.co/bartowski/Llama-3.2-3B-Instruct-GGUF/resolve/main/Llama-3.2-3B-Instruct-Q4_0.gguf
+```
+
+## How to Run
+Next, since we have setup the environment variables, we can run the llama-cli with the Hexagon backends:
+```
+$ ./bin/llama-cli -m Llama-3.2-3B-Instruct-Q4_0.gguf --device HTP0 -ngl 99 -p "what is the most popular cookie in the world?"
+```

docs/build.md

@@ -281,6 +281,12 @@ Use `GGML_CUDA_FORCE_CUBLAS_COMPUTE_16F` environment variable to force use FP16
 
 The environment variable `GGML_CUDA_ENABLE_UNIFIED_MEMORY=1` can be used to enable unified memory in Linux. This allows swapping to system RAM instead of crashing when the GPU VRAM is exhausted. In Windows this setting is available in the NVIDIA control panel as `System Memory Fallback`.
 
+### Peer Access
+
+The environment variable `GGML_CUDA_P2P` can be set to enable peer-to-peer access between multiple GPUs, allowing them to transfer data directly rather than to go through system memory.
+Requires driver support (usually restricted to workstation/datacenter GPUs).
+May cause crashes or corrupted outputs for some motherboards and BIOS settings (e.g. IOMMU).
+
 ### Performance Tuning
 
 The following compilation options are also available to tweak performance:
@@ -456,7 +462,8 @@ pacman -S git \
     mingw-w64-ucrt-x86_64-gcc \
     mingw-w64-ucrt-x86_64-cmake \
     mingw-w64-ucrt-x86_64-vulkan-devel \
-    mingw-w64-ucrt-x86_64-shaderc
+    mingw-w64-ucrt-x86_64-shaderc \
+    mingw-w64-ucrt-x86_64-spirv-headers
 ```
 
 Switch into the `llama.cpp` directory and build using CMake.
@@ -490,9 +497,11 @@ First, follow the official LunarG instructions for the installation and setup of
 
 On Debian / Ubuntu, you can install the required dependencies using:
 ```sh
-sudo apt-get install libvulkan-dev glslc
+sudo apt-get install libvulkan-dev glslc spirv-headers
 ```
 
+SPIRV-Headers (`spirv/unified1/spirv.hpp`) are required for the Vulkan backend and are **not** always pulled in by the Vulkan loader dev package alone. Other distros use names such as `spirv-headers` (Ubuntu / Debian / Arch), or `spirv-headers-devel` (Fedora / openSUSE). On Windows, the LunarG Vulkan SDK’s `Include` directory already contains these headers.
+
 #### Common steps
 
 Second, after verifying that you have followed all of the SDK installation/setup steps, use this command to make sure before proceeding:

docs/development/HOWTO-add-model.md

@@ -5,6 +5,7 @@ Adding a model requires few steps:
 1. Convert the model to GGUF
 2. Define the model architecture in `llama.cpp`
 3. Build the GGML graph implementation
+4. Optional: Add multimodal encoder implementation
 
 After following these steps, you can open PR.
 
@@ -114,6 +115,38 @@ Some `ggml` backends do not support all operations. Backend implementations can
 
 Note: to debug the inference graph: you can use [llama-eval-callback](/examples/eval-callback/).
 
+### 4. Optional: Add multimodal encoder implementation
+
+If the new model supports multimodal inputs, you will need to add a new encoder definition in `libmtmd`. You can find more information about llama.cpp's multimodal support in [the docs](../multimodal.md) and in the `tools/mtmd` source directory.
+
+1. In the conversion script, make sure you add a subclass that extends `MmprojModel` or another class that inherits from the same base class.
+2. Add the encoder definition in `clip.cpp`.
+3. Implement the preprocessor in `mtmd.cpp`. In most cases, you can reuse an existing preprocessor.
+4. Implement the encoder GGML graph, either in a dedicated file if the model is truly different from existing ones, or by reusing an existing implementation (for example: siglip, pixtral, or qwen) and adding a model-specific projector.
+
+Note:
+- Many multimodal encoders are based on models that are already supported. Make sure to read the existing encoder definitions in `tools/mtmd/models` before adding a new one. In `libmtmd`, it is generally better to extend an existing model than to duplicate code.
+- To debug the multimodal preprocessor and encoder, you can use [llama-mtmd-debug](tools/mtmd/debug/mtmd-debug.cpp).
+- Adding a model-specific API or CLI is an anti-pattern in `libmtmd`. The goal of `libmtmd` is to provide an easy-to-use, model-agnostic library for multimodal pipeline.
+- In most cases, `llama-mtmd-cli` should not be modified. If a model requires a specific prompt, either let the user provide it or bake it into the Jinja chat template.
+
+## Tips and tricks
+
+### Working with ggml_rope_ext
+
+PyTorch implementations usually prefer explicitly calculating `freq_cis`/`sin`/`cos` components. However, in llama.cpp, most RoPE operations can be handled via `ggml_rope_ext`, which does not require a sin/cos matrix. This saves memory while allowing the GGML RoPE kernel to be fused with other ops.
+
+However, since `ggml_rope_ext` only provides a subset of the RoPE implementations that models use, converting models from PyTorch to llama.cpp may require some creative adaptations.
+
+For more information about `ggml_rope_ext`, please refer to the in-code documentation in `ggml.h`.
+
+Examples:
+- `libmtmd` implements 2D RoPE with `GGML_ROPE_TYPE_NORMAL` ordering by splitting the input tensor in half, applying `ggml_rope_ext` separately to each half, then joining them back together using `ggml_concat`.
+- The [Kimi-K2.5](https://github.com/ggml-org/llama.cpp/pull/19170) vision encoder uses vision RoPE with interleaved frequencies. The weights must be permuted during conversion in order to reuse the `build_rope_2d()` function.
+- [Gemma 4](https://github.com/ggml-org/llama.cpp/pull/21309) uses "proportional" RoPE. We employ a trick where `rope_freqs` is set to a very large value in the last dimensions to prevent those dimensions from being rotated. See the `Gemma4Model` class in `convert_hf_to_gguf.py`.
+- Some models require scaling the input position. For example, `[0, 1, 2, ...]` becomes `[0, 0.5, 1, ...]`. In this case, you can provide the scaling via `freq_scale = 0.5f`.
+- Some models use learned RoPE frequencies instead of relying on `powf(freq_base, -2.0 * i / n_dims)`. In this case, you can provide the learned frequencies via the `rope_freqs` tensor (corresponding to the `c` argument in `ggml_rope_ext`), then set `freq_base = 1.0f`. An important note is that `rope_freqs` in GGML is the **inverse** (`theta = pos[i] / rope_freqs`), so you may need to invert `rope_freqs` during conversion.
+
 ## GGUF specification
 
 https://github.com/ggml-org/ggml/blob/master/docs/gguf.md

docs/multimodal.md

@@ -37,6 +37,7 @@ llama-server -hf ggml-org/gemma-3-4b-it-GGUF --no-mmproj-offload
 > - PaddleOCR-VL: https://github.com/ggml-org/llama.cpp/pull/18825
 > - GLM-OCR: https://github.com/ggml-org/llama.cpp/pull/19677
 > - Deepseek-OCR: https://github.com/ggml-org/llama.cpp/pull/17400
+> - Dots.OCR: https://github.com/ggml-org/llama.cpp/pull/17575
 > - HunyuanOCR: https://github.com/ggml-org/llama.cpp/pull/21395
 
 ## Pre-quantized models
@@ -93,6 +94,11 @@ NOTE: some models may require large context window, for example: `-c 8192`
 # Moondream2 20250414 version
 (tool_name) -hf ggml-org/moondream2-20250414-GGUF
 
+# Gemma 4
+(tool_name) -hf ggml-org/gemma-4-E2B-it-GGUF
+(tool_name) -hf ggml-org/gemma-4-E4B-it-GGUF
+(tool_name) -hf ggml-org/gemma-4-26B-A4B-it-GGUF
+(tool_name) -hf ggml-org/gemma-4-31B-it-GGUF
 ```
 
 **Audio models**:
@@ -108,6 +114,10 @@ NOTE: some models may require large context window, for example: `-c 8192`
 
 # Mistral's Voxtral
 (tool_name) -hf ggml-org/Voxtral-Mini-3B-2507-GGUF
+
+# Qwen3-ASR
+(tool_name) -hf ggml-org/Qwen3-ASR-0.6B-GGUF
+(tool_name) -hf ggml-org/Qwen3-ASR-1.7B-GGUF
 ```
 
 **Mixed modalities**:
@@ -117,6 +127,16 @@ NOTE: some models may require large context window, for example: `-c 8192`
 # Capabilities: audio input, vision input
 (tool_name) -hf ggml-org/Qwen2.5-Omni-3B-GGUF
 (tool_name) -hf ggml-org/Qwen2.5-Omni-7B-GGUF
+
+# Qwen3 Omni
+# Capabilities: audio input, vision input
+(tool_name) -hf ggml-org/Qwen3-Omni-30B-A3B-Instruct-GGUF
+(tool_name) -hf ggml-org/Qwen3-Omni-30B-A3B-Thinking-GGUF
+
+# Gemma 4
+# Capabilities: audio input, vision input
+(tool_name) -hf ggml-org/gemma-4-E2B-it-GGUF
+(tool_name) -hf ggml-org/gemma-4-E4B-it-GGUF
 ```
 
 ## Finding more models:

examples/debug/debug.cpp

@@ -9,6 +9,7 @@
 #include <vector>
 #include <filesystem>
 #include <fstream>
+#include <optional>
 #include <regex>
 
 static void print_usage(int /*argc*/, char ** argv) {
@@ -222,7 +223,10 @@ int main(int argc, char ** argv) {
     llama_backend_init();
     llama_numa_init(params.numa);
 
-    base_callback_data cb_data(params, params.tensor_filter);
+    std::optional<base_callback_data> cb_data;
+    if (!params.save_logits) {
+        cb_data.emplace(params, params.tensor_filter);
+    }
 
     auto llama_init = common_init_from_params(params);
 

examples/diffusion/diffusion-cli.cpp

@@ -602,8 +602,8 @@ int main(int argc, char ** argv) {
 
     int n_input = input_tokens.size();
 
-    if (n_input >= params.n_ctx) {
-        LOG_ERR("error: input too long (%d tokens), max context is %d\n", n_input, params.n_ctx);
+    if (static_cast<uint32_t>(n_input) >= llama_n_ctx(ctx)) {
+        LOG_ERR("error: input too long (%d tokens), max context is %d\n", n_input, llama_n_ctx(ctx));
         llama_free(ctx);
         llama_model_free(model);
         return 1;

examples/model-conversion/scripts/causal/run-casual-gen-embeddings-org.py

@@ -53,10 +53,10 @@ model_name = os.path.basename(model_path)
 print(f"Model name: {model_name}")
 
 prompt = "Hello world today"
-input_ids = tokenizer(prompt, return_tensors="pt").input_ids
+input_ids = tokenizer(prompt, return_tensors="pt").input_ids  # ty: ignore[call-non-callable]
 print(f"Input tokens: {input_ids}")
 print(f"Input text: {repr(prompt)}")
-print(f"Tokenized: {tokenizer.convert_ids_to_tokens(input_ids[0])}")
+print(f"Tokenized: {tokenizer.convert_ids_to_tokens(input_ids[0])}")  # ty: ignore[unresolved-attribute]
 
 with torch.no_grad():
     outputs = model(input_ids, output_hidden_states=True)
@@ -92,7 +92,7 @@ with torch.no_grad():
 
     # Print embeddings per token in the requested format
     print("\nToken embeddings:")
-    tokens = tokenizer.convert_ids_to_tokens(input_ids[0])
+    tokens = tokenizer.convert_ids_to_tokens(input_ids[0])  # ty: ignore[unresolved-attribute]
     for i, embedding in enumerate(token_embeddings):
         # Format: show first few values, ..., then last few values
         if len(embedding) > 10:

examples/model-conversion/scripts/utils/semantic_check.py

@@ -207,8 +207,8 @@ def main():
         else:
             model = AutoModel.from_pretrained(args.model_path, trust_remote_code=True)
 
-    encoded = tokenizer(prompt, return_tensors="pt")
-    tokens = tokenizer.convert_ids_to_tokens(encoded['input_ids'][0])
+    encoded = tokenizer(prompt, return_tensors="pt")  # ty: ignore[call-non-callable]
+    tokens = tokenizer.convert_ids_to_tokens(encoded['input_ids'][0])  # ty: ignore[unresolved-attribute]
     n_tokens = len(tokens)
     print(f"n_tokens: {n_tokens}");
     print(f"hidden_size: {model.config.hidden_size}")

ggml/CMakeLists.txt

@@ -1,4 +1,11 @@
 cmake_minimum_required(VERSION 3.14...3.28) # for add_link_options and implicit target directories.
+
+# ref: https://cmake.org/cmake/help/latest/policy/CMP0194.html
+# MSVC is not a valid assembler for the ASM language.
+# Set to NEW to avoid a warning on CMake 4.1+ with MSVC.
+if (POLICY CMP0194)
+    cmake_policy(SET CMP0194 NEW)
+endif()
 project("ggml" C CXX ASM)
 
 ### GGML Version
@@ -7,6 +14,8 @@ set(GGML_VERSION_MINOR 9)
 set(GGML_VERSION_PATCH 11)
 set(GGML_VERSION_BASE "${GGML_VERSION_MAJOR}.${GGML_VERSION_MINOR}.${GGML_VERSION_PATCH}")
 
+list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/cmake/")
+
 find_program(GIT_EXE NAMES git git.exe NO_CMAKE_FIND_ROOT_PATH)
 if(GIT_EXE)
     # Get current git commit hash
@@ -204,12 +213,14 @@ option(GGML_CUDA_NO_VMM                     "ggml: do not try to use CUDA VMM"
 option(GGML_CUDA_FA                         "ggml: compile ggml FlashAttention CUDA kernels"  ON)
 option(GGML_CUDA_FA_ALL_QUANTS              "ggml: compile all quants for FlashAttention"     OFF)
 option(GGML_CUDA_GRAPHS                     "ggml: use CUDA graphs (llama.cpp only)"          ${GGML_CUDA_GRAPHS_DEFAULT})
+option(GGML_CUDA_NCCL                       "ggml: use NVIDIA Collective Comm. Library"       ON)
 set   (GGML_CUDA_COMPRESSION_MODE "size" CACHE STRING
                                             "ggml: cuda link binary compression mode; requires cuda 12.8+")
 set_property(CACHE GGML_CUDA_COMPRESSION_MODE PROPERTY STRINGS "none;speed;balance;size")
 
 option(GGML_HIP                             "ggml: use HIP"                                   OFF)
 option(GGML_HIP_GRAPHS                      "ggml: use HIP graph, experimental, slow"         OFF)
+option(GGML_HIP_RCCL                        "ggml: use ROCm Collective Comm. Library"         OFF)
 option(GGML_HIP_NO_VMM                      "ggml: do not try to use HIP VMM"                 ON)
 option(GGML_HIP_ROCWMMA_FATTN               "ggml: enable rocWMMA for FlashAttention"         OFF)
 option(GGML_HIP_MMQ_MFMA                    "ggml: enable MFMA MMA for CDNA in MMQ"           ON)
@@ -243,6 +254,7 @@ option(GGML_RPC                             "ggml: use RPC"
 option(GGML_SYCL                            "ggml: use SYCL"                                  OFF)
 option(GGML_SYCL_F16                        "ggml: use 16 bit floats for sycl calculations"   OFF)
 option(GGML_SYCL_GRAPH                      "ggml: enable graphs in the SYCL backend"         ON)
+option(GGML_SYCL_HOST_MEM_FALLBACK          "ggml: allow host memory fallback in SYCL reorder (requires kernel 6.8+)" ON)
 option(GGML_SYCL_DNN                        "ggml: enable oneDNN in the SYCL backend"         ON)
 set   (GGML_SYCL_TARGET "INTEL" CACHE STRING
                                             "ggml: sycl target device")

ggml/cmake/FindNCCL.cmake

@@ -0,0 +1,36 @@
+# cmake/FindNCCL.cmake
+
+# NVIDIA does not distribute CMake files with NCCl, therefore use this file to find it instead.
+
+find_path(NCCL_INCLUDE_DIR
+    NAMES nccl.h
+    HINTS ${NCCL_ROOT} $ENV{NCCL_ROOT} $ENV{CUDA_HOME} /usr/local/cuda
+    PATH_SUFFIXES include
+)
+
+find_library(NCCL_LIBRARY
+    NAMES nccl
+    HINTS ${NCCL_ROOT} $ENV{NCCL_ROOT} $ENV{CUDA_HOME} /usr/local/cuda
+    PATH_SUFFIXES lib lib64
+)
+
+include(FindPackageHandleStandardArgs)
+find_package_handle_standard_args(NCCL
+    DEFAULT_MSG
+    NCCL_LIBRARY NCCL_INCLUDE_DIR
+)
+
+if(NCCL_FOUND)
+    set(NCCL_LIBRARIES ${NCCL_LIBRARY})
+    set(NCCL_INCLUDE_DIRS ${NCCL_INCLUDE_DIR})
+
+    if(NOT TARGET NCCL::NCCL)
+        add_library(NCCL::NCCL UNKNOWN IMPORTED)
+        set_target_properties(NCCL::NCCL PROPERTIES
+            IMPORTED_LOCATION "${NCCL_LIBRARY}"
+            INTERFACE_INCLUDE_DIRECTORIES "${NCCL_INCLUDE_DIR}"
+        )
+    endif()
+endif()
+
+mark_as_advanced(NCCL_INCLUDE_DIR NCCL_LIBRARY)

ggml/include/ggml-backend.h

@@ -68,7 +68,7 @@ extern "C" {
     GGML_API void                           ggml_backend_buffer_reset         (ggml_backend_buffer_t buffer);
 
     // tensor copy between different backends
-    GGML_API void ggml_backend_tensor_copy(struct ggml_tensor * src, struct ggml_tensor * dst);
+    GGML_API void ggml_backend_tensor_copy(const struct ggml_tensor * src, struct ggml_tensor * dst);
 
     //
     // Backend (stream)
@@ -83,13 +83,17 @@ extern "C" {
     GGML_API size_t                     ggml_backend_get_alignment(ggml_backend_t backend);
     GGML_API size_t                     ggml_backend_get_max_size(ggml_backend_t backend);
 
-    GGML_API void ggml_backend_tensor_set_async(ggml_backend_t backend,       struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
-    GGML_API void ggml_backend_tensor_get_async(ggml_backend_t backend, const struct ggml_tensor * tensor,       void * data, size_t offset, size_t size);
+    GGML_API void ggml_backend_tensor_set_async   (ggml_backend_t backend,       struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
+    GGML_API void ggml_backend_tensor_get_async   (ggml_backend_t backend, const struct ggml_tensor * tensor,       void * data, size_t offset, size_t size);
+    GGML_API void ggml_backend_tensor_set_2d_async(ggml_backend_t backend,       struct ggml_tensor * tensor, const void * data, size_t offset, size_t size, size_t n_copies, size_t stride_tensor, size_t stride_data);
+    GGML_API void ggml_backend_tensor_get_2d_async(ggml_backend_t backend, const struct ggml_tensor * tensor,       void * data, size_t offset, size_t size, size_t n_copies, size_t stride_tensor, size_t stride_data);
 
     // "offset" refers to the offset in tensor->data for setting/getting data
-    GGML_API void ggml_backend_tensor_set(      struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
-    GGML_API void ggml_backend_tensor_get(const struct ggml_tensor * tensor,       void * data, size_t offset, size_t size);
-    GGML_API void ggml_backend_tensor_memset(   struct ggml_tensor * tensor,     uint8_t value, size_t offset, size_t size);
+    GGML_API void ggml_backend_tensor_set   (      struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
+    GGML_API void ggml_backend_tensor_get   (const struct ggml_tensor * tensor,       void * data, size_t offset, size_t size);
+    GGML_API void ggml_backend_tensor_set_2d(      struct ggml_tensor * tensor, const void * data, size_t offset, size_t size, size_t n_copies, size_t stride_tensor, size_t stride_data);
+    GGML_API void ggml_backend_tensor_get_2d(const struct ggml_tensor * tensor,       void * data, size_t offset, size_t size, size_t n_copies, size_t stride_tensor, size_t stride_data);
+    GGML_API void ggml_backend_tensor_memset(      struct ggml_tensor * tensor,     uint8_t value, size_t offset, size_t size);
 
     GGML_API void ggml_backend_synchronize(ggml_backend_t backend);
 
@@ -109,7 +113,7 @@ extern "C" {
     // the copy is performed after all the currently queued operations in backend_src
     // backend_dst will wait for the copy to complete before performing other operations
     // automatic fallback to sync copy if async is not supported
-    GGML_API void ggml_backend_tensor_copy_async(ggml_backend_t backend_src, ggml_backend_t backend_dst, struct ggml_tensor * src, struct ggml_tensor * dst);
+    GGML_API void ggml_backend_tensor_copy_async(ggml_backend_t backend_src, ggml_backend_t backend_dst, const struct ggml_tensor * src, struct ggml_tensor * dst);
 
     GGML_API ggml_backend_dev_t ggml_backend_get_device(ggml_backend_t backend);
 
@@ -135,7 +139,9 @@ extern "C" {
         // integrated GPU device using host memory
         GGML_BACKEND_DEVICE_TYPE_IGPU,
         // accelerator devices intended to be used together with the CPU backend (e.g. BLAS or AMX)
-        GGML_BACKEND_DEVICE_TYPE_ACCEL
+        GGML_BACKEND_DEVICE_TYPE_ACCEL,
+        // "meta" device wrapping multiple other devices for tensor parallelism
+        GGML_BACKEND_DEVICE_TYPE_META,
     };
 
     // functionality supported by the device
@@ -196,7 +202,12 @@ extern "C" {
 
     // Common functions that may be obtained using ggml_backend_reg_get_proc_address
 
-    // Split buffer type for tensor parallelism
+    // Context management and operations for faster communication between backends, used for tensor parallelism (meta backend)
+    typedef void * (*ggml_backend_comm_init_t)(ggml_backend_t * backends, size_t n_backends);
+    typedef void   (*ggml_backend_comm_free_t)(void * comm_ctx);
+    typedef bool   (*ggml_backend_comm_allreduce_tensor_t)(void * comm_ctx, struct ggml_tensor ** tensors);
+
+    // Split buffer type for tensor parallelism (old)
     typedef ggml_backend_buffer_type_t   (*ggml_backend_split_buffer_type_t)(int main_device, const float * tensor_split);
     // Set the number of threads for the backend
     typedef void                         (*ggml_backend_set_n_threads_t)(ggml_backend_t backend, int n_threads);
@@ -340,6 +351,53 @@ extern "C" {
     // Set a callback to be called for each resulting node during graph compute
     GGML_API void                 ggml_backend_sched_set_eval_callback(ggml_backend_sched_t sched, ggml_backend_sched_eval_callback callback, void * user_data);
 
+    //
+    // Meta backend
+    //
+
+#define GGML_BACKEND_META_MAX_DEVICES 16
+
+    enum ggml_backend_meta_split_axis {
+        // tensor split by tensor dimensions:
+        GGML_BACKEND_SPLIT_AXIS_0 = 0,
+        GGML_BACKEND_SPLIT_AXIS_1 = 1,
+        GGML_BACKEND_SPLIT_AXIS_2 = 2,
+        GGML_BACKEND_SPLIT_AXIS_3 = 3,
+
+        GGML_BACKEND_SPLIT_AXIS_MIRRORED = 10, // all values on all backends
+        GGML_BACKEND_SPLIT_AXIS_PARTIAL  = 11, // each backend has a partial sum
+
+        // for internal bookkeeping only:
+        GGML_BACKEND_SPLIT_AXIS_NONE    = 98,
+        GGML_BACKEND_SPLIT_AXIS_UNKNOWN = 99,
+    };
+    GGML_API const char * ggml_backend_meta_split_axis_name(enum ggml_backend_meta_split_axis split_axis);
+
+    struct ggml_backend_meta_split_state {
+        enum ggml_backend_meta_split_axis axis;
+
+        // for tensors with axis >= 0 && axis < GGML_MAX_DIMS:
+        //   - each device has a slice of the tensor along the split axis
+        //   - most tensors have n_segments == 1 and a contiguous slice of the tensor data
+        //   - some tensors have an inhomogenenous data layout along the split axis,
+        //     those tensors are divided into segments which are each individually split across devices
+        //   - ne has one entry per segment and device that add up to ggml_tensor::ne for that axis,
+        //     the outer/inner loops are over segments/devices like [seg0_dev0, seg0_dev1, seg1_dev0, seg1_dev1],
+        //   - for example, a transformer may have a fused QKV matrix rather than 3 matrices, those would be 3 separate segments
+        //     that each need to be split individually across devices so that each device gets a slice of Q, K, and V
+        int64_t  ne[16*GGML_BACKEND_META_MAX_DEVICES];
+        uint32_t n_segments;
+    };
+
+    // function to assign split states for statically allocated tensors, compute tensor split states will be assigned to be compatible:
+    typedef struct ggml_backend_meta_split_state(*ggml_backend_meta_get_split_state_t)(const struct ggml_tensor * tensor, void * userdata);
+
+    // create a new meta device from "simple" devices, meta buffer type/buffer/backend is then derived from this:
+    // TODO: this looks a bit strange - a backend API creates a device. I think we should try
+    //       express this as a backend registry functionality instead
+    GGML_API ggml_backend_dev_t ggml_backend_meta_device(
+        ggml_backend_dev_t * devs, size_t n_devs, ggml_backend_meta_get_split_state_t get_split_state, void * get_split_state_ud);
+
     //
     // Utils
     //

ggml/include/ggml-cuda.h

@@ -27,6 +27,9 @@ GGML_BACKEND_API bool ggml_backend_is_cuda(ggml_backend_t backend);
 // device buffer
 GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_cuda_buffer_type(int device);
 
+// conduct allreduce operation between devices
+GGML_BACKEND_API bool ggml_backend_cuda_allreduce_tensor(ggml_backend_t * backends, struct ggml_tensor ** tensors, size_t n_backends);
+
 // split tensor buffer that splits matrices by rows across multiple devices
 GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_cuda_split_buffer_type(int main_device, const float * tensor_split);
 

ggml/include/ggml-rpc.h

@@ -6,9 +6,9 @@
 extern "C" {
 #endif
 
-#define RPC_PROTO_MAJOR_VERSION    3
-#define RPC_PROTO_MINOR_VERSION    6
-#define RPC_PROTO_PATCH_VERSION    1
+#define RPC_PROTO_MAJOR_VERSION    4
+#define RPC_PROTO_MINOR_VERSION    0
+#define RPC_PROTO_PATCH_VERSION    0
 
 #ifdef  __cplusplus
 static_assert(GGML_OP_COUNT == 96, "GGML_OP_COUNT has changed - update RPC_PROTO_PATCH_VERSION");

ggml/include/ggml.h

@@ -1773,8 +1773,32 @@ extern "C" {
             int                   n_dims,
             int                   mode);
 
-    // custom RoPE
+    // RoPE operations with extended options
+    // a is the input tensor to apply RoPE to, shape [n_embd, n_head, n_token]
+    // b is an int32 vector with size n_token
     // c is freq factors (e.g. phi3-128k), (optional)
+    // mode can be GGML_ROPE_TYPE_NORMAL or NEOX; for MROPE and VISION mode, use ggml_rope_multi
+    //
+    // pseudo-code for computing theta:
+    //   for i in [0, n_dims/2):
+    //     theta[i] = b[i] * powf(freq_base, -2.0 * i / n_dims);
+    //     theta[i] = theta[i] / c[i];  # if c is provided, divide theta by c
+    //     theta[i] = rope_yarn(theta[i], ...);  # note: theta = theta * freq_scale is applied here
+    //
+    // other params are used by YaRN RoPE scaling, these default values will disable YaRN:
+    //   freq_scale  = 1.0f
+    //   ext_factor  = 0.0f
+    //   attn_factor = 1.0f
+    //   beta_fast   = 0.0f
+    //   beta_slow   = 0.0f
+    //
+    // example:
+    //   (marking: c = cos, s = sin, 0 = unrotated)
+    //   given a single head with size = 8 --> [00000000]
+    //   GGML_ROPE_TYPE_NORMAL  n_dims = 4 --> [cscs0000]
+    //   GGML_ROPE_TYPE_NORMAL  n_dims = 8 --> [cscscscs]
+    //   GGML_ROPE_TYPE_NEOX    n_dims = 4 --> [ccss0000]
+    //   GGML_ROPE_TYPE_NEOX    n_dims = 8 --> [ccccssss]
     GGML_API struct ggml_tensor * ggml_rope_ext(
             struct ggml_context * ctx,
             struct ggml_tensor  * a,
@@ -1790,6 +1814,36 @@ extern "C" {
             float                 beta_fast,
             float                 beta_slow);
 
+    // multi-dimensional RoPE, for Qwen-VL and similar vision models
+    // mode can be either VISION, MROPE, IMROPE, cannot be combined with NORMAL or NEOX
+    // sections specify how many dimensions to rotate in each section:
+    //   section length is equivalent to number of cos/sin pairs, NOT the number of dims
+    //   (i.e. sum of 4 sections are expected to be n_dims/2)
+    //   last sections can be 0, means ignored
+    // all other options are identical to ggml_rope_ext
+    //
+    // important note:
+    //   - NEOX ordering is automatically applied and cannot be disabled for MROPE and VISION
+    //     if you need normal ordering, there are 2 methods:
+    //     (1) split the tensor manually using ggml_view
+    //     (2) permute the weight upon conversion
+    //   - for VISION, n_dims must be head_size/2
+    //
+    // example M-RoPE:
+    //  given sections = [t=4, y=2, x=2, 0]
+    //  given a single head with size = 18 --> [000000000000000000]
+    //  GGML_ROPE_TYPE_MROPE   n_dims = 16 --> [ttttyyxxttttyyxx00] (cos/sin are applied in NEOX ordering)
+    //  GGML_ROPE_TYPE_IMROPE  n_dims = 16 --> [ttyxttyxttyxttyx00] (interleaved M-RoPE, still NEOX ordering)
+    //  note: the theta for each dim is computed the same way as ggml_rope_ext, no matter the section
+    //        in other words, idx used for theta: [0123456789... until n_dims/2], not reset for each section
+    //
+    // example vision RoPE:
+    //  given sections = [y=4, x=4, 0, 0] (last 2 sections are ignored)
+    //  given a single head with size = 8 --> [00000000]
+    //  GGML_ROPE_TYPE_VISION  n_dims = 4 --> [yyyyxxxx]
+    //  other values of n_dims are untested and is undefined behavior
+    //  note: unlike MROPE, the theta for each dim is computed differently for each section
+    //        in other words, idx used for theta: [0123] for y section, then [0123] for x section
     GGML_API struct ggml_tensor * ggml_rope_multi(
             struct ggml_context * ctx,
             struct ggml_tensor  * a,

ggml/src/CMakeLists.txt

@@ -200,6 +200,7 @@ add_library(ggml-base
             ggml.cpp
             ggml-alloc.c
             ggml-backend.cpp
+            ggml-backend-meta.cpp
             ggml-opt.cpp
             ggml-threading.cpp
             ggml-threading.h

ggml/src/ggml-alloc.c

@@ -2,6 +2,7 @@
 #include "ggml-backend-impl.h"
 #include "ggml.h"
 #include "ggml-impl.h"
+
 #include <assert.h>
 #include <limits.h>
 #include <stdarg.h>
@@ -1236,6 +1237,9 @@ size_t ggml_backend_alloc_ctx_tensors_from_buft_size(struct ggml_context * ctx,
 
 ggml_backend_buffer_t ggml_backend_alloc_ctx_tensors_from_buft(struct ggml_context * ctx, ggml_backend_buffer_type_t buft) {
     size_t nbytes_total = 0;
+    if (ggml_backend_buft_is_meta(buft)) {
+        return ggml_backend_meta_alloc_ctx_tensors_from_buft(ctx, buft);
+    }
     return ggml_backend_alloc_ctx_tensors_from_buft_impl(ctx, buft, &nbytes_total, /*no_alloc =*/ false);
 }
 

ggml/src/ggml-backend-impl.h

@@ -49,6 +49,10 @@ extern "C" {
         void         (*memset_tensor)(ggml_backend_buffer_t buffer,       struct ggml_tensor * tensor,     uint8_t value, size_t offset, size_t size);
         void         (*set_tensor)   (ggml_backend_buffer_t buffer,       struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
         void         (*get_tensor)   (ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor,       void * data, size_t offset, size_t size);
+        // (optional) 2d data copies
+        void         (*set_tensor_2d)(ggml_backend_buffer_t buffer,       struct ggml_tensor * tensor, const void * data, size_t offset, size_t size, size_t n_copies, size_t stride_tensor, size_t stride_data);
+        void         (*get_tensor_2d)(ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor,       void * data, size_t offset, size_t size, size_t n_copies, size_t stride_tensor, size_t stride_data);
+
         // (optional) tensor copy: dst is in the buffer, src may be in any buffer, including buffers from a different backend (return false if not supported)
         bool         (*cpy_tensor)   (ggml_backend_buffer_t buffer, const struct ggml_tensor * src, struct ggml_tensor * dst);
         // clear the entire buffer
@@ -80,6 +84,20 @@ extern "C" {
     GGML_API bool                  ggml_backend_buffer_is_multi_buffer(ggml_backend_buffer_t buffer);
     GGML_API void                  ggml_backend_multi_buffer_set_usage(ggml_backend_buffer_t buffer, enum ggml_backend_buffer_usage usage);
 
+    //
+    // Backend (meta)
+    //
+
+    GGML_API bool ggml_backend_is_meta       (ggml_backend_t backend);
+    GGML_API bool ggml_backend_buffer_is_meta(ggml_backend_buffer_t buf);
+    GGML_API bool ggml_backend_buft_is_meta  (ggml_backend_buffer_type_t buft);
+
+    GGML_API size_t         ggml_backend_meta_n_backends    (ggml_backend_t meta_backend);
+    GGML_API ggml_backend_t ggml_backend_meta_simple_backend(ggml_backend_t meta_backend, size_t index);
+
+    // temporary workaround to statically allocate tensors from a context in a deduplicated way:
+    GGML_API struct ggml_backend_buffer * ggml_backend_meta_alloc_ctx_tensors_from_buft(struct ggml_context * ctx, ggml_backend_buffer_type_t buft);
+
     //
     // Backend (stream)
     //
@@ -90,8 +108,10 @@ extern "C" {
         void (*free)(ggml_backend_t backend);
 
