ggml : add Flash Attention (#5021 )

* ggml : add ggml_flash_attn_ext API * ggml : fix GQA support in ggml_flash_attn_ext * ggml : online attention (CPU) * metal : initial implementation * metal : f16 precision * metal : reduce branches * metal : specialize for head size * wip : 8 rows per simd group * wip : 4 rows per simd group * wip : template for rows per warp * metal : parallelize across KV size * metal : parallel reduce across heads * metal : efficient flash_attn_f16 implementation * metal : avoid redundant loads of the attention * metal : scale and mask in matrix form * metal : fix comment * llama : avoid ggml_cast, use F32 query * metal : add parallel reduce version (disabled) * metal : move output into local memory + optimize - the result from each simdgroup now stays in the registers - significantly reduced SRAM usage - more efficient skipping of -INF blocks - avoid simdgroup barrier in hot loop - add comments * metal : add tests, fix scaling, support C > 32 * metal : improve precision * ggml : fix f16 mad * metal : minor * metal : support Q > 8 * tests : add ATTN tests * metal : disable buffer allocation logs * tests : more * metal : faster inner loop for C == 32 * metal : fix array initialization * tests : ifdef * ggml : switch to padded F16 mask for ggml_soft_max, ggml_flash_attn_ext * ggml : fix ggml_soft_max mask requirement * cuda : fix soft_max to use correct mask size * cuda : add flash_attn kernel (wip) * metal : optimize softmax for C > 32 * metal : optimize softmax * tests : minor fix * cuda : avoid zeroing fragments * tests : update dims * cuda : fix __hisinf() result check * cuda : avoid warp_reduce for smax * cuda : use int instead of int64_t Noticeably improves performance (thanks to Johannes) * cuda : make loops use the same loop values Thanks Johannes again for the tip * cuda : unroll some of the loops * cuda : avoid __hisinf branches * cuda : use half2 in softmax * cuda : switch to 1 warp for bs > 16 * cuda : speed-up reduce part of the kernel * cuda : unroll Q*K^T loop * cuda : fix -INF block check * cuda : simplify softmax * cuda : fix matrix names * cuda : minor * llama : adapt to F16 KQ_pos * llama : adapt new models to F16 KQ_mask * ggml : fix F16 store (ARM NEON) * llama : fix type of KQ_mask and KQ_pos * ggml : fix CPU soft_max * tests : add hs=256 * cuda : fix build * metal : improve perf via smaller int registers * cuda : adapt soft_max to F16 mask and pos * CUDA: faster FlashAttention, kernel for bs == 1 * 16 cols for Phi-2 * no vec for hs, no hs==256 ncols==32 for Volta * adjust kernel selection logic * 4 warps, 256 stride for all D * no ncols == 64 * Multiple parallel blocks for batch size 1 * fix compile warnings * fix excessive KQ_b loads * fix cmake build * fix KV cache padding, NaN from INFINITY (#6438) * llama : flash_attn cparam + fix defrag * server: support flash_attn param * server: bench: enable flash_attn param * CUDA: refactor host code, dyn. par. blocks * fix flash_attn_vec_f16 race condition * flush softmax exp below threshold to 0 * store temp KQ in registers * Calculate KQ as FP32 if KQV has GGML_PREC_F32 * Add __hgt2_mask implementation for CUDA 11 * fix KQ FP32 precision fpr parallel_blocks > 1 * llama-bench : add -fa,--flash-attn arg * metal : add BS=1 kernel for flash attention (#6508) * metal : add BS=1 kernel for flash attention (wip) * metal : support more than 1 warps * metal : opts * metal : opt * metal : switch to parallel reduce * metal : reduce registers * metal : simplify * metal : initial FA vec kernel * metal : use F32 attention accumulators * batched-bench : add fattn arg * llama : simplify llama_build_kv_store ggml-ci * llama : adapt build_olmo to changes * ggml : fix arm fp16 store on windows * metal : clean-up * metal : clean-up kernel code * metal : minor * tests : remove benchmarks ggml-ci * ggml : fix avx512 const correctness ggml-ci * ggml : fix soft_max with bias on CPU ggml-ci * common : print --flash-attn in help * ggml : fix num dimensions in ggml_flash_attn_ext * llama : force disable flash attention for incompatible models * ggml : ggml_soft_max support F16/F32 mask/pos ggml-ci * cuda : uint -> uint32_t * cuda : "constexpr dim3" -> "const dim3" ggml-ci * cuda : try to fix __hgt2_mask ggml-ci * ggml : add TODO's for F16/F32 mask/pos support in other backends * llama : replace bool need_kq_pos with use_alibi * llama : prep ALiBi support for BERT models ggml-ci * llama : fix n_batch requirements ggml-ci * cont * server : add help for --flash-attn arg * llama : disable FA for AMD * tests : remove TMP_ATTN_BENCH ggml-ci * llama : support save/load state with FA enabled ggml-ci * ci : add CUDA save-load-state tests ggml-ci * llama : llama_kv_cache_clear zeroes data + fix save-load seq ggml-ci * llama : fix copy-paste errors, add TODO * llama : disallow incompatible states * llama : update llama_state_get_size after v_trans field * metal : remove tmp log * llama : add static reminder for llama_state_get_size * metal : fix max nsg ggml-ci * ci : fix arg order ggml-ci --------- Co-authored-by: Johannes Gäßler <johannesg@5d6.de> Co-authored-by: Pierrick HYMBERT <pierrick.hymbert@gmail.com>
convert : use utf8 encoding (#7000 )
2026-07-11 06:55:54 +02:00 · 2024-04-30 12:16:08 +03:00 · 2024-04-30 11:05:25 +03:00 · 2024-04-30 00:52:50 +01:00 · 2024-04-29 14:40:14 -04:00 · 2024-04-29 13:56:59 -04:00
155 changed files with 11801 additions and 11044 deletions
@@ -10,14 +10,12 @@ WORKDIR /app

 COPY . .

-RUN mkdir build && \
-    cd build && \
-    if [ "${LLAMA_SYCL_F16}" = "ON" ]; then \
+RUN if [ "${LLAMA_SYCL_F16}" = "ON" ]; then \
        echo "LLAMA_SYCL_F16 is set" && \
        export OPT_SYCL_F16="-DLLAMA_SYCL_F16=ON"; \
    fi && \
-    cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx ${OPT_SYCL_F16} && \
-    cmake --build . --config Release --target main
+    cmake -B build -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx ${OPT_SYCL_F16} && \
+    cmake --build build --config Release --target main

 FROM intel/oneapi-basekit:$ONEAPI_VERSION as runtime

@@ -14,10 +14,8 @@ RUN wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key
 # Build it
 WORKDIR /app
 COPY . .
-RUN mkdir build && \
-    cd build && \
-    cmake .. -DLLAMA_VULKAN=1 && \
-    cmake --build . --config Release --target main
+RUN cmake -B build -DLLAMA_VULKAN=1 && \
+    cmake --build build --config Release --target main

 # Clean up
 WORKDIR /
@@ -10,14 +10,12 @@ WORKDIR /app

 COPY . .

-RUN mkdir build && \
-    cd build && \
-    if [ "${LLAMA_SYCL_F16}" = "ON" ]; then \
+RUN if [ "${LLAMA_SYCL_F16}" = "ON" ]; then \
        echo "LLAMA_SYCL_F16 is set" && \
        export OPT_SYCL_F16="-DLLAMA_SYCL_F16=ON"; \
    fi && \
-    cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_CURL=ON ${OPT_SYCL_F16} && \
-    cmake --build . --config Release --target server
+    cmake -B build -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_CURL=ON ${OPT_SYCL_F16} && \
+    cmake --build build --config Release --target server

 FROM intel/oneapi-basekit:$ONEAPI_VERSION as runtime

@@ -18,10 +18,8 @@ RUN apt-get update && \
 # Build it
 WORKDIR /app
 COPY . .
-RUN mkdir build && \
-    cd build && \
-    cmake .. -DLLAMA_VULKAN=1 -DLLAMA_CURL=1 && \
-    cmake --build . --config Release --target server
+RUN cmake -B build -DLLAMA_VULKAN=1 -DLLAMA_CURL=1 && \
+    cmake --build build --config Release --target server

 # Clean up
 WORKDIR /
@@ -32,7 +32,7 @@ on:
    -  cron: '04 2 * * *'

 concurrency:
-  group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}-${{ github.event.inputs.sha }}
+  group: ${{ github.workflow }}-${{ github.ref }}-${{ github.head_ref || github.run_id }}-${{ github.event.inputs.sha }}
  cancel-in-progress: true

 jobs:
@@ -96,9 +96,7 @@ jobs:
        id: cmake_build
        run: |
          set -eux
-          mkdir build
-          cd build
-          cmake .. \
+          cmake -B build \
              -DLLAMA_NATIVE=OFF \
              -DLLAMA_BUILD_SERVER=ON \
              -DLLAMA_CURL=ON \
@@ -109,7 +107,7 @@ jobs:
              -DLLAMA_FATAL_WARNINGS=OFF \
              -DLLAMA_ALL_WARNINGS=OFF \
              -DCMAKE_BUILD_TYPE=Release;
-          cmake --build . --config Release -j $(nproc) --target server
+          cmake --build build --config Release -j $(nproc) --target server

      - name: Download the dataset
        id: download_dataset
@@ -32,6 +32,8 @@ jobs:
      - name: Clone
        id: checkout
        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0

      - name: Dependencies
        id: depends
@@ -88,6 +90,8 @@ jobs:
      - name: Clone
        id: checkout
        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0

      - name: Dependencies
        id: depends
@@ -206,6 +210,8 @@ jobs:
      - name: Clone
        id: checkout
        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0

      - name: Dependencies
        id: depends
@@ -238,6 +244,33 @@ jobs:
          ./bin/convert-llama2c-to-ggml --copy-vocab-from-model ./tok512.bin --llama2c-model stories260K.bin --llama2c-output-model stories260K.gguf
          ./bin/main -m stories260K.gguf -p "One day, Lily met a Shoggoth" -n 500 -c 256

+      - name: Determine tag name
+        id: tag
+        shell: bash
+        run: |
+          BUILD_NUMBER="$(git rev-list --count HEAD)"
+          SHORT_HASH="$(git rev-parse --short=7 HEAD)"
+          if [[ "${{ env.BRANCH_NAME }}" == "master" ]]; then
+            echo "name=b${BUILD_NUMBER}" >> $GITHUB_OUTPUT
+          else
+            SAFE_NAME=$(echo "${{ env.BRANCH_NAME }}" | tr '/' '-')
+            echo "name=${SAFE_NAME}-b${BUILD_NUMBER}-${SHORT_HASH}" >> $GITHUB_OUTPUT
+          fi
+
+      - name: Pack artifacts
+        id: pack_artifacts
+        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
+        run: |
+          cp LICENSE ./build/bin/
+          zip -r llama-${{ steps.tag.outputs.name }}-bin-ubuntu-x64.zip ./build/bin/*
+
+      - name: Upload artifacts
+        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
+        uses: actions/upload-artifact@v4
+        with:
+          path: llama-${{ steps.tag.outputs.name }}-bin-ubuntu-x64.zip
+          name: llama-bin-ubuntu-x64.zip
+
 #  ubuntu-latest-cmake-sanitizer:
 #    runs-on: ubuntu-latest
 #
@@ -560,6 +593,63 @@ jobs:
        run: |
            make swift

+  windows-msys2:
+    runs-on: windows-latest
+
+    strategy:
+      fail-fast: false
+      matrix:
+        include:
+          - { sys: UCRT64,  env: ucrt-x86_64,  build: Release }
+          - { sys: CLANG64, env: clang-x86_64, build: Release }
+
+    steps:
+      - name: Clone
+        uses: actions/checkout@v4
+
+      - name: Setup ${{ matrix.sys }}
+        uses: msys2/setup-msys2@v2
+        with:
+          update: true
+          msystem: ${{matrix.sys}}
+          install: >-
+            base-devel
+            mingw-w64-${{matrix.env}}-toolchain
+            mingw-w64-${{matrix.env}}-cmake
+            mingw-w64-${{matrix.env}}-openblas
+
+      - name: Build using make
+        shell: msys2 {0}
+        run: |
+            make -j $(nproc)
+
+      - name: Clean after building using make
+        shell: msys2 {0}
+        run: |
+            make clean
+
+      - name: Build using make w/ OpenBLAS
+        shell: msys2 {0}
+        run: |
+            make LLAMA_OPENBLAS=1 -j $(nproc)
+
+      - name: Build using CMake
+        shell: msys2 {0}
+        run: |
+            cmake -B build
+            cmake --build build --config ${{ matrix.build }} -j $(nproc)
+
+      - name: Clean after building using CMake
+        shell: msys2 {0}
+        run: |
+            rm -rf build
+
+      - name: Build using CMake w/ OpenBLAS
+        shell: msys2 {0}
+        run: |
+            cmake -B build -DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS
+            cmake --build build --config ${{ matrix.build }} -j $(nproc)
+
  windows-latest-cmake:
    runs-on: windows-latest

@@ -21,4 +21,4 @@ jobs:
        uses: py-actions/flake8@v2
        with:
            ignore: "E203,E211,E221,E225,E231,E241,E251,E261,E266,E501,E701,E704,W503"
-            exclude: "examples/*,examples/*/**,*/**/__init__.py"
+            exclude: "examples/*,examples/*/**,*/**/__init__.py,convert-hf-to-gguf-update.py"
@@ -23,7 +23,7 @@ on:
    -  cron: '2 4 * * *'

 concurrency:
-  group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
+  group: ${{ github.workflow }}-${{ github.ref }}-${{ github.head_ref || github.run_id }}
  cancel-in-progress: true

 jobs:
@@ -41,23 +41,16 @@ jobs:
            sanitizer: ""
      fail-fast: false # While -DLLAMA_SANITIZE_THREAD=ON is broken

-    container:
-      image: ubuntu:latest
-      ports:
-        - 8888
-      options: --cpus 4
-
    steps:
      - name: Dependencies
        id: depends
        run: |
-          apt-get update
-          apt-get -y install \
+          sudo apt-get update
+          sudo apt-get -y install \
            build-essential \
            xxd \
            git \
            cmake \
-            python3-pip \
            curl \
            wget \
            language-pack-en \
@@ -70,6 +63,17 @@ jobs:
          fetch-depth: 0
          ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }}

+      - name: Python setup
+        id: setup_python
+        uses: actions/setup-python@v5
+        with:
+          python-version: '3.11'
+
+      - name: Tests dependencies
+        id: test_dependencies
+        run: |
+          pip install -r examples/server/tests/requirements.txt
+
      - name: Verify server deps
        id: verify_server_deps
        run: |
@@ -90,20 +94,14 @@ jobs:
      - name: Build
        id: cmake_build
        run: |
-          mkdir build
-          cd build
-          cmake .. \
+          cmake -B build \
              -DLLAMA_NATIVE=OFF \
              -DLLAMA_BUILD_SERVER=ON \
              -DLLAMA_CURL=ON \
              -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \
              -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON ;
-          cmake --build . --config ${{ matrix.build_type }} -j $(nproc) --target server
+          cmake --build build --config ${{ matrix.build_type }} -j $(nproc) --target server

-      - name: Tests dependencies
-        id: test_dependencies
-        run: |
-          pip install -r examples/server/tests/requirements.txt

      - name: Tests
        id: server_integration_tests
@@ -129,6 +127,7 @@ jobs:
        uses: actions/checkout@v4
        with:
          fetch-depth: 0
+          ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }}

      - name: libCURL
        id: get_libcurl
@@ -142,10 +141,8 @@ jobs:
      - name: Build
        id: cmake_build
        run: |
-          mkdir build
-          cd build
-          cmake .. -DLLAMA_CURL=ON -DCURL_LIBRARY="$env:RUNNER_TEMP/libcurl/lib/libcurl.dll.a" -DCURL_INCLUDE_DIR="$env:RUNNER_TEMP/libcurl/include"
-          cmake --build . --config Release -j ${env:NUMBER_OF_PROCESSORS} --target server
+          cmake -B build -DLLAMA_CURL=ON -DCURL_LIBRARY="$env:RUNNER_TEMP/libcurl/lib/libcurl.dll.a" -DCURL_INCLUDE_DIR="$env:RUNNER_TEMP/libcurl/include"
+          cmake --build build --config Release -j ${env:NUMBER_OF_PROCESSORS} --target server

      - name: Python setup
        id: setup_python
@@ -2,6 +2,7 @@
 *.a
 *.so
 *.gguf
+*.gguf.json
 *.bin
 *.exe
 *.dll
@@ -34,6 +35,7 @@ lcov-report/
 gcovr-report/

 build*
+!build.zig
 cmake-build-*
 out/
 tmp/
@@ -100,7 +102,25 @@ qnt-*.txt
 perf-*.txt

 examples/jeopardy/results.txt
+examples/server/*.html.hpp
+examples/server/*.js.hpp
+examples/server/*.mjs.hpp

 poetry.lock
 poetry.toml
 nppBackup
+
+# Test binaries
+/tests/test-grammar-parser
+/tests/test-llama-grammar
+/tests/test-double-float
+/tests/test-grad0
+/tests/test-opt
+/tests/test-quantize-fns
+/tests/test-quantize-perf
+/tests/test-sampling
+/tests/test-tokenizer-0
+/tests/test-tokenizer-1-spm
+/tests/test-tokenizer-1-bpe
+/tests/test-rope
+/tests/test-backend-ops
@@ -43,6 +43,8 @@ else()
    set(LLAMA_METAL_DEFAULT OFF)
 endif()

+set(LLAMA_LLAMAFILE_DEFAULT ON)
+
 # general
 option(BUILD_SHARED_LIBS                "build shared libraries"                                OFF)
 option(LLAMA_STATIC                     "llama: static link libraries"                          OFF)
@@ -88,6 +90,7 @@ endif()
 # 3rd party libs
 option(LLAMA_ACCELERATE                      "llama: enable Accelerate framework"               ON)
 option(LLAMA_BLAS                            "llama: use BLAS"                                  OFF)
+option(LLAMA_LLAMAFILE                       "llama: use llamafile SGEMM"                       ${LLAMA_LLAMAFILE_DEFAULT})
 set(LLAMA_BLAS_VENDOR "Generic" CACHE STRING "llama: BLAS library vendor")
 option(LLAMA_CUDA                            "llama: use CUDA"                                  OFF)
 option(LLAMA_CUBLAS                          "llama: use CUDA (deprecated, use LLAMA_CUDA)"     OFF)
@@ -286,6 +289,7 @@ if (LLAMA_METAL)
        ${METALKIT_FRAMEWORK}
        )
 endif()
+
 if (LLAMA_BLAS)
    if (LLAMA_STATIC)
        set(BLA_STATIC ON)
@@ -368,6 +372,13 @@ if (LLAMA_BLAS)
    endif()
 endif()

+if (LLAMA_LLAMAFILE)
+    add_compile_definitions(GGML_USE_LLAMAFILE)
+
+    set(GGML_HEADERS_LLAMAFILE sgemm.h)
+    set(GGML_SOURCES_LLAMAFILE sgemm.cpp)
+endif()
+
 if (LLAMA_QKK_64)
    add_compile_definitions(GGML_QKK_64)
 endif()
@@ -1151,17 +1162,16 @@ add_library(ggml OBJECT
            ggml-backend.h
            ggml-quants.c
            ggml-quants.h
-            sgemm.cpp
-            sgemm.h
-            ${GGML_SOURCES_CUDA}    ${GGML_HEADERS_CUDA}
-            ${GGML_SOURCES_OPENCL}  ${GGML_HEADERS_OPENCL}
-            ${GGML_SOURCES_METAL}   ${GGML_HEADERS_METAL}
-            ${GGML_SOURCES_MPI}     ${GGML_HEADERS_MPI}
-            ${GGML_SOURCES_EXTRA}   ${GGML_HEADERS_EXTRA}
-            ${GGML_SOURCES_SYCL}    ${GGML_HEADERS_SYCL}
-            ${GGML_SOURCES_KOMPUTE} ${GGML_HEADERS_KOMPUTE}
-            ${GGML_SOURCES_VULKAN}  ${GGML_HEADERS_VULKAN}
-            ${GGML_SOURCES_ROCM}    ${GGML_HEADERS_ROCM}
+            ${GGML_SOURCES_CUDA}      ${GGML_HEADERS_CUDA}
+            ${GGML_SOURCES_OPENCL}    ${GGML_HEADERS_OPENCL}
+            ${GGML_SOURCES_METAL}     ${GGML_HEADERS_METAL}
+            ${GGML_SOURCES_MPI}       ${GGML_HEADERS_MPI}
+            ${GGML_SOURCES_EXTRA}     ${GGML_HEADERS_EXTRA}
+            ${GGML_SOURCES_SYCL}      ${GGML_HEADERS_SYCL}
+            ${GGML_SOURCES_KOMPUTE}   ${GGML_HEADERS_KOMPUTE}
+            ${GGML_SOURCES_VULKAN}    ${GGML_HEADERS_VULKAN}
+            ${GGML_SOURCES_ROCM}      ${GGML_HEADERS_ROCM}
+            ${GGML_SOURCES_LLAMAFILE} ${GGML_HEADERS_LLAMAFILE}
            )

 target_include_directories(ggml PUBLIC . ${LLAMA_EXTRA_INCLUDES})
@@ -6,11 +6,23 @@ BUILD_TARGETS = \

 # Binaries only useful for tests
 TEST_TARGETS = \
-	tests/test-llama-grammar tests/test-grammar-parser tests/test-double-float tests/test-grad0 tests/test-opt \
-	tests/test-quantize-fns tests/test-quantize-perf tests/test-sampling tests/test-tokenizer-0-llama          \
-	tests/test-tokenizer-0-falcon tests/test-tokenizer-1-llama tests/test-tokenizer-1-bpe tests/test-rope      \
-	tests/test-backend-ops tests/test-model-load-cancel tests/test-autorelease                                 \
-	tests/test-json-schema-to-grammar tests/test-grammar-integration
+	tests/test-autorelease \
+	tests/test-backend-ops \
+	tests/test-double-float \
+	tests/test-grad0 \
+	tests/test-grammar-integration \
+	tests/test-grammar-parser \
+	tests/test-json-schema-to-grammar \
+	tests/test-llama-grammar \
+	tests/test-model-load-cancel \
+	tests/test-opt \
+	tests/test-quantize-fns \
+	tests/test-quantize-perf \
+	tests/test-rope \
+	tests/test-sampling \
+	tests/test-tokenizer-0 \
+	tests/test-tokenizer-1-bpe \
+	tests/test-tokenizer-1-spm

 # Code coverage output files
 COV_TARGETS = *.gcno tests/*.gcno *.gcda tests/*.gcda *.gcov tests/*.gcov lcov-report gcovr-report
@@ -27,6 +39,17 @@ ifndef UNAME_M
 UNAME_M := $(shell uname -m)
 endif

+# In GNU make default CXX is g++ instead of c++.  Let's fix that so that users
+# of non-gcc compilers don't have to provide g++ alias or wrapper.
+DEFCC  := cc
+DEFCXX := c++
+ifeq ($(origin CC),default)
+CC  := $(DEFCC)
+endif
+ifeq ($(origin CXX),default)
+CXX := $(DEFCXX)
+endif
+
 # Mac OS + Arm can report x86_64
 # ref: https://github.com/ggerganov/whisper.cpp/issues/66#issuecomment-1282546789
 ifeq ($(UNAME_S),Darwin)
@@ -49,11 +72,17 @@ default: $(BUILD_TARGETS)
 test: $(TEST_TARGETS)
 	@failures=0; \
 	for test_target in $(TEST_TARGETS); do \
-		if [ "$$test_target" = "tests/test-tokenizer-0-llama" ]; then \
-			./$$test_target $(CURDIR)/models/ggml-vocab-llama.gguf; \
-		elif [ "$$test_target" = "tests/test-tokenizer-0-falcon" ]; then \
+		if [ "$$test_target" = "tests/test-tokenizer-0" ]; then \
+			./$$test_target $(CURDIR)/models/ggml-vocab-llama-spm.gguf; \
+			./$$test_target $(CURDIR)/models/ggml-vocab-llama-bpe.gguf; \
+			./$$test_target $(CURDIR)/models/ggml-vocab-phi-3.gguf; \
 			./$$test_target $(CURDIR)/models/ggml-vocab-falcon.gguf; \
-		elif [ "$$test_target" = "tests/test-tokenizer-1-llama" ]; then \
+			./$$test_target $(CURDIR)/models/ggml-vocab-deepseek-coder.gguf; \
+			./$$test_target $(CURDIR)/models/ggml-vocab-deepseek-llm.gguf; \
+			./$$test_target $(CURDIR)/models/ggml-vocab-bert-bge.gguf; \
+			./$$test_target $(CURDIR)/models/ggml-vocab-starcoder.gguf; \
+			./$$test_target $(CURDIR)/models/ggml-vocab-gpt-2.gguf; \
+		elif [ "$$test_target" = "tests/test-tokenizer-1-spm" ]; then \
 			continue; \
 		elif [ "$$test_target" = "tests/test-tokenizer-1-bpe" ]; then \
 			continue; \
@@ -219,11 +248,6 @@ ifdef LLAMA_DISABLE_LOGS
 	MK_CPPFLAGS += -DLOG_DISABLE_LOGS
 endif # LLAMA_DISABLE_LOGS

-# disable ggml.c's use of sgemm.cpp
-ifdef LLAMA_NO_LLAMAFILE
-	MK_CPPFLAGS += -DGGML_USE_LLAMAFILE=0
-endif
-
 # warnings
 WARN_FLAGS    = -Wall -Wextra -Wpedantic -Wcast-qual -Wno-unused-function
 MK_CFLAGS    += $(WARN_FLAGS) -Wshadow -Wstrict-prototypes -Wpointer-arith -Wmissing-prototypes -Werror=implicit-int \
@@ -389,6 +413,11 @@ ifdef LLAMA_OPENBLAS
 	MK_LDFLAGS  += $(shell pkg-config --libs openblas)
 endif # LLAMA_OPENBLAS

+ifndef LLAMA_NO_LLAMAFILE
+	MK_CPPFLAGS += -DGGML_USE_LLAMAFILE
+	OBJS        += sgemm.o
+endif
+
 ifdef LLAMA_BLIS
 	MK_CPPFLAGS += -DGGML_USE_OPENBLAS -I/usr/local/include/blis -I/usr/include/blis
 	MK_LDFLAGS  += -lblis -L/usr/local/lib
@@ -485,11 +514,9 @@ ggml-cuda/%.o: ggml-cuda/%.cu ggml-cuda/%.cuh ggml.h ggml-common.h ggml-cuda/com

 ggml-cuda.o: ggml-cuda.cu ggml-cuda.h ggml.h ggml-backend.h ggml-backend-impl.h ggml-common.h $(wildcard ggml-cuda/*.cuh)
 	$(NVCC_COMPILE)
-
 endif # LLAMA_CUDA

 ifdef LLAMA_CLBLAST
-
 	MK_CPPFLAGS += -DGGML_USE_CLBLAST $(shell pkg-config --cflags-only-I clblast OpenCL)
 	MK_CFLAGS   += $(shell pkg-config --cflags-only-other clblast OpenCL)
 	MK_CXXFLAGS += $(shell pkg-config --cflags-only-other clblast OpenCL)
@@ -608,6 +635,11 @@ ggml-mpi.o: ggml-mpi.c ggml-mpi.h
 	$(CC) $(CFLAGS) -c $< -o $@
 endif # LLAMA_MPI

+ifndef LLAMA_NO_LLAMAFILE
+sgemm.o: sgemm.cpp sgemm.h ggml.h
+	$(CXX) $(CXXFLAGS) -c $< -o $@
+endif
+
 GF_CC := $(CC)
 include scripts/get-flags.mk

@@ -681,21 +713,18 @@ ggml-backend.o: ggml-backend.c ggml.h ggml-backend.h
 ggml-quants.o: ggml-quants.c ggml.h ggml-quants.h ggml-common.h
 	$(CC) $(CFLAGS)    -c $< -o $@

-sgemm.o: sgemm.cpp sgemm.h ggml.h
-	$(CXX) $(CXXFLAGS) -c $< -o $@
-
 unicode.o: unicode.cpp unicode.h
 	$(CXX) $(CXXFLAGS) -c $< -o $@

 unicode-data.o: unicode-data.cpp unicode-data.h
 	$(CXX) $(CXXFLAGS) -c $< -o $@

-OBJS += ggml-alloc.o ggml-backend.o ggml-quants.o unicode.o unicode-data.o sgemm.o
+OBJS += ggml-alloc.o ggml-backend.o ggml-quants.o unicode.o unicode-data.o

 llama.o: llama.cpp unicode.h ggml.h ggml-alloc.h ggml-backend.h ggml-cuda.h ggml-metal.h llama.h
 	$(CXX) $(CXXFLAGS) -c $< -o $@

-COMMON_H_DEPS = common/common.h common/sampling.h common/log.h
+COMMON_H_DEPS = common/common.h common/sampling.h common/log.h llama.h
 COMMON_DEPS   = common.o sampling.o grammar-parser.o build-info.o json-schema-to-grammar.o

 common.o: common/common.cpp $(COMMON_H_DEPS)
@@ -768,7 +797,7 @@ batched-bench: examples/batched-bench/batched-bench.cpp       build-info.o ggml.
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)

-quantize: examples/quantize/quantize.cpp                      build-info.o ggml.o llama.o $(OBJS)
+quantize: examples/quantize/quantize.cpp                      ggml.o llama.o $(COMMON_DEPS) $(OBJS)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)

@@ -796,10 +825,19 @@ save-load-state: examples/save-load-state/save-load-state.cpp ggml.o llama.o $(C
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)

-server: examples/server/server.cpp examples/server/utils.hpp examples/server/httplib.h common/json.hpp examples/server/index.html.hpp examples/server/index.js.hpp examples/server/completion.js.hpp common/stb_image.h ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS)
+server: examples/server/server.cpp examples/server/utils.hpp examples/server/httplib.h common/json.hpp examples/server/index.html.hpp examples/server/index.js.hpp examples/server/completion.js.hpp examples/server/json-schema-to-grammar.mjs.hpp common/stb_image.h ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h %.hpp $<,$^) -Iexamples/server $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) $(LWINSOCK2)

+# Portable equivalent of `cd examples/server/public && xxd -i $(notdir $<) ../$(notdir $<).hpp`:
+examples/server/%.hpp: examples/server/public/% Makefile
+	@( export NAME=$(subst .,_,$(subst -,_,$(notdir $<))) && \
+		echo "unsigned char $${NAME}[] = {" && \
+		cat $< | od -v -t x1 -An | sed -E 's/([0-9a-fA-F]+)/0x\1, /g' && \
+		echo "};" && \
+		echo "unsigned int $${NAME}_len = $(shell cat $< | wc -c );" \
+	) > $@
+
 gguf: examples/gguf/gguf.cpp ggml.o $(OBJS)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
@@ -962,11 +1000,7 @@ tests/test-sampling: tests/test-sampling.cpp ggml.o llama.o $(OBJS)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)

-tests/test-tokenizer-0-falcon: tests/test-tokenizer-0-falcon.cpp ggml.o llama.o $(COMMON_DEPS) console.o $(OBJS)
-	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
-
-tests/test-tokenizer-0-llama: tests/test-tokenizer-0-llama.cpp ggml.o llama.o $(COMMON_DEPS) console.o $(OBJS)
+tests/test-tokenizer-0: tests/test-tokenizer-0.cpp ggml.o llama.o $(COMMON_DEPS) console.o $(OBJS)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)

@@ -974,7 +1008,7 @@ tests/test-tokenizer-1-bpe: tests/test-tokenizer-1-bpe.cpp ggml.o llama.o $(COMM
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)

-tests/test-tokenizer-1-llama: tests/test-tokenizer-1-llama.cpp ggml.o llama.o $(COMMON_DEPS) console.o $(OBJS)
+tests/test-tokenizer-1-spm: tests/test-tokenizer-1-spm.cpp ggml.o llama.o $(COMMON_DEPS) console.o $(OBJS)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)

@@ -185,9 +185,8 @@ Upon a successful installation, SYCL is enabled for the available intel devices,
 ```sh
 git clone https://github.com/oneapi-src/oneMKL
 cd oneMKL
-mkdir -p buildWithCublas && cd buildWithCublas
-cmake ../ -DCMAKE_CXX_COMPILER=icpx -DCMAKE_C_COMPILER=icx -DENABLE_MKLGPU_BACKEND=OFF -DENABLE_MKLCPU_BACKEND=OFF -DENABLE_CUBLAS_BACKEND=ON -DTARGET_DOMAINS=blas
-make
+cmake -B buildWithCublas -DCMAKE_CXX_COMPILER=icpx -DCMAKE_C_COMPILER=icx -DENABLE_MKLGPU_BACKEND=OFF -DENABLE_MKLCPU_BACKEND=OFF -DENABLE_CUBLAS_BACKEND=ON -DTARGET_DOMAINS=blas
+cmake --build buildWithCublas --config Release
 ```


@@ -227,17 +226,15 @@ Similarly, user targeting Nvidia GPUs should expect at least one SYCL-CUDA devic
 source /opt/intel/oneapi/setvars.sh

 # Build LLAMA with MKL BLAS acceleration for intel GPU
-mkdir -p build && cd build

-# Option 1: Use FP16 for better performance in long-prompt  inference
-cmake --build .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_SYCL_F16=ON
-# Or without "--build", run "make" next
+# Option 1: Use FP32 (recommended for better performance in most cases)
+cmake -B build -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx

-# Option 2: Use FP32 by default
-cmake --build .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
+# Option 2: Use FP16
+cmake -B build -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_SYCL_F16=ON

-#build all binary
-cmake --build . --config Release -j -v
+# build all binary
+cmake --build build --config Release -j -v
 ```

 #### Nvidia GPU
@@ -249,16 +246,15 @@ export CPLUS_INCLUDE_DIR=/path/to/oneMKL/buildWithCublas/include:$CPLUS_INCLUDE_
 export CPLUS_INCLUDE_DIR=/path/to/oneMKL/include:$CPLUS_INCLUDE_DIR

 # Build LLAMA with Nvidia BLAS acceleration through SYCL
-mkdir -p build && cd build

-# Option 1: Use FP16 for better performance in long-prompt  inference
-cmake --build .. -DLLAMA_SYCL=ON -DLLAMA_SYCL_TARGET=NVIDIA -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_SYCL_F16=ON
+# Option 1: Use FP32 (recommended for better performance in most cases)
+cmake -B build -DLLAMA_SYCL=ON -DLLAMA_SYCL_TARGET=NVIDIA -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx

-# Option 2: Use FP32 by default
-cmake --build .. -DLLAMA_SYCL=ON -DLLAMA_SYCL_TARGET=NVIDIA -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
+# Option 2: Use FP16
+cmake -B build -DLLAMA_SYCL=ON -DLLAMA_SYCL_TARGET=NVIDIA -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_SYCL_F16=ON

-#build all binary
-cmake --build . --config Release -j -v
+# build all binary
+cmake --build build --config Release -j -v

 ```

@@ -413,13 +409,15 @@ b. Download & install mingw-w64 make for Windows provided by w64devkit
 On the oneAPI command line window, step into the llama.cpp main directory and run the following:

 ```
-mkdir -p build
-cd build
@call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64 --force

-cmake -G "MinGW Makefiles" ..  -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icx  -DCMAKE_BUILD_TYPE=Release -DLLAMA_SYCL_F16=ON
+# Option 1: Use FP32 (recommended for better performance in most cases)
+cmake -B build -G "MinGW Makefiles" -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icx  -DCMAKE_BUILD_TYPE=Release

-make -j
+# Option 2: Or FP16
+cmake -B build -G "MinGW Makefiles" -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icx  -DCMAKE_BUILD_TYPE=Release -DLLAMA_SYCL_F16=ON
+
+cmake --build build --config Release -j
 ```

 Otherwise, run the `win-build-sycl.bat` wrapper which encapsulates the former instructions:
@@ -10,6 +10,7 @@ Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others)

 ### Recent API changes

+- [2024 Apr 21] `llama_token_to_piece` can now optionally render special tokens https://github.com/ggerganov/llama.cpp/pull/6807
 - [2024 Apr 4] State and session file functions reorganized under `llama_state_*` https://github.com/ggerganov/llama.cpp/pull/6341
 - [2024 Mar 26] Logits and embeddings API updated for compactness https://github.com/ggerganov/llama.cpp/pull/6122
 - [2024 Mar 13] Add `llama_synchronize()` + `llama_context_params.n_ubatch` https://github.com/ggerganov/llama.cpp/pull/6017
@@ -19,7 +20,8 @@ Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others)

 ### Hot topics

- **MoE memory layout has been updated - reconvert models for `mmap` support and regenerate `imatrix` https://github.com/ggerganov/llama.cpp/pull/6387**
+- **BPE pre-tokenization support has been added: https://github.com/ggerganov/llama.cpp/pull/6920**
+- MoE memory layout has been updated - reconvert models for `mmap` support and regenerate `imatrix` https://github.com/ggerganov/llama.cpp/pull/6387
 - Model sharding instructions using `gguf-split` https://github.com/ggerganov/llama.cpp/discussions/6404
 - Fix major bug in Metal batched inference https://github.com/ggerganov/llama.cpp/pull/6225
 - Multi-GPU pipeline parallelism support https://github.com/ggerganov/llama.cpp/pull/6017
@@ -92,10 +94,11 @@ Typically finetunes of the base models below are supported as well.

 - [X] LLaMA 🦙
 - [x] LLaMA 2 🦙🦙
+- [x] LLaMA 3 🦙🦙🦙
 - [X] [Mistral 7B](https://huggingface.co/mistralai/Mistral-7B-v0.1)
 - [x] [Mixtral MoE](https://huggingface.co/models?search=mistral-ai/Mixtral)
 - [x] [DBRX](https://huggingface.co/databricks/dbrx-instruct)
- [X] Falcon
+- [X] [Falcon](https://huggingface.co/models?search=tiiuae/falcon)
 - [X] [Chinese LLaMA / Alpaca](https://github.com/ymcui/Chinese-LLaMA-Alpaca) and [Chinese LLaMA-2 / Alpaca-2](https://github.com/ymcui/Chinese-LLaMA-Alpaca-2)
 - [X] [Vigogne (French)](https://github.com/bofenghuang/vigogne)
 - [X] [Koala](https://bair.berkeley.edu/blog/2023/04/03/koala/)
@@ -118,10 +121,12 @@ Typically finetunes of the base models below are supported as well.
 - [x] [CodeShell](https://github.com/WisdomShell/codeshell)
 - [x] [Gemma](https://ai.google.dev/gemma)
 - [x] [Mamba](https://github.com/state-spaces/mamba)
+- [x] [Grok-1](https://huggingface.co/keyfan/grok-1-hf)
 - [x] [Xverse](https://huggingface.co/models?search=xverse)
- [x] [Command-R](https://huggingface.co/CohereForAI/c4ai-command-r-v01)
+- [x] [Command-R models](https://huggingface.co/models?search=CohereForAI/c4ai-command-r)
 - [x] [SEA-LION](https://huggingface.co/models?search=sea-lion)
 - [x] [GritLM-7B](https://huggingface.co/GritLM/GritLM-7B) + [GritLM-8x7B](https://huggingface.co/GritLM/GritLM-8x7B)
+- [x] [OLMo](https://allenai.org/olmo)

 (instructions for supporting more models: [HOWTO-add-model.md](./docs/HOWTO-add-model.md))

@@ -133,6 +138,8 @@ Typically finetunes of the base models below are supported as well.
 - [x] [ShareGPT4V](https://huggingface.co/models?search=Lin-Chen/ShareGPT4V)
 - [x] [MobileVLM 1.7B/3B models](https://huggingface.co/models?search=mobileVLM)
 - [x] [Yi-VL](https://huggingface.co/models?search=Yi-VL)
+- [x] [Mini CPM](https://huggingface.co/models?search=MiniCPM)
+- [x] [Moondream](https://huggingface.co/vikhyatk/moondream2)

 **HTTP server**

@@ -189,6 +196,8 @@ Unless otherwise noted these projects are open-source with permissive licensing:
 - [MindMac](https://mindmac.app) (proprietary)
 - [KodiBot](https://github.com/firatkiral/kodibot) (GPL)
 - [eva](https://github.com/ylsdamxssjxxdd/eva) (MIT)
+- [AI Sublime Text plugin](https://github.com/yaroslavyaroslav/OpenAI-sublime-text) (MIT)
+
 *(to have a project listed here, it should clearly state that it depends on `llama.cpp`)*

 ---
@@ -299,6 +308,8 @@ In order to build llama.cpp you have three different options.
      make
      ```

+      **Note**: for `Debug` builds, run `make LLAMA_DEBUG=1`
+
  - On Windows:

    1. Download the latest fortran version of [w64devkit](https://github.com/skeeto/w64devkit/releases).
@@ -313,12 +324,26 @@ In order to build llama.cpp you have three different options.
 - Using `CMake`:

    ```bash
-    mkdir build
-    cd build
-    cmake ..
-    cmake --build . --config Release
+    cmake -B build
+    cmake --build build --config Release
    ```

+    **Note**: for `Debug` builds, there are two cases:
+
+    - Single-config generators (e.g. default = `Unix Makefiles`; note that they just ignore the `--config` flag):
+
+      ```bash
+      cmake -B build -DCMAKE_BUILD_TYPE=Debug
+      cmake --build build
+      ```
+
+    - Multi-config generators (`-G` param set to Visual Studio, XCode...):
+
+      ```bash
+      cmake -B build -G "Xcode"
+      cmake --build build --config Debug
+      ```
+
 - Using `Zig` (version 0.11 or later):

    Building for optimization levels and CPU features can be accomplished using standard build arguments, for example AVX2, FMA, F16C,
@@ -430,10 +455,8 @@ Building the program with BLAS support may lead to some performance improvements
  - Using `CMake` on Linux:

      ```bash
-      mkdir build
-      cd build
-      cmake .. -DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS
-      cmake --build . --config Release
+      cmake -B build -DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS
+      cmake --build build --config Release
      ```

 - #### BLIS
@@ -453,11 +476,9 @@ Building the program with BLAS support may lead to some performance improvements
  - Using manual oneAPI installation:
    By default, `LLAMA_BLAS_VENDOR` is set to `Generic`, so if you already sourced intel environment script and assign `-DLLAMA_BLAS=ON` in cmake, the mkl version of Blas will automatically been selected. Otherwise please install oneAPI and follow the below steps:
      ```bash
-      mkdir build
-      cd build
      source /opt/intel/oneapi/setvars.sh # You can skip this step if  in oneapi-basekit docker image, only required for manual installation
-      cmake .. -DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=Intel10_64lp -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_NATIVE=ON
-      cmake --build . --config Release
+      cmake -B build -DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=Intel10_64lp -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_NATIVE=ON
+      cmake --build build --config Release
      ```

  - Using oneAPI docker image:
@@ -478,10 +499,8 @@ Building the program with BLAS support may lead to some performance improvements
  - Using `CMake`:

    ```bash
-    mkdir build
-    cd build
-    cmake .. -DLLAMA_CUDA=ON
-    cmake --build . --config Release
+    cmake -B build -DLLAMA_CUDA=ON
+    cmake --build build --config Release
    ```

  The environment variable [`CUDA_VISIBLE_DEVICES`](https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#env-vars) can be used to specify which GPU(s) will be used. The following compilation options are also available to tweak performance:
@@ -508,8 +527,8 @@ Building the program with BLAS support may lead to some performance improvements
  - Using `CMake` for Linux (assuming a gfx1030-compatible AMD GPU):
    ```bash
    CC=/opt/rocm/llvm/bin/clang CXX=/opt/rocm/llvm/bin/clang++ \
-        cmake -H. -Bbuild -DLLAMA_HIPBLAS=ON -DAMDGPU_TARGETS=gfx1030 -DCMAKE_BUILD_TYPE=Release \
-        && cmake --build build -- -j 16
+        cmake -B build -DLLAMA_HIPBLAS=ON -DAMDGPU_TARGETS=gfx1030 -DCMAKE_BUILD_TYPE=Release \
+        && cmake --build build --config Release -- -j 16
    ```
    On Linux it is also possible to use unified memory architecture (UMA) to share main memory between the CPU and integrated GPU by setting `-DLLAMA_HIP_UMA=ON"`.
    However, this hurts performance for non-integrated GPUs (but enables working with integrated GPUs).
@@ -546,7 +565,7 @@ Building the program with BLAS support may lead to some performance improvements
  OpenCL acceleration is provided by the matrix multiplication kernels from the [CLBlast](https://github.com/CNugteren/CLBlast) project and custom kernels for ggml that can generate tokens on the GPU.

  You will need the [OpenCL SDK](https://github.com/KhronosGroup/OpenCL-SDK).
-    - For Ubuntu or Debian, the packages `opencl-headers`, `ocl-icd` may be needed.
+    - For Ubuntu, Debian, and Fedora the packages `opencl-headers`, `ocl-icd` may be needed.

    - For Windows, a pre-built SDK is available on the [OpenCL Releases](https://github.com/KhronosGroup/OpenCL-SDK/releases) page.

@@ -555,15 +574,14 @@ Building the program with BLAS support may lead to some performance improvements

        ```sh
        git clone --recurse-submodules https://github.com/KhronosGroup/OpenCL-SDK.git
-        mkdir OpenCL-SDK/build
-        cd OpenCL-SDK/build
-        cmake .. -DBUILD_DOCS=OFF \
+        cd OpenCL-SDK
+        cmake -B build -DBUILD_DOCS=OFF \
          -DBUILD_EXAMPLES=OFF \
          -DBUILD_TESTING=OFF \
          -DOPENCL_SDK_BUILD_SAMPLES=OFF \
          -DOPENCL_SDK_TEST_SAMPLES=OFF
-        cmake --build . --config Release
-        cmake --install . --prefix /some/path
+        cmake --build build
+        cmake --install build --prefix /some/path
        ```
      </details>

@@ -571,6 +589,12 @@ Building the program with BLAS support may lead to some performance improvements

  Pre-built CLBlast binaries may be found on the [CLBlast Releases](https://github.com/CNugteren/CLBlast/releases) page. For Unix variants, it may also be found in your operating system's packages.

+  Linux packaging:
+  Fedora Linux:
+  ```bash
+  sudo dnf install clblast
+  ```
+
  Alternatively, they may be built from source.

  - <details>
@@ -579,23 +603,23 @@ Building the program with BLAS support may lead to some performance improvements
      ```cmd
      set OPENCL_SDK_ROOT="C:/OpenCL-SDK-v2023.04.17-Win-x64"
      git clone https://github.com/CNugteren/CLBlast.git
-      mkdir CLBlast\build
-      cd CLBlast\build
-      cmake .. -DBUILD_SHARED_LIBS=OFF -DOVERRIDE_MSVC_FLAGS_TO_MT=OFF -DTUNERS=OFF -DOPENCL_ROOT=%OPENCL_SDK_ROOT% -G "Visual Studio 17 2022" -A x64
-      cmake --build . --config Release
-      cmake --install . --prefix C:/CLBlast
+      cd CLBlast
+      cmake -B build -DBUILD_SHARED_LIBS=OFF -DOVERRIDE_MSVC_FLAGS_TO_MT=OFF -DTUNERS=OFF -DOPENCL_ROOT=%OPENCL_SDK_ROOT% -G "Visual Studio 17 2022" -A x64
+      cmake --build build --config Release
+      cmake --install build --prefix C:/CLBlast
      ```

+      (note: `--config Release` at build time is the default and only relevant for Visual Studio builds - or multi-config Ninja builds)
+
  - <details>
    <summary>Unix:</summary>

      ```sh
      git clone https://github.com/CNugteren/CLBlast.git
-      mkdir CLBlast/build
-      cd CLBlast/build
-      cmake .. -DBUILD_SHARED_LIBS=OFF -DTUNERS=OFF
-      cmake --build . --config Release
-      cmake --install . --prefix /some/path
+      cd CLBlast
+      cmake -B build -DBUILD_SHARED_LIBS=OFF -DTUNERS=OFF
+      cmake --build build --config Release
+      cmake --install build --prefix /some/path
      ```

      Where `/some/path` is where the built library will be installed (default is `/usr/local`).
@@ -609,21 +633,17 @@ Building the program with BLAS support may lead to some performance improvements
    ```
  - CMake (Unix):
    ```sh
-    mkdir build
-    cd build
-    cmake .. -DLLAMA_CLBLAST=ON -DCLBlast_DIR=/some/path
-    cmake --build . --config Release
+    cmake -B build -DLLAMA_CLBLAST=ON -DCLBlast_DIR=/some/path
+    cmake --build build --config Release
    ```
  - CMake (Windows):
    ```cmd
    set CL_BLAST_CMAKE_PKG="C:/CLBlast/lib/cmake/CLBlast"
    git clone https://github.com/ggerganov/llama.cpp
    cd llama.cpp
-    mkdir build
-    cd build
-    cmake .. -DBUILD_SHARED_LIBS=OFF -DLLAMA_CLBLAST=ON -DCMAKE_PREFIX_PATH=%CL_BLAST_CMAKE_PKG% -G "Visual Studio 17 2022" -A x64
-    cmake --build . --config Release
-    cmake --install . --prefix C:/LlamaCPP
+    cmake -B build -DBUILD_SHARED_LIBS=OFF -DLLAMA_CLBLAST=ON -DCMAKE_PREFIX_PATH=%CL_BLAST_CMAKE_PKG% -G "Visual Studio 17 2022" -A x64
+    cmake --build build --config Release
+    cmake --install build --prefix C:/LlamaCPP
    ```

