ops : update BLAS

.devops/cann.Dockerfile

@@ -1,130 +0,0 @@
-# ==============================================================================
-# ARGUMENTS
-# ==============================================================================
-
-# Define the CANN base image for easier version updates later
-ARG CANN_BASE_IMAGE=quay.io/ascend/cann:8.1.rc1-910b-openeuler22.03-py3.10
-
-# ==============================================================================
-# BUILD STAGE
-# Compile all binary files and libraries
-# ==============================================================================
-FROM ${CANN_BASE_IMAGE} AS build
-
-# Define the Ascend chip model for compilation. Default is Ascend910B3
-ARG ASCEND_SOC_TYPE=Ascend910B3
-
-# -- Install build dependencies --
-RUN yum install -y gcc g++ cmake make git libcurl-devel python3 python3-pip && \
-    yum clean all && \
-    rm -rf /var/cache/yum
-
-# -- Set the working directory --
-WORKDIR /app
-
-# -- Copy project files --
-COPY . .
-
-# -- Set CANN environment variables (required for compilation) --
-# Using ENV instead of `source` allows environment variables to persist across the entire image layer
-ENV ASCEND_TOOLKIT_HOME=/usr/local/Ascend/ascend-toolkit/latest
-ENV LD_LIBRARY_PATH=${ASCEND_TOOLKIT_HOME}/lib64:${LD_LIBRARY_PATH}
-ENV PATH=${ASCEND_TOOLKIT_HOME}/bin:${PATH}
-ENV ASCEND_OPP_PATH=${ASCEND_TOOLKIT_HOME}/opp
-ENV LD_LIBRARY_PATH=${ASCEND_TOOLKIT_HOME}/runtime/lib64/stub:$LD_LIBRARY_PATH
-# ... You can add other environment variables from the original file as needed ...
-# For brevity, only core variables are listed here. You can paste the original ENV list here.
-
-# -- Build llama.cpp --
-# Use the passed ASCEND_SOC_TYPE argument and add general build options
-RUN source /usr/local/Ascend/ascend-toolkit/set_env.sh --force \
-    && \
-    cmake -B build \
-        -DGGML_CANN=ON \
-        -DCMAKE_BUILD_TYPE=Release \
-        -DSOC_TYPE=${ASCEND_SOC_TYPE} \
-        . && \
-    cmake --build build --config Release -j$(nproc)
-
-# -- Organize build artifacts for copying in later stages --
-# Create a lib directory to store all .so files
-RUN mkdir -p /app/lib && \
-    find build -name "*.so" -exec cp {} /app/lib \;
-
-# Create a full directory to store all executables and Python scripts
-RUN mkdir -p /app/full && \
-    cp build/bin/* /app/full/ && \
-    cp *.py /app/full/ && \
-    cp -r gguf-py /app/full/ && \
-    cp -r requirements /app/full/ && \
-    cp requirements.txt /app/full/
-    # If you have a tools.sh script, make sure it is copied here
-    # cp .devops/tools.sh /app/full/tools.sh
-
-# ==============================================================================
-# BASE STAGE
-# Create a minimal base image with CANN runtime and common libraries
-# ==============================================================================
-FROM ${CANN_BASE_IMAGE} AS base
-
-# -- Install runtime dependencies --
-RUN yum install -y libgomp curl && \
-    yum clean all && \
-    rm -rf /var/cache/yum
-
-# -- Set CANN environment variables (required for runtime) --
-ENV ASCEND_TOOLKIT_HOME=/usr/local/Ascend/ascend-toolkit/latest
-ENV LD_LIBRARY_PATH=/app:${ASCEND_TOOLKIT_HOME}/lib64:${LD_LIBRARY_PATH}
-ENV PATH=${ASCEND_TOOLKIT_HOME}/bin:${PATH}
-ENV ASCEND_OPP_PATH=${ASCEND_TOOLKIT_HOME}/opp
-# ... You can add other environment variables from the original file as needed ...
-
-WORKDIR /app
-
-# Copy compiled .so files from the build stage
-COPY --from=build /app/lib/ /app
-
-# ==============================================================================
-# FINAL STAGES (TARGETS)
-# ==============================================================================
-
-### Target: full
-# Complete image with all tools, Python bindings, and dependencies
-# ==============================================================================
-FROM base AS full
-
-COPY --from=build /app/full /app
-
-# Install Python dependencies
-RUN yum install -y git python3 python3-pip && \
-    pip3 install --no-cache-dir --upgrade pip setuptools wheel && \
-    pip3 install --no-cache-dir -r requirements.txt && \
-    yum clean all && \
-    rm -rf /var/cache/yum
-
-# You need to provide a tools.sh script as the entrypoint
-ENTRYPOINT ["/app/tools.sh"]
-# If there is no tools.sh, you can set the default to start the server
-# ENTRYPOINT ["/app/llama-server"]
-
-### Target: light
-# Lightweight image containing only llama-cli
-# ==============================================================================
-FROM base AS light
-
-COPY --from=build /app/full/llama-cli /app
-
-ENTRYPOINT [ "/app/llama-cli" ]
-
-### Target: server
-# Dedicated server image containing only llama-server
-# ==============================================================================
-FROM base AS server
-
-ENV LLAMA_ARG_HOST=0.0.0.0
-
-COPY --from=build /app/full/llama-server /app
-
-HEALTHCHECK --interval=5m CMD [ "curl", "-f", "http://localhost:8080/health" ]
-
-ENTRYPOINT [ "/app/llama-server" ]

.github/workflows/build.yml

@@ -159,15 +159,31 @@ jobs:
       - name: Dawn Dependency
         id: dawn-depends
         run: |
-          DAWN_VERSION="v1.0.0"
-          DAWN_OWNER="reeselevine"
-          DAWN_REPO="dawn"
-          DAWN_ASSET_NAME="Dawn-a1a6b45cced25a3b7f4fb491e0ae70796cc7f22b-macos-latest-Release.tar.gz"
-          echo "Fetching release asset from https://github.com/${DAWN_OWNER}/${DAWN_REPO}/releases/download/${DAWN_VERSION}/${DAWN_ASSET_NAME}"
-          curl -L -o artifact.tar.gz \
-            "https://github.com/${DAWN_OWNER}/${DAWN_REPO}/releases/download/${DAWN_VERSION}/${DAWN_ASSET_NAME}"
+          ARTIFACTS_JSON=$(curl -s -L \
+            -H "Accept: application/vnd.github+json" \
+            -H "Authorization: Bearer ${{ secrets.GITHUB_TOKEN }}" \
+            -H "X-GitHub-Api-Version: 2022-11-28" \
+            "https://api.github.com/repos/google/dawn/actions/artifacts")
+          echo "Finding latest macos-latest-Release artifact..."
+          DOWNLOAD_URL=$(echo "$ARTIFACTS_JSON" | jq -r '.artifacts
+            | sort_by(.created_at)
+            | reverse
+            | map(select(.name | test("macos-latest-Release$")))
+            | .[0].archive_download_url')
+          if [ "$DOWNLOAD_URL" = "null" ] || [ -z "$DOWNLOAD_URL" ]; then
+            echo "No suitable Dawn artifact found!"
+            exit 1
+          fi
+          echo "Downloading from: $DOWNLOAD_URL"
+          curl -L \
+            -H "Accept: application/vnd.github+json" \
+            -H "Authorization: Bearer ${{ secrets.GITHUB_TOKEN }}" \
+            -o artifact.zip "$DOWNLOAD_URL"
+          unzip artifact.zip
           mkdir dawn
-          tar -xvf artifact.tar.gz -C dawn --strip-components=1
+          tar_file=$(find . -name '*.tar.gz' | head -n 1)
+          echo "Extracting: $tar_file"
+          tar -xvf "$tar_file" -C dawn --strip-components=1
 
       - name: Build
         id: cmake_build
@@ -417,15 +433,31 @@ jobs:
         id: dawn-depends
         run: |
           sudo apt-get install -y libxrandr-dev libxinerama-dev libxcursor-dev mesa-common-dev libx11-xcb-dev libxi-dev
-          DAWN_VERSION="v1.0.0"
-          DAWN_OWNER="reeselevine"
-          DAWN_REPO="dawn"
-          DAWN_ASSET_NAME="Dawn-a1a6b45cced25a3b7f4fb491e0ae70796cc7f22b-ubuntu-latest-Release.tar.gz"
-          echo "Fetching release asset from https://github.com/${DAWN_OWNER}/${DAWN_REPO}/releases/download/${DAWN_VERSION}/${DAWN_ASSET_NAME}"
-          curl -L -o artifact.tar.gz \
-            "https://github.com/${DAWN_OWNER}/${DAWN_REPO}/releases/download/${DAWN_VERSION}/${DAWN_ASSET_NAME}"
+          ARTIFACTS_JSON=$(curl -s -L \
+            -H "Accept: application/vnd.github+json" \
+            -H "Authorization: Bearer ${{ secrets.GITHUB_TOKEN }}" \
+            -H "X-GitHub-Api-Version: 2022-11-28" \
+            "https://api.github.com/repos/google/dawn/actions/artifacts")
+          echo "Finding latest ubuntu-latest-Release artifact..."
+          DOWNLOAD_URL=$(echo "$ARTIFACTS_JSON" | jq -r '.artifacts
+            | sort_by(.created_at)
+            | reverse
+            | map(select(.name | test("ubuntu-latest-Release$")))
+            | .[0].archive_download_url')
+          if [ "$DOWNLOAD_URL" = "null" ] || [ -z "$DOWNLOAD_URL" ]; then
+            echo "No suitable Dawn artifact found!"
+            exit 1
+          fi
+          echo "Downloading from: $DOWNLOAD_URL"
+          curl -L \
+            -H "Accept: application/vnd.github+json" \
+            -H "Authorization: Bearer ${{ secrets.GITHUB_TOKEN }}" \
+            -o artifact.zip "$DOWNLOAD_URL"
+          unzip artifact.zip
           mkdir dawn
-          tar -xvf artifact.tar.gz -C dawn --strip-components=1
+          tar_file=$(find . -name '*.tar.gz' | head -n 1)
+          echo "Extracting: $tar_file"
+          tar -xvf "$tar_file" -C dawn --strip-components=1
 
       - name: Build
         id: cmake_build

.github/workflows/pre-tokenizer-hashes.yml

@@ -1,45 +0,0 @@
-name: Check Pre-Tokenizer Hashes
-
-on:
-    push:
-        paths:
-            - 'convert_hf_to_gguf.py'
-            - 'convert_hf_to_gguf_update.py'
-    pull_request:
-        paths:
-            - 'convert_hf_to_gguf.py'
-            - 'convert_hf_to_gguf_update.py'
-
-jobs:
-    pre-tokenizer-hashes:
-        runs-on: ubuntu-latest
-
-        steps:
-        - name: Checkout repository
-          uses: actions/checkout@v4
-
-        - name: Set up Python
-          uses: actions/setup-python@v5
-          with:
-              python-version: '3.11'
-
-        - name: Install Python dependencies
-          run: |
-              python3 -m venv .venv
-              .venv/bin/pip install -r requirements/requirements-convert_hf_to_gguf_update.txt
-
-        - name: Update pre-tokenizer hashes
-          run: |
-              cp convert_hf_to_gguf.py /tmp
-              .venv/bin/python convert_hf_to_gguf_update.py --check-missing
-
-        - name: Check if committed pre-tokenizer hashes matches generated version
-          run: |
-              if ! diff -q convert_hf_to_gguf.py /tmp/convert_hf_to_gguf.py; then
-                  echo "Model pre-tokenizer hashes (in convert_hf_to_gguf.py) do not match generated hashes (from convert_hf_to_gguf_update.py)."
-                  echo "To fix: run ./convert_hf_to_gguf_update.py and commit the updated convert_hf_to_gguf.py along with your changes"
-                  echo "Differences found:"
-                  diff convert_hf_to_gguf.py /tmp/convert_hf_to_gguf.py || true
-                  exit 1
-              fi
-              echo "Model pre-tokenizer hashes are up to date."

.gitignore

@@ -82,7 +82,6 @@ models/*
 models-mnt
 !models/.editorconfig
 !models/ggml-vocab-*.gguf*
-!models/templates
 
 # Zig
 zig-out/

README.md

@@ -17,7 +17,6 @@ LLM inference in C/C++
 
 ## Hot topics
 
-- Support for the `gpt-oss` model with native MXFP4 format has been added | [PR](https://github.com/ggml-org/llama.cpp/pull/15091) | [Collaboration with NVIDIA](https://blogs.nvidia.com/blog/rtx-ai-garage-openai-oss) | [Comment](https://github.com/ggml-org/llama.cpp/discussions/15095)
 - Hot PRs: [All](https://github.com/ggml-org/llama.cpp/pulls?q=is%3Apr+label%3Ahot+) | [Open](https://github.com/ggml-org/llama.cpp/pulls?q=is%3Apr+label%3Ahot+is%3Aopen)
 - Multimodal support arrived in `llama-server`: [#12898](https://github.com/ggml-org/llama.cpp/pull/12898) | [documentation](./docs/multimodal.md)
 - VS Code extension for FIM completions: https://github.com/ggml-org/llama.vscode

common/arg.cpp

@@ -24,7 +24,6 @@
 #include <cstdarg>
 #include <filesystem>
 #include <fstream>
-#include <list>
 #include <regex>
 #include <set>
 #include <string>
@@ -978,10 +977,6 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context
         for (auto & seq_breaker : params.sampling.dry_sequence_breakers) {
             string_process_escapes(seq_breaker);
         }
-        for (auto & pair : params.speculative.replacements) {
-            string_process_escapes(pair.first);
-            string_process_escapes(pair.second);
-        }
     }
 
     if (!params.kv_overrides.empty()) {
@@ -2096,13 +2091,6 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.no_kv_offload = true;
         }
     ).set_env("LLAMA_ARG_NO_KV_OFFLOAD"));
-    add_opt(common_arg(
-        {"-nr", "--no-repack"},
-        "disable weight repacking",
-        [](common_params & params) {
-            params.no_extra_bufts = true;
-        }
-    ).set_env("LLAMA_ARG_NO_REPACK"));
     add_opt(common_arg(
         {"-ctk", "--cache-type-k"}, "TYPE",
         string_format(
@@ -2376,35 +2364,11 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
                     }
                     throw std::invalid_argument("unknown buffer type");
                 }
-                // keep strings alive and avoid leaking memory by storing them in a static vector
-                static std::list<std::string> buft_overrides;
-                buft_overrides.push_back(tensor_name);
-                params.tensor_buft_overrides.push_back({buft_overrides.back().c_str(), buft_list.at(buffer_type)});
+                // FIXME: this leaks memory
+                params.tensor_buft_overrides.push_back({strdup(tensor_name.c_str()), buft_list.at(buffer_type)});
             }
         }
     ));
-    add_opt(common_arg(
-        {"--cpu-moe", "-cmoe"},
-        "keep all Mixture of Experts (MoE) weights in the CPU",
-        [](common_params & params) {
-            params.tensor_buft_overrides.push_back({"\\.ffn_(up|down|gate)_exps", ggml_backend_cpu_buffer_type()});
-        }
-    ).set_env("LLAMA_ARG_CPU_MOE"));
-    add_opt(common_arg(
-        {"--n-cpu-moe", "-ncmoe"}, "N",
-        "keep the Mixture of Experts (MoE) weights of the first N layers in the CPU",
-        [](common_params & params, int value) {
-            if (value < 0) {
-                throw std::invalid_argument("invalid value");
-            }
-            for (int i = 0; i < value; ++i) {
-                // keep strings alive and avoid leaking memory by storing them in a static vector
-                static std::list<std::string> buft_overrides;
-                buft_overrides.push_back(string_format("blk\\.%d\\.ffn_(up|down|gate)_exps", i));
-                params.tensor_buft_overrides.push_back({buft_overrides.back().c_str(), ggml_backend_cpu_buffer_type()});
-            }
-        }
-    ).set_env("LLAMA_ARG_N_CPU_MOE"));
     add_opt(common_arg(
         {"-ngl", "--gpu-layers", "--n-gpu-layers"}, "N",
         "number of layers to store in VRAM",
@@ -2663,15 +2627,6 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.n_out_freq = value;
         }
     ).set_examples({LLAMA_EXAMPLE_IMATRIX}));
-    add_opt(common_arg(
-        {"--output-format"}, "{gguf,dat}",
-        string_format("output format for imatrix file (default: %s)", params.imat_dat > 0 ? "dat" : "gguf"),
-        [](common_params & params, const std::string & value) {
-            /**/ if (value == "gguf") { params.imat_dat = -1; }
-            else if (value == "dat")  { params.imat_dat = 1;  }
-            else { throw std::invalid_argument("invalid output format"); }
-        }
-    ).set_examples({LLAMA_EXAMPLE_IMATRIX}));
     add_opt(common_arg(
         {"--save-frequency"}, "N",
         string_format("save an imatrix copy every N iterations (default: %d)", params.n_save_freq),
@@ -2947,12 +2902,11 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         "controls whether thought tags are allowed and/or extracted from the response, and in which format they're returned; one of:\n"
         "- none: leaves thoughts unparsed in `message.content`\n"
         "- deepseek: puts thoughts in `message.reasoning_content` (except in streaming mode, which behaves as `none`)\n"
-        "(default: auto)",
+        "(default: deepseek)",
         [](common_params & params, const std::string & value) {
             /**/ if (value == "deepseek") { params.reasoning_format = COMMON_REASONING_FORMAT_DEEPSEEK; }
             else if (value == "deepseek-legacy") { params.reasoning_format = COMMON_REASONING_FORMAT_DEEPSEEK_LEGACY; }
             else if (value == "none") {     params.reasoning_format = COMMON_REASONING_FORMAT_NONE; }
-            else if (value == "auto") {     params.reasoning_format = COMMON_REASONING_FORMAT_AUTO; }
             else { throw std::invalid_argument("invalid value"); }
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_MAIN}).set_env("LLAMA_ARG_THINK"));
@@ -3295,13 +3249,6 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.speculative.model.path = value;
         }
     ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_MODEL_DRAFT"));
-    add_opt(common_arg(
-        {"--spec-replace"}, "TARGET", "DRAFT",
-        "translate the string in TARGET into DRAFT if the draft model and main model are not compatible",
-        [](common_params & params, const std::string & tgt, const std::string & dft) {
-            params.speculative.replacements.push_back({ tgt, dft });
-        }
-    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}));
     add_opt(common_arg(
         {"-ctkd", "--cache-type-k-draft"}, "TYPE",
         string_format(
@@ -3491,11 +3438,28 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER}));
 
+    // diffusion parameters
     add_opt(common_arg(
         { "--diffusion-steps" }, "N",
         string_format("number of diffusion steps (default: %d)", params.diffusion.steps),
         [](common_params & params, int value) { params.diffusion.steps = value; }
     ).set_examples({ LLAMA_EXAMPLE_DIFFUSION }));
+    add_opt(common_arg(
+        { "--diffusion-eps" }, "F",
+        string_format("epsilon for timesteps (default: %.6f)", (double) params.diffusion.eps),
+        [](common_params & params, const std::string & value) { params.diffusion.eps = std::stof(value); }
+    ).set_examples({ LLAMA_EXAMPLE_DIFFUSION }));
+    add_opt(common_arg(
+        { "--diffusion-algorithm" }, "N",
+        string_format("diffusion algorithm: 0=ORIGIN, 1=MASKGIT_PLUS, 2=TOPK_MARGIN, 3=ENTROPY (default: %d)",
+                      params.diffusion.algorithm),
+        [](common_params & params, int value) { params.diffusion.algorithm = value; }
+    ).set_examples({ LLAMA_EXAMPLE_DIFFUSION }));
+    add_opt(common_arg(
+        { "--diffusion-alg-temp" }, "F",
+        string_format("algorithm temperature (default: %.3f)", (double) params.diffusion.alg_temp),
+        [](common_params & params, const std::string & value) { params.diffusion.alg_temp = std::stof(value); }
+    ).set_examples({ LLAMA_EXAMPLE_DIFFUSION }));
     add_opt(common_arg(
         { "--diffusion-visual" },
         string_format("enable visual diffusion mode (show progressive generation) (default: %s)",
@@ -3503,39 +3467,5 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params) { params.diffusion.visual_mode = true; }
     ).set_examples({ LLAMA_EXAMPLE_DIFFUSION }));
 
-    add_opt(common_arg(
-        { "--diffusion-eps" }, "F",
-        string_format("epsilon for timesteps (default: %.6f)", (double) params.diffusion.eps),
-        [](common_params & params, const std::string & value) { params.diffusion.eps = std::stof(value); }
-    ).set_examples({ LLAMA_EXAMPLE_DIFFUSION }));
-    add_opt(common_arg(
-        { "--diffusion-algorithm" }, "N",
-        string_format("diffusion algorithm: 0=ORIGIN, 1=ENTROPY_BASED, 2=MARGIN_BASED, 3=RANDOM, 4=LOW_CONFIDENCE (default: %d)",
-                      params.diffusion.algorithm),
-        [](common_params & params, int value) { params.diffusion.algorithm = value; }
-    ).set_examples({ LLAMA_EXAMPLE_DIFFUSION }));
-    add_opt(common_arg(
-        { "--diffusion-alg-temp" }, "F",
-        string_format("dream algorithm temperature (default: %.3f)", (double) params.diffusion.alg_temp),
-        [](common_params & params, const std::string & value) { params.diffusion.alg_temp = std::stof(value); }
-    ).set_examples({ LLAMA_EXAMPLE_DIFFUSION }));
-
-    add_opt(common_arg(
-        { "--diffusion-block-length" }, "N",
-        string_format("llada block length for generation (default: %d)", params.diffusion.block_length),
-        [](common_params & params, int value) { params.diffusion.block_length = value; }
-    ).set_examples({ LLAMA_EXAMPLE_DIFFUSION }));
-    add_opt(common_arg(
-        { "--diffusion-cfg-scale" }, "F",
-        string_format("llada classifier-free guidance scale (default: %.3f)", (double) params.diffusion.cfg_scale),
-        [](common_params & params, const std::string & value) { params.diffusion.cfg_scale = std::stof(value); }
-    ).set_examples({ LLAMA_EXAMPLE_DIFFUSION }));
-    add_opt(common_arg(
-        { "--diffusion-add-gumbel-noise" }, "F",
-        string_format("add gumbel noise to the logits if temp > 0.0 (default: %s)", params.diffusion.add_gumbel_noise ? "true" : "false"),
-        [](common_params & params, const std::string & value) { params.diffusion.add_gumbel_noise = std::stof(value); }
-    ).set_examples({ LLAMA_EXAMPLE_DIFFUSION }));
-
-
     return ctx_arg;
 }

common/chat-parser.cpp

@@ -55,15 +55,7 @@ bool common_chat_msg_parser::add_tool_call(const std::string & name, const std::
 bool common_chat_msg_parser::add_tool_call(const json & tool_call) {
     std::string name = tool_call.contains("name") ? tool_call.at("name") : "";
     std::string id = tool_call.contains("id") ? tool_call.at("id") : "";
-    std::string arguments = "";
-    if (tool_call.contains("arguments")) {
-        if (tool_call.at("arguments").is_object()) {
-            arguments = tool_call.at("arguments").dump();
-        } else {
-            arguments = tool_call.at("arguments");
-        }
-    }
-
+    std::string arguments = tool_call.contains("arguments") ? tool_call.at("arguments") : "";
     return add_tool_call(name, id, arguments);
 }
 

common/chat.cpp

@@ -126,8 +126,6 @@ std::vector<common_chat_msg_diff> common_chat_msg_diff::compute_diffs(const comm
 typedef minja::chat_template common_chat_template;
 
 struct common_chat_templates {
-    bool add_bos;
-    bool add_eos;
     bool has_explicit_template; // Model had builtin template or template overridde was specified.
     std::unique_ptr<common_chat_template> template_default; // always set (defaults to chatml)
     std::unique_ptr<common_chat_template> template_tool_use;
@@ -145,8 +143,6 @@ struct templates_params {
     bool enable_thinking = true;
     std::chrono::system_clock::time_point now = std::chrono::system_clock::now();
     json extra_context;
-    bool add_bos;
-    bool add_eos;
 };
 
 common_chat_tool_choice common_chat_tool_choice_parse_oaicompat(const std::string & tool_choice) {
@@ -449,8 +445,6 @@ std::string common_chat_format_single(
 
     common_chat_templates_inputs inputs;
     inputs.use_jinja = use_jinja;
-    inputs.add_bos = tmpls->add_bos;
-    inputs.add_eos = tmpls->add_eos;
 
     std::string fmt_past_msg;
     if (!past_msg.empty()) {
@@ -475,8 +469,6 @@ std::string common_chat_format_single(
 std::string common_chat_format_example(const struct common_chat_templates * tmpls, bool use_jinja) {
     common_chat_templates_inputs inputs;
     inputs.use_jinja = use_jinja;
-    inputs.add_bos = tmpls->add_bos;
-    inputs.add_eos = tmpls->add_eos;
     auto add_simple_msg = [&](auto role, auto content) {
         common_chat_msg msg;
         msg.role = role;
@@ -554,8 +546,6 @@ common_chat_templates_ptr common_chat_templates_init(
     }
     std::string token_bos = bos_token_override;
     std::string token_eos = eos_token_override;
-    bool add_bos = false;
-    bool add_eos = false;
     if (model) {
         const auto * vocab = llama_model_get_vocab(model);
         const auto get_token = [&](llama_token token, const char * name, const char * jinja_variable_name) {
@@ -570,13 +560,9 @@ common_chat_templates_ptr common_chat_templates_init(
         };
         token_bos = get_token(llama_vocab_bos(vocab), "BOS", "bos_token");
         token_eos = get_token(llama_vocab_eos(vocab), "EOS", "eos_token");
-        add_bos = llama_vocab_get_add_bos(vocab);
-        add_eos = llama_vocab_get_add_eos(vocab);
     }
     common_chat_templates_ptr tmpls(new common_chat_templates());
     tmpls->has_explicit_template = has_explicit_template;
-    tmpls->add_bos = add_bos;
-    tmpls->add_eos = add_eos;
     try {
         tmpls->template_default = std::make_unique<minja::chat_template>(default_template_src, token_bos, token_eos);
     } catch (const std::exception & e) {
@@ -606,8 +592,6 @@ const char * common_chat_format_name(common_chat_format format) {
         case COMMON_CHAT_FORMAT_FUNCTIONARY_V3_1_LLAMA_3_1: return "Functionary v3.1 Llama 3.1";
         case COMMON_CHAT_FORMAT_HERMES_2_PRO: return "Hermes 2 Pro";
         case COMMON_CHAT_FORMAT_COMMAND_R7B: return "Command R7B";
-        case COMMON_CHAT_FORMAT_GRANITE: return "Granite";
-        case COMMON_CHAT_FORMAT_GPT_OSS: return "GPT-OSS";
         default:
             throw std::runtime_error("Unknown chat format");
     }
@@ -616,10 +600,8 @@ const char * common_chat_format_name(common_chat_format format) {
 const char * common_reasoning_format_name(common_reasoning_format format) {
     switch (format) {
         case COMMON_REASONING_FORMAT_NONE:     return "none";
-        case COMMON_REASONING_FORMAT_AUTO:     return "auto";
         case COMMON_REASONING_FORMAT_DEEPSEEK: return "deepseek";
         case COMMON_REASONING_FORMAT_DEEPSEEK_LEGACY: return "deepseek-legacy";
-        case COMMON_REASONING_FORMAT_GRANITE: return "granite";
         default:
             throw std::runtime_error("Unknown reasoning format");
     }
@@ -766,10 +748,10 @@ static std::string apply(
     // instead of using `chat_template_options.use_bos_token = false`, since these tokens
     // may be needed inside the template / between messages too.
     auto result = tmpl.apply(tmpl_inputs, tmpl_opts);
-    if (inputs.add_bos && string_starts_with(result, tmpl.bos_token())) {
+    if (string_starts_with(result, tmpl.bos_token())) {
         result = result.substr(tmpl.bos_token().size());
     }
-    if (inputs.add_eos && string_ends_with(result, tmpl.eos_token())) {
+    if (string_ends_with(result, tmpl.eos_token())) {
         result = result.substr(0, result.size() - tmpl.eos_token().size());
     }
     return result;
@@ -1307,26 +1289,6 @@ static void common_chat_parse_deepseek_r1(common_chat_msg_parser & builder) {
         tool_calls_end);
 }
 
-static common_chat_params common_chat_params_init_gpt_oss(const common_chat_template & tmpl, const struct templates_params & inputs) {
-    common_chat_params data;
-    auto prompt = apply(tmpl, inputs);
-
-    data.prompt = prompt;
-    data.format = COMMON_CHAT_FORMAT_GPT_OSS;
-
-    // TODO: support tool calls in GPT-OSS?
-
-    return data;
-}
-static void common_chat_parse_gpt_oss(common_chat_msg_parser & builder) {
-    // TODO @ngxson : this won't work with --special enabled, we should fix that
-    builder.try_parse_reasoning("<|channel|>analysis<|message|>", "<|start|>assistant<|channel|>final<|message|>");
-    if (!builder.syntax().parse_tool_calls) {
-        builder.add_content(builder.consume_rest());
-        return;
-    }
-}
-
 static common_chat_params common_chat_params_init_firefunction_v2(const common_chat_template & tmpl, const struct templates_params & inputs) {
     LOG_DBG("%s\n", __func__);
     common_chat_params data;
@@ -1684,7 +1646,7 @@ static void common_chat_parse_hermes_2_pro(common_chat_msg_parser & builder) {
         "|<function name=\"([^\"]+)\">"  // match 5 (function name again)
     );
 
-    while (auto res = builder.try_find_regex(open_regex)) {
+    if (auto res = builder.try_find_regex(open_regex)) {
         const auto & block_start = res->groups[1];
         std::string block_end = block_start.empty() ? "" : "```";
 
@@ -1706,6 +1668,7 @@ static void common_chat_parse_hermes_2_pro(common_chat_msg_parser & builder) {
                     builder.consume_literal(block_end);
                     builder.consume_spaces();
                 }
+                builder.add_content(builder.consume_rest());
             } else {
                 throw common_chat_msg_partial_exception("failed to parse tool call");
             }
@@ -1730,124 +1693,7 @@ static void common_chat_parse_hermes_2_pro(common_chat_msg_parser & builder) {
                     builder.consume_spaces();
                 }
             }
-        }
-    }
-
-    builder.add_content(builder.consume_rest());
-}
-
-static common_chat_params common_chat_params_init_granite(const common_chat_template & tmpl, const struct templates_params & inputs) {
-    common_chat_params data;
-
-    // Pass thinking context for Granite template
-    json additional_context = {
-        {"thinking", inputs.enable_thinking},
-    };
-
-    data.prompt = apply(tmpl, inputs, /* messages_override= */ std::nullopt, /* tools_override= */ std::nullopt, additional_context);
-    data.format = COMMON_CHAT_FORMAT_GRANITE;
-
-    if (string_ends_with(data.prompt, "<think>\n") || string_ends_with(data.prompt, "<think>")) {
-        if (!inputs.enable_thinking) {
-            data.prompt += "</think>";
-        } else {
-            data.thinking_forced_open = true;
-        }
-    }
-
-    if (!inputs.tools.is_null()) {
-        // Granite uses <|tool_call|> followed by JSON list
-        data.grammar_lazy = inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_REQUIRED;
-        data.grammar = build_grammar([&](const common_grammar_builder & builder) {
-            std::vector<std::string> tool_rules;
-            foreach_function(inputs.tools, [&](const json & tool) {
-                const auto & function = tool.at("function");
-                std::string name = function.at("name");
-                auto parameters = function.at("parameters");
-                builder.resolve_refs(parameters);
-                tool_rules.push_back(builder.add_rule(name + "-call", builder.add_schema(name +
-"-args", {
-                    {"type", "object"},
-                    {"properties", {
-                        {"name", {{"const", name}}},
-                        {"arguments", parameters},
-                    }},
-                    {"required", json::array({"name", "arguments"})},
-                })));
-            });
-
-            auto tool_call = builder.add_rule("tool_call", string_join(tool_rules, " | "));
-            auto tool_list = builder.add_rule("tool_list", "\"[\" space " + tool_call + " (\",\" space " + tool_call + ")* space \"]\"");
-
-            if (data.thinking_forced_open) {
-                builder.add_rule("root", "\"</think>\" space \"<response>\" space [^<]* \"</response>\" space \"<|tool_call|>\" space " + tool_list);
-            } else {
-                builder.add_rule("root", "\"<|tool_call|>\" space " + tool_list);
-            }
-
-            data.grammar_triggers.push_back({
-                COMMON_GRAMMAR_TRIGGER_TYPE_WORD,
-                "<|tool_call|>"
-            });
-
-            data.preserved_tokens = {
-                "<think>",
-                "</think>",
-                "<response>",
-                "</response>",
-                "<|tool_call|>",
-            };
-        });
-    } else {
-        // Handle thinking tags for non-tool responses
-        if (data.thinking_forced_open && inputs.enable_thinking) {
-            data.grammar_lazy = false;
-            data.grammar = build_grammar([&](const common_grammar_builder & builder) {
-                builder.add_rule("root", "\"</think>\" space \"<response>\" space .* \"</response>\" space");
-            });
-            data.preserved_tokens = {
-                "<think>",
-                "</think>",
-                "<response>",
-                "</response>",
-            };
-        }
-    }
-
-    return data;
-}
-
-static void common_chat_parse_granite(common_chat_msg_parser & builder) {
-    // Parse thinking tags
-    builder.try_parse_reasoning("<think>", "</think>");
-
-    // Parse response tags using regex
-    static const common_regex response_regex("<response>([\\s\\S]*?)</response>");
-    if (auto res = builder.try_find_regex(response_regex)) {
-        // Extract the content between the tags (capture group 1)
-        auto content = builder.str(res->groups[1]);
-        builder.add_content(content);
-        builder.move_to(res->groups[0].end);
-    }
-
-    if (!builder.syntax().parse_tool_calls) {
-        builder.add_content(builder.consume_rest());
-        return;
-    }
-
-    // Look for tool calls
-    static const common_regex tool_call_regex(regex_escape("<|tool_call|>"));
-    if (auto res = builder.try_find_regex(tool_call_regex)) {
-        builder.move_to(res->groups[0].end);
-
-        // Expect JSON array of tool calls
-        auto tool_calls_data = builder.consume_json();
-        if (tool_calls_data.json.is_array()) {
-            if (!builder.add_tool_calls(tool_calls_data.json)) {
-                builder.add_content("<|tool_call|>" + tool_calls_data.json.dump());
-            }
-        } else {
-            builder.add_content("<|tool_call|>" + tool_calls_data.json.dump());
+            builder.add_content(builder.consume_rest());
         }
     } else {
         builder.add_content(builder.consume_rest());
@@ -1887,8 +1733,6 @@ static common_chat_params common_chat_templates_apply_jinja(
     params.enable_thinking = inputs.enable_thinking;
     params.grammar = inputs.grammar;
     params.now = inputs.now;
-    params.add_bos = inputs.add_bos;
-    params.add_eos = inputs.add_eos;
 
     params.extra_context = json::object();
     for (auto el : inputs.chat_template_kwargs) {
@@ -1925,21 +1769,11 @@ static common_chat_params common_chat_templates_apply_jinja(
         return common_chat_params_init_command_r7b(tmpl, params);
     }
 
-    // Granite (IBM) - detects thinking / tools support
-    if (src.find("elif thinking") != std::string::npos && src.find("<|tool_call|>") != std::string::npos) {
-        return common_chat_params_init_granite(tmpl, params);
-    }
-
     // Hermes 2/3 Pro, Qwen 2.5 Instruct (w/ tools)
     if (src.find("<tool_call>") != std::string::npos && params.json_schema.is_null()) {
         return common_chat_params_init_hermes_2_pro(tmpl, params);
     }
 
-    // GPT-OSS
-    if (src.find("<|channel|>") != std::string::npos && params.json_schema.is_null()) {
-        return common_chat_params_init_gpt_oss(tmpl, params);
-    }
-
     // Use generic handler when mixing tools + JSON schema.
     // TODO: support that mix in handlers below.
     if ((params.tools.is_array() && params.json_schema.is_object())) {
@@ -1990,7 +1824,6 @@ static common_chat_params common_chat_templates_apply_legacy(
     int alloc_size = 0;
     std::vector<llama_chat_message> chat;
     std::vector<std::string> contents;
-
     for (const auto & msg : inputs.messages) {
         auto content = msg.content;
         for (const auto & part : msg.content_parts) {
@@ -2092,12 +1925,6 @@ static void common_chat_parse(common_chat_msg_parser & builder) {
         case COMMON_CHAT_FORMAT_COMMAND_R7B:
             common_chat_parse_command_r7b(builder);
             break;
-        case COMMON_CHAT_FORMAT_GRANITE:
-            common_chat_parse_granite(builder);
-            break;
-        case COMMON_CHAT_FORMAT_GPT_OSS:
-            common_chat_parse_gpt_oss(builder);
-            break;
         default:
             throw std::runtime_error(std::string("Unsupported format: ") + common_chat_format_name(builder.syntax().format));
     }
@@ -2117,8 +1944,6 @@ common_chat_msg common_chat_parse(const std::string & input, bool is_partial, co
         }
     }
     auto msg = builder.result();
-    if (!is_partial) {
-        LOG_DBG("Parsed message: %s\n", common_chat_msgs_to_json_oaicompat<json>({msg}).at(0).dump().c_str());
-    }
+    LOG_DBG("Parsed message: %s\n", common_chat_msgs_to_json_oaicompat<json>({msg}).at(0).dump().c_str());
     return msg;
 }

common/chat.h

@@ -109,8 +109,6 @@ enum common_chat_format {
     COMMON_CHAT_FORMAT_FUNCTIONARY_V3_1_LLAMA_3_1,
     COMMON_CHAT_FORMAT_HERMES_2_PRO,
     COMMON_CHAT_FORMAT_COMMAND_R7B,
-    COMMON_CHAT_FORMAT_GRANITE,
-    COMMON_CHAT_FORMAT_GPT_OSS,
 
     COMMON_CHAT_FORMAT_COUNT, // Not a format, just the # formats
 };
@@ -129,8 +127,6 @@ struct common_chat_templates_inputs {
     bool enable_thinking = true;
     std::chrono::system_clock::time_point now = std::chrono::system_clock::now();
     std::map<std::string, std::string> chat_template_kwargs;
-    bool add_bos = false;
-    bool add_eos = false;
 };
 
 struct common_chat_params {

common/common.cpp

@@ -1122,7 +1122,6 @@ struct llama_model_params common_model_params_to_llama(common_params & params) {
     mparams.use_mmap        = params.use_mmap;
     mparams.use_mlock       = params.use_mlock;
     mparams.check_tensors   = params.check_tensors;
-    mparams.use_extra_bufts = !params.no_extra_bufts;
 
     if (params.kv_overrides.empty()) {
         mparams.kv_overrides = NULL;

common/common.h

@@ -201,7 +201,6 @@ struct common_params_speculative {
     int32_t n_gpu_layers =    -1; // number of layers to store in VRAM for the draft model (-1 - use default)
     float   p_split      =  0.1f; // speculative decoding split probability
     float   p_min        = 0.75f; // minimum speculative decoding probability (greedy)
-    std::vector<std::pair<std::string, std::string>> replacements; // main to speculative model replacements
 
     ggml_type cache_type_k = GGML_TYPE_F16; // KV cache data type for the K
     ggml_type cache_type_v = GGML_TYPE_F16; // KV cache data type for the V
@@ -221,25 +220,17 @@ struct common_params_vocoder {
 };
 
 struct common_params_diffusion {
-    int32_t steps         = 128;
-    bool    visual_mode   = false;
-
-    float   eps           = 0;        // epsilon for timesteps
-    int32_t block_length  = 0;        // block length for generation
-
-    int32_t algorithm     = 4;        // default algorithm: low-confidence
-    float   alg_temp      = 0.0f;     // algorithm temperature
-
-    float   cfg_scale     = 0;        // classifier-free guidance scale
-    bool    add_gumbel_noise = false; // add gumbel noise to the logits if temp > 0.0
+    int32_t steps       = 64;     // number of diffusion steps
+    float   eps         = 1e-3f;  // epsilon for timesteps
+    int32_t algorithm   = 0;      // diffusion algorithm (0=ORIGIN, 1=MASKGIT_PLUS, 2=TOPK_MARGIN, 3=ENTROPY)
+    float   alg_temp    = 0.0f;   // algorithm temperature
+    bool    visual_mode = false;  // show progressive diffusion on screen
 };
 
 enum common_reasoning_format {
     COMMON_REASONING_FORMAT_NONE,
-    COMMON_REASONING_FORMAT_AUTO,
     COMMON_REASONING_FORMAT_DEEPSEEK_LEGACY, // Extract thinking tag contents and return as `message.reasoning_content`, or leave inline in <think> tags in stream mode
     COMMON_REASONING_FORMAT_DEEPSEEK,        // Extract thinking tag contents and return as `message.reasoning_content`, including in streaming deltas.
-    COMMON_REASONING_FORMAT_GRANITE,         // Extract thinking tag contents and return as `message.reasoning_content`, including in streaming deltas.
 };
 
 struct common_params {
@@ -361,7 +352,6 @@ struct common_params {
     bool warmup            = true;  // warmup run
     bool check_tensors     = false; // validate tensor data
     bool no_op_offload     = false; // globally disable offload host tensor operations to device
-    bool no_extra_bufts    = false; // disable extra buffer types (used for weight repacking)
 
     bool single_turn       = false; // single turn chat conversation
 
@@ -396,7 +386,7 @@ struct common_params {
     std::string chat_template = "";                                                                         // NOLINT
     bool use_jinja = false;                                                                                 // NOLINT
     bool enable_chat_template = true;
-    common_reasoning_format reasoning_format = COMMON_REASONING_FORMAT_AUTO;
+    common_reasoning_format reasoning_format = COMMON_REASONING_FORMAT_DEEPSEEK;
     int reasoning_budget = -1;
     bool prefill_assistant = true;                                                                          // if true, any trailing assistant message will be prefilled into the response
 
@@ -441,7 +431,6 @@ struct common_params {
     int32_t n_out_freq  = 10; // output the imatrix every n_out_freq iterations
     int32_t n_save_freq =  0; // save the imatrix every n_save_freq iterations
     int32_t i_chunk     =  0; // start processing from this chunk
-    int8_t  imat_dat    =  0; // whether the legacy imatrix.dat format should be output (gguf <= 0 < dat)
 
     bool process_output  = false; // collect data for the output tensor
     bool compute_ppl     = true;  // whether to compute perplexity

common/speculative.cpp

@@ -1,39 +1,30 @@
 #include "speculative.h"
 
-#include "ggml.h"
-#include "llama.h"
 #include "log.h"
 #include "common.h"
 #include "sampling.h"
 
 #include <cstring>
 #include <algorithm>
-#include <map>
 
 #define SPEC_VOCAB_MAX_SIZE_DIFFERENCE  128
 #define SPEC_VOCAB_CHECK_START_TOKEN_ID 5
 
 struct common_speculative {
-    struct llama_context * ctx_tgt; // only used for retokenizing from ctx_dft
-    struct llama_context * ctx_dft;
+    struct llama_context * ctx;
     struct common_sampler * smpl;
 
     llama_batch batch;
-    llama_tokens prompt_dft;
-    bool vocab_dft_compatible = true; // whether retokenization is needed
-    std::map<std::string, std::string> tgt_dft_replacements = {};
+    llama_tokens prompt;
 };
 
 struct common_speculative * common_speculative_init(
-        struct llama_context * ctx_tgt,
         struct llama_context * ctx_dft) {
     auto * result = new common_speculative {
-        /* .ctx_tgt    = */ ctx_tgt,
-        /* .ctx_dft    = */ ctx_dft,
-        /* .smpl       = */ nullptr,
-        /* .batch      = */ llama_batch_init(llama_n_batch(ctx_dft), 0, 1),
-        /* .prompt_dft = */ {},
-        /* .vocab_dft_compatible = */ false,
+        /* .ctx    = */ ctx_dft,
+        /* .smpl   = */ nullptr,
+        /* .batch  = */ llama_batch_init(llama_n_batch(ctx_dft), 0, 1),
+        /* .prompt = */ {},
     };
 
     // TODO: optimize or pass from outside?
@@ -68,9 +59,6 @@ struct common_speculative * common_speculative_init(
     }
 #endif
 
-    result->vocab_dft_compatible = common_speculative_are_compatible(ctx_tgt, ctx_dft);
-    LOG_DBG("vocab_dft_compatible = %d\n", result->vocab_dft_compatible);
-
     return result;
 }
 
@@ -87,8 +75,8 @@ void common_speculative_free(struct common_speculative * spec) {
 }
 
 bool common_speculative_are_compatible(
-    const struct llama_context * ctx_tgt,
-    const struct llama_context * ctx_dft) {
+        const struct llama_context * ctx_tgt,
+        const struct llama_context * ctx_dft) {
     const struct llama_model * model_tgt = llama_get_model(ctx_tgt);
     const struct llama_model * model_dft = llama_get_model(ctx_dft);
 
@@ -102,32 +90,31 @@ bool common_speculative_are_compatible(
     LOG_DBG("%s: vocab_type dft: %d\n", __func__, vocab_type_dft);
 
     if (vocab_type_tgt != vocab_type_dft) {
-        LOG_DBG("%s: draft model vocab type must match target model to use speculation but ", __func__);
-        LOG_DBG("vocab_type_dft = %d while vocab_type_tgt = %d\n", vocab_type_dft, vocab_type_tgt);
+        LOG_ERR("%s: draft model vocab type must match target model to use speculation but "
+                     "vocab_type_dft = %d while vocab_type_tgt = %d\n", __func__, vocab_type_dft, vocab_type_tgt);
         return false;
     }
 
-    if (
-        llama_vocab_get_add_bos(vocab_tgt) != llama_vocab_get_add_bos(vocab_dft) ||
+    if (llama_vocab_get_add_bos(vocab_tgt) != llama_vocab_get_add_bos(vocab_dft) ||
         llama_vocab_get_add_eos(vocab_tgt) != llama_vocab_get_add_eos(vocab_dft) ||
         llama_vocab_bos(vocab_tgt) != llama_vocab_bos(vocab_dft) ||
-        llama_vocab_eos(vocab_tgt) != llama_vocab_eos(vocab_dft)
-    ) {
-        LOG_DBG("%s: draft model special tokens must match target model to use speculation\n", __func__);
+        llama_vocab_eos(vocab_tgt) != llama_vocab_eos(vocab_dft)) {
+        LOG_ERR("%s: draft vocab special tokens must match target vocab to use speculation\n", __func__);
+        LOG_ERR("%s: tgt: bos = %d (%d), eos = %d (%d)\n", __func__, llama_vocab_bos(vocab_tgt), llama_vocab_get_add_bos(vocab_tgt), llama_vocab_eos(vocab_tgt), llama_vocab_get_add_eos(vocab_tgt));
+        LOG_ERR("%s: dft: bos = %d (%d), eos = %d (%d)\n", __func__, llama_vocab_bos(vocab_dft), llama_vocab_get_add_bos(vocab_dft), llama_vocab_eos(vocab_dft), llama_vocab_get_add_eos(vocab_dft));
         return false;
     }
 
     {
         const int n_vocab_tgt = llama_vocab_n_tokens(vocab_tgt);
         const int n_vocab_dft = llama_vocab_n_tokens(vocab_dft);
-        const int vocab_diff  = n_vocab_tgt > n_vocab_dft
-            ? n_vocab_tgt - n_vocab_dft
-            : n_vocab_dft - n_vocab_tgt;
+
+        const int vocab_diff = std::abs(n_vocab_tgt - n_vocab_dft);
 
         if (vocab_diff > SPEC_VOCAB_MAX_SIZE_DIFFERENCE) {
-            LOG_DBG("%s: draft model vocab must closely match target model to use speculation but ", __func__);
-            LOG_DBG("target vocab size %d does not match draft vocab size %d - difference %d, max allowed %d\n",
-                    n_vocab_tgt, llama_vocab_n_tokens(vocab_dft), vocab_diff, SPEC_VOCAB_MAX_SIZE_DIFFERENCE);
+            LOG_ERR("%s: draft model vocab must closely match target model to use speculation but "
+                         "target vocab size %d does not match draft vocab size %d - difference %d, max allowed %d\n",
+                    __func__, n_vocab_tgt, llama_vocab_n_tokens(vocab_dft), vocab_diff, SPEC_VOCAB_MAX_SIZE_DIFFERENCE);
             return false;
         }
 
@@ -135,8 +122,8 @@ bool common_speculative_are_compatible(
             const char * token_text_tgt = llama_vocab_get_text(vocab_tgt, i);
             const char * token_text_dft = llama_vocab_get_text(vocab_dft, i);
             if (std::strcmp(token_text_tgt, token_text_dft) != 0) {
-                LOG_DBG("%s: draft model vocab must match target model to use speculation but ", __func__);
-                LOG_DBG("token %d content differs - target '%s', draft '%s'\n", i,
+                LOG_ERR("%s: draft vocab vocab must match target vocab to use speculation but "
+                             "token %d content differs - target '%s', draft '%s'\n", __func__, i,
                         common_token_to_piece(ctx_tgt, i).c_str(),
                         common_token_to_piece(ctx_dft, i).c_str());
                 return false;
@@ -147,93 +134,32 @@ bool common_speculative_are_compatible(
     return true;
 }
 
-void common_speculative_add_replacement_tgt_dft(
-        struct common_speculative * spec,
-        const char *source, const char *dest) {
-    spec->tgt_dft_replacements[source] = dest;
-}
-
-static std::string replace_to_dft(
-        struct common_speculative * spec,
-        const std::string& input) {
-    std::string result = input;
-    for (const auto & pair : spec->tgt_dft_replacements) {
-        size_t pos = result.find(pair.first);
-        while (pos != std::string::npos) {
-            result.replace(pos, pair.first.length(), pair.second);
-            pos = result.find(pair.first, pos + pair.second.length());
-        }
-    }
-    return result;
-}
-
-static std::string replace_to_tgt(
-        struct common_speculative * spec,
-        const std::string& input) {
-    std::string result = input;
-    for (const auto& pair : spec->tgt_dft_replacements) {
-        size_t pos = result.find(pair.second);
-        while (pos != std::string::npos) {
-            result.replace(pos, pair.second.length(), pair.first);
-            pos = result.find(pair.second, pos + pair.first.length());
-        }
-    }
-    return result;
-}
-
-
 llama_tokens common_speculative_gen_draft(
         struct common_speculative * spec,
         struct common_speculative_params params,
-        const llama_tokens & prompt_tgt_main_model, // specified in target model vocab
+        const llama_tokens & prompt_tgt,
         llama_token id_last) {
     auto & batch  = spec->batch;
-    auto & ctx_tgt = spec->ctx_tgt;
-    auto & ctx_dft = spec->ctx_dft;
+    auto & ctx    = spec->ctx;
     auto & smpl   = spec->smpl;
-    auto & prompt_dft = spec->prompt_dft;
+    auto & prompt = spec->prompt;
 
-    auto * mem_dft = llama_get_memory(ctx_dft);
+    auto * mem = llama_get_memory(ctx);
 
     int reuse_i = 0;
     int reuse_n = 0;
 
-    const int n_ctx = llama_n_ctx(ctx_dft) - params.n_draft;
-
-    llama_tokens prompt_tgt_draft_model;
-    if (!spec->vocab_dft_compatible) {
-        std::string text;
-        text = common_detokenize(ctx_tgt, prompt_tgt_main_model, true);
-        text = replace_to_dft(spec, text);
-        LOG_DBG("%s: main->draft detokenized string: '%s'\n", __func__, text.c_str());
-        prompt_tgt_draft_model = common_tokenize(ctx_dft, text, false, true);
-
-        // convert id_last to draft vocab. llama_detokenize is called directly to avoid an allocation
-        const auto * model_tgt = llama_get_model(ctx_tgt);
-        const auto * vocab_tgt = llama_model_get_vocab(model_tgt);
-
-        int32_t n_chars = llama_detokenize(vocab_tgt, &id_last, 1, nullptr, 0, false, false);
-        GGML_ASSERT(n_chars < 0 && "failed to detokenize id_last");
-        text.resize(-n_chars);
-        llama_detokenize(vocab_tgt, &id_last, 1, text.data(), text.size(), false, false);
-        text = replace_to_dft(spec, text);
-
-        LOG_DBG("main->draft detokenized id_last(%d): '%s'\n", id_last, text.c_str());
-        id_last = common_tokenize(ctx_dft, text, false, true)[0];
-    }
-    // prompt_tgt's tokens will always be compatible with ctx_dft
-    const llama_tokens &prompt_tgt =
-        spec->vocab_dft_compatible ? prompt_tgt_main_model : prompt_tgt_draft_model;
+    const int n_ctx = llama_n_ctx(ctx) - params.n_draft;
 
     const int i_start = std::max<int>(0, (int) prompt_tgt.size() - n_ctx);
 
     // reuse as much as possible from the old draft context
     // ideally, the draft context should be as big as the target context and we will always reuse the entire prompt
-    for (int i = 0; i < (int) prompt_dft.size(); ++i) {
+    for (int i = 0; i < (int) prompt.size(); ++i) {
         int cur = 0;
         while (i_start + cur < (int) prompt_tgt.size() &&
-               i       + cur < (int) prompt_dft.size() &&
-               prompt_tgt[i_start + cur] == prompt_dft[i + cur]) {
+               i       + cur < (int) prompt.size() &&
+               prompt_tgt[i_start + cur] == prompt[i + cur]) {
             cur++;
         }
 
@@ -243,20 +169,21 @@ llama_tokens common_speculative_gen_draft(
         }
     }
 
-    LOG_DBG("%s: reuse_i = %d, reuse_n = %d, prompt = %d\n", __func__, reuse_i, reuse_n, (int) prompt_dft.size());
+    LOG_DBG("%s: reuse_i = %d, reuse_n = %d, prompt = %d\n", __func__, reuse_i, reuse_n, (int) prompt.size());
 
     llama_tokens result;
     result.reserve(params.n_draft);
 
     if (reuse_n == 0) {
-        llama_memory_clear(mem_dft, false);
-        prompt_dft.clear();
+        llama_memory_clear(mem, false);
+
+        prompt.clear();
     } else {
         // this happens when a previous draft has been discarded (for example, due to being too small), but the
         // target model agreed with it. in this case, we simply pass back the previous results to save compute
-        if (reuse_i + reuse_n < (int) prompt_dft.size() && prompt_dft[reuse_i + reuse_n] == id_last) {
-            for (int i = reuse_i + reuse_n + 1; i < (int) prompt_dft.size(); ++i) {
-                result.push_back(prompt_dft[i]);
+        if (reuse_i + reuse_n < (int) prompt.size() && prompt[reuse_i + reuse_n] == id_last) {
+            for (int i = reuse_i + reuse_n + 1; i < (int) prompt.size(); ++i) {
+                result.push_back(prompt[i]);
 
                 if (params.n_draft <= (int) result.size()) {
                     break;
@@ -267,15 +194,16 @@ llama_tokens common_speculative_gen_draft(
         }
 
         if (reuse_i > 0) {
-            llama_memory_seq_rm (mem_dft, 0, 0, reuse_i);
-            llama_memory_seq_add(mem_dft, 0, reuse_i, -1, -reuse_i);
+            llama_memory_seq_rm (mem, 0, 0, reuse_i);
+            llama_memory_seq_add(mem, 0, reuse_i, -1, -reuse_i);
 
-            prompt_dft.erase(prompt_dft.begin(), prompt_dft.begin() + reuse_i);
+            prompt.erase(prompt.begin(), prompt.begin() + reuse_i);
         }
 
-        if (reuse_n < (int) prompt_dft.size()) {
-            llama_memory_seq_rm (mem_dft, 0, reuse_n, -1);
-            prompt_dft.erase(prompt_dft.begin() + reuse_n, prompt_dft.end());
+        if (reuse_n < (int) prompt.size()) {
+            llama_memory_seq_rm (mem, 0, reuse_n, -1);
+
+            prompt.erase(prompt.begin() + reuse_n, prompt.end());
         }
     }
 
@@ -286,28 +214,28 @@ llama_tokens common_speculative_gen_draft(
         //LOG_DBG("i = %d, i_start = %d, reuse_n = %d, i - i_start = %d, id = %6d\n", i, i_start, reuse_n, i - i_start, prompt_tgt[i]);
         common_batch_add(batch, prompt_tgt[i], i - i_start, { 0 }, false);
 
-        prompt_dft.push_back(prompt_tgt[i]);
+        prompt.push_back(prompt_tgt[i]);
     }
 
     // we should rarely end-up here during normal decoding
     if (batch.n_tokens > 0) {
         //LOG_DBG("%s: draft prompt batch: %s\n", __func__, string_from(ctx, batch).c_str());
 
-        llama_decode(ctx_dft, batch);
+        llama_decode(ctx, batch);
     }
 
-    const llama_pos n_past = prompt_dft.size();
+    const llama_pos n_past = prompt.size();
 
     LOG_DBG("%s: n_past = %d\n", __func__, n_past);
 
     common_batch_clear(batch);
     common_batch_add  (batch, id_last, n_past, { 0 }, true);
 
-    prompt_dft.push_back(id_last);
+    prompt.push_back(id_last);
 
-    LOG_DBG("%s: draft prompt: %s\n", __func__, string_from(ctx_dft, prompt_dft).c_str());
+    //LOG_DBG("%s: draft prompt: %s\n", __func__, string_from(ctx, prompt).c_str());
 
-    llama_decode(ctx_dft, batch);
+    llama_decode(ctx, batch);
 
     common_sampler_reset(smpl);
 
@@ -315,13 +243,13 @@ llama_tokens common_speculative_gen_draft(
     for (int i = 0; i < params.n_draft; ++i) {
         common_batch_clear(batch);
 
-        common_sampler_sample(smpl, ctx_dft, 0, true);
+        common_sampler_sample(smpl, ctx, 0, true);
 
         const auto * cur_p = common_sampler_get_candidates(smpl);
 
         for (int k = 0; k < std::min(3, (int) cur_p->size); ++k) {
             LOG_DBG(" - draft candidate %3d, pos %3d: %6d (%8.3f) '%s'\n",
-                    k, i, cur_p->data[k].id, cur_p->data[k].p, common_token_to_piece(ctx_dft, cur_p->data[k].id).c_str());
+                    k, i, cur_p->data[k].id, cur_p->data[k].p, common_token_to_piece(ctx, cur_p->data[k].id).c_str());
         }
 
         // add drafted token for each sequence
@@ -343,19 +271,10 @@ llama_tokens common_speculative_gen_draft(
         common_batch_add(batch, id, n_past + i + 1, { 0 }, true);
 
         // evaluate the drafted tokens on the draft model
-        llama_decode(ctx_dft, batch);
+        llama_decode(ctx, batch);
 
-        prompt_dft.push_back(id);
+        prompt.push_back(id);
     }
 
-    if (!spec->vocab_dft_compatible) {
-        std::string detokenized = common_detokenize(ctx_dft, result, true);
-        detokenized = replace_to_tgt(spec, detokenized);
-        LOG_DBG("draft->main detokenized string: '%s'\n", detokenized.c_str());
-        result = common_tokenize(ctx_tgt, detokenized, false, true);
-        if (result.size() > (size_t)params.n_draft) {
-            result.resize(params.n_draft);
-        }
-    }
     return result;
 }

common/speculative.h

@@ -12,10 +12,7 @@ struct common_speculative_params {
     float p_min = 0.75f; // min probability required to accept a token in the draft
 };
 
-struct common_speculative * common_speculative_init(
-        struct llama_context * ctx_tgt,
-        struct llama_context * ctx_dft
-);
+struct common_speculative * common_speculative_init(struct llama_context * ctx_dft);
 
 void common_speculative_free(struct common_speculative * spec);
 
@@ -23,10 +20,6 @@ bool common_speculative_are_compatible(
         const struct llama_context * ctx_tgt,
         const struct llama_context * ctx_dft);
 
-void common_speculative_add_replacement_tgt_dft(
-        struct common_speculative * spec,
-        const char *source, const char *dest);
-
 // sample up to n_draft tokens and add them to the batch using the draft model
 llama_tokens common_speculative_gen_draft(
                struct common_speculative * spec,

convert_hf_to_gguf.py

@@ -678,18 +678,12 @@ class TextModel(ModelBase):
         if chkhsh == "a1336059768a55c99a734006ffb02203cd450fed003e9a71886c88acf24fdbc2":
             # ref: https://huggingface.co/THUDM/glm-4-9b-hf
             res = "glm4"
-        if chkhsh == "9ca2dd618e8afaf09731a7cf6e2105b373ba6a1821559f258b272fe83e6eb902":
-            # ref: https://huggingface.co/zai-org/GLM-4.5-Air
-            res = "glm4"
         if chkhsh == "1431a23e583c97432bc230bff598d103ddb5a1f89960c8f1d1051aaa944d0b35":
             # ref: https://huggingface.co/sapienzanlp/Minerva-7B-base-v1.0
             res = "minerva-7b"
         if chkhsh == "7e57df22b1fe23a7b1e1c7f3dc4e3f96d43a4eb0836d0c6bdc3436d7b2f1c664":
             # ref: https://huggingface.co/tencent/Hunyuan-A13B-Instruct
             res = "hunyuan"
-        if chkhsh == "bba3b3366b646dbdded5dbc42d59598b849371afc42f7beafa914afaa5b70aa6":
-            # ref: https://huggingface.co/tencent/Hunyuan-4B-Instruct
-            res = "hunyuan-dense"
         if chkhsh == "a6b57017d60e6edb4d88ecc2845188e0eb333a70357e45dcc9b53964a73bbae6":
             # ref: https://huggingface.co/tiiuae/Falcon-H1-0.5B-Base
             res = "falcon-h1"
@@ -705,9 +699,6 @@ class TextModel(ModelBase):
         if chkhsh == "81212dc7cdb7e0c1074ca62c5aeab0d43c9f52b8a737be7b12a777c953027890":
             # ref: https://huggingface.co/moonshotai/Kimi-K2-Base
             res = "kimi-k2"
-        if chkhsh == "d4540891389ea895b53b399da6ac824becc30f2fba0e9ddbb98f92e55ca0e97c":
-            # ref: https://huggingface.co/Qwen/Qwen3-Embedding-0.6B
-            res = "qwen2"
         if chkhsh == "0ef9807a4087ebef797fc749390439009c3b9eda9ad1a097abbe738f486c01e5":
             # ref: https://huggingface.co/meta-llama/Meta-Llama-3-8B
             res = "llama-bpe"
@@ -855,9 +846,6 @@ class TextModel(ModelBase):
         if chkhsh == "2085e1638f6c377a0aa4ead21b27bb4cb941bf800df86ed391011769c1758dfb":
             # ref: https://huggingface.co/LGAI-EXAONE/EXAONE-4.0-32B
             res = "exaone4"
-        if chkhsh == "a1e163ecab2e718a4c829d1148b6e86824ec36163bb71941c3dca9cd5ac25756":
-            # ref: https://huggingface.co/JetBrains/Mellum-4b-base
-            res = "mellum"
 
         if res is None:
             logger.warning("\n")
@@ -1912,7 +1900,6 @@ class StableLMModel(TextModel):
     "MixtralForCausalLM",
     "VLlama3ForCausalLM",
     "LlavaForConditionalGeneration",
-    "VoxtralForConditionalGeneration",
     "LlamaModel")
 class LlamaModel(TextModel):
     model_arch = gguf.MODEL_ARCH.LLAMA
@@ -1925,11 +1912,6 @@ class LlamaModel(TextModel):
             self.hparams["num_attention_heads"] = self.hparams.get("num_attention_heads", 32)
 
     def set_vocab(self):
-        path_tekken_json = self.dir_model / "tekken.json"
-        path_tokenizer_json = self.dir_model / "tokenizer.json"
-        if path_tekken_json.is_file() and not path_tokenizer_json.is_file():
-            return self.set_vocab_tekken()
-
         try:
             self._set_vocab_sentencepiece()
         except FileNotFoundError:
@@ -1962,52 +1944,6 @@ class LlamaModel(TextModel):
         if self.hparams.get("vocab_size", 32000) == 49152:
             self.gguf_writer.add_add_bos_token(False)
 
-    def set_vocab_tekken(self):
-        vocab = gguf.vocab.MistralVocab(self.dir_model)
-        self.gguf_writer.add_tokenizer_model(vocab.gguf_tokenizer_model)
-
-        tokens = []
-        scores = []
-        toktypes = []
-
-        for text, score, toktype in vocab.all_tokens():
-            tokens.append(text)
-            scores.append(score)
-            toktypes.append(toktype)
-
-        assert len(tokens) == vocab.vocab_size, (
-            f"token count ({len(tokens)}) != vocab size ({vocab.vocab_size})"
-        )
-
-        if vocab.tokenizer_type == gguf.vocab.MistralTokenizerType.tekken:
-            self.gguf_writer.add_tokenizer_pre("tekken")
-            self.gguf_writer.add_token_merges(
-                vocab.extract_vocab_merges_from_model()
-            )
-
-        logger.info(
-            f"Setting bos, eos, unk and pad token IDs to {vocab.bos_id}, {vocab.eos_id}, {vocab.unk_id}, {vocab.pad_id}."
-        )
-
-        self.gguf_writer.add_bos_token_id(vocab.bos_id)
-        self.gguf_writer.add_eos_token_id(vocab.eos_id)
-        self.gguf_writer.add_unk_token_id(vocab.unk_id)
-        self.gguf_writer.add_pad_token_id(vocab.pad_id)
-
-        self.gguf_writer.add_token_list(tokens)
-        self.gguf_writer.add_token_scores(scores)
-        self.gguf_writer.add_token_types(toktypes)
-        self.gguf_writer.add_vocab_size(vocab.vocab_size)
-
-        self.gguf_writer.add_add_bos_token(True)
-        self.gguf_writer.add_add_eos_token(False)
-
-        script_dir = Path(__file__).parent
-        template_path = script_dir / "models/templates/unsloth-mistral-Devstral-Small-2507.jinja"
-        with open(template_path, "r", encoding="utf-8") as f:
-            template = f.read()
-            self.gguf_writer.add_chat_template(template)
-
     def set_gguf_parameters(self):
         super().set_gguf_parameters()
         hparams = self.hparams
@@ -2035,13 +1971,12 @@ class LlamaModel(TextModel):
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
         n_head = self.hparams["num_attention_heads"]
         n_kv_head = self.hparams.get("num_key_value_heads")
-        is_multimodal_tensor = "vision_tower" in name \
+        is_vision_tensor = "vision_tower" in name \
             or "vision_model" in name \
-            or "audio_tower" in name \
             or "model.connector" in name \
             or "multi_modal_projector" in name
 
-        if is_multimodal_tensor:
+        if is_vision_tensor:
             return [] # skip vision tensors
         elif self.hf_arch == "LlamaModel":
             name = "model." + name
@@ -2916,107 +2851,6 @@ class DreamModel(TextModel):
         yield from super().modify_tensors(data_torch, name, bid)
 
 
-@ModelBase.register("LLaDAModelLM")
-class LLaDAModel(TextModel):
-    model_arch = gguf.MODEL_ARCH.LLADA
-    undo_permute = True
-
-    def get_vocab_base(self) -> tuple[list[str], list[int], str]:
-        tokens: list[str] = []
-        toktypes: list[int] = []
-
-        from transformers import AutoTokenizer
-        tokenizer = AutoTokenizer.from_pretrained(self.dir_model, trust_remote_code=True)
-
-        vocab_dict = tokenizer.get_vocab()
-        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()
-
-        for i in range(vocab_size):
-            if i not in reverse_vocab:
-                tokens.append(f"[PAD{i}]")
-                toktypes.append(gguf.TokenType.UNUSED)
-            elif reverse_vocab[i] in added_vocab:
-                tokens.append(reverse_vocab[i])
-                # Check if it's a special token - treat special tokens as CONTROL tokens
-                if hasattr(tokenizer, 'added_tokens_decoder') and i in tokenizer.added_tokens_decoder:
-                    if tokenizer.added_tokens_decoder[i].special:
-                        toktypes.append(gguf.TokenType.CONTROL)
-                    else:
-                        toktypes.append(gguf.TokenType.USER_DEFINED)
-                else:
-                    # Fallback: treat all added vocab as control tokens for special tokens like <|im_start|>
-                    toktypes.append(gguf.TokenType.CONTROL)
-            else:
-                tokens.append(reverse_vocab[i])
-                toktypes.append(gguf.TokenType.NORMAL)
-
-        return tokens, toktypes, tokpre
-
-    def set_vocab(self):
-        self._set_vocab_gpt2()
-
-        # LLaDA specific parameters
-        self.gguf_writer.add_add_bos_token(True)
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        self._try_set_pooling_type()
-
-        # Add parameters similar to LlamaModel
-        hparams = self.hparams
-        self.gguf_writer.add_vocab_size(hparams["vocab_size"])
-
-        if (rope_dim := hparams.get("head_dim")) is None:
-            n_heads = hparams.get("num_attention_heads", hparams.get("n_heads"))
-            rope_dim = hparams.get("hidden_size", hparams.get("d_model")) // n_heads
-        self.gguf_writer.add_rope_dimension_count(rope_dim)
-
-        # Set context length for LLaDA
-        context_length = self.hparams.get("max_sequence_length", 4096)
-        self.gguf_writer.add_context_length(context_length)
-
-        # Set embedding length (dimension size)
-        embedding_length = self.hparams.get("d_model", 4096)
-        self.gguf_writer.add_embedding_length(embedding_length)
-
-        # Set feed forward length (MLP hidden size)
-        feed_forward_length = self.hparams.get("mlp_hidden_size", 12288)
-        self.gguf_writer.add_feed_forward_length(feed_forward_length)
-
-        # LLaDA models use non-causal attention for diffusion, similar to Dream
-        self.gguf_writer.add_causal_attention(False)
-
-        # LLaDA models don't shift their logits
-        self.gguf_writer.add_diffusion_shift_logits(False)
-
-    @staticmethod
-    def permute(weights: Tensor, n_head: int, n_head_kv: int | None):
-        if n_head_kv is not None and n_head != n_head_kv:
-            n_head = n_head_kv
-        return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
-                .swapaxes(1, 2)
-                .reshape(weights.shape))
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        n_head = self.hparams.get("num_attention_heads", self.hparams.get("n_heads"))
-        n_kv_head = self.hparams.get("num_key_value_heads", self.hparams.get("n_kv_heads"))
-
-        if self.undo_permute:
-            if name.endswith(("q_proj.weight", "q_proj.bias")):
-                data_torch = LLaDAModel.permute(data_torch, n_head, n_head)
-            if name.endswith(("k_proj.weight", "k_proj.bias")):
-                data_torch = LLaDAModel.permute(data_torch, n_head, n_kv_head)
-
-        # LLaDA model tensors should be mapped directly since it's the base model
-        yield from super().modify_tensors(data_torch, name, bid)
-
-
 @ModelBase.register("Ernie4_5_ForCausalLM")
 class Ernie4_5Model(TextModel):
     model_arch = gguf.MODEL_ARCH.ERNIE4_5
@@ -3328,13 +3162,7 @@ class Qwen25OmniModel(Qwen2VLVisionModel):
 @ModelBase.register("InternVisionModel")
 class InternVisionModel(MmprojModel):
     def set_gguf_parameters(self):
-        assert self.hparams_vision is not None
-        if isinstance(self.hparams_vision['image_size'], list):
-            self.hparams_vision['image_size'] = self.hparams_vision['image_size'][0]
-        if isinstance(self.hparams_vision['patch_size'], list):
-            self.hparams_vision['patch_size'] = self.hparams_vision['patch_size'][0]
         super().set_gguf_parameters()
-
         hparams = self.hparams
         self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.INTERNVL)
         self.gguf_writer.add_vision_attention_layernorm_eps(hparams["layer_norm_eps"])
@@ -3358,30 +3186,14 @@ class InternVisionModel(MmprojModel):
             return gguf.GGMLQuantizationType.F32
         return False
 
-    def _mapping_interns1_name(self, name):
-        names_map = {
-            "model.multi_modal_projector.layer_norm.bias": "mlp1.0.bias",
-            "model.multi_modal_projector.layer_norm.weight": "mlp1.0.weight",
-            "model.multi_modal_projector.linear_1.bias": "mlp1.1.bias",
-            "model.multi_modal_projector.linear_1.weight": "mlp1.1.weight",
-            "model.multi_modal_projector.linear_2.bias": "mlp1.3.bias",
-            "model.multi_modal_projector.linear_2.weight": "mlp1.3.weight",
-        }
-        if name in names_map:
-            name = names_map[name]
-        return name
-
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
         del bid  # unused
-        vision_prefix = ['vision_model', 'mlp', 'model.vision_tower', 'model.multi_modal_projector']
-        # deal with intern-s1 special case
-        name = self._mapping_interns1_name(name)
-        if any([name.startswith(prefix) for prefix in vision_prefix]):
+        if name.startswith("vision_model") or name.startswith("mlp"):
             # process visual tensors
             # correct name
             if name.startswith("vision_model"):
                 name = "vision_tower." + name
-            if (".ls" in name or ".lambda_" in name or "position_embedding" in name) and not name.endswith(".weight"):
+            if (".ls" in name or "position_embedding" in name) and not name.endswith(".weight"):
                 name += ".weight"
             # split QKV tensors if needed
             if ".qkv." in name:
@@ -3467,10 +3279,6 @@ class Qwen2MoeModel(TextModel):
 
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
         # process the experts separately
-        name = name.replace("language_model.", "") # InternVL
-        if name.startswith("mlp") or name.startswith("vision_model") or name.startswith("model.vision_tower") or name.startswith("model.multi_modal_projector"):
-            # skip visual tensors
-            return []
         if name.find("experts") != -1:
             n_experts = self.hparams["num_experts"]
             assert bid is not None
@@ -3524,85 +3332,6 @@ class Qwen3Model(Qwen2Model):
 class Qwen3MoeModel(Qwen2MoeModel):
     model_arch = gguf.MODEL_ARCH.QWEN3MOE
 
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        hparams = ModelBase.load_hparams(self.dir_model)
-        self.origin_hf_arch = hparams.get('architectures', [None])[0]
-
-    def set_vocab(self):
-        # deal with intern-s1
-        if self.origin_hf_arch == 'InternS1ForConditionalGeneration':
-            self._set_vocab_interns1()
-            return
-
-        try:
-            self._set_vocab_sentencepiece()
-        except FileNotFoundError:
-            self._set_vocab_gpt2()
-
-    def _set_vocab_interns1(self):
-        tokens: list[str] = []
-        toktypes: list[int] = []
-
-        from transformers import AutoTokenizer
-        tokenizer = AutoTokenizer.from_pretrained(self.dir_model, trust_remote_code=True)
-        vocab = getattr(tokenizer, 'vocab', tokenizer.get_vocab())
-        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_tokens_decoder = tokenizer.added_tokens_decoder
-
-        for i in range(vocab_size):
-            if i not in reverse_vocab:
-                tokens.append(f"[PAD{i}]")
-                toktypes.append(gguf.TokenType.UNUSED)
-            else:
-                token: str = reverse_vocab[i]
-                if token in added_vocab:
-                    # The tokenizer in llama.cpp assumes the CONTROL and USER_DEFINED tokens are pre-normalized.
-                    # 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))
-                        if previous_token != token:
-                            logger.info(f"{repr(previous_token)} is encoded and decoded back to {repr(token)} using AutoTokenizer")
-
-                    if added_tokens_decoder[i].special or self.does_token_look_special(token):
-                        toktypes.append(gguf.TokenType.CONTROL)
-                    else:
-                        toktypes.append(gguf.TokenType.USER_DEFINED)
-                else:
-                    toktypes.append(gguf.TokenType.NORMAL)
-                tokens.append(token)
-
-        self.gguf_writer.add_tokenizer_model("gpt2")
-        self.gguf_writer.add_tokenizer_pre(tokpre)
-        self.gguf_writer.add_token_list(tokens)
-        self.gguf_writer.add_token_types(toktypes)
-
-        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
-        special_tokens_map_file = self.dir_model / 'special_tokens_map.json'
-        additional_special_tokens = []
-        if special_tokens_map_file.is_file():
-            with open(special_tokens_map_file, encoding = 'utf-8') as f:
-                additional_special_tokens = json.load(f).get('additional_special_tokens', [])
-        tokenizer_cfg_file = self.dir_model / 'special_tokens_map.json'
-        if tokenizer_cfg_file.is_file():
-            with open(tokenizer_cfg_file, encoding = 'utf-8') as f:
-                added_tokens_decoder = json.load(f).get('added_tokens_decoder', {})
-                token2ids_map = {data['content'] : int(token) for token, data in added_tokens_decoder.items() if data['special']}
-                for token in additional_special_tokens:
-                    if token in token2ids_map:
-                        special_vocab._set_special_token(token, token2ids_map[token])
-        special_vocab._set_special_token('eos', 151645)
-        special_vocab._set_special_token("bos", 151643)
-        special_vocab.add_to_gguf(self.gguf_writer)
-
 
 @ModelBase.register("GPT2LMHeadModel")
 class GPT2Model(TextModel):
@@ -6170,7 +5899,6 @@ class DeepseekModel(TextModel):
 
 @ModelBase.register("DeepseekV2ForCausalLM")
 @ModelBase.register("DeepseekV3ForCausalLM")
-@ModelBase.register("KimiVLForConditionalGeneration")
 class DeepseekV2Model(TextModel):
     model_arch = gguf.MODEL_ARCH.DEEPSEEK2
 
@@ -6273,13 +6001,6 @@ class DeepseekV2Model(TextModel):
     _experts: list[dict[str, Tensor]] | None = None
 
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        # skip vision tensors and remove "language_model." for Kimi-VL
-        if "vision_tower" in name or "multi_modal_projector" in name:
-            return []
-
-        if name.startswith("language_model."):
-            name = name.replace("language_model.", "")
-
         # rename e_score_correction_bias tensors
         if name.endswith("e_score_correction_bias"):
             name = name.replace("e_score_correction_bias", "e_score_correction.bias")
@@ -6804,139 +6525,6 @@ class Glm4Model(TextModel):
         return super().modify_tensors(data_torch, name, bid)
 