         // (optional) asynchronous tensor data access
-        void (*set_tensor_async)(ggml_backend_t backend,       struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
-        void (*get_tensor_async)(ggml_backend_t backend, const struct ggml_tensor * tensor,       void * data, size_t offset, size_t size);
+        void (*set_tensor_async)   (ggml_backend_t backend,       struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
+        void (*get_tensor_async)   (ggml_backend_t backend, const struct ggml_tensor * tensor,       void * data, size_t offset, size_t size);
+        void (*set_tensor_2d_async)(ggml_backend_t backend,       struct ggml_tensor * tensor, const void * data, size_t offset, size_t size, size_t n_copies, size_t stride_tensor, size_t stride_data);
+        void (*get_tensor_2d_async)(ggml_backend_t backend, const struct ggml_tensor * tensor,       void * data, size_t offset, size_t size, size_t n_copies, size_t stride_tensor, size_t stride_data);
         bool (*cpy_tensor_async)(ggml_backend_t backend_src, ggml_backend_t backend_dst, const struct ggml_tensor * src, struct ggml_tensor * dst);
 
         // (optional) complete all pending operations (required if the backend supports async operations)

ggml/src/ggml-backend.cpp

@@ -123,7 +123,7 @@ size_t ggml_backend_buffer_get_size(ggml_backend_buffer_t buffer) {
 void * ggml_backend_buffer_get_base(ggml_backend_buffer_t buffer) {
     GGML_ASSERT(buffer);
     // get_base is optional if the buffer is zero-sized
-    if (buffer->size == 0) {
+    if (!ggml_backend_buffer_is_meta(buffer) && buffer->size == 0) {
         return NULL;
     }
 
@@ -279,15 +279,57 @@ void ggml_backend_tensor_get_async(ggml_backend_t backend, const struct ggml_ten
     }
 }
 
+void ggml_backend_tensor_set_2d_async(ggml_backend_t backend, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size,
+            size_t n_copies, size_t stride_tensor, size_t stride_data) {
+    GGML_ASSERT(backend);
+    GGML_ASSERT(tensor);
+    GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
+
+    if (n_copies <= 1 || backend->iface.set_tensor_2d_async == NULL) {
+        for (size_t i = 0; i < n_copies; i++) {
+            ggml_backend_tensor_set_async(backend, tensor, (const char *) data + i*stride_data, offset + i*stride_tensor, size);
+        }
+        return;
+    }
+    if (size == 0) {
+        return;
+    }
+
+    GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
+    GGML_ASSERT(offset + (n_copies-1)*stride_tensor + size <= ggml_nbytes(tensor) && "tensor write out of bounds");
+    backend->iface.set_tensor_2d_async(backend, tensor, data, offset, size, n_copies, stride_tensor, stride_data);
+}
+
+void ggml_backend_tensor_get_2d_async(ggml_backend_t backend, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size,
+            size_t n_copies, size_t stride_tensor, size_t stride_data) {
+    GGML_ASSERT(backend);
+    GGML_ASSERT(tensor);
+    GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
+
+    if (n_copies <= 1 || backend->iface.set_tensor_2d_async == NULL) {
+        for (size_t i = 0; i < n_copies; i++) {
+            ggml_backend_tensor_get_async(backend, tensor, (char *) data + i*stride_data, offset + i*stride_tensor, size);
+        }
+        return;
+    }
+    if (size == 0) {
+        return;
+    }
+
+    GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
+    GGML_ASSERT(offset + (n_copies-1)*stride_tensor + size <= ggml_nbytes(tensor) && "tensor write out of bounds");
+    backend->iface.get_tensor_2d_async(backend, tensor, data, offset, size, n_copies, stride_tensor, stride_data);
+}
+
 void ggml_backend_tensor_set(struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
     GGML_ASSERT(tensor);
     ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer;
+    GGML_ASSERT(buf != NULL && "tensor buffer not set");
 
     if (size == 0) {
         return;
     }
 
-    GGML_ASSERT(buf != NULL && "tensor buffer not set");
     GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
     GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor write out of bounds");
 
@@ -297,18 +339,62 @@ void ggml_backend_tensor_set(struct ggml_tensor * tensor, const void * data, siz
 void ggml_backend_tensor_get(const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) {
     GGML_ASSERT(tensor);
     ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer;
+    GGML_ASSERT(buf != NULL && "tensor buffer not set");
 
     if (size == 0) {
         return;
     }
 
-    GGML_ASSERT(buf != NULL && "tensor buffer not set");
     GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
     GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor read out of bounds");
 
     buf->iface.get_tensor(buf, tensor, data, offset, size);
 }
 
+void ggml_backend_tensor_set_2d(struct ggml_tensor * tensor, const void * data, size_t offset, size_t size,
+            size_t n_copies, size_t stride_tensor, size_t stride_data) {
+    GGML_ASSERT(tensor);
+    ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer;
+    GGML_ASSERT(buf != NULL && "tensor buffer not set");
+
+    if (n_copies <= 1 || buf->iface.set_tensor_2d == NULL) {
+        for (size_t i = 0; i < n_copies; i++) {
+            ggml_backend_tensor_set(tensor, (const char *) data + i*stride_data, offset + i*stride_tensor, size);
+        }
+        return;
+    }
+    if (size == 0) {
+        return;
+    }
+
+    GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
+    GGML_ASSERT(offset + (n_copies-1)*stride_tensor + size <= ggml_nbytes(tensor) && "tensor write out of bounds");
+
+    buf->iface.set_tensor_2d(buf, tensor, data, offset, size, n_copies, stride_tensor, stride_data);
+}
+
+void ggml_backend_tensor_get_2d(const struct ggml_tensor * tensor, void * data, size_t offset, size_t size,
+            size_t n_copies, size_t stride_tensor, size_t stride_data) {
+    GGML_ASSERT(tensor);
+    ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer;
+    GGML_ASSERT(buf != NULL && "tensor buffer not set");
+
+    if (n_copies <= 1 || buf->iface.set_tensor_2d == NULL) {
+        for (size_t i = 0; i < n_copies; i++) {
+            ggml_backend_tensor_get(tensor, (char *) data + i*stride_data, offset + i*stride_tensor, size);
+        }
+        return;
+    }
+    if (size == 0) {
+        return;
+    }
+
+    GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
+    GGML_ASSERT(offset + (n_copies-1)*stride_tensor + size <= ggml_nbytes(tensor) && "tensor read out of bounds");
+
+    buf->iface.get_tensor_2d(buf, tensor, data, offset, size, n_copies, stride_tensor, stride_data);
+}
+
 void ggml_backend_tensor_memset(struct ggml_tensor * tensor, uint8_t value, size_t offset, size_t size) {
     GGML_ASSERT(tensor);
     ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer;
@@ -388,7 +474,7 @@ ggml_backend_dev_t ggml_backend_get_device(ggml_backend_t backend) {
 
 // backend copy
 
-void ggml_backend_tensor_copy(struct ggml_tensor * src, struct ggml_tensor * dst) {
+void ggml_backend_tensor_copy(const struct ggml_tensor * src, struct ggml_tensor * dst) {
     GGML_ASSERT(ggml_are_same_layout(src, dst) && "cannot copy tensors with different layouts");
 
     if (src == dst) {
@@ -402,7 +488,7 @@ void ggml_backend_tensor_copy(struct ggml_tensor * src, struct ggml_tensor * dst
     } else if (!ggml_backend_buffer_copy_tensor(src, dst)) {
 #ifndef NDEBUG
         GGML_LOG_DEBUG("%s: warning: slow copy from %s to %s\n", __func__, ggml_backend_buffer_name(src->buffer), ggml_backend_buffer_name(dst->buffer));
-#endif
+#endif // NDEBUG
         size_t nbytes = ggml_nbytes(src);
         void * data = malloc(nbytes);
         ggml_backend_tensor_get(src, data, 0, nbytes);
@@ -411,7 +497,7 @@ void ggml_backend_tensor_copy(struct ggml_tensor * src, struct ggml_tensor * dst
     }
 }
 
-void ggml_backend_tensor_copy_async(ggml_backend_t backend_src, ggml_backend_t backend_dst, struct ggml_tensor * src, struct ggml_tensor * dst) {
+void ggml_backend_tensor_copy_async(ggml_backend_t backend_src, ggml_backend_t backend_dst, const struct ggml_tensor * src, struct ggml_tensor * dst) {
     GGML_ASSERT(ggml_are_same_layout(src, dst) && "cannot copy tensors with different layouts");
 
     if (src == dst) {
@@ -500,6 +586,7 @@ enum ggml_backend_dev_type ggml_backend_dev_type(ggml_backend_dev_t device) {
 }
 
 void ggml_backend_dev_get_props(ggml_backend_dev_t device, struct ggml_backend_dev_props * props) {
+    GGML_ASSERT(device);
     memset(props, 0, sizeof(*props));
     device->iface.get_props(device, props);
 }
@@ -610,6 +697,8 @@ static const struct ggml_backend_buffer_i ggml_backend_multi_buffer_i = {
     /* .memset_tensor   = */ NULL,
     /* .set_tensor      = */ NULL,
     /* .get_tensor      = */ NULL,
+    /* .set_tensor_2d   = */ NULL,
+    /* .get_tensor_2d   = */ NULL,
     /* .cpy_tensor      = */ NULL,
     /* .clear           = */ ggml_backend_multi_buffer_clear,
     /* .reset           = */ NULL,
@@ -1899,8 +1988,9 @@ enum ggml_status ggml_backend_tensor_alloc(ggml_backend_buffer_t buffer, struct
     GGML_ASSERT(tensor->data == NULL);
     GGML_ASSERT(tensor->view_src == NULL);
     GGML_ASSERT(addr >= ggml_backend_buffer_get_base(buffer));
-    GGML_ASSERT((char *)addr + ggml_backend_buffer_get_alloc_size(buffer, tensor) <=
-                (char *)ggml_backend_buffer_get_base(buffer) + ggml_backend_buffer_get_size(buffer));
+    GGML_ASSERT(ggml_backend_buffer_is_meta(buffer) ||
+        (char *) addr + ggml_backend_buffer_get_alloc_size(buffer, tensor) <=
+        (char *) ggml_backend_buffer_get_base(buffer) + ggml_backend_buffer_get_size(buffer));
 
     tensor->buffer = buffer;
     tensor->data = addr;
@@ -2174,6 +2264,8 @@ static const struct ggml_backend_buffer_i ggml_backend_cpu_buffer_i = {
     /* .memset_tensor   = */ ggml_backend_cpu_buffer_memset_tensor,
     /* .set_tensor      = */ ggml_backend_cpu_buffer_set_tensor,
     /* .get_tensor      = */ ggml_backend_cpu_buffer_get_tensor,
+    /* .set_tensor_2d   = */ NULL,
+    /* .get_tensor_2d   = */ NULL,
     /* .cpy_tensor      = */ ggml_backend_cpu_buffer_cpy_tensor,
     /* .clear           = */ ggml_backend_cpu_buffer_clear,
     /* .reset           = */ NULL,
@@ -2186,6 +2278,8 @@ static const struct ggml_backend_buffer_i ggml_backend_cpu_buffer_from_ptr_i = {
     /* .memset_tensor   = */ ggml_backend_cpu_buffer_memset_tensor,
     /* .set_tensor      = */ ggml_backend_cpu_buffer_set_tensor,
     /* .get_tensor      = */ ggml_backend_cpu_buffer_get_tensor,
+    /* .set_tensor_2d   = */ NULL,
+    /* .get_tensor_2d   = */ NULL,
     /* .cpy_tensor      = */ ggml_backend_cpu_buffer_cpy_tensor,
     /* .clear           = */ ggml_backend_cpu_buffer_clear,
     /* .reset           = */ NULL,

ggml/src/ggml-blas/ggml-blas.cpp

@@ -262,6 +262,8 @@ static struct ggml_backend_i blas_backend_i = {
     /* .get_name                = */ ggml_backend_blas_get_name,
     /* .free                    = */ ggml_backend_blas_free,
     /* .set_tensor_async        = */ NULL,
+    /* .get_tensor_2d_async     = */ NULL,
+    /* .set_tensor_2d_async     = */ NULL,
     /* .get_tensor_async        = */ NULL,
     /* .cpy_tensor_async        = */ NULL,
     /* .synchronize             = */ NULL,

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

@@ -1457,6 +1457,8 @@ static const ggml_backend_buffer_i ggml_backend_cann_buffer_interface = {
     /* .memset_tensor   = */ NULL,
     /* .set_tensor      = */ ggml_backend_cann_buffer_set_tensor,
     /* .get_tensor      = */ ggml_backend_cann_buffer_get_tensor,
+    /* .set_tensor_2d   = */ NULL,
+    /* .get_tensor_2d   = */ NULL,
     /* .cpy_tensor      = */ ggml_backend_cann_buffer_cpy_tensor,
     /* .clear           = */ ggml_backend_cann_buffer_clear,
     /* .reset           = */ NULL,
@@ -2698,6 +2700,8 @@ static const ggml_backend_i ggml_backend_cann_interface = {
     /* .free                    = */ ggml_backend_cann_free,
     /* .set_tensor_async        = */ ggml_backend_cann_set_tensor_async,
     /* .get_tensor_async        = */ ggml_backend_cann_get_tensor_async,
+    /* .get_tensor_2d_async     = */ NULL,
+    /* .set_tensor_2d_async     = */ NULL,
     /* .cpy_tensor_async        = */ ggml_backend_cann_cpy_tensor_async,
     /* .synchronize             = */ ggml_backend_cann_synchronize,
     /* .graph_plan_create       = */ NULL,

ggml/src/ggml-cpu/amx/amx.cpp

@@ -111,6 +111,8 @@ static ggml_backend_buffer_i ggml_backend_amx_buffer_interface = {
     /* .memset_tensor   = */ ggml_backend_amx_buffer_memset_tensor,
     /* .set_tensor      = */ ggml_backend_amx_buffer_set_tensor,
     /* .get_tensor      = */ nullptr,
+    /* .set_tensor_2d   = */ nullptr,
+    /* .get_tensor_2d   = */ nullptr,
     /* .cpy_tensor      = */ nullptr,
     /* .clear           = */ ggml_backend_amx_buffer_clear,
     /* .reset           = */ nullptr,

ggml/src/ggml-cpu/arch/arm/quants.c

@@ -783,6 +783,7 @@ void ggml_vec_dot_nvfp4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const vo
         const int8x16_t q4_lo_1 = ggml_vqtbl1q_s8(values, vandq_u8  (q4bits_1, m4b));
         const int8x16_t q4_hi_1 = ggml_vqtbl1q_s8(values, vshrq_n_u8(q4bits_1, 4));
 
+#if defined(__ARM_FEATURE_DOTPROD)
         const int8x16_t q8_0a = vld1q_s8(y[2*ib].qs);
         const int8x16_t q8_0b = vld1q_s8(y[2*ib].qs + 16);
         const int8x16_t q8_lo_0 = vcombine_s8(vget_low_s8(q8_0a), vget_low_s8(q8_0b));
@@ -794,15 +795,40 @@ void ggml_vec_dot_nvfp4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const vo
         const int8x16_t q8_hi_1 = vcombine_s8(vget_high_s8(q8_1a), vget_high_s8(q8_1b));
 
         const int32x4_t p0 = vaddq_s32(
-            ggml_vdotq_s32(vdupq_n_s32(0), q4_lo_0, q8_lo_0),
-            ggml_vdotq_s32(vdupq_n_s32(0), q4_hi_0, q8_hi_0));
+            vdotq_s32(vdupq_n_s32(0), q4_lo_0, q8_lo_0),
+            vdotq_s32(vdupq_n_s32(0), q4_hi_0, q8_hi_0));
         const int32x4_t p1 = vaddq_s32(
-            ggml_vdotq_s32(vdupq_n_s32(0), q4_lo_1, q8_lo_1),
-            ggml_vdotq_s32(vdupq_n_s32(0), q4_hi_1, q8_hi_1));
+            vdotq_s32(vdupq_n_s32(0), q4_lo_1, q8_lo_1),
+            vdotq_s32(vdupq_n_s32(0), q4_hi_1, q8_hi_1));
 
-        const int32x4_t sums = vpaddq_s32(p0, p1);
+        const int32x4_t sumi = vpaddq_s32(p0, p1);
+#else
+        const int8x8_t q4_0_lo = vget_low_s8(q4_lo_0);
+        const int8x8_t q4_0_hi = vget_low_s8(q4_hi_0);
+        const int8x8_t q4_1_lo = vget_high_s8(q4_lo_0);
+        const int8x8_t q4_1_hi = vget_high_s8(q4_hi_0);
+        const int8x8_t q4_2_lo = vget_low_s8(q4_lo_1);
+        const int8x8_t q4_2_hi = vget_low_s8(q4_hi_1);
+        const int8x8_t q4_3_lo = vget_high_s8(q4_lo_1);
+        const int8x8_t q4_3_hi = vget_high_s8(q4_hi_1);
+
+        const int8x8_t q8_0_lo = vld1_s8(y[2*ib].qs);
+        const int8x8_t q8_0_hi = vld1_s8(y[2*ib].qs + 8);
+        const int8x8_t q8_1_lo = vld1_s8(y[2*ib].qs + 16);
+        const int8x8_t q8_1_hi = vld1_s8(y[2*ib].qs + 24);
+        const int8x8_t q8_2_lo = vld1_s8(y[2*ib+1].qs);
+        const int8x8_t q8_2_hi = vld1_s8(y[2*ib+1].qs + 8);
+        const int8x8_t q8_3_lo = vld1_s8(y[2*ib+1].qs + 16);
+        const int8x8_t q8_3_hi = vld1_s8(y[2*ib+1].qs + 24);
+
+        const int32x4_t sumi = (int32x4_t){
+            vaddvq_s32(ggml_nvfp4_dot8(q4_0_lo, q8_0_lo, q4_0_hi, q8_0_hi)),
+            vaddvq_s32(ggml_nvfp4_dot8(q4_1_lo, q8_1_lo, q4_1_hi, q8_1_hi)),
+            vaddvq_s32(ggml_nvfp4_dot8(q4_2_lo, q8_2_lo, q4_2_hi, q8_2_hi)),
+            vaddvq_s32(ggml_nvfp4_dot8(q4_3_lo, q8_3_lo, q4_3_hi, q8_3_hi)),
+        };
+#endif
 
-        // Decode 4 UE4M3 scales to f32 and multiply with q8 scales
         const float dy0 = GGML_CPU_FP16_TO_FP32(y[2*ib].d);
         const float dy1 = GGML_CPU_FP16_TO_FP32(y[2*ib+1].d);
         const float32x4_t nvsc = {
@@ -813,7 +839,7 @@ void ggml_vec_dot_nvfp4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const vo
         };
         const float32x4_t scales = vmulq_f32(nvsc, (float32x4_t){dy0, dy0, dy1, dy1});
 
-        acc = vfmaq_f32(acc, vcvtq_f32_s32(sums), scales);
+        acc = vfmaq_f32(acc, vcvtq_f32_s32(sumi), scales);
     }
     sumf = vaddvq_f32(acc);
 #else

ggml/src/ggml-cpu/ggml-cpu-impl.h

@@ -306,6 +306,7 @@ inline static uint8x16_t ggml_vqtbl1q_u8(uint8x16_t a, uint8x16_t b) {
 
 #if !defined(__ARM_FEATURE_DOTPROD)
 
+// NOTE: this fallback produces the same total sum as native vdotq_s32 but with different per-lane grouping — do not use when individual lane values matter.
 inline static int32x4_t ggml_vdotq_s32(int32x4_t acc, int8x16_t a, int8x16_t b) {
     const int16x8_t p0 = vmull_s8(vget_low_s8 (a), vget_low_s8 (b));
     const int16x8_t p1 = vmull_s8(vget_high_s8(a), vget_high_s8(b));
@@ -319,6 +320,15 @@ inline static int32x4_t ggml_vdotq_s32(int32x4_t acc, int8x16_t a, int8x16_t b)
 
 #endif // !defined(__ARM_FEATURE_DOTPROD)
 
+static inline int32x4_t ggml_nvfp4_dot8(const int8x8_t q4_lo, const int8x8_t q8_lo,
+                                         const int8x8_t q4_hi, const int8x8_t q8_hi) {
+    const int16x8_t p_lo = vmull_s8(q4_lo, q8_lo);
+    const int16x8_t p_hi = vmull_s8(q4_hi, q8_hi);
+    const int32x4_t sum_lo = vpaddlq_s16(p_lo);
+    const int32x4_t sum_hi = vpaddlq_s16(p_hi);
+    return vaddq_s32(sum_lo, sum_hi);
+}
+
 #endif // defined(__ARM_NEON)
 
 #ifdef __wasm_simd128__

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

@@ -195,6 +195,8 @@ static const struct ggml_backend_i ggml_backend_cpu_i = {
     /* .free                    = */ ggml_backend_cpu_free,
     /* .set_tensor_async        = */ NULL,
     /* .get_tensor_async        = */ NULL,
+    /* .get_tensor_2d_async     = */ NULL,
+    /* .set_tensor_2d_async     = */ NULL,
     /* .cpy_tensor_async        = */ NULL,
     /* .synchronize             = */ NULL,
     /* .graph_plan_create       = */ ggml_backend_cpu_graph_plan_create,

ggml/src/ggml-cpu/ops.cpp

@@ -664,6 +664,7 @@ void ggml_compute_forward_add(
             {
                 ggml_compute_forward_add_non_quantized(params, dst);
             } break;
+        case GGML_TYPE_Q1_0:
         case GGML_TYPE_Q4_0:
         case GGML_TYPE_Q4_1:
         case GGML_TYPE_Q5_0:
@@ -1113,6 +1114,7 @@ void ggml_compute_forward_add1(
                     GGML_ABORT("fatal error");
                 }
             } break;
+        case GGML_TYPE_Q1_0:
         case GGML_TYPE_Q4_0:
         case GGML_TYPE_Q4_1:
         case GGML_TYPE_Q5_0:
@@ -1242,6 +1244,7 @@ void ggml_compute_forward_acc(
             } break;
         case GGML_TYPE_F16:
         case GGML_TYPE_BF16:
+        case GGML_TYPE_Q1_0:
         case GGML_TYPE_Q4_0:
         case GGML_TYPE_Q4_1:
         case GGML_TYPE_Q5_0:
@@ -4331,6 +4334,7 @@ void ggml_compute_forward_out_prod(
     const ggml_tensor * src0 = dst->src[0];
 
     switch (src0->type) {
+        case GGML_TYPE_Q1_0:
         case GGML_TYPE_Q4_0:
         case GGML_TYPE_Q4_1:
         case GGML_TYPE_Q5_0:
@@ -4606,6 +4610,7 @@ void ggml_compute_forward_set(
             } break;
         case GGML_TYPE_F16:
         case GGML_TYPE_BF16:
+        case GGML_TYPE_Q1_0:
         case GGML_TYPE_Q4_0:
         case GGML_TYPE_Q4_1:
         case GGML_TYPE_Q5_0:

ggml/src/ggml-cuda/CMakeLists.txt

@@ -181,6 +181,16 @@ if (CUDAToolkit_FOUND)
         target_link_libraries(ggml-cuda PRIVATE CUDA::cuda_driver)
     endif()
 
+    if (GGML_CUDA_NCCL)
+        find_package(NCCL)
+        if (NCCL_FOUND)
+            add_compile_definitions(GGML_USE_NCCL)
+            target_link_libraries(ggml-cuda PRIVATE NCCL::NCCL)
+        else()
+            message(STATUS "Warning: NCCL not found, performance for multiple CUDA GPUs will be suboptimal")
+        endif()
+    endif()
+
     set(CUDA_CXX_FLAGS "")
 
     set(CUDA_FLAGS -use_fast_math -extended-lambda)

ggml/src/ggml-cuda/argsort.cu

@@ -58,26 +58,48 @@ void argsort_f32_i32_cuda_cub(ggml_cuda_pool & pool,
 
     size_t temp_storage_bytes = 0;
 
+    bool is_capturing = false;
+#ifdef USE_CUDA_GRAPH
+    // Currently (confirmed for CCCL <= 3.2) DeviceSegmentedSort does not support stream capture, while DeviceSegmentedRadixSort does.
+    // See https://github.com/NVIDIA/cccl/issues/5661#issuecomment-3229037149
+    // TODO: constrain this to the CCCL versions that have this issue once it's resolved in a future CCCL release.
+    cudaStreamCaptureStatus capture_status;
+    CUDA_CHECK(cudaStreamIsCapturing(stream, &capture_status));
+    is_capturing = (capture_status != cudaStreamCaptureStatusNone);
+#endif  // USE_CUDA_GRAPH
+
     if (order == GGML_SORT_ORDER_ASC) {
         if (nrows == 1) {
-            DeviceRadixSort::SortPairs(nullptr, temp_storage_bytes, temp_keys, temp_keys,  // keys (in-place)
-                                       temp_indices, dst,                                  // values (indices)
-                                       ncols, 0, sizeof(float) * 8, stream);
+            CUDA_CHECK(DeviceRadixSort::SortPairs(nullptr, temp_storage_bytes, temp_keys, temp_keys,  // keys (in-place)
+                                                  temp_indices, dst,  // values (indices)
+                                                  ncols, 0, sizeof(float) * 8, stream));
+        } else if (is_capturing) {
+            CUDA_CHECK(DeviceSegmentedRadixSort::SortPairs(
+                nullptr, temp_storage_bytes, temp_keys, temp_keys,  // keys (in-place)
+                temp_indices, dst,                                  // values (indices)
+                ncols * nrows, nrows,                               // num items, num segments
+                offset_iterator, offset_iterator + 1, 0, sizeof(float) * 8, stream));
         } else {
-            DeviceSegmentedSort::SortPairs(nullptr, temp_storage_bytes, temp_keys, temp_keys,  // keys (in-place)
-                                           temp_indices, dst,                                  // values (indices)
-                                           ncols * nrows, nrows,  // num items, num segments
-                                           offset_iterator, offset_iterator + 1, stream);
+            CUDA_CHECK(DeviceSegmentedSort::SortPairs(nullptr, temp_storage_bytes, temp_keys,
+                                                      temp_keys,             // keys (in-place)
+                                                      temp_indices, dst,     // values (indices)
+                                                      ncols * nrows, nrows,  // num items, num segments
+                                                      offset_iterator, offset_iterator + 1, stream));
         }
     } else {
         if (nrows == 1) {
-            DeviceRadixSort::SortPairsDescending(nullptr, temp_storage_bytes, temp_keys, temp_keys,  // keys (in-place)
-                                                 temp_indices, dst,                                  // values (indices)
-                                                 ncols, 0, sizeof(float) * 8, stream);
+            CUDA_CHECK(DeviceRadixSort::SortPairsDescending(nullptr, temp_storage_bytes, temp_keys,
+                                                            temp_keys,          // keys (in-place)
+                                                            temp_indices, dst,  // values (indices)
+                                                            ncols, 0, sizeof(float) * 8, stream));
+        } else if (is_capturing) {
+            CUDA_CHECK(DeviceSegmentedRadixSort::SortPairsDescending(
+                nullptr, temp_storage_bytes, temp_keys, temp_keys, temp_indices, dst, ncols * nrows, nrows,
+                offset_iterator, offset_iterator + 1, 0, sizeof(float) * 8, stream));
         } else {
-            DeviceSegmentedSort::SortPairsDescending(nullptr, temp_storage_bytes, temp_keys, temp_keys, temp_indices,
-                                                     dst, ncols * nrows, nrows, offset_iterator, offset_iterator + 1,
-                                                     stream);
+            CUDA_CHECK(DeviceSegmentedSort::SortPairsDescending(nullptr, temp_storage_bytes, temp_keys, temp_keys,
+                                                                temp_indices, dst, ncols * nrows, nrows,
+                                                                offset_iterator, offset_iterator + 1, stream));
         }
     }
 
@@ -86,22 +108,33 @@ void argsort_f32_i32_cuda_cub(ggml_cuda_pool & pool,
 
     if (order == GGML_SORT_ORDER_ASC) {
         if (nrows == 1) {
-            DeviceRadixSort::SortPairs(d_temp_storage, temp_storage_bytes, temp_keys, temp_keys,  // keys (in-place)
-                                       temp_indices, dst,  // values (indices)
-                                       ncols, 0, sizeof(float) * 8, stream);
+            CUDA_CHECK(DeviceRadixSort::SortPairs(d_temp_storage, temp_storage_bytes, temp_keys,
+                                                  temp_keys,          // keys (in-place)
+                                                  temp_indices, dst,  // values (indices)
+                                                  ncols, 0, sizeof(float) * 8, stream));
+        } else if (is_capturing) {
+            CUDA_CHECK(DeviceSegmentedRadixSort::SortPairs(d_temp_storage, temp_storage_bytes, temp_keys, temp_keys,
+                                                           temp_indices, dst, ncols * nrows, nrows, offset_iterator,
+                                                           offset_iterator + 1, 0, sizeof(float) * 8, stream));
         } else {
-            DeviceSegmentedSort::SortPairs(d_temp_storage, temp_storage_bytes, temp_keys, temp_keys, temp_indices, dst,
-                                           ncols * nrows, nrows, offset_iterator, offset_iterator + 1, stream);
+            CUDA_CHECK(DeviceSegmentedSort::SortPairs(d_temp_storage, temp_storage_bytes, temp_keys, temp_keys,
+                                                      temp_indices, dst, ncols * nrows, nrows, offset_iterator,
+                                                      offset_iterator + 1, stream));
         }
     } else {
         if (nrows == 1) {
-            DeviceRadixSort::SortPairsDescending(d_temp_storage, temp_storage_bytes, temp_keys, temp_keys,  // keys (in-place)
-                                                 temp_indices, dst,                                  // values (indices)
-                                                 ncols, 0, sizeof(float) * 8, stream);
+            CUDA_CHECK(DeviceRadixSort::SortPairsDescending(d_temp_storage, temp_storage_bytes, temp_keys,
+                                                            temp_keys,          // keys (in-place)
+                                                            temp_indices, dst,  // values (indices)
+                                                            ncols, 0, sizeof(float) * 8, stream));
+        } else if (is_capturing) {
+            CUDA_CHECK(DeviceSegmentedRadixSort::SortPairsDescending(
+                d_temp_storage, temp_storage_bytes, temp_keys, temp_keys, temp_indices, dst, ncols * nrows, nrows,
+                offset_iterator, offset_iterator + 1, 0, sizeof(float) * 8, stream));
         } else {
-            DeviceSegmentedSort::SortPairsDescending(d_temp_storage, temp_storage_bytes, temp_keys, temp_keys,
-                                                     temp_indices, dst, ncols * nrows, nrows, offset_iterator,
-                                                     offset_iterator + 1, stream);
+            CUDA_CHECK(DeviceSegmentedSort::SortPairsDescending(d_temp_storage, temp_storage_bytes, temp_keys,
+                                                                temp_keys, temp_indices, dst, ncols * nrows, nrows,
+                                                                offset_iterator, offset_iterator + 1, stream));
         }
     }
 }

ggml/src/ggml-cuda/binbcast.cu

@@ -472,6 +472,36 @@ void ggml_cuda_op_fused_add(ggml_backend_cuda_context & ctx, ggml_tensor * dst,
     }
 }
 
+void ggml_cuda_op_fused_mul(ggml_backend_cuda_context & ctx, ggml_tensor * dst, int n_fuse) {
+    GGML_ASSERT(2 <= n_fuse && n_fuse <= 8);
+
+    switch (n_fuse) {
+        case 2:
+            ggml_cuda_op_fused_binbcast_impl<op_mul, 2>(ctx, dst);
+            break;
+        case 3:
+            ggml_cuda_op_fused_binbcast_impl<op_mul, 3>(ctx, dst);
+            break;
+        case 4:
+            ggml_cuda_op_fused_binbcast_impl<op_mul, 4>(ctx, dst);
+            break;
+        case 5:
+            ggml_cuda_op_fused_binbcast_impl<op_mul, 5>(ctx, dst);
+            break;
+        case 6:
+            ggml_cuda_op_fused_binbcast_impl<op_mul, 6>(ctx, dst);
+            break;
+        case 7:
+            ggml_cuda_op_fused_binbcast_impl<op_mul, 7>(ctx, dst);
+            break;
+        case 8:
+            ggml_cuda_op_fused_binbcast_impl<op_mul, 8>(ctx, dst);
+            break;
+        default:
+            GGML_ASSERT(false && "Unsupported n_fuse value");
+    }
+}
+
 void ggml_cuda_op_repeat_back(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
     const ggml_tensor * src0 = dst->src[0];
 

ggml/src/ggml-cuda/binbcast.cuh

@@ -9,3 +9,4 @@ void ggml_cuda_op_div(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
 void ggml_cuda_op_repeat_back(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
 
 void ggml_cuda_op_fused_add(ggml_backend_cuda_context & ctx, ggml_tensor * dst, int n_fuse);
+void ggml_cuda_op_fused_mul(ggml_backend_cuda_context & ctx, ggml_tensor * dst, int n_fuse);

ggml/src/ggml-cuda/common.cuh

@@ -67,6 +67,7 @@
 #define GGML_CUDA_CC_CDNA1      (GGML_CUDA_CC_OFFSET_AMD + 0x908)  // MI100, minimum for MFMA, acc registers
 #define GGML_CUDA_CC_CDNA2      (GGML_CUDA_CC_OFFSET_AMD + 0x90a)  // MI210 (gfx90a), minimum acc register renaming
 #define GGML_CUDA_CC_CDNA3      (GGML_CUDA_CC_OFFSET_AMD + 0x942)  // MI300
+#define GGML_CUDA_CC_CDNA4      (GGML_CUDA_CC_OFFSET_AMD + 0x950)  // MI350X/MI355X
 