  ##### Running Llama with CLBlast
@@ -679,10 +699,8 @@ Building the program with BLAS support may lead to some performance improvements
  Then, build llama.cpp using the cmake command below:

  ```bash
-  mkdir -p build
-  cd build
-  cmake .. -DLLAMA_VULKAN=1
-  cmake --build . --config Release
+  cmake -B build -DLLAMA_VULKAN=1
+  cmake --build build --config Release
  # Test the output binary (with "-ngl 33" to offload all layers to GPU)
  ./bin/main -m "PATH_TO_MODEL" -p "Hi you how are you" -n 50 -e -ngl 33 -t 4

@@ -1107,7 +1125,9 @@ docker run --gpus all -v /path/to/models:/models local/llama.cpp:server-cuda -m
 - Clean-up any trailing whitespaces, use 4 spaces for indentation, brackets on the same line, `void * ptr`, `int & a`
 - See [good first issues](https://github.com/ggerganov/llama.cpp/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22) for tasks suitable for first contributions
 - Tensors store data in row-major order. We refer to dimension 0 as columns, 1 as rows, 2 as matrices
- Matrix multiplication is unconventional: [`z = ggml_mul_mat(ctx, x, y)`](https://github.com/ggerganov/llama.cpp/blob/880e352277fc017df4d5794f0c21c44e1eae2b84/ggml.h#L1058-L1064) means `zT = x @ yT`
+- Matrix multiplication is unconventional: [`C = ggml_mul_mat(ctx, A, B)`](https://github.com/ggerganov/llama.cpp/blob/880e352277fc017df4d5794f0c21c44e1eae2b84/ggml.h#L1058-L1064) means $C^T = A B^T \Leftrightarrow C = B A^T.$
+
+![matmul](media/matmul.png)

 ### Docs

@@ -140,4 +140,33 @@ pub fn build(b: *std.build.Builder) !void {
    if (server.target.isWindows()) {
        server.linkSystemLibrary("ws2_32");
    }
+
+    const server_assets = [_][]const u8{ "index.html", "index.js", "completion.js", "json-schema-to-grammar.mjs" };
+    for (server_assets) |asset| {
+        const input_path = b.fmt("examples/server/public/{s}", .{asset});
+        const output_path = b.fmt("examples/server/{s}.hpp", .{asset});
+
+        // Portable equivalent of `b.addSystemCommand(&.{ "xxd", "-n", asset, "-i", input_path, output_path }) })`:
+
+        const input = try std.fs.cwd().readFileAlloc(b.allocator, input_path, std.math.maxInt(usize));
+        defer b.allocator.free(input);
+
+        var buf = std.ArrayList(u8).init(b.allocator);
+        defer buf.deinit();
+
+        for (input) |byte| {
+            try std.fmt.format(buf.writer(), "0x{X:0>2}, ", .{byte});
+        }
+
+        var name = try std.mem.replaceOwned(u8, b.allocator, asset, "-", "_");
+        defer b.allocator.free(name);
+        std.mem.replaceScalar(u8, name, '.', '_');
+
+        try std.fs.cwd().writeFile(output_path, b.fmt(
+            "unsigned char {s}[] = {{{s}}};\nunsigned int {s}_len = {d};\n",
+            .{ name, buf.items, name, input.len },
+        ));
+
+        std.debug.print("Dumped hex of \"{s}\" ({s}) to {s}\n", .{ input_path, name, output_path });
+    }
 }
@@ -160,7 +160,9 @@ function gg_run_test_scripts_debug {

    set -e

-    (cd ./examples/gguf-split && time bash tests.sh "$SRC/build-ci-debug/bin" "$MNT/models") 2>&1 | tee -a $OUT/${ci}-scripts.log
+    # TODO: too slow, run on dedicated node
+   #(cd ./examples/gguf-split && time bash tests.sh "$SRC/build-ci-debug/bin" "$MNT/models") 2>&1 | tee -a $OUT/${ci}-scripts.log
+   #(cd ./examples/quantize   && time bash tests.sh "$SRC/build-ci-debug/bin" "$MNT/models") 2>&1 | tee -a $OUT/${ci}-scripts.log

    set +e
 }
@@ -184,6 +186,7 @@ function gg_run_test_scripts_release {
    set -e

    (cd ./examples/gguf-split && time bash tests.sh "$SRC/build-ci-release/bin" "$MNT/models") 2>&1 | tee -a $OUT/${ci}-scripts.log
+    (cd ./examples/quantize   && time bash tests.sh "$SRC/build-ci-release/bin" "$MNT/models") 2>&1 | tee -a $OUT/${ci}-scripts.log

    set +e
 }
@@ -333,7 +336,8 @@ function gg_run_open_llama_3b_v2 {

    (time ./bin/imatrix --model ${model_f16} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-imatrix.log

-    (time ./bin/save-load-state --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
+    (time ./bin/save-load-state     --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
+    (time ./bin/save-load-state -fa --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log

    function check_ppl {
        qnt="$1"
@@ -514,7 +518,10 @@ function gg_run_open_llama_7b_v2 {

    (time ./bin/imatrix --model ${model_f16} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-imatrix.log

-    (time ./bin/save-load-state --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
+    (time ./bin/save-load-state     -ngl 10 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
+    (time ./bin/save-load-state -fa -ngl 10 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
+    (time ./bin/save-load-state     -ngl 99 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
+    (time ./bin/save-load-state -fa -ngl 99 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log

    function check_ppl {
        qnt="$1"
@@ -67,7 +67,6 @@
 #include <sys/syslimits.h>
 #endif
 #define LLAMA_CURL_MAX_URL_LENGTH 2084 // Maximum URL Length in Chrome: 2083
-#define LLAMA_CURL_MAX_HEADER_LENGTH 256
 #endif // LLAMA_USE_CURL

 using json = nlohmann::ordered_json;
@@ -108,7 +107,7 @@ int32_t get_num_physical_cores() {
    return n_threads > 0 ? (n_threads <= 4 ? n_threads : n_threads / 2) : 4;
 }

-#if defined(__x86_64__) && defined(__linux__)
+#if defined(__x86_64__) && defined(__linux__) && !defined(__ANDROID__)
 #include <pthread.h>

 static void cpuid(unsigned leaf, unsigned subleaf,
@@ -162,7 +161,7 @@ static int count_math_cpus(int cpu_count) {
 * Returns number of CPUs on system that are useful for math.
 */
 int get_math_cpu_count() {
-#if defined(__x86_64__) && defined(__linux__)
+#if defined(__x86_64__) && defined(__linux__) && !defined(__ANDROID__)
    int cpu_count = sysconf(_SC_NPROCESSORS_ONLN);
    if (cpu_count < 1) {
        return get_num_physical_cores();
@@ -234,15 +233,63 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
    return result;
 }

+bool parse_kv_override(const char * data, std::vector<llama_model_kv_override> & overrides) {
+    const char * sep = strchr(data, '=');
+    if (sep == nullptr || sep - data >= 128) {
+        fprintf(stderr, "%s: malformed KV override '%s'\n", __func__, data);
+        return false;
+    }
+    llama_model_kv_override kvo;
+    std::strncpy(kvo.key, data, sep - data);
+    kvo.key[sep - data] = 0;
+    sep++;
+    if (strncmp(sep, "int:", 4) == 0) {
+        sep += 4;
+        kvo.tag = LLAMA_KV_OVERRIDE_TYPE_INT;
+        kvo.val_i64 = std::atol(sep);
+    } else if (strncmp(sep, "float:", 6) == 0) {
+        sep += 6;
+        kvo.tag = LLAMA_KV_OVERRIDE_TYPE_FLOAT;
+        kvo.val_f64 = std::atof(sep);
+    } else if (strncmp(sep, "bool:", 5) == 0) {
+        sep += 5;
+        kvo.tag = LLAMA_KV_OVERRIDE_TYPE_BOOL;
+        if (std::strcmp(sep, "true") == 0) {
+            kvo.val_bool = true;
+        } else if (std::strcmp(sep, "false") == 0) {
+            kvo.val_bool = false;
+        } else {
+            fprintf(stderr, "%s: invalid boolean value for KV override '%s'\n", __func__, data);
+            return false;
+        }
+    } else if (strncmp(sep, "str:", 4) == 0) {
+        sep += 4;
+        kvo.tag = LLAMA_KV_OVERRIDE_TYPE_STR;
+        if (strlen(sep) > 127) {
+            fprintf(stderr, "%s: malformed KV override '%s', value cannot exceed 127 chars\n", __func__, data);
+            return false;
+        }
+        strncpy(kvo.val_str, sep, 127);
+        kvo.val_str[127] = '\0';
+    } else {
+        fprintf(stderr, "%s: invalid type for KV override '%s'\n", __func__, data);
+        return false;
+    }
+    overrides.emplace_back(std::move(kvo));
+    return true;
+}
+
 bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_params & params, int & i, bool & invalid_param) {
-    llama_sampling_params& sparams = params.sparams;
+    llama_sampling_params & sparams = params.sparams;

    if (arg == "-s" || arg == "--seed") {
        if (++i >= argc) {
            invalid_param = true;
            return true;
        }
+        // This is temporary, in the future the samplign state will be moved fully to llama_sampling_context.
        params.seed = std::stoul(argv[i]);
+        sparams.seed = std::stoul(argv[i]);
        return true;
    }
    if (arg == "-t" || arg == "--threads") {
@@ -845,7 +892,7 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
            invalid_param = true;
            return true;
        }
-        params.image = argv[i];
+        params.image.emplace_back(argv[i]);
        return true;
    }
    if (arg == "-i" || arg == "--interactive") {
@@ -900,6 +947,10 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
        params.cont_batching = true;
        return true;
    }
+    if (arg == "-fa" || arg == "--flash-attn") {
+        params.flash_attn = true;
+        return true;
+    }
    if (arg == "--color") {
        params.use_color = true;
        return true;
@@ -1087,6 +1138,10 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
        params.n_print = std::stoi(argv[i]);
        return true;
    }
+    if (arg == "--check-tensors") {
+        params.check_tensors = true;
+        return true;
+    }
    if (arg == "--ppl-output-type") {
        if (++i >= argc) {
            invalid_param = true;
@@ -1238,47 +1293,11 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
            invalid_param = true;
            return true;
        }
-        char* sep = strchr(argv[i], '=');
-        if (sep == nullptr || sep - argv[i] >= 128) {
-            fprintf(stderr, "error: Malformed KV override: %s\n", argv[i]);
-            invalid_param = true;
-            return true;
-        }
-        struct llama_model_kv_override kvo;
-        std::strncpy(kvo.key, argv[i], sep - argv[i]);
-        kvo.key[sep - argv[i]] = 0;
-        sep++;
-        if (strncmp(sep, "int:", 4) == 0) {
-            sep += 4;
-            kvo.tag = LLAMA_KV_OVERRIDE_TYPE_INT;
-            kvo.int_value = std::atol(sep);
-        }
-        else if (strncmp(sep, "float:", 6) == 0) {
-            sep += 6;
-            kvo.tag = LLAMA_KV_OVERRIDE_TYPE_FLOAT;
-            kvo.float_value = std::atof(sep);
-        }
-        else if (strncmp(sep, "bool:", 5) == 0) {
-            sep += 5;
-            kvo.tag = LLAMA_KV_OVERRIDE_TYPE_BOOL;
-            if (std::strcmp(sep, "true") == 0) {
-                kvo.bool_value = true;
-            }
-            else if (std::strcmp(sep, "false") == 0) {
-                kvo.bool_value = false;
-            }
-            else {
-                fprintf(stderr, "error: Invalid boolean value for KV override: %s\n", argv[i]);
-                invalid_param = true;
-                return true;
-            }
-        }
-        else {
+        if (!parse_kv_override(argv[i], params.kv_overrides)) {
            fprintf(stderr, "error: Invalid type for KV override: %s\n", argv[i]);
            invalid_param = true;
            return true;
        }
-        params.kv_overrides.push_back(kvo);
        return true;
    }
 #ifndef LOG_DISABLE_LOGS
@@ -1308,6 +1327,29 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
    return false;
 }

+void gpt_params_handle_model_default(gpt_params & params) {
+    if (!params.hf_repo.empty()) {
+        // short-hand to avoid specifying --hf-file -> default it to --model
+        if (params.hf_file.empty()) {
+            if (params.model.empty()) {
+                throw std::invalid_argument("error: --hf-repo requires either --hf-file or --model\n");
+            }
+            params.hf_file = params.model;
+        } else if (params.model.empty()) {
+            params.model = "models/" + string_split(params.hf_file, '/').back();
+        }
+    } else if (!params.model_url.empty()) {
+        if (params.model.empty()) {
+            auto f = string_split(params.model_url, '#').front();
+            f = string_split(f, '?').front();
+            f = string_split(f, '/').back();
+            params.model =  "models/" + f;
+        }
+    } else if (params.model.empty()) {
+        params.model = DEFAULT_MODEL_PATH;
+    }
+}
+
 bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
    bool invalid_param = false;
    std::string arg;
@@ -1336,10 +1378,7 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
        throw std::invalid_argument("error: --prompt-cache-all not supported in interactive mode yet\n");
    }

-    // short-hand to avoid specifying --hf-file -> default it to --model
-    if (!params.hf_repo.empty() && params.hf_file.empty()) {
-        params.hf_file = params.model;
-    }
+    gpt_params_handle_model_default(params);

    if (params.escape) {
        process_escapes(params.prompt);
@@ -1478,8 +1517,9 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
    printf("  -ns N, --sequences N  number of sequences to decode (default: %d)\n", params.n_sequences);
    printf("  -ps N, --p-split N    speculative decoding split probability (default: %.1f)\n", (double)params.p_split);
    printf("  -cb, --cont-batching  enable continuous batching (a.k.a dynamic batching) (default: disabled)\n");
+    printf("  -fa, --flash-attn     enable Flash Attention (default: %s)\n", params.flash_attn ? "enabled" : "disabled");
    printf("  --mmproj MMPROJ_FILE  path to a multimodal projector file for LLaVA. see examples/llava/README.md\n");
-    printf("  --image IMAGE_FILE    path to an image file. use with multimodal models\n");
+    printf("  --image IMAGE_FILE    path to an image file. use with multimodal models. Specify multiple times for batching\n");
    if (llama_supports_mlock()) {
        printf("  --mlock               force system to keep model in RAM rather than swapping or compressing\n");
    }
@@ -1532,7 +1572,7 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
    printf("  --control-vector-layer-range START END\n");
    printf("                        layer range to apply the control vector(s) to, start and end inclusive\n");
    printf("  -m FNAME, --model FNAME\n");
-    printf("                        model path (default: %s)\n", params.model.c_str());
+    printf("                        model path (default: models/$filename with filename from --hf-file or --model-url if set, otherwise %s)\n", DEFAULT_MODEL_PATH);
    printf("  -md FNAME, --model-draft FNAME\n");
    printf("                        draft model for speculative decoding (default: unused)\n");
    printf("  -mu MODEL_URL, --model-url MODEL_URL\n");
@@ -1549,9 +1589,10 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
    printf("                        path to dynamic lookup cache to use for lookup decoding (updated by generation)\n");
    printf("  --override-kv KEY=TYPE:VALUE\n");
    printf("                        advanced option to override model metadata by key. may be specified multiple times.\n");
-    printf("                        types: int, float, bool. example: --override-kv tokenizer.ggml.add_bos_token=bool:false\n");
+    printf("                        types: int, float, bool, str. example: --override-kv tokenizer.ggml.add_bos_token=bool:false\n");
    printf("  -ptc N, --print-token-count N\n");
    printf("                        print token count every N tokens (default: %d)\n", params.n_print);
+    printf("  --check-tensors       check model tensor data for invalid values\n");
    printf("\n");
 #ifndef LOG_DISABLE_LOGS
    log_print_usage();
@@ -1676,6 +1717,18 @@ std::vector<std::string> string_split(std::string input, char separator) {
    return parts;
 }

+std::string string_strip(const std::string & str) {
+    size_t start = 0;
+    size_t end = str.size();
+    while (start < end && std::isspace(str[start])) {
+        start++;
+    }
+    while (end > start && std::isspace(str[end - 1])) {
+        end--;
+    }
+    return str.substr(start, end - start);
+}
+
 std::vector<llama_sampler_type> sampler_types_from_names(const std::vector<std::string> & names, bool allow_alt_names) {
    std::unordered_map<std::string, llama_sampler_type> sampler_canonical_name_map {
        {"top_k",       llama_sampler_type::TOP_K},
@@ -1772,6 +1825,7 @@ struct llama_model_params llama_model_params_from_gpt_params(const gpt_params &
    mparams.tensor_split    = params.tensor_split;
    mparams.use_mmap        = params.use_mmap;
    mparams.use_mlock       = params.use_mlock;
+    mparams.check_tensors   = params.check_tensors;
    if (params.kv_overrides.empty()) {
        mparams.kv_overrides = NULL;
    } else {
@@ -1836,6 +1890,7 @@ struct llama_context_params llama_context_params_from_gpt_params(const gpt_param
    cparams.cb_eval           = params.cb_eval;
    cparams.cb_eval_user_data = params.cb_eval_user_data;
    cparams.offload_kqv       = !params.no_kv_offload;
+    cparams.flash_attn        = params.flash_attn;

    cparams.type_k = kv_cache_type_from_str(params.cache_type_k);
    cparams.type_v = kv_cache_type_from_str(params.cache_type_v);
@@ -1866,59 +1921,75 @@ void llama_batch_add(

 #ifdef LLAMA_USE_CURL

-static bool llama_download_file(CURL * curl, const char * url, const char * path) {
+static bool starts_with(const std::string & str, const std::string & prefix) {
+    // While we wait for C++20's std::string::starts_with...
+    return str.rfind(prefix, 0) == 0;
+}
+
+static bool llama_download_file(const std::string & url, const std::string & path) {
+
+    // Initialize libcurl
+    std::unique_ptr<CURL, decltype(&curl_easy_cleanup)> curl(curl_easy_init(), &curl_easy_cleanup);
+    if (!curl) {
+        fprintf(stderr, "%s: error initializing libcurl\n", __func__);
+        return false;
+    }
+
    bool force_download = false;

    // Set the URL, allow to follow http redirection
-    curl_easy_setopt(curl, CURLOPT_URL, url);
-    curl_easy_setopt(curl, CURLOPT_FOLLOWLOCATION, 1L);
+    curl_easy_setopt(curl.get(), CURLOPT_URL, url.c_str());
+    curl_easy_setopt(curl.get(), CURLOPT_FOLLOWLOCATION, 1L);

 #if defined(_WIN32)
    // CURLSSLOPT_NATIVE_CA tells libcurl to use standard certificate store of
    //   operating system. Currently implemented under MS-Windows.
-    curl_easy_setopt(curl, CURLOPT_SSL_OPTIONS, CURLSSLOPT_NATIVE_CA);
+    curl_easy_setopt(curl.get(), CURLOPT_SSL_OPTIONS, CURLSSLOPT_NATIVE_CA);
 #endif

    // Check if the file already exists locally
    struct stat model_file_info;
-    auto file_exists = (stat(path, &model_file_info) == 0);
+    auto file_exists = (stat(path.c_str(), &model_file_info) == 0);

-    // If the file exists, check for ${path_model}.etag or ${path_model}.lastModified files
-    char etag[LLAMA_CURL_MAX_HEADER_LENGTH] = {0};
-    char etag_path[PATH_MAX] = {0};
-    snprintf(etag_path, sizeof(etag_path), "%s.etag", path);
-
-    char last_modified[LLAMA_CURL_MAX_HEADER_LENGTH] = {0};
-    char last_modified_path[PATH_MAX] = {0};
-    snprintf(last_modified_path, sizeof(last_modified_path), "%s.lastModified", path);
+    // If the file exists, check its JSON metadata companion file.
+    std::string metadata_path = path + ".json";
+    nlohmann::json metadata;
+    std::string etag;
+    std::string last_modified;

    if (file_exists) {
-        auto * f_etag = fopen(etag_path, "r");
-        if (f_etag) {
-            if (!fgets(etag, sizeof(etag), f_etag)) {
-                fprintf(stderr, "%s: unable to read file %s\n", __func__, etag_path);
-            } else {
-                fprintf(stderr, "%s: previous file found %s: %s\n", __func__, etag_path, etag);
+        // Try and read the JSON metadata file (note: stream autoclosed upon exiting this block).
+        std::ifstream metadata_in(metadata_path);
+        if (metadata_in.good()) {
+            try {
+                metadata_in >> metadata;
+                fprintf(stderr, "%s: previous metadata file found %s: %s\n", __func__, metadata_path.c_str(), metadata.dump().c_str());
+                if (metadata.contains("url") && metadata["url"].is_string()) {
+                    auto previous_url = metadata["url"].get<std::string>();
+                    if (previous_url != url) {
+                        fprintf(stderr, "%s: Model URL mismatch: %s != %s\n", __func__, url.c_str(), previous_url.c_str());
+                        return false;
+                    }
+                }
+                if (metadata.contains("etag") && metadata["etag"].is_string()) {
+                    etag = metadata["etag"];
+                }
+                if (metadata.contains("lastModified") && metadata["lastModified"].is_string()) {
+                    last_modified = metadata["lastModified"];
+                }
+            } catch (const nlohmann::json::exception & e) {
+                fprintf(stderr, "%s: error reading metadata file %s: %s\n", __func__, metadata_path.c_str(), e.what());
+                return false;
            }
-            fclose(f_etag);
-        }
-
-        auto * f_last_modified = fopen(last_modified_path, "r");
-        if (f_last_modified) {
-            if (!fgets(last_modified, sizeof(last_modified), f_last_modified)) {
-                fprintf(stderr, "%s: unable to read file %s\n", __func__, last_modified_path);
-            } else {
-                fprintf(stderr, "%s: previous file found %s: %s\n", __func__, last_modified_path,
-                        last_modified);
-            }
-            fclose(f_last_modified);
        }
+    } else {
+        fprintf(stderr, "%s: no previous model file found %s\n", __func__, path.c_str());
    }

    // Send a HEAD request to retrieve the etag and last-modified headers
    struct llama_load_model_from_url_headers {
-        char etag[LLAMA_CURL_MAX_HEADER_LENGTH] = {0};
-        char last_modified[LLAMA_CURL_MAX_HEADER_LENGTH] = {0};
+        std::string etag;
+        std::string last_modified;
    };
    llama_load_model_from_url_headers headers;
    {
@@ -1926,38 +1997,37 @@ static bool llama_download_file(CURL * curl, const char * url, const char * path
        auto header_callback = [](char * buffer, size_t /*size*/, size_t n_items, void * userdata) -> size_t {
            llama_load_model_from_url_headers *headers = (llama_load_model_from_url_headers *) userdata;

-            // Convert header field name to lowercase
-            for (size_t i = 0; i < n_items && buffer[i] != ':'; ++i) {
-                buffer[i] = tolower(buffer[i]);
-            }
+            static std::regex header_regex("([^:]+): (.*)\r\n");
+            static std::regex etag_regex("ETag", std::regex_constants::icase);
+            static std::regex last_modified_regex("Last-Modified", std::regex_constants::icase);

-            const char * etag_prefix = "etag: ";
-            if (strncmp(buffer, etag_prefix, strlen(etag_prefix)) == 0) {
-                strncpy(headers->etag, buffer + strlen(etag_prefix), n_items - strlen(etag_prefix) - 2); // Remove CRLF
-            }
-
-            const char * last_modified_prefix = "last-modified: ";
-            if (strncmp(buffer, last_modified_prefix, strlen(last_modified_prefix)) == 0) {
-                strncpy(headers->last_modified, buffer + strlen(last_modified_prefix),
-                        n_items - strlen(last_modified_prefix) - 2); // Remove CRLF
+            std::string header(buffer, n_items);
+            std::smatch match;
+            if (std::regex_match(header, match, header_regex)) {
+                const std::string & key = match[1];
+                const std::string & value = match[2];
+                if (std::regex_match(key, match, etag_regex)) {
+                    headers->etag = value;
+                } else if (std::regex_match(key, match, last_modified_regex)) {
+                    headers->last_modified = value;
+                }
            }
            return n_items;
        };

-        curl_easy_setopt(curl, CURLOPT_NOBODY, 1L); // will trigger the HEAD verb
-        curl_easy_setopt(curl, CURLOPT_NOPROGRESS, 1L); // hide head request progress
-        curl_easy_setopt(curl, CURLOPT_HEADERFUNCTION, static_cast<CURLOPT_HEADERFUNCTION_PTR>(header_callback));
-        curl_easy_setopt(curl, CURLOPT_HEADERDATA, &headers);
+        curl_easy_setopt(curl.get(), CURLOPT_NOBODY, 1L); // will trigger the HEAD verb
+        curl_easy_setopt(curl.get(), CURLOPT_NOPROGRESS, 1L); // hide head request progress
+        curl_easy_setopt(curl.get(), CURLOPT_HEADERFUNCTION, static_cast<CURLOPT_HEADERFUNCTION_PTR>(header_callback));
+        curl_easy_setopt(curl.get(), CURLOPT_HEADERDATA, &headers);

-        CURLcode res = curl_easy_perform(curl);
+        CURLcode res = curl_easy_perform(curl.get());
        if (res != CURLE_OK) {
-            curl_easy_cleanup(curl);
            fprintf(stderr, "%s: curl_easy_perform() failed: %s\n", __func__, curl_easy_strerror(res));
            return false;
        }

        long http_code = 0;
-        curl_easy_getinfo(curl, CURLINFO_RESPONSE_CODE, &http_code);
+        curl_easy_getinfo(curl.get(), CURLINFO_RESPONSE_CODE, &http_code);
        if (http_code != 200) {
            // HEAD not supported, we don't know if the file has changed
            // force trigger downloading
@@ -1966,28 +2036,30 @@ static bool llama_download_file(CURL * curl, const char * url, const char * path
        }
    }

-    // If the ETag or the Last-Modified headers are different: trigger a new download
-    bool should_download = !file_exists
-        || force_download
-        || (strlen(headers.etag) > 0 && strcmp(etag, headers.etag) != 0)
-        || (strlen(headers.last_modified) > 0 && strcmp(last_modified, headers.last_modified) != 0);
+    bool should_download = !file_exists || force_download;
+    if (!should_download) {
+        if (!etag.empty() && etag != headers.etag) {
+            fprintf(stderr, "%s: ETag header is different (%s != %s): triggering a new download\n", __func__, etag.c_str(), headers.etag.c_str());
+            should_download = true;
+        } else if (!last_modified.empty() && last_modified != headers.last_modified) {
+            fprintf(stderr, "%s: Last-Modified header is different (%s != %s): triggering a new download\n", __func__, last_modified.c_str(), headers.last_modified.c_str());
+            should_download = true;
+        }
+    }
    if (should_download) {
-        char path_temporary[PATH_MAX] = {0};
-        snprintf(path_temporary, sizeof(path_temporary), "%s.downloadInProgress", path);
+        std::string path_temporary = path + ".downloadInProgress";
        if (file_exists) {
-            fprintf(stderr, "%s: deleting previous downloaded file: %s\n", __func__, path);
-            if (remove(path) != 0) {
-                curl_easy_cleanup(curl);
-                fprintf(stderr, "%s: unable to delete file: %s\n", __func__, path);
+            fprintf(stderr, "%s: deleting previous downloaded file: %s\n", __func__, path.c_str());
+            if (remove(path.c_str()) != 0) {
+                fprintf(stderr, "%s: unable to delete file: %s\n", __func__, path.c_str());
                return false;
            }
        }

        // Set the output file
-        auto * outfile = fopen(path_temporary, "wb");
+        std::unique_ptr<FILE, decltype(&fclose)> outfile(fopen(path_temporary.c_str(), "wb"), fclose);
        if (!outfile) {
-            curl_easy_cleanup(curl);
-            fprintf(stderr, "%s: error opening local file for writing: %s\n", __func__, path);
+            fprintf(stderr, "%s: error opening local file for writing: %s\n", __func__, path.c_str());
            return false;
        }

@@ -1995,12 +2067,12 @@ static bool llama_download_file(CURL * curl, const char * url, const char * path
        auto write_callback = [](void * data, size_t size, size_t nmemb, void * fd) -> size_t {
            return fwrite(data, size, nmemb, (FILE *)fd);
        };
-        curl_easy_setopt(curl, CURLOPT_NOBODY, 0L);
-        curl_easy_setopt(curl, CURLOPT_WRITEFUNCTION, static_cast<CURLOPT_WRITEFUNCTION_PTR>(write_callback));
-        curl_easy_setopt(curl, CURLOPT_WRITEDATA, outfile);
+        curl_easy_setopt(curl.get(), CURLOPT_NOBODY, 0L);
+        curl_easy_setopt(curl.get(), CURLOPT_WRITEFUNCTION, static_cast<CURLOPT_WRITEFUNCTION_PTR>(write_callback));
+        curl_easy_setopt(curl.get(), CURLOPT_WRITEDATA, outfile.get());

        //  display download progress
-        curl_easy_setopt(curl, CURLOPT_NOPROGRESS, 0L);
+        curl_easy_setopt(curl.get(), CURLOPT_NOPROGRESS, 0L);

        // helper function to hide password in URL
        auto llama_download_hide_password_in_url = [](const std::string & url) -> std::string {
@@ -2019,51 +2091,34 @@ static bool llama_download_file(CURL * curl, const char * url, const char * path

        // start the download
        fprintf(stderr, "%s: downloading from %s to %s (server_etag:%s, server_last_modified:%s)...\n", __func__,
-                llama_download_hide_password_in_url(url).c_str(), path, headers.etag, headers.last_modified);
-        auto res = curl_easy_perform(curl);
+                llama_download_hide_password_in_url(url).c_str(), path.c_str(), headers.etag.c_str(), headers.last_modified.c_str());
+        auto res = curl_easy_perform(curl.get());
        if (res != CURLE_OK) {
-            fclose(outfile);
-            curl_easy_cleanup(curl);
            fprintf(stderr, "%s: curl_easy_perform() failed: %s\n", __func__, curl_easy_strerror(res));
            return false;
        }

        long http_code = 0;
-        curl_easy_getinfo (curl, CURLINFO_RESPONSE_CODE, &http_code);
+        curl_easy_getinfo (curl.get(), CURLINFO_RESPONSE_CODE, &http_code);
        if (http_code < 200 || http_code >= 400) {
-            fclose(outfile);
-            curl_easy_cleanup(curl);
            fprintf(stderr, "%s: invalid http status code received: %ld\n", __func__, http_code);
            return false;
        }

-        // Clean up
-        fclose(outfile);
+        // Causes file to be closed explicitly here before we rename it.
+        outfile.reset();

-        // Write the new ETag to the .etag file
-        if (strlen(headers.etag) > 0) {
-            auto * etag_file = fopen(etag_path, "w");
-            if (etag_file) {
-                fputs(headers.etag, etag_file);
-                fclose(etag_file);
-                fprintf(stderr, "%s: file etag saved %s: %s\n", __func__, etag_path, headers.etag);
-            }
-        }
+        // Write the updated JSON metadata file.
+        metadata.update({
+            {"url", url},
+            {"etag", headers.etag},
+            {"lastModified", headers.last_modified}
+        });
+        std::ofstream(metadata_path) << metadata.dump(4);
+        fprintf(stderr, "%s: file metadata saved: %s\n", __func__, metadata_path.c_str());

-        // Write the new lastModified to the .etag file
-        if (strlen(headers.last_modified) > 0) {
-            auto * last_modified_file = fopen(last_modified_path, "w");
-            if (last_modified_file) {
-                fputs(headers.last_modified, last_modified_file);
-                fclose(last_modified_file);
-                fprintf(stderr, "%s: file last modified saved %s: %s\n", __func__, last_modified_path,
-                        headers.last_modified);
-            }
-        }
-
-        if (rename(path_temporary, path) != 0) {
-            curl_easy_cleanup(curl);
-            fprintf(stderr, "%s: unable to rename file: %s to %s\n", __func__, path_temporary, path);
+        if (rename(path_temporary.c_str(), path.c_str()) != 0) {
+            fprintf(stderr, "%s: unable to rename file: %s to %s\n", __func__, path_temporary.c_str(), path.c_str());
            return false;
        }
    }
@@ -2081,15 +2136,7 @@ struct llama_model * llama_load_model_from_url(
        return NULL;
    }

-    // Initialize libcurl
-    auto * curl = curl_easy_init();
-
-    if (!curl) {
-        fprintf(stderr, "%s: error initializing libcurl\n", __func__);
-        return NULL;
-    }
-
-    if (!llama_download_file(curl, model_url, path_model)) {
+    if (!llama_download_file(model_url, path_model)) {
        return NULL;
    }

@@ -2103,7 +2150,6 @@ struct llama_model * llama_load_model_from_url(
        auto * ctx_gguf = gguf_init_from_file(path_model, gguf_params);
        if (!ctx_gguf) {
            fprintf(stderr, "\n%s:  failed to load input GGUF from %s\n", __func__, path_model);
-            curl_easy_cleanup(curl);
            return NULL;
        }

@@ -2115,8 +2161,6 @@ struct llama_model * llama_load_model_from_url(
        gguf_free(ctx_gguf);
    }

-    curl_easy_cleanup(curl);
-
    if (n_split > 1) {
        char split_prefix[PATH_MAX] = {0};
        char split_url_prefix[LLAMA_CURL_MAX_URL_LENGTH] = {0};
@@ -2147,11 +2191,7 @@ struct llama_model * llama_load_model_from_url(
                char split_url[LLAMA_CURL_MAX_URL_LENGTH] = {0};
                llama_split_path(split_url, sizeof(split_url), split_url_prefix, download_idx, n_split);

-                auto * curl = curl_easy_init();
-                bool res = llama_download_file(curl, split_url, split_path);
-                curl_easy_cleanup(curl);
-
-                return res;
+                return llama_download_file(split_url, split_path);
            }, idx));
        }

@@ -2326,12 +2366,12 @@ std::vector<llama_token> llama_tokenize(
    return result;
 }

-std::string llama_token_to_piece(const struct llama_context * ctx, llama_token token) {
+std::string llama_token_to_piece(const struct llama_context * ctx, llama_token token, bool special) {
    std::vector<char> result(8, 0);
-    const int n_tokens = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size());
+    const int n_tokens = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size(), special);
    if (n_tokens < 0) {
        result.resize(-n_tokens);
-        int check = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size());
+        int check = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size(), special);
        GGML_ASSERT(check == -n_tokens);
    } else {
        result.resize(n_tokens);
@@ -2638,7 +2678,7 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l
    fprintf(stream, "mirostat_ent: %f # default: 5.0\n", sparams.mirostat_tau);
    fprintf(stream, "mirostat_lr: %f # default: 0.1\n", sparams.mirostat_eta);
    fprintf(stream, "mlock: %s # default: false\n", params.use_mlock ? "true" : "false");
-    fprintf(stream, "model: %s # default: models/7B/ggml-model.bin\n", params.model.c_str());
+    fprintf(stream, "model: %s # default: %s\n", params.model.c_str(), DEFAULT_MODEL_PATH);
    fprintf(stream, "model_draft: %s # default:\n", params.model_draft.c_str());
    fprintf(stream, "multiline_input: %s # default: false\n", params.multiline_input ? "true" : "false");
    fprintf(stream, "n_gpu_layers: %d # default: -1\n", params.n_gpu_layers);
@@ -2673,6 +2713,7 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l
    fprintf(stream, "seed: %u # default: -1 (random seed)\n", params.seed);
    fprintf(stream, "simple_io: %s # default: false\n", params.simple_io ? "true" : "false");
    fprintf(stream, "cont_batching: %s # default: false\n", params.cont_batching ? "true" : "false");
+    fprintf(stream, "flash_attn: %s # default: false\n", params.flash_attn ? "true" : "false");
    fprintf(stream, "temp: %f # default: 0.8\n", sparams.temp);

    const std::vector<float> tensor_split_vector(params.tensor_split, params.tensor_split + llama_max_devices());
@@ -31,6 +31,8 @@
    fprintf(stderr, "%s: built with %s for %s\n", __func__, LLAMA_COMPILER, LLAMA_BUILD_TARGET);    \
 } while(0)

+#define DEFAULT_MODEL_PATH "models/7B/ggml-model-f16.gguf"
+
 // build info
 extern int LLAMA_BUILD_NUMBER;
 extern char const *LLAMA_COMMIT;
@@ -86,13 +88,13 @@ struct gpt_params {

    ggml_numa_strategy numa = GGML_NUMA_STRATEGY_DISABLED;

-    llama_rope_scaling_type rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED;
-    llama_pooling_type      pooling_type      = LLAMA_POOLING_TYPE_UNSPECIFIED; // pooling type for embeddings
+    enum llama_rope_scaling_type rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED;
+    enum llama_pooling_type      pooling_type      = LLAMA_POOLING_TYPE_UNSPECIFIED; // pooling type for embeddings

    // // sampling parameters
    struct llama_sampling_params sparams;

-    std::string model                = "models/7B/ggml-model-f16.gguf"; // model path
+    std::string model                = "";  // model path
    std::string model_draft          = "";  // draft model for speculative decoding
    std::string model_alias          = "unknown"; // model alias
    std::string model_url            = "";  // model url to download
@@ -148,6 +150,7 @@ struct gpt_params {
    bool multiline_input   = false; // reverse the usage of `\`
    bool simple_io         = false; // improves compatibility with subprocesses and limited consoles
    bool cont_batching     = true;  // insert new sequences for decoding on-the-fly
+    bool flash_attn        = false; // flash attention

    bool input_prefix_bos  = false; // prefix BOS to user inputs, preceding input_prefix
    bool ignore_eos        = false; // ignore generated EOS tokens
@@ -161,15 +164,20 @@ struct gpt_params {
    bool dump_kv_cache     = false; // dump the KV cache contents for debugging purposes
    bool no_kv_offload     = false; // disable KV offloading
    bool warmup            = true;  // warmup run
+    bool check_tensors     = false; // validate tensor data

    std::string cache_type_k = "f16"; // KV cache data type for the K
    std::string cache_type_v = "f16"; // KV cache data type for the V

    // multimodal models (see examples/llava)
-    std::string mmproj = ""; // path to multimodal projector
-    std::string image  = ""; // path to an image file
+    std::string mmproj = "";        // path to multimodal projector
+    std::vector<std::string> image; // path to image file(s)
 };

+void gpt_params_handle_model_default(gpt_params & params);
+
+bool parse_kv_override(const char * data, std::vector<llama_model_kv_override> & overrides);
+
 bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params);

 bool gpt_params_parse(int argc, char ** argv, gpt_params & params);
@@ -193,6 +201,7 @@ bool validate_file_name(const std::string & filename);
 std::vector<llama_sampler_type> sampler_types_from_names(const std::vector<std::string> & names, bool allow_alt_names);
 std::vector<llama_sampler_type> sampler_types_from_chars(const std::string & names_string);
 std::vector<std::string> string_split(std::string input, char separator);
+std::string string_strip(const std::string & str);
 std::string sampler_type_to_name_string(llama_sampler_type sampler_type);

 //
@@ -237,11 +246,12 @@ std::vector<llama_token> llama_tokenize(
                        bool   add_special,
                        bool   parse_special = false);

-// tokenizes a token into a piece
+// tokenizes a token into a piece, optionally renders special/control tokens
 // should work similar to Python's `tokenizer.id_to_piece`
 std::string llama_token_to_piece(
        const struct llama_context * ctx,
-                       llama_token   token);
+                       llama_token   token,
+                       bool          special = true);

 // TODO: these should be moved in llama.h C-style API under single `llama_detokenize` function
 //       that takes into account the tokenizer type and decides how to handle the leading space
@@ -234,7 +234,7 @@ inline std::string log_filename_generator_impl(LogTriState multilog, const std::
 // INTERNAL, DO NOT USE
 //  USE LOG() INSTEAD
 //
-#if !defined(_MSC_VER) or defined(__INTEL_LLVM_COMPILER)
+#if !defined(_MSC_VER) || defined(__INTEL_LLVM_COMPILER)
    #define LOG_IMPL(str, ...)                                                                                      \
    do {                                                                                                            \
        if (LOG_TARGET != nullptr)                                                                                  \
@@ -257,7 +257,7 @@ inline std::string log_filename_generator_impl(LogTriState multilog, const std::
 // INTERNAL, DO NOT USE
 //  USE LOG_TEE() INSTEAD
 //
-#if !defined(_MSC_VER) or defined(__INTEL_LLVM_COMPILER)
+#if !defined(_MSC_VER) || defined(__INTEL_LLVM_COMPILER)
    #define LOG_TEE_IMPL(str, ...)                                                                                                      \
    do {                                                                                                                                \
        if (LOG_TARGET != nullptr)                                                                                                      \
@@ -1,4 +1,6 @@
+#define LLAMA_API_INTERNAL
 #include "sampling.h"
+#include <random>

 struct llama_sampling_context * llama_sampling_init(const struct llama_sampling_params & params) {
    struct llama_sampling_context * result = new llama_sampling_context();
@@ -33,6 +35,8 @@ struct llama_sampling_context * llama_sampling_init(const struct llama_sampling_

    result->prev.resize(params.n_prev);

+    llama_sampling_set_rng_seed(result, params.seed);
+
    return result;
 }

@@ -62,6 +66,13 @@ void llama_sampling_reset(llama_sampling_context * ctx) {
    ctx->cur.clear();
 }

+void llama_sampling_set_rng_seed(struct llama_sampling_context * ctx, uint32_t seed) {
+    if (seed == LLAMA_DEFAULT_SEED) {
+        seed = std::random_device{}();
+    }
+    ctx->rng.seed(seed);
+}
+
 void llama_sampling_cp(llama_sampling_context * src, llama_sampling_context * dst) {
    if (dst->grammar) {
        llama_grammar_free(dst->grammar);
@@ -203,7 +214,7 @@ static llama_token llama_sampling_sample_impl(

            sampler_queue(ctx_main, params, cur_p, min_keep);

-            id = llama_sample_token(ctx_main, &cur_p);
+            id = llama_sample_token_with_rng(ctx_main, &cur_p, ctx_sampling->rng);

            //{
            //    const int n_top = 10;
@@ -4,9 +4,10 @@

 #include "grammar-parser.h"

+#include <random>
 #include <string>
-#include <vector>
 #include <unordered_map>
+#include <vector>

 // sampler types
 enum class llama_sampler_type : char {
@@ -20,25 +21,26 @@ enum class llama_sampler_type : char {

 // sampling parameters
 typedef struct llama_sampling_params {
-    int32_t     n_prev                = 64;       // number of previous tokens to remember
-    int32_t     n_probs               = 0;        // if greater than 0, output the probabilities of top n_probs tokens.
-    int32_t     min_keep              = 0;        // 0 = disabled, otherwise samplers should return at least min_keep tokens
-    int32_t     top_k                 = 40;       // <= 0 to use vocab size
-    float       top_p                 = 0.95f;    // 1.0 = disabled
-    float       min_p                 = 0.05f;    // 0.0 = disabled
-    float       tfs_z                 = 1.00f;    // 1.0 = disabled
-    float       typical_p             = 1.00f;    // 1.0 = disabled
-    float       temp                  = 0.80f;    // <= 0.0 to sample greedily, 0.0 to not output probabilities
-    float       dynatemp_range        = 0.00f;    // 0.0 = disabled
-    float       dynatemp_exponent     = 1.00f;    // controls how entropy maps to temperature in dynamic temperature sampler
-    int32_t     penalty_last_n        = 64;       // last n tokens to penalize (0 = disable penalty, -1 = context size)
-    float       penalty_repeat        = 1.00f;    // 1.0 = disabled
-    float       penalty_freq          = 0.00f;    // 0.0 = disabled
-    float       penalty_present       = 0.00f;    // 0.0 = disabled
-    int32_t     mirostat              = 0;        // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0
-    float       mirostat_tau          = 5.00f;    // target entropy
-    float       mirostat_eta          = 0.10f;    // learning rate
-    bool        penalize_nl           = false;     // consider newlines as a repeatable token
+    int32_t     n_prev                = 64;                 // number of previous tokens to remember
+    int32_t     n_probs               = 0;                  // if greater than 0, output the probabilities of top n_probs tokens.
+    int32_t     min_keep              = 0;                  // 0 = disabled, otherwise samplers should return at least min_keep tokens
+    int32_t     top_k                 = 40;                 // <= 0 to use vocab size
+    float       top_p                 = 0.95f;              // 1.0 = disabled
+    float       min_p                 = 0.05f;              // 0.0 = disabled
+    float       tfs_z                 = 1.00f;              // 1.0 = disabled
+    float       typical_p             = 1.00f;              // 1.0 = disabled
+    float       temp                  = 0.80f;              // <= 0.0 to sample greedily, 0.0 to not output probabilities
+    float       dynatemp_range        = 0.00f;              // 0.0 = disabled
+    float       dynatemp_exponent     = 1.00f;              // controls how entropy maps to temperature in dynamic temperature sampler
+    int32_t     penalty_last_n        = 64;                 // last n tokens to penalize (0 = disable penalty, -1 = context size)
+    float       penalty_repeat        = 1.00f;              // 1.0 = disabled
+    float       penalty_freq          = 0.00f;              // 0.0 = disabled
+    float       penalty_present       = 0.00f;              // 0.0 = disabled
+    int32_t     mirostat              = 0;                  // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0
+    float       mirostat_tau          = 5.00f;              // target entropy
+    float       mirostat_eta          = 0.10f;              // learning rate
+    bool        penalize_nl           = false;              // consider newlines as a repeatable token
+    uint32_t    seed                  = LLAMA_DEFAULT_SEED; // the seed used to initialize llama_sampling_context

    std::vector<llama_sampler_type> samplers_sequence = {
        llama_sampler_type::TOP_K,
@@ -79,6 +81,8 @@ struct llama_sampling_context {
    // TODO: replace with ring-buffer
    std::vector<llama_token>      prev;
    std::vector<llama_token_data> cur;
+
+    std::mt19937 rng;
 };

 #include "common.h"
@@ -93,6 +97,9 @@ void llama_sampling_free(struct llama_sampling_context * ctx);
 // - reset grammar
 void llama_sampling_reset(llama_sampling_context * ctx);

+// Set the sampler seed
+void llama_sampling_set_rng_seed(struct llama_sampling_context * ctx, uint32_t seed);
+
 // Copy the sampler context
 void llama_sampling_cp(llama_sampling_context * src, llama_sampling_context * dst);

@@ -0,0 +1,279 @@
+# This script downloads the tokenizer models of the specified models from Huggingface and
+# generates the get_vocab_base_pre() function for convert-hf-to-gguf.py
+#
+# This is necessary in order to analyze the type of pre-tokenizer used by the model and
+# provide the necessary information to llama.cpp via the GGUF header in order to implement
+# the same pre-tokenizer.
+#
+# ref: https://github.com/ggerganov/llama.cpp/pull/6920
+#
+# Instructions:
+#
+# - Add a new model to the "models" list
+# - Run the script with your huggingface token:
+#
+#   python3 convert-hf-to-gguf-update.py <huggingface_token>
+#
+# - Copy-paste the generated get_vocab_base_pre() function into convert-hf-to-gguf.py
+# - Update llama.cpp with the new pre-tokenizer if necessary
+#
+# TODO: generate tokenizer tests for llama.cpp
+# TODO: automate the update of convert-hf-to-gguf.py
+#
+
+import os
+import requests
+import sys
+import json
+
+from hashlib import sha256
+from enum import IntEnum, auto
+
+class TOKENIZER_TYPE(IntEnum):
+    SPM = auto()
+    BPE = auto()
+    WPM = auto()
+
+# TODO: this string has to exercise as much pre-tokenizer functionality as possible
+#       will be updated with time - contributions welcome
+chktxt = '\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'
+
+if len(sys.argv) == 2:
+    token = sys.argv[1]
+else:
+    print("Usage: python convert-hf-to-gguf-update.py <huggingface_token>")
+    sys.exit(1)
+
+# TODO: add models here, base models preferred
+models = [
+        { "name": "llama-spm",      "tokt": TOKENIZER_TYPE.SPM, "repo": "https://huggingface.co/meta-llama/Llama-2-7b-hf", },
+        { "name": "llama-bpe",      "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/meta-llama/Meta-Llama-3-8B", },
+        { "name": "phi-3",          "tokt": TOKENIZER_TYPE.SPM, "repo": "https://huggingface.co/microsoft/Phi-3-mini-4k-instruct", },
+        { "name": "deepseek-llm",   "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/deepseek-ai/deepseek-llm-7b-base", },
+        { "name": "deepseek-coder", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-base", },
+        { "name": "falcon",         "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/falcon-7b", },
+        { "name": "bert-bge",       "tokt": TOKENIZER_TYPE.WPM, "repo": "https://huggingface.co/BAAI/bge-small-en-v1.5", },
+        { "name": "mpt",            "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/mosaicml/mpt-7b", },
+        { "name": "starcoder",      "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/bigcode/starcoder2-3b", },
+        { "name": "gpt-2",          "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/openai-community/gpt2", },
+        ]
+
+# make directory "models/tokenizers" if it doesn't exist
+if not os.path.exists("models/tokenizers"):
+    os.makedirs("models/tokenizers")
+
+def download_file_with_auth(url, token, save_path):
+    headers = {"Authorization": f"Bearer {token}"}
+    response = requests.get(url, headers=headers)
+    if response.status_code == 200:
+        with open(save_path, 'wb') as f:
+            f.write(response.content)
+        print(f"File {save_path} downloaded successfully")
+    else:
+        print(f"Failed to download file. Status code: {response.status_code}")
+
+# download the tokenizer models
+for model in models:
+    name = model["name"]
+    repo = model["repo"]
+    tokt = model["tokt"]
+
+    if not os.path.exists(f"models/tokenizers/{name}"):
+        os.makedirs(f"models/tokenizers/{name}")
+    else:
+        print(f"Directory models/tokenizers/{name} already exists - skipping")
+        continue
+
+    print(f"Downloading {name} to models/tokenizers/{name}")
+
+    url = f"{repo}/raw/main/config.json"
+    save_path = f"models/tokenizers/{name}/config.json"
+    download_file_with_auth(url, token, save_path)
+
+    url = f"{repo}/raw/main/tokenizer.json"
+    save_path = f"models/tokenizers/{name}/tokenizer.json"
+    download_file_with_auth(url, token, save_path)
+
+    if tokt == TOKENIZER_TYPE.SPM:
+        url = f"{repo}/resolve/main/tokenizer.model"
+        save_path = f"models/tokenizers/{name}/tokenizer.model"
+        download_file_with_auth(url, token, save_path)
+
+    url = f"{repo}/raw/main/tokenizer_config.json"
+    save_path = f"models/tokenizers/{name}/tokenizer_config.json"
+    download_file_with_auth(url, token, save_path)
+
+# generate the source code for the convert-hf-to-gguf.py:get_vocab_base_pre() function:
+# TODO: auto-update convert-hf-to-gguf.py with the generated function
+
+src_ifs = ""
+for model in models:
+    name = model["name"]
+    tokt = model["tokt"]
+
+    if tokt == TOKENIZER_TYPE.SPM:
+        continue
+
+    # create the tokenizer
+    from transformers import AutoTokenizer
+    tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}")
+
+    chktok = tokenizer.encode(chktxt)
+    chkhsh = sha256(str(chktok).encode()).hexdigest()
+
+    print(f"model: {name}")
+    print(f"tokt: {tokt}")
+    print(f"repo: {model['repo']}")
+    print(f"chktok: {chktok}")
+    print(f"chkhsh: {chkhsh}")
+
+    # print the "pre_tokenizer" content from the tokenizer.json
+    with open(f"models/tokenizers/{name}/tokenizer.json", "r", encoding="utf-8") as f:
+        cfg = json.load(f)
+        pre_tokenizer = cfg["pre_tokenizer"]
+        print("pre_tokenizer: " + json.dumps(pre_tokenizer, indent=4))
+
+    print(f"\n")
+
+    src_ifs += f"        if chkhsh == \"{chkhsh}\":\n"
+    src_ifs += f"            # ref: {model['repo']}\n"
+    src_ifs += f"            res = \"{name}\"\n"
+
+src_func = ""
+src_func +=  "    def get_vocab_base_pre(self, tokenizer) -> str:\n"
+src_func +=  "        # encoding this string and hashing the resulting tokens would (hopefully) give us a unique identifier that\n"
+src_func +=  "        # is specific for the BPE pre-tokenizer used by the model\n"
+src_func +=  "        # we will use this unique identifier to write a \"tokenizer.ggml.pre\" entry in the GGUF file which we can\n"
+src_func +=  "        # use in llama.cpp to implement the same pre-tokenizer\n"
+src_func +=  "\n"
+src_func += f"        chktxt = {repr(chktxt)}\n"
+src_func +=  "\n"
+src_func +=  "        chktok = tokenizer.encode(chktxt)\n"
+src_func +=  "        chkhsh = sha256(str(chktok).encode()).hexdigest()\n"
+src_func +=  "\n"
+src_func +=  "        print(f\"chktok: {chktok}\")\n"
+src_func +=  "        print(f\"chkhsh: {chkhsh}\")\n"
+src_func +=  "\n"
+src_func +=  "        res = None\n"
+src_func +=  "\n"
+src_func +=  "        # NOTE: if you get an error here, you need to update the convert-hf-to-gguf-update.py script\n"
+src_func +=  "        #       or pull the latest version of the model from Huggingface\n"
+src_func +=  "        #       don't edit the hashes manually!\n"
+src_func += f"{src_ifs}\n"
+src_func +=  "        if res is None:\n"
+src_func +=  "            print(\"\\n\")\n"
+src_func +=  "            print(\"**************************************************************************************\")\n"
+src_func +=  "            print(\"** WARNING: The BPE pre-tokenizer was not recognized!\")\n"
+src_func +=  "            print(\"**          There are 2 possible reasons for this:\")\n"
+src_func +=  "            print(\"**          - the model has not been added to convert-hf-to-gguf-update.py yet\")\n"
+src_func +=  "            print(\"**          - the pre-tokenization config has changed upstream\")\n"
+src_func +=  "            print(\"**          Check your model files and convert-hf-to-gguf-update.py and update them accordingly.\")\n"
+src_func +=  "            print(\"** ref:     https://github.com/ggerganov/llama.cpp/pull/6920\")\n"
+src_func +=  "            print(\"**\")\n"
+src_func +=  "            print(f\"** chkhsh:  {chkhsh}\")\n"
+src_func +=  "            print(\"**************************************************************************************\")\n"
+src_func +=  "            print(\"\\n\")\n"
+src_func +=  "            raise NotImplementedError(\"BPE pre-tokenizer was not recognized - update get_vocab_base_pre()\")\n"
+src_func +=  "\n"
+src_func +=  "        print(f\"tokenizer.ggml.pre: {res}\")\n"
+src_func +=  "        print(f\"chkhsh: {chkhsh}\")\n"
+src_func +=  "\n"
+src_func +=  "        return res\n"
+
+print(src_func)
+
+print("\n")
+print("!!! Copy-paste the function above into convert-hf-to-gguf.py !!!")
+print("\n")
+
+# generate tests for each tokenizer model
+
+tests = [
+    "",
+    " ",
+    "  ",
+    "   ",
+    "\t",
+    "\n",
+    "\n\n",
+    "\n\n\n",
+    "\t\n",
+    "Hello world",
+    " Hello world",
+    "Hello World",
+    " Hello World",
+    " Hello World!",
+    "Hello, world!",
+    " Hello, world!",
+    " this is 🦙.cpp",
+    "w048 7tuijk dsdfhu",
+    "нещо на Български",
+    "កាន់តែពិសេសអាចខលចេញ",
+    "🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token)",
+    "Hello",
+    " Hello",
+    "  Hello",
+    "   Hello",
+    "    Hello",
+    "    Hello\n    Hello",
+    " (",
+    "\n =",
+    "' era",
+    "Hello, y'all! How are you 😁 ?我想在apple工作1314151天～",
+    "3",
+    "33",
+    "333",
+    "3333",
+    "33333",
+    "333333",
+    "3333333",
+    "33333333",
+    "333333333",
+    chktxt,
+]
+
+# write the tests to ./models/ggml-vocab-{name}.gguf.inp
+# the format is:
+#
+# test0
+# __ggml_vocab_test__
+# test1
+# __ggml_vocab_test__
+# ...
+#
+
+# with each model, encode all tests and write the results in ./models/ggml-vocab-{name}.gguf.out
+# for each test, write the resulting tokens on a separate line
+
+for model in models:
+    name = model["name"]
+    tokt = model["tokt"]
+
+    # create the tokenizer
+    from transformers import AutoTokenizer
+    tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}")
+
+    with open(f"models/ggml-vocab-{name}.gguf.inp", "w", encoding="utf-8") as f:
+        for text in tests:
+            f.write(f"{text}")
+            f.write("\n__ggml_vocab_test__\n")
+
+    with open(f"models/ggml-vocab-{name}.gguf.out", "w") as f:
+        for text in tests:
+            res = tokenizer.encode(text, add_special_tokens=False)
+            for r in res:
+                f.write(f" {r}")
+            f.write("\n")
+
+    print(f"Tests for {name} written in ./models/ggml-vocab-{name}.gguf.*")
+
+# generate commands for creating vocab files
+
+print("\nRun the following commands to generate the vocab files for testing:\n")
+
+for model in models:
+    name = model["name"]
+
+    print(f"python3 convert-hf-to-gguf.py models/tokenizers/{name}/ --outfile models/ggml-vocab-{name}.gguf --vocab-only")
+
+print("\n")
@@ -11,6 +11,7 @@ import sys
 from abc import ABC, abstractmethod
 from enum import IntEnum
 from pathlib import Path
+from hashlib import sha256
 from typing import TYPE_CHECKING, Any, Callable, ContextManager, Iterator, Sequence, TypeVar, cast

 import numpy as np
@@ -229,7 +230,7 @@ class Model(ABC):
        return (f"pytorch_model-{n:05}-of-{self.num_parts:05}.bin" for n in range(1, self.num_parts + 1))

    # used for GPT-2 BPE and WordPiece vocabs
-    def get_basic_vocab(self) -> tuple[list[str], list[int]]:
+    def get_vocab_base(self) -> tuple[list[str], list[int], str]:
        tokens: list[str] = []
        toktypes: list[int] = []

@@ -238,6 +239,8 @@ class Model(ABC):
        vocab_size = self.hparams.get("vocab_size", len(tokenizer.vocab))
        assert max(tokenizer.vocab.values()) < vocab_size

+        tokpre = self.get_vocab_base_pre(tokenizer)
+
        reverse_vocab = {id_: encoded_tok for encoded_tok, id_ in tokenizer.vocab.items()}
        added_vocab = tokenizer.get_added_vocab()

@@ -255,11 +258,79 @@ class Model(ABC):
                tokens.append(reverse_vocab[i])
                toktypes.append(gguf.TokenType.NORMAL)

-        return tokens, toktypes
+        return tokens, toktypes, tokpre
+
+    # NOTE: this function is generated by convert-hf-to-gguf-update.py
+    #       do not modify it manually!
+    # ref:  https://github.com/ggerganov/llama.cpp/pull/6920
+    def get_vocab_base_pre(self, tokenizer) -> str:
+        # encoding this string and hashing the resulting tokens would (hopefully) give us a unique identifier that
+        # is specific for the BPE pre-tokenizer used by the model
+        # we will use this unique identifier to write a "tokenizer.ggml.pre" entry in the GGUF file which we can
+        # use in llama.cpp to implement the same pre-tokenizer
+
+        chktxt = '\n \n\n \n\n\n \t \t\t \t\n  \n   \n    \n     \n🚀 (normal) 😶\u200d🌫️ (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'
+
+        chktok = tokenizer.encode(chktxt)
+        chkhsh = sha256(str(chktok).encode()).hexdigest()
+
+        print(f"chktok: {chktok}")
+        print(f"chkhsh: {chkhsh}")
+
+        res = None
+
+        # NOTE: if you get an error here, you need to update the convert-hf-to-gguf-update.py script
+        #       or pull the latest version of the model from Huggingface
+        #       don't edit the hashes manually!
+        if chkhsh == "0ef9807a4087ebef797fc749390439009c3b9eda9ad1a097abbe738f486c01e5":
+            # ref: https://huggingface.co/meta-llama/Meta-Llama-3-8B
+            res = "llama-bpe"
+        if chkhsh == "049ecf7629871e3041641907f3de7c733e4dbfdc736f57d882ba0b0845599754":
+            # ref: https://huggingface.co/deepseek-ai/deepseek-llm-7b-base
+            res = "deepseek-llm"
+        if chkhsh == "347715f544604f9118bb75ed199f68779f423cabb20db6de6f31b908d04d7821":
+            # ref: https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-base
+            res = "deepseek-coder"
+        if chkhsh == "8aeee3860c56296a157a1fe2fad249ec40aa59b1bb5709f4ade11c4e6fe652ed":
+            # ref: https://huggingface.co/tiiuae/falcon-7b
+            res = "falcon"
+        if chkhsh == "0876d13b50744004aa9aeae05e7b0647eac9d801b5ba4668afc01e709c15e19f":
+            # ref: https://huggingface.co/BAAI/bge-small-en-v1.5
+            res = "bert-bge"
+        if chkhsh == "b6dc8df998e1cfbdc4eac8243701a65afe638679230920b50d6f17d81c098166":
+            # ref: https://huggingface.co/mosaicml/mpt-7b
+            res = "mpt"
+        if chkhsh == "35d91631860c815f952d711435f48d356ebac988362536bed955d43bfa436e34":
+            # ref: https://huggingface.co/bigcode/starcoder2-3b
+            res = "starcoder"
+        if chkhsh == "3ce83efda5659b07b1ad37ca97ca5797ea4285d9b9ab0dc679e4a720c9da7454":
+            # ref: https://huggingface.co/openai-community/gpt2
+            res = "gpt-2"
+
+        if res is None:
+            print("\n")
+            print("**************************************************************************************")
+            print("** WARNING: The BPE pre-tokenizer was not recognized!")
+            print("**          There are 2 possible reasons for this:")
+            print("**          - the model has not been added to convert-hf-to-gguf-update.py yet")
+            print("**          - the pre-tokenization config has changed upstream")
+            print("**          Check your model files and convert-hf-to-gguf-update.py and update them accordingly.")
+            print("** ref:     https://github.com/ggerganov/llama.cpp/pull/6920")
+            print("**")
+            print(f"** chkhsh:  {chkhsh}")
+            print("**************************************************************************************")
+            print("\n")
+            raise NotImplementedError("BPE pre-tokenizer was not recognized - update get_vocab_base_pre()")
+
+        print(f"tokenizer.ggml.pre: {res}")
+        print(f"chkhsh: {chkhsh}")
+
+        return res

    def _set_vocab_gpt2(self) -> None:
-        tokens, toktypes = self.get_basic_vocab()
+        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)

@@ -277,6 +348,8 @@ class Model(ABC):
        vocab_size = hparams["vocab_size"]
        assert max(tokenizer.get_vocab().values()) < vocab_size

+        tokpre = self.get_vocab_base_pre(tokenizer)
+
        merges = []
        vocab = {}
        mergeable_ranks = tokenizer.mergeable_ranks
@@ -304,6 +377,7 @@ class Model(ABC):
                toktypes.append(gguf.TokenType.NORMAL)

        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)

@@ -363,9 +437,20 @@ class Model(ABC):
                        scores.append(-1000.0)
                        toktypes.append(SentencePieceTokenTypes.USER_DEFINED)

+        if vocab_size > len(tokens):
+            pad_count = vocab_size - len(tokens)
+            print(
+                f"Padding vocab with {pad_count} token(s) - [PAD1] through [PAD{pad_count}]"
+            )
+            for i in range(1, pad_count + 1):
+                tokens.append(f"[PAD{i}]")
+                scores.append(-1000.0)
+                toktypes.append(SentencePieceTokenTypes.UNUSED)
+
        assert len(tokens) == vocab_size

        self.gguf_writer.add_tokenizer_model("llama")
+        self.gguf_writer.add_tokenizer_pre("default")
        self.gguf_writer.add_token_list(tokens)
        self.gguf_writer.add_token_scores(scores)
        self.gguf_writer.add_token_types(toktypes)
@@ -387,6 +472,7 @@ class Model(ABC):
        assert len(tokens) == vocab.vocab_size

        self.gguf_writer.add_tokenizer_model("llama")
+        self.gguf_writer.add_tokenizer_pre("default")
        self.gguf_writer.add_token_list(tokens)
        self.gguf_writer.add_token_scores(scores)
        self.gguf_writer.add_token_types(toktypes)
@@ -830,6 +916,7 @@ class XverseModel(Model):
            toktypes.append(toktype)

        self.gguf_writer.add_tokenizer_model("llama")
+        self.gguf_writer.add_tokenizer_pre("default")
        self.gguf_writer.add_token_list(tokens)
        self.gguf_writer.add_token_types(toktypes)

@@ -1301,15 +1388,23 @@ class LlamaModel(Model):
        try:
            self. _set_vocab_sentencepiece()
        except FileNotFoundError:
-            self._set_vocab_llama_hf()
+            try:
+                self._set_vocab_llama_hf()
+            except (FileNotFoundError, TypeError):
+                # Llama 3
+                self._set_vocab_gpt2()

-        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=False,
-                                          special_token_types = ['prefix', 'suffix', 'middle', 'eot'])
-        special_vocab._set_special_token("prefix", 32007)
-        special_vocab._set_special_token("suffix", 32008)
-        special_vocab._set_special_token("middle", 32009)
-        special_vocab._set_special_token("eot",    32010)
-        special_vocab.add_to_gguf(self.gguf_writer)
+        # Apply to CodeLlama only (and ignore for Llama 3 with a vocab size of 128256)
+        if self.hparams.get("vocab_size", 32000) == 32016:
+            special_vocab = gguf.SpecialVocab(
+                self.dir_model, load_merges=False,
+                special_token_types = ['prefix', 'suffix', 'middle', 'eot']
+            )
+            special_vocab._set_special_token("prefix", 32007)
+            special_vocab._set_special_token("suffix", 32008)
+            special_vocab._set_special_token("middle", 32009)
+            special_vocab._set_special_token("eot",    32010)
+            special_vocab.add_to_gguf(self.gguf_writer)

    def set_gguf_parameters(self):
        super().set_gguf_parameters()
@@ -1317,6 +1412,11 @@ class LlamaModel(Model):
        self.gguf_writer.add_vocab_size(hparams["vocab_size"])
        self.gguf_writer.add_rope_dimension_count(hparams["hidden_size"] // hparams["num_attention_heads"])

+        if self.hparams.get("rope_scaling") is not None and "factor" in self.hparams["rope_scaling"]:
+            if self.hparams["rope_scaling"].get("type") == "linear":
+                self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR)
+                self.gguf_writer.add_rope_scaling_factor(self.hparams["rope_scaling"]["factor"])
+
    # Same as super class, but permuting q_proj, k_proj
    def write_tensors(self):
        block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers", self.hparams.get("n_layer")))
@@ -1781,6 +1881,12 @@ class QwenModel(Model):
 class Qwen2Model(Model):
    model_arch = gguf.MODEL_ARCH.QWEN2

+    def set_vocab(self):
+        try:
+            self._set_vocab_sentencepiece()
+        except FileNotFoundError:
+            self._set_vocab_gpt2()
+

@Model.register("Qwen2MoeForCausalLM")
 class Qwen2MoeModel(Model):
@@ -1971,6 +2077,92 @@ class Phi2Model(Model):
        self.gguf_writer.add_add_bos_token(False)


+@Model.register("Phi3ForCausalLM")
+class Phi3MiniModel(Model):
+    model_arch = gguf.MODEL_ARCH.PHI3
+
+    def set_vocab(self):
+        from sentencepiece import SentencePieceProcessor
+
+        tokenizer_path = self.dir_model / 'tokenizer.model'
+
+        if not tokenizer_path.is_file():
+            print(f'Error: Missing {tokenizer_path}', file=sys.stderr)
+            sys.exit(1)
+
+        tokenizer = SentencePieceProcessor(str(tokenizer_path))
+
+        vocab_size = self.hparams.get('vocab_size', tokenizer.vocab_size())
+
+        tokens: list[bytes] = [f"[PAD{i}]".encode("utf-8") for i in range(vocab_size)]
+        scores: list[float] = [-10000.0] * vocab_size
+        toktypes: list[int] = [SentencePieceTokenTypes.UNKNOWN] * vocab_size
+
+        for token_id in range(tokenizer.vocab_size()):
+
+            piece = tokenizer.id_to_piece(token_id)
+            text = piece.encode("utf-8")
+            score = tokenizer.get_score(token_id)
+
+            toktype = SentencePieceTokenTypes.NORMAL
+            if tokenizer.is_unknown(token_id):
+                toktype = SentencePieceTokenTypes.UNKNOWN
+            elif tokenizer.is_control(token_id):
+                toktype = SentencePieceTokenTypes.CONTROL
+            elif tokenizer.is_unused(token_id):
+                toktype = SentencePieceTokenTypes.UNUSED
+            elif tokenizer.is_byte(token_id):
+                toktype = SentencePieceTokenTypes.BYTE
+
+            tokens[token_id] = text
+            scores[token_id] = score
+            toktypes[token_id] = toktype
+
+        added_tokens_file = self.dir_model / 'added_tokens.json'
+        if added_tokens_file.is_file():
+            with open(added_tokens_file, "r", encoding="utf-8") as f:
+                added_tokens_json = json.load(f)
+
+                for key in added_tokens_json:
+                    token_id = added_tokens_json[key]
+                    if (token_id >= vocab_size):
+                        print(f'ignore token {token_id}: id is out of range, max={vocab_size - 1}')
+                        continue
+
+                    tokens[token_id] = key.encode("utf-8")
+                    scores[token_id] = -1000.0
+                    toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED
+
+        self.gguf_writer.add_tokenizer_model("llama")
+        self.gguf_writer.add_tokenizer_pre("default")
+        self.gguf_writer.add_token_list(tokens)
+        self.gguf_writer.add_token_scores(scores)
+        self.gguf_writer.add_token_types(toktypes)
+
+        special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
+        special_vocab.add_to_gguf(self.gguf_writer)
+
+    def set_gguf_parameters(self):
+        block_count = self.find_hparam(["num_hidden_layers", "n_layer"])
+
+        rot_pct = 1.0
+        n_embd = self.find_hparam(["hidden_size", "n_embd"])
+        n_head = self.find_hparam(["num_attention_heads", "n_head"])
+        rms_eps = self.find_hparam(["rms_norm_eps"])
+
+        self.gguf_writer.add_name("Phi3")
+        self.gguf_writer.add_context_length(self.find_hparam(["n_positions", "max_position_embeddings"]))
+
+        self.gguf_writer.add_embedding_length(n_embd)
+        self.gguf_writer.add_feed_forward_length(8192)
+        self.gguf_writer.add_block_count(block_count)
+        self.gguf_writer.add_head_count(n_head)
+        self.gguf_writer.add_head_count_kv(n_head)
+        self.gguf_writer.add_layer_norm_rms_eps(rms_eps)
+        self.gguf_writer.add_rope_dimension_count(int(rot_pct * n_embd) // n_head)
+        self.gguf_writer.add_file_type(self.ftype)
+
+
@Model.register("PlamoForCausalLM")
 class PlamoModel(Model):
    model_arch = gguf.MODEL_ARCH.PLAMO
@@ -2185,6 +2377,7 @@ class InternLM2Model(Model):
                    toktypes.append(SentencePieceTokenTypes.USER_DEFINED)

        self.gguf_writer.add_tokenizer_model("llama")
+        self.gguf_writer.add_tokenizer_pre("default")
        self.gguf_writer.add_token_list(tokens)
        self.gguf_writer.add_token_scores(scores)
        self.gguf_writer.add_token_types(toktypes)
@@ -2194,6 +2387,8 @@ class InternLM2Model(Model):
        old_eos = special_vocab.special_token_ids["eos"]
        if "chat" in os.path.basename(self.dir_model.absolute()):
            # For the chat model, we replace the eos with '<|im_end|>'.
+            # TODO: this is a hack, should be fixed
+            #       https://github.com/ggerganov/llama.cpp/pull/6745#issuecomment-2067687048
            special_vocab.special_token_ids["eos"] = self._try_get_sft_eos(tokenizer)
            print(f"Replace eos:{old_eos} with a special token:{special_vocab.special_token_ids['eos']} \
 in chat mode so that the conversation can end normally.")
@@ -2332,7 +2527,7 @@ class BertModel(Model):
            self.gguf_writer.add_pooling_type(pooling_type)

    def set_vocab(self):
-        tokens, toktypes = self.get_basic_vocab()
+        tokens, toktypes, tokpre = self.get_vocab_base()
        self.vocab_size = len(tokens)

        # we need this to validate the size of the token_type embeddings
@@ -2350,6 +2545,7 @@ class BertModel(Model):

        # add vocab to gguf
        self.gguf_writer.add_tokenizer_model("bert")
+        self.gguf_writer.add_tokenizer_pre(tokpre)
        self.gguf_writer.add_token_list(tokens)
        self.gguf_writer.add_token_types(toktypes)

@@ -2371,6 +2567,10 @@ class BertModel(Model):
                print(f"Can not map tensor {name!r}")
                sys.exit()

+            # convert any unsupported data types to float32
+            if data_torch.dtype not in (torch.float16, torch.float32):
+                data_torch = data_torch.to(torch.float32)
+
            data = data_torch.squeeze().numpy()
            n_dims = len(data.shape)
            new_dtype: type[np.floating[Any]]
@@ -2429,12 +2629,15 @@ class GemmaModel(Model):

    def set_vocab(self):
        self._set_vocab_sentencepiece()
+
+        # TODO: these special tokens should be exported only for the CodeGemma family
        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=False,
-                                          special_token_types = ['prefix', 'suffix', 'middle', 'eot'])
+                                          special_token_types = ['prefix', 'suffix', 'middle', 'fsep', 'eot'])
        special_vocab._set_special_token("prefix", 67)
        special_vocab._set_special_token("suffix", 69)
        special_vocab._set_special_token("middle", 68)
-        special_vocab._set_special_token("eot",    70)
+        special_vocab._set_special_token("fsep",   70)
+        special_vocab._set_special_token("eot",    107)
        special_vocab.add_to_gguf(self.gguf_writer)

    def set_gguf_parameters(self):
@@ -2458,6 +2661,12 @@ class GemmaModel(Model):
        tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)

        for name, data_torch in self.get_tensors():
+            # lm_head is not used in llama.cpp, while autoawq will include this tensor in model
+            # To prevent errors, skip loading lm_head.weight.
+            if name == "lm_head.weight":
+                print(f"Skipping get tensor {name!r} in safetensors so that convert can end normally.")
+                continue
+
            old_dtype = data_torch.dtype

            # convert any unsupported data types to float32
@@ -2517,28 +2726,37 @@ class MambaModel(Model):

            field = neox_reader.get_field(gguf.Keys.Tokenizer.MODEL)
            self.gguf_writer.add_tokenizer_model(bytes(field.parts[-1]))
+
+            field = neox_reader.get_field(gguf.Keys.Tokenizer.PRE)
+            self.gguf_writer.add_tokenizer_pre(bytes(field.parts[-1]))
+
            field = neox_reader.get_field(gguf.Keys.Tokenizer.LIST)
            self.gguf_writer.add_token_list([bytes(field.parts[i]) for i in field.data][:vocab_size])
+
            field = neox_reader.get_field(gguf.Keys.Tokenizer.TOKEN_TYPE)
            self.gguf_writer.add_token_types([field.parts[i].tolist()[0] for i in field.data][:vocab_size])
+
            field = neox_reader.get_field(gguf.Keys.Tokenizer.MERGES)
            self.gguf_writer.add_token_merges([bytes(field.parts[i]) for i in field.data])
+
            field = neox_reader.get_field(gguf.Keys.Tokenizer.BOS_ID)
            self.gguf_writer.add_bos_token_id(field.parts[-1].tolist()[0])
+
            field = neox_reader.get_field(gguf.Keys.Tokenizer.EOS_ID)
            self.gguf_writer.add_eos_token_id(field.parts[-1].tolist()[0])
+
            field = neox_reader.get_field(gguf.Keys.Tokenizer.UNK_ID)
            self.gguf_writer.add_unk_token_id(field.parts[-1].tolist()[0])

    def set_gguf_parameters(self):
-        d_model = self.find_hparam(["hidden_size", "d_model"])
-        d_conv  = self.find_hparam(["conv_kernel", "d_conv"], optional=True) or 4
+        d_model = self.find_hparam(["hidden_size",       "d_model"])
+        d_conv  = self.find_hparam(["conv_kernel",       "d_conv"],  optional=True) or 4
        d_inner = self.find_hparam(["intermediate_size", "d_inner"], optional=True) or 2 * d_model
-        d_state = self.find_hparam(["state_size", "d_state"], optional=True) or 16
+        d_state = self.find_hparam(["state_size",        "d_state"], optional=True) or 16
        # ceiling division
        # ref: https://stackoverflow.com/a/17511341/22827863
        # ref: https://github.com/state-spaces/mamba/blob/ce59daea3a090d011d6476c6e5b97f6d58ddad8b/mamba_ssm/modules/mamba_simple.py#L58
-        dt_rank = self.find_hparam(["time_step_rank", "dt_rank"], optional=True) or -(d_model // -16)
+        dt_rank      = self.find_hparam(["time_step_rank",     "dt_rank"],      optional=True) or -(d_model // -16)
        rms_norm_eps = self.find_hparam(["layer_norm_epsilon", "rms_norm_eps"], optional=True) or 1e-5

        # Fail early for models which don't have a block expansion factor of 2
@@ -2630,6 +2848,66 @@ class CommandR2Model(Model):
        self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)


+@Model.register("OlmoForCausalLM")
+@Model.register("OLMoForCausalLM")
+class OlmoModel(Model):
+    model_arch = gguf.MODEL_ARCH.OLMO