 
-@ModelBase.register("Glm4MoeForCausalLM")
-class Glm4MoeModel(TextModel):
-    model_arch = gguf.MODEL_ARCH.GLM4_MOE
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        # GLM4_MOE has num_hidden_layers + 1 actual layers (including NextN layer)
-        self.block_count = self.hparams["num_hidden_layers"] + self.hparams.get("num_nextn_predict_layers", 0)
-        self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
-
-    def set_vocab(self):
-        from transformers import AutoTokenizer
-
-        tokenizer = AutoTokenizer.from_pretrained(self.dir_model)
-        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
-        tokens, toktypes, tokpre = self.get_vocab_base()
-        self.gguf_writer.add_tokenizer_model("gpt2")
-        self.gguf_writer.add_tokenizer_pre(tokpre)
-        self.gguf_writer.add_token_list(tokens)
-        self.gguf_writer.add_token_types(toktypes)
-
-        # Special tokens
-        # Note: Using <|endoftext|> (151329) for eot causes endless generation
-        special_vocab._set_special_token("bos", tokenizer.get_added_vocab()["[gMASK]"])  # 151331
-        special_vocab._set_special_token("eot", tokenizer.get_added_vocab()["<|user|>"])  # 151336
-        special_vocab._set_special_token("unk", tokenizer.get_added_vocab()["<|endoftext|>"]) # 151329
-        special_vocab._set_special_token("eom", tokenizer.get_added_vocab()["<|observation|>"])  # 151338
-
-        # Patch broken chat template
-        if isinstance(special_vocab.chat_template, str) and "visible_text(m.content).endswith" in special_vocab.chat_template:
-            special_vocab.chat_template = special_vocab.chat_template.replace(
-                """{{ visible_text(m.content) }}\n{{- '/nothink' if (enable_thinking is defined and not enable_thinking and not visible_text(m.content).endswith("/nothink")) else '' -}}""",
-                """{% set content = visible_text(m.content) %}{{ content }}\n{{- '/nothink' if (enable_thinking is defined and not enable_thinking and not content.endswith("/nothink")) else '' -}}""")
-
-        special_vocab.add_to_gguf(self.gguf_writer)
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        if (rope_dim := self.hparams.get("head_dim")) is None:
-            rope_dim = (
-                self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
-            )
-        self.gguf_writer.add_rope_dimension_count(
-            int(rope_dim * self.hparams.get("partial_rotary_factor", 0.5))
-        )
-
-        # MoE parameters - Use only routed expert count (shared experts handled separately)
-        if (n_routed_experts := self.hparams.get("n_routed_experts")) is not None:
-            self.gguf_writer.add_expert_count(n_routed_experts)
-        if (moe_intermediate_size := self.hparams.get("moe_intermediate_size")) is not None:
-            self.gguf_writer.add_expert_feed_forward_length(moe_intermediate_size)
-        if (n_shared_experts := self.hparams.get("n_shared_experts")) is not None:
-            self.gguf_writer.add_expert_shared_count(n_shared_experts)
-        if (first_k_dense_replace := self.hparams.get("first_k_dense_replace")) is not None:
-            self.gguf_writer.add_leading_dense_block_count(first_k_dense_replace)
-
-        # Expert gating function (sigmoid for GLM4_MOE)
-        self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SIGMOID)
-
-        # Routed scaling factor
-        if (routed_scaling_factor := self.hparams.get("routed_scaling_factor")) is not None:
-            self.gguf_writer.add_expert_weights_scale(routed_scaling_factor)
-
-        # Normalise topk probabilities
-        if (norm_topk_prob := self.hparams.get("norm_topk_prob")) is not None:
-            self.gguf_writer.add_expert_weights_norm(norm_topk_prob)
-
-        # NextN/MTP prediction layers
-        if (num_nextn_predict_layers := self.hparams.get("num_nextn_predict_layers")) is not None:
-            self.gguf_writer.add_nextn_predict_layers(num_nextn_predict_layers)
-
-    _experts: list[dict[str, Tensor]] | None = None
-
-    def modify_tensors(
-        self, data_torch: Tensor, name: str, bid: int | None
-    ) -> Iterable[tuple[str, Tensor]]:
-        if name.startswith("model.visual."):  # ignore visual part
-            return []
-        elif name.startswith("model.language_model."):
-            name = name.replace("language_model.", "")  # for multimodal variants
-
-        # Handle main token embedding (but not layer-specific NextN embeddings)
-        if name == "model.embed_tokens.weight" and ".layers." not in name:
-            return [(self.map_tensor_name("token_embd.weight"), data_torch)]
-
-        # Handle routed experts
-        if name.find("mlp.experts") != -1:
-            n_experts = self.hparams["n_routed_experts"]
-            assert bid is not None
-
-            if self._experts is None:
-                self._experts = [{} for _ in range(self.block_count)]
-
-            self._experts[bid][name] = data_torch
-
-            if len(self._experts[bid]) >= n_experts * 3:
-                tensors: list[tuple[str, Tensor]] = []
-
-                # merge the experts into a single 3d tensor
-                for w_name in ["down_proj", "gate_proj", "up_proj"]:
-                    datas: list[Tensor] = []
-
-                    for xid in range(n_experts):
-                        ename = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight"
-                        datas.append(self._experts[bid][ename])
-                        del self._experts[bid][ename]
-
-                    data_torch = torch.stack(datas, dim=0)
-
-                    merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight"
-
-                    new_name = self.map_tensor_name(merged_name)
-                    tensors.append((new_name, data_torch))
-                return tensors
-            else:
-                return []
-
-        if name.endswith("e_score_correction_bias"):
-            name = name.replace("e_score_correction_bias", "e_score_correction.bias")
-
-        new_name = self.map_tensor_name(name)
-
-        return [(new_name, data_torch)]
-
-    def prepare_tensors(self):
-        super().prepare_tensors()
-        if self._experts is not None:
-            # flatten `list[dict[str, Tensor]]` into `list[str]`
-            experts = [k for d in self._experts for k in d.keys()]
-            if len(experts) > 0:
-                raise ValueError(f"Unprocessed experts: {experts}")
-
-
 @ModelBase.register("GlmForCausalLM", "ChatGLMModel", "ChatGLMForConditionalGeneration")
 class ChatGLMModel(TextModel):
     model_arch = gguf.MODEL_ARCH.CHATGLM
@@ -7643,10 +7231,9 @@ class WhisperEncoderModel(MmprojModel):
 
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
-        if "hidden_size" not in self.hparams and "intermediate_size" not in self.hparams:
-            self.hparams["hidden_size"] = self.hparams["d_model"]
-            self.hparams["intermediate_size"] = self.hparams["encoder_ffn_dim"]
-            self.hparams["num_attention_heads"] = self.hparams["encoder_attention_heads"]
+        self.hparams["hidden_size"] = self.hparams["d_model"]
+        self.hparams["intermediate_size"] = self.hparams["encoder_ffn_dim"]
+        self.hparams["num_attention_heads"] = self.hparams["encoder_attention_heads"]
 
     def set_gguf_parameters(self):
         super().set_gguf_parameters()
@@ -7685,21 +7272,9 @@ class UltravoxWhisperEncoderModel(WhisperEncoderModel):
 
     def set_gguf_parameters(self):
         super().set_gguf_parameters()
-        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.ULTRAVOX)
         self.gguf_writer.add_audio_stack_factor(self.global_config["stack_factor"])
 
 
-@ModelBase.register("VoxtralForConditionalGeneration")
-class VoxtralWhisperEncoderModel(WhisperEncoderModel):
-    has_vision_encoder = False # no vision encoder
-    has_audio_encoder = True
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.VOXTRAL)
-        self.gguf_writer.add_audio_stack_factor(4) # == intermediate_size // hidden_size
-
-
 @ModelBase.register("FalconH1ForCausalLM")
 class FalconH1Model(Mamba2Model):
     model_arch = gguf.MODEL_ARCH.FALCON_H1
@@ -7811,6 +7386,11 @@ class FalconH1Model(Mamba2Model):
 class HunYuanMoEModel(TextModel):
     model_arch = gguf.MODEL_ARCH.HUNYUAN_MOE
 
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        # For handling tied embeddings
+        self._tok_embd = None
+
     def set_vocab(self):
         from transformers import AutoTokenizer
         tokenizer = AutoTokenizer.from_pretrained(self.dir_model, trust_remote_code=True)
@@ -7904,6 +7484,9 @@ class HunYuanMoEModel(TextModel):
     _experts: list[dict[str, Tensor]] | None = None
 
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        if name == "model.embed_tokens.weight":
+            self._tok_embd = data_torch.clone()
+
         if name == "lm_head.weight":
             if self.hparams.get("tie_word_embeddings", False):
                 logger.info("Skipping tied output layer 'lm_head.weight'")
@@ -7948,98 +7531,6 @@ class HunYuanMoEModel(TextModel):
                 raise ValueError(f"Unprocessed experts: {experts}")
 
 
-@ModelBase.register("HunYuanDenseV1ForCausalLM")
-class HunYuanModel(TextModel):
-    model_arch = gguf.MODEL_ARCH.HUNYUAN_DENSE
-
-    def set_vocab(self):
-        if (self.dir_model / "tokenizer.json").is_file():
-            self._set_vocab_gpt2()
-        else:
-            from transformers import AutoTokenizer
-            tokenizer = AutoTokenizer.from_pretrained(self.dir_model, trust_remote_code=True)
-
-            # 1. Get the pre-tokenizer identifier hash
-            tokpre = self.get_vocab_base_pre(tokenizer)
-
-            # 2. Reverse-engineer the merges list from mergeable_ranks
-            merges = []
-            vocab = {}
-            mergeable_ranks = tokenizer.mergeable_ranks
-            for token, rank in mergeable_ranks.items():
-                vocab[QwenModel.token_bytes_to_string(token)] = rank
-                if len(token) == 1:
-                    continue
-                merged = QwenModel.bpe(mergeable_ranks, token, max_rank=rank)
-                if len(merged) == 2:
-                    merges.append(' '.join(map(QwenModel.token_bytes_to_string, merged)))
-
-            # 3. Generate the tokens and toktypes lists
-            vocab_size = self.hparams["vocab_size"]
-            assert tokenizer.vocab_size == vocab_size
-            special_tokens = tokenizer.special_tokens
-            reverse_vocab = {id_ : encoded_tok for encoded_tok, id_ in {**vocab, **special_tokens}.items()}
-            tokens: list[str] = []
-            toktypes: list[int] = []
-            for i in range(vocab_size):
-                if i not in reverse_vocab:
-                    tokens.append(f"[PAD{i}]")
-                    toktypes.append(gguf.TokenType.UNUSED)
-                else:
-                    token = reverse_vocab[i]
-                    tokens.append(token)
-                    if i in special_tokens.values():
-                        toktypes.append(gguf.TokenType.CONTROL)
-                    else:
-                        toktypes.append(gguf.TokenType.NORMAL)
-
-            # 4. Write all vocab-related fields to the GGUF writer
-            self.gguf_writer.add_tokenizer_model("gpt2")
-            self.gguf_writer.add_tokenizer_pre(tokpre)
-            self.gguf_writer.add_token_list(tokens)
-            self.gguf_writer.add_token_types(toktypes)
-            self.gguf_writer.add_token_merges(merges)
-
-            # 5. Add special tokens and chat templates
-            special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=False)
-            special_vocab.add_to_gguf(self.gguf_writer)
-            # FIX for BOS token: Overwrite incorrect id read from config.json
-            if self.hparams['hidden_size'] == 4096:
-                self.gguf_writer.add_bos_token_id(127958) # only for 7b dense, fix <|bos|> token
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        hparams = self.hparams
-
-        # Rope
-        rope_scaling = hparams.get("rope_scaling", {})
-        if rope_scaling.get("type") == "dynamic":
-            # HunYuan uses NTK Aware Alpha based scaling. Original implementation: https://www.reddit.com/r/LocalLLaMA/comments/14lz7j5/ntkaware_scaled_rope_allows_llama_models_to_have/
-            # 1000 corresponds to a usable context length of 256k (https://github.com/Tencent-Hunyuan/Hunyuan-A13B/blob/main/report/Hunyuan_A13B_Technical_Report.pdf)
-            alpha = rope_scaling.get("alpha", 50)
-            base = hparams.get("rope_theta", 10000.0)
-            dim = hparams["head_dim"]
-            scaled_base = base * (alpha ** (dim / (dim - 2)))
-            self.gguf_writer.add_rope_freq_base(scaled_base)
-            self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)
-            self.gguf_writer.add_rope_scaling_factor(1)
-            # There is no consistent way to calculate ctx from alpha, and the config is incorrectly set to 32k
-            self.gguf_writer.add_rope_scaling_orig_ctx_len(256 * 1024) # 256k context length
-            self.gguf_writer.add_context_length(256 * 1024) # 256k context length
-
-            # if any of our assumptions about the values are wrong, something has changed and this may need to be updated
-            assert base == 10000.0 and self.hparams["max_position_embeddings"] in [32 * 1024, 256 * 1024] , \
-                "HunYuan dynamic RoPE scaling assumptions changed, please update the logic or context length manually"
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        if name == "lm_head.weight":
-            if self.hparams.get("tie_word_embeddings", False):
-                logger.info("Skipping tied output layer 'lm_head.weight'")
-                return []
-
-        return [(self.map_tensor_name(name), data_torch)]
-
-
 @ModelBase.register("SmolLM3ForCausalLM")
 class SmolLM3Model(LlamaModel):
     model_arch = gguf.MODEL_ARCH.SMOLLM3
@@ -8055,130 +7546,6 @@ class SmolLM3Model(LlamaModel):
             self.gguf_writer.add_chat_template(chat_template)
 
 
-@ModelBase.register("GptOssForCausalLM")
-class GptOssModel(TextModel):
-    model_arch = gguf.MODEL_ARCH.GPT_OSS
-
-    def transform_nibble_layout(self, tensor):
-        assert tensor.dtype == torch.uint8
-        assert tensor.shape[-1] == 16
-        # swap nibbles
-        t_lo = tensor & 0x0F
-        t_hi = tensor & 0xF0
-        t_swapped = (t_lo << 4) | (t_hi >> 4)
-        tensor = t_swapped
-        # transform aaaa...bbbb... to abababab...
-        blk_a, blk_b = tensor.chunk(2, dim=-1)
-        # get a_
-        blk_a0 = (blk_a & 0xF0).view(-1, 1)
-        blk_a1 = (blk_a << 4).view(-1, 1)
-        blk_a = torch.stack((blk_a0, blk_a1), dim=2).view(tensor.shape)
-        # get _b
-        blk_b0 = (blk_b >> 4).view(-1, 1)
-        blk_b1 = (blk_b & 0x0F).view(-1, 1)
-        blk_b = torch.stack((blk_b0, blk_b1), dim=2).view(tensor.shape)
-        # swap once more
-        out = blk_a | blk_b
-        out_h = out & 0xF0
-        out_l = out & 0x0F
-        out = (out_h >> 4) | (out_l << 4)
-        return out
-
-    def repack_mxfp4(self, new_name: str, blocks: Tensor, scales: Tensor):
-        assert blocks.dtype == torch.uint8
-        assert scales.dtype == torch.uint8
-        scales = scales.unsqueeze(-1)
-        assert len(blocks.shape) == 4
-        assert len(scales.shape) == 4
-        blocks = self.transform_nibble_layout(blocks)
-        new_data = torch.concat((scales, blocks), dim=-1)
-        new_shape = [new_data.shape[0], new_data.shape[1], new_data.shape[2] * 32]
-        logger.info(f"Repacked {new_name} with shape {new_shape} and quantization MXFP4")
-        # flatten last dim
-        new_data = new_data.view(new_data.shape[0], new_data.shape[1], new_data.shape[2] * new_data.shape[3])
-        new_data = new_data.numpy()
-        self.gguf_writer.add_tensor(new_name, new_data, raw_dtype=gguf.GGMLQuantizationType.MXFP4)
-
-    def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
-        blocks0: Tensor = torch.zeros(1)
-        blocks1: Tensor = torch.zeros(1)
-        # we assume that tensors are loaded in the correct order
-        for name, data_torch in self.get_tensors():
-            if "mlp.experts.down_proj_blocks" in name:
-                blocks0 = data_torch
-            elif "mlp.experts.down_proj_scales" in name:
-                new_name = self.map_tensor_name(name.replace("_scales", ".weight"))
-                self.repack_mxfp4(new_name, blocks0, data_torch)
-            elif "mlp.experts.gate_up_proj_blocks" in name:
-                blocks0, blocks1 = data_torch[:, ::2, :, :], data_torch[:, 1::2, :, :]
-            elif "mlp.experts.gate_up_proj_scales" in name:
-                scales0, scales1 = data_torch[:, ::2, :], data_torch[:, 1::2, :]
-                new_name_gate = self.map_tensor_name(name.replace("gate_up_proj_scales", "gate_proj.weight"))
-                new_name_up = self.map_tensor_name(name.replace("gate_up_proj_scales", "up_proj.weight"))
-                self.repack_mxfp4(new_name_gate, blocks0, scales0)
-                self.repack_mxfp4(new_name_up, blocks1, scales1)
-        return []
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        del bid  # unused
-
-        if "sinks" in name:
-            name += ".weight"
-
-        # correct naming for down_proj
-        if "down_proj" in name:
-            if name.endswith("_bias"):
-                name = name.replace("down_proj_bias", "down_proj.bias")
-            elif "_blocks" not in name and "_scales" not in name:
-                logger.warning(f"{name} is not in MXFP4, performance may be degraded")
-                name = name.replace("down_proj", "down_proj.weight")
-                data_torch = data_torch.transpose(-1, -2)
-            else:
-                # otherwise, it should already be repacked to ggml MXFP4 format
-                return []
-
-        # split the gate_up into gate and up
-        if "gate_up_proj" in name:
-            if name.endswith("_bias"):
-                name_up = name.replace("gate_up_proj_bias", "up_proj.bias")
-                name_gate = name.replace("gate_up_proj_bias", "gate_proj.bias")
-                gate_proj_bias, up_proj_bias = data_torch[..., ::2], data_torch[..., 1::2]
-                return [
-                    (self.map_tensor_name(name_gate), gate_proj_bias),
-                    (self.map_tensor_name(name_up), up_proj_bias)
-                ]
-            elif "_blocks" not in name and "_scales" not in name:
-                logger.warning(f"{name} is not in MXFP4, performance may be degraded")
-                name_up = name.replace("gate_up_proj", "up_proj.weight")
-                name_gate = name.replace("gate_up_proj", "gate_proj.weight")
-                data_torch = data_torch.transpose(-1, -2)
-                gate_proj_weight, up_proj_weight = data_torch[:, ::2, :], data_torch[:, 1::2, :]
-                return [
-                    (self.map_tensor_name(name_gate), gate_proj_weight),
-                    (self.map_tensor_name(name_up), up_proj_weight)
-                ]
-            else:
-                # otherwise, it should already be repacked to ggml MXFP4 format
-                return []
-
-        return [(self.map_tensor_name(name), data_torch)]
-
-    def set_vocab(self):
-        self._set_vocab_gpt2()
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        self.gguf_writer.add_sliding_window(self.hparams["sliding_window"])
-        self.gguf_writer.add_expert_feed_forward_length(self.hparams["intermediate_size"])
-
-        rope_scaling = self.hparams.get("rope_scaling") or {}
-        rope_type = rope_scaling.get("rope_type", rope_scaling.get("type"))
-        assert rope_type == "yarn", f"GPT-OSS only supports yarn rope scaling, got {rope_type}"
-        self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN)
-        self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"])
-        self.gguf_writer.add_rope_scaling_orig_ctx_len(rope_scaling.get("original_max_position_embeddings", 4096))
-
-
 @ModelBase.register("Lfm2ForCausalLM")
 @ModelBase.register("LFM2ForCausalLM")
 class LFM2Model(TextModel):
@@ -8222,88 +7589,6 @@ class LFM2Model(TextModel):
         return [(self.map_tensor_name(name), data_torch)]
 
 
-@ModelBase.register("SmallThinkerForCausalLM")
-class SmallThinkerModel(TextModel):
-    model_arch = gguf.MODEL_ARCH.SMALLTHINKER
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        if (n_experts := self.hparams.get("num_experts", self.hparams.get("moe_num_primary_experts"))) is not None:
-            self.gguf_writer.add_expert_count(n_experts)
-        if (n_experts_used := self.hparams.get("num_experts_per_tok", self.hparams.get("moe_num_active_primary_experts"))) is not None:
-            self.gguf_writer.add_expert_used_count(n_experts_used)
-        if (moe_intermediate_size := self.hparams.get("moe_ffn_hidden_size")) is not None:
-            self.gguf_writer.add_expert_feed_forward_length(moe_intermediate_size)
-            self.gguf_writer.add_feed_forward_length(moe_intermediate_size)
-            logger.info(f"gguf: expert feed forward length = {moe_intermediate_size}")
-        if (self.hparams.get('moe_primary_router_apply_softmax')):
-            self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SOFTMAX)
-        else:
-            self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SIGMOID)
-        # YaRN is not enabled by default
-        # To enable it, please refer to this guide: https://huggingface.co/Qwen/Qwen3-30B-A3B#processing-long-texts
-        rope_scaling = self.hparams.get("rope_scaling") or {}
-        if rope_scaling.get("rope_type", rope_scaling.get("type")) == "yarn" and "factor" in rope_scaling:
-            self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN)
-            self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"])
-            self.gguf_writer.add_rope_scaling_orig_ctx_len(rope_scaling["original_max_position_embeddings"])
-
-        sliding_window_layout = self.hparams.get("sliding_window_layout")
-        if sliding_window_layout:
-            for i in sliding_window_layout:
-                if i != 0:
-                    sliding_window = self.hparams.get("sliding_window_size")
-                    if sliding_window:
-                        self.gguf_writer.add_sliding_window(sliding_window)
-                    break
-
-    _experts: list[dict[str, Tensor]] | None = None
-
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        # process the experts separately
-        if name.find("experts") != -1:
-            n_experts = self.hparams.get("num_experts", self.hparams.get("moe_num_primary_experts"))
-            assert bid is not None
-
-            if self._experts is None:
-                self._experts = [{} for _ in range(self.block_count)]
-
-            self._experts[bid][name] = data_torch
-
-            if len(self._experts[bid]) >= n_experts * 3:
-                tensors: list[tuple[str, Tensor]] = []
-
-                # merge the experts into a single 3d tensor
-                for w_name in ["down", "gate", "up"]:
-                    datas: list[Tensor] = []
-
-                    for xid in range(n_experts):
-                        ename = f"model.layers.{bid}.block_sparse_moe.experts.{xid}.{w_name}.weight"
-                        datas.append(self._experts[bid][ename])
-                        del self._experts[bid][ename]
-
-                    data_torch = torch.stack(datas, dim=0)
-
-                    merged_name = f"model.layers.{bid}.block_sparse_moe.experts.{w_name}.weight"
-
-                    new_name = self.map_tensor_name(merged_name)
-
-                    tensors.append((new_name, data_torch))
-                return tensors
-            else:
-                return []
-
-        return [(self.map_tensor_name(name), data_torch)]
-
-    def prepare_tensors(self):
-        super().prepare_tensors()
-
-        if self._experts is not None:
-            # flatten `list[dict[str, Tensor]]` into `list[str]`
-            experts = [k for d in self._experts for k in d.keys()]
-            if len(experts) > 0:
-                raise ValueError(f"Unprocessed experts: {experts}")
-
 ###### CONVERSION LOGIC ######
 
 
@@ -8318,7 +7603,6 @@ class LazyTorchTensor(gguf.LazyBase):
     _dtype_map: dict[torch.dtype, type] = {
         torch.float16: np.float16,
         torch.float32: np.float32,
-        torch.uint8: np.uint8,
     }
 
     # used for safetensors slices

convert_hf_to_gguf_update.py

@@ -59,10 +59,6 @@ parser.add_argument(
     "--full", action="store_true",
     help="download full list of models - make sure you have access to all of them",
 )
-parser.add_argument(
-    "--check-missing", action="store_true",
-    help="only check for missing pre-tokenizer hashes",
-)
 parser.add_argument(
     "hf_token",
     help="optional HF token",
@@ -74,10 +70,6 @@ hf_token = args.hf_token if args.hf_token is not None else hf_token
 if hf_token is None:
     logger.warning("HF token not found. You can provide it as an argument or set it in ~/.cache/huggingface/token")
 
-if args.check_missing and args.full:
-    logger.warning("Downloading full list of models requested, ignoring --check-missing!")
-    args.check_missing = False
-
 # TODO: this string has to exercise as much pre-tokenizer functionality as possible
 #       will be updated with time - contributions welcome
 CHK_TXT = '\n \n\n \n\n\n \t \t\t \t\n  \n   \n    \n     \n🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ 🦙🦙 3 33 333 3333 33333 333333 3333333 33333333 3.3 3..3 3...3 កាន់តែពិសេសអាច😁 ?我想在apple工作1314151天～ ------======= нещо на Български \'\'\'\'\'\'```````\"\"\"\"......!!!!!!?????? I\'ve been \'told he\'s there, \'RE you sure? \'M not sure I\'ll make it, \'D you like some tea? We\'Ve a\'lL'
@@ -138,7 +130,6 @@ models = [
     {"name": "midm-2.0",         "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/K-intelligence/Midm-2.0-Base-Instruct", },
     {"name": "lfm2",             "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LiquidAI/LFM2-Tokenizer"},
     {"name": "exaone4",          "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LGAI-EXAONE/EXAONE-4.0-32B", },
-    {"name": "mellum",           "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/JetBrains/Mellum-4b-base", },
 ]
 
 # some models are known to be broken upstream, so we will skip them as exceptions
@@ -147,17 +138,14 @@ pre_computed_hashes = [
     {"name": "chatglm-bpe", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/THUDM/glm-4-9b-chat", "chkhsh": "b6e8e1518dc4305be2fe39c313ed643381c4da5db34a98f6a04c093f8afbe99b"},
     {"name": "chatglm-bpe", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/THUDM/glm-4-9b-chat", "chkhsh": "81d72c7348a9f0ebe86f23298d37debe0a5e71149e29bd283904c02262b27516"},
     {"name": "glm4", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/THUDM/glm-4-9b-hf", "chkhsh": "a1336059768a55c99a734006ffb02203cd450fed003e9a71886c88acf24fdbc2"},
-    {"name": "glm4", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/zai-org/GLM-4.5-Air", "chkhsh": "9ca2dd618e8afaf09731a7cf6e2105b373ba6a1821559f258b272fe83e6eb902"},
     {"name": "minerva-7b", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/sapienzanlp/Minerva-7B-base-v1.0", "chkhsh": "1431a23e583c97432bc230bff598d103ddb5a1f89960c8f1d1051aaa944d0b35"},
     {"name": "hunyuan", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tencent/Hunyuan-A13B-Instruct", "chkhsh": "7e57df22b1fe23a7b1e1c7f3dc4e3f96d43a4eb0836d0c6bdc3436d7b2f1c664"},
-    {"name": "hunyuan-dense", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tencent/Hunyuan-4B-Instruct", "chkhsh": "bba3b3366b646dbdded5dbc42d59598b849371afc42f7beafa914afaa5b70aa6"},
     # falcon-h1 series uses 4 different tokenizers across model sizes (0.5b - 34b), hence we need to define 4 different hashes
     {"name": "falcon-h1", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/Falcon-H1-0.5B-Base", "chkhsh": "a6b57017d60e6edb4d88ecc2845188e0eb333a70357e45dcc9b53964a73bbae6"},
     {"name": "falcon-h1", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/Falcon-H1-1B-Base", "chkhsh": "60476e1243776c4fb1b993dbd7a5f15ac22f83c80afdf425fa5ae01c8d44ef86"},
     {"name": "falcon-h1", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/Falcon-H1-7B-Base", "chkhsh": "3eda48b4c4dc7de733d1a8b3e3b4a85243dbbf704da2ee9d42c6beced8897896"},
     {"name": "falcon-h1", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/Falcon-H1-34B-Base", "chkhsh": "48f8e02c0359c0bbdd82f26909171fac1c18a457bb47573ed1fe3bbb2c1cfd4b"},
     {"name": "kimi-k2",   "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/moonshotai/Kimi-K2-Base",   "chkhsh": "81212dc7cdb7e0c1074ca62c5aeab0d43c9f52b8a737be7b12a777c953027890"},
-    {"name": "qwen2",     "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/Qwen/Qwen3-Embedding-0.6B", "chkhsh": "d4540891389ea895b53b399da6ac824becc30f2fba0e9ddbb98f92e55ca0e97c"},
 ]
 
 
@@ -232,13 +220,12 @@ if not args.full:
     all_models = models.copy()
     models = [model for model in all_models if model["name"] not in existing_models]
 
-if not args.check_missing:
-    logging.info(f"Downloading {len(models)} models...")
-    for model in models:
-        try:
-            download_model(model)
-        except Exception as e:
-            logger.error(f"Failed to download model {model['name']}. Error: {e}")
+logging.info(f"Downloading {len(models)} models...")
+for model in models:
+    try:
+        download_model(model)
+    except Exception as e:
+        logger.error(f"Failed to download model {model['name']}. Error: {e}")
 
 
 # generate the source code for the convert_hf_to_gguf.py:get_vocab_base_pre() function:

docs/backend/CANN.md

@@ -310,7 +310,5 @@ Specifies the memory pool management strategy:
 
 Controls automatic cleanup of the memory pool. This option is only effective when using the prio or leg memory pool strategies.
 
-### GGML_CANN_WEIGHT_NZ
-
-Converting the matmul weight format from ND to NZ can significantly improve performance on the 310I DUO NPU.
-
+## TODO
+- Support more models and data types.

docs/multimodal.md

@@ -97,9 +97,6 @@ NOTE: some models may require large context window, for example: `-c 8192`
 # Qwen2-Audio and SeaLLM-Audio
 # note: no pre-quantized GGUF this model, as they have very poor result
 # ref: https://github.com/ggml-org/llama.cpp/pull/13760
-
-# Mistral's Voxtral
-(tool_name) -hf ggml-org/Voxtral-Mini-3B-2507-GGUF
 ```
 
 **Mixed modalities**:

docs/multimodal/minicpmo2.6.md

@@ -29,8 +29,8 @@ cmake --build build --config Release
 Convert PyTorch model to gguf files (You can also download the converted [gguf](https://huggingface.co/openbmb/MiniCPM-o-2_6-gguf) by us)
 
 ```bash
-python ./tools/mtmd/legacy-models/minicpmv-surgery.py -m ../MiniCPM-o-2_6
-python ./tools/mtmd/legacy-models/minicpmv-convert-image-encoder-to-gguf.py -m ../MiniCPM-o-2_6 --minicpmv-projector ../MiniCPM-o-2_6/minicpmv.projector --output-dir ../MiniCPM-o-2_6/ --minicpmv_version 4
+python ./tools/mtmd/minicpmv-surgery.py -m ../MiniCPM-o-2_6
+python ./tools/mtmd/minicpmv-convert-image-encoder-to-gguf.py -m ../MiniCPM-o-2_6 --minicpmv-projector ../MiniCPM-o-2_6/minicpmv.projector --output-dir ../MiniCPM-o-2_6/ --image-mean 0.5 0.5 0.5 --image-std 0.5 0.5 0.5 --minicpmv_version 4
 python ./convert_hf_to_gguf.py ../MiniCPM-o-2_6/model
 
 # quantize int4 version

docs/multimodal/minicpmo4.0.md

@@ -1,47 +0,0 @@
-## MiniCPM-o 4
-
-### Prepare models and code
-
-Download [MiniCPM-o-4](https://huggingface.co/openbmb/MiniCPM-o-4) PyTorch model from huggingface to "MiniCPM-o-4" folder.
-
-
-### Build llama.cpp
-Readme modification time: 20250206
-
-If there are differences in usage, please refer to the official build [documentation](https://github.com/ggerganov/llama.cpp/blob/master/docs/build.md)
-
-Clone llama.cpp:
-```bash
-git clone https://github.com/ggerganov/llama.cpp
-cd llama.cpp
-```
-
-Build llama.cpp using `CMake`:
-```bash
-cmake -B build
-cmake --build build --config Release
-```
-
-
-### Usage of MiniCPM-o 4
-
-Convert PyTorch model to gguf files (You can also download the converted [gguf](https://huggingface.co/openbmb/MiniCPM-o-4-gguf) by us)
-
-```bash
-python ./tools/mtmd/legacy-models/minicpmv-surgery.py -m ../MiniCPM-o-4
-python ./tools/mtmd/legacy-models/minicpmv-convert-image-encoder-to-gguf.py -m ../MiniCPM-o-4 --minicpmv-projector ../MiniCPM-o-4/minicpmv.projector --output-dir ../MiniCPM-o-4/ --minicpmv_version 6
-python ./convert_hf_to_gguf.py ../MiniCPM-o-4/model
-
-# quantize int4 version
-./build/bin/llama-quantize ../MiniCPM-o-4/model/ggml-model-f16.gguf ../MiniCPM-o-4/model/ggml-model-Q4_K_M.gguf Q4_K_M
-```
-
-
-Inference on Linux or Mac
-```bash
-# run in single-turn mode
-./build/bin/llama-mtmd-cli -m ../MiniCPM-o-4/model/ggml-model-f16.gguf --mmproj ../MiniCPM-o-4/mmproj-model-f16.gguf -c 4096 --temp 0.7 --top-p 0.8 --top-k 100 --repeat-penalty 1.05 --image xx.jpg -p "What is in the image?"
-
-# run in conversation mode
-./build/bin/llama-mtmd-cli -m ../MiniCPM-o-4/model/ggml-model-Q4_K_M.gguf --mmproj ../MiniCPM-o-4/mmproj-model-f16.gguf
-```

docs/multimodal/minicpmv2.5.md

@@ -28,8 +28,8 @@ cmake --build build --config Release
 Convert PyTorch model to gguf files (You can also download the converted [gguf](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5-gguf) by us)
 
 ```bash
-python ./tools/mtmd/legacy-models/minicpmv-surgery.py -m ../MiniCPM-Llama3-V-2_5
-python ./tools/mtmd/legacy-models/minicpmv-convert-image-encoder-to-gguf.py -m ../MiniCPM-Llama3-V-2_5 --minicpmv-projector ../MiniCPM-Llama3-V-2_5/minicpmv.projector --output-dir ../MiniCPM-Llama3-V-2_5/ --minicpmv_version 2
+python ./tools/mtmd/minicpmv-surgery.py -m ../MiniCPM-Llama3-V-2_5
+python ./tools/mtmd/minicpmv-convert-image-encoder-to-gguf.py -m ../MiniCPM-Llama3-V-2_5 --minicpmv-projector ../MiniCPM-Llama3-V-2_5/minicpmv.projector --output-dir ../MiniCPM-Llama3-V-2_5/ --image-mean 0.5 0.5 0.5 --image-std 0.5 0.5 0.5 --minicpmv_version 2
 python ./convert_hf_to_gguf.py ../MiniCPM-Llama3-V-2_5/model
 
 # quantize int4 version

docs/multimodal/minicpmv2.6.md

@@ -28,8 +28,8 @@ cmake --build build --config Release
 Convert PyTorch model to gguf files (You can also download the converted [gguf](https://huggingface.co/openbmb/MiniCPM-V-2_6-gguf) by us)
 
 ```bash
-python ./tools/mtmd/legacy-models/minicpmv-surgery.py -m ../MiniCPM-V-2_6
-python ./tools/mtmd/legacy-models/minicpmv-convert-image-encoder-to-gguf.py -m ../MiniCPM-V-2_6 --minicpmv-projector ../MiniCPM-V-2_6/minicpmv.projector --output-dir ../MiniCPM-V-2_6/ --minicpmv_version 3
+python ./tools/mtmd/minicpmv-surgery.py -m ../MiniCPM-V-2_6
+python ./tools/mtmd/minicpmv-convert-image-encoder-to-gguf.py -m ../MiniCPM-V-2_6 --minicpmv-projector ../MiniCPM-V-2_6/minicpmv.projector --output-dir ../MiniCPM-V-2_6/ --image-mean 0.5 0.5 0.5 --image-std 0.5 0.5 0.5 --minicpmv_version 3
 python ./convert_hf_to_gguf.py ../MiniCPM-V-2_6/model
 
 # quantize int4 version

docs/multimodal/minicpmv4.0.md

@@ -1,47 +0,0 @@
-## MiniCPM-V 4
-
-### Prepare models and code
-
-Download [MiniCPM-V-4](https://huggingface.co/openbmb/MiniCPM-V-4) PyTorch model from huggingface to "MiniCPM-V-4" folder.
-
-
-### Build llama.cpp
-Readme modification time: 20250206
-
-If there are differences in usage, please refer to the official build [documentation](https://github.com/ggerganov/llama.cpp/blob/master/docs/build.md)
-
-Clone llama.cpp:
-```bash
-git clone https://github.com/ggerganov/llama.cpp
-cd llama.cpp
-```
-
-Build llama.cpp using `CMake`:
-```bash
-cmake -B build
-cmake --build build --config Release
-```
-
-
-### Usage of MiniCPM-V 4
-
-Convert PyTorch model to gguf files (You can also download the converted [gguf](https://huggingface.co/openbmb/MiniCPM-V-4-gguf) by us)
-
-```bash
-python ./tools/mtmd/legacy-models/minicpmv-surgery.py -m ../MiniCPM-V-4
-python ./tools/mtmd/legacy-models/minicpmv-convert-image-encoder-to-gguf.py -m ../MiniCPM-V-4 --minicpmv-projector ../MiniCPM-V-4/minicpmv.projector --output-dir ../MiniCPM-V-4/ --minicpmv_version 5
-python ./convert_hf_to_gguf.py ../MiniCPM-V-4/model
-
-# quantize int4 version
-./build/bin/llama-quantize ../MiniCPM-V-4/model/ggml-model-f16.gguf ../MiniCPM-V-4/model/ggml-model-Q4_K_M.gguf Q4_K_M
-```
-
-
-Inference on Linux or Mac
-```bash
-# run in single-turn mode
-./build/bin/llama-mtmd-cli -m ../MiniCPM-V-4/model/ggml-model-f16.gguf --mmproj ../MiniCPM-V-4/mmproj-model-f16.gguf -c 4096 --temp 0.7 --top-p 0.8 --top-k 100 --repeat-penalty 1.05 --image xx.jpg -p "What is in the image?"
-
-# run in conversation mode
-./build/bin/llama-mtmd-cli -m ../MiniCPM-V-4/model/ggml-model-Q4_K_M.gguf --mmproj ../MiniCPM-V-4/mmproj-model-f16.gguf
-```

docs/ops.md

@@ -12,91 +12,91 @@ Legend:
 - 🟡 Partially supported by this backend
 - ❌ Not supported by this backend
 
-| Operation | BLAS | CANN | CPU | CUDA | Metal | OpenCL | SYCL | Vulkan |
-|-----------|------|------|------|------|------|------|------|------|
-|                              ABS | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ |
-|                              ACC | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ |
-|                              ADD | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ |
-|                             ADD1 | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ |
-|                           ARANGE | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                           ARGMAX | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ |
-|                          ARGSORT | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
-|                            CLAMP | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 |
-|                           CONCAT | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | 🟡 | ✅ |
-|                             CONT | ❌ | 🟡 | ✅ | ✅ | ✅ | 🟡 | 🟡 | 🟡 |
-|                          CONV_2D | ❌ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ✅ |
-|                       CONV_2D_DW | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ |
-|                CONV_TRANSPOSE_1D | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ |
-|                CONV_TRANSPOSE_2D | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ |
-|                              COS | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | 🟡 |
-|                      COUNT_EQUAL | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ |
-|                              CPY | ❌ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 |
-|               CROSS_ENTROPY_LOSS | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ |
-|          CROSS_ENTROPY_LOSS_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ |
-|                    DIAG_MASK_INF | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ |
-|                              DIV | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ |
-|                              DUP | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 |
-|                              ELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ |
-|                              EXP | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ |
-|                   FLASH_ATTN_EXT | ❌ | 🟡 | ✅ | 🟡 | 🟡 | ❌ | ❌ | 🟡 |
-|                GATED_LINEAR_ATTN | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ |
-|                            GEGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 |
-|                        GEGLU_ERF | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 |
-|                      GEGLU_QUICK | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 |
-|                             GELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 |
-|                         GELU_ERF | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 |
-|                       GELU_QUICK | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 |
-|                         GET_ROWS | ❌ | 🟡 | ✅ | 🟡 | ✅ | 🟡 | 🟡 | 🟡 |
-|                    GET_ROWS_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ | ❌ |
-|                       GROUP_NORM | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
-|                      HARDSIGMOID | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ |
-|                        HARDSWISH | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ |
-|                           IM2COL | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ |
-|                          L2_NORM | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ |
-|                       LEAKY_RELU | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ |
-|                              LOG | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ |
-|                             MEAN | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                              MUL | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ |
-|                          MUL_MAT | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 |
-|                       MUL_MAT_ID | ❌ | 🟡 | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ |
-|                              NEG | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ |
-|                             NORM | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 |
-|                   OPT_STEP_ADAMW | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ |
-|                         OUT_PROD | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ❌ |
-|                              PAD | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
-|                   PAD_REFLECT_1D | ❌ | ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ |
-|                          POOL_2D | ❌ | 🟡 | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ |
-|                            REGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 |
-|                             RELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 |
-|                           REPEAT | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | 🟡 |
-|                      REPEAT_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ |
-|                         RMS_NORM | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ |
-|                    RMS_NORM_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ |
-|                 RMS_NORM_MUL_ADD | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
-|                             ROLL | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ✅ |
-|                             ROPE | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
-|                        ROPE_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ |
-|                        RWKV_WKV6 | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ |
-|                        RWKV_WKV7 | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ |
-|                            SCALE | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
-|                              SET | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ |
-|                         SET_ROWS | ❌ | ❌ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 |
-|                              SGN | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ |
-|                          SIGMOID | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 |
-|                             SILU | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 |
-|                        SILU_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ |
-|                              SIN | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | 🟡 |
-|                         SOFT_MAX | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | 🟡 | ✅ |
-|                    SOFT_MAX_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ | ✅ |
-|                              SQR | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | 🟡 |
-|                             SQRT | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | ❌ |
-|                         SSM_CONV | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                         SSM_SCAN | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                             STEP | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ |
-|                              SUB | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ |
-|                              SUM | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ |
-|                         SUM_ROWS | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
-|                           SWIGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 |
-|                             TANH | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | 🟡 |
-|               TIMESTEP_EMBEDDING | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
-|                          UPSCALE | ❌ | 🟡 | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ |
+| Operation | BLAS | CPU | CUDA | Metal | SYCL | Vulkan |
+|-----------|------|------|------|------|------|------|
+|                              ABS | ❌ | ✅ | 🟡 | 🟡 | 🟡 | ❌ |
+|                              ACC | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ |
+|                              ADD | ❌ | ✅ | ✅ | 🟡 | ✅ | ✅ |
+|                             ADD1 | ❌ | ✅ | ✅ | ❌ | ✅ | ❌ |
+|                           ARANGE | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                           ARGMAX | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ |
+|                          ARGSORT | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ |
+|                            CLAMP | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 |
+|                           CONCAT | ❌ | ✅ | 🟡 | ✅ | 🟡 | ✅ |
+|                             CONT | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 |
+|                          CONV_2D | ❌ | ✅ | ❌ | ❌ | ❌ | ✅ |
+|                       CONV_2D_DW | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ |
+|                CONV_TRANSPOSE_1D | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ |
+|                CONV_TRANSPOSE_2D | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
+|                              COS | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 |
+|                      COUNT_EQUAL | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ |
+|                              CPY | ❌ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 |
+|               CROSS_ENTROPY_LOSS | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
+|          CROSS_ENTROPY_LOSS_BACK | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
+|                    DIAG_MASK_INF | ❌ | ✅ | ✅ | 🟡 | ✅ | ✅ |
+|                              DIV | ❌ | ✅ | ✅ | 🟡 | ✅ | ✅ |
+|                              DUP | ❌ | ✅ | 🟡 | 🟡 | ✅ | 🟡 |
+|                              ELU | ❌ | ✅ | 🟡 | 🟡 | 🟡 | ❌ |
+|                              EXP | ❌ | ✅ | 🟡 | 🟡 | 🟡 | ❌ |
+|                   FLASH_ATTN_EXT | ❌ | ✅ | 🟡 | 🟡 | ❌ | 🟡 |
+|                GATED_LINEAR_ATTN | ❌ | ✅ | ✅ | ❌ | ✅ | ❌ |
+|                            GEGLU | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 |
+|                        GEGLU_ERF | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 |
+|                      GEGLU_QUICK | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 |
+|                             GELU | ❌ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 |
+|                         GELU_ERF | ❌ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 |
+|                       GELU_QUICK | ❌ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 |
+|                         GET_ROWS | ❌ | ✅ | 🟡 | ✅ | 🟡 | 🟡 |
+|                    GET_ROWS_BACK | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ |
+|                       GROUP_NORM | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ |
+|                      HARDSIGMOID | ❌ | ✅ | 🟡 | 🟡 | 🟡 | ❌ |
+|                        HARDSWISH | ❌ | ✅ | 🟡 | 🟡 | 🟡 | ❌ |
+|                           IM2COL | ❌ | ✅ | ✅ | 🟡 | ✅ | ✅ |
+|                          L2_NORM | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ |
+|                       LEAKY_RELU | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ |
+|                              LOG | ❌ | ✅ | ✅ | ❌ | ✅ | ❌ |
+|                             MEAN | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                              MUL | ❌ | ✅ | ✅ | 🟡 | ✅ | ✅ |
+|                          MUL_MAT | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 |
+|                       MUL_MAT_ID | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ |
+|                              NEG | ❌ | ✅ | 🟡 | 🟡 | 🟡 | ❌ |
+|                             NORM | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 |
+|                   OPT_STEP_ADAMW | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ |
+|                         OUT_PROD | 🟡 | 🟡 | 🟡 | ❌ | 🟡 | ❌ |
+|                              PAD | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ |
+|                   PAD_REFLECT_1D | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ |
+|                          POOL_2D | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ |
+|                            REGLU | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 |
+|                             RELU | ❌ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 |
+|                           REPEAT | ❌ | ✅ | 🟡 | ✅ | ✅ | 🟡 |
+|                      REPEAT_BACK | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ |
+|                         RMS_NORM | ❌ | ✅ | ✅ | 🟡 | ✅ | ✅ |
+|                    RMS_NORM_BACK | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ |
+|                 RMS_NORM_MUL_ADD | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ |
+|                             ROLL | ❌ | ✅ | ❌ | ❌ | ❌ | ✅ |
+|                             ROPE | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ |
+|                        ROPE_BACK | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ |
+|                        RWKV_WKV6 | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ |
+|                        RWKV_WKV7 | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ |
+|                            SCALE | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ |
+|                              SET | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ |
+|                         SET_ROWS | ❌ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 |
+|                              SGN | ❌ | ✅ | 🟡 | 🟡 | 🟡 | ❌ |
+|                          SIGMOID | ❌ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 |
+|                             SILU | ❌ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 |
+|                        SILU_BACK | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ |
+|                              SIN | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 |
+|                         SOFT_MAX | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ |
+|                    SOFT_MAX_BACK | ❌ | 🟡 | 🟡 | ❌ | ❌ | ✅ |
+|                              SQR | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 |
+|                             SQRT | ❌ | ✅ | ✅ | 🟡 | ✅ | ❌ |
+|                         SSM_CONV | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                         SSM_SCAN | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                             STEP | ❌ | ✅ | 🟡 | 🟡 | 🟡 | ❌ |
+|                              SUB | ❌ | ✅ | ✅ | 🟡 | ✅ | ✅ |
+|                              SUM | ❌ | ✅ | ✅ | ❌ | ✅ | ✅ |
+|                         SUM_ROWS | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ |
+|                           SWIGLU | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 |
+|                             TANH | ❌ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 |
+|               TIMESTEP_EMBEDDING | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ |
+|                          UPSCALE | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ |

examples/diffusion/README.md

@@ -1,13 +0,0 @@
-# Diffusion Text Generation
-
-This directory contains implementations for Diffusion LLMs (DLLMs)
-
-More Info:
-- https://github.com/ggml-org/llama.cpp/pull/14644
-- https://github.com/ggml-org/llama.cpp/pull/14771
-
-
-Example of using Dream architechture: `llama-diffusion-cli -m dream7b.gguf -p "write code to train MNIST in pytorch" -ub 512 --diffusion-eps 0.001 --diffusion-algorithm 3 --diffusion-steps 256 --diffusion-visual`
-
-Example of using LLaDA architechture: `llama-diffusion-cli -m llada-8b.gguf -p "write code to train MNIST in pytorch" -ub 512 --diffusion-block-length 32 --diffusion-steps 256 --diffusion-visual`
-

examples/diffusion/diffusion-cli.cpp

@@ -5,128 +5,344 @@
 #include "log.h"
 
 #include <limits.h>
-
-#include <algorithm>
-#include <cmath>
-#include <cstring>
-#include <limits>
-#include <random>
 #include <string>
 #include <vector>
+#include <algorithm>
+#include <cmath>
+#include <limits>
+#include <random>
 
-enum diffusion_algorithm { ORIGIN = 0, ENTROPY_BASED = 1, MARGIN_BASED = 2, RANDOM = 3, CONFIDENCE_BASED = 4 };
-
-// Unified transfer scheduling methods
-enum transfer_schedule {
-    TIMESTEP_BASED = 0,  // Dream-style: (1.0 - s/t) * remaining
-    BLOCK_BASED    = 1,  // LLaDA-style: process in blocks with get_num_transfer_tokens
-};
-
-typedef bool (*diffusion_step_callback_t)(int32_t             step,
-                                          int32_t             total_steps,
+typedef bool (*diffusion_step_callback_t)(int32_t step,
+                                          int32_t total_steps,
                                           const llama_token * tokens,
-                                          int32_t             n_tokens,
-                                          void *              user_data);
+                                          int32_t n_tokens,
+                                          void * user_data);
+
+enum diffusion_alg {
+    DIFFUSION_ALG_ORIGIN       = 0,
+    DIFFUSION_ALG_MASKGIT_PLUS = 1,
+    DIFFUSION_ALG_TOPK_MARGIN  = 2,
+    DIFFUSION_ALG_ENTROPY      = 3,
+};
 
 struct diffusion_params {
-    int32_t                   steps                   = 0;
-    float                     temperature             = 0;
-    llama_token               mask_token_id           = LLAMA_TOKEN_NULL;
-    diffusion_step_callback_t step_callback           = nullptr;
-    void *                    step_callback_user_data = nullptr;
-    int32_t                   seed                    = 0;
-    bool                      visual_mode             = false;
-    bool                      shift_logits            = false;  // Shift logits by -1 after decode
-
-    float   top_p = 0.;
-    int32_t top_k = 0.;
-
-    diffusion_algorithm algorithm = CONFIDENCE_BASED;
-    transfer_schedule   schedule  = TIMESTEP_BASED;
-
-    float   cfg_scale        = 0.;     // Config scale for classifier-free guidance
-    float   eps              = 0.;     // Timestep scheduling
-    int32_t block_length     = 0;      // Block size (for block scheduling)
-    float   alg_temp         = 0;      // algorithm temperature (0.0 = deterministic)
-    bool    add_gumbel_noise = false;  // Add gumbel noise to the logits if temp > 0.0
-
-    int32_t max_length = 0;            // Maximum sequence length
+    int32_t                   steps;
+    float                     eps;
+    float                     temperature;
+    float                     top_p;
+    int32_t                   top_k;
+    llama_token               mask_token_id;
+    enum diffusion_alg        algorithm;
+    float                     alg_temp;
+    diffusion_step_callback_t step_callback;
+    void *                    step_callback_user_data;
+    int32_t                   seed;
 };
 
+
+static diffusion_params diffusion_default_params() {
+    diffusion_params params        = {};
+    params.steps                   = 64;
+    params.eps                     = 1e-3f;
+    params.temperature             = 0.2f;
+    params.top_p                   = 0.95f;
+    params.top_k                   = 0;
+    params.mask_token_id           = LLAMA_TOKEN_NULL;
+    params.algorithm               = DIFFUSION_ALG_ORIGIN;
+    params.alg_temp                = 0.0f;
+    params.step_callback           = nullptr;
+    params.step_callback_user_data = nullptr;
+    params.seed                    = 0;
+    return params;
+}
+
+static void diffusion_generate(llama_context * ctx,
+                        const llama_token * input_tokens,
+                        llama_token * output_tokens,
+                        int32_t n_input,
+                        int32_t max_length,
+                        struct diffusion_params params,
+                        int32_t & n_generated) {
+
+    n_generated = 0;
+    if (!ctx || !input_tokens || !output_tokens || n_input <= 0 || max_length <= n_input) {
+        return;
+    }
+
+    const llama_model * model = llama_get_model(ctx);
+
+    // Initialize with input and pad with mask tokens
+    std::copy(input_tokens, input_tokens + n_input, output_tokens);
+    std::fill(output_tokens + n_input, output_tokens + max_length, params.mask_token_id);
+
+    std::mt19937 rng(params.seed);
+
+    std::vector<float> timesteps(params.steps + 1);
+    for (int32_t i = 0; i <= params.steps; i++) {
+        timesteps[i] = 1.0f - (float) i / params.steps * (1.0f - params.eps);
+    }
+
+    llama_set_causal_attn(ctx, false);
+
+    int32_t n_vocab = llama_vocab_n_tokens(llama_model_get_vocab(model));
+
+    std::vector<llama_token_data> candidates(n_vocab);
+
+    std::vector<llama_token_data> conf_candidates;
+    conf_candidates.reserve(max_length);
+
+    std::vector<int32_t> mask_positions;
+    mask_positions.reserve(max_length);
+
+    struct llama_sampler * sampler = llama_sampler_chain_init(llama_sampler_chain_default_params());
+    if (params.top_k > 0) {
+        llama_sampler_chain_add(sampler, llama_sampler_init_top_k(params.top_k));
+    }
+    if (params.top_p < 1.0f) {
+        llama_sampler_chain_add(sampler, llama_sampler_init_top_p(params.top_p, 1));
+    }
+    if (params.temperature > 0.0f) {
+        llama_sampler_chain_add(sampler, llama_sampler_init_temp(params.temperature));
+    }
+    llama_sampler_chain_add(sampler, llama_sampler_init_dist(params.seed));
+
+    struct llama_sampler * dist_sampler = llama_sampler_init_dist(params.seed);
+
+    llama_batch batch = llama_batch_init(max_length, 0, 1);
+    batch.n_tokens    = max_length;
+
+    int64_t total_sampling_time = 0;
+    int64_t total_time = 0;
+
+    int64_t time_start = ggml_time_us();
+    for (int32_t step = 0; step < params.steps; step++) {
+        if (params.step_callback) {
+            if (!params.step_callback(step, params.steps, output_tokens, max_length, params.step_callback_user_data)) {
+                break;
+            }
+        }
+
+        for (int32_t i = 0; i < max_length; i++) {
+            batch.token[i]     = output_tokens[i];
+            batch.pos[i]       = i;
+            batch.n_seq_id[i]  = 1;
+            batch.seq_id[i][0] = 0;
+            batch.logits[i]    = 1;
+        }
+
+        int ret = llama_decode(ctx, batch);
+        if (ret != 0) {
+            LOG_ERR("%s: failed to decode at step %d, ret = %d\n", __func__, step, ret);
+            break;
+        }
+
+        float * raw_logits = llama_get_logits(ctx);
+        if (!raw_logits) {
+            LOG_ERR("%s: failed to get logits at step %d\n", __func__, step);
+            break;
+        }
+
+        auto get_logits_for_pos = [&](int32_t pos) -> const float * {
+            return pos == 0 ? raw_logits : raw_logits + (pos - 1) * n_vocab;
+        };
+
+        int64_t time_start_sampling = ggml_time_us();
+
+        mask_positions.clear();
+        for (int32_t i = 0; i < max_length; i++) {
+            if (output_tokens[i] == params.mask_token_id) {
+                mask_positions.push_back(i);
+            }
+        }
+
+        if (mask_positions.empty()) {
+            break;
+        }
+
+        float t = timesteps[step];
+        float s = timesteps[step + 1];
+
+        if (params.algorithm == DIFFUSION_ALG_ORIGIN) {
+            float p_transfer = (step < params.steps - 1) ? (1.0f - s / t) : 1.0f;
+
+            for (int32_t pos : mask_positions) {
+                if (std::uniform_real_distribution<float>(0.0f, 1.0f)(rng) < p_transfer) {
+                    const float * pos_logits = get_logits_for_pos(pos);
+                    for (int32_t token_id = 0; token_id < n_vocab; token_id++) {
+                        candidates[token_id].id    = token_id;
+                        candidates[token_id].logit = pos_logits[token_id];
+                        candidates[token_id].p     = 0.0f;
+                    }
+
+                    llama_token_data_array cur_p = {
+                        /* .data       = */ candidates.data(),
+                        /* .size       = */ (size_t) n_vocab,  // Reset size to full vocab
+                        /* .selected   = */ -1,
+                        /* .sorted     = */ false,
+                    };
+
+                    llama_sampler_apply(sampler, &cur_p);
+                    output_tokens[pos] = cur_p.data[cur_p.selected].id;
+                }
+            }
+        } else {
+            std::vector<std::pair<float, int32_t>> confidences;
+            std::vector<llama_token>               sampled_tokens(mask_positions.size());
+
+            for (size_t i = 0; i < mask_positions.size(); i++) {
+                int32_t       pos        = mask_positions[i];
+                const float * pos_logits = get_logits_for_pos(pos);
+
+                for (int32_t token_id = 0; token_id < n_vocab; token_id++) {
+                    candidates[token_id].logit = pos_logits[token_id];
+                    candidates[token_id].p     = 0.0f;
+                    candidates[token_id].id    = token_id;
+                }
+
+                llama_token_data_array cur_p = {
+                    /* .data       = */ candidates.data(),
+                    /* .size       = */ candidates.size(),
+                    /* .selected   = */ -1,
+                    /* .sorted     = */ false,
+                };
+
+                llama_sampler_apply(sampler, &cur_p);
+
+                llama_token sampled_token = cur_p.data[cur_p.selected].id;
+
+                float confidence = 0.0f;
+                if (params.algorithm == DIFFUSION_ALG_ENTROPY) {
+                    const float epsilon = 1e-10f;
+                    for (size_t j = 0; j < cur_p.size; j++) {
+                        float prob = cur_p.data[j].p;
+                        confidence += prob * logf(prob + epsilon);
+                    }
+                } else if (params.algorithm == DIFFUSION_ALG_TOPK_MARGIN) {
+                    confidence = cur_p.data[0].p - cur_p.data[1].p;
+                } else {
+                    confidence = cur_p.data[cur_p.selected].p;
+                }
+
+                sampled_tokens[i] = sampled_token;
+                confidences.emplace_back(confidence, i);
+            }
+
+            int32_t num_transfer =
+                (step < params.steps - 1) ? (int32_t) (mask_positions.size() * (1.0f - s / t)) : mask_positions.size();
+
+            if (num_transfer > 0) {
+                if (params.alg_temp == 0.0f) {
+                    std::partial_sort(confidences.begin(), confidences.begin() + num_transfer, confidences.end(),
+                                      [](const std::pair<float, int32_t> & a, const std::pair<float, int32_t> & b) {
+                                          if (a.first != b.first) {
+                                              return a.first > b.first;
+                                          }
+                                          return a.second < b.second;
+                                      });
+                } else {
+                    conf_candidates.clear();
+
+                    for (int32_t pos = 0; pos < max_length; pos++) {
+                        float conf_logit = -std::numeric_limits<float>::infinity();
+
+                        auto it = std::find(mask_positions.begin(), mask_positions.end(), pos);
+                        if (it != mask_positions.end()) {
+                            size_t mask_idx = std::distance(mask_positions.begin(), it);
+                            conf_logit = confidences[mask_idx].first / params.alg_temp;  // Apply temperature scaling
+                        }
+
+                        conf_candidates.emplace_back(llama_token_data{ pos, conf_logit, 0.0f });
+                    }
+
+                    llama_token_data_array conf_array = {
+                        /* .data       = */ conf_candidates.data(),
+                        /* .size       = */ conf_candidates.size(),
+                        /* .selected   = */ -1,
+                        /* .sorted     = */ false,
+                    };
+
+                    for (int32_t i = 0; i < num_transfer; i++) {
+                        // Apply distribution sampler to get selected index
+                        llama_sampler_apply(dist_sampler, &conf_array);
+                        int selected_idx      = conf_array.selected;
+                        confidences[i].second = conf_candidates[selected_idx].id;
+
+                        conf_candidates[selected_idx].p = 0.0f;
+                        conf_array.selected             = -1;
+                    }
+                }
+
+                if (params.alg_temp == 0.0f) {
+                    // Deterministic - use confidence order
+                    for (int32_t i = 0; i < num_transfer; i++) {
+                        int32_t     mask_idx = confidences[i].second;
+                        int32_t     pos      = mask_positions[mask_idx];
+                        llama_token token    = sampled_tokens[mask_idx];
+                        output_tokens[pos]   = token;
+                    }
+                } else {
+                    for (int32_t i = 0; i < num_transfer; i++) {
+                        int32_t pos = confidences[i].second;
+                        auto    it  = std::find(mask_positions.begin(), mask_positions.end(), pos);
+                        if (it != mask_positions.end()) {
+                            int32_t mask_idx   = std::distance(mask_positions.begin(), it);
+                            output_tokens[pos] = sampled_tokens[mask_idx];
+                        }
+                    }
+                }
+            }
+        }
+        int64_t time_end_sampling = ggml_time_us();
+        total_sampling_time += time_end_sampling - time_start_sampling;
+    }
+    int64_t time_end = ggml_time_us();
+    total_time += time_end - time_start;
+
+    LOG_INF("\ntotal time: %0.2fms, time per step: %0.2fms, sampling time per step: %0.2fms\n",
+            total_time / 1000.0, total_time / 1000.0 / params.steps, total_sampling_time / 1000.0 / params.steps);
+
+
+    llama_batch_free(batch);
+    llama_sampler_free(sampler);
+    llama_sampler_free(dist_sampler);
+
+    n_generated = max_length;
+}
+
+
+
+
+static std::string format_input_text(const std::string & prompt, bool use_chat_template, llama_model * model) {
+    if (!use_chat_template) {
+        return prompt;
+    }
+
+    auto chat_templates = common_chat_templates_init(model, "");
+
+    common_chat_templates_inputs inputs;
+    common_chat_msg              user_msg;
+    user_msg.role                = "user";
+    user_msg.content             = prompt;
+    inputs.add_generation_prompt = true;
+    inputs.messages.push_back(user_msg);
+
+    auto result = common_chat_templates_apply(chat_templates.get(), inputs);
+
+    return result.prompt;
+}
+
 struct callback_data {
-    diffusion_params *  diff_params;
-    const llama_vocab * vocab;
-    int32_t             n_input;
+    const common_params_diffusion * diff_params;
+    const llama_vocab *             vocab;
+    int32_t                         n_input;
 };
 
-static float calculate_confidence(const llama_token_data_array & cur_p,
-                                  diffusion_algorithm            algorithm,
-                                  std::mt19937 &                 rng) {
-    switch (algorithm) {
-        case CONFIDENCE_BASED:
-            return cur_p.data[cur_p.selected].p;  // Selected token probability
-
-        case ENTROPY_BASED:
-            {
-                float       entropy = 0.0f;
-                const float epsilon = 1e-10f;
-                for (size_t i = 0; i < cur_p.size; i++) {
-                    float prob = cur_p.data[i].p;
-                    entropy += prob * logf(prob + epsilon);
-                }
-                return -entropy;  // Higher entropy = lower confidence
-            }
-
-        case MARGIN_BASED:
-            return (cur_p.size > 1) ? cur_p.data[0].p - cur_p.data[1].p : cur_p.data[0].p;
-
-        case RANDOM:
-            {
-                std::uniform_real_distribution<float> uniform(0.0f, 1.0f);
-                return uniform(rng);  // Random confidence
-            }
-
-        case ORIGIN:
-            return cur_p.data[cur_p.selected].p;
-
-        default:
-            return 0.0f;
-    }
-}
-
-// Unified transfer count calculation function
-static int32_t calculate_transfer_count(int32_t                      step,
-                                        int32_t                      total_steps,
-                                        int32_t                      remaining_masked,
-                                        transfer_schedule            schedule,
-                                        float                        eps,
-                                        const std::vector<int32_t> & num_transfer_tokens = {}) {
-    switch (schedule) {
-        case TIMESTEP_BASED:
-            {
-                float t          = 1.0f - (float) step / total_steps * (1.0f - eps);
-                float s          = 1.0f - (float) (step + 1) / total_steps * (1.0f - eps);
-                float p_transfer = (step < total_steps - 1) ? (1.0f - s / t) : 1.0f;
-                return (int32_t) (remaining_masked * p_transfer);
-            }
-
-        case BLOCK_BASED:
-            if (!num_transfer_tokens.empty() && step < (int32_t) num_transfer_tokens.size()) {
-                return num_transfer_tokens[step];
-            }
-            return remaining_masked / (total_steps - step);  // Fallback
-
-        default:
-            return remaining_masked / (total_steps - step);
-    }
-}
-
-static bool diffusion_step_callback(int32_t             step,
-                                    int32_t             total_steps,
+static bool diffusion_step_callback(int32_t step,
+                                    int32_t total_steps,
                                     const llama_token * tokens,
-                                    int32_t             n_tokens,
-                                    void *              user_data) {
-    (void) user_data;
+                                    int32_t n_tokens,
+                                    void * user_data) {
+    (void)user_data;
 
     callback_data * data = static_cast<callback_data *>(user_data);
 
@@ -134,11 +350,11 @@ static bool diffusion_step_callback(int32_t             step,
         int progress_percent = (step * 100) / total_steps;
         int progress_bars    = (step * 50) / total_steps;
         LOG_INF("\rdiffusion step: %d/%d [%s%s] %d%%",
-                step,
-                total_steps,
-                std::string(progress_bars, '=').c_str(),
-                std::string(50 - progress_bars, ' ').c_str(),
-                progress_percent);
+            step,
+            total_steps,
+            std::string(progress_bars, '=').c_str(),
+            std::string(50 - progress_bars, ' ').c_str(),
+            progress_percent);
     };
 
     if (data->diff_params->visual_mode) {
@@ -175,360 +391,6 @@ static bool diffusion_step_callback(int32_t             step,
     return true;
 }
 
-static void add_gumbel_noise(float * logits, int32_t n_vocab, float temperature, std::mt19937 & rng) {
-    if (temperature == 0.0f) {
-        return;
-    }
-
-    std::uniform_real_distribution<double> uniform(0.0, 1.0);
-    for (int32_t i = 0; i < n_vocab; i++) {
-        double noise        = uniform(rng);
-        // Prevent log(0)
-        noise               = std::max(noise, 1e-20);
-        double gumbel_noise = std::pow(-std::log(noise), temperature);
-        logits[i]           = std::exp(logits[i]) / gumbel_noise;
-    }
-}
-
-static std::vector<int32_t> get_num_transfer_tokens(int32_t mask_count, int32_t steps) {
-    std::vector<int32_t> num_transfer_tokens(steps);
-
-    int32_t base      = mask_count / steps;
-    int32_t remainder = mask_count % steps;
-
-    for (int32_t i = 0; i < steps; i++) {
-        num_transfer_tokens[i] = base + (i < remainder ? 1 : 0);
-    }
-
-    return num_transfer_tokens;
-}
-
-static void diffusion_generate(llama_context *          ctx,
-                               const llama_token *      input_tokens,
-                               llama_token *            output_tokens,
-                               int32_t                  n_input,
-                               const diffusion_params & params,
-                               int32_t &                n_generated) {
-    n_generated = 0;
-    if (!ctx || !input_tokens || !output_tokens || n_input <= 0 || params.max_length <= n_input) {
-        return;
-    }
-
-    const llama_model * model = llama_get_model(ctx);
-
-    // Initialize with input and pad with mask tokens
-    std::copy(input_tokens, input_tokens + n_input, output_tokens);
-    std::fill(output_tokens + n_input, output_tokens + params.max_length, params.mask_token_id);
-
-    std::mt19937 rng(params.seed);
-
-    llama_set_causal_attn(ctx, false);
-
-    int32_t n_vocab = llama_vocab_n_tokens(llama_model_get_vocab(model));
-
-    std::vector<llama_token_data> candidates(n_vocab);
-    std::vector<llama_token_data> conf_candidates;
-    conf_candidates.reserve(params.max_length);
-    std::vector<int32_t> mask_positions;
-    mask_positions.reserve(params.max_length);
-
-    // Setup sampler chain
-    struct llama_sampler * sampler = llama_sampler_chain_init(llama_sampler_chain_default_params());
-    if (params.top_k > 0) {
-        llama_sampler_chain_add(sampler, llama_sampler_init_top_k(params.top_k));
-    }
-    if (params.top_p < 1.0f) {
-        llama_sampler_chain_add(sampler, llama_sampler_init_top_p(params.top_p, 1));
-    }
-    if (params.temperature > 0.0f) {
-        llama_sampler_chain_add(sampler, llama_sampler_init_temp(params.temperature));
-    }
-    llama_sampler_chain_add(sampler, llama_sampler_init_dist(params.seed));
-
-    struct llama_sampler * dist_sampler = llama_sampler_init_dist(params.seed);
-
-    llama_batch batch = llama_batch_init(params.max_length, 0, 1);
-    batch.n_tokens    = params.max_length;
-
-    // Pre-allocate buffers for CFG if needed
-    int32_t                  logits_size = n_vocab * params.max_length;
-    std::vector<float>       cond_logits_buffer;
-    std::vector<llama_token> un_x_buffer;
-    if (params.cfg_scale > 0.0f) {
-        cond_logits_buffer.resize(logits_size);
-        un_x_buffer.resize(params.max_length);
-    }
-
-    // For block-based processing
-    std::vector<int32_t> num_transfer_tokens;
-    int32_t              num_blocks      = 1;
-    int32_t              steps_per_block = params.steps;
-
-    if (params.schedule == BLOCK_BASED) {
-        GGML_ASSERT(params.max_length % params.block_length == 0);
-        num_blocks = params.max_length / params.block_length;
-        GGML_ASSERT(params.steps % num_blocks == 0);
-        steps_per_block = params.steps / num_blocks;
-    }
-
-    std::vector<float> confidence(params.max_length);
-
-    int64_t total_sampling_time = 0;
-    int64_t total_time          = 0;
-    int64_t time_start          = ggml_time_us();
-
-    for (int block_num = 0; block_num < num_blocks; block_num++) {
-        int32_t block_start = (params.schedule == BLOCK_BASED) ? n_input + block_num * params.block_length : 0;
-        int32_t block_end   = (params.schedule == BLOCK_BASED) ?
-                                  std::min(n_input + (block_num + 1) * params.block_length, params.max_length) :
-                                  params.max_length;
-
-        // Count masked tokens in current block for block-based processing
-        if (params.schedule == BLOCK_BASED) {
-            int32_t block_mask_count = 0;
-            for (int i = block_start; i < block_end; i++) {
-                if (output_tokens[i] == params.mask_token_id) {
-                    block_mask_count++;
-                }
-            }
-            num_transfer_tokens = get_num_transfer_tokens(block_mask_count, steps_per_block);
-        }
-
-        for (int32_t step = 0; step < steps_per_block; step++) {
-            int32_t global_step = block_num * steps_per_block + step;
-
-            if (params.step_callback) {
-                if (!params.step_callback(
-                        global_step, params.steps, output_tokens, params.max_length, params.step_callback_user_data)) {
-                    break;
-                }
-            }
-
-            // Setup batch
-            for (int32_t i = 0; i < params.max_length; i++) {
-                batch.token[i]     = output_tokens[i];
-                batch.pos[i]       = i;
-                batch.n_seq_id[i]  = 1;
-                batch.seq_id[i][0] = 0;
-                batch.logits[i]    = 1;
-            }
-
-            float * logits = nullptr;
-
-            if (params.cfg_scale > 0.0f) {
-                int ret = llama_decode(ctx, batch);
-                if (ret != 0) {
-                    LOG_ERR("Failed to generate conditional");
-                    break;
-                }
-                float * cond_logits_ptr = llama_get_logits(ctx);
-                std::memcpy(cond_logits_buffer.data(), cond_logits_ptr, logits_size * sizeof(float));
-
-                // Unconditional generation (mask input)
-                std::copy(output_tokens, output_tokens + params.max_length, un_x_buffer.begin());
-                for (int32_t i = 0; i < n_input; i++) {
-                    un_x_buffer[i] = params.mask_token_id;
-                }
-
-                for (int32_t i = 0; i < params.max_length; i++) {
-                    batch.token[i] = un_x_buffer[i];
-                }
-                ret = llama_decode(ctx, batch);
-                if (ret != 0) {
-                    LOG_ERR("Failed to generate unconditional");
-                    break;
-                }
-                float * uncond_logits = llama_get_logits(ctx);
-
-                // Apply CFG
-                for (int32_t i = 0; i < logits_size; i++) {
-                    cond_logits_buffer[i] =
-                        uncond_logits[i] + (params.cfg_scale + 1.0f) * (cond_logits_buffer[i] - uncond_logits[i]);
-                }
-                logits = cond_logits_buffer.data();
-            } else {
-                int ret = llama_decode(ctx, batch);
-                if (ret != 0) {
-                    LOG_ERR("%s: failed to decode at step %d, ret = %d\n", __func__, global_step, ret);
-                    break;
-                }
-                logits = llama_get_logits(ctx);
-            }
-
-            if (!logits) {
-                LOG_ERR("%s: failed to get logits at step %d\n", __func__, global_step);
-                break;
-            }
-
-            auto get_logits_for_pos = [&](int32_t pos) -> const float * {
-                if (params.shift_logits) {
-                    return pos == 0 ? logits : logits + (pos - 1) * n_vocab;
-                }
-                return logits + (pos) *n_vocab;
-            };
-
-            int64_t time_start_sampling = ggml_time_us();
-
-            mask_positions.clear();
-            for (int32_t i = 0; i < params.max_length; i++) {
-                if (output_tokens[i] == params.mask_token_id) {
-                    // For block-based, only consider current block
-                    if (params.schedule != BLOCK_BASED || (i >= block_start && i < block_end)) {
-                        mask_positions.push_back(i);
-                    }
-                }
-            }
-
-            if (mask_positions.empty()) {
-                break;
-            }
-
-            if (params.add_gumbel_noise && params.temperature > 0.0f) {
-                add_gumbel_noise(logits, n_vocab, params.temperature, rng);
-            }
-
-            if (params.algorithm == ORIGIN) {
-                int32_t transfer_count = calculate_transfer_count(
-                    step, steps_per_block, mask_positions.size(), params.schedule, params.eps, num_transfer_tokens);
-                float p_transfer = (float) transfer_count / mask_positions.size();
-
-                for (int32_t pos : mask_positions) {
-                    if (std::uniform_real_distribution<float>(0.0f, 1.0f)(rng) < p_transfer) {
-                        const float * pos_logits = get_logits_for_pos(pos);
-                        for (int32_t token_id = 0; token_id < n_vocab; token_id++) {
-                            candidates[token_id].id    = token_id;
-                            candidates[token_id].logit = pos_logits[token_id];
-                            candidates[token_id].p     = 0.0f;
-                        }
-
-                        llama_token_data_array cur_p = {
-                            candidates.data(),
-                            (size_t) n_vocab,
-                            -1,
-                            false,
-                        };
-
-                        llama_sampler_apply(sampler, &cur_p);
-                        output_tokens[pos] = cur_p.data[cur_p.selected].id;
-                    }
-                }
-            } else {
-                std::vector<std::pair<float, int32_t>> confidences;
-                std::vector<llama_token>               sampled_tokens(mask_positions.size());
-
-                for (size_t i = 0; i < mask_positions.size(); i++) {
-                    int32_t       pos        = mask_positions[i];
-                    const float * pos_logits = get_logits_for_pos(pos);
-
-                    for (int32_t token_id = 0; token_id < n_vocab; token_id++) {
-                        candidates[token_id].logit = pos_logits[token_id];
-                        candidates[token_id].p     = 0.0f;
-                        candidates[token_id].id    = token_id;
-                    }
-
-                    llama_token_data_array cur_p = {
-                        candidates.data(),
-                        candidates.size(),
-                        -1,
-                        false,
-                    };
-
-                    llama_sampler_apply(sampler, &cur_p);
-                    llama_token sampled_token = cur_p.data[cur_p.selected].id;
-
-                    float conf = calculate_confidence(cur_p, params.algorithm, rng);
-
-                    sampled_tokens[i] = sampled_token;
-                    confidences.emplace_back(conf, i);
-                }
-
-                int32_t transfer_count = calculate_transfer_count(
-                    step, steps_per_block, mask_positions.size(), params.schedule, params.eps, num_transfer_tokens);
-
-                if (transfer_count > 0) {
-                    if (params.alg_temp == 0.0f) {
-                        std::partial_sort(confidences.begin(),
-                                          confidences.begin() + std::min(transfer_count, (int32_t) confidences.size()),
-                                          confidences.end(),
-                                          [](const std::pair<float, int32_t> & a, const std::pair<float, int32_t> & b) {
-                                              if (a.first != b.first) {
-                                                  return a.first > b.first;
-                                              }
-                                              return a.second < b.second;
-                                          });
-
-                        for (int32_t i = 0; i < std::min(transfer_count, (int32_t) confidences.size()); i++) {
-                            int32_t mask_idx   = confidences[i].second;
-                            int32_t pos        = mask_positions[mask_idx];
-                            output_tokens[pos] = sampled_tokens[mask_idx];
-                        }
-                    } else {
-                        conf_candidates.clear();
-                        for (size_t i = 0; i < confidences.size(); i++) {
-                            float conf_logit = confidences[i].first / params.alg_temp;
-                            conf_candidates.emplace_back(llama_token_data{ (int32_t) i, conf_logit, 0.0f });
-                        }
-
-                        llama_token_data_array conf_array = {
-                            conf_candidates.data(),
-                            conf_candidates.size(),
-                            -1,
-                            false,
-                        };
-
-                        for (int32_t i = 0; i < std::min(transfer_count, (int32_t) confidences.size()); i++) {
-                            llama_sampler_apply(dist_sampler, &conf_array);
-                            int32_t selected_idx = conf_array.selected;
-                            int32_t mask_idx     = selected_idx;
-                            int32_t pos          = mask_positions[mask_idx];
-                            output_tokens[pos]   = sampled_tokens[mask_idx];
-
-                            conf_candidates[selected_idx].p = 0.0f;
-                            conf_array.selected             = -1;
-                        }
-                    }
-                }
-            }
-
-            int64_t time_end_sampling = ggml_time_us();
-            total_sampling_time += time_end_sampling - time_start_sampling;
-        }
-    }
-
-    int64_t time_end = ggml_time_us();
-    total_time += time_end - time_start;
-
-    LOG_INF("\ntotal time: %0.2fms, time per step: %0.2fms, sampling time per step: %0.2fms\n",
-            total_time / 1000.0,
-            total_time / 1000.0 / params.steps,
-            total_sampling_time / 1000.0 / params.steps);
-
-    llama_batch_free(batch);
-    llama_sampler_free(sampler);
-    llama_sampler_free(dist_sampler);
-
-    n_generated = params.max_length;
-}
-
-static std::string format_input_text(const std::string & prompt, bool use_chat_template, llama_model * model) {
-    if (!use_chat_template) {
-        return prompt;
-    }
-
-    auto chat_templates = common_chat_templates_init(model, "");
-
-    common_chat_templates_inputs inputs;
-    common_chat_msg              user_msg;
-    user_msg.role                = "user";
-    user_msg.content             = prompt;
-    inputs.add_generation_prompt = true;
-    inputs.messages.push_back(user_msg);
-
-    auto result = common_chat_templates_apply(chat_templates.get(), inputs);
-
-    return result.prompt;
-}
-
 int main(int argc, char ** argv) {
     ggml_time_init();
 
@@ -538,6 +400,11 @@ int main(int argc, char ** argv) {
         return 1;
     }
 
+    const char * alg_names[] = { "ORIGIN", "MASKGIT_PLUS", "TOPK_MARGIN", "ENTROPY" };
+    const char * alg_name    = (params.diffusion.algorithm >= 0 && params.diffusion.algorithm <= 3) ?
+                                   alg_names[params.diffusion.algorithm] :
+                                   "UNKNOWN";
+
     common_init();
     llama_backend_init();
 
@@ -554,12 +421,6 @@ int main(int argc, char ** argv) {
         return 1;
     }
 
-    if (!llama_model_is_diffusion(model)) {
-        LOG_ERR("error: unsupported model for diffusion");
-        llama_model_free(model);
-        return 1;
-    }
-
     llama_context_params ctx_params = llama_context_default_params();
     ctx_params.n_ctx                = params.n_ctx;
     ctx_params.n_batch              = params.n_batch;
@@ -581,12 +442,10 @@ int main(int argc, char ** argv) {
     const llama_vocab * vocab            = llama_model_get_vocab(model);
     std::string         formatted_prompt = format_input_text(params.prompt, params.enable_chat_template, model);
 
-    std::vector<llama_token> input_tokens = common_tokenize(vocab,
-                                                            formatted_prompt,
+    std::vector<llama_token> input_tokens = common_tokenize(vocab, formatted_prompt,
                                                             /*add special tokens*/ true,
                                                             /*parse special*/ true);
-
-    int n_input = input_tokens.size();
+    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);
@@ -595,79 +454,44 @@ int main(int argc, char ** argv) {
         return 1;
     }
 
+    struct diffusion_params ldiff_params = diffusion_default_params();
+    ldiff_params.steps                   = params.diffusion.steps;
+    ldiff_params.eps                     = params.diffusion.eps;
+    ldiff_params.temperature             = params.sampling.temp;
+    ldiff_params.top_p                   = params.sampling.top_p;
+    ldiff_params.top_k                   = params.sampling.top_k;
+    ldiff_params.algorithm               = static_cast<enum diffusion_alg>(params.diffusion.algorithm);
+    ldiff_params.alg_temp                = params.diffusion.alg_temp;
+    ldiff_params.seed                    = params.sampling.seed;
+
     llama_token mask_token_id = llama_vocab_mask(vocab);
     GGML_ASSERT(mask_token_id != LLAMA_TOKEN_NULL);
 