 // RDNA removes MFMA, dp4a, xnack, acc registers, wave size is 32
 #define GGML_CUDA_CC_RDNA1      (GGML_CUDA_CC_OFFSET_AMD + 0x1010) // RX 5000
@@ -87,7 +88,8 @@
 #define GGML_CUDA_CC_IS_CDNA(cc)    (cc >= GGML_CUDA_CC_CDNA1 && cc < GGML_CUDA_CC_RDNA1)
 #define GGML_CUDA_CC_IS_CDNA1(cc)   (cc >= GGML_CUDA_CC_CDNA1 && cc < GGML_CUDA_CC_CDNA2)
 #define GGML_CUDA_CC_IS_CDNA2(cc)   (cc >= GGML_CUDA_CC_CDNA2 && cc < GGML_CUDA_CC_CDNA3)
-#define GGML_CUDA_CC_IS_CDNA3(cc)   (cc >= GGML_CUDA_CC_CDNA3 && cc < GGML_CUDA_CC_RDNA1)
+#define GGML_CUDA_CC_IS_CDNA3(cc)   (cc >= GGML_CUDA_CC_CDNA3 && cc < GGML_CUDA_CC_CDNA4)
+#define GGML_CUDA_CC_IS_CDNA4(cc)   (cc >= GGML_CUDA_CC_CDNA4 && cc < GGML_CUDA_CC_RDNA1)
 
 // Moore Threads
 #define MUSART_HMASK 40300 // MUSA rc4.3, min. ver. for half2 -> uint mask comparisons
@@ -186,6 +188,10 @@ void ggml_cuda_error(const char * stmt, const char * func, const char * file, in
 
 #define CUBLAS_CHECK(err) CUDA_CHECK_GEN(err, CUBLAS_STATUS_SUCCESS, cublas_get_error_str)
 
+#ifdef GGML_USE_NCCL
+#define NCCL_CHECK(err) CUDA_CHECK_GEN(err, ncclSuccess, ncclGetErrorString)
+#endif // GGML_USE_NCCL
+
 #if !defined(GGML_USE_HIP) && !defined(GGML_CUDA_NO_VMM)
 static const char * cu_get_error_str(CUresult err) {
     const char * err_str;
@@ -918,6 +924,13 @@ struct ggml_cuda_type_traits<GGML_TYPE_F16> {
     static constexpr int qr = 1;
 };
 
+template<>
+struct ggml_cuda_type_traits<GGML_TYPE_Q1_0> {
+    static constexpr int qk = QK1_0;
+    static constexpr int qr = QR1_0;
+    static constexpr int qi = QI1_0;
+};
+
 template<>
 struct ggml_cuda_type_traits<GGML_TYPE_Q4_0> {
     static constexpr int qk = QK4_0;
@@ -1173,7 +1186,13 @@ struct ggml_cuda_graph {
     std::vector<cudaGraphNode_t> nodes;
     bool disable_due_to_gpu_arch = false;
     bool warmup_complete = false;
-    std::vector<ggml_tensor> nodes_copy;
+    struct node_properties {
+        ggml_tensor node;
+        void *   node_src_data_ptrs[GGML_MAX_SRC];
+        int64_t  node_src_ne[GGML_MAX_SRC][GGML_MAX_DIMS];
+        size_t   node_src_nb[GGML_MAX_SRC][GGML_MAX_DIMS];
+    };
+    std::vector<node_properties> node_props;
 
     bool is_enabled() const {
         static const bool disable_cuda_graphs_due_to_env = (getenv("GGML_CUDA_DISABLE_GRAPHS") != nullptr);

ggml/src/ggml-cuda/convert.cu

@@ -711,6 +711,8 @@ to_bf16_cuda_t ggml_get_to_bf16_cuda(ggml_type type) {
 
 to_fp16_cuda_t ggml_get_to_fp16_cuda(ggml_type type) {
     switch (type) {
+        case GGML_TYPE_Q1_0:
+            return dequantize_block_cont_cuda<QK1_0, QR1_0, dequantize_q1_0>;
         case GGML_TYPE_Q4_0:
             return dequantize_row_q4_0_cuda;
         case GGML_TYPE_Q4_1:
@@ -767,6 +769,8 @@ to_fp16_cuda_t ggml_get_to_fp16_cuda(ggml_type type) {
 
 to_fp32_cuda_t ggml_get_to_fp32_cuda(ggml_type type) {
     switch (type) {
+        case GGML_TYPE_Q1_0:
+            return dequantize_block_cont_cuda<QK1_0, QR1_0, dequantize_q1_0>;
         case GGML_TYPE_Q4_0:
             return dequantize_row_q4_0_cuda;
         case GGML_TYPE_Q4_1:
@@ -822,6 +826,8 @@ to_fp16_nc_cuda_t ggml_get_to_fp16_nc_cuda(ggml_type type) {
     switch (type) {
         case GGML_TYPE_F32:
             return convert_unary_cuda<float>;
+        case GGML_TYPE_Q1_0:
+            return dequantize_block_cuda<QK1_0, QR1_0, dequantize_q1_0>;
         case GGML_TYPE_Q4_0:
             return dequantize_block_cuda<QK4_0, QR4_0, dequantize_q4_0>;
         case GGML_TYPE_Q4_1:
@@ -843,6 +849,8 @@ to_bf16_nc_cuda_t ggml_get_to_bf16_nc_cuda(ggml_type type) {
     switch (type) {
         case GGML_TYPE_F32:
             return convert_unary_cuda<float, nv_bfloat16>;
+        case GGML_TYPE_Q1_0:
+            return dequantize_block_cuda<QK1_0, QR1_0, dequantize_q1_0>;
         case GGML_TYPE_Q4_0:
             return dequantize_block_cuda<QK4_0, QR4_0, dequantize_q4_0>;
         case GGML_TYPE_Q4_1:
@@ -864,6 +872,8 @@ to_fp32_nc_cuda_t ggml_get_to_fp32_nc_cuda(ggml_type type) {
     switch (type) {
         case GGML_TYPE_F16:
             return convert_unary_cuda<half, float>;
+        case GGML_TYPE_Q1_0:
+            return dequantize_block_cuda<QK1_0, QR1_0, dequantize_q1_0>;
         case GGML_TYPE_Q4_0:
             return dequantize_block_cuda<QK4_0, QR4_0, dequantize_q4_0>;
         case GGML_TYPE_Q4_1:

ggml/src/ggml-cuda/dequantize.cuh

@@ -1,5 +1,27 @@
 #include "common.cuh"
 
+static __device__ __forceinline__ void dequantize_q1_0(const void * vx, const int64_t ib, const int iqs, float2 & v){
+    const block_q1_0 * x = (const block_q1_0 *) vx;
+
+    const float d = x[ib].d;
+
+    const int bit_index_0 = iqs;
+    const int bit_index_1 = iqs + 1;
+
+    const int byte_index_0 = bit_index_0 / 8;
+    const int bit_offset_0 = bit_index_0 % 8;
+
+    const int byte_index_1 = bit_index_1 / 8;
+    const int bit_offset_1 = bit_index_1 % 8;
+
+    // Extract bits: 1 = +d, 0 = -d (branchless)
+    const int bit_0 = (x[ib].qs[byte_index_0] >> bit_offset_0) & 1;
+    const int bit_1 = (x[ib].qs[byte_index_1] >> bit_offset_1) & 1;
+
+    v.x = (2*bit_0 - 1) * d;
+    v.y = (2*bit_1 - 1) * d;
+}
+
 static __device__ __forceinline__ void dequantize_q4_0(const void * vx, const int64_t ib, const int iqs, float2 & v){
     const block_q4_0 * x = (const block_q4_0 *) vx;
 

ggml/src/ggml-cuda/fattn.cu

@@ -75,13 +75,17 @@ static void ggml_cuda_flash_attn_ext_mma_f16_switch_ncols2(ggml_backend_cuda_con
             return;
         }
 
-        if (use_gqa_opt && gqa_ratio % 2 == 0) {
-            ggml_cuda_flash_attn_ext_mma_f16_switch_ncols1<DKQ, DV, 2>(ctx, dst);
-            return;
-        }
+        if constexpr (DKQ <= 256) {
+            if (use_gqa_opt && gqa_ratio % 2 == 0) {
+                ggml_cuda_flash_attn_ext_mma_f16_switch_ncols1<DKQ, DV, 2>(ctx, dst);
+                return;
+            }
 
-        ggml_cuda_flash_attn_ext_mma_f16_switch_ncols1<DKQ, DV, 1>(ctx, dst);
-        return;
+            ggml_cuda_flash_attn_ext_mma_f16_switch_ncols1<DKQ, DV, 1>(ctx, dst);
+            return;
+        } else {
+            GGML_ABORT("fatal error");
+        }
     }
 
     if (use_gqa_opt && gqa_ratio > 4) {
@@ -94,12 +98,16 @@ static void ggml_cuda_flash_attn_ext_mma_f16_switch_ncols2(ggml_backend_cuda_con
         return;
     }
 
-    if (use_gqa_opt && gqa_ratio > 1) {
-        ggml_cuda_flash_attn_ext_mma_f16_switch_ncols1<DKQ, DV, 2>(ctx, dst);
-        return;
-    }
+    if constexpr (DKQ <= 256) {
+        if (use_gqa_opt && gqa_ratio > 1) {
+            ggml_cuda_flash_attn_ext_mma_f16_switch_ncols1<DKQ, DV, 2>(ctx, dst);
+            return;
+        }
 
-    ggml_cuda_flash_attn_ext_mma_f16_switch_ncols1<DKQ, DV, 1>(ctx, dst);
+        ggml_cuda_flash_attn_ext_mma_f16_switch_ncols1<DKQ, DV, 1>(ctx, dst);
+    } else {
+        GGML_ABORT("fatal error");
+    }
 }
 
 static void ggml_cuda_flash_attn_ext_mma_f16(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {

ggml/src/ggml-cuda/getrows.cu

@@ -179,6 +179,10 @@ static void ggml_cuda_get_rows_switch_src0_type(
             get_rows_cuda_float((const nv_bfloat16 *) src0_d, src1_d, dst_d,
                 ne00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb1, nb2, nb3, stream);
             break;
+        case GGML_TYPE_Q1_0:
+            get_rows_cuda_q<QK1_0, QR1_0, dequantize_q1_0>(src0_d, src1_d, dst_d,
+                ne00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb1, nb2, nb3, stream);
+            break;
         case GGML_TYPE_Q4_0:
             get_rows_cuda_q<QK4_0, QR4_0, dequantize_q4_0>(src0_d, src1_d, dst_d,
                 ne00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb1, nb2, nb3, stream);

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

@@ -324,6 +324,22 @@ static ggml_cuda_device_info ggml_cuda_init() {
     // configure logging to stdout
     // CUBLAS_CHECK(cublasLoggerConfigure(1, 1, 0, nullptr));
 
+    if (getenv("GGML_CUDA_P2P") != nullptr) {
+        for (int id = 0; id < info.device_count; ++id) {
+            ggml_cuda_set_device(id);
+            for (int id_other = 0; id_other < info.device_count; ++id_other) {
+                if (id == id_other) {
+                    continue;
+                }
+                int can_access_peer;
+                CUDA_CHECK(cudaDeviceCanAccessPeer(&can_access_peer, id, id_other));
+                if (can_access_peer) {
+                    CUDA_CHECK(cudaDeviceEnablePeerAccess(id_other, 0));
+                }
+            }
+        }
+    }
+
     return info;
 }
 
@@ -632,26 +648,46 @@ static enum ggml_status ggml_backend_cuda_buffer_init_tensor(ggml_backend_buffer
 }
 
 static void ggml_backend_cuda_buffer_memset_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, uint8_t value, size_t offset, size_t size) {
-    ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context;
+    ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *) buffer->context;
 
     ggml_cuda_set_device(ctx->device);
-    CUDA_CHECK(cudaMemsetAsync((char *)tensor->data + offset, value, size, cudaStreamPerThread));
+    CUDA_CHECK(cudaMemsetAsync((char *) tensor->data + offset, value, size, cudaStreamPerThread));
     CUDA_CHECK(cudaStreamSynchronize(cudaStreamPerThread));
 }
 
 static void ggml_backend_cuda_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
-    ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context;
+    ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *) buffer->context;
 
     ggml_cuda_set_device(ctx->device);
-    CUDA_CHECK(cudaMemcpyAsync((char *)tensor->data + offset, data, size, cudaMemcpyHostToDevice, cudaStreamPerThread));
+    CUDA_CHECK(cudaMemcpyAsync((char *) tensor->data + offset, data, size, cudaMemcpyHostToDevice, cudaStreamPerThread));
     CUDA_CHECK(cudaStreamSynchronize(cudaStreamPerThread));
 }
 
 static void ggml_backend_cuda_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) {
+    ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *) buffer->context;
+
+    ggml_cuda_set_device(ctx->device);
+    CUDA_CHECK(cudaMemcpyAsync(data, (const char *) tensor->data + offset, size, cudaMemcpyDeviceToHost, cudaStreamPerThread));
+    CUDA_CHECK(cudaStreamSynchronize(cudaStreamPerThread));
+}
+
+static void ggml_backend_cuda_buffer_set_tensor_2d(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, const void * data,
+        size_t offset, size_t size, size_t n_copies, size_t stride_tensor, size_t stride_data) {
+    ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *) buffer->context;
+
+    ggml_cuda_set_device(ctx->device);
+    CUDA_CHECK(cudaMemcpy2DAsync(
+        (char *) tensor->data + offset, stride_tensor, data, stride_data, size, n_copies, cudaMemcpyHostToDevice, cudaStreamPerThread));
+    CUDA_CHECK(cudaStreamSynchronize(cudaStreamPerThread));
+}
+
+static void ggml_backend_cuda_buffer_get_tensor_2d(ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor, void * data,
+        size_t offset, size_t size, size_t n_copies, size_t stride_tensor, size_t stride_data) {
     ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context;
 
     ggml_cuda_set_device(ctx->device);
-    CUDA_CHECK(cudaMemcpyAsync(data, (const char *)tensor->data + offset, size, cudaMemcpyDeviceToHost, cudaStreamPerThread));
+    CUDA_CHECK(cudaMemcpy2DAsync(
+        data, stride_data, (const char *) tensor->data + offset, stride_tensor, size, n_copies, cudaMemcpyDeviceToHost, cudaStreamPerThread));
     CUDA_CHECK(cudaStreamSynchronize(cudaStreamPerThread));
 }
 
@@ -691,6 +727,8 @@ static const ggml_backend_buffer_i ggml_backend_cuda_buffer_interface = {
     /* .memset_tensor   = */ ggml_backend_cuda_buffer_memset_tensor,
     /* .set_tensor      = */ ggml_backend_cuda_buffer_set_tensor,
     /* .get_tensor      = */ ggml_backend_cuda_buffer_get_tensor,
+    /* .set_tensor_2d   = */ ggml_backend_cuda_buffer_set_tensor_2d,
+    /* .get_tensor_2d   = */ ggml_backend_cuda_buffer_get_tensor_2d,
     /* .cpy_tensor      = */ ggml_backend_cuda_buffer_cpy_tensor,
     /* .clear           = */ ggml_backend_cuda_buffer_clear,
     /* .reset           = */ NULL,
@@ -1003,6 +1041,8 @@ static const ggml_backend_buffer_i ggml_backend_cuda_split_buffer_interface = {
     /* .memset_tensor   = */ NULL,
     /* .set_tensor      = */ ggml_backend_cuda_split_buffer_set_tensor,
     /* .get_tensor      = */ ggml_backend_cuda_split_buffer_get_tensor,
+    /* .set_tensor_2d   = */ NULL,
+    /* .get_tensor_2d   = */ NULL,
     /* .cpy_tensor      = */ NULL,
     /* .clear           = */ ggml_backend_cuda_split_buffer_clear,
     /* .reset           = */ NULL,
@@ -1079,6 +1119,137 @@ static const ggml_backend_buffer_type_i ggml_backend_cuda_split_buffer_type_inte
     /* .is_host          = */ ggml_backend_cuda_split_buffer_type_is_host,
 };
 
+#ifdef GGML_USE_NCCL
+struct ggml_backend_cuda_comm_context {
+    std::vector<ggml_backend_t> backends;
+    std::vector<ncclComm_t> comms;
+
+    ~ggml_backend_cuda_comm_context() {
+        for (ncclComm_t comm : comms) {
+            NCCL_CHECK(ncclCommDestroy(comm));
+        }
+    }
+};
+#endif // GGML_USE_NCCL
+
+static void ggml_backend_cuda_comm_free(void * comm_ctx_v) {
+#ifdef GGML_USE_NCCL
+    if (comm_ctx_v == nullptr) {
+        return;
+    }
+    ggml_backend_cuda_comm_context * comm_ctx = (ggml_backend_cuda_comm_context *) comm_ctx_v;
+    delete comm_ctx;
+#else
+    GGML_UNUSED(comm_ctx_v);
+#endif // GGML_USE_NCCL
+}
+
+static void * ggml_backend_cuda_comm_init(ggml_backend_t * backends, size_t n_backends) {
+#ifdef GGML_USE_NCCL
+    for (size_t i = 0; i < n_backends; i++) {
+        if (!ggml_backend_is_cuda(backends[i])) {
+            return nullptr;
+        }
+    }
+    ggml_backend_cuda_comm_context * ret = new ggml_backend_cuda_comm_context;
+    std::vector<int> dev_ids;
+    ret->backends.reserve(n_backends);
+    dev_ids.reserve(n_backends);
+    for (size_t i = 0; i < n_backends; i++) {
+        ret->backends.push_back(backends[i]);
+        ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *) backends[i]->context;
+        dev_ids.push_back(cuda_ctx->device);
+    }
+
+    ret->comms.resize(n_backends);
+    NCCL_CHECK(ncclCommInitAll(ret->comms.data(), n_backends, dev_ids.data()));
+    return ret;
+#else
+    // If NCCL is installed it is used by default for optimal performance.
+    // However, NVIDIA does not distribute NCCL with CUDA so users may be unwittingly missing this package.
+    // RCCL is disabled by default, users are explicitly opting in.
+    // Therefore print no warning for RCCL.
+#if !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)
+    static bool warning_printed = false;
+    if (!warning_printed) {
+        GGML_LOG_WARN("%s: NVIDIA Collective Communications Library (NCCL) is unavailable, multi GPU performance will be suboptimal\n", __func__);
+        warning_printed = true;
+    }
+#endif // !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)
+    GGML_UNUSED_VARS(backends, n_backends);
+    return nullptr;
+#endif // GGML_USE_NCCL
+}
+
+static bool ggml_backend_cuda_comm_allreduce_tensor(void * comm_ctx_v, struct ggml_tensor ** tensors) {
+#ifdef GGML_USE_NCCL
+    const int64_t ne = ggml_nelements(tensors[0]);
+    // FIXME the input of llm_graph_context::build_in_out_ids can produce a tensor with 0 elements if n_outputs == 0
+    // This then causes a crash in this function
+    if (ne == 0) {
+        return true;
+    }
+
+    GGML_ASSERT(comm_ctx_v != nullptr);
+    ggml_backend_cuda_comm_context * comm_ctx = (ggml_backend_cuda_comm_context *) comm_ctx_v;
+    const size_t n_backends = comm_ctx->backends.size();
+
+    for (size_t i = 0; i < n_backends; ++i) {
+        GGML_ASSERT(tensors[i] != nullptr);
+        GGML_ASSERT(ggml_nelements(tensors[i]) == ne);
+        GGML_ASSERT(ggml_is_contiguously_allocated(tensors[i]));
+    }
+
+    // For small tensors, simply reduce them as FP32.
+    // The following heuristic for how "small" a tensor should be is based on RTX 4090s connected via 16x PCIe 4.0.
+    if ((n_backends <= 2 && ne < 32768) || (n_backends == 3 && ne < 131072) || (n_backends >= 4 && ne < 262144)) {
+        NCCL_CHECK(ncclGroupStart());
+        for (size_t i = 0; i < n_backends; ++i) {
+            ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *) comm_ctx->backends[i]->context;
+            NCCL_CHECK(ncclAllReduce(tensors[i]->data, tensors[i]->data, ne, ncclFloat, ncclSum, comm_ctx->comms[i], cuda_ctx->stream()));
+        }
+        NCCL_CHECK(ncclGroupEnd());
+
+        return true;
+    }
+
+    // For large tensors it's faster to compress them to BF16 for the reduction:
+    to_bf16_cuda_t to_bf16 = ggml_get_to_bf16_cuda(GGML_TYPE_F32);
+    to_fp32_cuda_t to_fp32 = ggml_get_to_fp32_cuda(GGML_TYPE_BF16);
+
+    ggml_cuda_pool_alloc<nv_bfloat16> tmp[GGML_CUDA_MAX_DEVICES];
+    for (size_t i = 0; i < n_backends; ++i) {
+        ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *) comm_ctx->backends[i]->context;
+        tmp[i].pool = &cuda_ctx->pool();
+        tmp[i].alloc(ne);
+
+        ggml_cuda_set_device(cuda_ctx->device);
+        to_bf16(tensors[i]->data, tmp[i].get(), ne, cuda_ctx->stream());
+        CUDA_CHECK(cudaGetLastError());
+    }
+
+    NCCL_CHECK(ncclGroupStart());
+    for (size_t i = 0; i < n_backends; ++i) {
+        ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *) comm_ctx->backends[i]->context;
+        NCCL_CHECK(ncclAllReduce(tmp[i].get(), tmp[i].get(), ne, ncclBfloat16, ncclSum, comm_ctx->comms[i], cuda_ctx->stream()));
+    }
+    NCCL_CHECK(ncclGroupEnd());
+
+    for (size_t i = 0; i < n_backends; ++i) {
+        ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *) comm_ctx->backends[i]->context;
+
+        ggml_cuda_set_device(cuda_ctx->device);
+        to_fp32(tmp[i].get(), (float *) tensors[i]->data, ne, cuda_ctx->stream());
+        CUDA_CHECK(cudaGetLastError());
+    }
+
+    return true;
+#else
+    GGML_UNUSED_VARS(comm_ctx_v, tensors);
+    return false;
+#endif // GGML_USE_NCCL
+}
+
 ggml_backend_buffer_type_t ggml_backend_cuda_split_buffer_type(int main_device, const float * tensor_split) {
     static std::mutex mutex;
     std::lock_guard<std::mutex> lock(mutex);
@@ -1425,64 +1596,6 @@ static void ggml_cuda_op_mul_mat_cublas(
     GGML_UNUSED_VARS(dst, src1_ddq_i, src1_padded_row_size);
 }
 
-static void ggml_cuda_set_peer_access(const int n_tokens, int main_device) {
-    static bool peer_access_enabled = false;
-
-    const bool enable_peer_access = n_tokens <= GGML_CUDA_PEER_MAX_BATCH_SIZE;
-
-    if (peer_access_enabled == enable_peer_access) {
-        return;
-    }
-
-#ifdef NDEBUG
-    for (int id = 0; id < ggml_backend_cuda_get_device_count(); ++id) {
-        ggml_cuda_set_device(id);
-        CUDA_CHECK(cudaDeviceSynchronize());
-    }
-
-    for (int id = 0; id < ggml_backend_cuda_get_device_count(); ++id) {
-        ggml_cuda_set_device(id);
-
-        for (int id_other = 0; id_other < ggml_backend_cuda_get_device_count(); ++id_other) {
-            if (id == id_other) {
-                continue;
-            }
-            if (id != main_device && id_other != main_device) {
-                continue;
-            }
-
-            int can_access_peer;
-            CUDA_CHECK(cudaDeviceCanAccessPeer(&can_access_peer, id, id_other));
-            if (can_access_peer) {
-                if (enable_peer_access) {
-                    cudaError_t err = cudaDeviceEnablePeerAccess(id_other, 0);
-                    if (err != cudaErrorPeerAccessAlreadyEnabled) {
-                        CUDA_CHECK(err);
-                    } else {
-                        // reset the error
-                        (void)cudaGetLastError();
-                    }
-                } else {
-                    cudaError_t err = cudaDeviceDisablePeerAccess(id_other);
-                    if (err != cudaErrorPeerAccessNotEnabled) {
-                        CUDA_CHECK(err);
-                    } else {
-                        // reset the error
-                        (void)cudaGetLastError();
-                    }
-                }
-            }
-        }
-    }
-
-    ggml_cuda_set_device(main_device);
-#endif // NDEBUG
-
-    peer_access_enabled = enable_peer_access;
-
-    GGML_UNUSED(main_device);
-}
-
 static cudaError_t ggml_cuda_Memcpy2DPeerAsync(
     void * dst, int dstDevice, size_t dpitch, void * src, int srcDevice, size_t spitch, size_t width, size_t height, cudaStream_t stream) {
 
@@ -2483,11 +2596,6 @@ static void ggml_cuda_mul_mat_id(ggml_backend_cuda_context & ctx, ggml_tensor *
 }
 
 static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct ggml_tensor * dst) {
-    // why is this here instead of mul_mat?
-    if (dst->src[0] != nullptr && ggml_backend_buft_is_cuda_split(dst->src[0]->buffer->buft)) {
-        ggml_cuda_set_peer_access(dst->src[1]->ne[1], ctx.device);
-    }
-
     switch (dst->op) {
         case GGML_OP_ARGMAX:
             ggml_cuda_argmax(ctx, dst);
@@ -2845,21 +2953,43 @@ static void ggml_backend_cuda_free(ggml_backend_t backend) {
 }
 
 static void ggml_backend_cuda_set_tensor_async(ggml_backend_t backend, ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
-    ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context;
+    ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *) backend->context;
     ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer;
 
     GGML_ASSERT(buf->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) && "unsupported buffer type");
 
-    CUDA_CHECK(cudaMemcpyAsync((char *)tensor->data + offset, data, size, cudaMemcpyHostToDevice, cuda_ctx->stream()));
+    CUDA_CHECK(cudaMemcpyAsync((char *) tensor->data + offset, data, size, cudaMemcpyHostToDevice, cuda_ctx->stream()));
 }
 
 static void ggml_backend_cuda_get_tensor_async(ggml_backend_t backend, const ggml_tensor * tensor, void * data, size_t offset, size_t size) {
-    ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context;
+    ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *) backend->context;
     ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer;
 
     GGML_ASSERT(buf->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) && "unsupported buffer type");
 
-    CUDA_CHECK(cudaMemcpyAsync(data, (const char *)tensor->data + offset, size, cudaMemcpyDeviceToHost, cuda_ctx->stream()));
+    CUDA_CHECK(cudaMemcpyAsync(data, (const char *) tensor->data + offset, size, cudaMemcpyDeviceToHost, cuda_ctx->stream()));
+}
+
+static void ggml_backend_cuda_set_tensor_2d_async(ggml_backend_t backend, struct ggml_tensor * tensor, const void * data,
+        size_t offset, size_t size, size_t n_copies, size_t stride_tensor, size_t stride_data) {
+    ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *) backend->context;
+    ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer;
+
+    GGML_ASSERT(buf->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) && "unsupported buffer type");
+
+    CUDA_CHECK(cudaMemcpy2DAsync(
+        (char *) tensor->data + offset, stride_tensor, data, stride_data, size, n_copies, cudaMemcpyHostToDevice, cuda_ctx->stream()));
+}
+
+static void ggml_backend_cuda_get_tensor_2d_async(ggml_backend_t backend, const struct ggml_tensor * tensor, void * data,
+        size_t offset, size_t size, size_t n_copies, size_t stride_tensor, size_t stride_data) {
+    ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *) backend->context;
+    ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer;
+
+    GGML_ASSERT(buf->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) && "unsupported buffer type");
+
+    CUDA_CHECK(cudaMemcpy2DAsync(
+        data, stride_data, (const char *) tensor->data + offset, stride_tensor, size, n_copies, cudaMemcpyDeviceToHost, cuda_ctx->stream()));
 }
 
 static bool ggml_backend_cuda_cpy_tensor_async(ggml_backend_t backend_src, ggml_backend_t backend_dst, const ggml_tensor * src, ggml_tensor * dst) {
@@ -2870,21 +3000,21 @@ static bool ggml_backend_cuda_cpy_tensor_async(ggml_backend_t backend_src, ggml_
         return false;
     }
 
-    if (!ggml_backend_buffer_is_cuda(src->buffer) || !ggml_backend_buffer_is_cuda(dst->buffer)) {
+    if (!ggml_backend_buffer_is_cuda(buf_src) || !ggml_backend_buffer_is_cuda(buf_dst)) {
         return false;
     }
 
     // device -> device copy
-    ggml_backend_cuda_context * cuda_ctx_src = (ggml_backend_cuda_context *)backend_src->context;
-    ggml_backend_cuda_context * cuda_ctx_dst = (ggml_backend_cuda_context *)backend_dst->context;
+    ggml_backend_cuda_context * cuda_ctx_src = (ggml_backend_cuda_context *) backend_src->context;
+    ggml_backend_cuda_context * cuda_ctx_dst = (ggml_backend_cuda_context *) backend_dst->context;
 
-    ggml_backend_cuda_buffer_context * buf_ctx_src = (ggml_backend_cuda_buffer_context *)buf_src->context;
-    ggml_backend_cuda_buffer_context * buf_ctx_dst = (ggml_backend_cuda_buffer_context *)buf_dst->context;
+    ggml_backend_cuda_buffer_context * buf_ctx_src = (ggml_backend_cuda_buffer_context *) buf_src->context;
+    ggml_backend_cuda_buffer_context * buf_ctx_dst = (ggml_backend_cuda_buffer_context *) buf_dst->context;
 
     if (cuda_ctx_src->device != buf_ctx_src->device || cuda_ctx_dst->device != buf_ctx_dst->device) {
 #ifndef NDEBUG
         GGML_LOG_DEBUG("%s: backend and buffer devices do not match\n", __func__);
-#endif
+#endif // NDEBUG
         return false;
     }
 
@@ -2897,7 +3027,7 @@ static bool ggml_backend_cuda_cpy_tensor_async(ggml_backend_t backend_src, ggml_
             return false;
 #else
             CUDA_CHECK(cudaMemcpyPeerAsync(dst->data, cuda_ctx_dst->device, src->data, cuda_ctx_src->device, ggml_nbytes(dst), cuda_ctx_src->stream()));
-#endif
+#endif // GGML_CUDA_NO_PEER_COPY
         }
 
         // record event on src stream after the copy
@@ -2979,18 +3109,27 @@ static bool ggml_cuda_graph_update_required(ggml_backend_cuda_context * cuda_ctx
     ggml_cuda_graph * graph = cuda_ctx->cuda_graph(graph_key);
 
     // Check if the graph size has changed
-    if ((int)graph->nodes_copy.size() != cgraph->n_nodes) {
+    if ((int)graph->node_props.size() != cgraph->n_nodes) {
         res = true;
-        graph->nodes_copy.resize(cgraph->n_nodes);
+        graph->node_props.resize(cgraph->n_nodes);
     }
 
     for (int i = 0; i < cgraph->n_nodes; i++) {
-        if (!res) {
-            if (memcmp(&graph->nodes_copy[i], cgraph->nodes[i], sizeof(ggml_tensor)) != 0) {
-                res = true;
+        ggml_cuda_graph::node_properties prop = {};
+        memcpy(&prop.node, cgraph->nodes[i], sizeof(ggml_tensor));
+
+        for (int j = 0; j < GGML_MAX_SRC; ++j) {
+            if (cgraph->nodes[i]->src[j]) {
+                prop.node_src_data_ptrs[j] = cgraph->nodes[i]->src[j]->data;
+                memcpy(prop.node_src_ne[j], cgraph->nodes[i]->src[j]->ne, sizeof(prop.node_src_ne[j]));
+                memcpy(prop.node_src_nb[j], cgraph->nodes[i]->src[j]->nb, sizeof(prop.node_src_nb[j]));
             }
         }
-        memcpy(&graph->nodes_copy[i], cgraph->nodes[i], sizeof(ggml_tensor));
+
+        if (res || memcmp(&graph->node_props[i], &prop, sizeof(prop)) != 0) {
+            graph->node_props[i] = prop;
+            res = true;
+        }
     }
 
     return res;
@@ -3669,10 +3808,10 @@ static void ggml_cuda_graph_evaluate_and_capture(ggml_backend_cuda_context * cud
                         continue;
                     }
 
-                    if (node->op == GGML_OP_ADD) {
+                    if (node->op == GGML_OP_ADD || node->op == GGML_OP_MUL) {
                         int n_fuse = 0;
                         ggml_op ops[8];
-                        std::fill(ops, ops + 8, GGML_OP_ADD);
+                        std::fill(ops, ops + 8, node->op);
 
                         for (; n_fuse <= 6; ++n_fuse){
                             if (!ggml_can_fuse(cgraph, i + n_fuse, ops + n_fuse, 2)) {
@@ -3689,13 +3828,17 @@ static void ggml_cuda_graph_evaluate_and_capture(ggml_backend_cuda_context * cud
                         n_fuse++;
 
                         if (n_fuse > 1) {
-                            ggml_tensor fused_add_node;
-                            memcpy(&fused_add_node, node, sizeof(ggml_tensor));
+                            ggml_tensor fused_node;
+                            memcpy(&fused_node, node, sizeof(ggml_tensor));
                             for (int j = 0; j < n_fuse - 1; ++j) {
-                                fused_add_node.src[j + 2] = cgraph->nodes[i + j + 1]->src[1];
+                                fused_node.src[j + 2] = cgraph->nodes[i + j + 1]->src[1];
+                            }
+                            fused_node.data = cgraph->nodes[i + n_fuse - 1]->data;
+                            if (node->op == GGML_OP_ADD) {
+                                ggml_cuda_op_fused_add(*cuda_ctx, &fused_node, n_fuse);
+                            } else {
+                                ggml_cuda_op_fused_mul(*cuda_ctx, &fused_node, n_fuse);
                             }
-                            fused_add_node.data = cgraph->nodes[i + n_fuse - 1]->data;
-                            ggml_cuda_op_fused_add(*cuda_ctx, &fused_add_node, n_fuse);
                             i += n_fuse - 1;
 