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self.gguf_writer.add_layer_norm_eps(1e-5)
+        if "clip_qkv" in self.hparams is not None:
+            self.gguf_writer.add_clamp_kqv(self.hparams["clip_qkv"])
+
+    # Same as super class, but permuting q_proj, k_proj
+    # Copied from: LlamaModel
+    def write_tensors(self):
+        block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers", self.hparams.get("n_layer")))
+        tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
+        n_head = self.hparams.get("num_attention_heads")
+        n_kv_head = self.hparams.get("num_key_value_heads")
+        for name, data_torch in self.get_tensors():
+            old_dtype = data_torch.dtype
+
+            # convert any unsupported data types to float32
+            if data_torch.dtype not in (torch.float16, torch.float32):
+                data_torch = data_torch.to(torch.float32)
+
+            data = data_torch.numpy()
+
+            if name.endswith("q_proj.weight"):
+                data = permute(data, n_head, n_head)
+            if name.endswith("k_proj.weight"):
+                data = permute(data, n_head, n_kv_head)
+
+            data = data.squeeze()
+
+            # map tensor names
+            new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
+            if new_name is None:
+                print(f"Can not map tensor {name!r}")
+                sys.exit()
+
+            n_dims = len(data.shape)
+            data_dtype = data.dtype
+
+            # if f32 desired, convert any float16 to float32
+            if self.ftype == 0 and data_dtype == np.float16:
+                data = data.astype(np.float32)
+
+            # 1d tensors need to be converted to float32
+            if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1:
+                data = data.astype(np.float32)
+
+            # if f16 desired, convert any float32 2-dim weight tensors to float16
+            if self.ftype == 1 and data_dtype == np.float32 and n_dims == 2:
+                data = data.astype(np.float16)
+
+            print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")
+
+            self.gguf_writer.add_tensor(new_name, data)
+
+
 ###### CONVERSION LOGIC ######


@@ -2657,6 +2935,7 @@ def parse_args() -> argparse.Namespace:
        help="directory containing model file",
    )
    parser.add_argument("--use-temp-file", action="store_true", help="use the tempfile library while processing (helpful when running out of memory, process killed)")
+    parser.add_argument("--model-name", type=str, default=None, help="name of the model")

    return parser.parse_args()

@@ -281,6 +281,7 @@ class GGMLToGGUF:
    def add_vocab(self, gguf_writer):
        hp = self.model.hyperparameters
        gguf_writer.add_tokenizer_model('llama')
+        gguf_writer.add_tokenizer_pre('default')
        tokens = []
        scores = []
        toktypes = []
@@ -99,6 +99,7 @@ def main():

    tokens, scores, toktypes = _get_sentencepiece_tokenizer_info(args.model_dir)
    gguf_writer.add_tokenizer_model('llama')
+    gguf_writer.add_tokenizer_pre('default')
    gguf_writer.add_token_list(tokens)
    gguf_writer.add_token_scores(scores)
    gguf_writer.add_token_types(toktypes)
@@ -525,7 +525,14 @@ class LlamaHfVocab(Vocab):

        # pre-check so we know if we need transformers
        tokenizer_model: dict[str, Any] = tokenizer_json['model']
-        if (
+        is_llama3 = (
+            tokenizer_model['type'] == 'BPE' and tokenizer_model.get('ignore_merges', False)
+            and not tokenizer_model.get('byte_fallback', True)
+        )
+        if is_llama3:
+            raise TypeError('Llama 3 must be converted with BpeVocab')
+
+        if not is_llama3 and (
            tokenizer_model['type'] != 'BPE' or not tokenizer_model.get('byte_fallback', False)
            or tokenizer_json['decoder']['type'] != 'Sequence'
        ):
@@ -32,7 +32,7 @@ int main(int argc, char ** argv) {
    gpt_params params;

    if (argc == 1 || argv[1][0] == '-') {
-        printf("usage: %s MODEL_PATH [N_KV_MAX] [N_BATCH] [N_UBATCH] [IS_PP_SHARED] [NGL] <PP> <TG> <PL>\n" , argv[0]);
+        printf("usage: %s MODEL_PATH [N_KV_MAX] [N_BATCH] [N_UBATCH] [FATTN] [IS_PP_SHARED] [NGL] <PP> <TG> <PL>\n" , argv[0]);
        printf("  <PP>, <TG> and PL are comma-separated lists of numbers without spaces\n\n");
        printf("  example: %s ggml-model-f16.gguf 2048 2048 512 0 999 128,256,512 128,256 1,2,4,8,16,32\n\n", argv[0]);
        return 1 ;
@@ -41,6 +41,7 @@ int main(int argc, char ** argv) {
    int n_kv_max     = 2048;
    int n_batch      = 2048;
    int n_ubatch     = 512;
+    bool flash_attn  = false;
    int is_pp_shared = 0;
    int n_gpu_layers = 0;

@@ -66,23 +67,27 @@ int main(int argc, char ** argv) {
    }

    if (argc >= 6) {
-        is_pp_shared = std::atoi(argv[5]);
+        flash_attn = std::atoi(argv[5]);
    }

    if (argc >= 7) {
-        n_gpu_layers = std::atoi(argv[6]);
+        is_pp_shared = std::atoi(argv[6]);
    }

    if (argc >= 8) {
-        n_pp = parse_list(argv[7]);
+        n_gpu_layers = std::atoi(argv[7]);
    }

    if (argc >= 9) {
-        n_tg = parse_list(argv[8]);
+        n_pp = parse_list(argv[8]);
    }

    if (argc >= 10) {
-        n_pl = parse_list(argv[9]);
+        n_tg = parse_list(argv[9]);
+    }
+
+    if (argc >= 11) {
+        n_pl = parse_list(argv[10]);
    }

    // init LLM
@@ -108,10 +113,11 @@ int main(int argc, char ** argv) {

    llama_context_params ctx_params = llama_context_default_params();

-    ctx_params.seed      = 1234;
-    ctx_params.n_ctx     = n_kv_max;
-    ctx_params.n_batch   = n_batch;
-    ctx_params.n_ubatch  = n_ubatch;
+    ctx_params.seed       = 1234;
+    ctx_params.n_ctx      = n_kv_max;
+    ctx_params.n_batch    = n_batch;
+    ctx_params.n_ubatch   = n_ubatch;
+    ctx_params.flash_attn = flash_attn;

    ctx_params.n_threads       = params.n_threads;
    ctx_params.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch;
@@ -169,7 +175,7 @@ int main(int argc, char ** argv) {
    }

    LOG_TEE("\n");
-    LOG_TEE("%s: n_kv_max = %d, n_batch = %d, n_ubatch = %d, is_pp_shared = %d, n_gpu_layers = %d, n_threads = %u, n_threads_batch = %u\n", __func__, n_kv_max, n_batch, n_ubatch, is_pp_shared, n_gpu_layers, ctx_params.n_threads, ctx_params.n_threads_batch);
+    LOG_TEE("%s: n_kv_max = %d, n_batch = %d, n_ubatch = %d, flash_attn = %d, is_pp_shared = %d, n_gpu_layers = %d, n_threads = %u, n_threads_batch = %u\n", __func__, n_kv_max, n_batch, n_ubatch, flash_attn, is_pp_shared, n_gpu_layers, ctx_params.n_threads, ctx_params.n_threads_batch);
    LOG_TEE("\n");

    LOG_TEE("|%6s | %6s | %4s | %6s | %8s | %8s | %8s | %8s | %8s | %8s |\n", "PP",     "TG",     "B",    "N_KV",     "T_PP s",   "S_PP t/s", "T_TG s",   "S_TG t/s", "T s",      "S t/s");
@@ -153,7 +153,7 @@ while n_cur <= n_len {
        // const llama_token new_token_id = llama_sample_token_greedy(ctx, &candidates_p);

        // is it an end of stream? -> mark the stream as finished
-        if new_token_id == llama_token_eos(model) || n_cur == n_len {
+        if llama_token_is_eog(model, new_token_id) || n_cur == n_len {
            i_batch[i] = -1
            // print("")
            if n_parallel > 1 {
@@ -229,7 +229,7 @@ private func tokenize(text: String, add_bos: Bool) -> [llama_token] {

 private func token_to_piece(token: llama_token, buffer: inout [CChar]) -> String? {
    var result = [CChar](repeating: 0, count: 8)
-    let nTokens = llama_token_to_piece(model, token, &result, Int32(result.count))
+    let nTokens = llama_token_to_piece(model, token, &result, Int32(result.count), false)
    if nTokens < 0 {
        let actualTokensCount = -Int(nTokens)
        result = .init(repeating: 0, count: actualTokensCount)
@@ -237,7 +237,8 @@ private func token_to_piece(token: llama_token, buffer: inout [CChar]) -> String
            model,
            token,
            &result,
-            Int32(result.count)
+            Int32(result.count),
+            false
        )
        assert(check == actualTokensCount)
    } else {
@@ -191,8 +191,8 @@ int main(int argc, char ** argv) {

            //const llama_token new_token_id = llama_sample_token_greedy(ctx, &candidates_p);

-            // is it an end of stream? -> mark the stream as finished
-            if (new_token_id == llama_token_eos(model) || n_cur == n_len) {
+            // is it an end of generation? -> mark the stream as finished
+            if (llama_token_is_eog(model, new_token_id) || n_cur == n_len) {
                i_batch[i] = -1;
                LOG_TEE("\n");
                if (n_parallel > 1) {
@@ -47,7 +47,7 @@ struct beam_search_callback_data {
 // In this case, end-of-beam (eob) is equivalent to end-of-sentence (eos) but this need not always be the same.
 // For example, eob can be flagged due to maximum token length, stop words, etc.
 static bool is_at_eob(const beam_search_callback_data & callback_data, const llama_token * tokens, size_t n_tokens) {
-    return n_tokens && tokens[n_tokens-1] == llama_token_eos(llama_get_model(callback_data.ctx));
+    return n_tokens && llama_token_is_eog(llama_get_model(callback_data.ctx), tokens[n_tokens-1]);
 }

 // Function matching type llama_beam_search_callback_fn_t.
@@ -4,16 +4,16 @@ set -eu

 if [ $# -lt 1 ]
 then
-  echo "usage:   $0 path_to_build_binary [path_to_temp_folder]"
-  echo "example: $0 ../../build/bin ../../tmp"
-  exit 1
+    echo "usage:   $0 path_to_build_binary [path_to_temp_folder]"
+    echo "example: $0 ../../build/bin ../../tmp"
+    exit 1
 fi

 if [ $# -gt 1 ]
 then
-  TMP_DIR=$2
+    TMP_DIR=$2
 else
-  TMP_DIR=/tmp
+    TMP_DIR=/tmp
 fi

 set -x
@@ -21,7 +21,7 @@ set -x
 SPLIT=$1/gguf-split
 MAIN=$1/main
 WORK_PATH=$TMP_DIR/gguf-split
-CUR_DIR=$(pwd)
+ROOT_DIR=$(realpath $(dirname $0)/../../)

 mkdir -p "$WORK_PATH"

@@ -30,8 +30,8 @@ rm -f $WORK_PATH/ggml-model-split*.gguf $WORK_PATH/ggml-model-merge*.gguf

 # 1. Get a model
 (
-  cd $WORK_PATH
-  "$CUR_DIR"/../../scripts/hf.sh --repo ggml-org/gemma-1.1-2b-it-Q8_0-GGUF --file gemma-1.1-2b-it.Q8_0.gguf
+cd $WORK_PATH
+"$ROOT_DIR"/scripts/hf.sh --repo ggml-org/gemma-1.1-2b-it-Q8_0-GGUF --file gemma-1.1-2b-it.Q8_0.gguf
 )
 echo PASS

@@ -23,6 +23,7 @@ struct Stats {
 };

 struct StatParams {
+    std::string dataset;
    std::string ofile = "imatrix.dat";
    int         n_output_frequency = 10;
    int         verbosity = 1;
@@ -44,9 +45,9 @@ private:
    std::mutex                             m_mutex;
    int                                    m_last_call = 0;
    std::vector<float>                     m_src1_data;
-    std::vector<int>                       m_ids; // the expert ids from ggml_mul_mat_id
+    std::vector<char>                      m_ids; // the expert ids from ggml_mul_mat_id
                                                  //
-    void save_imatrix(const char * file_name) const;
+    void save_imatrix(const char * file_name, const char * dataset) const;
    void keep_imatrix(int ncall) const;
 };

@@ -81,6 +82,7 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
    if (ask) {
        if (t->op == GGML_OP_MUL_MAT_ID) return true; // collect all indirect matrix multiplications
        if (t->op != GGML_OP_MUL_MAT) return false;
+        // why are small batches ignored (<16 tokens)?
        if (src1->ne[1] < 16 || src1->type != GGML_TYPE_F32) return false;
        if (!(wname.substr(0, 4) == "blk." || (m_params.collect_output_weight && wname == "output.weight"))) return false;
        return true;
@@ -101,14 +103,19 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
    // this has been adapted to the new format of storing merged experts in a single 3d tensor
    // ref: https://github.com/ggerganov/llama.cpp/pull/6387
    if (t->op == GGML_OP_MUL_MAT_ID) {
-        const int idx  = ((int32_t *) t->op_params)[0];
+        //   ids  -> [n_experts_used, n_tokens]
+        //   src1 -> [cols, n_expert_used, n_tokens]
        const ggml_tensor * ids = t->src[2];
        const int n_as = src0->ne[2];
+        const int n_ids = ids->ne[0];

        // the top-k selected expert ids are stored in the ids tensor
        // for simplicity, always copy ids to host, because it is small
-        GGML_ASSERT(ids->ne[1] == src1->ne[1]);
-        m_ids.resize(ggml_nbytes(ids)/sizeof(int));
+        // take into account that ids is not contiguous!
+
+        GGML_ASSERT(ids->ne[1] == src1->ne[2]);
+
+        m_ids.resize(ggml_nbytes(ids));
        ggml_backend_tensor_get(ids, m_ids.data(), 0, ggml_nbytes(ids));

        auto & e = m_stats[wname];
@@ -118,26 +125,35 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
        //       using the following line, we can correct for that if needed by replacing the line above with:
        //if (idx == t->src[0]->ne[0] - 1) ++e.ncall;

+        if (e.values.empty()) {
+            e.values.resize(src1->ne[0]*n_as, 0);
+        }
+        else if (e.values.size() != (size_t)src1->ne[0]*n_as) {
+            fprintf(stderr, "Oops: inconsistent size for %s (%d vs %d)\n", wname.c_str(), (int)e.values.size(), (int)src1->ne[0]*n_as);
+            exit(1); //GGML_ASSERT(false);
+        }
+        if (m_params.verbosity > 1) {
+            printf("%s[%d]: %32s, %s, %5d x %5d, %d\n", __func__, m_last_call, wname.c_str(), ggml_op_name(t->op), (int)src1->ne[0], (int)src1->ne[2], (int)src1->type);
+        }
        // loop over all possible experts, regardless if they are used or not in the batch
        for (int ex = 0; ex < n_as; ++ex) {
            size_t e_start = ex*src1->ne[0];
-            if (e.values.empty()) {
-                e.values.resize(src1->ne[0]*n_as, 0);
-            }
-            else if (e.values.size() != (size_t)src1->ne[0]*n_as) {
-                fprintf(stderr, "Oops: inconsistent size for %s (%d vs %d)\n", wname.c_str(), (int)e.values.size(), (int)src1->ne[0]*n_as);
-                exit(1); //GGML_ASSERT(false);
-            }
-            if (m_params.verbosity > 1) {
-                printf("%s[%d]: %32s, %s, %5d x %5d, %d\n", __func__, m_last_call, wname.c_str(), ggml_op_name(t->op), (int)src1->ne[0], (int)src1->ne[1], (int)src1->type);
-            }
-            for (int row = 0; row < (int)src1->ne[1]; ++row) {
-                const int excur = m_ids[row*n_as + idx];
-                GGML_ASSERT(excur >= 0 && excur < n_as); // sanity check
-                if (excur != ex) continue;
-                const float * x = data + row * src1->ne[0];
-                for (int j = 0; j < (int)src1->ne[0]; ++j) {
-                    e.values[e_start + j] += x[j]*x[j];
+
+            for (int idx = 0; idx < n_ids; ++idx) {
+                for (int row = 0; row < (int)src1->ne[2]; ++row) {
+                    const int excur = *(const int32_t *) (m_ids.data() + row*ids->nb[1] + idx*ids->nb[0]);
+
+                    GGML_ASSERT(excur >= 0 && excur < n_as); // sanity check
+
+                    if (excur != ex) continue;
+
+                    const int64_t i11 = idx % src1->ne[1];
+                    const int64_t i12 = row;
+                    const float * x = (const float *)((const char *)data + i11*src1->nb[1] + i12*src1->nb[2]);
+
+                    for (int j = 0; j < (int)src1->ne[0]; ++j) {
+                        e.values[e_start + j] += x[j]*x[j];
+                    }
                }
            }
            if (e.ncall > m_last_call) {
@@ -184,7 +200,7 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
 }

 void IMatrixCollector::save_imatrix() const {
-    save_imatrix(m_params.ofile.empty() ? "imatrix.dat" : m_params.ofile.c_str());
+    save_imatrix(m_params.ofile.empty() ? "imatrix.dat" : m_params.ofile.c_str(), m_params.dataset.c_str());
 }

 void IMatrixCollector::keep_imatrix(int ncall) const {
@@ -192,24 +208,33 @@ void IMatrixCollector::keep_imatrix(int ncall) const {
    if (file_name.empty()) file_name = "imatrix.dat";
    file_name += ".at_";
    file_name += std::to_string(ncall);
-    save_imatrix(file_name.c_str());
+    save_imatrix(file_name.c_str(), m_params.dataset.c_str());
 }

-void IMatrixCollector::save_imatrix(const char * fname) const {
+void IMatrixCollector::save_imatrix(const char * fname, const char * dataset) const {
    std::ofstream out(fname, std::ios::binary);
    int n_entries = m_stats.size();
-    out.write((const char*)&n_entries, sizeof(n_entries));
-    for (auto& p : m_stats) {
+    out.write((const char *) &n_entries, sizeof(n_entries));
+    for (const auto & p : m_stats) {
        int len = p.first.size();
-        out.write((const char*)&len, sizeof(len));
+        out.write((const char *) &len, sizeof(len));
        out.write(p.first.c_str(), len);
-        out.write((const char*)&p.second.ncall, sizeof(p.second.ncall));
+        out.write((const char *) &p.second.ncall, sizeof(p.second.ncall));
        int nval = p.second.values.size();
-        out.write((const char*)&nval, sizeof(nval));
-        if (nval > 0) out.write((const char*)p.second.values.data(), nval*sizeof(float));
+        out.write((const char *) &nval, sizeof(nval));
+        if (nval > 0) out.write((const char *) p.second.values.data(), nval * sizeof(float));
    }
+
+    // Write the number of call the matrix was computed with
+    out.write((const char *) &m_last_call, sizeof(m_last_call));
+
+    // Write the dataset name at the end of the file to later on specify it in quantize
+    int n_dataset = strlen(dataset);
+    out.write((const char *) &n_dataset, sizeof(n_dataset));
+    out.write(dataset, n_dataset);
+
    if (m_params.verbosity > 0) {
-        fprintf(stderr, "\n%s: stored collected data after %d chunks in %s\n",__func__,m_last_call,fname);
+        fprintf(stderr, "\n%s: stored collected data after %d chunks in %s\n", __func__, m_last_call, fname);
    }
 }

@@ -532,6 +557,29 @@ int main(int argc, char ** argv) {
        }
    }

+    gpt_params params;
+    params.n_batch = 512;
+    if (!gpt_params_parse(args.size(), args.data(), params)) {
+        return 1;
+    }
+
+    params.logits_all = true;
+    params.n_batch = std::min(params.n_batch, params.n_ctx);
+
+    print_build_info();
+
+    if (params.seed == LLAMA_DEFAULT_SEED) {
+        params.seed = time(NULL);
+    }
+
+    fprintf(stderr, "%s: seed  = %u\n", __func__, params.seed);
+
+    std::mt19937 rng(params.seed);
+    if (params.random_prompt) {
+        params.prompt = gpt_random_prompt(rng);
+    }
+
+    sparams.dataset = params.prompt_file;
    g_collector.set_parameters(std::move(sparams));

    if (!combine_files.empty()) {
@@ -570,28 +618,6 @@ int main(int argc, char ** argv) {
        }
    }

-    gpt_params params;
-    params.n_batch = 512;
-    if (!gpt_params_parse(args.size(), args.data(), params)) {
-        return 1;
-    }
-
-    params.logits_all = true;
-    params.n_batch = std::min(params.n_batch, params.n_ctx);
-
-    print_build_info();
-
-    if (params.seed == LLAMA_DEFAULT_SEED) {
-        params.seed = time(NULL);
-    }
-
-    fprintf(stderr, "%s: seed  = %u\n", __func__, params.seed);
-
-    std::mt19937 rng(params.seed);
-    if (params.random_prompt) {
-        params.prompt = gpt_random_prompt(rng);
-    }
-
    llama_backend_init();
    llama_numa_init(params.numa);

@@ -586,7 +586,7 @@ int main(int argc, char ** argv) {

            // deal with eot token in infill mode
            if ((llama_sampling_last(ctx_sampling) == llama_token_eot(model) || is_interacting) && params.interactive){
-                if(is_interacting && !params.interactive_first) {
+                if (is_interacting && !params.interactive_first) {
                    // print an eot token
                    printf("%s", llama_token_to_piece(ctx, llama_token_eot(model)).c_str());
                }
@@ -651,8 +651,8 @@ int main(int argc, char ** argv) {
                // LOG_TEE("took new input\n");
                is_interacting = false;
            }
-            // deal with end of text token in interactive mode
-            else if (llama_sampling_last(ctx_sampling) == llama_token_eos(model)) {
+            // deal with end of generation tokens in interactive mode
+            else if (llama_token_is_eog(model, llama_sampling_last(ctx_sampling))) {
                LOG("found EOS token\n");

                if (params.interactive) {
@@ -731,8 +731,8 @@ int main(int argc, char ** argv) {
            }
        }

-        // end of text token
-        if (!embd.empty() && embd.back() == llama_token_eos(model) && !params.interactive) {
+        // end of generation
+        if (!embd.empty() && llama_token_is_eog(model, embd.back()) && !params.interactive) {
            break;
        }

@@ -174,6 +174,7 @@ struct cmd_params {
    std::vector<llama_split_mode> split_mode;
    std::vector<int> main_gpu;
    std::vector<bool> no_kv_offload;
+    std::vector<bool> flash_attn;
    std::vector<std::vector<float>> tensor_split;
    std::vector<bool> use_mmap;
    std::vector<bool> embeddings;
@@ -195,6 +196,7 @@ static const cmd_params cmd_params_defaults = {
    /* split_mode    */ {LLAMA_SPLIT_MODE_LAYER},
    /* main_gpu      */ {0},
    /* no_kv_offload */ {false},
+    /* flash_attn    */ {false},
    /* tensor_split  */ {std::vector<float>(llama_max_devices(), 0.0f)},
    /* use_mmap      */ {true},
    /* embeddings    */ {false},
@@ -220,6 +222,7 @@ static void print_usage(int /* argc */, char ** argv) {
    printf("  -sm, --split-mode <none|layer|row>  (default: %s)\n", join(transform_to_str(cmd_params_defaults.split_mode, split_mode_str), ",").c_str());
    printf("  -mg, --main-gpu <i>                 (default: %s)\n", join(cmd_params_defaults.main_gpu, ",").c_str());
    printf("  -nkvo, --no-kv-offload <0|1>        (default: %s)\n", join(cmd_params_defaults.no_kv_offload, ",").c_str());
+    printf("  -fa, --flash-attn <0|1>             (default: %s)\n", join(cmd_params_defaults.flash_attn, ",").c_str());
    printf("  -mmp, --mmap <0|1>                  (default: %s)\n", join(cmd_params_defaults.use_mmap, ",").c_str());
    printf("  -embd, --embeddings <0|1>           (default: %s)\n", join(cmd_params_defaults.embeddings, ",").c_str());
    printf("  -ts, --tensor-split <ts0/ts1/..>    (default: 0)\n");
@@ -393,6 +396,13 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
            }
            auto p = split<bool>(argv[i], split_delim);
            params.no_kv_offload.insert(params.no_kv_offload.end(), p.begin(), p.end());
+        } else if (arg == "-fa" || arg == "--flash-attn") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            auto p = split<bool>(argv[i], split_delim);
+            params.flash_attn.insert(params.flash_attn.end(), p.begin(), p.end());
        } else if (arg == "-mmp" || arg == "--mmap") {
            if (++i >= argc) {
                invalid_param = true;
@@ -477,6 +487,7 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
    if (params.split_mode.empty())   { params.split_mode = cmd_params_defaults.split_mode; }
    if (params.main_gpu.empty())     { params.main_gpu = cmd_params_defaults.main_gpu; }
    if (params.no_kv_offload.empty()){ params.no_kv_offload = cmd_params_defaults.no_kv_offload; }
+    if (params.flash_attn.empty())   { params.flash_attn = cmd_params_defaults.flash_attn; }
    if (params.tensor_split.empty()) { params.tensor_split = cmd_params_defaults.tensor_split; }
    if (params.use_mmap.empty())     { params.use_mmap = cmd_params_defaults.use_mmap; }
    if (params.embeddings.empty())   { params.embeddings = cmd_params_defaults.embeddings; }
@@ -498,6 +509,7 @@ struct cmd_params_instance {
    llama_split_mode split_mode;
    int main_gpu;
    bool no_kv_offload;
+    bool flash_attn;
    std::vector<float> tensor_split;
    bool use_mmap;
    bool embeddings;
@@ -532,6 +544,7 @@ struct cmd_params_instance {
        cparams.type_k = type_k;
        cparams.type_v = type_v;
        cparams.offload_kqv = !no_kv_offload;
+        cparams.flash_attn = flash_attn;
        cparams.embeddings = embeddings;

        return cparams;
@@ -554,6 +567,7 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
    for (const auto & tk : params.type_k)
    for (const auto & tv : params.type_v)
    for (const auto & nkvo : params.no_kv_offload)
+    for (const auto & fa : params.flash_attn)
    for (const auto & nt : params.n_threads) {
        for (const auto & n_prompt : params.n_prompt) {
            if (n_prompt == 0) {
@@ -572,6 +586,7 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
                /* .split_mode   = */ sm,
                /* .main_gpu     = */ mg,
                /* .no_kv_offload= */ nkvo,
+                /* .flash_attn   = */ fa,
                /* .tensor_split = */ ts,
                /* .use_mmap     = */ mmp,
                /* .embeddings   = */ embd,
@@ -596,6 +611,7 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
                /* .split_mode   = */ sm,
                /* .main_gpu     = */ mg,
                /* .no_kv_offload= */ nkvo,
+                /* .flash_attn   = */ fa,
                /* .tensor_split = */ ts,
                /* .use_mmap     = */ mmp,
                /* .embeddings   = */ embd,
@@ -633,6 +649,7 @@ struct test {
    llama_split_mode split_mode;
    int main_gpu;
    bool no_kv_offload;
+    bool flash_attn;
    std::vector<float> tensor_split;
    bool use_mmap;
    bool embeddings;
@@ -657,6 +674,7 @@ struct test {
        split_mode = inst.split_mode;
        main_gpu = inst.main_gpu;
        no_kv_offload = inst.no_kv_offload;
+        flash_attn = inst.flash_attn;
        tensor_split = inst.tensor_split;
        use_mmap = inst.use_mmap;
        embeddings = inst.embeddings;
@@ -731,7 +749,7 @@ struct test {
            "n_batch", "n_ubatch",
            "n_threads", "type_k", "type_v",
            "n_gpu_layers", "split_mode",
-            "main_gpu", "no_kv_offload",
+            "main_gpu", "no_kv_offload", "flash_attn",
            "tensor_split", "use_mmap", "embeddings",
            "n_prompt", "n_gen", "test_time",
            "avg_ns", "stddev_ns",
@@ -753,7 +771,7 @@ struct test {
        }
        if (field == "cuda" || field == "opencl"  || field == "vulkan" || field == "kompute" || field == "metal" ||
            field == "gpu_blas" || field == "blas" || field == "sycl" ||field == "f16_kv" || field == "no_kv_offload" ||
-            field == "use_mmap" || field == "embeddings") {
+            field == "flash_attn" || field == "use_mmap" || field == "embeddings") {
            return BOOL;
        }
        if (field == "avg_ts" || field == "stddev_ts") {
@@ -787,7 +805,7 @@ struct test {
            std::to_string(n_batch), std::to_string(n_ubatch),
            std::to_string(n_threads), ggml_type_name(type_k), ggml_type_name(type_v),
            std::to_string(n_gpu_layers), split_mode_str(split_mode),
-            std::to_string(main_gpu), std::to_string(no_kv_offload),
+            std::to_string(main_gpu), std::to_string(no_kv_offload), std::to_string(flash_attn),
            tensor_split_str, std::to_string(use_mmap), std::to_string(embeddings),
            std::to_string(n_prompt), std::to_string(n_gen), test_time,
            std::to_string(avg_ns()), std::to_string(stdev_ns()),
@@ -955,6 +973,9 @@ struct markdown_printer : public printer {
        if (field == "no_kv_offload") {
            return "nkvo";
        }
+        if (field == "flash_attn") {
+            return "fa";
+        }
        if (field == "use_mmap") {
            return "mmap";
        }
@@ -1001,6 +1022,9 @@ struct markdown_printer : public printer {
        if (params.no_kv_offload.size() > 1 || params.no_kv_offload != cmd_params_defaults.no_kv_offload) {
            fields.emplace_back("no_kv_offload");
        }
+        if (params.flash_attn.size() > 1 || params.flash_attn != cmd_params_defaults.flash_attn) {
+            fields.emplace_back("flash_attn");
+        }
        if (params.tensor_split.size() > 1 || params.tensor_split != cmd_params_defaults.tensor_split) {
            fields.emplace_back("tensor_split");
        }
@@ -408,7 +408,7 @@ Java_com_example_llama_Llm_completion_1loop(
    const auto new_token_id = llama_sample_token_greedy(context, &candidates_p);

    const auto n_cur = env->CallIntMethod(intvar_ncur, la_int_var_value);
-    if (new_token_id == llama_token_eos(model) || n_cur == n_len) {
+    if (llama_token_is_eog(model, new_token_id) || n_cur == n_len) {
        return env->NewStringUTF("");
    }

@@ -158,7 +158,7 @@ actor LlamaContext {
            new_token_id = llama_sample_token_greedy(context, &candidates_p)
        }

-        if new_token_id == llama_token_eos(model) || n_cur == n_len {
+        if llama_token_is_eog(model, new_token_id) || n_cur == n_len {
            print("\n")
            let new_token_str = String(cString: temporary_invalid_cchars + [0])
            temporary_invalid_cchars.removeAll()
@@ -322,7 +322,7 @@ actor LlamaContext {
        defer {
            result.deallocate()
        }
-        let nTokens = llama_token_to_piece(model, token, result, 8)
+        let nTokens = llama_token_to_piece(model, token, result, 8, false)

        if nTokens < 0 {
            let newResult = UnsafeMutablePointer<Int8>.allocate(capacity: Int(-nTokens))
@@ -330,7 +330,7 @@ actor LlamaContext {
            defer {
                newResult.deallocate()
            }
-            let nNewTokens = llama_token_to_piece(model, token, newResult, -nTokens)
+            let nNewTokens = llama_token_to_piece(model, token, newResult, -nTokens, false)
            let bufferPointer = UnsafeBufferPointer(start: newResult, count: Int(nNewTokens))
            return Array(bufferPointer)
        } else {
@@ -3,6 +3,7 @@
 // I'll gradually clean and extend it
 // Note: Even when using identical normalized image inputs (see normalize_image_u8_to_f32()) we have a significant difference in resulting embeddings compared to pytorch
 #include "clip.h"
+#include "log.h"
 #include "ggml.h"
 #include "ggml-alloc.h"
 #include "ggml-backend.h"
@@ -23,7 +24,6 @@
 #include <cstdlib>
 #include <cstring>
 #include <fstream>
-#include <iostream>
 #include <map>
 #include <regex>
 #include <stdexcept>
@@ -104,6 +104,7 @@ static std::string format(const char * fmt, ...) {
 #define TN_POS_EMBD        "%s.position_embd.weight"
 #define TN_CLASS_EMBD      "v.class_embd"
 #define TN_PATCH_EMBD      "v.patch_embd.weight"
+#define TN_PATCH_BIAS      "v.patch_embd.bias"
 #define TN_ATTN_K          "%s.blk.%d.attn_k.%s"
 #define TN_ATTN_Q          "%s.blk.%d.attn_q.%s"
 #define TN_ATTN_V          "%s.blk.%d.attn_v.%s"
@@ -145,7 +146,7 @@ static std::map<projector_type, std::string> PROJECTOR_TYPE_NAMES = {
 static int get_key_idx(const gguf_context * ctx, const char * key) {
    int i = gguf_find_key(ctx, key);
    if (i == -1) {
-        fprintf(stderr, "key %s not found in file\n", key);
+        LOG_TEE("key %s not found in file\n", key);
        throw std::runtime_error(format("Missing required key: %s", key));
    }

@@ -247,7 +248,7 @@ static std::string gguf_kv_to_str(const struct gguf_context * ctx_gguf, int i) {

 static void print_tensor_info(const ggml_tensor * tensor, const char * prefix = "") {
    size_t tensor_size = ggml_nbytes(tensor);
-    printf("%s: n_dims = %d, name = %s, tensor_size=%zu, shape:[%" PRId64 ", %" PRId64 ", %" PRId64 ", %" PRId64 "], type = %s\n",
+    LOG_TEE("%s: n_dims = %d, name = %s, tensor_size=%zu, shape:[%" PRId64 ", %" PRId64 ", %" PRId64 ", %" PRId64 "], type = %s\n",
            prefix, ggml_n_dims(tensor), tensor->name, tensor_size,
            tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3], ggml_type_name(tensor->type));
 }
@@ -265,7 +266,7 @@ static projector_type clip_projector_type_from_string(const std::string & name)
 static void clip_image_write_image_to_ppm(const clip_image_u8& img, const std::string& filename) {
    std::ofstream file(filename, std::ios::binary);
    if (!file.is_open()) {
-        std::cerr << "Failed to open file for writing: " << filename << std::endl;
+        LOG_TEE("Failed to open file for writing: %s\n", filename.c_str());
        return;
    }

@@ -284,7 +285,7 @@ static void clip_image_write_image_to_ppm(const clip_image_u8& img, const std::s
 static void clip_image_save_to_bmp(const clip_image_u8& img, const std::string& filename) {
    std::ofstream file(filename, std::ios::binary);
    if (!file.is_open()) {
-        std::cerr << "Failed to open file for writing: " << filename << std::endl;
+        LOG_TEE("Failed to open file for writing: %s\n", filename.c_str());
        return;
    }

@@ -425,6 +426,7 @@ struct clip_vision_model {
    // embeddings
    struct ggml_tensor * class_embedding;
    struct ggml_tensor * patch_embeddings;
+    struct ggml_tensor * patch_bias;
    struct ggml_tensor * position_embeddings;

    struct ggml_tensor * pre_ln_w;
@@ -501,6 +503,11 @@ struct clip_ctx {
    bool use_gelu = false;
    int32_t ftype = 1;

+    bool has_class_embedding = true;
+    bool has_pre_norm = true;
+    bool has_post_norm = false;
+    bool has_patch_bias = false;
+
    struct gguf_context * ctx_gguf;
    struct ggml_context * ctx_data;

@@ -515,7 +522,7 @@ struct clip_ctx {

 static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32_batch * imgs) {
    if (!ctx->has_vision_encoder) {
-        printf("This gguf file seems to have no vision encoder\n");
+        LOG_TEE("This gguf file seems to have no vision encoder\n");
        return nullptr;
    }

@@ -526,7 +533,7 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
    const int patch_size           = hparams.patch_size;
    const int num_patches          = ((image_size / patch_size) * (image_size / patch_size));
    const int num_patches_per_side = image_size / patch_size; GGML_UNUSED(num_patches_per_side);
-    const int num_positions        = num_patches + 1;
+    const int num_positions        = num_patches + (ctx->has_class_embedding ? 1 : 0);
    const int hidden_size          = hparams.hidden_size;
    const int n_head               = hparams.n_head;
    const int d_head               = hidden_size / n_head;
@@ -557,16 +564,23 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
    inp = ggml_reshape_3d(ctx0, inp, num_patches, hidden_size, batch_size);
    inp = ggml_cont(ctx0, ggml_permute(ctx0, inp, 1, 0, 2, 3));

+    if (ctx->has_patch_bias) {
+        // inp = ggml_add(ctx0, inp, ggml_repeat(ctx0, model.patch_bias, inp));
+        inp = ggml_add(ctx0, inp, model.patch_bias);
+    }
+
    // concat class_embeddings and patch_embeddings
-    struct ggml_tensor * embeddings = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, hidden_size, num_positions, batch_size);
+    struct ggml_tensor * embeddings = inp;
+    if (ctx->has_class_embedding) {
+        embeddings = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, hidden_size, num_positions, batch_size);
+        embeddings = ggml_acc(ctx0, embeddings, model.class_embedding,
+                embeddings->nb[1], embeddings->nb[2], embeddings->nb[3], 0);
+        embeddings = ggml_acc(ctx0, embeddings, inp,
+                embeddings->nb[1], embeddings->nb[2], embeddings->nb[3], model.class_embedding->nb[1]);
+    }
    ggml_set_name(embeddings, "embeddings");
    ggml_set_input(embeddings);

-    embeddings = ggml_acc(ctx0, embeddings, model.class_embedding,
-            embeddings->nb[1], embeddings->nb[2], embeddings->nb[3], 0);
-
-    embeddings = ggml_acc(ctx0, embeddings, inp,
-            embeddings->nb[1], embeddings->nb[2], embeddings->nb[3], model.class_embedding->nb[1]);

    struct ggml_tensor * positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_positions);
    ggml_set_name(positions, "positions");
@@ -576,7 +590,7 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
        ggml_add(ctx0, embeddings, ggml_get_rows(ctx0, model.position_embeddings, positions));

    // pre-layernorm
-    {
+    if (ctx->has_pre_norm) {
        embeddings = ggml_norm(ctx0, embeddings, eps);
        ggml_set_name(embeddings, "pre_ln");

@@ -664,6 +678,14 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
        embeddings = cur;
    }

+    // post-layernorm
+    if (ctx->has_post_norm) {
+        embeddings = ggml_norm(ctx0, embeddings, eps);
+        ggml_set_name(embeddings, "post_ln");
+
+        embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.post_ln_w), model.post_ln_b);
+    }
+
    // llava projector
    {
        embeddings = ggml_reshape_2d(ctx0, embeddings, embeddings->ne[0], embeddings->ne[1]);
@@ -879,21 +901,21 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
        const int idx_name = gguf_find_key(ctx, KEY_NAME);
        if (idx_name != -1) { // make name optional temporarily as some of the uploaded models missing it due to a bug
            const std::string name = gguf_get_val_str(ctx, idx_name);
-            printf("%s: model name:   %s\n", __func__, name.c_str());
+            LOG_TEE("%s: model name:   %s\n", __func__, name.c_str());
        }
-        printf("%s: description:  %s\n", __func__, description.c_str());
-        printf("%s: GGUF version: %d\n", __func__, gguf_get_version(ctx));
-        printf("%s: alignment:    %zu\n", __func__, gguf_get_alignment(ctx));
-        printf("%s: n_tensors:    %d\n", __func__, n_tensors);
-        printf("%s: n_kv:         %d\n", __func__, n_kv);
-        printf("%s: ftype:        %s\n", __func__, ftype_str.c_str());
-        printf("\n");
+        LOG_TEE("%s: description:  %s\n", __func__, description.c_str());
+        LOG_TEE("%s: GGUF version: %d\n", __func__, gguf_get_version(ctx));
+        LOG_TEE("%s: alignment:    %zu\n", __func__, gguf_get_alignment(ctx));
+        LOG_TEE("%s: n_tensors:    %d\n", __func__, n_tensors);
+        LOG_TEE("%s: n_kv:         %d\n", __func__, n_kv);
+        LOG_TEE("%s: ftype:        %s\n", __func__, ftype_str.c_str());
+        LOG_TEE("\n");
    }
    const int n_tensors = gguf_get_n_tensors(ctx);

    // kv
    const int n_kv = gguf_get_n_kv(ctx);
-    printf("%s: loaded meta data with %d key-value pairs and %d tensors from %s\n",
+    LOG_TEE("%s: loaded meta data with %d key-value pairs and %d tensors from %s\n",
        __func__, n_kv, n_tensors, fname);
    {
        std::map<enum ggml_type, uint32_t> n_type;
@@ -904,7 +926,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
            n_type[type]++;
        }

-        printf("%s: Dumping metadata keys/values. Note: KV overrides do not apply in this output.\n", __func__);
+        LOG_TEE("%s: Dumping metadata keys/values. Note: KV overrides do not apply in this output.\n", __func__);
        for (int i = 0; i < n_kv; i++) {
            const char * name           = gguf_get_key(ctx, i);
            const enum gguf_type type   = gguf_get_kv_type(ctx, i);
@@ -920,7 +942,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
            }
            replace_all(value, "\n", "\\n");

-            printf("%s: - kv %3d: %42s %-16s = %s\n", __func__, i, name, type_name.c_str(), value.c_str());
+            LOG_TEE("%s: - kv %3d: %42s %-16s = %s\n", __func__, i, name, type_name.c_str(), value.c_str());
        }

        // print type counts
@@ -929,7 +951,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
                continue;
            }

-            printf("%s: - type %4s: %4d tensors\n", __func__, ggml_type_name(kv.first), kv.second);
+            LOG_TEE("%s: - type %4s: %4d tensors\n", __func__, ggml_type_name(kv.first), kv.second);
        }
    }

@@ -944,7 +966,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
            size_t tensor_size = ggml_nbytes(cur);
            model_size += tensor_size;
            if (verbosity >= 3) {
-                printf("%s: tensor[%d]: n_dims = %d, name = %s, tensor_size=%zu, offset=%zu, shape:[%" PRIu64 ", %" PRIu64 ", %" PRIu64 ", %" PRIu64 "], type = %s\n",
+                LOG_TEE("%s: tensor[%d]: n_dims = %d, name = %s, tensor_size=%zu, offset=%zu, shape:[%" PRIu64 ", %" PRIu64 ", %" PRIu64 ", %" PRIu64 "], type = %s\n",
                       __func__, i, ggml_n_dims(cur), cur->name, tensor_size, offset, cur->ne[0], cur->ne[1], cur->ne[2], cur->ne[3], ggml_type_name(type));
            }
        }
@@ -971,18 +993,18 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {

 #ifdef GGML_USE_CUDA
    new_clip->backend = ggml_backend_cuda_init(0);
-    printf("%s: CLIP using CUDA backend\n", __func__);
+    LOG_TEE("%s: CLIP using CUDA backend\n", __func__);
 #endif

 #ifdef GGML_USE_METAL
    new_clip->backend = ggml_backend_metal_init();
-    printf("%s: CLIP using Metal backend\n", __func__);
+    LOG_TEE("%s: CLIP using Metal backend\n", __func__);
 #endif


    if (!new_clip->backend) {
        new_clip->backend = ggml_backend_cpu_init();
-        printf("%s: CLIP using CPU backend\n", __func__);
+        LOG_TEE("%s: CLIP using CPU backend\n", __func__);
    }

    // model size and capabilities
@@ -1006,15 +1028,15 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
        new_clip->use_gelu = gguf_get_val_bool(ctx, idx);

        if (verbosity >= 1) {
-            printf("%s: text_encoder:   %d\n", __func__, new_clip->has_text_encoder);
-            printf("%s: vision_encoder: %d\n", __func__, new_clip->has_vision_encoder);
-            printf("%s: llava_projector:  %d\n", __func__, new_clip->has_llava_projector);
-            printf("%s: model size:     %.2f MB\n", __func__, model_size / 1024.0 / 1024.0);
-            printf("%s: metadata size:  %.2f MB\n", __func__, ggml_get_mem_size(meta) / 1024.0 / 1024.0);
+            LOG_TEE("%s: text_encoder:   %d\n", __func__, new_clip->has_text_encoder);
+            LOG_TEE("%s: vision_encoder: %d\n", __func__, new_clip->has_vision_encoder);
+            LOG_TEE("%s: llava_projector:  %d\n", __func__, new_clip->has_llava_projector);
+            LOG_TEE("%s: model size:     %.2f MB\n", __func__, model_size / 1024.0 / 1024.0);
+            LOG_TEE("%s: metadata size:  %.2f MB\n", __func__, ggml_get_mem_size(meta) / 1024.0 / 1024.0);
        }
    }

-    printf("%s: params backend buffer size = % 6.2f MB (%i tensors)\n", __func__, model_size / (1024.0 * 1024.0), n_tensors);
+    LOG_TEE("%s: params backend buffer size = % 6.2f MB (%i tensors)\n", __func__, model_size / (1024.0 * 1024.0), n_tensors);

    // load tensors
    {
@@ -1027,7 +1049,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {

        new_clip->ctx_data = ggml_init(params);
        if (!new_clip->ctx_data) {
-            fprintf(stderr, "%s: ggml_init() failed\n", __func__);
+            LOG_TEE("%s: ggml_init() failed\n", __func__);
            clip_free(new_clip);
            gguf_free(ctx);
            return nullptr;
@@ -1035,7 +1057,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {

        auto fin = std::ifstream(fname, std::ios::binary);
        if (!fin) {
-            printf("cannot open model file for loading tensors\n");
+            LOG_TEE("cannot open model file for loading tensors\n");
            clip_free(new_clip);
            gguf_free(ctx);
            return nullptr;
@@ -1057,7 +1079,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
            const size_t offset = gguf_get_data_offset(ctx) + gguf_get_tensor_offset(ctx, i);
            fin.seekg(offset, std::ios::beg);
            if (!fin) {
-                printf("%s: failed to seek for tensor %s\n", __func__, name);
+                LOG_TEE("%s: failed to seek for tensor %s\n", __func__, name);
                clip_free(new_clip);
                gguf_free(ctx);
                return nullptr;
@@ -1128,34 +1150,61 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
        }

        if (verbosity >= 2) {
-            printf("\n%s: vision model hparams\n", __func__);
-            printf("image_size         %d\n", hparams.image_size);
-            printf("patch_size         %d\n", hparams.patch_size);
-            printf("v_hidden_size      %d\n", hparams.hidden_size);
-            printf("v_n_intermediate   %d\n", hparams.n_intermediate);
-            printf("v_projection_dim   %d\n", hparams.projection_dim);
-            printf("v_n_head           %d\n", hparams.n_head);
-            printf("v_n_layer          %d\n", hparams.n_layer);
-            printf("v_eps              %f\n", hparams.eps);
-            printf("v_image_mean       %f %f %f\n", new_clip->image_mean[0], new_clip->image_mean[1], new_clip->image_mean[2]);
-            printf("v_image_std        %f %f %f\n", new_clip->image_std[0], new_clip->image_std[1], new_clip->image_std[2]);
-            printf("v_image_grid_pinpoints: ");
+            LOG_TEE("\n%s: vision model hparams\n", __func__);
+            LOG_TEE("image_size         %d\n", hparams.image_size);
+            LOG_TEE("patch_size         %d\n", hparams.patch_size);
+            LOG_TEE("v_hidden_size      %d\n", hparams.hidden_size);
+            LOG_TEE("v_n_intermediate   %d\n", hparams.n_intermediate);
+            LOG_TEE("v_projection_dim   %d\n", hparams.projection_dim);
+            LOG_TEE("v_n_head           %d\n", hparams.n_head);
+            LOG_TEE("v_n_layer          %d\n", hparams.n_layer);
+            LOG_TEE("v_eps              %f\n", hparams.eps);
+            LOG_TEE("v_image_mean       %f %f %f\n", new_clip->image_mean[0], new_clip->image_mean[1], new_clip->image_mean[2]);
+            LOG_TEE("v_image_std        %f %f %f\n", new_clip->image_std[0], new_clip->image_std[1], new_clip->image_std[2]);
+            LOG_TEE("v_image_grid_pinpoints: ");
            for (int i = 0; i < 32 && (hparams.image_grid_pinpoints[i] != 0); ++i) {
-                printf("%d ", hparams.image_grid_pinpoints[i]);
+                LOG_TEE("%d ", hparams.image_grid_pinpoints[i]);
            }
-            printf("\n");
-            printf("v_mm_patch_merge_type: %s\n", hparams.mm_patch_merge_type);
+            LOG_TEE("\n");
+            LOG_TEE("v_mm_patch_merge_type: %s\n", hparams.mm_patch_merge_type);

        }

+        try {
+            vision_model.class_embedding  = get_tensor(new_clip->ctx_data, TN_CLASS_EMBD);
+            new_clip->has_class_embedding = true;
+        } catch (const std::exception& e) {
+            new_clip->has_class_embedding = false;
+        }
+
+        try {
+            vision_model.pre_ln_w  = get_tensor(new_clip->ctx_data, format(TN_LN_PRE, "v", "weight"));
+            vision_model.pre_ln_b  = get_tensor(new_clip->ctx_data, format(TN_LN_PRE, "v", "bias"));
+            new_clip->has_pre_norm = true;
+        } catch (std::exception & e) {
+            new_clip->has_pre_norm = false;
+        }
+
+        try {
+            vision_model.post_ln_w  = get_tensor(new_clip->ctx_data, format(TN_LN_POST, "v", "weight"));
+            vision_model.post_ln_b  = get_tensor(new_clip->ctx_data, format(TN_LN_POST, "v", "bias"));
+            new_clip->has_post_norm = true;
+        } catch (std::exception & e) {
+            new_clip->has_post_norm = false;
+        }
+
+        try {
+            vision_model.patch_bias = get_tensor(new_clip->ctx_data, TN_PATCH_BIAS);
+            new_clip->has_patch_bias = true;
+        } catch (std::exception & e) {
+            new_clip->has_patch_bias = false;
+        }
+
        try {
            vision_model.patch_embeddings    = get_tensor(new_clip->ctx_data, TN_PATCH_EMBD);
-            vision_model.class_embedding     = get_tensor(new_clip->ctx_data, TN_CLASS_EMBD);
            vision_model.position_embeddings = get_tensor(new_clip->ctx_data, format(TN_POS_EMBD, "v"));
-            vision_model.pre_ln_w            = get_tensor(new_clip->ctx_data, format(TN_LN_PRE, "v", "weight"));
-            vision_model.pre_ln_b            = get_tensor(new_clip->ctx_data, format(TN_LN_PRE, "v", "bias"));
        } catch(const std::exception& e) {
-            fprintf(stderr, "%s: failed to load vision model tensors\n", __func__);
+            LOG_TEE("%s: failed to load vision model tensors\n", __func__);
        }

        // LLaVA projection
@@ -1184,7 +1233,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
            } catch (std::runtime_error & e) {  }
            try {
                vision_model.image_newline = get_tensor(new_clip->ctx_data, TN_IMAGE_NEWLINE);
-                // fprintf(stderr, "%s: image_newline tensor (llava-1.6) found\n", __func__);
+                // LOG_TEE("%s: image_newline tensor (llava-1.6) found\n", __func__);
            } catch (std::runtime_error & e) {  }
        } else if (new_clip->proj_type == PROJECTOR_TYPE_LDP) {
            // MobileVLM projection
@@ -1264,7 +1313,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
        ggml_cgraph * gf = clip_image_build_graph(new_clip, &batch);
        ggml_gallocr_reserve(new_clip->compute_alloc, gf);
        size_t compute_memory_buffer_size = ggml_gallocr_get_buffer_size(new_clip->compute_alloc, 0);
-        printf("%s: compute allocated memory: %.2f MB\n", __func__, compute_memory_buffer_size /1024.0/1024.0);
+        LOG_TEE("%s: compute allocated memory: %.2f MB\n", __func__, compute_memory_buffer_size /1024.0/1024.0);
    }

    return new_clip;
@@ -1304,7 +1353,7 @@ bool clip_image_load_from_file(const char * fname, clip_image_u8 * img) {
    int nx, ny, nc;
    auto * data = stbi_load(fname, &nx, &ny, &nc, 3);
    if (!data) {
-        fprintf(stderr, "%s: failed to load image '%s'\n", __func__, fname);
+        LOG_TEE("%s: failed to load image '%s'\n", __func__, fname);
        return false;
    }
    build_clip_img_from_data(data, nx, ny, img);
@@ -1316,7 +1365,7 @@ bool clip_image_load_from_bytes(const unsigned char * bytes, size_t bytes_length
    int nx, ny, nc;
    auto * data = stbi_load_from_memory(bytes, bytes_length, &nx, &ny, &nc, 3);
    if (!data) {
-        fprintf(stderr, "%s: failed to decode image bytes\n", __func__);
+        LOG_TEE("%s: failed to decode image bytes\n", __func__);
        return false;
    }
    build_clip_img_from_data(data, nx, ny, img);
@@ -1325,7 +1374,7 @@ bool clip_image_load_from_bytes(const unsigned char * bytes, size_t bytes_length
 }

 // Linear interpolation between two points
-inline float lerp(float s, float e, float t) {
+inline float clip_lerp(float s, float e, float t) {
    return s + (e - s) * t;
 }
 // Bilinear resize function
@@ -1347,17 +1396,17 @@ static void bilinear_resize(const clip_image_u8& src, clip_image_u8& dst, int ta
            float y_lerp = py - y_floor;

            for (int c = 0; c < 3; c++) {
-                float top = lerp(
+                float top = clip_lerp(
                    static_cast<float>(src.buf[3 * (y_floor * src.nx + x_floor) + c]),
                    static_cast<float>(src.buf[3 * (y_floor * src.nx + (x_floor + 1)) + c]),
                    x_lerp
                );
-                float bottom = lerp(
+                float bottom = clip_lerp(
                    static_cast<float>(src.buf[3 * ((y_floor + 1) * src.nx + x_floor) + c]),
                    static_cast<float>(src.buf[3 * ((y_floor + 1) * src.nx + (x_floor + 1)) + c]),
                    x_lerp
                );
-                dst.buf[3 * (y * target_width + x) + c] = static_cast<uint8_t>(lerp(top, bottom, y_lerp));
+                dst.buf[3 * (y * target_width + x) + c] = static_cast<uint8_t>(clip_lerp(top, bottom, y_lerp));
            }
        }
    }
@@ -1506,7 +1555,7 @@ static std::pair<int, int> select_best_resolution(const std::pair<int, int> & or
        int downscaled_height = static_cast<int>(original_height * scale);
        int effective_resolution = std::min(downscaled_width * downscaled_height, original_width * original_height);
        int wasted_resolution = (width * height) - effective_resolution;
-        // fprintf(stderr, "resolution: %d %d, scale: %f, downscaled: %d %d, effective: %d, wasted: %d\n", width, height, scale, downscaled_width, downscaled_height, effective_resolution, wasted_resolution);
+        // LOG_TEE("resolution: %d %d, scale: %f, downscaled: %d %d, effective: %d, wasted: %d\n", width, height, scale, downscaled_width, downscaled_height, effective_resolution, wasted_resolution);
        if (effective_resolution > max_effective_resolution || (effective_resolution == max_effective_resolution && wasted_resolution < min_wasted_resolution)) {
            max_effective_resolution = effective_resolution;
            min_wasted_resolution = wasted_resolution;
@@ -1545,7 +1594,7 @@ static std::vector<clip_image_u8*> divide_to_patches_u8(const clip_image_u8 & im
 bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, clip_image_f32_batch * res_imgs) {
    bool pad_to_square = true;
    if (!ctx->has_vision_encoder) {
-        printf("This gguf file seems to have no vision encoder\n");
+        LOG_TEE("This gguf file seems to have no vision encoder\n");
        return false;
    }
    auto & params = ctx->vision_model.hparams;
@@ -1622,7 +1671,7 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, cli
            }

            for (size_t i = 0; i < patches.size(); i++) {
-                // printf("patch %d: %d %d\n", i, patches[i]->nx, patches[i]->ny);
+                // LOG_TEE("patch %d: %d %d\n", i, patches[i]->nx, patches[i]->ny);
                clip_image_u8_free(patches[i]);
            }

@@ -1765,7 +1814,7 @@ int clip_n_patches(const struct clip_ctx * ctx) {

 bool clip_image_encode(struct clip_ctx * ctx, const int n_threads, clip_image_f32 * img, float * vec) {
    if (!ctx->has_vision_encoder) {
-        printf("This gguf file seems to have no vision encoder\n");
+        LOG_TEE("This gguf file seems to have no vision encoder\n");
        return false;
    }

@@ -1777,7 +1826,7 @@ bool clip_image_encode(struct clip_ctx * ctx, const int n_threads, clip_image_f3

 bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_image_f32_batch * imgs, float * vec) {
    if (!ctx->has_vision_encoder) {
-        printf("This gguf file seems to have no vision encoder\n");
+        LOG_TEE("This gguf file seems to have no vision encoder\n");
        return false;
    }

@@ -1939,7 +1988,7 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i
            new_type = type;
            if (new_type >= GGML_TYPE_Q2_K && name.find("embd") != std::string::npos) {
                new_type = GGML_TYPE_Q8_0; // ggml_get_rows needs non K type
-                // fprintf(stderr, "%s: quantizing %s to %s\n", __func__, name.c_str(), ggml_type_name(new_type));
+                // LOG_TEE("%s: quantizing %s to %s\n", __func__, name.c_str(), ggml_type_name(new_type));
            }
            const size_t n_elms = ggml_nelements(cur);
            float * f32_data;
@@ -1958,7 +2007,7 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i
                f32_data = (float *)conv_buf.data();
                break;
            default:
-                printf("Please use an input file in f32 or f16\n");
+                LOG_TEE("Please use an input file in f32 or f16\n");
                gguf_free(ctx_out);
                return false;
            }
@@ -1985,7 +2034,7 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i
            fout.put(0);
        }

-        printf("%s: n_dims = %d | quantize=%d | size = %f MB -> %f MB\n", name.c_str(), ggml_n_dims(cur), quantize,
+        LOG_TEE("%s: n_dims = %d | quantize=%d | size = %f MB -> %f MB\n", name.c_str(), ggml_n_dims(cur), quantize,
               orig_size / 1024.0 / 1024.0, new_size / 1024.0 / 1024.0);
    }

@@ -2001,8 +2050,8 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i
    gguf_free(ctx_out);

    {
-        printf("%s: original  size = %8.2f MB\n", __func__, total_size_org / 1024.0 / 1024.0);
-        printf("%s: quantized size = %8.2f MB\n", __func__, total_size_new / 1024.0 / 1024.0);
+        LOG_TEE("%s: original  size = %8.2f MB\n", __func__, total_size_org / 1024.0 / 1024.0);
+        LOG_TEE("%s: quantized size = %8.2f MB\n", __func__, total_size_new / 1024.0 / 1024.0);
    }

    return true;
@@ -1,4 +1,5 @@
 #include "ggml.h"
+#include "log.h"
 #include "common.h"
 #include "clip.h"
 #include "llava.h"
@@ -18,7 +19,7 @@ static bool eval_tokens(struct llama_context * ctx_llama, std::vector<llama_toke
            n_eval = n_batch;
        }
        if (llama_decode(ctx_llama, llama_batch_get_one(&tokens[i], n_eval, *n_past, 0))) {
-            fprintf(stderr, "%s : failed to eval. token %d/%d (batch size %d, n_past %d)\n", __func__, i, N, n_batch, *n_past);
+            LOG_TEE("%s : failed to eval. token %d/%d (batch size %d, n_past %d)\n", __func__, i, N, n_batch, *n_past);
            return false;
        }
        *n_past += n_eval;
@@ -45,7 +46,7 @@ static const char * sample(struct llama_sampling_context * ctx_sampling,
    const llama_token id = llama_sampling_sample(ctx_sampling, ctx_llama, NULL);
    llama_sampling_accept(ctx_sampling, ctx_llama, id, true);
    static std::string ret;
-    if (id == llama_token_eos(llama_get_model(ctx_llama))) {
+    if (llama_token_is_eog(llama_get_model(ctx_llama), id)) {
        ret = "</s>";
    } else {
        ret = llama_token_to_piece(ctx_llama, id);
@@ -73,7 +74,7 @@ static llava_image_embed * llava_image_embed_make_with_prompt_base64(struct clip
    size_t img_base64_str_start, img_base64_str_end;
    find_image_tag_in_prompt(prompt, img_base64_str_start, img_base64_str_end);
    if (img_base64_str_start == std::string::npos || img_base64_str_end == std::string::npos) {
-        fprintf(stderr, "%s: invalid base64 image tag. must be %s<base64 byte string>%s\n", __func__, IMG_BASE64_TAG_BEGIN, IMG_BASE64_TAG_END);
+        LOG_TEE("%s: invalid base64 image tag. must be %s<base64 byte string>%s\n", __func__, IMG_BASE64_TAG_BEGIN, IMG_BASE64_TAG_END);
        return NULL;
    }

@@ -87,7 +88,7 @@ static llava_image_embed * llava_image_embed_make_with_prompt_base64(struct clip

    auto embed = llava_image_embed_make_with_bytes(ctx_clip, n_threads, img_bytes.data(), img_bytes.size());
    if (!embed) {
-        fprintf(stderr, "%s: could not load image from base64 string.\n", __func__);
+        LOG_TEE("%s: could not load image from base64 string.\n", __func__);
        return NULL;
    }

@@ -112,29 +113,29 @@ struct llava_context {
 };

 static void show_additional_info(int /*argc*/, char ** argv) {
-    fprintf(stderr, "\n example usage: %s -m <llava-v1.5-7b/ggml-model-q5_k.gguf> --mmproj <llava-v1.5-7b/mmproj-model-f16.gguf> --image <path/to/an/image.jpg> [--temp 0.1] [-p \"describe the image in detail.\"]\n", argv[0]);
-    fprintf(stderr, "  note: a lower temperature value like 0.1 is recommended for better quality.\n");
+    LOG_TEE("\n example usage: %s -m <llava-v1.5-7b/ggml-model-q5_k.gguf> --mmproj <llava-v1.5-7b/mmproj-model-f16.gguf> --image <path/to/an/image.jpg> --image <path/to/another/image.jpg> [--temp 0.1] [-p \"describe the image in detail.\"]\n", argv[0]);
+    LOG_TEE("  note: a lower temperature value like 0.1 is recommended for better quality.\n");
 }

-static struct llava_image_embed * load_image(llava_context * ctx_llava, gpt_params * params) {
+static struct llava_image_embed * load_image(llava_context * ctx_llava, gpt_params * params, const std::string & fname) {

    // load and preprocess the image
    llava_image_embed * embed = NULL;
    auto prompt = params->prompt;
    if (prompt_contains_image(prompt)) {
        if (!params->image.empty()) {
-            fprintf(stderr, "using base64 encoded image instead of command line image path\n");
+            LOG_TEE("using base64 encoded image instead of command line image path\n");
        }
        embed = llava_image_embed_make_with_prompt_base64(ctx_llava->ctx_clip, params->n_threads, prompt);
        if (!embed) {
-            fprintf(stderr, "%s: can't load image from prompt\n", __func__);
+            LOG_TEE("%s: can't load image from prompt\n", __func__);
            return NULL;
        }
        params->prompt = remove_image_from_prompt(prompt);
    } else {
-        embed = llava_image_embed_make_with_filename(ctx_llava->ctx_clip, params->n_threads, params->image.c_str());
+        embed = llava_image_embed_make_with_filename(ctx_llava->ctx_clip, params->n_threads, fname.c_str());
        if (!embed) {
-            fprintf(stderr, "%s: is %s really an image file?\n", __func__, params->image.c_str());
+            fprintf(stderr, "%s: is %s really an image file?\n", __func__, fname.c_str());
            return NULL;
        }
    }
@@ -153,18 +154,18 @@ static void process_prompt(struct llava_context * ctx_llava, struct llava_image_
        // new templating mode: Provide the full prompt including system message and use <image> as a placeholder for the image
        system_prompt = prompt.substr(0, image_pos);
        user_prompt = prompt.substr(image_pos + std::string("<image>").length());
-        printf("system_prompt: %s\n", system_prompt.c_str());
+        LOG_TEE("system_prompt: %s\n", system_prompt.c_str());
        if (params->verbose_prompt) {
            auto tmp = ::llama_tokenize(ctx_llava->ctx_llama, system_prompt, true, true);
            for (int i = 0; i < (int) tmp.size(); i++) {
-                printf("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str());
+                LOG_TEE("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str());
            }
        }
-        printf("user_prompt: %s\n", user_prompt.c_str());
+        LOG_TEE("user_prompt: %s\n", user_prompt.c_str());
        if (params->verbose_prompt) {
            auto tmp = ::llama_tokenize(ctx_llava->ctx_llama, user_prompt, true, true);
            for (int i = 0; i < (int) tmp.size(); i++) {
-                printf("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str());
+                LOG_TEE("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str());
            }
        }
    } else {
@@ -174,7 +175,7 @@ static void process_prompt(struct llava_context * ctx_llava, struct llava_image_
        if (params->verbose_prompt) {
            auto tmp = ::llama_tokenize(ctx_llava->ctx_llama, user_prompt, true, true);
            for (int i = 0; i < (int) tmp.size(); i++) {
-                printf("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str());
+                LOG_TEE("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str());
            }
        }
    }
@@ -185,7 +186,7 @@ static void process_prompt(struct llava_context * ctx_llava, struct llava_image_

    // generate the response

-    fprintf(stderr, "\n");
+    LOG_TEE("\n");

    struct llama_sampling_context * ctx_sampling = llama_sampling_init(params->sparams);
    std::string response = "";
@@ -206,8 +207,21 @@ static void process_prompt(struct llava_context * ctx_llava, struct llava_image_
    printf("\n");
 }

+static struct llama_model * llava_init(gpt_params * params) {
+    llama_backend_init();
+    llama_numa_init(params->numa);

-static struct llava_context * llava_init(gpt_params * params) {
+    llama_model_params model_params = llama_model_params_from_gpt_params(*params);
+
+    llama_model * model = llama_load_model_from_file(params->model.c_str(), model_params);
+    if (model == NULL) {
+        LOG_TEE("%s: error: unable to load model\n" , __func__);
+        return NULL;
+    }
+    return model;
+}
+
+static struct llava_context * llava_init_context(gpt_params * params, llama_model * model) {
    const char * clip_path = params->mmproj.c_str();

    auto prompt = params->prompt;
@@ -217,16 +231,6 @@ static struct llava_context * llava_init(gpt_params * params) {

    auto ctx_clip = clip_model_load(clip_path, /*verbosity=*/ 1);

-    llama_backend_init();
-    llama_numa_init(params->numa);
-
-    llama_model_params model_params = llama_model_params_from_gpt_params(*params);
-
-    llama_model * model = llama_load_model_from_file(params->model.c_str(), model_params);
-    if (model == NULL) {
-        fprintf(stderr , "%s: error: unable to load model\n" , __func__);
-        return NULL;
-    }

    llama_context_params ctx_params = llama_context_params_from_gpt_params(*params);
    ctx_params.n_ctx           = params->n_ctx < 2048 ? 2048 : params->n_ctx; // we need a longer context size to process image embeddings
@@ -234,7 +238,7 @@ static struct llava_context * llava_init(gpt_params * params) {
    llama_context * ctx_llama = llama_new_context_with_model(model, ctx_params);

    if (ctx_llama == NULL) {
-        fprintf(stderr , "%s: error: failed to create the llama_context\n" , __func__);
+        LOG_TEE("%s: error: failed to create the llama_context\n" , __func__);
        return NULL;
    }

@@ -257,6 +261,12 @@ static void llava_free(struct llava_context * ctx_llava) {
    llama_backend_free();
 }

+static void llama_log_callback_logTee(ggml_log_level level, const char * text, void * user_data) {
+    (void) level;
+    (void) user_data;
+    LOG_TEE("%s", text);
+}
+
 int main(int argc, char ** argv) {
    ggml_time_init();

@@ -266,29 +276,43 @@ int main(int argc, char ** argv) {
        show_additional_info(argc, argv);
        return 1;
    }
+
+#ifndef LOG_DISABLE_LOGS
+    log_set_target(log_filename_generator("llava", "log"));
+    LOG_TEE("Log start\n");
+    log_dump_cmdline(argc, argv);
+    llama_log_set(llama_log_callback_logTee, nullptr);
+#endif // LOG_DISABLE_LOGS
+
    if (params.mmproj.empty() || (params.image.empty() && !prompt_contains_image(params.prompt))) {
        gpt_print_usage(argc, argv, params);
        show_additional_info(argc, argv);
        return 1;
    }
-
-    auto ctx_llava = llava_init(&params);
-    if (ctx_llava == NULL) {
-        fprintf(stderr, "%s: error: failed to init llava\n", __func__);
+    auto model = llava_init(&params);
+    if (model == NULL) {
+        fprintf(stderr, "%s: error: failed to init llava model\n", __func__);
        return 1;
    }

-    auto image_embed = load_image(ctx_llava, &params);
-    if (!image_embed) {
-        return 1;
+    for (auto & image : params.image) {
+        auto ctx_llava = llava_init_context(&params, model);
+
+        auto image_embed = load_image(ctx_llava, &params, image);
+        if (!image_embed) {
+            std::cerr << "error: failed to load image " << image << ". Terminating\n\n";
+            return 1;
+        }
+
+        // process the prompt
+        process_prompt(ctx_llava, image_embed, &params, params.prompt);
+
+        llama_print_timings(ctx_llava->ctx_llama);
+        llava_image_embed_free(image_embed);
+        ctx_llava->model = NULL;
+        llava_free(ctx_llava);
    }
+    llama_free_model(model);

-    // process the prompt
-    process_prompt(ctx_llava, image_embed, &params, params.prompt);
-
-    llama_print_timings(ctx_llava->ctx_llama);
-
-    llava_image_embed_free(image_embed);
-    llava_free(ctx_llava);
    return 0;
 }
@@ -54,7 +54,7 @@ static std::pair<int, int> select_best_resolution(const std::pair<int, int>& ori
        int downscaled_height = static_cast<int>(original_height * scale);
        int effective_resolution = std::min(downscaled_width * downscaled_height, original_width * original_height);
        int wasted_resolution = (width * height) - effective_resolution;
-        // fprintf(stderr, "resolution: %d %d, scale: %f, downscaled: %d %d, effective: %d, wasted: %d\n", width, height, scale, downscaled_width, downscaled_height, effective_resolution, wasted_resolution);
+        // LOG_TEE("resolution: %d %d, scale: %f, downscaled: %d %d, effective: %d, wasted: %d\n", width, height, scale, downscaled_width, downscaled_height, effective_resolution, wasted_resolution);
        if (effective_resolution > max_effective_resolution || (effective_resolution == max_effective_resolution && wasted_resolution < min_wasted_resolution)) {
            max_effective_resolution = effective_resolution;
            min_wasted_resolution = wasted_resolution;
@@ -154,13 +154,13 @@ static bool clip_llava_handle_patches(clip_ctx * ctx_clip, std::vector<float *>
    model.newline = ggml_new_tensor_1d(model.ctx, GGML_TYPE_F32, newline_tmp->ne[0]);
    if (newline_tmp->backend != GGML_BACKEND_TYPE_CPU) {
        if (newline_tmp->buffer == NULL) {
-            printf("newline_tmp tensor buffer is NULL\n");
+            LOG_TEE("newline_tmp tensor buffer is NULL\n");
        }
        ggml_backend_tensor_get(newline_tmp, model.newline->data, 0, ggml_nbytes(newline_tmp));
    } else {
        model.newline->data = newline_tmp->data;
        if (model.newline->data == NULL) {
-            printf("newline_tmp tensor data is NULL\n");
+            LOG_TEE("newline_tmp tensor data is NULL\n");
        }
    }

@@ -224,7 +224,7 @@ static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const cli
    img_res_v.size = 0;
    img_res_v.data = nullptr;
    if (!clip_image_preprocess(ctx_clip, img, &img_res_v)) {
-        fprintf(stderr, "%s: unable to preprocess image\n", __func__);
+        LOG_TEE("%s: unable to preprocess image\n", __func__);
        delete[] img_res_v.data;
        return false;
    }
@@ -239,7 +239,7 @@ static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const cli
        bool encoded = clip_image_encode(ctx_clip, n_threads, &img_res_v.data[0], image_embd); // image_embd shape is 576 x 4096
        delete[] img_res_v.data;
        if (!encoded) {
-            fprintf(stderr, "Unable to encode image\n");
+            LOG_TEE("Unable to encode image\n");

            return false;
        }
@@ -252,12 +252,12 @@ static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const cli
            image_embd_v[i] = (float *)malloc(clip_embd_nbytes(ctx_clip)); // 576 patches * 4096 embeddings * 4 bytes = 9437184
            const bool encoded = clip_image_encode(ctx_clip, n_threads, &img_res_v.data[i], image_embd_v[i]); // image data is in 3x336x336 format and will be converted to 336x336x3 inside
            if (!encoded) {
-                fprintf(stderr, "Unable to encode image - spatial_unpad - subimage %d of %d\n", (int) i+1, (int) img_res_v.size);
+                LOG_TEE("Unable to encode image - spatial_unpad - subimage %d of %d\n", (int) i+1, (int) img_res_v.size);
                return false;
            }
        }
        const int64_t t_img_enc_batch_us = ggml_time_us();
-        printf("%s: %d segments encoded in %8.2f ms\n", __func__, (int)img_res_v.size, (t_img_enc_batch_us - t_img_enc_start_us) / 1000.0);
+        LOG_TEE("%s: %d segments encoded in %8.2f ms\n", __func__, (int)img_res_v.size, (t_img_enc_batch_us - t_img_enc_start_us) / 1000.0);

        const int32_t * image_grid = clip_image_grid(ctx_clip);

@@ -290,12 +290,12 @@ static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const cli
        // clip_image_save_to_bmp(*tmp, "image_feature.bmp");
    }

-    printf("%s: image embedding created: %d tokens\n", __func__, *n_img_pos);
+    LOG_TEE("%s: image embedding created: %d tokens\n", __func__, *n_img_pos);

    const int64_t t_img_enc_end_us = ggml_time_us();
    float t_img_enc_ms = (t_img_enc_end_us - t_img_enc_start_us) / 1000.0;

-    printf("\n%s: image encoded in %8.2f ms by CLIP (%8.2f ms per image patch)\n", __func__, t_img_enc_ms, t_img_enc_ms / *n_img_pos);
+    LOG_TEE("\n%s: image encoded in %8.2f ms by CLIP (%8.2f ms per image patch)\n", __func__, t_img_enc_ms, t_img_enc_ms / *n_img_pos);

    return true;
 }
@@ -305,7 +305,7 @@ bool llava_validate_embed_size(const llama_context * ctx_llama, const clip_ctx *
    int n_llama_embd = llama_n_embd(llama_get_model(ctx_llama));
    auto n_image_embd = clip_n_mmproj_embd(ctx_clip);
    if (n_image_embd != n_llama_embd) {
-        printf("%s: embedding dim of the multimodal projector (%d) is not equal to that of LLaMA (%d). Make sure that you use the correct mmproj file.\n", __func__, n_image_embd, n_llama_embd);
+        LOG_TEE("%s: embedding dim of the multimodal projector (%d) is not equal to that of LLaMA (%d). Make sure that you use the correct mmproj file.\n", __func__, n_image_embd, n_llama_embd);
        return false;
    }
    return true;
@@ -314,13 +314,13 @@ bool llava_validate_embed_size(const llama_context * ctx_llama, const clip_ctx *
 bool llava_image_embed_make_with_clip_img(clip_ctx * ctx_clip, int n_threads, const clip_image_u8 * img, float ** image_embd_out, int * n_img_pos_out) {
    float * image_embd = (float *)malloc(clip_embd_nbytes(ctx_clip)*6); // TODO: base on gridsize/llava model
    if (!image_embd) {
-        fprintf(stderr, "Unable to allocate memory for image embeddings\n");
+        LOG_TEE("Unable to allocate memory for image embeddings\n");
        return false;
    }

    int n_img_pos;
    if (!encode_image_with_clip(ctx_clip, n_threads, img, image_embd, &n_img_pos)) {
-        fprintf(stderr, "%s: cannot encode image, aborting\n", __func__);
+        LOG_TEE("%s: cannot encode image, aborting\n", __func__);
        free(image_embd);
        return false;
    }
@@ -340,7 +340,7 @@ bool llava_eval_image_embed(llama_context * ctx_llama, const struct llava_image_
        }
        llama_batch batch = {int32_t(n_eval), nullptr, (image_embed->embed+i*n_embd), nullptr, nullptr, nullptr, nullptr, *n_past, 1, 0, };
        if (llama_decode(ctx_llama, batch)) {
-            fprintf(stderr, "%s : failed to eval\n", __func__);
+            LOG_TEE("%s : failed to eval\n", __func__);
            return false;
        }
        *n_past += n_eval;
@@ -352,7 +352,7 @@ struct llava_image_embed * llava_image_embed_make_with_bytes(struct clip_ctx * c
    clip_image_u8 * img = clip_image_u8_init();
    if (!clip_image_load_from_bytes(image_bytes, image_bytes_length, img)) {
        clip_image_u8_free(img);
-        fprintf(stderr, "%s: can't load image from bytes, is it a valid image?", __func__);
+        LOG_TEE("%s: can't load image from bytes, is it a valid image?", __func__);
        return NULL;
    }

@@ -361,7 +361,7 @@ struct llava_image_embed * llava_image_embed_make_with_bytes(struct clip_ctx * c
    bool image_embed_result = llava_image_embed_make_with_clip_img(ctx_clip, n_threads, img, &image_embed, &n_image_pos);
    if (!image_embed_result) {
        clip_image_u8_free(img);
-        fprintf(stderr, "%s: coulnd't embed the image\n", __func__);
+        LOG_TEE("%s: coulnd't embed the image\n", __func__);
        return NULL;
    }

@@ -375,7 +375,7 @@ struct llava_image_embed * llava_image_embed_make_with_bytes(struct clip_ctx * c
 static bool load_file_to_bytes(const char* path, unsigned char** bytesOut, long *sizeOut) {
    auto file = fopen(path, "rb");
    if (file == NULL) {
-        fprintf(stderr, "%s: can't read file %s\n", __func__, path);
+        LOG_TEE("%s: can't read file %s\n", __func__, path);
        return false;
    }

@@ -385,7 +385,7 @@ static bool load_file_to_bytes(const char* path, unsigned char** bytesOut, long

    auto buffer = (unsigned char *)malloc(fileSize); // Allocate memory to hold the file data
    if (buffer == NULL) {
-        fprintf(stderr, "%s: failed to alloc %ld bytes for file %s\n", __func__, fileSize, path);
+        LOG_TEE("%s: failed to alloc %ld bytes for file %s\n", __func__, fileSize, path);
        perror("Memory allocation error");
        fclose(file);
        return false;
@@ -410,7 +410,7 @@ struct llava_image_embed * llava_image_embed_make_with_filename(struct clip_ctx
    long image_bytes_length;
    auto loaded = load_file_to_bytes(image_path, &image_bytes, &image_bytes_length);
    if (!loaded) {
-        fprintf(stderr, "%s: failed to load %s\n", __func__, image_path);
+        LOG_TEE("%s: failed to load %s\n", __func__, image_path);
        return NULL;
    }

@@ -299,7 +299,7 @@ int main(int argc, char ** argv) {
                }
                fflush(stdout);

-                if (id == llama_token_eos(model)) {
+                if (llama_token_is_eog(model, id)) {
                    has_eos = true;
                }

@@ -30,7 +30,6 @@ int main(int argc, char ** argv){

    // load the model
    std::tie(model, ctx) = llama_init_from_gpt_params(params);
-    llama_set_rng_seed(ctx, params.seed);
    GGML_ASSERT(llama_n_vocab(model) < (1 << 16));

    // tokenize the prompt
@@ -38,7 +38,6 @@ int main(int argc, char ** argv){

    // load the model
    std::tie(model, ctx) = llama_init_from_gpt_params(params);
-    llama_set_rng_seed(ctx, params.seed);
    GGML_ASSERT(llama_n_vocab(model) < (1 << 16));

    // tokenize the prompt
@@ -141,7 +140,7 @@ int main(int argc, char ** argv){
                printf("%s", token_str.c_str());
            }

-            if (id == llama_token_eos(model)) {
+            if (llama_token_is_eog(model, id)) {
                has_eos = true;
            }

@@ -17,11 +17,9 @@ In this case, CLBlast was already installed so the CMake package is referenced i
 ```cmd
 git clone https://github.com/ggerganov/llama.cpp
 cd llama.cpp
-mkdir build
-cd build
-cmake .. -DBUILD_SHARED_LIBS=OFF -DLLAMA_CLBLAST=ON -DCMAKE_PREFIX_PATH=C:/CLBlast/lib/cmake/CLBlast -G "Visual Studio 17 2022" -A x64
-cmake --build . --config Release
-cmake --install . --prefix C:/LlamaCPP
+cmake -B build -DBUILD_SHARED_LIBS=OFF -DLLAMA_CLBLAST=ON -DCMAKE_PREFIX_PATH=C:/CLBlast/lib/cmake/CLBlast -G "Visual Studio 17 2022" -A x64
+cmake --build build --config Release
+cmake --install build --prefix C:/LlamaCPP
 ```

 ### Build main-cmake-pkg
@@ -29,9 +27,7 @@ cmake --install . --prefix C:/LlamaCPP