-    bool visual_mode = params.diffusion.visual_mode;
-
-    int32_t                  n_generated = 0;
-    std::vector<llama_token> output_tokens(params.n_ubatch);
-
-    struct diffusion_params diff_params;
-
-    char shift_logits_str[8];
-    if (llama_model_meta_val_str(model, "diffusion.shift_logits", shift_logits_str, sizeof(shift_logits_str)) >= 0) {
-        diff_params.shift_logits = (strcmp(shift_logits_str, "true") == 0);
-    } else {
-        diff_params.shift_logits = true;
-    }
-
-    //Use either eps or block length, but not both
-    GGML_ASSERT((params.diffusion.eps == 0) ^ (params.diffusion.block_length == 0));
-
-    if (params.diffusion.eps) {
-        diff_params.schedule = TIMESTEP_BASED;
-        diff_params.eps      = params.diffusion.eps;
-    } else if (params.diffusion.block_length) {
-        diff_params.schedule     = BLOCK_BASED;
-        diff_params.block_length = params.diffusion.block_length;
-    }
-
-    diff_params.mask_token_id    = mask_token_id;
-    diff_params.seed             = params.sampling.seed;
-    diff_params.temperature      = params.sampling.temp;
-    diff_params.steps            = params.diffusion.steps;
-    diff_params.algorithm        = static_cast<diffusion_algorithm>(params.diffusion.algorithm);
-    diff_params.max_length       = params.n_ubatch;
-    diff_params.top_p            = params.sampling.top_p;
-    diff_params.top_k            = params.sampling.top_k;
-    diff_params.visual_mode      = params.diffusion.visual_mode;
-    diff_params.add_gumbel_noise = params.diffusion.add_gumbel_noise;
-
-    diff_params.step_callback           = diffusion_step_callback;
-    callback_data cb_data               = { &diff_params, vocab, n_input };
-    diff_params.step_callback_user_data = &cb_data;
-
-    const char * alg_names[]   = { "ORIGIN", "ENTROPY_BASED", "MARGIN_BASED", "RANDOM", "CONFIDENCE_BASED" };
-    const char * sched_names[] = { "TIMESTEP_BASED", "BLOCK_BASED" };
-    const char * alg_name =
-        (diff_params.algorithm >= 0 && diff_params.algorithm <= 4) ? alg_names[diff_params.algorithm] : "UNKNOWN";
-    const char * sched_name =
-        (diff_params.schedule >= 0 && diff_params.schedule <= 1) ? sched_names[diff_params.schedule] : "UNKNOWN";
-
     LOG_INF("diffusion_params: - %-25s llama_token      = %d\n", "mask_token_id", mask_token_id);
-    LOG_INF("diffusion_params: - %-25s u32              = %d\n", "steps", diff_params.steps);
-    LOG_INF("diffusion_params: - %-25s u32              = %d\n", "max_length", diff_params.max_length);
-    LOG_INF("diffusion_params: - %-25s enum             = %d (%s)\n", "algorithm", diff_params.algorithm, alg_name);
-    LOG_INF("diffusion_params: - %-25s enum             = %d (%s)\n", "schedule", diff_params.schedule, sched_name);
-    LOG_INF("diffusion_params: - %-25s f32              = %.3f\n", "temperature", diff_params.temperature);
-    if (diff_params.schedule == TIMESTEP_BASED) {
-        LOG_INF("diffusion_params: - %-25s f32              = %.6f\n", "eps", diff_params.eps);
-        LOG_INF("diffusion_params: - %-25s f32              = %.3f\n", "alg_temp", diff_params.alg_temp);
-    }
-    if (diff_params.schedule == BLOCK_BASED) {
-        LOG_INF("diffusion_params: - %-25s u32              = %d\n", "block_length", diff_params.block_length);
-        LOG_INF("diffusion_params: - %-25s f32              = %.3f\n", "cfg_scale", diff_params.cfg_scale);
-    }
+    LOG_INF("diffusion_params: - %-25s u32              = %d\n", "steps", params.diffusion.steps);
+    LOG_INF("diffusion_params: - %-25s f32              = %.6f\n", "eps", params.diffusion.eps);
+    LOG_INF("diffusion_params: - %-25s u32              = %d (%s)\n", "algorithm", params.diffusion.algorithm,
+            alg_name);
+    LOG_INF("diffusion_params: - %-25s f32              = %.3f\n", "alg_temp", params.diffusion.alg_temp);
 
-    diffusion_generate(ctx, input_tokens.data(), output_tokens.data(), n_input, diff_params, n_generated);
+    ldiff_params.mask_token_id = mask_token_id;
+
+    callback_data cb_data = { &params.diffusion, vocab, n_input };
+
+    ldiff_params.step_callback           = diffusion_step_callback;
+    ldiff_params.step_callback_user_data = &cb_data;
+
+    int32_t n_generated = 0;
+
+    std::vector<llama_token> output_tokens(params.n_ubatch);
+    diffusion_generate(ctx, input_tokens.data(), output_tokens.data(), n_input, params.n_ubatch,
+                       ldiff_params, n_generated);
 
     if (n_generated > 0) {
-        if (visual_mode) {
+        if (params.diffusion.visual_mode) {
             //clear screen and move cursor to top-left
             LOG_INF("\033[2J\033[H");
         }
-
         output_tokens.erase(output_tokens.begin(), output_tokens.begin() + n_input);
         std::string output_data = common_detokenize(vocab, output_tokens, false);
         LOG_INF("\n%s\n", output_data.c_str());

examples/embedding/embedding.cpp

@@ -81,14 +81,6 @@ int main(int argc, char ** argv) {
 
     params.embedding = true;
 
-    // if the number of prompts that would be encoded is known in advance, it's more efficient to specify the
-    //   --parallel argument accordingly. for convenience, if not specified, we fallback to unified KV cache
-    //   in order to support any number of prompts
-    if (params.n_parallel == 1) {
-        LOG_INF("%s: n_parallel == 1 -> unified KV cache is enabled\n", __func__);
-        params.kv_unified = true;
-    }
-
     // utilize the full context
     if (params.n_batch < params.n_ctx) {
         LOG_WRN("%s: setting batch size to %d\n", __func__, params.n_ctx);

examples/save-load-state/save-load-state.cpp

@@ -15,12 +15,6 @@ int main(int argc, char ** argv) {
         return 1;
     }
 
-    if (params.n_parallel == 1) {
-        // the example uses 2 sequences, so when n_parallel == 1, we need to enable unified kv cache
-        printf("%s: n_parallel == 1, enabling unified kv cache\n", __func__);
-        params.kv_unified = true;
-    }
-
     common_init();
 
     if (params.n_predict < 0) {

examples/speculative-simple/speculative-simple.cpp

@@ -65,7 +65,7 @@ int main(int argc, char ** argv) {
     ctx_dft   = llama_init_dft.context.get();
 
     if (!common_speculative_are_compatible(ctx_tgt, ctx_dft)) {
-        LOG_INF("the draft model '%s' is not compatible with the target model '%s'. tokens will be translated between the draft and target models.\n", params.speculative.model.path.c_str(), params.model.path.c_str());
+        return 1;
     }
 
     // Tokenize the prompt
@@ -130,10 +130,7 @@ int main(int argc, char ** argv) {
     params_spec.n_reuse = llama_n_ctx(ctx_dft) - n_draft;
     params_spec.p_min   = p_min;
 
-    struct common_speculative * spec = common_speculative_init(ctx_tgt, ctx_dft);
-    for (auto &pair : params.speculative.replacements) {
-        common_speculative_add_replacement_tgt_dft(spec, pair.first.c_str(), pair.second.c_str());
-    }
+    struct common_speculative * spec = common_speculative_init(ctx_dft);
 
     llama_batch batch_tgt = llama_batch_init(llama_n_batch(ctx_tgt), 0, 1);
 

ggml/CMakeLists.txt

@@ -39,9 +39,8 @@ if (WIN32)
     set(CMAKE_SHARED_MODULE_PREFIX  "")
 endif()
 
-option(BUILD_SHARED_LIBS           "ggml: build shared libraries" ${BUILD_SHARED_LIBS_DEFAULT})
-option(GGML_BACKEND_DL             "ggml: build backends as dynamic libraries (requires BUILD_SHARED_LIBS)" OFF)
-set(GGML_BACKEND_DIR "" CACHE PATH "ggml: directory to load dynamic backends from (requires GGML_BACKEND_DL")
+option(BUILD_SHARED_LIBS "ggml: build shared libraries" ${BUILD_SHARED_LIBS_DEFAULT})
+option(GGML_BACKEND_DL   "ggml: build backends as dynamic libraries (requires BUILD_SHARED_LIBS)" OFF)
 
 #
 # option list
@@ -175,8 +174,6 @@ option(GGML_HIP_GRAPHS                      "ggml: use HIP graph, experimental,
 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_FORCE_ROCWMMA_FATTN_GFX12   "ggml: enable rocWMMA FlashAttention on GFX12"    OFF)
-option(GGML_HIP_MMQ_MFMA                    "ggml: enable MFMA MMA for CDNA in MMQ"           ON)
-option(GGML_HIP_EXPORT_METRICS              "ggml: enable kernel perf metrics output"         OFF)
 option(GGML_MUSA_GRAPHS                     "ggml: use MUSA graph, experimental, unstable"    OFF)
 option(GGML_MUSA_MUDNN_COPY                 "ggml: enable muDNN for accelerated copy"         OFF)
 option(GGML_VULKAN                          "ggml: use Vulkan"                                OFF)

ggml/cmake/ggml-config.cmake.in

@@ -34,8 +34,8 @@ if (NOT GGML_SHARED_LIB)
 
     if (GGML_BLAS)
         find_dependency(BLAS)
-        list(APPEND GGML_BLAS_INTERFACE_LINK_LIBRARIES ${BLAS_LIBRARIES})
-        list(APPEND GGML_BLAS_INTERFACE_LINK_OPTIONS   ${BLAS_LINKER_FLAGS})
+        list(APPEND GGML_CPU_INTERFACE_LINK_LIBRARIES ${BLAS_LIBRARIES})
+        list(APPEND GGML_CPU_INTERFACE_LINK_OPTIONS   ${BLAS_LINKER_FLAGS})
     endif()
 
     if (GGML_CUDA)
@@ -125,56 +125,54 @@ if(NOT TARGET ggml::ggml)
             IMPORTED_LOCATION "${GGML_BASE_LIBRARY}")
 
     set(_ggml_all_targets "")
-    if (NOT GGML_BACKEND_DL)
-        foreach(_ggml_backend ${GGML_AVAILABLE_BACKENDS})
-            string(REPLACE "-" "_" _ggml_backend_pfx "${_ggml_backend}")
-            string(TOUPPER "${_ggml_backend_pfx}" _ggml_backend_pfx)
+    foreach(_ggml_backend ${GGML_AVAILABLE_BACKENDS})
+        string(REPLACE "-" "_" _ggml_backend_pfx "${_ggml_backend}")
+        string(TOUPPER "${_ggml_backend_pfx}" _ggml_backend_pfx)
 
-            find_library(${_ggml_backend_pfx}_LIBRARY ${_ggml_backend}
-                REQUIRED
-                HINTS ${GGML_LIB_DIR}
-                NO_CMAKE_FIND_ROOT_PATH)
+        find_library(${_ggml_backend_pfx}_LIBRARY ${_ggml_backend}
+            REQUIRED
+            HINTS ${GGML_LIB_DIR}
+            NO_CMAKE_FIND_ROOT_PATH)
 
-            message(STATUS "Found ${${_ggml_backend_pfx}_LIBRARY}")
+        message(STATUS "Found ${${_ggml_backend_pfx}_LIBRARY}")
 
-            add_library(ggml::${_ggml_backend} UNKNOWN IMPORTED)
+        add_library(ggml::${_ggml_backend} UNKNOWN IMPORTED)
+        set_target_properties(ggml::${_ggml_backend}
+            PROPERTIES
+                INTERFACE_INCLUDE_DIRECTORIES "${GGML_INCLUDE_DIR}"
+                IMPORTED_LINK_INTERFACE_LANGUAGES "CXX"
+                IMPORTED_LOCATION "${${_ggml_backend_pfx}_LIBRARY}"
+                INTERFACE_COMPILE_FEATURES c_std_90
+                POSITION_INDEPENDENT_CODE ON)
+
+        string(REGEX MATCH "^ggml-cpu" is_cpu_variant "${_ggml_backend}")
+        if(is_cpu_variant)
+            list(APPEND GGML_CPU_INTERFACE_LINK_LIBRARIES "ggml::ggml-base")
             set_target_properties(ggml::${_ggml_backend}
-                PROPERTIES
-                    INTERFACE_INCLUDE_DIRECTORIES "${GGML_INCLUDE_DIR}"
-                    IMPORTED_LINK_INTERFACE_LANGUAGES "CXX"
-                    IMPORTED_LOCATION "${${_ggml_backend_pfx}_LIBRARY}"
-                    INTERFACE_COMPILE_FEATURES c_std_90
-                    POSITION_INDEPENDENT_CODE ON)
+            PROPERTIES
+                INTERFACE_LINK_LIBRARIES "${GGML_CPU_INTERFACE_LINK_LIBRARIES}")
 
-            string(REGEX MATCH "^ggml-cpu" is_cpu_variant "${_ggml_backend}")
-            if(is_cpu_variant)
-                list(APPEND GGML_CPU_INTERFACE_LINK_LIBRARIES "ggml::ggml-base")
-                set_target_properties(ggml::${_ggml_backend}
-                PROPERTIES
-                    INTERFACE_LINK_LIBRARIES "${GGML_CPU_INTERFACE_LINK_LIBRARIES}")
-
-                if(GGML_CPU_INTERFACE_LINK_OPTIONS)
-                    set_target_properties(ggml::${_ggml_backend}
-                        PROPERTIES
-                            INTERFACE_LINK_OPTIONS "${GGML_CPU_INTERFACE_LINK_OPTIONS}")
-                endif()
-
-            else()
-                list(APPEND ${_ggml_backend_pfx}_INTERFACE_LINK_LIBRARIES "ggml::ggml-base")
+            if(GGML_CPU_INTERFACE_LINK_OPTIONS)
                 set_target_properties(ggml::${_ggml_backend}
                     PROPERTIES
-                        INTERFACE_LINK_LIBRARIES "${${_ggml_backend_pfx}_INTERFACE_LINK_LIBRARIES}")
-
-                if(${_ggml_backend_pfx}_INTERFACE_LINK_OPTIONS)
-                    set_target_properties(ggml::${_ggml_backend}
-                        PROPERTIES
-                            INTERFACE_LINK_OPTIONS "${${_ggml_backend_pfx}_INTERFACE_LINK_OPTIONS}")
-                endif()
+                        INTERFACE_LINK_OPTIONS "${GGML_CPU_INTERFACE_LINK_OPTIONS}")
             endif()
 
-            list(APPEND _ggml_all_targets ggml::${_ggml_backend})
-        endforeach()
-    endif()
+        else()
+            list(APPEND ${_ggml_backend_pfx}_INTERFACE_LINK_LIBRARIES "ggml::ggml-base")
+            set_target_properties(ggml::${_ggml_backend}
+                PROPERTIES
+                    INTERFACE_LINK_LIBRARIES "${${_ggml_backend_pfx}_INTERFACE_LINK_LIBRARIES}")
+
+            if(${_ggml_backend_pfx}_INTERFACE_LINK_OPTIONS)
+                set_target_properties(ggml::${_ggml_backend}
+                    PROPERTIES
+                        INTERFACE_LINK_OPTIONS "${${_ggml_backend_pfx}_INTERFACE_LINK_OPTIONS}")
+            endif()
+        endif()
+
+        list(APPEND _ggml_all_targets ggml::${_ggml_backend})
+    endforeach()
 
     list(APPEND GGML_INTERFACE_LINK_LIBRARIES ggml::ggml-base "${_ggml_all_targets}")
     set_target_properties(ggml::ggml

ggml/include/ggml.h

@@ -304,16 +304,6 @@
     GGML_TENSOR_LOCALS(int64_t, ne,  dst,  ne) \
     GGML_TENSOR_LOCALS(size_t,  nb,  dst,  nb)
 
-#define GGML_TENSOR_TERNARY_OP_LOCALS \
-    GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne) \
-    GGML_TENSOR_LOCALS(size_t,  nb0, src0, nb) \
-    GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne) \
-    GGML_TENSOR_LOCALS(size_t,  nb1, src1, nb) \
-    GGML_TENSOR_LOCALS(int64_t, ne2, src2, ne) \
-    GGML_TENSOR_LOCALS(size_t,  nb2, src2, nb) \
-    GGML_TENSOR_LOCALS(int64_t, ne,  dst,  ne) \
-    GGML_TENSOR_LOCALS(size_t,  nb,  dst,  nb)
-
 #define GGML_TENSOR_BINARY_OP_LOCALS01 \
     GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne) \
     GGML_TENSOR_LOCALS(size_t,  nb0, src0, nb) \
@@ -405,8 +395,7 @@ extern "C" {
         // GGML_TYPE_IQ4_NL_4_4 = 36,
         // GGML_TYPE_IQ4_NL_4_8 = 37,
         // GGML_TYPE_IQ4_NL_8_8 = 38,
-        GGML_TYPE_MXFP4   = 39, // MXFP4 (1 block)
-        GGML_TYPE_COUNT   = 40,
+        GGML_TYPE_COUNT   = 39,
     };
 
     // precision
@@ -441,7 +430,6 @@ extern "C" {
         GGML_FTYPE_MOSTLY_IQ4_XS  = 22, // except 1d tensors
         GGML_FTYPE_MOSTLY_IQ1_M   = 23, // except 1d tensors
         GGML_FTYPE_MOSTLY_BF16    = 24, // except 1d tensors
-        GGML_FTYPE_MOSTLY_MXFP4   = 25, // except 1d tensors
     };
 
     // available tensor operations:
@@ -450,7 +438,6 @@ extern "C" {
 
         GGML_OP_DUP,
         GGML_OP_ADD,
-        GGML_OP_ADD_ID,
         GGML_OP_ADD1,
         GGML_OP_ACC,
         GGML_OP_SUB,
@@ -570,7 +557,6 @@ extern "C" {
         GGML_GLU_OP_REGLU,
         GGML_GLU_OP_GEGLU,
         GGML_GLU_OP_SWIGLU,
-        GGML_GLU_OP_SWIGLU_OAI,
         GGML_GLU_OP_GEGLU_ERF,
         GGML_GLU_OP_GEGLU_QUICK,
 
@@ -845,13 +831,6 @@ extern "C" {
             struct ggml_tensor  * b,
             enum   ggml_type      type);
 
-    // dst[i0, i1, i2] = a[i0, i1, i2] + b[i0, ids[i1, i2]]
-    GGML_API struct ggml_tensor * ggml_add_id(
-            struct ggml_context * ctx,
-            struct ggml_tensor  * a,
-            struct ggml_tensor  * b,
-            struct ggml_tensor  * ids);
-
     GGML_API struct ggml_tensor * ggml_add1(
             struct ggml_context * ctx,
             struct ggml_tensor  * a,
@@ -1219,13 +1198,6 @@ extern "C" {
             struct ggml_tensor  * a,
             struct ggml_tensor  * b);
 
-    GGML_API struct ggml_tensor * ggml_swiglu_oai(
-            struct ggml_context * ctx,
-            struct ggml_tensor  * a,
-            struct ggml_tensor  * b,
-            float                 alpha,
-            float                 limit);
-
     // normalize along rows
     GGML_API struct ggml_tensor * ggml_norm(
             struct ggml_context * ctx,
@@ -1598,10 +1570,6 @@ extern "C" {
             float                 scale,
             float                 max_bias);
 
-    GGML_API void ggml_soft_max_add_sinks(
-            struct ggml_tensor * a,
-            struct ggml_tensor * sinks);
-
     GGML_API struct ggml_tensor * ggml_soft_max_ext_back(
             struct ggml_context * ctx,
             struct ggml_tensor  * a,
@@ -2084,10 +2052,6 @@ extern "C" {
     GGML_API enum ggml_prec ggml_flash_attn_ext_get_prec(
             const struct ggml_tensor * a);
 
-    GGML_API void ggml_flash_attn_ext_add_sinks(
-            struct ggml_tensor * a,
-            struct ggml_tensor * sinks);
-
     // TODO: needs to be adapted to ggml_flash_attn_ext
     GGML_API struct ggml_tensor * ggml_flash_attn_back(
            struct ggml_context * ctx,

ggml/src/CMakeLists.txt

@@ -214,13 +214,6 @@ add_library(ggml
             ggml-backend-reg.cpp)
 add_library(ggml::ggml ALIAS ggml)
 
-if (GGML_BACKEND_DIR)
-    if (NOT GGML_BACKEND_DL)
-        message(FATAL_ERROR "GGML_BACKEND_DIR requires GGML_BACKEND_DL")
-    endif()
-    target_compile_definitions(ggml PUBLIC GGML_BACKEND_DIR="${GGML_BACKEND_DIR}")
-endif()
-
 target_link_libraries(ggml PUBLIC ggml-base)
 
 if (CMAKE_SYSTEM_NAME MATCHES "Linux")
@@ -234,11 +227,7 @@ function(ggml_add_backend_library backend)
         set_target_properties(${backend} PROPERTIES LIBRARY_OUTPUT_DIRECTORY ${CMAKE_RUNTIME_OUTPUT_DIRECTORY})
         target_compile_definitions(${backend} PRIVATE GGML_BACKEND_DL)
         add_dependencies(ggml ${backend})
-        if (GGML_BACKEND_DIR)
-            install(TARGETS ${backend} LIBRARY DESTINATION ${GGML_BACKEND_DIR})
-        else()
-            install(TARGETS ${backend} LIBRARY DESTINATION ${CMAKE_INSTALL_BINDIR})
-        endif()
+        install(TARGETS ${backend} LIBRARY DESTINATION ${CMAKE_INSTALL_BINDIR})
     else()
         add_library(${backend} ${ARGN})
         target_link_libraries(ggml PUBLIC ${backend})

ggml/src/ggml-alloc.c

@@ -29,7 +29,6 @@ static bool ggml_op_can_inplace(enum ggml_op op) {
         case GGML_OP_DIAG_MASK_ZERO:
         case GGML_OP_DIAG_MASK_INF:
         case GGML_OP_ADD:
-        case GGML_OP_ADD_ID:
         case GGML_OP_ADD1:
         case GGML_OP_SUB:
         case GGML_OP_MUL:

ggml/src/ggml-backend-reg.cpp

@@ -498,9 +498,6 @@ static ggml_backend_reg_t ggml_backend_load_best(const char * name, bool silent,
 
     std::vector<fs::path> search_paths;
     if (user_search_path == nullptr) {
-#ifdef GGML_BACKEND_DIR
-        search_paths.push_back(fs::u8path(GGML_BACKEND_DIR));
-#endif
         // default search paths: executable directory, current directory
         search_paths.push_back(get_executable_path());
         search_paths.push_back(fs::current_path());

ggml/src/ggml-backend.cpp

@@ -1071,11 +1071,6 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg
                 }
             }
         }
-        // if the node is still unassigned, assign it to the first backend that supports it
-        for (int b = 0; b < sched->n_backends && *cur_backend_id == -1; b++) {
-            ggml_backend_sched_set_if_supported(sched, node, b, cur_backend_id);
-        }
-        GGML_ASSERT(*cur_backend_id != -1);
     }
 
     // pass 5: split graph, find tensors that need to be copied
@@ -1103,7 +1098,7 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg
 
             const int node_backend_id = tensor_backend_id(node);
 
-            GGML_ASSERT(node_backend_id != -1); // all nodes should be assigned by now, this can happen if there is no CPU fallback
+            assert(node_backend_id != -1); // all nodes should be assigned by now, this can happen if there is no CPU fallback
 
             // check if we should start a new split based on the sources of the current node
             bool need_new_split = false;
@@ -1161,7 +1156,7 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg
 
                 size_t src_id = hash_id(src);
                 const int src_backend_id = sched->hv_tensor_backend_ids[src_id];
-                GGML_ASSERT(src_backend_id != -1); // all inputs should be assigned by now
+                assert(src_backend_id != -1); // all inputs should be assigned by now
 
                 if (src->flags & GGML_TENSOR_FLAG_INPUT && sched->n_copies > 1) {
                     if (tensor_id_copy(src_id, src_backend_id, 0) == NULL) {

ggml/src/ggml-cann/CMakeLists.txt

@@ -31,13 +31,6 @@ string(REGEX MATCH "[0-9]+[a-zA-Z]" SOC_TYPE_MAJOR_SN "${SOC_VERSION}")
 set(SOC_TYPE_COMPILE_OPTION "ASCEND_${SOC_TYPE_MAJOR_SN}")
 string(TOUPPER ${SOC_TYPE_COMPILE_OPTION} SOC_TYPE_COMPILE_OPTION)
 message(STATUS "CANN: SOC_VERSION =  ${SOC_VERSION}")
-option(USE_ACL_GRAPH "Enable CANN graph execution (ACL graph mode)" OFF)
-
-if(USE_ACL_GRAPH AND (SOC_TYPE_MAJOR_SN STREQUAL "310P" OR SOC_TYPE_COMPILE_OPTION STREQUAL "ASCEND_310P"))
-    message(FATAL_ERROR
-        "CANN Graph (ACL graph mode) is not supported on 310P devices. "
-        "Please build with -DUSE_ACL_GRAPH=OFF or use a supported SOC.")
-endif()
 
 if (CANN_INSTALL_DIR)
     # Only Support Linux.
@@ -75,13 +68,6 @@ if (CANN_INSTALL_DIR)
 
     target_compile_definitions(ggml-cann PRIVATE "-D${SOC_TYPE_COMPILE_OPTION}")
 
-    if (USE_ACL_GRAPH)
-        target_compile_definitions(ggml-cann PRIVATE USE_ACL_GRAPH)
-        message(STATUS "CANN: USE_ACL_GRAPH is enabled.")
-    else()
-        message(STATUS "CANN: USE_ACL_GRAPH is disabled.")
-    endif()
-
     message(STATUS "CANN: CANN_INCLUDE_DIRS =  ${CANN_INCLUDE_DIRS}")
     message(STATUS "CANN: CANN_LIBRARIES =  ${CANN_LIBRARIES}")
 else()

ggml/src/ggml-cann/aclnn_ops.cpp

@@ -68,8 +68,6 @@
 #include <aclnnop/aclnn_grouped_matmul_v3.h>
 #include <aclnnop/aclnn_fused_infer_attention_score_v2.h>
 #include <aclnnop/aclnn_zero.h>
-#include <aclnnop/aclnn_index_copy.h>
-#include <aclnnop/aclnn_index_select.h>
 #include <float.h>
 
 #include <cmath>
@@ -1616,97 +1614,50 @@ void ggml_cann_softmax(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
 }
 
 /**
- * @brief Performs index select operation on a 4D tensor using the CANN backend.
+ * @brief Performs embedding operation on a 4D tensor using the CANN backend.
  *
- * This function applies the `IndexSelect` operation along a specific dimension
- * of the source tensor (`src_buffer`) using the indices from the index tensor (`index`).
- * It iterates over the last two dimensions of the source tensor, creates the corresponding
- * CANN tensors for the source, index, and output slices, and executes the `IndexSelect`
- * operation for each slice.
+ * This function extracts slices from the source tensor (`src_buffer`),
+ * index tensor (`index`), and destination tensor (`dst`), and performs an
+ * embedding operation on them. The embedding operation is applied by iterating
+ * over the last two dimensions of the source tensor, creating the necessary
+ * tensors for the source, index, and output, and executing the embedding operation.
  *
  * @param ctx The context for CANN backend operations.
- * @param src_buffer The source buffer containing the 4D input tensor data.
+ * @param src_buffer The source buffer holding the data for the source tensor.
  * @param src_ne The dimensions of the source tensor.
  * @param src_nb The strides (byte offsets) of the source tensor.
- * @param dst_buffer The destination buffer where the output tensor data will be written.
- * @param dst_ne The dimensions of the destination tensor.
- * @param dst_nb The strides (byte offsets) of the destination tensor.
- * @param index The index tensor specifying the indices to select from the source tensor.
- * @param type The data type of the source and destination tensors.
+ * @param index The index tensor used in the embedding operation.
+ * @param dst The destination tensor where the result will be stored.
  */
-static void aclnn_index_select_4d(ggml_backend_cann_context& ctx,
-                                void* src_buffer,int64_t* src_ne, size_t* src_nb,
-                                void* dst_buffer, int64_t* dst_ne, size_t* dst_nb,
-                                ggml_tensor* index, ggml_type type) {
+static void aclnn_embedding_4d(ggml_backend_cann_context& ctx, void* src_buffer,
+                            int64_t* src_ne, size_t* src_nb, ggml_tensor* index,
+                            ggml_tensor* dst) {
     for (int64_t i = 0; i < src_ne[3]; i++) {
         for (int64_t j = 0; j < src_ne[2]; j++) {
             // src
+            int64_t acl_src_ne[2] = {src_ne[0], src_ne[1]};
+            size_t acl_src_nb[2] = {src_nb[0], src_nb[1]};
             aclTensor* acl_src_tensor = ggml_cann_create_tensor(
                 (char*)src_buffer + i * src_nb[3] + j * src_nb[2],
-                ggml_cann_type_mapping(type), ggml_type_size(type),
-                src_ne, src_nb, 2);
+                ggml_cann_type_mapping(dst->type), ggml_element_size(dst),
+                acl_src_ne, acl_src_nb, 2);
 
             // index
+            int64_t acl_index_ne[1] = {index->ne[0]};
+            size_t acl_index_nb[1] = {index->nb[0]};
             aclTensor* acl_index = ggml_cann_create_tensor(
-                (char*)index->data + (i % index->ne[2]) * index->nb[2] + (j % index->ne[1]) * index->nb[1],
+                (char*)index->data + i * index->nb[2] + j * index->nb[1],
                 ggml_cann_type_mapping(index->type), ggml_element_size(index),
-                index->ne, index->nb, 1);
+                acl_index_ne, acl_index_nb, 1);
 
             // out
+            int64_t acl_out_ne[2] = {dst->ne[0], dst->ne[1]};
+            size_t acl_out_nb[2] = {dst->nb[0], dst->nb[1]};
             aclTensor* acl_out = ggml_cann_create_tensor(
-                (char*)dst_buffer + i * dst_nb[3] + j * dst_nb[2],
-                ggml_cann_type_mapping(type), ggml_type_size(type),
-                dst_ne, dst_nb, 2);
-            GGML_CANN_CALL_ACLNN_OP(ctx, IndexSelect, acl_src_tensor, 0, acl_index, acl_out);
-            ggml_cann_release_resources(ctx, acl_src_tensor, acl_index, acl_out);
-        }
-    }
-}
-
-/**
- * @brief Performs inplace index copy operation on a 4D tensor using the CANN backend.
- *
- * This function applies the `IndexCopy` operation along a specific dimension of the
- * destination tensor (`dst_buffer`) by copying elements from the source tensor (`src_buffer`)
- * to positions specified by the index tensor (`index`).
- * It iterates over the last two dimensions of the tensors, creates the corresponding
- * CANN tensors for source, index, and destination slices, and performs the index copy
- * operation for each slice.
- *
- * @param ctx The context for CANN backend operations.
- * @param src_buffer The source buffer containing the 4D input tensor data to be copied.
- * @param src_ne The dimensions of the source tensor.
- * @param src_nb The strides (byte offsets) of the source tensor.
- * @param dst_buffer The destination buffer where values will be copied to.
- * @param dst_ne The dimensions of the destination tensor.
- * @param dst_nb The strides (byte offsets) of the destination tensor.
- * @param index The index tensor specifying target positions in the destination tensor.
- * @param type The data type of the source and destination tensors.
- */
-static void aclnn_index_copy_4d(ggml_backend_cann_context& ctx,
-                                void* src_buffer,int64_t* src_ne, size_t* src_nb,
-                                void* dst_buffer, int64_t* dst_ne, size_t* dst_nb,
-                                ggml_tensor* index, ggml_type type) {
-    for (int64_t i = 0; i < src_ne[3]; i++) {
-        for (int64_t j = 0; j < src_ne[2]; j++) {
-            // src
-            aclTensor* acl_src_tensor = ggml_cann_create_tensor(
-                (char*)src_buffer + i * src_nb[3] + j * src_nb[2],
-                ggml_cann_type_mapping(type), ggml_type_size(type),
-                src_ne, src_nb, 2);
-
-            // index
-            aclTensor* acl_index = ggml_cann_create_tensor(
-                (char*)index->data + (i % index->ne[2]) * index->nb[2] + (j % index->ne[1]) * index->nb[1],
-                ggml_cann_type_mapping(index->type), ggml_element_size(index),
-                index->ne, index->nb, 1);
-
-            // out
-            aclTensor* acl_out = ggml_cann_create_tensor(
-                (char*)dst_buffer + i * dst_nb[3] + j * dst_nb[2],
-                ggml_cann_type_mapping(type), ggml_type_size(type),
-                dst_ne, dst_nb, 2);
-            GGML_CANN_CALL_ACLNN_OP(ctx, InplaceIndexCopy, acl_out, 0, acl_index, acl_src_tensor);
+                (char*)dst->data + i * dst->nb[3] + j * dst->nb[2],
+                ggml_cann_type_mapping(dst->type), ggml_element_size(dst),
+                acl_out_ne, acl_out_nb, 2);
+            GGML_CANN_CALL_ACLNN_OP(ctx, Embedding, acl_src_tensor, acl_index, acl_out);
             ggml_cann_release_resources(ctx, acl_src_tensor, acl_index, acl_out);
         }
     }
@@ -1718,9 +1669,8 @@ void ggml_cann_get_rows(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
 
     switch (src0->type) {
         case GGML_TYPE_F32: {
-            aclnn_index_select_4d(ctx, src0->data, src0->ne, src0->nb,
-                                dst->data, dst->ne, dst->nb,
-                                src1, dst->type);
+            aclnn_embedding_4d(ctx, src0->data, src0->ne, src0->nb, src1,
+                                   dst);
             break;
         }
         case GGML_TYPE_F16: {
@@ -1737,9 +1687,8 @@ void ggml_cann_get_rows(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
                 src_trans_buffer, ACL_FLOAT, ggml_type_size(dst->type),
                 src0->ne, src_trans_nb, GGML_MAX_DIMS);
             aclnn_cast(ctx, acl_src0, src_trans_tensor, ggml_cann_type_mapping(dst->type));
-            aclnn_index_select_4d(ctx, src_trans_buffer, src0->ne, src_trans_nb,
-                                dst->data, dst->ne, dst->nb,
-                                src1, dst->type);
+            aclnn_embedding_4d(ctx, src_trans_buffer, src0->ne,
+                                   src_trans_nb, src1, dst);
             ggml_cann_release_resources(ctx, acl_src0, src_trans_tensor);
             break;
         }
@@ -1799,10 +1748,8 @@ void ggml_cann_get_rows(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
                 dequant_nb[i] = dequant_nb[i - 1] * src0->ne[i - 1];
             }
 
-            aclnn_index_select_4d(ctx, dequant_buffer_allocator.get(),
-                                   dequant_ne, dequant_nb,
-                                   dst->data, dst->ne, dst->nb,
-                                   src1, dst->type);
+            aclnn_embedding_4d(ctx, dequant_buffer_allocator.get(),
+                                   dequant_ne, dequant_nb, src1, dst);
 
             ggml_cann_release_resources(ctx, dequant_tensor);
             break;
@@ -1813,43 +1760,6 @@ void ggml_cann_get_rows(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
     }
 }
 
-void ggml_cann_set_rows(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
-    ggml_tensor* src0 = dst->src[0];  // src
-    ggml_tensor* src1 = dst->src[1];  // index
-
-    switch (dst->type) {
-        case GGML_TYPE_F32: {
-            aclnn_index_copy_4d(ctx, src0->data, src0->ne, src0->nb,
-                                dst->data, dst->ne, dst->nb,
-                                src1, dst->type);
-            break;
-        }
-        case GGML_TYPE_F16: {
-            aclTensor* acl_src0 = ggml_cann_create_tensor(src0);
-            ggml_cann_pool_alloc src_buffer_allocator(
-                ctx.pool(), ggml_nelements(src0) * sizeof(uint16_t));
-            void* src_trans_buffer = src_buffer_allocator.get();
-            size_t src_trans_nb[GGML_MAX_DIMS];
-            src_trans_nb[0] = sizeof(uint16_t);
-            for (int i = 1; i < GGML_MAX_DIMS; i++) {
-                src_trans_nb[i] = src_trans_nb[i - 1] * src0->ne[i - 1];
-            }
-            aclTensor* src_trans_tensor = ggml_cann_create_tensor(
-                src_trans_buffer, ACL_FLOAT16, ggml_type_size(dst->type),
-                src0->ne, src_trans_nb, GGML_MAX_DIMS);
-            aclnn_cast(ctx, acl_src0, src_trans_tensor, ggml_cann_type_mapping(dst->type));
-            aclnn_index_copy_4d(ctx, src_trans_buffer, src0->ne, src_trans_nb,
-                                dst->data, dst->ne, dst->nb,
-                                src1, dst->type);
-            ggml_cann_release_resources(ctx, acl_src0, src_trans_tensor);
-            break;
-        }
-        default:
-            GGML_ABORT("Unsupported tensor type for GGML_OP_SET_ROWS");
-            break;
-    }
-}
-
 /**
  * @brief Repeats elements of a tensor along a specified dimension.
  *
@@ -1913,9 +1823,11 @@ static void ggml_cann_mat_mul_fp(ggml_backend_cann_context& ctx,
                              bcast_weight_nb[4], bcast_weight_nb[5]};
     aclTensor* acl_weight_tensor;
 
-    // Only check env once.
-    static bool weight_to_nz = parse_bool(get_env("GGML_CANN_WEIGHT_NZ").value_or(""));
-    if (weight_to_nz && is_matmul_weight(weight)) {
+    bool weightToNZ = false;
+#ifdef ASCEND_310P
+    weightToNZ = (getenv("GGML_CANN_WEIGHT_NZ") != nullptr);
+#endif
+    if (weightToNZ && is_matmul_weight(weight)) {
         int64_t acl_stride[2] = {1, transpose_ne[1]};
 
         // Reverse ne.

ggml/src/ggml-cann/aclnn_ops.h

@@ -424,25 +424,15 @@ void ggml_cann_softmax(ggml_backend_cann_context& ctx, ggml_tensor* dst);
  *
  * @details This function retrieves rows from a source tensor src0 according to
  *          the indices provided in another tensor src1 and stores the result in
- *          a destination tensor (\p dst).
+ *          a destination tensor (\p dst). It supports different data types
+ *          including F32, F16, Q4_0, and Q8_0.
  *
  * @param ctx The backend CANN context for executing operations.
  * @param dst The destination tensor where the extracted rows will be stored.
+ *            dst->op is `GGML_OP_GET_ROWS`.
  */
 void ggml_cann_get_rows(ggml_backend_cann_context& ctx, ggml_tensor* dst);
 
-/**
- * @brief   Writes specific rows into a tensor at positions specified by indices.
- *
- * @details This function copies rows from a source tensor into a destination
- *          tensor (\p dst) at the positions indicated by the indices in another
- *          tensor.
- *
- * @param ctx The backend CANN context for executing operations.
- * @param dst The destination tensor where the specified rows will be updated.
- */
-void ggml_cann_set_rows(ggml_backend_cann_context& ctx, ggml_tensor* dst);
-
 /**
  * @brief   Executes matrix multiplication for the given tensor.
  *

ggml/src/ggml-cann/common.h

@@ -337,29 +337,6 @@ private:
     int32_t device_;
 };
 
-#ifdef USE_ACL_GRAPH
-struct ggml_graph_node_properties {
-    void * node_address;
-    ggml_op node_op;
-    int64_t ne[GGML_MAX_DIMS];
-    size_t nb[GGML_MAX_DIMS];
-    void * src_address[GGML_MAX_SRC];
-    int32_t op_params[GGML_MAX_OP_PARAMS / sizeof(int32_t)];
-};
-
-struct ggml_cann_graph {
-    ~ggml_cann_graph() {
-        if (graph != nullptr) {
-            aclmdlRIDestroy(graph);
-        }
-    }
-
-    aclmdlRI graph = nullptr;
-
-    std::vector<ggml_graph_node_properties> ggml_graph_properties;
-};
-#endif  // USE_ACL_GRAPH
-
 /**
  * @brief Context for managing CANN backend operations.
  */
@@ -368,13 +345,8 @@ struct ggml_backend_cann_context {
     std::string name;                /**< Name of the device. */
     std::string description;         /**< Description of the device. */
     aclrtEvent copy_event = nullptr; /**< Event for managing copy operations. */
-#ifdef USE_ACL_GRAPH
-    /// Cached CANN ACL graph used for executing the current ggml computation graph.
-    std::unique_ptr<ggml_cann_graph> cann_graph;
-#endif
     cann_task_queue task_queue;
     bool async_mode;
-    bool support_set_rows;
 
     aclrtStream streams[GGML_CANN_MAX_STREAMS] = {nullptr}; /**< Array of streams for the device. */
 
@@ -390,14 +362,6 @@ struct ggml_backend_cann_context {
         async_mode = parse_bool(get_env("GGML_CANN_ASYNC_MODE").value_or(""));
         GGML_LOG_INFO("%s: device %d async operator submission is %s\n", __func__,
             device, async_mode ? "ON" : "OFF");
-
-        support_set_rows = parse_bool(get_env("LLAMA_SET_ROWS").value_or(""));
-        GGML_LOG_INFO("%s: LLAMA_SET_ROWS is %s\n", __func__, support_set_rows ? "ON" : "OFF");
-
-        if (!support_set_rows) {
-            GGML_LOG_INFO("%s: CANN Graph currently only supports execution when LLAMA_SET_ROWS is ON. "
-                    "Falling back to eager mode.\n", __func__);
-        }
     }
 
     /**

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

@@ -1116,59 +1116,61 @@ static enum ggml_status ggml_backend_cann_buffer_init_tensor(
     return GGML_STATUS_SUCCESS;
 }
 
-// ND to NZ Workspace Cache Management. Thread-safety: Not guaranteed
-namespace {
-    void* g_nz_workspace = nullptr;
-    size_t g_nz_workspace_allocated = 0;
-
-    void release_nz_workspace() {
-        if (g_nz_workspace) {
-            aclrtFree(g_nz_workspace);
-            g_nz_workspace = nullptr;
-            g_nz_workspace_allocated = 0;
-        }
+static int CreateAclTensorWeight(const void *hostData, const std::vector<int64_t> &shape, void **deviceAddr,
+                      aclDataType dataType, aclTensor **tensor)
+{
+    uint64_t size = 1;
+    for (auto i : shape) {
+        size *= i;
     }
 
-    void relloc_nz_workspace(size_t new_size) {
-        if (new_size > g_nz_workspace_allocated) {
-        if (g_nz_workspace) {
-            aclrtFree(g_nz_workspace);
-            g_nz_workspace = nullptr;
-        }
-        ACL_CHECK(aclrtMalloc(&g_nz_workspace, new_size, ACL_MEM_MALLOC_HUGE_FIRST));
-        g_nz_workspace_allocated = new_size;
-    }
+    const aclIntArray *mat2Size = aclCreateIntArray(shape.data(), shape.size());
+    ACL_CHECK(aclnnCalculateMatmulWeightSizeV2(mat2Size, dataType, &size));
+
+    size *= sizeof(int16_t);
+
+    ACL_CHECK(aclrtMalloc(deviceAddr, size, ACL_MEM_MALLOC_HUGE_FIRST));
+    aclrtMemcpy(*deviceAddr, size, hostData, size, ACL_MEMCPY_HOST_TO_DEVICE);
+
+    std::vector<int64_t> strides(shape.size(), 1);
+    for (int64_t i = shape.size() - 2; i >= 0; i--) {
+        strides[i] = shape[i + 1] * strides[i + 1];
     }
+
+    *tensor = aclCreateTensor(shape.data(), shape.size(), dataType, strides.data(), 0, aclFormat::ACL_FORMAT_ND,
+                              shape.data(), shape.size(), *deviceAddr);
+    return 0;
 }
 
-/**
- * @brief Convert tensor weights to NZ format using Ascend CANN API.
- *
- * This function creates a transposed tensor descriptor and performs the
- * TransMatmulWeight operation. Converting tensor formats can significantly
- * improve performance on certain hardware.
- *
- * @param tensor Pointer to the input ggml_tensor containing the weights.
- * @param data Pointer to the raw data buffer for the tensor weights.
- * @param offset Byte offset within the tensor data buffer where weights start.
- *
- * @note The workspace buffer used in this function is managed globally and reused
- *       across calls. This reduces overhead from repeated memory allocation and deallocation.
- */
 static void weight_format_to_nz(ggml_tensor *tensor, const void *data, size_t offset) {
-    aclTensor* weightTransposed = ggml_cann_create_tensor(tensor, tensor->ne,
-                                    tensor->nb, 2, ACL_FORMAT_ND, offset);
+    aclrtStream stream;
+    ACL_CHECK(aclrtCreateStream(&stream));
+
+    std::vector<int64_t> weightTransposedShape = {tensor->ne[1], tensor->ne[0]};
+    void *weightTransposedDeviceAddr = nullptr;
+    aclTensor *weightTransposed = nullptr;
+    CreateAclTensorWeight(data, weightTransposedShape, &weightTransposedDeviceAddr,
+                          ggml_cann_type_mapping(tensor->type), &weightTransposed);
+
     uint64_t workspaceSize = 0;
     aclOpExecutor *executor;
+    void *workspaceAddr = nullptr;
 
     // TransMatmulWeight
-    ACL_CHECK(aclnnTransMatmulWeightGetWorkspaceSize(weightTransposed,
-                                                    &workspaceSize, &executor));
-    // Avoid frequent malloc/free of the workspace.
-    relloc_nz_workspace(workspaceSize);
+    ACL_CHECK(aclnnTransMatmulWeightGetWorkspaceSize(weightTransposed, &workspaceSize, &executor));
+    std::unique_ptr<void, aclError (*)(void *)> workspaceAddrPtrTrans(nullptr, aclrtFree);
+    if (workspaceSize > 0) {
+        ACL_CHECK(aclrtMalloc(&workspaceAddr, workspaceSize, ACL_MEM_MALLOC_HUGE_FIRST));
+        workspaceAddrPtrTrans.reset(workspaceAddr);
+    }
+    ACL_CHECK(aclnnTransMatmulWeight(workspaceAddr, workspaceSize, executor, stream));
 
-    ACL_CHECK(aclnnTransMatmulWeight(g_nz_workspace, workspaceSize, executor, nullptr));
+    size_t size = ggml_nelements(tensor) * ggml_element_size(tensor);
+
+    aclrtMemcpy((char *)tensor->data + offset, size,
+                weightTransposedDeviceAddr, size, ACL_MEMCPY_HOST_TO_DEVICE);
     ACL_CHECK(aclDestroyTensor(weightTransposed));
+    aclrtFree(weightTransposedDeviceAddr);
 }
 
 // TODO: need handle tensor which has paddings.
@@ -1195,14 +1197,14 @@ static void ggml_backend_cann_buffer_set_tensor(
     // For acl, synchronous functions use this default stream.
     // Why aclrtSynchronizeDevice?
 
-    // Only check env once.
-    static bool weight_to_nz = parse_bool(get_env("GGML_CANN_WEIGHT_NZ").value_or(""));
+    bool weightToNZ = false;
+#ifdef ASCEND_310P
+    weightToNZ = (getenv("GGML_CANN_WEIGHT_NZ") != nullptr);
+#endif
     if (!need_transform(tensor->type)) {
         ACL_CHECK(aclrtMemcpy((char *)tensor->data + offset, size, data, size,
                               ACL_MEMCPY_HOST_TO_DEVICE));
-        if (weight_to_nz && is_matmul_weight((const ggml_tensor*)tensor)) {
-            GGML_ASSERT(tensor->ne[2] == 1);
-            GGML_ASSERT(tensor->ne[3] == 1);
+        if (weightToNZ && is_matmul_weight((const ggml_tensor*)tensor)) {
             weight_format_to_nz(tensor, data, offset);
         }
     } else {
@@ -1438,32 +1440,20 @@ static size_t ggml_backend_cann_buffer_type_get_alloc_size(
     size_t size = ggml_nbytes(tensor);
     int64_t ne0 = tensor->ne[0];
 
-    // Only check env once.
-    static bool weight_to_nz = parse_bool(get_env("GGML_CANN_WEIGHT_NZ").value_or(""));
-
     // last line must bigger than 32, because every single op deal at
     // least 32 bytes.
     // TODO: quantized type?
     // int64_t line_size = ne0 * ggml_element_size(tensor);
     // int64_t line_size_align_32 = (line_size + 31) & ~31;
     // size += (line_size_align_32 - line_size);
+
+    // TODO: not support quantized yet.
+    // TODO: consider un-continue tensor.
     if (ggml_is_quantized(tensor->type)) {
         if (ne0 % MATRIX_ROW_PADDING != 0) {
             size += ggml_row_size(
                 tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING);
         }
-    } else if (weight_to_nz && is_matmul_weight((const ggml_tensor*)tensor)) {
-        // NZ format weight are not support quantized yet.
-        // If ND tensor transform to NZ, size may changed.
-        int64_t shape[] = {tensor->ne[1], tensor->ne[0]};
-        GGML_ASSERT(tensor->ne[2] == 1);
-        GGML_ASSERT(tensor->ne[3] == 1);
-        const aclIntArray *acl_shape = aclCreateIntArray(shape, 2);
-        size_t new_size;
-        ACL_CHECK(aclnnCalculateMatmulWeightSizeV2(acl_shape,
-                    ggml_cann_type_mapping(tensor->type), &new_size));
-        ACL_CHECK(aclDestroyIntArray(acl_shape));
-        size = std::max(size, new_size);
     }
 
     return size;
@@ -1669,9 +1659,6 @@ static bool ggml_cann_compute_forward(ggml_backend_cann_context& ctx,
         case GGML_OP_GET_ROWS:
             ggml_cann_get_rows(ctx, dst);
             break;
-        case GGML_OP_SET_ROWS:
-            ggml_cann_set_rows(ctx, dst);
-            break;
         case GGML_OP_DUP:
             ggml_cann_dup(ctx, dst);
             break;
@@ -2016,9 +2003,6 @@ static bool ggml_backend_cann_cpy_tensor_async(
         (ggml_backend_cann_context*)backend_dst->context;
 
     size_t copy_size = ggml_nbytes(dst);
-    if (copy_size == 0) {
-        return true;
-    }
     if (backend_src != backend_dst) {
         ggml_backend_cann_buffer_context* buf_ctx_src =
             (ggml_backend_cann_buffer_context*)buf_src->context;
@@ -2075,160 +2059,6 @@ static void ggml_backend_cann_synchronize(ggml_backend_t backend) {
     ACL_CHECK(aclrtSynchronizeStream(cann_ctx->stream()));
 }
 
-#ifdef USE_ACL_GRAPH
-/**
- * @brief Populate the internal CANN graph node properties from the ggml computation graph.
- *
- * This function copies all node attributes (operation type, dimensions, strides, input sources,
- * and operation parameters) into the cached CANN graph structure for later reuse or comparison.
- *
- * @param cann_ctx  The CANN backend context.
- * @param cgraph    The ggml computational graph.
- */
-static void set_ggml_graph_node_properties(ggml_backend_cann_context * cann_ctx, ggml_cgraph * cgraph) {
-    for (int node_idx = 0; node_idx < cgraph->n_nodes; node_idx++) {
-        ggml_tensor * node = cgraph->nodes[node_idx];
-        cann_ctx->cann_graph->ggml_graph_properties[node_idx].node_address = node->data;
-        cann_ctx->cann_graph->ggml_graph_properties[node_idx].node_op = node->op;
-
-        for (int dim = 0; dim < GGML_MAX_DIMS; dim++) {
-            cann_ctx->cann_graph->ggml_graph_properties[node_idx].ne[dim] = node->ne[dim];
-            cann_ctx->cann_graph->ggml_graph_properties[node_idx].nb[dim] = node->nb[dim];
-        }
-        for (int src = 0; src < GGML_MAX_SRC; src++) {
-            cann_ctx->cann_graph->ggml_graph_properties[node_idx].src_address[src] =
-                node->src[src] ? node->src[src]->data : nullptr;
-        }
-        memcpy(cann_ctx->cann_graph->ggml_graph_properties[node_idx].op_params, node->op_params, GGML_MAX_OP_PARAMS);
-    }
-}
-
-/**
- * @brief Check if a ggml tensor node matches a previously captured CANN graph node.
- *
- * This function compares all relevant fields (address, op type, shape, source inputs, op params)
- * to determine whether the current node matches a previously recorded version.
- *
- * @param node                  The current ggml tensor node.
- * @param graph_node_properties The stored properties of a CANN graph node.
- * @return true if all fields match (excluding GGML_OP_VIEW); false otherwise.
- */
-static bool ggml_graph_node_has_matching_properties(ggml_tensor * node, ggml_graph_node_properties * graph_node_properties) {
-    if (node->data != graph_node_properties->node_address &&
-           node->op != GGML_OP_VIEW) {
-        return false;
-    }
-    if (node->op != graph_node_properties->node_op) {
-        return false;
-    }
-    for (int i = 0; i < GGML_MAX_DIMS; i++) {
-        if (node->ne[i] != graph_node_properties->ne[i]) {
-            return false;
-        }
-        if (node->nb[i] != graph_node_properties->nb[i]) {
-            return false;
-        }
-    }
-    for (int i = 0; i < GGML_MAX_SRC; i++) {
-        if (node->src[i] &&
-            node->src[i]->data != graph_node_properties->src_address[i] &&
-            node->op != GGML_OP_VIEW
-        ) {
-            return false;
-        }
-    }
-    if (node->op == GGML_OP_SCALE &&
-        memcmp(graph_node_properties->op_params, node->op_params, GGML_MAX_OP_PARAMS) != 0) {
-        return false;
-    }
-    return true;
-}
-
-/**
- * @brief Determine if the CANN graph needs to be rebuilt due to graph changes.
- *
- * This checks whether the number or properties of ggml graph nodes have changed
- * compared to the last captured CANN graph. If so, the CANN graph must be re-captured.
- *
- * @param cann_ctx  The CANN backend context.
- * @param cgraph    The current ggml computation graph.
- * @return true if an update is required; false otherwise.
- */
-static bool is_cann_graph_update_required(ggml_backend_cann_context * cann_ctx, ggml_cgraph * cgraph) {
-    // The number of nodes is different, so the graph needs to be reconstructed.
-    if (cann_ctx->cann_graph->ggml_graph_properties.size() != (size_t)cgraph->n_nodes) {
-        cann_ctx->cann_graph->ggml_graph_properties.resize(cgraph->n_nodes);
-        return true;
-    }
-
-    // The number of nodes is the same; iterate over each node to check whether they match.
-    for (int i = 0; i < cgraph->n_nodes; i++) {
-        bool has_matching_properties = ggml_graph_node_has_matching_properties(
-            cgraph->nodes[i], &cann_ctx->cann_graph->ggml_graph_properties[i]);
-        if(!has_matching_properties) {
-            return true;
-        }
-    }
-    return false;
-}
-#endif  // USE_ACL_GRAPH
-
-/**
- * @brief Evaluate the computation graph and optionally capture or execute it using CANN graph API.
- *
- * If CANN graph execution is enabled and graph capture is required, this function begins
- * graph capture, runs the graph, ends capture, and stores the captured graph.
- *
- * Otherwise, it falls back to op-by-op execution using the CANN compute kernel dispatcher.
- *
- * @param cann_ctx                 The CANN backend context.
- * @param cgraph                   The ggml computation graph.
- * @param use_cann_graph           Whether to use CANN graph execution.
- * @param cann_graph_update_required Whether graph capture is needed due to graph changes.
- */
-static void evaluate_and_capture_cann_graph(ggml_backend_cann_context * cann_ctx, ggml_cgraph * cgraph,
-    bool & use_cann_graph, bool & cann_graph_update_required) {
-#ifdef USE_ACL_GRAPH
-    if (use_cann_graph && cann_graph_update_required) {
-        if (cann_ctx->cann_graph->graph != nullptr) {
-            ACL_CHECK(aclmdlRIDestroy(cann_ctx->cann_graph->graph));
-            cann_ctx->cann_graph->graph = nullptr;
-        }
-        ACL_CHECK(aclmdlRICaptureBegin(cann_ctx->stream(), ACL_MODEL_RI_CAPTURE_MODE_GLOBAL));
-    }
-#endif // USE_ACL_GRAPH
-
-    // Only perform the graph execution if CANN graphs are not enabled, or we are capturing the graph.
-    // With the use of CANN graphs, the execution will be performed by the graph launch.
-    if (!use_cann_graph || cann_graph_update_required) {
-        for (int i = 0; i < cgraph->n_nodes; i++) {
-            ggml_tensor * node = cgraph->nodes[i];
-
-            if (ggml_is_empty(node) || node->op == GGML_OP_RESHAPE || node->op == GGML_OP_TRANSPOSE || node->op == GGML_OP_VIEW || node->op == GGML_OP_PERMUTE || node->op == GGML_OP_NONE) {
-                continue;
-            }
-
-            bool ok = ggml_cann_compute_forward(*cann_ctx, node);
-            if (!ok) {
-                GGML_LOG_ERROR("%s: op not supported %s (%s)\n", __func__, node->name, ggml_op_name(node->op));
-            }
-            GGML_ASSERT(ok);
-        }
-    }
-
-#ifdef USE_ACL_GRAPH
-    if (use_cann_graph && cann_graph_update_required) { // End CANN graph capture
-        ACL_CHECK(aclmdlRICaptureEnd(cann_ctx->stream(), &cann_ctx->cann_graph->graph));
-    }
-
-    if (use_cann_graph) {
-        // Execute graph
-        ACL_CHECK(aclmdlRIExecuteAsync(cann_ctx->cann_graph->graph, cann_ctx->stream()));
-    }
-#endif // USE_ACL_GRAPH
-}
-
-
 /**
  * @brief Computes a computational graph using a CANN backend.
  *
@@ -2245,37 +2075,24 @@ static enum ggml_status ggml_backend_cann_graph_compute(
     ggml_backend_t backend, ggml_cgraph* cgraph) {
     ggml_backend_cann_context* cann_ctx =
         (ggml_backend_cann_context*)backend->context;
+
     ggml_cann_set_device(cann_ctx->device);
-    release_nz_workspace();
-#ifdef USE_ACL_GRAPH
-    bool use_cann_graph = true;
-    bool cann_graph_update_required = false;
 
-    // check environment LLAMA_SET_ROWS
-    if (!cann_ctx->support_set_rows) {
-        use_cann_graph = false;
-    }
+    for (int i = 0; i < cgraph->n_nodes; i++) {
+        ggml_tensor* node = cgraph->nodes[i];
 
-    if (use_cann_graph) {
-        if (cann_ctx->cann_graph == nullptr) {
-            cann_ctx->cann_graph.reset(new ggml_cann_graph());
-            cann_graph_update_required = true;
+        if (ggml_is_empty(node) || node->op == GGML_OP_NONE) {
+            continue;
         }
 
-        cann_graph_update_required = is_cann_graph_update_required(cann_ctx, cgraph);
-        set_ggml_graph_node_properties(cann_ctx, cgraph);
-    }
-#else
-    bool use_cann_graph = false;
-    bool cann_graph_update_required = false;
-#endif  // USE_ACL_GRAPH
+        bool ok = ggml_cann_compute_forward(*cann_ctx, node);
 
-    evaluate_and_capture_cann_graph(
-        cann_ctx,
-        cgraph,
-        use_cann_graph,
-        cann_graph_update_required
-    );
+        if (!ok) {
+            GGML_LOG_ERROR("%s: error: op not supported %s (%s)\n", __func__,
+                    node->name, ggml_op_name(node->op));
+        }
+        GGML_ASSERT(ok);
+    }
 
     return GGML_STATUS_SUCCESS;
 }
@@ -2374,15 +2191,13 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev,
                     return false;
             }
         } break;
-        case GGML_OP_SET_ROWS: {
-            switch (op->type) {
-                case GGML_TYPE_F32:
-                case GGML_TYPE_F16:
-                    return true;
-                default:
-                    return false;
-            }
-        } break;
+        case GGML_OP_SET_ROWS:
+            {
+                // TODO: add support
+                // ref: https://github.com/ggml-org/llama.cpp/pull/14274
+#pragma message("TODO: implement F32, F16, BF16, Q4_0, Q4_1, Q5_0, Q5_1, Q8_0, IQ4_NL support (https://github.com/ggml-org/llama.cpp/pull/14661)")
+                return false;
+            } break;
         case GGML_OP_CPY: {
             ggml_tensor *src = op->src[0];
             if ((op->type != GGML_TYPE_F32 && op->type != GGML_TYPE_F16) ||
@@ -2391,6 +2206,12 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev,
                 // only support F32 and F16.
                 return false;
             }
+
+            if (!ggml_are_same_shape(op, src) && !ggml_is_contiguous(op)) {
+                // unsupport dst is not contiguous.
+                return false;
+            }
+
             return true;
         } break;
         case GGML_OP_CONT: {
@@ -2499,10 +2320,6 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev,
             memcpy(&bias, (float*)op->op_params + 1, sizeof(float));
             return bias == 0.0f; // TODO: support bias != 0.0f
         case GGML_OP_SOFT_MAX:
-            // TODO: support attention sinks [TAG_ATTN_SINKS]
-            if (op->src[2]) {
-                return false;
-            }
             // TODO: support broadcast
             // ref: https://github.com/ggml-org/llama.cpp/pull/14435
             return !op->src[1] || (op->src[1]->ne[2] == 1 && op->src[1]->ne[3] == 1);
@@ -2517,10 +2334,6 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev,
             if(op->type != GGML_TYPE_F16 && op->type != GGML_TYPE_F32 && op->type != GGML_TYPE_BF16){
                 return false;
             }
-            // TODO: support attention sinks [TAG_ATTN_SINKS]
-            if (op->src[4]) {
-                return false;
-            }
             if (op->src[1]->ne[0] != op->src[2]->ne[0]) {
                 // different head sizes of K and V are not supported yet
                 return false;

ggml/src/ggml-common.h

@@ -99,9 +99,6 @@ typedef sycl::half2 ggml_half2;
 #define QI4_1 (QK4_1 / (4 * QR4_1))
 #define QR4_1 2
 
-#define QI_MXFP4 (QK_MXFP4 / (4 * QR_MXFP4))
-#define QR_MXFP4 2
-
 #define QI5_0 (QK5_0 / (4 * QR5_0))
 #define QR5_0 2
 
@@ -187,13 +184,6 @@ typedef struct {
 } block_q4_1;
 static_assert(sizeof(block_q4_1) == 2 * sizeof(ggml_half) + QK4_1 / 2, "wrong q4_1 block size/padding");
 
-#define QK_MXFP4 32
-typedef struct {
-    uint8_t e; // E8M0
-    uint8_t qs[QK_MXFP4/2];
-} block_mxfp4;
-static_assert(sizeof(block_mxfp4) == sizeof(uint8_t) + QK_MXFP4/2, "wrong mxfp4 block size/padding");
-
 #define QK5_0 32
 typedef struct {
     ggml_half d;           // delta
@@ -1084,17 +1074,10 @@ GGML_TABLE_BEGIN(uint32_t, iq3s_grid, 512)
     0x0f090307, 0x0f090501, 0x0f090b01, 0x0f0b0505, 0x0f0b0905, 0x0f0d0105, 0x0f0d0703, 0x0f0f0101,
 GGML_TABLE_END()
 
-// TODO: fix name to kvalues_iq4_nl
 GGML_TABLE_BEGIN(int8_t, kvalues_iq4nl, 16)
     -127, -104, -83, -65, -49, -35, -22, -10, 1, 13, 25, 38, 53, 69, 89, 113,
 GGML_TABLE_END()
 
-// e2m1 values (doubled)
-// ref: https://www.opencompute.org/documents/ocp-microscaling-formats-mx-v1-0-spec-final-pdf
-GGML_TABLE_BEGIN(int8_t, kvalues_mxfp4, 16)
-    0, 1, 2, 3, 4, 6, 8, 12, 0, -1, -2, -3, -4, -6, -8, -12,
-GGML_TABLE_END()
-
 #define NGRID_IQ1S 2048
 #define IQ1S_DELTA 0.125f
 #define IQ1M_DELTA 0.125f

ggml/src/ggml-cpu/arch-fallback.h

@@ -13,7 +13,6 @@
 #define ggml_vec_dot_q5_0_q8_0_generic ggml_vec_dot_q5_0_q8_0
 #define ggml_vec_dot_q5_1_q8_1_generic ggml_vec_dot_q5_1_q8_1
 #define ggml_vec_dot_q8_0_q8_0_generic ggml_vec_dot_q8_0_q8_0
-#define ggml_vec_dot_mxfp4_q8_0_generic ggml_vec_dot_mxfp4_q8_0
 #define ggml_vec_dot_tq1_0_q8_K_generic ggml_vec_dot_tq1_0_q8_K
 #define ggml_vec_dot_tq2_0_q8_K_generic ggml_vec_dot_tq2_0_q8_K
 #define ggml_vec_dot_q2_K_q8_K_generic ggml_vec_dot_q2_K_q8_K
@@ -38,21 +37,17 @@
 #define ggml_gemv_q4_0_4x8_q8_0_generic ggml_gemv_q4_0_4x8_q8_0
 #define ggml_gemv_q4_0_8x8_q8_0_generic ggml_gemv_q4_0_8x8_q8_0
 #define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K
-#define ggml_gemv_q2_K_8x8_q8_K_generic ggml_gemv_q2_K_8x8_q8_K
 #define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
 #define ggml_gemm_q4_0_4x4_q8_0_generic ggml_gemm_q4_0_4x4_q8_0
 #define ggml_gemm_q4_0_4x8_q8_0_generic ggml_gemm_q4_0_4x8_q8_0
 #define ggml_gemm_q4_0_8x8_q8_0_generic ggml_gemm_q4_0_8x8_q8_0
 #define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K
-#define ggml_gemm_q2_K_8x8_q8_K_generic ggml_gemm_q2_K_8x8_q8_K
 #define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
 #elif defined(__aarch64__) || defined(__arm__) || defined(_M_ARM) || defined(_M_ARM64)
 // repack.cpp
 #define ggml_quantize_mat_q8_K_4x8_generic ggml_quantize_mat_q8_K_4x8
 #define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K
-#define ggml_gemv_q2_K_8x8_q8_K_generic ggml_gemv_q2_K_8x8_q8_K
 #define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K
-#define ggml_gemm_q2_K_8x8_q8_K_generic ggml_gemm_q2_K_8x8_q8_K
 #elif defined(__x86_64__) || defined(__i386__) || defined(_M_IX86) || defined(_M_X64)
 // repack.cpp
 #define ggml_quantize_mat_q8_0_4x4_generic ggml_quantize_mat_q8_0_4x4
@@ -69,7 +64,6 @@
 #define ggml_vec_dot_tq1_0_q8_K_generic ggml_vec_dot_tq1_0_q8_K
 #define ggml_vec_dot_tq2_0_q8_K_generic ggml_vec_dot_tq2_0_q8_K
 #define ggml_vec_dot_iq1_m_q8_K_generic ggml_vec_dot_iq1_m_q8_K
-#define ggml_vec_dot_mxfp4_q8_0_generic ggml_vec_dot_mxfp4_q8_0
 // repack.cpp
 #define ggml_quantize_mat_q8_0_4x4_generic ggml_quantize_mat_q8_0_4x4
 #define ggml_quantize_mat_q8_0_4x8_generic ggml_quantize_mat_q8_0_4x8
@@ -78,13 +72,11 @@
 #define ggml_gemv_q4_0_4x8_q8_0_generic ggml_gemv_q4_0_4x8_q8_0
 #define ggml_gemv_q4_0_8x8_q8_0_generic ggml_gemv_q4_0_8x8_q8_0
 #define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K
-#define ggml_gemv_q2_K_8x8_q8_K_generic ggml_gemv_q2_K_8x8_q8_K
 #define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
 #define ggml_gemm_q4_0_4x4_q8_0_generic ggml_gemm_q4_0_4x4_q8_0
 #define ggml_gemm_q4_0_4x8_q8_0_generic ggml_gemm_q4_0_4x8_q8_0
 #define ggml_gemm_q4_0_8x8_q8_0_generic ggml_gemm_q4_0_8x8_q8_0
 #define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K
-#define ggml_gemm_q2_K_8x8_q8_K_generic ggml_gemm_q2_K_8x8_q8_K
 #define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
 #elif defined(__loongarch64)
 // quants.c
@@ -92,7 +84,6 @@
 #define ggml_vec_dot_tq1_0_q8_K_generic ggml_vec_dot_tq1_0_q8_K
 #define ggml_vec_dot_tq2_0_q8_K_generic ggml_vec_dot_tq2_0_q8_K
 #define ggml_vec_dot_iq1_m_q8_K_generic ggml_vec_dot_iq1_m_q8_K
-#define ggml_vec_dot_mxfp4_q8_0_generic ggml_vec_dot_mxfp4_q8_0
 // repack.cpp
 #define ggml_quantize_mat_q8_0_4x4_generic ggml_quantize_mat_q8_0_4x4
 #define ggml_quantize_mat_q8_0_4x8_generic ggml_quantize_mat_q8_0_4x8
@@ -101,13 +92,11 @@
 #define ggml_gemv_q4_0_4x8_q8_0_generic ggml_gemv_q4_0_4x8_q8_0
 #define ggml_gemv_q4_0_8x8_q8_0_generic ggml_gemv_q4_0_8x8_q8_0
 #define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K
-#define ggml_gemv_q2_K_8x8_q8_K_generic ggml_gemv_q2_K_8x8_q8_K
 #define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
 #define ggml_gemm_q4_0_4x4_q8_0_generic ggml_gemm_q4_0_4x4_q8_0
 #define ggml_gemm_q4_0_4x8_q8_0_generic ggml_gemm_q4_0_4x8_q8_0
 #define ggml_gemm_q4_0_8x8_q8_0_generic ggml_gemm_q4_0_8x8_q8_0
 #define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K
-#define ggml_gemm_q2_K_8x8_q8_K_generic ggml_gemm_q2_K_8x8_q8_K
 #define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
 #elif defined(__riscv)
 // quants.c
@@ -123,7 +112,6 @@
 #define ggml_vec_dot_iq1_m_q8_K_generic ggml_vec_dot_iq1_m_q8_K
 #define ggml_vec_dot_iq4_nl_q8_0_generic ggml_vec_dot_iq4_nl_q8_0
 #define ggml_vec_dot_iq4_xs_q8_K_generic ggml_vec_dot_iq4_xs_q8_K
-#define ggml_vec_dot_mxfp4_q8_0_generic ggml_vec_dot_mxfp4_q8_0
 // repack.cpp
 #define ggml_quantize_mat_q8_0_4x4_generic ggml_quantize_mat_q8_0_4x4
 #define ggml_quantize_mat_q8_0_4x8_generic ggml_quantize_mat_q8_0_4x8
@@ -131,12 +119,10 @@
 #define ggml_gemv_q4_0_4x4_q8_0_generic ggml_gemv_q4_0_4x4_q8_0
 #define ggml_gemv_q4_0_4x8_q8_0_generic ggml_gemv_q4_0_4x8_q8_0
 #define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K
-#define ggml_gemv_q2_K_8x8_q8_K_generic ggml_gemv_q2_K_8x8_q8_K
 #define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
 #define ggml_gemm_q4_0_4x4_q8_0_generic ggml_gemm_q4_0_4x4_q8_0
 #define ggml_gemm_q4_0_4x8_q8_0_generic ggml_gemm_q4_0_4x8_q8_0
 #define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K
-#define ggml_gemm_q2_K_8x8_q8_K_generic ggml_gemm_q2_K_8x8_q8_K
 #define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
 #elif defined(__s390x__)
 // quants.c
@@ -153,7 +139,6 @@
 #define ggml_vec_dot_iq3_s_q8_K_generic ggml_vec_dot_iq3_s_q8_K
 #define ggml_vec_dot_iq1_s_q8_K_generic ggml_vec_dot_iq1_s_q8_K
 #define ggml_vec_dot_iq1_m_q8_K_generic ggml_vec_dot_iq1_m_q8_K
-#define ggml_vec_dot_mxfp4_q8_0_generic ggml_vec_dot_mxfp4_q8_0
 // repack.cpp
 #define ggml_quantize_mat_q8_0_4x4_generic ggml_quantize_mat_q8_0_4x4
 #define ggml_quantize_mat_q8_0_4x8_generic ggml_quantize_mat_q8_0_4x8
@@ -162,13 +147,11 @@
 #define ggml_gemv_q4_0_4x8_q8_0_generic ggml_gemv_q4_0_4x8_q8_0
 #define ggml_gemv_q4_0_8x8_q8_0_generic ggml_gemv_q4_0_8x8_q8_0
 #define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K
-#define ggml_gemv_q2_K_8x8_q8_K_generic ggml_gemv_q2_K_8x8_q8_K
 #define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
 #define ggml_gemm_q4_0_4x4_q8_0_generic ggml_gemm_q4_0_4x4_q8_0
 #define ggml_gemm_q4_0_4x8_q8_0_generic ggml_gemm_q4_0_4x8_q8_0
 #define ggml_gemm_q4_0_8x8_q8_0_generic ggml_gemm_q4_0_8x8_q8_0
 #define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K
-#define ggml_gemm_q2_K_8x8_q8_K_generic ggml_gemm_q2_K_8x8_q8_K
 #define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
 #elif defined(__wasm__)
 // quants.c
@@ -184,7 +167,6 @@
 #define ggml_vec_dot_iq1_m_q8_K_generic ggml_vec_dot_iq1_m_q8_K
 #define ggml_vec_dot_iq4_nl_q8_0_generic ggml_vec_dot_iq4_nl_q8_0
 #define ggml_vec_dot_iq4_xs_q8_K_generic ggml_vec_dot_iq4_xs_q8_K
-#define ggml_vec_dot_mxfp4_q8_0_generic ggml_vec_dot_mxfp4_q8_0
 // repack.cpp
 #define ggml_quantize_mat_q8_0_4x4_generic ggml_quantize_mat_q8_0_4x4
 #define ggml_quantize_mat_q8_0_4x8_generic ggml_quantize_mat_q8_0_4x8
@@ -193,12 +175,10 @@
 #define ggml_gemv_q4_0_4x8_q8_0_generic ggml_gemv_q4_0_4x8_q8_0
 #define ggml_gemv_q4_0_8x8_q8_0_generic ggml_gemv_q4_0_8x8_q8_0
 #define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K
-#define ggml_gemv_q2_K_8x8_q8_K_generic ggml_gemv_q2_K_8x8_q8_K
 #define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
 #define ggml_gemm_q4_0_4x4_q8_0_generic ggml_gemm_q4_0_4x4_q8_0
 #define ggml_gemm_q4_0_4x8_q8_0_generic ggml_gemm_q4_0_4x8_q8_0
 #define ggml_gemm_q4_0_8x8_q8_0_generic ggml_gemm_q4_0_8x8_q8_0
 #define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K
-#define ggml_gemm_q2_K_8x8_q8_K_generic ggml_gemm_q2_K_8x8_q8_K
 #define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
 #endif

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

@@ -589,67 +589,6 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * GGML_RESTRICT s, size_t bs, const voi
     *s = sumf;
 }
 
-void ggml_vec_dot_mxfp4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
-    assert(nrc == 1);
-    UNUSED(nrc);
-    UNUSED(bx);
-    UNUSED(by);
-    UNUSED(bs);
-    assert(n % QK_MXFP4 == 0);
-    static_assert(QK_MXFP4 == QK8_0, "QK_MXFP4 and QK8_0 must be the same");
-
-    const block_mxfp4 * GGML_RESTRICT x = vx;
-    const block_q8_0 * GGML_RESTRICT y = vy;
-
-    const int nb = n / QK_MXFP4;
-
-    int ib = 0;
-    float sumf = 0;
-
-#if defined __ARM_NEON
-    const int8x16_t values = vld1q_s8(kvalues_mxfp4);
-    const uint8x16_t m4b = vdupq_n_u8(0x0f);
-    uint8x16x2_t q4bits;
-    int8x16x4_t q4b;
-    int8x16x4_t q8b;
-    int32x4_t prod_1;
-    int32x4_t prod_2;
-
-    for (; ib + 1 < nb; ib += 2) {
-        q4bits.val[0] = vld1q_u8(x[ib + 0].qs);
-        q4bits.val[1] = vld1q_u8(x[ib + 1].qs);
-        q8b.val[0]    = vld1q_s8(y[ib + 0].qs);
-        q8b.val[1]    = vld1q_s8(y[ib + 0].qs + 16);
-        q8b.val[2]    = vld1q_s8(y[ib + 1].qs);
-        q8b.val[3]    = vld1q_s8(y[ib + 1].qs + 16);
-
-        q4b.val[0] = ggml_vqtbl1q_s8(values, vandq_u8  (q4bits.val[0], m4b));
-        q4b.val[1] = ggml_vqtbl1q_s8(values, vshrq_n_u8(q4bits.val[0], 4));
-        q4b.val[2] = ggml_vqtbl1q_s8(values, vandq_u8  (q4bits.val[1], m4b));
-        q4b.val[3] = ggml_vqtbl1q_s8(values, vshrq_n_u8(q4bits.val[1], 4));
-
-        prod_1 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q4b.val[0], q8b.val[0]), q4b.val[1], q8b.val[1]);
-        prod_2 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q4b.val[2], q8b.val[2]), q4b.val[3], q8b.val[3]);
-
-        sumf +=
-            GGML_E8M0_TO_FP32_HALF(x[ib + 0].e) * GGML_CPU_FP16_TO_FP32(y[ib + 0].d) * vaddvq_s32(prod_1) +
-            GGML_E8M0_TO_FP32_HALF(x[ib + 1].e) * GGML_CPU_FP16_TO_FP32(y[ib + 1].d) * vaddvq_s32(prod_2);
-    }
-
-#endif
-    for (; ib < nb; ++ib) {
-        const float d = GGML_CPU_FP16_TO_FP32(y[ib].d)*GGML_E8M0_TO_FP32_HALF(x[ib].e);
-        int sumi1 = 0;
-        int sumi2 = 0;
-        for (int j = 0; j < QK_MXFP4/2; ++j) {
-            sumi1 += y[ib].qs[j +          0] * kvalues_mxfp4[x[ib].qs[j] & 0xf];
-            sumi2 += y[ib].qs[j + QK_MXFP4/2] * kvalues_mxfp4[x[ib].qs[j] >>  4];
-        }
-        sumf += d * (sumi1 + sumi2);
-    }
-    *s = sumf;
-}
-
 void ggml_vec_dot_q5_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
     const int qk = QK8_0;
     const int nb = n / qk;
@@ -1297,10 +1236,44 @@ void ggml_vec_dot_tq1_0_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const vo
     *s = sumf;
 
 #else
-    UNUSED(x);
-    UNUSED(y);
-    UNUSED(nb);
-    ggml_vec_dot_tq1_0_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
+    const uint8_t pow3[6] = {1, 3, 9, 27, 81, 243};
+
+    float sumf = 0.0f;
+
+    for (int i = 0; i < nb; ++i) {
+        int sum = 0;
+
+        for (size_t j = 0; j < sizeof(x->qs) - sizeof(x->qs) % 32; j += 32) {
+            for (size_t l = 0; l < 5; ++l) {
+                for (size_t m = 0; m < 32; ++m) {
+                    uint8_t q = x[i].qs[j + m] * pow3[l];
+                    uint16_t xi = ((uint16_t) q * 3) >> 8;
+                    sum += (xi - 1) * y[i].qs[j*5 + l*32 + m];
+                }
+            }
+        }
+        for (size_t j = sizeof(x->qs) - sizeof(x->qs) % 32; j < sizeof(x->qs); j += 16) {
+            for (size_t l = 0; l < 5; ++l) {
+                for (size_t m = 0; m < 16; ++m) {
+                    uint8_t q = x[i].qs[j + m] * pow3[l];
+                    uint16_t xi = ((uint16_t) q * 3) >> 8;
+                    sum += (xi - 1) * y[i].qs[j*5 + l*16 + m];
+                }
+            }
+        }
+
+        for (size_t l = 0; l < 4; ++l) {
+            for (size_t j = 0; j < sizeof(x->qh); ++j) {
+                uint8_t q = x[i].qh[j] * pow3[l];
+                uint16_t xi = ((uint16_t) q * 3) >> 8;
+                sum += (xi - 1) * y[i].qs[sizeof(x->qs)*5 + l*sizeof(x->qh) + j];
+            }
+        }
+
+        sumf += (float) sum * (GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d);
+    }
+
+    *s = sumf;
 #endif
 }
 