                             continue;
@@ -4336,6 +4479,8 @@ static const ggml_backend_i ggml_backend_cuda_interface = {
     /* .free                    = */ ggml_backend_cuda_free,
     /* .set_tensor_async        = */ ggml_backend_cuda_set_tensor_async,
     /* .get_tensor_async        = */ ggml_backend_cuda_get_tensor_async,
+    /* .get_tensor_2d_async     = */ ggml_backend_cuda_set_tensor_2d_async,
+    /* .set_tensor_2d_async     = */ ggml_backend_cuda_get_tensor_2d_async,
     /* .cpy_tensor_async        = */ ggml_backend_cuda_cpy_tensor_async,
     /* .synchronize             = */ ggml_backend_cuda_synchronize,
     /* .graph_plan_create       = */ NULL,
@@ -4686,6 +4831,7 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
                 switch (a->type) {
                     case GGML_TYPE_F32:
                     case GGML_TYPE_F16:
+                    case GGML_TYPE_Q1_0:
                     case GGML_TYPE_Q4_0:
                     case GGML_TYPE_Q4_1:
                     case GGML_TYPE_Q5_0:
@@ -4723,6 +4869,7 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
                     case GGML_TYPE_F32:
                     case GGML_TYPE_BF16:
                     case GGML_TYPE_I32:
+                    case GGML_TYPE_Q1_0:
                     case GGML_TYPE_Q4_0:
                     case GGML_TYPE_Q4_1:
                     case GGML_TYPE_Q5_0:
@@ -5123,6 +5270,15 @@ static ggml_backend_feature * ggml_backend_cuda_get_features(ggml_backend_reg_t
 
 static void * ggml_backend_cuda_reg_get_proc_address(ggml_backend_reg_t reg, const char * name) {
     GGML_UNUSED(reg);
+    if (strcmp(name, "ggml_backend_comm_init") == 0) {
+        return (void *)ggml_backend_cuda_comm_init;
+    }
+    if (strcmp(name, "ggml_backend_comm_free") == 0) {
+        return (void *)ggml_backend_cuda_comm_free;
+    }
+    if (strcmp(name, "ggml_backend_comm_allreduce_tensor") == 0) {
+        return (void *)ggml_backend_cuda_comm_allreduce_tensor;
+    }
     if (strcmp(name, "ggml_backend_split_buffer_type") == 0) {
         return (void *)ggml_backend_cuda_split_buffer_type;
     }

ggml/src/ggml-cuda/mma.cuh

@@ -1025,7 +1025,8 @@ namespace ggml_cuda_mma {
         const floatx2_t& a_frag = reinterpret_cast<const floatx2_t&>(A.x[0]);
         const floatx2_t& b_frag = reinterpret_cast<const floatx2_t&>(B.x[0]);
         acc_frag = __builtin_amdgcn_mfma_f32_16x16x8_xf32(a_frag, b_frag, acc_frag, 0, 0, 0);
-#elif defined(CDNA2) || defined(CDNA1)
+#elif defined(CDNA4) || defined(CDNA2) || defined(CDNA1)
+        // CDNA4 (gfx950) does not support xf32 MFMA, use f32 path like CDNA2/CDNA1
 #pragma unroll
         for (int i = 0; i < 2; ++i) {
             acc_frag = __builtin_amdgcn_mfma_f32_16x16x4f32(A.x[i], B.x[i], acc_frag, 0, 0, 0);
@@ -1187,7 +1188,7 @@ namespace ggml_cuda_mma {
 #elif defined(AMD_MFMA_AVAILABLE)
         using floatx4_t = __attribute__((ext_vector_type(4))) float;
         floatx4_t& acc_frag = reinterpret_cast<floatx4_t&>(D.x[0]);
-#if defined(CDNA3) || defined(CDNA2)
+#if defined(CDNA4) || defined(CDNA3) || defined(CDNA2)
         using bf16x4_t = __attribute__((ext_vector_type(4))) __bf16;
         const bf16x4_t& a_frag = reinterpret_cast<const bf16x4_t&>(A.x[0]);
         const bf16x4_t& b_frag = reinterpret_cast<const bf16x4_t&>(B.x[0]);
@@ -1216,12 +1217,12 @@ namespace ggml_cuda_mma {
 #if defined(AMD_MFMA_AVAILABLE)
         using int32x4_t = __attribute__((__vector_size__(4 * sizeof(int)))) int;
         int32x4_t * acc = (int32x4_t *) D.x;
-#if defined(CDNA3)
+#if defined(CDNA4) || defined(CDNA3)
         acc[0] = __builtin_amdgcn_mfma_i32_16x16x32_i8(((int64_t *) A.x)[0],
                                                        ((int64_t *) B.x)[0],
                                                        acc[0],
                                                        0, 0, 0);
-#elif defined(CDNA2) || defined(CDNA)
+#elif defined(CDNA2) || defined(CDNA1)
         acc[0] = __builtin_amdgcn_mfma_i32_16x16x16i8(A.x[0],
                                                       B.x[0],
                                                       acc[0],
@@ -1230,7 +1231,7 @@ namespace ggml_cuda_mma {
                                                       B.x[1],
                                                       acc[0],
                                                       0, 0, 0);
-#endif // defined(CDNA3)
+#endif // defined(CDNA4) || defined(CDNA3)
 
 #elif defined(AMD_WMMA_AVAILABLE)
 
@@ -1295,12 +1296,12 @@ namespace ggml_cuda_mma {
 #if defined(AMD_MFMA_AVAILABLE)
         using int32x16_t = __attribute__((__vector_size__(16 * sizeof(int)))) int;
         int32x16_t * acc = (int32x16_t *) D.x;
-#if defined(CDNA3)
+#if defined(CDNA4) || defined(CDNA3)
         acc[0] = __builtin_amdgcn_mfma_i32_32x32x16_i8(((int64_t *) A.x)[0],
                                                        ((int64_t *) B.x)[0],
                                                        acc[0],
                                                        0, 0, 0);
-#elif defined(CDNA2) || defined(CDNA)
+#elif defined(CDNA2) || defined(CDNA1)
         acc[0] = __builtin_amdgcn_mfma_i32_32x32x8i8(A.x[0],
                                                      B.x[0],
                                                      acc[0],
@@ -1309,7 +1310,7 @@ namespace ggml_cuda_mma {
                                                      B.x[1],
                                                      acc[0],
                                                      0, 0, 0);
-#endif // defined(CDNA3)
+#endif // defined(CDNA4) || defined(CDNA3)
 
 #else
         GGML_UNUSED_VARS(D, A, B);

ggml/src/ggml-cuda/mmq.cu

@@ -5,6 +5,9 @@
 
 static void ggml_cuda_mul_mat_q_switch_type(ggml_backend_cuda_context & ctx, const mmq_args & args, cudaStream_t stream) {
     switch (args.type_x) {
+        case GGML_TYPE_Q1_0:
+            mul_mat_q_case<GGML_TYPE_Q1_0>(ctx, args, stream);
+            break;
         case GGML_TYPE_Q4_0:
             mul_mat_q_case<GGML_TYPE_Q4_0>(ctx, args, stream);
             break;
@@ -270,6 +273,7 @@ bool ggml_cuda_should_use_mmq(enum ggml_type type, int cc, int64_t ne11, int64_t
     bool mmq_supported;
 
     switch (type) {
+        case GGML_TYPE_Q1_0:
         case GGML_TYPE_Q4_0:
         case GGML_TYPE_Q4_1:
         case GGML_TYPE_Q5_0:

ggml/src/ggml-cuda/mmq.cuh

@@ -57,6 +57,8 @@ static_assert(sizeof(block_fp4_mmq)  == sizeof(block_q8_1_mmq),    "Unexpected b
 
 static mmq_q8_1_ds_layout mmq_get_q8_1_ds_layout(const ggml_type type_x) {
     switch (type_x) {
+        case GGML_TYPE_Q1_0:
+            return MMQ_Q8_1_DS_LAYOUT_D4;
         case GGML_TYPE_Q4_0:
         case GGML_TYPE_Q4_1:
             return MMQ_Q8_1_DS_LAYOUT_DS4;
@@ -185,6 +187,7 @@ static constexpr __device__ int get_mmq_y_device() {
 
 static constexpr __host__ __device__ tile_x_sizes mmq_get_dp4a_tile_x_sizes(ggml_type type, int mmq_y) {
     switch (type) {
+        case GGML_TYPE_Q1_0:    return MMQ_DP4A_TXS_Q8_0;
         case GGML_TYPE_Q4_0:    return MMQ_DP4A_TXS_Q4_0;
         case GGML_TYPE_Q4_1:    return MMQ_DP4A_TXS_Q4_1;
         case GGML_TYPE_Q5_0:    return MMQ_DP4A_TXS_Q8_0;
@@ -229,6 +232,7 @@ static_assert(MMQ_MMA_TILE_X_K_NVFP4 % 8 == 4, "Wrong padding.");
 
 static constexpr __host__ __device__ int mmq_get_mma_tile_x_k(ggml_type type) {
     switch (type) {
+        case GGML_TYPE_Q1_0:    return MMQ_MMA_TILE_X_K_Q8_0;
         case GGML_TYPE_Q4_0:    return MMQ_MMA_TILE_X_K_Q8_0;
         case GGML_TYPE_Q4_1:    return MMQ_MMA_TILE_X_K_Q8_1;
         case GGML_TYPE_Q5_0:    return MMQ_MMA_TILE_X_K_Q8_0;
@@ -302,6 +306,87 @@ static constexpr __device__ int mmq_get_nwarps_device() {
 
 // ------------------------------------------------------------
 
+template <int mmq_y, bool need_check> static __device__ __forceinline__ void load_tiles_q1_0(
+    const char * __restrict__ x, int * __restrict__ x_tile, const int kbx0, const int i_max, const int stride) {
+    constexpr int nwarps = mmq_get_nwarps_device();
+    constexpr int warp_size = ggml_cuda_get_physical_warp_size();
+
+#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE)
+    int   * x_qs = (int   *)  x_tile;
+    float * x_df = (float *) (x_qs + 2*MMQ_TILE_NE_K);
+#else
+    constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_Q8_0, mmq_y);
+    int   * x_qs = (int   *)  x_tile;
+    float * x_df = (float *) (x_qs + txs.qs);
+#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE)
+
+    constexpr int blocks_per_iter = MMQ_ITER_K / QK1_0;
+    constexpr int threads_per_row = blocks_per_iter * QI1_0;
+    constexpr int nrows = warp_size / threads_per_row;
+    constexpr int scale_entries_per_block = QK1_0 / QK8_1;
+    constexpr int scale_entries_per_row = blocks_per_iter * scale_entries_per_block;
+
+    const int txi  = threadIdx.x % threads_per_row;
+    const int kbx  = txi / QI1_0;
+    const int kqsx = txi % QI1_0;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nrows*nwarps) {
+        int i = i0 + threadIdx.y*nrows + threadIdx.x/threads_per_row;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q1_0 * bxi = (const block_q1_0 *) x + kbx0 + i*stride + kbx;
+        const int qs_offset = 4*kqsx;
+        const int qs0 = bxi->qs[qs_offset + 0] | (bxi->qs[qs_offset + 1] << 8) |
+                        (bxi->qs[qs_offset + 2] << 16) | (bxi->qs[qs_offset + 3] << 24);
+
+        int unpacked_bytes[8];
+#pragma unroll
+        for (int j = 0; j < 8; ++j) {
+            const int shift = j * 4;
+            const int bits4 = (qs0 >> shift) & 0x0F;
+            const int b0 = (bits4 & 0x01) ? 1 : -1;
+            const int b1 = (bits4 & 0x02) ? 1 : -1;
+            const int b2 = (bits4 & 0x04) ? 1 : -1;
+            const int b3 = (bits4 & 0x08) ? 1 : -1;
+            unpacked_bytes[j] = (b0 & 0xFF) | ((b1 & 0xFF) << 8) | ((b2 & 0xFF) << 16) | ((b3 & 0xFF) << 24);
+        }
+
+        const int dst_offset = kbx*(scale_entries_per_block*QI8_0) + kqsx*QI8_0;
+#pragma unroll
+        for (int j = 0; j < 8; ++j) {
+#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE)
+            x_qs[i*MMQ_MMA_TILE_X_K_Q8_0 + dst_offset + j] = unpacked_bytes[j];
+#else
+            x_qs[i*(2*MMQ_TILE_NE_K + 1) + dst_offset + j] = unpacked_bytes[j];
+#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE)
+        }
+    }
+
+    const int ksx = threadIdx.x % scale_entries_per_row;
+    const int scale_block = ksx / scale_entries_per_block;
+
+#pragma unroll
+    for (int i0 = 0; i0 < mmq_y; i0 += nwarps) {
+        int i = i0 + threadIdx.y;
+
+        if (need_check) {
+            i = min(i, i_max);
+        }
+
+        const block_q1_0 * bxi = (const block_q1_0 *) x + kbx0 + i*stride + scale_block;
+
+#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE)
+        x_df[i*MMQ_MMA_TILE_X_K_Q8_0 + ksx] = bxi->d;
+#else
+        x_df[i*(2*MMQ_TILE_NE_K/QI8_0) + i/(QI8_0/2) + ksx] = bxi->d;
+#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE)
+    }
+}
+
 template <int mmq_y, bool need_check> static __device__ __forceinline__ void load_tiles_q4_0(
     const char * __restrict__ x, int * __restrict__ x_tile, const int kbx0, const int i_max, const int stride) {
     constexpr int nwarps = mmq_get_nwarps_device();
@@ -3290,6 +3375,14 @@ static __device__ __forceinline__ void mmq_write_back_mma(
 template <int mmq_x, int mmq_y, bool need_check, ggml_type type>
 struct mmq_type_traits;
 
+template <int mmq_x, int mmq_y, bool need_check>
+struct mmq_type_traits<mmq_x, mmq_y, need_check, GGML_TYPE_Q1_0> {
+    static constexpr int              vdr          = VDR_Q1_0_Q8_1_MMQ;
+    static constexpr load_tiles_mmq_t load_tiles   = load_tiles_q1_0<mmq_y, need_check>;
+    static constexpr vec_dot_mmq_t    vec_dot_mma  = vec_dot_q8_0_q8_1_mma<mmq_x, mmq_y, MMQ_Q8_1_DS_LAYOUT_D4>;
+    static constexpr vec_dot_mmq_t    vec_dot_dp4a = vec_dot_q8_0_q8_1_dp4a<mmq_x, mmq_y>;
+};
+
 template <int mmq_x, int mmq_y, bool need_check>
 struct mmq_type_traits<mmq_x, mmq_y, need_check, GGML_TYPE_Q4_0> {
     static constexpr int              vdr          = VDR_Q4_0_Q8_1_MMQ;
@@ -3645,7 +3738,7 @@ static __global__ void mul_mat_q(
              tile_x_max_i, tile_y_max_j, 0, ncols_x/qk);
         return;
     }
-#endif // (defined(GGML_USE_HIP) && !defined(CDNA3)) || __CUDA_ARCH__ < GGML_CUDA_CC_VOLTA
+#endif // (defined(GGML_USE_HIP) && !defined(CDNA4) && !defined(CDNA3)) || __CUDA_ARCH__ < GGML_CUDA_CC_VOLTA
 
     constexpr int ITER_K = get_iter_k(type);
 

ggml/src/ggml-cuda/mmvq.cu

@@ -9,6 +9,7 @@ typedef float (*vec_dot_q_cuda_t)(const void * __restrict__ vbq, const block_q8_
 
 static constexpr __device__ vec_dot_q_cuda_t get_vec_dot_q_cuda(ggml_type type) {
     switch (type) {
+        case GGML_TYPE_Q1_0:    return vec_dot_q1_0_q8_1;
         case GGML_TYPE_Q4_0:    return vec_dot_q4_0_q8_1;
         case GGML_TYPE_Q4_1:    return vec_dot_q4_1_q8_1;
         case GGML_TYPE_Q5_0:    return vec_dot_q5_0_q8_1;
@@ -36,6 +37,7 @@ static constexpr __device__ vec_dot_q_cuda_t get_vec_dot_q_cuda(ggml_type type)
 
 static constexpr __host__ __device__ int get_vdr_mmvq(ggml_type type) {
     switch (type) {
+        case GGML_TYPE_Q1_0:    return VDR_Q1_0_Q8_1_MMVQ;
         case GGML_TYPE_Q4_0:    return VDR_Q4_0_Q8_1_MMVQ;
         case GGML_TYPE_Q4_1:    return VDR_Q4_1_Q8_1_MMVQ;
         case GGML_TYPE_Q5_0:    return VDR_Q5_0_Q8_1_MMVQ;
@@ -886,6 +888,12 @@ static void mul_mat_vec_q_switch_type(
         const int nsamples_x, const int nsamples_dst, const int stride_sample_x, const int stride_sample_y, const int stride_sample_dst,
         const int ids_stride, cudaStream_t stream) {
     switch (type_x) {
+        case GGML_TYPE_Q1_0:
+            mul_mat_vec_q_switch_ncols_dst<GGML_TYPE_Q1_0>
+                (vx, vy, ids, fusion, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, stride_col_y, stride_col_dst,
+                 nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
+                 nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, ids_stride, stream);
+            break;
         case GGML_TYPE_Q4_0:
             mul_mat_vec_q_switch_ncols_dst<GGML_TYPE_Q4_0>
                 (vx, vy, ids, fusion, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, stride_col_y, stride_col_dst,

ggml/src/ggml-cuda/ssm-conv.cu

@@ -134,8 +134,9 @@ static void ssm_conv_f32_cuda(const float * src0, const float * src1, const int
     switch (nc) {
         case 3: launch_kernel(std::integral_constant<int, 3>{}); break;
         case 4: launch_kernel(std::integral_constant<int, 4>{}); break;
+        case 5: launch_kernel(std::integral_constant<int, 5>{}); break;
         case 9: launch_kernel(std::integral_constant<int, 9>{}); break;
-        default: GGML_ABORT("Only support kernel sizes 3, 4, 9 right now.");
+        default: GGML_ABORT("Only support kernel sizes 3, 4, 5, 9 right now.");
     }
 }
 

ggml/src/ggml-cuda/template-instances/generate_cu_files.py

@@ -32,6 +32,7 @@ SOURCE_FATTN_MMA_START = """// This file has been autogenerated by generate_cu_f
 SOURCE_FATTN_MMA_CASE = "DECL_FATTN_MMA_F16_CASE({head_size_kq}, {head_size_v}, {ncols1}, {ncols2});\n"
 
 TYPES_MMQ = [
+    "GGML_TYPE_Q1_0",
     "GGML_TYPE_Q4_0", "GGML_TYPE_Q4_1", "GGML_TYPE_Q5_0", "GGML_TYPE_Q5_1", "GGML_TYPE_Q8_0",
     "GGML_TYPE_Q2_K", "GGML_TYPE_Q3_K", "GGML_TYPE_Q4_K", "GGML_TYPE_Q5_K", "GGML_TYPE_Q6_K",
     "GGML_TYPE_IQ2_XXS", "GGML_TYPE_IQ2_XS", "GGML_TYPE_IQ2_S", "GGML_TYPE_IQ3_XXS", "GGML_TYPE_IQ3_S",

ggml/src/ggml-cuda/template-instances/mmq-instance-q1_0.cu

@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../mmq.cuh"
+
+DECL_MMQ_CASE(GGML_TYPE_Q1_0);

ggml/src/ggml-cuda/top-k.cu

@@ -25,14 +25,14 @@ static void top_k_cub(ggml_cuda_pool & pool,
     auto indexes_in = cuda::make_counting_iterator(0);
 
     size_t temp_storage_bytes = 0;
-    DeviceTopK::MaxPairs(nullptr, temp_storage_bytes, src, cuda::discard_iterator(), indexes_in, dst, ncols, k,
-                         env);
+    CUDA_CHECK(DeviceTopK::MaxPairs(nullptr, temp_storage_bytes, src, cuda::discard_iterator(), indexes_in, dst, ncols, k,
+                         env));
 
     ggml_cuda_pool_alloc<uint8_t> temp_storage_alloc(pool, temp_storage_bytes);
     void *                        d_temp_storage = temp_storage_alloc.get();
 
-    DeviceTopK::MaxPairs(d_temp_storage, temp_storage_bytes, src, cuda::discard_iterator(), indexes_in, dst,
-                         ncols, k, env);
+    CUDA_CHECK(DeviceTopK::MaxPairs(d_temp_storage, temp_storage_bytes, src, cuda::discard_iterator(), indexes_in, dst,
+                         ncols, k, env));
 }
 
 #elif defined(GGML_CUDA_USE_CUB)  // CUB_TOP_K_AVAILABLE

ggml/src/ggml-cuda/vecdotq.cuh

@@ -106,6 +106,9 @@ static __device__ __forceinline__ uint32_t unpack_ksigns(const uint8_t v) {
 // VDR = vec dot ratio, how many contiguous integers each thread processes when the vec dot kernel is called
 // MMVQ = mul_mat_vec_q, MMQ = mul_mat_q
 
+#define VDR_Q1_0_Q8_1_MMVQ 1  // Process one 32-element chunk at a time for parallelism
+#define VDR_Q1_0_Q8_1_MMQ  4  // Q1_0 has 128 bits (4 ints) per block
+
 #define VDR_Q4_0_Q8_1_MMVQ 2
 #define VDR_Q4_0_Q8_1_MMQ  4
 
@@ -669,6 +672,51 @@ static __device__ __forceinline__ float vec_dot_q6_K_q8_1_impl_mmq(
     return d6 * sumf_d;
 }
 
+static __device__ __forceinline__ float vec_dot_q1_0_q8_1(
+    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs) {
+
+    const block_q1_0 * bq1_0 = (const block_q1_0 *) vbq + kbx;
+
+    // Q1_0: 128 elements with ONE scale
+    // Q8_1: 32 elements per block with individual scales
+    // iqs selects which of the 4 chunks of 32 elements to process (0-3)
+
+    const float d1 = bq1_0->d;
+
+    // Process only the chunk specified by iqs
+    const block_q8_1 * bq8_1_chunk = bq8_1 + iqs;
+
+    // Load 32 bits (4 bytes) for this chunk from Q1_0
+    const int offset = iqs * 4;
+    const int v = bq1_0->qs[offset + 0] | (bq1_0->qs[offset + 1] << 8) |
+                  (bq1_0->qs[offset + 2] << 16) | (bq1_0->qs[offset + 3] << 24);
+
+    // Unpack 32 bits into 32 signed values (-1 or +1)
+    int vi_bytes[8];
+#pragma unroll
+    for (int j = 0; j < 8; ++j) {
+        const int shift = j * 4;
+        const int bits4 = (v >> shift) & 0x0F;
+        const int b0 = (bits4 & 0x01) ? 1 : -1;
+        const int b1 = (bits4 & 0x02) ? 1 : -1;
+        const int b2 = (bits4 & 0x04) ? 1 : -1;
+        const int b3 = (bits4 & 0x08) ? 1 : -1;
+        vi_bytes[j] = (b0 & 0xFF) | ((b1 & 0xFF) << 8) | ((b2 & 0xFF) << 16) | ((b3 & 0xFF) << 24);
+    }
+
+    // Compute dot product for this 32-element chunk
+    int sumi = 0;
+#pragma unroll
+    for (int j = 0; j < 8; ++j) {
+        const int u = get_int_b4(bq8_1_chunk->qs, j);
+        sumi = ggml_cuda_dp4a(vi_bytes[j], u, sumi);
+    }
+
+    // Apply Q1_0's single scale and this chunk's Q8_1 scale
+    const float d8 = __low2float(bq8_1_chunk->ds);
+    return d1 * d8 * sumi;
+}
+
 static __device__ __forceinline__ float vec_dot_q4_0_q8_1(
     const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs) {
 

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

@@ -6,6 +6,10 @@
 #include <cuda_bf16.h>
 #include <cuda_fp16.h>
 
+#ifdef GGML_USE_NCCL
+#include <nccl.h>
+#endif // GGML_USE_NCCL
+
 #if CUDART_VERSION >= 11080
 #include <cuda_fp8.h>
 #define FP8_AVAILABLE

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

@@ -10,6 +10,11 @@
 #include <rocwmma/rocwmma-version.hpp>
 #endif // defined(GGML_HIP_ROCWMMA_FATTN)
 
+#ifdef GGML_USE_NCCL
+#include <rccl/rccl.h>
+#endif // GGML_USE_NCCL
+
+
 #define CUBLAS_GEMM_DEFAULT HIPBLAS_GEMM_DEFAULT
 #define CUBLAS_GEMM_DEFAULT_TENSOR_OP HIPBLAS_GEMM_DEFAULT
 #define CUBLAS_OP_N HIPBLAS_OP_N
@@ -28,6 +33,7 @@
 #define CU_MEM_LOCATION_TYPE_DEVICE hipMemLocationTypeDevice
 #define CU_MEM_ACCESS_FLAGS_PROT_READWRITE hipMemAccessFlagsProtReadWrite
 #define CU_CHECK(fn) {hipError_t err = fn; if(err != hipSuccess) { GGML_ABORT("HipVMM Failure: %s\n", hipGetErrorString(err)); }}
+#define NCCL_CHECK(fn) {ncclResult_t err = fn; if(err != ncclSuccess) { GGML_ABORT("RCCL Failure RCCL returned: %i\n", err); }}
 #define __shfl_sync(mask, var, laneMask, width) __shfl(var, laneMask, width)
 #define __shfl_up_sync(mask, var, laneMask, width) __shfl_up(var, laneMask, width)
 #define __shfl_xor_sync(mask, var, laneMask, width) __shfl_xor(var, laneMask, width)
@@ -183,6 +189,10 @@
 #define GCN
 #endif // defined(GCN5) || defined(GCN4)
 
+#if defined(__gfx950__)
+#define CDNA4
+#endif // defined(__gfx950__)
+
 #if defined(__gfx942__)
 #define CDNA3
 #endif // defined(__gfx942__)
@@ -195,9 +205,9 @@
 #define CDNA1
 #endif // defined(__gfx908__)
 
-#if defined(CDNA3) || defined(CDNA2) || defined(CDNA1)
+#if defined(CDNA4) || defined(CDNA3) || defined(CDNA2) || defined(CDNA1)
 #define CDNA // For the entire family
-#endif // defined(CDNA3) || defined(CDNA2) || defined(CDNA1)
+#endif // defined(CDNA4) || defined(CDNA3) || defined(CDNA2) || defined(CDNA1)
 
 #if defined(__GFX12__)
 #define RDNA4

ggml/src/ggml-hexagon/htp/CMakeLists.txt

@@ -47,6 +47,7 @@ list(FIND HTP_HMX_VERSIONS ${DSP_VERSION} _hmx_idx)
 
 if (_hmx_idx GREATER_EQUAL 0)
     target_sources(${HTP_LIB} PRIVATE
+        hmx-queue.c
         hmx-matmul-ops.c
     )
 

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

@@ -14,59 +14,42 @@
 #define GGML_COMMON_DECL_C
 #include "ggml-common.h"
 #include "htp-ctx.h"
-#include "htp-msg.h"
+#include "htp-ops.h"
 #include "htp-ops.h"
 
-#define htp_act_preamble3              \
-    const uint32_t ne00 = src0->ne[0]; \
-    const uint32_t ne01 = src0->ne[1]; \
-    const uint32_t ne02 = src0->ne[2]; \
-    const uint32_t ne03 = src0->ne[3]; \
-                                       \
-    const uint32_t ne10 = src1->ne[0]; \
-    const uint32_t ne11 = src1->ne[1]; \
-    const uint32_t ne12 = src1->ne[2]; \
-    const uint32_t ne13 = src1->ne[3]; \
-                                       \
-    const uint32_t ne0 = dst->ne[0];   \
-    const uint32_t ne1 = dst->ne[1];   \
-    const uint32_t ne2 = dst->ne[2];   \
-    const uint32_t ne3 = dst->ne[3];   \
-                                       \
-    const uint32_t nb00 = src0->nb[0]; \
-    const uint32_t nb01 = src0->nb[1]; \
-    const uint32_t nb02 = src0->nb[2]; \
-    const uint32_t nb03 = src0->nb[3]; \
-                                       \
-    const uint32_t nb10 = src1->nb[0]; \
-    const uint32_t nb11 = src1->nb[1]; \
-    const uint32_t nb12 = src1->nb[2]; \
-    const uint32_t nb13 = src1->nb[3]; \
-                                       \
-    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];
-
-#define htp_act_preamble2              \
-    const uint32_t ne00 = src0->ne[0]; \
-    const uint32_t ne01 = src0->ne[1]; \
-    const uint32_t ne02 = src0->ne[2]; \
-    const uint32_t ne03 = src0->ne[3]; \
-                                       \
-    const uint32_t ne0 = dst->ne[0];   \
-    const uint32_t ne1 = dst->ne[1];   \
-    const uint32_t ne2 = dst->ne[2];   \
-    const uint32_t ne3 = dst->ne[3];   \
-                                       \
-    const uint32_t nb00 = src0->nb[0]; \
-    const uint32_t nb01 = src0->nb[1]; \
-    const uint32_t nb02 = src0->nb[2]; \
-    const uint32_t nb03 = src0->nb[3]; \
-                                       \
-    const uint32_t nb0 = dst->nb[0];   \
-    const uint32_t nb1 = dst->nb[1];   \
-    const uint32_t nb2 = dst->nb[2];   \
+#define htp_act_preamble                                 \
+    const struct htp_tensor * src0 = actx->octx->src[0]; \
+    const struct htp_tensor * src1 = actx->octx->src[1]; \
+    const struct htp_tensor * dst  = actx->octx->dst;    \
+                                                         \
+    const uint32_t ne00 = src0->ne[0];                   \
+    const uint32_t ne01 = src0->ne[1];                   \
+    const uint32_t ne02 = src0->ne[2];                   \
+    const uint32_t ne03 = src0->ne[3];                   \
+                                                         \
+    const uint32_t nb00 = src0->nb[0];                   \
+    const uint32_t nb01 = src0->nb[1];                   \
+    const uint32_t nb02 = src0->nb[2];                   \
+    const uint32_t nb03 = src0->nb[3];                   \
+                                                         \
+    const uint32_t ne10 = src1 ? src1->ne[0] : 0;        \
+    const uint32_t ne11 = src1 ? src1->ne[1] : 0;        \
+    const uint32_t ne12 = src1 ? src1->ne[2] : 0;        \
+    const uint32_t ne13 = src1 ? src1->ne[3] : 0;        \
+                                                         \
+    const uint32_t nb10 = src1 ? src1->nb[0] : 0;        \
+    const uint32_t nb11 = src1 ? src1->nb[1] : 0;        \
+    const uint32_t nb12 = src1 ? src1->nb[2] : 0;        \
+    const uint32_t nb13 = src1 ? src1->nb[3] : 0;        \
+                                                         \
+    const uint32_t ne0 = dst->ne[0];                     \
+    const uint32_t ne1 = dst->ne[1];                     \
+    const uint32_t ne2 = dst->ne[2];                     \
+    const uint32_t ne3 = dst->ne[3];                     \
+                                                         \
+    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];
 
 struct htp_act_context {
@@ -97,10 +80,7 @@ struct htp_act_context {
 
 static void glu_swiglu_f32_per_thread(unsigned int nth, unsigned int ith, void * data) {
     struct htp_act_context * actx = (struct htp_act_context *) data;
-    const struct htp_tensor * src0 = &actx->octx->src0;
-    const struct htp_tensor * src1 = &actx->octx->src1;
-    const struct htp_tensor * dst  = &actx->octx->dst;
-    htp_act_preamble3;
+    htp_act_preamble;
 
     size_t src0_row_size = actx->src0_row_size;
     size_t src1_row_size = actx->src1_row_size;
@@ -207,10 +187,7 @@ static void glu_swiglu_f32_per_thread(unsigned int nth, unsigned int ith, void *
 
 static void glu_swiglu_oai_f32_per_thread(unsigned int nth, unsigned int ith, void * data) {
     struct htp_act_context * actx = (struct htp_act_context *) data;
-    const struct htp_tensor * src0 = &actx->octx->src0;
-    const struct htp_tensor * src1 = &actx->octx->src1;
-    const struct htp_tensor * dst  = &actx->octx->dst;
-    htp_act_preamble3;
+    htp_act_preamble;
 
     uint64_t t1, t2;
     t1 = HAP_perf_get_qtimer_count();
@@ -332,9 +309,7 @@ static void glu_swiglu_oai_f32_per_thread(unsigned int nth, unsigned int ith, vo
 
 static void unary_gelu_f32_per_thread(unsigned int nth, unsigned int ith, void * data) {
     struct htp_act_context * actx = (struct htp_act_context *) data;
-    const struct htp_tensor * src0 = &actx->octx->src0;
-    const struct htp_tensor * dst  = &actx->octx->dst;
-    htp_act_preamble2;
+    htp_act_preamble;
 
     uint64_t t1, t2;
     t1 = HAP_perf_get_qtimer_count();
@@ -433,9 +408,7 @@ static void unary_gelu_f32_per_thread(unsigned int nth, unsigned int ith, void *
 
 static void unary_silu_f32_per_thread(unsigned int nth, unsigned int ith, void * data) {
     struct htp_act_context * actx = (struct htp_act_context *) data;
-    const struct htp_tensor * src0 = &actx->octx->src0;
-    const struct htp_tensor * dst  = &actx->octx->dst;
-    htp_act_preamble2;
+    htp_act_preamble;
 
     uint64_t t1, t2;
     t1 = HAP_perf_get_qtimer_count();
@@ -533,10 +506,7 @@ static const float SQRT_2_OVER_PI  = 0.79788456080286535587989211986876f;
 
 static void glu_geglu_f32_per_thread(unsigned int nth, unsigned int ith, void * data) {
     struct htp_act_context * actx = (struct htp_act_context *) data;
-    const struct htp_tensor * src0 = &actx->octx->src0;
-    const struct htp_tensor * src1 = &actx->octx->src1;
-    const struct htp_tensor * dst  = &actx->octx->dst;
-    htp_act_preamble3;
+    htp_act_preamble;
 
     size_t src0_row_size = actx->src0_row_size;
     size_t src1_row_size = actx->src1_row_size;
@@ -652,9 +622,9 @@ static void glu_geglu_f32_per_thread(unsigned int nth, unsigned int ith, void *
 }
 
 static int execute_op_activations_f32(struct htp_ops_context * octx) {
-    const struct htp_tensor * src0 = &octx->src0;
-    const struct htp_tensor * src1 = &octx->src1;
-    struct htp_tensor *       dst  = &octx->dst;
+    const struct htp_tensor * src0 = octx->src[0];
+    const struct htp_tensor * src1 = octx->src[1];
+    const struct htp_tensor * dst  = octx->dst;
 
     if (((src0->ne[0] * SIZEOF_FP32) != src0->nb[1]) || ((dst->ne[0] * SIZEOF_FP32) != dst->nb[1])) {
         FARF(ERROR, "Non-contiguous tensors are not supported at this time \n");
@@ -697,25 +667,20 @@ static int execute_op_activations_f32(struct htp_ops_context * octx) {
     const uint32_t n_threads  = MIN(octx->n_threads, src0_nrows);
 
     size_t src0_row_size = src0->nb[1];
-    size_t src1_row_size = src1->nb[1]; // zero bytes if src1 is not used
+    size_t src1_row_size = src1 ? src1->nb[1] : src0->nb[1];
     size_t dst_row_size  = dst->nb[1];
 