 ```cmd
 cd ..\examples\main-cmake-pkg
-mkdir build
-cd build
-cmake .. -DBUILD_SHARED_LIBS=OFF -DCMAKE_PREFIX_PATH="C:/CLBlast/lib/cmake/CLBlast;C:/LlamaCPP/lib/cmake/Llama" -G "Visual Studio 17 2022" -A x64
-cmake --build . --config Release
-cmake --install . --prefix C:/MyLlamaApp
+cmake -B build -DBUILD_SHARED_LIBS=OFF -DCMAKE_PREFIX_PATH="C:/CLBlast/lib/cmake/CLBlast;C:/LlamaCPP/lib/cmake/Llama" -G "Visual Studio 17 2022" -A x64
+cmake --build build --config Release
+cmake --install build --prefix C:/MyLlamaApp
 ```
@@ -66,7 +66,7 @@ main.exe -m models\7B\ggml-model.bin --ignore-eos -n -1 --random-prompt

 In this section, we cover the most commonly used options for running the `main` program with the LLaMA models:

-   `-m FNAME, --model FNAME`: Specify the path to the LLaMA model file (e.g., `models/7B/ggml-model.bin`).
+-   `-m FNAME, --model FNAME`: Specify the path to the LLaMA model file (e.g., `models/7B/ggml-model.gguf`; inferred from `--model-url` if set).
 -   `-mu MODEL_URL --model-url MODEL_URL`: Specify a remote http url to download the file (e.g https://huggingface.co/ggml-org/models/resolve/main/phi-2/ggml-model-q4_0.gguf).
 -   `-i, --interactive`: Run the program in interactive mode, allowing you to provide input directly and receive real-time responses.
 -   `-ins, --instruct`: Run the program in instruction mode, which is particularly useful when working with Alpaca models.
@@ -240,7 +240,6 @@ int main(int argc, char ** argv) {
                return 1;
            }
            session_tokens.resize(n_token_count_out);
-            llama_set_rng_seed(ctx, params.seed);
            LOG_TEE("%s: loaded a session with prompt size of %d tokens\n", __func__, (int)session_tokens.size());
        }
    }
@@ -325,7 +324,7 @@ int main(int argc, char ** argv) {
            log_tostr(embd_inp.empty()), n_matching_session_tokens, embd_inp.size(), session_tokens.size(), embd_inp.size());

    // if we will use the cache for the full prompt without reaching the end of the cache, force
-    // reevaluation of the last token token to recalculate the cached logits
+    // reevaluation of the last token to recalculate the cached logits
    if (!embd_inp.empty() && n_matching_session_tokens == embd_inp.size() && session_tokens.size() > embd_inp.size()) {
        LOGLN("recalculate the cached logits (do): session_tokens.resize( %zu )", embd_inp.size() - 1);

@@ -795,8 +794,8 @@ int main(int argc, char ** argv) {
                }
            }

-            // deal with end of text token in interactive mode
-            if (llama_sampling_last(ctx_sampling) == llama_token_eos(model)) {
+            // deal with end of generation tokens in interactive mode
+            if (llama_token_is_eog(model, llama_sampling_last(ctx_sampling))) {
                LOG("found EOS token\n");

                if (params.interactive) {
@@ -920,8 +919,8 @@ int main(int argc, char ** argv) {
            }
        }

-        // end of text token
-        if (!embd.empty() && embd.back() == llama_token_eos(model) && !(params.instruct || params.interactive || params.chatml)) {
+        // end of generation
+        if (!embd.empty() && llama_token_is_eog(model, embd.back()) && !(params.instruct || params.interactive || params.chatml)) {
            LOG_TEE(" [end of text]\n");
            break;
        }
@@ -359,7 +359,7 @@ int main(int argc, char ** argv) {
                //        client.id, client.seq_id, id, client.n_decoded, client.i_batch, token_str.c_str());

                if (client.n_decoded > 2 &&
-                        (id == llama_token_eos(model) ||
+                        (llama_token_is_eog(model, id) ||
                         (params.n_predict > 0 && client.n_decoded + client.n_prompt >= params.n_predict) ||
                         client.response.find("User:") != std::string::npos ||
                         client.response.find('\n') != std::string::npos)) {
@@ -252,8 +252,8 @@ int main(int argc, char ** argv) {
            // sample the most likely token
            const llama_token new_token_id = llama_sample_token_greedy(ctx, &candidates_p);

-            // is it an end of stream?
-            if (new_token_id == llama_token_eos(model) || n_cur == n_len) {
+            // is it an end of generation?
+            if (llama_token_is_eog(model, new_token_id) || n_cur == n_len) {
                LOG_TEE("\n");

                break;
@@ -23,7 +23,7 @@
 #endif

 struct quantize_stats_params {
-    std::string model = "models/7B/ggml-model-f16.gguf";
+    std::string model = DEFAULT_MODEL_PATH;
    bool verbose = false;
    bool per_layer_stats = false;
    bool print_histogram = false;
@@ -1,6 +1,6 @@
 set(TARGET quantize)
 add_executable(${TARGET} quantize.cpp)
 install(TARGETS ${TARGET} RUNTIME)
-target_link_libraries(${TARGET} PRIVATE llama build_info ${CMAKE_THREAD_LIBS_INIT})
+target_link_libraries(${TARGET} PRIVATE llama common ${CMAKE_THREAD_LIBS_INIT})
 target_include_directories(${TARGET} PRIVATE ../../common)
 target_compile_features(${TARGET} PRIVATE cxx_std_11)
@@ -8,7 +8,6 @@
 #include <unordered_map>
 #include <fstream>
 #include <cmath>
-#include <algorithm>

 struct quant_option {
    std::string name;
@@ -53,6 +52,10 @@ static const std::vector<struct quant_option> QUANT_OPTIONS = {
    { "COPY",   LLAMA_FTYPE_ALL_F32,       "only copy tensors, no quantizing", },
 };

+static const char * const LLM_KV_QUANTIZE_IMATRIX_FILE       = "quantize.imatrix.file";
+static const char * const LLM_KV_QUANTIZE_IMATRIX_DATASET    = "quantize.imatrix.dataset";
+static const char * const LLM_KV_QUANTIZE_IMATRIX_N_ENTRIES  = "quantize.imatrix.entries_count";
+static const char * const LLM_KV_QUANTIZE_IMATRIX_N_CHUNKS   = "quantize.imatrix.chunks_count";

 static bool try_parse_ftype(const std::string & ftype_str_in, llama_ftype & ftype, std::string & ftype_str_out) {
    std::string ftype_str;
@@ -97,6 +100,7 @@ static void usage(const char * executable) {
    printf("  --exclude-weights tensor_name: use importance matrix for this/these tensor(s)\n");
    printf("  --output-tensor-type ggml_type: use this ggml_type for the output.weight tensor\n");
    printf("  --token-embedding-type ggml_type: use this ggml_type for the token embeddings tensor\n");
+    printf("  --keep-split: will generate quatized model in the same shards as input");
    printf("  --override-kv KEY=TYPE:VALUE\n");
    printf("      Advanced option to override model metadata by key in the quantized model. May be specified multiple times.\n");
    printf("Note: --include-weights and --exclude-weights cannot be used together\n");
@@ -112,7 +116,7 @@ static void usage(const char * executable) {
    exit(1);
 }

-static void load_imatrix(const std::string & imatrix_file, std::unordered_map<std::string, std::vector<float>> & imatrix_data) {
+static int load_imatrix(const std::string & imatrix_file, std::string & imatrix_dataset, std::unordered_map<std::string, std::vector<float>> & imatrix_data) {
    std::ifstream in(imatrix_file.c_str(), std::ios::binary);
    if (!in) {
        printf("%s: failed to open %s\n",__func__, imatrix_file.c_str());
@@ -159,18 +163,33 @@ static void load_imatrix(const std::string & imatrix_file, std::unordered_map<st
            printf("%s: loaded data (size = %6d, ncall = %6d) for '%s'\n", __func__, int(e.size()), ncall, name.c_str());
        }
    }
-    printf("%s: loaded %d importance matrix entries from %s\n", __func__, int(imatrix_data.size()), imatrix_file.c_str());
+
+    // latest imatrix version contains the dataset filename at the end of the file
+    int m_last_call = 0;
+    if (in.peek() != EOF) {
+        in.read((char *)&m_last_call, sizeof(m_last_call));
+        int dataset_len;
+        in.read((char *)&dataset_len, sizeof(dataset_len));
+        std::vector<char> dataset_as_vec(dataset_len);
+        in.read(dataset_as_vec.data(), dataset_len);
+        imatrix_dataset.assign(dataset_as_vec.begin(), dataset_as_vec.end());
+        printf("%s: imatrix dataset='%s'\n", __func__, imatrix_dataset.c_str());
+    }
+    printf("%s: loaded %d importance matrix entries from %s computed on %d chunks\n", __func__, int(imatrix_data.size()), imatrix_file.c_str(), m_last_call);
+    return m_last_call;
 }

-static void prepare_imatrix(const std::string & imatrix_file,
+static int prepare_imatrix(const std::string & imatrix_file,
+        std::string & imatrix_dataset,
        const std::vector<std::string> & included_weights,
        const std::vector<std::string> & excluded_weights,
        std::unordered_map<std::string, std::vector<float>> & imatrix_data) {
+    int m_last_call = -1;
    if (!imatrix_file.empty()) {
-        load_imatrix(imatrix_file, imatrix_data);
+        m_last_call = load_imatrix(imatrix_file, imatrix_dataset, imatrix_data);
    }
    if (imatrix_data.empty()) {
-        return;
+        return m_last_call;
    }
    if (!excluded_weights.empty()) {
        for (auto& name : excluded_weights) {
@@ -196,6 +215,7 @@ static void prepare_imatrix(const std::string & imatrix_file,
    if (!imatrix_data.empty()) {
        printf("%s: have %d importance matrix entries\n", __func__, int(imatrix_data.size()));
    }
+    return m_last_call;
 }

 static ggml_type parse_ggml_type(const char * arg) {
@@ -210,43 +230,6 @@ static ggml_type parse_ggml_type(const char * arg) {
    return result;
 }

-static bool parse_kv_override(const char * data, std::vector<llama_model_kv_override> & overrides) {
-    const char* sep = strchr(data, '=');
-    if (sep == nullptr || sep - data >= 128) {
-        fprintf(stderr, "%s: malformed KV override '%s'\n", __func__, data);
-        return false;
-    }
-    llama_model_kv_override kvo;
-    std::strncpy(kvo.key, data, sep - data);
-    kvo.key[sep - data] = 0;
-    sep++;
-    if (strncmp(sep, "int:", 4) == 0) {
-        sep += 4;
-        kvo.tag = LLAMA_KV_OVERRIDE_TYPE_INT;
-        kvo.int_value = std::atol(sep);
-    } else if (strncmp(sep, "float:", 6) == 0) {
-        sep += 6;
-        kvo.tag = LLAMA_KV_OVERRIDE_TYPE_FLOAT;
-        kvo.float_value = std::atof(sep);
-    } else if (strncmp(sep, "bool:", 5) == 0) {
-        sep += 5;
-        kvo.tag = LLAMA_KV_OVERRIDE_TYPE_BOOL;
-        if (std::strcmp(sep, "true") == 0) {
-            kvo.bool_value = true;
-        } else if (std::strcmp(sep, "false") == 0) {
-            kvo.bool_value = false;
-        } else {
-            fprintf(stderr, "%s: invalid boolean value for KV override '%s'\n", __func__, data);
-            return false;
-        }
-    } else {
-        fprintf(stderr, "%s: invalid type for KV override '%s'\n", __func__, data);
-        return false;
-    }
-    overrides.emplace_back(std::move(kvo));
-    return true;
-}
-
 int main(int argc, char ** argv) {
    if (argc < 3) {
        usage(argv[0]);
@@ -300,6 +283,8 @@ int main(int argc, char ** argv) {
            } else {
                usage(argv[0]);
            }
+        } else if (strcmp(argv[arg_idx], "--keep-split")) {
+            params.keep_split = true;
        } else {
            usage(argv[0]);
        }
@@ -313,10 +298,43 @@ int main(int argc, char ** argv) {
        usage(argv[0]);
    }

+    std::string imatrix_dataset;
    std::unordered_map<std::string, std::vector<float>> imatrix_data;
-    prepare_imatrix(imatrix_file, included_weights, excluded_weights, imatrix_data);
+    int m_last_call = prepare_imatrix(imatrix_file, imatrix_dataset, included_weights, excluded_weights, imatrix_data);
    if (!imatrix_data.empty()) {
        params.imatrix = &imatrix_data;
+        {
+            llama_model_kv_override kvo;
+            std::strcpy(kvo.key, LLM_KV_QUANTIZE_IMATRIX_FILE);
+            kvo.tag = LLAMA_KV_OVERRIDE_TYPE_STR;
+            strncpy(kvo.val_str, imatrix_file.c_str(), 127);
+            kvo.val_str[127] = '\0';
+            kv_overrides.emplace_back(std::move(kvo));
+        }
+        if (!imatrix_dataset.empty()) {
+            llama_model_kv_override kvo;
+            std::strcpy(kvo.key, LLM_KV_QUANTIZE_IMATRIX_DATASET);
+            kvo.tag = LLAMA_KV_OVERRIDE_TYPE_STR;
+            strncpy(kvo.val_str, imatrix_dataset.c_str(), 127);
+            kvo.val_str[127] = '\0';
+            kv_overrides.emplace_back(std::move(kvo));
+        }
+
+        {
+            llama_model_kv_override kvo;
+            std::strcpy(kvo.key, LLM_KV_QUANTIZE_IMATRIX_N_ENTRIES);
+            kvo.tag = LLAMA_KV_OVERRIDE_TYPE_INT;
+            kvo.val_i64 = imatrix_data.size();
+            kv_overrides.emplace_back(std::move(kvo));
+        }
+
+        if (m_last_call > 0) {
+            llama_model_kv_override kvo;
+            std::strcpy(kvo.key, LLM_KV_QUANTIZE_IMATRIX_N_CHUNKS);
+            kvo.tag = LLAMA_KV_OVERRIDE_TYPE_INT;
+            kvo.val_i64 = m_last_call;
+            kv_overrides.emplace_back(std::move(kvo));
+        }
    }
    if (!kv_overrides.empty()) {
        kv_overrides.emplace_back();
@@ -332,20 +350,28 @@ int main(int argc, char ** argv) {
    std::string fname_out;

    std::string ftype_str;
+    std::string suffix = ".gguf";
    if (try_parse_ftype(argv[arg_idx], params.ftype, ftype_str)) {
        std::string fpath;
        const size_t pos = fname_inp.find_last_of("/\\");
        if (pos != std::string::npos) {
            fpath = fname_inp.substr(0, pos + 1);
        }
-        // export as [inp path]/ggml-model-[ftype].gguf
-        fname_out = fpath + "ggml-model-" + ftype_str + ".gguf";
+
+        // export as [inp path]/ggml-model-[ftype]. Only add extension if there is no splitting
+        fname_out = fpath + "ggml-model-" + ftype_str;
+        if (!params.keep_split) {
+            fname_out += suffix;
+        }
        arg_idx++;
        if (ftype_str == "COPY") {
            params.only_copy = true;
        }
    } else {
        fname_out = argv[arg_idx];
+        if (params.keep_split && fname_out.find(suffix) != std::string::npos) {
+            fname_out = fname_out.substr(0, fname_out.length() - suffix.length());
+        }
        arg_idx++;

        if (argc <= arg_idx) {
@@ -0,0 +1,65 @@
+#!/bin/bash
+
+set -eu
+
+if [ $# -lt 1 ]
+then
+    echo "usage:   $0 path_to_build_binary [path_to_temp_folder]"
+    echo "example: $0 ../../build/bin ../../tmp"
+    exit 1
+fi
+
+if [ $# -gt 1 ]
+then
+    TMP_DIR=$2
+else
+    TMP_DIR=/tmp
+fi
+
+set -x
+
+SPLIT=$1/gguf-split
+QUANTIZE=$1/quantize
+MAIN=$1/main
+WORK_PATH=$TMP_DIR/quantize
+ROOT_DIR=$(realpath $(dirname $0)/../../)
+
+mkdir -p "$WORK_PATH"
+
+# Clean up in case of previously failed test
+rm -f $WORK_PATH/ggml-model-split*.gguf $WORK_PATH/ggml-model-requant*.gguf
+
+# 1. Get a model
+(
+cd $WORK_PATH
+"$ROOT_DIR"/scripts/hf.sh --repo ggml-org/gemma-1.1-2b-it-Q8_0-GGUF --file gemma-1.1-2b-it.Q8_0.gguf
+)
+echo PASS
+
+# 2. Split model
+$SPLIT --split-max-tensors 28  $WORK_PATH/gemma-1.1-2b-it.Q8_0.gguf $WORK_PATH/ggml-model-split
+echo PASS
+echo
+
+# 3. Requant model with '--keep_split'
+$QUANTIZE --allow-requantize --keep_split $WORK_PATH/ggml-model-split-00001-of-00006.gguf $WORK_PATH/ggml-model-requant.gguf Q4_K
+echo PASS
+echo
+
+# 3a. Test the requanted model is loading properly
+$MAIN --model $WORK_PATH/ggml-model-requant-00001-of-00006.gguf --random-prompt --n-predict 32
+echo PASS
+echo
+
+# 4. Requant mode without '--keep_split'
+$QUANTIZE --allow-requantize $WORK_PATH/ggml-model-split-00001-of-00006.gguf $WORK_PATH/ggml-model-requant-merge.gguf Q4_K
+echo PASS
+echo
+
+# 4b. Test the requanted model is loading properly
+$MAIN --model $WORK_PATH/ggml-model-requant-merge.gguf --random-prompt --n-predict 32
+echo PASS
+echo
+
+# Clean up
+rm -f $WORK_PATH/ggml-model-split*.gguf $WORK_PATH/ggml-model-requant*.gguf
@@ -1,12 +1,29 @@
 set(TARGET server)
 option(LLAMA_SERVER_VERBOSE "Build verbose logging option for Server" ON)
 option(LLAMA_SERVER_SSL "Build SSL support for the server" OFF)
-include_directories(${CMAKE_CURRENT_SOURCE_DIR})
-add_executable(${TARGET}
+include_directories(${CMAKE_CURRENT_SOURCE_DIR} ${CMAKE_CURRENT_BINARY_DIR})
+set(TARGET_SRCS
    server.cpp
    utils.hpp
    httplib.h
 )
+set(PUBLIC_ASSETS
+    index.html
+    index.js
+    completion.js
+    json-schema-to-grammar.mjs
+)
+foreach(asset ${PUBLIC_ASSETS})
+    set(input "${CMAKE_CURRENT_SOURCE_DIR}/public/${asset}")
+    set(output "${CMAKE_CURRENT_BINARY_DIR}/${asset}.hpp")
+    list(APPEND TARGET_SRCS ${output})
+    add_custom_command(
+        DEPENDS "${input}"
+        OUTPUT "${output}"
+        COMMAND "${CMAKE_COMMAND}" "-DINPUT=${input}" "-DOUTPUT=${output}" -P "${PROJECT_SOURCE_DIR}/scripts/xxd.cmake"
+    )
+endforeach()
+add_executable(${TARGET} ${TARGET_SRCS})
 install(TARGETS ${TARGET} RUNTIME)
 target_compile_definitions(${TARGET} PRIVATE
    SERVER_VERBOSE=$<BOOL:${LLAMA_SERVER_VERBOSE}>
@@ -74,15 +74,18 @@ page cache before using this. See https://github.com/ggerganov/llama.cpp/issues/
 - Using `make`:

  ```bash
-  make
+  make server
  ```

 - Using `CMake`:

  ```bash
-  cmake --build . --config Release
+  cmake -B build
+  cmake --build build --config Release -t server
  ```

+  Binary is at `./build/bin/server`
+
 ## Build with SSL

 `server` can also be built with SSL support using OpenSSL 3
@@ -99,10 +102,8 @@ page cache before using this. See https://github.com/ggerganov/llama.cpp/issues/
 - Using `CMake`:

  ```bash
-  mkdir build
-  cd build
-  cmake .. -DLLAMA_SERVER_SSL=ON
-  make server
+  cmake -B build -DLLAMA_SERVER_SSL=ON
+  cmake --build build --config Release -t server
  ```

 ## Quick Start
@@ -268,6 +268,7 @@ def start_server_background(args):
    server_args.extend(['--defrag-thold', "0.1"])
    server_args.append('--cont-batching')
    server_args.append('--metrics')
+    server_args.append('--flash-attn')
    server_args.extend(['--log-format', "text"])
    args = [str(arg) for arg in [server_path, *server_args]]
    print(f"bench: starting server with: {' '.join(args)}")
@@ -90,7 +90,8 @@ export default function () {
        "model": model,
        "stream": true,
        "seed": 42,
-        "max_tokens": max_tokens
+        "max_tokens": max_tokens,
+        "stop": ["<|im_end|>"] // This is temporary for phi-2 base (i.e. not instructed) since the server expects that the model always to emit BOS
    }

    const params = {method: 'POST', body: JSON.stringify(payload)};
@@ -1,496 +0,0 @@
-unsigned char completion_js[] = {
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-  0x73, 0x3b, 0x0a, 0x7d, 0x0a
-};
-unsigned int completion_js_len = 5909;
@@ -8,13 +8,3 @@ PUBLIC=$DIR/public
 echo "download js bundle files"
 curl https://npm.reversehttp.com/@preact/signals-core,@preact/signals,htm/preact,preact,preact/hooks > $PUBLIC/index.js
 echo >> $PUBLIC/index.js # add newline
-
-FILES=$(ls $PUBLIC)
-
-cd $PUBLIC
-for FILE in $FILES; do
-  echo "generate $FILE.hpp"
-
-  # use simple flag for old version of xxd
-  xxd -i $FILE > $DIR/$FILE.hpp
-done
@@ -881,11 +881,11 @@
        .replace(/&/g, '&amp;')
        .replace(/</g, '&lt;')
        .replace(/>/g, '&gt;')
-        .replace(/^#{1,6} (.*)$/gim, '<h3>$1</h3>')
-        .replace(/\*\*(.*?)\*\*/g, '<strong>$1</strong>')
-        .replace(/__(.*?)__/g, '<strong>$1</strong>')
-        .replace(/\*(.*?)\*/g, '<em>$1</em>')
-        .replace(/_(.*?)_/g, '<em>$1</em>')
+        .replace(/(^|\n)#{1,6} ([^\n]*)(?=([^`]*`[^`]*`)*[^`]*$)/g, '$1<h3>$2</h3>')
+        .replace(/\*\*(.*?)\*\*(?=([^`]*`[^`]*`)*[^`]*$)/g, '<strong>$1</strong>')
+        .replace(/__(.*?)__(?=([^`]*`[^`]*`)*[^`]*$)/g, '<strong>$1</strong>')
+        .replace(/\*(.*?)\*(?=([^`]*`[^`]*`)*[^`]*$)/g, '<em>$1</em>')
+        .replace(/_(.*?)_(?=([^`]*`[^`]*`)*[^`]*$)/g, '<em>$1</em>')
        .replace(/```.*?\n([\s\S]*?)```/g, '<pre><code>$1</code></pre>')
        .replace(/`(.*?)`/g, '<code>$1</code>')
        .replace(/\n/gim, '<br />');
@@ -854,7 +854,7 @@ struct server_context {
        slot.sparams.penalize_nl       = json_value(data, "penalize_nl",       default_sparams.penalize_nl);
        slot.params.n_keep             = json_value(data, "n_keep",            slot.params.n_keep);
        slot.params.n_discard          = json_value(data, "n_discard",         default_params.n_discard);
-        slot.params.seed               = json_value(data, "seed",              default_params.seed);
+        slot.sparams.seed              = json_value(data, "seed",              default_sparams.seed);
        slot.sparams.n_probs           = json_value(data, "n_probs",           default_sparams.n_probs);
        slot.sparams.min_keep          = json_value(data, "min_keep",          default_sparams.min_keep);

@@ -1028,7 +1028,6 @@ struct server_context {
                send_error(task, "Failed to parse grammar", ERROR_TYPE_INVALID_REQUEST);
                return false;
            }
-            llama_set_rng_seed(ctx, slot.params.seed);
        }

        slot.command = SLOT_COMMAND_LOAD_PROMPT;
@@ -1118,7 +1117,7 @@ struct server_context {

    bool process_token(completion_token_output & result, server_slot & slot) {
        // remember which tokens were sampled - used for repetition penalties during sampling
-        const std::string token_str = llama_token_to_piece(ctx, result.tok);
+        const std::string token_str = llama_token_to_piece(ctx, result.tok, false);
        slot.sampled = result.tok;

        // search stop word and delete it
@@ -1201,13 +1200,34 @@ struct server_context {
            });
        }

-        if (result.tok == llama_token_eos(model)) {
+        if (llama_token_is_eog(model, result.tok)) {
            slot.stopped_eos    = true;
            slot.has_next_token = false;

            LOG_VERBOSE("eos token found", {});
        }

+        auto n_ctx_train = llama_n_ctx_train(model);
+        if (slot.params.n_predict < 1 && slot.n_predict < 1 && slot.ga_n == 1
+                    && slot.n_prompt_tokens + slot.n_decoded >= n_ctx_train) {
+            LOG_WARNING("n_predict is not set and self-context extend is disabled."
+                        " Limiting generated tokens to n_ctx_train to avoid EOS-less generation infinite loop", {
+                    { "id_slot",              slot.id },
+                    { "params.n_predict",     slot.params.n_predict },
+                    { "slot.n_prompt_tokens", slot.n_prompt_tokens },
+                    { "slot.n_decoded",       slot.n_decoded },
+                    { "slot.n_predict",       slot.n_predict },
+                    { "n_slots",              params.n_parallel },
+                    { "slot.n_ctx",           slot.n_ctx },
+                    { "n_ctx",                n_ctx },
+                    { "n_ctx_train",          n_ctx_train },
+                    { "ga_n",                 slot.ga_n },
+                });
+            slot.truncated      = true;
+            slot.stopped_limit  = true;
+            slot.has_next_token = false; // stop prediction
+        }
+
        LOG_VERBOSE("next token", {
            {"id_slot",        slot.id},
            {"id_task",        slot.id_task},
@@ -2142,7 +2162,7 @@ struct server_context {
        });

        // process the created batch of tokens
-        for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += n_batch) {
+        for (int32_t i = 0; i < batch.n_tokens; i += n_batch) {
            const int32_t n_tokens = std::min(n_batch, batch.n_tokens - i);

            for (auto & slot : slots) {
@@ -2333,7 +2353,7 @@ static void server_print_usage(const char * argv0, const gpt_params & params, co
        printf("                            disable KV offload\n");
    }
    printf("  -m FNAME, --model FNAME\n");
-    printf("                            model path (default: %s)\n", params.model.c_str());
+    printf("                            model path (default: models/$filename with filename from --hf-file or --model-url if set, otherwise %s)\n", DEFAULT_MODEL_PATH);
    printf("  -mu MODEL_URL, --model-url MODEL_URL\n");
    printf("                            model download url (default: unused)\n");
    printf("  -hfr REPO, --hf-repo REPO\n");
@@ -2357,6 +2377,7 @@ static void server_print_usage(const char * argv0, const gpt_params & params, co
    printf("  --embeddings              enable embedding vector output (default: %s)\n", params.embedding ? "enabled" : "disabled");
    printf("  -np N, --parallel N       number of slots for process requests (default: %d)\n", params.n_parallel);
    printf("  -cb, --cont-batching      enable continuous batching (a.k.a dynamic batching) (default: enabled)\n");
+    printf("  -fa, --flash-attn         enable Flash Attention (default: %s)\n", params.flash_attn ? "enabled" : "disabled");
    printf("  -spf FNAME, --system-prompt-file FNAME\n");
    printf("                            set a file to load a system prompt (initial prompt of all slots), this is useful for chat applications.\n");
    printf("  -ctk TYPE, --cache-type-k TYPE\n");
@@ -2372,7 +2393,7 @@ static void server_print_usage(const char * argv0, const gpt_params & params, co
    printf("  -n, --n-predict           maximum tokens to predict (default: %d)\n", params.n_predict);
    printf("  --override-kv KEY=TYPE:VALUE\n");
    printf("                            advanced option to override model metadata by key. may be specified multiple times.\n");
-    printf("                            types: int, float, bool. example: --override-kv tokenizer.ggml.add_bos_token=bool:false\n");
+    printf("                            types: int, float, bool, str. example: --override-kv tokenizer.ggml.add_bos_token=bool:false\n");
    printf("  -gan N, --grp-attn-n N    set the group attention factor to extend context size through self-extend(default: 1=disabled), used together with group attention width `--grp-attn-w`\n");
    printf("  -gaw N, --grp-attn-w N    set the group attention width to extend context size through self-extend(default: 512), used together with group attention factor `--grp-attn-n`\n");
    printf("  --chat-template JINJA_TEMPLATE\n");
@@ -2722,6 +2743,8 @@ static void server_params_parse(int argc, char ** argv, server_params & sparams,
            params.embedding = true;
        } else if (arg == "-cb" || arg == "--cont-batching") {
            params.cont_batching = true;
+        } else if (arg == "-fa" || arg == "--flash-attn") {
+            params.flash_attn = true;
        } else if (arg == "-np" || arg == "--parallel") {
            if (++i >= argc) {
                invalid_param = true;
@@ -2803,43 +2826,11 @@ static void server_params_parse(int argc, char ** argv, server_params & sparams,
                invalid_param = true;
                break;
            }
-            char * sep = strchr(argv[i], '=');
-            if (sep == nullptr || sep - argv[i] >= 128) {
-                fprintf(stderr, "error: Malformed KV override: %s\n", argv[i]);
-                invalid_param = true;
-                break;
-            }
-
-            struct llama_model_kv_override kvo;
-            std::strncpy(kvo.key, argv[i], sep - argv[i]);
-            kvo.key[sep - argv[i]] = 0;
-            sep++;
-            if (strncmp(sep, "int:", 4) == 0) {
-                sep += 4;
-                kvo.tag = LLAMA_KV_OVERRIDE_TYPE_INT;
-                kvo.int_value = std::atol(sep);
-            } else if (strncmp(sep, "float:", 6) == 0) {
-                sep += 6;
-                kvo.tag = LLAMA_KV_OVERRIDE_TYPE_FLOAT;
-                kvo.float_value = std::atof(sep);
-            } else if (strncmp(sep, "bool:", 5) == 0) {
-                sep += 5;
-                kvo.tag = LLAMA_KV_OVERRIDE_TYPE_BOOL;
-                if (std::strcmp(sep, "true") == 0) {
-                    kvo.bool_value = true;
-                } else if (std::strcmp(sep, "false") == 0) {
-                    kvo.bool_value = false;
-                } else {
-                    fprintf(stderr, "error: Invalid boolean value for KV override: %s\n", argv[i]);
-                    invalid_param = true;
-                    break;
-                }
-            } else {
+            if (!parse_kv_override(argv[i], params.kv_overrides)) {
                fprintf(stderr, "error: Invalid type for KV override: %s\n", argv[i]);
                invalid_param = true;
                break;
            }
-            params.kv_overrides.push_back(kvo);
        } else {
            fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
            server_print_usage(argv[0], default_params, default_sparams);
@@ -2847,6 +2838,8 @@ static void server_params_parse(int argc, char ** argv, server_params & sparams,
        }
    }

+    gpt_params_handle_model_default(params);
+
    if (!params.kv_overrides.empty()) {
        params.kv_overrides.emplace_back();
        params.kv_overrides.back().key[0] = 0;
@@ -29,7 +29,7 @@ To mitigate it, you can increase values in `n_predict`, `kv_size`.
 cd ../../..
 mkdir build
 cd build
-cmake ../
+cmake -DLLAMA_CURL=ON ../
 cmake --build . --target server
 ```

@@ -5,7 +5,7 @@ Feature: llama.cpp server
  Background: Server startup
    Given a server listening on localhost:8080
    And   a model url https://huggingface.co/ggml-org/models/resolve/main/bert-bge-small/ggml-model-f16.gguf
-    And   a model file ggml-model-f16.gguf
+    And   a model file bert-bge-small.gguf
    And   a model alias bert-bge-small
    And   42 as server seed
    And   2 slots
@@ -0,0 +1,57 @@
+@llama.cpp
+@results
+Feature: Results
+
+  Background: Server startup
+    Given a server listening on localhost:8080
+    And   a model file tinyllamas/split/stories15M-00001-of-00003.gguf from HF repo ggml-org/models
+    And   a model file test-model-00001-of-00003.gguf
+    And   128 as batch size
+    And   256 KV cache size
+    And   128 max tokens to predict
+
+  Scenario Outline: Multi users completion
+    Given <n_slots> slots
+    And   continuous batching
+    Then  the server is starting
+    Then  the server is healthy
+
+    Given 42 as seed
+    And a prompt:
+      """
+      Write a very long story about AI.
+      """
+
+    Given 42 as seed
+    And a prompt:
+      """
+      Write a very long story about AI.
+      """
+
+    Given 42 as seed
+    And a prompt:
+      """
+      Write a very long story about AI.
+      """
+
+    Given 42 as seed
+    And a prompt:
+      """
+      Write a very long story about AI.
+      """
+
+    Given 42 as seed
+    And a prompt:
+      """
+      Write a very long story about AI.
+      """
+
+    Given concurrent completion requests
+    Then the server is busy
+    Then the server is idle
+    And  all slots are idle
+    Then all predictions are equal
+    Examples:
+      | n_slots |
+      | 1       |
+      | 2       |
@@ -61,6 +61,7 @@ def step_server_config(context, server_fqdn, server_port):
    context.server_metrics = False
    context.server_process = None
    context.seed = None
+    context.draft = None
    context.server_seed = None
    context.user_api_key = None
    context.response_format = None
@@ -107,6 +108,11 @@ def step_n_gpu_layer(context, ngl):
    context.n_gpu_layer = ngl


+@step('{draft:d} as draft')
+def step_draft(context, draft):
+    context.draft = draft
+
+
@step('{n_ctx:d} KV cache size')
 def step_n_ctx(context, n_ctx):
    context.n_ctx = n_ctx
@@ -254,6 +260,15 @@ def step_n_tokens_predicted(context, predicted_n):
    assert_n_tokens_predicted(context.completion, predicted_n)


+@step('all predictions are equal')
+@async_run_until_complete
+async def step_predictions_equal(context):
+    n_completions = await gather_tasks_results(context)
+    assert n_completions >= 2, "need at least 2 completions"
+    assert_all_predictions_equal(context.tasks_result)
+    context.tasks_result = []
+
+
@step('the completion is  truncated')
 def step_assert_completion_truncated(context):
    step_assert_completion_truncated(context, '')
@@ -1020,6 +1035,23 @@ def assert_n_tokens_predicted(completion_response, expected_predicted_n=None, re
        assert n_predicted == expected_predicted_n, (f'invalid number of tokens predicted:'
                                                     f' {n_predicted} <> {expected_predicted_n}')

+def assert_all_predictions_equal(completion_responses):
+    content_0 = completion_responses[0]['content']
+
+    if 'DEBUG' in os.environ and os.environ['DEBUG'] == 'ON':
+        print(f"content 0: {content_0}")
+
+    i = 1
+    for response in completion_responses[1:]:
+        content = response['content']
+
+        if 'DEBUG' in os.environ and os.environ['DEBUG'] == 'ON':
+            print(f"content {i}: {content}")
+
+        assert content == content_0, "contents not equal"
+
+        i += 1
+

 async def gather_tasks_results(context):
    n_tasks = len(context.concurrent_tasks)
@@ -1148,6 +1180,8 @@ def start_server_background(context):
        server_args.extend(['--ubatch-size', context.n_ubatch])
    if context.n_gpu_layer:
        server_args.extend(['--n-gpu-layers', context.n_gpu_layer])
+    if context.draft is not None:
+        server_args.extend(['--draft', context.draft])
    if context.server_continuous_batching:
        server_args.append('--cont-batching')
    if context.server_embeddings:
@@ -4,9 +4,8 @@ set -eu

 if [ $# -lt 1 ]
 then
-  # Start @llama.cpp scenario
-  behave --summary --stop --no-capture --exclude 'issues|wrong_usages|passkey' --tags llama.cpp
+    # Start @llama.cpp scenario
+    behave --summary --stop --no-capture --exclude 'issues|wrong_usages|passkey' --tags llama.cpp
 else
-  behave "$@"
+    behave "$@"
 fi
-
@@ -381,10 +381,6 @@ static json oaicompat_completion_params_parse(
    } else {
        llama_params["stop"] = json_value(body, "stop", json::array());
    }
-    // Some chat templates don't use EOS token to stop generation
-    // We must add their end sequences to list of stop words
-    llama_params["stop"].push_back("<|im_end|>"); // chatml
-    llama_params["stop"].push_back("<end_of_turn>"); // gemma

    // Handle "response_format" field
    if (body.contains("response_format")) {
@@ -133,8 +133,8 @@ int main(int argc, char ** argv) {
            // sample the most likely token
            const llama_token new_token_id = llama_sample_token_greedy(ctx, &candidates_p);

-            // is it an end of stream?
-            if (new_token_id == llama_token_eos(model) || n_cur == n_len) {
+            // is it an end of generation?
+            if (llama_token_is_eog(model, new_token_id) || n_cur == n_len) {
                LOG_TEE("\n");

                break;
@@ -360,7 +360,7 @@ int main(int argc, char ** argv) {
                    }
                }

-                if (token_id == llama_token_eos(model_tgt)) {
+                if (llama_token_is_eog(model_tgt, token_id)) {
                    has_eos = true;
                }
                ++n_predict;
@@ -73,6 +73,7 @@ struct my_llama_model {
 static const char * LLM_KV_TRAINING_TYPE_TRAIN_MODEL     = "train_model";
 static const char * LLM_KV_TRAINING_TYPE                 = "training.type";

+static const char * LLM_KV_GENERAL_NAME                = "general.name";
 static const char * LLM_KV_GENERAL_ARCHITECTURE        = "general.architecture";
 static const char * LLM_KV_GENERAL_FILE_TYPE           = "general.file_type";

@@ -529,6 +530,7 @@ static void load_llama_model_gguf(struct gguf_context * fctx, struct ggml_contex

 static void save_llama_model_gguf(struct gguf_context * fctx, const char * fn_vocab_model, struct my_llama_model * model) {
    const char * arch = "llama";
+
    enum llama_ftype ftype = LLAMA_FTYPE_ALL_F32;

    std::vector<char> keybuf;
@@ -540,6 +542,7 @@ static void save_llama_model_gguf(struct gguf_context * fctx, const char * fn_vo

    // set arch
    gguf_set_val_str(fctx, LLM_KV_GENERAL_ARCHITECTURE, arch);
+    gguf_set_val_str(fctx, LLM_KV_GENERAL_NAME, arch);
    gguf_set_val_u32(fctx, LLM_KV_GENERAL_FILE_TYPE, ftype);

    // set hparams
@@ -20,11 +20,11 @@
    },
    "nixpkgs": {
      "locked": {
-        "lastModified": 1712791164,
-        "narHash": "sha256-3sbWO1mbpWsLepZGbWaMovSO7ndZeFqDSdX0hZ9nVyw=",
+        "lastModified": 1714076141,
+        "narHash": "sha256-Drmja/f5MRHZCskS6mvzFqxEaZMeciScCTFxWVLqWEY=",
        "owner": "NixOS",
        "repo": "nixpkgs",
-        "rev": "1042fd8b148a9105f3c0aca3a6177fd1d9360ba5",
+        "rev": "7bb2ccd8cdc44c91edba16c48d2c8f331fb3d856",
        "type": "github"
      },
      "original": {
@@ -371,16 +371,16 @@ struct ggml_gallocr {
 };

 ggml_gallocr_t ggml_gallocr_new_n(ggml_backend_buffer_type_t * bufts, int n_bufs) {
-    ggml_gallocr_t galloc = (ggml_gallocr_t)calloc(sizeof(struct ggml_gallocr), 1);
+    ggml_gallocr_t galloc = (ggml_gallocr_t)calloc(1, sizeof(struct ggml_gallocr));
    GGML_ASSERT(galloc != NULL);

-    galloc->bufts = calloc(sizeof(ggml_backend_buffer_type_t) * n_bufs, 1);
+    galloc->bufts = calloc(n_bufs, sizeof(ggml_backend_buffer_type_t));
    GGML_ASSERT(galloc->bufts != NULL);

-    galloc->buffers = calloc(sizeof(ggml_backend_buffer_t) * n_bufs, 1);
+    galloc->buffers = calloc(n_bufs, sizeof(ggml_backend_buffer_t) * n_bufs);
    GGML_ASSERT(galloc->buffers != NULL);

-    galloc->buf_tallocs = calloc(sizeof(struct ggml_dyn_tallocr *) * n_bufs, 1);
+    galloc->buf_tallocs = calloc(n_bufs, sizeof(struct ggml_dyn_tallocr *));
    GGML_ASSERT(galloc->buf_tallocs != NULL);

    for (int i = 0; i < n_bufs; i++) {
@@ -646,8 +646,8 @@ bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, c
        free(galloc->hash_set.keys);
        free(galloc->hash_values);
        galloc->hash_set.size = hash_size;
-        galloc->hash_set.keys = calloc(sizeof(struct ggml_tensor *), hash_size);
-        galloc->hash_values   = calloc(sizeof(struct hash_node), hash_size);
+        galloc->hash_set.keys = calloc(hash_size, sizeof(struct ggml_tensor *));
+        galloc->hash_values   = calloc(hash_size, sizeof(struct hash_node));
        GGML_ASSERT(galloc->hash_set.keys != NULL);
        GGML_ASSERT(galloc->hash_values != NULL);
    } else {
@@ -667,7 +667,7 @@ bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, c
    // set the node_allocs from the hash table
    if (galloc->n_nodes < graph->n_nodes) {
        free(galloc->node_allocs);
-        galloc->node_allocs = calloc(sizeof(struct node_alloc), graph->n_nodes);
+        galloc->node_allocs = calloc(graph->n_nodes, sizeof(struct node_alloc));
        GGML_ASSERT(galloc->node_allocs != NULL);
    }
    galloc->n_nodes = graph->n_nodes;
@@ -697,7 +697,7 @@ bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, c
    }
    if (galloc->n_leafs < graph->n_leafs) {
        free(galloc->leaf_allocs);
-        galloc->leaf_allocs = calloc(sizeof(galloc->leaf_allocs[0]), graph->n_leafs);
+        galloc->leaf_allocs = calloc(graph->n_leafs, sizeof(galloc->leaf_allocs[0]));
        GGML_ASSERT(galloc->leaf_allocs != NULL);
    }
    galloc->n_leafs = graph->n_leafs;
@@ -822,7 +822,11 @@ GGML_CALL static enum ggml_status ggml_backend_cpu_graph_compute(ggml_backend_t
 GGML_CALL static bool ggml_backend_cpu_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) {
    switch (op->op) {
        case GGML_OP_CPY:
-            return op->type != GGML_TYPE_IQ2_XXS && op->type != GGML_TYPE_IQ2_XS && op->type != GGML_TYPE_IQ1_S; // missing type_traits.from_float
+            return
+                op->type != GGML_TYPE_IQ2_XXS &&
+                op->type != GGML_TYPE_IQ2_XS  &&
+                op->type != GGML_TYPE_IQ1_S   &&
+                op->type != GGML_TYPE_IQ1_M; // missing type_traits.from_float
        case GGML_OP_MUL_MAT:
            return op->src[1]->type == GGML_TYPE_F32 || op->src[1]->type == ggml_internal_get_type_traits(op->src[0]->type).vec_dot_type;
        default:
@@ -1721,23 +1725,23 @@ ggml_backend_sched_t ggml_backend_sched_new(
    GGML_ASSERT(n_backends <= GGML_SCHED_MAX_BACKENDS);
    GGML_ASSERT(ggml_backend_is_cpu(backends[n_backends - 1])); // last backend must be CPU

-    struct ggml_backend_sched * sched = calloc(sizeof(struct ggml_backend_sched), 1);
+    struct ggml_backend_sched * sched = calloc(1, sizeof(struct ggml_backend_sched));

    // initialize hash table
    sched->hash_set          = ggml_hash_set_new(graph_size);
-    sched->tensor_backend_id = calloc(sizeof(sched->tensor_backend_id[0]), sched->hash_set.size);
-    sched->tensor_copies     = calloc(sizeof(sched->tensor_copies[0]), sched->hash_set.size);
+    sched->tensor_backend_id = calloc(sched->hash_set.size, sizeof(sched->tensor_backend_id[0]));
+    sched->tensor_copies     = calloc(sched->hash_set.size, sizeof(sched->tensor_copies[0]));

    const size_t nodes_size = graph_size + GGML_SCHED_MAX_SPLITS*GGML_SCHED_MAX_SPLIT_INPUTS*2;
-    sched->node_backend_ids  = calloc(sizeof(sched->node_backend_ids[0]), nodes_size);
-    sched->leaf_backend_ids  = calloc(sizeof(sched->leaf_backend_ids[0]), nodes_size);
+    sched->node_backend_ids  = calloc(nodes_size, sizeof(sched->node_backend_ids[0]));
+    sched->leaf_backend_ids  = calloc(nodes_size, sizeof(sched->leaf_backend_ids[0]));

    sched->n_backends = n_backends;

    sched->n_copies = parallel ? GGML_SCHED_MAX_COPIES : 1;

    const int initial_splits_capacity = 16;
-    sched->splits = calloc(sizeof(sched->splits[0]), initial_splits_capacity);
+    sched->splits = calloc(initial_splits_capacity, sizeof(sched->splits[0]));
    sched->splits_capacity = initial_splits_capacity;

    for (int b = 0; b < n_backends; b++) {
@@ -1780,12 +1784,14 @@ void ggml_backend_sched_free(ggml_backend_sched_t sched) {

 void ggml_backend_sched_reset(ggml_backend_sched_t sched) {
    // reset state for the next run
-    size_t hash_size = sched->hash_set.size;
-    memset(sched->hash_set.keys,      0, sizeof(sched->hash_set.keys[0])     * hash_size); // NOLINT
-    memset(sched->tensor_backend_id, -1, sizeof(sched->tensor_backend_id[0]) * hash_size);
-    memset(sched->tensor_copies,      0, sizeof(sched->tensor_copies[0])     * hash_size);
+    if (!sched->is_reset) {
+        size_t hash_size = sched->hash_set.size;
+        memset(sched->hash_set.keys,      0, sizeof(sched->hash_set.keys[0])     * hash_size); // NOLINT
+        memset(sched->tensor_backend_id, -1, sizeof(sched->tensor_backend_id[0]) * hash_size);
+        memset(sched->tensor_copies,      0, sizeof(sched->tensor_copies[0])     * hash_size);

-    sched->is_reset = true;
+        sched->is_reset = true;
+    }
    sched->is_alloc = false;
 }

@@ -1968,10 +1974,10 @@ static void graph_copy_init_tensor(struct ggml_hash_set hash_set, struct ggml_te
 struct ggml_backend_graph_copy ggml_backend_graph_copy(ggml_backend_t backend, struct ggml_cgraph * graph) {
    struct ggml_hash_set hash_set = {
        /* .size = */ graph->visited_hash_table.size,
-        /* .keys = */ calloc(sizeof(hash_set.keys[0]), graph->visited_hash_table.size) // NOLINT
+        /* .keys = */ calloc(graph->visited_hash_table.size, sizeof(hash_set.keys[0])) // NOLINT
    };
-    struct ggml_tensor ** node_copies = calloc(sizeof(node_copies[0]), hash_set.size); // NOLINT
-    bool * node_init = calloc(sizeof(node_init[0]), hash_set.size);
+    struct ggml_tensor ** node_copies = calloc(hash_set.size, sizeof(node_copies[0])); // NOLINT
+    bool * node_init = calloc(hash_set.size, sizeof(node_init[0]));

    struct ggml_init_params params = {
        /* .mem_size   = */ ggml_tensor_overhead()*hash_set.size + ggml_graph_overhead_custom(graph->size, false),
@@ -14,6 +14,7 @@
 #include "ggml-cuda/cpy.cuh"
 #include "ggml-cuda/diagmask.cuh"
 #include "ggml-cuda/dmmv.cuh"
+#include "ggml-cuda/fattn.cuh"
 #include "ggml-cuda/getrows.cuh"
 #include "ggml-cuda/im2col.cuh"
 #include "ggml-cuda/mmq.cuh"
@@ -140,6 +141,7 @@ static ggml_cuda_device_info ggml_cuda_init() {
        info.devices[id].cc = 100*prop.major + 10*prop.minor;
 #endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
        info.devices[id].smpb = prop.sharedMemPerBlock;
+        info.devices[id].nsm  = prop.multiProcessorCount;
    }

    for (int id = 0; id < info.device_count; ++id) {
@@ -1231,7 +1233,7 @@ static void ggml_cuda_op_mul_mat_cublas(

    if (compute_capability >= CC_VOLTA && (src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type)) && ggml_is_contiguous(src0) && row_diff == src0->ne[1] && dst->op_params[0] == GGML_PREC_DEFAULT) {
        // convert src0 and src1 to fp16, multiply as fp16, convert dst to fp32
-        ggml_cuda_pool_alloc<half> src0_as_f16(ctx.pool());
+        ggml_cuda_pool_alloc<half> src0_as_f16(ctx.pool(id));
        if (src0->type != GGML_TYPE_F16) {
            const to_fp16_cuda_t to_fp16_cuda = ggml_get_to_fp16_cuda(src0->type);
            GGML_ASSERT(to_fp16_cuda != nullptr);
@@ -1241,7 +1243,7 @@ static void ggml_cuda_op_mul_mat_cublas(
        }
        const half * src0_ptr = src0->type == GGML_TYPE_F16 ? (const half *) src0_dd_i : src0_as_f16.get();

-        ggml_cuda_pool_alloc<half> src1_as_f16(ctx.pool());
+        ggml_cuda_pool_alloc<half> src1_as_f16(ctx.pool(id));
        if (src1->type != GGML_TYPE_F16) {
            const to_fp16_cuda_t to_fp16_cuda = ggml_get_to_fp16_cuda(src1->type);
            GGML_ASSERT(to_fp16_cuda != nullptr);
@@ -1250,7 +1252,7 @@ static void ggml_cuda_op_mul_mat_cublas(
            to_fp16_cuda(src1_ddf_i, src1_as_f16.get(), ne, stream);
        }
        const half * src1_ptr = src1->type == GGML_TYPE_F16 ? (const half *) src1_ddf_i : src1_as_f16.get();
-        ggml_cuda_pool_alloc<half> dst_f16(ctx.pool(), row_diff*src1_ncols);
+        ggml_cuda_pool_alloc<half> dst_f16(ctx.pool(id), row_diff*src1_ncols);

        const half alpha_f16 = 1.0f;
        const half beta_f16 = 0.0f;
@@ -1960,20 +1962,73 @@ static void ggml_cuda_mul_mat(ggml_backend_cuda_context & ctx, const ggml_tensor
    }
 }

+struct mmid_row_mapping {
+    int32_t i1;
+    int32_t i2;
+};
+
+static __global__ void k_copy_src1_to_contiguous(const char * __restrict__ src1_original, char * __restrict__ src1_contiguous,
+                                                 int * __restrict__ cur_src1_row, mmid_row_mapping * __restrict__ row_mapping,
+                                                 const char * __restrict ids, int64_t i02, size_t ids_nb1, size_t ids_nb0,
+                                                 int64_t ne11, int64_t ne10,
+                                                 size_t nb11, size_t nb12) {
+    int32_t iid1 = blockIdx.x;
+    int32_t id = blockIdx.y;
+
+    const int32_t row_id_i = *(const int32_t *) (ids + iid1*ids_nb1 + id*ids_nb0);
+
+    if (row_id_i != i02) {
+        return;
+    }
+
+    const int64_t i11 = id % ne11;
+    const int64_t i12 = iid1;
+
+    __shared__ int src1_row;
+    if (threadIdx.x == 0) {
+        src1_row = atomicAdd(cur_src1_row, 1);
+        row_mapping[src1_row] = {id, iid1};
+    }
+    __syncthreads();
+
+    const float * src1_row_original = (const float *)(src1_original + i11*nb11 + i12*nb12);
+    float * src1_row_contiguous = (float *)(src1_contiguous + src1_row*nb11);
+
+    for (int i = threadIdx.x; i < ne10; i += blockDim.x) {
+        src1_row_contiguous[i] = src1_row_original[i];
+    }
+}
+
+static __global__ void k_copy_dst_from_contiguous(char * __restrict__ dst_original, const char * __restrict__ dst_contiguous,
+                                                  const mmid_row_mapping * __restrict__ row_mapping,
+                                                  int64_t ne0,
+                                                  size_t nb1, size_t nb2) {
+    int32_t i = blockIdx.x;
+
+    const int32_t i1 = row_mapping[i].i1;
+    const int32_t i2 = row_mapping[i].i2;
+
+    const float * dst_row_contiguous = (const float *)(dst_contiguous + i*nb1);
+    float * dst_row_original = (float *)(dst_original + i1*nb1 + i2*nb2);
+
+    for (int j = threadIdx.x; j < ne0; j += blockDim.x) {
+        dst_row_original[j] = dst_row_contiguous[j];
+    }
+}
+
 static void ggml_cuda_mul_mat_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 * ids  = dst->src[2];

+    GGML_TENSOR_BINARY_OP_LOCALS
+
    GGML_ASSERT(!ggml_backend_buffer_is_cuda_split(src0->buffer) && "mul_mat_id does not support split buffers");

    cudaStream_t stream = ctx.stream();

-    const size_t nb11 = src1->nb[1];
-    const size_t nb1  =  dst->nb[1];
-
-    const int32_t id = ((int32_t *) dst->op_params)[0];
-    const int32_t n_as = src0->ne[2];
+    const int64_t n_as = ne02;
+    const int64_t n_ids = ids->ne[0];

    std::vector<char> ids_host(ggml_nbytes(ids));
    const char * ids_dev = (const char *) ids->data;
@@ -1982,7 +2037,7 @@ static void ggml_cuda_mul_mat_id(ggml_backend_cuda_context & ctx, ggml_tensor *

    ggml_tensor src0_row = *src0;
    ggml_tensor src1_row = *src1;
-    ggml_tensor dst_row = *dst;
+    ggml_tensor dst_row  = *dst;

    char * src0_original = (char *) src0->data;
    char * src1_original = (char *) src1->data;
@@ -1990,19 +2045,39 @@ static void ggml_cuda_mul_mat_id(ggml_backend_cuda_context & ctx, ggml_tensor *

    src0_row.ne[2] = 1;
    src0_row.ne[3] = 1;
-    src0_row.nb[3] = src0->nb[2];
+    src0_row.nb[3] = nb02;

-    if (src1->ne[1] == 1) {
-        for (int64_t i01 = 0; i01 < ids->ne[1]; i01++) {
-            const int32_t row_id = *(const int32_t *) (ids_host.data() + i01*ids->nb[1] + id*ids->nb[0]);
+    src1_row.ne[1] = 1;
+    src1_row.ne[2] = 1;
+    src1_row.ne[3] = 1;
+    src1_row.nb[2] = nb11;
+    src1_row.nb[3] = nb11;

-            GGML_ASSERT(row_id >= 0 && row_id < n_as);
+    dst_row.ne[1] = 1;
+    dst_row.ne[2] = 1;
+    dst_row.ne[3] = 1;
+    dst_row.nb[2] = nb1;
+    dst_row.nb[3] = nb1;

-            src0_row.data = src0_original + row_id*src0->nb[2];
-            src1_row.data = src1_original + i01*src1->nb[1];
-            dst_row.data  =  dst_original + i01*dst->nb[1];
+    if (ne12 == 1) {
+        for (int64_t iid1 = 0; iid1 < ids->ne[1]; iid1++) {
+            for (int64_t id = 0; id < n_ids; id++) {
+                const int32_t i02 = *(const int32_t *) (ids_host.data() + iid1*ids->nb[1] + id*ids->nb[0]);

-            ggml_cuda_mul_mat(ctx, &src0_row, &src1_row, &dst_row);
+                GGML_ASSERT(i02 >= 0 && i02 < n_as);
+
+                const int64_t i11 = id % ne11;
+                const int64_t i12 = iid1;
+
+                const int64_t i1 = id;
+                const int64_t i2 = i12;
+
+                src0_row.data = src0_original + i02*nb02;
+                src1_row.data = src1_original + i11*nb11 + i12*nb12;
+                dst_row.data  =  dst_original + i1*nb1   + i2*nb2;
+
+                ggml_cuda_mul_mat(ctx, &src0_row, &src1_row, &dst_row);
+            }
        }
    } else {
        ggml_cuda_pool_alloc<char> src1_contiguous(ctx.pool(), sizeof(float)*ggml_nelements(src1));
@@ -2011,54 +2086,69 @@ static void ggml_cuda_mul_mat_id(ggml_backend_cuda_context & ctx, ggml_tensor *
        src1_row.data = src1_contiguous.get();
        dst_row.data  =  dst_contiguous.get();

-        for (int32_t row_id = 0; row_id < n_as; ++row_id) {
+        for (int64_t i02 = 0; i02 < n_as; i02++) {
            int64_t num_src1_rows = 0;
-            for (int64_t i01 = 0; i01 < ids->ne[1]; i01++) {
-                const int32_t row_id_i = *(const int32_t *) (ids_host.data() + i01*ids->nb[1] + id*ids->nb[0]);

-                if (row_id_i != row_id) {
-                    continue;
+            for (int64_t iid1 = 0; iid1 < ids->ne[1]; iid1++) {
+                for (int64_t id = 0; id < n_ids; id++) {
+                    const int32_t row_id_i = *(const int32_t *) (ids_host.data() + iid1*ids->nb[1] + id*ids->nb[0]);
+
+                    GGML_ASSERT(row_id_i >= 0 && row_id_i < n_as);
+
+                    if (row_id_i != i02) {
+                        continue;
+                    }
+
+                    num_src1_rows++;
                }
-
-                GGML_ASSERT(row_id >= 0 && row_id < n_as);
-
-                CUDA_CHECK(cudaMemcpyAsync(src1_contiguous.get() + num_src1_rows*nb11, src1_original + i01*nb11,
-                                        nb11, cudaMemcpyDeviceToDevice, stream));
-                num_src1_rows++;
            }

            if (num_src1_rows == 0) {
                continue;
            }

-            src0_row.data = src0_original + row_id*src0->nb[2];
+            ggml_cuda_pool_alloc<int> dev_cur_src1_row(ctx.pool(), 1);
+            ggml_cuda_pool_alloc<mmid_row_mapping> dev_row_mapping(ctx.pool(), num_src1_rows);
+            CUDA_CHECK(cudaMemsetAsync(dev_cur_src1_row.get(), 0, sizeof(int), stream));
+
+            {
+                dim3 block_dims(std::min((unsigned int)ne10, 768u));
+                dim3 grid_dims(ids->ne[1], n_ids);
+                k_copy_src1_to_contiguous<<<grid_dims, block_dims, 0, stream>>>(
+                        src1_original, src1_contiguous.get(),
+                        dev_cur_src1_row.get(), dev_row_mapping.get(),
+                        ids_dev, i02, ids->nb[1], ids->nb[0],
+                        ne11, ne10,
+                        nb11, nb12);
+                CUDA_CHECK(cudaGetLastError());
+            }
+
+            src0_row.data = src0_original + i02*nb02;
+
+            GGML_ASSERT(nb11 == sizeof(float)*ne10);
+            GGML_ASSERT(nb1 == sizeof(float)*ne0);

            src1_row.ne[1] = num_src1_rows;
-            dst_row.ne[1] = num_src1_rows;
-
            src1_row.nb[1] = nb11;
            src1_row.nb[2] = num_src1_rows*nb11;
            src1_row.nb[3] = num_src1_rows*nb11;

+            dst_row.ne[1] = num_src1_rows;
            dst_row.nb[1] = nb1;
            dst_row.nb[2] = num_src1_rows*nb1;
            dst_row.nb[3] = num_src1_rows*nb1;

            ggml_cuda_mul_mat(ctx, &src0_row, &src1_row, &dst_row);

-            num_src1_rows = 0;
-            for (int64_t i01 = 0; i01 < ids->ne[1]; i01++) {
-                const int32_t row_id_i = *(const int32_t *) (ids_host.data() + i01*ids->nb[1] + id*ids->nb[0]);
-
-                if (row_id_i != row_id) {
-                    continue;
-                }
-
-                GGML_ASSERT(row_id >= 0 && row_id < n_as);
-
-                CUDA_CHECK(cudaMemcpyAsync(dst_original + i01*nb1, dst_contiguous.get() + num_src1_rows*nb1,
-                                        nb1, cudaMemcpyDeviceToDevice, stream));
-                num_src1_rows++;
+            {
+                dim3 block_dims(std::min((unsigned int)ne0, 768u));
+                dim3 grid_dims(num_src1_rows);
+                k_copy_dst_from_contiguous<<<grid_dims, block_dims, 0, stream>>>(
+                        dst_original, dst_contiguous.get(),
+                        dev_row_mapping.get(),
+                        ne0,
+                        nb1, nb2);
+                CUDA_CHECK(cudaGetLastError());
            }
        }
    }
@@ -2202,6 +2292,9 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg
        case GGML_OP_ARGSORT:
            ggml_cuda_op_argsort(ctx, dst);
            break;
+        case GGML_OP_FLASH_ATTN_EXT:
+            ggml_cuda_flash_attn_ext(ctx, dst);
+            break;
        default:
            return false;
    }
@@ -2476,6 +2569,7 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons
        case GGML_OP_ARANGE:
        case GGML_OP_TIMESTEP_EMBEDDING:
        case GGML_OP_LEAKY_RELU:
+        case GGML_OP_FLASH_ATTN_EXT:
            return true;
        default:
            return false;
@@ -2487,7 +2581,8 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons
 GGML_CALL static bool ggml_backend_cuda_offload_op(ggml_backend_t backend, const ggml_tensor * op) {
    const int min_batch_size = 32;

-    return op->ne[1] >= min_batch_size && op->op != GGML_OP_GET_ROWS;
+    return (op->ne[1] >= min_batch_size && op->op != GGML_OP_GET_ROWS) ||
+           (op->ne[2] >= min_batch_size && op->op == GGML_OP_MUL_MAT_ID);

    GGML_UNUSED(backend);
 }
@@ -22,6 +22,7 @@ static __global__ void k_bin_bcast(const src0_t * src0, const src1_t * src1, dst
        int ne0, int ne1, int ne2, int ne3,
        int ne10, int ne11, int ne12, int ne13,
        /*int s0, */ int s1,  int s2,  int s3,
+        /*int s00,*/ int s01, int s02, int s03,
        /*int s10,*/ int s11, int s12, int s13) {
    const int i0s = blockDim.x*blockIdx.x + threadIdx.x;
    const int i1 = (blockDim.y*blockIdx.y + threadIdx.y);
@@ -36,9 +37,9 @@ static __global__ void k_bin_bcast(const src0_t * src0, const src1_t * src1, dst
    const int i12 = i2 % ne12;
    const int i13 = i3 % ne13;

-    const size_t i_src0 = i3*s3 + i2*s2 + i1*s1;
+    const size_t i_src0 =  i3*s03 +  i2*s02 +  i1*s01;
    const size_t i_src1 = i13*s13 + i12*s12 + i11*s11;
-    const size_t i_dst  = i_src0;
+    const size_t i_dst  =  i3*s3  +  i2*s2  +  i1*s1;

    const src0_t * src0_row = src0 + i_src0;
    const src1_t * src1_row = src1 + i_src1;
@@ -55,6 +56,7 @@ static __global__ void k_bin_bcast_unravel(const src0_t * src0, const src1_t * s
        int ne0, int ne1, int ne2, int ne3,
        int ne10, int ne11, int ne12, int ne13,
        /*int s0, */ int s1,  int s2,  int s3,
+        /*int s00,*/ int s01, int s02, int s03,
        /*int s10,*/ int s11, int s12, int s13) {

    const int i = blockDim.x*blockIdx.x + threadIdx.x;
@@ -72,9 +74,9 @@ static __global__ void k_bin_bcast_unravel(const src0_t * src0, const src1_t * s
    const int i12 = i2 % ne12;
    const int i13 = i3 % ne13;

-    const size_t i_src0 = i3*s3 + i2*s2 + i1*s1;
+    const size_t i_src0 =  i3*s03 +  i2*s02 +  i1*s01;
    const size_t i_src1 = i13*s13 + i12*s12 + i11*s11;
-    const size_t i_dst  = i_src0;
+    const size_t i_dst  =  i3*s3  +  i2*s2  +  i1*s1;

    const src0_t * src0_row = src0 + i_src0;
    const src1_t * src1_row = src1 + i_src1;
@@ -101,10 +103,14 @@ struct bin_bcast_cuda {
        int nr[4] = { nr0, nr1, nr2, nr3 };

        // collapse dimensions until first broadcast dimension
-        int64_t cne0[] = {ne0, ne1, ne2, ne3};
+        int64_t cne[] = {ne0, ne1, ne2, ne3};
+        int64_t cne0[] = {ne00, ne01, ne02, ne03};
        int64_t cne1[] = {ne10, ne11, ne12, ne13};
-        size_t cnb0[] = {nb0, nb1, nb2, nb3};
+
+        size_t cnb[] = {nb0, nb1, nb2, nb3};
+        size_t cnb0[] = {nb00, nb01, nb02, nb03};
        size_t cnb1[] = {nb10, nb11, nb12, nb13};
+
        auto collapse = [](int64_t cne[]) {
            cne[0] *= cne[1];
            cne[1] = cne[2];
@@ -118,32 +124,47 @@ struct bin_bcast_cuda {
            cnb[3] *= cne[3];
        };

-        for (int i = 0; i < 4; i++) {
-            if (nr[i] != 1) {
-                break;
-            }
-            if (i > 0) {
-                collapse_nb(cnb0, cne0);
-                collapse_nb(cnb1, cne1);
-                collapse(cne0);
-                collapse(cne1);
+        if (ggml_is_contiguous(src0) && ggml_is_contiguous(src1) && ggml_is_contiguous(dst)) {
+            for (int i = 0; i < 4; i++) {
+                if (nr[i] != 1) {
+                    break;
+                }
+                if (i > 0) {
+                    collapse_nb(cnb, cne);
+                    collapse_nb(cnb0, cne0);
+                    collapse_nb(cnb1, cne1);
+                    collapse(cne);
+                    collapse(cne0);
+                    collapse(cne1);
+                }
            }
        }
+
        {
-            int64_t ne0 = cne0[0];
-            int64_t ne1 = cne0[1];
-            int64_t ne2 = cne0[2];
-            int64_t ne3 = cne0[3];
+            int64_t ne0 = cne[0];
+            int64_t ne1 = cne[1];
+            int64_t ne2 = cne[2];
+            int64_t ne3 = cne[3];
+
+            //int64_t ne00 = cne0[0]; GGML_UNUSED(ne00);
+            //int64_t ne01 = cne0[1]; GGML_UNUSED(ne01);
+            //int64_t ne02 = cne0[2]; GGML_UNUSED(ne02);
+            //int64_t ne03 = cne0[3]; GGML_UNUSED(ne03);

            int64_t ne10 = cne1[0];
            int64_t ne11 = cne1[1];
            int64_t ne12 = cne1[2];
            int64_t ne13 = cne1[3];

-            size_t nb0 = cnb0[0];
-            size_t nb1 = cnb0[1];
-            size_t nb2 = cnb0[2];
-            size_t nb3 = cnb0[3];
+            size_t nb0 = cnb[0];
+            size_t nb1 = cnb[1];
+            size_t nb2 = cnb[2];
+            size_t nb3 = cnb[3];
+
+            size_t nb00 = cnb0[0];
+            size_t nb01 = cnb0[1];
+            size_t nb02 = cnb0[2];
+            size_t nb03 = cnb0[3];

            size_t nb10 = cnb1[0];
            size_t nb11 = cnb1[1];
@@ -160,7 +181,28 @@ struct bin_bcast_cuda {
            size_t s12 = nb12 / sizeof(src1_t);
            size_t s13 = nb13 / sizeof(src1_t);

+            size_t s00 = nb00 / sizeof(src0_t);
+            size_t s01 = nb01 / sizeof(src0_t);
+            size_t s02 = nb02 / sizeof(src0_t);
+            size_t s03 = nb03 / sizeof(src0_t);
+
+            GGML_ASSERT(nb0 % sizeof(dst_t) == 0);
+            GGML_ASSERT(nb1 % sizeof(dst_t) == 0);
+            GGML_ASSERT(nb2 % sizeof(dst_t) == 0);
+            GGML_ASSERT(nb3 % sizeof(dst_t) == 0);
+
+            GGML_ASSERT(nb00 % sizeof(src0_t) == 0);
+            GGML_ASSERT(nb01 % sizeof(src0_t) == 0);
+            GGML_ASSERT(nb02 % sizeof(src0_t) == 0);
+            GGML_ASSERT(nb03 % sizeof(src0_t) == 0);
+
+            GGML_ASSERT(nb10 % sizeof(src1_t) == 0);
+            GGML_ASSERT(nb11 % sizeof(src1_t) == 0);
+            GGML_ASSERT(nb12 % sizeof(src1_t) == 0);
+            GGML_ASSERT(nb13 % sizeof(src1_t) == 0);
+
            GGML_ASSERT(s0 == 1);
+            GGML_ASSERT(s00 == 1);
            GGML_ASSERT(s10 == 1);

            const int block_size = 128;
@@ -179,13 +221,14 @@ struct bin_bcast_cuda {
            );

            if (block_nums.z > 65535) {
-                // this is the maximum number of blocks in z direction, fallback to 1D grid kernel
+                // this is the maximum number of blocks in z dimension, fallback to 1D grid kernel
                int block_num = (ne0*ne1*ne2*ne3 + block_size - 1) / block_size;
                k_bin_bcast_unravel<bin_op><<<block_num, block_size, 0, stream>>>(
                    src0_dd, src1_dd, dst_dd,
                    ne0, ne1, ne2, ne3,
                    ne10, ne11, ne12, ne13,
                    /* s0, */ s1, s2, s3,
+                    /* s00, */ s01, s02, s03,
                    /* s10, */ s11, s12, s13);
            } else {
                k_bin_bcast<bin_op><<<block_nums, block_dims, 0, stream>>>(
@@ -193,6 +236,7 @@ struct bin_bcast_cuda {
                    ne0, ne1, ne2, ne3,
                    ne10, ne11, ne12, ne13,
                    /* s0, */ s1, s2, s3,
+                    /* s00, */ s01, s02, s03,
                    /* s10, */ s11, s12, s13);
            }
        }
@@ -142,6 +142,7 @@
 #define CC_PASCAL     600
 #define MIN_CC_DP4A   610 // minimum compute capability for __dp4a, an intrinsic for byte-wise dot products
 #define CC_VOLTA      700
+#define CC_AMPERE     800
 #define CC_OFFSET_AMD 1000000
 #define CC_RDNA1      (CC_OFFSET_AMD + 1010)
 #define CC_RDNA2      (CC_OFFSET_AMD + 1030)
@@ -271,7 +272,6 @@ static __device__ __forceinline__ float2 warp_reduce_sum(float2 a) {
    return a;
 }

-#ifdef GGML_CUDA_F16
 static __device__ __forceinline__ half2 warp_reduce_sum(half2 a) {
 #if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL
 #pragma unroll
@@ -284,7 +284,6 @@ static __device__ __forceinline__ half2 warp_reduce_sum(half2 a) {
   NO_DEVICE_CODE;
 #endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL
 }
-#endif // GGML_CUDA_F16

 static __device__ __forceinline__ float warp_reduce_max(float x) {
 #pragma unroll
@@ -294,19 +293,26 @@ static __device__ __forceinline__ float warp_reduce_max(float x) {
    return x;
 }

-//static __device__ __forceinline__ half2 warp_reduce_max(half2 x) {
-//#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL && CUDART_VERSION >= CUDART_HMAX
-//#pragma unroll
-//    for (int mask = 16; mask > 0; mask >>= 1) {
-//        x = __hmax2(x, __shfl_xor_sync(0xffffffff, x, mask, 32));
-//    }
-//    return x;
-//#else
-//    GGML_UNUSED(x);
-//    NO_DEVICE_CODE;
-//#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL && CUDART_VERSION >= CUDART_HMAX
-//}
+static __device__ __forceinline__ half2 warp_reduce_max(half2 x) {
+#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL && CUDART_VERSION >= CUDART_HMAX
+#pragma unroll
+   for (int mask = 16; mask > 0; mask >>= 1) {
+       x = __hmax2(x, __shfl_xor_sync(0xffffffff, x, mask, 32));
+   }
+   return x;
+#else
+   GGML_UNUSED(x);
+   NO_DEVICE_CODE;
+#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL && CUDART_VERSION >= CUDART_HMAX
+}

+#if CUDART_VERSION < 12000
+static __device__ __forceinline__ uint32_t __hgt2_mask(const half2 a, const half2 b) {
+    const uint32_t mask_low  = 0x0000FFFF * (float( __low2half(a)) > float( __low2half(b)));
+    const uint32_t mask_high = 0xFFFF0000 * (float(__high2half(a)) > float(__high2half(b)));
+    return mask_low | mask_high;
+}
+#endif // CUDART_VERSION < 12000

 #if defined(GGML_USE_HIPBLAS)
 #define __CUDA_ARCH__ 1300
@@ -391,6 +397,11 @@ static __device__ __forceinline__ int __dp4a(const int a, const int b, int c) {
 }
 #endif // defined(GGML_USE_HIPBLAS)

+#define FP16_AVAILABLE     defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) ? \
+    defined(RDNA1) || defined(RDNA2) || defined(RDNA3) : __CUDA_ARCH__ >= CC_PASCAL
+
+#define FP16_MMA_AVAILABLE !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_VOLTA
+
 // TODO: move to ggml-common.h
 static const __device__ int8_t kvalues_iq4nl[16] = {-127, -104, -83, -65, -49, -35, -22, -10, 1, 13, 25, 38, 53, 69, 89, 113};

@@ -404,6 +415,7 @@ struct ggml_cuda_device_info {

    struct cuda_device_info {
        int     cc;                 // compute capability
+        int     nsm;                // number of streaming multiprocessors
        size_t  smpb;               // max. shared memory per block
        bool    vmm;                // virtual memory support
        size_t  vmm_granularity;    // granularity of virtual memory
@@ -5,16 +5,16 @@

 template <int qk, int qr, dequantize_kernel_t dequantize_kernel, typename dst_t>
 static __global__ void dequantize_block(const void * __restrict__ vx, dst_t * __restrict__ y, const int64_t k) {
-    const int64_t i = 2*(blockDim.x*blockIdx.x + threadIdx.x);
+    const int64_t i = (int64_t)2*(blockDim.x*blockIdx.x + threadIdx.x);

    if (i >= k) {
        return;
    }

    const int64_t ib = i/qk; // block index
-    const int iqs = (i%qk)/qr; // quant index
-    const int iybs = i - i%qk; // y block start index
-    const int y_offset = qr == 1 ? 1 : qk/2;
+    const int64_t iqs = (i%qk)/qr; // quant index
+    const int64_t iybs = i - i%qk; // y block start index
+    const int64_t y_offset = qr == 1 ? 1 : qk/2;

    // dequantize
    dfloat2 v;
@@ -29,7 +29,7 @@ static __global__ void dequantize_block_q8_0_f16(const void * __restrict__ vx, h
 #if __CUDA_ARCH__ >= CC_PASCAL
    constexpr int nint = CUDA_Q8_0_NE_ALIGN/sizeof(int) + WARP_SIZE;

-    const int   i0 = CUDA_Q8_0_NE_ALIGN*blockIdx.x;
+    const int64_t   i0 = CUDA_Q8_0_NE_ALIGN*blockIdx.x;
    const int * x0 = ((int *) vx) + blockIdx.x * nint;
    half2 * y2 = (half2 *) (y + i0);

@@ -45,6 +45,8 @@ static __global__ void dequantize_block_q8_0_f16(const void * __restrict__ vx, h
        vals[ix] = x0[ix];
    }

+    __syncthreads();
+
 #pragma unroll
    for (int iy = 0; iy < CUDA_Q8_0_NE_ALIGN; iy += 2*WARP_SIZE) {
        if (need_check && i0 + iy + 2*threadIdx.x >= k) {
@@ -71,9 +73,9 @@ static __global__ void dequantize_block_q4_0(const void * __restrict__ vx, dst_t
    const int64_t i = blockIdx.x;

    // assume 32 threads
-    const int tid = threadIdx.x;
-    const int il  = tid/8;
-    const int ir  = tid%8;
+    const int64_t tid = threadIdx.x;
+    const int64_t il  = tid/8;
+    const int64_t ir  = tid%8;
    const int64_t ib = 8*i + ir;
    if (ib >= nb32) {
        return;
@@ -99,9 +101,9 @@ static __global__ void dequantize_block_q4_1(const void * __restrict__ vx, dst_t
    const int64_t i = blockIdx.x;

    // assume 32 threads
-    const int tid = threadIdx.x;
-    const int il  = tid/8;
-    const int ir  = tid%8;
+    const int64_t tid = threadIdx.x;
+    const int64_t il  = tid/8;
+    const int64_t ir  = tid%8;
    const int64_t ib = 8*i + ir;
    if (ib >= nb32) {
        return;
@@ -125,14 +127,14 @@ static __global__ void dequantize_block_q4_1(const void * __restrict__ vx, dst_t
 template<typename dst_t>
 static __global__ void dequantize_block_q2_K(const void * __restrict__ vx, dst_t * __restrict__ yy) {

-    const int i   = blockIdx.x;
+    const int64_t i   = blockIdx.x;
    const block_q2_K * x = (const block_q2_K *) vx;

-    const int tid = threadIdx.x;
+    const int64_t tid = threadIdx.x;
 #if QK_K == 256
-    const int n   = tid/32;
-    const int l   = tid - 32*n;
-    const int is  = 8*n + l/16;
+    const int64_t n   = tid/32;
+    const int64_t l   = tid - 32*n;
+    const int64_t is  = 8*n + l/16;

    const uint8_t q = x[i].qs[32*n + l];
    dst_t * y = yy + i*QK_K + 128*n;
@@ -144,8 +146,8 @@ static __global__ void dequantize_block_q2_K(const void * __restrict__ vx, dst_t
    y[l+64] = dall * (x[i].scales[is+4] & 0xF) * ((q >> 4) & 3) - dmin * (x[i].scales[is+4] >> 4);
    y[l+96] = dall * (x[i].scales[is+6] & 0xF) * ((q >> 6) & 3) - dmin * (x[i].scales[is+6] >> 4);
 #else
-    const int is = tid/16;  // 0 or 1
-    const int il = tid%16;  // 0...15
+    const int64_t is = tid/16;  // 0 or 1
+    const int64_t il = tid%16;  // 0...15
    const uint8_t q = x[i].qs[il] >> (2*is);
    dst_t * y = yy + i*QK_K + 16*is + il;
    float dall = __low2half(x[i].dm);
@@ -159,19 +161,19 @@ static __global__ void dequantize_block_q2_K(const void * __restrict__ vx, dst_t
 template<typename dst_t>
 static __global__ void dequantize_block_q3_K(const void * __restrict__ vx, dst_t * __restrict__ yy) {

-    const int i = blockIdx.x;
+    const int64_t i = blockIdx.x;
    const block_q3_K * x = (const block_q3_K *) vx;

 #if QK_K == 256
-    const int r = threadIdx.x/4;
-    const int tid = r/2;
-    const int is0 = r%2;
-    const int l0 = 16*is0 + 4*(threadIdx.x%4);
-    const int n = tid / 4;
-    const int j = tid - 4*n;
+    const int64_t r = threadIdx.x/4;
+    const int64_t tid = r/2;
+    const int64_t is0 = r%2;
+    const int64_t l0 = 16*is0 + 4*(threadIdx.x%4);
+    const int64_t n = tid / 4;
+    const int64_t j = tid - 4*n;

    uint8_t m = 1 << (4*n + j);
-    int is = 8*n + 2*j + is0;
+    int64_t is = 8*n + 2*j + is0;
    int shift = 2*j;

    int8_t us = is <  4 ? (x[i].scales[is-0] & 0xF) | (((x[i].scales[is+8] >> 0) & 3) << 4) :
@@ -187,11 +189,11 @@ static __global__ void dequantize_block_q3_K(const void * __restrict__ vx, dst_t

    for (int l = l0; l < l0+4; ++l) y[l] = dl * ((int8_t)((q[l] >> shift) & 3) - ((hm[l] & m) ? 0 : 4));
 #else
-    const int tid = threadIdx.x;
-    const int is  = tid/16;  // 0 or 1
-    const int il  = tid%16;  // 0...15
-    const int im  = il/8;    // 0...1
-    const int in  = il%8;    // 0...7
+    const int64_t tid = threadIdx.x;
+    const int64_t is  = tid/16;  // 0 or 1
+    const int64_t il  = tid%16;  // 0...15
+    const int64_t im  = il/8;    // 0...1
+    const int64_t in  = il%8;    // 0...7

    dst_t * y = yy + i*QK_K + 16*is + il;