@@ -1408,10 +1381,25 @@ void ggml_vec_dot_tq2_0_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const vo
     *s = sumf;
 
 #else
-    UNUSED(x);
-    UNUSED(y);
-    UNUSED(nb);
-    ggml_vec_dot_tq2_0_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
+    float sumf = 0.0f;
+
+    for (int i = 0; i < nb; ++i) {
+        int32_t sumi = 0;
+
+        for (size_t j = 0; j < sizeof(x->qs); j += 32) {
+            for (size_t l = 0; l < 4; ++l) {
+                for (size_t k = 0; k < 32; ++k) {
+                    sumi += y[i].qs[j*4 + l*32 + k] * (((x[i].qs[j + k] >> (l*2)) & 3) - 1);
+                }
+            }
+        }
+
+        const float d = y[i].d * GGML_CPU_FP16_TO_FP32(x[i].d);
+
+        sumf += (float) sumi * d;
+    }
+
+    *s = sumf;
 #endif
 }
 
@@ -1741,10 +1729,45 @@ void ggml_vec_dot_q2_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
     *s = sum;
 
 #else
-    UNUSED(x);
-    UNUSED(y);
-    UNUSED(nb);
-    ggml_vec_dot_q2_K_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
+
+    float sumf = 0;
+
+    for (int i = 0; i < nb; ++i) {
+
+        const uint8_t * q2 = x[i].qs;
+        const  int8_t * q8 = y[i].qs;
+        const uint8_t * sc = x[i].scales;
+
+        int summs = 0;
+        for (int j = 0; j < 16; ++j) {
+            summs += y[i].bsums[j] * (sc[j] >> 4);
+        }
+
+        const float dall = y[i].d * GGML_CPU_FP16_TO_FP32(x[i].d);
+        const float dmin = y[i].d * GGML_CPU_FP16_TO_FP32(x[i].dmin);
+
+        int isum = 0;
+        int is = 0;
+        int d;
+        for (int k = 0; k < QK_K/128; ++k) {
+            int shift = 0;
+            for (int j = 0; j < 4; ++j) {
+                d = sc[is++] & 0xF;
+                int isuml = 0;
+                for (int l =  0; l < 16; ++l) isuml += q8[l] * ((q2[l] >> shift) & 3);
+                isum += d * isuml;
+                d = sc[is++] & 0xF;
+                isuml = 0;
+                for (int l = 16; l < 32; ++l) isuml += q8[l] * ((q2[l] >> shift) & 3);
+                isum += d * isuml;
+                shift += 2;
+                q8 += 32;
+            }
+            q2 += 32;
+        }
+        sumf += dall * isum - dmin * summs;
+    }
+    *s = sumf;
 #endif
 }
 
@@ -2034,12 +2057,68 @@ void ggml_vec_dot_q3_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
     *s = sum;
 
 #else
-    UNUSED(kmask1);
-    UNUSED(kmask2);
-    UNUSED(x);
-    UNUSED(y);
-    UNUSED(nb);
-    ggml_vec_dot_q3_K_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
+    // scalar version
+    // This function is written like this so the compiler can manage to vectorize most of it
+    // Using -Ofast, GCC and clang manage to produce code that is within a factor of 2 or so from the
+    // manually vectorized version above. Every other version I tried would run at least 4 times slower.
+    // The ideal situation would be if we could just write the code once, and the compiler would
+    // automatically produce the best possible set of machine instructions, instead of us having to manually
+    // write vectorized versions for AVX, ARM_NEON, etc.
+
+    int8_t  aux8[QK_K];
+    int16_t aux16[8];
+    float   sums [8];
+    int32_t aux32[8];
+    memset(sums, 0, 8*sizeof(float));
+
+    uint32_t auxs[4];
+    const int8_t * scales = (const int8_t*)auxs;
+
+    float sumf = 0;
+    for (int i = 0; i < nb; ++i) {
+        const uint8_t * GGML_RESTRICT q3 = x[i].qs;
+        const uint8_t * GGML_RESTRICT hm = x[i].hmask;
+        const  int8_t * GGML_RESTRICT q8 = y[i].qs;
+        memset(aux32, 0, 8*sizeof(int32_t));
+        int8_t * GGML_RESTRICT a = aux8;
+        uint8_t m = 1;
+        for (int j = 0; j < QK_K; j += 128) {
+            for (int l = 0; l < 32; ++l) a[l] = q3[l] & 3;
+            for (int l = 0; l < 32; ++l) a[l] -= (hm[l] & m ? 0 : 4);
+            a += 32; m <<= 1;
+            for (int l = 0; l < 32; ++l) a[l] = (q3[l] >> 2) & 3;
+            for (int l = 0; l < 32; ++l) a[l] -= (hm[l] & m ? 0 : 4);
+            a += 32; m <<= 1;
+            for (int l = 0; l < 32; ++l) a[l] = (q3[l] >> 4) & 3;
+            for (int l = 0; l < 32; ++l) a[l] -= (hm[l] & m ? 0 : 4);
+            a += 32; m <<= 1;
+            for (int l = 0; l < 32; ++l) a[l] = (q3[l] >> 6) & 3;
+            for (int l = 0; l < 32; ++l) a[l] -= (hm[l] & m ? 0 : 4);
+            a += 32; m <<= 1;
+            q3 += 32;
+        }
+        a = aux8;
+
+        memcpy(auxs, x[i].scales, 12);
+        uint32_t tmp = auxs[2];
+        auxs[2] = ((auxs[0] >> 4) & kmask2) | (((tmp >> 4) & kmask1) << 4);
+        auxs[3] = ((auxs[1] >> 4) & kmask2) | (((tmp >> 6) & kmask1) << 4);
+        auxs[0] = (auxs[0] & kmask2) | (((tmp >> 0) & kmask1) << 4);
+        auxs[1] = (auxs[1] & kmask2) | (((tmp >> 2) & kmask1) << 4);
+        for (int j = 0; j < QK_K/16; ++j) {
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += (scales[j] - 32) * aux16[l];
+            q8 += 8; a += 8;
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += (scales[j] - 32) * aux16[l];
+            q8 += 8; a += 8;
+        }
+        const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
+        for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l];
+    }
+    for (int l = 0; l < 8; ++l) sumf += sums[l];
+    *s = sumf;
+
 #endif
 
 }
@@ -2352,14 +2431,61 @@ void ggml_vec_dot_q4_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
     *s = sumf;
 
 #else
-    UNUSED(x);
-    UNUSED(y);
-    UNUSED(nb);
-    UNUSED(kmask1);
-    UNUSED(kmask2);
-    UNUSED(kmask3);
-    UNUSED(utmp);
-    ggml_vec_dot_q4_K_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
+
+    const uint8_t * scales = (const uint8_t*)&utmp[0];
+    const uint8_t * mins   = (const uint8_t*)&utmp[2];
+
+    int8_t  aux8[QK_K];
+    int16_t aux16[8];
+    float   sums [8];
+    int32_t aux32[8];
+    memset(sums, 0, 8*sizeof(float));
+
+    float sumf = 0;
+    for (int i = 0; i < nb; ++i) {
+        const uint8_t * GGML_RESTRICT q4 = x[i].qs;
+        const  int8_t * GGML_RESTRICT q8 = y[i].qs;
+        memset(aux32, 0, 8*sizeof(int32_t));
+        int8_t * GGML_RESTRICT a = aux8;
+        for (int j = 0; j < QK_K/64; ++j) {
+            for (int l = 0; l < 32; ++l) a[l] = (int8_t)(q4[l] & 0xF);
+            a += 32;
+            for (int l = 0; l < 32; ++l) a[l] = (int8_t)(q4[l]  >> 4);
+            a += 32; q4 += 32;
+        }
+        memcpy(utmp, x[i].scales, 12);
+        utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4);
+        const uint32_t uaux = utmp[1] & kmask1;
+        utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4);
+        utmp[2] = uaux;
+        utmp[0] &= kmask1;
+
+        int sumi = 0;
+        for (int j = 0; j < QK_K/16; ++j) sumi += y[i].bsums[j] * mins[j/2];
+        a = aux8;
+        int is = 0;
+        for (int j = 0; j < QK_K/32; ++j) {
+            int32_t scale = scales[is++];
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
+            q8 += 8; a += 8;
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
+            q8 += 8; a += 8;
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
+            q8 += 8; a += 8;
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
+            q8 += 8; a += 8;
+        }
+        const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
+        for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l];
+        const float dmin = GGML_CPU_FP16_TO_FP32(x[i].dmin) * y[i].d;
+        sumf -= dmin * sumi;
+    }
+    for (int l = 0; l < 8; ++l) sumf += sums[l];
+    *s = sumf;
 #endif
 }
 
@@ -2452,14 +2578,66 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
     *s = sumf;
 
 #else
-    UNUSED(x);
-    UNUSED(y);
-    UNUSED(nb);
-    UNUSED(kmask1);
-    UNUSED(kmask2);
-    UNUSED(kmask3);
-    UNUSED(utmp);
-    ggml_vec_dot_q5_K_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
+
+    const uint8_t * scales = (const uint8_t*)&utmp[0];
+    const uint8_t * mins   = (const uint8_t*)&utmp[2];
+
+    int8_t  aux8[QK_K];
+    int16_t aux16[8];
+    float   sums [8];
+    int32_t aux32[8];
+    memset(sums, 0, 8*sizeof(float));
+
+    float sumf = 0;
+    for (int i = 0; i < nb; ++i) {
+        const uint8_t * GGML_RESTRICT q4 = x[i].qs;
+        const uint8_t * GGML_RESTRICT hm = x[i].qh;
+        const  int8_t * GGML_RESTRICT q8 = y[i].qs;
+        memset(aux32, 0, 8*sizeof(int32_t));
+        int8_t * GGML_RESTRICT a = aux8;
+        uint8_t m = 1;
+        for (int j = 0; j < QK_K/64; ++j) {
+            for (int l = 0; l < 32; ++l) a[l] = (int8_t)(q4[l] & 0xF);
+            for (int l = 0; l < 32; ++l) a[l] += (hm[l] & m ? 16 : 0);
+            a += 32; m <<= 1;
+            for (int l = 0; l < 32; ++l) a[l] = (int8_t)(q4[l]  >> 4);
+            for (int l = 0; l < 32; ++l) a[l] += (hm[l] & m ? 16 : 0);
+            a += 32; m <<= 1;
+            q4 += 32;
+        }
+        memcpy(utmp, x[i].scales, 12);
+        utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4);
+        const uint32_t uaux = utmp[1] & kmask1;
+        utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4);
+        utmp[2] = uaux;
+        utmp[0] &= kmask1;
+
+        int sumi = 0;
+        for (int j = 0; j < QK_K/16; ++j) sumi += y[i].bsums[j] * mins[j/2];
+        a = aux8;
+        int is = 0;
+        for (int j = 0; j < QK_K/32; ++j) {
+            int32_t scale = scales[is++];
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
+            q8 += 8; a += 8;
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
+            q8 += 8; a += 8;
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
+            q8 += 8; a += 8;
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
+            q8 += 8; a += 8;
+        }
+        const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
+        for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l];
+        const float dmin = GGML_CPU_FP16_TO_FP32(x[i].dmin) * y[i].d;
+        sumf -= dmin * sumi;
+    }
+    for (int l = 0; l < 8; ++l) sumf += sums[l];
+    *s = sumf;
 #endif
 }
 
@@ -2915,10 +3093,47 @@ void ggml_vec_dot_q6_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
     }
     *s = sum;
 #else
-    UNUSED(x);
-    UNUSED(y);
-    UNUSED(nb);
-    ggml_vec_dot_q6_K_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
+
+    int8_t  aux8[QK_K];
+    int16_t aux16[8];
+    float   sums [8];
+    int32_t aux32[8];
+    memset(sums, 0, 8*sizeof(float));
+
+    float sumf = 0;
+    for (int i = 0; i < nb; ++i) {
+        const uint8_t * GGML_RESTRICT q4 = x[i].ql;
+        const uint8_t * GGML_RESTRICT qh = x[i].qh;
+        const  int8_t * GGML_RESTRICT q8 = y[i].qs;
+        memset(aux32, 0, 8*sizeof(int32_t));
+        int8_t * GGML_RESTRICT a = aux8;
+        for (int j = 0; j < QK_K; j += 128) {
+            for (int l = 0; l < 32; ++l) {
+                a[l +  0] = (int8_t)((q4[l +  0] & 0xF) | (((qh[l] >> 0) & 3) << 4)) - 32;
+                a[l + 32] = (int8_t)((q4[l + 32] & 0xF) | (((qh[l] >> 2) & 3) << 4)) - 32;
+                a[l + 64] = (int8_t)((q4[l +  0] >>  4) | (((qh[l] >> 4) & 3) << 4)) - 32;
+                a[l + 96] = (int8_t)((q4[l + 32] >>  4) | (((qh[l] >> 6) & 3) << 4)) - 32;
+            }
+            a  += 128;
+            q4 += 64;
+            qh += 32;
+        }
+        a = aux8;
+        int is = 0;
+        for (int j = 0; j < QK_K/16; ++j) {
+            int scale = x[i].scales[is++];
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
+            q8 += 8; a += 8;
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
+            q8 += 8; a += 8;
+        }
+        const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
+        for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l];
+    }
+    for (int l = 0; l < 8; ++l) sumf += sums[l];
+    *s = sumf;
 #endif
 }
 
@@ -3014,10 +3229,34 @@ void ggml_vec_dot_iq2_xxs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const
     *s = 0.25f * sumf;
 
 #else
-    UNUSED(x);
-    UNUSED(y);
-    UNUSED(nb);
-    ggml_vec_dot_iq2_xxs_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
+
+    uint32_t aux32[2];
+    const uint8_t * aux8 = (const uint8_t *)aux32;
+
+    float sumf = 0.f;
+    for (int i = 0; i < nb; ++i) {
+        const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
+        const uint16_t * GGML_RESTRICT q2 = x[i].qs;
+        const int8_t   * GGML_RESTRICT q8 = y[i].qs;
+        int32_t bsum = 0;
+        for (int ib32 = 0; ib32 < QK_K/32; ++ib32) {
+            memcpy(aux32, q2, 2*sizeof(uint32_t));
+            q2 += 4;
+            const uint32_t ls = 2*(aux32[1] >> 28) + 1;
+            int32_t sumi = 0;
+            for (int l = 0; l < 4; ++l) {
+                const uint8_t * grid = (const uint8_t *)(iq2xxs_grid + aux8[l]);
+                const uint8_t  signs = ksigns_iq2xs[(aux32[1] >> 7*l) & 127];
+                for (int j = 0; j < 8; ++j) {
+                    sumi += grid[j] * q8[j] * (signs & kmask_iq2xs[j] ? -1 : 1);
+                }
+                q8 += 8;
+            }
+            bsum += sumi * ls;
+        }
+        sumf += d * bsum;
+    }
+    *s = 0.125f * sumf;
 #endif
 }
 
@@ -3088,10 +3327,42 @@ void ggml_vec_dot_iq2_xs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const v
     *s = 0.125f * sumf;
 
 #else
-    UNUSED(x);
-    UNUSED(y);
-    UNUSED(nb);
-    ggml_vec_dot_iq2_xs_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
+
+    float sumf = 0.f;
+    for (int i = 0; i < nb; ++i) {
+        const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
+        const uint16_t * GGML_RESTRICT q2 = x[i].qs;
+        const uint8_t  * GGML_RESTRICT sc = x[i].scales;
+        const int8_t   * GGML_RESTRICT q8 = y[i].qs;
+        int32_t bsum = 0;
+        for (int ib32 = 0; ib32 < QK_K/32; ++ib32) {
+            const uint16_t ls1 = 2*(sc[ib32] & 0xf) + 1;
+            const uint16_t ls2 = 2*(sc[ib32] >>  4) + 1;
+            int32_t sumi = 0;
+            for (int l = 0; l < 2; ++l) {
+                const uint8_t * grid = (const uint8_t *)(iq2xs_grid + (q2[l] & 511));
+                const uint8_t  signs = ksigns_iq2xs[q2[l] >> 9];
+                for (int j = 0; j < 8; ++j) {
+                    sumi += grid[j] * q8[j] * (signs & kmask_iq2xs[j] ? -1 : 1);
+                }
+                q8 += 8;
+            }
+            bsum += sumi * ls1;
+            sumi = 0;
+            for (int l = 2; l < 4; ++l) {
+                const uint8_t * grid = (const uint8_t *)(iq2xs_grid + (q2[l] & 511));
+                const uint8_t  signs = ksigns_iq2xs[q2[l] >> 9];
+                for (int j = 0; j < 8; ++j) {
+                    sumi += grid[j] * q8[j] * (signs & kmask_iq2xs[j] ? -1 : 1);
+                }
+                q8 += 8;
+            }
+            bsum += sumi * ls2;
+            q2 += 4;
+        }
+        sumf += d * bsum;
+    }
+    *s = 0.125f * sumf;
 #endif
 }
 
@@ -3184,10 +3455,45 @@ void ggml_vec_dot_iq2_s_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const vo
     *s = 0.125f * sumf;
 
 #else
-    UNUSED(x);
-    UNUSED(y);
-    UNUSED(nb);
-    ggml_vec_dot_iq2_s_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
+
+    float sumf = 0;
+    for (int i = 0; i < nb; i++) {
+
+        const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
+        const int8_t  * q8 = y[i].qs;
+        const uint8_t * qs = x[i].qs;
+        const uint8_t * qh = x[i].qh;
+        const uint8_t * signs = qs + QK_K/8;
+
+        int bsum = 0;
+        for (int ib32 = 0; ib32 < QK_K/32; ++ib32) {
+            int ls1 = 1 + 2*(x[i].scales[ib32] & 0xf);
+            int ls2 = 1 + 2*(x[i].scales[ib32] >>  4);
+            int sumi1 = 0, sumi2 = 0;
+            for (int l = 0; l < 2; ++l) {
+                const uint8_t * grid = (const uint8_t *)(iq2s_grid + (qs[l] | (qh[ib32] << (8-2*l) & 0x300)));
+                for (int j = 0; j < 8; ++j) {
+                    sumi1 += q8[j] * grid[j] * (signs[l] & kmask_iq2xs[j] ? -1 : 1);
+                }
+                q8 += 8;
+            }
+            for (int l = 2; l < 4; ++l) {
+                const uint8_t * grid = (const uint8_t *)(iq2s_grid + (qs[l] | (qh[ib32] << (8-2*l) & 0x300)));
+                for (int j = 0; j < 8; ++j) {
+                    sumi2 += q8[j] * grid[j] * (signs[l] & kmask_iq2xs[j] ? -1 : 1);
+                }
+                q8 += 8;
+            }
+            bsum += ls1 * sumi1 + ls2 * sumi2;
+            qs += 4;
+            signs += 4;
+        }
+
+        sumf += d * bsum;
+    }
+
+    *s = 0.125f * sumf;
+
 #endif
 
 }
@@ -3247,10 +3553,36 @@ void ggml_vec_dot_iq3_xxs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const
     *s = 0.5f * sumf;
 
 #else
-    UNUSED(x);
-    UNUSED(y);
-    UNUSED(nb);
-    ggml_vec_dot_iq3_xxs_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
+
+    uint32_t aux32;
+
+    float sumf = 0.f;
+    for (int i = 0; i < nb; ++i) {
+        const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
+        const uint8_t * GGML_RESTRICT q3 = x[i].qs;
+        const uint8_t * GGML_RESTRICT gas = x[i].qs + QK_K/4;
+        const int8_t  * GGML_RESTRICT q8 = y[i].qs;
+        int32_t bsum = 0;
+        for (int ib32 = 0; ib32 < QK_K/32; ++ib32) {
+            memcpy(&aux32, gas, sizeof(uint32_t)); gas += sizeof(uint32_t);
+            const uint32_t ls = 2*(aux32 >> 28) + 1;
+            int32_t sumi = 0;
+            for (int l = 0; l < 4; ++l) {
+                const uint8_t * grid1 = (const uint8_t *)(iq3xxs_grid + q3[2*l+0]);
+                const uint8_t * grid2 = (const uint8_t *)(iq3xxs_grid + q3[2*l+1]);
+                const uint8_t  signs = ksigns_iq2xs[(aux32 >> 7*l) & 127];
+                for (int j = 0; j < 4; ++j) {
+                    sumi += grid1[j] * q8[j+0] * (signs & kmask_iq2xs[j+0] ? -1 : 1);
+                    sumi += grid2[j] * q8[j+4] * (signs & kmask_iq2xs[j+4] ? -1 : 1);
+                }
+                q8 += 8;
+            }
+            q3 += 8;
+            bsum += sumi * ls;
+        }
+        sumf += d * bsum;
+    }
+    *s = 0.25f * sumf;
 #endif
 }
 
@@ -3357,10 +3689,48 @@ void ggml_vec_dot_iq3_s_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const vo
     *s = sumf;
 
 #else
-    UNUSED(x);
-    UNUSED(y);
-    UNUSED(nb);
-    ggml_vec_dot_iq3_s_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
+
+    float sumf = 0.f;
+    for (int i = 0; i < nb; ++i) {
+        const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
+        const uint8_t * GGML_RESTRICT qs = x[i].qs;
+        const uint8_t * GGML_RESTRICT qh = x[i].qh;
+        const uint8_t * GGML_RESTRICT signs = x[i].signs;
+        const int8_t  * GGML_RESTRICT q8 = y[i].qs;
+        int32_t bsum = 0;
+        for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) {
+            const uint32_t ls1 = 2*(x[i].scales[ib32/2] & 0xf) + 1;
+            const uint32_t ls2 = 2*(x[i].scales[ib32/2] >>  4) + 1;
+            int32_t sumi = 0;
+            for (int l = 0; l < 4; ++l) {
+                const uint8_t * grid1 = (const uint8_t *)(iq3s_grid + (qs[2*l+0] | ((qh[ib32+0] << (8-2*l)) & 256)));
+                const uint8_t * grid2 = (const uint8_t *)(iq3s_grid + (qs[2*l+1] | ((qh[ib32+0] << (7-2*l)) & 256)));
+                for (int j = 0; j < 4; ++j) {
+                    sumi += grid1[j] * q8[j+0] * (signs[l] & kmask_iq2xs[j+0] ? -1 : 1);
+                    sumi += grid2[j] * q8[j+4] * (signs[l] & kmask_iq2xs[j+4] ? -1 : 1);
+                }
+                q8 += 8;
+            }
+            qs += 8;
+            signs += 4;
+            bsum += sumi * ls1;
+            sumi = 0;
+            for (int l = 0; l < 4; ++l) {
+                const uint8_t * grid1 = (const uint8_t *)(iq3s_grid + (qs[2*l+0] | ((qh[ib32+1] << (8-2*l)) & 256)));
+                const uint8_t * grid2 = (const uint8_t *)(iq3s_grid + (qs[2*l+1] | ((qh[ib32+1] << (7-2*l)) & 256)));
+                for (int j = 0; j < 4; ++j) {
+                    sumi += grid1[j] * q8[j+0] * (signs[l] & kmask_iq2xs[j+0] ? -1 : 1);
+                    sumi += grid2[j] * q8[j+4] * (signs[l] & kmask_iq2xs[j+4] ? -1 : 1);
+                }
+                q8 += 8;
+            }
+            qs += 8;
+            signs += 4;
+            bsum += sumi * ls2;
+        }
+        sumf += d * bsum;
+    }
+    *s = sumf;
 #endif
 }
 
@@ -3423,10 +3793,36 @@ void ggml_vec_dot_iq1_s_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const vo
     *s = sumf;
 
 #else
-    UNUSED(x);
-    UNUSED(y);
-    UNUSED(nb);
-    ggml_vec_dot_iq1_s_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
+
+    float sumf = 0;
+    for (int i = 0; i < nb; i++) {
+
+        const int8_t   * q8 = y[i].qs;
+        const uint8_t  * qs = x[i].qs;
+        const uint16_t * qh = x[i].qh;
+
+        int sumi = 0, sumi1 = 0;
+        for (int ib = 0; ib < QK_K/32; ++ib) {
+            const int ls = 2*((qh[ib] >> 12) & 7) + 1;
+            const int delta = qh[ib] & 0x8000 ? -1 : 1;
+            int lsum = 0;
+            for (int l = 0; l < 4; ++l) {
+                const int8_t * grid = (const int8_t *)(iq1s_grid + (qs[l] | (((qh[ib] >> 3*l) & 7) << 8)));
+                for (int j = 0; j < 8; ++j) {
+                    lsum += q8[j] * grid[j];
+                }
+                q8 += 8;
+            }
+            sumi  += ls * lsum;
+            sumi1 += ls * delta * (y[i].bsums[2*ib+0] + y[i].bsums[2*ib+1]);
+            qs += 4;
+        }
+
+        sumf += GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d * (sumi + IQ1S_DELTA * sumi1);
+    }
+
+    *s = sumf;
+
 #endif
 }
 
@@ -3516,11 +3912,52 @@ void ggml_vec_dot_iq1_m_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const vo
     *s = sumf;
 
 #else
-    UNUSED(x);
-    UNUSED(y);
-    UNUSED(nb);
-    UNUSED(scale);
-    ggml_vec_dot_iq1_m_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
+
+    int sum1[2], sum2[2], delta[4];
+
+    float sumf = 0;
+    for (int i = 0; i < nb; i++) {
+
+        const int8_t   * q8 = y[i].qs;
+        const uint8_t  * qs = x[i].qs;
+        const uint8_t  * qh = x[i].qh;
+        const uint16_t * sc = (const uint16_t *)x[i].scales;
+
+        scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000);
+
+        int sumi1 = 0, sumi2 = 0;
+        for (int ib = 0; ib < QK_K/32; ++ib) {
+            delta[0] = qh[0] & 0x08 ? -1 : 1;
+            delta[1] = qh[0] & 0x80 ? -1 : 1;
+            delta[2] = qh[1] & 0x08 ? -1 : 1;
+            delta[3] = qh[1] & 0x80 ? -1 : 1;
+            sum1[0] = sum1[1] = sum2[0] = sum2[1] = 0;
+            for (int l = 0; l < 4; ++l) {
+                const int8_t * grid = (const int8_t *)(iq1s_grid + (qs[l] | (((uint16_t)qh[l/2] << (8 - 4*(l%2))) & 0x700)));
+                int lsum1 = 0, lsum2 = 0;
+                for (int j = 0; j < 8; ++j) {
+                    lsum1 += q8[j] * grid[j];
+                    lsum2 += q8[j];
+                }
+                q8 += 8;
+                sum1[l/2] += lsum1;
+                sum2[l/2] += lsum2*delta[l];
+            }
+
+            const int ls1 = 2*((sc[ib/2] >> (6*(ib%2)+0)) & 0x7) + 1;
+            const int ls2 = 2*((sc[ib/2] >> (6*(ib%2)+3)) & 0x7) + 1;
+
+            sumi1 += sum1[0] * ls1 + sum1[1] * ls2;
+            sumi2 += sum2[0] * ls1 + sum2[1] * ls2;
+            qs += 4;
+            qh += 2;
+        }
+
+        sumf += GGML_CPU_FP16_TO_FP32(scale.f16) * y[i].d * (sumi1 + IQ1M_DELTA * sumi2);
+    }
+
+    *s = sumf;
+
 #endif
 }
 
@@ -3641,10 +4078,37 @@ void ggml_vec_dot_iq4_xs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const v
     *s = sumf;
 
 #else
-    UNUSED(x);
-    UNUSED(y);
-    UNUSED(nb);
-    ggml_vec_dot_iq4_xs_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
+    float sumf = 0;
+    for (int ibl = 0; ibl < nb; ++ibl) {
+        const float d4d8 = GGML_CPU_FP16_TO_FP32(x[ibl].d) * y[ibl].d;
+        uint16_t h = x[ibl].scales_h;
+        const uint8_t * qs = x[ibl].qs;
+        const int8_t  * q8 = y[ibl].qs;
+        for (int ib = 0; ib < QK_K/32; ib += 2) {
+            const uint8_t ls1 = (x[ibl].scales_l[ib/2] & 0xf) | ((h << 4) & 0x30);
+            const uint8_t ls2 = (x[ibl].scales_l[ib/2] >>  4) | ((h << 2) & 0x30);
+            h >>= 4;
+            const float d1 = d4d8*(ls1 - 32);
+            const float d2 = d4d8*(ls2 - 32);
+            int sumi1 = 0, sumi2 = 0;
+            for (int j = 0; j < 16; ++j) {
+                sumi1 += q8[j+ 0] * kvalues_iq4nl[qs[j] & 0xf];
+                sumi2 += q8[j+16] * kvalues_iq4nl[qs[j] >>  4];
+            }
+            sumf += d1 * (sumi1 + sumi2);
+            qs += 16;
+            q8 += 32;
+            sumi1 = sumi2 = 0;
+            for (int j = 0; j < 16; ++j) {
+                sumi1 += q8[j+ 0] * kvalues_iq4nl[qs[j] & 0xf];
+                sumi2 += q8[j+16] * kvalues_iq4nl[qs[j] >>  4];
+            }
+            sumf += d2 * (sumi1 + sumi2);
+            qs += 16;
+            q8 += 32;
+        }
+    }
+    *s = sumf;
 #endif
 }
 

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

@@ -86,9 +86,35 @@ void ggml_quantize_mat_q8_0_4x4(const float * GGML_RESTRICT x, void * GGML_RESTR
         }
     }
 #else
-    UNUSED(nb);
-    UNUSED(y);
-    ggml_quantize_mat_q8_0_4x4_generic(x, vy, k);
+    // scalar
+    const int blck_size_interleave = 4;
+    float srcv[4][QK8_0];
+    float id[4];
+
+    for (int i = 0; i < nb; i++) {
+        for (int row_iter = 0; row_iter < 4; row_iter++) {
+            float amax = 0.0f; // absolute max
+
+            for (int j = 0; j < QK8_0; j++) {
+                srcv[row_iter][j] = x[row_iter * k + i * QK8_0 + j];
+                amax = MAX(amax, fabsf(srcv[row_iter][j]));
+            }
+
+            const float d = amax / ((1 << 7) - 1);
+            id[row_iter] = d ? 1.0f / d : 0.0f;
+
+            y[i].d[row_iter] = GGML_CPU_FP32_TO_FP16(d);
+        }
+
+        for (int j = 0; j < QK8_0 * 4; j++) {
+            int src_offset = (j / (4 * blck_size_interleave)) * blck_size_interleave;
+            int src_id = (j % (4 * blck_size_interleave)) / blck_size_interleave;
+            src_offset += (j % blck_size_interleave);
+
+            float x0 = srcv[src_id][src_offset] * id[src_id];
+            y[i].qs[j] = roundf(x0);
+        }
+    }
 #endif
 }
 
@@ -179,9 +205,35 @@ void ggml_quantize_mat_q8_0_4x8(const float * GGML_RESTRICT x, void * GGML_RESTR
     }
 
 #else
-    UNUSED(nb);
-    UNUSED(y);
-    ggml_quantize_mat_q8_0_4x8_generic(x, vy, k);
+    // scalar
+    const int blck_size_interleave = 8;
+    float srcv[4][QK8_0];
+    float id[4];
+
+    for (int i = 0; i < nb; i++) {
+        for (int row_iter = 0; row_iter < 4; row_iter++) {
+            float amax = 0.0f; // absolute max
+
+            for (int j = 0; j < QK8_0; j++) {
+                srcv[row_iter][j] = x[row_iter * k + i * QK8_0 + j];
+                amax = MAX(amax, fabsf(srcv[row_iter][j]));
+            }
+
+            const float d = amax / ((1 << 7) - 1);
+            id[row_iter] = d ? 1.0f / d : 0.0f;
+
+            y[i].d[row_iter] = GGML_CPU_FP32_TO_FP16(d);
+        }
+
+        for (int j = 0; j < QK8_0 * 4; j++) {
+            int src_offset = (j / (4 * blck_size_interleave)) * blck_size_interleave;
+            int src_id = (j % (4 * blck_size_interleave)) / blck_size_interleave;
+            src_offset += (j % blck_size_interleave);
+
+            float x0 = srcv[src_id][src_offset] * id[src_id];
+            y[i].qs[j] = roundf(x0);
+        }
+    }
 #endif
 }
 
@@ -243,7 +295,29 @@ void ggml_gemv_q4_0_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const vo
     }
     return;
 #endif // #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_DOTPROD)
-    ggml_gemv_q4_0_4x4_q8_0_generic(n, s, bs, vx, vy, nr, nc);
+    float sumf[4];
+    int sumi;
+
+    const block_q8_0 * a_ptr = (const block_q8_0 *) vy;
+    for (int x = 0; x < nc / ncols_interleaved; x++) {
+        const block_q4_0x4 * b_ptr = (const block_q4_0x4 *) vx + (x * nb);
+
+        for (int j = 0; j < ncols_interleaved; j++) sumf[j] = 0.0;
+        for (int l = 0; l < nb; l++) {
+            for (int k = 0; k < (qk / (2 * blocklen)); k++) {
+                for (int j = 0; j < ncols_interleaved; j++) {
+                    sumi = 0;
+                    for (int i = 0; i < blocklen; ++i) {
+                        const int v0 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] << 4);
+                        const int v1 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0xF0);
+                        sumi += ((v0 * a_ptr[l].qs[k * blocklen + i]) + (v1 * a_ptr[l].qs[k * blocklen + i + qk / 2])) >> 4;
+                    }
+                    sumf[j] += sumi * GGML_CPU_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_CPU_FP16_TO_FP32(a_ptr[l].d);
+                }
+            }
+        }
+        for (int j = 0; j < ncols_interleaved; j++) s[x * ncols_interleaved + j] = sumf[j];
+    }
 }
 
 void ggml_gemv_q4_0_4x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
@@ -309,7 +383,29 @@ void ggml_gemv_q4_0_4x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const vo
     }
     return;
 #endif // #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_DOTPROD)
-    ggml_gemv_q4_0_4x8_q8_0_generic(n, s, bs, vx, vy, nr, nc);
+    float sumf[4];
+    int sumi;
+
+    const block_q8_0 * a_ptr = (const block_q8_0 *) vy;
+    for (int x = 0; x < nc / ncols_interleaved; x++) {
+        const block_q4_0x4 * b_ptr = (const block_q4_0x4 *) vx + (x * nb);
+
+        for (int j = 0; j < ncols_interleaved; j++) sumf[j] = 0.0;
+        for (int l = 0; l < nb; l++) {
+            for (int k = 0; k < (qk / (2 * blocklen)); k++) {
+                for (int j = 0; j < ncols_interleaved; j++) {
+                    sumi = 0;
+                    for (int i = 0; i < blocklen; ++i) {
+                        const int v0 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] << 4);
+                        const int v1 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0xF0);
+                        sumi += ((v0 * a_ptr[l].qs[k * blocklen + i]) + (v1 * a_ptr[l].qs[k * blocklen + i + qk / 2])) >> 4;
+                    }
+                    sumf[j] += sumi * GGML_CPU_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_CPU_FP16_TO_FP32(a_ptr[l].d);
+                }
+            }
+        }
+        for (int j = 0; j < ncols_interleaved; j++) s[x * ncols_interleaved + j] = sumf[j];
+    }
 }
 
 void ggml_gemv_q4_0_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
@@ -401,7 +497,31 @@ void ggml_gemv_q4_0_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const vo
 #endif // #if defined(__ARM_FEATURE_SVE)
 
 #endif // #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__)
-    ggml_gemv_q4_0_8x8_q8_0_generic(n, s, bs, vx, vy, nr, nc);
+    {
+        float sumf[8];
+        int sumi;
+
+        const block_q8_0 * a_ptr = (const block_q8_0 *) vy;
+        for (int x = 0; x < nc / ncols_interleaved; x++) {
+            const block_q4_0x8 * b_ptr = (const block_q4_0x8 *) vx + (x * nb);
+
+            for (int j = 0; j < ncols_interleaved; j++) sumf[j] = 0.0;
+            for (int l = 0; l < nb; l++) {
+                for (int k = 0; k < (qk / (2 * blocklen)); k++) {
+                    for (int j = 0; j < ncols_interleaved; j++) {
+                        sumi = 0;
+                        for (int i = 0; i < blocklen; ++i) {
+                            const int v0 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] << 4);
+                            const int v1 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0xF0);
+                            sumi += ((v0 * a_ptr[l].qs[k * blocklen + i]) + (v1 * a_ptr[l].qs[k * blocklen + i + qk / 2])) >> 4;
+                        }
+                        sumf[j] += sumi * GGML_CPU_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_CPU_FP16_TO_FP32(a_ptr[l].d);
+                    }
+                }
+            }
+            for (int j = 0; j < ncols_interleaved; j++) s[x * ncols_interleaved + j] = sumf[j];
+        }
+    }
 }
 
 void ggml_gemv_iq4_nl_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
@@ -471,7 +591,31 @@ void ggml_gemv_iq4_nl_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const
     }
     return;
 #endif // #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON)
-    ggml_gemv_iq4_nl_4x4_q8_0_generic(n, s, bs, vx, vy, nr, nc);
+    {
+        float sumf[4];
+        int sumi;
+
+        const block_q8_0 * a_ptr = (const block_q8_0 *) vy;
+        for (int x = 0; x < nc / ncols_interleaved; x++) {
+            const block_iq4_nlx4 * b_ptr = (const block_iq4_nlx4 *) vx + (x * nb);
+
+            for (int j = 0; j < ncols_interleaved; j++) sumf[j] = 0.0;
+            for (int l = 0; l < nb; l++) {
+                for (int k = 0; k < (qk / (2 * blocklen)); k++) {
+                    for (int j = 0; j < ncols_interleaved; j++) {
+                        sumi = 0;
+                        for (int i = 0; i < blocklen; ++i) {
+                            const int v0 = kvalues_iq4nl[b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0x0F];
+                            const int v1 = kvalues_iq4nl[b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] >> 4];
+                            sumi += ((v0 * a_ptr[l].qs[k * blocklen + i]) + (v1 * a_ptr[l].qs[k * blocklen + i + qk / 2]));
+                        }
+                        sumf[j] += sumi * GGML_CPU_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_CPU_FP16_TO_FP32(a_ptr[l].d);
+                    }
+                }
+            }
+            for (int j = 0; j < ncols_interleaved; j++) s[x * ncols_interleaved + j] = sumf[j];
+        }
+    }
 }
 
 void ggml_gemm_q4_0_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
@@ -952,7 +1096,40 @@ void ggml_gemm_q4_0_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const vo
     );
     return;
 #endif // #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON)
-    ggml_gemm_q4_0_4x4_q8_0_generic(n, s, bs, vx, vy, nr, nc);
+    {
+        float sumf[4][4];
+        int sumi;
+
+        for (int y = 0; y < nr / 4; y++) {
+            const block_q8_0x4 * a_ptr = (const block_q8_0x4 *) vy + (y * nb);
+            for (int x = 0; x < nc / ncols_interleaved; x++) {
+                const block_q4_0x4 * b_ptr = (const block_q4_0x4 *) vx + (x * nb);
+                for (int m = 0; m < 4; m++) {
+                    for (int j = 0; j < ncols_interleaved; j++) sumf[m][j] = 0.0;
+                }
+                for (int l = 0; l < nb; l++) {
+                    for (int k = 0; k < (qk / (2 * blocklen)); k++) {
+                        for (int m = 0; m < 4; m++) {
+                            for (int j = 0; j < ncols_interleaved; j++) {
+                                sumi = 0;
+                                for (int i = 0; i < blocklen; ++i) {
+                                    const int v0 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] << 4);
+                                    const int v1 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0xF0);
+                                    sumi += ((v0 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i]) +
+                                            (v1 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i + qk / 2 * 4])) >> 4;
+                                }
+                                sumf[m][j] += sumi * GGML_CPU_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_CPU_FP16_TO_FP32(a_ptr[l].d[m]);
+                            }
+                        }
+                    }
+                }
+                for (int m = 0; m < 4; m++) {
+                    for (int j = 0; j < ncols_interleaved; j++)
+                        s[(y * 4 + m) * bs + x * ncols_interleaved + j] = sumf[m][j];
+                }
+            }
+        }
+    }
 }
 
 void ggml_gemm_q4_0_4x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
@@ -1373,7 +1550,38 @@ void ggml_gemm_q4_0_4x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const vo
     );
     return;
 #endif // #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_MATMUL_INT8)
-    ggml_gemm_q4_0_4x8_q8_0_generic(n, s, bs, vx, vy, nr, nc);
+    float sumf[4][4];
+    int sumi;
+
+    for (int y = 0; y < nr / 4; y++) {
+        const block_q8_0x4 * a_ptr = (const block_q8_0x4 *) vy + (y * nb);
+        for (int x = 0; x < nc / ncols_interleaved; x++) {
+            const block_q4_0x4 * b_ptr = (const block_q4_0x4 *) vx + (x * nb);
+            for (int m = 0; m < 4; m++) {
+                for (int j = 0; j < ncols_interleaved; j++) sumf[m][j] = 0.0;
+            }
+            for (int l = 0; l < nb; l++) {
+                for (int k = 0; k < (qk / (2 * blocklen)); k++) {
+                    for (int m = 0; m < 4; m++) {
+                        for (int j = 0; j < ncols_interleaved; j++) {
+                            sumi = 0;
+                            for (int i = 0; i < blocklen; ++i) {
+                                const int v0 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] << 4);
+                                const int v1 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0xF0);
+                                sumi += ((v0 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i]) +
+                                        (v1 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i + qk / 2 * 4])) >> 4;
+                            }
+                            sumf[m][j] += sumi * GGML_CPU_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_CPU_FP16_TO_FP32(a_ptr[l].d[m]);
+                        }
+                    }
+                }
+            }
+            for (int m = 0; m < 4; m++) {
+                for (int j = 0; j < ncols_interleaved; j++)
+                    s[(y * 4 + m) * bs + x * ncols_interleaved + j] = sumf[m][j];
+            }
+        }
+    }
 }
 
 void ggml_gemm_q4_0_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
@@ -1811,7 +2019,38 @@ void ggml_gemm_q4_0_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const vo
 #endif // #if defined(__ARM_FEATURE_SVE) && defined(__ARM_FEATURE_MATMUL_INT8)
 
 #endif // #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__)
-    ggml_gemm_q4_0_8x8_q8_0_generic(n, s, bs, vx, vy, nr, nc);
+    float sumf[4][8];
+    int sumi;
+
+    for (int y = 0; y < nr / 4; y++) {
+        const block_q8_0x4 * a_ptr = (const block_q8_0x4 *) vy + (y * nb);
+        for (int x = 0; x < nc / ncols_interleaved; x++) {
+            const block_q4_0x8 * b_ptr = (const block_q4_0x8 *) vx + (x * nb);
+            for (int m = 0; m < 4; m++) {
+                for (int j = 0; j < ncols_interleaved; j++) sumf[m][j] = 0.0;
+            }
+            for (int l = 0; l < nb; l++) {
+                for (int k = 0; k < (qk / (2 * blocklen)); k++) {
+                    for (int m = 0; m < 4; m++) {
+                        for (int j = 0; j < ncols_interleaved; j++) {
+                            sumi = 0;
+                            for (int i = 0; i < blocklen; ++i) {
+                                const int v0 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] << 4);
+                                const int v1 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0xF0);
+                                sumi += ((v0 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i]) +
+                                         (v1 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i + qk / 2 * 4])) >> 4;
+                            }
+                            sumf[m][j] += sumi * GGML_CPU_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_CPU_FP16_TO_FP32(a_ptr[l].d[m]);
+                        }
+                    }
+                }
+            }
+            for (int m = 0; m < 4; m++) {
+                for (int j = 0; j < ncols_interleaved; j++)
+                    s[(y * 4 + m) * bs + x * ncols_interleaved + j] = sumf[m][j];
+            }
+        }
+    }
 }
 
 void ggml_gemm_iq4_nl_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
@@ -1887,5 +2126,38 @@ void ggml_gemm_iq4_nl_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const
     }
     return;
 #endif // #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__) && defined(__ARM_NEON)
-    ggml_gemm_iq4_nl_4x4_q8_0_generic(n, s, bs, vx, vy, nr, nc);
+    {
+        float sumf[4][4];
+        int sumi;
+
+        for (int y = 0; y < nr / 4; y++) {
+            const block_q8_0x4 * a_ptr = (const block_q8_0x4 *) vy + (y * nb);
+            for (int x = 0; x < nc / ncols_interleaved; x++) {
+                const block_iq4_nlx4 * b_ptr = (const block_iq4_nlx4 *) vx + (x * nb);
+                for (int m = 0; m < 4; m++) {
+                    for (int j = 0; j < ncols_interleaved; j++) sumf[m][j] = 0.0;
+                }
+                for (int l = 0; l < nb; l++) {
+                    for (int k = 0; k < (qk / (2 * blocklen)); k++) {
+                        for (int m = 0; m < 4; m++) {
+                            for (int j = 0; j < ncols_interleaved; j++) {
+                                sumi = 0;
+                                for (int i = 0; i < blocklen; ++i) {
+                                    const int v0 = kvalues_iq4nl[b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0x0F];
+                                    const int v1 = kvalues_iq4nl[b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] >> 4];
+                                    sumi += ((v0 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i]) +
+                                            (v1 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i + qk / 2 * 4]));
+                                }
+                                sumf[m][j] += sumi * GGML_CPU_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_CPU_FP16_TO_FP32(a_ptr[l].d[m]);
+                            }
+                        }
+                    }
+                }
+                for (int m = 0; m < 4; m++) {
+                    for (int j = 0; j < ncols_interleaved; j++)
+                        s[(y * 4 + m) * bs + x * ncols_interleaved + j] = sumf[m][j];
+                }
+            }
+        }
+    }
 }

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

@@ -821,15 +821,24 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * GGML_RESTRICT s, size_t bs, const voi
 
     sumf = hsum_float_8(acc) + summs;
 
-    *s = sumf;
-#else
-    UNUSED(nb);
-    UNUSED(x);
-    UNUSED(y);
-    UNUSED(ib);
-    UNUSED(sumf);
-    ggml_vec_dot_q4_1_q8_1_generic(n, s, bs, vx, bx, vy, by, nrc);
 #endif
+    for (; ib < nb; ++ib) {
+        int sumi0 = 0;
+        int sumi1 = 0;
+
+        for (int j = 0; j < qk/2; ++j) {
+            const int v0 = (x[ib].qs[j] & 0x0F);
+            const int v1 = (x[ib].qs[j] >>   4);
+
+            sumi0 += (v0 * y[ib].qs[j]);
+            sumi1 += (v1 * y[ib].qs[j + qk/2]);
+        }
+
+        int sumi = sumi0 + sumi1;
+        sumf += (GGML_CPU_FP16_TO_FP32(x[ib].d)*GGML_CPU_FP16_TO_FP32(y[ib].d))*sumi + GGML_CPU_FP16_TO_FP32(x[ib].m)*GGML_CPU_FP16_TO_FP32(y[ib].s);
+    }
+
+    *s = sumf;
 }
 
 void ggml_vec_dot_q5_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
@@ -874,15 +883,30 @@ void ggml_vec_dot_q5_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const voi
 
     sumf = hsum_float_8(acc);
 
-    *s = sumf;
-#else
-    UNUSED(nb);
-    UNUSED(ib);
-    UNUSED(sumf);
-    UNUSED(x);
-    UNUSED(y);
-    ggml_vec_dot_q5_0_q8_0_generic(n, s, bs, vx, bx, vy, by, nrc);
 #endif
+    for (; ib < nb; ++ib) {
+        uint32_t qh;
+        memcpy(&qh, x[ib].qh, sizeof(qh));
+
+        int sumi0 = 0;
+        int sumi1 = 0;
+
+        for (int j = 0; j < qk/2; ++j) {
+            const uint8_t xh_0 = ((qh & (1u << (j + 0 ))) >> (j + 0 )) << 4;
+            const uint8_t xh_1 = ((qh & (1u << (j + 16))) >> (j + 12));
+
+            const int32_t x0 = (int8_t)(((x[ib].qs[j] & 0x0F) | xh_0) - 16);
+            const int32_t x1 = (int8_t)(((x[ib].qs[j] >>   4) | xh_1) - 16);
+
+            sumi0 += (x0 * y[ib].qs[j]);
+            sumi1 += (x1 * y[ib].qs[j + qk/2]);
+        }
+
+        int sumi = sumi0 + sumi1;
+        sumf += (GGML_CPU_FP16_TO_FP32(x[ib].d)*GGML_CPU_FP16_TO_FP32(y[ib].d)) * sumi;
+    }
+
+    *s = sumf;
 }
 
 void ggml_vec_dot_q5_1_q8_1(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
@@ -930,15 +954,30 @@ void ggml_vec_dot_q5_1_q8_1(int n, float * GGML_RESTRICT s, size_t bs, const voi
 
     sumf = hsum_float_8(acc) + summs;
 
-    *s = sumf;
-#else
-    UNUSED(nb);
-    UNUSED(ib);
-    UNUSED(sumf);
-    UNUSED(x);
-    UNUSED(y);
-    ggml_vec_dot_q5_1_q8_1_generic(n, s, bs, vx, bx, vy, by, nrc);
 #endif
+    for (; ib < nb; ++ib) {
+        uint32_t qh;
+        memcpy(&qh, x[ib].qh, sizeof(qh));
+
+        int sumi0 = 0;
+        int sumi1 = 0;
+
+        for (int j = 0; j < qk/2; ++j) {
+            const uint8_t xh_0 = ((qh >> (j +  0)) << 4) & 0x10;
+            const uint8_t xh_1 = ((qh >> (j + 12))     ) & 0x10;
+
+            const int32_t x0 = (x[ib].qs[j] & 0xF) | xh_0;
+            const int32_t x1 = (x[ib].qs[j] >>  4) | xh_1;
+
+            sumi0 += (x0 * y[ib].qs[j]);
+            sumi1 += (x1 * y[ib].qs[j + qk/2]);
+        }
+
+        int sumi = sumi0 + sumi1;
+        sumf += (GGML_CPU_FP16_TO_FP32(x[ib].d)*GGML_CPU_FP16_TO_FP32(y[ib].d))*sumi + GGML_CPU_FP16_TO_FP32(x[ib].m)*GGML_CPU_FP16_TO_FP32(y[ib].s);
+    }
+
+    *s = sumf;
 }
 
 void ggml_vec_dot_q8_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
@@ -977,15 +1016,18 @@ void ggml_vec_dot_q8_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const voi
 
     sumf = hsum_float_8(acc);
 
-    *s = sumf;
-#else
-    UNUSED(nb);
-    UNUSED(ib);
-    UNUSED(sumf);
-    UNUSED(x);
-    UNUSED(y);
-    ggml_vec_dot_q8_0_q8_0_generic(n, s, bs, vx, bx, vy, by, nrc);
 #endif
+    for (; ib < nb; ++ib) {
+        int sumi = 0;
+
+        for (int j = 0; j < qk; j++) {
+            sumi += x[ib].qs[j]*y[ib].qs[j];
+        }
+
+        sumf += sumi*(GGML_CPU_FP16_TO_FP32(x[ib].d)*GGML_CPU_FP16_TO_FP32(y[ib].d));
+    }
+
+    *s = sumf;
 }
 
 void ggml_vec_dot_q2_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
@@ -1061,10 +1103,45 @@ void ggml_vec_dot_q2_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
     *s = hsum_float_8(acc);
 
 #else
-    UNUSED(x);
-    UNUSED(y);
-    UNUSED(nb);
-    ggml_vec_dot_q2_K_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
+
+    float sumf = 0;
+
+    for (int i = 0; i < nb; ++i) {
+
+        const uint8_t * q2 = x[i].qs;
+        const  int8_t * q8 = y[i].qs;
+        const uint8_t * sc = x[i].scales;
+
+        int summs = 0;
+        for (int j = 0; j < 16; ++j) {
+            summs += y[i].bsums[j] * (sc[j] >> 4);
+        }
+
+        const float dall = y[i].d * GGML_CPU_FP16_TO_FP32(x[i].d);
+        const float dmin = y[i].d * GGML_CPU_FP16_TO_FP32(x[i].dmin);
+
+        int isum = 0;
+        int is = 0;
+        int d;
+        for (int k = 0; k < QK_K/128; ++k) {
+            int shift = 0;
+            for (int j = 0; j < 4; ++j) {
+                d = sc[is++] & 0xF;
+                int isuml = 0;
+                for (int l =  0; l < 16; ++l) isuml += q8[l] * ((q2[l] >> shift) & 3);
+                isum += d * isuml;
+                d = sc[is++] & 0xF;
+                isuml = 0;
+                for (int l = 16; l < 32; ++l) isuml += q8[l] * ((q2[l] >> shift) & 3);
+                isum += d * isuml;
+                shift += 2;
+                q8 += 32;
+            }
+            q2 += 32;
+        }
+        sumf += dall * isum - dmin * summs;
+    }
+    *s = sumf;
 #endif
 }
 
@@ -1162,13 +1239,70 @@ void ggml_vec_dot_q3_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
     *s = hsum_float_8(acc);
 
 #else
-    UNUSED(kmask1);
-    UNUSED(kmask2);
-    UNUSED(x);
-    UNUSED(y);
-    UNUSED(nb);
-    ggml_vec_dot_q3_K_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
+    // scalar version
+    // This function is written like this so the compiler can manage to vectorize most of it
+    // Using -Ofast, GCC and clang manage to produce code that is within a factor of 2 or so from the
+    // manually vectorized version above. Every other version I tried would run at least 4 times slower.
+    // The ideal situation would be if we could just write the code once, and the compiler would
+    // automatically produce the best possible set of machine instructions, instead of us having to manually
+    // write vectorized versions for AVX, ARM_NEON, etc.
+
+    int8_t  aux8[QK_K];
+    int16_t aux16[8];
+    float   sums [8];
+    int32_t aux32[8];
+    memset(sums, 0, 8*sizeof(float));
+
+    uint32_t auxs[4];
+    const int8_t * scales = (const int8_t*)auxs;
+
+    float sumf = 0;
+    for (int i = 0; i < nb; ++i) {
+        const uint8_t * GGML_RESTRICT q3 = x[i].qs;
+        const uint8_t * GGML_RESTRICT hm = x[i].hmask;
+        const  int8_t * GGML_RESTRICT q8 = y[i].qs;
+        memset(aux32, 0, 8*sizeof(int32_t));
+        int8_t * GGML_RESTRICT a = aux8;
+        uint8_t m = 1;
+        for (int j = 0; j < QK_K; j += 128) {
+            for (int l = 0; l < 32; ++l) a[l] = q3[l] & 3;
+            for (int l = 0; l < 32; ++l) a[l] -= (hm[l] & m ? 0 : 4);
+            a += 32; m <<= 1;
+            for (int l = 0; l < 32; ++l) a[l] = (q3[l] >> 2) & 3;
+            for (int l = 0; l < 32; ++l) a[l] -= (hm[l] & m ? 0 : 4);
+            a += 32; m <<= 1;
+            for (int l = 0; l < 32; ++l) a[l] = (q3[l] >> 4) & 3;
+            for (int l = 0; l < 32; ++l) a[l] -= (hm[l] & m ? 0 : 4);
+            a += 32; m <<= 1;
+            for (int l = 0; l < 32; ++l) a[l] = (q3[l] >> 6) & 3;
+            for (int l = 0; l < 32; ++l) a[l] -= (hm[l] & m ? 0 : 4);
+            a += 32; m <<= 1;
+            q3 += 32;
+        }
+        a = aux8;
+
+        memcpy(auxs, x[i].scales, 12);
+        uint32_t tmp = auxs[2];
+        auxs[2] = ((auxs[0] >> 4) & kmask2) | (((tmp >> 4) & kmask1) << 4);
+        auxs[3] = ((auxs[1] >> 4) & kmask2) | (((tmp >> 6) & kmask1) << 4);
+        auxs[0] = (auxs[0] & kmask2) | (((tmp >> 0) & kmask1) << 4);
+        auxs[1] = (auxs[1] & kmask2) | (((tmp >> 2) & kmask1) << 4);
+        for (int j = 0; j < QK_K/16; ++j) {
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += (scales[j] - 32) * aux16[l];
+            q8 += 8; a += 8;
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += (scales[j] - 32) * aux16[l];
+            q8 += 8; a += 8;
+        }
+        const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
+        for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l];
+    }
+    for (int l = 0; l < 8; ++l) sumf += sums[l];
+    *s = sumf;
+
 #endif
+
 }
 
 void ggml_vec_dot_q4_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
@@ -1257,14 +1391,61 @@ void ggml_vec_dot_q4_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
     *s = hsum_float_8(acc) + ((v4f32)acc_m)[0];
 
 #else
-    UNUSED(x);
-    UNUSED(y);
-    UNUSED(nb);
-    UNUSED(kmask1);
-    UNUSED(kmask2);
-    UNUSED(kmask3);
-    UNUSED(utmp);
-    ggml_vec_dot_q4_K_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
+
+    const uint8_t * scales = (const uint8_t*)&utmp[0];
+    const uint8_t * mins   = (const uint8_t*)&utmp[2];
+
+    int8_t  aux8[QK_K];
+    int16_t aux16[8];
+    float   sums [8];
+    int32_t aux32[8];
+    memset(sums, 0, 8*sizeof(float));
+
+    float sumf = 0;
+    for (int i = 0; i < nb; ++i) {
+        const uint8_t * GGML_RESTRICT q4 = x[i].qs;
+        const  int8_t * GGML_RESTRICT q8 = y[i].qs;
+        memset(aux32, 0, 8*sizeof(int32_t));
+        int8_t * GGML_RESTRICT a = aux8;
+        for (int j = 0; j < QK_K/64; ++j) {
+            for (int l = 0; l < 32; ++l) a[l] = (int8_t)(q4[l] & 0xF);
+            a += 32;
+            for (int l = 0; l < 32; ++l) a[l] = (int8_t)(q4[l]  >> 4);
+            a += 32; q4 += 32;
+        }
+        memcpy(utmp, x[i].scales, 12);
+        utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4);
+        const uint32_t uaux = utmp[1] & kmask1;
+        utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4);
+        utmp[2] = uaux;
+        utmp[0] &= kmask1;
+
+        int sumi = 0;
+        for (int j = 0; j < QK_K/16; ++j) sumi += y[i].bsums[j] * mins[j/2];
+        a = aux8;
+        int is = 0;
+        for (int j = 0; j < QK_K/32; ++j) {
+            int32_t scale = scales[is++];
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
+            q8 += 8; a += 8;
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
+            q8 += 8; a += 8;
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
+            q8 += 8; a += 8;
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
+            q8 += 8; a += 8;
+        }
+        const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
+        for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l];
+        const float dmin = GGML_CPU_FP16_TO_FP32(x[i].dmin) * y[i].d;
+        sumf -= dmin * sumi;
+    }
+    for (int l = 0; l < 8; ++l) sumf += sums[l];
+    *s = sumf;
 #endif
 }
 
@@ -1360,14 +1541,66 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
     *s = hsum_float_8(acc) + ((v4f32)acc_m)[0];
 
 #else
-    UNUSED(x);
-    UNUSED(y);
-    UNUSED(nb);
-    UNUSED(kmask1);
-    UNUSED(kmask2);
-    UNUSED(kmask3);
-    UNUSED(utmp);
-    ggml_vec_dot_q5_K_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
+
+    const uint8_t * scales = (const uint8_t*)&utmp[0];
+    const uint8_t * mins   = (const uint8_t*)&utmp[2];
+
+    int8_t  aux8[QK_K];
+    int16_t aux16[8];
+    float   sums [8];
+    int32_t aux32[8];
+    memset(sums, 0, 8*sizeof(float));
+
+    float sumf = 0;
+    for (int i = 0; i < nb; ++i) {
+        const uint8_t * GGML_RESTRICT q4 = x[i].qs;
+        const uint8_t * GGML_RESTRICT hm = x[i].qh;
+        const  int8_t * GGML_RESTRICT q8 = y[i].qs;
+        memset(aux32, 0, 8*sizeof(int32_t));
+        int8_t * GGML_RESTRICT a = aux8;
+        uint8_t m = 1;
+        for (int j = 0; j < QK_K/64; ++j) {
+            for (int l = 0; l < 32; ++l) a[l] = (int8_t)(q4[l] & 0xF);
+            for (int l = 0; l < 32; ++l) a[l] += (hm[l] & m ? 16 : 0);
+            a += 32; m <<= 1;
+            for (int l = 0; l < 32; ++l) a[l] = (int8_t)(q4[l]  >> 4);
+            for (int l = 0; l < 32; ++l) a[l] += (hm[l] & m ? 16 : 0);
+            a += 32; m <<= 1;
+            q4 += 32;
+        }
+        memcpy(utmp, x[i].scales, 12);
+        utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4);
+        const uint32_t uaux = utmp[1] & kmask1;
+        utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4);
+        utmp[2] = uaux;
+        utmp[0] &= kmask1;
+
+        int sumi = 0;
+        for (int j = 0; j < QK_K/16; ++j) sumi += y[i].bsums[j] * mins[j/2];
+        a = aux8;
+        int is = 0;
+        for (int j = 0; j < QK_K/32; ++j) {
+            int32_t scale = scales[is++];
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
+            q8 += 8; a += 8;
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
+            q8 += 8; a += 8;
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
+            q8 += 8; a += 8;
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
+            q8 += 8; a += 8;
+        }
+        const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
+        for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l];
+        const float dmin = GGML_CPU_FP16_TO_FP32(x[i].dmin) * y[i].d;
+        sumf -= dmin * sumi;
+    }
+    for (int l = 0; l < 8; ++l) sumf += sums[l];
+    *s = sumf;
 #endif
 }
 
@@ -1445,10 +1678,47 @@ void ggml_vec_dot_q6_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
     *s = hsum_float_8(acc);
 
 #else
-    UNUSED(x);
-    UNUSED(y);
-    UNUSED(nb);
-    ggml_vec_dot_q6_K_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
+
+    int8_t  aux8[QK_K];
+    int16_t aux16[8];
+    float   sums [8];
+    int32_t aux32[8];
+    memset(sums, 0, 8*sizeof(float));
+
+    float sumf = 0;
+    for (int i = 0; i < nb; ++i) {
+        const uint8_t * GGML_RESTRICT q4 = x[i].ql;
+        const uint8_t * GGML_RESTRICT qh = x[i].qh;
+        const  int8_t * GGML_RESTRICT q8 = y[i].qs;
+        memset(aux32, 0, 8*sizeof(int32_t));
+        int8_t * GGML_RESTRICT a = aux8;
+        for (int j = 0; j < QK_K; j += 128) {
+            for (int l = 0; l < 32; ++l) {
+                a[l +  0] = (int8_t)((q4[l +  0] & 0xF) | (((qh[l] >> 0) & 3) << 4)) - 32;
+                a[l + 32] = (int8_t)((q4[l + 32] & 0xF) | (((qh[l] >> 2) & 3) << 4)) - 32;
+                a[l + 64] = (int8_t)((q4[l +  0] >>  4) | (((qh[l] >> 4) & 3) << 4)) - 32;
+                a[l + 96] = (int8_t)((q4[l + 32] >>  4) | (((qh[l] >> 6) & 3) << 4)) - 32;
+            }
+            a  += 128;
+            q4 += 64;
+            qh += 32;
+        }
+        a = aux8;
+        int is = 0;
+        for (int j = 0; j < QK_K/16; ++j) {
+            int scale = x[i].scales[is++];
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
+            q8 += 8; a += 8;
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
+            q8 += 8; a += 8;
+        }
+        const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
+        for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l];
+    }
+    for (int l = 0; l < 8; ++l) sumf += sums[l];
+    *s = sumf;
 #endif
 }
 
@@ -1545,10 +1815,34 @@ void ggml_vec_dot_iq2_xxs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const
     *s = 0.125f * hsum_float_8(accumf);
 
 #else
-    UNUSED(x);
-    UNUSED(y);
-    UNUSED(nb);
-    ggml_vec_dot_iq2_xxs_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
+
+    uint32_t aux32[2];
+    const uint8_t * aux8 = (const uint8_t *)aux32;
+
+    float sumf = 0.f;
+    for (int i = 0; i < nb; ++i) {
+        const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
+        const uint16_t * GGML_RESTRICT q2 = x[i].qs;
+        const int8_t   * GGML_RESTRICT q8 = y[i].qs;
+        int32_t bsum = 0;
+        for (int ib32 = 0; ib32 < QK_K/32; ++ib32) {
+            memcpy(aux32, q2, 2*sizeof(uint32_t));
+            q2 += 4;
+            const uint32_t ls = 2*(aux32[1] >> 28) + 1;
+            int32_t sumi = 0;
+            for (int l = 0; l < 4; ++l) {
+                const uint8_t * grid = (const uint8_t *)(iq2xxs_grid + aux8[l]);
+                const uint8_t  signs = ksigns_iq2xs[(aux32[1] >> 7*l) & 127];
+                for (int j = 0; j < 8; ++j) {
+                    sumi += grid[j] * q8[j] * (signs & kmask_iq2xs[j] ? -1 : 1);
+                }
+                q8 += 8;
+            }
+            bsum += sumi * ls;
+        }
+        sumf += d * bsum;
+    }
+    *s = 0.125f * sumf;
 #endif
 }
 
@@ -1684,10 +1978,42 @@ void ggml_vec_dot_iq2_xs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const v
     *s = 0.125f * hsum_float_8(accumf);
 
 #else
-    UNUSED(x);
-    UNUSED(y);
-    UNUSED(nb);
-    ggml_vec_dot_iq2_xs_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
+
+    float sumf = 0.f;
+    for (int i = 0; i < nb; ++i) {
+        const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
+        const uint16_t * GGML_RESTRICT q2 = x[i].qs;
+        const uint8_t  * GGML_RESTRICT sc = x[i].scales;
+        const int8_t   * GGML_RESTRICT q8 = y[i].qs;
+        int32_t bsum = 0;
+        for (int ib32 = 0; ib32 < QK_K/32; ++ib32) {
+            const uint16_t ls1 = 2*(sc[ib32] & 0xf) + 1;
+            const uint16_t ls2 = 2*(sc[ib32] >>  4) + 1;
+            int32_t sumi = 0;
+            for (int l = 0; l < 2; ++l) {
+                const uint8_t * grid = (const uint8_t *)(iq2xs_grid + (q2[l] & 511));
+                const uint8_t  signs = ksigns_iq2xs[q2[l] >> 9];
+                for (int j = 0; j < 8; ++j) {
+                    sumi += grid[j] * q8[j] * (signs & kmask_iq2xs[j] ? -1 : 1);
+                }
+                q8 += 8;
+            }
+            bsum += sumi * ls1;
+            sumi = 0;
+            for (int l = 2; l < 4; ++l) {
+                const uint8_t * grid = (const uint8_t *)(iq2xs_grid + (q2[l] & 511));
+                const uint8_t  signs = ksigns_iq2xs[q2[l] >> 9];
+                for (int j = 0; j < 8; ++j) {
+                    sumi += grid[j] * q8[j] * (signs & kmask_iq2xs[j] ? -1 : 1);
+                }
+                q8 += 8;
+            }
+            bsum += sumi * ls2;
+            q2 += 4;
+        }
+        sumf += d * bsum;
+    }
+    *s = 0.125f * sumf;
 #endif
 }
 
@@ -1779,11 +2105,47 @@ void ggml_vec_dot_iq2_s_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const vo
     *s = 0.125f * hsum_float_8(accumf);
 
 #else
-    UNUSED(x);
-    UNUSED(y);
-    UNUSED(nb);
-    ggml_vec_dot_iq2_s_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
+
+    float sumf = 0;
+    for (int i = 0; i < nb; i++) {
+
+        const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
+        const int8_t  * q8 = y[i].qs;
+        const uint8_t * qs = x[i].qs;
+        const uint8_t * qh = x[i].qh;
+        const uint8_t * signs = qs + QK_K/8;
+
+        int bsum = 0;
+        for (int ib32 = 0; ib32 < QK_K/32; ++ib32) {
+            int ls1 = 1 + 2*(x[i].scales[ib32] & 0xf);
+            int ls2 = 1 + 2*(x[i].scales[ib32] >>  4);
+            int sumi1 = 0, sumi2 = 0;
+            for (int l = 0; l < 2; ++l) {
+                const uint8_t * grid = (const uint8_t *)(iq2s_grid + (qs[l] | (qh[ib32] << (8-2*l) & 0x300)));
+                for (int j = 0; j < 8; ++j) {
+                    sumi1 += q8[j] * grid[j] * (signs[l] & kmask_iq2xs[j] ? -1 : 1);
+                }
+                q8 += 8;
+            }
+            for (int l = 2; l < 4; ++l) {
+                const uint8_t * grid = (const uint8_t *)(iq2s_grid + (qs[l] | (qh[ib32] << (8-2*l) & 0x300)));
+                for (int j = 0; j < 8; ++j) {
+                    sumi2 += q8[j] * grid[j] * (signs[l] & kmask_iq2xs[j] ? -1 : 1);
+                }
+                q8 += 8;
+            }
+            bsum += ls1 * sumi1 + ls2 * sumi2;
+            qs += 4;
+            signs += 4;
+        }
+
+        sumf += d * bsum;
+    }
+
+    *s = 0.125f * sumf;
+
 #endif
+
 }
 
 void ggml_vec_dot_iq3_xxs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
@@ -1847,10 +2209,36 @@ void ggml_vec_dot_iq3_xxs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const
     *s = 0.25f * hsum_float_8(accumf);
 
 #else
-    UNUSED(x);
-    UNUSED(y);
-    UNUSED(nb);
-    ggml_vec_dot_iq3_xxs_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
+
+    uint32_t aux32;
+
+    float sumf = 0.f;
+    for (int i = 0; i < nb; ++i) {
+        const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
+        const uint8_t * GGML_RESTRICT q3 = x[i].qs;
+        const uint8_t * GGML_RESTRICT gas = x[i].qs + QK_K/4;
+        const int8_t  * GGML_RESTRICT q8 = y[i].qs;
+        int32_t bsum = 0;
+        for (int ib32 = 0; ib32 < QK_K/32; ++ib32) {
+            memcpy(&aux32, gas, sizeof(uint32_t)); gas += sizeof(uint32_t);
+            const uint32_t ls = 2*(aux32 >> 28) + 1;
+            int32_t sumi = 0;
+            for (int l = 0; l < 4; ++l) {
+                const uint8_t * grid1 = (const uint8_t *)(iq3xxs_grid + q3[2*l+0]);
+                const uint8_t * grid2 = (const uint8_t *)(iq3xxs_grid + q3[2*l+1]);
+                const uint8_t  signs = ksigns_iq2xs[(aux32 >> 7*l) & 127];
+                for (int j = 0; j < 4; ++j) {
+                    sumi += grid1[j] * q8[j+0] * (signs & kmask_iq2xs[j+0] ? -1 : 1);
+                    sumi += grid2[j] * q8[j+4] * (signs & kmask_iq2xs[j+4] ? -1 : 1);
+                }
+                q8 += 8;
+            }
+            q3 += 8;
+            bsum += sumi * ls;
+        }
+        sumf += d * bsum;
+    }
+    *s = 0.25f * sumf;
 #endif
 }
 
@@ -1950,10 +2338,48 @@ void ggml_vec_dot_iq3_s_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const vo
     *s = hsum_float_8(accumf);
 
 #else
-    UNUSED(x);
-    UNUSED(y);
-    UNUSED(nb);
-    ggml_vec_dot_iq3_s_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
+
+    float sumf = 0.f;
+    for (int i = 0; i < nb; ++i) {
+        const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
+        const uint8_t * GGML_RESTRICT qs = x[i].qs;
+        const uint8_t * GGML_RESTRICT qh = x[i].qh;
+        const uint8_t * GGML_RESTRICT signs = x[i].signs;
+        const int8_t  * GGML_RESTRICT q8 = y[i].qs;
+        int32_t bsum = 0;
+        for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) {
+            const uint32_t ls1 = 2*(x[i].scales[ib32/2] & 0xf) + 1;
+            const uint32_t ls2 = 2*(x[i].scales[ib32/2] >>  4) + 1;
+            int32_t sumi = 0;
+            for (int l = 0; l < 4; ++l) {
+                const uint8_t * grid1 = (const uint8_t *)(iq3s_grid + (qs[2*l+0] | ((qh[ib32+0] << (8-2*l)) & 256)));
+                const uint8_t * grid2 = (const uint8_t *)(iq3s_grid + (qs[2*l+1] | ((qh[ib32+0] << (7-2*l)) & 256)));
+                for (int j = 0; j < 4; ++j) {
+                    sumi += grid1[j] * q8[j+0] * (signs[l] & kmask_iq2xs[j+0] ? -1 : 1);
+                    sumi += grid2[j] * q8[j+4] * (signs[l] & kmask_iq2xs[j+4] ? -1 : 1);
+                }
+                q8 += 8;
+            }
+            qs += 8;
+            signs += 4;
+            bsum += sumi * ls1;
+            sumi = 0;
+            for (int l = 0; l < 4; ++l) {
+                const uint8_t * grid1 = (const uint8_t *)(iq3s_grid + (qs[2*l+0] | ((qh[ib32+1] << (8-2*l)) & 256)));
+                const uint8_t * grid2 = (const uint8_t *)(iq3s_grid + (qs[2*l+1] | ((qh[ib32+1] << (7-2*l)) & 256)));
+                for (int j = 0; j < 4; ++j) {
+                    sumi += grid1[j] * q8[j+0] * (signs[l] & kmask_iq2xs[j+0] ? -1 : 1);
+                    sumi += grid2[j] * q8[j+4] * (signs[l] & kmask_iq2xs[j+4] ? -1 : 1);
+                }
+                q8 += 8;
+            }
+            qs += 8;
+            signs += 4;
+            bsum += sumi * ls2;
+        }
+        sumf += d * bsum;
+    }
+    *s = sumf;
 #endif
 }
 
@@ -2034,10 +2460,36 @@ void ggml_vec_dot_iq1_s_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const vo
     *s = hsum_float_8(accum) + IQ1S_DELTA * accum1;
 
 #else
-    UNUSED(x);
-    UNUSED(y);
-    UNUSED(nb);
-    ggml_vec_dot_iq1_s_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
+
+    float sumf = 0;
+    for (int i = 0; i < nb; i++) {
+
+        const int8_t   * q8 = y[i].qs;
+        const uint8_t  * qs = x[i].qs;
+        const uint16_t * qh = x[i].qh;
+
+        int sumi = 0, sumi1 = 0;
+        for (int ib = 0; ib < QK_K/32; ++ib) {
+            const int ls = 2*((qh[ib] >> 12) & 7) + 1;
+            const int delta = qh[ib] & 0x8000 ? -1 : 1;
+            int lsum = 0;
+            for (int l = 0; l < 4; ++l) {
+                const int8_t * grid = (const int8_t *)(iq1s_grid + (qs[l] | (((qh[ib] >> 3*l) & 7) << 8)));
+                for (int j = 0; j < 8; ++j) {
+                    lsum += q8[j] * grid[j];
+                }
+                q8 += 8;
+            }
+            sumi  += ls * lsum;
+            sumi1 += ls * delta * (y[i].bsums[2*ib+0] + y[i].bsums[2*ib+1]);
+            qs += 4;
+        }
+
+        sumf += GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d * (sumi + IQ1S_DELTA * sumi1);
+    }
+
+    *s = sumf;
+
 #endif
 }
 
@@ -2151,10 +2603,37 @@ void ggml_vec_dot_iq4_xs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const v
     *s = hsum_float_8(accum);
 
 #else
-    UNUSED(x);
-    UNUSED(y);
-    UNUSED(nb);
-    ggml_vec_dot_iq4_xs_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
+    float sumf = 0;
+    for (int ibl = 0; ibl < nb; ++ibl) {
+        const float d4d8 = GGML_CPU_FP16_TO_FP32(x[ibl].d) * y[ibl].d;
+        uint16_t h = x[ibl].scales_h;
+        const uint8_t * qs = x[ibl].qs;
+        const int8_t  * q8 = y[ibl].qs;
+        for (int ib = 0; ib < QK_K/32; ib += 2) {
+            const uint8_t ls1 = (x[ibl].scales_l[ib/2] & 0xf) | ((h << 4) & 0x30);
+            const uint8_t ls2 = (x[ibl].scales_l[ib/2] >>  4) | ((h << 2) & 0x30);
+            h >>= 4;
+            const float d1 = d4d8*(ls1 - 32);
+            const float d2 = d4d8*(ls2 - 32);
+            int sumi1 = 0, sumi2 = 0;
+            for (int j = 0; j < 16; ++j) {
+                sumi1 += q8[j+ 0] * kvalues_iq4nl[qs[j] & 0xf];
+                sumi2 += q8[j+16] * kvalues_iq4nl[qs[j] >>  4];
+            }
+            sumf += d1 * (sumi1 + sumi2);
+            qs += 16;
+            q8 += 32;
+            sumi1 = sumi2 = 0;
+            for (int j = 0; j < 16; ++j) {
+                sumi1 += q8[j+ 0] * kvalues_iq4nl[qs[j] & 0xf];
+                sumi2 += q8[j+16] * kvalues_iq4nl[qs[j] >>  4];
+            }
+            sumf += d2 * (sumi1 + sumi2);
+            qs += 16;
+            q8 += 32;
+        }
+    }
+    *s = sumf;
 #endif
 }
 

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

@@ -201,14 +201,24 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const voi
 
     sumf = vec_extract(vsumf0, 0);
 
-    *s = sumf;
-#else
-    UNUSED(x);
-    UNUSED(y);
-    UNUSED(ib);
-    UNUSED(sumf);
-    ggml_vec_dot_q4_0_q8_0_generic(n, s, bs, vx, bx, vy, by, nrc);
 #endif
+    for (; ib < nb; ++ib) {
+        int sumi0 = 0;
+        int sumi1 = 0;
+
+        for (int j = 0; j < qk/2; ++j) {
+            const int v0 = (x[ib].qs[j] & 0x0F) - 8;
+            const int v1 = (x[ib].qs[j] >>   4) - 8;
+
+            sumi0 += (v0 * y[ib].qs[j]);
+            sumi1 += (v1 * y[ib].qs[j + qk/2]);
+        }
+
+        int sumi = sumi0 + sumi1;
+        sumf += sumi*GGML_CPU_FP16_TO_FP32(x[ib].d)*GGML_CPU_FP16_TO_FP32(y[ib].d);
+    }
+
+    *s = sumf;
 }
 
 void ggml_vec_dot_q4_1_q8_1(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
@@ -268,14 +278,24 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * GGML_RESTRICT s, size_t bs, const voi
 
     sumf = vec_extract(vsumf0, 0);
 
-    *s = sumf;
-#else
-    UNUSED(x);
-    UNUSED(y);
-    UNUSED(ib);
-    UNUSED(sumf);
-    ggml_vec_dot_q4_1_q8_1_generic(n, s, bs, vx, bx, vy, by, nrc);
 #endif
+    for (; ib < nb; ++ib) {
+        int sumi0 = 0;
+        int sumi1 = 0;
+
+        for (int j = 0; j < qk/2; ++j) {
+            const int v0 = (x[ib].qs[j] & 0x0F);
+            const int v1 = (x[ib].qs[j] >>   4);
+
+            sumi0 += (v0 * y[ib].qs[j]);
+            sumi1 += (v1 * y[ib].qs[j + qk/2]);
+        }
+
+        int sumi = sumi0 + sumi1;
+        sumf += (GGML_CPU_FP16_TO_FP32(x[ib].d)*GGML_CPU_FP16_TO_FP32(y[ib].d))*sumi + GGML_CPU_FP16_TO_FP32(x[ib].m)*GGML_CPU_FP16_TO_FP32(y[ib].s);
+    }
+
+    *s = sumf;
 }
 
 void ggml_vec_dot_q5_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
@@ -340,14 +360,30 @@ void ggml_vec_dot_q5_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const voi
 
     sumf = vec_extract(vsumf0, 0);
 