-    const bool src1_valid = src1->ne[0];
-    if (!src1_valid) {
-        src1_row_size = src0_row_size;
-    }
-
     const size_t src0_row_size_aligned = hex_round_up(src0_row_size, VLEN);
     const size_t src1_row_size_aligned = hex_round_up(src1_row_size, VLEN);
     const size_t dst_row_size_aligned  = hex_round_up(dst_row_size, VLEN);
+
     // VTCM scratchpads for all tensors
     // N rows per thread, padded to HVX vector size
-
     size_t spad_size_per_row   = (src0_row_size_aligned + src1_row_size_aligned) + dst_row_size_aligned;
     size_t vtcm_row_per_thread = (octx->ctx->vtcm_size)/ (n_threads* spad_size_per_row);
 
     // Make sure the reserved vtcm size is sufficient
-    if(vtcm_row_per_thread ==0){
+    if (vtcm_row_per_thread == 0) {
         FARF(ERROR, "act-%s : current VTCM reservation %zu is too small for even 1 row per thread, needed at least %zu\n", op_type, octx->ctx->vtcm_size,
              spad_size_per_row * n_threads);
         return HTP_STATUS_VTCM_TOO_SMALL;
@@ -733,7 +698,11 @@ static int execute_op_activations_f32(struct htp_ops_context * octx) {
     octx->src1_spad.data = octx->src0_spad.data + octx->src0_spad.size;
     octx->dst_spad.data  = octx->src1_spad.data + octx->src1_spad.size;
 
-    if (src1->ne[0]) {
+    octx->src0_spad.src = NULL;
+    octx->src1_spad.src = NULL;
+    octx->dst_spad.src  = NULL;
+
+    if (src1) {
         FARF(HIGH, "%s: %ux%ux%ux%u x %ux%ux%ux%u -> %ux%ux%ux%u : src0-spad-size %u src1-spad-size %u dst-spad-size %u\n",
              op_type, src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3], src1->ne[0], src1->ne[1], src1->ne[2],
              src1->ne[3], dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3], octx->src0_spad.size, octx->src1_spad.size,
@@ -773,9 +742,9 @@ static int execute_op_activations_f32(struct htp_ops_context * octx) {
 
     // Pointers and GLU logic
     const uint8_t * data_src0 = (const uint8_t *) src0->data;
-    const uint8_t * data_src1 = (const uint8_t *) src1->data;
+    const uint8_t * data_src1 = src1 ? (const uint8_t *) src1->data : NULL;
 
-    if (!src1_valid && (octx->op == HTP_OP_GLU_SWIGLU || octx->op == HTP_OP_GLU_SWIGLU_OAI || octx->op == HTP_OP_GLU_GEGLU)) {
+    if (!src1 && (octx->op == HTP_OP_GLU_SWIGLU || octx->op == HTP_OP_GLU_SWIGLU_OAI || octx->op == HTP_OP_GLU_GEGLU)) {
          const int32_t swapped = octx->op_params[1];
          data_src1 = data_src0;
          actx.src1_row_size = actx.src0_row_size;
@@ -799,7 +768,7 @@ static int execute_op_activations_f32(struct htp_ops_context * octx) {
 int op_activations(struct htp_ops_context * octx) {
     int err = HTP_STATUS_OK;
 
-    switch (octx->src0.type) {
+    switch (octx->src[0]->type) {
         case HTP_TYPE_F32:
             err = execute_op_activations_f32(octx);
             break;

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

@@ -12,7 +12,7 @@
 #include "hex-dma.h"
 
 #include "htp-ctx.h"
-#include "htp-msg.h"
+#include "htp-ops.h"
 #include "htp-ops.h"
 
 #ifndef MIN
@@ -175,8 +175,8 @@ static void htp_argsort_f32(unsigned int n, unsigned int i, void * data) {
     struct htp_ops_context * octx = actx->octx;
 
     // Unpack context
-    const struct htp_tensor * src0 = &octx->src0;
-    const struct htp_tensor * dst = &octx->dst;
+    const struct htp_tensor * src0 = octx->src[0];
+    const struct htp_tensor * dst = octx->dst;
 
     // Scratchpad memory
     uint8_t * spad = octx->src0_spad.data + octx->src0_spad.size_per_thread * i;
@@ -249,16 +249,16 @@ static void htp_argsort_f32(unsigned int n, unsigned int i, void * data) {
 
 int op_argsort(struct htp_ops_context * octx) {
     // Check supported types
-    if (octx->src0.type != HTP_TYPE_F32) {
+    if (octx->src[0]->type != HTP_TYPE_F32) {
         return HTP_STATUS_NO_SUPPORT;
     }
 
-    const uint32_t total_rows = octx->src0.ne[1] * octx->src0.ne[2] * octx->src0.ne[3];
+    const uint32_t total_rows = octx->src[0]->ne[1] * octx->src[0]->ne[2] * octx->src[0]->ne[3];
     const uint32_t n_threads = MIN(total_rows, octx->n_threads);
 
     // Allocate scratchpad
     // We need 1 row of float + 1 row of int32 per thread.
-    uint32_t ne00 = octx->src0.ne[0];
+    uint32_t ne00 = octx->src[0]->ne[0];
     size_t values_size  = hex_round_up(ne00 * sizeof(float), 128);
     size_t indices_size = hex_round_up(ne00 * sizeof(int32_t), 128);
     size_t spad_per_thread = values_size + indices_size;
@@ -278,9 +278,9 @@ int op_argsort(struct htp_ops_context * octx) {
     octx->src0_spad.size_per_thread = spad_per_thread;
 
     FARF(HIGH, "argsort: %ux%ux%ux%u -> %ux%ux%ux%u (0x%x, 0x%x)",
-         octx->src0.ne[0], octx->src0.ne[1], octx->src0.ne[2], octx->src0.ne[3],
-         octx->dst.ne[0], octx->dst.ne[1], octx->dst.ne[2], octx->dst.ne[3],
-         octx->src0.data, octx->dst.data);
+         octx->src[0]->ne[0], octx->src[0]->ne[1], octx->src[0]->ne[2], octx->src[0]->ne[3],
+         octx->dst->ne[0], octx->dst->ne[1], octx->dst->ne[2], octx->dst->ne[3],
+         octx->src[0]->data, octx->dst->data);
 
     struct htp_argsort_context actx;
     actx.octx = octx;

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

@@ -14,7 +14,7 @@
 #define GGML_COMMON_DECL_C
 #include "ggml-common.h"
 #include "htp-ctx.h"
-#include "htp-msg.h"
+#include "htp-ops.h"
 #include "htp-ops.h"
 
 #ifndef MIN
@@ -43,10 +43,10 @@ struct htp_binary_context {
     bool split_at_ne02;
 };
 
-#define htp_binary_preamble                       \
-    const struct htp_tensor * src0 = &octx->src0; \
-    const struct htp_tensor * src1 = &octx->src1; \
-    struct htp_tensor *       dst  = &octx->dst;  \
+#define htp_binary_preamble                        \
+    const struct htp_tensor * src0 = octx->src[0]; \
+    const struct htp_tensor * src1 = octx->src[1]; \
+    const struct htp_tensor * dst  = octx->dst;    \
                                        \
     const uint32_t ne00 = src0->ne[0]; \
     const uint32_t ne01 = src0->ne[1]; \
@@ -181,7 +181,7 @@ static void binary_job_scalar(unsigned int nth, unsigned int ith, void * data) {
     struct htp_ops_context * octx = bctx->octx;
     htp_binary_preamble;
 
-    const uint32_t src0_type = octx->src0.type;
+    const uint32_t src0_type = octx->src[0]->type;
     const uint32_t row_size_bytes = (src0_type == HTP_TYPE_F32) ? ne00 * sizeof(float) : ne00 * sizeof(_Float16);
     const uint32_t total_rows = ne01 * ne02 * ne03;
     const uint32_t start_row = bctx->nrows_per_thread * ith;
@@ -274,7 +274,7 @@ static void binary_job_vector_same_shape(unsigned int nth, unsigned int ith, voi
     struct htp_ops_context * octx = bctx->octx;
     htp_binary_preamble;
 
-    const uint32_t src0_type = octx->src0.type;
+    const uint32_t src0_type = octx->src[0]->type;
     const uint32_t row_size_bytes = (src0_type == HTP_TYPE_F32) ? ne00 * sizeof(float) : ne00 * sizeof(_Float16);
     const uint32_t total_rows = ne01 * ne02 * ne03;
     const uint32_t start_row = bctx->nrows_per_thread * ith;
@@ -374,7 +374,7 @@ static void binary_job_vector_row_broadcast(unsigned int nth, unsigned int ith,
     struct htp_ops_context * octx = bctx->octx;
     htp_binary_preamble;
 
-    const uint32_t src0_type  = octx->src0.type;
+    const uint32_t src0_type  = octx->src[0]->type;
     const uint32_t row_size_bytes = (src0_type == HTP_TYPE_F32) ? ne00 * sizeof(float) : ne00 * sizeof(_Float16);
     const uint32_t total_rows = ne01 * ne02 * ne03;
     const uint32_t start_row  = bctx->nrows_per_thread * ith;
@@ -455,7 +455,7 @@ static void binary_job_vector_complex(unsigned int nth, unsigned int ith, void *
     struct htp_ops_context * octx = bctx->octx;
     htp_binary_preamble;
 
-    const uint32_t src0_type = octx->src0.type;
+    const uint32_t src0_type = octx->src[0]->type;
     const uint32_t row_size_bytes = (src0_type == HTP_TYPE_F32) ? ne00 * sizeof(float) : ne00 * sizeof(_Float16);
     const uint32_t total_rows = ne01 * ne02 * ne03;
     const uint32_t start_row  = bctx->nrows_per_thread * ith;
@@ -540,7 +540,7 @@ static void binary_job_element_repeat(unsigned int nth, unsigned int ith, void *
     struct htp_ops_context * octx = bctx->octx;
     htp_binary_preamble;
 
-    const uint32_t src0_type = octx->src0.type;
+    const uint32_t src0_type = octx->src[0]->type;
     const uint32_t elem_size_bytes = (src0_type == HTP_TYPE_F32) ? sizeof(float) : sizeof(_Float16);
     const uint32_t row_size_bytes = ne00 * elem_size_bytes;;
     const uint32_t total_rows = ne01 * ne02 * ne03;
@@ -629,10 +629,10 @@ static void binary_job_add_id(unsigned int nth, unsigned int ith, void * data) {
     struct htp_binary_context * bctx = (struct htp_binary_context *) data;
     struct htp_ops_context * octx = bctx->octx;
 
-    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 struct htp_tensor * src0 = octx->src[0];
+    const struct htp_tensor * src1 = octx->src[1];
+    const struct htp_tensor * src2 = octx->src[2];
+    const struct htp_tensor * dst  = octx->dst;
 
     const uint32_t ne00 = src0->ne[0];
     const uint32_t ne01 = src0->ne[1];
@@ -723,15 +723,15 @@ static void binary_job_add_id(unsigned int nth, unsigned int ith, void * data) {
 }
 
 static int execute_op_binary(struct htp_ops_context * octx) {
-    const struct htp_tensor * src0 = &octx->src0;
-    const struct htp_tensor * src1 = &octx->src1;
-    struct htp_tensor *       dst  = &octx->dst;
+    const struct htp_tensor * src0 = octx->src[0];
+    const struct htp_tensor * src1 = octx->src[1];
+    const struct htp_tensor * dst  = octx->dst;
 
     const uint32_t src0_nrows = src0->ne[1] * src0->ne[2] * src0->ne[3];
     const uint32_t n_threads  = MIN(octx->n_threads, src0_nrows);
 
     // Use packed row sizes for VTCM allocation
-    const uint32_t src0_type = octx->src0.type;
+    const uint32_t src0_type = octx->src[0]->type;
     const size_t elem_size = (src0_type == HTP_TYPE_F32) ? sizeof(float) : sizeof(_Float16);
     const size_t src0_row_size = src0->ne[0] * elem_size;
     const size_t src1_row_size = src1->ne[0] * elem_size;
@@ -799,9 +799,9 @@ static int execute_op_binary(struct htp_ops_context * octx) {
         return HTP_STATUS_VTCM_TOO_SMALL;
     }
 
-    octx->src0_spad.data = octx->ctx->vtcm_base;
-    octx->src1_spad.data = octx->src0_spad.data + octx->src0_spad.size;
-    octx->dst_spad.data  = octx->src1_spad.data + octx->src1_spad.size;
+    octx->src0_spad.data = octx->ctx->vtcm_base;                        octx->src0_spad.src = NULL;
+    octx->src1_spad.data = octx->src0_spad.data + octx->src0_spad.size; octx->src1_spad.src = NULL;
+    octx->dst_spad.data  = octx->src1_spad.data + octx->src1_spad.size; octx->dst_spad.src  = NULL;
 
     if ((octx->flags & HTP_OPFLAGS_SKIP_COMPUTE)) {
         return HTP_STATUS_OK;
@@ -857,12 +857,12 @@ static int execute_op_binary(struct htp_ops_context * octx) {
 int op_binary(struct htp_ops_context * octx) {
 
     // Does not support permutations of src1
-    const struct htp_tensor * src1 = &octx->src1;
+    const struct htp_tensor * src1 = octx->src[1];
     if (src1->nb[1] < src1->nb[0]) {
         return HTP_STATUS_NO_SUPPORT;
     }
 
-    const uint32_t src0_type = octx->src0.type;
+    const uint32_t src0_type = octx->src[0]->type;
     if ((src0_type == HTP_TYPE_F32) || (src0_type == HTP_TYPE_F16)) {
         return execute_op_binary(octx);
     }

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

@@ -11,7 +11,7 @@
 #define GGML_COMMON_DECL_C
 #include "ggml-common.h"
 #include "htp-ctx.h"
-#include "htp-msg.h"
+#include "htp-ops.h"
 #include "htp-ops.h"
 #include "hvx-utils.h"
 
@@ -32,10 +32,10 @@ struct htp_copy_context {
     void (*copy)(struct htp_copy_context * ct, struct htp_ops_context * octx, int nth, int ith);
 };
 
-#define cpy_preamble                       \
-    struct htp_tensor *src0 = &octx->src0; \
-    struct htp_tensor *dst  = &octx->dst;  \
-                                           \
+#define cpy_preamble                              \
+    const struct htp_tensor *src0 = octx->src[0]; \
+    const 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];     \

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

@@ -13,9 +13,9 @@
 #include "hvx-utils.h"
 #include "hex-dma.h"
 
-#define htp_cumsum_tensors_preamble                  \
-    struct htp_tensor * restrict src0 = &octx->src0; \
-    struct htp_tensor * restrict dst  = &octx->dst;  \
+#define htp_cumsum_tensors_preamble                         \
+    const struct htp_tensor * restrict src0 = octx->src[0]; \
+    const struct htp_tensor * restrict dst  = octx->dst;    \
                                                      \
     const uint32_t ne00 = src0->ne[0];               \
     const uint32_t ne01 = src0->ne[1];               \
@@ -206,8 +206,8 @@ static void cumsum_thread_f32(unsigned int nth, unsigned int ith, void * data) {
 }
 
 int op_cumsum_f32(struct htp_ops_context * octx) {
-    const struct htp_tensor * src0 = &octx->src0;
-    const struct htp_tensor * dst  = &octx->dst;
+    const struct htp_tensor * src0 = octx->src[0];
+    const struct htp_tensor * dst  = octx->dst;
 
     if (octx->flags & HTP_OPFLAGS_SKIP_COMPUTE) {
         return HTP_STATUS_OK;
@@ -226,10 +226,12 @@ int op_cumsum_f32(struct htp_ops_context * octx) {
 
     octx->src0_spad.size_per_thread = src_row_size_aligned * 2;
     octx->dst_spad.size_per_thread  = dst_row_size_aligned * 2;
-    octx->src0_spad.size            = n_threads * octx->src0_spad.size_per_thread;
-    octx->dst_spad.size             = n_threads * octx->dst_spad.size_per_thread;
-    octx->src0_spad.data            = octx->ctx->vtcm_base;
-    octx->dst_spad.data             = octx->src0_spad.data + octx->src0_spad.size;
+
+    octx->src0_spad.size  = n_threads * octx->src0_spad.size_per_thread;
+    octx->dst_spad.size   = n_threads * octx->dst_spad.size_per_thread;
+
+    octx->src0_spad.data  = octx->ctx->vtcm_base;                        octx->src0_spad.src = NULL;
+    octx->dst_spad.data   = octx->src0_spad.data + octx->src0_spad.size; octx->dst_spad.src  = NULL;
 
     struct htp_cumsum_context cctx = {
         .octx                 = octx,
@@ -251,8 +253,9 @@ int op_cumsum_f32(struct htp_ops_context * octx) {
 }
 
 int op_cumsum(struct htp_ops_context * octx) {
-    int                 err = HTP_STATUS_OK;
-    struct htp_tensor * dst = &octx->dst;
+    const struct htp_tensor * dst = octx->dst;
+
+    int err = HTP_STATUS_OK;
 
     switch (dst->type) {
         case HTP_TYPE_F32:

ggml/src/ggml-hexagon/htp/flash-attn-ops.c

@@ -15,7 +15,7 @@
 #define GGML_COMMON_DECL_C
 #include "ggml-common.h"
 #include "htp-ctx.h"
-#include "htp-msg.h"
+#include "htp-ops.h"
 #include "htp-ops.h"
 
 // Must be multiple of 32
@@ -278,12 +278,12 @@ static inline void hvx_scale_vec_f32_aa(uint8_t * restrict dst, const uint8_t *
 static void flash_attn_ext_f16_thread(unsigned int nth, unsigned int ith, void * data) {
     struct htp_fa_context * factx = (struct htp_fa_context *) data;
     const struct htp_ops_context * octx = factx->octx;
-    const struct htp_tensor * q = &octx->src0;
-    const struct htp_tensor * k = &octx->src1;
-    const struct htp_tensor * v = &octx->src2;
-    const struct htp_tensor * mask  = (octx->src3.data) ? &octx->src3 : NULL;
-    const struct htp_tensor * sinks = (octx->src4.data) ? &octx->src4 : NULL;
-    const struct htp_tensor * dst = &octx->dst;
+    const struct htp_tensor * q     = octx->src[0];
+    const struct htp_tensor * k     = octx->src[1];
+    const struct htp_tensor * v     = octx->src[2];
+    const struct htp_tensor * mask  = octx->src[3];
+    const struct htp_tensor * sinks = octx->src[4];
+    const struct htp_tensor * dst   = octx->dst;
 
     const uint32_t neq0 = q->ne[0];
     const uint32_t neq1 = q->ne[1];
@@ -610,11 +610,11 @@ static void flash_attn_ext_f16_thread(unsigned int nth, unsigned int ith, void *
 }
 
 int op_flash_attn_ext(struct htp_ops_context * octx) {
-    const struct htp_tensor * q = &octx->src0;
-    const struct htp_tensor * k = &octx->src1;
-    const struct htp_tensor * v = &octx->src2;
-    const struct htp_tensor * mask = (octx->src3.data) ? &octx->src3 : NULL;
-    const struct htp_tensor * dst = &octx->dst;
+    const struct htp_tensor * q    = octx->src[0];
+    const struct htp_tensor * k    = octx->src[1];
+    const struct htp_tensor * v    = octx->src[2];
+    const struct htp_tensor * mask = octx->src[3];
+    const struct htp_tensor * dst  = octx->dst;
 
     // Check support
     if ((q->type != HTP_TYPE_F16 && q->type != HTP_TYPE_F32) || k->type != HTP_TYPE_F16 || v->type != HTP_TYPE_F16) {
@@ -701,13 +701,11 @@ int op_flash_attn_ext(struct htp_ops_context * octx) {
         return HTP_STATUS_VTCM_TOO_SMALL;
     }
 
-    octx->src0_spad.data = octx->ctx->vtcm_base;
-    octx->src1_spad.data = octx->src0_spad.data + octx->src0_spad.size;
-    octx->src2_spad.data = octx->src1_spad.data + octx->src1_spad.size;
-    octx->src3_spad.data = octx->src2_spad.data + octx->src2_spad.size;
-    octx->dst_spad.data  = octx->src3_spad.data + octx->src3_spad.size;
-
-    // FARF(ERROR, "fa: qrows-per-thread %u", factx.qrows_per_thread);
+    octx->src0_spad.data = octx->ctx->vtcm_base;                        octx->src0_spad.src = NULL;
+    octx->src1_spad.data = octx->src0_spad.data + octx->src0_spad.size; octx->src1_spad.src = NULL;
+    octx->src2_spad.data = octx->src1_spad.data + octx->src1_spad.size; octx->src2_spad.src = NULL;
+    octx->src3_spad.data = octx->src2_spad.data + octx->src2_spad.size; octx->src3_spad.src = NULL;
+    octx->dst_spad.data  = octx->src3_spad.data + octx->src3_spad.size; octx->dst_spad.src  = NULL;
 
     if (!(octx->flags & HTP_OPFLAGS_SKIP_COMPUTE)) {
         worker_pool_run_func(octx->ctx->worker_pool, flash_attn_ext_f16_thread, &factx, octx->n_threads);

ggml/src/ggml-hexagon/htp/get-rows-ops.c

@@ -11,7 +11,7 @@
 #define GGML_COMMON_DECL_C
 #include "ggml-common.h"
 #include "htp-ctx.h"
-#include "htp-msg.h"
+#include "htp-ops.h"
 #include "htp-ops.h"
 #include "hvx-utils.h"
 
@@ -23,27 +23,33 @@ struct get_rows_context {
 };
 
 #define get_rows_preamble \
-    const uint32_t ne00 = octx->src0.ne[0]; \
-    const uint32_t ne01 = octx->src0.ne[1]; \
-    const uint32_t ne02 = octx->src0.ne[2]; \
-    const uint32_t ne03 = octx->src0.ne[3]; \
-                                            \
-    const uint32_t ne10 = octx->src1.ne[0]; \
-    const uint32_t ne11 = octx->src1.ne[1]; \
-    const uint32_t ne12 = octx->src1.ne[2]; \
-                                            \
-    const uint32_t nb01 = octx->src0.nb[1]; \
-    const uint32_t nb02 = octx->src0.nb[2]; \
-    const uint32_t nb03 = octx->src0.nb[3]; \
-                                            \
-    const uint32_t nb10 = octx->src1.nb[0]; \
-    const uint32_t nb11 = octx->src1.nb[1]; \
-    const uint32_t nb12 = octx->src1.nb[2]; \
-                                            \
-    const uint32_t nb1 = octx->dst.nb[1];   \
-    const uint32_t nb2 = octx->dst.nb[2];   \
-    const uint32_t nb3 = octx->dst.nb[3];   \
-                                            \
+    const uint32_t ne00 = octx->src[0]->ne[0]; \
+    const uint32_t ne01 = octx->src[0]->ne[1]; \
+    const uint32_t ne02 = octx->src[0]->ne[2]; \
+    const uint32_t ne03 = octx->src[0]->ne[3]; \
+                                               \
+    const uint32_t ne10 = octx->src[1]->ne[0]; \
+    const uint32_t ne11 = octx->src[1]->ne[1]; \
+    const uint32_t ne12 = octx->src[1]->ne[2]; \
+    const uint32_t ne13 = octx->src[1]->ne[3]; \
+                                               \
+    const uint32_t ne0 = octx->dst->ne[0];     \
+    const uint32_t ne1 = octx->dst->ne[1];     \
+    const uint32_t ne2 = octx->dst->ne[2];     \
+    const uint32_t ne3 = octx->dst->ne[3];     \
+                                               \
+    const uint32_t nb01 = octx->src[0]->nb[1]; \
+    const uint32_t nb02 = octx->src[0]->nb[2]; \
+    const uint32_t nb03 = octx->src[0]->nb[3]; \
+                                               \
+    const uint32_t nb10 = octx->src[1]->nb[0]; \
+    const uint32_t nb11 = octx->src[1]->nb[1]; \
+    const uint32_t nb12 = octx->src[1]->nb[2]; \
+                                               \
+    const uint32_t nb1 = octx->dst->nb[1];     \
+    const uint32_t nb2 = octx->dst->nb[2];     \
+    const uint32_t nb3 = octx->dst->nb[3];     \
+                                               \
     const uint32_t nr = ne10 * ne11 * ne12;
 
 static void get_rows_thread_f32_f32(unsigned int nth, unsigned int ith, void *data) {
@@ -51,12 +57,14 @@ static void get_rows_thread_f32_f32(unsigned int nth, unsigned int ith, void *da
     struct htp_ops_context * octx = grctx->octx;
     get_rows_preamble;
 
+    uint64_t qt = HAP_perf_get_qtimer_count();
+
     // parallelize by src1 elements (which correspond to dst rows)
     const uint32_t dr  = grctx->src1_nrows_per_thread;
     const uint32_t ir0 = dr * ith;
     const uint32_t ir1 = (ir0 + dr < nr) ? (ir0 + dr) : nr;
 
-    const bool is_i32 = (octx->src1.type == HTP_TYPE_I32);
+    const bool is_i32 = (octx->src[1]->type == HTP_TYPE_I32);
 
     for (uint32_t i = ir0; i < ir1; ++i) {
         const uint32_t i12 = fastdiv(i, &grctx->get_rows_div_ne10_ne11);
@@ -64,7 +72,7 @@ static void get_rows_thread_f32_f32(unsigned int nth, unsigned int ith, void *da
         const uint32_t i11 = fastdiv(rem, &grctx->get_rows_div_ne10);
         const uint32_t i10 = rem - i11 * ne10;
 
-        const uintptr_t src1_addr = octx->src1.data + i10*nb10 + i11*nb11 + i12*nb12;
+        const uintptr_t src1_addr = octx->src[1]->data + i10*nb10 + i11*nb11 + i12*nb12;
 
         uint32_t i01 = is_i32 ? *(int32_t *)src1_addr : *(int64_t *)src1_addr;
 
@@ -73,10 +81,14 @@ static void get_rows_thread_f32_f32(unsigned int nth, unsigned int ith, void *da
             continue;
         }
 
-        const uintptr_t src0_ptr = octx->src0.data + i01*nb01 + i11*nb02 + i12*nb03;
-        const uintptr_t dst_ptr  = octx->dst.data  + i10*nb1  + i11*nb2  + i12*nb3;
+        const uintptr_t src0_ptr = octx->src[0]->data + i01*nb01 + i11*nb02 + i12*nb03;
+        const uintptr_t dst_ptr  = octx->dst->data    + i10*nb1  + i11*nb2  + i12*nb3;
         hvx_copy_f32_uu((uint8_t *)dst_ptr, (const uint8_t *)src0_ptr, ne00);
     }
+
+    qt = HAP_perf_qtimer_count_to_us(HAP_perf_get_qtimer_count() - qt);
+    FARF(HIGH, "get-rows-f32-f32 %d/%d: %ux%ux%ux%u (%u:%u) x %ux%ux%ux%u -> %ux%ux%ux%u usec %u\n", ith, nth,
+         ne00, ne01, ne02, ne03, ir0, ir1, ne10, ne11, ne12, ne13, ne0, ne1, ne2, ne3, (unsigned) qt);
 }
 
 int op_get_rows(struct htp_ops_context * octx) {
@@ -84,15 +96,15 @@ int op_get_rows(struct htp_ops_context * octx) {
 
     const uint32_t n_threads = MIN(nr, octx->n_threads);
 
-    if (octx->src0.type != HTP_TYPE_F32) {
+    if (octx->src[0]->type != HTP_TYPE_F32) {
         return HTP_STATUS_NO_SUPPORT;
     }
 
-    if (octx->dst.type != HTP_TYPE_F32) {
+    if (octx->dst->type != HTP_TYPE_F32) {
         return HTP_STATUS_NO_SUPPORT;
     }
 
-    if (octx->src1.type != HTP_TYPE_I32 && octx->src1.type != HTP_TYPE_I64) {
+    if (octx->src[1]->type != HTP_TYPE_I32 && octx->src[1]->type != HTP_TYPE_I64) {
         return HTP_STATUS_NO_SUPPORT;
     }
 
@@ -102,8 +114,8 @@ int op_get_rows(struct htp_ops_context * octx) {
 
     struct get_rows_context grctx;
     grctx.octx = octx;
-    grctx.get_rows_div_ne10      = init_fastdiv_values(octx->src1.ne[0]);
-    grctx.get_rows_div_ne10_ne11 = init_fastdiv_values(octx->src1.ne[0] * octx->src1.ne[1]);
+    grctx.get_rows_div_ne10      = init_fastdiv_values(octx->src[1]->ne[0]);
+    grctx.get_rows_div_ne10_ne11 = init_fastdiv_values(octx->src[1]->ne[0] * octx->src[1]->ne[1]);
 
     grctx.src1_nrows_per_thread = (nr + n_threads - 1) / n_threads;
 

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

@@ -3,8 +3,10 @@
 
 #include <stdbool.h>
 #include <stdint.h>
+#include <qurt_memory.h>
 
 #include "hexagon_types.h"
+#include "hexagon_protos.h"
 
 #include "hex-fastdiv.h"
 #include "hex-dump.h"
@@ -29,6 +31,14 @@ static inline uint64_t hex_get_pktcnt() {
     return pktcnt;
 }
 
+static inline uint32_t hex_ceil_pow2(uint32_t x) {
+    if (x <= 1) { return 1; }
+    int p = 2;
+    x--;
+    while (x >>= 1) { p <<= 1; }
+    return p;
+}
+
 static inline size_t hmx_ceil_div(size_t num, size_t den) {
     return (num + den - 1) / den;
 }
@@ -68,4 +78,26 @@ static inline void hex_l2fetch(const void * p, uint32_t width, uint32_t stride,
     Q6_l2fetch_AP((void *) p, control);
 }
 
+#define HEX_L2_LINE_SIZE  64
+#define HEX_L2_FLUSH_SIZE (128 * 1024)
+
+static inline void hex_l2flush(void * addr, size_t size) {
+    if (size > HEX_L2_FLUSH_SIZE) {
+        qurt_mem_cache_clean((qurt_addr_t) 0, 0, QURT_MEM_CACHE_FLUSH_INVALIDATE_ALL, QURT_MEM_DCACHE);
+    } else {
+        const uint32_t s = (uint32_t) addr;
+        const uint32_t e = s + size;
+        for (uint32_t i = s; i < e; i += HEX_L2_LINE_SIZE * 4) {
+            Q6_dccleaninva_A((void *) i + HEX_L2_LINE_SIZE * 0);
+            Q6_dccleaninva_A((void *) i + HEX_L2_LINE_SIZE * 1);
+            Q6_dccleaninva_A((void *) i + HEX_L2_LINE_SIZE * 2);
+            Q6_dccleaninva_A((void *) i + HEX_L2_LINE_SIZE * 3);
+        }
+    }
+}
+
+static inline void hex_pause() {
+    asm volatile(" pause(#255)\n");
+}
+
 #endif /* HEX_UTILS_H */

ggml/src/ggml-hexagon/htp/hmx-matmul-ops.c

@@ -16,14 +16,16 @@
 #include "ggml-common.h"
 
 #include "hex-dma.h"
+#include "worker-pool.h"
+
 #include "hvx-utils.h"
 #include "hvx-dump.h"
-#include "worker-pool.h"
 #include "htp-ctx.h"
-#include "htp-msg.h"
+#include "htp-ops.h"
 
-#include "hmx-utils.h"
 #include "hmx-ops.h"
+#include "hmx-utils.h"
+#include "hmx-queue.h"
 #include "hmx-profile.h"
 
 static const __fp16 q4_0_to_fp16_lut[64] __attribute__((aligned(VLEN))) = {
@@ -47,7 +49,8 @@ static const __fp16 iq4_nl_to_fp16_lut[64] __attribute__((aligned(VLEN))) = {
 static const int32_t weight_transpose_scatter_offsets[32] __attribute__((aligned(VLEN))) = {
     0*128,  1*128,  2*128,  3*128,  4*128,  5*128,  6*128,  7*128,
     8*128,  9*128, 10*128, 11*128, 12*128, 13*128, 14*128, 15*128,
-    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    16*128, 17*128, 18*128, 19*128, 20*128, 21*128, 22*128, 23*128,
+    24*128, 25*128, 26*128, 27*128, 28*128, 29*128, 30*128, 31*128
 };
 
 // Scales per x4x2 logical block: 8 × sizeof(__fp16) = 16 bytes
@@ -109,36 +112,45 @@ static inline bool hmx_add_overflow(size_t a, size_t b, size_t *out) {
     return false;
 }
 
-// Search for optimal (mc, nc) chunk sizes that maximize mc * nc within VTCM budget.
+// Search for optimal (mc, nc) chunk sizes within VTCM budget.
 //
-// Cost model: total = nc * per_n_cost + mc * per_m_cost + mc * nc * per_mn_cost + overhead
-//   per_n_cost:  bytes per nc column (weight + scratch buffers)
-//   per_m_cost:  bytes per mc row (activation)
-//   per_mn_cost: bytes per mc*nc element (output)
-//   overhead:    fixed bytes (scales 256B, eye_tile 2048B, etc.)
+// VTCM model: nc * per_n_cost + mc * per_m_cost + mc * nc * per_mn_cost + overhead
+//
+// Minimize ceil(m/mc) * m_block_cost + ceil(n/nc) * n_block_cost.
+// All matmul paths repeat weight processing per M-block and activation loading
+// per N-block, so discrete block counts drive total overhead.
+// Tie-break: when cost is equal, prefer larger mc * nc.
+//
+// Caller-provided coefficients:
+//   m_block_cost: penalty per extra M-block (weight redundancy, scales with n).
+//   n_block_cost: penalty per extra N-block (activation redundancy, scales with m).
 //
 // Algorithm: nc sweeps from n_max down by 32, analytically solving for mc_max.
 // Returns 0 on success, -1 if VTCM is insufficient.
-static int hmx_compute_chunks(
-    size_t vtcm_total, size_t overhead,
-    size_t per_n_cost, size_t per_m_cost, size_t per_mn_cost,
-    int m, int n,
-    size_t *m_chunk_out, size_t *n_chunk_out,
-    size_t *total_out)
-{
+static int hmx_compute_chunks(size_t   vtcm_total,
+                              size_t   overhead,
+                              size_t   per_n_cost,
+                              size_t   per_m_cost,
+                              size_t   per_mn_cost,
+                              int      m,
+                              int      n,
+                              size_t   m_block_cost,
+                              size_t   n_block_cost,
+                              size_t * m_chunk_out,
+                              size_t * n_chunk_out,
+                              size_t * total_out) {
     if (m <= 0 || n <= 0) return -1;
     if (vtcm_total <= overhead) return -1;
     if (per_n_cost == 0 || per_m_cost == 0 || per_mn_cost == 0) return -1;
 
     const size_t usable = vtcm_total - overhead;
-    size_t best_mn = 0, best_m = 0, best_n = 0;
+
+    size_t best_cost = SIZE_MAX;
+    size_t best_mn   = 0;
+    size_t best_m = 0, best_n = 0;
 
     const size_t n_max = hex_align_down((size_t)n, HMX_FP16_TILE_N_COLS);
     for (size_t nc = n_max; nc >= HMX_FP16_TILE_N_COLS; nc -= HMX_FP16_TILE_N_COLS) {
-        // Early exit: if nc * m_max cannot beat best, smaller nc won't either
-        if (nc * hex_align_down((size_t)m, HMX_FP16_TILE_N_ROWS) <= best_mn)
-            break;
-
         size_t n_fixed = 0, ncmn = 0, mc_denom = 0;
         if (hmx_mul_overflow(nc, per_n_cost, &n_fixed)) continue;
         if (n_fixed >= usable) goto next_nc;
@@ -152,10 +164,19 @@ static int hmx_compute_chunks(
             mc = hex_align_down(mc, HMX_FP16_TILE_N_ROWS);
             mc = hex_smin(mc, (size_t)m);
 