@@ -225,15 +227,15 @@ template<typename dst_t>
 static __global__ void dequantize_block_q4_K(const void * __restrict__ vx, dst_t * __restrict__ yy) {
    const block_q4_K * x = (const block_q4_K *) vx;

-    const int i = blockIdx.x;
+    const int64_t i = blockIdx.x;

 #if QK_K == 256
    // assume 32 threads
-    const int tid = threadIdx.x;
-    const int il  = tid/8;
-    const int ir  = tid%8;
-    const int is  = 2*il;
-    const int n   = 4;
+    const int64_t tid = threadIdx.x;
+    const int64_t il  = tid/8;
+    const int64_t ir  = tid%8;
+    const int64_t is  = 2*il;
+    const int64_t n   = 4;

    dst_t * y = yy + i*QK_K + 64*il + n*ir;

@@ -252,7 +254,7 @@ static __global__ void dequantize_block_q4_K(const void * __restrict__ vx, dst_t
        y[l +32] = d2 * (q[l] >>  4) - m2;
    }
 #else
-    const int tid = threadIdx.x;
+    const int64_t tid = threadIdx.x;
    const uint8_t * q = x[i].qs;
    dst_t * y = yy + i*QK_K;
    const float d = (float)x[i].dm[0];
@@ -266,14 +268,14 @@ template<typename dst_t>
 static __global__ void dequantize_block_q5_K(const void * __restrict__ vx, dst_t * __restrict__ yy) {
    const block_q5_K * x = (const block_q5_K *) vx;

-    const int i = blockIdx.x;
+    const int64_t i = blockIdx.x;

 #if QK_K == 256
    // assume 64 threads - this is very slightly better than the one below
-    const int tid = threadIdx.x;
-    const int il  = tid/16;   // il is in 0...3
-    const int ir  = tid%16;   // ir is in 0...15
-    const int is  = 2*il;     // is is in 0...6
+    const int64_t tid = threadIdx.x;
+    const int64_t il  = tid/16;   // il is in 0...3
+    const int64_t ir  = tid%16;   // ir is in 0...15
+    const int64_t is  = 2*il;     // is is in 0...6

    dst_t * y = yy + i*QK_K + 64*il + 2*ir;

@@ -296,11 +298,11 @@ static __global__ void dequantize_block_q5_K(const void * __restrict__ vx, dst_t
    y[32] = d2 * ((ql[ 0] >>  4) + (qh[ 0] & hm ? 16 : 0)) - m2;
    y[33] = d2 * ((ql[ 1] >>  4) + (qh[ 1] & hm ? 16 : 0)) - m2;
 #else
-    const int tid = threadIdx.x;
+    const int64_t tid = threadIdx.x;
    const uint8_t q = x[i].qs[tid];
-    const int im = tid/8;  // 0...3
-    const int in = tid%8;  // 0...7
-    const int is = tid/16; // 0 or 1
+    const int64_t im = tid/8;  // 0...3
+    const int64_t in = tid%8;  // 0...7
+    const int64_t is = tid/16; // 0 or 1
    const uint8_t h = x[i].qh[in] >> im;
    const float d = x[i].d;
    dst_t * y = yy + i*QK_K + tid;
@@ -357,13 +359,13 @@ static __global__ void dequantize_block_q6_K(const void * __restrict__ vx, dst_t
 template<typename dst_t>
 static __global__ void dequantize_block_iq2_xxs(const void * __restrict__ vx, dst_t * __restrict__ yy) {

-    const int i   = blockIdx.x;
+    const int64_t i   = blockIdx.x;
    const block_iq2_xxs * x = (const block_iq2_xxs  *) vx;

-    const int tid = threadIdx.x;
+    const int64_t tid = threadIdx.x;
 #if QK_K == 256
-    const int il = tid/8; // 0...3
-    const int ib = tid%8; // 0...7
+    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 + 8*il;
    const uint16_t * q2 = x[i].qs + 4*ib;
    const uint8_t  * aux8 = (const uint8_t *)q2;
@@ -381,13 +383,13 @@ static __global__ void dequantize_block_iq2_xxs(const void * __restrict__ vx, ds
 template<typename dst_t>
 static __global__ void dequantize_block_iq2_xs(const void * __restrict__ vx, dst_t * __restrict__ yy) {

-    const int i   = blockIdx.x;
+    const int64_t i   = blockIdx.x;
    const block_iq2_xs * x = (const block_iq2_xs *) vx;

-    const int tid = threadIdx.x;
+    const int64_t tid = threadIdx.x;
 #if QK_K == 256
-    const int il = tid/8; // 0...3
-    const int ib = tid%8; // 0...7
+    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 + 8*il;
    const uint16_t * q2 = x[i].qs + 4*ib;
    const uint8_t  * grid = (const uint8_t *)(iq2xs_grid + (q2[il] & 511));
@@ -403,13 +405,13 @@ static __global__ void dequantize_block_iq2_xs(const void * __restrict__ vx, dst
 template<typename dst_t>
 static __global__ void dequantize_block_iq2_s(const void * __restrict__ vx, dst_t * __restrict__ yy) {

-    const int i   = blockIdx.x;
+    const int64_t i   = blockIdx.x;
    const block_iq2_s * x = (const block_iq2_s *) vx;

-    const int tid = threadIdx.x;
+    const int64_t tid = threadIdx.x;
 #if QK_K == 256
-    const int il = tid/8; // 0...3
-    const int ib = tid%8; // 0...7
+    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 + 8*il;
    const uint8_t * grid = (const uint8_t *)(iq2s_grid + (x[i].qs[4*ib+il] | ((x[i].qh[ib] << (8-2*il)) & 0x300)));
    const float d = (float)x[i].d * (0.5f + ((x[i].scales[ib] >> 4*(il/2)) & 0xf)) * 0.25f;
@@ -424,13 +426,13 @@ static __global__ void dequantize_block_iq2_s(const void * __restrict__ vx, dst_
 template<typename dst_t>
 static __global__ void dequantize_block_iq3_xxs(const void * __restrict__ vx, dst_t * __restrict__ yy) {

-    const int i   = blockIdx.x;
+    const int64_t i   = blockIdx.x;
    const block_iq3_xxs * x = (const block_iq3_xxs  *) vx;

-    const int tid = threadIdx.x;
+    const int64_t tid = threadIdx.x;
 #if QK_K == 256
-    const int il = tid/8; // 0...3
-    const int ib = tid%8; // 0...7
+    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 + 8*il;
    const uint8_t  * q3 = x[i].qs + 8*ib;
    const uint16_t * gas = (const uint16_t *)(x[i].qs + QK_K/4) + 2*ib;
@@ -452,13 +454,13 @@ static __global__ void dequantize_block_iq3_xxs(const void * __restrict__ vx, ds
 template<typename dst_t>
 static __global__ void dequantize_block_iq3_s(const void * __restrict__ vx, dst_t * __restrict__ yy) {

-    const int i   = blockIdx.x;
+    const int64_t i   = blockIdx.x;
    const block_iq3_s * x = (const block_iq3_s *) vx;

-    const int tid = threadIdx.x;
+    const int64_t tid = threadIdx.x;
 #if QK_K == 256
-    const int il = tid/8; // 0...3
-    const int ib = tid%8; // 0...7
+    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 + 8*il;
    const uint8_t * qs = x[i].qs + 8*ib;
    const uint8_t * grid1 = (const uint8_t *)(iq3s_grid + (qs[2*il+0] | ((x[i].qh[ib] << (8-2*il)) & 256)));
@@ -478,13 +480,13 @@ static __global__ void dequantize_block_iq3_s(const void * __restrict__ vx, dst_
 template<typename dst_t>
 static __global__ void dequantize_block_iq1_s(const void * __restrict__ vx, dst_t * __restrict__ yy) {

-    const int i   = blockIdx.x;
+    const int64_t i   = blockIdx.x;
    const block_iq1_s * x = (const block_iq1_s  *) vx;

-    const int tid = threadIdx.x;
+    const int64_t tid = threadIdx.x;
 #if QK_K == 256
-    const int il = tid/8; // 0...3
-    const int ib = tid%8; // 0...7
+    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 + 8*il;
    const float delta = x[i].qh[ib] & 0x8000 ? -1 - IQ1S_DELTA : -1 + IQ1S_DELTA;
    const float d = (float)x[i].d * (2*((x[i].qh[ib] >> 12) & 7) + 1);
@@ -504,18 +506,18 @@ static __global__ void dequantize_block_iq1_s(const void * __restrict__ vx, dst_
 template<typename dst_t>
 static __global__ void dequantize_block_iq1_m(const void * __restrict__ vx, dst_t * __restrict__ yy) {

-    const int i   = blockIdx.x;
+    const int64_t i   = blockIdx.x;
    const block_iq1_m * x = (const block_iq1_m  *) vx;

-    const int tid = threadIdx.x;
+    const int64_t tid = threadIdx.x;
 #if QK_K == 256
-    const int il = tid/8; // 0...3
-    const int ib = tid%8; // 0...7
+    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 + 8*il;
    const uint16_t * sc = (const uint16_t *)x[i].scales;
    iq1m_scale_t scale;
    scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000);
-    const int ib16 = 2*ib + il/2; // sc[ib16/4] >> 3*(ib16%4) -> sc[ib/2] >> 3*((2*ib+il/2)%4);
+    const int64_t ib16 = 2*ib + il/2; // sc[ib16/4] >> 3*(ib16%4) -> sc[ib/2] >> 3*((2*ib+il/2)%4);
    const float d = (float)scale.f16 * (2*((sc[ib16/4] >> 3*(ib16%4)) & 0x7) + 1);
    const float delta = x[i].qh[2*ib+il/2] & (0x08 << 4*(il%2)) ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA;
    uint32_t grid32[2]; const int8_t * q = (const int8_t *)grid32;
@@ -535,12 +537,12 @@ static __global__ void dequantize_block_iq1_m(const void * __restrict__ vx, dst_
 template<typename dst_t>
 static __global__ void dequantize_block_iq4_nl(const void * __restrict__ vx, dst_t * __restrict__ yy) {

-    const int i   = blockIdx.x;
+    const int64_t i   = blockIdx.x;
    const block_iq4_nl * x = (const block_iq4_nl *) vx + i*(QK_K/QK4_NL);

-    const int tid = threadIdx.x;
-    const int il = tid/8; // 0...3
-    const int ib = tid%8; // 0...7
+    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 = (float)x[ib].d;
@@ -554,12 +556,12 @@ static __global__ void dequantize_block_iq4_nl(const void * __restrict__ vx, dst
 #if QK_K != 64
 template<typename dst_t>
 static __global__ void dequantize_block_iq4_xs(const void * __restrict__ vx, dst_t * __restrict__ yy) {
-    const int i   = blockIdx.x;
+    const int64_t i   = blockIdx.x;
    const block_iq4_xs * x = (const block_iq4_xs *)vx;

-    const int tid = threadIdx.x;
-    const int il = tid/8; // 0...3
-    const int ib = tid%8; // 0...7
+    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[i].qs + 16*ib + 4*il;
    const float d = (float)x[i].d * ((((x[i].scales_l[ib/2] >> 4*(ib%2)) & 0xf) | (((x[i].scales_h >> 2*ib) & 3) << 4)) - 32);
@@ -0,0 +1,944 @@
+#include "common.cuh"
+#include "fattn.cuh"
+
+#include <cstdint>
+
+#if FP16_MMA_AVAILABLE
+#include <mma.h>
+#endif
+
+#define FATTN_KQ_STRIDE       256
+#define HALF_MAX_HALF         __float2half(65504.0f/2) // Use neg. of this instead of -INFINITY to initialize KQ max vals to avoid NaN upon subtraction.
+#define SOFTMAX_FTZ_THRESHOLD -20.0f                   // Softmax exp. of values smaller than this are flushed to zero to avoid NaNs.
+
+template<int D, int parallel_blocks> // D == head size
+__launch_bounds__(((D + WARP_SIZE - 1) / WARP_SIZE)*WARP_SIZE, 1)
+static __global__ void flash_attn_vec_ext_f16(
+        const char * __restrict__ Q,
+        const char * __restrict__ K,
+        const char * __restrict__ V,
+        const char * __restrict__ mask,
+        float      * __restrict__ dst,
+        float2     * __restrict__ dst_meta,
+        const float scale,
+        const int ne00,
+        const int ne01,
+        const int ne02,
+        const int ne03,
+        const int ne10,
+        const int ne11,
+        const int ne12,
+        const int ne13,
+        const int ne31,
+        const int nb31,
+        const int nb01,
+        const int nb02,
+        const int nb03,
+        const int nb11,
+        const int nb12,
+        const int nb13,
+        const int ne0,
+        const int ne1,
+        const int ne2,
+        const int ne3) {
+#if FP16_AVAILABLE
+    //In this kernel Q, K, V are matrices while i, j, k are matrix indices.
+
+    const int ic = blockIdx.x / parallel_blocks; // Index of the Q/QKV column to work on.
+    const int ip = blockIdx.x % parallel_blocks; // Index in group of blocks running for the same column in parallel.
+
+    const int gqa_ratio = ne02 / ne12; // With grouped query attention there are > 1 Q matrices per K, V matrix.
+    const float2 * Q_f2  = (const float2 *) (Q    + nb02* blockIdx.y              + nb01*ic);
+    const half2  * K_h2  = (const half2  *) (K    + nb12*(blockIdx.y / gqa_ratio));
+    const half   * V_h   = (const half   *) (V    + nb12*(blockIdx.y / gqa_ratio)); // K and V have same shape
+    const half   * maskh = (const half   *)  mask + ne11*ic;
+
+    const int stride_KV  = nb11 / sizeof(half);
+    const int stride_KV2 = nb11 / sizeof(half2);
+
+    constexpr int nwarps = (D + WARP_SIZE - 1) / WARP_SIZE;
+    const int tid = WARP_SIZE*threadIdx.y + threadIdx.x;
+    __builtin_assume(tid < nwarps*WARP_SIZE);
+
+    __shared__ half KQ[nwarps*WARP_SIZE];
+    KQ[tid] = -INFINITY;
+    half2 * KQ2 = (half2 *) KQ;
+
+    half kqmax = -HALF_MAX_HALF;
+    half kqsum = 0.0f;
+
+    __shared__ half kqmax_shared[WARP_SIZE];
+    __shared__ half kqsum_shared[WARP_SIZE];
+    if (threadIdx.y == 0) {
+        kqmax_shared[threadIdx.x] = -HALF_MAX_HALF;
+        kqsum_shared[threadIdx.x] = 0.0f;
+    }
+    __syncthreads();
+
+    // Convert Q to half2 and store in registers:
+    half2 Q_h2[(D/2 + WARP_SIZE - 1) / WARP_SIZE];
+#pragma unroll
+    for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) {
+        const int i = i0 + threadIdx.x;
+        if (i0 + WARP_SIZE > D/2 && i >= D/2) {
+            break;
+        }
+
+        Q_h2[i0/WARP_SIZE] = make_half2(scale, scale) * make_half2(Q_f2[i].x, Q_f2[i].y);
+    }
+
+    half2 VKQ = make_half2(0.0f, 0.0f); // Each thread calculates a single VKQ value.
+
+    const int k_start  = parallel_blocks == 1 ? 0 : ip*D;
+    for (int k_VKQ_0 = k_start; k_VKQ_0 < ne11; k_VKQ_0 += parallel_blocks*D) {
+        // Calculate KQ tile and keep track of new maximum KQ values:
+        half kqmax_new = kqmax;
+#pragma unroll
+        for (int i_KQ_0 = 0; i_KQ_0 < D; i_KQ_0 += nwarps) {
+            const int i_KQ = i_KQ_0 + threadIdx.y;
+
+            if ((i_KQ_0 + nwarps > D && i_KQ >= D) || (FATTN_KQ_STRIDE % D != 0 && k_VKQ_0 + i_KQ >= ne11)) {
+                break;
+            }
+
+            half2 sum2 = make_half2(0.0f, 0.0f);
+#pragma unroll
+            for (int k_KQ_0 = 0; k_KQ_0 < D/2; k_KQ_0 += WARP_SIZE) {
+                const int k_KQ = k_KQ_0 + threadIdx.x;
+                if (k_KQ_0 + WARP_SIZE > D/2 && k_KQ >= D/2) {
+                    break;
+                }
+
+                const half2 K_ik = K_h2[(k_VKQ_0 + i_KQ)*stride_KV2 + k_KQ];
+                sum2 += K_ik * Q_h2[k_KQ_0/WARP_SIZE];
+            }
+
+            sum2 = warp_reduce_sum(sum2);
+            half sum = __low2half(sum2) + __high2half(sum2);
+            sum += mask ? maskh[k_VKQ_0 + i_KQ] : __float2half(0.0f);
+            kqmax_new = __hmax(kqmax_new, sum);
+            if (threadIdx.x == 0) {
+                KQ[i_KQ] = sum;
+            }
+        }
+
+        kqmax_new = warp_reduce_max(kqmax_new);
+        if (threadIdx.x == 0) {
+            kqmax_shared[threadIdx.y] = kqmax_new;
+        }
+        __syncthreads();
+        kqmax_new = kqmax_shared[threadIdx.x];
+        kqmax_new = warp_reduce_max(kqmax_new);
+
+        const half KQ_max_scale = hexp(kqmax - kqmax_new);
+        kqmax = kqmax_new;
+
+        const half val = hexp(KQ[tid] - kqmax);
+        kqsum = kqsum*KQ_max_scale + val;
+        KQ[tid] = val;
+
+        VKQ *= __half2half2(KQ_max_scale);
+
+        __syncthreads();
+
+        if (tid < D) {
+#pragma unroll
+            for (int k0 = 0; k0 < D; k0 += 2) {
+                if (FATTN_KQ_STRIDE % D != 0 && k_VKQ_0 + k0 >= ne11) {
+                    break;
+                }
+
+                half2 V_k;
+                reinterpret_cast<half&>(V_k.x) = V_h[(k_VKQ_0 + k0 + 0)*stride_KV + tid];
+                reinterpret_cast<half&>(V_k.y) = V_h[(k_VKQ_0 + k0 + 1)*stride_KV + tid];
+                VKQ += V_k*KQ2[k0/2];
+            }
+        }
+
+        __syncthreads();
+    }
+
+    if (tid >= D) {
+        kqsum = 0.0f;
+    }
+
+    kqsum = warp_reduce_sum(kqsum);
+    if (threadIdx.x == 0) {
+        kqsum_shared[threadIdx.y] = kqsum;
+    }
+    __syncthreads();
+    kqsum = kqsum_shared[threadIdx.x];
+    kqsum = warp_reduce_sum(kqsum);
+
+    if (tid >= D) {
+        return;
+    }
+
+    half dst_val = (__low2half(VKQ) + __high2half(VKQ));
+    if (parallel_blocks == 1) {
+        dst_val /= kqsum;
+    }
+    dst[D*gridDim.y*blockIdx.x + D*blockIdx.y + tid] = dst_val;
+
+    if (parallel_blocks == 1 || tid != 0) {
+        return;
+    }
+    dst_meta[ic*gridDim.y*parallel_blocks + blockIdx.y*parallel_blocks + ip] = make_float2(kqmax, kqsum);
+#else
+   NO_DEVICE_CODE;
+#endif // FP16_AVAILABLE
+}
+
+// D == head size, VKQ_stride == num VKQ rows calculated in parallel:
+template<int D, int ncols, int nwarps, int VKQ_stride, int parallel_blocks, typename KQ_acc_t>
+__launch_bounds__(nwarps*WARP_SIZE, 1)
+static __global__ void flash_attn_ext_f16(
+        const char * __restrict__ Q,
+        const char * __restrict__ K,
+        const char * __restrict__ V,
+        const char * __restrict__ mask,
+        float      * __restrict__ dst,
+        float2     * __restrict__ dst_meta,
+        const float scale,
+        const int ne00,
+        const int ne01,
+        const int ne02,
+        const int ne03,
+        const int ne10,
+        const int ne11,
+        const int ne12,
+        const int ne13,
+        const int ne31,
+        const int nb31,
+        const int nb01,
+        const int nb02,
+        const int nb03,
+        const int nb11,
+        const int nb12,
+        const int nb13,
+        const int ne0,
+        const int ne1,
+        const int ne2,
+        const int ne3) {
+#if FP16_MMA_AVAILABLE
+    //In this kernel Q, K, V are matrices while i, j, k are matrix indices.
+
+    const int ic0 = ncols*(blockIdx.x / parallel_blocks); // Index of the first Q/QKV column to work on.
+    const int ip  =        blockIdx.x % parallel_blocks;  // Index in group of blocks running for the same column in parallel.
+
+    static_assert(D <= FATTN_KQ_STRIDE, "D must be <= FATTN_KQ_STRIDE.");
+    static_assert(ncols == 8 || ncols % 16 == 0, "ncols must be 8 or a multiple of 16.");
+    constexpr int frag_m = ncols == 8 ? 32 : 16;
+    constexpr int frag_n = ncols == 8 ?  8 : 16;
+    static_assert(D % frag_m == 0, "If ncols == 8 then D % frag_m must be 0.");
+    typedef nvcuda::wmma::fragment<nvcuda::wmma::matrix_a,    frag_m, frag_n, 16, half, nvcuda::wmma::row_major> frag_a_K;
+    typedef nvcuda::wmma::fragment<nvcuda::wmma::matrix_a,    frag_m, frag_n, 16, half, nvcuda::wmma::col_major> frag_a_V;
+    typedef nvcuda::wmma::fragment<nvcuda::wmma::matrix_b,    frag_m, frag_n, 16, half, nvcuda::wmma::col_major> frag_b;
+    typedef nvcuda::wmma::fragment<nvcuda::wmma::accumulator, frag_m, frag_n, 16, KQ_acc_t>                      frag_c_KQ;
+    typedef nvcuda::wmma::fragment<nvcuda::wmma::accumulator, frag_m, frag_n, 16, half>                          frag_c_VKQ;
+
+    constexpr int KQ_stride_tc  = nwarps*frag_m; // Number of KQ rows calculated in parallel.
+    constexpr int VKQ_ratio = KQ_stride_tc/VKQ_stride; // Number of parallel VKQ accumulators needed to keep all warps busy.
+    static_assert(VKQ_ratio <= nwarps, "VKQ_ratio must be <= nwarps.");
+
+    // Pad internal representation of KQ, KQV to reduce shared memory bank conflicts:
+    constexpr int D_padded = D + 8;
+    constexpr int kqs_padded = FATTN_KQ_STRIDE + 8;
+    constexpr int kqar = sizeof(KQ_acc_t)/sizeof(half);
+
+    const int gqa_ratio = ne02 / ne12; // With grouped query attention there are > 1 Q matrices per K, V matrix.
+    const float * Q_f   = (const float *) (Q + nb02* blockIdx.y              + nb01*ic0);
+    const half  * K_h   = (const half  *) (K + nb12*(blockIdx.y / gqa_ratio));
+    const half  * V_h   = (const half  *) (V + nb12*(blockIdx.y / gqa_ratio)); // K and V have same shape
+    const half  * maskh = (const half  *)  mask + (nb31/sizeof(half))* ic0;
+    const half2 * mask2 = (const half2 *)  mask + (nb31/sizeof(half))*(ic0/2);
+
+    const int stride_Q  = nb01 / sizeof(float);
+    const int stride_KV = nb11 / sizeof(half);
+
+    frag_b Q_b[D/16][ncols/frag_n];
+
+    // A single buffer for temporarily holding tiles of KQ and VKQ parts:
+    constexpr int mem_KQ = ncols*kqs_padded*kqar;
+    constexpr int mem_VKQ_parts = VKQ_ratio*ncols*D_padded;
+    __shared__ half KQ[mem_KQ >= mem_VKQ_parts ? mem_KQ : mem_VKQ_parts];
+    float * KQ_f = (float *) KQ;
+    half2 * KQ2 = (half2 *) KQ;
+
+    float    KQ_rowsum_f[ncols/nwarps] = {0.0f};
+    float       KQ_max_f[ncols/nwarps];
+    float KQ_max_scale_f[ncols/nwarps] = {0.0f};
+
+#pragma unroll
+    for (int j = 0; j < ncols/nwarps; ++j) {
+        KQ_max_f[j] = -FLT_MAX/2.0f;
+    }
+
+    half2    KQ_rowsum_h2[ncols/nwarps] = {{0.0f, 0.0f}};
+    half2       KQ_max_h2[ncols/nwarps];
+    half2 KQ_max_scale_h2[ncols/nwarps] = {{0.0f, 0.0f}};
+
+#pragma unroll
+    for (int j = 0; j < ncols/nwarps; ++j) {
+        KQ_max_h2[j] = make_half2(-HALF_MAX_HALF, -HALF_MAX_HALF);
+    }
+
+    __shared__ half VKQ[ncols*D_padded]; // Accumulator for final VKQ slice.
+    half2 * VKQ2 = (half2 *) VKQ;
+#pragma unroll
+    for (int j0 = 0; j0 < ncols; j0 += nwarps) {
+        const int j = j0 + threadIdx.y;
+#pragma unroll
+        for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) {
+            const int i = i0 + threadIdx.x;
+            if (i0 + WARP_SIZE > D/2 && i >= D/2) {
+                break;
+            }
+            VKQ2[j*(D_padded/2) + i] = make_half2(0.0f, 0.0f);
+        }
+    }
+
+    // Convert Q to half and apply scale, temporarily store in KQ:
+#pragma unroll
+    for (int j0 = 0; j0 < ncols; j0 += nwarps) {
+        const int j = j0 + threadIdx.y;
+#pragma unroll
+        for (int i0 = 0; i0 < D; i0 += WARP_SIZE) {
+            const int i = i0 + threadIdx.x;
+            if (i0 + WARP_SIZE > D && i >= D) {
+                break;
+            }
+            KQ[j*D_padded + i] = ic0 + j < ne01 ? Q_f[j*stride_Q + i] * scale : 0.0f;
+        }
+    }
+
+    __syncthreads();
+
+    // Load Q into tensor core fragments/registers since it will be used frequently:
+#pragma unroll
+    for (int i0 = 0; i0 < D; i0 += 16) {
+#pragma unroll
+        for (int j0 = 0; j0 < ncols; j0 += frag_n) {
+            nvcuda::wmma::load_matrix_sync(Q_b[i0/16][j0/frag_n], KQ + j0*D_padded + i0, D_padded);
+        }
+    }
+
+    __syncthreads();
+
+    // Iterate over ne11 == previous tokens:
+    for (int k_VKQ_0 = ip*FATTN_KQ_STRIDE; k_VKQ_0 < ne11; k_VKQ_0 += parallel_blocks*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) {
+            frag_c_KQ KQ_c[ncols/frag_n];
+#pragma unroll
+            for (int j = 0; j < ncols/frag_n; ++j) {
+                nvcuda::wmma::fill_fragment(KQ_c[j], 0.0f);
+            }
+#pragma unroll
+            for (int k_KQ_0 = 0; k_KQ_0 < D; k_KQ_0 += 16) {
+                frag_a_K K_a;
+                nvcuda::wmma::load_matrix_sync(K_a, K_h + (k_VKQ_0 + i_KQ_0 + frag_m*threadIdx.y)*stride_KV + k_KQ_0, stride_KV);
+#pragma unroll
+                for (int j = 0; j < ncols/frag_n; ++j) {
+                    nvcuda::wmma::mma_sync(KQ_c[j], K_a, Q_b[k_KQ_0/16][j], KQ_c[j]);
+                }
+            }
+#pragma unroll
+            for (int j0 = 0; j0 < ncols; j0 += frag_n) {
+                nvcuda::wmma::store_matrix_sync((KQ_acc_t *) KQ + j0*kqs_padded + i_KQ_0 + frag_m*threadIdx.y, KQ_c[j0/frag_n], kqs_padded, nvcuda::wmma::mem_col_major);
+            }
+        }
+
+        __syncthreads();
+
+        // Calculate softmax for each KQ column using the current max. value.
+        // The divisor is stored in KQ_rowsum and will be applied at the end.
+#pragma unroll
+        for (int j0 = 0; j0 < ncols; j0 += nwarps) {
+            const int j = j0 + threadIdx.y;
+
+            if (std::is_same<KQ_acc_t, float>::value) {
+                float KQ_f_tmp[FATTN_KQ_STRIDE / WARP_SIZE];
+#pragma unroll
+                for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += WARP_SIZE) {
+                    const int k = k0 + threadIdx.x;
+
+                    KQ_f_tmp[k0/WARP_SIZE] = KQ_f[j*kqs_padded + k];
+                }
+
+                float KQ_max_new = KQ_max_f[j0/nwarps];
+#pragma unroll
+                for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += WARP_SIZE) {
+                    const int k = k0 + threadIdx.x;
+
+                    KQ_f_tmp[k0/WARP_SIZE] += mask ? __half2float(maskh[j*(nb31/sizeof(half)) + k_VKQ_0 + k]) : 0.0f;
+                    KQ_max_new = max(KQ_max_new, KQ_f_tmp[k0/WARP_SIZE]);
+                }
+                KQ_max_new = warp_reduce_max(KQ_max_new);
+
+                const float diff = KQ_max_f[j0/nwarps] - KQ_max_new;
+                KQ_max_scale_f[j0/nwarps] = expf(diff);
+                if (diff <= SOFTMAX_FTZ_THRESHOLD) {
+                    KQ_max_scale_f[j0/nwarps] = 0.0f;
+                }
+                KQ_max_f[j0/nwarps] = KQ_max_new;
+
+                float KQ_rowsum_add = 0.0f;
+#pragma unroll
+                for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += WARP_SIZE) {
+                    const int k = k0 + threadIdx.x;
+
+                    const float diff = KQ_f_tmp[k0/WARP_SIZE] - KQ_max_f[j0/nwarps];
+                    KQ_f_tmp[k0/WARP_SIZE] = expf(diff);
+                    if (diff <= SOFTMAX_FTZ_THRESHOLD) {
+                        KQ_f_tmp[k0/WARP_SIZE] = 0.0f;
+                    }
+                    KQ_rowsum_add += KQ_f_tmp[k0/WARP_SIZE];
+                    KQ[j*(kqar*kqs_padded) + k] = KQ_f_tmp[k0/WARP_SIZE];
+                }
+                KQ_rowsum_add = warp_reduce_sum(KQ_rowsum_add);
+
+                // Scale previous KQ_rowsum to account for a potential increase in KQ_max:
+                KQ_rowsum_f[j0/nwarps] = KQ_max_scale_f[j0/nwarps]*KQ_rowsum_f[j0/nwarps] + KQ_rowsum_add;
+            } else {
+                half2 KQ2_tmp[FATTN_KQ_STRIDE/(2*WARP_SIZE)];
+#pragma unroll
+                for (int k0 = 0; k0 < FATTN_KQ_STRIDE/2; k0 += WARP_SIZE) {
+                    const int k = k0 + threadIdx.x;
+
+                    KQ2_tmp[k0/WARP_SIZE] = KQ2[j*(kqs_padded/2) + k];
+                }
+
+                half2 KQ_max_new = KQ_max_h2[j0/nwarps];
+#pragma unroll
+                for (int k0 = 0; k0 < FATTN_KQ_STRIDE/2; k0 += WARP_SIZE) {
+                    const int k = k0 + threadIdx.x;
+
+                    KQ2_tmp[k0/WARP_SIZE] += mask ? mask2[(j*ne11 + k_VKQ_0)/2 + k] : make_half2(0.0f, 0.0f);
+                    KQ_max_new = __hmax2(KQ_max_new, KQ2_tmp[k0/WARP_SIZE]);
+                }
+                KQ_max_new = __half2half2(warp_reduce_max(__hmax(__low2half(KQ_max_new), __high2half(KQ_max_new))));
+                const half2 diff = KQ_max_h2[j0/nwarps] - KQ_max_new;
+                KQ_max_scale_h2[j0/nwarps] = h2exp(diff);
+                const uint32_t ftz_mask = __hgt2_mask(diff, make_half2(SOFTMAX_FTZ_THRESHOLD, SOFTMAX_FTZ_THRESHOLD));
+                *((uint32_t *) &KQ_max_scale_h2[j0/nwarps]) &= ftz_mask;
+                KQ_max_h2[j0/nwarps] = KQ_max_new;
+
+                half2 KQ_rowsum_add = make_half2(0.0f, 0.0f);
+#pragma unroll
+                for (int k0 = 0; k0 < FATTN_KQ_STRIDE/2; k0 += WARP_SIZE) {
+                    const int k = k0 + threadIdx.x;
+
+                    const half2 diff = KQ2_tmp[k0/WARP_SIZE] - KQ_max_h2[j0/nwarps];
+                    KQ2_tmp[k0/WARP_SIZE] = h2exp(diff);
+                    const uint32_t ftz_mask = __hgt2_mask(diff, make_half2(SOFTMAX_FTZ_THRESHOLD, SOFTMAX_FTZ_THRESHOLD));
+                    *((uint32_t *) &KQ2_tmp[k0/WARP_SIZE]) &= ftz_mask;
+                    KQ_rowsum_add += KQ2_tmp[k0/WARP_SIZE];
+                    KQ2[j*(kqs_padded/2) + k] = KQ2_tmp[k0/WARP_SIZE];
+                }
+                KQ_rowsum_add = warp_reduce_sum(KQ_rowsum_add);
+
+                // Scale previous KQ_rowsum to account for a potential increase in KQ_max:
+                KQ_rowsum_h2[j0/nwarps] = KQ_max_scale_h2[j0/nwarps]*KQ_rowsum_h2[j0/nwarps] + KQ_rowsum_add;
+            }
+        }
+
+        __syncthreads();
+
+        frag_b KQ_b[FATTN_KQ_STRIDE/(VKQ_ratio*16)][ncols/frag_n];
+#pragma unroll
+        for (int j0 = 0; j0 < ncols; j0 += frag_n) {
+#pragma unroll
+            for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += VKQ_ratio*16) {
+                const int k = k0 + (threadIdx.y % VKQ_ratio)*16;
+                nvcuda::wmma::load_matrix_sync(
+                    KQ_b[k0/(VKQ_ratio*16)][j0/frag_n],
+                    KQ + j0*(kqar*kqs_padded) + k,
+                    kqar*kqs_padded);
+            }
+        }
+
+        frag_c_VKQ VKQ_c[D/VKQ_stride][ncols/frag_n];
+#pragma unroll
+        for (int i_VKQ_0 = 0; i_VKQ_0 < D; i_VKQ_0 += VKQ_stride) {
+#pragma unroll
+            for (int j = 0; j < ncols/frag_n; ++j) {
+                nvcuda::wmma::fill_fragment(VKQ_c[i_VKQ_0/VKQ_stride][j], 0.0f);
+            }
+
+#pragma unroll
+            for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += VKQ_ratio*16) {
+                const int k = k0 + (threadIdx.y % VKQ_ratio)*16;
+
+                frag_a_V v_a;
+                nvcuda::wmma::load_matrix_sync(v_a, V_h + (k_VKQ_0 + k)*stride_KV + i_VKQ_0 + frag_m*(threadIdx.y/VKQ_ratio), stride_KV);
+#pragma unroll
+                for (int j = 0; j < ncols/frag_n; ++j) {
+                    nvcuda::wmma::mma_sync(VKQ_c[i_VKQ_0/VKQ_stride][j], v_a, KQ_b[k0/(VKQ_ratio*16)][j], VKQ_c[i_VKQ_0/VKQ_stride][j]);
+                }
+            }
+        }
+
+        __syncthreads();
+
+        const int offset_k = (threadIdx.y % VKQ_ratio) * (ncols*D_padded);
+#pragma unroll
+        for (int i_KQ_0 = 0; i_KQ_0 < D; i_KQ_0 += VKQ_stride) {
+#pragma unroll
+            for (int j0 = 0; j0 < ncols; j0 += frag_n) {
+                nvcuda::wmma::store_matrix_sync(
+                    KQ + offset_k + j0*D_padded + i_KQ_0 + frag_m*(threadIdx.y/VKQ_ratio),
+                    VKQ_c[i_KQ_0/VKQ_stride][j0/frag_n],
+                    D_padded, nvcuda::wmma::mem_col_major);
+            }
+        }
+
+        __syncthreads();
+
+#pragma unroll
+        for (int j0 = 0; j0 < ncols; j0 += nwarps) {
+            const int j = j0 + threadIdx.y;
+
+            half2 VKQ_scale;
+            if (std::is_same<KQ_acc_t, float>::value) {
+                VKQ_scale = make_half2(KQ_max_scale_f[j0/nwarps], KQ_max_scale_f[j0/nwarps]);
+            } else {
+                VKQ_scale = KQ_max_scale_h2[j0/nwarps];
+            }
+
+#pragma unroll
+            for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) {
+                const int i = i0 + threadIdx.x;
+                if (i0 + WARP_SIZE > D/2 && i >= D/2) {
+                    break;
+                }
+
+                half2 VKQ_add = make_half2(0.0f, 0.0f);
+#pragma unroll
+                for (int l = 0; l < VKQ_ratio; ++l) {
+                    VKQ_add += KQ2[l*(ncols*D_padded/2) + j*(D_padded/2) + i];
+                }
+                VKQ2[j*(D_padded/2) + i] = VKQ_scale*VKQ2[j*(D_padded/2) + i] + VKQ_add;
+            }
+        }
+
+        __syncthreads();
+    }
+
+#pragma unroll
+    for (int j0 = 0; j0 < ncols; j0 += nwarps) {
+        const int j_VKQ = j0 + threadIdx.y;
+        if (ic0 + j_VKQ >= ne01) {
+            return;
+        }
+        const int j_dst = (ic0 + j_VKQ)*parallel_blocks + ip;
+
+        float KQ_rowsum_j;
+        if (std::is_same<KQ_acc_t, float>::value) {
+            KQ_rowsum_j = KQ_rowsum_f[j0/nwarps];
+        } else {
+            KQ_rowsum_j = __low2float(KQ_rowsum_h2[j0/nwarps]) + __high2float(KQ_rowsum_h2[j0/nwarps]);
+        }
+
+#pragma unroll
+        for (int i0 = 0; i0 < D; i0 += WARP_SIZE) {
+            const int i = i0 + threadIdx.x;
+            if (i0 + WARP_SIZE > D && i >= D) {
+                break;
+            }
+            float dst_val = VKQ[j_VKQ*D_padded + i];
+            if (parallel_blocks == 1) {
+                dst_val /= KQ_rowsum_j;
+            }
+            dst[j_dst*gridDim.y*D + blockIdx.y*D + i] = dst_val;
+        }
+
+        if (parallel_blocks == 1 || threadIdx.x != 0) {
+            continue;
+        }
+
+        float2 dst_meta_val;
+        if (std::is_same<KQ_acc_t, float>::value) {
+            dst_meta_val.x = KQ_max_f[j0/nwarps];
+        } else {
+            dst_meta_val.x = __low2float(KQ_max_h2[j0/nwarps]);
+        }
+        dst_meta_val.y = KQ_rowsum_j;
+        dst_meta[(ic0 + j_VKQ)*gridDim.y*parallel_blocks + blockIdx.y*parallel_blocks + ip] = dst_meta_val;
+    }
+#else
+   NO_DEVICE_CODE;
+#endif // FP16_MMA_AVAILABLE
+}
+
+template<int D, int parallel_blocks> // D == head size
+__launch_bounds__(D, 1)
+static __global__ void flash_attn_combine_results(
+        const float  * __restrict__ VKQ_parts,
+        const float2 * __restrict__ VKQ_meta,
+        float * __restrict__ dst) {
+#if FP16_AVAILABLE
+    VKQ_parts += parallel_blocks*D * gridDim.y*blockIdx.x;
+    VKQ_meta  += parallel_blocks   * gridDim.y*blockIdx.x;
+    dst       +=                 D * gridDim.y*blockIdx.x;
+
+    const int tid = threadIdx.x;
+    __builtin_assume(tid < D);
+
+    __shared__ float2 meta[parallel_blocks];
+    if (tid < 2*parallel_blocks) {
+        ((float *) meta)[threadIdx.x] = ((const float *)VKQ_meta) [blockIdx.y*(2*parallel_blocks) + tid];
+    }
+
+    __syncthreads();
+
+    float kqmax = meta[0].x;
+#pragma unroll
+    for (int l = 1; l < parallel_blocks; ++l) {
+        kqmax = max(kqmax, meta[l].x);
+    }
+
+    float VKQ_numerator   = 0.0f;
+    float VKQ_denominator = 0.0f;
+#pragma unroll
+    for (int l = 0; l < parallel_blocks; ++l) {
+        const float diff = meta[l].x - kqmax;
+        const float KQ_max_scale = expf(diff);
+        const uint32_t ftz_mask = 0xFFFFFFFF * (diff > SOFTMAX_FTZ_THRESHOLD);
+        *((uint32_t *) &KQ_max_scale) &= ftz_mask;
+
+        VKQ_numerator   += KQ_max_scale * VKQ_parts[l*gridDim.y*D + blockIdx.y*D + tid];
+        VKQ_denominator += KQ_max_scale * meta[l].y;
+    }
+
+    dst[blockIdx.y*D + tid] = VKQ_numerator / VKQ_denominator;
+#else
+   NO_DEVICE_CODE;
+#endif // FP16_AVAILABLE
+}
+
+constexpr int get_max_power_of_2(int x) {
+    return x % 2 == 0 ? 2*get_max_power_of_2(x/2) : 1;
+}
+
+static_assert(get_max_power_of_2(1) == 1, "Test failed.");
+static_assert(get_max_power_of_2(2) == 2, "Test failed.");
+static_assert(get_max_power_of_2(4) == 4, "Test failed.");
+static_assert(get_max_power_of_2(6) == 2, "Test failed.");
+
+// Number of VKQ rows calculated in parallel:
+constexpr int get_VKQ_stride(int D, int nwarps, int frag_m) {
+    return (get_max_power_of_2(D/frag_m) < nwarps ? get_max_power_of_2(D/frag_m) : nwarps)*frag_m;
+}
+
+static_assert(get_VKQ_stride(128, 1, 32) ==  32, "Test failed.");
+static_assert(get_VKQ_stride(128, 2, 32) ==  64, "Test failed.");
+static_assert(get_VKQ_stride(128, 4, 32) == 128, "Test failed.");
+static_assert(get_VKQ_stride( 64, 1, 32) ==  32, "Test failed.");
+static_assert(get_VKQ_stride( 64, 2, 32) ==  64, "Test failed.");
+static_assert(get_VKQ_stride( 64, 4, 32) ==  64, "Test failed.");
+static_assert(get_VKQ_stride( 80, 1, 16) ==  16, "Test failed.");
+static_assert(get_VKQ_stride( 80, 2, 16) ==  16, "Test failed.");
+static_assert(get_VKQ_stride( 80, 4, 16) ==  16, "Test failed.");
+
+template <int D, int parallel_blocks> void launch_fattn_vec_f16(
+        const ggml_tensor * Q, const ggml_tensor * K, const ggml_tensor * V, ggml_tensor * KQV, const ggml_tensor * mask,
+        ggml_cuda_pool & pool, cudaStream_t main_stream
+) {
+    ggml_cuda_pool_alloc<float>  dst_tmp(pool);
+    ggml_cuda_pool_alloc<float2> dst_tmp_meta(pool);
+
+    if (parallel_blocks > 1) {
+        dst_tmp.alloc(parallel_blocks*ggml_nelements(KQV));
+        dst_tmp_meta.alloc(parallel_blocks*ggml_nrows(KQV));
+    }
+
+    constexpr int  nwarps = (D + WARP_SIZE - 1) / WARP_SIZE;
+    const     dim3 block_dim(WARP_SIZE, nwarps, 1);
+    const     dim3 blocks_num(parallel_blocks*Q->ne[1], Q->ne[2], Q->ne[3]);
+    const     int  shmem = 0;
+
+    float scale;
+    memcpy(&scale, KQV->op_params, sizeof(float));
+
+    flash_attn_vec_ext_f16<D, parallel_blocks>
+        <<<blocks_num, block_dim, shmem, main_stream>>> (
+                (const char *) Q->data,
+                (const char *) K->data,
+                (const char *) V->data,
+                mask ? ((const char *) mask->data) : nullptr,
+                parallel_blocks == 1 ? (float *) KQV->data : dst_tmp.ptr, dst_tmp_meta.ptr,
+                scale,
+                Q->ne[0], Q->ne[1], Q->ne[2], Q->ne[3],
+                K->ne[0], K->ne[1], K->ne[2], K->ne[3],
+                mask ? mask->ne[1] : 0, mask ?  mask->nb[1] : 0,
+                Q->nb[1], Q->nb[2], Q->nb[3],
+                K->nb[1], K->nb[2], K->nb[3],
+                KQV->ne[0], KQV->ne[1], KQV->ne[2], KQV->ne[3]
+                );
+    CUDA_CHECK(cudaGetLastError());
+
+    if (parallel_blocks == 1) {
+        return;
+    }
+
+    const dim3 block_dim_combine(D, 1, 1);
+    const dim3 blocks_num_combine(Q->ne[1], blocks_num.y, blocks_num.z);
+    const int  shmem_combine = 0;
+
+    flash_attn_combine_results<D, parallel_blocks>
+        <<<blocks_num_combine, block_dim_combine, shmem_combine, main_stream>>>
+        (dst_tmp.ptr, dst_tmp_meta.ptr, (float *) KQV->data);
+    CUDA_CHECK(cudaGetLastError());
+}
+
+template <int D, int cols_per_block, int nwarps, int parallel_blocks, typename KQ_acc_t> void launch_fattn_f16_impl(
+        const ggml_tensor * Q, const ggml_tensor * K, const ggml_tensor * V, ggml_tensor * KQV, const ggml_tensor * mask,
+        ggml_cuda_pool & pool, cudaStream_t main_stream
+) {
+    ggml_cuda_pool_alloc<float>  dst_tmp(pool);
+    ggml_cuda_pool_alloc<float2> dst_tmp_meta(pool);
+
+    if (parallel_blocks > 1) {
+        dst_tmp.alloc(parallel_blocks*ggml_nelements(KQV));
+        dst_tmp_meta.alloc(parallel_blocks*ggml_nrows(KQV));
+    }
+
+    constexpr int  frag_m = (cols_per_block) == 8 && (D) % 32 == 0 ? 32 : 16;
+    const     dim3 block_dim(WARP_SIZE, nwarps, 1);
+    const     dim3 blocks_num(parallel_blocks*(Q->ne[1] + cols_per_block - 1) / cols_per_block, Q->ne[2], Q->ne[3]);
+    const     int  shmem = 0;
+
+    float scale;
+    memcpy(&scale, KQV->op_params, sizeof(float));
+
+    flash_attn_ext_f16<D, cols_per_block, nwarps, get_VKQ_stride(D, nwarps, frag_m), parallel_blocks, KQ_acc_t>
+        <<<blocks_num, block_dim, shmem, main_stream>>> (
+                (const char *) Q->data,
+                (const char *) K->data,
+                (const char *) V->data,
+                mask ? ((const char *) mask->data) : nullptr,
+                (parallel_blocks) == 1 ? (float *) KQV->data : dst_tmp.ptr, dst_tmp_meta.ptr,
+                scale,
+                Q->ne[0], Q->ne[1], Q->ne[2], Q->ne[3],
+                K->ne[0], K->ne[1], K->ne[2], K->ne[3],
+                mask ? mask->ne[1] : 0, mask ?  mask->nb[1] : 0,
+                Q->nb[1], Q->nb[2], Q->nb[3],
+                K->nb[1], K->nb[2], K->nb[3],
+                KQV->ne[0], KQV->ne[1], KQV->ne[2], KQV->ne[3]
+                );
+    CUDA_CHECK(cudaGetLastError());
+
+    if ((parallel_blocks) == 1) {
+        return;
+    }
+
+    const dim3 block_dim_combine(D, 1, 1);
+    const dim3 blocks_num_combine(Q->ne[1], blocks_num.y, blocks_num.z);
+    const int  shmem_combine = 0;
+
+    flash_attn_combine_results<D, parallel_blocks>
+        <<<blocks_num_combine, block_dim_combine, shmem_combine, main_stream>>>
+        (dst_tmp.ptr, dst_tmp_meta.ptr, (float *) KQV->data);
+    CUDA_CHECK(cudaGetLastError());
+}
+
+template <int D, int cols_per_block, int nwarps, typename KQ_acc_t> void launch_fattn_f16(
+        const ggml_tensor * Q, const ggml_tensor * K, const ggml_tensor * V, ggml_tensor * KQV, const ggml_tensor * mask,
+        const int nsm, ggml_cuda_pool & pool, cudaStream_t main_stream
+) {
+    const int blocks_num_pb1 = ((Q->ne[1] + cols_per_block - 1) / cols_per_block)*Q->ne[2]*Q->ne[3];
+
+    if (4*blocks_num_pb1 < 2*nsm) {
+        launch_fattn_f16_impl<D, cols_per_block, nwarps, 4, KQ_acc_t>(Q, K, V, KQV, mask, pool, main_stream);
+        return;
+    }
+    if (2*blocks_num_pb1 < 2*nsm) {
+        launch_fattn_f16_impl<D, cols_per_block, nwarps, 2, KQ_acc_t>(Q, K, V, KQV, mask, pool, main_stream);
+        return;
+    }
+    launch_fattn_f16_impl<D, cols_per_block, nwarps, 1, KQ_acc_t>(Q, K, V, KQV, mask, pool, main_stream);
+}
+
+void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, 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];
+
+    ggml_tensor * KQV = dst;
+
+    GGML_ASSERT(Q->type == GGML_TYPE_F32);
+    GGML_ASSERT(K->type == GGML_TYPE_F16);
+    GGML_ASSERT(V->type == GGML_TYPE_F16);
+    GGML_ASSERT(KQV->type == GGML_TYPE_F32);
+
+    GGML_ASSERT(!mask || mask->type == GGML_TYPE_F16);
+    GGML_ASSERT(!mask || mask->ne[1] >= GGML_PAD(Q->ne[1], 16) &&
+                                "the Flash-Attention CUDA kernel requires the mask to be padded to 16 and at least n_queries big");
+
+    GGML_ASSERT(K->ne[1] % FATTN_KQ_STRIDE == 0 && "Incorrect KV cache padding.");
+
+    ggml_cuda_set_device(ctx.device);
+
+    const int nsm = ggml_cuda_info().devices[ggml_cuda_get_device()].nsm;
+
+    const int32_t precision = KQV->op_params[1];
+
+    if (precision != GGML_PREC_DEFAULT) {
+        if (Q->ne[1] <= 32 || Q->ne[0] > 128) {
+            constexpr int cols_per_block = 16;
+            constexpr int nwarps         =  4;
+            switch (Q->ne[0]) {
+                case 64:
+                    launch_fattn_f16< 64, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
+                    break;
+                case 80:
+                    launch_fattn_f16< 80, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
+                    break;
+                case 96:
+                    launch_fattn_f16< 96, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
+                    break;
+                case 112:
+                    launch_fattn_f16<112, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
+                    break;
+                case 128:
+                    launch_fattn_f16<128, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
+                    break;
+                case 256:
+                    launch_fattn_f16<256, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
+                    break;
+                default:
+                    GGML_ASSERT(false);
+                    break;
+            }
+        } else {
+            constexpr int cols_per_block = 32;
+            constexpr int nwarps         =  4;
+            switch (Q->ne[0]) {
+                case 64:
+                    launch_fattn_f16< 64, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
+                    break;
+                case 80:
+                    launch_fattn_f16< 80, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
+                    break;
+                case 96:
+                    launch_fattn_f16< 96, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
+                    break;
+                case 112:
+                    launch_fattn_f16<112, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
+                    break;
+                case 128:
+                    launch_fattn_f16<128, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
+                    break;
+                // case 256:
+                //     launch_fattn_f16<256, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
+                //     break;
+                default:
+                    GGML_ASSERT(false);
+                    break;
+            }
+        }
+        return;
+    }
+
+    if (Q->ne[1] == 1 && Q->ne[0] % (2*WARP_SIZE) == 0) {
+        constexpr int parallel_blocks = 4;
+        switch (Q->ne[0]) {
+            case 64:
+                launch_fattn_vec_f16< 64, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
+                break;
+            case 128:
+                launch_fattn_vec_f16<128, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
+                break;
+            case 256:
+                launch_fattn_vec_f16<256, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream());
+                break;
+            default:
+                GGML_ASSERT(false);
+                break;
+        }
+        return;
+    }
+
+    if (Q->ne[1] <= 8 && Q->ne[0] % WARP_SIZE == 0) {
+        constexpr int cols_per_block = 8;
+        constexpr int nwarps         = 4;
+        switch (Q->ne[0]) {
+            case 64:
+                launch_fattn_f16< 64, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
+                break;
+            case 96:
+                launch_fattn_f16< 96, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
+                break;
+            case 128:
+                launch_fattn_f16<128, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
+                break;
+            case 256:
+                launch_fattn_f16<256, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
+                break;
+            default:
+                GGML_ASSERT(false);
+                break;
+        }
+        return;
+    }
+
+    if (Q->ne[1] <= 32) {
+        constexpr int cols_per_block = 16;
+        constexpr int nwarps         =  4;
+        switch (Q->ne[0]) {
+            case 64:
+                launch_fattn_f16< 64, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
+                break;
+            case 80:
+                launch_fattn_f16< 80, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
+                break;
+            case 96:
+                launch_fattn_f16< 96, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
+                break;
+            case 112:
+                launch_fattn_f16<112, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
+                break;
+            case 128:
+                launch_fattn_f16<128, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
+                break;
+            case 256:
+                launch_fattn_f16<256, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
+                break;
+            default:
+                GGML_ASSERT(false);
+                break;
+        }
+        return;
+    }
+
+    constexpr int cols_per_block = 32;
+    constexpr int nwarps         =  4;
+    switch (Q->ne[0]) {
+        case 64:
+            launch_fattn_f16< 64, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
+            break;
+        case 80:
+            launch_fattn_f16< 80, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
+            break;
+        case 96:
+            launch_fattn_f16< 96, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
+            break;
+        case 112:
+            launch_fattn_f16<112, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
+            break;
+        case 128:
+            launch_fattn_f16<128, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
+            break;
+        case 256:
+            launch_fattn_f16<256, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream());
+            break;
+        default:
+            GGML_ASSERT(false);
+            break;
+    }
+    return;
+}
@@ -0,0 +1,3 @@
+#include "common.cuh"
+
+void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
@@ -1,7 +1,17 @@
 #include "softmax.cuh"

-template <bool vals_smem, int ncols_template, int block_size_template>
-static __global__ void soft_max_f32(const float * x, const float * mask, const float * pos, float * dst, const int ncols_par, const int nrows_y, const float scale, const float max_bias, const float m0, const float m1, uint32_t n_head_log2) {
+template <typename T>
+static __device__ __forceinline__ float t2f32(T val) {
+    return (float) val;
+}
+
+template <>
+__device__ float __forceinline__ t2f32<half>(half val) {
+    return __half2float(val);
+}
+
+template <bool vals_smem, int ncols_template, int block_size_template, typename T>
+static __global__ void soft_max_f32(const float * x, const T * mask, const T * pos, float * dst, const int ncols_par, const int nrows_y, const float scale, const float max_bias, const float m0, const float m1, uint32_t n_head_log2) {
    const int ncols = ncols_template == 0 ? ncols_par : ncols_template;

    const int tid  = threadIdx.x;
@@ -28,7 +38,7 @@ static __global__ void soft_max_f32(const float * x, const float * mask, const f
    extern __shared__ float data_soft_max_f32[];
    float * buf_iw = data_soft_max_f32; // shared memory buffer for inter-warp communication
    // shared memory buffer to cache values between iterations:
-    float * vals = vals_smem ? buf_iw + WARP_SIZE : dst + rowx*ncols;
+    float * vals = vals_smem ? buf_iw + WARP_SIZE : dst + (int64_t)rowx*ncols;

    float max_val = -INFINITY;

@@ -40,10 +50,10 @@ static __global__ void soft_max_f32(const float * x, const float * mask, const f
            break;
        }

-        const int ix = rowx*ncols + col;
-        const int iy = rowy*ncols + col;
+        const int64_t ix = (int64_t)rowx*ncols + col;
+        const int64_t iy = (int64_t)rowy*ncols + col;

-        const float val = x[ix]*scale + (mask ? mask[iy] : 0.0f) + (pos ? slope*pos[col] : 0.0f);
+        const float val = x[ix]*scale + (mask ? t2f32(mask[iy]) : 0.0f) + (pos ? slope*t2f32(pos[col]) : 0.0f);

        vals[col] = val;
        max_val = max(max_val, val);
@@ -109,12 +119,13 @@ static __global__ void soft_max_f32(const float * x, const float * mask, const f
            return;
        }

-        const int idst = rowx*ncols + col;
+        const int64_t idst = (int64_t)rowx*ncols + col;
        dst[idst] = vals[col] * inv_sum;
    }
 }

-static void soft_max_f32_cuda(const float * x, const float * mask, const float * pos, float * dst, const int ncols_x, const int nrows_x, const int nrows_y, const float scale, const float max_bias, cudaStream_t stream) {
+template<typename T>
+static void soft_max_f32_cuda(const float * x, const T * mask, const T * pos, float * dst, const int ncols_x, const int nrows_x, const int nrows_y, const float scale, const float max_bias, cudaStream_t stream) {
    int nth = WARP_SIZE;
    while (nth < ncols_x && nth < CUDA_SOFT_MAX_BLOCK_SIZE) nth *= 2;
    const dim3 block_dims(nth,     1, 1);
@@ -167,15 +178,19 @@ static void soft_max_f32_cuda(const float * x, const float * mask, const float *
 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 float * src1_d = src1 ? (const float *)src1->data : nullptr;
+    const void  * src1_d = src1 ? (const void *)src1->data : nullptr;
+
    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);