-    *s = sumf;
-#else
-    UNUSED(ib);
-    UNUSED(sumf);
-    UNUSED(x);
-    UNUSED(y);
-    ggml_vec_dot_q5_0_q8_0_generic(n, s, bs, vx, bx, vy, by, nrc);
 #endif
+    for (; ib < nb; ++ib) {
+        uint32_t qh;
+        memcpy(&qh, x[ib].qh, sizeof(qh));
+
+        int sumi0 = 0;
+        int sumi1 = 0;
+
+        for (int j = 0; j < qk/2; ++j) {
+            const uint8_t xh_0 = ((qh & (1u << (j + 0 ))) >> (j + 0 )) << 4;
+            const uint8_t xh_1 = ((qh & (1u << (j + 16))) >> (j + 12));
+
+            const int32_t x0 = (int8_t)(((x[ib].qs[j] & 0x0F) | xh_0) - 16);
+            const int32_t x1 = (int8_t)(((x[ib].qs[j] >>   4) | xh_1) - 16);
+
+            sumi0 += (x0 * y[ib].qs[j]);
+            sumi1 += (x1 * y[ib].qs[j + qk/2]);
+        }
+
+        int sumi = sumi0 + sumi1;
+        sumf += (GGML_CPU_FP16_TO_FP32(x[ib].d)*GGML_CPU_FP16_TO_FP32(y[ib].d)) * sumi;
+    }
+
+    *s = sumf;
 }
 
 void ggml_vec_dot_q5_1_q8_1(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
@@ -415,15 +451,30 @@ void ggml_vec_dot_q5_1_q8_1(int n, float * GGML_RESTRICT s, size_t bs, const voi
 
     sumf = vec_extract(vsumf0, 0);
 
-    *s = sumf;
-#else
-    UNUSED(nb);
-    UNUSED(ib);
-    UNUSED(sumf);
-    UNUSED(x);
-    UNUSED(y);
-    ggml_vec_dot_q5_1_q8_1_generic(n, s, bs, vx, bx, vy, by, nrc);
 #endif
+    for (; ib < nb; ++ib) {
+        uint32_t qh;
+        memcpy(&qh, x[ib].qh, sizeof(qh));
+
+        int sumi0 = 0;
+        int sumi1 = 0;
+
+        for (int j = 0; j < qk/2; ++j) {
+            const uint8_t xh_0 = ((qh >> (j +  0)) << 4) & 0x10;
+            const uint8_t xh_1 = ((qh >> (j + 12))     ) & 0x10;
+
+            const int32_t x0 = (x[ib].qs[j] & 0xF) | xh_0;
+            const int32_t x1 = (x[ib].qs[j] >>  4) | xh_1;
+
+            sumi0 += (x0 * y[ib].qs[j]);
+            sumi1 += (x1 * y[ib].qs[j + qk/2]);
+        }
+
+        int sumi = sumi0 + sumi1;
+        sumf += (GGML_CPU_FP16_TO_FP32(x[ib].d)*GGML_CPU_FP16_TO_FP32(y[ib].d))*sumi + GGML_CPU_FP16_TO_FP32(x[ib].m)*GGML_CPU_FP16_TO_FP32(y[ib].s);
+    }
+
+    *s = sumf;
 }
 
 void ggml_vec_dot_q8_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
@@ -484,15 +535,18 @@ void ggml_vec_dot_q8_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const voi
 
     sumf = vec_extract(vsumf0, 0);
 
-    *s = sumf;
-#else
-    UNUSED(nb);
-    UNUSED(x);
-    UNUSED(y);
-    UNUSED(ib);
-    UNUSED(sumf);
-    ggml_vec_dot_q8_0_q8_0_generic(n, s, bs, vx, bx, vy, by, nrc);
 #endif
+    for (; ib < nb; ++ib) {
+        int sumi = 0;
+
+        for (int j = 0; j < qk; j++) {
+            sumi += x[ib].qs[j]*y[ib].qs[j];
+        }
+
+        sumf += sumi*(GGML_CPU_FP16_TO_FP32(x[ib].d)*GGML_CPU_FP16_TO_FP32(y[ib].d));
+    }
+
+    *s = sumf;
 }
 
 void ggml_vec_dot_q2_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
@@ -641,10 +695,45 @@ void ggml_vec_dot_q2_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
     *s = vec_extract(vsumf0, 0);
 
 #else
-    UNUSED(x);
-    UNUSED(y);
-    UNUSED(nb);
-    ggml_vec_dot_q2_K_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
+
+    float sumf = 0;
+
+    for (int i = 0; i < nb; ++i) {
+
+        const uint8_t * q2 = x[i].qs;
+        const  int8_t * q8 = y[i].qs;
+        const uint8_t * sc = x[i].scales;
+
+        int summs = 0;
+        for (int j = 0; j < 16; ++j) {
+            summs += y[i].bsums[j] * (sc[j] >> 4);
+        }
+
+        const float dall = y[i].d * GGML_CPU_FP16_TO_FP32(x[i].d);
+        const float dmin = y[i].d * GGML_CPU_FP16_TO_FP32(x[i].dmin);
+
+        int isum = 0;
+        int is = 0;
+        int d;
+        for (int k = 0; k < QK_K/128; ++k) {
+            int shift = 0;
+            for (int j = 0; j < 4; ++j) {
+                d = sc[is++] & 0xF;
+                int isuml = 0;
+                for (int l =  0; l < 16; ++l) isuml += q8[l] * ((q2[l] >> shift) & 3);
+                isum += d * isuml;
+                d = sc[is++] & 0xF;
+                isuml = 0;
+                for (int l = 16; l < 32; ++l) isuml += q8[l] * ((q2[l] >> shift) & 3);
+                isum += d * isuml;
+                shift += 2;
+                q8 += 32;
+            }
+            q2 += 32;
+        }
+        sumf += dall * isum - dmin * summs;
+    }
+    *s = sumf;
 #endif
 }
 
@@ -818,13 +907,70 @@ void ggml_vec_dot_q3_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
     *s = vec_extract(vsumf0, 0);
 
 #else
-    UNUSED(kmask1);
-    UNUSED(kmask2);
-    UNUSED(x);
-    UNUSED(y);
-    UNUSED(nb);
-    ggml_vec_dot_q3_K_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
+    // scalar version
+    // This function is written like this so the compiler can manage to vectorize most of it
+    // Using -Ofast, GCC and clang manage to produce code that is within a factor of 2 or so from the
+    // manually vectorized version above. Every other version I tried would run at least 4 times slower.
+    // The ideal situation would be if we could just write the code once, and the compiler would
+    // automatically produce the best possible set of machine instructions, instead of us having to manually
+    // write vectorized versions for AVX, ARM_NEON, etc.
+
+    int8_t  aux8[QK_K];
+    int16_t aux16[8];
+    float   sums [8];
+    int32_t aux32[8];
+    memset(sums, 0, 8*sizeof(float));
+
+    uint32_t auxs[4];
+    const int8_t * scales = (const int8_t*)auxs;
+
+    float sumf = 0;
+    for (int i = 0; i < nb; ++i) {
+        const uint8_t * GGML_RESTRICT q3 = x[i].qs;
+        const uint8_t * GGML_RESTRICT hm = x[i].hmask;
+        const  int8_t * GGML_RESTRICT q8 = y[i].qs;
+        memset(aux32, 0, 8*sizeof(int32_t));
+        int8_t * GGML_RESTRICT a = aux8;
+        uint8_t m = 1;
+        for (int j = 0; j < QK_K; j += 128) {
+            for (int l = 0; l < 32; ++l) a[l] = q3[l] & 3;
+            for (int l = 0; l < 32; ++l) a[l] -= (hm[l] & m ? 0 : 4);
+            a += 32; m <<= 1;
+            for (int l = 0; l < 32; ++l) a[l] = (q3[l] >> 2) & 3;
+            for (int l = 0; l < 32; ++l) a[l] -= (hm[l] & m ? 0 : 4);
+            a += 32; m <<= 1;
+            for (int l = 0; l < 32; ++l) a[l] = (q3[l] >> 4) & 3;
+            for (int l = 0; l < 32; ++l) a[l] -= (hm[l] & m ? 0 : 4);
+            a += 32; m <<= 1;
+            for (int l = 0; l < 32; ++l) a[l] = (q3[l] >> 6) & 3;
+            for (int l = 0; l < 32; ++l) a[l] -= (hm[l] & m ? 0 : 4);
+            a += 32; m <<= 1;
+            q3 += 32;
+        }
+        a = aux8;
+
+        memcpy(auxs, x[i].scales, 12);
+        uint32_t tmp = auxs[2];
+        auxs[2] = ((auxs[0] >> 4) & kmask2) | (((tmp >> 4) & kmask1) << 4);
+        auxs[3] = ((auxs[1] >> 4) & kmask2) | (((tmp >> 6) & kmask1) << 4);
+        auxs[0] = (auxs[0] & kmask2) | (((tmp >> 0) & kmask1) << 4);
+        auxs[1] = (auxs[1] & kmask2) | (((tmp >> 2) & kmask1) << 4);
+        for (int j = 0; j < QK_K/16; ++j) {
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += (scales[j] - 32) * aux16[l];
+            q8 += 8; a += 8;
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += (scales[j] - 32) * aux16[l];
+            q8 += 8; a += 8;
+        }
+        const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
+        for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l];
+    }
+    for (int l = 0; l < 8; ++l) sumf += sums[l];
+    *s = sumf;
+
 #endif
+
 }
 
 void ggml_vec_dot_q4_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
@@ -984,14 +1130,61 @@ void ggml_vec_dot_q4_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
     *s = vec_extract(vsumf0, 0);
 
 #else
-    UNUSED(x);
-    UNUSED(y);
-    UNUSED(nb);
-    UNUSED(kmask1);
-    UNUSED(kmask2);
-    UNUSED(kmask3);
-    UNUSED(utmp);
-    ggml_vec_dot_q4_K_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
+
+    const uint8_t * scales = (const uint8_t*)&utmp[0];
+    const uint8_t * mins   = (const uint8_t*)&utmp[2];
+
+    int8_t  aux8[QK_K];
+    int16_t aux16[8];
+    float   sums [8];
+    int32_t aux32[8];
+    memset(sums, 0, 8*sizeof(float));
+
+    float sumf = 0;
+    for (int i = 0; i < nb; ++i) {
+        const uint8_t * GGML_RESTRICT q4 = x[i].qs;
+        const  int8_t * GGML_RESTRICT q8 = y[i].qs;
+        memset(aux32, 0, 8*sizeof(int32_t));
+        int8_t * GGML_RESTRICT a = aux8;
+        for (int j = 0; j < QK_K/64; ++j) {
+            for (int l = 0; l < 32; ++l) a[l] = (int8_t)(q4[l] & 0xF);
+            a += 32;
+            for (int l = 0; l < 32; ++l) a[l] = (int8_t)(q4[l]  >> 4);
+            a += 32; q4 += 32;
+        }
+        memcpy(utmp, x[i].scales, 12);
+        utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4);
+        const uint32_t uaux = utmp[1] & kmask1;
+        utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4);
+        utmp[2] = uaux;
+        utmp[0] &= kmask1;
+
+        int sumi = 0;
+        for (int j = 0; j < QK_K/16; ++j) sumi += y[i].bsums[j] * mins[j/2];
+        a = aux8;
+        int is = 0;
+        for (int j = 0; j < QK_K/32; ++j) {
+            int32_t scale = scales[is++];
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
+            q8 += 8; a += 8;
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
+            q8 += 8; a += 8;
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
+            q8 += 8; a += 8;
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
+            q8 += 8; a += 8;
+        }
+        const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
+        for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l];
+        const float dmin = GGML_CPU_FP16_TO_FP32(x[i].dmin) * y[i].d;
+        sumf -= dmin * sumi;
+    }
+    for (int l = 0; l < 8; ++l) sumf += sums[l];
+    *s = sumf;
 #endif
 }
 
@@ -1149,14 +1342,66 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
     *s = vec_extract(vsumf0, 0);
 
 #else
-    UNUSED(x);
-    UNUSED(y);
-    UNUSED(nb);
-    UNUSED(kmask1);
-    UNUSED(kmask2);
-    UNUSED(kmask3);
-    UNUSED(utmp);
-    ggml_vec_dot_q5_K_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
+
+    const uint8_t * scales = (const uint8_t*)&utmp[0];
+    const uint8_t * mins   = (const uint8_t*)&utmp[2];
+
+    int8_t  aux8[QK_K];
+    int16_t aux16[8];
+    float   sums [8];
+    int32_t aux32[8];
+    memset(sums, 0, 8*sizeof(float));
+
+    float sumf = 0;
+    for (int i = 0; i < nb; ++i) {
+        const uint8_t * GGML_RESTRICT q4 = x[i].qs;
+        const uint8_t * GGML_RESTRICT hm = x[i].qh;
+        const  int8_t * GGML_RESTRICT q8 = y[i].qs;
+        memset(aux32, 0, 8*sizeof(int32_t));
+        int8_t * GGML_RESTRICT a = aux8;
+        uint8_t m = 1;
+        for (int j = 0; j < QK_K/64; ++j) {
+            for (int l = 0; l < 32; ++l) a[l] = (int8_t)(q4[l] & 0xF);
+            for (int l = 0; l < 32; ++l) a[l] += (hm[l] & m ? 16 : 0);
+            a += 32; m <<= 1;
+            for (int l = 0; l < 32; ++l) a[l] = (int8_t)(q4[l]  >> 4);
+            for (int l = 0; l < 32; ++l) a[l] += (hm[l] & m ? 16 : 0);
+            a += 32; m <<= 1;
+            q4 += 32;
+        }
+        memcpy(utmp, x[i].scales, 12);
+        utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4);
+        const uint32_t uaux = utmp[1] & kmask1;
+        utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4);
+        utmp[2] = uaux;
+        utmp[0] &= kmask1;
+
+        int sumi = 0;
+        for (int j = 0; j < QK_K/16; ++j) sumi += y[i].bsums[j] * mins[j/2];
+        a = aux8;
+        int is = 0;
+        for (int j = 0; j < QK_K/32; ++j) {
+            int32_t scale = scales[is++];
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
+            q8 += 8; a += 8;
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
+            q8 += 8; a += 8;
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
+            q8 += 8; a += 8;
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
+            q8 += 8; a += 8;
+        }
+        const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
+        for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l];
+        const float dmin = GGML_CPU_FP16_TO_FP32(x[i].dmin) * y[i].d;
+        sumf -= dmin * sumi;
+    }
+    for (int l = 0; l < 8; ++l) sumf += sums[l];
+    *s = sumf;
 #endif
 }
 
@@ -1311,10 +1556,47 @@ void ggml_vec_dot_q6_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
     *s = vec_extract(vsumf0, 0);
 
 #else
-    UNUSED(x);
-    UNUSED(y);
-    UNUSED(nb);
-    ggml_vec_dot_q6_K_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
+
+    int8_t  aux8[QK_K];
+    int16_t aux16[8];
+    float   sums [8];
+    int32_t aux32[8];
+    memset(sums, 0, 8*sizeof(float));
+
+    float sumf = 0;
+    for (int i = 0; i < nb; ++i) {
+        const uint8_t * GGML_RESTRICT q4 = x[i].ql;
+        const uint8_t * GGML_RESTRICT qh = x[i].qh;
+        const  int8_t * GGML_RESTRICT q8 = y[i].qs;
+        memset(aux32, 0, 8*sizeof(int32_t));
+        int8_t * GGML_RESTRICT a = aux8;
+        for (int j = 0; j < QK_K; j += 128) {
+            for (int l = 0; l < 32; ++l) {
+                a[l +  0] = (int8_t)((q4[l +  0] & 0xF) | (((qh[l] >> 0) & 3) << 4)) - 32;
+                a[l + 32] = (int8_t)((q4[l + 32] & 0xF) | (((qh[l] >> 2) & 3) << 4)) - 32;
+                a[l + 64] = (int8_t)((q4[l +  0] >>  4) | (((qh[l] >> 4) & 3) << 4)) - 32;
+                a[l + 96] = (int8_t)((q4[l + 32] >>  4) | (((qh[l] >> 6) & 3) << 4)) - 32;
+            }
+            a  += 128;
+            q4 += 64;
+            qh += 32;
+        }
+        a = aux8;
+        int is = 0;
+        for (int j = 0; j < QK_K/16; ++j) {
+            int scale = x[i].scales[is++];
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
+            q8 += 8; a += 8;
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
+            q8 += 8; a += 8;
+        }
+        const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
+        for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l];
+    }
+    for (int l = 0; l < 8; ++l) sumf += sums[l];
+    *s = sumf;
 #endif
 }
 
@@ -1455,10 +1737,34 @@ void ggml_vec_dot_iq2_xxs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const
     *s = 0.125f * vec_extract(vsumf0, 0);
 
 #else
-    UNUSED(x);
-    UNUSED(y);
-    UNUSED(nb);
-    ggml_vec_dot_iq2_xxs_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
+
+    uint32_t aux32[2];
+    const uint8_t * aux8 = (const uint8_t *)aux32;
+
+    float sumf = 0.f;
+    for (int i = 0; i < nb; ++i) {
+        const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
+        const uint16_t * GGML_RESTRICT q2 = x[i].qs;
+        const int8_t   * GGML_RESTRICT q8 = y[i].qs;
+        int32_t bsum = 0;
+        for (int ib32 = 0; ib32 < QK_K/32; ++ib32) {
+            memcpy(aux32, q2, 2*sizeof(uint32_t));
+            q2 += 4;
+            const uint32_t ls = 2*(aux32[1] >> 28) + 1;
+            int32_t sumi = 0;
+            for (int l = 0; l < 4; ++l) {
+                const uint8_t * grid = (const uint8_t *)(iq2xxs_grid + aux8[l]);
+                const uint8_t  signs = ksigns_iq2xs[(aux32[1] >> 7*l) & 127];
+                for (int j = 0; j < 8; ++j) {
+                    sumi += grid[j] * q8[j] * (signs & kmask_iq2xs[j] ? -1 : 1);
+                }
+                q8 += 8;
+            }
+            bsum += sumi * ls;
+        }
+        sumf += d * bsum;
+    }
+    *s = 0.125f * sumf;
 #endif
 }
 
@@ -1563,10 +1869,42 @@ void ggml_vec_dot_iq2_xs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const v
     *s = 0.125f * vec_extract(vsumf0, 0);
 
 #else
-    UNUSED(x);
-    UNUSED(y);
-    UNUSED(nb);
-    ggml_vec_dot_iq2_xs_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
+
+    float sumf = 0.f;
+    for (int i = 0; i < nb; ++i) {
+        const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
+        const uint16_t * GGML_RESTRICT q2 = x[i].qs;
+        const uint8_t  * GGML_RESTRICT sc = x[i].scales;
+        const int8_t   * GGML_RESTRICT q8 = y[i].qs;
+        int32_t bsum = 0;
+        for (int ib32 = 0; ib32 < QK_K/32; ++ib32) {
+            const uint16_t ls1 = 2*(sc[ib32] & 0xf) + 1;
+            const uint16_t ls2 = 2*(sc[ib32] >>  4) + 1;
+            int32_t sumi = 0;
+            for (int l = 0; l < 2; ++l) {
+                const uint8_t * grid = (const uint8_t *)(iq2xs_grid + (q2[l] & 511));
+                const uint8_t  signs = ksigns_iq2xs[q2[l] >> 9];
+                for (int j = 0; j < 8; ++j) {
+                    sumi += grid[j] * q8[j] * (signs & kmask_iq2xs[j] ? -1 : 1);
+                }
+                q8 += 8;
+            }
+            bsum += sumi * ls1;
+            sumi = 0;
+            for (int l = 2; l < 4; ++l) {
+                const uint8_t * grid = (const uint8_t *)(iq2xs_grid + (q2[l] & 511));
+                const uint8_t  signs = ksigns_iq2xs[q2[l] >> 9];
+                for (int j = 0; j < 8; ++j) {
+                    sumi += grid[j] * q8[j] * (signs & kmask_iq2xs[j] ? -1 : 1);
+                }
+                q8 += 8;
+            }
+            bsum += sumi * ls2;
+            q2 += 4;
+        }
+        sumf += d * bsum;
+    }
+    *s = 0.125f * sumf;
 #endif
 }
 
@@ -1692,11 +2030,47 @@ void ggml_vec_dot_iq2_s_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const vo
     *s = 0.125f * vec_extract(vsumf0, 0);
 
 #else
-    UNUSED(x);
-    UNUSED(y);
-    UNUSED(nb);
-    ggml_vec_dot_iq2_s_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
+
+    float sumf = 0;
+    for (int i = 0; i < nb; i++) {
+
+        const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
+        const int8_t  * q8 = y[i].qs;
+        const uint8_t * qs = x[i].qs;
+        const uint8_t * qh = x[i].qh;
+        const uint8_t * signs = qs + QK_K/8;
+
+        int bsum = 0;
+        for (int ib32 = 0; ib32 < QK_K/32; ++ib32) {
+            int ls1 = 1 + 2*(x[i].scales[ib32] & 0xf);
+            int ls2 = 1 + 2*(x[i].scales[ib32] >>  4);
+            int sumi1 = 0, sumi2 = 0;
+            for (int l = 0; l < 2; ++l) {
+                const uint8_t * grid = (const uint8_t *)(iq2s_grid + (qs[l] | (qh[ib32] << (8-2*l) & 0x300)));
+                for (int j = 0; j < 8; ++j) {
+                    sumi1 += q8[j] * grid[j] * (signs[l] & kmask_iq2xs[j] ? -1 : 1);
+                }
+                q8 += 8;
+            }
+            for (int l = 2; l < 4; ++l) {
+                const uint8_t * grid = (const uint8_t *)(iq2s_grid + (qs[l] | (qh[ib32] << (8-2*l) & 0x300)));
+                for (int j = 0; j < 8; ++j) {
+                    sumi2 += q8[j] * grid[j] * (signs[l] & kmask_iq2xs[j] ? -1 : 1);
+                }
+                q8 += 8;
+            }
+            bsum += ls1 * sumi1 + ls2 * sumi2;
+            qs += 4;
+            signs += 4;
+        }
+
+        sumf += d * bsum;
+    }
+
+    *s = 0.125f * sumf;
+
 #endif
+
 }
 
 void ggml_vec_dot_iq3_xxs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
@@ -1798,10 +2172,36 @@ void ggml_vec_dot_iq3_xxs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const
     *s = 0.25f * vec_extract(vsumf0, 0);
 
 #else
-    UNUSED(x);
-    UNUSED(y);
-    UNUSED(nb);
-    ggml_vec_dot_iq3_xxs_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
+
+    uint32_t aux32;
+
+    float sumf = 0.f;
+    for (int i = 0; i < nb; ++i) {
+        const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
+        const uint8_t * GGML_RESTRICT q3 = x[i].qs;
+        const uint8_t * GGML_RESTRICT gas = x[i].qs + QK_K/4;
+        const int8_t  * GGML_RESTRICT q8 = y[i].qs;
+        int32_t bsum = 0;
+        for (int ib32 = 0; ib32 < QK_K/32; ++ib32) {
+            memcpy(&aux32, gas, sizeof(uint32_t)); gas += sizeof(uint32_t);
+            const uint32_t ls = 2*(aux32 >> 28) + 1;
+            int32_t sumi = 0;
+            for (int l = 0; l < 4; ++l) {
+                const uint8_t * grid1 = (const uint8_t *)(iq3xxs_grid + q3[2*l+0]);
+                const uint8_t * grid2 = (const uint8_t *)(iq3xxs_grid + q3[2*l+1]);
+                const uint8_t  signs = ksigns_iq2xs[(aux32 >> 7*l) & 127];
+                for (int j = 0; j < 4; ++j) {
+                    sumi += grid1[j] * q8[j+0] * (signs & kmask_iq2xs[j+0] ? -1 : 1);
+                    sumi += grid2[j] * q8[j+4] * (signs & kmask_iq2xs[j+4] ? -1 : 1);
+                }
+                q8 += 8;
+            }
+            q3 += 8;
+            bsum += sumi * ls;
+        }
+        sumf += d * bsum;
+    }
+    *s = 0.25f * sumf;
 #endif
 }
 
@@ -1927,10 +2327,48 @@ void ggml_vec_dot_iq3_s_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const vo
     *s = vec_extract(vsumf0, 0);
 
 #else
-    UNUSED(x);
-    UNUSED(y);
-    UNUSED(nb);
-    ggml_vec_dot_iq3_s_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
+
+    float sumf = 0.f;
+    for (int i = 0; i < nb; ++i) {
+        const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
+        const uint8_t * GGML_RESTRICT qs = x[i].qs;
+        const uint8_t * GGML_RESTRICT qh = x[i].qh;
+        const uint8_t * GGML_RESTRICT signs = x[i].signs;
+        const int8_t  * GGML_RESTRICT q8 = y[i].qs;
+        int32_t bsum = 0;
+        for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) {
+            const uint32_t ls1 = 2*(x[i].scales[ib32/2] & 0xf) + 1;
+            const uint32_t ls2 = 2*(x[i].scales[ib32/2] >>  4) + 1;
+            int32_t sumi = 0;
+            for (int l = 0; l < 4; ++l) {
+                const uint8_t * grid1 = (const uint8_t *)(iq3s_grid + (qs[2*l+0] | ((qh[ib32+0] << (8-2*l)) & 256)));
+                const uint8_t * grid2 = (const uint8_t *)(iq3s_grid + (qs[2*l+1] | ((qh[ib32+0] << (7-2*l)) & 256)));
+                for (int j = 0; j < 4; ++j) {
+                    sumi += grid1[j] * q8[j+0] * (signs[l] & kmask_iq2xs[j+0] ? -1 : 1);
+                    sumi += grid2[j] * q8[j+4] * (signs[l] & kmask_iq2xs[j+4] ? -1 : 1);
+                }
+                q8 += 8;
+            }
+            qs += 8;
+            signs += 4;
+            bsum += sumi * ls1;
+            sumi = 0;
+            for (int l = 0; l < 4; ++l) {
+                const uint8_t * grid1 = (const uint8_t *)(iq3s_grid + (qs[2*l+0] | ((qh[ib32+1] << (8-2*l)) & 256)));
+                const uint8_t * grid2 = (const uint8_t *)(iq3s_grid + (qs[2*l+1] | ((qh[ib32+1] << (7-2*l)) & 256)));
+                for (int j = 0; j < 4; ++j) {
+                    sumi += grid1[j] * q8[j+0] * (signs[l] & kmask_iq2xs[j+0] ? -1 : 1);
+                    sumi += grid2[j] * q8[j+4] * (signs[l] & kmask_iq2xs[j+4] ? -1 : 1);
+                }
+                q8 += 8;
+            }
+            qs += 8;
+            signs += 4;
+            bsum += sumi * ls2;
+        }
+        sumf += d * bsum;
+    }
+    *s = sumf;
 #endif
 }
 
@@ -2043,10 +2481,36 @@ void ggml_vec_dot_iq1_s_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const vo
     *s = vec_extract(vsumf0, 0);
 
 #else
-    UNUSED(x);
-    UNUSED(y);
-    UNUSED(nb);
-    ggml_vec_dot_iq1_s_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
+
+    float sumf = 0;
+    for (int i = 0; i < nb; i++) {
+
+        const int8_t   * q8 = y[i].qs;
+        const uint8_t  * qs = x[i].qs;
+        const uint16_t * qh = x[i].qh;
+
+        int sumi = 0, sumi1 = 0;
+        for (int ib = 0; ib < QK_K/32; ++ib) {
+            const int ls = 2*((qh[ib] >> 12) & 7) + 1;
+            const int delta = qh[ib] & 0x8000 ? -1 : 1;
+            int lsum = 0;
+            for (int l = 0; l < 4; ++l) {
+                const int8_t * grid = (const int8_t *)(iq1s_grid + (qs[l] | (((qh[ib] >> 3*l) & 7) << 8)));
+                for (int j = 0; j < 8; ++j) {
+                    lsum += q8[j] * grid[j];
+                }
+                q8 += 8;
+            }
+            sumi  += ls * lsum;
+            sumi1 += ls * delta * (y[i].bsums[2*ib+0] + y[i].bsums[2*ib+1]);
+            qs += 4;
+        }
+
+        sumf += GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d * (sumi + IQ1S_DELTA * sumi1);
+    }
+
+    *s = sumf;
+
 #endif
 }
 
@@ -2117,15 +2581,17 @@ void ggml_vec_dot_iq4_nl_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const v
 
     sumf = vec_extract(vsumf0, 0);
 
-    *s = sumf;
-#else
-    UNUSED(x);
-    UNUSED(y);
-    UNUSED(nb);
-    UNUSED(ib);
-    UNUSED(sumf);
-    ggml_vec_dot_iq4_nl_q8_0_generic(n, s, bs, vx, bx, vy, by, nrc);
 #endif
+    for (; ib < nb; ++ib) {
+        const float d = GGML_CPU_FP16_TO_FP32(y[ib].d)*GGML_CPU_FP16_TO_FP32(x[ib].d);
+        int sumi1 = 0, sumi2 = 0;
+        for (int j = 0; j < QK4_NL/2; ++j) {
+            sumi1 += y[ib].qs[j+       0] * kvalues_iq4nl[x[ib].qs[j] & 0xf];
+            sumi2 += y[ib].qs[j+QK4_NL/2] * kvalues_iq4nl[x[ib].qs[j] >>  4];
+        }
+        sumf += d * (sumi1 + sumi2);
+    }
+    *s = sumf;
 }
 
 void ggml_vec_dot_iq4_xs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
@@ -2230,10 +2696,37 @@ void ggml_vec_dot_iq4_xs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const v
     *s = vec_extract(vsumf0, 0);
 
 #else
-    UNUSED(x);
-    UNUSED(y);
-    UNUSED(nb);
-    ggml_vec_dot_iq4_xs_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
+    float sumf = 0;
+    for (int ibl = 0; ibl < nb; ++ibl) {
+        const float d4d8 = GGML_CPU_FP16_TO_FP32(x[ibl].d) * y[ibl].d;
+        uint16_t h = x[ibl].scales_h;
+        const uint8_t * qs = x[ibl].qs;
+        const int8_t  * q8 = y[ibl].qs;
+        for (int ib = 0; ib < QK_K/32; ib += 2) {
+            const uint8_t ls1 = (x[ibl].scales_l[ib/2] & 0xf) | ((h << 4) & 0x30);
+            const uint8_t ls2 = (x[ibl].scales_l[ib/2] >>  4) | ((h << 2) & 0x30);
+            h >>= 4;
+            const float d1 = d4d8*(ls1 - 32);
+            const float d2 = d4d8*(ls2 - 32);
+            int sumi1 = 0, sumi2 = 0;
+            for (int j = 0; j < 16; ++j) {
+                sumi1 += q8[j+ 0] * kvalues_iq4nl[qs[j] & 0xf];
+                sumi2 += q8[j+16] * kvalues_iq4nl[qs[j] >>  4];
+            }
+            sumf += d1 * (sumi1 + sumi2);
+            qs += 16;
+            q8 += 32;
+            sumi1 = sumi2 = 0;
+            for (int j = 0; j < 16; ++j) {
+                sumi1 += q8[j+ 0] * kvalues_iq4nl[qs[j] & 0xf];
+                sumi2 += q8[j+16] * kvalues_iq4nl[qs[j] >>  4];
+            }
+            sumf += d2 * (sumi1 + sumi2);
+            qs += 16;
+            q8 += 32;
+        }
+    }
+    *s = sumf;
 #endif
 }
 

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

@@ -116,7 +116,6 @@ void quantize_row_q8_1(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, i
 //===================================== Dot products =================================
 
 void ggml_vec_dot_q4_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
-#if defined(__riscv_v)
     const int qk = QK8_0;
     const int nb = n / qk;
 
@@ -133,6 +132,7 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const voi
     int ib = 0;
     float sumf = 0;
 
+#if defined(__riscv_v)
     size_t vl = qk / 2;
 
     for (; ib < nb; ++ib) {
@@ -164,14 +164,27 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const voi
         sumf += sumi*GGML_CPU_FP16_TO_FP32(x[ib].d)*GGML_CPU_FP16_TO_FP32(y[ib].d);
     }
 
-    *s = sumf;
-#else
-    ggml_vec_dot_q4_0_q8_0_generic(n, s, bs, vx, bx, vy, by, nrc);
 #endif
+    for (; ib < nb; ++ib) {
+        int sumi0 = 0;
+        int sumi1 = 0;
+
+        for (int j = 0; j < qk/2; ++j) {
+            const int v0 = (x[ib].qs[j] & 0x0F) - 8;
+            const int v1 = (x[ib].qs[j] >>   4) - 8;
+
+            sumi0 += (v0 * y[ib].qs[j]);
+            sumi1 += (v1 * y[ib].qs[j + qk/2]);
+        }
+
+        int sumi = sumi0 + sumi1;
+        sumf += sumi*GGML_CPU_FP16_TO_FP32(x[ib].d)*GGML_CPU_FP16_TO_FP32(y[ib].d);
+    }
+
+    *s = sumf;
 }
 
 void ggml_vec_dot_q4_1_q8_1(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
-#if defined(__riscv_v)
     const int qk = QK8_1;
     const int nb = n / qk;
 
@@ -188,6 +201,7 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * GGML_RESTRICT s, size_t bs, const voi
     int ib = 0;
     float sumf = 0;
 
+#if defined(__riscv_v)
     size_t vl = qk / 2;
 
     for (; ib < nb; ++ib) {
@@ -215,14 +229,27 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * GGML_RESTRICT s, size_t bs, const voi
         sumf += (GGML_CPU_FP16_TO_FP32(x[ib].d)*GGML_CPU_FP16_TO_FP32(y[ib].d))*sumi + GGML_CPU_FP16_TO_FP32(x[ib].m)*GGML_CPU_FP16_TO_FP32(y[ib].s);
     }
 
-    *s = sumf;
-#else
-    ggml_vec_dot_q4_1_q8_1_generic(n, s, bs, vx, bx, vy, by, nrc);
 #endif
+    for (; ib < nb; ++ib) {
+        int sumi0 = 0;
+        int sumi1 = 0;
+
+        for (int j = 0; j < qk/2; ++j) {
+            const int v0 = (x[ib].qs[j] & 0x0F);
+            const int v1 = (x[ib].qs[j] >>   4);
+
+            sumi0 += (v0 * y[ib].qs[j]);
+            sumi1 += (v1 * y[ib].qs[j + qk/2]);
+        }
+
+        int sumi = sumi0 + sumi1;
+        sumf += (GGML_CPU_FP16_TO_FP32(x[ib].d)*GGML_CPU_FP16_TO_FP32(y[ib].d))*sumi + GGML_CPU_FP16_TO_FP32(x[ib].m)*GGML_CPU_FP16_TO_FP32(y[ib].s);
+    }
+
+    *s = sumf;
 }
 
 void ggml_vec_dot_q5_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
-#if defined(__riscv_v)
     const int qk = QK8_0;
     const int nb = n / qk;
 
@@ -240,6 +267,7 @@ void ggml_vec_dot_q5_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const voi
     const block_q5_0 * GGML_RESTRICT x = vx;
     const block_q8_0 * GGML_RESTRICT y = vy;
 
+#if defined(__riscv_v)
     size_t vl;
     size_t vlenb = __riscv_vlenb();
 
@@ -269,14 +297,33 @@ void ggml_vec_dot_q5_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const voi
         sumf += (GGML_CPU_FP16_TO_FP32(x[ib].d) * GGML_CPU_FP16_TO_FP32(y[ib].d)) * sumi;
     }
 
-    *s = sumf;
-#else
-    ggml_vec_dot_q5_0_q8_0_generic(n, s, bs, vx, bx, vy, by, nrc);
 #endif
+    for (; ib < nb; ++ib) {
+        uint32_t qh;
+        memcpy(&qh, x[ib].qh, sizeof(qh));
+
+        int sumi0 = 0;
+        int sumi1 = 0;
+
+        for (int j = 0; j < qk/2; ++j) {
+            const uint8_t xh_0 = ((qh & (1u << (j + 0 ))) >> (j + 0 )) << 4;
+            const uint8_t xh_1 = ((qh & (1u << (j + 16))) >> (j + 12));
+
+            const int32_t x0 = (int8_t)(((x[ib].qs[j] & 0x0F) | xh_0) - 16);
+            const int32_t x1 = (int8_t)(((x[ib].qs[j] >>   4) | xh_1) - 16);
+
+            sumi0 += (x0 * y[ib].qs[j]);
+            sumi1 += (x1 * y[ib].qs[j + qk/2]);
+        }
+
+        int sumi = sumi0 + sumi1;
+        sumf += (GGML_CPU_FP16_TO_FP32(x[ib].d)*GGML_CPU_FP16_TO_FP32(y[ib].d)) * sumi;
+    }
+
+    *s = sumf;
 }
 
 void ggml_vec_dot_q5_1_q8_1(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
-#if defined(__riscv_v)
     const int qk = QK8_1;
     const int nb = n / qk;
 
@@ -294,6 +341,7 @@ void ggml_vec_dot_q5_1_q8_1(int n, float * GGML_RESTRICT s, size_t bs, const voi
     const block_q5_1 * GGML_RESTRICT x = vx;
     const block_q8_1 * GGML_RESTRICT y = vy;
 
+#if defined(__riscv_v)
     size_t vl;
     size_t vlenb = __riscv_vlenb();
 
@@ -322,10 +370,30 @@ void ggml_vec_dot_q5_1_q8_1(int n, float * GGML_RESTRICT s, size_t bs, const voi
         sumf += (GGML_CPU_FP16_TO_FP32(x[ib].d)*GGML_CPU_FP16_TO_FP32(y[ib].d))*sumi + GGML_CPU_FP16_TO_FP32(x[ib].m)*GGML_CPU_FP16_TO_FP32(y[ib].s);
     }
 
-    *s = sumf;
-#else
-    ggml_vec_dot_q5_1_q8_1_generic(n, s, bs, vx, bx, vy, by, nrc);
 #endif
+    for (; ib < nb; ++ib) {
+        uint32_t qh;
+        memcpy(&qh, x[ib].qh, sizeof(qh));
+
+        int sumi0 = 0;
+        int sumi1 = 0;
+
+        for (int j = 0; j < qk/2; ++j) {
+            const uint8_t xh_0 = ((qh >> (j +  0)) << 4) & 0x10;
+            const uint8_t xh_1 = ((qh >> (j + 12))     ) & 0x10;
+
+            const int32_t x0 = (x[ib].qs[j] & 0xF) | xh_0;
+            const int32_t x1 = (x[ib].qs[j] >>  4) | xh_1;
+
+            sumi0 += (x0 * y[ib].qs[j]);
+            sumi1 += (x1 * y[ib].qs[j + qk/2]);
+        }
+
+        int sumi = sumi0 + sumi1;
+        sumf += (GGML_CPU_FP16_TO_FP32(x[ib].d)*GGML_CPU_FP16_TO_FP32(y[ib].d))*sumi + GGML_CPU_FP16_TO_FP32(x[ib].m)*GGML_CPU_FP16_TO_FP32(y[ib].s);
+    }
+
+    *s = sumf;
 }
 
 void ggml_vec_dot_q8_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
@@ -363,17 +431,18 @@ void ggml_vec_dot_q8_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const voi
         sumf += sumi*(GGML_CPU_FP16_TO_FP32(x[ib].d)*GGML_CPU_FP16_TO_FP32(y[ib].d));
     }
 
-    *s = sumf;
-#else
-
-    UNUSED(nb);
-    UNUSED(x);
-    UNUSED(y);
-    UNUSED(ib);
-    UNUSED(sumf);
-
-    ggml_vec_dot_q8_0_q8_0_generic(n, s, bs, vx, bx, vy, by, nrc);
 #endif
+    for (; ib < nb; ++ib) {
+        int sumi = 0;
+
+        for (int j = 0; j < qk; j++) {
+            sumi += x[ib].qs[j]*y[ib].qs[j];
+        }
+
+        sumf += sumi*(GGML_CPU_FP16_TO_FP32(x[ib].d)*GGML_CPU_FP16_TO_FP32(y[ib].d));
+    }
+
+    *s = sumf;
 }
 
 void ggml_vec_dot_q2_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
@@ -669,11 +738,44 @@ void ggml_vec_dot_q2_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
 
 #else
 
-    UNUSED(x);
-    UNUSED(y);
-    UNUSED(nb);
+    float sumf = 0;
 
-    ggml_vec_dot_q2_K_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
+    for (int i = 0; i < nb; ++i) {
+
+        const uint8_t * q2 = x[i].qs;
+        const  int8_t * q8 = y[i].qs;
+        const uint8_t * sc = x[i].scales;
+
+        int summs = 0;
+        for (int j = 0; j < 16; ++j) {
+            summs += y[i].bsums[j] * (sc[j] >> 4);
+        }
+
+        const float dall = y[i].d * GGML_CPU_FP16_TO_FP32(x[i].d);
+        const float dmin = y[i].d * GGML_CPU_FP16_TO_FP32(x[i].dmin);
+
+        int isum = 0;
+        int is = 0;
+        int d;
+        for (int k = 0; k < QK_K/128; ++k) {
+            int shift = 0;
+            for (int j = 0; j < 4; ++j) {
+                d = sc[is++] & 0xF;
+                int isuml = 0;
+                for (int l =  0; l < 16; ++l) isuml += q8[l] * ((q2[l] >> shift) & 3);
+                isum += d * isuml;
+                d = sc[is++] & 0xF;
+                isuml = 0;
+                for (int l = 16; l < 32; ++l) isuml += q8[l] * ((q2[l] >> shift) & 3);
+                isum += d * isuml;
+                shift += 2;
+                q8 += 32;
+            }
+            q2 += 32;
+        }
+        sumf += dall * isum - dmin * summs;
+    }
+    *s = sumf;
 #endif
 }
 
@@ -1045,14 +1147,68 @@ void ggml_vec_dot_q3_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
     *s = sumf;
 
 #else
+    // scalar version
+    // This function is written like this so the compiler can manage to vectorize most of it
+    // Using -Ofast, GCC and clang manage to produce code that is within a factor of 2 or so from the
+    // manually vectorized version above. Every other version I tried would run at least 4 times slower.
+    // The ideal situation would be if we could just write the code once, and the compiler would
+    // automatically produce the best possible set of machine instructions, instead of us having to manually
+    // write vectorized versions for AVX, ARM_NEON, etc.
 
-    UNUSED(kmask1);
-    UNUSED(kmask2);
-    UNUSED(x);
-    UNUSED(y);
-    UNUSED(nb);
+    int8_t  aux8[QK_K];
+    int16_t aux16[8];
+    float   sums [8];
+    int32_t aux32[8];
+    memset(sums, 0, 8*sizeof(float));
+
+    uint32_t auxs[4];
+    const int8_t * scales = (const int8_t*)auxs;
+
+    float sumf = 0;
+    for (int i = 0; i < nb; ++i) {
+        const uint8_t * GGML_RESTRICT q3 = x[i].qs;
+        const uint8_t * GGML_RESTRICT hm = x[i].hmask;
+        const  int8_t * GGML_RESTRICT q8 = y[i].qs;
+        memset(aux32, 0, 8*sizeof(int32_t));
+        int8_t * GGML_RESTRICT a = aux8;
+        uint8_t m = 1;
+        for (int j = 0; j < QK_K; j += 128) {
+            for (int l = 0; l < 32; ++l) a[l] = q3[l] & 3;
+            for (int l = 0; l < 32; ++l) a[l] -= (hm[l] & m ? 0 : 4);
+            a += 32; m <<= 1;
+            for (int l = 0; l < 32; ++l) a[l] = (q3[l] >> 2) & 3;
+            for (int l = 0; l < 32; ++l) a[l] -= (hm[l] & m ? 0 : 4);
+            a += 32; m <<= 1;
+            for (int l = 0; l < 32; ++l) a[l] = (q3[l] >> 4) & 3;
+            for (int l = 0; l < 32; ++l) a[l] -= (hm[l] & m ? 0 : 4);
+            a += 32; m <<= 1;
+            for (int l = 0; l < 32; ++l) a[l] = (q3[l] >> 6) & 3;
+            for (int l = 0; l < 32; ++l) a[l] -= (hm[l] & m ? 0 : 4);
+            a += 32; m <<= 1;
+            q3 += 32;
+        }
+        a = aux8;
+
+        memcpy(auxs, x[i].scales, 12);
+        uint32_t tmp = auxs[2];
+        auxs[2] = ((auxs[0] >> 4) & kmask2) | (((tmp >> 4) & kmask1) << 4);
+        auxs[3] = ((auxs[1] >> 4) & kmask2) | (((tmp >> 6) & kmask1) << 4);
+        auxs[0] = (auxs[0] & kmask2) | (((tmp >> 0) & kmask1) << 4);
+        auxs[1] = (auxs[1] & kmask2) | (((tmp >> 2) & kmask1) << 4);
+        for (int j = 0; j < QK_K/16; ++j) {
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += (scales[j] - 32) * aux16[l];
+            q8 += 8; a += 8;
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += (scales[j] - 32) * aux16[l];
+            q8 += 8; a += 8;
+        }
+        const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
+        for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l];
+    }
+    for (int l = 0; l < 8; ++l) sumf += sums[l];
+    *s = sumf;
 
-    ggml_vec_dot_q3_K_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
 #endif
 
 }
@@ -1378,15 +1534,60 @@ void ggml_vec_dot_q4_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
 
 #else
 
-    UNUSED(x);
-    UNUSED(y);
-    UNUSED(kmask1);
-    UNUSED(kmask2);
-    UNUSED(kmask3);
-    UNUSED(nb);
-    UNUSED(utmp);
+    const uint8_t * scales = (const uint8_t*)&utmp[0];
+    const uint8_t * mins   = (const uint8_t*)&utmp[2];
 
-    ggml_vec_dot_q4_K_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
+    int8_t  aux8[QK_K];
+    int16_t aux16[8];
+    float   sums [8];
+    int32_t aux32[8];
+    memset(sums, 0, 8*sizeof(float));
+
+    float sumf = 0;
+    for (int i = 0; i < nb; ++i) {
+        const uint8_t * GGML_RESTRICT q4 = x[i].qs;
+        const  int8_t * GGML_RESTRICT q8 = y[i].qs;
+        memset(aux32, 0, 8*sizeof(int32_t));
+        int8_t * GGML_RESTRICT a = aux8;
+        for (int j = 0; j < QK_K/64; ++j) {
+            for (int l = 0; l < 32; ++l) a[l] = (int8_t)(q4[l] & 0xF);
+            a += 32;
+            for (int l = 0; l < 32; ++l) a[l] = (int8_t)(q4[l]  >> 4);
+            a += 32; q4 += 32;
+        }
+        memcpy(utmp, x[i].scales, 12);
+        utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4);
+        const uint32_t uaux = utmp[1] & kmask1;
+        utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4);
+        utmp[2] = uaux;
+        utmp[0] &= kmask1;
+
+        int sumi = 0;
+        for (int j = 0; j < QK_K/16; ++j) sumi += y[i].bsums[j] * mins[j/2];
+        a = aux8;
+        int is = 0;
+        for (int j = 0; j < QK_K/32; ++j) {
+            int32_t scale = scales[is++];
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
+            q8 += 8; a += 8;
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
+            q8 += 8; a += 8;
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
+            q8 += 8; a += 8;
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
+            q8 += 8; a += 8;
+        }
+        const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
+        for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l];
+        const float dmin = GGML_CPU_FP16_TO_FP32(x[i].dmin) * y[i].d;
+        sumf -= dmin * sumi;
+    }
+    for (int l = 0; l < 8; ++l) sumf += sums[l];
+    *s = sumf;
 #endif
 }
 
@@ -1497,15 +1698,65 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
 
 #else
 
-    UNUSED(x);
-    UNUSED(y);
-    UNUSED(kmask1);
-    UNUSED(kmask2);
-    UNUSED(kmask3);
-    UNUSED(nb);
-    UNUSED(utmp);
+    const uint8_t * scales = (const uint8_t*)&utmp[0];
+    const uint8_t * mins   = (const uint8_t*)&utmp[2];
 
-    ggml_vec_dot_q5_K_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
+    int8_t  aux8[QK_K];
+    int16_t aux16[8];
+    float   sums [8];
+    int32_t aux32[8];
+    memset(sums, 0, 8*sizeof(float));
+
+    float sumf = 0;
+    for (int i = 0; i < nb; ++i) {
+        const uint8_t * GGML_RESTRICT q4 = x[i].qs;
+        const uint8_t * GGML_RESTRICT hm = x[i].qh;
+        const  int8_t * GGML_RESTRICT q8 = y[i].qs;
+        memset(aux32, 0, 8*sizeof(int32_t));
+        int8_t * GGML_RESTRICT a = aux8;
+        uint8_t m = 1;
+        for (int j = 0; j < QK_K/64; ++j) {
+            for (int l = 0; l < 32; ++l) a[l] = (int8_t)(q4[l] & 0xF);
+            for (int l = 0; l < 32; ++l) a[l] += (hm[l] & m ? 16 : 0);
+            a += 32; m <<= 1;
+            for (int l = 0; l < 32; ++l) a[l] = (int8_t)(q4[l]  >> 4);
+            for (int l = 0; l < 32; ++l) a[l] += (hm[l] & m ? 16 : 0);
+            a += 32; m <<= 1;
+            q4 += 32;
+        }
+        memcpy(utmp, x[i].scales, 12);
+        utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4);
+        const uint32_t uaux = utmp[1] & kmask1;
+        utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4);
+        utmp[2] = uaux;
+        utmp[0] &= kmask1;
+
+        int sumi = 0;
+        for (int j = 0; j < QK_K/16; ++j) sumi += y[i].bsums[j] * mins[j/2];
+        a = aux8;
+        int is = 0;
+        for (int j = 0; j < QK_K/32; ++j) {
+            int32_t scale = scales[is++];
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
+            q8 += 8; a += 8;
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
+            q8 += 8; a += 8;
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
+            q8 += 8; a += 8;
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
+            q8 += 8; a += 8;
+        }
+        const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
+        for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l];
+        const float dmin = GGML_CPU_FP16_TO_FP32(x[i].dmin) * y[i].d;
+        sumf -= dmin * sumi;
+    }
+    for (int l = 0; l < 8; ++l) sumf += sums[l];
+    *s = sumf;
 #endif
 }
 
@@ -1773,11 +2024,46 @@ void ggml_vec_dot_q6_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
 
 #else
 
-    UNUSED(x);
-    UNUSED(y);
-    UNUSED(nb);
+    int8_t  aux8[QK_K];
+    int16_t aux16[8];
+    float   sums [8];
+    int32_t aux32[8];
+    memset(sums, 0, 8*sizeof(float));
 
-    ggml_vec_dot_q6_K_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
+    float sumf = 0;
+    for (int i = 0; i < nb; ++i) {
+        const uint8_t * GGML_RESTRICT q4 = x[i].ql;
+        const uint8_t * GGML_RESTRICT qh = x[i].qh;
+        const  int8_t * GGML_RESTRICT q8 = y[i].qs;
+        memset(aux32, 0, 8*sizeof(int32_t));
+        int8_t * GGML_RESTRICT a = aux8;
+        for (int j = 0; j < QK_K; j += 128) {
+            for (int l = 0; l < 32; ++l) {
+                a[l +  0] = (int8_t)((q4[l +  0] & 0xF) | (((qh[l] >> 0) & 3) << 4)) - 32;
+                a[l + 32] = (int8_t)((q4[l + 32] & 0xF) | (((qh[l] >> 2) & 3) << 4)) - 32;
+                a[l + 64] = (int8_t)((q4[l +  0] >>  4) | (((qh[l] >> 4) & 3) << 4)) - 32;
+                a[l + 96] = (int8_t)((q4[l + 32] >>  4) | (((qh[l] >> 6) & 3) << 4)) - 32;
+            }
+            a  += 128;
+            q4 += 64;
+            qh += 32;
+        }
+        a = aux8;
+        int is = 0;
+        for (int j = 0; j < QK_K/16; ++j) {
+            int scale = x[i].scales[is++];
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
+            q8 += 8; a += 8;
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
+            q8 += 8; a += 8;
+        }
+        const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
+        for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l];
+    }
+    for (int l = 0; l < 8; ++l) sumf += sums[l];
+    *s = sumf;
 #endif
 }
 

ggml/src/ggml-cpu/arch/riscv/repack.cpp

@@ -112,7 +112,31 @@ void ggml_gemv_q4_0_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const vo
     }
 
 #endif
-    ggml_gemv_q4_0_8x8_q8_0_generic(n, s, bs, vx, vy, nr, nc);
+    {
+        float sumf[8];
+        int sumi;
+
+        const block_q8_0 * a_ptr = (const block_q8_0 *) vy;
+        for (int x = 0; x < nc / ncols_interleaved; x++) {
+            const block_q4_0x8 * b_ptr = (const block_q4_0x8 *) vx + (x * nb);
+
+            for (int j = 0; j < ncols_interleaved; j++) sumf[j] = 0.0;
+            for (int l = 0; l < nb; l++) {
+                for (int k = 0; k < (qk / (2 * blocklen)); k++) {
+                    for (int j = 0; j < ncols_interleaved; j++) {
+                        sumi = 0;
+                        for (int i = 0; i < blocklen; ++i) {
+                            const int v0 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] << 4);
+                            const int v1 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0xF0);
+                            sumi += ((v0 * a_ptr[l].qs[k * blocklen + i]) + (v1 * a_ptr[l].qs[k * blocklen + i + qk / 2])) >> 4;
+                        }
+                        sumf[j] += sumi * GGML_CPU_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_CPU_FP16_TO_FP32(a_ptr[l].d);
+                    }
+                }
+            }
+            for (int j = 0; j < ncols_interleaved; j++) s[x * ncols_interleaved + j] = sumf[j];
+        }
+    }
 }
 
 void ggml_gemm_q4_0_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
@@ -337,6 +361,37 @@ void ggml_gemm_q4_0_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const vo
         return;
     }
 
-#endif
-    ggml_gemm_q4_0_8x8_q8_0_generic(n, s, bs, vx, vy, nr, nc);
+#endif // #if ! ((defined(_MSC_VER)) && ! defined(__clang__)) && defined(__aarch64__)
+    float sumf[4][8];
+    int sumi;
+
+    for (int y = 0; y < nr / 4; y++) {
+        const block_q8_0x4 * a_ptr = (const block_q8_0x4 *) vy + (y * nb);
+        for (int x = 0; x < nc / ncols_interleaved; x++) {
+            const block_q4_0x8 * b_ptr = (const block_q4_0x8 *) vx + (x * nb);
+            for (int m = 0; m < 4; m++) {
+                for (int j = 0; j < ncols_interleaved; j++) sumf[m][j] = 0.0;
+            }
+            for (int l = 0; l < nb; l++) {
+                for (int k = 0; k < (qk / (2 * blocklen)); k++) {
+                    for (int m = 0; m < 4; m++) {
+                        for (int j = 0; j < ncols_interleaved; j++) {
+                            sumi = 0;
+                            for (int i = 0; i < blocklen; ++i) {
+                                const int v0 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] << 4);
+                                const int v1 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 0xF0);
+                                sumi += ((v0 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i]) +
+                                         (v1 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i + qk / 2 * 4])) >> 4;
+                            }
+                            sumf[m][j] += sumi * GGML_CPU_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_CPU_FP16_TO_FP32(a_ptr[l].d[m]);
+                        }
+                    }
+                }
+            }
+            for (int m = 0; m < 4; m++) {
+                for (int j = 0; j < ncols_interleaved; j++)
+                    s[(y * 4 + m) * bs + x * ncols_interleaved + j] = sumf[m][j];
+            }
+        }
+    }
 }

ggml/src/ggml-cpu/arch/s390/quants.c

@@ -172,15 +172,24 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const voi
 
     sumf = acc[0] + acc[1] + acc[2] + acc[3];
 
-    *s = sumf;
-#else
-    UNUSED(nb);
-    UNUSED(x);
-    UNUSED(y);
-    UNUSED(ib);
-    UNUSED(sumf);
-    ggml_vec_dot_q4_0_q8_0_generic(n, s, bs, vx, bx, vy, by, nrc);
 #endif
+    for (; ib < nb; ++ib) {
+        int sumi0 = 0;
+        int sumi1 = 0;
+
+        for (int j = 0; j < qk/2; ++j) {
+            const int v0 = (x[ib].qs[j] & 0x0F) - 8;
+            const int v1 = (x[ib].qs[j] >>   4) - 8;
+
+            sumi0 += (v0 * y[ib].qs[j]);
+            sumi1 += (v1 * y[ib].qs[j + qk/2]);
+        }
+
+        int sumi = sumi0 + sumi1;
+        sumf += sumi*GGML_CPU_FP16_TO_FP32(x[ib].d)*GGML_CPU_FP16_TO_FP32(y[ib].d);
+    }
+
+    *s = sumf;
 }
 
 void ggml_vec_dot_q4_1_q8_1(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
@@ -230,15 +239,24 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * GGML_RESTRICT s, size_t bs, const voi
 
     sumf = acc[0] + acc[1] + acc[2] + acc[3] + summs;
 
-    *s = sumf;
-#else
-    UNUSED(nb);
-    UNUSED(x);
-    UNUSED(y);
-    UNUSED(ib);
-    UNUSED(sumf);
-    ggml_vec_dot_q4_1_q8_1_generic(n, s, bs, vx, bx, vy, by, nrc);
 #endif
+    for (; ib < nb; ++ib) {
+        int sumi0 = 0;
+        int sumi1 = 0;
+
+        for (int j = 0; j < qk/2; ++j) {
+            const int v0 = (x[ib].qs[j] & 0x0F);
+            const int v1 = (x[ib].qs[j] >>   4);
+
+            sumi0 += (v0 * y[ib].qs[j]);
+            sumi1 += (v1 * y[ib].qs[j + qk/2]);
+        }
+
+        int sumi = sumi0 + sumi1;
+        sumf += (GGML_CPU_FP16_TO_FP32(x[ib].d)*GGML_CPU_FP16_TO_FP32(y[ib].d))*sumi + GGML_CPU_FP16_TO_FP32(x[ib].m)*GGML_CPU_FP16_TO_FP32(y[ib].s);
+    }
+
+    *s = sumf;
 }
 
 void ggml_vec_dot_q8_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
@@ -280,15 +298,18 @@ void ggml_vec_dot_q8_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const voi
 
     sumf = acc[0] + acc[1] + acc[2] + acc[3];
 
-    *s = sumf;
-#else
-    UNUSED(nb);
-    UNUSED(x);
-    UNUSED(y);
-    UNUSED(ib);
-    UNUSED(sumf);
-    ggml_vec_dot_q8_0_q8_0_generic(n, s, bs, vx, bx, vy, by, nrc);
 #endif
+    for (; ib < nb; ++ib) {
+        int sumi = 0;
+
+        for (int j = 0; j < qk; j++) {
+            sumi += x[ib].qs[j]*y[ib].qs[j];
+        }
+
+        sumf += sumi*(GGML_CPU_FP16_TO_FP32(x[ib].d)*GGML_CPU_FP16_TO_FP32(y[ib].d));
+    }
+
+    *s = sumf;
 }
 
 void ggml_vec_dot_q3_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
@@ -421,13 +442,70 @@ void ggml_vec_dot_q3_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
     *s = sum;
 
 #else
-    UNUSED(kmask1);
-    UNUSED(kmask2);
-    UNUSED(x);
-    UNUSED(y);
-    UNUSED(nb);
-    ggml_vec_dot_q3_K_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
+    // scalar version
+    // This function is written like this so the compiler can manage to vectorize most of it
+    // Using -Ofast, GCC and clang manage to produce code that is within a factor of 2 or so from the
+    // manually vectorized version above. Every other version I tried would run at least 4 times slower.
+    // The ideal situation would be if we could just write the code once, and the compiler would
+    // automatically produce the best possible set of machine instructions, instead of us having to manually
+    // write vectorized versions for AVX, ARM_NEON, etc.
+
+    int8_t  aux8[QK_K];
+    int16_t aux16[8];
+    float   sums [8];
+    int32_t aux32[8];
+    memset(sums, 0, 8*sizeof(float));
+
+    uint32_t auxs[4];
+    const int8_t * scales = (const int8_t*)auxs;
+
+    float sumf = 0;
+    for (int i = 0; i < nb; ++i) {
+        const uint8_t * GGML_RESTRICT q3 = x[i].qs;
+        const uint8_t * GGML_RESTRICT hm = x[i].hmask;
+        const  int8_t * GGML_RESTRICT q8 = y[i].qs;
+        memset(aux32, 0, 8*sizeof(int32_t));
+        int8_t * GGML_RESTRICT a = aux8;
+        uint8_t m = 1;
+        for (int j = 0; j < QK_K; j += 128) {
+            for (int l = 0; l < 32; ++l) a[l] = q3[l] & 3;
+            for (int l = 0; l < 32; ++l) a[l] -= (hm[l] & m ? 0 : 4);
+            a += 32; m <<= 1;
+            for (int l = 0; l < 32; ++l) a[l] = (q3[l] >> 2) & 3;
+            for (int l = 0; l < 32; ++l) a[l] -= (hm[l] & m ? 0 : 4);
+            a += 32; m <<= 1;
+            for (int l = 0; l < 32; ++l) a[l] = (q3[l] >> 4) & 3;
+            for (int l = 0; l < 32; ++l) a[l] -= (hm[l] & m ? 0 : 4);
+            a += 32; m <<= 1;
+            for (int l = 0; l < 32; ++l) a[l] = (q3[l] >> 6) & 3;
+            for (int l = 0; l < 32; ++l) a[l] -= (hm[l] & m ? 0 : 4);
+            a += 32; m <<= 1;
+            q3 += 32;
+        }
+        a = aux8;
+
+        memcpy(auxs, x[i].scales, 12);
+        uint32_t tmp = auxs[2];
+        auxs[2] = ((auxs[0] >> 4) & kmask2) | (((tmp >> 4) & kmask1) << 4);
+        auxs[3] = ((auxs[1] >> 4) & kmask2) | (((tmp >> 6) & kmask1) << 4);
+        auxs[0] = (auxs[0] & kmask2) | (((tmp >> 0) & kmask1) << 4);
+        auxs[1] = (auxs[1] & kmask2) | (((tmp >> 2) & kmask1) << 4);
+        for (int j = 0; j < QK_K/16; ++j) {
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += (scales[j] - 32) * aux16[l];
+            q8 += 8; a += 8;
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += (scales[j] - 32) * aux16[l];
+            q8 += 8; a += 8;
+        }
+        const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
+        for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l];
+    }
+    for (int l = 0; l < 8; ++l) sumf += sums[l];
+    *s = sumf;
+
 #endif
+
 }
 
 void ggml_vec_dot_q4_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
@@ -522,14 +600,61 @@ void ggml_vec_dot_q4_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
     *s = sumf;
 
 #else
-    UNUSED(x);
-    UNUSED(y);
-    UNUSED(nb);
-    UNUSED(kmask1);
-    UNUSED(kmask2);
-    UNUSED(kmask3);
-    UNUSED(utmp);
-    ggml_vec_dot_q4_K_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
+
+    const uint8_t * scales = (const uint8_t*)&utmp[0];
+    const uint8_t * mins   = (const uint8_t*)&utmp[2];
+
+    int8_t  aux8[QK_K];
+    int16_t aux16[8];
+    float   sums [8];
+    int32_t aux32[8];
+    memset(sums, 0, 8*sizeof(float));
+
+    float sumf = 0;
+    for (int i = 0; i < nb; ++i) {
+        const uint8_t * GGML_RESTRICT q4 = x[i].qs;
+        const  int8_t * GGML_RESTRICT q8 = y[i].qs;
+        memset(aux32, 0, 8*sizeof(int32_t));
+        int8_t * GGML_RESTRICT a = aux8;
+        for (int j = 0; j < QK_K/64; ++j) {
+            for (int l = 0; l < 32; ++l) a[l] = (int8_t)(q4[l] & 0xF);
+            a += 32;
+            for (int l = 0; l < 32; ++l) a[l] = (int8_t)(q4[l]  >> 4);
+            a += 32; q4 += 32;
+        }
+        memcpy(utmp, x[i].scales, 12);
+        utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4);
+        const uint32_t uaux = utmp[1] & kmask1;
+        utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4);
+        utmp[2] = uaux;
+        utmp[0] &= kmask1;
+
+        int sumi = 0;
+        for (int j = 0; j < QK_K/16; ++j) sumi += y[i].bsums[j] * mins[j/2];
+        a = aux8;
+        int is = 0;
+        for (int j = 0; j < QK_K/32; ++j) {
+            int32_t scale = scales[is++];
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
+            q8 += 8; a += 8;
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
+            q8 += 8; a += 8;
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
+            q8 += 8; a += 8;
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
+            q8 += 8; a += 8;
+        }
+        const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
+        for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l];
+        const float dmin = GGML_CPU_FP16_TO_FP32(x[i].dmin) * y[i].d;
+        sumf -= dmin * sumi;
+    }
+    for (int l = 0; l < 8; ++l) sumf += sums[l];
+    *s = sumf;
 #endif
 }
 
@@ -642,14 +767,66 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
     *s = sumf;
 
 #else
-    UNUSED(x);
-    UNUSED(y);
-    UNUSED(nb);
-    UNUSED(kmask1);
-    UNUSED(kmask2);
-    UNUSED(kmask3);
-    UNUSED(utmp);
-    ggml_vec_dot_q5_K_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
+
+    const uint8_t * scales = (const uint8_t*)&utmp[0];
+    const uint8_t * mins   = (const uint8_t*)&utmp[2];
+
+    int8_t  aux8[QK_K];
+    int16_t aux16[8];
+    float   sums [8];
+    int32_t aux32[8];
+    memset(sums, 0, 8*sizeof(float));
+
+    float sumf = 0;
+    for (int i = 0; i < nb; ++i) {
+        const uint8_t * GGML_RESTRICT q4 = x[i].qs;
+        const uint8_t * GGML_RESTRICT hm = x[i].qh;
+        const  int8_t * GGML_RESTRICT q8 = y[i].qs;
+        memset(aux32, 0, 8*sizeof(int32_t));
+        int8_t * GGML_RESTRICT a = aux8;
+        uint8_t m = 1;
+        for (int j = 0; j < QK_K/64; ++j) {
+            for (int l = 0; l < 32; ++l) a[l] = (int8_t)(q4[l] & 0xF);
+            for (int l = 0; l < 32; ++l) a[l] += (hm[l] & m ? 16 : 0);
+            a += 32; m <<= 1;
+            for (int l = 0; l < 32; ++l) a[l] = (int8_t)(q4[l]  >> 4);
+            for (int l = 0; l < 32; ++l) a[l] += (hm[l] & m ? 16 : 0);
+            a += 32; m <<= 1;
+            q4 += 32;
+        }
+        memcpy(utmp, x[i].scales, 12);
+        utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4);
+        const uint32_t uaux = utmp[1] & kmask1;
+        utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4);
+        utmp[2] = uaux;
+        utmp[0] &= kmask1;
+
+        int sumi = 0;
+        for (int j = 0; j < QK_K/16; ++j) sumi += y[i].bsums[j] * mins[j/2];
+        a = aux8;
+        int is = 0;
+        for (int j = 0; j < QK_K/32; ++j) {
+            int32_t scale = scales[is++];
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
+            q8 += 8; a += 8;
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
+            q8 += 8; a += 8;
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
+            q8 += 8; a += 8;
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
+            q8 += 8; a += 8;
+        }
+        const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
+        for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l];
+        const float dmin = GGML_CPU_FP16_TO_FP32(x[i].dmin) * y[i].d;
+        sumf -= dmin * sumi;
+    }
+    for (int l = 0; l < 8; ++l) sumf += sums[l];
+    *s = sumf;
 #endif
 }
 
@@ -792,10 +969,47 @@ void ggml_vec_dot_q6_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
     *s = sum;
 
 #else
-    UNUSED(x);
-    UNUSED(y);
-    UNUSED(nb);
-    ggml_vec_dot_q6_K_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
+
+    int8_t  aux8[QK_K];
+    int16_t aux16[8];
+    float   sums [8];
+    int32_t aux32[8];
+    memset(sums, 0, 8*sizeof(float));
+
+    float sumf = 0;
+    for (int i = 0; i < nb; ++i) {
+        const uint8_t * GGML_RESTRICT q4 = x[i].ql;
+        const uint8_t * GGML_RESTRICT qh = x[i].qh;
+        const  int8_t * GGML_RESTRICT q8 = y[i].qs;
+        memset(aux32, 0, 8*sizeof(int32_t));
+        int8_t * GGML_RESTRICT a = aux8;
+        for (int j = 0; j < QK_K; j += 128) {
+            for (int l = 0; l < 32; ++l) {
+                a[l +  0] = (int8_t)((q4[l +  0] & 0xF) | (((qh[l] >> 0) & 3) << 4)) - 32;
+                a[l + 32] = (int8_t)((q4[l + 32] & 0xF) | (((qh[l] >> 2) & 3) << 4)) - 32;
+                a[l + 64] = (int8_t)((q4[l +  0] >>  4) | (((qh[l] >> 4) & 3) << 4)) - 32;
+                a[l + 96] = (int8_t)((q4[l + 32] >>  4) | (((qh[l] >> 6) & 3) << 4)) - 32;
+            }
+            a  += 128;
+            q4 += 64;
+            qh += 32;
+        }
+        a = aux8;
+        int is = 0;
+        for (int j = 0; j < QK_K/16; ++j) {
+            int scale = x[i].scales[is++];
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
+            q8 += 8; a += 8;
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
+            q8 += 8; a += 8;
+        }
+        const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
+        for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l];
+    }
+    for (int l = 0; l < 8; ++l) sumf += sums[l];
+    *s = sumf;
 #endif
 }
 
@@ -972,15 +1186,17 @@ void ggml_vec_dot_iq4_nl_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const v
         sumf += GGML_CPU_FP16_TO_FP32(x0->d) * GGML_CPU_FP16_TO_FP32(y0->d) * (v_xy[0] + v_xy[1] + v_xy[2] + v_xy[3]);
     }
 
-    *s = sumf;
-#else
-    UNUSED(x);
-    UNUSED(y);
-    UNUSED(nb);
-    UNUSED(ib);
-    UNUSED(sumf);
-    ggml_vec_dot_iq4_nl_q8_0_generic(n, s, bs, vx, bx, vy, by, nrc);
 #endif
+    for (; ib < nb; ++ib) {
+        const float d = GGML_CPU_FP16_TO_FP32(y[ib].d)*GGML_CPU_FP16_TO_FP32(x[ib].d);
+        int sumi1 = 0, sumi2 = 0;
+        for (int j = 0; j < QK4_NL/2; ++j) {
+            sumi1 += y[ib].qs[j+       0] * kvalues_iq4nl[x[ib].qs[j] & 0xf];
+            sumi2 += y[ib].qs[j+QK4_NL/2] * kvalues_iq4nl[x[ib].qs[j] >>  4];
+        }
+        sumf += d * (sumi1 + sumi2);
+    }
+    *s = sumf;
 }
 
 void ggml_vec_dot_iq4_xs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
@@ -1048,10 +1264,37 @@ void ggml_vec_dot_iq4_xs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const v
     *s = sumf;
 
 #else
-    UNUSED(x);
-    UNUSED(y);
-    UNUSED(nb);
-    ggml_vec_dot_iq4_xs_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
+    float sumf = 0;
+    for (int ibl = 0; ibl < nb; ++ibl) {
+        const float d4d8 = GGML_CPU_FP16_TO_FP32(x[ibl].d) * y[ibl].d;
+        uint16_t h = x[ibl].scales_h;
+        const uint8_t * qs = x[ibl].qs;
+        const int8_t  * q8 = y[ibl].qs;
+        for (int ib = 0; ib < QK_K/32; ib += 2) {
+            const uint8_t ls1 = (x[ibl].scales_l[ib/2] & 0xf) | ((h << 4) & 0x30);
+            const uint8_t ls2 = (x[ibl].scales_l[ib/2] >>  4) | ((h << 2) & 0x30);
+            h >>= 4;
+            const float d1 = d4d8*(ls1 - 32);
+            const float d2 = d4d8*(ls2 - 32);
+            int sumi1 = 0, sumi2 = 0;
+            for (int j = 0; j < 16; ++j) {
+                sumi1 += q8[j+ 0] * kvalues_iq4nl[qs[j] & 0xf];
+                sumi2 += q8[j+16] * kvalues_iq4nl[qs[j] >>  4];
+            }
+            sumf += d1 * (sumi1 + sumi2);
+            qs += 16;
+            q8 += 32;
+            sumi1 = sumi2 = 0;
+            for (int j = 0; j < 16; ++j) {
+                sumi1 += q8[j+ 0] * kvalues_iq4nl[qs[j] & 0xf];
+                sumi2 += q8[j+16] * kvalues_iq4nl[qs[j] >>  4];
+            }
+            sumf += d2 * (sumi1 + sumi2);
+            qs += 16;
+            q8 += 32;
+        }
+    }
+    *s = sumf;
 #endif
 }
 

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

@@ -435,15 +435,30 @@ void ggml_vec_dot_q5_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const voi
     sumf = wasm_f32x4_extract_lane(sumv, 0) + wasm_f32x4_extract_lane(sumv, 1) +
            wasm_f32x4_extract_lane(sumv, 2) + wasm_f32x4_extract_lane(sumv, 3);
 
-    *s = sumf;
-#else
-    UNUSED(nb);
-    UNUSED(ib);
-    UNUSED(sumf);
-    UNUSED(x);
-    UNUSED(y);
-    ggml_vec_dot_q5_0_q8_0_generic(n, s, bs, vx, bx, vy, by, nrc);
 #endif
+    for (; ib < nb; ++ib) {
+        uint32_t qh;
+        memcpy(&qh, x[ib].qh, sizeof(qh));
+
+        int sumi0 = 0;
+        int sumi1 = 0;
+
+        for (int j = 0; j < qk/2; ++j) {
+            const uint8_t xh_0 = ((qh & (1u << (j + 0 ))) >> (j + 0 )) << 4;
+            const uint8_t xh_1 = ((qh & (1u << (j + 16))) >> (j + 12));
+
+            const int32_t x0 = (int8_t)(((x[ib].qs[j] & 0x0F) | xh_0) - 16);
+            const int32_t x1 = (int8_t)(((x[ib].qs[j] >>   4) | xh_1) - 16);
+
+            sumi0 += (x0 * y[ib].qs[j]);
+            sumi1 += (x1 * y[ib].qs[j + qk/2]);
+        }
+
+        int sumi = sumi0 + sumi1;
+        sumf += (GGML_CPU_FP16_TO_FP32(x[ib].d)*GGML_CPU_FP16_TO_FP32(y[ib].d)) * sumi;
+    }
+
+    *s = sumf;
 }
 
 void ggml_vec_dot_q5_1_q8_1(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
@@ -530,15 +545,30 @@ void ggml_vec_dot_q5_1_q8_1(int n, float * GGML_RESTRICT s, size_t bs, const voi
     sumf = wasm_f32x4_extract_lane(sumv, 0) + wasm_f32x4_extract_lane(sumv, 1) +
            wasm_f32x4_extract_lane(sumv, 2) + wasm_f32x4_extract_lane(sumv, 3) + summs;
 
-    *s = sumf;
-#else
-    UNUSED(nb);
-    UNUSED(ib);
-    UNUSED(sumf);
-    UNUSED(x);
-    UNUSED(y);
-    ggml_vec_dot_q5_1_q8_1_generic(n, s, bs, vx, bx, vy, by, nrc);
 #endif
+    for (; ib < nb; ++ib) {
+        uint32_t qh;
+        memcpy(&qh, x[ib].qh, sizeof(qh));
+
+        int sumi0 = 0;
+        int sumi1 = 0;
+
+        for (int j = 0; j < qk/2; ++j) {
+            const uint8_t xh_0 = ((qh >> (j +  0)) << 4) & 0x10;
+            const uint8_t xh_1 = ((qh >> (j + 12))     ) & 0x10;
+
+            const int32_t x0 = (x[ib].qs[j] & 0xF) | xh_0;
+            const int32_t x1 = (x[ib].qs[j] >>  4) | xh_1;
+
+            sumi0 += (x0 * y[ib].qs[j]);
+            sumi1 += (x1 * y[ib].qs[j + qk/2]);
+        }
+
+        int sumi = sumi0 + sumi1;
+        sumf += (GGML_CPU_FP16_TO_FP32(x[ib].d)*GGML_CPU_FP16_TO_FP32(y[ib].d))*sumi + GGML_CPU_FP16_TO_FP32(x[ib].m)*GGML_CPU_FP16_TO_FP32(y[ib].s);
+    }
+
+    *s = sumf;
 }
 
 void ggml_vec_dot_q8_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
@@ -598,15 +628,18 @@ void ggml_vec_dot_q8_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const voi
     sumf = wasm_f32x4_extract_lane(sumv, 0) + wasm_f32x4_extract_lane(sumv, 1) +
            wasm_f32x4_extract_lane(sumv, 2) + wasm_f32x4_extract_lane(sumv, 3);
 
-    *s = sumf;
-#else
-    UNUSED(nb);
-    UNUSED(x);
-    UNUSED(y);
-    UNUSED(ib);
-    UNUSED(sumf);
-    ggml_vec_dot_q8_0_q8_0_generic(n, s, bs, vx, bx, vy, by, nrc);
 #endif
+    for (; ib < nb; ++ib) {
+        int sumi = 0;
+
+        for (int j = 0; j < qk; j++) {
+            sumi += x[ib].qs[j]*y[ib].qs[j];
+        }
+
+        sumf += sumi*(GGML_CPU_FP16_TO_FP32(x[ib].d)*GGML_CPU_FP16_TO_FP32(y[ib].d));
+    }
+
+    *s = sumf;
 }
 
 void ggml_vec_dot_q2_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
@@ -722,10 +755,45 @@ void ggml_vec_dot_q2_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
     *s = sumf;
 
 #else
-    UNUSED(x);
-    UNUSED(y);
-    UNUSED(nb);
-    ggml_vec_dot_q2_K_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
+
+    float sumf = 0;
+
+    for (int i = 0; i < nb; ++i) {
+
+        const uint8_t * q2 = x[i].qs;
+        const  int8_t * q8 = y[i].qs;
+        const uint8_t * sc = x[i].scales;
+
+        int summs = 0;
+        for (int j = 0; j < 16; ++j) {
+            summs += y[i].bsums[j] * (sc[j] >> 4);
+        }
+
+        const float dall = y[i].d * GGML_CPU_FP16_TO_FP32(x[i].d);
+        const float dmin = y[i].d * GGML_CPU_FP16_TO_FP32(x[i].dmin);
+
+        int isum = 0;
+        int is = 0;
+        int d;
+        for (int k = 0; k < QK_K/128; ++k) {
+            int shift = 0;
+            for (int j = 0; j < 4; ++j) {
+                d = sc[is++] & 0xF;
+                int isuml = 0;
+                for (int l =  0; l < 16; ++l) isuml += q8[l] * ((q2[l] >> shift) & 3);
+                isum += d * isuml;
+                d = sc[is++] & 0xF;
+                isuml = 0;
+                for (int l = 16; l < 32; ++l) isuml += q8[l] * ((q2[l] >> shift) & 3);
+                isum += d * isuml;
+                shift += 2;
+                q8 += 32;
+            }
+            q2 += 32;
+        }
+        sumf += dall * isum - dmin * summs;
+    }
+    *s = sumf;
 #endif
 }
 
@@ -834,12 +902,68 @@ void ggml_vec_dot_q3_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
     *s = sumf;
 