-            if (mc > 0 && mc * nc > best_mn) {
-                best_mn = mc * nc;
-                best_m  = mc;
-                best_n  = nc;
+            if (mc == 0) {
+                goto next_nc;
+            }
+
+            size_t mblocks = ((size_t) m + mc - 1) / mc;
+            size_t nblocks = ((size_t) n + nc - 1) / nc;
+            size_t cost    = mblocks * m_block_cost + nblocks * n_block_cost;
+            size_t mn      = mc * nc;
+            if (cost < best_cost || (cost == best_cost && mn > best_mn)) {
+                best_cost = cost;
+                best_mn   = mn;
+                best_m    = mc;
+                best_n    = nc;
             }
         }
 
@@ -233,7 +254,7 @@ static inline HVX_Vector dequantize_x4x2_q4_0_group_hvx(
     const HVX_Vector mask_h4 = Q6_Vb_vsplat_R(0x0F);
     HVX_Vector v_scales = hvx_vec_splat_f16(*scale);
     // q4x4x2 stores two int4 values per byte. Keep only the selected nibble.
-    HVX_Vector v_quants = upper_nibbles ? Q6_Vub_vlsr_VubR(vq, 4) : vq;
+    HVX_Vector v_quants =  Q6_Vub_vlsr_VubR(vq, 4 * upper_nibbles);
     v_quants = Q6_V_vand_VV(v_quants, mask_h4);
     // Shuffle before LUT
     v_quants = Q6_Vb_vshuff_Vb(v_quants);
@@ -257,7 +278,7 @@ static inline void dequantize_x4x2_q4_0_x4groups_hvx(
     // Load all 128 packed bytes (4 contiguous 32-byte groups)
     HVX_Vector vq = hvx_vmemu(packed_128);
     const HVX_Vector mask_h4 = Q6_Vb_vsplat_R(0x0F);
-    HVX_Vector v_quants = upper_nibbles ? Q6_Vub_vlsr_VubR(vq, 4) : vq;
+    HVX_Vector v_quants = Q6_Vub_vlsr_VubR(vq, 4 * upper_nibbles);
     v_quants = Q6_V_vand_VV(v_quants, mask_h4);
 
     // Shuffle before LUT
@@ -277,10 +298,8 @@ static inline void dequantize_x4x2_q4_0_x4groups_hvx(
     v_hi = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(v_hi, v_sc23));
 
     // Extract individual groups: scatter uses q_mask64 so only first 64 bytes matter
-    out[0] = v_lo;                      // group0 already in [0:63]
-    out[1] = Q6_V_vror_VR(v_lo, 64);    // group1 rotated to [0:63]
-    out[2] = v_hi;                      // group2 already in [0:63]
-    out[3] = Q6_V_vror_VR(v_hi, 64);    // group3 rotated to [0:63]
+    out[0] = v_lo; // group0 already in [0:63]
+    out[1] = v_hi; // group2 already in [0:63]
 }
 
 // Dequantize one x4x2 Q8_0 group (32 int8 quants) -> 32 FP16 in first 64 bytes.
@@ -384,8 +403,9 @@ static void dequantize_x4x2_weight_to_fp16_tiles_task(
         size_t row_stride, int weight_type,
         int start_tile, int end_tile) {
 
-    const int n_k_tiles = k_block / HMX_FP16_TILE_N_COLS;
-    const int qrow_size = (weight_type == HTP_TYPE_Q8_0) ? k_block : (k_block / 2);
+    const int n_k_tiles = (unsigned)k_block / HMX_FP16_TILE_N_COLS;
+    const bool is_q4 = (weight_type == HTP_TYPE_Q4_0 || weight_type == HTP_TYPE_IQ4_NL);
+    const int qrow_size = is_q4 ? ((unsigned)k_block / 2) : k_block;
 
     const HVX_Vector vlut_cvt = (weight_type == HTP_TYPE_IQ4_NL) ? hvx_vmem(iq4_nl_to_fp16_lut) :
                                 (weight_type == HTP_TYPE_MXFP4)  ? hvx_vmem(mxfp4_to_fp16_lut) :
@@ -398,47 +418,46 @@ static void dequantize_x4x2_weight_to_fp16_tiles_task(
     const HVX_Vector v_scat_step = Q6_V_vsplat_R(4);  // 4 bytes = 1 column step
     const HVX_VectorPred q_mask64 = Q6_Q_vsetq_R(64);  // first 16 words (64 bytes)
 
-    for (int t = start_tile; t < end_tile; ) {
-        int ct = t / n_k_tiles;  // column tile index
-        int kt = t % n_k_tiles;  // K tile index
+    unsigned ct = (unsigned)start_tile / n_k_tiles;  // column tile index
+    unsigned kt = (unsigned)start_tile % n_k_tiles;  // K tile index
+    for (unsigned t = start_tile; t < end_tile; ) {
+        if (kt >= n_k_tiles) { kt = 0; ct++; }
 
-        // --- Batch-4 fast path for Q4_0/IQ4_NL: process 4 contiguous K-tiles with one vlut16 per row ---
-        if ((weight_type == HTP_TYPE_Q4_0 || weight_type == HTP_TYPE_IQ4_NL) && (kt % 4 == 0) && (t + 4 <= end_tile) &&
-            ((t + 3) / n_k_tiles == ct)) {
-            int blk_idx      = (kt * 32) / QK_Q4_0x4x2;
-            int sub_blk_base = ((kt * 32) % QK_Q4_0x4x2) / 32;  // 0 or 4
-            bool upper       = (sub_blk_base >= 4);
-            int packed_off   = blk_idx * (QK_Q4_0x4x2 / 2);     // 128 contiguous packed bytes
-            int scale_off    = qrow_size + blk_idx * HMX_X4X2_DBLK_SIZE
-                             + sub_blk_base * (int)sizeof(__fp16);   // 4 consecutive scales
+        // --- Batch-4 fast path for Q4: process 4 contiguous K-tiles with one vlut16 per row ---
+        if (is_q4 && (kt % 4 == 0) && (t + 4 <= end_tile) && ((t + 3) / n_k_tiles == ct)) {
+            unsigned blk_idx      = (kt * 32) / QK_Q4_0x4x2;
+            unsigned sub_blk_base = ((kt * 32) % QK_Q4_0x4x2) / 32;  // 0 or 4
+            bool upper            = (sub_blk_base >= 4);
+            unsigned packed_off   = blk_idx * (QK_Q4_0x4x2 / 2);     // 128 contiguous packed bytes
+            unsigned scale_off    = qrow_size + blk_idx * HMX_X4X2_DBLK_SIZE
+                                  + sub_blk_base * (int)sizeof(__fp16);   // 4 consecutive scales
 
             __fp16 *tile_bases[4];
-            for (int g = 0; g < 4; g++) { tile_bases[g] = vtcm_dst + (t + g) * HMX_FP16_TILE_N_ELMS; }
+            for (unsigned g = 0; g < 4; g++) { tile_bases[g] = vtcm_dst + (t + g) * HMX_FP16_TILE_N_ELMS; }
 
             HVX_Vector v_off = v_scat_base;
-            for (int r = 0; r < HMX_FP16_TILE_N_ROWS; r += 2) {
-                int row0 = ct * HMX_FP16_TILE_N_COLS + r;
-                int row1 = row0 + 1;
-                const uint8_t *r0 = vtcm_src + row0 * row_stride;
-                const uint8_t *r1 = vtcm_src + row1 * row_stride;
 
-                HVX_Vector v0[4], v1[4];
+            unsigned row_offset = ct * HMX_FP16_TILE_N_COLS * row_stride;
+            unsigned row1 = ct * HMX_FP16_TILE_N_COLS + 1;
+
+            for (int r = 0; r < HMX_FP16_TILE_N_ROWS; r += 2, row1 += 2) {
+                HVX_Vector v0[2];
+                const uint8_t *r0 = vtcm_src + row_offset; row_offset += row_stride;
                 dequantize_x4x2_q4_0_x4groups_hvx(r0 + packed_off, upper, (const __fp16 *)(r0 + scale_off), vlut_cvt, v0);
-                if (row1 < n_cols) {
-                    dequantize_x4x2_q4_0_x4groups_hvx(r1 + packed_off, upper, (const __fp16 *)(r1 + scale_off), vlut_cvt, v1);
-                } else {
-                    v1[0] = v1[1] = v1[2] = v1[3] = Q6_V_vzero();
-                }
-
-                for (int g = 0; g < 4; g++) { Q6_vscatter_QRMVwV(q_mask64, (size_t)tile_bases[g], HMX_FP16_TILE_SIZE - 1, v_off, v0[g]); }
+                Q6_vscatter_RMVwV((size_t)tile_bases[0], 2 * HMX_FP16_TILE_SIZE - 1, v_off, v0[0]);
+                Q6_vscatter_RMVwV((size_t)tile_bases[2], 2 * HMX_FP16_TILE_SIZE - 1, v_off, v0[1]);
                 v_off = Q6_Vw_vadd_VwVw(v_off, v_scat_step);
-                for (int g = 0; g < 4; g++) { Q6_vscatter_QRMVwV(q_mask64, (size_t)tile_bases[g], HMX_FP16_TILE_SIZE - 1, v_off, v1[g]); }
+
+
+                r0 = vtcm_src + row_offset; row_offset += row_stride;
+                dequantize_x4x2_q4_0_x4groups_hvx(r0 + packed_off, upper, (const __fp16 *)(r0 + scale_off), vlut_cvt, v0);
+                Q6_vscatter_RMVwV((size_t)tile_bases[0], 2 * HMX_FP16_TILE_SIZE - 1, v_off, v0[0]);
+                Q6_vscatter_RMVwV((size_t)tile_bases[2], 2 * HMX_FP16_TILE_SIZE - 1, v_off, v0[1]);
                 v_off = Q6_Vw_vadd_VwVw(v_off, v_scat_step);
             }
 
             for (int g = 0; g < 4; g++) { (void) *(volatile HVX_Vector *)(tile_bases[g]); }
-
-            t += 4;
+            t += 4; kt += 4;
             continue;
         }
 
@@ -495,20 +514,19 @@ static void dequantize_x4x2_weight_to_fp16_tiles_task(
         // --- Single-tile fallback ---
         __fp16 *tile_base = vtcm_dst + t * HMX_FP16_TILE_N_ELMS;
 
-        if (weight_type == HTP_TYPE_Q4_0 || weight_type == HTP_TYPE_IQ4_NL) {
-            int blk_idx  = (kt * 32) / QK_Q4_0x4x2;
-            int sub_blk  = ((kt * 32) % QK_Q4_0x4x2) / 32;
-            bool upper   = (sub_blk >= 4);
-            int byte_off  = blk_idx * (QK_Q4_0x4x2 / 2) + (upper ? (sub_blk - 4) : sub_blk) * 32;
-            int scale_off = qrow_size + blk_idx * HMX_X4X2_DBLK_SIZE + sub_blk * (int)sizeof(__fp16);
+        if (is_q4) {
+            unsigned blk_idx   = (kt * 32) / QK_Q4_0x4x2;
+            unsigned sub_blk   = ((kt * 32) % QK_Q4_0x4x2) / 32;
+            bool upper         = (sub_blk >= 4);
+            unsigned byte_off  = blk_idx * (QK_Q4_0x4x2 / 2) + (upper ? (sub_blk - 4) : sub_blk) * 32;
+            unsigned scale_off = qrow_size + blk_idx * HMX_X4X2_DBLK_SIZE + sub_blk * (int)sizeof(__fp16);
 
             HVX_Vector v_off = v_scat_base;  // reset to column 0
-            for (int r = 0; r < HMX_FP16_TILE_N_ROWS; r += 2) {
-                int row0 = ct * HMX_FP16_TILE_N_COLS + r;
-                int row1 = row0 + 1;
-
-                const uint8_t *r0 = vtcm_src + row0 * row_stride;
-                const uint8_t *r1 = vtcm_src + row1 * row_stride;
+            unsigned row_offset = ct * HMX_FP16_TILE_N_COLS * row_stride;
+            unsigned row1 = ct * HMX_FP16_TILE_N_COLS + 1;
+            for (int r = 0; r < HMX_FP16_TILE_N_ROWS; r += 2, row1 += 2) {
+                const uint8_t *r0 = vtcm_src + row_offset; row_offset += row_stride;
+                const uint8_t *r1 = vtcm_src + row_offset; row_offset += row_stride;
 
                 HVX_Vector v0 = dequantize_x4x2_q4_0_group_hvx(
                     r0 + byte_off, upper, (const __fp16 *)(r0 + scale_off), vlut_cvt);
@@ -585,7 +603,7 @@ static void dequantize_x4x2_weight_to_fp16_tiles_task(
             }
             (void) *(volatile HVX_Vector *)(tile_base);
         }
-        ++t;
+        ++t; ++kt;
     }
 
     // Drain HVX scatter write buffer: a vmem load on the same HW thread retires
@@ -653,9 +671,13 @@ static void dequantize_x4x2_weight_chunk_to_fp16_tiles(
 // --- End x4x2 dequantizers ---
 
 // requires external HMX lock
-static void core_dot_chunk_fp16(__fp16 *output, const __fp16 *activation, const __fp16 *weight, const __fp16 *scales,
+static void core_dot_chunk_fp16(__fp16 *restrict output, const __fp16 *restrict activation, const __fp16 *restrict weight, const __fp16 *restrict scales,
                                 int n_row_tiles, int n_col_tiles, int n_dot_tiles) {
-    hmx_set_output_scales(scales);
+    __builtin_assume(n_row_tiles > 0);
+    __builtin_assume(n_col_tiles > 0);
+    __builtin_assume(n_dot_tiles > 0);
+
+    Q6_bias_mxmem2_A((void *)scales);
 
     for (int r = 0; r < n_row_tiles; ++r) {
         for (int c = 0; c < n_col_tiles; ++c) {
@@ -665,16 +687,55 @@ static void core_dot_chunk_fp16(__fp16 *output, const __fp16 *activation, const
             const __fp16 *col_tiles = weight + c * n_dot_tiles * HMX_FP16_TILE_N_ELMS;
 
             for (int k = 0; k < n_dot_tiles; ++k) {
-                int offset = k * HMX_FP16_TILE_N_ELMS;
-                hmx_load_tile_pair_fp16(row_tiles + offset, col_tiles + offset);
+                Q6_activation_hf_mxmem_RR((unsigned int)row_tiles, 2047);
+                Q6_weight_hf_mxmem_RR((unsigned int)col_tiles, 2047);
+                row_tiles += HMX_FP16_TILE_N_ELMS;
+                col_tiles += HMX_FP16_TILE_N_ELMS;
             }
 
             __fp16 *out_tile = output + (r * n_col_tiles + c) * HMX_FP16_TILE_N_ELMS;
-            hmx_consume_accumulator_fp16(out_tile);
+            Q6_mxmem_AR_after_hf(out_tile, 0);
         }
     }
 }
 
+// --- Async HMX matmul job (for pipeline overlap) ---
+
+typedef struct {
+    __fp16 *       output;
+    const __fp16 * activation;
+    const __fp16 * weight;
+    const __fp16 * scales;
+    uint32_t       n_row_tiles;
+    uint32_t       n_col_tiles;
+    uint32_t       n_dot_tiles;
+} hmx_matmul_job_t;
+
+static void hmx_matmul_worker_fn(void * data) {
+    hmx_matmul_job_t * job = (hmx_matmul_job_t *) data;
+    FARF(HIGH, "hmx-mm-job: n_row_tiles %u n_col_tiles %u n_dot_tiles %u", job->n_row_tiles, job->n_col_tiles, job->n_dot_tiles);
+    core_dot_chunk_fp16(job->output, job->activation, job->weight, job->scales, job->n_row_tiles, job->n_col_tiles, job->n_dot_tiles);
+}
+
+static inline void hmx_matmul_job_init(hmx_matmul_job_t * job,
+                                       __fp16 *           output,
+                                       const __fp16 *     activation,
+                                       const __fp16 *     weight,
+                                       const __fp16 *     scales,
+                                       int                n_row_tiles,
+                                       int                n_col_tiles,
+                                       int                n_dot_tiles) {
+    job->output      = output;
+    job->activation  = activation;
+    job->weight      = weight;
+    job->scales      = scales;
+    job->n_row_tiles = n_row_tiles;
+    job->n_col_tiles = n_col_tiles;
+    job->n_dot_tiles = n_dot_tiles;
+}
+
+// --- End async HMX matmul job ---
+
 static void transfer_output_chunk_fp16_to_fp32(float *restrict dst, const __fp16 *restrict vtcm_src, int n_rows, int n_cols, int n) {
     assert(n_cols % HMX_FP16_TILE_N_COLS == 0);
     const int n_col_tiles = n_cols / HMX_FP16_TILE_N_COLS;
@@ -821,7 +882,7 @@ int hmx_mat_mul_permuted_w16a32_batched(struct htp_context *ctx, const hmx_matmu
     // and each q_head is computed individually to avoid tile-major packing
     // issues.  m_chunk_n_rows is always a multiple of 32 (from
     // hmx_compute_chunks), so per-head tile arrays don't overlap.
-    const size_t vtcm_budget  = ctx->vtcm_scratch_size;
+    const size_t vtcm_budget  = ctx->vtcm_size;
     const size_t vec_dot_size = params->k * sizeof(__fp16);
 
     // When the activation has a large stride (e.g. permuted Q tensor with
@@ -832,12 +893,13 @@ int hmx_mat_mul_permuted_w16a32_batched(struct htp_context *ctx, const hmx_matmu
     const size_t f32_scratch_per_m = use_dma_activation ? (size_t) params->k * sizeof(float) : 0;
 
     size_t m_chunk_n_rows = 0, n_chunk_n_cols = 0, vtcm_used = 0;
+    // FP16 weight: interleave and activation load have similar per-element cost.
     if (hmx_compute_chunks(vtcm_budget, /*overhead=*/256,
-                             /*per_n=*/3 * vec_dot_size,
-                             /*per_m=*/group_size * vec_dot_size + f32_scratch_per_m,
-                             /*per_mn=*/sizeof(__fp16),
-                             params->m, params->n,
-                             &m_chunk_n_rows, &n_chunk_n_cols, &vtcm_used) != 0) {
+                           /*per_n=*/3 * vec_dot_size,
+                           /*per_m=*/group_size * vec_dot_size + f32_scratch_per_m,
+                           /*per_mn=*/sizeof(__fp16), params->m, params->n,
+                           /*m_block_cost=*/(size_t) params->n,
+                           /*n_block_cost=*/(size_t) params->m, &m_chunk_n_rows, &n_chunk_n_cols, &vtcm_used) != 0) {
         FARF(HIGH, "%s: grouped path does not fit VTCM, falling back to legacy batched loop", __func__);
         return hmx_mat_mul_permuted_w16a32_batched_legacy(ctx, params);
     }
@@ -998,7 +1060,7 @@ int hmx_mat_mul_permuted_w16a32(struct htp_context *ctx, float *restrict dst, co
     }
 
     // --- Dynamic VTCM layout ---
-    const size_t vtcm_budget  = ctx->vtcm_scratch_size;
+    const size_t vtcm_budget  = ctx->vtcm_size;
     const size_t vec_dot_size = k * sizeof(__fp16);
 
     // DMA-based activation gather for strided tensors (see batched path comment).
@@ -1006,13 +1068,15 @@ int hmx_mat_mul_permuted_w16a32(struct htp_context *ctx, float *restrict dst, co
     const size_t f32_scratch_per_m = use_dma_activation ? (size_t) k * sizeof(float) : 0;
 
     size_t m_chunk_n_rows = 0, n_chunk_n_cols = 0, vtcm_used = 0;
+    // FP16 weight: interleave and activation load have similar per-element cost.
     if (hmx_compute_chunks(vtcm_budget,
-                              /*overhead=*/ 256,
-                              /*per_n=*/    3 * vec_dot_size,  // W + S0 + S1
-                              /*per_m=*/    vec_dot_size + f32_scratch_per_m,  // A + optional F32 scratch
-                              /*per_mn=*/   sizeof(__fp16),     // O
-                              m, n,
-                              &m_chunk_n_rows, &n_chunk_n_cols, &vtcm_used) != 0) {
+                           /*overhead=*/256,
+                           /*per_n=*/3 * vec_dot_size,                  // W + S0 + S1
+                           /*per_m=*/vec_dot_size + f32_scratch_per_m,  // A + optional F32 scratch
+                           /*per_mn=*/sizeof(__fp16),                   // O
+                           m, n,
+                           /*m_block_cost=*/(size_t) n,
+                           /*n_block_cost=*/(size_t) m, &m_chunk_n_rows, &n_chunk_n_cols, &vtcm_used) != 0) {
         FARF(HIGH, "%s: VTCM too small (m=%d k=%d n=%d budget=%zu)", __func__, m, k, n, vtcm_budget);
         return -1;
     }
@@ -1157,6 +1221,8 @@ int hmx_mat_mul_permuted_w16a32(struct htp_context *ctx, float *restrict dst, co
 int mat_mul_qk_0_d16a32_out_stationary(struct htp_context *ctx, float *restrict out, const float *restrict x, const uint8_t *restrict w, int m,
                                        int k, int n, int w_type);
 
+#define FALLBACK_TO_STANDARD 1
+
 int hmx_mat_mul_permuted_qk_0_d16a32(struct htp_context *ctx, float *restrict dst, const float *restrict activation,
                                      const uint8_t *restrict permuted_weight, int m, int k, int n,
                                      int weight_type) {
@@ -1169,9 +1235,12 @@ int hmx_mat_mul_permuted_qk_0_d16a32(struct htp_context *ctx, float *restrict ds
 
     // for large m, k (e.g. prefill FFN Down), use out-stationary version
     if (m >= 128 && k > n && n > 1024) {
-        FARF(MEDIUM, "hmx_matmul_qk: OUT-STATIONARY path m=%d k=%d n=%d type=%d (K_BLOCK=512, %d K-iters with fp16 intermediate)",
-             m, k, n, weight_type, (k + 511) / 512);
-        return mat_mul_qk_0_d16a32_out_stationary(ctx, dst, activation, permuted_weight, m, k, n, weight_type);
+        int rc = mat_mul_qk_0_d16a32_out_stationary(ctx, dst, activation, permuted_weight, m, k, n, weight_type);
+        if (rc != FALLBACK_TO_STANDARD) {
+            return rc;  // 0 success, -1 error
+        }
+        FARF(MEDIUM, "hmx_matmul_qk: out-stationary fallback to standard m=%d k=%d n=%d", m, k, n);
+        // fall through to standard path
     }
 
     size_t row_stride = get_x4x2_row_stride(weight_type, k);
@@ -1182,7 +1251,7 @@ int hmx_mat_mul_permuted_qk_0_d16a32(struct htp_context *ctx, float *restrict ds
     FARF(MEDIUM, "hmx_matmul_qk: STANDARD path m=%d k=%d n=%d type=%d", m, k, n, weight_type);
 
     // --- Dynamic VTCM layout ---
-    const size_t vtcm_budget   = ctx->vtcm_scratch_size;
+    const size_t vtcm_budget   = ctx->vtcm_size;
     const size_t vec_dot_size  = k * sizeof(__fp16);
     const bool   use_pipeline  = (m >= 128) && (k <= n);
 
@@ -1197,9 +1266,10 @@ int hmx_mat_mul_permuted_qk_0_d16a32(struct htp_context *ctx, float *restrict ds
     }
 
     size_t m_chunk_n_rows = 0, n_chunk_n_cols = 0, vtcm_used = 0;
-    if (hmx_compute_chunks(vtcm_budget, /*overhead=*/256,
-                              per_n_cost, /*per_m=*/vec_dot_size, per_mn_cost,
-                              m, n, &m_chunk_n_rows, &n_chunk_n_cols, &vtcm_used) != 0) {
+    // Quantized weight: dequant ~1.5x more expensive per element than activation load.
+    if (hmx_compute_chunks(vtcm_budget, /*overhead=*/256, per_n_cost, /*per_m=*/vec_dot_size, per_mn_cost, m, n,
+                           /*m_block_cost=*/(size_t) n * 3,
+                           /*n_block_cost=*/(size_t) m * 2, &m_chunk_n_rows, &n_chunk_n_cols, &vtcm_used) != 0) {
         FARF(HIGH, "%s: VTCM too small (m=%d k=%d n=%d pipe=%d budget=%zu)",
              __func__, m, k, n, use_pipeline, vtcm_budget);
         return -1;
@@ -1256,9 +1326,8 @@ int hmx_mat_mul_permuted_qk_0_d16a32(struct htp_context *ctx, float *restrict ds
          use_pipeline ? "PIPELINE" : "SEQUENTIAL", m_chunk_n_rows, n_chunk_n_cols,
          (size_t)(vtcm_ptr - (uint8_t *)ctx->vtcm_base), vtcm_budget);
 
-    HAP_compute_res_hmx_lock(ctx->vtcm_rctx);
-
     if (!use_pipeline) {
+        HAP_compute_res_hmx_lock(ctx->vtcm_rctx);
         for (size_t mr = 0; mr < m; mr += m_chunk_n_rows) {
             // transfer activation matrix chunk into VTCM
             size_t n_rows = hex_smin(m - mr, m_chunk_n_rows);
@@ -1273,9 +1342,6 @@ int hmx_mat_mul_permuted_qk_0_d16a32(struct htp_context *ctx, float *restrict ds
             void *buf_curr = vtcm_scratch0;
             void *buf_next = vtcm_scratch1;
 
-            // issue async DDR data transfer for the first weight chunk
-            // NOTE: use 2D DMA (n_cols rows x row_stride bytes) instead of 1D
-            // because UDMA roiwidth is 16-bit and total size can exceed 65535.
             {
                 const size_t n_cols_first = hex_smin(n, n_chunk_n_cols);
                 dma_queue_push(ctx->dma[0], dma_make_ptr(buf_curr, permuted_weight), row_stride, row_stride, row_stride, n_cols_first);
@@ -1321,20 +1387,22 @@ int hmx_mat_mul_permuted_qk_0_d16a32(struct htp_context *ctx, float *restrict ds
                 TIMER_STOP(output_store);
             }
         }
+        HAP_compute_res_hmx_unlock(ctx->vtcm_rctx);
     } else {
         // 4-stage pipeline: DMA load (A), dequantize (B), HMX matmul (C), store (D)
-        // stage B and D (dequantize and store) are expected to be on the critical path
+        // HMX compute (C) runs on dedicated worker thread, overlapping with HVX stages (B, D).
 
         // A --> B: vtcm_qweight, 1 buffer
         // B --> C: vtcm_weight0/vtcm_weight1, 2 buffers
         // C --> D: vtcm_output0/vtcm_output1, 2 buffers
 
-        //
-        // LD ||A3|  | B3 ||
-        // MM ||    C2    ||
-        // ST || D1 |     ||
+        // Async timeline (C overlaps B+D):
+        //   main+HVX:   [A0][Act][B0][A1][sub C0][B1‖C0][A2][wait,sub C1][D0+B2‖C1][wait,sub C2][D1‖C2][wait][D2]
+        //   HMX queue:                   [████ C0 ████████][████ C1 ████████████][████ C2 ████████]
 
         int n_chunk_cnt = hmx_ceil_div(n, n_chunk_n_cols);
+        hmx_matmul_job_t job_slots[2];  // persistent double-buffered job descriptors
+
         for (size_t mr = 0; mr < m; mr += m_chunk_n_rows) {
             const size_t n_rows = hex_smin(m - mr, m_chunk_n_rows);
 
@@ -1355,31 +1423,34 @@ int hmx_mat_mul_permuted_qk_0_d16a32(struct htp_context *ctx, float *restrict ds
                 transfer_activation_chunk_threaded(ctx, vtcm_activation, activation_chunk, n_rows, k, k);
             }
 
-            // prologue: B0, A1, C0, B1
+            // prologue: B0, A1, submit C0 (async), B1 (overlaps C0)
             {
-                // B0
+                // B0: wait for DMA, dequant weight chunk 0
                 dma_queue_pop(ctx->dma[0]);
                 dequantize_x4x2_weight_chunk_to_fp16_tiles(ctx, vtcm_weight_bufs[0], vtcm_qweight, n_cols_A0, k, row_stride, weight_type);
 
-                // A1
+                // A1: issue DMA for weight chunk 1
                 const size_t n_cols_A1 = hex_smin(n - 1 * n_chunk_n_cols, n_chunk_n_cols);
                 if (1 < n_chunk_cnt) {
                     const uint8_t *qweight_chunk_A1 = permuted_weight + n_chunk_n_cols * row_stride;
                     dma_queue_push(ctx->dma[0], dma_make_ptr(vtcm_qweight, qweight_chunk_A1), row_stride, row_stride, row_stride, n_cols_A1);
                 }
 
-                // C0
-                core_dot_chunk_fp16((__fp16 *) vtcm_output_bufs[0], (__fp16 *) vtcm_activation, (__fp16 *) vtcm_weight_bufs[0], vtcm_scales,
-                         hmx_ceil_div(n_rows, HMX_FP16_TILE_N_ROWS), hmx_ceil_div(n_cols_A0, HMX_FP16_TILE_N_COLS), k / HMX_FP16_TILE_N_ROWS);
+                // submit C0 (non-blocking — HMX worker executes in parallel)
+                hmx_matmul_job_init(&job_slots[0], (__fp16 *) vtcm_output_bufs[0], (__fp16 *) vtcm_activation,
+                                    (__fp16 *) vtcm_weight_bufs[0], vtcm_scales,
+                                    hmx_ceil_div(n_rows, HMX_FP16_TILE_N_ROWS),
+                                    hmx_ceil_div(n_cols_A0, HMX_FP16_TILE_N_COLS), k / HMX_FP16_TILE_N_ROWS);
+                hmx_queue_push(ctx->hmx_queue, hmx_queue_make_desc(hmx_matmul_worker_fn, &job_slots[0]));
 
-                // B1
+                // B1: DMA pop + dequant (runs in parallel with C0 on HMX worker)
                 if (1 < n_chunk_cnt) {
                     dma_queue_pop(ctx->dma[0]);
                     dequantize_x4x2_weight_chunk_to_fp16_tiles(ctx, vtcm_weight_bufs[1], vtcm_qweight, n_cols_A1, k, row_stride, weight_type);
                 }
             }
 
-            // main loop
+            // main loop: wait C_i → submit C_{i+1} → D_i + B_{i+2} (parallel with C_{i+1})
             for (int i = 0; i < n_chunk_cnt; ++i) {
                 const size_t nc    = i * n_chunk_n_cols;
                 const size_t nc_p1 = nc + 1 * n_chunk_n_cols;
@@ -1389,36 +1460,41 @@ int hmx_mat_mul_permuted_qk_0_d16a32(struct htp_context *ctx, float *restrict ds
                 const size_t n_cols_p1 = hex_smin(n - nc_p1, n_chunk_n_cols);
                 const size_t n_cols_p2 = hex_smin(n - nc_p2, n_chunk_n_cols);
 
-                // issue A_{i+2}
+                // issue A_{i+2}: DMA push (non-blocking)
                 if (i + 2 < n_chunk_cnt) {
                     const uint8_t *qweight_chunk_p2 = permuted_weight + nc_p2 * row_stride;
                     dma_queue_push(ctx->dma[0], dma_make_ptr(vtcm_qweight, qweight_chunk_p2), row_stride, row_stride, row_stride, n_cols_p2);
                 }
 
-                // wait for HMX (C_{i}) -- C_{i} is done
+                // wait C_i: block until prologue/previous C completes
+                hmx_queue_pop(ctx->hmx_queue);
 
-                // result of B_{i+1} (input of C_{i+1}) should be ready now
-
-                // issue C_{i+1}
+                // submit C_{i+1} (non-blocking, overlaps with D_i + B_{i+2} below)
+                // job_slots[(i+1)%2] is safe: C_i just completed, freeing slot i%2's
+                // counterpart — and (i+1)%2 was last used by C_{i-1} which completed
+                // before C_i was submitted.
                 if (i + 1 < n_chunk_cnt) {
-                    core_dot_chunk_fp16((__fp16 *) vtcm_output_bufs[(i + 1) % 2], (__fp16 *) vtcm_activation, (__fp16 *) vtcm_weight_bufs[(i + 1) % 2], vtcm_scales,
-                        hmx_ceil_div(n_rows, HMX_FP16_TILE_N_ROWS), hmx_ceil_div(n_cols_p1, HMX_FP16_TILE_N_COLS), k / HMX_FP16_TILE_N_ROWS);
+                    hmx_matmul_job_init(&job_slots[(i + 1) % 2], (__fp16 *) vtcm_output_bufs[(i + 1) % 2],
+                                        (__fp16 *) vtcm_activation, (__fp16 *) vtcm_weight_bufs[(i + 1) % 2],
+                                        vtcm_scales, hmx_ceil_div(n_rows, HMX_FP16_TILE_N_ROWS),
+                                        hmx_ceil_div(n_cols_p1, HMX_FP16_TILE_N_COLS), k / HMX_FP16_TILE_N_ROWS);
+                    hmx_queue_push(ctx->hmx_queue, hmx_queue_make_desc(hmx_matmul_worker_fn, &job_slots[(i + 1) % 2]));
                 }
 