-    GGML_ASSERT(!src1 || src1->type == GGML_TYPE_F32); // src1 contains mask and it is optional
+    GGML_ASSERT(!src1 || src1->type == GGML_TYPE_F16 || src1->type == GGML_TYPE_F32); // src1 contains mask and it is optional
+    GGML_ASSERT(!src2 || src2->type == GGML_TYPE_F16 || src2->type == GGML_TYPE_F32); // src2 contains positions and it is optional

    const int64_t ne00    = src0->ne[0];
    const int64_t nrows_x = ggml_nrows(src0);
@@ -188,14 +203,25 @@ void ggml_cuda_op_soft_max(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
    memcpy(&max_bias, (float *) dst->op_params + 1, sizeof(float));

    // positions tensor
-    float * src2_dd = nullptr;
+    void * src2_d = nullptr;

-    ggml_tensor * src2 = dst->src[2];
    const bool use_src2 = src2 != nullptr;

    if (use_src2) {
-        src2_dd = (float *)src2->data;
+        src2_d = (void *)src2->data;
    }

-    soft_max_f32_cuda(src0_d, src1_d, src2_dd, dst_d, ne00, nrows_x, nrows_y, scale, max_bias, stream);
+    const bool use_f16 = (src1 && src1->type == GGML_TYPE_F16) || (src2 && src2->type == GGML_TYPE_F16);
+
+    if (use_f16) {
+        const half * src1_dd = (const half *)src1_d;
+        const half * src2_dd = (const half *)src2_d;
+
+        soft_max_f32_cuda(src0_d, src1_dd, src2_dd, dst_d, ne00, nrows_x, nrows_y, scale, max_bias, stream);
+    } else {
+        const float * src1_dd = (const float *)src1_d;
+        const float * src2_dd = (const float *)src2_d;
+
+        soft_max_f32_cuda(src0_d, src1_dd, src2_dd, dst_d, ne00, nrows_x, nrows_y, scale, max_bias, stream);
+    }
 }
@@ -11,6 +11,12 @@
 #include <string.h> // memcpy
 #include <math.h>   // fabsf

+#undef MIN
+#undef MAX
+
+#define MIN(a, b) ((a) < (b) ? (a) : (b))
+#define MAX(a, b) ((a) > (b) ? (a) : (b))
+
 #ifdef __cplusplus
 extern "C" {
 #endif
@@ -45,7 +51,7 @@ extern "C" {
 // 16-bit float
 // on Arm, we use __fp16
 // on x86, we use uint16_t
-#if defined(__ARM_NEON) && !defined(_MSC_VER)
+#if defined(__ARM_NEON)

 // if YCM cannot find <arm_neon.h>, make a symbolic link to it, for example:
 //
@@ -53,8 +59,262 @@ extern "C" {
 //
 #include <arm_neon.h>

+#ifdef _MSC_VER
+
+typedef uint16_t ggml_fp16_internal_t;
+
+#define ggml_vld1q_u32(w,x,y,z) { ((w) + ((uint64_t)(x) << 32)), ((y) + ((uint64_t)(z) << 32)) }
+
+#else
+
 typedef __fp16 ggml_fp16_internal_t;

+#define ggml_vld1q_u32(w,x,y,z) { (w), (x), (y), (z) }
+
+#endif // _MSC_VER
+
+#if !defined(__aarch64__)
+
+// 32-bit ARM compatibility
+
+// vaddvq_s16
+// vpaddq_s16
+// vpaddq_s32
+// vaddvq_s32
+// vaddvq_f32
+// vmaxvq_f32
+// vcvtnq_s32_f32
+// vzip1_u8
+// vzip2_u8
+
+inline static int32_t vaddvq_s16(int16x8_t v) {
+    return
+        (int32_t)vgetq_lane_s16(v, 0) + (int32_t)vgetq_lane_s16(v, 1) +
+        (int32_t)vgetq_lane_s16(v, 2) + (int32_t)vgetq_lane_s16(v, 3) +
+        (int32_t)vgetq_lane_s16(v, 4) + (int32_t)vgetq_lane_s16(v, 5) +
+        (int32_t)vgetq_lane_s16(v, 6) + (int32_t)vgetq_lane_s16(v, 7);
+}
+
+inline static int16x8_t vpaddq_s16(int16x8_t a, int16x8_t b) {
+    int16x4_t a0 = vpadd_s16(vget_low_s16(a), vget_high_s16(a));
+    int16x4_t b0 = vpadd_s16(vget_low_s16(b), vget_high_s16(b));
+    return vcombine_s16(a0, b0);
+}
+
+inline static int32x4_t vpaddq_s32(int32x4_t a, int32x4_t b) {
+    int32x2_t a0 = vpadd_s32(vget_low_s32(a), vget_high_s32(a));
+    int32x2_t b0 = vpadd_s32(vget_low_s32(b), vget_high_s32(b));
+    return vcombine_s32(a0, b0);
+}
+
+inline static int32_t vaddvq_s32(int32x4_t v) {
+    return vgetq_lane_s32(v, 0) + vgetq_lane_s32(v, 1) + vgetq_lane_s32(v, 2) + vgetq_lane_s32(v, 3);
+}
+
+inline static float vaddvq_f32(float32x4_t v) {
+    return vgetq_lane_f32(v, 0) + vgetq_lane_f32(v, 1) + vgetq_lane_f32(v, 2) + vgetq_lane_f32(v, 3);
+}
+
+inline static float vmaxvq_f32(float32x4_t v) {
+    return
+        MAX(MAX(vgetq_lane_f32(v, 0), vgetq_lane_f32(v, 1)),
+            MAX(vgetq_lane_f32(v, 2), vgetq_lane_f32(v, 3)));
+}
+
+inline static int32x4_t vcvtnq_s32_f32(float32x4_t v) {
+    int32x4_t res;
+
+    res[0] = roundf(vgetq_lane_f32(v, 0));
+    res[1] = roundf(vgetq_lane_f32(v, 1));
+    res[2] = roundf(vgetq_lane_f32(v, 2));
+    res[3] = roundf(vgetq_lane_f32(v, 3));
+
+    return res;
+}
+
+inline static uint8x8_t vzip1_u8(uint8x8_t a, uint8x8_t b) {
+    uint8x8_t res;
+
+    res[0] = a[0]; res[1] = b[0];
+    res[2] = a[1]; res[3] = b[1];
+    res[4] = a[2]; res[5] = b[2];
+    res[6] = a[3]; res[7] = b[3];
+
+    return res;
+}
+
+inline static uint8x8_t vzip2_u8(uint8x8_t a, uint8x8_t b) {
+    uint8x8_t res;
+
+    res[0] = a[4]; res[1] = b[4];
+    res[2] = a[5]; res[3] = b[5];
+    res[4] = a[6]; res[5] = b[6];
+    res[6] = a[7]; res[7] = b[7];
+
+    return res;
+}
+
+// vld1q_s16_x2
+// vld1q_u8_x2
+// vld1q_u8_x4
+// vld1q_s8_x2
+// vld1q_s8_x4
+// TODO: double-check these work correctly
+
+typedef struct ggml_int16x8x2_t {
+    int16x8_t val[2];
+} ggml_int16x8x2_t;
+
+inline static ggml_int16x8x2_t ggml_vld1q_s16_x2(const int16_t * ptr) {
+    ggml_int16x8x2_t res;
+
+    res.val[0] = vld1q_s16(ptr + 0);
+    res.val[1] = vld1q_s16(ptr + 8);
+
+    return res;
+}
+
+typedef struct ggml_uint8x16x2_t {
+    uint8x16_t val[2];
+} ggml_uint8x16x2_t;
+
+inline static ggml_uint8x16x2_t ggml_vld1q_u8_x2(const uint8_t * ptr) {
+    ggml_uint8x16x2_t res;
+
+    res.val[0] = vld1q_u8(ptr + 0);
+    res.val[1] = vld1q_u8(ptr + 16);
+
+    return res;
+}
+
+typedef struct ggml_uint8x16x4_t {
+    uint8x16_t val[4];
+} ggml_uint8x16x4_t;
+
+inline static ggml_uint8x16x4_t ggml_vld1q_u8_x4(const uint8_t * ptr) {
+    ggml_uint8x16x4_t res;
+
+    res.val[0] = vld1q_u8(ptr + 0);
+    res.val[1] = vld1q_u8(ptr + 16);
+    res.val[2] = vld1q_u8(ptr + 32);
+    res.val[3] = vld1q_u8(ptr + 48);
+
+    return res;
+}
+
+typedef struct ggml_int8x16x2_t {
+    int8x16_t val[2];
+} ggml_int8x16x2_t;
+
+inline static ggml_int8x16x2_t ggml_vld1q_s8_x2(const int8_t * ptr) {
+    ggml_int8x16x2_t res;
+
+    res.val[0] = vld1q_s8(ptr + 0);
+    res.val[1] = vld1q_s8(ptr + 16);
+
+    return res;
+}
+
+typedef struct ggml_int8x16x4_t {
+    int8x16_t val[4];
+} ggml_int8x16x4_t;
+
+inline static ggml_int8x16x4_t ggml_vld1q_s8_x4(const int8_t * ptr) {
+    ggml_int8x16x4_t res;
+
+    res.val[0] = vld1q_s8(ptr + 0);
+    res.val[1] = vld1q_s8(ptr + 16);
+    res.val[2] = vld1q_s8(ptr + 32);
+    res.val[3] = vld1q_s8(ptr + 48);
+
+    return res;
+}
+
+// NOTE: not tested
+inline static int8x16_t ggml_vqtbl1q_s8(int8x16_t a, uint8x16_t b) {
+    int8x16_t res;
+
+    res[ 0] = a[b[ 0]];
+    res[ 1] = a[b[ 1]];
+    res[ 2] = a[b[ 2]];
+    res[ 3] = a[b[ 3]];
+    res[ 4] = a[b[ 4]];
+    res[ 5] = a[b[ 5]];
+    res[ 6] = a[b[ 6]];
+    res[ 7] = a[b[ 7]];
+    res[ 8] = a[b[ 8]];
+    res[ 9] = a[b[ 9]];
+    res[10] = a[b[10]];
+    res[11] = a[b[11]];
+    res[12] = a[b[12]];
+    res[13] = a[b[13]];
+    res[14] = a[b[14]];
+    res[15] = a[b[15]];
+
+    return res;
+}
+
+// NOTE: not tested
+inline static uint8x16_t ggml_vqtbl1q_u8(uint8x16_t a, uint8x16_t b) {
+    uint8x16_t res;
+
+    res[ 0] = a[b[ 0]];
+    res[ 1] = a[b[ 1]];
+    res[ 2] = a[b[ 2]];
+    res[ 3] = a[b[ 3]];
+    res[ 4] = a[b[ 4]];
+    res[ 5] = a[b[ 5]];
+    res[ 6] = a[b[ 6]];
+    res[ 7] = a[b[ 7]];
+    res[ 8] = a[b[ 8]];
+    res[ 9] = a[b[ 9]];
+    res[10] = a[b[10]];
+    res[11] = a[b[11]];
+    res[12] = a[b[12]];
+    res[13] = a[b[13]];
+    res[14] = a[b[14]];
+    res[15] = a[b[15]];
+
+    return res;
+}
+
+#else
+
+#define ggml_int16x8x2_t  int16x8x2_t
+#define ggml_uint8x16x2_t uint8x16x2_t
+#define ggml_uint8x16x4_t uint8x16x4_t
+#define ggml_int8x16x2_t  int8x16x2_t
+#define ggml_int8x16x4_t  int8x16x4_t
+
+#define ggml_vld1q_s16_x2 vld1q_s16_x2
+#define ggml_vld1q_u8_x2  vld1q_u8_x2
+#define ggml_vld1q_u8_x4  vld1q_u8_x4
+#define ggml_vld1q_s8_x2  vld1q_s8_x2
+#define ggml_vld1q_s8_x4  vld1q_s8_x4
+#define ggml_vqtbl1q_s8   vqtbl1q_s8
+#define ggml_vqtbl1q_u8   vqtbl1q_u8
+
+#endif // !defined(__aarch64__)
+
+#if !defined(__ARM_FEATURE_DOTPROD)
+
+inline static int32x4_t ggml_vdotq_s32(int32x4_t acc, int8x16_t a, int8x16_t b) {
+    const int16x8_t p0 = vmull_s8(vget_low_s8 (a), vget_low_s8 (b));
+    const int16x8_t p1 = vmull_s8(vget_high_s8(a), vget_high_s8(b));
+
+    return vaddq_s32(acc, vaddq_s32(vpaddlq_s16(p0), vpaddlq_s16(p1)));
+}
+
+#else
+
+#define ggml_vdotq_s32(a, b, c) vdotq_s32(a, b, c)
+
+#endif // !defined(__ARM_FEATURE_DOTPROD)
+
+#endif // defined(__ARM_NEON)
+
+#if defined(__ARM_NEON) && !defined(_MSC_VER)
+
 #define GGML_COMPUTE_FP16_TO_FP32(x) ggml_compute_fp16_to_fp32(x)
 #define GGML_COMPUTE_FP32_TO_FP16(x) ggml_compute_fp32_to_fp16(x)

@@ -75,8 +335,6 @@ static inline ggml_fp16_t ggml_compute_fp32_to_fp16(float f) {

 #else

-typedef uint16_t ggml_fp16_internal_t;
-
 #ifdef __wasm_simd128__
 #include <wasm_simd128.h>
 #else
@@ -221,7 +479,7 @@ static inline ggml_fp16_t ggml_compute_fp32_to_fp16(float f) {

 #endif // __F16C__

-#endif // __ARM_NEON
+#endif // defined(__ARM_NEON) && (!defined(__MSC_VER)

 // precomputed f32 table for f16 (256 KB)
 // defined in ggml.c, initialized in ggml_init()
@@ -1427,6 +1427,7 @@ static void ggml_vk_graph_compute(struct ggml_kompute_context * ctx, struct ggml
        for (int i = node_start; i < node_end; ++i) {
            struct ggml_tensor * src0 = gf->nodes[i]->src[0];
            struct ggml_tensor * src1 = gf->nodes[i]->src[1];
+            struct ggml_tensor * src2 = gf->nodes[i]->src[2]; GGML_UNUSED(src2);
            struct ggml_tensor * dst = gf->nodes[i];
            GGML_ASSERT(dst->data != nullptr);

@@ -1559,6 +1560,12 @@ static void ggml_vk_graph_compute(struct ggml_kompute_context * ctx, struct ggml
                    {
                        float scale;
                        memcpy(&scale, dst->op_params, sizeof(float));
+
+#pragma message("TODO: add ggml_vk_soft_max() F16/F32 src1 and src2 support")
+#pragma message("ref:  https://github.com/ggerganov/llama.cpp/pull/5021")
+                        GGML_ASSERT(!src1 || src1t == GGML_TYPE_F32);
+                        GGML_ASSERT(src2 == nullptr);
+
                        ggml_vk_soft_max(seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, ne00, ne01, ne02, ne03, scale);
                    } break;
                case GGML_OP_DIAG_MASK_INF:
@@ -46,8 +46,10 @@ enum ggml_metal_kernel_type {
    GGML_METAL_KERNEL_TYPE_GELU_QUICK_4,
    GGML_METAL_KERNEL_TYPE_SILU,
    GGML_METAL_KERNEL_TYPE_SILU_4,
-    GGML_METAL_KERNEL_TYPE_SOFT_MAX,
-    GGML_METAL_KERNEL_TYPE_SOFT_MAX_4,
+    GGML_METAL_KERNEL_TYPE_SOFT_MAX_F16,
+    GGML_METAL_KERNEL_TYPE_SOFT_MAX_F16_4,
+    GGML_METAL_KERNEL_TYPE_SOFT_MAX_F32,
+    GGML_METAL_KERNEL_TYPE_SOFT_MAX_F32_4,
    GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF,
    GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF_8,
    GGML_METAL_KERNEL_TYPE_GET_ROWS_F32,
@@ -177,6 +179,14 @@ enum ggml_metal_kernel_type {
    GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_ASC,
    GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_DESC,
    GGML_METAL_KERNEL_TYPE_LEAKY_RELU_F32,
+    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H64,
+    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H80,
+    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H96,
+    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H112,
+    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H128,
+    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H256,
+    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H128,
+    GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H256,
    GGML_METAL_KERNEL_TYPE_CPY_F32_F16,
    GGML_METAL_KERNEL_TYPE_CPY_F32_F32,
    GGML_METAL_KERNEL_TYPE_CPY_F32_Q8_0,
@@ -443,7 +453,7 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) {
        }

        /*
-            GGML_METAL_LOG_INFO("%s: loaded %-32s %16p | th_max = %4d | th_width = %4d\n", __func__, "kernel_"#name, (void *) kernel->pipeline, \
+            GGML_METAL_LOG_INFO("%s: loaded %-40s %16p | th_max = %4d | th_width = %4d\n", __func__, "kernel_"#name, (void *) kernel->pipeline, \
                    (int) kernel->pipeline.maxTotalThreadsPerThreadgroup, \
                    (int) kernel->pipeline.threadExecutionWidth); \
        */
@@ -459,172 +469,182 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) {
                return NULL; \
            } \
        } else { \
-            GGML_METAL_LOG_WARN("%s: skipping %-32s (not supported)\n", __func__, "kernel_"#name); \
+            GGML_METAL_LOG_WARN("%s: skipping %-40s (not supported)\n", __func__, "kernel_"#name); \
        }

        // simd_sum and simd_max requires MTLGPUFamilyApple7

-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ADD,                       add,                    true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ADD_ROW,                   add_row,                true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL,                       mul,                    true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_ROW,                   mul_row,                true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIV,                       div,                    true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIV_ROW,                   div_row,                true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SCALE,                     scale,                  true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SCALE_4,                   scale_4,                true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CLAMP,                     clamp,                  true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_TANH,                      tanh,                   true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_RELU,                      relu,                   true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU,                      gelu,                   true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU_4,                    gelu_4,                 true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU_QUICK,                gelu_quick,             true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU_QUICK_4,              gelu_quick_4,           true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SILU,                      silu,                   true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SILU_4,                    silu_4,                 true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SOFT_MAX,                  soft_max,               ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SOFT_MAX_4,                soft_max_4,             ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF,             diag_mask_inf,          true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF_8,           diag_mask_inf_8,        true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_F32,              get_rows_f32,           true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_F16,              get_rows_f16,           true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_0,             get_rows_q4_0,          true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_1,             get_rows_q4_1,          true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_0,             get_rows_q5_0,          true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_1,             get_rows_q5_1,          true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q8_0,             get_rows_q8_0,          true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q2_K,             get_rows_q2_K,          true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q3_K,             get_rows_q3_K,          true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_K,             get_rows_q4_K,          true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_K,             get_rows_q5_K,          true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q6_K,             get_rows_q6_K,          true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XXS,          get_rows_iq2_xxs,       true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XS,           get_rows_iq2_xs,        true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_XXS,          get_rows_iq3_xxs,       true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_S,            get_rows_iq3_s,         true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_S,            get_rows_iq2_s,         true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_S,            get_rows_iq1_s,         true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_M,            get_rows_iq1_m,         true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_NL,           get_rows_iq4_nl,        true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_XS,           get_rows_iq4_xs,        true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_I32,              get_rows_i32,           true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_RMS_NORM,                  rms_norm,               ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GROUP_NORM,                group_norm,             ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_NORM,                      norm,                   true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F32_F32,            mul_mv_f32_f32,         ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F16,            mul_mv_f16_f16,         ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32,            mul_mv_f16_f32,         ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_1ROW,       mul_mv_f16_f32_1row,    ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_L4,         mul_mv_f16_f32_l4,      ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_0_F32,           mul_mv_q4_0_f32,        ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_1_F32,           mul_mv_q4_1_f32,        ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_0_F32,           mul_mv_q5_0_f32,        ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_1_F32,           mul_mv_q5_1_f32,        ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q8_0_F32,           mul_mv_q8_0_f32,        ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q2_K_F32,           mul_mv_q2_K_f32,        ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q3_K_F32,           mul_mv_q3_K_f32,        ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_K_F32,           mul_mv_q4_K_f32,        ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_K_F32,           mul_mv_q5_K_f32,        ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q6_K_F32,           mul_mv_q6_K_f32,        ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XXS_F32,        mul_mv_iq2_xxs_f32,     ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XS_F32,         mul_mv_iq2_xs_f32,      ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_XXS_F32,        mul_mv_iq3_xxs_f32,     ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_S_F32,          mul_mv_iq3_s_f32,       ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_S_F32,          mul_mv_iq2_s_f32,       ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_S_F32,          mul_mv_iq1_s_f32,       ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_M_F32,          mul_mv_iq1_m_f32,       ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_NL_F32,         mul_mv_iq4_nl_f32,      ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_XS_F32,         mul_mv_iq4_xs_f32,      ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F32_F32,         mul_mv_id_f32_f32,      ctx->support_simdgroup_reduction);
-      //GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F16,         mul_mv_id_f16_f16,      ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32,         mul_mv_id_f16_f32,      ctx->support_simdgroup_reduction);
-      //GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32_1ROW,    mul_mv_id_f16_f32_1row, ctx->support_simdgroup_reduction);
-      //GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32_L4,      mul_mv_id_f16_f32_l4,   ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_0_F32,        mul_mv_id_q4_0_f32,     ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_1_F32,        mul_mv_id_q4_1_f32,     ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_0_F32,        mul_mv_id_q5_0_f32,     ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_1_F32,        mul_mv_id_q5_1_f32,     ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q8_0_F32,        mul_mv_id_q8_0_f32,     ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q2_K_F32,        mul_mv_id_q2_K_f32,     ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q3_K_F32,        mul_mv_id_q3_K_f32,     ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_K_F32,        mul_mv_id_q4_K_f32,     ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_K_F32,        mul_mv_id_q5_K_f32,     ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q6_K_F32,        mul_mv_id_q6_K_f32,     ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XXS_F32,     mul_mv_id_iq2_xxs_f32,  ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XS_F32,      mul_mv_id_iq2_xs_f32,   ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_XXS_F32,     mul_mv_id_iq3_xxs_f32,  ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_S_F32,       mul_mv_id_iq3_s_f32,    ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_S_F32,       mul_mv_id_iq2_s_f32,    ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_S_F32,       mul_mv_id_iq1_s_f32,    ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_M_F32,       mul_mv_id_iq1_m_f32,    ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_NL_F32,      mul_mv_id_iq4_nl_f32,   ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_XS_F32,      mul_mv_id_iq4_xs_f32,   ctx->support_simdgroup_reduction);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_F32_F32,            mul_mm_f32_f32,         ctx->support_simdgroup_mm);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_F16_F32,            mul_mm_f16_f32,         ctx->support_simdgroup_mm);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_0_F32,           mul_mm_q4_0_f32,        ctx->support_simdgroup_mm);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_1_F32,           mul_mm_q4_1_f32,        ctx->support_simdgroup_mm);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_0_F32,           mul_mm_q5_0_f32,        ctx->support_simdgroup_mm);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_1_F32,           mul_mm_q5_1_f32,        ctx->support_simdgroup_mm);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q8_0_F32,           mul_mm_q8_0_f32,        ctx->support_simdgroup_mm);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q2_K_F32,           mul_mm_q2_K_f32,        ctx->support_simdgroup_mm);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q3_K_F32,           mul_mm_q3_K_f32,        ctx->support_simdgroup_mm);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_K_F32,           mul_mm_q4_K_f32,        ctx->support_simdgroup_mm);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_K_F32,           mul_mm_q5_K_f32,        ctx->support_simdgroup_mm);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q6_K_F32,           mul_mm_q6_K_f32,        ctx->support_simdgroup_mm);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XXS_F32,        mul_mm_iq2_xxs_f32,     ctx->support_simdgroup_mm);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XS_F32,         mul_mm_iq2_xs_f32,      ctx->support_simdgroup_mm);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_XXS_F32,        mul_mm_iq3_xxs_f32,     ctx->support_simdgroup_mm);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_S_F32,          mul_mm_iq3_s_f32,       ctx->support_simdgroup_mm);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_S_F32,          mul_mm_iq2_s_f32,       ctx->support_simdgroup_mm);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_S_F32,          mul_mm_iq1_s_f32,       ctx->support_simdgroup_mm);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_M_F32,          mul_mm_iq1_m_f32,       ctx->support_simdgroup_mm);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_NL_F32,         mul_mm_iq4_nl_f32,      ctx->support_simdgroup_mm);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_XS_F32,         mul_mm_iq4_xs_f32,      ctx->support_simdgroup_mm);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F32_F32,         mul_mm_id_f32_f32,      ctx->support_simdgroup_mm);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F16_F32,         mul_mm_id_f16_f32,      ctx->support_simdgroup_mm);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_0_F32,        mul_mm_id_q4_0_f32,     ctx->support_simdgroup_mm);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_1_F32,        mul_mm_id_q4_1_f32,     ctx->support_simdgroup_mm);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_0_F32,        mul_mm_id_q5_0_f32,     ctx->support_simdgroup_mm);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_1_F32,        mul_mm_id_q5_1_f32,     ctx->support_simdgroup_mm);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q8_0_F32,        mul_mm_id_q8_0_f32,     ctx->support_simdgroup_mm);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q2_K_F32,        mul_mm_id_q2_K_f32,     ctx->support_simdgroup_mm);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q3_K_F32,        mul_mm_id_q3_K_f32,     ctx->support_simdgroup_mm);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_K_F32,        mul_mm_id_q4_K_f32,     ctx->support_simdgroup_mm);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_K_F32,        mul_mm_id_q5_K_f32,     ctx->support_simdgroup_mm);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q6_K_F32,        mul_mm_id_q6_K_f32,     ctx->support_simdgroup_mm);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XXS_F32,     mul_mm_id_iq2_xxs_f32,  ctx->support_simdgroup_mm);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XS_F32,      mul_mm_id_iq2_xs_f32,   ctx->support_simdgroup_mm);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_XXS_F32,     mul_mm_id_iq3_xxs_f32,  ctx->support_simdgroup_mm);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_S_F32,       mul_mm_id_iq3_s_f32,    ctx->support_simdgroup_mm);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_S_F32,       mul_mm_id_iq2_s_f32,    ctx->support_simdgroup_mm);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_S_F32,       mul_mm_id_iq1_s_f32,    ctx->support_simdgroup_mm);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_M_F32,       mul_mm_id_iq1_m_f32,    ctx->support_simdgroup_mm);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_NL_F32,      mul_mm_id_iq4_nl_f32,   ctx->support_simdgroup_mm);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_XS_F32,      mul_mm_id_iq4_xs_f32,   ctx->support_simdgroup_mm);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_F32,                  rope_f32,               true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_F16,                  rope_f16,               true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ALIBI_F32,                 alibi_f32,              true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_IM2COL_F16,                im2col_f16,             true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_IM2COL_F32,                im2col_f32,             true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_UPSCALE_F32,               upscale_f32,            true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_PAD_F32,                   pad_f32,                true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_TIMESTEP_EMBEDDING_F32,    timestep_embedding_f32, true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARANGE_F32,                arange_f32,             true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_ASC,       argsort_f32_i32_asc,    true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_DESC,      argsort_f32_i32_desc,   true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_LEAKY_RELU_F32,            leaky_relu_f32,         true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_F16,               cpy_f32_f16,            true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_F32,               cpy_f32_f32,            true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q8_0,              cpy_f32_q8_0,           true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_0,              cpy_f32_q4_0,           true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_1,              cpy_f32_q4_1,           true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_0,              cpy_f32_q5_0,           true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_1,              cpy_f32_q5_1,           true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_IQ4_NL,            cpy_f32_iq4_nl,         true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F16_F16,               cpy_f16_f16,            true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F16_F32,               cpy_f16_f32,            true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CONCAT,                    concat,                 true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SQR,                       sqr,                    true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SUM_ROWS,                  sum_rows,               true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ADD,                           add,                            true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ADD_ROW,                       add_row,                        true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL,                           mul,                            true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_ROW,                       mul_row,                        true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIV,                           div,                            true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIV_ROW,                       div_row,                        true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SCALE,                         scale,                          true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SCALE_4,                       scale_4,                        true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CLAMP,                         clamp,                          true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_TANH,                          tanh,                           true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_RELU,                          relu,                           true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU,                          gelu,                           true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU_4,                        gelu_4,                         true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU_QUICK,                    gelu_quick,                     true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU_QUICK_4,                  gelu_quick_4,                   true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SILU,                          silu,                           true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SILU_4,                        silu_4,                         true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SOFT_MAX_F16,                  soft_max_f16,                   ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SOFT_MAX_F16_4,                soft_max_f16_4,                 ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SOFT_MAX_F32,                  soft_max_f32,                   ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SOFT_MAX_F32_4,                soft_max_f32_4,                 ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF,                 diag_mask_inf,                  true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF_8,               diag_mask_inf_8,                true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_F32,                  get_rows_f32,                   true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_F16,                  get_rows_f16,                   true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_0,                 get_rows_q4_0,                  true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_1,                 get_rows_q4_1,                  true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_0,                 get_rows_q5_0,                  true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_1,                 get_rows_q5_1,                  true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q8_0,                 get_rows_q8_0,                  true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q2_K,                 get_rows_q2_K,                  true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q3_K,                 get_rows_q3_K,                  true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_K,                 get_rows_q4_K,                  true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_K,                 get_rows_q5_K,                  true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q6_K,                 get_rows_q6_K,                  true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XXS,              get_rows_iq2_xxs,               true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XS,               get_rows_iq2_xs,                true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_XXS,              get_rows_iq3_xxs,               true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_S,                get_rows_iq3_s,                 true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_S,                get_rows_iq2_s,                 true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_S,                get_rows_iq1_s,                 true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_M,                get_rows_iq1_m,                 true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_NL,               get_rows_iq4_nl,                true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_XS,               get_rows_iq4_xs,                true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_I32,                  get_rows_i32,                   true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_RMS_NORM,                      rms_norm,                       ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GROUP_NORM,                    group_norm,                     ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_NORM,                          norm,                           true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F32_F32,                mul_mv_f32_f32,                 ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F16,                mul_mv_f16_f16,                 ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32,                mul_mv_f16_f32,                 ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_1ROW,           mul_mv_f16_f32_1row,            ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_L4,             mul_mv_f16_f32_l4,              ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_0_F32,               mul_mv_q4_0_f32,                ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_1_F32,               mul_mv_q4_1_f32,                ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_0_F32,               mul_mv_q5_0_f32,                ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_1_F32,               mul_mv_q5_1_f32,                ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q8_0_F32,               mul_mv_q8_0_f32,                ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q2_K_F32,               mul_mv_q2_K_f32,                ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q3_K_F32,               mul_mv_q3_K_f32,                ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_K_F32,               mul_mv_q4_K_f32,                ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_K_F32,               mul_mv_q5_K_f32,                ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q6_K_F32,               mul_mv_q6_K_f32,                ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XXS_F32,            mul_mv_iq2_xxs_f32,             ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XS_F32,             mul_mv_iq2_xs_f32,              ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_XXS_F32,            mul_mv_iq3_xxs_f32,             ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_S_F32,              mul_mv_iq3_s_f32,               ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_S_F32,              mul_mv_iq2_s_f32,               ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_S_F32,              mul_mv_iq1_s_f32,               ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_M_F32,              mul_mv_iq1_m_f32,               ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_NL_F32,             mul_mv_iq4_nl_f32,              ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_XS_F32,             mul_mv_iq4_xs_f32,              ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F32_F32,             mul_mv_id_f32_f32,              ctx->support_simdgroup_reduction);
+      //GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F16,             mul_mv_id_f16_f16,              ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32,             mul_mv_id_f16_f32,              ctx->support_simdgroup_reduction);
+      //GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32_1ROW,        mul_mv_id_f16_f32_1row,         ctx->support_simdgroup_reduction);
+      //GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32_L4,          mul_mv_id_f16_f32_l4,           ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_0_F32,            mul_mv_id_q4_0_f32,             ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_1_F32,            mul_mv_id_q4_1_f32,             ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_0_F32,            mul_mv_id_q5_0_f32,             ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_1_F32,            mul_mv_id_q5_1_f32,             ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q8_0_F32,            mul_mv_id_q8_0_f32,             ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q2_K_F32,            mul_mv_id_q2_K_f32,             ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q3_K_F32,            mul_mv_id_q3_K_f32,             ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_K_F32,            mul_mv_id_q4_K_f32,             ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_K_F32,            mul_mv_id_q5_K_f32,             ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q6_K_F32,            mul_mv_id_q6_K_f32,             ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XXS_F32,         mul_mv_id_iq2_xxs_f32,          ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XS_F32,          mul_mv_id_iq2_xs_f32,           ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_XXS_F32,         mul_mv_id_iq3_xxs_f32,          ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_S_F32,           mul_mv_id_iq3_s_f32,            ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_S_F32,           mul_mv_id_iq2_s_f32,            ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_S_F32,           mul_mv_id_iq1_s_f32,            ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_M_F32,           mul_mv_id_iq1_m_f32,            ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_NL_F32,          mul_mv_id_iq4_nl_f32,           ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_XS_F32,          mul_mv_id_iq4_xs_f32,           ctx->support_simdgroup_reduction);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_F32_F32,                mul_mm_f32_f32,                 ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_F16_F32,                mul_mm_f16_f32,                 ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_0_F32,               mul_mm_q4_0_f32,                ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_1_F32,               mul_mm_q4_1_f32,                ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_0_F32,               mul_mm_q5_0_f32,                ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_1_F32,               mul_mm_q5_1_f32,                ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q8_0_F32,               mul_mm_q8_0_f32,                ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q2_K_F32,               mul_mm_q2_K_f32,                ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q3_K_F32,               mul_mm_q3_K_f32,                ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_K_F32,               mul_mm_q4_K_f32,                ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_K_F32,               mul_mm_q5_K_f32,                ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q6_K_F32,               mul_mm_q6_K_f32,                ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XXS_F32,            mul_mm_iq2_xxs_f32,             ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XS_F32,             mul_mm_iq2_xs_f32,              ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_XXS_F32,            mul_mm_iq3_xxs_f32,             ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_S_F32,              mul_mm_iq3_s_f32,               ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_S_F32,              mul_mm_iq2_s_f32,               ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_S_F32,              mul_mm_iq1_s_f32,               ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_M_F32,              mul_mm_iq1_m_f32,               ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_NL_F32,             mul_mm_iq4_nl_f32,              ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_XS_F32,             mul_mm_iq4_xs_f32,              ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F32_F32,             mul_mm_id_f32_f32,              ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F16_F32,             mul_mm_id_f16_f32,              ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_0_F32,            mul_mm_id_q4_0_f32,             ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_1_F32,            mul_mm_id_q4_1_f32,             ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_0_F32,            mul_mm_id_q5_0_f32,             ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_1_F32,            mul_mm_id_q5_1_f32,             ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q8_0_F32,            mul_mm_id_q8_0_f32,             ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q2_K_F32,            mul_mm_id_q2_K_f32,             ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q3_K_F32,            mul_mm_id_q3_K_f32,             ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_K_F32,            mul_mm_id_q4_K_f32,             ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_K_F32,            mul_mm_id_q5_K_f32,             ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q6_K_F32,            mul_mm_id_q6_K_f32,             ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XXS_F32,         mul_mm_id_iq2_xxs_f32,          ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XS_F32,          mul_mm_id_iq2_xs_f32,           ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_XXS_F32,         mul_mm_id_iq3_xxs_f32,          ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_S_F32,           mul_mm_id_iq3_s_f32,            ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_S_F32,           mul_mm_id_iq2_s_f32,            ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_S_F32,           mul_mm_id_iq1_s_f32,            ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_M_F32,           mul_mm_id_iq1_m_f32,            ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_NL_F32,          mul_mm_id_iq4_nl_f32,           ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_XS_F32,          mul_mm_id_iq4_xs_f32,           ctx->support_simdgroup_mm);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_F32,                      rope_f32,                       true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_F16,                      rope_f16,                       true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ALIBI_F32,                     alibi_f32,                      true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_IM2COL_F16,                    im2col_f16,                     true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_IM2COL_F32,                    im2col_f32,                     true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_UPSCALE_F32,                   upscale_f32,                    true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_PAD_F32,                       pad_f32,                        true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_TIMESTEP_EMBEDDING_F32,        timestep_embedding_f32,         true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARANGE_F32,                    arange_f32,                     true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_ASC,           argsort_f32_i32_asc,            true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_DESC,          argsort_f32_i32_desc,           true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_LEAKY_RELU_F32,                leaky_relu_f32,                 true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H64,        flash_attn_ext_f16_h64,         true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H80,        flash_attn_ext_f16_h80,         true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H96,        flash_attn_ext_f16_h96,         true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H112,       flash_attn_ext_f16_h112,        true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H128,       flash_attn_ext_f16_h128,        true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H256,       flash_attn_ext_f16_h256,        true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H128,   flash_attn_ext_vec_f16_h128,    true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H256,   flash_attn_ext_vec_f16_h256,    true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_F16,                   cpy_f32_f16,                    true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_F32,                   cpy_f32_f32,                    true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q8_0,                  cpy_f32_q8_0,                   true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_0,                  cpy_f32_q4_0,                   true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_1,                  cpy_f32_q4_1,                   true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_0,                  cpy_f32_q5_0,                   true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_1,                  cpy_f32_q5_1,                   true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_IQ4_NL,                cpy_f32_iq4_nl,                 true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F16_F16,                   cpy_f16_f16,                    true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F16_F32,                   cpy_f16_f32,                    true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CONCAT,                        concat,                         true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SQR,                           sqr,                            true);
+        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SUM_ROWS,                      sum_rows,                       true);
    }

    [metal_library release];
@@ -743,6 +763,7 @@ static bool ggml_metal_supports_op(const struct ggml_metal_context * ctx, const
        case GGML_OP_TIMESTEP_EMBEDDING:
        case GGML_OP_ARGSORT:
        case GGML_OP_LEAKY_RELU:
+        case GGML_OP_FLASH_ATTN_EXT:
            return true;
        case GGML_OP_MUL_MAT:
        case GGML_OP_MUL_MAT_ID:
@@ -1326,20 +1347,33 @@ static enum ggml_status ggml_metal_graph_compute(
                    } break;
                case GGML_OP_SOFT_MAX:
                    {
+                        GGML_ASSERT(!src1 || src1->type == GGML_TYPE_F16 || src1->type == GGML_TYPE_F32);
+                        GGML_ASSERT(!src2 || src2->type == GGML_TYPE_F16 || src2->type == GGML_TYPE_F32);
+
                        int nth = 32; // SIMD width

                        id<MTLComputePipelineState> pipeline = nil;

+                        const bool use_f16 = (src1 && src1->type == GGML_TYPE_F16) || (src2 && src2->type == GGML_TYPE_F16);
+
                        if (ne00%4 == 0) {
                            while (nth < ne00/4 && nth < 256) {
                                nth *= 2;
                            }
-                            pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SOFT_MAX_4].pipeline;
+                            if (use_f16) {
+                                pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SOFT_MAX_F16_4].pipeline;
+                            } else {
+                                pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SOFT_MAX_F32_4].pipeline;
+                            }
                        } else {
                            while (nth < ne00 && nth < 1024) {
                                nth *= 2;
                            }
-                            pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SOFT_MAX].pipeline;
+                            if (use_f16) {
+                                pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SOFT_MAX_F16].pipeline;
+                            } else {
+                                pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SOFT_MAX_F32].pipeline;
+                            }
                        }

                        float scale;
@@ -1732,15 +1766,10 @@ static enum ggml_status ggml_metal_graph_compute(
                    } break;
                case GGML_OP_MUL_MAT_ID:
                    {
-                        //GGML_ASSERT(ne00 == ne10);
-                        //GGML_ASSERT(ne03 == ne13);
                        const int n_as = src0->ne[2];

-                        // max size of the src1ids array in the kernel shared buffer
-                        GGML_ASSERT(ne11 <= 4096);
-
                        // src2 = ids
-                        const int64_t  ne20 = src2->ne[0]; GGML_UNUSED(ne20);
+                        const int64_t  ne20 = src2->ne[0];
                        const int64_t  ne21 = src2->ne[1];
                        const int64_t  ne22 = src2->ne[2]; GGML_UNUSED(ne22);
                        const int64_t  ne23 = src2->ne[3]; GGML_UNUSED(ne23);
@@ -1761,15 +1790,13 @@ static enum ggml_status ggml_metal_graph_compute(

                        // find the break-even point where the matrix-matrix kernel becomes more efficient compared
                        // to the matrix-vector kernel
-                        int ne11_mm_min = n_as;
+                        // ne20 = n_used_experts
+                        // ne21 = n_rows
+                        const int dst_rows = ne20*ne21;
+                        const int dst_rows_min = n_as;

-                        const int idx = ((int32_t *) dst->op_params)[0];
-
-                        // batch size
-                        GGML_ASSERT(ne21 == ne11); // ?
-                        GGML_ASSERT(ne12 == 1 && ne13 == 1); // no broadcasting
-                        const uint r2 = 1;
-                        const uint r3 = 1;
+                        // max size of the rowids array in the kernel shared buffer
+                        GGML_ASSERT(dst_rows <= 2048);

                        // for now the matrix-matrix multiplication kernel only works on A14+/M1+ SoCs
                        // AMD GPU and older A-chips will reuse matrix-vector multiplication kernel
@@ -1779,7 +1806,7 @@ static enum ggml_status ggml_metal_graph_compute(
                        // !!!
                        if ([ctx->device supportsFamily:MTLGPUFamilyApple7] &&
                            ne00 % 32 == 0 && ne00 >= 64 &&
-                            ne11 > ne11_mm_min) {
+                            dst_rows > dst_rows_min) {

                            // some Metal matrix data types require aligned pointers
                            // ref: https://developer.apple.com/metal/Metal-Shading-Language-Specification.pdf (Table 2.5)
@@ -1821,26 +1848,26 @@ static enum ggml_status ggml_metal_graph_compute(
                            [encoder setBuffer:id_src1 offset:offs_src1    atIndex:1];
                            [encoder setBuffer:id_dst  offset:offs_dst     atIndex:2];
                            [encoder setBuffer:id_src2 offset:offs_src2    atIndex:3];
-                            [encoder setBytes:&nb21    length:sizeof(nb21) atIndex:4];
-                            [encoder setBytes:&ne00    length:sizeof(ne00) atIndex:5];
-                            [encoder setBytes:&ne02    length:sizeof(ne02) atIndex:6];
-                            [encoder setBytes:&nb01    length:sizeof(nb01) atIndex:7];
-                            [encoder setBytes:&nb02    length:sizeof(nb02) atIndex:8];
-                            [encoder setBytes:&ne12    length:sizeof(ne12) atIndex:9];
-                            [encoder setBytes:&ne13    length:sizeof(ne13) atIndex:10];
-                            [encoder setBytes:&nb10    length:sizeof(nb10) atIndex:11];
-                            [encoder setBytes:&nb11    length:sizeof(nb11) atIndex:12];
-                            [encoder setBytes:&nb12    length:sizeof(nb12) atIndex:13];
-                            [encoder setBytes:&ne0     length:sizeof(ne0)  atIndex:14];
-                            [encoder setBytes:&ne1     length:sizeof(ne1)  atIndex:15];
-                            [encoder setBytes:&nb1     length:sizeof(nb1)  atIndex:16];
-                            [encoder setBytes:&r2      length:sizeof(r2)   atIndex:17];
-                            [encoder setBytes:&r3      length:sizeof(r3)   atIndex:18];
-                            [encoder setBytes:&idx     length:sizeof(idx)  atIndex:19];
+                            [encoder setBytes:&ne20    length:sizeof(ne20) atIndex:4];
+                            [encoder setBytes:&ne21    length:sizeof(ne21) atIndex:5];
+                            [encoder setBytes:&nb21    length:sizeof(nb21) atIndex:6];
+                            [encoder setBytes:&ne00    length:sizeof(ne00) atIndex:7];
+                            [encoder setBytes:&ne02    length:sizeof(ne02) atIndex:8];
+                            [encoder setBytes:&nb01    length:sizeof(nb01) atIndex:9];
+                            [encoder setBytes:&nb02    length:sizeof(nb02) atIndex:10];
+                            [encoder setBytes:&ne11    length:sizeof(ne11) atIndex:11];
+                            [encoder setBytes:&ne12    length:sizeof(ne12) atIndex:12];
+                            [encoder setBytes:&ne13    length:sizeof(ne13) atIndex:13];
+                            [encoder setBytes:&nb10    length:sizeof(nb10) atIndex:14];
+                            [encoder setBytes:&nb11    length:sizeof(nb11) atIndex:15];
+                            [encoder setBytes:&nb12    length:sizeof(nb12) atIndex:16];
+                            [encoder setBytes:&ne0     length:sizeof(ne0)  atIndex:17];
+                            [encoder setBytes:&ne1     length:sizeof(ne1)  atIndex:18];
+                            [encoder setBytes:&nb1     length:sizeof(nb1)  atIndex:19];

-                            [encoder setThreadgroupMemoryLength:GGML_PAD(8192 + 2*ne11, 16) atIndex:0];
+                            [encoder setThreadgroupMemoryLength:GGML_PAD(8192 + dst_rows*4/*sizeof(ushort2)*/, 16) atIndex:0];

-                            [encoder dispatchThreadgroups:MTLSizeMake((ne11 + 31)/32, (ne01 + 63)/64, n_as*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(128, 1, 1)];
+                            [encoder dispatchThreadgroups:MTLSizeMake((ne21 + 31)/32, (ne01 + 63)/64, n_as) threadsPerThreadgroup:MTLSizeMake(128, 1, 1)];
                        } else {
                            int nth0 = 32;
                            int nth1 = 1;
@@ -1993,72 +2020,72 @@ static enum ggml_status ggml_metal_graph_compute(
                                GGML_ASSERT(ne00 >= nth0*nth1);
                            }

-                            const int64_t _ne1 = 1; // kernels needs a reference in constant memory
-
                            [encoder setComputePipelineState:pipeline];
                            [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
                            [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1];
                            [encoder setBuffer:id_dst  offset:offs_dst  atIndex:2];
                            [encoder setBuffer:id_src2 offset:offs_src2 atIndex:3];
-                            [encoder setBytes:&nb21 length:sizeof(nb21) atIndex:4];
-                            [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:5];
-                            [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:6];
-                            [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:7];
-                            [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:8];
-                            [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:9];
-                            [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:10];
-                            [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:11];
-                            [encoder setBytes:&_ne1 length:sizeof(_ne1) atIndex:12];
-                            [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:13];
-                            [encoder setBytes:&ne13 length:sizeof(ne13) atIndex:14];
-                            [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:15];
-                            [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:16];
-                            [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:17];
-                            [encoder setBytes:&ne0  length:sizeof(ne0)  atIndex:18];
-                            [encoder setBytes:&_ne1 length:sizeof(_ne1) atIndex:19];
-                            [encoder setBytes:&nb1  length:sizeof(nb1)  atIndex:20];
-                            [encoder setBytes:&r2   length:sizeof(r2)   atIndex:21];
-                            [encoder setBytes:&r3   length:sizeof(r3)   atIndex:22];
-                            [encoder setBytes:&idx  length:sizeof(idx)  atIndex:23];
+                            [encoder setBytes:&ne20 length:sizeof(ne20) atIndex:4];
+                            [encoder setBytes:&ne21 length:sizeof(ne21) atIndex:5];
+                            [encoder setBytes:&nb21 length:sizeof(nb21) atIndex:6];
+                            [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:7];
+                            [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:8];
+                            [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:9];
+                            [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:10];
+                            [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:11];
+                            [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:12];
+                            [encoder setBytes:&ne10 length:sizeof(ne10) atIndex:13];
+                            [encoder setBytes:&ne11 length:sizeof(ne11) atIndex:14];
+                            [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:15];
+                            [encoder setBytes:&ne13 length:sizeof(ne13) atIndex:16];
+                            [encoder setBytes:&nb10 length:sizeof(nb10) atIndex:17];
+                            [encoder setBytes:&nb11 length:sizeof(nb11) atIndex:18];
+                            [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:19];
+                            [encoder setBytes:&ne0  length:sizeof(ne0)  atIndex:20];
+                            [encoder setBytes:&ne1  length:sizeof(ne1)  atIndex:21];
+                            [encoder setBytes:&nb1  length:sizeof(nb1)  atIndex:22];
+
+                            const int64_t _ne1 = 1;
+                            const int tgz = dst_rows;

                            if (src0t == GGML_TYPE_Q4_0  || src0t == GGML_TYPE_Q4_1  || src0t == GGML_TYPE_Q5_0 ||
                                src0t == GGML_TYPE_Q5_1  || src0t == GGML_TYPE_Q8_0  || src0t == GGML_TYPE_Q2_K ||
                                src0t == GGML_TYPE_IQ1_S || src0t == GGML_TYPE_IQ1_M || src0t == GGML_TYPE_IQ2_S) {
-                                [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, _ne1, ne21*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
+                                [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
                            }
                            else if (src0t == GGML_TYPE_IQ2_XXS || src0t == GGML_TYPE_IQ2_XS) {
                                const int mem_size = src0t == GGML_TYPE_IQ2_XXS ? 256*8+128 : 512*8+128;
                                [encoder setThreadgroupMemoryLength:mem_size atIndex:0];
-                                [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, _ne1, ne21*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
+                                [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
                            }
                            else if (src0t == GGML_TYPE_IQ3_XXS || src0t == GGML_TYPE_IQ3_S) {
                                const int mem_size = src0t == GGML_TYPE_IQ3_XXS ? 256*4+128 : 512*4;
                                [encoder setThreadgroupMemoryLength:mem_size atIndex:0];
-                                [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, _ne1, ne21*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
+                                [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
                            }
                            else if (src0t == GGML_TYPE_IQ4_NL || src0t == GGML_TYPE_IQ4_XS) {
                                const int mem_size = 32*sizeof(float);
                                [encoder setThreadgroupMemoryLength:mem_size atIndex:0];
-                                [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, _ne1, ne21*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
+                                [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
                            }
                            else if (src0t == GGML_TYPE_Q4_K) {
-                                [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, _ne1, ne21*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
+                                [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
                            }
                            else if (src0t == GGML_TYPE_Q3_K) {
 #ifdef GGML_QKK_64
-                                [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 1)/2, _ne1, ne21*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
+                                [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 1)/2, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
 #else
-                                [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, _ne1, ne21*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
+                                [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
 #endif
                            }
                            else if (src0t == GGML_TYPE_Q5_K) {
-                                [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, _ne1, ne21*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
+                                [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
                            }
                            else if (src0t == GGML_TYPE_Q6_K) {
-                                [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 1)/2, _ne1, ne21*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
+                                [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 1)/2, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
                            } else {
-                                const int64_t ny = (_ne1 + nrows - 1)/nrows;
-                                [encoder dispatchThreadgroups:MTLSizeMake(ne01, ny, ne21*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
+                                const int64_t ny = (_ne1 + nrows - 1)/nrows; // = _ne1
+                                [encoder dispatchThreadgroups:MTLSizeMake(ne01, ny, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
                            }
                        }
                    } break;
@@ -2510,6 +2537,161 @@ static enum ggml_status ggml_metal_graph_compute(

                        [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
                    } break;
+                case GGML_OP_FLASH_ATTN_EXT:
+                    {
+                        GGML_ASSERT(ne00 % 4 == 0);
+                        GGML_ASSERT(src0->type == GGML_TYPE_F32);
+
+                        struct ggml_tensor * src3 = gf->nodes[i]->src[3];
+
+                        GGML_ASSERT(ggml_are_same_shape(src1, src2));
+                        GGML_ASSERT(src3);
+
+                        size_t offs_src3 = 0;
+
+                        id<MTLBuffer> id_src3 = src3 ? ggml_metal_get_buffer(src3, &offs_src3) : nil;
+
+                        GGML_ASSERT(!src3 || src3->type == GGML_TYPE_F16);
+                        GGML_ASSERT(!src3 || src3->ne[1] >= GGML_PAD(src0->ne[1], 8) &&
+                                "the Flash-Attention Metal kernel requires the mask to be padded to 8 and at least n_queries big");
+
+                        const int64_t  ne30 = src3 ? src3->ne[0] : 0; GGML_UNUSED(ne30);
+                        const int64_t  ne31 = src3 ? src3->ne[1] : 0;
+                        const int64_t  ne32 = src3 ? src3->ne[2] : 0; GGML_UNUSED(ne32);
+                        const int64_t  ne33 = src3 ? src3->ne[3] : 0; GGML_UNUSED(ne33);
+
+                        const uint64_t nb30 = src3 ? src3->nb[0] : 0; GGML_UNUSED(nb30);
+                        const uint64_t nb31 = src3 ? src3->nb[1] : 0;
+                        const uint64_t nb32 = src3 ? src3->nb[2] : 0; GGML_UNUSED(nb32);
+                        const uint64_t nb33 = src3 ? src3->nb[3] : 0; GGML_UNUSED(nb33);
+
+                        const enum ggml_type src2t = src2 ? src2->type : GGML_TYPE_COUNT; GGML_UNUSED(src2t);
+
+                        float scale;
+                        memcpy(&scale, dst->op_params, sizeof(float));
+
+                        id<MTLComputePipelineState> pipeline = nil;
+
+                        bool use_vec_kernel = false;
+
+                        if (ne01 >= 4 || (ne00%128 != 0)) {
+                            switch (ne00) {
+                                case 64:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H64 ].pipeline; break;
+                                case 80:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H80 ].pipeline; break;
+                                case 96:  pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H96 ].pipeline; break;
+                                case 112: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H112].pipeline; break;
+                                case 128: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H128].pipeline; break;
+                                case 256: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H256].pipeline; break;
+                                default:
+                                          {
+                                              GGML_METAL_LOG_ERROR("unsupported size: %lld\n", ne00);
+                                              GGML_METAL_LOG_ERROR("add template specialization for this size\n");
+                                              GGML_ASSERT(false && "add template specialization for this size");
+                                          }
+                            }
+                        } else {
+                            use_vec_kernel = true;
+
+                            switch (ne00) {
+                                case 128: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H128].pipeline; break;
+                                case 256: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H256].pipeline; break;
+                                default:
+                                          {
+                                              GGML_METAL_LOG_ERROR("unsupported size: %lld\n", ne00);
+                                              GGML_METAL_LOG_ERROR("add template specialization for this size\n");
+                                              GGML_ASSERT(false && "add template specialization for this size");
+                                          }
+                            }
+                        }
+
+                        [encoder setComputePipelineState:pipeline];
+                        [encoder setBuffer:id_src0 offset:offs_src0        atIndex:0];
+                        [encoder setBuffer:id_src1 offset:offs_src1        atIndex:1];
+                        [encoder setBuffer:id_src2 offset:offs_src2        atIndex:2];
+                        [encoder setBuffer:id_src3 offset:offs_src3        atIndex:3];
+                        [encoder setBuffer:id_dst  offset:offs_dst         atIndex:4];
+                        [encoder setBytes:&ne00    length:sizeof( int64_t) atIndex:5];
+                        [encoder setBytes:&ne01    length:sizeof( int64_t) atIndex:6];
+                        [encoder setBytes:&ne02    length:sizeof( int64_t) atIndex:7];
+                        [encoder setBytes:&ne03    length:sizeof( int64_t) atIndex:8];
+                        [encoder setBytes:&nb00    length:sizeof(uint64_t) atIndex:9];
+                        [encoder setBytes:&nb01    length:sizeof(uint64_t) atIndex:10];
+                        [encoder setBytes:&nb02    length:sizeof(uint64_t) atIndex:11];
+                        [encoder setBytes:&nb03    length:sizeof(uint64_t) atIndex:12];
+                        [encoder setBytes:&ne10    length:sizeof( int64_t) atIndex:13];
+                        [encoder setBytes:&ne11    length:sizeof( int64_t) atIndex:14];
+                        [encoder setBytes:&ne12    length:sizeof( int64_t) atIndex:15];
+                        [encoder setBytes:&ne13    length:sizeof( int64_t) atIndex:16];
+                        [encoder setBytes:&nb10    length:sizeof(uint64_t) atIndex:17];
+                        [encoder setBytes:&nb11    length:sizeof(uint64_t) atIndex:18];
+                        [encoder setBytes:&nb12    length:sizeof(uint64_t) atIndex:19];
+                        [encoder setBytes:&nb13    length:sizeof(uint64_t) atIndex:20];
+                        [encoder setBytes:&ne31    length:sizeof( int64_t) atIndex:21];
+                        [encoder setBytes:&nb31    length:sizeof(uint64_t) atIndex:22];
+                        [encoder setBytes:&ne0     length:sizeof( int64_t) atIndex:23];
+                        [encoder setBytes:&ne1     length:sizeof( int64_t) atIndex:24];
+                        [encoder setBytes:&ne2     length:sizeof( int64_t) atIndex:25];
+                        [encoder setBytes:&ne3     length:sizeof( int64_t) atIndex:26];
+                        [encoder setBytes:&scale   length:sizeof(   float) atIndex:27];
+
+                        if (!use_vec_kernel) {
+                            // half8x8 kernel
+                            const int64_t nqptg = 8;  // queries per threadgroup    !! sync with kernel template arguments !!
+                            const int64_t ncpsg = 32; // cache values per simdgroup !! sync with kernel template arguments !!
+
+                            GGML_ASSERT(nqptg <= 32);
+                            GGML_ASSERT(nqptg  % 8  == 0);
+                            GGML_ASSERT(ncpsg  % 32 == 0);
+
+                            int64_t nsgmax = 2;
+
+                            while (true) {
+                                const size_t smem = nqptg*(ne00 + 2*nsgmax*(ncpsg + nqptg))*(sizeof(float)/2);
+                                if (smem > ctx->device.maxThreadgroupMemoryLength) {
+                                    break;
+                                }
+                                nsgmax *= 2;
+                            }
+                            nsgmax /= 2;
+
+                            // simdgroups per threadgroup (a.k.a. warps)
+                            const int64_t nsg = ne01 <= nqptg ? MAX(4, MIN(nsgmax, MIN(ne11/ncpsg, (int64_t) pipeline.maxTotalThreadsPerThreadgroup/32))) : 4;
+
+                            const size_t smem = nqptg*(ne00 + 2*nsg*(ncpsg + nqptg))*(sizeof(float)/2);
+
+                            //printf("smem: %zu, max: %zu\n", smem, ctx->device.maxThreadgroupMemoryLength);
+                            GGML_ASSERT(smem <= ctx->device.maxThreadgroupMemoryLength);
+
+                            [encoder setThreadgroupMemoryLength:GGML_PAD(smem, 16) atIndex:0];
+
+                            [encoder dispatchThreadgroups:MTLSizeMake((ne01 + nqptg - 1)/nqptg, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(32, nsg, 1)];
+                        } else {
+                            // half1x4 kernel
+                            const int64_t nqptg = 1;  // queries per threadgroup    !! sync with kernel template arguments !!
+                            const int64_t ncpsg = 32; // cache values per simdgroup !! sync with kernel template arguments !!
+
+                            GGML_ASSERT(nqptg <= 32);
+                            GGML_ASSERT(nqptg  % 1  == 0);
+                            GGML_ASSERT(ncpsg  % 32 == 0);
+
+                            // simdgroups per threadgroup (a.k.a. warps)
+                            const int64_t nsgt = MAX(2, MIN(ne11/ncpsg, (int64_t) pipeline.maxTotalThreadsPerThreadgroup/32));
+
+                            int64_t nsg = 1;
+                            while (nsg <= nsgt) {
+                                nsg *= 2;
+                            }
+                            nsg /= 2;
+
+                            const size_t smem = (nqptg*(ne00 + 2*nsg*(ncpsg + nqptg)) + nsg*ne00)*(sizeof(float)/2);
+
+                            //printf("smem: %zu, max: %zu\n", smem, ctx->device.maxThreadgroupMemoryLength);
+                            GGML_ASSERT(smem <= ctx->device.maxThreadgroupMemoryLength);
+                            [encoder setThreadgroupMemoryLength:GGML_PAD(smem, 16) atIndex:0];
+
+                            [encoder dispatchThreadgroups:MTLSizeMake((ne01 + nqptg - 1)/nqptg, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(32, nsg, 1)];
+                        }
+                    } break;
                case GGML_OP_DUP:
                case GGML_OP_CPY:
                case GGML_OP_CONT:
@@ -2713,10 +2895,13 @@ GGML_CALL static const char * ggml_backend_metal_buffer_type_get_name(ggml_backe
    UNUSED(buft);
 }