 #else
-    UNUSED(kmask1);
-    UNUSED(kmask2);
-    UNUSED(x);
-    UNUSED(y);
-    UNUSED(nb);
-    ggml_vec_dot_q3_K_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
+    // scalar version
+    // This function is written like this so the compiler can manage to vectorize most of it
+    // Using -Ofast, GCC and clang manage to produce code that is within a factor of 2 or so from the
+    // manually vectorized version above. Every other version I tried would run at least 4 times slower.
+    // The ideal situation would be if we could just write the code once, and the compiler would
+    // automatically produce the best possible set of machine instructions, instead of us having to manually
+    // write vectorized versions for AVX, ARM_NEON, etc.
+
+    int8_t  aux8[QK_K];
+    int16_t aux16[8];
+    float   sums [8];
+    int32_t aux32[8];
+    memset(sums, 0, 8*sizeof(float));
+
+    uint32_t auxs[4];
+    const int8_t * scales = (const int8_t*)auxs;
+
+    float sumf = 0;
+    for (int i = 0; i < nb; ++i) {
+        const uint8_t * GGML_RESTRICT q3 = x[i].qs;
+        const uint8_t * GGML_RESTRICT hm = x[i].hmask;
+        const  int8_t * GGML_RESTRICT q8 = y[i].qs;
+        memset(aux32, 0, 8*sizeof(int32_t));
+        int8_t * GGML_RESTRICT a = aux8;
+        uint8_t m = 1;
+        for (int j = 0; j < QK_K; j += 128) {
+            for (int l = 0; l < 32; ++l) a[l] = q3[l] & 3;
+            for (int l = 0; l < 32; ++l) a[l] -= (hm[l] & m ? 0 : 4);
+            a += 32; m <<= 1;
+            for (int l = 0; l < 32; ++l) a[l] = (q3[l] >> 2) & 3;
+            for (int l = 0; l < 32; ++l) a[l] -= (hm[l] & m ? 0 : 4);
+            a += 32; m <<= 1;
+            for (int l = 0; l < 32; ++l) a[l] = (q3[l] >> 4) & 3;
+            for (int l = 0; l < 32; ++l) a[l] -= (hm[l] & m ? 0 : 4);
+            a += 32; m <<= 1;
+            for (int l = 0; l < 32; ++l) a[l] = (q3[l] >> 6) & 3;
+            for (int l = 0; l < 32; ++l) a[l] -= (hm[l] & m ? 0 : 4);
+            a += 32; m <<= 1;
+            q3 += 32;
+        }
+        a = aux8;
+
+        memcpy(auxs, x[i].scales, 12);
+        uint32_t tmp = auxs[2];
+        auxs[2] = ((auxs[0] >> 4) & kmask2) | (((tmp >> 4) & kmask1) << 4);
+        auxs[3] = ((auxs[1] >> 4) & kmask2) | (((tmp >> 6) & kmask1) << 4);
+        auxs[0] = (auxs[0] & kmask2) | (((tmp >> 0) & kmask1) << 4);
+        auxs[1] = (auxs[1] & kmask2) | (((tmp >> 2) & kmask1) << 4);
+        for (int j = 0; j < QK_K/16; ++j) {
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += (scales[j] - 32) * aux16[l];
+            q8 += 8; a += 8;
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += (scales[j] - 32) * aux16[l];
+            q8 += 8; a += 8;
+        }
+        const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
+        for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l];
+    }
+    for (int l = 0; l < 8; ++l) sumf += sums[l];
+    *s = sumf;
+
 #endif
 
 }
@@ -965,14 +1089,61 @@ void ggml_vec_dot_q4_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
     *s = sumf;
 
 #else
-    UNUSED(x);
-    UNUSED(y);
-    UNUSED(nb);
-    UNUSED(kmask1);
-    UNUSED(kmask2);
-    UNUSED(kmask3);
-    UNUSED(utmp);
-    ggml_vec_dot_q4_K_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
+
+    const uint8_t * scales = (const uint8_t*)&utmp[0];
+    const uint8_t * mins   = (const uint8_t*)&utmp[2];
+
+    int8_t  aux8[QK_K];
+    int16_t aux16[8];
+    float   sums [8];
+    int32_t aux32[8];
+    memset(sums, 0, 8*sizeof(float));
+
+    float sumf = 0;
+    for (int i = 0; i < nb; ++i) {
+        const uint8_t * GGML_RESTRICT q4 = x[i].qs;
+        const  int8_t * GGML_RESTRICT q8 = y[i].qs;
+        memset(aux32, 0, 8*sizeof(int32_t));
+        int8_t * GGML_RESTRICT a = aux8;
+        for (int j = 0; j < QK_K/64; ++j) {
+            for (int l = 0; l < 32; ++l) a[l] = (int8_t)(q4[l] & 0xF);
+            a += 32;
+            for (int l = 0; l < 32; ++l) a[l] = (int8_t)(q4[l]  >> 4);
+            a += 32; q4 += 32;
+        }
+        memcpy(utmp, x[i].scales, 12);
+        utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4);
+        const uint32_t uaux = utmp[1] & kmask1;
+        utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4);
+        utmp[2] = uaux;
+        utmp[0] &= kmask1;
+
+        int sumi = 0;
+        for (int j = 0; j < QK_K/16; ++j) sumi += y[i].bsums[j] * mins[j/2];
+        a = aux8;
+        int is = 0;
+        for (int j = 0; j < QK_K/32; ++j) {
+            int32_t scale = scales[is++];
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
+            q8 += 8; a += 8;
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
+            q8 += 8; a += 8;
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
+            q8 += 8; a += 8;
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
+            q8 += 8; a += 8;
+        }
+        const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
+        for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l];
+        const float dmin = GGML_CPU_FP16_TO_FP32(x[i].dmin) * y[i].d;
+        sumf -= dmin * sumi;
+    }
+    for (int l = 0; l < 8; ++l) sumf += sums[l];
+    *s = sumf;
 #endif
 }
 
@@ -1108,14 +1279,66 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
     *s = sumf;
 
 #else
-    UNUSED(x);
-    UNUSED(y);
-    UNUSED(nb);
-    UNUSED(kmask1);
-    UNUSED(kmask2);
-    UNUSED(kmask3);
-    UNUSED(utmp);
-    ggml_vec_dot_q5_K_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
+
+    const uint8_t * scales = (const uint8_t*)&utmp[0];
+    const uint8_t * mins   = (const uint8_t*)&utmp[2];
+
+    int8_t  aux8[QK_K];
+    int16_t aux16[8];
+    float   sums [8];
+    int32_t aux32[8];
+    memset(sums, 0, 8*sizeof(float));
+
+    float sumf = 0;
+    for (int i = 0; i < nb; ++i) {
+        const uint8_t * GGML_RESTRICT q4 = x[i].qs;
+        const uint8_t * GGML_RESTRICT hm = x[i].qh;
+        const  int8_t * GGML_RESTRICT q8 = y[i].qs;
+        memset(aux32, 0, 8*sizeof(int32_t));
+        int8_t * GGML_RESTRICT a = aux8;
+        uint8_t m = 1;
+        for (int j = 0; j < QK_K/64; ++j) {
+            for (int l = 0; l < 32; ++l) a[l] = (int8_t)(q4[l] & 0xF);
+            for (int l = 0; l < 32; ++l) a[l] += (hm[l] & m ? 16 : 0);
+            a += 32; m <<= 1;
+            for (int l = 0; l < 32; ++l) a[l] = (int8_t)(q4[l]  >> 4);
+            for (int l = 0; l < 32; ++l) a[l] += (hm[l] & m ? 16 : 0);
+            a += 32; m <<= 1;
+            q4 += 32;
+        }
+        memcpy(utmp, x[i].scales, 12);
+        utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4);
+        const uint32_t uaux = utmp[1] & kmask1;
+        utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4);
+        utmp[2] = uaux;
+        utmp[0] &= kmask1;
+
+        int sumi = 0;
+        for (int j = 0; j < QK_K/16; ++j) sumi += y[i].bsums[j] * mins[j/2];
+        a = aux8;
+        int is = 0;
+        for (int j = 0; j < QK_K/32; ++j) {
+            int32_t scale = scales[is++];
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
+            q8 += 8; a += 8;
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
+            q8 += 8; a += 8;
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
+            q8 += 8; a += 8;
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
+            q8 += 8; a += 8;
+        }
+        const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
+        for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l];
+        const float dmin = GGML_CPU_FP16_TO_FP32(x[i].dmin) * y[i].d;
+        sumf -= dmin * sumi;
+    }
+    for (int l = 0; l < 8; ++l) sumf += sums[l];
+    *s = sumf;
 #endif
 }
 
@@ -1212,10 +1435,47 @@ void ggml_vec_dot_q6_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
     *s = sumf;
 
 #else
-    UNUSED(x);
-    UNUSED(y);
-    UNUSED(nb);
-    ggml_vec_dot_q6_K_q8_K_generic(n, s, bs, vx, bx, vy, by, nrc);
+
+    int8_t  aux8[QK_K];
+    int16_t aux16[8];
+    float   sums [8];
+    int32_t aux32[8];
+    memset(sums, 0, 8*sizeof(float));
+
+    float sumf = 0;
+    for (int i = 0; i < nb; ++i) {
+        const uint8_t * GGML_RESTRICT q4 = x[i].ql;
+        const uint8_t * GGML_RESTRICT qh = x[i].qh;
+        const  int8_t * GGML_RESTRICT q8 = y[i].qs;
+        memset(aux32, 0, 8*sizeof(int32_t));
+        int8_t * GGML_RESTRICT a = aux8;
+        for (int j = 0; j < QK_K; j += 128) {
+            for (int l = 0; l < 32; ++l) {
+                a[l +  0] = (int8_t)((q4[l +  0] & 0xF) | (((qh[l] >> 0) & 3) << 4)) - 32;
+                a[l + 32] = (int8_t)((q4[l + 32] & 0xF) | (((qh[l] >> 2) & 3) << 4)) - 32;
+                a[l + 64] = (int8_t)((q4[l +  0] >>  4) | (((qh[l] >> 4) & 3) << 4)) - 32;
+                a[l + 96] = (int8_t)((q4[l + 32] >>  4) | (((qh[l] >> 6) & 3) << 4)) - 32;
+            }
+            a  += 128;
+            q4 += 64;
+            qh += 32;
+        }
+        a = aux8;
+        int is = 0;
+        for (int j = 0; j < QK_K/16; ++j) {
+            int scale = x[i].scales[is++];
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
+            q8 += 8; a += 8;
+            for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l];
+            for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l];
+            q8 += 8; a += 8;
+        }
+        const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
+        for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l];
+    }
+    for (int l = 0; l < 8; ++l) sumf += sums[l];
+    *s = sumf;
 #endif
 }
 

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

@@ -253,12 +253,6 @@ static const struct ggml_type_traits_cpu type_traits_cpu[GGML_TYPE_COUNT] = {
         .vec_dot_type             = GGML_TYPE_Q8_1,
         .nrows                    = 1,
     },
-    [GGML_TYPE_MXFP4] = {
-        .from_float               = quantize_row_mxfp4,
-        .vec_dot                  = ggml_vec_dot_mxfp4_q8_0,
-        .vec_dot_type             = GGML_TYPE_Q8_0,
-        .nrows                    = 1,
-    },
     [GGML_TYPE_Q2_K] = {
         .from_float               = quantize_row_q2_K,
         .vec_dot                  = ggml_vec_dot_q2_K_q8_K,
@@ -1676,10 +1670,6 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
             {
                 ggml_compute_forward_add(params, tensor);
             } break;
-        case GGML_OP_ADD_ID:
-            {
-                ggml_compute_forward_add_id(params, tensor);
-            } break;
         case GGML_OP_ADD1:
             {
                 ggml_compute_forward_add1(params, tensor);
@@ -1934,7 +1924,7 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
             } break;
         case GGML_OP_FLASH_ATTN_EXT:
             {
-                ggml_compute_forward_flash_attn_ext(params, tensor);
+                ggml_compute_forward_flash_attn_ext(params, tensor->src[0], tensor->src[1], tensor->src[2], tensor->src[3], tensor);
             } break;
         case GGML_OP_FLASH_ATTN_BACK:
             {
@@ -2121,7 +2111,6 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
         case GGML_OP_DUP:
         case GGML_OP_CONT:
         case GGML_OP_ADD:
-        case GGML_OP_ADD_ID:
         case GGML_OP_ADD1:
         case GGML_OP_ACC:
             {
@@ -2183,7 +2172,6 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
                 case GGML_GLU_OP_REGLU:
                 case GGML_GLU_OP_GEGLU:
                 case GGML_GLU_OP_SWIGLU:
-                case GGML_GLU_OP_SWIGLU_OAI:
                 case GGML_GLU_OP_GEGLU_ERF:
                 case GGML_GLU_OP_GEGLU_QUICK:
                     {
@@ -2685,7 +2673,6 @@ struct ggml_cplan ggml_graph_plan(
                         }
                     } break;
                 case GGML_OP_ADD:
-                case GGML_OP_ADD_ID:
                 case GGML_OP_ADD1:
                     {
                         if (ggml_is_quantized(node->src[0]->type)) {

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

@@ -35,7 +35,7 @@
 
 // ggml-backend interface
 
-std::vector<ggml_backend_buffer_type_t> & ggml_backend_cpu_get_extra_buffer_types() {
+std::vector<ggml_backend_buffer_type_t>& ggml_backend_cpu_get_extra_buffers_type() {
     static std::vector<ggml_backend_buffer_type_t> bufts = []() {
         std::vector<ggml_backend_buffer_type_t> bufts;
 
@@ -57,6 +57,8 @@ std::vector<ggml_backend_buffer_type_t> & ggml_backend_cpu_get_extra_buffer_type
         }
 #endif
 
+        bufts.push_back(NULL);
+
         return bufts;
     }();
 
@@ -64,20 +66,14 @@ std::vector<ggml_backend_buffer_type_t> & ggml_backend_cpu_get_extra_buffer_type
 }
 
 static ggml_backend_buffer_type_t * ggml_backend_cpu_device_get_extra_buffers_type(ggml_backend_dev_t device) {
-    static std::vector<ggml_backend_buffer_type_t> extra_bufts = [] {
-        std::vector<ggml_backend_buffer_type_t> bufts = ggml_backend_cpu_get_extra_buffer_types();
-        bufts.push_back(nullptr);
-        return bufts;
-    }();
-
-    return extra_bufts.data();
+    return ggml_backend_cpu_get_extra_buffers_type().data();
 
     GGML_UNUSED(device);
 }
 
 static bool ggml_backend_cpu_is_extra_buffer_type(ggml_backend_buffer_type_t buft) {
-    for (auto * extra : ggml_backend_cpu_get_extra_buffer_types()) {
-        if (extra == buft) {
+    for (auto * extra : ggml_backend_cpu_get_extra_buffers_type()) {
+        if (extra && extra == buft) {
             return true;
         }
     }
@@ -401,13 +397,20 @@ static bool ggml_backend_cpu_device_supports_op(ggml_backend_dev_t dev, const st
         return true;
     }
 
-    // check extra buffer types
-    // note: only the first sources are checked for extra buffer types to reduce overhead, increase if necessary
-    for (int i = 0; i < 4; i++) {
-        if (op->src[i] && op->src[i]->buffer &&
-            ggml_backend_cpu_is_extra_buffer_type(op->src[i]->buffer->buft)) {
-            auto * buf_extra = (ggml::cpu::extra_buffer_type *) op->src[i]->buffer->buft->context;
-            return buf_extra->supports_op(dev, op);
+    // extra_buffer_op?
+    for (auto extra : ggml_backend_cpu_get_extra_buffers_type()) {
+        if (extra) {
+            auto buf_extra = (ggml::cpu::extra_buffer_type*) extra->context;
+            if (buf_extra && buf_extra->supports_op(dev, op)) {
+                return true;
+            }
+        }
+    }
+
+    // the other case need host buffer.
+    for (int i = 0; i < GGML_MAX_SRC; i++) {
+        if (op->src[i] && op->src[i]->buffer && !ggml_backend_buft_is_host(op->src[i]->buffer->buft)) {
+            return false;
         }
     }
 

ggml/src/ggml-cpu/ops.cpp

@@ -8,7 +8,6 @@
 #include "vec.h"
 
 #include <float.h>
-#include <algorithm>
 
 // ggml_compute_forward_dup
 
@@ -1284,7 +1283,6 @@ void ggml_compute_forward_add(
         case GGML_TYPE_Q5_0:
         case GGML_TYPE_Q5_1:
         case GGML_TYPE_Q8_0:
-        case GGML_TYPE_MXFP4:
         case GGML_TYPE_Q2_K:
         case GGML_TYPE_Q3_K:
         case GGML_TYPE_Q4_K:
@@ -1311,77 +1309,6 @@ void ggml_compute_forward_add(
     }
 }
 
-// ggml_compute_forward_add_id
-
-static void ggml_compute_forward_add_id_f32(
-        const ggml_compute_params * params,
-        ggml_tensor * dst) {
-
-    const ggml_tensor * src0 = dst->src[0];
-    const ggml_tensor * src1 = dst->src[1];
-    const ggml_tensor * src2 = dst->src[2];
-
-    GGML_ASSERT(dst->type  == GGML_TYPE_F32);
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT(src1->type == GGML_TYPE_F32);
-    GGML_ASSERT(src2->type == GGML_TYPE_I32);
-
-    GGML_ASSERT(src0->nb[0] == sizeof(float));
-    GGML_ASSERT(src1->nb[0] == sizeof(float));
-
-    const int ith = params->ith;
-    const int nth = params->nth;
-
-    const int nr  = ggml_nrows(src0);
-
-    GGML_TENSOR_TERNARY_OP_LOCALS
-
-    GGML_ASSERT( nb0 == sizeof(float));
-    GGML_ASSERT(nb10 == sizeof(float));
-
-    // rows per thread
-    const int dr = (nr + nth - 1)/nth;
-
-    // row range for this thread
-    const int ir0 = dr*ith;
-    const int ir1 = MIN(ir0 + dr, nr);
-
-    for (int ir = ir0; ir < ir1; ++ir) {
-        // src0 indices
-        const int i3 = ir/(ne2*ne1);
-        const int i2 = (ir - i3*ne2*ne1)/ne1;
-        const int i1 = (ir - i3*ne2*ne1 - i2*ne1);
-
-        // src1 indices
-        const int i11 = *(int32_t *) ((char *) src2->data + i1*nb20 + i2*nb21);
-
-        GGML_ASSERT(i11 >= 0 && i11 < ne11);
-
-        ggml_vec_add_f32(ne0,
-                (float *) ((char *) dst->data  + i3*nb3  + i2*nb2  + i1*nb1 ),
-                (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01),
-                (float *) ((char *) src1->data + i11*nb11));
-    }
-}
-
-void ggml_compute_forward_add_id(
-        const ggml_compute_params * params,
-        ggml_tensor * dst) {
-
-    const ggml_tensor * src0 = dst->src[0];
-
-    switch (src0->type) {
-        case GGML_TYPE_F32:
-            {
-                ggml_compute_forward_add_id_f32(params, dst);
-            } break;
-        default:
-            {
-                GGML_ABORT("unsupported type for ggml_compute_forward_add_id: %s", ggml_type_name(src0->type));
-            }
-    }
-}
-
 // ggml_compute_forward_add1
 
 static void ggml_compute_forward_add1_f32(
@@ -1733,7 +1660,6 @@ void ggml_compute_forward_add1(
         case GGML_TYPE_Q5_1:
         case GGML_TYPE_Q8_0:
         case GGML_TYPE_Q8_1:
-        case GGML_TYPE_MXFP4:
         case GGML_TYPE_Q2_K:
         case GGML_TYPE_Q3_K:
         case GGML_TYPE_Q4_K:
@@ -1861,7 +1787,6 @@ void ggml_compute_forward_acc(
         case GGML_TYPE_Q5_1:
         case GGML_TYPE_Q8_0:
         case GGML_TYPE_Q8_1:
-        case GGML_TYPE_MXFP4:
         case GGML_TYPE_Q2_K:
         case GGML_TYPE_Q3_K:
         case GGML_TYPE_Q4_K:
@@ -3689,93 +3614,6 @@ static void ggml_compute_forward_swiglu(
     }
 }
 
-// ggml_compute_forward_swiglu_oai
-
-static void ggml_compute_forward_swiglu_oai_f32(
-        const ggml_compute_params * params,
-        ggml_tensor * dst) {
-
-    const ggml_tensor * src0 = dst->src[0];
-    const ggml_tensor * src1 = dst->src[1];
-    char * src0_d = (char *) src0->data;
-    char * src1_d = (char *) (src1 ? src1->data : src0->data);
-    const size_t src0_o = src0->nb[1];
-    const size_t src1_o = src1 ? src1->nb[1] : src0->nb[1];
-
-    GGML_ASSERT(ggml_is_contiguous_1(src0));
-    GGML_ASSERT(ggml_is_contiguous_1(dst));
-
-    if (src1) {
-        GGML_ASSERT(ggml_is_contiguous_1(src1));
-        GGML_ASSERT(src0->type == src1->type);
-    }
-
-    const int ith = params->ith;
-    const int nth = params->nth;
-
-    const int nc = src1 ? src0->ne[0] : src0->ne[0] / 2;
-    const int nr = ggml_nrows(src0);
-
-    GGML_ASSERT(dst->ne[0] == nc);
-    GGML_ASSERT(ggml_nrows(dst) == nr);
-
-    const int32_t swapped = ggml_get_op_params_i32(dst, 1);
-    const float alpha = ggml_get_op_params_f32(dst, 2);
-    const float limit = ggml_get_op_params_f32(dst, 3);
-
-    // rows per thread
-    const int dr = (nr + nth - 1)/nth;
-
-    // row range for this thread
-    const int ir0 = dr*ith;
-    const int ir1 = MIN(ir0 + dr, nr);
-
-    for (int i1 = ir0; i1 < ir1; i1++) {
-        float * src0_p = (float *) (src0_d + i1*src0_o);
-        float * src1_p = (float *) (src1_d + i1*src1_o);
-        float * dst_p  = (float *) ((char *) dst->data + i1*(dst->nb[1]));
-
-        if (!src1) {
-            src0_p += swapped ? nc : 0;
-            src1_p += swapped ? 0 : nc;
-        }
-
-        for (int k = 0; k < nc; k++) {
-            const float x = std::min(src0_p[k], limit);
-            const float y = std::clamp(src1_p[k], -limit, limit);
-            const float out_glu = x / (1.f + expf(alpha * (-x)));
-            dst_p[k] = out_glu * (y + 1.f);
-        }
-
-#ifndef NDEBUG
-        for (int k = 0; k < nc; k++) {
-            const float x = dst_p[k];
-            GGML_UNUSED(x);
-            assert(!isnan(x));
-            assert(!isinf(x));
-        }
-#endif
-    }
-}
-
-static void ggml_compute_forward_swiglu_oai(
-        const ggml_compute_params * params,
-        ggml_tensor * dst) {
-
-    const ggml_tensor * src0 = dst->src[0];
-
-    switch (src0->type) {
-        case GGML_TYPE_F32:
-            {
-                ggml_compute_forward_swiglu_oai_f32(params, dst);
-            } break;
-        default:
-            {
-                GGML_ABORT("fatal error");
-            }
-    }
-}
-
 // ggml_compute_forward_geglu_erf
 
 static void ggml_compute_forward_geglu_erf_f32(
@@ -4761,7 +4599,6 @@ void ggml_compute_forward_out_prod(
         case GGML_TYPE_Q5_0:
         case GGML_TYPE_Q5_1:
         case GGML_TYPE_Q8_0:
-        case GGML_TYPE_MXFP4:
         case GGML_TYPE_Q2_K:
         case GGML_TYPE_Q3_K:
         case GGML_TYPE_Q4_K:
@@ -5036,7 +4873,6 @@ void ggml_compute_forward_set(
         case GGML_TYPE_Q5_1:
         case GGML_TYPE_Q8_0:
         case GGML_TYPE_Q8_1:
-        case GGML_TYPE_MXFP4:
         case GGML_TYPE_Q2_K:
         case GGML_TYPE_Q3_K:
         case GGML_TYPE_Q4_K:
@@ -5298,7 +5134,6 @@ void ggml_compute_forward_get_rows(
         case GGML_TYPE_Q5_1:
         case GGML_TYPE_Q8_0:
         case GGML_TYPE_Q8_1:
-        case GGML_TYPE_MXFP4:
         case GGML_TYPE_Q2_K:
         case GGML_TYPE_Q3_K:
         case GGML_TYPE_Q4_K:
@@ -5688,7 +5523,6 @@ static void ggml_compute_forward_soft_max_f32(
 
     const ggml_tensor * src0 = dst->src[0];
     const ggml_tensor * src1 = dst->src[1];
-    const ggml_tensor * src2 = dst->src[2];
 
     assert(ggml_is_contiguous(dst));
     assert(ggml_are_same_shape(src0, dst));
@@ -5723,9 +5557,6 @@ static void ggml_compute_forward_soft_max_f32(
 
     const bool use_f16 = (src1 && src1->type == GGML_TYPE_F16);
 
-    // sinks
-    const float * sk = src2 ? (float *)((char *) src2->data) : nullptr;
-
     for (int64_t i03 = 0; i03 < ne03; i03++) {
         for (int64_t i02 = 0; i02 < ne02; i02++) {
             for (int64_t i01 = ith; i01 < ne01; i01 += nth) {
@@ -5768,18 +5599,9 @@ static void ggml_compute_forward_soft_max_f32(
                 float max = -INFINITY;
                 ggml_vec_max_f32(ne00, &max, wp);
 
-                // if we have sinks, make a correction as if they were included in the softmax
-                if (sk) {
-                    max = MAX(max, sk[i02]);
-                }
-
                 ggml_float sum = ggml_vec_soft_max_f32(ne00, dp, wp, max);
                 assert(sum > 0.0);
 
-                if (sk) {
-                    sum += (ggml_float) expf(sk[i02] - max);
-                }
-
                 sum = 1.0/sum;
                 ggml_vec_scale_f32(ne00, dp, sum);
 
@@ -6014,7 +5836,6 @@ void ggml_compute_forward_clamp(
         case GGML_TYPE_Q5_1:
         case GGML_TYPE_Q8_0:
         case GGML_TYPE_Q8_1:
-        case GGML_TYPE_MXFP4:
         case GGML_TYPE_Q2_K:
         case GGML_TYPE_Q3_K:
         case GGML_TYPE_Q4_K:
@@ -8168,14 +7989,12 @@ void ggml_compute_forward_argsort(
 
 static void ggml_compute_forward_flash_attn_ext_f16(
         const ggml_compute_params * params,
+        const ggml_tensor * q,
+        const ggml_tensor * k,
+        const ggml_tensor * v,
+        const ggml_tensor * mask,
         ggml_tensor * dst) {
 
-    const ggml_tensor * q     = dst->src[0];
-    const ggml_tensor * k     = dst->src[1];
-    const ggml_tensor * v     = dst->src[2];
-    const ggml_tensor * mask  = dst->src[3];
-    const ggml_tensor * sinks = dst->src[4];
-
     GGML_TENSOR_LOCALS(int64_t, neq, q,   ne)
     GGML_TENSOR_LOCALS(size_t,  nbq, q,   nb)
     GGML_TENSOR_LOCALS(int64_t, nek, k,   ne)
@@ -8370,23 +8189,6 @@ static void ggml_compute_forward_flash_attn_ext_f16(
             }
         }
 
-        // sinks
-        if (sinks) {
-            const float s = ((float *)((char *) sinks->data))[h];
-
-            float ms = 1.0f;
-            float vs = 1.0f;
-
-            if (s > M) {
-                ms = expf(M - s);
-                ggml_vec_scale_f32(DV, VKQ32, ms);
-            } else {
-                vs = expf(s - M);
-            }
-
-            S = S*ms + vs;
-        }
-
         // V /= S
         const float S_inv = 1.0f/S;
         ggml_vec_scale_f32(DV, VKQ32, S_inv);
@@ -8406,13 +8208,17 @@ static void ggml_compute_forward_flash_attn_ext_f16(
 
 void ggml_compute_forward_flash_attn_ext(
         const ggml_compute_params * params,
+        const ggml_tensor * q,
+        const ggml_tensor * k,
+        const ggml_tensor * v,
+        const ggml_tensor * mask,
         ggml_tensor * dst) {
     switch (dst->op_params[3]) {
         case GGML_PREC_DEFAULT:
         case GGML_PREC_F32:
             {
                 // uses F32 accumulators
-                ggml_compute_forward_flash_attn_ext_f16(params, dst);
+                ggml_compute_forward_flash_attn_ext_f16(params, q, k, v, mask, dst);
             } break;
         default:
             {
@@ -9274,10 +9080,6 @@ void ggml_compute_forward_glu(
             {
                 ggml_compute_forward_swiglu(params, dst);
             } break;
-        case GGML_GLU_OP_SWIGLU_OAI:
-            {
-                ggml_compute_forward_swiglu_oai(params, dst);
-            } break;
         case GGML_GLU_OP_GEGLU_ERF:
             {
                 ggml_compute_forward_geglu_erf(params, dst);

ggml/src/ggml-cpu/ops.h

@@ -29,7 +29,6 @@ extern "C" {
 
 void ggml_compute_forward_dup(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_add(const struct ggml_compute_params * params, struct ggml_tensor * dst);
-void ggml_compute_forward_add_id(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_add1(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_acc(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_sum(const struct ggml_compute_params * params, struct ggml_tensor * dst);
@@ -83,7 +82,13 @@ void ggml_compute_forward_arange(const struct ggml_compute_params * params, stru
 void ggml_compute_forward_timestep_embedding(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_argsort(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_leaky_relu(const struct ggml_compute_params * params, struct ggml_tensor * dst);
-void ggml_compute_forward_flash_attn_ext(const struct ggml_compute_params * params, struct ggml_tensor * dst);
+void ggml_compute_forward_flash_attn_ext(
+    const struct ggml_compute_params * params,
+    const struct ggml_tensor * q,
+    const struct ggml_tensor * k,
+    const struct ggml_tensor * v,
+    const struct ggml_tensor * mask,
+    struct ggml_tensor * dst);
 void ggml_compute_forward_flash_attn_back(
         const struct ggml_compute_params * params,
         const bool masked,

ggml/src/ggml-cpu/quants.c

@@ -46,10 +46,6 @@ void quantize_row_q8_1_generic(const float * GGML_RESTRICT x, void * GGML_RESTRI
     quantize_row_q8_1_ref(x, y, k);
 }
 
-void quantize_row_mxfp4(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k) {
-    quantize_row_mxfp4_ref(x, y, k);
-}
-
 //
 // 2-6 bit quantization in super-blocks
 //
@@ -185,37 +181,6 @@ void ggml_vec_dot_q4_1_q8_1_generic(int n, float * GGML_RESTRICT s, size_t bs, c
     *s = sumf;
 }
 
-void ggml_vec_dot_mxfp4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
-    assert(nrc == 1);
-    UNUSED(nrc);
-    UNUSED(bx);
-    UNUSED(by);
-    UNUSED(bs);
-    assert(n % QK_MXFP4 == 0);
-    static_assert(QK_MXFP4 == QK8_0, "QK_MXFP4 and QK8_0 must be the same");
-
-    const block_mxfp4 * GGML_RESTRICT x = vx;
-    const block_q8_0 * GGML_RESTRICT y = vy;
-
-    const int nb = n / QK_MXFP4;
-
-    int ib = 0;
-    float sumf = 0;
-
-    for (; ib < nb; ++ib) {
-        const float d = GGML_CPU_FP16_TO_FP32(y[ib].d)*GGML_E8M0_TO_FP32_HALF(x[ib].e);
-
-        int sumi1 = 0;
-        int sumi2 = 0;
-        for (int j = 0; j < QK_MXFP4/2; ++j) {
-            sumi1 += y[ib].qs[j +          0] * kvalues_mxfp4[x[ib].qs[j] & 0xf];
-            sumi2 += y[ib].qs[j + QK_MXFP4/2] * kvalues_mxfp4[x[ib].qs[j] >>  4];
-        }
-        sumf += d * (sumi1 + sumi2);
-    }
-    *s = sumf;
-}
-
 void ggml_vec_dot_q5_0_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
     const int qk = QK8_0;
     const int nb = n / qk;

ggml/src/ggml-cpu/quants.h

@@ -19,8 +19,6 @@ void quantize_row_q5_1(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, in
 void quantize_row_q8_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
 void quantize_row_q8_1(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
 
-void quantize_row_mxfp4(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
-
 void quantize_row_q2_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
 void quantize_row_q3_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
 void quantize_row_q4_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
@@ -41,8 +39,6 @@ void ggml_vec_dot_q5_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const voi
 void ggml_vec_dot_q5_1_q8_1(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
 void ggml_vec_dot_q8_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
 
-void ggml_vec_dot_mxfp4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
-
 void ggml_vec_dot_q2_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
 void ggml_vec_dot_q3_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
 void ggml_vec_dot_q4_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
@@ -71,12 +67,8 @@ void ggml_vec_dot_q4_1_q8_1_generic(int n, float * GGML_RESTRICT s, size_t bs, c
 void ggml_vec_dot_q5_0_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
 void ggml_vec_dot_q5_1_q8_1_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
 void ggml_vec_dot_q8_0_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
-
-void ggml_vec_dot_mxfp4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
-
 void ggml_vec_dot_tq1_0_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
 void ggml_vec_dot_tq2_0_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
-
 void ggml_vec_dot_q2_K_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
 void ggml_vec_dot_q3_K_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
 void ggml_vec_dot_q4_K_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);

ggml/src/ggml-cpu/repack.cpp

@@ -412,82 +412,6 @@ void ggml_gemv_q4_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs,
     }
 }
 
-void ggml_gemv_q2_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
-    const int qk = QK_K;
-    const int nb = n / qk;
-    const int ncols_interleaved = 8;
-    const int blocklen = 8;
-
-    assert (n % qk == 0);
-    assert (nc % ncols_interleaved == 0);
-
-    UNUSED(s);
-    UNUSED(bs);
-    UNUSED(vx);
-    UNUSED(vy);
-    UNUSED(nr);
-    UNUSED(nc);
-    UNUSED(nb);
-    UNUSED(ncols_interleaved);
-    UNUSED(blocklen);
-
-    float sumf[8];
-    float sum_minf[8];
-    int sumi1,sumi2,sumi3,sumi4;
-    int sumi;
-
-    const block_q8_K * a_ptr = (const block_q8_K *)vy;
-    for(int x = 0; x < nc / ncols_interleaved; x++) {
-        const block_q2_Kx8 * b_ptr = (const block_q2_Kx8 *) vx + (x * nb);
-        for (int j = 0; j < ncols_interleaved; j++) {
-            sumf[j] = 0.0;
-            sum_minf[j] = 0.0;
-        }
-        for (int l = 0; l < nb; l++) {
-            for (int k = 0; k < (qk / (4 * blocklen)); k++) {
-                const uint8_t *scales_0 = b_ptr[l].scales + (k / 4) * 64 ;
-                const uint8_t *scales_1 = b_ptr[l].scales + (k / 4) * 64 + 16;
-                const uint8_t *scales_2 = b_ptr[l].scales + (k / 4) * 64 + 32;
-                const uint8_t *scales_3 = b_ptr[l].scales + (k / 4) * 64 + 48;
-                for (int j = 0; j < ncols_interleaved; j++) {
-                    sumi1 = 0;
-                    sumi2 = 0;
-                    sumi3 = 0;
-                    sumi4 = 0;
-                    sumi = 0;
-                    int offset = ((k / 2) % 2) + j * 2;
-                    for (int i = 0; i < blocklen; ++i){
-                        const int v0 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 3);
-                        const int v1 = (int8_t) ((b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] >> 2 ) & 3);
-                        const int v2 = (int8_t) ((b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] >> 4 ) & 3);
-                        const int v3 = (int8_t) ((b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] >> 6 ) & 3);
-                        sumi1 = (v0 * a_ptr[l].qs[(k >> 2) * 128 + (k % 4) * blocklen + i]);
-                        sumi2 = (v1 * a_ptr[l].qs[(k >> 2) * 128 + (k % 4) * blocklen + i + 32]);
-                        sumi3 = (v2 * a_ptr[l].qs[(k >> 2) * 128 + (k % 4) * blocklen + i + 64]);
-                        sumi4 = (v3 * a_ptr[l].qs[(k >> 2) * 128 + (k % 4) * blocklen + i + 96]);
-
-                        sumi1 = sumi1 * (scales_0[offset] & 0xF);
-                        sumi2 = sumi2 * (scales_1[offset] & 0xF);
-                        sumi3 = sumi3 * (scales_2[offset] & 0xF);
-                        sumi4 = sumi4 * (scales_3[offset] & 0xF);
-                        sumi += sumi1 + sumi2 + sumi3 + sumi4;
-                    }
-                    sumf[j] += sumi * GGML_FP16_TO_FP32(b_ptr[l].d[j]) * a_ptr[l].d;
-                }
-            }
-            for(int sb = 0; sb < 8; sb++) {
-                const uint8_t *mins = b_ptr[l].scales + sb * 16;
-                for(int j = 0; j < ncols_interleaved; j++){
-                    sum_minf[j] += ((mins[j * 2] >> 4) * a_ptr[l].bsums[sb * 2] + (mins[(j * 2)+ 1] >> 4) * a_ptr[l].bsums[sb * 2 + 1]) * GGML_FP16_TO_FP32(b_ptr[l].dmin[j]) * a_ptr[l].d;
-                }
-            }
-        }
-        for (int j = 0; j < ncols_interleaved; j++) {
-            s[x * ncols_interleaved + j] = sumf[j] - sum_minf[j];
-        }
-    }
-}
-
 void ggml_gemv_iq4_nl_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
     const int qk = QK8_0;
     const int nb = n / qk;
@@ -787,97 +711,6 @@ void ggml_gemm_q4_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs,
     }
 }
 
-void ggml_gemm_q2_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
-    const int qk = QK_K;
-    const int nb = n / qk;
-    const int ncols_interleaved = 8;
-    const int blocklen = 8;
-
-    assert (n % qk == 0);
-    assert (nr % 4 == 0);
-    assert (nc % ncols_interleaved == 0);
-
-    UNUSED(s);
-    UNUSED(bs);
-    UNUSED(vx);
-    UNUSED(vy);
-    UNUSED(nr);
-    UNUSED(nc);
-    UNUSED(nb);
-    UNUSED(ncols_interleaved);
-    UNUSED(blocklen);
-
-    float sumf[4][8];
-    float sum_minf[4][8];
-    int sumi1, sumi2, sumi3, sumi4;
-    int sumi;
-
-    for (int y = 0; y < nr / 4; y++) {
-        const block_q8_Kx4 * a_ptr = (const block_q8_Kx4 *) vy + (y * nb);
-        for (int x = 0; x < nc / ncols_interleaved; x++) {
-            const block_q2_Kx8 * b_ptr = (const block_q2_Kx8 *) vx + (x * nb);
-            for (int m = 0; m < 4; m++) {
-                for (int j = 0; j < ncols_interleaved; j++) {
-                    sumf[m][j] = 0.0;
-                    sum_minf[m][j] = 0.0;
-                }
-            }
-            for (int l = 0; l < nb; l++) {
-                for (int k = 0; k < (qk / (4 * blocklen)); k++) {
-
-                    const uint8_t *scales_0 = b_ptr[l].scales + (k / 4) * 64 ;
-                    const uint8_t *scales_1 = b_ptr[l].scales + (k / 4) * 64 + 16;
-                    const uint8_t *scales_2 = b_ptr[l].scales + (k / 4) * 64 + 32;
-                    const uint8_t *scales_3 = b_ptr[l].scales + (k / 4) * 64 + 48;
-                    for (int m = 0; m < 4; m++) {
-                        for (int j = 0; j < ncols_interleaved; j++) {
-                            sumi1 = 0;
-                            sumi2 = 0;
-                            sumi3 = 0;
-                            sumi4 = 0;
-                            sumi = 0;
-                            int offset = ((k / 2) % 2) + j * 2;
-                            for (int i = 0; i < blocklen; ++i){
-                                const int v0 = (int8_t) (b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] & 3);
-                                const int v1 = (int8_t) ((b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] >> 2 ) & 3);
-                                const int v2 = (int8_t) ((b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] >> 4 ) & 3);
-                                const int v3 = (int8_t) ((b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i] >> 6 ) & 3);
-                                sumi1 = (v0 * a_ptr[l].qs[(k >> 2) * 512 + (k % 4) * 4 * blocklen + m * blocklen + i]);
-                                sumi2 = (v1 * a_ptr[l].qs[(k >> 2) * 512  + (k % 4) * 4 * blocklen + m * blocklen + i + 128]);
-                                sumi3 = (v2 * a_ptr[l].qs[(k >> 2) * 512  + (k % 4) * 4 * blocklen + m * blocklen + i + 256]);
-                                sumi4 = (v3 * a_ptr[l].qs[(k >> 2) * 512  + (k % 4) * 4 * blocklen + m * blocklen + i + 384]);
-                                sumi1 = sumi1 * (scales_0[offset] & 0xF);
-                                sumi2 = sumi2 * (scales_1[offset] & 0xF);
-                                sumi3 = sumi3 * (scales_2[offset] & 0xF);
-                                sumi4 = sumi4 * (scales_3[offset] & 0xF);
-                                sumi += sumi1 + sumi2 + sumi3 + sumi4;
-                            }
-                            sumf[m][j] += sumi * GGML_FP16_TO_FP32(b_ptr[l].d[j]) * a_ptr[l].d[m];
-                        }
-                    }
-                }
-                for(int sb = 0; sb < 8; sb++) {
-                    const uint8_t *mins = b_ptr[l].scales + sb * 16;
-                    for(int m = 0; m < 4; m++) {
-                        const int16_t *bsums = a_ptr[l].bsums + (sb * 8) + (m * 4) - ((sb % 2) *  6);
-                        for(int j = 0; j < ncols_interleaved; j++) {
-                            int mins_prod = ((mins[j * 2] >> 4) * bsums[0] + (mins[(j * 2)+ 1] >> 4) * bsums[1]);
-                            sum_minf[m][j] += (mins_prod) * GGML_FP16_TO_FP32(b_ptr[l].dmin[j]) * a_ptr[l].d[m];
-                        }
-                    }
-                }
-            }
-
-            for (int m = 0; m < 4; m++) {
-                for (int j = 0; j < ncols_interleaved; j++) {
-                    s[(y * 4 + m) * bs + x * ncols_interleaved + j] = sumf[m][j] - sum_minf[m][j];
-                }
-            }
-        }
-    }
-}
-
-
 void ggml_gemm_iq4_nl_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
     const int qk = QK8_0;
     const int nb = n / qk;
@@ -1081,50 +914,6 @@ static block_q4_Kx8 make_block_q4_Kx8(block_q4_K * in, unsigned int blck_size_in
     return out;
 }
 
-static block_q2_Kx8 make_block_q2_Kx8(block_q2_K * in, unsigned int blck_size_interleave) {
-    block_q2_Kx8 out;
-
-    // Delta(scale) and dmin values of the eight Q2_K structures are copied onto the output interleaved structure
-    for (int i = 0; i < 8; i++) {
-        out.d[i] = in[i].GGML_COMMON_AGGR_U.GGML_COMMON_AGGR_S.d;
-    }
-
-    for (int i = 0; i < 8; i++) {
-        out.dmin[i] = in[i].GGML_COMMON_AGGR_U.GGML_COMMON_AGGR_S.dmin;
-    }
-
-    const int end = QK_K * 2 / blck_size_interleave;
-
-    // Interleave Q2_K quants by taking 8 bytes at a time
-    for (int i = 0; i < end; ++i) {
-        int src_id = i % 8;
-        int src_offset = (i / 8) * blck_size_interleave;
-        int dst_offset = i * blck_size_interleave;
-
-        uint64_t elems;
-        memcpy(&elems, &in[src_id].qs[src_offset], sizeof(uint64_t));
-        memcpy(&out.qs[dst_offset], &elems, sizeof(uint64_t));
-    }
-
-    // The below logic is designed so as to unpack and rearrange scales and mins values in Q2_K
-    // Currently the Q2_K structure has 16 scales and 16 mins packed in 16 bytes ( 4 bits for each value)
-    // The output Q2_Kx8 structure has 128 bytes for storing scales and mins
-    // Every 16 byte is packed such that it contains scales and mins for corresponding sub blocks from Q2_K structure
-    // For eg - First 16 bytes contains 16 scales and 16 mins - each of first and second sub blocks from different Q2_K structures
-
-    for(int i = 0; i < 128; i++){
-
-        // Index for selecting which q2k super block
-        int src1 = (i % 16) / 2;
-        // Index for selecting scale
-        int src2 = ((i / 16) * 2) + (i % 2);
-
-        out.scales[i] = in[src1].scales[src2];
-    }
-    return out;
-
-}
-
 static int repack_q4_0_to_q4_0_4_bl(struct ggml_tensor * t, int interleave_block, const void * GGML_RESTRICT data, size_t data_size) {
     GGML_ASSERT(t->type == GGML_TYPE_Q4_0);
     GGML_ASSERT(interleave_block == 4 || interleave_block == 8);
@@ -1186,37 +975,6 @@ static int repack_q4_K_to_q4_K_8_bl(struct ggml_tensor * t, int interleave_block
     GGML_UNUSED(data_size);
 }
 
-static int repack_q2_K_to_q2_K_8_bl(struct ggml_tensor * t, int interleave_block, const void * GGML_RESTRICT data, size_t data_size) {
-    GGML_ASSERT(t->type == GGML_TYPE_Q2_K);
-    GGML_ASSERT(interleave_block == 8);
-    constexpr int nrows_interleaved = 8;
-
-    block_q2_Kx8 * dst = (block_q2_Kx8*)t->data;
-    const block_q2_K * src = (const block_q2_K*) data;
-    block_q2_K dst_tmp[8];
-    int nrow = ggml_nrows(t);
-    int nblocks = t->ne[0] / QK_K;
-
-    GGML_ASSERT(data_size == nrow * nblocks * sizeof(block_q2_K));
-
-    if (t->ne[1] % nrows_interleaved != 0 || t->ne[0] % 8 != 0) {
-        return -1;
-    }
-
-    for (int b = 0; b < nrow; b += nrows_interleaved) {
-        for (int64_t x = 0; x < nblocks; x++) {
-            for (int i  = 0; i < nrows_interleaved; i++ ) {
-                dst_tmp[i] = src[x + i * nblocks];
-            }
-            *dst++ = make_block_q2_Kx8(dst_tmp, interleave_block);
-        }
-        src += nrows_interleaved * nblocks;
-    }
-    return 0;
-
-    GGML_UNUSED(data_size);
-}
-
 static int repack_q4_0_to_q4_0_8_bl(struct ggml_tensor * t, int interleave_block, const void * GGML_RESTRICT data, size_t data_size) {
     GGML_ASSERT(t->type == GGML_TYPE_Q4_0);
     GGML_ASSERT(interleave_block == 8);
@@ -1337,10 +1095,6 @@ template <> int repack<block_q4_K, 8, 8>(struct ggml_tensor * t, const void * da
     return repack_q4_K_to_q4_K_8_bl(t, 8, data, data_size);
 }
 
-template <> int repack<block_q2_K, 8, 8>(struct ggml_tensor * t, const void * data, size_t data_size) {
-    return repack_q2_K_to_q2_K_8_bl(t, 8, data, data_size);
-}
-
 template <> int repack<block_iq4_nl, 4, 4>(struct ggml_tensor * t, const void * data, size_t data_size) {
     return repack_iq4_nl_to_iq4_nl_4_bl(t, 4, data, data_size);
 }
@@ -1370,10 +1124,6 @@ template <> void gemv<block_q4_K, 8, 8, GGML_TYPE_Q8_K>(int n, float * s, size_t
     ggml_gemv_q4_K_8x8_q8_K(n, s, bs, vx, vy, nr, nc);
 }
 
-template <> void gemv<block_q2_K, 8, 8, GGML_TYPE_Q8_K>(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) {
-    ggml_gemv_q2_K_8x8_q8_K(n, s, bs, vx, vy, nr, nc);
-}
-
 template <> void gemv<block_iq4_nl, 4, 4, GGML_TYPE_Q8_0>(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) {
     ggml_gemv_iq4_nl_4x4_q8_0(n, s, bs, vx, vy, nr, nc);
 }
@@ -1398,10 +1148,6 @@ template <> void gemm<block_q4_K, 8, 8, GGML_TYPE_Q8_K>(int n, float * s, size_t
     ggml_gemm_q4_K_8x8_q8_K(n, s, bs, vx, vy, nr, nc);
 }
 
-template <> void gemm<block_q2_K, 8, 8, GGML_TYPE_Q8_K>(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) {
-    ggml_gemm_q2_K_8x8_q8_K(n, s, bs, vx, vy, nr, nc);
-}
-
 template <> void gemm<block_iq4_nl, 4, 4, GGML_TYPE_Q8_0>(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) {
     ggml_gemm_iq4_nl_4x4_q8_0(n, s, bs, vx, vy, nr, nc);
 }
@@ -1675,9 +1421,6 @@ static const ggml::cpu::tensor_traits * ggml_repack_get_optimal_repack_type(cons
     static const ggml::cpu::repack::tensor_traits<block_q4_0, 8, 8, GGML_TYPE_Q8_0> q4_0_8x8_q8_0;
     static const ggml::cpu::repack::tensor_traits<block_q4_K, 8, 8, GGML_TYPE_Q8_K> q4_K_8x8_q8_K;
 
-    // instance for Q2
-    static const ggml::cpu::repack::tensor_traits<block_q2_K, 8, 8, GGML_TYPE_Q8_K> q2_K_8x8_q8_K;
-
     // instance for IQ4
     static const ggml::cpu::repack::tensor_traits<block_iq4_nl, 4, 4, GGML_TYPE_Q8_0> iq4_nl_4x4_q8_0;
 
@@ -1703,12 +1446,6 @@ static const ggml::cpu::tensor_traits * ggml_repack_get_optimal_repack_type(cons
                 return &q4_K_8x8_q8_K;
             }
         }
-    } else if (cur->type == GGML_TYPE_Q2_K) {
-        if (ggml_cpu_has_avx512()) {
-            if (cur->ne[1] % 8 == 0) {
-                return &q2_K_8x8_q8_K;
-            }
-        }
     } else if (cur->type == GGML_TYPE_IQ4_NL) {
         if (ggml_cpu_has_neon() && ggml_cpu_has_dotprod()) {
             if (cur->ne[1] % 4 == 0) {

ggml/src/ggml-cpu/repack.h

@@ -44,14 +44,7 @@ struct block_q4_Kx8 {
 };
 
 static_assert(sizeof(block_q4_Kx8) == sizeof(ggml_half) * 16 + K_SCALE_SIZE * 8 + QK_K * 4, "wrong q4_K block size/padding");
-struct block_q2_Kx8 {
-    ggml_half d[8];      // super-block scale for quantized scales
-    ggml_half dmin[8];   // super-block scale for quantized mins
-    uint8_t scales[128];  // scales and mins, quantized with 4 bits
-    uint8_t qs[512];    // 2--bit quants
-};
 
-static_assert(sizeof(block_q2_Kx8) == sizeof(ggml_half) * 16 + QK_K/2 + QK_K * 2, "wrong q2_K block size/padding");
 struct block_q8_Kx4 {
     float d[4];              // delta
     int8_t qs[QK_K * 4];     // quants
@@ -78,13 +71,11 @@ void ggml_gemv_q4_0_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const vo
 void ggml_gemv_q4_0_4x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemv_q4_0_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemv_q4_K_8x8_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
-void ggml_gemv_q2_K_8x8_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemv_iq4_nl_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_q4_0_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_q4_0_4x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_q4_0_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_q4_K_8x8_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
-void ggml_gemm_q2_K_8x8_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_iq4_nl_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 
 // Native implementations
@@ -95,13 +86,11 @@ void ggml_gemv_q4_0_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs,
 void ggml_gemv_q4_0_4x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemv_q4_0_8x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemv_q4_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
-void ggml_gemv_q2_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemv_iq4_nl_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_q4_0_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_q4_0_4x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_q4_0_8x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_q4_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
-void ggml_gemm_q2_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 void ggml_gemm_iq4_nl_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
 
 #if defined(__cplusplus)

ggml/src/ggml-cpu/traits.cpp

@@ -10,7 +10,7 @@ extra_buffer_type::~extra_buffer_type() {}
 }  // namespace ggml::cpu
 
 bool ggml_cpu_extra_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * op) {
-    for (auto extra : ggml_backend_cpu_get_extra_buffer_types()) {
+    for (auto extra : ggml_backend_cpu_get_extra_buffers_type()) {
         if (extra && extra->context) {
             auto buf_extra     = (ggml::cpu::extra_buffer_type *) extra->context;
             auto tensor_traits = buf_extra->get_tensor_traits(op);
@@ -23,7 +23,7 @@ bool ggml_cpu_extra_compute_forward(struct ggml_compute_params * params, struct
 }
 
 bool ggml_cpu_extra_work_size(int n_threads, const struct ggml_tensor * op, size_t * size) {
-    for (auto extra : ggml_backend_cpu_get_extra_buffer_types()) {
+    for (auto extra : ggml_backend_cpu_get_extra_buffers_type()) {
         if (extra && extra->context) {
             auto buf_extra     = (ggml::cpu::extra_buffer_type *) extra->context;
             auto tensor_traits = buf_extra->get_tensor_traits(op);

ggml/src/ggml-cpu/traits.h

@@ -33,6 +33,6 @@ class extra_buffer_type {
 }  // namespace ggml::cpu
 
 // implemented in ggml-cpu.cpp.
-std::vector<ggml_backend_buffer_type_t> & ggml_backend_cpu_get_extra_buffer_types();
+std::vector<ggml_backend_buffer_type_t> & ggml_backend_cpu_get_extra_buffers_type();
 
 #endif

ggml/src/ggml-cpu/vec.h

@@ -55,22 +55,7 @@ inline static void ggml_vec_cpy_i32(const int n, int32_t * y, const int32_t * x)
 
 inline static void ggml_vec_set_f16(const int n, ggml_fp16_t * x, const ggml_fp16_t v) { for (int i = 0; i < n; ++i) x[i] = v; }
 inline static void ggml_vec_set_bf16(const int n, ggml_bf16_t * x, const ggml_bf16_t v) { for (int i = 0; i < n; ++i) x[i] = v; }
-
-inline static void ggml_vec_add_f32 (const int n, float * z, const float * x, const float * y) {
-    int i = 0;
-#if defined(__AVX2__)
-    for (; i + 7 < n; i += 8) {
-        __m256 vx = _mm256_loadu_ps(x + i);
-        __m256 vy = _mm256_loadu_ps(y + i);
-        __m256 vz = _mm256_add_ps(vx, vy);
-        _mm256_storeu_ps(z + i, vz);
-    }
-#endif
-    for (; i < n; ++i) {
-        z[i] = x[i] + y[i];
-    }
-}
-
+inline static void ggml_vec_add_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i]  = x[i] + y[i]; }
 inline static void ggml_vec_add_f16 (const int n, ggml_fp16_t * z, const ggml_fp16_t * x, const ggml_fp16_t * y) {
     for (int i = 0; i < n; ++i) {
         z[i] = GGML_CPU_FP32_TO_FP16(GGML_CPU_FP16_TO_FP32(x[i]) + GGML_CPU_FP16_TO_FP32(y[i]));
@@ -1007,9 +992,9 @@ void ggml_vec_swiglu_f32(const int n, float * y, const float * x, const float *
 
 inline static void ggml_vec_swiglu_f16(const int n, ggml_fp16_t * y, const ggml_fp16_t * x, const ggml_fp16_t * g) {
     for (int i = 0; i < n; ++i) {
-        float xi = GGML_CPU_FP16_TO_FP32(x[i]);
-        float gi = GGML_CPU_FP16_TO_FP32(g[i]);
-        y[i] = GGML_CPU_FP32_TO_FP16((xi/(1.0f + expf(-xi))) * gi);
+        float v = GGML_CPU_FP16_TO_FP32(x[i]);
+        float w = GGML_CPU_FP16_TO_FP32(g[i]);
+        y[i] = GGML_CPU_FP32_TO_FP16((v/(1.0f + expf(-v))) * w);
     }
 }
 

ggml/src/ggml-cuda/add-id.cu

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

ggml/src/ggml-cuda/add-id.cuh

@@ -1,3 +0,0 @@
-#include "common.cuh"
-
-void ggml_cuda_op_add_id(ggml_backend_cuda_context & ctx, ggml_tensor * dst);

ggml/src/ggml-cuda/common.cuh

@@ -1,7 +1,6 @@
 #pragma once
 
 #include "ggml.h"
-#include "ggml-impl.h"
 #include "ggml-cuda.h"
 
 #include <cstdint>
@@ -177,7 +176,7 @@ static const char * cu_get_error_str(CUresult err) {
 #define CU_CHECK(err) CUDA_CHECK_GEN(err, CUDA_SUCCESS, cu_get_error_str)
 #endif
 
-#if !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)
+#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && !defined(GGML_USE_MUSA)
 #    define CUDA_SET_SHARED_MEMORY_LIMIT(kernel, nbytes)                                                       \
         do {                                                                                                   \
             static bool shared_memory_limit_raised[GGML_CUDA_MAX_DEVICES] = { false };                         \
@@ -192,7 +191,7 @@ static const char * cu_get_error_str(CUresult err) {
         do {                                             \
             GGML_UNUSED(nbytes);                         \
         } while (0)
-#endif // !(defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)
+#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && !defined(GGML_USE_MUSA)
 
 #if CUDART_VERSION >= 11010 || defined(GGML_USE_MUSA)
 #define GGML_CUDA_ASSUME(x) __builtin_assume(x)
@@ -212,9 +211,9 @@ typedef float2 dfloat2;
 #define GGML_USE_VMM
 #endif // (!defined(GGML_USE_HIP) && !defined(GGML_CUDA_NO_VMM)) || (defined(GGML_USE_HIP) && !defined(GGML_HIP_NO_VMM))
 
-#if defined(GGML_USE_HIP) || __CUDA_ARCH__ >= GGML_CUDA_CC_PASCAL
+#if (defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) || __CUDA_ARCH__ >= GGML_CUDA_CC_PASCAL
 #define FP16_AVAILABLE
-#endif // defined(GGML_USE_HIP) || __CUDA_ARCH__ >= GGML_CUDA_CC_PASCAL
+#endif // (defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) || __CUDA_ARCH__ >= GGML_CUDA_CC_PASCAL
 
 #if defined(FP16_AVAILABLE) && __CUDA_ARCH__ != 610
 #define FAST_FP16_AVAILABLE
@@ -228,21 +227,17 @@ typedef float2 dfloat2;
 #define FP16_MMA_AVAILABLE
 #endif // defined(GGML_HIP_ROCWMMA_FATTN) && (defined(CDNA) || defined(RDNA3) || (defined(GGML_HIP_ROCWMMA_FATTN_GFX12) && defined(RDNA4)))
 
-#if defined(GGML_USE_HIP) && defined(CDNA) && !defined(GGML_HIP_NO_MMQ_MFMA)
+#if defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__) && defined(CDNA3)
 #define AMD_MFMA_AVAILABLE
-#endif // defined(GGML_USE_HIP) && defined(CDNA) && !defined(GGML_HIP_NO_MMQ_MFMA)
+#endif // defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__) && defined(CDNA3)
 
-#if !defined(GGML_USE_HIP) && __CUDA_ARCH__ >= GGML_CUDA_CC_TURING
-#define TURING_MMA_AVAILABLE
-#endif // !defined(GGML_USE_HIP) && __CUDA_ARCH__ >= GGML_CUDA_CC_TURING
+#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= GGML_CUDA_CC_TURING
+#define NEW_MMA_AVAILABLE
+#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= GGML_CUDA_CC_TURING
 
-#if !defined(GGML_USE_HIP) && __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE
-#define AMPERE_MMA_AVAILABLE
-#endif // !defined(GGML_USE_HIP) && __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE
-
-#if !defined(GGML_USE_HIP) && __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE
+#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE
 #define CP_ASYNC_AVAILABLE
-#endif // !defined(GGML_USE_HIP) && __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE
+#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE
 
 #if !defined(GGML_CUDA_NO_FA) && !(defined(GGML_USE_MUSA) && __MUSA_ARCH__ < 220)
 #define FLASH_ATTN_AVAILABLE
@@ -264,7 +259,7 @@ static bool fast_fp16_hardware_available(const int cc) {
 
 // Any FP16 tensor core instructions are available for ggml code.
 static bool fp16_mma_available(const int cc) {
-#if defined(GGML_USE_HIP) && !defined(GGML_HIP_ROCWMMA_FATTN)
+#if defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__) && !defined(GGML_HIP_ROCWMMA_FATTN)
     return false;
 #else
     if ((GGML_CUDA_CC_IS_NVIDIA(cc) && ggml_cuda_highest_compiled_arch(cc) >= GGML_CUDA_CC_VOLTA) ||
@@ -280,7 +275,7 @@ static bool fp16_mma_available(const int cc) {
     } else {
         return false;
     }
-#endif // defined(GGML_USE_HIP) && !defined(GGML_HIP_ROCWMMA_FATTN)
+#endif // defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__) && !defined(GGML_HIP_ROCWMMA_FATTN)
 }
 
 // To be used for feature selection of external libraries, e.g. cuBLAS.
@@ -298,47 +293,40 @@ static bool fp32_mma_hardware_available(const int cc) {
     return GGML_CUDA_CC_IS_CDNA(cc);
 }
 
+// AMD CDNA3 matrix cores.. Will add support for other CDNA generations later.
 static bool amd_mfma_available(const int cc) {
-#if !defined(GGML_HIP_NO_MMQ_MFMA)
-    return GGML_CUDA_CC_IS_CDNA(cc);
-#else
-    return false;
-#endif //!defined(GGML_HIP_NO_MMQ_MFMA)
+    return cc >= GGML_CUDA_CC_OFFSET_AMD && GGML_CUDA_CC_IS_CDNA3(cc);
 }
 
 // Volta technically had FP16 tensor cores but they work very differently compared to Turing and later.
-static bool turing_mma_available(const int cc) {
+static bool new_mma_available(const int cc) {
     return GGML_CUDA_CC_IS_NVIDIA(cc) && ggml_cuda_highest_compiled_arch(cc) >= GGML_CUDA_CC_TURING;
 }
 
-static bool ampere_mma_available(const int cc) {
-    return cc < GGML_CUDA_CC_OFFSET_AMD && ggml_cuda_highest_compiled_arch(cc) >= GGML_CUDA_CC_AMPERE;
-}
-
 static bool cp_async_available(const int cc) {
     return cc < GGML_CUDA_CC_OFFSET_AMD && ggml_cuda_highest_compiled_arch(cc) >= GGML_CUDA_CC_AMPERE;
 }
 
 static constexpr __device__ int ggml_cuda_get_physical_warp_size() {
-#if defined(GGML_USE_HIP) && (defined(__GFX9__) || defined(__GFX8__))
+#if defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__) && (defined(__GFX9__) || defined(__GFX8__))
     return 64;
 #else
     return 32;
-#endif // defined(GGML_USE_HIP) && (defined(__GFX9__) || defined(__GFX8__))
+#endif // defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__) && (defined(__GFX9__) || defined(__GFX8__))
 }
 
 [[noreturn]]
 static __device__ void no_device_code(
     const char * file_name, const int line, const char * function_name, const int arch, const char * arch_list) {
 
-#if defined(GGML_USE_HIP)
+#if defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)
     printf("%s:%d: ERROR: HIP kernel %s has no device code compatible with HIP arch %d.\n",
            file_name, line, function_name, arch);
     GGML_UNUSED(arch_list);
 #else
     printf("%s:%d: ERROR: CUDA kernel %s has no device code compatible with CUDA arch %d. ggml-cuda.cu was compiled for: %s\n",
            file_name, line, function_name, arch, arch_list);
-#endif // defined(GGML_USE_HIP)
+#endif // defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)
     __trap();
 
     GGML_UNUSED(no_device_code); // suppress unused function warning
@@ -375,7 +363,7 @@ struct ggml_cuda_unroll<1> {
 
 template<int width = WARP_SIZE>
 static __device__ __forceinline__ int warp_reduce_sum(int x) {
-#if !defined(GGML_USE_HIP) && __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE
+#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE
     return __reduce_add_sync(0xffffffff, x);
 #else
 #pragma unroll
@@ -383,7 +371,7 @@ static __device__ __forceinline__ int warp_reduce_sum(int x) {
         x += __shfl_xor_sync(0xffffffff, x, offset, width);
     }
     return x;
-#endif // !defined(GGML_USE_HIP) && __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE
+#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE
 }
 
 template<int width = WARP_SIZE>
@@ -440,20 +428,6 @@ static __global__ void reduce_rows_f32(const float * x, float * dst, const int n
     dst[row] = norm ? sum / ncols : sum;
 }
 
-template<int width = WARP_SIZE>
-static __device__ __forceinline__ int warp_reduce_all(int x) {
-#ifdef GGML_USE_HIP
-#pragma unroll
-    for (int offset = width/2; offset > 0; offset >>= 1) {
-        x = x && __shfl_xor_sync(0xffffffff, x, offset, width);
-    }
-    return x;
-#else
-    static_assert(width == WARP_SIZE, "width != WARP_SIZE not implemented");
-    return __all_sync(0xffffffff, x);
-#endif // GGML_USE_HIP
-}
-
 template<int width = WARP_SIZE>
 static __device__ __forceinline__ float warp_reduce_max(float x) {
 #pragma unroll
@@ -466,11 +440,11 @@ static __device__ __forceinline__ float warp_reduce_max(float x) {
 static __device__ __forceinline__ half ggml_cuda_hmax(const half a, const half b) {
 #ifdef FP16_AVAILABLE
 
-#if !defined(GGML_USE_HIP) && CUDART_VERSION < CUDART_HMAX
+#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && CUDART_VERSION < CUDART_HMAX
     return __float2half(fmaxf(__half2float(a), __half2float(b)));
 #else
     return __hmax(a, b);
-#endif // !defined(GGML_USE_HIP) && CUDART_VERSION < CUDART_HMAX
+#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && CUDART_VERSION < CUDART_HMAX
 
 #else
    NO_DEVICE_CODE;
@@ -498,7 +472,7 @@ static __device__ __forceinline__ half2 ggml_cuda_hmax2(const half2 a, const hal
 
 template<int width = WARP_SIZE>
 static __device__ __forceinline__ half2 warp_reduce_max(half2 x) {
-#if !defined(GGML_USE_HIP) && __CUDA_ARCH__ >= GGML_CUDA_CC_PASCAL || (defined(GGML_USE_HIP) && HIP_VERSION >= 50700000)
+#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= GGML_CUDA_CC_PASCAL || (defined(GGML_USE_HIP) && HIP_VERSION >= 50700000)
 #pragma unroll
    for (int offset = width/2; offset > 0; offset >>= 1) {
        x = ggml_cuda_hmax2(x, __shfl_xor_sync(0xffffffff, x, offset, width));
@@ -507,7 +481,7 @@ static __device__ __forceinline__ half2 warp_reduce_max(half2 x) {
 #else
    GGML_UNUSED(x);
    NO_DEVICE_CODE;
-#endif // !defined(GGML_USE_HIP) && __CUDA_ARCH__ >= GGML_CUDA_CC_PASCAL || (defined(GGML_USE_HIP) && HIP_VERSION >= 50700000)
+#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= GGML_CUDA_CC_PASCAL || (defined(GGML_USE_HIP) && HIP_VERSION >= 50700000)
 }
 
 #if CUDART_VERSION < CUDART_HMASK
@@ -519,7 +493,7 @@ static __device__ __forceinline__ uint32_t __hgt2_mask(const half2 a, const half
 #endif // CUDART_VERSION < CUDART_HMASK
 
 static __device__ __forceinline__ int ggml_cuda_dp4a(const int a, const int b, int c) {
-#if defined(GGML_USE_HIP)
+#if defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)
 #if defined(CDNA) || defined(RDNA2) || defined(__gfx906__)
     c = __builtin_amdgcn_sdot4(a, b, c, false);
 #elif defined(RDNA3) || defined(RDNA4)
@@ -545,7 +519,7 @@ static __device__ __forceinline__ int ggml_cuda_dp4a(const int a, const int b, i
 #endif
     return c;
 
-#else // defined(GGML_USE_HIP)
+#else // defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)
 
 #if __CUDA_ARCH__ >= GGML_CUDA_CC_DP4A || defined(GGML_USE_MUSA)
     return __dp4a(a, b, c);
@@ -555,25 +529,7 @@ static __device__ __forceinline__ int ggml_cuda_dp4a(const int a, const int b, i
     return c + a8[0]*b8[0] + a8[1]*b8[1] + a8[2]*b8[2] + a8[3]*b8[3];
 #endif // __CUDA_ARCH__ >= GGML_CUDA_CC_DP4A || defined(GGML_USE_MUSA)
 
-#endif // defined(GGML_USE_HIP)
-}
-
-static __device__ __forceinline__ float ggml_cuda_e8m0_to_fp32(uint8_t x) {
-#if CUDART_VERSION >= 12080
-    const nv_bfloat16 e = __nv_cvt_e8m0_to_bf16raw(x);
-    return (float) e;
-#else
-    uint32_t bits;
-    if (x == 0) {
-        bits = 0x00400000;
-    } else {
-        bits = (uint32_t) x << 23;
-    }
-
-    float result;
-    memcpy(&result, &bits, sizeof(float));
-    return result;
-#endif // CUDART_VERSION >= 12050
+#endif // defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)
 }
 
 typedef void (*dequantize_kernel_t)(const void * vx, const int64_t ib, const int iqs, dfloat2 & v);
@@ -634,13 +590,6 @@ struct ggml_cuda_type_traits<GGML_TYPE_Q8_0> {
     static constexpr int qi = QI8_0;
 };
 
-template<>
-struct ggml_cuda_type_traits<GGML_TYPE_MXFP4> {
-    static constexpr int qk = QK_MXFP4;
-    static constexpr int qr = QR_MXFP4;
-    static constexpr int qi = QI_MXFP4;
-};
-
 template<>
 struct ggml_cuda_type_traits<GGML_TYPE_Q2_K> {
     static constexpr int qk = QK_K;

ggml/src/ggml-cuda/convert.cu

@@ -465,24 +465,6 @@ static __global__ void dequantize_block_iq4_xs(const void * __restrict__ vx, dst
     }
 }
 
-template<typename dst_t>
-static __global__ void dequantize_block_mxfp4(const void * __restrict__ vx, dst_t * __restrict__ yy) {
-
-    const int64_t i   = blockIdx.x;
-    const block_mxfp4 * x = (const block_mxfp4 *) vx + i*(QK_K/QK_MXFP4);
-
-    const int64_t tid = threadIdx.x;
-    const int64_t il = tid/8; // 0...3
-    const int64_t ib = tid%8; // 0...7
-    dst_t * y = yy + i*QK_K + 32*ib + 4*il;
-    const uint8_t  * q4 = x[ib].qs + 4*il;
-    const float d = ggml_cuda_e8m0_to_fp32(x[ib].e);
-    for (int j = 0; j < 4; ++j) {
-        y[j+ 0] = d * kvalues_mxfp4[q4[j] & 0xf]*0.5f;
-        y[j+16] = d * kvalues_mxfp4[q4[j] >>  4]*0.5f;
-    }
-}
-
 template <int qk, int qr, dequantize_kernel_t dequantize_kernel, typename dst_t>
 static void dequantize_block_cuda(const void * vx, dst_t * y,
         const int64_t ne00, const int64_t ne01, const int64_t ne02, const int64_t ne03,
@@ -606,12 +588,6 @@ static void dequantize_row_iq4_xs_cuda(const void * vx, dst_t * y, const int64_t
     dequantize_block_iq4_xs<<<nb, 32, 0, stream>>>(vx, y);
 }
 
-template<typename dst_t>
-static void dequantize_row_mxfp4_cuda(const void * vx, dst_t * y, const int64_t k, cudaStream_t stream) {
-    const int nb = (k + QK_K - 1) / QK_K;
-    dequantize_block_mxfp4<<<nb, 32, 0, stream>>>(vx, y);
-}
-
 template <typename src_t, typename dst_t>
 static __global__ void convert_unary(
         const void * __restrict__ vx, dst_t * __restrict__ y, const int64_t ne00, const int64_t ne01, const int64_t ne02,
@@ -701,8 +677,6 @@ to_fp16_cuda_t ggml_get_to_fp16_cuda(ggml_type type) {
             return dequantize_row_iq4_xs_cuda;
         case GGML_TYPE_IQ3_S:
             return dequantize_row_iq3_s_cuda;
-        case GGML_TYPE_MXFP4:
-            return dequantize_row_mxfp4_cuda;
         case GGML_TYPE_F32:
             return convert_unary_cont_cuda<float>;
         case GGML_TYPE_BF16:
@@ -752,8 +726,6 @@ to_fp32_cuda_t ggml_get_to_fp32_cuda(ggml_type type) {
             return dequantize_row_iq4_xs_cuda;
         case GGML_TYPE_IQ3_S:
             return dequantize_row_iq3_s_cuda;
-        case GGML_TYPE_MXFP4:
-            return dequantize_row_mxfp4_cuda;
         case GGML_TYPE_F16:
             return convert_unary_cont_cuda<half>;
         case GGML_TYPE_BF16:

ggml/src/ggml-cuda/fattn-common.cuh

@@ -15,8 +15,6 @@ typedef void (* fattn_kernel_t)(
         const char * __restrict__ K,
         const char * __restrict__ V,
         const char * __restrict__ mask,
-        const char * __restrict__ sinks,
-        const int  * __restrict__ KV_max,
         float      * __restrict__ dst,
         float2     * __restrict__ dst_meta,
         const float scale,
@@ -502,55 +500,6 @@ constexpr __device__ dequantize_1_f32_t get_dequantize_1_f32(ggml_type type_V) {
         nullptr;
 }
 
-template <int ncols1>
-__launch_bounds__(FATTN_KQ_STRIDE/2, 1)
-static __global__ void flash_attn_mask_to_KV_max(
-        const half2 * __restrict__ mask, int * __restrict__ KV_max, const int ne30, const int s31, const int s33) {
-    const int ne31     = gridDim.x;
-    const int tid      = threadIdx.x;
-    const int sequence = blockIdx.y;
-    const int jt       = blockIdx.x;
-
-    mask += sequence*s33 + jt*ncols1*s31;
-
-    __shared__ int buf_iw[WARP_SIZE];
-    if (tid < WARP_SIZE) {
-        buf_iw[tid] = 1;
-    }
-    __syncthreads();
-
-    int KV_max_sj = (ne30 - 1) * FATTN_KQ_STRIDE;
-    for (; KV_max_sj >= 0; KV_max_sj -= FATTN_KQ_STRIDE) {
-        int all_inf = 1;
-
-#pragma unroll
-        for (int j = 0; j < ncols1; ++j) {
-            const float2 tmp = __half22float2(mask[j*s31 + KV_max_sj/2 + tid]);
-            all_inf = all_inf && int(isinf(tmp.x)) && int(isinf(tmp.y));
-        }
-
-        all_inf = warp_reduce_all(all_inf);
-        if (tid % WARP_SIZE == 0) {
-            buf_iw[tid / WARP_SIZE] = all_inf;
-        }
-        __syncthreads();
-        all_inf = buf_iw[tid % WARP_SIZE];
-        __syncthreads();
-        all_inf = warp_reduce_all(all_inf);
-
-        if (!all_inf) {
-            KV_max_sj += FATTN_KQ_STRIDE;
-            break;
-        }
-    }
-
-    if (threadIdx.x != 0) {
-        return;
-    }
-
-    KV_max[sequence*ne31 + jt] = KV_max_sj;
-}
-
 template<int D, int ncols1, int ncols2> // D == head size
 __launch_bounds__(D, 1)
 static __global__ void flash_attn_stream_k_fixup(
@@ -643,9 +592,9 @@ static __global__ void flash_attn_stream_k_fixup(
 }
 
 template<int D> // D == head size
-#if !defined(GGML_USE_HIP)
+#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__))
 __launch_bounds__(D, 1)
-#endif // !(defined(GGML_USE_HIP)
+#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__))
 static __global__ void flash_attn_combine_results(
         const float  * __restrict__ VKQ_parts,
         const float2 * __restrict__ VKQ_meta,
@@ -737,8 +686,7 @@ void launch_fattn(
 
     GGML_ASSERT(V || is_mla);
 
-    const ggml_tensor * mask  = dst->src[3];
-    const ggml_tensor * sinks = dst->src[4];
+    const ggml_tensor * mask = dst->src[3];
 
     ggml_tensor * KQV = dst;
 
@@ -763,7 +711,6 @@ void launch_fattn(
 
     ggml_cuda_pool_alloc<half>   K_f16(pool);
     ggml_cuda_pool_alloc<half>   V_f16(pool);
-    ggml_cuda_pool_alloc<int>    KV_max(pool);
     ggml_cuda_pool_alloc<float>  dst_tmp(pool);
     ggml_cuda_pool_alloc<float2> dst_tmp_meta(pool);
 
@@ -832,30 +779,11 @@ void launch_fattn(
         V_data = (char *) V_f16.ptr;
     }
 
+    int parallel_blocks = 1;
+
     const int ntiles_x = ((Q->ne[1] + ncols1 - 1) / ncols1);
     const int ntiles_total = ntiles_x * (Q->ne[2] / ncols2) * Q->ne[3];
 
-    // Optional optimization where the mask is scanned to determine whether part of the calculation can be skipped.
-    // Only worth the overhead if there is at lease one FATTN_KQ_STRIDE x FATTN_KQ_STRIDE square to be skipped or
-    //     multiple sequences of possibly different lengths.
-    if (mask && (Q->ne[1] >= 1024 || Q->ne[3] > 1)) {
-        const int s31 = mask->nb[1] / sizeof(half2);
-        const int s33 = mask->nb[3] / sizeof(half2);
-
-        const dim3 blocks_num_KV_max(ntiles_x, Q->ne[3], 1);
-        const dim3 block_dim_KV_max(FATTN_KQ_STRIDE/2, 1, 1);
-
-        const int ne_KV_max = blocks_num_KV_max.x*blocks_num_KV_max.y;
-        const int iter_k = K->ne[1] / FATTN_KQ_STRIDE;
-
-        KV_max.alloc(ne_KV_max);
-        flash_attn_mask_to_KV_max<ncols1><<<blocks_num_KV_max, block_dim_KV_max, 0, main_stream>>>
-            ((const half2 *) mask->data, KV_max.ptr, iter_k, s31, s33);
-        CUDA_CHECK(cudaGetLastError());
-    }
-
-    int parallel_blocks = 1;
-
     const dim3 block_dim(warp_size, nwarps, 1);
     int max_blocks_per_sm = 1; // Max. number of active blocks limited by occupancy.
     CUDA_CHECK(cudaOccupancyMaxActiveBlocksPerMultiprocessor(&max_blocks_per_sm, fattn_kernel, block_dim.x * block_dim.y * block_dim.z, nbytes_shared));
@@ -942,8 +870,6 @@ void launch_fattn(
         K_data,
         V_data,
         mask ? ((const char *) mask->data) : nullptr,
-        sinks ? ((const char *) sinks->data) : nullptr,
-        KV_max.ptr,
         !stream_k && parallel_blocks > 1 ? dst_tmp.ptr : (float *) KQV->data, dst_tmp_meta.ptr,
         scale, max_bias, m0, m1, n_head_log2, logit_softcap,
         Q->ne[0], Q->ne[1], Q->ne[2], Q->ne[3], Q->nb[1], Q->nb[2], Q->nb[3],

ggml/src/ggml-cuda/fattn-mma-f16.cuh

@@ -392,8 +392,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_load_mask(
     }
 }
 
-template<int DKQ, int DV, int ncols1, int ncols2, int nwarps, int ntiles,
-    bool use_logit_softcap, bool mla, bool needs_fixup, bool is_fixup, bool last_iter>
+template<int DKQ, int DV, int ncols1, int ncols2, int nwarps, int ntiles, bool use_logit_softcap, bool mla, bool needs_fixup, bool is_fixup, bool last_iter>
 static __device__ __forceinline__ void flash_attn_ext_f16_iter(
         const float2 * const __restrict__ Q_f2,
         const half2  * const __restrict__ K_h2,
@@ -418,7 +417,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter(
         float        * const __restrict__ KQ_max,
         float        * const __restrict__ KQ_rowsum,
         const int kb0) {
-#ifdef TURING_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
     typedef fattn_mma_f16_config<DKQ, DV> c;
 
 #ifdef CP_ASYNC_AVAILABLE
@@ -776,7 +775,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_iter(
     GGML_UNUSED(VKQ_C); GGML_UNUSED(KQ_max); GGML_UNUSED(KQ_rowsum);
     GGML_UNUSED(kb0); GGML_UNUSED(tile_Q);
     NO_DEVICE_CODE;
-#endif // TURING_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
 }
 
 template<int DKQ, int DV, int ncols1, int ncols2, int nwarps, int ntiles, bool use_logit_softcap, bool mla, bool needs_fixup, bool is_fixup>
@@ -785,7 +784,6 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile(
         const half2  * const __restrict__ K_h2,
         const half2  * const __restrict__ V_h2,
         const half2  * const __restrict__ mask_h2,
-        const float  * const __restrict__ sinks_f,
         float2       * const __restrict__ dstk,
         float2       * const __restrict__ dstk_fixup,
         const float scale,
@@ -801,7 +799,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile(
         const int jt,
         const int kb0_start,
         const int kb0_stop) {
-#ifdef TURING_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
     //In this kernel Q, K, V are matrices while i, j, k are matrix indices.
 
     typedef fattn_mma_f16_config<DKQ, DV> c;
@@ -924,8 +922,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile(
     }
 
     // Iterate over ne11 == previous tokens:
-    int kb0 = kb0_start;
-    for (; kb0 < kb0_stop-1; ++kb0) {
+    for (int kb0 = kb0_start; kb0 < kb0_stop-1; ++kb0) {
         constexpr bool last_iter = false;
         flash_attn_ext_f16_iter<DKQ, DV, ncols1, ncols2, nwarps, ntiles, use_logit_softcap, mla, needs_fixup, is_fixup, last_iter>
             (Q_f2, K_h2, V_h2, mask_h2, dstk, dstk_fixup, scale, slope, logit_softcap,
@@ -935,7 +932,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile(
         constexpr bool last_iter = true;
         flash_attn_ext_f16_iter<DKQ, DV, ncols1, ncols2, nwarps, ntiles, use_logit_softcap, mla, needs_fixup, is_fixup, last_iter>
             (Q_f2, K_h2, V_h2, mask_h2, dstk, dstk_fixup, scale, slope, logit_softcap,
-             ne01, ne02, stride_K, stride_V, stride_mask, tile_Q, tile_K, tile_V, tile_mask, Q_B, VKQ_C, KQ_max, KQ_rowsum, kb0);
+             ne01, ne02, stride_K, stride_V, stride_mask, tile_Q, tile_K, tile_V, tile_mask, Q_B, VKQ_C, KQ_max, KQ_rowsum, kb0_stop-1);
     }
 
     // With multi-stage loading there is no __syncthreads at the end of the iter,
@@ -958,52 +955,6 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile(
         }
     }
 
-    // If attention sinks are used, potentially re-scale if KQ_max is small.
-    // Also add the sink as a value to KQ_rowsum, this is done after synchonization of KQ_rowsum
-    //     so it's being done unconditionally for every thread.
-    if (!is_fixup && (np == 1 || threadIdx.y % np == 0) && sinks_f) {
-        float KQ_max_scale[cols_per_thread];
-#pragma unroll
-        for (int col = 0; col < cols_per_thread; ++col) {
-            static_assert(ntiles == 1 || ntiles == 2, "ntiles > 2 not implemented");
-            const int jc = ntiles == 1 ? 2*tile_C_VKQ::get_j(col/2) + col % 2 : tile_C_VKQ_16::get_i(col);
-            const float sink = sinks_f[jc % ncols2];
-
-            const float KQ_max_new = fmaxf(KQ_max[col], sink);
-            const float KQ_max_diff = KQ_max[col] - KQ_max_new;
-            KQ_max_scale[col] = expf(KQ_max_diff);
-            KQ_max[col] = KQ_max_new;
-
-            *((uint32_t *) &KQ_max_scale[col]) *= KQ_max_diff >= SOFTMAX_FTZ_THRESHOLD;
-
-            const float KQ_max_add = expf(sink - KQ_max_new);
-            KQ_rowsum[col] = KQ_max_scale[col]*KQ_rowsum[col] + KQ_max_add;
-        }
-
-        if (ntiles == 1) {
-            const half2 KQ_max_scale_h2 = make_half2(KQ_max_scale[0], KQ_max_scale[1]);
-#pragma unroll
-            for (int i = 0; i < DV/tile_C_VKQ::I; ++i) {
-#pragma unroll
-                for (int l = 0; l < tile_C_VKQ::ne; ++l) {
-                    VKQ_C[i].x[l] *= KQ_max_scale_h2;
-                }
-            }
-        } else {
-#pragma unroll
-            for (int col = 0; col < cols_per_thread; ++col) {
-                const half2 KQ_max_scale_h2 = make_half2(KQ_max_scale[col], KQ_max_scale[col]);
-#pragma unroll
-                for (int i = 0; i < DV/tile_C_VKQ_16::J; ++i) {
-#pragma unroll
-                    for (int l0 = 0; l0 < tile_C_VKQ_16::ne; l0 += 2) {
-                        VKQ_C_16[i*ntiles/2 + col/2].x[l0 + col % 2] *= KQ_max_scale_h2;
-                    }
-                }
-            }
-        }
-    }
-
     // Combine VKQ accumulator values if np > 1.
     // It's also faster to do small writes to shared memory, then large write to VRAM than to do small writes to VRAM.
     // So also write VKQ accumulators to shared memory in column-major format if np == 1.
@@ -1243,7 +1194,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile(
     GGML_UNUSED(stride_Q2); GGML_UNUSED(stride_K); GGML_UNUSED(stride_V); GGML_UNUSED(stride_mask);
     GGML_UNUSED(jt); GGML_UNUSED(kb0_start); GGML_UNUSED(kb0_stop);
     NO_DEVICE_CODE;
-#endif // TURING_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
 }
 
 template<int DKQ, int DV, int ncols1, int ncols2, int nwarps, int ntiles, bool use_logit_softcap, bool mla>
@@ -1253,8 +1204,6 @@ static __global__ void flash_attn_ext_f16(
         const char * __restrict__ K,
         const char * __restrict__ V,
         const char * __restrict__ mask,
-        const char * __restrict__ sinks,
-        const int  * __restrict__ KV_max,
         float      * __restrict__ dst,
         float2     * __restrict__ dst_meta,
         const float scale,
@@ -1270,7 +1219,7 @@ static __global__ void flash_attn_ext_f16(
                             const int32_t nb21, const int32_t nb22, const int64_t nb23,
                             const int32_t ne31, const int32_t ne32, const int32_t ne33,
                             const int32_t nb31, const int32_t nb32, const int64_t nb33) {
-#if defined(FLASH_ATTN_AVAILABLE) && defined(TURING_MMA_AVAILABLE)
+#if defined(FLASH_ATTN_AVAILABLE) && defined(NEW_MMA_AVAILABLE)
 
     // Skip unused kernel variants for faster compilation:
     if (use_logit_softcap && !(DKQ == 128 || DKQ == 256)) {
@@ -1315,42 +1264,34 @@ static __global__ void flash_attn_ext_f16(
     // kb0 == k start index when in the output tile.
     int kb0_start = kbc % iter_k;
     int kb0_stop  = min(iter_k, kb0_start + kbc_stop - kbc);
-
     while (kbc < kbc_stop && kb0_stop == iter_k) {
         const int sequence = kbc / (iter_k*iter_j*(ne02/ncols2));
-        const int zt = (kbc - iter_k*iter_j*(ne02/ncols2)*sequence) / (iter_k*iter_j); // head in units of ncols2
-        const int jt = (kbc - iter_k*iter_j*(ne02/ncols2)*sequence - iter_k*iter_j*zt) / iter_k; // j index of current tile.
+        const int head = (kbc - iter_k*iter_j*(ne02/ncols2)*sequence) / (iter_k*iter_j);
+        const int jt = (kbc - iter_k*iter_j*(ne02/ncols2)*sequence - iter_k*iter_j*head) / iter_k; // j index of current tile.
 