-                // compute D_{i}
+                // D_i: store output (multi-thread HVX, parallel with C_{i+1})
                 float *output_chunk = dst + (mr * n + nc);
                 transfer_output_chunk_threaded(ctx, output_chunk, vtcm_output_bufs[i % 2], n_rows, n_cols, n);
 
-                // wait for DMA (A_{i+2}), compute B_{i+2}
+                // B_{i+2}: DMA pop + dequant (multi-thread HVX, parallel with C_{i+1})
                 if (i + 2 < n_chunk_cnt) {
                     dma_queue_pop(ctx->dma[0]);
                     dequantize_x4x2_weight_chunk_to_fp16_tiles(ctx, vtcm_weight_bufs[(i + 2) % 2], vtcm_qweight, n_cols_p2, k, row_stride, weight_type);
                 }
             }
         }
-    }
 
-    HAP_compute_res_hmx_unlock(ctx->vtcm_rctx);
+        hmx_queue_suspend(ctx->hmx_queue);
+    }
 
     TIMER_STOP(total);
 
@@ -1437,10 +1513,13 @@ int hmx_mat_mul_permuted_qk_0_d16a32(struct htp_context *ctx, float *restrict ds
 }
 
 // C += AB
-void core_mma_chunk_fp16(__fp16 *c, const __fp16 *a, const __fp16 *b, const __fp16 *col_scales, const __fp16 *eye_tile,
+void core_mma_chunk_fp16(__fp16 *restrict c, const __fp16 *restrict a, const __fp16 *restrict b, const __fp16 *restrict col_scales, const __fp16 *restrict eye_tile,
                          int n_row_tiles, int n_col_tiles, int n_dot_tiles, bool zero_init) {
+    __builtin_assume(n_row_tiles > 0);
+    __builtin_assume(n_col_tiles > 0);
+    __builtin_assume(n_dot_tiles > 0);
 
-    hmx_set_output_scales(col_scales);
+    Q6_bias_mxmem2_A((void *)col_scales);
 
     for (int i = 0; i < n_row_tiles; ++i) {
         for (int j = 0; j < n_col_tiles; ++j) {
@@ -1451,15 +1530,17 @@ void core_mma_chunk_fp16(__fp16 *c, const __fp16 *a, const __fp16 *b, const __fp
 
             __fp16 *accum_tile = c + (i * n_col_tiles + j) * HMX_FP16_TILE_N_ELMS;
             if (!zero_init) {
-                hmx_load_tile_pair_fp16(accum_tile, eye_tile);
+                Q6_activation_hf_mxmem_RR((unsigned int)accum_tile, 2047);
+                Q6_weight_hf_mxmem_RR((unsigned int)eye_tile, 2047);
             }
 
             for (int k = 0; k < n_dot_tiles; ++k) {
-                int offset = k * HMX_FP16_TILE_N_ELMS;
-                hmx_load_tile_pair_fp16(row_tiles + offset, col_tiles + offset);
+                Q6_activation_hf_mxmem_RR((unsigned int)row_tiles, 2047);
+                Q6_weight_hf_mxmem_RR((unsigned int)col_tiles, 2047);
+                row_tiles += HMX_FP16_TILE_N_ELMS;
+                col_tiles += HMX_FP16_TILE_N_ELMS;
             }
-
-            hmx_consume_accumulator_fp16(accum_tile);
+            Q6_mxmem_AR_after_hf(accum_tile, 0);
         }
     }
 }
@@ -1533,27 +1614,51 @@ void transfer_activation_chunk_threaded(struct htp_context *ctx, __fp16 *dst, co
     worker_pool_run_func(ctx->worker_pool, transfer_activation_chunk_worker_fn, &state, ctx->n_threads);
 }
 
-int mat_mul_qk_0_d16a32_out_stationary(struct htp_context *ctx, float *restrict out, const float *restrict x, const uint8_t *restrict w, int m,
-                                       int k, int n, int weight_type) {
-    // Runtime check -- k >= 16384 exceeds 2D DMA limit
-    if (k >= 16384) {
-        FARF(HIGH, "%s: k=%d exceeds 2D DMA limit", __func__, k);
-        return -1;
-    }
+int mat_mul_qk_0_d16a32_out_stationary(struct htp_context *ctx, float *restrict out, const float *restrict x, const uint8_t *restrict w,
+                                       int m, int k, int n, int weight_type) {
     // assume k % 32 == 0 && n % 32 == 0
     const size_t row_stride = get_x4x2_row_stride(weight_type, k);
     if (row_stride == 0) {
         return -1;
     }
 
-    const size_t vtcm_budget = ctx->vtcm_scratch_size;
+    const size_t vtcm_budget = ctx->vtcm_size;
 
-    const size_t M_BLOCK_SIZE = 512;
-    const size_t N_BLOCK_SIZE = 512;
-    const size_t K_BLOCK_SIZE = 512;
+    const size_t K_BLOCK_SIZE = 1024;
 
-    // Compute precise buffer sizes
+    // Fallback: if k doesn't need K-blocking, out-stationary has no advantage
+    const size_t k_iters_check = (k + K_BLOCK_SIZE - 1) / K_BLOCK_SIZE;
+    if (k_iters_check <= 1) {
+        FARF(MEDIUM, "%s: K_BLK=%zu >= k=%d, fallback to standard path", __func__, K_BLOCK_SIZE, k);
+        return FALLBACK_TO_STANDARD;
+    }
+
+    // Dynamic M,N search via hmx_compute_chunks
     const size_t sub_row_stride_alloc = get_x4x2_row_stride(weight_type, K_BLOCK_SIZE);
+    const size_t per_m                = K_BLOCK_SIZE * sizeof(float)  // scratch1: M×K×4 (act DMA staging F32)
+                         + K_BLOCK_SIZE * sizeof(__fp16);             // activation: M×K×2 (F16 tiles)
+    const size_t per_n = sub_row_stride_alloc                         // scratch0: N×sub_row(K) (packed quant)
+                         + K_BLOCK_SIZE * sizeof(__fp16);             // weight: N×K×2 (F16 tiles)
+    const size_t per_mn       = sizeof(__fp16);                       // output: M×N×2 (out-stationary)
+    // Alignment margin: hex_align_up can add up to 2047 bytes per buffer;
+    // scratch1 (mc×6144) is naturally 2048-aligned, remaining 4 buffers need margin
+    const size_t align_margin = 4 * HMX_FP16_TILE_SIZE;
+    const size_t overhead     = HMX_FP16_TILE_SIZE + 256 + align_margin;  // eye_tile + scales + alignment
+
+    size_t       M_BLOCK_SIZE, N_BLOCK_SIZE, vtcm_used;
+    // Cost-based search: minimize ceil(m/mc)*m_block_cost + ceil(n/nc)*n_block_cost.
+    // From profiling: wt_dequant per element ≈ 1.5× activation load per element.
+    // m_block_cost = n*3: each extra M-block re-dequants all N×K weight (expensive).
+    // n_block_cost = m*2: each extra N-block re-loads all M×K activation (cheaper).
+    const size_t m_block_cost = (size_t) n * 3;
+    const size_t n_block_cost = (size_t) m * 2;
+    if (hmx_compute_chunks(vtcm_budget, overhead, per_n, per_m, per_mn, m, n, m_block_cost, n_block_cost, &M_BLOCK_SIZE,
+                           &N_BLOCK_SIZE, &vtcm_used) != 0) {
+        FARF(HIGH, "%s: VTCM too small (m=%d k=%d n=%d budget=%zu)", __func__, m, k, n, vtcm_budget);
+        return -1;
+    }
+
+    // Compute precise buffer sizes from searched M,N and fixed K
     const size_t weight_size  = hex_align_up(N_BLOCK_SIZE * K_BLOCK_SIZE * sizeof(__fp16), HMX_FP16_TILE_SIZE);
     const size_t act_size     = hex_align_up(M_BLOCK_SIZE * K_BLOCK_SIZE * sizeof(__fp16), HMX_FP16_TILE_SIZE);
     const size_t out_size     = hex_align_up(M_BLOCK_SIZE * N_BLOCK_SIZE * sizeof(__fp16), HMX_FP16_TILE_SIZE);
@@ -1562,7 +1667,8 @@ int mat_mul_qk_0_d16a32_out_stationary(struct htp_context *ctx, float *restrict
 
     const size_t total_vtcm = weight_size + act_size + out_size + scratch0_sz + scratch1_sz + HMX_FP16_TILE_SIZE + 256;
     if (total_vtcm > vtcm_budget) {
-        FARF(HIGH, "%s: VTCM too small: need %zu have %zu (m=%d k=%d n=%d)", __func__, total_vtcm, vtcm_budget, m, k, n);
+        FARF(HIGH, "%s: VTCM overflow after search: need %zu have %zu (M=%zu N=%zu K=%zu)", __func__, total_vtcm,
+             vtcm_budget, M_BLOCK_SIZE, N_BLOCK_SIZE, K_BLOCK_SIZE);
         return -1;
     }
 
@@ -1576,9 +1682,8 @@ int mat_mul_qk_0_d16a32_out_stationary(struct htp_context *ctx, float *restrict
     __fp16  *vtcm_scales     = (__fp16 *) vtcm_seq_alloc(&vtcm_ptr, 256);
     assert((size_t)(vtcm_ptr - (uint8_t *)ctx->vtcm_base) <= vtcm_budget);
 
-    FARF(MEDIUM, "%s: m=%d k=%d n=%d wtype=%d vtcm=%zu/%zu",
-         __func__, m, k, n, weight_type,
-         (size_t)(vtcm_ptr - (uint8_t *)ctx->vtcm_base), vtcm_budget);
+    FARF(HIGH, "hmx-mm: m=%d k=%d n=%d wtype=%d block M=%zu N=%zu K=%zu vtcm=%zu/%zu", __func__, m, k, n, weight_type,
+         M_BLOCK_SIZE, N_BLOCK_SIZE, K_BLOCK_SIZE, (size_t) (vtcm_ptr - (uint8_t *) ctx->vtcm_base), vtcm_budget);
 
     // initialize eye tile (32x32 identity matrix)
     {

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

@@ -7,16 +7,12 @@
 #include <stddef.h>
 #include <stdint.h>
 
-#ifndef restrict
-#  define restrict __restrict
-#endif
+#include "htp-ops.h"
 
 #ifdef __cplusplus
 extern "C" {
 #endif
 
-struct htp_context;  // forward declaration
-
 typedef struct {
     float        *dst;
     const float  *activation;

ggml/src/ggml-hexagon/htp/hmx-queue.c

@@ -0,0 +1,158 @@
+#pragma clang diagnostic ignored "-Wunused-function"
+
+#include <stdbool.h>
+#include <stdlib.h>
+#include <string.h>
+
+#include <qurt_thread.h>
+#include <qurt_futex.h>
+
+#include <HAP_compute_res.h>
+
+#include "hmx-queue.h"
+
+#define QURT_LOWEST_PRIO (254)
+
+static inline void hmx_lock(struct hmx_queue *q)
+{
+    if (!q->hmx_locked) {
+        HAP_compute_res_hmx_lock(q->hap_rctx);
+        q->hmx_locked = true;
+    }
+}
+
+static inline void hmx_unlock(struct hmx_queue *q)
+{
+    if (q->hmx_locked) {
+        HAP_compute_res_hmx_unlock(q->hap_rctx);
+        q->hmx_locked = false;
+    }
+}
+
+static inline void hmx_queue_process(struct hmx_queue *q, bool* killed) {
+    unsigned int ir = atomic_load(&q->idx_read);
+
+    while (ir != atomic_load(&q->idx_write)) {
+        struct hmx_queue_desc *d = &q->desc[ir];
+        if (!d->done) {
+            FARF(HIGH, "hmx-queue-process: ir %u func %p data %p", ir, d->func, d->data);
+
+            enum hmx_queue_signal sig = (enum hmx_queue_signal) (unsigned int) d->func;
+            switch (sig) {
+                case HMX_QUEUE_NOOP:    /* noop */;     break;
+                case HMX_QUEUE_KILL:    *killed = true; break;
+                case HMX_QUEUE_SUSPEND: hmx_unlock(q);  break;
+                default:
+                    hmx_lock(q);
+                    d->func(d->data);
+                    break;
+            }
+
+            atomic_fetch_add(&d->done, 1);
+        }
+
+        ir = (ir + 1) & q->idx_mask;
+        atomic_store(&q->idx_read, ir);
+    }
+}
+
+static void hmx_queue_thread(void * arg) {
+    struct hmx_queue * q = (struct hmx_queue *) arg;
+
+    FARF(HIGH, "hmx-queue-thread: started");
+
+    bool killed = false;
+
+    unsigned int poll_cnt  = HMX_QUEUE_POLL_COUNT;
+    unsigned int prev_seqn = 0;
+    while (!killed) {
+        unsigned int seqn = atomic_load(&q->seqn);
+        if (seqn == prev_seqn) {
+            if (--poll_cnt) { hex_pause(); continue; }
+            FARF(HIGH, "hmx-queue-thread: sleeping");
+            qurt_futex_wait(&q->seqn, prev_seqn);
+            continue;
+        }
+        prev_seqn = seqn;
+        poll_cnt  = HMX_QUEUE_POLL_COUNT;
+
+        FARF(HIGH, "hmx-queue-thread: new work");
+
+        hmx_queue_process(q, &killed);
+    }
+
+    FARF(HIGH, "hmx-queue-thread: stopped");
+}
+
+struct hmx_queue * hmx_queue_create(size_t capacity, uint32_t hap_rctx) {
+    capacity = hex_ceil_pow2(capacity);
+
+    struct hmx_queue * q = (struct hmx_queue *) memalign(32, sizeof(struct hmx_queue));
+    if (q == NULL) {
+        FARF(ERROR, "%s: failed to allocate DMA queue\n", __FUNCTION__);
+        return NULL;
+    }
+    memset(q, 0, sizeof(struct hmx_queue));
+    q->capacity = capacity;
+    q->idx_mask = capacity - 1;
+    q->hap_rctx = hap_rctx;
+
+    q->desc = (struct hmx_queue_desc *) memalign(64, capacity * sizeof(struct hmx_queue_desc));
+    if (!q->desc) {
+        FARF(ERROR, "hmx-queue: failed to allocate HMX queue descriptors\n");
+        return NULL;
+    }
+    memset(q->desc, 0, capacity * sizeof(struct hmx_queue_desc));
+
+    const size_t stack_size = HMX_QUEUE_THREAD_STACK_SIZE;
+    q->stack = (unsigned char *) memalign(64, stack_size);
+    if (!q->stack) {
+        FARF(ERROR, "hmx-queue: thread stack allocation failed (%zu bytes)", stack_size);
+        return NULL;
+    }
+    memset(q->stack, 0, stack_size);
+
+    // Match caller thread priority (same pattern as worker-pool.c).
+    int prio = qurt_thread_get_priority(qurt_thread_get_id());
+    if (prio < 1) {
+        prio = 1;
+    }
+    if (prio > QURT_LOWEST_PRIO) {
+        prio = QURT_LOWEST_PRIO;
+    }
+
+    qurt_thread_attr_t attr;
+    qurt_thread_attr_init(&attr);
+    qurt_thread_attr_set_stack_addr(&attr, q->stack);
+    qurt_thread_attr_set_stack_size(&attr, stack_size);
+    qurt_thread_attr_set_priority(&attr, prio);
+    qurt_thread_attr_set_name(&attr, "hmx-queue");
+
+    int err = qurt_thread_create(&q->thread, &attr, hmx_queue_thread, q);
+    if (err) {
+        FARF(ERROR, "hmx-worker: thread create failed (%d)", err);
+        return NULL;
+    }
+
+    FARF(HIGH, "hmx-queue: capacity %u\n", capacity);
+
+    return q;
+}
+
+void hmx_queue_delete(struct hmx_queue * q) {
+    if (!q) {
+        return;
+    }
+
+    // Tell the worker to exit.
+    hmx_queue_flush(q);
+    hmx_queue_signal(q, HMX_QUEUE_KILL);
+    hmx_queue_flush(q);
+
+    int status;
+    qurt_thread_join(q->thread, &status);
+
+    free(q->desc);
+    free(q->stack);
+    free(q);
+}

ggml/src/ggml-hexagon/htp/hmx-queue.h

@@ -0,0 +1,134 @@
+#ifndef HMX_QUEUE_H
+#define HMX_QUEUE_H
+
+#include <stdbool.h>
+#include <stdint.h>
+#include <stdatomic.h>
+
+#include <hexagon_types.h>
+#include <qurt_thread.h>
+#include <qurt_futex.h>
+#include <HAP_farf.h>
+
+#include "hex-utils.h"
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+#define HMX_QUEUE_THREAD_STACK_SIZE (16 * 1024)
+#define HMX_QUEUE_POLL_COUNT        2000
+
+typedef void (*hmx_queue_func)(void *);
+
+// Dummy funcs used as signals
+enum hmx_queue_signal {
+    HMX_QUEUE_NOOP = 0, // aka NULL
+    HMX_QUEUE_SUSPEND,
+    HMX_QUEUE_KILL
+};
+
+struct hmx_queue_desc {
+    hmx_queue_func   func;
+    void *           data;
+    atomic_uint      done;
+};
+
+struct hmx_queue {
+    struct hmx_queue_desc * desc;
+    atomic_uint      idx_write; // updated by producer (push)
+    atomic_uint      idx_read;  // updated by consumer (process)
+    unsigned int     idx_pop;   // updated by producer (pop)
+    uint32_t         idx_mask;
+    uint32_t         capacity;
+
+    atomic_uint      seqn;      // incremented for all pushes, used with futex
+    qurt_thread_t    thread;
+    void *           stack;
+    uint32_t         hap_rctx;
+    bool             hmx_locked;
+};
+
+struct hmx_queue * hmx_queue_create(size_t capacity, uint32_t hap_rctx);
+void hmx_queue_delete(struct hmx_queue * q);
+
+static inline struct hmx_queue_desc hmx_queue_make_desc(hmx_queue_func func, void * data) {
+    struct hmx_queue_desc d = { func, data };
+    return d;
+}
+
+static inline bool hmx_queue_push(struct hmx_queue * q, struct hmx_queue_desc d) {
+    unsigned int ir = atomic_load(&q->idx_read);
+    unsigned int iw = q->idx_write;
+
+    if (((iw + 1) & q->idx_mask) == ir) {
+        FARF(HIGH, "hmx-queue-push: queue is full\n");
+        return false;
+    }
+
+    atomic_store(&d.done, 0);
+
+    FARF(HIGH, "hmx-queue-push: iw %u func %p data %p\n", iw, d.func, d.data);
+
+    q->desc[iw] = d;
+    atomic_store(&q->idx_write, (iw + 1) & q->idx_mask);
+    // wake up our thread
+    atomic_fetch_add(&q->seqn, 1);
+    qurt_futex_wake(&q->seqn, 1);
+
+    return true;
+}
+
+static inline bool hmx_queue_signal(struct hmx_queue *q, enum hmx_queue_signal sig) {
+    return hmx_queue_push(q, hmx_queue_make_desc((hmx_queue_func) sig, NULL));
+}
+
+static inline bool hmx_queue_empty(struct hmx_queue * q) {
+    return q->idx_pop == q->idx_write;
+}
+
+static inline uint32_t hmx_queue_depth(struct hmx_queue * q) {
+    return (q->idx_read - q->idx_read) & q->idx_mask;
+}
+
+static inline uint32_t hmx_queue_capacity(struct hmx_queue * q) {
+    return q->capacity;
+}
+
+static inline struct hmx_queue_desc hmx_queue_pop(struct hmx_queue * q) {
+    unsigned int ip = q->idx_pop;
+    unsigned int iw = q->idx_write;
+
+    struct hmx_queue_desc rd = { NULL, NULL };
+    if (ip == iw) {
+        return rd;
+    }
+
+    // Wait for desc to complete
+    struct hmx_queue_desc * d = &q->desc[ip];
+    while (!atomic_load(&d->done)) {
+        FARF(HIGH, "hmx-queue-pop: waiting for HMX queue : %u\n", ip);
+        hex_pause();
+    }
+
+    rd = *d;
+    q->idx_pop = (ip + 1) & q->idx_mask;
+
+    FARF(HIGH, "hmx-queue-pop: ip %u func %p data %p\n", ip, rd.func, rd.data);
+    return rd;
+}
+
+static inline void hmx_queue_flush(struct hmx_queue * q) {
+    while (hmx_queue_pop(q).func != NULL) ;
+}
+
+static inline void hmx_queue_suspend(struct hmx_queue *q) {
+    hmx_queue_signal(q, HMX_QUEUE_SUSPEND);
+    hmx_queue_flush(q);
+}
+
+#ifdef __cplusplus
+}  // extern "C"
+#endif
+
+#endif /* HMX_QUEUE_H */

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

@@ -14,10 +14,6 @@
 
 #define HMX_INLINE_ALWAYS inline __attribute__((unused, always_inline))
 
-static HMX_INLINE_ALWAYS void hmx_set_output_scales(const void *scales) {
-    asm volatile("bias = mxmem2(%0)" :: "r"(scales));
-}
-
 // Initialise aligned 256-byte area with scale vector + zero padding.
 static HMX_INLINE_ALWAYS void hmx_init_column_scales(void *out_scales, HVX_Vector v_scale) {
     HVX_Vector *pv = (HVX_Vector *)out_scales;
@@ -25,58 +21,6 @@ static HMX_INLINE_ALWAYS void hmx_init_column_scales(void *out_scales, HVX_Vecto
     *pv   = Q6_V_vzero();
 }
 
-// Load multiple contiguous tiles with :deep streaming.
-// Rt = total region size - 1; the hardware streams through [Rs, Rs + Rt].
-// IMPORTANT: the tile region [Rs, Rs + Rt] must NOT cross a VTCM 4 MB bank
-// boundary, otherwise the mxmem instruction will raise a precise bus error.
-// Callers must ensure their VTCM layout satisfies this constraint.
-static HMX_INLINE_ALWAYS void hmx_load_tiles_fp16(const __fp16 *row_tiles,
-                                                   const __fp16 *col_tiles,
-                                                   size_t n_tiles) {
-    size_t limit = n_tiles * HMX_FP16_TILE_SIZE - 1;
-    asm volatile(
-        "{ activation.hf = mxmem(%0, %1):deep\n"
-        "weight.hf = mxmem(%2, %3) }\n"
-        :: "r"(row_tiles), "r"(limit), "r"(col_tiles), "r"(limit)
-        : "memory");
-}
-
-// Load a single activation+weight tile pair (no :deep streaming).
-// Rt defines the accessible region [Rs, Rs+Rt].  Following the reference formula
-// (limit = n_tiles * HMX_FP16_TILE_SIZE - 1), for a single tile Rt = 2047.
-// The original code used Rt=0x7FFF (32 KB region); when dynamic VTCM allocation
-// places a tile near a 4 MB bank boundary, the oversized region crosses it and
-// triggers a precise bus error (0x2601).  Rt=2047 confines accesses to exactly
-// one 2048-byte tile while covering all 16 HVX vectors (offsets 0..2047).
-static HMX_INLINE_ALWAYS void hmx_load_tile_pair_fp16(const __fp16 *act_tile,
-                                                       const __fp16 *wt_tile) {
-    asm volatile(
-        "{ activation.hf = mxmem(%0, %1)\n"
-        "weight.hf = mxmem(%2, %3) }\n"
-        :: "r"(act_tile), "r"(2047),
-           "r"(wt_tile),  "r"(2047)
-        : "memory");
-}
-
-static HMX_INLINE_ALWAYS void hmx_consume_accumulator_fp16(__fp16 *out) {
-    // Use the combined convert-and-store instruction (matches the reference
-    // Q6_mxmem_AR_after_hf intrinsic).  The previous two-instruction sequence
-    // "cvt.hf = acc(2); mxmem = cvt" used an undocumented Rs=2 parameter.
-    asm volatile(
-        "mxmem(%0, %1):after.hf = acc\n"
-        :: "r"(out), "r"(0)
-        : "memory");
-}
-
-// Compute inner product of two vectors of tiles and store result.
-static HMX_INLINE_ALWAYS void hmx_dot_fp16(__fp16 *out,
-                                            const __fp16 *row_tiles,
-                                            const __fp16 *col_tiles,
-                                            size_t n_tiles) {
-    hmx_load_tiles_fp16(row_tiles, col_tiles, n_tiles);
-    hmx_consume_accumulator_fp16(out);
-}
-
 // --- VTCM sequential allocator (from htp-ops-lib/include/dsp/vtcm_mgr.h) ---
 
 static inline uint8_t *vtcm_seq_alloc(uint8_t **vtcm_ptr, size_t size) {

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

@@ -2,6 +2,8 @@
 #define HTP_CTX_H
 
 #include "hex-dma.h"
+#include "hmx-queue.h"
+#include "htp-ops.h"
 #include "worker-pool.h"
 
 #include <assert.h>
@@ -10,38 +12,91 @@
 #include <stdint.h>
 
 #define HTP_MAX_NTHREADS 10
+#define HTP_MAX_MMAPS    16
+
+// Memory mapping
+struct htp_mmap {
+    uint64_t size;
+    uint64_t base;
+    uint32_t fd;
+    uint32_t pinned;
+};
+
+// Scratchpad state
+struct htp_spad {
+    const struct htp_tensor * src;             // original src of the data (for reuse)
+    uint8_t *                 data;            // pointer to an area in vtcm
+    uint32_t                  stride;          // stride used inside this spad
+    uint32_t                  size;            // total size
+    uint32_t                  size_per_thread; // size per thread
+};
+
+struct htp_context;
+
+// Context while processing an Op
+// TODO: fold this into the main context
+struct htp_ops_context {
+    struct htp_context * ctx;
+
+    enum htp_op_code    op; // FIXME: rename to opcode
+    int32_t             op_params[HTP_OP_MAX_PARAMS];
+
+    const struct htp_tensor * src[HTP_OP_MAX_INPUTS];
+    const struct htp_tensor * dst;
+
+    // TODO convert these to an array
+    struct htp_spad src0_spad;
+    struct htp_spad src1_spad;
+    struct htp_spad src2_spad;
+    struct htp_spad src3_spad;
+    struct htp_spad dst_spad;
+
+    uint32_t n_threads;
+    uint32_t flags;
+};
 
 // Main context for htp DSP backend
 struct htp_context {
-    dspqueue_t            queue;
-    dma_queue *           dma[HTP_MAX_NTHREADS];
-    worker_pool_context_t worker_pool;
-    uint32_t              n_threads;
+    dspqueue_t             queue;
+    dma_queue *            dma[HTP_MAX_NTHREADS];
+    struct htp_mmap        mmap[HTP_MAX_MMAPS];
+    worker_pool_context_t  worker_pool;
+    uint32_t               n_threads;
 
-    int thread_id;
-    int thread_prio;
+    int                    thread_id;
+    int                    thread_prio;
 
-    uint8_t * vtcm_base;
-    size_t    vtcm_size;
-    uint32_t  vtcm_rctx;
+    int                    hmx_enabled;
 
-    atomic_bool vtcm_valid;
-    atomic_bool vtcm_inuse;
-    atomic_bool vtcm_needs_release;
+    uint8_t *              vtcm_base;
+    size_t                 vtcm_size;
+    uint32_t               vtcm_rctx;
+    atomic_bool            vtcm_valid;
+    atomic_bool            vtcm_needs_release;
 
-    uint32_t opmask;
+    struct htp_ops_context octx;
 
-    // Cached src1 spad position from the last quantize pass.
-    // When SKIP_QUANTIZE is set the Q8 activation data is already in VTCM
-    // at this address; the matmul must read from here instead of recomputing
-    // the offset (which depends on the current op's src0 size).
-    uint8_t * prev_src1_spad;
-
-    // HMX acceleration fields (v73+, enabled by compile-time HTP_HAS_HMX)
 #ifdef HTP_HAS_HMX
-    int        hmx_enabled;       // Runtime flag: HMX initialisation succeeded
-    size_t     vtcm_scratch_size; // Usable dynamic scratch (vtcm_size minus tail reservation)
+    struct hmx_queue *     hmx_queue; // Async HMX queue for pipeline overlap
 #endif
 };
 
+int op_matmul(struct htp_ops_context * octx);
+int op_matmul_id(struct htp_ops_context * octx);
+int op_binary(struct htp_ops_context * octx);
+int op_unary(struct htp_ops_context * octx);
+int op_sum_rows(struct htp_ops_context * octx);
+int op_activations(struct htp_ops_context * octx);
+int op_softmax(struct htp_ops_context * octx);
+int op_add_id(struct htp_ops_context * octx);
+int op_rope(struct htp_ops_context * octx);
+int op_flash_attn_ext(struct htp_ops_context * octx);
+int op_set_rows(struct htp_ops_context * octx);
+int op_get_rows(struct htp_ops_context * octx);
+int op_cpy(struct htp_ops_context * octx);
+int op_repeat(struct htp_ops_context * octx);
+int op_argsort(struct htp_ops_context * octx);
+int op_ssm_conv(struct htp_ops_context * octx);
+int op_cumsum(struct htp_ops_context * octx);
+
 #endif /* HTP_CTX_H */

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

@@ -1,166 +0,0 @@
-#ifndef HTP_MSG_H
-#define HTP_MSG_H
-
-#include <assert.h>
-
-// ggml-common.h must be included prio to this header
-
-// Mask to enable various stages of the Ops.
-// Used for debugging and profiling.
-enum {
-    HTP_OPMASK_QUEUE    = (1 << 0),  // Enable Queueing (ie calls into the DSP)
-    HTP_OPMASK_QUANTIZE = (1 << 1),  // Enable Quantize
-    HTP_OPMASK_COMPUTE  = (1 << 2),  // Enable Compute
-};
-
-// Op flags
-enum {
-    HTP_OPFLAGS_SKIP_QUANTIZE = (1 << 0),  // Skip dynamic quantization (reuse quantized tensors)
-    HTP_OPFLAGS_SKIP_COMPUTE  = (1 << 1),  // Skip actual computation (used for profiling)
-    HTP_OPFLAGS_EARLY_WAKEUP  = (1 << 2)   // Send early wakeup notification
-};
-
-enum htp_status {
-    HTP_STATUS_OK             = 1,
-    HTP_STATUS_INTERNAL_ERR   = 2,
-    HTP_STATUS_NO_SUPPORT     = 3,
-    HTP_STATUS_INVAL_PARAMS   = 4,
-    HTP_STATUS_VTCM_TOO_SMALL = 5,
-};
-
-// The values must match the ggml_type.
-// Duplicated here because we can't include full ggml.h in the htp build.
-// We have some static_asserts in the cpp code to ensure things are in sync.
-enum htp_data_type {
-    HTP_TYPE_F32    = 0,
-    HTP_TYPE_F16    = 1,
-    HTP_TYPE_Q4_0   = 2,
-    HTP_TYPE_Q8_0   = 8,
-    HTP_TYPE_IQ4_NL = 20,
-    HTP_TYPE_I32    = 26,
-    HTP_TYPE_I64    = 27,
-    HTP_TYPE_MXFP4  = 39,
-    HTP_TYPE_COUNT
-};
-
-// Do not reorder first 4 (used as an index)
-enum htp_op {
-    HTP_OP_MUL = 0,
-    HTP_OP_ADD = 1,
-    HTP_OP_SUB = 2,
-    HTP_OP_DIV = 3,
-    HTP_OP_MUL_MAT,
-    HTP_OP_MUL_MAT_ID,
-    HTP_OP_RMS_NORM,
-    HTP_OP_UNARY_SILU,
-    HTP_OP_UNARY_GELU,
-    HTP_OP_UNARY_SIGMOID,
-    HTP_OP_UNARY_EXP,
-    HTP_OP_UNARY_NEG,
-    HTP_OP_UNARY_SOFTPLUS,
-    HTP_OP_GLU_SWIGLU,
-    HTP_OP_GLU_SWIGLU_OAI,
-    HTP_OP_GLU_GEGLU,
-    HTP_OP_SOFTMAX,
-    HTP_OP_ADD_ID,
-    HTP_OP_ROPE,
-    HTP_OP_FLASH_ATTN_EXT,
-    HTP_OP_SET_ROWS,
-    HTP_OP_GET_ROWS,
-    HTP_OP_SCALE,
-    HTP_OP_CPY,
-    HTP_OP_ARGSORT,
-    HTP_OP_SQR,
-    HTP_OP_SQRT,
-    HTP_OP_SUM_ROWS,
-    HTP_OP_SSM_CONV,
-    HTP_OP_REPEAT,
-    HTP_OP_CUMSUM,
-    INVALID
-};
-
-static inline size_t htp_t_block_size(uint32_t t) {
-    switch (t) {
-        case HTP_TYPE_F32:
-            return 1;
-        case HTP_TYPE_F16:
-            return 1;
-        case HTP_TYPE_Q4_0:
-            return QK4_0;
-        case HTP_TYPE_Q8_0:
-            return QK8_0;
-        case HTP_TYPE_IQ4_NL:
-            return QK4_NL;
-        case HTP_TYPE_MXFP4:
-            return QK_MXFP4;
-        default:
-            assert(0 && "unsupported HTP data type");
-    }
-    return 0;
-}
-
-static inline size_t htp_type_nbytes(uint32_t t) {
-    switch (t) {
-        case HTP_TYPE_F32:
-            return 4;
-        case HTP_TYPE_F16:
-            return 2;
-        case HTP_TYPE_Q4_0:
-            return sizeof(block_q4_0);
-        case HTP_TYPE_Q8_0:
-            return sizeof(block_q8_0);
-        case HTP_TYPE_IQ4_NL:
-            return sizeof(block_iq4_nl);
-        case HTP_TYPE_MXFP4:
-            return sizeof(block_mxfp4);
-        default:
-            assert(0 && "unsupported HTP data type");
-    }
-    return 0;
-}
-
-// Internal types
-#define QK_Q4_0x4x2  256  // 4x Q4_0 blocks packed with next 4x Q4_0 blocks (size in bytes 128)
-#define QK_Q8_0x4x2  256  // 4x Q8_0 blocks concat with next 4x Q8_0 blocks
-#define QK_MXFP4x4x2 256  // 4x MXFP4 blocks concat with next 4x MXFP4 blocks
-
-#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)
-};
-
-#define HTP_MAX_OP_PARAMS 64
-
-struct htp_general_req {
-    uint32_t op;  // GGML/HTP Op
-    int32_t  op_params[HTP_MAX_OP_PARAMS / sizeof(int32_t)];
-    // Params for the op, e.g. epsilon of RMS norm
-    uint32_t flags;          // Request flags
-
-    struct htp_tensor src0;  // Input0 tensor
-    struct htp_tensor src1;  // Input1 tensor
-    struct htp_tensor src2;  // Input2 tensor
-    struct htp_tensor src3;  // Input3 tensor
-    struct htp_tensor src4;  // Input4 tensor
-    struct htp_tensor dst;   // Output tensor
-
-    // should be multiple of 64 bytes (cacheline)
-};
-
-struct htp_general_rsp {
-    uint32_t op;           // GGML/HTP Op
-    uint32_t status;       // HTP_STATUS_...
-    uint32_t prof_usecs;   // Number of usec per request
-    uint32_t prof_cycles;  // Number of cycles per request
-    uint32_t prof_pkts;    // Number of instruction packets per request
-    uint8_t  unused[44];   // Pad to 64 bytes
-};
-
-#define HTP_MAX_MESSAGE_SIZE   sizeof(struct htp_general_req)
-#define HTP_MAX_PACKET_BUFFERS 8
-
-#endif /* HTP_MSG_H */