-        const int head0 = zt * ncols2;
-
-        const float2 * Q_f2    = (const float2 *) (Q + nb03*sequence + nb02* head0);
-        const half2  * K_h2    = (const half2  *) (K + nb13*sequence + nb12*(head0 / gqa_ratio));
+        const float2 * Q_f2    = (const float2 *) (Q + nb03*sequence + nb02*(head*ncols2));
+        const half2  * K_h2    = (const half2  *) (K + nb13*sequence + nb12*(head*ncols2 / gqa_ratio));
         const half2  * mask_h2 = ncols2 == 1 && !mask ? nullptr :
             (const half2  *) (mask + nb33*(sequence % ne33) + nb31*jt*ncols1);
-        float2       * dstk    = ((float2 *) dst) + (sequence*ne01*ne02 + head0) * (DV/2);
+        float2       * dstk    = ((float2 *) dst) + (sequence*ne01*ne02 + head*ncols2) * (DV/2);
 
-        const half2 * V_h2 = mla ? K_h2 + (DKQ/2 - DV/2) : (const half2 *) (V + nb23*sequence + nb22*(head0 / gqa_ratio));
-        const float * sinks_f = sinks ? (const float *) sinks + head0 : nullptr;
+        const half2 * V_h2 = mla ? K_h2 + (DKQ/2 - DV/2) : (const half2 *) (V + nb23*sequence + nb22*(head*ncols2 / gqa_ratio));
 
-        const float slope = ncols2 == 1 ? get_alibi_slope(max_bias, head0, n_head_log2, m0, m1) : 1.0f;
+        const float slope = ncols2 == 1 ? get_alibi_slope(max_bias, head, n_head_log2, m0, m1) : 1.0f;
 
         const int kb0_start_kernel = kb0_start * kb_niter;
-        int       kb0_stop_kernel  = kb0_stop  * kb_niter;
-
-        if (KV_max) {
-            kb0_stop_kernel = min(kb0_stop_kernel, KV_max[sequence*iter_j + jt] / c::nbatch_fa);
-        }
+        const int kb0_stop_kernel  = kb0_stop  * kb_niter;
 
         constexpr bool is_fixup = false; // All but (potentially) the last iterations write their data to dst rather than the fixup buffer.
         if (kb0_start == 0) {
             constexpr bool needs_fixup = false; // CUDA block is working on an entire tile.
             flash_attn_ext_f16_process_tile<DKQ, DV, ncols1, ncols2, nwarps, ntiles, use_logit_softcap, mla, needs_fixup, is_fixup>
-                (Q_f2, K_h2, V_h2, mask_h2, sinks_f, dstk, dst_meta, scale, slope, logit_softcap,
+                (Q_f2, K_h2, V_h2, mask_h2, dstk, dst_meta, scale, slope, logit_softcap,
                  ne01, ne02, stride_Q1, stride_Q2, stride_K, stride_V, stride_mask, jt, kb0_start_kernel, kb0_stop_kernel);
         } else {
             constexpr bool needs_fixup = true; // CUDA block is working on the beginning of a tile.
             flash_attn_ext_f16_process_tile<DKQ, DV, ncols1, ncols2, nwarps, ntiles, use_logit_softcap, mla, needs_fixup, is_fixup>
-                (Q_f2, K_h2, V_h2, mask_h2, sinks_f, dstk, dst_meta, scale, slope, logit_softcap,
+                (Q_f2, K_h2, V_h2, mask_h2, dstk, dst_meta, scale, slope, logit_softcap,
                  ne01, ne02, stride_Q1, stride_Q2, stride_K, stride_V, stride_mask, jt, kb0_start_kernel, kb0_stop_kernel);
         }
 
@@ -1366,36 +1307,29 @@ static __global__ void flash_attn_ext_f16(
     }
 
     const int sequence = kbc / (iter_k*iter_j*(ne02/ncols2));
-    const int zt = (kbc - iter_k*iter_j*(ne02/ncols2)*sequence) / (iter_k*iter_j); // head in units of ncols2
-    const int jt = (kbc - iter_k*iter_j*(ne02/ncols2)*sequence - iter_k*iter_j*zt) / iter_k; // j index of current tile.
+    const int head = (kbc - iter_k*iter_j*(ne02/ncols2)*sequence) / (iter_k*iter_j);
+    const int jt = (kbc - iter_k*iter_j*(ne02/ncols2)*sequence - iter_k*iter_j*head) / iter_k; // j index of current tile.
 
-    const int head0 = zt * ncols2;
-
-    const float2 * Q_f2    = (const float2 *) (Q + nb03*sequence + nb02* head0);
-    const half2  * K_h2    = (const half2  *) (K + nb13*sequence + nb12*(head0 / gqa_ratio));
+    const float2 * Q_f2    = (const float2 *) (Q + nb03*sequence + nb02*(head*ncols2));
+    const half2  * K_h2    = (const half2  *) (K + nb13*sequence + nb12*(head*ncols2 / gqa_ratio));
     const half2  * mask_h2 = ncols2 == 1 && !mask ? nullptr :
         (const half2  *) (mask + nb33*(sequence % ne33) + nb31*jt*ncols1);
-    float2       * dstk    = ((float2 *) dst) + (sequence*ne01*ne02 + head0) * (DV/2);
+    float2       * dstk    = ((float2 *) dst) + (sequence*ne01*ne02 + head*ncols2) * (DV/2);
 
-    const half2 * V_h2 = mla ? K_h2 + (DKQ/2 - DV/2) : (const half2 *) (V + nb23*sequence + nb22*(head0 / gqa_ratio));
-    const float * sinks_f = sinks ? (const float *) sinks + head0 : nullptr;
+    const half2 * V_h2 = mla ? K_h2 + (DKQ/2 - DV/2) : (const half2 *) (V + nb23*sequence + nb22*(head*ncols2 / gqa_ratio));
 
-    const float slope = ncols2 == 1 ? get_alibi_slope(max_bias, head0, n_head_log2, m0, m1) : 1.0f;
+    const float slope = ncols2 == 1 ? get_alibi_slope(max_bias, head, n_head_log2, m0, m1) : 1.0f;
 
     const int kb0_start_kernel = kb0_start * kb_niter;
-    int       kb0_stop_kernel  = kb0_stop  * kb_niter;
-
-    if (KV_max) {
-        kb0_stop_kernel = min(kb0_stop_kernel, KV_max[sequence*iter_j + jt] / c::nbatch_fa);
-    }
+    const int kb0_stop_kernel  = kb0_stop  * kb_niter;
 
     constexpr bool is_fixup = true; // Last index writes its data to fixup buffer to avoid data races with other blocks.
     constexpr bool needs_fixup = false;
     flash_attn_ext_f16_process_tile<DKQ, DV, ncols1, ncols2, nwarps, ntiles, use_logit_softcap, mla, needs_fixup, is_fixup>
-        (Q_f2, K_h2, V_h2, mask_h2, sinks_f, dstk, dst_meta, scale, slope, logit_softcap,
+        (Q_f2, K_h2, V_h2, mask_h2, dstk, dst_meta, scale, slope, logit_softcap,
          ne01, ne02, stride_Q1, stride_Q2, stride_K, stride_V, stride_mask, jt, kb0_start_kernel, kb0_stop_kernel);
 #else
-    GGML_UNUSED(Q); GGML_UNUSED(K); GGML_UNUSED(V); GGML_UNUSED(mask); GGML_UNUSED(sinks);
+    GGML_UNUSED(Q); GGML_UNUSED(K); GGML_UNUSED(V); GGML_UNUSED(mask);
     GGML_UNUSED(dst); GGML_UNUSED(dst_meta);
     GGML_UNUSED(scale); GGML_UNUSED(max_bias); GGML_UNUSED(m0); GGML_UNUSED(m1);
     GGML_UNUSED(n_head_log2); GGML_UNUSED(logit_softcap);
@@ -1407,7 +1341,7 @@ static __global__ void flash_attn_ext_f16(
     GGML_UNUSED(ne31); GGML_UNUSED(ne32); GGML_UNUSED(ne33);
     GGML_UNUSED(nb31); GGML_UNUSED(nb32); GGML_UNUSED(nb33);
     NO_DEVICE_CODE;
-#endif // defined(FLASH_ATTN_AVAILABLE) && defined(TURING_MMA_AVAILABLE)
+#endif // defined(FLASH_ATTN_AVAILABLE) && defined(NEW_MMA_AVAILABLE)
 }
 
 template <int DKQ, int DV, int ncols1, int ncols2>
@@ -1457,24 +1391,24 @@ void ggml_cuda_flash_attn_ext_mma_f16_case(ggml_backend_cuda_context & ctx, ggml
         constexpr bool use_logit_softcap = false;
         fattn_kernel = flash_attn_ext_f16<DKQ, DV, ncols1, ncols2, nwarps, ntiles, use_logit_softcap, mla>;
 
-#if !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)
+#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && !defined(GGML_USE_MUSA)
         static bool shared_memory_limit_raised[GGML_CUDA_MAX_DEVICES] = {false};
         if (!shared_memory_limit_raised[id]) {
             CUDA_CHECK(cudaFuncSetAttribute(fattn_kernel, cudaFuncAttributeMaxDynamicSharedMemorySize, nbytes_shared_total));
             shared_memory_limit_raised[id] = true;
         }
-#endif // !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)
+#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && !defined(GGML_USE_MUSA)
     } else {
         constexpr bool use_logit_softcap = true;
         fattn_kernel = flash_attn_ext_f16<DKQ, DV, ncols1, ncols2, nwarps, ntiles, use_logit_softcap, mla>;
 
-#if !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)
+#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && !defined(GGML_USE_MUSA)
         static bool shared_memory_limit_raised[GGML_CUDA_MAX_DEVICES] = {false};
         if (!shared_memory_limit_raised[id]) {
             CUDA_CHECK(cudaFuncSetAttribute(fattn_kernel, cudaFuncAttributeMaxDynamicSharedMemorySize, nbytes_shared_total));
             shared_memory_limit_raised[id] = true;
         }
-#endif // !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)
+#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && !defined(GGML_USE_MUSA)
     }
 
     launch_fattn<DV, ncols1, ncols2>

ggml/src/ggml-cuda/fattn-tile-f16.cu

@@ -5,16 +5,14 @@
 #define FATTN_KQ_STRIDE_TILE_F16 64
 
 template<int D, int ncols, int nwarps, bool use_logit_softcap> // D == head size
-#if !defined(GGML_USE_HIP)
+#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__))
 __launch_bounds__(nwarps*WARP_SIZE, 2)
-#endif // !defined(GGML_USE_HIP)
+#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__))
 static __global__ void flash_attn_tile_ext_f16(
         const char * __restrict__ Q,
         const char * __restrict__ K,
         const char * __restrict__ V,
         const char * __restrict__ mask,
-        const char * __restrict__ sinks,
-        const int  * __restrict__ KV_max,
         float      * __restrict__ dst,
         float2     * __restrict__ dst_meta,
         const float scale,
@@ -92,8 +90,7 @@ static __global__ void flash_attn_tile_ext_f16(
 
     __syncthreads();
 
-    const int k_VKQ_max = KV_max ? KV_max[sequence*gridDim.x + blockIdx.x] : ne11;
-    for (int k_VKQ_0 = blockIdx.y*FATTN_KQ_STRIDE_TILE_F16; k_VKQ_0 < k_VKQ_max; k_VKQ_0 += gridDim.y*FATTN_KQ_STRIDE_TILE_F16) {
+    for (int k_VKQ_0 = blockIdx.y*FATTN_KQ_STRIDE_TILE_F16; k_VKQ_0 < ne11; k_VKQ_0 += gridDim.y*FATTN_KQ_STRIDE_TILE_F16) {
         // Calculate KQ tile and keep track of new maximum KQ values:
 
         half kqmax_new[ncols/nwarps];
@@ -273,7 +270,7 @@ static __global__ void flash_attn_tile_ext_f16(
         }
     }
 #else
-    GGML_UNUSED(Q); GGML_UNUSED(K); GGML_UNUSED(V); GGML_UNUSED(mask); GGML_UNUSED(sinks);
+    GGML_UNUSED(Q); GGML_UNUSED(K); GGML_UNUSED(V); GGML_UNUSED(mask);
     GGML_UNUSED(dst); GGML_UNUSED(dst_meta); GGML_UNUSED(scale);
     GGML_UNUSED(max_bias); GGML_UNUSED(m0); GGML_UNUSED(m1);
     GGML_UNUSED(n_head_log2); GGML_UNUSED(logit_softcap);

ggml/src/ggml-cuda/fattn-tile-f32.cu

@@ -5,16 +5,14 @@
 #define FATTN_KQ_STRIDE_TILE_F32 32
 
 template<int D, int ncols, int nwarps, bool use_logit_softcap> // D == head size
-#if !defined(GGML_USE_HIP)
+#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__))
 __launch_bounds__(nwarps*WARP_SIZE, 2)
-#endif // !defined(GGML_USE_HIP)
+#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__))
 static __global__ void flash_attn_tile_ext_f32(
         const char * __restrict__ Q,
         const char * __restrict__ K,
         const char * __restrict__ V,
         const char * __restrict__ mask,
-        const char * __restrict__ sinks,
-        const int  * __restrict__ KV_max,
         float      * __restrict__ dst,
         float2     * __restrict__ dst_meta,
         const float scale,
@@ -38,7 +36,7 @@ static __global__ void flash_attn_tile_ext_f32(
     return;
 #endif // FP16_MMA_AVAILABLE
     if (use_logit_softcap && !(D == 128 || D == 256)) {
-        GGML_UNUSED(Q); GGML_UNUSED(K); GGML_UNUSED(V); GGML_UNUSED(mask); GGML_UNUSED(sinks);
+        GGML_UNUSED(Q); GGML_UNUSED(K); GGML_UNUSED(V); GGML_UNUSED(mask);
         GGML_UNUSED(dst); GGML_UNUSED(dst_meta);
         GGML_UNUSED(scale); GGML_UNUSED(max_bias); GGML_UNUSED(m0); GGML_UNUSED(m1);
         GGML_UNUSED(n_head_log2); GGML_UNUSED(logit_softcap);
@@ -101,8 +99,7 @@ static __global__ void flash_attn_tile_ext_f32(
 
     __syncthreads();
 
-    const int k_VKQ_max = KV_max ? KV_max[sequence*gridDim.x + blockIdx.x] : ne11;
-    for (int k_VKQ_0 = blockIdx.y*FATTN_KQ_STRIDE_TILE_F32; k_VKQ_0 < k_VKQ_max; k_VKQ_0 += gridDim.y*FATTN_KQ_STRIDE_TILE_F32) {
+    for (int k_VKQ_0 = blockIdx.y*FATTN_KQ_STRIDE_TILE_F32; k_VKQ_0 < ne11; k_VKQ_0 += gridDim.y*FATTN_KQ_STRIDE_TILE_F32) {
         // Calculate KQ tile and keep track of new maximum KQ values:
 
         float kqmax_new[ncols/nwarps];

ggml/src/ggml-cuda/fattn-vec-f16.cuh

@@ -1,12 +1,6 @@
 #include "common.cuh"
 #include "fattn-common.cuh"
 
-// Currenlty llvm with the amdgcn target dose not support unrolling loops
-// that contain a break that can not be resolved at compile time.
-#ifdef __clang__
-#pragma clang diagnostic push
-#pragma clang diagnostic ignored "-Wpass-failed"
-#endif // __clang__
 template<int D, int ncols, ggml_type type_K, ggml_type type_V, bool use_logit_softcap> // D == head size
 #ifndef GGML_USE_HIP
 __launch_bounds__(D, 1)
@@ -16,8 +10,6 @@ static __global__ void flash_attn_vec_ext_f16(
         const char * __restrict__ K,
         const char * __restrict__ V,
         const char * __restrict__ mask,
-        const char * __restrict__ sinks,
-        const int  * __restrict__ KV_max,
         float      * __restrict__ dst,
         float2     * __restrict__ dst_meta,
         const float scale,
@@ -62,8 +54,7 @@ static __global__ void flash_attn_vec_ext_f16(
     K += nb13*sequence + nb12*(head / gqa_ratio);
     V += nb23*sequence + nb22*(head / gqa_ratio);
 
-    const half  * maskh  = (const half  *) (mask + nb33*(sequence % ne33) + nb31*ic0);
-    const float * sinksf = (const float *) (sinks);
+    const half * maskh = (const half *) (mask + nb33*(sequence % ne33) + nb31*ic0);
 
     const float slopef = get_alibi_slope(max_bias, head, n_head_log2, m0, m1);
     const half  slopeh = __float2half(slopef);
@@ -77,12 +68,11 @@ static __global__ void flash_attn_vec_ext_f16(
     half2 * KQ2 = (half2 *) KQ;
 
     half kqmax[ncols];
-    half kqsum[ncols];
 #pragma unroll
     for (int j = 0; j < ncols; ++j) {
         kqmax[j] = -HALF_MAX_HALF;
-        kqsum[j] = 0.0f;
     }
+    half kqsum[ncols] = {0.0f};
 
     __shared__ half kqmax_shared[ncols][WARP_SIZE];
     __shared__ half kqsum_shared[ncols][WARP_SIZE];
@@ -181,14 +171,10 @@ static __global__ void flash_attn_vec_ext_f16(
 
     half2 VKQ[ncols] = {{0.0f, 0.0f}};
 
-    const int k_VKQ_max = KV_max ? KV_max[sequence*gridDim.x + blockIdx.x] : ne11;
     K     += blockIdx.y*D * nb11;
     V     += blockIdx.y*D * nb21;
     maskh += blockIdx.y*D;
-    for (int k_VKQ_0 = blockIdx.y*D; k_VKQ_0 < k_VKQ_max; k_VKQ_0 += gridDim.y*D,
-             // Increment pointers after each loop:
-             K += gridDim.y*D*nb11, V += gridDim.y*D*nb21, maskh += gridDim.y*D) {
-
+    for (int k_VKQ_0 = blockIdx.y*D; k_VKQ_0 < ne11; k_VKQ_0 += gridDim.y*D) {
         // Calculate KQ tile and keep track of new maximum KQ values:
 
         if (mask) {
@@ -196,7 +182,29 @@ static __global__ void flash_attn_vec_ext_f16(
             for (int j = 0; j < ncols; ++j) {
                 maskh_shared[j*D + tid] = slopeh*maskh[j*ne11 + tid];
             }
+
             __syncthreads();
+
+            // When using multiple parallel sequences in llama.cpp, some KV slices can be fully masked out.
+            // In such cases, skip the KV slice.
+            // On AMD __all_sync would not work correctly because it assumes a warp size of 64.
+#ifndef GGML_USE_HIP
+            bool skip = true;
+#pragma unroll
+            for (int j = 0; j < ncols; ++j) {
+#pragma unroll
+                for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) {
+                    const int i = i0 + threadIdx.x;
+
+                    const float2 tmp = __half22float2(((const half2 *) maskh_shared)[j*(D/2) + i]);
+                    skip = skip && isinf(tmp.x) && isinf(tmp.y);
+                }
+            }
+            if (__all_sync(0xFFFFFFFF, skip)) {
+                __syncthreads();
+                continue;
+            }
+#endif // GGML_USE_HIP
         }
 
         // For unknown reasons using a half array of size 1 for kqmax_new causes a performance regression,
@@ -283,38 +291,9 @@ static __global__ void flash_attn_vec_ext_f16(
             }
         }
 
-        __syncthreads();
-    }
-
-    if (sinksf && blockIdx.y == 0) {
-        const half sink = __float2half(sinksf[head]);
-
-#pragma unroll
-        for (int j = 0; j < ncols; ++j) {
-            if (threadIdx.x == 0) {
-                kqmax_shared[j][threadIdx.y] = fmaxf(kqmax[j], sink);
-            }
-        }
-
-        __syncthreads();
-
-#pragma unroll
-        for (int j = 0; j < ncols; ++j) {
-            half kqmax_new_j = kqmax_shared[j][threadIdx.x];
-            kqmax_new_j = warp_reduce_max(kqmax_new_j);
-
-            const half KQ_max_scale = hexp(kqmax[j] - kqmax_new_j);
-            kqmax[j] = kqmax_new_j;
-
-            const half val = hexp(sink - kqmax[j]);
-            kqsum[j] = kqsum[j]*KQ_max_scale;
-
-            if (tid == 0) {
-                kqsum[j] += val;
-            }
-
-            VKQ[j] *= __half2half2(KQ_max_scale);
-        }
+        K     += gridDim.y*D * nb11;
+        V     += gridDim.y*D * nb21;
+        maskh += gridDim.y*D;
 
         __syncthreads();
     }
@@ -349,7 +328,7 @@ static __global__ void flash_attn_vec_ext_f16(
         dst_meta[((sequence*ne01 + ic0 + tid)*ne02 + head)*gridDim.y + blockIdx.y] = make_float2(kqmax[tid], kqsum[tid]);
     }
 #else
-    GGML_UNUSED(Q); GGML_UNUSED(K); GGML_UNUSED(V); GGML_UNUSED(mask); GGML_UNUSED(sinks);
+    GGML_UNUSED(Q); GGML_UNUSED(K); GGML_UNUSED(V); GGML_UNUSED(mask);
     GGML_UNUSED(dst); GGML_UNUSED(dst_meta);
     GGML_UNUSED(scale); GGML_UNUSED(max_bias); GGML_UNUSED(m0); GGML_UNUSED(m1);
     GGML_UNUSED(n_head_log2); GGML_UNUSED(logit_softcap);
@@ -363,9 +342,6 @@ static __global__ void flash_attn_vec_ext_f16(
     NO_DEVICE_CODE;
 #endif // defined(FLASH_ATTN_AVAILABLE) && defined(FP16_AVAILABLE)
 }
-#ifdef __clang__
-#pragma clang diagnostic pop
-#endif // __clang__
 
 template <int D, int cols_per_block, ggml_type type_K, ggml_type type_V, bool use_logit_softcap>
 void ggml_cuda_flash_attn_ext_vec_f16_case_impl(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {

ggml/src/ggml-cuda/fattn-vec-f32.cuh

@@ -1,12 +1,6 @@
 #include "common.cuh"
 #include "fattn-common.cuh"
 
-// Currenlty llvm with the amdgcn target dose not support unrolling loops
-// that contain a break that can not be resolved at compile time.
-#ifdef __clang__
-#pragma clang diagnostic push
-#pragma clang diagnostic ignored "-Wpass-failed"
-#endif // __clang__
 template<int D, int ncols, ggml_type type_K, ggml_type type_V, bool use_logit_softcap> // D == head size
 #ifndef GGML_USE_HIP
 __launch_bounds__(D, 1)
@@ -16,8 +10,6 @@ static __global__ void flash_attn_vec_ext_f32(
         const char * __restrict__ K,
         const char * __restrict__ V,
         const char * __restrict__ mask,
-        const char * __restrict__ sinks,
-        const int  * __restrict__ KV_max,
         float      * __restrict__ dst,
         float2     * __restrict__ dst_meta,
         const float scale,
@@ -73,8 +65,7 @@ static __global__ void flash_attn_vec_ext_f32(
     K += nb13*sequence + nb12*(head / gqa_ratio);
     V += nb23*sequence + nb22*(head / gqa_ratio);
 
-    const half  * maskh  = (const half  *) (mask + nb33*(sequence % ne33) + nb31*ic0);
-    const float * sinksf = (const float *) (sinks);
+    const half * maskh = (const half *) (mask + nb33*(sequence % ne33) + nb31*ic0);
 
     const float slope = get_alibi_slope(max_bias, head, n_head_log2, m0, m1);
 
@@ -90,12 +81,11 @@ static __global__ void flash_attn_vec_ext_f32(
     }
 
     float kqmax[ncols];
-    float kqsum[ncols];
 #pragma unroll
     for (int j = 0; j < ncols; ++j) {
         kqmax[j] = -FLT_MAX/2.0f;
-        kqsum[j] = 0.0f;
     }
+    float kqsum[ncols] = {0.0f};
 
     __shared__ float kqmax_shared[ncols][WARP_SIZE];
     __shared__ float kqsum_shared[ncols][WARP_SIZE];
@@ -187,14 +177,10 @@ static __global__ void flash_attn_vec_ext_f32(
 
     float VKQ[ncols] = {0.0f};
 
-    const int k_VKQ_max = KV_max ? KV_max[sequence*gridDim.x + blockIdx.x] : ne11;
     K     += blockIdx.y*D * nb11;
     V     += blockIdx.y*D * nb21;
     maskh += blockIdx.y*D;
-    for (int k_VKQ_0 = blockIdx.y*D; k_VKQ_0 < k_VKQ_max; k_VKQ_0 += gridDim.y*D,
-             // Increment pointers after each loop:
-             K += gridDim.y*D*nb11, V += gridDim.y*D*nb21, maskh += gridDim.y*D) {
-
+    for (int k_VKQ_0 = blockIdx.y*D; k_VKQ_0 < ne11; k_VKQ_0 += gridDim.y*D) {
         // Calculate KQ tile and keep track of new maximum KQ values:
 
         if (mask) {
@@ -202,7 +188,28 @@ static __global__ void flash_attn_vec_ext_f32(
             for (int j = 0; j < ncols; ++j) {
                 maskf_shared[j*D + tid] = slope*__half2float(maskh[j*ne11 + tid]);
             }
+
             __syncthreads();
+
+            // When using multiple parallel sequences in llama.cpp, some KV slices can be fully masked out.
+            // In such cases, skip the KV slice.
+            // On AMD __all_sync would not work correctly because it assumes a warp size of 64.
+#ifndef GGML_USE_HIP
+            bool skip = true;
+#pragma unroll
+            for (int j = 0; j < ncols; ++j) {
+#pragma unroll
+                for (int i0 = 0; i0 < D; i0 += WARP_SIZE) {
+                    const int i = i0 + threadIdx.x;
+
+                    skip = skip && isinf(maskf_shared[j*D + i]);
+                }
+            }
+            if (__all_sync(0xFFFFFFFF, skip)) {
+                __syncthreads();
+                continue;
+            }
+#endif // GGML_USE_HIP
         }
 
         float kqmax_new_arr[ncols];
@@ -279,38 +286,9 @@ static __global__ void flash_attn_vec_ext_f32(
             }
         }
 
-        __syncthreads();
-    }
-
-    if (sinksf && blockIdx.y == 0) {
-        const float sink = sinksf[head];
-
-#pragma unroll
-        for (int j = 0; j < ncols; ++j) {
-            if (threadIdx.x == 0) {
-                kqmax_shared[j][threadIdx.y] = fmaxf(kqmax[j], sink);
-            }
-        }
-
-        __syncthreads();
-
-#pragma unroll
-        for (int j = 0; j < ncols; ++j) {
-            float kqmax_new_j = kqmax_shared[j][threadIdx.x];
-            kqmax_new_j = warp_reduce_max(kqmax_new_j);
-
-            const float KQ_max_scale = expf(kqmax[j] - kqmax_new_j);
-            kqmax[j] = kqmax_new_j;
-
-            const float val = expf(sink - kqmax[j]);
-            kqsum[j] = kqsum[j]*KQ_max_scale;
-
-            if (tid == 0) {
-                kqsum[j] += val;
-            }
-
-            VKQ[j] *= KQ_max_scale;
-        }
+        K     += gridDim.y*D * nb11;
+        V     += gridDim.y*D * nb21;
+        maskh += gridDim.y*D;
 
         __syncthreads();
     }
@@ -359,9 +337,6 @@ static __global__ void flash_attn_vec_ext_f32(
     NO_DEVICE_CODE;
 #endif // FLASH_ATTN_AVAILABLE
 }
-#ifdef __clang__
-#pragma clang diagnostic pop
-#endif // __clang__
 
 template <int D, int cols_per_block, ggml_type type_K, ggml_type type_V, bool use_logit_softcap>
 void ggml_cuda_flash_attn_ext_vec_f32_case_impl(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {

ggml/src/ggml-cuda/fattn-wmma-f16.cu

@@ -7,7 +7,7 @@
 #include "fattn-wmma-f16.cuh"
 
 #ifdef FP16_MMA_AVAILABLE
-#if !defined(GGML_USE_HIP)
+#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__))
 #include <mma.h>
 #ifdef GGML_USE_MUSA
 namespace wmma = mtmusa::wmma;
@@ -18,7 +18,7 @@ namespace wmma = nvcuda::wmma;
 #undef HIP_ENABLE_WARP_SYNC_BUILTINS // conflicts with rocWMMA headers
 #include <rocwmma/rocwmma.hpp>
 namespace wmma = rocwmma;
-#endif // !defined(GGML_USE_HIP)
+#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__))
 #endif // FP16_MMA_AVAILABLE
 
 // D == head size, VKQ_stride == num VKQ rows calculated in parallel:
@@ -29,8 +29,6 @@ static __global__ void flash_attn_ext_f16(
         const char * __restrict__ K,
         const char * __restrict__ V,
         const char * __restrict__ mask,
-        const char * __restrict__ sinks,
-        const int  * __restrict__ KV_max,
         float      * __restrict__ dst,
         float2     * __restrict__ dst_meta,
         const float scale,
@@ -167,8 +165,7 @@ static __global__ void flash_attn_ext_f16(
     __syncthreads();
 
     // Iterate over ne11 == previous tokens:
-    const int k_VKQ_max = KV_max ? KV_max[sequence*gridDim.x + blockIdx.x] : ne11;
-    for (int k_VKQ_0 = blockIdx.y*FATTN_KQ_STRIDE; k_VKQ_0 < k_VKQ_max; k_VKQ_0 += gridDim.y*FATTN_KQ_STRIDE) {
+    for (int k_VKQ_0 = blockIdx.y*FATTN_KQ_STRIDE; k_VKQ_0 < ne11; k_VKQ_0 += gridDim.y*FATTN_KQ_STRIDE) {
         // Calculate tile of KQ:
 #pragma unroll
         for (int i_KQ_0 = 0; i_KQ_0 < FATTN_KQ_STRIDE; i_KQ_0 += KQ_stride_tc) {
@@ -424,7 +421,7 @@ static __global__ void flash_attn_ext_f16(
         dst_meta[j_dst_unrolled] = dst_meta_val;
     }
 #else
-    GGML_UNUSED(Q); GGML_UNUSED(K); GGML_UNUSED(V); GGML_UNUSED(mask); GGML_UNUSED(sinks);
+    GGML_UNUSED(Q); GGML_UNUSED(K); GGML_UNUSED(V); GGML_UNUSED(mask);
     GGML_UNUSED(dst); GGML_UNUSED(dst_meta); GGML_UNUSED(scale);
     GGML_UNUSED(max_bias); GGML_UNUSED(m0); GGML_UNUSED(m1);
     GGML_UNUSED(n_head_log2); GGML_UNUSED(logit_softcap);
@@ -549,7 +546,7 @@ void ggml_cuda_flash_attn_ext_wmma_f16(ggml_backend_cuda_context & ctx, ggml_ten
         return;
     }
 
-#if !defined(GGML_USE_HIP)
+#if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__))
     if (Q->ne[1] <= 8 && Q->ne[0] % warp_size == 0) {
         constexpr int cols_per_block = 8;
         switch (Q->ne[0]) {
@@ -571,7 +568,7 @@ void ggml_cuda_flash_attn_ext_wmma_f16(ggml_backend_cuda_context & ctx, ggml_ten
         }
         return;
     }
-#endif // !defined(GGML_USE_HIP)
+#endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__))
 
     if (Q->ne[1] <= 32) {
         constexpr int cols_per_block = 16;

ggml/src/ggml-cuda/fattn.cu

@@ -269,28 +269,17 @@ static void ggml_cuda_flash_attn_ext_vec_f32(ggml_backend_cuda_context & ctx, gg
 }
 
 void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
-    const ggml_tensor * KQV   = dst;
-    const ggml_tensor * Q     = dst->src[0];
-    const ggml_tensor * K     = dst->src[1];
-    const ggml_tensor * V     = dst->src[2];
-    const ggml_tensor * mask  = dst->src[3];
-    const ggml_tensor * sinks = dst->src[4];
+    const ggml_tensor * KQV  = dst;
+    const ggml_tensor * Q    = dst->src[0];
+    const ggml_tensor * K    = dst->src[1];
+    const ggml_tensor * V    = dst->src[2];
+    const ggml_tensor * mask = dst->src[3];
 
     ggml_cuda_set_device(ctx.device);
     const int cc = ggml_cuda_info().devices[ggml_cuda_get_device()].cc;
     const int warp_size = ggml_cuda_info().devices[ggml_cuda_get_device()].warp_size;
     const enum ggml_prec prec = ggml_flash_attn_ext_get_prec(KQV);
 
-    // TODO: currently only vec implementation for sinks is supported [TAG_ATTN_SINKS]
-    if (sinks && !fp16_mma_available(cc)) {
-        if (prec == GGML_PREC_DEFAULT && fast_fp16_available(cc)) {
-            ggml_cuda_flash_attn_ext_vec_f16(ctx, dst);
-        } else {
-            ggml_cuda_flash_attn_ext_vec_f32(ctx, dst);
-        }
-        return;
-    }
-
 #if defined(GGML_HIP_ROCWMMA_FATTN)
     if (GGML_CUDA_CC_IS_AMD(cc) && fp16_mma_available(cc)) {
         ggml_cuda_flash_attn_ext_wmma_f16(ctx, dst);
@@ -326,9 +315,7 @@ void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst
 
     const bool gqa_opt_applies = ((Q->ne[2] / K->ne[2]) % 2 == 0) && mask; // The mma-based kernels have GQA-specific optimizations
     const bool mma_needs_data_conversion = K->type != GGML_TYPE_F16 || V->type != GGML_TYPE_F16;
-    const bool mma_faster_for_rtx4000 = Q->ne[3] > 1 || (Q->ne[2] > 4*K->ne[2] && K->ne[1] >= 8192);
-    const bool mma_faster_for_bs1 = turing_mma_available(cc) && gqa_opt_applies && !mma_needs_data_conversion &&
-        (cc < GGML_CUDA_CC_ADA_LOVELACE || mma_faster_for_rtx4000);
+    const bool mma_faster_for_bs1 = new_mma_available(cc) && gqa_opt_applies && cc < GGML_CUDA_CC_ADA_LOVELACE && !mma_needs_data_conversion;
     const bool can_use_vector_kernel = Q->ne[0] <= 256 && Q->ne[0] % (2*warp_size) == 0;
     if (Q->ne[1] == 1 && can_use_vector_kernel && !mma_faster_for_bs1) {
         if (prec == GGML_PREC_DEFAULT) {
@@ -340,7 +327,7 @@ void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst
     }
 
     // The MMA implementation needs Turing or newer, use the old WMMA code for Volta:
-    if (fp16_mma_available(cc) && !turing_mma_available(cc)) {
+    if (fp16_mma_available(cc) && !new_mma_available(cc)) {
         ggml_cuda_flash_attn_ext_wmma_f16(ctx, dst);
         return;
     }

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

@@ -4,7 +4,6 @@
 
 #include "ggml-cuda/common.cuh"
 #include "ggml-cuda/acc.cuh"
-#include "ggml-cuda/add-id.cuh"
 #include "ggml-cuda/arange.cuh"
 #include "ggml-cuda/argmax.cuh"
 #include "ggml-cuda/argsort.cuh"
@@ -22,9 +21,8 @@
 #include "ggml-cuda/fattn.cuh"
 #include "ggml-cuda/getrows.cuh"
 #include "ggml-cuda/im2col.cuh"
-#include "ggml-cuda/mmf.cuh"
 #include "ggml-cuda/mmq.cuh"
-#include "ggml-cuda/mmvf.cuh"
+#include "ggml-cuda/mmv.cuh"
 #include "ggml-cuda/mmvq.cuh"
 #include "ggml-cuda/norm.cuh"
 #include "ggml-cuda/opt-step-adamw.cuh"
@@ -33,9 +31,7 @@
 #include "ggml-cuda/pool2d.cuh"
 #include "ggml-cuda/quantize.cuh"
 #include "ggml-cuda/rope.cuh"
-#include "ggml-cuda/roll.cuh"
 #include "ggml-cuda/scale.cuh"
-#include "ggml-cuda/softcap.cuh"
 #include "ggml-cuda/softmax.cuh"
 #include "ggml-cuda/ssm-conv.cuh"
 #include "ggml-cuda/ssm-scan.cuh"
@@ -130,7 +126,7 @@ static cudaError_t ggml_cuda_device_malloc(void ** ptr, size_t size, int device)
     return err;
 }
 
-#if defined(GGML_USE_HIP)
+#if defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)
 static int ggml_cuda_parse_id(char devName[]) {
     // A list of possible Target IDs can be found under the rocclr/clr repo in device.cpp
     // these values are not stable so this is susceptible to breakage
@@ -177,10 +173,10 @@ static int ggml_cuda_parse_id(char devName[]) {
     archNum += archMinor;
     return archNum;
 }
-#endif // defined(GGML_USE_HIP)
+#endif // defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)
 
 static ggml_cuda_device_info ggml_cuda_init() {
-#if defined(GGML_USE_HIP)
+#ifdef __HIP_PLATFORM_AMD__
     // Workaround for a rocBLAS bug when using multiple graphics cards:
     // https://github.com/ROCmSoftwarePlatform/rocBLAS/issues/1346
     {
@@ -253,7 +249,7 @@ static ggml_cuda_device_info ggml_cuda_init() {
         info.devices[id].nsm        = prop.multiProcessorCount;
         info.devices[id].smpb       = prop.sharedMemPerBlock;
         info.devices[id].warp_size  = prop.warpSize;
-#if defined(GGML_USE_HIP)
+#if defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)
         info.devices[id].smpbo = prop.sharedMemPerBlock;
 
         info.devices[id].cc = ggml_cuda_parse_id(prop.gcnArchName);
@@ -283,7 +279,7 @@ static ggml_cuda_device_info ggml_cuda_init() {
         info.devices[id].cc = 100*prop.major + 10*prop.minor;
         GGML_LOG_INFO("  Device %d: %s, compute capability %d.%d, VMM: %s\n",
                         id, prop.name, prop.major, prop.minor, device_vmm ? "yes" : "no");
-#endif // defined(GGML_USE_HIP)
+#endif // defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)
     }
 
     for (int id = 0; id < info.device_count; ++id) {
@@ -1854,9 +1850,6 @@ static void ggml_cuda_mul_mat_batched_cublas_impl(ggml_backend_cuda_context & ct
     ggml_cuda_pool_alloc<cuda_t> src0_alloc(ctx.pool());
     ggml_cuda_pool_alloc<cuda_t> src1_alloc(ctx.pool());
 
-    bool is_src0_cont_2 = ggml_is_contiguous_2(src0);
-    bool is_src1_cont_2 = ggml_is_contiguous_2(src1);
-
     // Handle src0
     src0_ptr = (const cuda_t *) src0->data;
 
@@ -1875,8 +1868,6 @@ static void ggml_cuda_mul_mat_batched_cublas_impl(ggml_backend_cuda_context & ct
         s11 = ne10;
         s12 = ne11*s11;
         s13 = ne12*s12;
-
-        is_src1_cont_2 = true;
     }
 
     // Setup destination buffer
@@ -1925,19 +1916,15 @@ static void ggml_cuda_mul_mat_batched_cublas_impl(ggml_backend_cuda_context & ct
     const int64_t r2 = ne12/ne02;
     const int64_t r3 = ne13/ne03;
 
-    if (r2 == 1 && r3 == 1 && is_src0_cont_2 && is_src1_cont_2) {
-        // with a [0, 2, 1, 3] perm. and ne02==1 the matrix strides need to be determined from dim 3:
-        const int64_t sma = ne02 == 1 ? nb03/nb00 : nb02/nb00;
-        const int64_t smb = ne12 == 1 ? s13       : s12;
-
+    if (r2 == 1 && r3 == 1 && ggml_is_contiguous_2(src0) && ggml_is_contiguous_2(src1)) {
         // there is no broadcast and src0, src1 are contiguous across dims 2, 3
         // use cublasGemmStridedBatchedEx
         CUBLAS_CHECK(
         cublasGemmStridedBatchedEx(ctx.cublas_handle(), CUBLAS_OP_T, CUBLAS_OP_N,
                 ne01, ne11, ne10,
-                alpha, src0_ptr, cu_data_type_a, nb01/nb00, sma,     // strideA
-                       src1_ptr, cu_data_type_b, s11,       smb,     // strideB
-                beta,     dst_t, cu_data_type,   ne0,       ne1*ne0, // strideC
+                alpha, src0_ptr, cu_data_type_a, nb01/nb00, nb02/nb00, // strideA
+                       src1_ptr, cu_data_type_b, s11,       s12,       // strideB
+                beta,     dst_t, cu_data_type,   ne0,       ne1*ne0,   // strideC
                 ne12*ne13,
                 cu_compute_type,
                 CUBLAS_GEMM_DEFAULT_TENSOR_OP));
@@ -2009,9 +1996,7 @@ static void ggml_cuda_mul_mat(ggml_backend_cuda_context & ctx, const ggml_tensor
     const bool bad_padding_clear = ggml_backend_buffer_get_usage(src0->buffer) == GGML_BACKEND_BUFFER_USAGE_COMPUTE
         && ggml_nbytes(src0) != ggml_backend_buffer_get_alloc_size(src0->buffer, src0) && src0->view_src;
 
-    bool use_mul_mat_vec_f = (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16 || src0->type == GGML_TYPE_BF16)
-        && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32;
-    bool use_mul_mat_f     = !ggml_is_quantized(src0->type)
+    bool use_mul_mat_vec   = (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16 || src0->type == GGML_TYPE_BF16)
         && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32;
     bool use_mul_mat_vec_q = ggml_is_quantized(src0->type) && !bad_padding_clear
         && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32
@@ -2031,18 +2016,14 @@ static void ggml_cuda_mul_mat(ggml_backend_cuda_context & ctx, const ggml_tensor
             }
 
             const int cc            = ggml_cuda_info().devices[id].cc;
-            const int warp_size     = ggml_cuda_info().devices[id].warp_size;
             use_mul_mat_q           = use_mul_mat_q             && ggml_cuda_should_use_mmq(src0->type, cc, src1->ne[1]);
-            use_mul_mat_f           = use_mul_mat_f             && ggml_cuda_should_use_mmf(src0->type, cc, warp_size, src0->ne, src1->ne[1]);
-            use_mul_mat_vec_f       = use_mul_mat_vec_f         && ggml_cuda_should_use_mmvf(src0->type, cc, src0->ne, src1->ne[1]);
+            use_mul_mat_vec         = use_mul_mat_vec           && ggml_cuda_should_use_mmv(src0->type, cc, src0->ne, src1->ne[1]);
             any_gpus_with_slow_fp16 = any_gpus_with_slow_fp16   || !fast_fp16_hardware_available(cc);
         }
     } else {
         const int cc            = ggml_cuda_info().devices[ctx.device].cc;
-        const int warp_size     = ggml_cuda_info().devices[ctx.device].warp_size;
         use_mul_mat_q           = use_mul_mat_q             && ggml_cuda_should_use_mmq(src0->type, cc, src1->ne[1]);
-        use_mul_mat_f           = use_mul_mat_f             && ggml_cuda_should_use_mmf(src0->type, cc, warp_size, src0->ne, src1->ne[1]);
-        use_mul_mat_vec_f       = use_mul_mat_vec_f         && ggml_cuda_should_use_mmvf(src0->type, cc, src0->ne, src1->ne[1]);
+        use_mul_mat_vec         = use_mul_mat_vec           && ggml_cuda_should_use_mmv(src0->type, cc, src0->ne, src1->ne[1]);
         any_gpus_with_slow_fp16 = any_gpus_with_slow_fp16   || !fast_fp16_hardware_available(cc);
     }
 
@@ -2055,17 +2036,15 @@ static void ggml_cuda_mul_mat(ggml_backend_cuda_context & ctx, const ggml_tensor
     //printf("src1 is contiguous %d, transposed %d, type = %s, name = %s\n", ggml_is_contiguous(src1), ggml_is_transposed(src1), ggml_type_name(src1->type), src1->name);
 
     //TODO update for generic tensor parallelism
-    const int cc                 = ggml_cuda_info().devices[ggml_cuda_get_device()].cc;
+    const int cc                     = ggml_cuda_info().devices[ggml_cuda_get_device()].cc;
     bool use_batched_cublas_f16  = src0->type == GGML_TYPE_F16 && (src1->type == GGML_TYPE_F16 || !any_gpus_with_slow_fp16);
     bool use_batched_cublas_bf16 = src0->type == GGML_TYPE_BF16 && bf16_mma_hardware_available(cc);
     bool use_batched_cublas_f32  = src0->type == GGML_TYPE_F32;
 
-    if (!split && use_mul_mat_vec_f) {
+    if (!split && use_mul_mat_vec) {
         // the custom F16 vector kernel can be used over batched cuBLAS GEMM
         // but this is only faster for GPUs without tensor cores or with a thin src0 matrix (particularly KQV in attention)
-        ggml_cuda_mul_mat_vec_f(ctx, src0, src1, nullptr, dst);
-    } else if (!split && use_mul_mat_f) {
-        ggml_cuda_mul_mat_f(ctx, src0, src1, nullptr, dst);
+        ggml_cuda_mul_mat_vec(ctx, src0, src1, nullptr, dst);
     } else if (!split && use_mul_mat_vec_q) {
         ggml_cuda_mul_mat_vec_q(ctx, src0, src1, nullptr, dst);
     } else if (!split && use_mul_mat_q) {
@@ -2074,8 +2053,8 @@ static void ggml_cuda_mul_mat(ggml_backend_cuda_context & ctx, const ggml_tensor
         && !ggml_is_transposed(src0) && !ggml_is_transposed(src1) && src1->ne[2]*src1->ne[3] > 1) {
         // general KQ + KQV multi-batch without FlashAttention
         ggml_cuda_mul_mat_batched_cublas(ctx, src0, src1, dst);
-    } else if (use_mul_mat_vec_f) {
-        ggml_cuda_op_mul_mat(ctx, src0, src1, dst, ggml_cuda_op_mul_mat_vec_f, nullptr);
+    } else if (use_mul_mat_vec) {
+        ggml_cuda_op_mul_mat(ctx, src0, src1, dst, ggml_cuda_op_mul_mat_vec, nullptr);
     } else if (use_mul_mat_vec_q) {
         ggml_cuda_op_mul_mat(ctx, src0, src1, dst, ggml_cuda_op_mul_mat_vec_q, quantize_row_q8_1_cuda);
     } else if (use_mul_mat_q) {
@@ -2103,7 +2082,7 @@ static void ggml_cuda_mul_mat_id(ggml_backend_cuda_context & ctx, ggml_tensor *
             if (ggml_is_quantized(src0->type)) {
                 ggml_cuda_mul_mat_vec_q(ctx, src0, src1, ids, dst);
             } else {
-                ggml_cuda_mul_mat_vec_f(ctx, src0, src1, ids, dst);
+                ggml_cuda_mul_mat_vec(ctx, src0, src1, ids, dst);
             }
             return;
         }
@@ -2269,9 +2248,6 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg
         case GGML_OP_ADD1: // TODO: more efficient implementation
             ggml_cuda_op_add(ctx, dst);
             break;
-        case GGML_OP_ADD_ID:
-            ggml_cuda_op_add_id(ctx, dst);
-            break;
         case GGML_OP_SUB:
             ggml_cuda_op_sub(ctx, dst);
             break;
@@ -2346,9 +2322,6 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg
                 case GGML_GLU_OP_SWIGLU:
                     ggml_cuda_op_swiglu(ctx, dst);
                     break;
-                case GGML_GLU_OP_SWIGLU_OAI:
-                    ggml_cuda_op_swiglu_oai(ctx, dst);
-                    break;
                 case GGML_GLU_OP_GEGLU_ERF:
                     ggml_cuda_op_geglu_erf(ctx, dst);
                     break;
@@ -2446,9 +2419,6 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg
         case GGML_OP_ROPE_BACK:
             ggml_cuda_op_rope_back(ctx, dst);
             break;
-        case GGML_OP_ROLL:
-            ggml_cuda_op_roll(ctx, dst);
-            break;
         case GGML_OP_IM2COL:
             ggml_cuda_op_im2col(ctx, dst);
             break;
@@ -2623,9 +2593,6 @@ static bool check_node_graph_compatibility_and_refresh_copy_ops(ggml_backend_cud
 
     const std::string gemma3n_per_layer_proj_src0_name = "inp_per_layer_selected";
     const std::string gemma3n_per_layer_proj_src1_name = "per_layer_proj";
-    const std::string ffn_moe_gate_bias_prefix = "ffn_moe_gate_biased";
-    const std::string ffn_moe_up_bias_prefix = "ffn_moe_up_biased";
-    const std::string ffn_moe_down_bias_prefix = "ffn_moe_down_biased";
 
     for (int i = 0; i < cgraph->n_nodes; i++) {
         ggml_tensor * node = cgraph->nodes[i];
@@ -2648,13 +2615,7 @@ static bool check_node_graph_compatibility_and_refresh_copy_ops(ggml_backend_cud
 #endif
         }
 
-        if (node->op == GGML_OP_ADD &&
-            node->src[1] && node->src[1]->ne[1] > 1 &&
-            (node->src[0] ? node->src[0]->name != gemma3n_per_layer_proj_src0_name : true) &&
-            (node->src[1] ? node->src[1]->name != gemma3n_per_layer_proj_src1_name : true) &&
-            strncmp(node->name, ffn_moe_gate_bias_prefix.c_str(), ffn_moe_gate_bias_prefix.size()) != 0 &&
-            strncmp(node->name, ffn_moe_up_bias_prefix.c_str(), ffn_moe_up_bias_prefix.size()) != 0 &&
-            strncmp(node->name, ffn_moe_down_bias_prefix.c_str(), ffn_moe_down_bias_prefix.size()) != 0) {
+        if (node->op == GGML_OP_ADD && node->src[1] && node->src[1]->ne[1] > 1 && (node->src[0] ? node->src[0]->name != gemma3n_per_layer_proj_src0_name : true) && (node->src[1] ? node->src[1]->name != gemma3n_per_layer_proj_src1_name : true)) {
             // disable CUDA graphs for batch size > 1 for now while excluding the matrix-matrix addition as part of Gemma3n's `project_per_layer_input` operation
             // by means of matching node names. See
             // https://github.com/ggml-org/llama.cpp/blob/f9a31eea06a859e34cecb88b4d020c7f03d86cc4/src/llama-model.cpp#L10199-L10241 and
@@ -2805,12 +2766,7 @@ static void update_cuda_graph_executable(ggml_backend_cuda_context * cuda_ctx) {
 }
 #endif
 
-static bool ggml_cuda_can_fuse(const struct ggml_cgraph * cgraph, int node_idx, std::initializer_list<enum ggml_op> ops, std::initializer_list<enum ggml_unary_op> unary_ops) {
-#ifndef NDEBUG
-    const size_t num_unary = std::count(ops.begin(), ops.end(), GGML_OP_UNARY);
-    GGML_ASSERT(unary_ops.size() == num_unary);
-#endif
-
+static bool ggml_cuda_can_fuse(const struct ggml_cgraph * cgraph, int node_idx, std::initializer_list<enum ggml_op> ops) {
     if (!ggml_can_fuse(cgraph, node_idx, ops)) {
         return false;
     }
@@ -2838,32 +2794,9 @@ static bool ggml_cuda_can_fuse(const struct ggml_cgraph * cgraph, int node_idx,
         if (!ggml_is_contiguous_rows(mul->src[0]) || !ggml_is_contiguous_rows(mul->src[1])) {
             return false;
         }
-
-        return true;
     }
 
-    if (ops.size() == 3 && ops.begin()[0] == GGML_OP_SCALE && ops.begin()[1] == GGML_OP_UNARY && ops.begin()[2] == GGML_OP_SCALE
-     && unary_ops.size() == 1 && unary_ops.begin()[0] == GGML_UNARY_OP_TANH) {
-        const ggml_tensor *scale  = cgraph->nodes[node_idx];
-        const ggml_tensor *tanh   = cgraph->nodes[node_idx+1];
-        const ggml_tensor *scale2 = cgraph->nodes[node_idx+2];
-
-        GGML_ASSERT(scale->src[0]->type == GGML_TYPE_F32);
-        GGML_ASSERT(scale->type == GGML_TYPE_F32);
-
-        if (ggml_get_unary_op(tanh) != GGML_UNARY_OP_TANH) {
-            return false;
-        }
-
-        // Check for bias
-        if (ggml_get_op_params_f32(scale, 1) != 0.0f || ggml_get_op_params_f32(scale2, 1) != 0.0f) {
-            return false;
-        }
-
-        return true;
-    }
-
-    return false;
+    return true;
 }
 
 static void evaluate_and_capture_cuda_graph(ggml_backend_cuda_context * cuda_ctx, ggml_cgraph * cgraph,
@@ -2884,18 +2817,10 @@ static void evaluate_and_capture_cuda_graph(ggml_backend_cuda_context * cuda_ctx
                 }
 
                 static bool disable_fusion = (getenv("GGML_CUDA_DISABLE_FUSION") != nullptr);
-                if (!disable_fusion) {
-                    if (ggml_cuda_can_fuse(cgraph, i, { GGML_OP_RMS_NORM, GGML_OP_MUL }, {})) {
-                        ggml_cuda_op_rms_norm_fused(*cuda_ctx, node, cgraph->nodes[i+1]);
-                        i++;
-                        continue;
-                    }
-
-                    if (ggml_cuda_can_fuse(cgraph, i, { GGML_OP_SCALE, GGML_OP_UNARY, GGML_OP_SCALE }, { GGML_UNARY_OP_TANH })) {
-                        i += 2;
-                        ggml_cuda_op_softcap(*cuda_ctx, cgraph->nodes[i], node);
-                        continue;
-                    }
+                if (!disable_fusion && ggml_cuda_can_fuse(cgraph, i, { GGML_OP_RMS_NORM, GGML_OP_MUL })) {
+                    ggml_cuda_op_rms_norm_fused(*cuda_ctx, node, cgraph->nodes[i+1]);
+                    i++;
+                    continue;
                 }
 #ifndef NDEBUG
                 assert(node->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device));
@@ -3252,7 +3177,6 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
                 case GGML_GLU_OP_REGLU:
                 case GGML_GLU_OP_GEGLU:
                 case GGML_GLU_OP_SWIGLU:
-                case GGML_GLU_OP_SWIGLU_OAI:
                 case GGML_GLU_OP_GEGLU_ERF:
                 case GGML_GLU_OP_GEGLU_QUICK:
                     return ggml_is_contiguous_1(op->src[0]);
@@ -3303,7 +3227,6 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
                     case GGML_TYPE_Q5_0:
                     case GGML_TYPE_Q5_1:
                     case GGML_TYPE_Q8_0:
-                    case GGML_TYPE_MXFP4:
                     case GGML_TYPE_Q2_K:
                     case GGML_TYPE_Q3_K:
                     case GGML_TYPE_Q4_K:
@@ -3450,7 +3373,6 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
         case GGML_OP_PERMUTE:
         case GGML_OP_TRANSPOSE:
         case GGML_OP_ADD:
-        case GGML_OP_ADD_ID:
         case GGML_OP_ADD1:
         case GGML_OP_SUB:
         case GGML_OP_MUL:
@@ -3489,11 +3411,6 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
             memcpy(&max_bias, (const float *) op->op_params + 1, sizeof(float));
             return max_bias == 0.0f;
         }
-        case GGML_OP_ROLL:
-            if(op->src[0]->type == GGML_TYPE_F32) {
-                return true;
-            }
-            return false;
         case GGML_OP_ROPE:
         case GGML_OP_ROPE_BACK: {
             return op->src[0]->nb[0] == ggml_type_size(op->src[0]->type) && ggml_is_contiguous_2(op->src[0]);
@@ -3525,17 +3442,12 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
 #endif // FLASH_ATTN_AVAILABLE
             if (op->src[1]->ne[0] != op->src[2]->ne[0]) {
                 const int cc = ggml_cuda_info().devices[dev_ctx->device].cc;
-                if (!turing_mma_available(cc)) {
+                if (!new_mma_available(cc)) {
                     return false;
                 }
                 const int gqa_ratio = op->src[0]->ne[2] / op->src[1]->ne[2];
                 return op->src[1]->ne[0] == 576 && op->src[2]->ne[0] == 512 && op->src[3] && gqa_ratio % 16 == 0;
             }
-            // TODO: more general-purpose attention sink support [TAG_ATTN_SINKS]
-            if (op->src[4] && !fp16_mma_available(ggml_cuda_info().devices[dev_ctx->device].cc)
-                    && op->src[0]->ne[0] != 64 && op->src[0]->ne[0] != 128) {
-                return false;
-            }
             if (op->src[0]->ne[0] == 192) {
                 return false;
             }

ggml/src/ggml-cuda/im2col.cu

@@ -1,76 +1,65 @@
 #include "im2col.cuh"
 
-#define MAX_GRIDDIM_Z 65535
-
 template <typename T>
 static  __global__ void im2col_kernel(
-        const float * x, T * dst,
-        int64_t IC, int64_t IW, int64_t IH, int64_t OH, int64_t OW, int64_t KW, int64_t KH,
-        int64_t IC_IH_IW, int64_t IH_IW, int64_t N_OH, int64_t KH_KW, int64_t IC_KH_KW,
+        const float * x, T * dst, int64_t batch_offset,
+        int64_t offset_delta, int64_t IC, int64_t IW, int64_t IH, int64_t OH, int64_t OW, int64_t KW, int64_t KH, int64_t pelements, int64_t CHW,
         int s0, int s1, int p0, int p1, int d0, int d1) {
     const int64_t i = threadIdx.x + blockIdx.x * blockDim.x;
-    if (i >= IC_KH_KW) {
+    if (i >= pelements) {
         return;
     }
 
-    const int64_t iic = i / (KH_KW);
-    const int64_t rem = i - iic * KH_KW;
-    const int64_t ikh = rem / KW;
-    const int64_t ikw = rem - ikh * KW;
+    const int64_t  ksize = OW * KH;
+    const int64_t  kx = i / ksize;
+    const int64_t  kd = kx * ksize;
+    const int64_t  ky = (i - kd) / OW;
+    const int64_t  ix = i % OW;
 
-    const int64_t  iow = blockIdx.y;
-    for (int64_t iz = blockIdx.z; iz < N_OH; iz+=MAX_GRIDDIM_Z) {
-        const int64_t  in = iz / OH;
-        const int64_t  ioh = iz - in * OH;
+    const int64_t  oh = blockIdx.y;
+    const int64_t  batch = blockIdx.z / IC;
+    const int64_t  ic = blockIdx.z % IC;
 
-        const int64_t iiw = iow * s0 + ikw * d0 - p0;
-        const int64_t iih = ioh * s1 + ikh * d1 - p1;
+    const int64_t iiw = ix * s0 + kx * d0 - p0;
+    const int64_t iih = oh * s1 + ky * d1 - p1;
 
-        const int64_t offset_dst =
-            ((in * OH + ioh) * OW + iow) * IC_KH_KW + iic * KH_KW + ikh * KW + ikw;
+    const int64_t offset_dst =
+        ((batch * OH + oh) * OW + ix) * CHW +
+        (ic * (KW * KH) + ky * KW + kx);
 
-        if (iih < 0 || iih >= IH || iiw < 0 || iiw >= IW) {
-            dst[offset_dst] = 0.0f;
-        } else {
-            const int64_t offset_src = iic * IC_IH_IW + in * IH_IW;
-            dst[offset_dst] = x[offset_src + iih * IW + iiw];
-        }
+    if (iih < 0 || iih >= IH || iiw < 0 || iiw >= IW) {
+        dst[offset_dst] = 0.0f;
+    } else {
+        const int64_t offset_src = ic * offset_delta + batch * batch_offset;
+        dst[offset_dst] = x[offset_src + iih * IW + iiw];
     }
-
-    GGML_UNUSED(IC);
-    GGML_UNUSED(KH);
 }
 
-// im2col: [N, IC, IH, IW] => [N, OH, OW, IC*KH*KW]
 template <typename T>
 static void im2col_cuda(const float * x, T* dst,
     int64_t IW, int64_t IH, int64_t OW, int64_t OH, int64_t KW, int64_t KH, int64_t IC,
-    int64_t N, int64_t IC_IH_IW, int64_t IH_IW,
+    int64_t batch, int64_t batch_offset, int64_t offset_delta,
     int s0,int s1,int p0,int p1,int d0,int d1, cudaStream_t stream) {
-    const int64_t IC_KH_KW = IC * KH * KW;
-    const int64_t num_blocks = (IC_KH_KW + CUDA_IM2COL_BLOCK_SIZE - 1) / CUDA_IM2COL_BLOCK_SIZE;
-    const int64_t N_OH = N * OH;
-    const int64_t KH_KW = KW*KH;
-    dim3 block_nums(num_blocks, OW, MIN(N_OH, MAX_GRIDDIM_Z));
-    im2col_kernel<<<block_nums, MIN(IC_KH_KW, CUDA_IM2COL_BLOCK_SIZE) , 0, stream>>>(x, dst, IC, IW, IH, OH, OW, KW, KH,
-                                                                                     IC_IH_IW, IH_IW, N_OH, KH_KW, IC_KH_KW,
-                                                                                     s0, s1, p0, p1, d0, d1);
+    const int parallel_elements = OW * KW * KH;
+    const int num_blocks = (parallel_elements + CUDA_IM2COL_BLOCK_SIZE - 1) / CUDA_IM2COL_BLOCK_SIZE;
+    dim3 block_nums(num_blocks, OH, batch * IC);
+    im2col_kernel<<<block_nums, CUDA_IM2COL_BLOCK_SIZE, 0, stream>>>(x, dst, batch_offset, offset_delta, IC, IW, IH, OH, OW, KW, KH, parallel_elements, (IC * KH * KW), s0, s1, p0, p1, d0, d1);
 }
 
 static void im2col_cuda_f16(const float * x, half * dst,
     int64_t IW, int64_t IH, int64_t OW, int64_t OH, int64_t KW, int64_t KH, int64_t IC,
-    int64_t N, int64_t IC_IH_IW, int64_t IH_IW,
+    int64_t batch, int64_t batch_offset, int64_t offset_delta,
     int s0,int s1,int p0,int p1,int d0,int d1, cudaStream_t stream) {
 
-    im2col_cuda<half>(x, dst, IW, IH, OW, OH, KW, KH, IC, N, IC_IH_IW, IH_IW, s0, s1, p0, p1, d0, d1, stream);
+    im2col_cuda<half>(x, dst, IW, IH, OW, OH, KW, KH, IC, batch, batch_offset, offset_delta, s0, s1, p0, p1, d0, d1, stream);
 }
 
 static void im2col_cuda_f32(const float * x, float * dst,
     int64_t IW, int64_t IH, int64_t OW, int64_t OH, int64_t KW, int64_t KH, int64_t IC,
-    int64_t N, int64_t IC_IH_IW, int64_t IH_IW,
+    int64_t batch, int64_t batch_offset, int64_t offset_delta,
     int s0,int s1,int p0,int p1,int d0,int d1, cudaStream_t stream) {
 
-    im2col_cuda<float>(x, dst, IW, IH, OW, OH, KW, KH, IC, N, IC_IH_IW, IH_IW, s0, s1, p0, p1, d0, d1, stream);
+    im2col_cuda<float>(x, dst, IW, IH, OW, OH, KW, KH, IC, batch, batch_offset, offset_delta, s0, s1, p0, p1, d0, d1, stream);
 }
 
 void ggml_cuda_op_im2col(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
@@ -102,13 +91,13 @@ void ggml_cuda_op_im2col(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
     const int64_t OH = is_2D ? dst->ne[2] : 1;
     const int64_t OW =         dst->ne[1];
 
-    const int64_t IC_IH_IW = src1->nb[is_2D ? 2 : 1] / 4; // nb is byte offset, src is type float32
-    const int64_t N        = src1->ne[is_2D ? 3 : 2];
-    const int64_t IH_IW    = src1->nb[is_2D ? 3 : 2] / 4; // nb is byte offset, src is type float32
+    const size_t  delta_offset = src1->nb[is_2D ? 2 : 1] / 4; // nb is byte offset, src is type float32
+    const int64_t batch        = src1->ne[is_2D ? 3 : 2];
+    const size_t  batch_offset = src1->nb[is_2D ? 3 : 2] / 4; // nb is byte offset, src is type float32
 
     if(dst->type == GGML_TYPE_F16) {
-        im2col_cuda_f16(src1_d, (half *) dst_d, IW, IH, OW, OH, KW, KH, IC, N, IC_IH_IW, IH_IW, s0, s1, p0, p1, d0, d1, stream);
+        im2col_cuda_f16(src1_d, (half *) dst_d, IW, IH, OW, OH, KW, KH, IC, batch, batch_offset, delta_offset, s0, s1, p0, p1, d0, d1, stream);
     } else {
-        im2col_cuda_f32(src1_d, (float *) dst_d, IW, IH, OW, OH, KW, KH, IC, N, IC_IH_IW, IH_IW, s0, s1, p0, p1, d0, d1, stream);
+        im2col_cuda_f32(src1_d, (float *) dst_d, IW, IH, OW, OH, KW, KH, IC, batch, batch_offset, delta_offset, s0, s1, p0, p1, d0, d1, stream);
     }
 }

ggml/src/ggml-cuda/mma.cuh

@@ -23,13 +23,13 @@
 static __device__ __forceinline__ int ggml_cuda_movmatrix(const int x) {
     int ret = 0;
 
-#ifdef TURING_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
     asm("movmatrix.sync.aligned.m8n8.trans.b16 %0, %1;"
         : "=r"(ret) : "r"(x));
 #else
     GGML_UNUSED(x);
     NO_DEVICE_CODE;
-#endif // defined(TURING_MMA_AVAILABLE)
+#endif // defined(NEW_MMA_AVAILABLE)
     return ret;
 }
 
@@ -68,7 +68,7 @@ namespace ggml_cuda_mma {
         static constexpr int I  = I_;
         static constexpr int J  = J_;
 
-#if defined(GGML_USE_HIP)
+#if defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)
         static constexpr int ne = I * J / 64;
         T x[ne] = {0};
 
@@ -132,7 +132,7 @@ namespace ggml_cuda_mma {
                 static_assert(I == -1 && J == -1, "template specialization not implemented");
             }
         }
-#endif // defined(GGML_USE_HIP)
+#endif // defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)
     };
 
     template <int I_, int J_>
@@ -167,38 +167,6 @@ namespace ggml_cuda_mma {
         }
     };
 