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

@@ -1,65 +1,159 @@
 #ifndef HTP_OPS_H
 #define HTP_OPS_H
 
-#include "htp-ctx.h"
-#include "htp-msg.h"
-#include "worker-pool.h"
-
 #include <assert.h>
-#include <stdint.h>
 
-#include <hex-fastdiv.h>
+// ggml-common.h must be included prio to this header
 
-// ggml-common.h must be included prior to this header
-
-struct htp_spad {
-    uint8_t * data;
-    size_t    stride;
-    size_t    size;
-    size_t    size_per_thread;
+enum htp_status {
+    HTP_STATUS_OK             = 1,
+    HTP_STATUS_INTERNAL_ERR   = 2,
+    HTP_STATUS_NO_SUPPORT     = 3,
+    HTP_STATUS_INVAL_PARAMS   = 4,
+    HTP_STATUS_VTCM_TOO_SMALL = 5,
 };
 
-struct htp_ops_context {
-    struct htp_context * ctx;
+// First set of values must match the ggml_type.
+// Duplicated here because we can't include full ggml.h in the htp build.
+// We have some static_asserts in the cpp code to ensure things are in sync.
+enum htp_data_type {
+    HTP_TYPE_F32    = 0,
+    HTP_TYPE_F16    = 1,
+    HTP_TYPE_Q4_0   = 2,
+    HTP_TYPE_Q8_0   = 8,
+    HTP_TYPE_IQ4_NL = 20,
+    HTP_TYPE_I32    = 26,
+    HTP_TYPE_I64    = 27,
+    HTP_TYPE_MXFP4  = 39,
 
-    enum htp_op op;
-    int32_t     op_params[HTP_MAX_OP_PARAMS / sizeof(int32_t)];
+    // types used internally for repack, dyn.quant, etc
+    HTP_TYPE_Q4_0x4x2 = 200,
+    HTP_TYPE_Q8_0x4x2,
+    HTP_TYPE_MXFP4x4x2,
 
-    struct htp_tensor src0;
-    struct htp_tensor src1;
-    struct htp_tensor src2;
-    struct htp_tensor src3;
-    struct htp_tensor src4;
-    struct htp_tensor dst;
-
-    struct htp_spad src0_spad;
-    struct htp_spad src1_spad;
-    struct htp_spad src2_spad;
-    struct htp_spad src3_spad;
-    struct htp_spad dst_spad;
-
-    worker_pool_context_t * wpool;      // worker pool
-    uint32_t                n_threads;  // num threads
-
-    uint32_t flags;
+    HTP_TYPE_INVALID
 };
 
-int op_matmul(struct htp_ops_context * octx);
-int op_matmul_id(struct htp_ops_context * octx);
-int op_binary(struct htp_ops_context * octx);
-int op_unary(struct htp_ops_context * octx);
-int op_sum_rows(struct htp_ops_context * octx);
-int op_activations(struct htp_ops_context * octx);
-int op_softmax(struct htp_ops_context * octx);
-int op_add_id(struct htp_ops_context * octx);
-int op_rope(struct htp_ops_context * octx);
-int op_flash_attn_ext(struct htp_ops_context * octx);
-int op_set_rows(struct htp_ops_context * octx);
-int op_get_rows(struct htp_ops_context * octx);
-int op_cpy(struct htp_ops_context * octx);
-int op_repeat(struct htp_ops_context * octx);
-int op_argsort(struct htp_ops_context * octx);
-int op_ssm_conv(struct htp_ops_context * octx);
-int op_cumsum(struct htp_ops_context * octx);
+// Constats for internal types
+#define QK_Q4_0x4x2  256  // 4x Q4_0  blocks packed with next 4x Q4_0 blocks (size in bytes 128)
+#define QK_Q8_0x4x2  256  // 4x Q8_0  blocks concat with next 4x Q8_0 blocks
+#define QK_MXFP4x4x2 256  // 4x MXFP4 blocks concat with next 4x MXFP4 blocks
+
+
+// Mask to enable various stages of the Ops.
+// Used for debugging and profiling.
+enum htp_op_mask {
+    HTP_OPMASK_QUEUE    = (1 << 0),  // Enable Queueing (ie calls into the DSP)
+    HTP_OPMASK_COMPUTE  = (1 << 1),  // Enable Compute
+};
+
+// Do not reorder first 4 (used as an index)
+enum htp_op_code {
+    HTP_OP_MUL = 0,
+    HTP_OP_ADD = 1,
+    HTP_OP_SUB = 2,
+    HTP_OP_DIV = 3,
+    HTP_OP_MUL_MAT,
+    HTP_OP_MUL_MAT_ID,
+    HTP_OP_RMS_NORM,
+    HTP_OP_UNARY_SILU,
+    HTP_OP_UNARY_GELU,
+    HTP_OP_UNARY_SIGMOID,
+    HTP_OP_UNARY_EXP,
+    HTP_OP_UNARY_NEG,
+    HTP_OP_UNARY_SOFTPLUS,
+    HTP_OP_GLU_SWIGLU,
+    HTP_OP_GLU_SWIGLU_OAI,
+    HTP_OP_GLU_GEGLU,
+    HTP_OP_SOFTMAX,
+    HTP_OP_ADD_ID,
+    HTP_OP_ROPE,
+    HTP_OP_FLASH_ATTN_EXT,
+    HTP_OP_SET_ROWS,
+    HTP_OP_GET_ROWS,
+    HTP_OP_SCALE,
+    HTP_OP_CPY,
+    HTP_OP_ARGSORT,
+    HTP_OP_SQR,
+    HTP_OP_SQRT,
+    HTP_OP_SUM_ROWS,
+    HTP_OP_SSM_CONV,
+    HTP_OP_REPEAT,
+    HTP_OP_CUMSUM,
+
+    HTP_OP_INVALID
+};
+
+#define HTP_OP_MAX_DIMS    4    // aka GGML_MAX_DIMS
+#define HTP_OP_MAX_INPUTS  6    // aka GGML_MAX_SRCS
+#define HTP_OP_MAX_PARAMS  16   // aka GGML_MAX_OP_PARAMS
+
+#define HTP_OP_MAX_BUFS    8
+#define HTP_OP_MAX_REQS    256
+#define HTP_OP_MAX_TENSORS (HTP_OP_MAX_REQS * HTP_OP_MAX_INPUTS + HTP_OP_MAX_REQS)
+
+#if __HVX_ARCH__ < 75
+#define HTP_OP_MAX_VMEM    (3167538380u)
+#else
+#define HTP_OP_MAX_VMEM    (3221225472u)
+#endif
+
+enum htp_tensor_flags {
+    HTP_TENSOR_COMPUTE = (1U << 0), // Tensor buffer temporal compute data (not weights)
+    HTP_TENSOR_FLUSHED = (1U << 1)  // Tensor buffer has been flushed (set by the NPU)
+};
+
+// Tensor descriptor
+struct htp_tensor {
+    uint32_t data;                 // Buffer offset in the messages, and data pointer on the NPU
+    uint32_t size;                 // Data size in bytes
+    uint32_t flags;                // Buffer / tensor flags
+    uint16_t type;                 // Data type
+    uint16_t bi;                   // Buffer index
+    uint32_t ne[HTP_OP_MAX_DIMS];  // Number of elements
+    uint32_t nb[HTP_OP_MAX_DIMS];  // Stride in bytes (see ggml.h ggml_tensor)
+};
+
+// Buffer descriptor
+struct htp_buf_desc {
+    uint64_t base;     // base address
+    uint64_t size;     // total size
+    uint32_t flags;    // buffer flags (unused)
+    uint32_t fd;       // file descriptor
+};
+
+enum htp_op_flags {
+    HTP_OPFLAGS_SKIP_COMPUTE  = (1U << 0), // Skip actual computation (used for profiling)
+};
+
+// Op descriptor
+struct htp_op_desc {
+    uint32_t opcode;                    // GGML/HTP Op
+    uint32_t flags;                     // Op flags
+    int32_t  params[HTP_OP_MAX_PARAMS]; // Params for the op, e.g. epsilon of RMS norm
+    uint16_t src[HTP_OP_MAX_INPUTS];    // Input tensors indices
+    uint16_t dst;                       // Output tensor index
+
+    // the rest is filled in-place by the NPU
+    uint32_t prof_usecs;                // Number of usec per request
+    uint32_t prof_cycles;               // Number of cycles per request
+    uint32_t prof_pkts;                 // Number of instruction packets per request
+    uint32_t unused;
+};
+
+struct htp_opbatch_req {
+    uint32_t n_bufs;      // Number of buffers
+    uint32_t n_tensors;   // Number of tensors
+    uint32_t n_ops;       // Number of ops
+    uint32_t flags;       // unused
+    // struct htp_buf_desc  bufs[];    -- dspqueue buf 0
+    // struct htp_tensor    tensors[]; -- dspqueue buf 0
+    // struct htp_op_desc   ops[];     -- dspqueue buf 0
+};
+
+struct htp_opbatch_rsp {
+    uint32_t status;     // HTP_STATUS_...
+    // struct htp_op_req ops[];     -- dspqueue buf 0
+};
 
 #endif /* HTP_OPS_H */

ggml/src/ggml-hexagon/htp/htp_iface.idl

@@ -9,6 +9,8 @@
 interface htp_iface : remote_handle64 {
     AEEResult start(in uint32 sess_id, in uint64 dsp_queue_id, in uint32 n_hvx, in uint32 use_hmx);
     AEEResult stop();
+    AEEResult mmap(in uint32 fd, in uint32 size, in uint32 pinned);
+    AEEResult munmap(in uint32 fd);
     AEEResult enable_etm();
     AEEResult disable_etm();
 };

ggml/src/ggml-hexagon/htp/hvx-base.h

@@ -116,9 +116,14 @@ static inline HVX_VectorPred hvx_vec_is_nan_f16(HVX_Vector v) {
 }
 
 static inline HVX_Vector hvx_vec_f32_to_f16_shuff(HVX_Vector v0, HVX_Vector v1) {
+#if __HVX_ARCH__ >= 81
+    HVX_Vector q0 = Q6_Vqf32_equals_Vsf(v0);
+    HVX_Vector q1 = Q6_Vqf32_equals_Vsf(v1);
+#else
     const HVX_Vector zero = Q6_V_vzero();
     HVX_Vector q0 = Q6_Vqf32_vadd_VsfVsf(v0, zero);
     HVX_Vector q1 = Q6_Vqf32_vadd_VsfVsf(v1, zero);
+#endif
     return Q6_Vhf_equals_Wqf32(Q6_W_vcombine_VV(q1, q0));
 }
 

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

@@ -16,8 +16,9 @@
 #define GGML_COMMON_DECL_C
 #include "ggml-common.h"
 #include "htp-ctx.h"
-#include "htp-msg.h"
 #include "htp-ops.h"
+#include "htp-ops.h"
+#include "hmx-ops.h"
 
 #define MM_SPAD_SRC0_NROWS 16
 #define MM_SPAD_SRC1_NROWS 16
@@ -1897,11 +1898,11 @@ static void vec_dot_f16_f32_uu_1x1(const int n, float * restrict s, const void *
     hvx_vec_store_u(&s[0], 4, rsum);
 }
 
-#define htp_matmul_tensors_preamble    \
-    struct htp_tensor * restrict src0    = &octx->src0;      \
-    struct htp_tensor * restrict src1    = &octx->src1;      \
-    struct htp_tensor * restrict src2    = &octx->src2;      \
-    struct htp_tensor * restrict dst     = &octx->dst;       \
+#define htp_matmul_tensors_preamble                          \
+    const struct htp_tensor * restrict src0 = octx->src[0];  \
+    const struct htp_tensor * restrict src1 = octx->src[1];  \
+    const struct htp_tensor * restrict src2 = octx->src[2];  \
+    const struct htp_tensor * restrict  dst = octx->dst;     \
     struct htp_spad * restrict src0_spad = &octx->src0_spad; \
     struct htp_spad * restrict src1_spad = &octx->src1_spad; \
     struct htp_spad * restrict dst_spad  = &octx->dst_spad;  \
@@ -2223,8 +2224,8 @@ struct mmid_row_mapping {
 static void matmul_id(unsigned int nth, unsigned int ith, void * data) {
     htp_matmul_preamble;
 
-    struct htp_tensor * restrict     ids = &octx->src2;
-    struct htp_spad * restrict src2_spad = &octx->src2_spad;
+    const struct htp_tensor * restrict ids = octx->src[2];
+    struct htp_spad * restrict   src2_spad = &octx->src2_spad;
 
     uint64_t t1, t2;
     t1 = HAP_perf_get_qtimer_count();
@@ -2342,8 +2343,8 @@ static void matmul_id(unsigned int nth, unsigned int ith, void * data) {
 static void matvec_id(unsigned int nth, unsigned int ith, void * data) {
     htp_matmul_preamble;
 
-    struct htp_tensor * restrict     ids = &octx->src2;
-    struct htp_spad * restrict src2_spad = &octx->src2_spad;
+    const struct htp_tensor * restrict ids = octx->src[2];
+    struct htp_spad * restrict   src2_spad = &octx->src2_spad;
 
     uint64_t t1, t2;
     t1 = HAP_perf_get_qtimer_count();
@@ -2612,7 +2613,7 @@ static void quantize_f32_q8x4x2(unsigned int nth, unsigned int ith, void * data)
     struct htp_matmul_context * mmctx = data;
     struct htp_ops_context * octx = mmctx->octx;
 
-    const struct htp_tensor * src = &octx->src1;
+    const struct htp_tensor * src = octx->src[1];
     uint8_t * restrict dst = octx->src1_spad.data;
     struct htp_spad * spad = &octx->src0_spad;
     uint32_t nrows_per_thread = mmctx->src1_nrows_per_thread;
@@ -2659,7 +2660,7 @@ static void quantize_f32_f16(unsigned int nth, unsigned int ith, void * data) {
     struct htp_matmul_context * mmctx = data;
     struct htp_ops_context * octx = mmctx->octx;
 
-    const struct htp_tensor * src = &octx->src1;
+    const struct htp_tensor * src = octx->src[1];
     uint8_t * restrict dst = octx->src1_spad.data;
     uint32_t nrows_per_thread = mmctx->src1_nrows_per_thread;
     uint32_t dst_stride = octx->src1_spad.stride;
@@ -2701,7 +2702,7 @@ static void quantize_f16_f16(unsigned int nth, unsigned int ith, void * data) {
     struct htp_matmul_context * mmctx = data;
     struct htp_ops_context * octx = mmctx->octx;
 
-    const struct htp_tensor * src = &octx->src1;
+    const struct htp_tensor * src = octx->src[1];
     uint8_t * restrict dst = octx->src1_spad.data;
     uint32_t nrows_per_thread = mmctx->src1_nrows_per_thread;
     uint32_t dst_stride = octx->src1_spad.stride;
@@ -2800,7 +2801,7 @@ static void htp_mminit_spad(struct htp_ops_context * octx,
     octx->dst_spad.size  = octx->dst_spad.size_per_thread * octx->n_threads;
 }
 
-int op_matmul(struct htp_ops_context * octx) {
+static int op_matmul_hvx(struct htp_ops_context * octx) {
     htp_matmul_tensors_preamble;
 
     struct htp_matmul_context mmctx_struct = {0};
@@ -2824,7 +2825,7 @@ int op_matmul(struct htp_ops_context * octx) {
     worker_callback_t quant_job_func;
     worker_callback_t matmul_job_func = src1_nrows > 1 ? matmul_2d : matvec_2d;
 
-    bool need_quant = !(octx->flags & HTP_OPFLAGS_SKIP_QUANTIZE);
+    bool need_quant = true;
 
     if (src0->type == HTP_TYPE_F16) {
         // Try optimized f16-f16 path first (src1 in VTCM)
@@ -2838,7 +2839,7 @@ int op_matmul(struct htp_ops_context * octx) {
         // Default matmul implementation does not support multi-batch src0 (N-vs-N broadcasting).
         // It only supports 1-vs-N broadcasting (src0 is 2D) or standard 2D matmul.
         const bool is_batched  = (ne02 > 1) || (ne03 > 1);
-        const bool is_permuted = htp_is_permuted(&octx->src0) || htp_is_permuted(&octx->src1);
+        const bool is_permuted = htp_is_permuted(octx->src[0]) || htp_is_permuted(octx->src[1]);
 
         if (!is_batched && !is_permuted && f16_total_size <= octx->ctx->vtcm_size) {
             // Optimized path
@@ -2915,34 +2916,172 @@ int op_matmul(struct htp_ops_context * octx) {
         return HTP_STATUS_VTCM_TOO_SMALL;
     }
 
-    octx->src0_spad.data = octx->ctx->vtcm_base;
-    octx->src1_spad.data = octx->src0_spad.data + octx->src0_spad.size;
-    octx->dst_spad.data  = octx->src1_spad.data + octx->src1_spad.size;
+    // Place src1 spad first. We use it for dyn.quant and may reuse between ops
+    octx->src1_spad.data = octx->ctx->vtcm_base;
+    octx->src0_spad.data = octx->src1_spad.data + octx->src1_spad.size;
+    octx->dst_spad.data  = octx->src0_spad.data + octx->src0_spad.size;
+
+    octx->src1_spad.src  = (src1 == octx->src1_spad.src) ? src1 : NULL;
+    octx->src0_spad.src  = NULL;
+    octx->dst_spad.src   = NULL;
 
     octx->src0_spad.stride = src0_row_size_padded;
     octx->src1_spad.stride = src1_row_size;
 
-    if (need_quant) {
+    if (octx->flags & HTP_OPFLAGS_SKIP_COMPUTE)
+        return HTP_STATUS_OK;
+
+    if (need_quant && !octx->src1_spad.src) {
         const uint32_t n_quant_jobs  = MIN(src1_nrows, octx->n_threads);
         mmctx->src1_nrows_per_thread = (src1_nrows + n_quant_jobs - 1) / n_quant_jobs;
         worker_pool_run_func(octx->ctx->worker_pool, quant_job_func, mmctx, n_quant_jobs);
-        // Cache where src1 was written so subsequent SKIP_QUANTIZE ops can find it
-        octx->ctx->prev_src1_spad = octx->src1_spad.data;
-    } else {
-        // SKIP_QUANTIZE: Q8 data lives at the address written by the previous
-        // quantize pass.  The current op may have a different src0 size (e.g.
-        // IQ4_NL vs MXFP4), so src1_spad.data computed above could be wrong.
-        octx->src1_spad.data = octx->ctx->prev_src1_spad;
+        octx->src1_spad.src = src1;
     }
 
-    if (!(octx->flags & HTP_OPFLAGS_SKIP_COMPUTE)) {
-        const uint32_t n_matmul_jobs = octx->n_threads;
-        worker_pool_run_func(octx->ctx->worker_pool, matmul_job_func, mmctx, n_matmul_jobs);
-    }
+    const uint32_t n_matmul_jobs = octx->n_threads;
+    worker_pool_run_func(octx->ctx->worker_pool, matmul_job_func, mmctx, n_matmul_jobs);
 
     return HTP_STATUS_OK;
 }
 
+int op_matmul(struct htp_ops_context * octx) {
+    htp_matmul_tensors_preamble;
+
+#ifndef HTP_HAS_HMX
+    return op_matmul_hvx(octx);
+#else
+    if (!octx->ctx->hmx_enabled) {
+        return op_matmul_hvx(octx);
+    }
+
+    // HMX weight tile requires N to be 32-aligned.
+    if (src0->ne[1] % 32 != 0) {
+        return op_matmul_hvx(octx);
+    }
+
+    // HMX supports F16, Q4_0, Q8_0, IQ4_NL, MXFP4 weights.
+    // Other types fall back to HVX.
+    uint32_t wtype = src0->type;
+    if (wtype != HTP_TYPE_F16 && wtype != HTP_TYPE_Q4_0 && wtype != HTP_TYPE_Q8_0 && wtype != HTP_TYPE_IQ4_NL && wtype != HTP_TYPE_MXFP4) {
+        return op_matmul_hvx(octx);
+    }
+
+    // Quantised HMX path requires K aligned to 256 (x4x2 super-block).
+    // F16 HMX path requires K aligned to 32 (tile width).
+    if (wtype != HTP_TYPE_F16 && src0->ne[0] % 256 != 0) {
+        return op_matmul_hvx(octx);
+    }
+
+    if (wtype == HTP_TYPE_F16 && src0->ne[0] % 32 != 0) {
+        return op_matmul_hvx(octx);
+    }
+
+    const bool is_batched = (src0->ne[2] * src0->ne[3] > 1 || src1->ne[2] * src1->ne[3] > 1);
+
+    // Quantised HMX kernels only handle flat 2D matmul (host already rejects
+    // batched quantised, but guard here too).  F16 batched matmul is handled
+    // by the dedicated wrapper in hmx-matmul-ops.c.
+    if (is_batched && src0->type != HTP_TYPE_F16) {
+        return op_matmul_hvx(octx);
+    }
+
+    // HMX assumes contiguous row-major layout.  Fall back for permuted
+    // tensors where strides are non-monotonic (e.g. transposed KV cache).
+    if (src0->nb[0] > src0->nb[1] || src1->nb[0] > src1->nb[1]) {
+        return op_matmul_hvx(octx);
+    }
+
+    // M alignment: when M > 32 but not 32-aligned, we split into
+    // HMX (first m_hmx = M & ~31 rows) + HVX (remaining m_tail rows).
+    // When M <= 32 and not 32-aligned, fall back entirely to HVX.
+    const int m_total = (int) src1->ne[1];
+    const int m_tail  = m_total % 32;
+    const int m_hmx   = m_total - m_tail;
+
+    if (m_hmx == 0) {
+        return op_matmul_hvx(octx);
+    }
+
+    // Always re-quantize src1 since HMX kernel overwrites vtcm/spad,
+    // so any previously cached quantized data is invalid.
+    octx->src1_spad.src = NULL;
+
+    int k = (int) src0->ne[0];  // inner dimension
+    int n = (int) src0->ne[1];  // weight columns
+
+    // --- Phase 1: HMX on the first m_hmx (32-aligned) rows ---
+    int ret = -1;
+
+    // Row strides in elements. For compact tensors these equal k; for
+    // permuted attention views they can be larger, so pass the real stride.
+    const int act_stride = (int)(src1->nb[1] / sizeof(float));
+    const int wgt_stride = (int)(src0->nb[1] / sizeof(__fp16));
+
+    if (src0->type == HTP_TYPE_F16) {
+        if (is_batched) {
+            hmx_matmul_w16a32_batched_params_t batch_params = {
+                .dst             = (float *) dst->data,
+                .activation      = (float *) src1->data,
+                .permuted_weight = (const __fp16 *) src0->data,
+                .m               = m_hmx,
+                .k               = k,
+                .n               = n,
+                .act_stride      = act_stride,
+                .weight_stride   = wgt_stride,
+                .dst_stride      = (int) (dst->nb[1] / sizeof(float)),
+                .ne02            = ne02,
+                .ne03            = ne03,
+                .ne12            = ne12,
+                .ne13            = ne13,
+                .src0_nb2        = src0->nb[2],
+                .src0_nb3        = src0->nb[3],
+                .src1_nb2        = src1->nb[2],
+                .src1_nb3        = src1->nb[3],
+                .dst_nb2         = dst->nb[2],
+                .dst_nb3         = dst->nb[3],
+            };
+            ret = hmx_mat_mul_permuted_w16a32_batched(octx->ctx, &batch_params);
+        } else {
+            ret = hmx_mat_mul_permuted_w16a32(octx->ctx,
+                    (float*) dst->data, (float*) src1->data, (const __fp16 *) src0->data,
+                    m_hmx, k, n, act_stride, wgt_stride);
+        }
+    } else {
+        ret = hmx_mat_mul_permuted_qk_0_d16a32(octx->ctx,
+                    (float*) dst->data, (float*) src1->data, (const uint8_t *) src0->data,
+                    m_hmx, k, n, (int) src0->type);
+    }
+
+    if (ret != 0) {
+        FARF(HIGH, "HMX matmul failed (ret=%d), falling back to HVX", ret);
+        return op_matmul(octx);
+    }
+
+    // --- Phase 2: HVX on the remaining m_tail rows ---
+    if (m_tail > 0) {
+        // copy of src1 and dst
+        struct htp_tensor src1_tail = *src1;
+        struct htp_tensor dst_tail  = *dst;
+
+        src1_tail.ne[1] = m_tail; // only tail rows
+        dst_tail.ne[1]  = m_tail; // only tail rows
+
+        // Offset activation and dst pointers past the HMX-processed rows.
+        // Use nb[1] (row stride in bytes) to compute the byte offset.
+        src1_tail.data += (uint32_t) m_hmx * src1->nb[1];
+        dst_tail.data  += (uint32_t) m_hmx * dst->nb[1];
+
+        octx->src[1] = &src1_tail;
+        octx->dst    = &dst_tail;
+
+        FARF(HIGH, "hmx-matmul: HVX tail m_tail %d src1 %p dst %p", m_tail, (void *) src1_tail.data, (void *) dst_tail.data);
+        return op_matmul_hvx(octx);
+    }
+
+    return 0;
+#endif // HTP_HAS_HMX
+}
+
 int op_matmul_id(struct htp_ops_context * octx) {
     htp_matmul_tensors_preamble;
 
@@ -2950,7 +3089,7 @@ int op_matmul_id(struct htp_ops_context * octx) {
     struct htp_matmul_context * mmctx = &mmctx_struct;
     mmctx->octx = octx;
 
-    struct htp_tensor * restrict ids = &octx->src2;
+    const struct htp_tensor * restrict ids = octx->src[2];
 
     const size_t src0_row_size = nb01;
     const size_t dst_row_size  = nb1;
@@ -3003,11 +3142,17 @@ int op_matmul_id(struct htp_ops_context * octx) {
         return HTP_STATUS_VTCM_TOO_SMALL;
     }
 
-    octx->src0_spad.data = octx->ctx->vtcm_base;
-    octx->src1_spad.data = octx->src0_spad.data + octx->src0_spad.size;
-    octx->src2_spad.data = octx->src1_spad.data + octx->src1_spad.size;
+    // Place src1 spad first. We use it for dyn.quant and may reuse in subseq ops.
+    octx->src1_spad.data = octx->ctx->vtcm_base;
+    octx->src0_spad.data = octx->src1_spad.data + octx->src1_spad.size;
+    octx->src2_spad.data = octx->src0_spad.data + octx->src0_spad.size;
     octx->dst_spad.data  = octx->src2_spad.data + octx->src2_spad.size;
 
+    octx->src1_spad.src  = (src1 == octx->src1_spad.src) ? src1 : NULL;
+    octx->src0_spad.src  = NULL;
+    octx->src2_spad.src  = NULL;
+    octx->dst_spad.src   = NULL;
+
     octx->src0_spad.stride = src0_row_size_padded;
     octx->src1_spad.stride = src1_row_size;
 
@@ -3031,20 +3176,18 @@ int op_matmul_id(struct htp_ops_context * octx) {
         }
     }
 
-    // Setup worker pool callbacks
-    if (!(octx->flags & HTP_OPFLAGS_SKIP_QUANTIZE)) {
+    if (octx->flags & HTP_OPFLAGS_SKIP_COMPUTE)
+        return HTP_STATUS_OK;
+
+    if (octx->src1_spad.src != src1) {
         const uint32_t n_quant_jobs = MIN(src1_nrows, octx->n_threads);
         mmctx->src1_nrows_per_thread = (src1_nrows + n_quant_jobs - 1) / n_quant_jobs;
         worker_pool_run_func(octx->ctx->worker_pool, quant_job_func, mmctx, n_quant_jobs);
-        octx->ctx->prev_src1_spad = octx->src1_spad.data;
-    } else {
-        octx->src1_spad.data = octx->ctx->prev_src1_spad;
+        octx->src1_spad.src = src1;
     }
 
-    if (!(octx->flags & HTP_OPFLAGS_SKIP_COMPUTE)) {
-        const uint32_t n_matmul_jobs = octx->n_threads;
-        worker_pool_run_func(octx->ctx->worker_pool, matmul_id_job_func, mmctx, n_matmul_jobs);
-    }
+    const uint32_t n_matmul_jobs = octx->n_threads;
+    worker_pool_run_func(octx->ctx->worker_pool, matmul_id_job_func, mmctx, n_matmul_jobs);
 
     return HTP_STATUS_OK;
 }

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

@@ -12,7 +12,7 @@
 #define GGML_COMMON_DECL_C
 #include "ggml-common.h"
 #include "htp-ctx.h"
-#include "htp-msg.h"
+#include "htp-ops.h"
 #include "htp-ops.h"
 
 struct htp_repeat_context {
@@ -32,8 +32,8 @@ struct htp_repeat_context {
 static void repeat_job_per_thread(unsigned int nth, unsigned int ith, void * data) {
     const struct htp_repeat_context * rctx = (const struct htp_repeat_context *) data;
     struct htp_ops_context * octx = rctx->octx;
-    const struct htp_tensor * src = &octx->src0;
-    const struct htp_tensor * dst = &octx->dst;
+    const struct htp_tensor * src = octx->src[0];
+    const struct htp_tensor * dst = octx->dst;
 
     const uint32_t ne00 = src->ne[0];
     const uint32_t ne01 = src->ne[1];
@@ -98,8 +98,8 @@ static void repeat_job_per_thread(unsigned int nth, unsigned int ith, void * dat
 }
 
 int op_repeat(struct htp_ops_context * octx) {
-    const struct htp_tensor * src0 = &octx->src0;
-    struct htp_tensor *       dst  = &octx->dst;
+    const struct htp_tensor * src0 = octx->src[0];
+    const struct htp_tensor * dst  = octx->dst;
 
     // Validate that dst dims are multiples of src dims
     if (dst->ne[0] % src0->ne[0] != 0 ||

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

@@ -15,7 +15,7 @@
 #define GGML_COMMON_DECL_C
 #include "ggml-common.h"
 #include "htp-ctx.h"
-#include "htp-msg.h"
+#include "htp-ops.h"
 #include "htp-ops.h"
 
 // Redefined the types GGML_ROPE_TYPE_NORMAL & GGML_ROPE_TYPE_NEOX as we can't include ggml.h
@@ -253,10 +253,10 @@ static void rope_job_f32(unsigned int nth, unsigned int ith, void * data) {
     struct htp_rope_context * rctx = (struct htp_rope_context *) data;
     struct htp_ops_context * octx = rctx->octx;
 
-    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 struct htp_tensor * src0 = octx->src[0];
+    const struct htp_tensor * src1 = octx->src[1];
+    const struct htp_tensor * src2 = octx->src[2];
+    const struct htp_tensor * dst  = octx->dst;
 
     htp_rope_preamble;
 
@@ -284,7 +284,7 @@ static void rope_job_f32(unsigned int nth, unsigned int ith, void * data) {
 
     dma_queue * dma_queue = octx->ctx->dma[ith];
     const int32_t * pos = (const int32_t *) src1->data;
-    const float * freq_factors = src2->data ? (const float *) src2->data : NULL;
+    const float * freq_factors = src2 ? (const float *) src2->data : NULL;
 
     uint32_t ir = 0;
     uint32_t prev_i2 = (uint32_t) -1;
@@ -384,10 +384,10 @@ done:
 static int execute_op_rope_f32(struct htp_ops_context * octx) {
     int err = HTP_STATUS_OK;
 
-    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 struct htp_tensor * src0 = octx->src[0];
+    const struct htp_tensor * src1 = octx->src[1];
+    const struct htp_tensor * src2 = octx->src[2];
+    const struct htp_tensor * dst  = octx->dst;
 
     const char * op_type = "rope-f32";
 
@@ -424,19 +424,16 @@ static int execute_op_rope_f32(struct htp_ops_context * octx) {
         return HTP_STATUS_VTCM_TOO_SMALL;
     }
 
-    // Assign sizes
     octx->src0_spad.size_per_thread = src0_spad_per_thread;
     octx->dst_spad.size_per_thread  = dst_spad_per_thread;
     octx->src0_spad.size = n_threads * src0_spad_per_thread;
     octx->dst_spad.size  = n_threads * dst_spad_per_thread;
     octx->src1_spad.size = 0;
 
-    // Assign pointers
-    octx->src0_spad.data = octx->ctx->vtcm_base;
-    octx->src1_spad.data = NULL;
-    octx->dst_spad.data  = octx->src0_spad.data + octx->src0_spad.size;
+    octx->src0_spad.data = octx->ctx->vtcm_base;                        octx->src0_spad.src = NULL;
+    octx->src1_spad.data = NULL;                                        octx->src1_spad.src = NULL;
+    octx->dst_spad.data  = octx->src0_spad.data + octx->src0_spad.size; octx->dst_spad.src  = NULL;
 
-    // Fill context
     struct htp_rope_context rctx;
     memset(&rctx, 0, sizeof(struct htp_rope_context));
 
@@ -483,7 +480,7 @@ static int execute_op_rope_f32(struct htp_ops_context * octx) {
 int op_rope(struct htp_ops_context * octx) {
     int err = HTP_STATUS_OK;
 
-    switch (octx->src0.type) {
+    switch (octx->src[0]->type) {
         case HTP_TYPE_F32:
             err = execute_op_rope_f32(octx);
             break;