-    template <int I_, int J_>
-    struct tile<I_, J_, nv_bfloat162> {
-        static constexpr int I  = I_;
-        static constexpr int J  = J_;
-        static constexpr int ne = I * J / WARP_SIZE;
-        nv_bfloat162 x[ne] = {{0.0f, 0.0f}};
-
-        static __device__ __forceinline__ int get_i(const int l) {
-            if constexpr (I == 8 && J == 8) {
-                return threadIdx.x / 4;
-            } else if constexpr (I == 16 && J == 4) {
-                return l * 8 + threadIdx.x / 4;
-            } else if constexpr (I == 16 && J == 8) {
-                return (l % 2) * 8 + threadIdx.x / 4;
-            } else {
-                static_assert(I == -1 && J == -1, "template specialization not implemented");
-            }
-        }
-
-        static __device__ __forceinline__ int get_j(const int l) {
-            if constexpr (I == 8 && J == 8) {
-                return l * 4 + threadIdx.x % 4;
-            } else if constexpr (I == 16 && J == 4) {
-                return threadIdx.x % 4;
-            } else if constexpr (I == 16 && J == 8) {
-                return (l / 2) * 4 + threadIdx.x % 4;
-            } else {
-                static_assert(I == -1 && J == -1, "template specialization not implemented");
-            }
-        }
-    };
-
     template <int I, int J>
     static __device__ __forceinline__ tile<I, J/2, half2> get_half2(const tile<I, J, float> & tile_float) {
         tile<I, J/2, half2> ret;
@@ -241,7 +209,7 @@ namespace ggml_cuda_mma {
     template <typename T>
     static __device__ __forceinline__ void load_ldmatrix(
             tile<8, 8, T> & t, const T * __restrict__ xs0, const int stride) {
-#ifdef TURING_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
         int * xi = (int *) t.x;
         const int * xs = (const int *) xs0 + (threadIdx.x % t.I) * stride + ((threadIdx.x / t.I) * (t.J / 2)) % t.J;
         asm volatile("ldmatrix.sync.aligned.m8n8.x2.b16 {%0, %1}, [%2];"
@@ -249,13 +217,13 @@ namespace ggml_cuda_mma {
             : "l"(xs));
 #else
         load_generic(t, xs0, stride);
-#endif // TURING_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
     }
 
     template <typename T>
     static __device__ __forceinline__ void load_ldmatrix(
             tile<16, 4, T> & t, const T * __restrict__ xs0, const int stride) {
-#ifdef TURING_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
         int * xi = (int *) t.x;
         const int * xs = (const int *) xs0 + (threadIdx.x % t.I) * stride;
         asm volatile("ldmatrix.sync.aligned.m8n8.x2.b16 {%0, %1}, [%2];"
@@ -264,13 +232,13 @@ namespace ggml_cuda_mma {
 #else
         load_generic(xs0, stride);
         GGML_UNUSED(t);
-#endif // TURING_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
     }
 
     template <typename T>
     static __device__ __forceinline__ void load_ldmatrix(
             tile<16, 8, T> & t, const T * __restrict__ xs0, const int stride) {
-#if defined(TURING_MMA_AVAILABLE)
+#if defined(NEW_MMA_AVAILABLE)
         int * xi = (int * ) t.x;
         const int * xs = (const int *) xs0 + (threadIdx.x % t.I) * stride + (threadIdx.x / t.I) * (t.J / 2);
         asm volatile("ldmatrix.sync.aligned.m8n8.x4.b16 {%0, %1, %2, %3}, [%4];"
@@ -278,13 +246,13 @@ namespace ggml_cuda_mma {
             : "l"(xs));
 #else
         load_generic(t, xs0, stride);
-#endif // TURING_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
     }
 
     template <typename T>
     static __device__ __forceinline__ void load_ldmatrix_trans(
             tile<16, 8, T> & t, const T * __restrict__ xs0, const int stride) {
-#ifdef TURING_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
         int * xi = (int * ) t.x;
         const int * xs = (const int *) xs0 + (threadIdx.x % t.I) * stride + (threadIdx.x / t.I) * (t.J / 2);
         asm volatile("ldmatrix.sync.aligned.m8n8.x4.trans.b16 {%0, %1, %2, %3}, [%4];"
@@ -295,12 +263,12 @@ namespace ggml_cuda_mma {
         GGML_UNUSED(xs0);
         GGML_UNUSED(stride);
         NO_DEVICE_CODE;
-#endif // TURING_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
     }
 
     static __device__ __forceinline__ void mma(
             tile<16, 8, int> & D, const tile<16, 4, int> & A, const tile<8, 4, int> & B) {
-#ifdef TURING_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
 #if __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE
         asm("mma.sync.aligned.m16n8k16.row.col.s32.s8.s8.s32 {%0, %1, %2, %3}, {%4, %5}, {%6}, {%0, %1, %2, %3};"
             : "+r"(D.x[0]), "+r"(D.x[1]), "+r"(D.x[2]), "+r"(D.x[3])
@@ -319,12 +287,12 @@ namespace ggml_cuda_mma {
         GGML_UNUSED(A);
         GGML_UNUSED(B);
         NO_DEVICE_CODE;
-#endif // TURING_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
     }
 
     static __device__ __forceinline__ void mma(
             tile<16, 8, int> & D, const tile<16, 8, int> & A, const tile<8, 8, int> & B) {
-#ifdef TURING_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
 #if __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE
         asm("mma.sync.aligned.m16n8k32.row.col.s32.s8.s8.s32 {%0, %1, %2, %3}, {%4, %5, %6, %7}, {%8, %9}, {%0, %1, %2, %3};"
             : "+r"(D.x[0]), "+r"(D.x[1]), "+r"(D.x[2]), "+r"(D.x[3])
@@ -349,12 +317,12 @@ namespace ggml_cuda_mma {
         GGML_UNUSED(A);
         GGML_UNUSED(B);
         NO_DEVICE_CODE;
-#endif // TURING_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
     }
 
     static __device__ __forceinline__ void mma(
             tile<16, 4, half2> & D, const tile<16, 8, half2> & A, const tile<8, 8, half2> & B) {
-#ifdef TURING_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
         const int * Axi = (const int *) A.x;
         const int * Bxi = (const int *) B.x;
         int       * Dxi = (int       *) D.x;
@@ -376,12 +344,12 @@ namespace ggml_cuda_mma {
         GGML_UNUSED(A);
         GGML_UNUSED(B);
         NO_DEVICE_CODE;
-#endif // TURING_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
     }
 
     static __device__ __forceinline__ void mma(
             tile<16, 8, half2> & D, const tile<16, 8, half2> & A, const tile<16, 8, half2> & B) {
-#ifdef TURING_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
         const int * Axi = (const int *) A.x;
         const int * Bxi = (const int *) B.x;
         int       * Dxi = (int       *) D.x;
@@ -412,29 +380,12 @@ namespace ggml_cuda_mma {
         GGML_UNUSED(A);
         GGML_UNUSED(B);
         NO_DEVICE_CODE;
-#endif // TURING_MMA_AVAILABLE
-    }
-
-    static __device__ __forceinline__ void mma(
-            tile<16, 8, float> & D, const tile<16, 8, float> & A, const tile<8, 8, float> & B) {
-#ifdef AMPERE_MMA_AVAILABLE
-        const int * Axi = (const int *) A.x;
-        const int * Bxi = (const int *) B.x;
-        int       * Dxi = (int       *) D.x;
-        asm("mma.sync.aligned.m16n8k8.row.col.f32.tf32.tf32.f32 {%0, %1, %2, %3}, {%4, %5, %6, %7}, {%8, %9}, {%0, %1, %2, %3};"
-            : "+r"(Dxi[0]), "+r"(Dxi[1]), "+r"(Dxi[2]), "+r"(Dxi[3])
-            : "r"(Axi[0]), "r"(Axi[1]), "r"(Axi[2]), "r"(Axi[3]), "r"(Bxi[0]), "r"(Bxi[1]));
-#else
-        GGML_UNUSED(D);
-        GGML_UNUSED(A);
-        GGML_UNUSED(B);
-        NO_DEVICE_CODE;
-#endif // AMPERE_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
     }
 
     static __device__ __forceinline__ void mma(
             tile<16, 8, float> & D, const tile<16, 8, half2> & A, const tile<8, 8, half2> & B) {
-#ifdef TURING_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
         const int * Axi = (const int *) A.x;
         const int * Bxi = (const int *) B.x;
         int       * Dxi = (int       *) D.x;
@@ -456,29 +407,12 @@ namespace ggml_cuda_mma {
         GGML_UNUSED(A);
         GGML_UNUSED(B);
         NO_DEVICE_CODE;
-#endif // TURING_MMA_AVAILABLE
-    }
-
-    static __device__ __forceinline__ void mma(
-            tile<16, 8, float> & D, const tile<16, 8, nv_bfloat162> & A, const tile<8, 8, nv_bfloat162> & B) {
-#ifdef AMPERE_MMA_AVAILABLE
-        const int * Axi = (const int *) A.x;
-        const int * Bxi = (const int *) B.x;
-        int       * Dxi = (int       *) D.x;
-        asm("mma.sync.aligned.m16n8k16.row.col.f32.bf16.bf16.f32 {%0, %1, %2, %3}, {%4, %5, %6, %7}, {%8, %9}, {%0, %1, %2, %3};"
-            : "+r"(Dxi[0]), "+r"(Dxi[1]), "+r"(Dxi[2]), "+r"(Dxi[3])
-            : "r"(Axi[0]), "r"(Axi[1]), "r"(Axi[2]), "r"(Axi[3]), "r"(Bxi[0]), "r"(Bxi[1]));
-#else
-        GGML_UNUSED(D);
-        GGML_UNUSED(A);
-        GGML_UNUSED(B);
-        NO_DEVICE_CODE;
-#endif // AMPERE_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
     }
 
     static __device__ __forceinline__ void mma(
             tile<16, 16, float> & D, const tile<16, 8, half2> & A, const tile<16, 8, half2> & B) {
-#ifdef TURING_MMA_AVAILABLE
+#ifdef NEW_MMA_AVAILABLE
         const int * Axi = (const int *) A.x;
         const int * Bxi = (const int *) B.x;
         int       * Dxi = (int       *) D.x;
@@ -509,7 +443,7 @@ namespace ggml_cuda_mma {
         GGML_UNUSED(A);
         GGML_UNUSED(B);
         NO_DEVICE_CODE;
-#endif // TURING_MMA_AVAILABLE
+#endif // NEW_MMA_AVAILABLE
     }
 
     static __device__ __forceinline__ void mma(

ggml/src/ggml-cuda/mmf.cu

@@ -1,431 +0,0 @@
-#include "ggml.h"
-#include "common.cuh"
-#include "mma.cuh"
-#include "mmf.cuh"
-
-using namespace ggml_cuda_mma;
-
-#define MMF_ROWS_PER_BLOCK 32
-
-template <typename T, int rows_per_block, int cols_per_block, int nwarps>
-__launch_bounds__(ggml_cuda_get_physical_warp_size()*nwarps, 1)
-static __global__ void mul_mat_f(
-        const T * __restrict__ x, const float * __restrict__ y, const int32_t * __restrict__ ids, float * __restrict__ dst,
-        const int ncols, const int nchannels_y, const int stride_row, const int stride_col_y, const int stride_col_dst,
-        const int channel_ratio, const int stride_channel_x, const int stride_channel_y, const int stride_channel_dst,
-        const int sample_ratio, const int stride_sample_x, const int stride_sample_y, const int stride_sample_dst) {
-#if !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)
-    typedef tile<16, 8, T>     tile_A;
-    typedef tile< 8, 8, T>     tile_B;
-    typedef tile<16, 8, float> tile_C;
-
-    constexpr int warp_size = ggml_cuda_get_physical_warp_size();
-    constexpr int tile_k_padded = warp_size + 4;
-    constexpr int ntA = rows_per_block / tile_A::I;
-    constexpr int ntB = (cols_per_block + tile_B::I - 1) / tile_B::I;
-
-    const int row0        = blockIdx.x * rows_per_block;
-    const int channel_dst = blockIdx.y;
-    const int channel_x   = channel_dst / channel_ratio;
-    const int channel_y   = channel_dst;
-    const int sample_dst  = blockIdx.z;
-    const int sample_x    = sample_dst / sample_ratio;
-    const int sample_y    = sample_dst;
-
-    x   += int64_t(sample_x)  *stride_sample_x   + channel_x  *stride_channel_x   + row0*stride_row ;
-    y   += int64_t(sample_y)  *stride_sample_y   + channel_y  *stride_channel_y;
-    dst += int64_t(sample_dst)*stride_sample_dst + channel_dst*stride_channel_dst;
-
-    const float2 * y2 = (const float2 *) y;
-
-    extern __shared__ char data_mmv[];
-
-    tile_C C[ntA][ntB];
-
-    T * tile_xy = (T *) data_mmv + threadIdx.y*(tile_A::I * tile_k_padded);
-
-    for (int col = threadIdx.y*warp_size + threadIdx.x; col < ncols; col += nwarps*warp_size) {
-        tile_A A[ntA][warp_size / tile_A::J];
-#pragma unroll
-        for (int itA = 0; itA < ntA; ++itA) {
-#pragma unroll
-            for (int i = 0; i < tile_A::I; ++i) {
-                tile_xy[i*tile_k_padded + threadIdx.x] = x[(itA*tile_A::I + i)*stride_row  + col];
-            }
-#pragma unroll
-            for (int k0 = 0; k0 < warp_size; k0 += tile_A::J) {
-                load_ldmatrix(A[itA][k0/tile_A::J], tile_xy + k0, tile_k_padded);
-            }
-        }
-
-#pragma unroll
-        for (int itB = 0; itB < ntB; ++itB) {
-            if constexpr (std::is_same_v<T, float>) {
-#pragma unroll
-                for (int j0 = 0; j0 < tile_B::I; ++j0) {
-                    const int j = j0 + itB*tile_B::I;
-
-                    tile_xy[j0*tile_k_padded + threadIdx.x] = j < cols_per_block ? y[j*stride_col_y + col] : 0.0f;
-                }
-            } else if constexpr (std::is_same_v<T, half2> || std::is_same_v<T, nv_bfloat162>) {
-#pragma unroll
-                for (int j0 = 0; j0 < tile_B::I; ++j0) {
-                    const int j = j0 + itB*tile_B::I;
-
-                    const float2 tmp = j < cols_per_block ? y2[j*stride_col_y + col] : make_float2(0.0f, 0.0f);
-                    tile_xy[j0*tile_k_padded + threadIdx.x] = {tmp.x, tmp.y};
-                }
-            } else {
-                static_assert(std::is_same_v<T, void>, "unsupported type");
-            }
-#pragma unroll
-            for (int k0 = 0; k0 < warp_size; k0 += tile_B::J) {
-                tile_B B;
-                load_ldmatrix(B, tile_xy + k0, tile_k_padded);
-#pragma unroll
-                for (int itA = 0; itA < ntA; ++itA) {
-                    mma(C[itA][itB], A[itA][k0/tile_B::J], B);
-                }
-            }
-        }
-    }
-
-    float * buf_iw = (float *) data_mmv;
-    constexpr int kiw = nwarps*rows_per_block + 4;
-
-    if (nwarps > 1) {
-        __syncthreads();
-    }
-#pragma unroll
-    for (int itB = 0; itB < ntB; ++itB) {
-#pragma unroll
-        for (int itA = 0; itA < ntA; ++itA) {
-#pragma unroll
-            for (int l = 0; l < tile_C::ne; ++l) {
-                const int i = threadIdx.y*rows_per_block + itA*tile_C::I + tile_C::get_i(l);
-                const int j = itB*tile_C::J + tile_C::get_j(l);
-                buf_iw[j*kiw + i] = C[itA][itB].x[l];
-            }
-        }
-    }
-
-    if (nwarps > 1) {
-        __syncthreads();
-    }
-
-#pragma unroll
-    for (int j0 = 0; j0 < cols_per_block; j0 += nwarps) {
-        const int j = j0 + threadIdx.y;
-
-        if (j0 + nwarps > cols_per_block && j >= cols_per_block) {
-            return;
-        }
-
-        float sum = 0.0f;
-        static_assert(rows_per_block == warp_size, "need loop/check");
-#pragma unroll
-        for (int i0 = 0; i0 < nwarps*rows_per_block; i0 += rows_per_block) {
-            const int i = i0 + threadIdx.x;
-
-            sum += buf_iw[j*kiw + i];
-        }
-        dst[j*stride_col_dst + row0 + threadIdx.x] = sum;
-    }
-#else
-    NO_DEVICE_CODE;
-    GGML_UNUSED(x); GGML_UNUSED(y); GGML_UNUSED(ids); GGML_UNUSED(dst);
-    GGML_UNUSED(ncols); GGML_UNUSED(nchannels_y); GGML_UNUSED(stride_row); GGML_UNUSED(stride_col_y); GGML_UNUSED(stride_col_dst);
-    GGML_UNUSED(channel_ratio); GGML_UNUSED(stride_channel_x); GGML_UNUSED(stride_channel_y); GGML_UNUSED(stride_channel_dst);
-    GGML_UNUSED(sample_ratio); GGML_UNUSED(stride_sample_x); GGML_UNUSED(stride_sample_y); GGML_UNUSED(stride_sample_dst);
-#endif // !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)
-}
-
-template <typename T, int cols_per_block>
-static void mul_mat_f_cuda(
-        const T * x, const float * y, const int32_t * ids, float * dst,
-        const int64_t ncols_x, const int64_t nrows_x,
-        const int64_t stride_row, const int64_t stride_col_y, const int64_t stride_col_dst,
-        const int64_t nchannels_x, const int64_t nchannels_y, const int64_t nchannels_dst,
-        const int64_t stride_channel_x, const int64_t stride_channel_y, const int64_t stride_channel_dst, const int64_t nsamples_x,
-        const int64_t nsamples_dst, const int64_t stride_sample_x, const int64_t stride_sample_y, const int64_t stride_sample_dst,
-        cudaStream_t stream) {
-    typedef tile<16, 8, T>     tile_A;
-    typedef tile< 8, 8, T>     tile_B;
-    typedef tile<16, 8, float> tile_C;
-
-    GGML_ASSERT(!ids && "mul_mat_id not implemented");
-
-    GGML_ASSERT(ncols_x      % 2 == 0);
-    GGML_ASSERT(stride_row   % 2 == 0);
-    GGML_ASSERT(stride_col_y % 2 == 0);
-    GGML_ASSERT(ids || nchannels_dst % nchannels_x == 0);
-    GGML_ASSERT(       nsamples_dst  % nsamples_x  == 0);
-    const int64_t channel_ratio = nchannels_dst / nchannels_x;
-    const int64_t sample_ratio  = nsamples_dst  / nsamples_x;
-
-    const int device = ggml_cuda_get_device();
-    const int warp_size = ggml_cuda_info().devices[device].warp_size;
-
-    int64_t nwarps_best     = 1;
-    int64_t niter_best      = (ncols_x + warp_size*2 - 1) / (warp_size*2);
-    int64_t max_block_size  = 256;
-    for (int64_t nwarps = 2; nwarps <= max_block_size/warp_size; nwarps++) {
-        const int64_t niter = (ncols_x + nwarps*warp_size*2 - 1) / (nwarps*warp_size*2);
-        if (niter < niter_best) {
-            niter_best  = niter;
-            nwarps_best = nwarps;
-        }
-    }
-
-    constexpr int rows_per_block = MMF_ROWS_PER_BLOCK;
-    const int nbytes_shared_iter = nwarps_best * tile_A::I * (warp_size + 4) * 4;
-    const int nbytes_shared_combine = GGML_PAD(cols_per_block, tile_B::I) * (nwarps_best*rows_per_block + 4) * 4;
-    const int nbytes_shared = std::max(nbytes_shared_iter, nbytes_shared_combine);
-    const dim3 block_nums(nrows_x/rows_per_block, nchannels_dst, nsamples_dst);
-    const dim3 block_dims(warp_size, nwarps_best, 1);
-    switch (nwarps_best) {
-        case 1: {
-            mul_mat_f<T, rows_per_block, cols_per_block, 1><<<block_nums, block_dims, nbytes_shared, stream>>>
-                (x, y, ids, dst, ncols_x, nchannels_y, stride_row, stride_col_y, stride_col_dst,
-                 channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
-                 sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
-        } break;
-        case 2: {
-            mul_mat_f<T, rows_per_block, cols_per_block, 2><<<block_nums, block_dims, nbytes_shared, stream>>>
-                (x, y, ids, dst, ncols_x, nchannels_y, stride_row, stride_col_y, stride_col_dst,
-                 channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
-                 sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
-        } break;
-        case 3: {
-            mul_mat_f<T, rows_per_block, cols_per_block, 3><<<block_nums, block_dims, nbytes_shared, stream>>>
-                (x, y, ids, dst, ncols_x, nchannels_y, stride_row, stride_col_y, stride_col_dst,
-                 channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
-                 sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
-        } break;
-        case 4: {
-            mul_mat_f<T, rows_per_block, cols_per_block, 4><<<block_nums, block_dims, nbytes_shared, stream>>>
-                (x, y, ids, dst, ncols_x, nchannels_y, stride_row, stride_col_y, stride_col_dst,
-                 channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
-                 sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
-        } break;
-        case 5: {
-            mul_mat_f<T, rows_per_block, cols_per_block, 5><<<block_nums, block_dims, nbytes_shared, stream>>>
-                (x, y, ids, dst, ncols_x, nchannels_y, stride_row, stride_col_y, stride_col_dst,
-                 channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
-                 sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
-        } break;
-        case 6: {
-            mul_mat_f<T, rows_per_block, cols_per_block, 6><<<block_nums, block_dims, nbytes_shared, stream>>>
-                (x, y, ids, dst, ncols_x, nchannels_y, stride_row, stride_col_y, stride_col_dst,
-                 channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
-                 sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
-        } break;
-        case 7: {
-            mul_mat_f<T, rows_per_block, cols_per_block, 7><<<block_nums, block_dims, nbytes_shared, stream>>>
-                (x, y, ids, dst, ncols_x, nchannels_y, stride_row, stride_col_y, stride_col_dst,
-                 channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
-                 sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
-        } break;
-        case 8: {
-            mul_mat_f<T, rows_per_block, cols_per_block, 8><<<block_nums, block_dims, nbytes_shared, stream>>>
-                (x, y, ids, dst, ncols_x, nchannels_y, stride_row, stride_col_y, stride_col_dst,
-                 channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
-                 sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
-        } break;
-        default: {
-            GGML_ABORT("fatal error");
-        } break;
-    }
-}
-
-template <typename T>
-static void mul_mat_f_switch_cols_per_block(
-        const T * x, const float * y, const int32_t * ids, float * dst,
-        const int64_t ncols_x, const int64_t nrows_x, const int64_t ncols_dst,
-        const int64_t stride_row, const int64_t stride_col_y, const int64_t stride_col_dst,
-        const int64_t nchannels_x, const int64_t nchannels_y, const int64_t nchannels_dst,
-        const int64_t stride_channel_x, const int64_t stride_channel_y, const int64_t stride_channel_dst, const int64_t nsamples_x,
-        const int64_t nsamples_dst, const int64_t stride_sample_x, const int64_t stride_sample_y, const int64_t stride_sample_dst,
-        cudaStream_t stream) {
-    switch (ncols_dst) {
-        case  1: {
-            mul_mat_f_cuda<T,  1>(x, y, ids, dst, ncols_x, nrows_x, stride_row, stride_col_y, stride_col_dst,
-                nchannels_x, nchannels_y,  nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
-                nsamples_x,                nsamples_dst,  stride_sample_x,  stride_sample_y,  stride_sample_dst,  stream);
-        } break;
-        case  2: {
-            mul_mat_f_cuda<T,  2>(x, y, ids, dst, ncols_x, nrows_x, stride_row, stride_col_y, stride_col_dst,
-                nchannels_x, nchannels_y,  nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
-                nsamples_x,                nsamples_dst,  stride_sample_x,  stride_sample_y,  stride_sample_dst,  stream);
-        } break;
-        case  3: {
-            mul_mat_f_cuda<T,  3>(x, y, ids, dst, ncols_x, nrows_x, stride_row, stride_col_y, stride_col_dst,
-                nchannels_x, nchannels_y,  nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
-                nsamples_x,                nsamples_dst,  stride_sample_x,  stride_sample_y,  stride_sample_dst,  stream);
-        } break;
-        case  4: {
-            mul_mat_f_cuda<T,  4>(x, y, ids, dst, ncols_x, nrows_x, stride_row, stride_col_y, stride_col_dst,
-                nchannels_x, nchannels_y,  nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
-                nsamples_x,                nsamples_dst,  stride_sample_x,  stride_sample_y,  stride_sample_dst,  stream);
-        } break;
-        case  5: {
-            mul_mat_f_cuda<T,  5>(x, y, ids, dst, ncols_x, nrows_x, stride_row, stride_col_y, stride_col_dst,
-                nchannels_x, nchannels_y,  nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
-                nsamples_x,                nsamples_dst,  stride_sample_x,  stride_sample_y,  stride_sample_dst,  stream);
-        } break;
-        case  6: {
-            mul_mat_f_cuda<T,  6>(x, y, ids, dst, ncols_x, nrows_x, stride_row, stride_col_y, stride_col_dst,
-                nchannels_x, nchannels_y,  nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
-                nsamples_x,                nsamples_dst,  stride_sample_x,  stride_sample_y,  stride_sample_dst,  stream);
-        } break;
-        case  7: {
-            mul_mat_f_cuda<T,  7>(x, y, ids, dst, ncols_x, nrows_x, stride_row, stride_col_y, stride_col_dst,
-                nchannels_x, nchannels_y,  nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
-                nsamples_x,                nsamples_dst,  stride_sample_x,  stride_sample_y,  stride_sample_dst,  stream);
-        } break;
-        case  8: {
-            mul_mat_f_cuda<T,  8>(x, y, ids, dst, ncols_x, nrows_x, stride_row, stride_col_y, stride_col_dst,
-                nchannels_x, nchannels_y,  nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
-                nsamples_x,                nsamples_dst,  stride_sample_x,  stride_sample_y,  stride_sample_dst,  stream);
-        } break;
-        case  9: {
-            mul_mat_f_cuda<T,  9>(x, y, ids, dst, ncols_x, nrows_x, stride_row, stride_col_y, stride_col_dst,
-                nchannels_x, nchannels_y,  nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
-                nsamples_x,                nsamples_dst,  stride_sample_x,  stride_sample_y,  stride_sample_dst,  stream);
-        } break;
-        case 10: {
-            mul_mat_f_cuda<T, 10>(x, y, ids, dst, ncols_x, nrows_x, stride_row, stride_col_y, stride_col_dst,
-                nchannels_x, nchannels_y,  nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
-                nsamples_x,                nsamples_dst,  stride_sample_x,  stride_sample_y,  stride_sample_dst,  stream);
-        } break;
-        case 11: {
-            mul_mat_f_cuda<T, 11>(x, y, ids, dst, ncols_x, nrows_x, stride_row, stride_col_y, stride_col_dst,
-                nchannels_x, nchannels_y,  nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
-                nsamples_x,                nsamples_dst,  stride_sample_x,  stride_sample_y,  stride_sample_dst,  stream);
-        } break;
-        case 12: {
-            mul_mat_f_cuda<T, 12>(x, y, ids, dst, ncols_x, nrows_x, stride_row, stride_col_y, stride_col_dst,
-                nchannels_x, nchannels_y,  nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
-                nsamples_x,                nsamples_dst,  stride_sample_x,  stride_sample_y,  stride_sample_dst,  stream);
-        } break;
-        case 13: {
-            mul_mat_f_cuda<T, 13>(x, y, ids, dst, ncols_x, nrows_x, stride_row, stride_col_y, stride_col_dst,
-                nchannels_x, nchannels_y,  nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
-                nsamples_x,                nsamples_dst,  stride_sample_x,  stride_sample_y,  stride_sample_dst,  stream);
-        } break;
-        case 14: {
-            mul_mat_f_cuda<T, 14>(x, y, ids, dst, ncols_x, nrows_x, stride_row, stride_col_y, stride_col_dst,
-                nchannels_x, nchannels_y,  nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
-                nsamples_x,                nsamples_dst,  stride_sample_x,  stride_sample_y,  stride_sample_dst,  stream);
-        } break;
-        case 15: {
-            mul_mat_f_cuda<T, 15>(x, y, ids, dst, ncols_x, nrows_x, stride_row, stride_col_y, stride_col_dst,
-                nchannels_x, nchannels_y,  nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
-                nsamples_x,                nsamples_dst,  stride_sample_x,  stride_sample_y,  stride_sample_dst,  stream);
-        } break;
-        case 16: {
-            mul_mat_f_cuda<T, 16>(x, y, ids, dst, ncols_x, nrows_x, stride_row, stride_col_y, stride_col_dst,
-                nchannels_x, nchannels_y,  nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
-                nsamples_x,                nsamples_dst,  stride_sample_x,  stride_sample_y,  stride_sample_dst,  stream);
-        } break;
-        default: {
-            GGML_ABORT("fatal error");
-        } break;
-    }
-}
-
-void ggml_cuda_mul_mat_f(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * ids, ggml_tensor * dst) {
-    GGML_ASSERT(        src1->type == GGML_TYPE_F32);
-    GGML_ASSERT(!ids ||  ids->type == GGML_TYPE_I32);
-    GGML_ASSERT(         dst->type == GGML_TYPE_F32);
-
-    GGML_TENSOR_BINARY_OP_LOCALS;
-
-    const size_t ts_src0 = ggml_type_size(src0->type);
-    const size_t ts_src1 = ggml_type_size(src1->type);
-    const size_t ts_dst  = ggml_type_size(dst->type);
-
-    GGML_ASSERT(ne13 == ne3);
-
-    GGML_ASSERT(        nb00       == ts_src0);
-    GGML_ASSERT(        nb10       == ts_src1);
-    GGML_ASSERT(!ids || ids->nb[0] == ggml_type_size(ids->type));
-    GGML_ASSERT(        nb0        == ts_dst);
-
-    const int cc = ggml_cuda_info().devices[ggml_cuda_get_device()].cc;
-    const enum ggml_prec prec = fast_fp16_available(cc) ? ggml_prec(dst->op_params[0]) : GGML_PREC_F32;
-
-    const float   * src1_d =       (const float   *) src1->data;
-    const int32_t *  ids_d = ids ? (const int32_t *)  ids->data : nullptr;
-    float         *  dst_d =       (float         *)  dst->data;
-
-    const int64_t s01 = src0->nb[1] / ts_src0;
-    const int64_t s11 = src1->nb[1] / ts_src1;
-    const int64_t s1  =  dst->nb[1] / ts_dst;
-    const int64_t s02 = src0->nb[2] / ts_src0;
-    const int64_t s12 = src1->nb[2] / ts_src1;
-    const int64_t s2  =  dst->nb[2] / ts_dst;
-    const int64_t s03 = src0->nb[3] / ts_src0;
-    const int64_t s13 = src1->nb[3] / ts_src1;
-    const int64_t s3  =  dst->nb[3] / ts_dst;
-
-    // For MUL_MAT_ID the memory layout is different than for MUL_MAT:
-    const int64_t ncols_dst          = ids ? ne2  : ne1;
-    const int64_t nchannels_y        = ids ? ne11 : ne12;
-    const int64_t nchannels_dst      = ids ? ne1  : ne2;
-    const int64_t stride_channel_dst = ids ? s1   : s2;
-    const int64_t stride_channel_y   = ids ? s11  : s12;
-
-    GGML_ASSERT(!ids || ncols_dst == 1);
-
-    switch (src0->type) {
-        case GGML_TYPE_F32: {
-            const float * src0_d = (const float *) src0->data;
-            constexpr int vals_per_T = 1;
-            mul_mat_f_switch_cols_per_block(
-                src0_d, src1_d, ids_d, dst_d, ne00/vals_per_T, ne01, ncols_dst, s01/vals_per_T, s11/vals_per_T, s1,
-                ne02, nchannels_y, nchannels_dst, s02/vals_per_T, stride_channel_y, stride_channel_dst,
-                ne03,              ne3,           s03/vals_per_T, s13,              s3,                 ctx.stream());
-        } break;
-        case GGML_TYPE_F16: {
-            const half2 * src0_d = (const half2 *) src0->data;
-            constexpr int vals_per_T = 2;
-            mul_mat_f_switch_cols_per_block(
-                src0_d, src1_d, ids_d, dst_d, ne00/vals_per_T, ne01, ncols_dst, s01/vals_per_T, s11/vals_per_T, s1,
-                ne02, nchannels_y, nchannels_dst, s02/vals_per_T, stride_channel_y, stride_channel_dst,
-                ne03,              ne3,           s03/vals_per_T, s13,              s3,                 ctx.stream());
-        } break;
-        case GGML_TYPE_BF16: {
-            const nv_bfloat162 * src0_d = (const nv_bfloat162 *) src0->data;
-            constexpr int vals_per_T = 2;
-            mul_mat_f_switch_cols_per_block(
-                src0_d, src1_d, ids_d, dst_d, ne00/vals_per_T, ne01, ncols_dst, s01/vals_per_T, s11/vals_per_T, s1,
-                ne02, nchannels_y, nchannels_dst, s02/vals_per_T, stride_channel_y, stride_channel_dst,
-                ne03,              ne3,           s03/vals_per_T, s13,              s3,                 ctx.stream());
-        } break;
-        default:
-            GGML_ABORT("unsupported type: %s", ggml_type_name(src0->type));
-    }
-}
-
-bool ggml_cuda_should_use_mmf(enum ggml_type type, int cc, int warp_size, const int64_t * src0_ne, int64_t ne11) {
-    if (src0_ne[0] % (warp_size * (4/ggml_type_size(type))) != 0) {
-        return false;
-    }
-    if (src0_ne[1] % MMF_ROWS_PER_BLOCK != 0) {
-        return false;
-    }
-    if (ne11 > 16) {
-        return false;
-    }
-    switch (type) {
-        case GGML_TYPE_F32:
-            return ampere_mma_available(cc);
-        case GGML_TYPE_F16:
-            return turing_mma_available(cc);
-        case GGML_TYPE_BF16:
-            return ampere_mma_available(cc);
-        default:
-            return false;
-    }
-}

ggml/src/ggml-cuda/mmf.cuh

@@ -1,5 +0,0 @@
-#include "common.cuh"
-
-void ggml_cuda_mul_mat_f(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * ids, ggml_tensor * dst);
-
-bool ggml_cuda_should_use_mmf(enum ggml_type type, int cc, int warp_size, const int64_t * scr0_ne, int64_t ne11);

ggml/src/ggml-cuda/mmq.cu

@@ -20,9 +20,6 @@ static void ggml_cuda_mul_mat_q_switch_type(ggml_backend_cuda_context & ctx, con
         case GGML_TYPE_Q8_0:
             mul_mat_q_case<GGML_TYPE_Q8_0>(ctx, args, stream);
             break;
-        case GGML_TYPE_MXFP4:
-            mul_mat_q_case<GGML_TYPE_MXFP4>(ctx, args, stream);
-            break;
         case GGML_TYPE_Q2_K:
             mul_mat_q_case<GGML_TYPE_Q2_K>(ctx, args, stream);
             break;
@@ -112,8 +109,8 @@ void ggml_cuda_mul_mat_q(
     const int64_t s03 = src0->nb[3] / ts_src0;
     const int64_t s3  =  dst->nb[3] / ts_dst;
 
-    const bool use_stream_k = (GGML_CUDA_CC_IS_NVIDIA(cc) && ggml_cuda_highest_compiled_arch(cc) >= GGML_CUDA_CC_VOLTA)
-                            || GGML_CUDA_CC_IS_CDNA(cc);
+    const bool use_stream_k = ((GGML_CUDA_CC_IS_NVIDIA(cc) && ggml_cuda_highest_compiled_arch(cc) >= GGML_CUDA_CC_VOLTA)
+                            || (GGML_CUDA_CC_IS_AMD(cc) && GGML_CUDA_CC_IS_CDNA3(cc)));
 
     if (!ids) {
         const size_t nbytes_src1_q8_1 = ne13*ne12 * ne11*ne10_padded * sizeof(block_q8_1)/QK8_1 +
@@ -255,7 +252,7 @@ void ggml_cuda_op_mul_mat_q(
     // Also its fixup needs to allocate a temporary buffer in the memory pool.
     // There are multiple parallel CUDA streams for src1_ncols != ne11 which would introduce a race condition for this buffer.
     const bool use_stream_k = ((GGML_CUDA_CC_IS_NVIDIA(cc) && ggml_cuda_highest_compiled_arch(cc) >= GGML_CUDA_CC_VOLTA)
-                            || GGML_CUDA_CC_IS_CDNA(cc))
+                            || (GGML_CUDA_CC_IS_AMD(cc) && GGML_CUDA_CC_IS_CDNA3(cc)))
                             && src1_ncols == ne11;
     const mmq_args args = {
         src0_dd_i, src0->type, (const int *) src1_ddq_i, nullptr, nullptr, dst_dd_i,
@@ -285,7 +282,6 @@ bool ggml_cuda_should_use_mmq(enum ggml_type type, int cc, int64_t ne11) {
         case GGML_TYPE_Q5_0:
         case GGML_TYPE_Q5_1:
         case GGML_TYPE_Q8_0:
-        case GGML_TYPE_MXFP4:
         case GGML_TYPE_Q2_K:
         case GGML_TYPE_Q3_K:
         case GGML_TYPE_Q4_K:
@@ -310,7 +306,7 @@ bool ggml_cuda_should_use_mmq(enum ggml_type type, int cc, int64_t ne11) {
         return false;
     }
 
-    if (turing_mma_available(cc)) {
+    if (new_mma_available(cc) || amd_mfma_available(cc)) {
         return true;
     }
 
@@ -326,21 +322,5 @@ bool ggml_cuda_should_use_mmq(enum ggml_type type, int cc, int64_t ne11) {
         return !fp16_mma_hardware_available(cc) || ne11 < MMQ_DP4A_MAX_BATCH_SIZE;
     }
 
-    if (amd_mfma_available(cc)) {
-        // As of ROCM 7.0 rocblas/tensile performs very poorly on CDNA3 and hipblaslt (via ROCBLAS_USE_HIPBLASLT)
-        // performs better but is currently suffering from a crash on this architecture.
-        // TODO: Revisit when hipblaslt is fixed on CDNA3
-        if (GGML_CUDA_CC_IS_CDNA3(cc)) {
-            return true;
-        }
-        if (ne11 <= 128 || type == GGML_TYPE_Q4_0 || type == GGML_TYPE_Q4_1 || type == GGML_TYPE_Q5_0 || type == GGML_TYPE_Q5_1) {
-            return true;
-        }
-        if (ne11 <= 256 && (type == GGML_TYPE_Q4_K || type == GGML_TYPE_Q5_K)) {
-            return true;
-        }
-        return false;
-    }
-
     return (!GGML_CUDA_CC_IS_RDNA4(cc) && !GGML_CUDA_CC_IS_RDNA3(cc) && !GGML_CUDA_CC_IS_CDNA(cc)) || ne11 < MMQ_DP4A_MAX_BATCH_SIZE;
 }

ggml/src/ggml-cuda/mmv.cu

@@ -1,9 +1,9 @@
 #include "ggml.h"
 #include "common.cuh"
-#include "mmvf.cuh"
+#include "mmv.cuh"
 
 template <typename T, typename type_acc, int ncols_dst, int block_size>
-static __global__ void mul_mat_vec_f(
+static __global__ void mul_mat_vec(
         const T * __restrict__ x, const float * __restrict__ y, const int32_t * __restrict__ ids, float * __restrict__ dst,
         const int ncols2, const int nchannels_y, const int stride_row, const int stride_col_y2, const int stride_col_dst,
         const int channel_ratio, const int stride_channel_x, const int stride_channel_y, const int stride_channel_dst,
@@ -37,7 +37,7 @@ static __global__ void mul_mat_vec_f(
 
     float sumf[ncols_dst] = {0.0f};
 
-    if constexpr (std::is_same_v<T, float>) {
+    if constexpr (std::is_same<T, float>::value) {
         const float2 * x2 = (const float2 *) x;
 
         for (int col2 = tid; col2 < ncols2; col2 += block_size) {
@@ -50,10 +50,10 @@ static __global__ void mul_mat_vec_f(
                 sumf[j] += tmpx.y*tmpy.y;
             }
         }
-    } else if constexpr (std::is_same_v<T, half>) {
+    } else if constexpr (std::is_same<T, half>::value) {
         const half2 * x2 = (const half2 *) x;
 
-        if (std::is_same_v<type_acc, float>) {
+        if (std::is_same<type_acc, float>::value) {
             for (int col2 = tid; col2 < ncols2; col2 += block_size) {
                 const float2 tmpx = __half22float2(x2[col2]);
 
@@ -86,7 +86,7 @@ static __global__ void mul_mat_vec_f(
             NO_DEVICE_CODE;
 #endif // FP16_AVAILABLE
         }
-    } else if constexpr (std::is_same_v<T, nv_bfloat16>) {
+    } else if constexpr (std::is_same<T, nv_bfloat16>::value) {
         const int * x2 = (const int *) x;
         for (int col2 = tid; col2 < ncols2; col2 += block_size) {
             const int tmpx = x2[col2];
@@ -98,7 +98,7 @@ static __global__ void mul_mat_vec_f(
             }
         }
     } else {
-        static_assert(std::is_same_v<T, void>, "unsupported type");
+        static_assert(std::is_same<T, void>::value, "unsupported type");
     }
 
 #pragma unroll
@@ -126,7 +126,7 @@ static __global__ void mul_mat_vec_f(
 }
 
 template <typename T, typename type_acc, int ncols_dst>
-static void launch_mul_mat_vec_f_cuda(
+static void launch_mul_mat_vec_cuda(
         const T * x, const float * y, const int32_t * ids, float * dst,
         const int64_t ncols, const int64_t nrows,
         const int64_t stride_row, const int64_t stride_col_y, const int64_t stride_col_dst,
@@ -141,9 +141,11 @@ static void launch_mul_mat_vec_f_cuda(
     GGML_ASSERT(       nsamples_dst  % nsamples_x  == 0);
     const int64_t channel_ratio = nchannels_dst / nchannels_x;
     const int64_t sample_ratio  = nsamples_dst  / nsamples_x;
+    int device;
+    int warp_size;
 
-    const int device = ggml_cuda_get_device();
-    const int warp_size = ggml_cuda_info().devices[device].warp_size;
+    CUDA_CHECK(cudaGetDevice(&device));
+    warp_size = ggml_cuda_info().devices[device].warp_size;
 
     int64_t block_size_best = warp_size;
     int64_t niter_best      = (ncols + 2*warp_size - 1) / (2*warp_size);
@@ -159,54 +161,54 @@ static void launch_mul_mat_vec_f_cuda(
         }
     }
 
-    const int nbytes_shared = warp_size*sizeof(float);
+    const int smem = warp_size*sizeof(float);
     const dim3 block_nums(nrows, nchannels_dst, nsamples_dst);
     const dim3 block_dims(block_size_best, 1, 1);
     switch (block_size_best) {
         case   32: {
-            mul_mat_vec_f<T, type_acc, ncols_dst,  32><<<block_nums, block_dims, nbytes_shared, stream>>>
+            mul_mat_vec<T, type_acc, ncols_dst,  32><<<block_nums, block_dims, smem, stream>>>
                 (x, y, ids, dst, ncols/2, nchannels_y, stride_row, stride_col_y/2, stride_col_dst,
                  channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
                  sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
         } break;
         case   64: {
-            mul_mat_vec_f<T, type_acc, ncols_dst,  64><<<block_nums, block_dims, nbytes_shared, stream>>>
+            mul_mat_vec<T, type_acc, ncols_dst,  64><<<block_nums, block_dims, smem, stream>>>
                 (x, y, ids, dst, ncols/2, nchannels_y, stride_row, stride_col_y/2, stride_col_dst,
                  channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
                  sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
         } break;
         case   96: {
-            mul_mat_vec_f<T, type_acc, ncols_dst,  96><<<block_nums, block_dims, nbytes_shared, stream>>>
+            mul_mat_vec<T, type_acc, ncols_dst,  96><<<block_nums, block_dims, smem, stream>>>
                 (x, y, ids, dst, ncols/2, nchannels_y, stride_row, stride_col_y/2, stride_col_dst,
                  channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
                  sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
         } break;
         case  128: {
-            mul_mat_vec_f<T, type_acc, ncols_dst, 128><<<block_nums, block_dims, nbytes_shared, stream>>>
+            mul_mat_vec<T, type_acc, ncols_dst, 128><<<block_nums, block_dims, smem, stream>>>
                 (x, y, ids, dst, ncols/2, nchannels_y, stride_row, stride_col_y/2, stride_col_dst,
                  channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
                  sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
         } break;
         case  160: {
-            mul_mat_vec_f<T, type_acc, ncols_dst, 160><<<block_nums, block_dims, nbytes_shared, stream>>>
+            mul_mat_vec<T, type_acc, ncols_dst, 160><<<block_nums, block_dims, smem, stream>>>
                 (x, y, ids, dst, ncols/2, nchannels_y, stride_row, stride_col_y/2, stride_col_dst,
                  channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
                  sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
         } break;
         case  192: {
-            mul_mat_vec_f<T, type_acc, ncols_dst, 192><<<block_nums, block_dims, nbytes_shared, stream>>>
+            mul_mat_vec<T, type_acc, ncols_dst, 192><<<block_nums, block_dims, smem, stream>>>
                 (x, y, ids, dst, ncols/2, nchannels_y, stride_row, stride_col_y/2, stride_col_dst,
                  channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
                  sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
         } break;
         case  224: {
-            mul_mat_vec_f<T, type_acc, ncols_dst, 224><<<block_nums, block_dims, nbytes_shared, stream>>>
+            mul_mat_vec<T, type_acc, ncols_dst, 224><<<block_nums, block_dims, smem, stream>>>
                 (x, y, ids, dst, ncols/2, nchannels_y, stride_row, stride_col_y/2, stride_col_dst,
                  channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
                  sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
         } break;
         case  256: {
-            mul_mat_vec_f<T, type_acc, ncols_dst, 256><<<block_nums, block_dims, nbytes_shared, stream>>>
+            mul_mat_vec<T, type_acc, ncols_dst, 256><<<block_nums, block_dims, smem, stream>>>
                 (x, y, ids, dst, ncols/2, nchannels_y, stride_row, stride_col_y/2, stride_col_dst,
                  channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
                  sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
@@ -218,7 +220,7 @@ static void launch_mul_mat_vec_f_cuda(
 }
 
 template <typename T, typename type_acc>
-static void mul_mat_vec_f_cuda_switch_ncols_dst(
+static void mul_mat_vec_cuda_switch_ncols_dst(
         const T * x, const float * y, const int32_t * ids, float * dst,
         const int64_t ncols, const int64_t nrows, const int64_t ncols_dst,
         const int64_t stride_row, const int64_t stride_col_y, const int64_t stride_col_dst,
@@ -228,49 +230,49 @@ static void mul_mat_vec_f_cuda_switch_ncols_dst(
         cudaStream_t stream) {
     switch (ncols_dst) {
         case 1:
-            launch_mul_mat_vec_f_cuda<T, type_acc, 1>
+            launch_mul_mat_vec_cuda<T, type_acc, 1>
                 (x, y, ids, dst, ncols, nrows, stride_row, stride_col_y, stride_col_dst,
                  nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y,
                  stride_channel_dst, nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
             break;
         case 2:
-            launch_mul_mat_vec_f_cuda<T, type_acc, 2>
+            launch_mul_mat_vec_cuda<T, type_acc, 2>
                 (x, y, ids, dst, ncols, nrows, stride_row, stride_col_y, stride_col_dst,
                  nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y,
                  stride_channel_dst, nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
             break;
         case 3:
-            launch_mul_mat_vec_f_cuda<T, type_acc, 3>
+            launch_mul_mat_vec_cuda<T, type_acc, 3>
                 (x, y, ids, dst, ncols, nrows, stride_row, stride_col_y, stride_col_dst,
                  nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y,
                  stride_channel_dst, nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
             break;
         case 4:
-            launch_mul_mat_vec_f_cuda<T, type_acc, 4>
+            launch_mul_mat_vec_cuda<T, type_acc, 4>
                 (x, y, ids, dst, ncols, nrows, stride_row, stride_col_y, stride_col_dst,
                  nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y,
                  stride_channel_dst, nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
             break;
         case 5:
-            launch_mul_mat_vec_f_cuda<T, type_acc, 5>
+            launch_mul_mat_vec_cuda<T, type_acc, 5>
                 (x, y, ids, dst, ncols, nrows, stride_row, stride_col_y, stride_col_dst,
                  nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y,
                  stride_channel_dst, nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
             break;
         case 6:
-            launch_mul_mat_vec_f_cuda<T, type_acc, 6>
+            launch_mul_mat_vec_cuda<T, type_acc, 6>
                 (x, y, ids, dst, ncols, nrows, stride_row, stride_col_y, stride_col_dst,
                  nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y,
                  stride_channel_dst, nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
             break;
         case 7:
-            launch_mul_mat_vec_f_cuda<T, type_acc, 7>
+            launch_mul_mat_vec_cuda<T, type_acc, 7>
                 (x, y, ids, dst, ncols, nrows, stride_row, stride_col_y, stride_col_dst,
                  nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y,
                  stride_channel_dst, nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
             break;
         case 8:
-            launch_mul_mat_vec_f_cuda<T, type_acc, 8>
+            launch_mul_mat_vec_cuda<T, type_acc, 8>
                 (x, y, ids, dst, ncols, nrows, stride_row, stride_col_y, stride_col_dst,
                  nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y,
                  stride_channel_dst, nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
@@ -282,7 +284,7 @@ static void mul_mat_vec_f_cuda_switch_ncols_dst(
 }
 
 template<typename T>
-static void mul_mat_vec_f_cuda(
+static void mul_mat_vec_cuda(
         const T * x, const float * y, const int32_t * ids, float * dst,
         const int64_t ncols, const int64_t nrows, const int64_t ncols_dst,
         const int64_t stride_row, const int64_t stride_col_y, const int stride_col_dst,
@@ -290,22 +292,22 @@ static void mul_mat_vec_f_cuda(
         const int64_t stride_channel_x, const int64_t stride_channel_y, const int64_t stride_channel_dst, const int64_t nsamples_x,
         const int64_t nsamples_dst, const int64_t stride_sample_x, const int64_t stride_sample_y, const int64_t stride_sample_dst,
         enum ggml_prec prec, cudaStream_t stream) {
-    if constexpr(std::is_same_v<T, half>) {
+    if constexpr(std::is_same<T, half>::value) {
         if (prec == GGML_PREC_DEFAULT) {
-            mul_mat_vec_f_cuda_switch_ncols_dst<T, half>
+            mul_mat_vec_cuda_switch_ncols_dst<T, half>
                 (x, y, ids, dst, ncols, nrows, ncols_dst, stride_row, stride_col_y, stride_col_dst,
                  nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y,
                  stride_channel_dst, nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
             return;
         }
     }
-    mul_mat_vec_f_cuda_switch_ncols_dst<T, float>
+    mul_mat_vec_cuda_switch_ncols_dst<T, float>
         (x, y, ids, dst, ncols, nrows, ncols_dst, stride_row, stride_col_y, stride_col_dst,
          nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y,
          stride_channel_dst, nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, stream);
 }
 
-void ggml_cuda_mul_mat_vec_f(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * ids, ggml_tensor * dst) {
+void ggml_cuda_mul_mat_vec(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * ids, ggml_tensor * dst) {
     GGML_ASSERT(        src1->type == GGML_TYPE_F32);
     GGML_ASSERT(!ids ||  ids->type == GGML_TYPE_I32);
     GGML_ASSERT(         dst->type == GGML_TYPE_F32);
@@ -353,19 +355,19 @@ void ggml_cuda_mul_mat_vec_f(ggml_backend_cuda_context & ctx, const ggml_tensor
     switch (src0->type) {
         case GGML_TYPE_F32: {
             const float * src0_d = (const float *) src0->data;
-            mul_mat_vec_f_cuda(src0_d, src1_d, ids_d, dst_d, ne00, ne01, ncols_dst, s01, s11, s1,
+            mul_mat_vec_cuda(src0_d, src1_d, ids_d, dst_d, ne00, ne01, ncols_dst, s01, s11, s1,
                 ne02, nchannels_y, nchannels_dst, s02, stride_channel_y, stride_channel_dst,
                 ne03,              ne3,           s03, s13,              s3,                 prec, ctx.stream());
         } break;
         case GGML_TYPE_F16: {
             const half * src0_d = (const half *) src0->data;
-            mul_mat_vec_f_cuda(src0_d, src1_d, ids_d, dst_d, ne00, ne01, ncols_dst, s01, s11, s1,
+            mul_mat_vec_cuda(src0_d, src1_d, ids_d, dst_d, ne00, ne01, ncols_dst, s01, s11, s1,
                 ne02, nchannels_y, nchannels_dst, s02, stride_channel_y, stride_channel_dst,
                 ne03,              ne3,           s03, s13,              s3,                 prec, ctx.stream());
         } break;
         case GGML_TYPE_BF16: {
             const nv_bfloat16 * src0_d = (const nv_bfloat16 *) src0->data;
-            mul_mat_vec_f_cuda(src0_d, src1_d, ids_d, dst_d, ne00, ne01, ncols_dst, s01, s11, s1,
+            mul_mat_vec_cuda(src0_d, src1_d, ids_d, dst_d, ne00, ne01, ncols_dst, s01, s11, s1,
                 ne02, nchannels_y, nchannels_dst, s02, stride_channel_y, stride_channel_dst,
                 ne03,              ne3,           s03, s13,              s3,                 prec, ctx.stream());
         } break;
@@ -374,7 +376,7 @@ void ggml_cuda_mul_mat_vec_f(ggml_backend_cuda_context & ctx, const ggml_tensor
     }
 }
 
-void ggml_cuda_op_mul_mat_vec_f(
+void ggml_cuda_op_mul_mat_vec(
     ggml_backend_cuda_context & ctx,
     const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const char * src0_dd_i, const float * src1_ddf_i,
     const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols,
@@ -412,19 +414,19 @@ void ggml_cuda_op_mul_mat_vec_f(
     switch (src0->type) {
         case GGML_TYPE_F32: {
             const float * src0_d = (const float *) src0_dd_i;
-            mul_mat_vec_f_cuda(src0_d, src1_ddf_i, nullptr, dst_dd_i, ne00, row_diff, src1_ncols, stride_row, stride_col_y, stride_col_dst,
+            mul_mat_vec_cuda(src0_d, src1_ddf_i, nullptr, dst_dd_i, ne00, row_diff, src1_ncols, stride_row, 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, prec, stream);
         } break;
         case GGML_TYPE_F16: {
             const half * src0_d = (const half *) src0_dd_i;
-            mul_mat_vec_f_cuda(src0_d, src1_ddf_i, nullptr, dst_dd_i, ne00, row_diff, src1_ncols, stride_row, stride_col_y, stride_col_dst,
+            mul_mat_vec_cuda(src0_d, src1_ddf_i, nullptr, dst_dd_i, ne00, row_diff, src1_ncols, stride_row, 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, prec, stream);
         } break;
         case GGML_TYPE_BF16: {
             const nv_bfloat16 * src0_d = (const nv_bfloat16 *) src0_dd_i;
-            mul_mat_vec_f_cuda(src0_d, src1_ddf_i, nullptr, dst_dd_i, ne00, row_diff, src1_ncols, stride_row, stride_col_y, stride_col_dst,
+            mul_mat_vec_cuda(src0_d, src1_ddf_i, nullptr, dst_dd_i, ne00, row_diff, src1_ncols, stride_row, 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, prec, stream);
         } break;
@@ -440,15 +442,15 @@ void ggml_cuda_op_mul_mat_vec_f(
     GGML_UNUSED(src1_padded_row_size);
 }
 
-bool ggml_cuda_should_use_mmvf(enum ggml_type type, int cc, const int64_t * src0_ne, int64_t ne11) {
+bool ggml_cuda_should_use_mmv(enum ggml_type type, int cc, const int64_t * src0_ne, int64_t ne11) {
     if (src0_ne[0] % 2 != 0) {
         return false;
     }
     switch (type) {
         case GGML_TYPE_F32:
             if (GGML_CUDA_CC_IS_NVIDIA(cc)) {
-                if (ampere_mma_available(cc)) {
-                    return ne11 <= 3;
+                if (cc >= GGML_CUDA_CC_ADA_LOVELACE) {
+                    return ne11 <= 8;
                 }
                 if (cc >= GGML_CUDA_CC_TURING) {
                     return ne11 <= 4;
@@ -464,9 +466,6 @@ bool ggml_cuda_should_use_mmvf(enum ggml_type type, int cc, const int64_t * src0
         case GGML_TYPE_F16:
             if (GGML_CUDA_CC_IS_NVIDIA(cc)) {
                 const bool src0_small = (src0_ne[1] <= 512 || src0_ne[2]*src0_ne[3] == 1);
-                if (ampere_mma_available(cc)) {
-                    return src0_small && ne11 == 1;
-                }
                 if (cc >= GGML_CUDA_CC_ADA_LOVELACE) {
                     return src0_small && ne11 <= 4;
                 }
@@ -487,9 +486,6 @@ bool ggml_cuda_should_use_mmvf(enum ggml_type type, int cc, const int64_t * src0
         case GGML_TYPE_BF16:
             if (GGML_CUDA_CC_IS_NVIDIA(cc)) {
                 const bool src0_small = (src0_ne[1] <= 512 || src0_ne[2]*src0_ne[3] == 1);
-                if (ampere_mma_available(cc)) {
-                    return src0_small && ne11 == 1;
-                }
                 if (cc >= GGML_CUDA_CC_ADA_LOVELACE) {
                     return src0_small && ne11 <= 4;
                 }

ggml/src/ggml-cuda/mmv.cuh

@@ -1,11 +1,11 @@
 #include "common.cuh"
 
-void ggml_cuda_mul_mat_vec_f(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * ids, ggml_tensor * dst);
+void ggml_cuda_mul_mat_vec(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * ids, ggml_tensor * dst);
 
-void ggml_cuda_op_mul_mat_vec_f(
+void ggml_cuda_op_mul_mat_vec(
     ggml_backend_cuda_context & ctx,
     const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const char * src0_dd_i, const float * src1_ddf_i,
     const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols,
     const int64_t src1_padded_row_size, cudaStream_t stream);
 
-bool ggml_cuda_should_use_mmvf(enum ggml_type type, int cc, const int64_t * src0_ne, int64_t ne11);
+bool ggml_cuda_should_use_mmv(enum ggml_type type, int cc, const int64_t * src0_ne, int64_t ne11);

ggml/src/ggml-cuda/mmvq.cu

@@ -13,7 +13,6 @@ static constexpr __device__ vec_dot_q_cuda_t get_vec_dot_q_cuda(ggml_type type)
         case GGML_TYPE_Q5_0:    return vec_dot_q5_0_q8_1;
         case GGML_TYPE_Q5_1:    return vec_dot_q5_1_q8_1;
         case GGML_TYPE_Q8_0:    return vec_dot_q8_0_q8_1;
-        case GGML_TYPE_MXFP4:   return vec_dot_mxfp4_q8_1;
         case GGML_TYPE_Q2_K:    return vec_dot_q2_K_q8_1;
         case GGML_TYPE_Q3_K:    return vec_dot_q3_K_q8_1;
         case GGML_TYPE_Q4_K:    return vec_dot_q4_K_q8_1;
@@ -39,7 +38,6 @@ static constexpr __device__ int get_vdr_mmvq(ggml_type type) {
         case GGML_TYPE_Q5_0:    return VDR_Q5_0_Q8_1_MMVQ;
         case GGML_TYPE_Q5_1:    return VDR_Q5_1_Q8_1_MMVQ;
         case GGML_TYPE_Q8_0:    return VDR_Q8_0_Q8_1_MMVQ;
-        case GGML_TYPE_MXFP4:   return VDR_MXFP4_Q8_1_MMVQ;
         case GGML_TYPE_Q2_K:    return VDR_Q2_K_Q8_1_MMVQ;
         case GGML_TYPE_Q3_K:    return VDR_Q3_K_Q8_1_MMVQ;
         case GGML_TYPE_Q4_K:    return VDR_Q4_K_Q8_1_MMVQ;
@@ -386,13 +384,6 @@ static void mul_mat_vec_q_switch_type(
                  nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst,
                  stream);
             break;
-        case GGML_TYPE_MXFP4:
-            mul_mat_vec_q_switch_ncols_dst<GGML_TYPE_MXFP4>
-                (vx, vy, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, stride_col_y, stride_col_dst,
-                 nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
-                 nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst,
-                 stream);
-            break;
         case GGML_TYPE_Q2_K:
             mul_mat_vec_q_switch_ncols_dst<GGML_TYPE_Q2_K>
                 (vx, vy, ids, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, stride_col_y, stride_col_dst,

ggml/src/ggml-cuda/roll.cu

@@ -1,67 +0,0 @@
-#include "ggml-cuda/common.cuh"
-#include "roll.cuh"
-
-static __forceinline__ __device__ int64_t wrap_index(const int64_t idx, const int64_t ne) {
-    if (idx < 0) {
-        return idx + ne;
-    }
-    if (idx >= ne) {
-        return idx - ne;
-    }
-    return idx;
-}
-
-static __global__ void roll_f32_cuda(const float * __restrict__ src,
-                                     float * __restrict__ dst,
-                                     const int64_t ne00,
-                                     const int64_t ne01,
-                                     const int64_t ne02,
-                                     const int64_t ne03,
-                                     const int     s0,
-                                     const int     s1,
-                                     const int     s2,
-                                     const int     s3) {
-    const int64_t idx        = int64_t(blockDim.x) * blockIdx.x + threadIdx.x;
-    const int64_t n_elements = ne00 * ne01 * ne02 * ne03;
-
-    if (idx >= n_elements) {
-        return;
-    }
-
-    const int64_t i0 = idx % ne00;
-    const int64_t i1 = (idx / ne00) % ne01;
-    const int64_t i2 = (idx / (ne00 * ne01)) % ne02;
-    const int64_t i3 = (idx / (ne00 * ne01 * ne02)) % ne03;
-
-    const int64_t d0 = wrap_index(i0 - s0, ne00);
-    const int64_t d1 = wrap_index(i1 - s1, ne01);
-    const int64_t d2 = wrap_index(i2 - s2, ne02);
-    const int64_t d3 = wrap_index(i3 - s3, ne03);
-
-    dst[i3 * (ne00 * ne01 * ne02) + i2 * (ne01 * ne00) + i1 * ne00 + i0] =
-        src[d3 * (ne00 * ne01 * ne02) + d2 * (ne01 * ne00) + d1 * ne00 + d0];
-}
-
-void ggml_cuda_op_roll(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
-    int s0 = dst->op_params[0];
-    int s1 = dst->op_params[1];
-    int s2 = dst->op_params[2];
-    int s3 = dst->op_params[3];
-
-    const ggml_tensor * src0   = dst->src[0];
-    const float *       src0_d = (const float *) dst->src[0]->data;
-    float *             dst_d  = (float *) dst->data;
-
-    GGML_TENSOR_UNARY_OP_LOCALS;
-
-    GGML_ASSERT(dst->src[0]->type == GGML_TYPE_F32);
-    GGML_ASSERT(ggml_are_same_shape(dst->src[0], dst));
-
-    cudaStream_t stream = ctx.stream();
-
-    int64_t sz         = (ne00 * ne01 * ne02 * ne03);
-    int64_t num_blocks = (sz + CUDA_ROLL_BLOCK_SIZE - 1) / CUDA_ROLL_BLOCK_SIZE;
-
-    roll_f32_cuda<<<num_blocks, CUDA_ROLL_BLOCK_SIZE, 0, stream>>>(
-        src0_d, dst_d, ne00, ne01, ne02, ne03, s0, s1, s2, s3);
-}

ggml/src/ggml-cuda/roll.cuh

@@ -1,5 +0,0 @@
-#include "common.cuh"
-
-#define CUDA_ROLL_BLOCK_SIZE 256
-
-void ggml_cuda_op_roll(ggml_backend_cuda_context & ctx, ggml_tensor * dst);

ggml/src/ggml-cuda/softcap.cu

@@ -1,34 +0,0 @@
-#include "softcap.cuh"
-
-static __global__ void softcap_f32(const float * x, float * dst, const float scale, const float softcap, const int k) {
-    const int i = blockDim.x*blockIdx.x + threadIdx.x;
-
-    if (i >= k) {
-        return;
-    }
-
-    dst[i] = tanhf(scale * x[i]) * softcap;
-}
-
-static void softcap_f32_cuda(const float * x, float * dst, const float scale, const float softcap, const int k, cudaStream_t stream) {
-    const int num_blocks = (k + CUDA_SOFTCAP_BLOCK_SIZE - 1) / CUDA_SOFTCAP_BLOCK_SIZE;
-    softcap_f32<<<num_blocks, CUDA_SOFTCAP_BLOCK_SIZE, 0, stream>>>(x, dst, scale, softcap, k);
-}
-
-// fused GGML_OP_SCALE + GGML_UNARY_OP_TANH + GGML_OP_SCALE
-void ggml_cuda_op_softcap(ggml_backend_cuda_context & ctx, ggml_tensor * dst, ggml_tensor * src) {
-    const ggml_tensor * src0 = src->src[0];
-    const float * src0_d = (const float *)src0->data;
-    float * dst_d = (float *)dst->data;
-    cudaStream_t stream = ctx.stream();
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT( dst->type == GGML_TYPE_F32);
-
-    float scale;
-    float softcap;
-    memcpy(&scale,   (float *) src->op_params + 0, sizeof(float));
-    memcpy(&softcap, (float *) dst->op_params + 0, sizeof(float));
-
-    softcap_f32_cuda(src0_d, dst_d, scale, softcap, ggml_nelements(src0), stream);
-}

ggml/src/ggml-cuda/softcap.cuh

@@ -1,5 +0,0 @@
-#include "common.cuh"
-
-#define CUDA_SOFTCAP_BLOCK_SIZE 256
-
-void ggml_cuda_op_softcap(ggml_backend_cuda_context & ctx, ggml_tensor * dst, ggml_tensor * src);

ggml/src/ggml-cuda/softmax.cu

@@ -45,7 +45,7 @@ struct soft_max_params {
 #endif // __clang__
 template <bool use_shared, int ncols_template, int block_size_template, typename T>
 static __global__ void soft_max_f32(
-        const float * x, const T * mask, const float * sinks, float * dst, const soft_max_params p) {
+        const float * x, const T * mask, float * dst, const soft_max_params p) {
     const int ncols = ncols_template == 0 ? p.ncols : ncols_template;
 
     const int tid  = threadIdx.x;
@@ -77,7 +77,7 @@ static __global__ void soft_max_f32(
     // shared memory buffer to cache values between iterations:
     float * vals = use_shared ? buf_iw + WARP_SIZE : dst;
 
-    float max_val = sinks ? sinks[i02] : -INFINITY;
+    float max_val = -INFINITY;
 
 #pragma unroll
     for (int col0 = 0; col0 < ncols; col0 += block_size) {
@@ -143,10 +143,6 @@ static __global__ void soft_max_f32(
         tmp = warp_reduce_sum(tmp);
     }
 
-    if (sinks) {
-        tmp += expf(sinks[i02] - max_val);
-    }
-
     const float inv_sum = 1.0f / tmp;
 
 #pragma unroll
@@ -187,7 +183,7 @@ static __global__ void soft_max_back_f32(
 }
 
 template<int... Ns, typename T>
-static void launch_soft_max_kernels(const float * x, const T * mask, const float * sinks, float * dst,
+static void launch_soft_max_kernels(const float * x, const T * mask, float * dst,
                              const soft_max_params & p, cudaStream_t stream, dim3 block_dims, dim3 block_nums, size_t nbytes_shared)
 {
     const int id       = ggml_cuda_get_device();
@@ -200,7 +196,7 @@ static void launch_soft_max_kernels(const float * x, const T * mask, const float
         if (p.ncols == ncols) {
             CUDA_SET_SHARED_MEMORY_LIMIT((soft_max_f32<true, ncols, block, T>), smpbo);
             soft_max_f32<true, ncols, block><<<block_nums, block_dims, nbytes_shared, stream>>>
-                (x, mask, sinks, dst, p);
+                (x, mask, dst, p);
             return true;
         }
         return false;
@@ -213,12 +209,12 @@ static void launch_soft_max_kernels(const float * x, const T * mask, const float
 
     //default case
     CUDA_SET_SHARED_MEMORY_LIMIT((soft_max_f32<true, 0, 0, T>), smpbo);
-    soft_max_f32<true, 0, 0><<<block_nums, block_dims, nbytes_shared, stream>>>(x, mask, sinks, dst, p);
+    soft_max_f32<true, 0, 0><<<block_nums, block_dims, nbytes_shared, stream>>>(x, mask, dst, p);
 }
 
 
 template<typename T>
-static void soft_max_f32_cuda(const float * x, const T * mask, const float * sinks, float * dst, const soft_max_params & params, cudaStream_t stream) {
+static void soft_max_f32_cuda(const float * x, const T * mask, float * dst, const soft_max_params & params, cudaStream_t stream) {
     int nth = WARP_SIZE;
     const int64_t ncols_x = params.ncols;
 
@@ -234,10 +230,10 @@ static void soft_max_f32_cuda(const float * x, const T * mask, const float * sin
 
 
     if (nbytes_shared <= smpbo) {
-        launch_soft_max_kernels<32, 64, 128, 256, 512, 1024, 2048, 4096>(x, mask, sinks, dst, params, stream, block_dims, block_nums, nbytes_shared);
+        launch_soft_max_kernels<32, 64, 128, 256, 512, 1024, 2048, 4096>(x, mask, dst, params, stream, block_dims, block_nums, nbytes_shared);
     } else {
         const size_t nbytes_shared_low = WARP_SIZE*sizeof(float);
-        soft_max_f32<false, 0, 0><<<block_nums, block_dims, nbytes_shared_low, stream>>>(x, mask, sinks, dst, params);
+        soft_max_f32<false, 0, 0><<<block_nums, block_dims, nbytes_shared_low, stream>>>(x, mask, dst, params);
     }
 }
 
@@ -253,11 +249,9 @@ static void soft_max_back_f32_cuda(
 void ggml_cuda_op_soft_max(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
     const ggml_tensor * src0 = dst->src[0];
     const ggml_tensor * src1 = dst->src[1];
-    const ggml_tensor * src2 = dst->src[2];
 
     const float * src0_d = (const float *) src0->data;
     const void  * src1_d = src1 ? (const void *) src1->data : nullptr;
-    const void  * src2_d = src2 ? (const void *) src2->data : nullptr;
     float       *  dst_d = (float *) dst->data;
 
     cudaStream_t stream = ctx.stream();
@@ -315,9 +309,9 @@ void ggml_cuda_op_soft_max(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
     params.m1 = m1;
 
     if (use_f16) {
-        soft_max_f32_cuda(src0_d, (const half  *) src1_d, (const float *) src2_d, dst_d, params, stream);
+        soft_max_f32_cuda(src0_d, (const half  *) src1_d, dst_d, params, stream);
     } else {
-        soft_max_f32_cuda(src0_d, (const float *) src1_d, (const float *) src2_d, dst_d, params, stream);
+        soft_max_f32_cuda(src0_d, (const float *) src1_d, dst_d, params, stream);
     }
 }
 

ggml/src/ggml-cuda/template-instances/mmq-instance-mxfp4.cu

@@ -1,5 +0,0 @@
-// This file has been autogenerated by generate_cu_files.py, do not edit manually.
-
-#include "../mmq.cuh"
-
-DECL_MMQ_CASE(GGML_TYPE_MXFP4);

ggml/src/ggml-cuda/unary.cu

@@ -300,81 +300,6 @@ void ggml_cuda_op_geglu_quick(ggml_backend_cuda_context & ctx, ggml_tensor * dst
     ggml_cuda_op_unary_gated<op_gelu_quick>(ctx, dst);
 }
 
-// swiglu_oai
-
-template <typename T>
-static __global__ void swiglu_oai_kernel(const T * x, const T * g, T * dst, const int64_t k, const int64_t n, const int64_t o0, const int64_t o1, float alpha, float limit) {
-    const int64_t i = int64_t(blockDim.x)*blockIdx.x + threadIdx.x;
-
-    if (i >= k) {
-        return;
-    }
-
-    // perform base op and multiply with gate (either offset in same tensor or a separate one)
-    const int64_t j0 = (i / n) * o0 + (i % n);
-    const int64_t j1 = o0 == o1 ? j0 : (i / n) * o1 + (i % n);
-
-    float xi = x[j0];
-    float gi = g[j1];
-    xi = fminf(xi, limit);
-    gi = fmaxf(fminf(gi, limit), -limit);
-
-    float out_glu = xi / (1.0f + expf(-xi * alpha));
-    out_glu = out_glu * (1.0f + gi);
-
-    dst[i] = out_glu;
-}
-
-template <typename T>
-static void swiglu_oai_cuda(const T * x, const T * g, T * dst, const int64_t k, const int64_t n, const int64_t o0, const int64_t o1, const float alpha, const float limit, cudaStream_t stream) {
-    const int64_t num_blocks = (k + CUDA_GLU_BLOCK_SIZE - 1) / CUDA_GLU_BLOCK_SIZE;
-    swiglu_oai_kernel<<<num_blocks, CUDA_GLU_BLOCK_SIZE, 0, stream>>>(x, g, dst, k, n, o0, o1, alpha, limit);
-}
-
-void ggml_cuda_op_swiglu_oai(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
-    const ggml_tensor * src0 = dst->src[0];
-    const ggml_tensor * src1 = dst->src[1];
-    void * src0_d = src0->data;
-    void * src1_d = src1 ? src1->data : src0->data;
-    const int64_t src0_o = src0->nb[1];
-    const int64_t src1_o = src1 ? src1->nb[1] : src0->nb[1];
-    void * dst_d = dst->data;
-    const int64_t nc = src1 ? src0->ne[0] : src0->ne[0] / 2;
-    cudaStream_t stream = ctx.stream();
-
-    GGML_ASSERT(ggml_is_contiguous_1(src0));
-    GGML_ASSERT(src0->nb[0] == ggml_element_size(src0));
-    GGML_ASSERT(ggml_is_contiguous(dst));
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT( dst->type == GGML_TYPE_F32);
-    GGML_ASSERT(src0->type == dst->type);
-    GGML_ASSERT(dst->ne[0] == nc);
-    GGML_ASSERT(ggml_nrows(dst) == ggml_nrows(src0));
-
-    if (src1) {
-        GGML_ASSERT(ggml_is_contiguous_1(src1));
-        GGML_ASSERT(src1->nb[0] == ggml_element_size(src1));
-        GGML_ASSERT(src1->ne[0] == nc);
-        GGML_ASSERT(src0->type == src1->type);
-    }
-
-    //const int32_t swapped = ((const int32_t *) dst->op_params)[1];
-    const int32_t swapped = ggml_get_op_params_i32(dst, 1);
-    const float alpha = ggml_get_op_params_f32(dst, 2);
-    const float limit = ggml_get_op_params_f32(dst, 3);
-
-    float * src0_p = (float *) src0_d;
-    float * src1_p = (float *) src1_d;
-
-    if (!src1) {
-        src0_p += swapped ? nc : 0;
-        src1_p += swapped ? 0 : nc;
-    }
-
-    swiglu_oai_cuda(src0_p, src1_p, (float *)dst_d, ggml_nelements(dst), nc, src0_o / sizeof(float), src1_o / sizeof(float), alpha, limit, stream);
-}
-
 /* silu_back */
 
 static __device__ __forceinline__ float op_silu_back(float grad, float x) {

ggml/src/ggml-cuda/unary.cuh

@@ -67,8 +67,6 @@ void ggml_cuda_op_geglu(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
 
 void ggml_cuda_op_swiglu(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
 
-void ggml_cuda_op_swiglu_oai(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
-
 void ggml_cuda_op_geglu_erf(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
 
 void ggml_cuda_op_geglu_quick(ggml_backend_cuda_context & ctx, ggml_tensor * dst);

ggml/src/ggml-cuda/vecdotq.cuh

@@ -1,20 +1,8 @@
 #pragma once
 
 #include "common.cuh"
-
 #include <cstdint>
 
-static __device__ __forceinline__ int get_int_b1(const void * x, const int & i32) {
-    const uint8_t * x8 = (const uint8_t *) x;
-
-    int x32  = x8[4*i32 + 0] <<  0;
-    x32     |= x8[4*i32 + 1] <<  8;
-    x32     |= x8[4*i32 + 2] << 16;
-    x32     |= x8[4*i32 + 3] << 24;
-
-    return x32;
-}
-
 static __device__ __forceinline__ int get_int_b2(const void * x, const int & i32) {
     const uint16_t * x16 = (const uint16_t *) x; // assume at least 2 byte alignment
 
@@ -28,20 +16,6 @@ static __device__ __forceinline__ int get_int_b4(const void * x, const int & i32
     return ((const int *) x)[i32]; // assume at least 4 byte alignment
 }
 
-static __device__ __forceinline__ int2 get_int_from_table_16(const int & q4, const int8_t * table) {
-    const int      q0_32  = (q4 >> 0) & 0x0F0F0F0F;
-    const int8_t * q0_8   = (const int8_t *) &q0_32;
-    const char4    val0_8 = make_char4(
-        table[q0_8[0]], table[q0_8[1]], table[q0_8[2]], table[q0_8[3]]);
-
-    const int      q1_32  = (q4 >> 4) & 0x0F0F0F0F;
-    const int8_t * q1_8   = (const int8_t *) &q1_32;
-    const char4    val1_8 = make_char4(
-        table[q1_8[0]], table[q1_8[1]], table[q1_8[2]], table[q1_8[3]]);
-
-    return make_int2(*((const int *) &val0_8), *((const int *) &val1_8));
-}
-
 // VDR = vec dot ratio, how many contiguous integers each thread processes when the vec dot kernel is called
 // MMVQ = mul_mat_vec_q, MMQ = mul_mat_q
 
@@ -237,30 +211,6 @@ template <int vdr> static __device__ __forceinline__ float vec_dot_q8_0_16_q8_1_
     return d8_1*sumf;
 }
 
-#define VDR_MXFP4_Q8_1_MMVQ 2
-#define VDR_MXFP4_Q8_1_MMQ  4
-
-static __device__ __forceinline__ float vec_dot_mxfp4_q8_1(
-    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs) {
-
-    const block_mxfp4 * bq4 = (const block_mxfp4 *) vbq + kbx;
-
-    const int * q8 = (const int *) bq8_1->qs + iqs;
-
-    int sumi = 0;
-#pragma unroll
-    for (int l = 0; l < VDR_MXFP4_Q8_1_MMVQ; ++l) {
-        const int aux_q4 = get_int_b1(bq4->qs, iqs + l);
-        const int2 v = get_int_from_table_16(aux_q4, kvalues_mxfp4);
-
-        sumi = ggml_cuda_dp4a(v.x, q8[l + 0], sumi);
-        sumi = ggml_cuda_dp4a(v.y, q8[l + 4], sumi);
-    }
-
-    const float d = ggml_cuda_e8m0_to_fp32(bq4->e) * 0.5f * __low2float(bq8_1->ds);
-    return d * sumi;
-}
-
 #define VDR_Q2_K_Q8_1_MMVQ 1
 #define VDR_Q2_K_Q8_1_MMQ  4
 
@@ -1118,6 +1068,20 @@ static __device__ __forceinline__ float vec_dot_iq1_m_q8_1(
     return d * ((sumi[0] + sumf[0]) * sc0 + (sumi[1] + sumf[1]) * sc1);
 }
 
+static __device__ __forceinline__ int2 get_int_from_table_16(const int & q4) {
+    const int      q0_32  = (q4 >> 0) & 0x0F0F0F0F;
+    const int8_t * q0_8   = (const int8_t *) &q0_32;
+    const char4    val0_8 = make_char4(
+        kvalues_iq4nl[q0_8[0]], kvalues_iq4nl[q0_8[1]], kvalues_iq4nl[q0_8[2]], kvalues_iq4nl[q0_8[3]]);
+
+    const int      q1_32  = (q4 >> 4) & 0x0F0F0F0F;
+    const int8_t * q1_8   = (const int8_t *) &q1_32;
+    const char4    val1_8 = make_char4(
+        kvalues_iq4nl[q1_8[0]], kvalues_iq4nl[q1_8[1]], kvalues_iq4nl[q1_8[2]], kvalues_iq4nl[q1_8[3]]);
+
+    return make_int2(*((const int *) &val0_8), *((const int *) &val1_8));
+}
+
 #define VDR_IQ4_NL_Q8_1_MMVQ 2
 #define VDR_IQ4_NL_Q8_1_MMQ  4
 
@@ -1132,7 +1096,7 @@ static __device__ __forceinline__ float vec_dot_iq4_nl_q8_1(
 #pragma unroll
     for (int l = 0; l < VDR_Q4_0_Q8_1_MMVQ; ++l) {
         const int aux_q4 = get_int_b2(bq4->qs, iqs + l);
-        const int2 v = get_int_from_table_16(aux_q4, kvalues_iq4nl);
+        const int2 v = get_int_from_table_16(aux_q4);
 
         sumi = ggml_cuda_dp4a(v.x, q8[l + 0], sumi);
         sumi = ggml_cuda_dp4a(v.y, q8[l + 4], sumi);
@@ -1154,7 +1118,7 @@ static __device__ __forceinline__ float vec_dot_iq4_xs_q8_1(
 #pragma unroll
     for (int j = 0; j < 4; ++j) {
         const int aux_q4 = get_int_b4(bq4->qs, iqs + j);
-        const int2 v = get_int_from_table_16(aux_q4, kvalues_iq4nl);
+        const int2 v = get_int_from_table_16(aux_q4);
 
         const int u0 = get_int_b4(bq8_1[iqs/4].qs, j + 0);
         const int u1 = get_int_b4(bq8_1[iqs/4].qs, j + 4);

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

@@ -6,10 +6,6 @@
 #include <cuda_bf16.h>
 #include <cuda_fp16.h>
 
-#if CUDART_VERSION >= 12050
-#include <cuda_fp8.h>
-#endif // CUDART_VERSION >= 12050
-
 #if CUDART_VERSION < 11020
 #define CU_DEVICE_ATTRIBUTE_VIRTUAL_MEMORY_MANAGEMENT_SUPPORTED CU_DEVICE_ATTRIBUTE_VIRTUAL_ADDRESS_MANAGEMENT_SUPPORTED
 #define CUBLAS_TF32_TENSOR_OP_MATH CUBLAS_TENSOR_OP_MATH

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

@@ -5,8 +5,10 @@
 #include <hipblas/hipblas.h>
 #include <hip/hip_fp16.h>
 #include <hip/hip_bfloat16.h>
+#ifdef __HIP_PLATFORM_AMD__
 // for rocblas_initialize()
 #include "rocblas/rocblas.h"
+#endif // __HIP_PLATFORM_AMD__
 
 #define CUBLAS_GEMM_DEFAULT HIPBLAS_GEMM_DEFAULT
 #define CUBLAS_GEMM_DEFAULT_TENSOR_OP HIPBLAS_GEMM_DEFAULT
@@ -137,7 +139,7 @@
 #define CUBLAS_STATUS_INTERNAL_ERROR HIPBLAS_STATUS_INTERNAL_ERROR
 #define CUBLAS_STATUS_NOT_SUPPORTED HIPBLAS_STATUS_NOT_SUPPORTED
 
-#if HIP_VERSION >= 70000000
+#if defined(__HIP_PLATFORM_AMD__) && HIP_VERSION >= 70000000
 #define CUBLAS_COMPUTE_16F HIPBLAS_COMPUTE_16F
 #define CUBLAS_COMPUTE_32F HIPBLAS_COMPUTE_32F
 #define CUBLAS_COMPUTE_32F_FAST_16F HIPBLAS_COMPUTE_32F_FAST_16F
@@ -149,11 +151,7 @@
 #define CUBLAS_COMPUTE_32F_FAST_16F HIPBLAS_R_32F
 #define cublasComputeType_t hipblasDatatype_t
 #define cudaDataType_t hipblasDatatype_t
-#endif // HIP_VERSION >= 7000000
-
-#if !defined(__HIP_PLATFORM_AMD__)
-#error "The HIP backend supports only AMD targets"
-#endif // !defined(__HIP_PLATFORM_AMD__)
+#endif
 
 #define __CUDA_ARCH__ 1300
 
@@ -200,7 +198,6 @@
 #endif
 
 typedef hip_bfloat16 nv_bfloat16;
-typedef short2 nv_bfloat162; // FIXME there is no 2x BF16 type being defined in bfloat16.h, ad-hoc compilation fix
 
 typedef int8_t int8x4_t __attribute__((ext_vector_type(4)));
 typedef uint8_t uint8x4_t __attribute__((ext_vector_type(4)));
@@ -252,7 +249,7 @@ static __device__ __forceinline__ unsigned int __vcmpne4(unsigned int a, unsigne
     return c;
 }
 
-#if HIP_VERSION < 50600000
+#if defined(__HIP_PLATFORM_AMD__) && HIP_VERSION < 50600000
 // __shfl_xor() for half2 was added in ROCm 5.6
 static __device__ __forceinline__ half2 __shfl_xor(half2 var, int laneMask, int width) {
     typedef union half2_b32 {
@@ -264,4 +261,4 @@ static __device__ __forceinline__ half2 __shfl_xor(half2 var, int laneMask, int
     tmp.b32 = __shfl_xor(tmp.b32, laneMask, width);
     return tmp.val;
 }
-#endif // HIP_VERSION < 50600000
+#endif // defined(__HIP_PLATFORM_AMD__) && HIP_VERSION < 50600000

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

@@ -137,5 +137,4 @@
 #define cudaStreamEndCapture musaStreamEndCapture
 #define cudaOccupancyMaxActiveBlocksPerMultiprocessor musaOccupancyMaxActiveBlocksPerMultiprocessor
 
-typedef __mt_bfloat16 nv_bfloat16;
-typedef __mt_bfloat162 nv_bfloat162;
+typedef mt_bfloat16 nv_bfloat16;

ggml/src/ggml-hip/CMakeLists.txt

@@ -113,18 +113,10 @@ if (GGML_HIP_ROCWMMA_FATTN)
     add_compile_definitions(GGML_HIP_ROCWMMA_FATTN)
 endif()
 
-if (NOT GGML_HIP_MMQ_MFMA)
-    add_compile_definitions(GGML_HIP_NO_MMQ_MFMA)
-endif()
-
 if (GGML_HIP_FORCE_ROCWMMA_FATTN_GFX12 OR ${hip_VERSION} VERSION_GREATER_EQUAL 7.0)
     add_compile_definitions(GGML_HIP_ROCWMMA_FATTN_GFX12)
 endif()
 
-if (GGML_HIP_EXPORT_METRICS)
-    set(CMAKE_HIP_FLAGS "${CMAKE_HIP_FLAGS} -Rpass-analysis=kernel-resource-usage --save-temps")
-endif()
-
 if (NOT GGML_CUDA_FA)
     add_compile_definitions(GGML_CUDA_NO_FA)
 endif()