-static void ggml_backend_metal_log_allocated_size(id<MTLDevice> device) {
+static void ggml_backend_metal_log_allocated_size(id<MTLDevice> device, size_t size_aligned) {
+#ifndef GGML_METAL_NDEBUG
 #if TARGET_OS_OSX || (TARGET_OS_IOS && __clang_major__ >= 15)
    if (@available(macOS 10.12, iOS 16.0, *)) {
-        GGML_METAL_LOG_INFO(", (%8.2f / %8.2f)",
+        GGML_METAL_LOG_INFO("%s: allocated buffer, size = %8.2f MiB, (%8.2f / %8.2f)",
+                __func__,
+                size_aligned / 1024.0 / 1024.0,
                device.currentAllocatedSize / 1024.0 / 1024.0,
                device.recommendedMaxWorkingSetSize / 1024.0 / 1024.0);

@@ -2726,10 +2911,15 @@ static void ggml_backend_metal_log_allocated_size(id<MTLDevice> device) {
            GGML_METAL_LOG_INFO("\n");
        }
    } else {
-        GGML_METAL_LOG_INFO(", (%8.2f)\n", device.currentAllocatedSize / 1024.0 / 1024.0);
+        GGML_METAL_LOG_INFO("%s: allocated buffer, size = %8.2f MiB, (%8.2f)\n",
+                __func__,
+                size_aligned / 1024.0 / 1024.0,
+                device.currentAllocatedSize / 1024.0 / 1024.0);
    }
+#endif
 #endif
    UNUSED(device);
+    UNUSED(size_aligned);
 }

 GGML_CALL static ggml_backend_buffer_t ggml_backend_metal_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
@@ -2763,8 +2953,7 @@ GGML_CALL static ggml_backend_buffer_t ggml_backend_metal_buffer_type_alloc_buff
        return NULL;
    }

-    GGML_METAL_LOG_INFO("%s: allocated buffer, size = %8.2f MiB", __func__, size_aligned / 1024.0 / 1024.0);
-    ggml_backend_metal_log_allocated_size(device);
+    //ggml_backend_metal_log_allocated_size(device, size_aligned);

    return ggml_backend_buffer_init(buft, ggml_backend_metal_buffer_i, ctx, size);
 }
@@ -2851,7 +3040,7 @@ GGML_CALL ggml_backend_buffer_t ggml_backend_metal_buffer_from_ptr(void * data,
            return false;
        }

-        GGML_METAL_LOG_INFO("%s: allocated buffer, size = %8.2f MiB", __func__, size_aligned / 1024.0 / 1024.0);
+        ggml_backend_metal_log_allocated_size(device, size_aligned);

        ++ctx->n_buffers;
    } else {
@@ -2874,7 +3063,8 @@ GGML_CALL ggml_backend_buffer_t ggml_backend_metal_buffer_from_ptr(void * data,
                return false;
            }

-            GGML_METAL_LOG_INFO("%s: allocated buffer, size = %8.2f MiB, offs = %12ld", __func__, size_step_aligned / 1024.0 / 1024.0, i);
+            ggml_backend_metal_log_allocated_size(device, size_step_aligned);
+
            if (i + size_step < size) {
                GGML_METAL_LOG_INFO("\n");
            }
@@ -2883,8 +3073,6 @@ GGML_CALL ggml_backend_buffer_t ggml_backend_metal_buffer_from_ptr(void * data,
        }
    }

-    ggml_backend_metal_log_allocated_size(device);
-
    return ggml_backend_buffer_init(ggml_backend_metal_buffer_type(), ggml_backend_metal_buffer_i, ctx, size);
 }

@@ -14,47 +14,6 @@
 #include <stdlib.h> // for qsort
 #include <stdio.h>  // for GGML_ASSERT

-#ifdef __ARM_NEON
-
-// if YCM cannot find <arm_neon.h>, make a symbolic link to it, for example:
-//
-//   $ ln -sfn /Library/Developer/CommandLineTools/usr/lib/clang/13.1.6/include/arm_neon.h ./src/
-//
-#include <arm_neon.h>
-
-#else
-
-#ifdef __wasm_simd128__
-#include <wasm_simd128.h>
-#else
-#if defined(__POWER9_VECTOR__) || defined(__powerpc64__)
-#include <altivec.h>
-#undef bool
-#define bool _Bool
-#else
-#if defined(_MSC_VER) || defined(__MINGW32__)
-#include <intrin.h>
-#else
-#if defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) || defined(__SSSE3__) || defined(__SSE3__)
-#if !defined(__riscv)
-#include <immintrin.h>
-#endif
-#endif
-#endif
-#endif
-#endif
-#endif
-
-#ifdef __riscv_v_intrinsic
-#include <riscv_vector.h>
-#endif
-
-#undef MIN
-#undef MAX
-
-#define MIN(a, b) ((a) < (b) ? (a) : (b))
-#define MAX(a, b) ((a) > (b) ? (a) : (b))
-
 #define UNUSED GGML_UNUSED

 // some compilers don't provide _mm256_set_m128i, e.g. gcc 7
@@ -276,258 +235,6 @@ static inline float hsum_float_4x4(const __m128 a, const __m128 b, const __m128
 #endif // __AVX__ || __AVX2__ || __AVX512F__
 #endif // defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) || defined(__SSSE3__)

-#if defined(__ARM_NEON)
-
-#ifdef _MSC_VER
-
-#define ggml_vld1q_u32(w,x,y,z) { ((w) + ((uint64_t)(x) << 32)), ((y) + ((uint64_t)(z) << 32)) }
-
-#else
-
-#define ggml_vld1q_u32(w,x,y,z) { (w), (x), (y), (z) }
-
-#endif
-
-#if !defined(__aarch64__)
-
-// 64-bit compatibility
-
-// vaddvq_s16
-// vpaddq_s16
-// vpaddq_s32
-// vaddvq_s32
-// vaddvq_f32
-// vmaxvq_f32
-// vcvtnq_s32_f32
-// vzip1_u8
-// vzip2_u8
-
-inline static int32_t vaddvq_s16(int16x8_t v) {
-    return
-        (int32_t)vgetq_lane_s16(v, 0) + (int32_t)vgetq_lane_s16(v, 1) +
-        (int32_t)vgetq_lane_s16(v, 2) + (int32_t)vgetq_lane_s16(v, 3) +
-        (int32_t)vgetq_lane_s16(v, 4) + (int32_t)vgetq_lane_s16(v, 5) +
-        (int32_t)vgetq_lane_s16(v, 6) + (int32_t)vgetq_lane_s16(v, 7);
-}
-
-inline static int16x8_t vpaddq_s16(int16x8_t a, int16x8_t b) {
-    int16x4_t a0 = vpadd_s16(vget_low_s16(a), vget_high_s16(a));
-    int16x4_t b0 = vpadd_s16(vget_low_s16(b), vget_high_s16(b));
-    return vcombine_s16(a0, b0);
-}
-
-inline static int32x4_t vpaddq_s32(int32x4_t a, int32x4_t b) {
-    int32x2_t a0 = vpadd_s32(vget_low_s32(a), vget_high_s32(a));
-    int32x2_t b0 = vpadd_s32(vget_low_s32(b), vget_high_s32(b));
-    return vcombine_s32(a0, b0);
-}
-
-inline static int32_t vaddvq_s32(int32x4_t v) {
-    return vgetq_lane_s32(v, 0) + vgetq_lane_s32(v, 1) + vgetq_lane_s32(v, 2) + vgetq_lane_s32(v, 3);
-}
-
-inline static float vaddvq_f32(float32x4_t v) {
-    return vgetq_lane_f32(v, 0) + vgetq_lane_f32(v, 1) + vgetq_lane_f32(v, 2) + vgetq_lane_f32(v, 3);
-}
-
-inline static float vmaxvq_f32(float32x4_t v) {
-    return
-        MAX(MAX(vgetq_lane_f32(v, 0), vgetq_lane_f32(v, 1)),
-            MAX(vgetq_lane_f32(v, 2), vgetq_lane_f32(v, 3)));
-}
-
-inline static int32x4_t vcvtnq_s32_f32(float32x4_t v) {
-    int32x4_t res;
-
-    res[0] = roundf(vgetq_lane_f32(v, 0));
-    res[1] = roundf(vgetq_lane_f32(v, 1));
-    res[2] = roundf(vgetq_lane_f32(v, 2));
-    res[3] = roundf(vgetq_lane_f32(v, 3));
-
-    return res;
-}
-
-inline static uint8x8_t vzip1_u8(uint8x8_t a, uint8x8_t b) {
-    uint8x8_t res;
-
-    res[0] = a[0]; res[1] = b[0];
-    res[2] = a[1]; res[3] = b[1];
-    res[4] = a[2]; res[5] = b[2];
-    res[6] = a[3]; res[7] = b[3];
-
-    return res;
-}
-
-inline static uint8x8_t vzip2_u8(uint8x8_t a, uint8x8_t b) {
-    uint8x8_t res;
-
-    res[0] = a[4]; res[1] = b[4];
-    res[2] = a[5]; res[3] = b[5];
-    res[4] = a[6]; res[5] = b[6];
-    res[6] = a[7]; res[7] = b[7];
-
-    return res;
-}
-
-// vld1q_s16_x2
-// vld1q_u8_x2
-// vld1q_u8_x4
-// vld1q_s8_x2
-// vld1q_s8_x4
-// TODO: double-check these work correctly
-
-typedef struct ggml_int16x8x2_t {
-    int16x8_t val[2];
-} ggml_int16x8x2_t;
-
-inline static ggml_int16x8x2_t ggml_vld1q_s16_x2(const int16_t * ptr) {
-    ggml_int16x8x2_t res;
-
-    res.val[0] = vld1q_s16(ptr + 0);
-    res.val[1] = vld1q_s16(ptr + 8);
-
-    return res;
-}
-
-typedef struct ggml_uint8x16x2_t {
-    uint8x16_t val[2];
-} ggml_uint8x16x2_t;
-
-inline static ggml_uint8x16x2_t ggml_vld1q_u8_x2(const uint8_t * ptr) {
-    ggml_uint8x16x2_t res;
-
-    res.val[0] = vld1q_u8(ptr + 0);
-    res.val[1] = vld1q_u8(ptr + 16);
-
-    return res;
-}
-
-typedef struct ggml_uint8x16x4_t {
-    uint8x16_t val[4];
-} ggml_uint8x16x4_t;
-
-inline static ggml_uint8x16x4_t ggml_vld1q_u8_x4(const uint8_t * ptr) {
-    ggml_uint8x16x4_t res;
-
-    res.val[0] = vld1q_u8(ptr + 0);
-    res.val[1] = vld1q_u8(ptr + 16);
-    res.val[2] = vld1q_u8(ptr + 32);
-    res.val[3] = vld1q_u8(ptr + 48);
-
-    return res;
-}
-
-typedef struct ggml_int8x16x2_t {
-    int8x16_t val[2];
-} ggml_int8x16x2_t;
-
-inline static ggml_int8x16x2_t ggml_vld1q_s8_x2(const int8_t * ptr) {
-    ggml_int8x16x2_t res;
-
-    res.val[0] = vld1q_s8(ptr + 0);
-    res.val[1] = vld1q_s8(ptr + 16);
-
-    return res;
-}
-
-typedef struct ggml_int8x16x4_t {
-    int8x16_t val[4];
-} ggml_int8x16x4_t;
-
-inline static ggml_int8x16x4_t ggml_vld1q_s8_x4(const int8_t * ptr) {
-    ggml_int8x16x4_t res;
-
-    res.val[0] = vld1q_s8(ptr + 0);
-    res.val[1] = vld1q_s8(ptr + 16);
-    res.val[2] = vld1q_s8(ptr + 32);
-    res.val[3] = vld1q_s8(ptr + 48);
-
-    return res;
-}
-
-// NOTE: not tested
-inline static int8x16_t ggml_vqtbl1q_s8(int8x16_t a, uint8x16_t b) {
-    int8x16_t res;
-
-    res[ 0] = a[b[ 0]];
-    res[ 1] = a[b[ 1]];
-    res[ 2] = a[b[ 2]];
-    res[ 3] = a[b[ 3]];
-    res[ 4] = a[b[ 4]];
-    res[ 5] = a[b[ 5]];
-    res[ 6] = a[b[ 6]];
-    res[ 7] = a[b[ 7]];
-    res[ 8] = a[b[ 8]];
-    res[ 9] = a[b[ 9]];
-    res[10] = a[b[10]];
-    res[11] = a[b[11]];
-    res[12] = a[b[12]];
-    res[13] = a[b[13]];
-    res[14] = a[b[14]];
-    res[15] = a[b[15]];
-
-    return res;
-}
-
-// NOTE: not tested
-inline static uint8x16_t ggml_vqtbl1q_u8(uint8x16_t a, uint8x16_t b) {
-    uint8x16_t res;
-
-    res[ 0] = a[b[ 0]];
-    res[ 1] = a[b[ 1]];
-    res[ 2] = a[b[ 2]];
-    res[ 3] = a[b[ 3]];
-    res[ 4] = a[b[ 4]];
-    res[ 5] = a[b[ 5]];
-    res[ 6] = a[b[ 6]];
-    res[ 7] = a[b[ 7]];
-    res[ 8] = a[b[ 8]];
-    res[ 9] = a[b[ 9]];
-    res[10] = a[b[10]];
-    res[11] = a[b[11]];
-    res[12] = a[b[12]];
-    res[13] = a[b[13]];
-    res[14] = a[b[14]];
-    res[15] = a[b[15]];
-
-    return res;
-}
-
-#else
-
-#define ggml_int16x8x2_t  int16x8x2_t
-#define ggml_uint8x16x2_t uint8x16x2_t
-#define ggml_uint8x16x4_t uint8x16x4_t
-#define ggml_int8x16x2_t  int8x16x2_t
-#define ggml_int8x16x4_t  int8x16x4_t
-
-#define ggml_vld1q_s16_x2 vld1q_s16_x2
-#define ggml_vld1q_u8_x2  vld1q_u8_x2
-#define ggml_vld1q_u8_x4  vld1q_u8_x4
-#define ggml_vld1q_s8_x2  vld1q_s8_x2
-#define ggml_vld1q_s8_x4  vld1q_s8_x4
-#define ggml_vqtbl1q_s8   vqtbl1q_s8
-#define ggml_vqtbl1q_u8   vqtbl1q_u8
-
-#endif
-
-#if !defined(__ARM_FEATURE_DOTPROD)
-
-inline static int32x4_t ggml_vdotq_s32(int32x4_t acc, int8x16_t a, int8x16_t b) {
-    const int16x8_t p0 = vmull_s8(vget_low_s8 (a), vget_low_s8 (b));
-    const int16x8_t p1 = vmull_s8(vget_high_s8(a), vget_high_s8(b));
-
-    return vaddq_s32(acc, vaddq_s32(vpaddlq_s16(p0), vpaddlq_s16(p1)));
-}
-
-#else
-
-#define ggml_vdotq_s32(a, b, c) vdotq_s32(a, b, c)
-
-#endif
-
-#endif
-
 #if defined(__ARM_NEON) || defined(__wasm_simd128__)
 #define B1(c,s,n)  0x ## n ## c ,  0x ## n ## s
 #define B2(c,s,n) B1(c,s,n ## c), B1(c,s,n ## s)
@@ -12676,3 +12383,287 @@ void quantize_row_iq2_s(const float * restrict x, void * restrict vy, int64_t k)
    block_iq2_s * restrict y = vy;
    quantize_row_iq2_s_reference(x, y, k);
 }
+
+static bool validate_float(float f, size_t i) {
+    if (isinf(f)) {
+        fprintf(stderr, "ggml_validate_row_data: found inf value at block %zu\n", i);
+        return false;
+    }
+
+    if (isnan(f)) {
+        fprintf(stderr, "ggml_validate_row_data: found nan value at block %zu\n", i);
+        return false;
+    }
+
+    return true;
+}
+
+static bool isinf_fp16(ggml_fp16_t f) {
+    return (f & 0x7c00) == 0x7c00 && (f & 0x03ff) == 0;
+}
+
+static bool isnan_fp16(ggml_fp16_t f) {
+    return (f & 0x7c00) == 0x7c00 && (f & 0x03ff) != 0;
+}
+
+static bool validate_fp16(ggml_fp16_t f, size_t i) {
+    if (isinf_fp16(f)) {
+        fprintf(stderr, "ggml_validate_row_data: found inf value at block %zu\n", i);
+        return false;
+    }
+
+    if (isnan_fp16(f)) {
+        fprintf(stderr, "ggml_validate_row_data: found nan value at block %zu\n", i);
+        return false;
+    }
+
+    return true;
+}
+
+#define VALIDATE_ROW_DATA_D_F16_IMPL(type, data, nb) \
+    const type * q = (const type *) (data); \
+    for (size_t i = 0; i < (nb); ++i) { \
+        if (!validate_fp16(q[i].d, i)) { \
+            return false; \
+        } \
+    }
+
+#define VALIDATE_ROW_DATA_DM_F16_IMPL(type, data, nb, d, m) \
+    const type * q = (const type *) (data); \
+    for (size_t i = 0; i < (nb); ++i) { \
+        if (!validate_fp16(q[i].d, i) || !validate_fp16(q[i].m, i)) { \
+            return false; \
+        } \
+    }
+
+bool ggml_validate_row_data(enum ggml_type type, const void * data, size_t nbytes) {
+    if (type < 0 || type >= GGML_TYPE_COUNT) {
+        fprintf(stderr, "%s: invalid type %d\n", __func__, type);
+        return false;
+    }
+
+    if (nbytes % ggml_type_size(type) != 0) {
+        fprintf(stderr, "%s: invalid size %zu for type %d\n", __func__, nbytes, type);
+        return false;
+    }
+
+    const size_t nb = nbytes/ggml_type_size(type);
+
+    switch (type) {
+        case GGML_TYPE_F16:
+            {
+                const ggml_fp16_t * f = (const ggml_fp16_t *) data;
+                size_t i = 0;
+#if defined(__AVX2__)
+                for (; i + 15 < nb; i += 16) {
+                    __m256i v = _mm256_loadu_si256((const __m256i *)(f + i));
+                    __m256i vexp = _mm256_and_si256(v, _mm256_set1_epi16(0x7c00));
+                    __m256i cmp = _mm256_cmpeq_epi16(vexp, _mm256_set1_epi16(0x7c00));
+                    int mask = _mm256_movemask_epi8(cmp);
+                    if (mask) {
+                        for (size_t j = 0; j < 16; ++j) {
+                            if (!validate_fp16(f[i + j], i + j)) {
+                                return false;
+                            }
+                        }
+                        GGML_UNREACHABLE();
+                    }
+                }
+#elif defined(__ARM_NEON)
+                for (; i + 7 < nb; i += 8) {
+                    uint16x8_t v = vld1q_u16(f + i);
+                    uint16x8_t vexp = vandq_u16(v, vdupq_n_u16(0x7c00));
+                    uint16x8_t cmp = vceqq_u16(vexp, vdupq_n_u16(0x7c00));
+                    uint64_t mask = vget_lane_u64(vreinterpret_u64_u8(vshrn_n_u16(cmp, 4)), 0);
+                    if (mask) {
+                        for (size_t j = 0; j < 8; ++j) {
+                            if (!validate_fp16(f[i + j], i + j)) {
+                                return false;
+                            }
+                        }
+                        GGML_UNREACHABLE();
+                    }
+                }
+#endif
+                for (; i < nb; ++i) {
+                    if (!validate_fp16(f[i], i)) {
+                        return false;
+                    }
+                }
+            } break;
+        case GGML_TYPE_F32:
+            {
+                const float * f = (const float *) data;
+                size_t i = 0;
+#if defined(__AVX2__)
+                for (; i + 7 < nb; i += 8) {
+                    __m256i v = _mm256_loadu_si256((const __m256i *)(f + i));
+                    __m256i vexp = _mm256_and_si256(v, _mm256_set1_epi32(0x7f800000));
+                    __m256i cmp = _mm256_cmpeq_epi32(vexp, _mm256_set1_epi32(0x7f800000));
+                    int mask = _mm256_movemask_epi8(cmp);
+                    if (mask) {
+                        for (size_t j = 0; j < 8; ++j) {
+                            if (!validate_float(f[i + j], i + j)) {
+                                return false;
+                            }
+                        }
+                        GGML_UNREACHABLE();
+                    }
+                }
+#elif defined(__ARM_NEON)
+                for (; i + 3 < nb; i += 4) {
+                    uint32x4_t v = vld1q_u32((const uint32_t *)f + i);
+                    uint32x4_t vexp = vandq_u32(v, vdupq_n_u32(0x7f800000));
+                    uint32x4_t cmp = vceqq_u32(vexp, vdupq_n_u32(0x7f800000));
+                    uint64_t mask = vget_lane_u64(vreinterpret_u64_u16(vshrn_n_u32(cmp, 8)), 0);
+                    if (mask) {
+                        for (size_t j = 0; j < 4; ++j) {
+                            if (!validate_float(f[i + j], i + j)) {
+                                return false;
+                            }
+                        }
+                        GGML_UNREACHABLE();
+                    }
+                }
+#endif
+                for (; i < nb; ++i) {
+                    if (!validate_float(f[i], i)) {
+                        return false;
+                    }
+                }
+            } break;
+        case GGML_TYPE_F64:
+            {
+                const double * f = (const double *) data;
+                for (size_t i = 0; i < nb; ++i) {
+                    if (!validate_float(f[i], i)) {
+                        return false;
+                    }
+                }
+            } break;
+        case GGML_TYPE_Q4_0:
+            {
+                VALIDATE_ROW_DATA_D_F16_IMPL(block_q4_0, data, nb);
+            } break;
+        case GGML_TYPE_Q4_1:
+            {
+                VALIDATE_ROW_DATA_DM_F16_IMPL(block_q4_1, data, nb, d, m);
+            } break;
+        case GGML_TYPE_Q5_0:
+            {
+                VALIDATE_ROW_DATA_D_F16_IMPL(block_q5_0, data, nb);
+            } break;
+        case GGML_TYPE_Q5_1:
+            {
+                VALIDATE_ROW_DATA_DM_F16_IMPL(block_q5_1, data, nb, d, m);
+            } break;
+        case GGML_TYPE_Q8_0:
+            {
+                VALIDATE_ROW_DATA_D_F16_IMPL(block_q8_0, data, nb);
+            } break;
+        case GGML_TYPE_Q2_K:
+            {
+                VALIDATE_ROW_DATA_DM_F16_IMPL(block_q2_K, data, nb, d, dmin);
+            } break;
+        case GGML_TYPE_Q3_K:
+            {
+                VALIDATE_ROW_DATA_D_F16_IMPL(block_q3_K, data, nb);
+            } break;
+        case GGML_TYPE_Q4_K:
+            {
+            #ifdef GGML_QKK_64
+                VALIDATE_ROW_DATA_DM_F16_IMPL(block_q4_K, data, nb, d[0], d[1]);
+            #else
+                VALIDATE_ROW_DATA_DM_F16_IMPL(block_q4_K, data, nb, d, dmin);
+            #endif
+            } break;
+        case GGML_TYPE_Q5_K:
+            {
+            #ifdef GGML_QKK_64
+                VALIDATE_ROW_DATA_D_F16_IMPL(block_q5_K, data, nb);
+            #else
+                VALIDATE_ROW_DATA_DM_F16_IMPL(block_q5_K, data, nb, d, dmin);
+            #endif
+            } break;
+        case GGML_TYPE_Q6_K:
+            {
+                VALIDATE_ROW_DATA_D_F16_IMPL(block_q6_K, data, nb);
+            } break;
+        case GGML_TYPE_Q8_K:
+            {
+                const block_q8_K * q = (const block_q8_K *) data;
+                for (size_t i = 0; i < nb; ++i) {
+                    if (!validate_float(q[i].d, i)) {
+                        return false;
+                    }
+                }
+            } break;
+        case GGML_TYPE_IQ1_S:
+            {
+                VALIDATE_ROW_DATA_D_F16_IMPL(block_iq1_s, data, nb);
+            } break;
+        case GGML_TYPE_IQ1_M:
+            {
+                const block_iq1_m * q = (const block_iq1_m *) data;
+                for (size_t i = 0; i < nb; ++i) {
+                #if QK_K == 64
+                    if (!validate_fp16(q[i].d, i)) {
+                        return false;
+                    }
+                #else
+                    iq1m_scale_t scale;
+                    const uint16_t * sc = (const uint16_t *)q[i].scales;
+                    scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000);
+                    if (!validate_fp16(scale.f16, i)) {
+                        return false;
+                    }
+                #endif
+                }
+            } break;
+        case GGML_TYPE_IQ2_XXS:
+            {
+                VALIDATE_ROW_DATA_D_F16_IMPL(block_iq2_xxs, data, nb);
+            } break;
+        case GGML_TYPE_IQ2_XS:
+            {
+                VALIDATE_ROW_DATA_D_F16_IMPL(block_iq2_xs, data, nb);
+            } break;
+        case GGML_TYPE_IQ2_S:
+            {
+                VALIDATE_ROW_DATA_D_F16_IMPL(block_iq2_s, data, nb);
+            } break;
+        case GGML_TYPE_IQ3_XXS:
+            {
+                VALIDATE_ROW_DATA_D_F16_IMPL(block_iq3_xxs, data, nb);
+            } break;
+
+        case GGML_TYPE_IQ3_S:
+            {
+                VALIDATE_ROW_DATA_D_F16_IMPL(block_iq3_s, data, nb);
+            } break;
+        case GGML_TYPE_IQ4_XS:
+        #if QK_K != 64
+            {
+                VALIDATE_ROW_DATA_D_F16_IMPL(block_iq4_xs, data, nb);
+            } break;
+        #endif
+        // with QK_K == 64, iq4_xs is iq4_nl
+        case GGML_TYPE_IQ4_NL:
+            {
+                VALIDATE_ROW_DATA_D_F16_IMPL(block_iq4_nl, data, nb);
+            } break;
+        case GGML_TYPE_I8:
+        case GGML_TYPE_I16:
+        case GGML_TYPE_I32:
+        case GGML_TYPE_I64:
+            // nothing to validate
+            break;
+        default:
+            {
+                fprintf(stderr, "%s: invalid type %d\n", __func__, type);
+                return false;
+            }
+    }
+
+    return true;
+}
@@ -13416,11 +13416,16 @@ void print_device_detail(int id, sycl::device &device, std::string device_type)
    version += std::to_string(prop.get_minor_version());

    device_type = std::regex_replace(device_type, std::regex("ext_oneapi_"), "");
+    std::string name = std::string(prop.get_name());
+    name = std::regex_replace(name, std::regex("\\(R\\)"), "");
+    name = std::regex_replace(name, std::regex("\\(TM\\)"), "");

-    fprintf(stderr, "|%2d|%18s|%45s|%10s|%11d|%8d|%7d|%15lu|\n", id, device_type.c_str(),
-            prop.get_name(), version.c_str(), prop.get_max_compute_units(),
+    auto global_mem_size = prop.get_global_mem_size()/1000000;
+
+    fprintf(stderr, "|%2d|%19s|%39s|%7s|%7d|%8d|%5d|%6luM|%21s|\n", id, device_type.c_str(),
+            name.c_str(), version.c_str(), prop.get_max_compute_units(),
            prop.get_max_work_group_size(), prop.get_max_sub_group_size(),
-            prop.get_global_mem_size());
+            global_mem_size, device.get_info<sycl::info::device::driver_version>().c_str());
 }

 void ggml_backend_sycl_print_sycl_devices() {
@@ -13428,9 +13433,10 @@ void ggml_backend_sycl_print_sycl_devices() {
    int device_count = dpct::dev_mgr::instance().device_count();
    std::map<std::string, size_t> DeviceNums;
    fprintf(stderr, "found %d SYCL devices:\n", device_count);
-    fprintf(stderr, "|  |                  |                                             |Compute   |Max compute|Max work|Max sub|               |\n");
-    fprintf(stderr, "|ID|       Device Type|                                         Name|capability|units      |group   |group  |Global mem size|\n");
-    fprintf(stderr, "|--|------------------|---------------------------------------------|----------|-----------|--------|-------|---------------|\n");
+    fprintf(stderr, "|  |                   |                                       |       |Max    |        |Max  |Global |                     |\n");
+    fprintf(stderr, "|  |                   |                                       |       |compute|Max work|sub  |mem    |                     |\n");
+    fprintf(stderr, "|ID|        Device Type|                                   Name|Version|units  |group   |group|size   |       Driver version|\n");
+    fprintf(stderr, "|--|-------------------|---------------------------------------|-------|-------|--------|-----|-------|---------------------|\n");
    for (int id = 0; id < device_count; ++id) {
        sycl::device device = dpct::dev_mgr::instance().get_device(id);
        sycl::backend backend = device.get_backend();
@@ -14738,7 +14744,12 @@ inline void ggml_sycl_op_soft_max(const ggml_tensor *src0,
    GGML_ASSERT(src0->type == GGML_TYPE_F32);
    GGML_ASSERT( dst->type == GGML_TYPE_F32);

+    const ggml_tensor * src2 = dst->src[2];
+
+#pragma message("TODO: add ggml_sycl_op_soft_max() F16 src1 and src2 support")
+#pragma message("ref:  https://github.com/ggerganov/llama.cpp/pull/5021")
    GGML_ASSERT(!src1 || src1->type == GGML_TYPE_F32); // src1 contains mask and it is optional
+    GGML_ASSERT(!src2 || src2->type == GGML_TYPE_F32); // src2 contains positions and it is optional

    const int64_t ne00 = src0->ne[0];
    const int64_t nrows_x = ggml_nrows(src0);
@@ -14754,7 +14765,6 @@ inline void ggml_sycl_op_soft_max(const ggml_tensor *src0,
    float * src2_dd = nullptr;
    sycl_pool_alloc<float> src2_f;

-    ggml_tensor * src2 = dst->src[2];
    const bool use_src2 = src2 != nullptr;

    if (use_src2) {
@@ -17752,7 +17762,7 @@ GGML_CALL static bool ggml_backend_sycl_supports_op(ggml_backend_t backend, cons

 GGML_CALL static bool ggml_backend_sycl_offload_op(ggml_backend_t backend, const ggml_tensor * op) {
    const int min_batch_size = 32;
-    return op->ne[1] >= min_batch_size && op->op != GGML_OP_GET_ROWS;
+    return op->ne[1] >= min_batch_size && op->op != GGML_OP_GET_ROWS && op->op != GGML_OP_MUL_MAT_ID;
    GGML_UNUSED(backend);
 }

@@ -3178,6 +3178,11 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const
        }
        return nullptr;
    case GGML_OP_SOFT_MAX:
+#pragma message("TODO: add ggml_vk_soft_max() F16 src1 and src2 support")
+#pragma message("ref:  https://github.com/ggerganov/llama.cpp/pull/5021")
+        GGML_ASSERT(!src1 || src1->type == GGML_TYPE_F32);
+        GGML_ASSERT(!src2 || src2->type == GGML_TYPE_F32);
+
        if (src0->type == GGML_TYPE_F32 && (src1 == nullptr || src1->type == GGML_TYPE_F32) && (src2 == nullptr || src2->type == GGML_TYPE_F32) && dst->type == GGML_TYPE_F32) {
            return ctx->device->pipeline_soft_max_f32;
        }
@@ -33,12 +33,8 @@
 #include <unistd.h>
 #endif

-#ifndef GGML_USE_LLAMAFILE
 #ifdef __ARM_FEATURE_MATMUL_INT8
-#define GGML_USE_LLAMAFILE 0
-#else
-#define GGML_USE_LLAMAFILE 1
-#endif
+#undef GGML_USE_LLAMAFILE
 #endif

 #if defined(_MSC_VER)
@@ -862,18 +858,6 @@ ggml_type_traits_t ggml_internal_get_type_traits(enum ggml_type type) {
 // simd mappings
 //

-#if defined(__ARM_NEON)
-#if !defined(__aarch64__)
-
-// 64-bit compatibility
-
-inline static float vaddvq_f32(float32x4_t v) {
-    return vgetq_lane_f32(v, 0) + vgetq_lane_f32(v, 1) + vgetq_lane_f32(v, 2) + vgetq_lane_f32(v, 3);
-}
-
-#endif
-#endif
-
 // we define a common set of C macros which map to specific intrinsics based on the current architecture
 // we then implement the fundamental computation operations below using only these macros
 // adding support for new architectures requires to define the corresponding SIMD macros
@@ -967,7 +951,7 @@ inline static float vaddvq_f32(float32x4_t v) {
    #define GGML_F16_VEC_ZERO           GGML_F16x8_ZERO
    #define GGML_F16_VEC_SET1           GGML_F16x8_SET1
    #define GGML_F16_VEC_LOAD(p, i)     GGML_F16x8_LOAD(p)
-    #define GGML_F16_VEC_STORE(p, r, i) GGML_F16x8_STORE(p, r[i])
+    #define GGML_F16_VEC_STORE(p, r, i) GGML_F16x8_STORE((ggml_fp16_internal_t *)(p), r[i])
    #define GGML_F16_VEC_FMA            GGML_F16x8_FMA
    #define GGML_F16_VEC_ADD            GGML_F16x8_ADD
    #define GGML_F16_VEC_MUL            GGML_F16x8_MUL
@@ -993,7 +977,7 @@ inline static float vaddvq_f32(float32x4_t v) {
    #define GGML_F16_VEC_ZERO           GGML_F32Cx4_ZERO
    #define GGML_F16_VEC_SET1           GGML_F32Cx4_SET1
    #define GGML_F16_VEC_LOAD(p, i)     GGML_F32Cx4_LOAD(p)
-    #define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx4_STORE(p, r[i])
+    #define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx4_STORE((ggml_fp16_internal_t *)(p), r[i])
    #define GGML_F16_VEC_FMA            GGML_F32Cx4_FMA
    #define GGML_F16_VEC_ADD            GGML_F32Cx4_ADD
    #define GGML_F16_VEC_MUL            GGML_F32Cx4_MUL
@@ -1062,7 +1046,7 @@ do {                                                                  \

 // unlike  _mm256_cvt intrinsics that require F16C, _mm512_cvt is defined in AVX512F
 // so F16C guard isn't required
-#define GGML_F32Cx16_LOAD(x)     _mm512_cvtph_ps(_mm256_loadu_si256((__m256i *)(x)))
+#define GGML_F32Cx16_LOAD(x)     _mm512_cvtph_ps(_mm256_loadu_si256((const __m256i *)(x)))
 #define GGML_F32Cx16_STORE(x, y) _mm256_storeu_si256((__m256i *)(x), _mm512_cvtps_ph(y, 0))

 #define GGML_F32Cx16_FMA(a, b, c) _mm512_fmadd_ps(b, c, a)
@@ -1160,7 +1144,7 @@ do {                                                              \

 #if defined(__F16C__)
 // the  _mm256_cvt intrinsics require F16C
-#define GGML_F32Cx8_LOAD(x)     _mm256_cvtph_ps(_mm_loadu_si128((__m128i *)(x)))
+#define GGML_F32Cx8_LOAD(x)     _mm256_cvtph_ps(_mm_loadu_si128((const __m128i *)(x)))
 #define GGML_F32Cx8_STORE(x, y) _mm_storeu_si128((__m128i *)(x), _mm256_cvtps_ph(y, 0))
 #else
 static inline __m256 __avx_f32cx8_load(ggml_fp16_t *x) {
@@ -1678,6 +1662,37 @@ inline static void ggml_vec_mad_f32(const int n, float * restrict y, const float
 #endif
 }

+inline static void ggml_vec_mad_f16(const int n, ggml_fp16_t * restrict y, const ggml_fp16_t * restrict x, const float v) {
+#if defined(GGML_SIMD)
+    const int np = (n & ~(GGML_F16_STEP - 1));
+
+    GGML_F16_VEC vx = GGML_F16_VEC_SET1(v);
+
+    GGML_F16_VEC ax[GGML_F16_ARR];
+    GGML_F16_VEC ay[GGML_F16_ARR];
+
+    for (int i = 0; i < np; i += GGML_F16_STEP) {
+        for (int j = 0; j < GGML_F16_ARR; j++) {
+            ax[j] = GGML_F16_VEC_LOAD(x + i + j*GGML_F16_EPR, j);
+            ay[j] = GGML_F16_VEC_LOAD(y + i + j*GGML_F16_EPR, j);
+            ay[j] = GGML_F16_VEC_FMA(ay[j], ax[j], vx);
+
+            GGML_F16_VEC_STORE(y + i + j*GGML_F16_EPR, ay, j);
+        }
+    }
+
+    // leftovers
+    for (int i = np; i < n; ++i) {
+        y[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(y[i]) + GGML_FP16_TO_FP32(x[i])*v);
+    }
+#else
+    // scalar
+    for (int i = 0; i < n; ++i) {
+        y[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(y[i]) + GGML_FP16_TO_FP32(x[i])*v);
+    }
+#endif
+}
+
 // xs and vs are byte strides of x and v
 inline static void ggml_vec_mad_f32_unroll(const int n, const int xs, const int vs, float * restrict y, const float * restrict xv, const float * restrict vv) {

@@ -1762,6 +1777,35 @@ inline static void ggml_vec_scale_f32(const int n, float * y, const float   v) {
 #endif
 }

+inline static void ggml_vec_scale_f16(const int n, ggml_fp16_t * y, const float v) {
+#if defined(GGML_SIMD)
+    const int np = (n & ~(GGML_F16_STEP - 1));
+
+    GGML_F16_VEC vx = GGML_F16_VEC_SET1(v);
+
+    GGML_F16_VEC ay[GGML_F16_ARR];
+
+    for (int i = 0; i < np; i += GGML_F16_STEP) {
+        for (int j = 0; j < GGML_F16_ARR; j++) {
+            ay[j] = GGML_F16_VEC_LOAD(y + i + j*GGML_F16_EPR, j);
+            ay[j] = GGML_F16_VEC_MUL(ay[j], vx);
+
+            GGML_F16_VEC_STORE(y + i + j*GGML_F16_EPR, ay, j);
+        }
+    }
+
+    // leftovers
+    for (int i = np; i < n; ++i) {
+        y[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(y[i])*v);
+    }
+#else
+    // scalar
+    for (int i = 0; i < n; ++i) {
+        y[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(y[i])*v);
+    }
+#endif
+}
+
 inline static void ggml_vec_norm_f32 (const int n, float * s, const float * x) { ggml_vec_dot_f32(n, s, 0, x, 0, x, 0, 1); *s = sqrtf(*s);   }
 inline static void ggml_vec_sqr_f32  (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = x[i]*x[i];   }
 inline static void ggml_vec_sqrt_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = sqrtf(x[i]); }
@@ -2016,6 +2060,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
    "LEAKY_RELU",

    "FLASH_ATTN",
+    "FLASH_ATTN_EXT",
    "FLASH_FF",
    "FLASH_ATTN_BACK",
    "SSM_CONV",
@@ -2042,7 +2087,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
    "CROSS_ENTROPY_LOSS_BACK",
 };

-static_assert(GGML_OP_COUNT == 76, "GGML_OP_COUNT != 76");
+static_assert(GGML_OP_COUNT == 77, "GGML_OP_COUNT != 77");

 static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
    "none",
@@ -2106,6 +2151,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
    "leaky_relu(x)",

    "flash_attn(x)",
+    "flash_attn_ext(x)",
    "flash_ff(x)",
    "flash_attn_back(x)",
    "ssm_conv(x)",
@@ -2132,7 +2178,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
    "cross_entropy_loss_back(x,y)",
 };

-static_assert(GGML_OP_COUNT == 76, "GGML_OP_COUNT != 76");
+static_assert(GGML_OP_COUNT == 77, "GGML_OP_COUNT != 77");

 static_assert(GGML_OP_POOL_COUNT == 2, "GGML_OP_POOL_COUNT != 2");

@@ -4575,6 +4621,8 @@ struct ggml_tensor * ggml_mul_mat(
 void ggml_mul_mat_set_prec(
        struct ggml_tensor * a,
        enum ggml_prec       prec) {
+    GGML_ASSERT(a->op == GGML_OP_MUL_MAT);
+
    const int32_t prec_i32 = (int32_t) prec;

    ggml_set_op_params_i32(a, 0, prec_i32);
@@ -4582,21 +4630,32 @@ void ggml_mul_mat_set_prec(

 // ggml_mul_mat_id

-// NOTE: id will be removed in the future and instead all the experts listed in ids will be computed
-//       this will allow computing all the used experts in a single matrix multiplication
+/*
+    c = ggml_mul_mat_id(ctx, as, b, ids);
+
+    as  -> [cols, rows, n_expert]
+    ids -> [n_experts_used, n_tokens] (i32)
+    b   -> [cols, n_expert_used, n_tokens]
+    c   -> [cols, n_expert_used, n_tokens]
+
+    in b, n_experts_used can be broadcasted to match the n_expert_used of ids
+
+    c ~= as[:,:,i] @ b[:,i%r,t], i = ids[e,t] for all e,t in ids
+*/
 struct ggml_tensor * ggml_mul_mat_id(
        struct ggml_context * ctx,
        struct ggml_tensor  * as,
-        struct ggml_tensor  * ids,
-        int                   id,
-        struct ggml_tensor  * b) {
-
+        struct ggml_tensor  * b,
+        struct ggml_tensor  * ids) {
+    GGML_ASSERT(!ggml_is_transposed(as));
    GGML_ASSERT(ids->type == GGML_TYPE_I32);
+
+    GGML_ASSERT(as->ne[3] == 1); // as is 3d (one matrix per expert)
+    GGML_ASSERT(b->ne[3] == 1); // b is 3d
    GGML_ASSERT(ids->ne[2] == 1 && ids->ne[3] == 1); // ids is 2d
-    GGML_ASSERT(ids->ne[1] == b->ne[1]); // must have an expert per b row
-    GGML_ASSERT(ids->ne[2] == b->ne[2] && ids->ne[3] == b->ne[3]);
-    GGML_ASSERT(id >= 0 && id < ids->ne[0]); // valid id
+    GGML_ASSERT(ids->ne[1] == b->ne[2]); // must have an expert list per b row
    GGML_ASSERT(as->ne[0] == b->ne[0]); // can_mul_mat
+    GGML_ASSERT(ids->ne[0] % b->ne[1] == 0); // can broadcast

    bool is_node = false;

@@ -4604,11 +4663,9 @@ struct ggml_tensor * ggml_mul_mat_id(
        is_node = true;
    }

-    const int64_t ne[4] = { as->ne[1], b->ne[1], b->ne[2], b->ne[3] };
+    const int64_t ne[4] = { as->ne[1], ids->ne[0], b->ne[2], 1 };
    struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne);

-    ggml_set_op_params_i32(result, 0, id);
-
    result->op   = GGML_OP_MUL_MAT_ID;
    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
    result->src[0] = as;
@@ -5404,17 +5461,23 @@ static struct ggml_tensor * ggml_soft_max_impl(
    GGML_ASSERT(ggml_is_contiguous(a));

    if (mask) {
+        GGML_ASSERT(mask->type == GGML_TYPE_F16 || mask->type == GGML_TYPE_F32);
        GGML_ASSERT(ggml_is_contiguous(mask));
        GGML_ASSERT(ggml_is_matrix(mask));
-        GGML_ASSERT(ggml_can_repeat_rows(mask, a));
+        GGML_ASSERT(mask->ne[0] == a->ne[0]);
+        GGML_ASSERT(mask->ne[1] >= a->ne[1]);
    }

    if (pos) {
        GGML_ASSERT(ggml_is_vector(pos));
-        GGML_ASSERT(pos->type == GGML_TYPE_F32);
+        GGML_ASSERT(pos->type == GGML_TYPE_F16 || pos->type == GGML_TYPE_F32);
        GGML_ASSERT(pos->ne[0] == a->ne[0]);
    }

+    if (pos && mask) {
+        GGML_ASSERT(pos->type == mask->type);
+    }
+
    if (max_bias > 0.0f) {
        GGML_ASSERT(pos);
    }
@@ -6223,6 +6286,59 @@ struct ggml_tensor * ggml_flash_attn(
    return result;
 }

+// ggml_flash_attn_ext
+
+struct ggml_tensor * ggml_flash_attn_ext(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * q,
+        struct ggml_tensor  * k,
+        struct ggml_tensor  * v,
+        struct ggml_tensor  * mask,
+        float                 scale) {
+    GGML_ASSERT(ggml_can_mul_mat(k, q));
+    // TODO: check if vT can be multiplied by (k*qT)
+    if (mask) {
+        GGML_ASSERT(ggml_is_contiguous(mask));
+        GGML_ASSERT(mask->ne[2] == 1);
+        GGML_ASSERT(mask->ne[3] == 1);
+        GGML_ASSERT(mask->ne[1] >= GGML_PAD(q->ne[1], GGML_KQ_MASK_PAD) &&
+                "the Flash-Attention kernel requires the mask to be padded to GGML_KQ_MASK_PAD and at least n_queries big");
+        //GGML_ASSERT(ggml_can_repeat_rows(mask, qk));
+    }
+
+    bool is_node = false;
+
+    if (q->grad || k->grad || v->grad) {
+        is_node = true;
+    }
+
+    // permute(0, 2, 1, 3)
+    int64_t ne[4] = { q->ne[0], q->ne[2], q->ne[1], q->ne[3] };
+    struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne);
+
+    float params[] = { scale };
+    ggml_set_op_params(result, params, sizeof(params));
+
+    result->op   = GGML_OP_FLASH_ATTN_EXT;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src[0] = q;
+    result->src[1] = k;
+    result->src[2] = v;
+    result->src[3] = mask;
+
+    return result;
+}
+
+void ggml_flash_attn_ext_set_prec(
+        struct ggml_tensor * a,
+        enum ggml_prec       prec) {
+    GGML_ASSERT(a->op == GGML_OP_FLASH_ATTN_EXT);
+
+    const int32_t prec_i32 = (int32_t) prec;
+
+    ggml_set_op_params_i32(a, 1, prec_i32); // scale is on first pos
+}
+
 // ggml_flash_ff

 struct ggml_tensor * ggml_flash_ff(
@@ -10820,7 +10936,7 @@ static void ggml_compute_forward_mul_mat(
 #endif

 #if GGML_USE_LLAMAFILE
-    if (nb10 == ggml_type_size(src1->type)) {
+    if (src1_cont) {
        for (int64_t i13 = 0; i13 < ne13; i13++)
            for (int64_t i12 = 0; i12 < ne12; i12++)
                if (!llamafile_sgemm(ne01, ne11, ne00/ggml_blck_size(src0->type),
@@ -10873,14 +10989,13 @@ UseGgmlGemm1:;
    const size_t row_size = ggml_row_size(vec_dot_type, ne10);

 #if GGML_USE_LLAMAFILE
-    if (nb10 == ggml_type_size(src1->type) || src1->type != vec_dot_type) {
+    if (src1->type != vec_dot_type) {
        for (int64_t i13 = 0; i13 < ne13; i13++)
            for (int64_t i12 = 0; i12 < ne12; i12++)
                if (!llamafile_sgemm(ne01, ne11, ne00/ggml_blck_size(src0->type),
                                     (const char *)src0->data + i12/r2*nb02 + i13/r3*nb03,
                                     nb01/ggml_type_size(src0->type),
-                                     (const char *)wdata + (nb12/ggml_type_size(src1->type)*ggml_type_size(vec_dot_type)*i12 +
-                                                            nb13/ggml_type_size(src1->type)*ggml_type_size(vec_dot_type)*i13),
+                                     (const char *)wdata + (i12*ne11 + i13*ne12*ne11)*row_size,
                                     row_size/ggml_type_size(vec_dot_type),
                                     (char *)dst->data + i12*nb2 + i13*nb3,
                                     nb1/ggml_type_size(dst->type),
@@ -11012,11 +11127,6 @@ static void ggml_compute_forward_mul_mat_id(
    enum ggml_type    const vec_dot_type          = type_traits[type].vec_dot_type;
    ggml_from_float_t const from_float_to_vec_dot = type_traits[vec_dot_type].from_float;

-    GGML_ASSERT(ne0 == ne01);
-    GGML_ASSERT(ne1 == ne11);
-    GGML_ASSERT(ne2 == ne12);
-    GGML_ASSERT(ne3 == ne13);
-
    // we don't support permuted src0 or src1
    GGML_ASSERT(nb00 == ggml_type_size(type));
    GGML_ASSERT(nb10 == ggml_type_size(src1->type));
@@ -11027,22 +11137,21 @@ static void ggml_compute_forward_mul_mat_id(
    GGML_ASSERT(nb1 <= nb2);
    GGML_ASSERT(nb2 <= nb3);

-    // broadcast is not supported with mmid
-    assert(ne12 == 1);
-    assert(ne13 == 1);
-
    // row groups
-    const int id   = ggml_get_op_params_i32(dst, 0);
-    const int n_as = src0->ne[2];
+    const int n_ids = ids->ne[0]; // n_expert_used
+    const int n_as  = ne02;       // n_expert

    char * wdata_src1_end = (src1->type == vec_dot_type) ?
            (char *) params->wdata :
            (char *) params->wdata + GGML_PAD(ggml_row_size(vec_dot_type, ggml_nelements(src1)), sizeof(int64_t));

-    int64_t * matrix_row_counts = (int64_t *) (wdata_src1_end); // [n_as]
-    int64_t * matrix_rows       = matrix_row_counts + n_as;     // [n_as][ne11]
+    struct mmid_row_mapping {
+        int32_t i1;
+        int32_t i2;
+    };

-    #define MMID_MATRIX_ROW(row_id, i1) matrix_rows[(row_id)*ne11 + (i1)]
+    int64_t * matrix_row_counts = (int64_t *) (wdata_src1_end); // [n_as]
+    struct mmid_row_mapping * matrix_rows = (struct mmid_row_mapping *)(matrix_row_counts + n_as); // [n_as][ne11]

   if (params->type == GGML_TASK_TYPE_INIT) {
        if (ith != 0) {
@@ -11068,13 +11177,18 @@ static void ggml_compute_forward_mul_mat_id(
        // initialize matrix_row_counts
        memset(matrix_row_counts, 0, n_as*sizeof(int64_t));

-        // group rows by src0 matrix
-        for (int64_t i01 = 0; i01 < ids->ne[1]; i01++) {
-            const int32_t row_id = *(const int32_t *) ((const char *) ids->data + i01*ids->nb[1] + id*ids->nb[0]);
+#define MMID_MATRIX_ROW(row_id, i1) matrix_rows[(row_id)*ne12 + (i1)]

-            GGML_ASSERT(row_id >= 0 && row_id < n_as);
-            MMID_MATRIX_ROW(row_id, matrix_row_counts[row_id]) = i01;
-            matrix_row_counts[row_id] += 1;
+        // group rows by src0 matrix
+        for (int64_t iid1 = 0; iid1 < ids->ne[1]; ++iid1) {
+            for (int id = 0; id < n_ids; ++id) {
+                const int32_t i02 = *(const int32_t *) ((const char *) ids->data + iid1*ids->nb[1] + id*ids->nb[0]);
+
+                assert(i02 >= 0 && i02 < n_as);
+
+                MMID_MATRIX_ROW(i02, matrix_row_counts[i02]) = (struct mmid_row_mapping) {id, iid1};
+                matrix_row_counts[i02] += 1;
+            }
        }

        return;
@@ -11092,15 +11206,13 @@ static void ggml_compute_forward_mul_mat_id(
            continue;
        }

-        size_t src0_offset = cur_a*src0->nb[2];
+        const char * src0_cur = (const char *) src0->data + cur_a*nb02;

        const void * wdata    = (src1->type == vec_dot_type) ? src1->data : params->wdata;
        const size_t row_size = ggml_row_size(vec_dot_type, ne10);

-        const int64_t nr0 = ne01;           // src0 rows
-        const int64_t nr1 = cne1*ne12*ne13; // src1 rows
-
-        //printf("nr0 = %lld, nr1 = %lld\n", nr0, nr1);
+        const int64_t nr0 = ne01; // src0 rows
+        const int64_t nr1 = cne1; // src1 rows

        // distribute the thread work across the inner or outer loop based on which one is larger

@@ -11119,13 +11231,11 @@ static void ggml_compute_forward_mul_mat_id(
        const int64_t ir110 = dr1*ith1;
        const int64_t ir111 = MIN(ir110 + dr1, nr1);

-        //printf("ir010 = %6lld, ir011 = %6lld, ir110 = %6lld, ir111 = %6lld\n", ir010, ir011, ir110, ir111);
-
        // threads with no work simply yield (not sure if it helps)
-        if (ir010 >= ir011 || ir110 >= ir111) {
-            sched_yield();
-            continue;
-        }
+        //if (ir010 >= ir011 || ir110 >= ir111) {
+        //    sched_yield();
+        //    continue;
+        //}

        // block-tiling attempt
        const int64_t blck_0 = 16;
@@ -11137,20 +11247,16 @@ static void ggml_compute_forward_mul_mat_id(
        for (int64_t iir1 = ir110; iir1 < ir111; iir1 += blck_1) {
            for (int64_t iir0 = ir010; iir0 < ir011; iir0 += blck_0) {
                for (int64_t ir1 = iir1; ir1 < iir1 + blck_1 && ir1 < ir111; ++ir1) {
-                    const int64_t  i13 = (ir1/(ne12*cne1)); // Note: currently, src1 is always a matrix
-                    const int64_t  i12 = (ir1 - i13*ne12*cne1)/cne1;
-                    const int64_t _i11 = (ir1 - i13*ne12*cne1 - i12*cne1);
-                    const int64_t  i11 = MMID_MATRIX_ROW(cur_a, _i11);
+                    const int64_t _i12 = ir1; // logical row index for this expert

-                    // broadcast src0 into src1
-                    //const int64_t i03 = i13/r3;
-                    //const int64_t i02 = i12/r2;
+                    struct mmid_row_mapping row_mapping = MMID_MATRIX_ROW(cur_a, _i12);
+                    const int id       = row_mapping.i1; // selected expert index

-                    const int64_t i1 = i11;
-                    const int64_t i2 = i12;
-                    const int64_t i3 = i13;
+                    const int64_t  i11 = id % ne11;
+                    const int64_t  i12 = row_mapping.i2; // row index in src1

-                    const char * src0_row = (const char *) src0->data + src0_offset;
+                    const int64_t  i1 = id;  // selected expert index
+                    const int64_t  i2 = i12; // row

                    // desc: when src1 is not a contiguous memory block we have to calculate the offset using the strides
                    //       if it is, then we have either copied the data to params->wdata and made it contiguous or we are using
@@ -11158,25 +11264,26 @@ static void ggml_compute_forward_mul_mat_id(
                    // TODO: this is a bit of a hack, we should probably have a better way to handle this
                    const char * src1_col = (const char *) wdata +
                        (src1_cont || src1->type != vec_dot_type
-                        ? (i11      + i12*ne11 + i13*ne12*ne11)*row_size
-                        : (i11*nb11 + i12*nb12 + i13*nb13));
+                        ? (i11      + i12*ne11)*row_size
+                        : (i11*nb11 + i12*nb12));

-                    float * dst_col = (float *) ((char *) dst->data + (i1*nb1 + i2*nb2 + i3*nb3));
+                    float * dst_col = (float *) ((char *) dst->data + (i1*nb1 + i2*nb2));

                    //for (int64_t ir0 = iir0; ir0 < iir0 + blck_0 && ir0 < ir011; ++ir0) {
                    //    vec_dot(ne00, &dst_col[ir0], src0_row + ir0*nb01, src1_col);
                    //}

                    for (int64_t ir0 = iir0; ir0 < iir0 + blck_0 && ir0 < ir011; ++ir0) {
-                        vec_dot(ne00, &tmp[ir0 - iir0], 0, src0_row + ir0*nb01, 0, src1_col, 0, 1);
+                        vec_dot(ne00, &tmp[ir0 - iir0], 0, src0_cur + ir0*nb01, 0, src1_col, 0, 1);
                    }
+
                    memcpy(&dst_col[iir0], tmp, (MIN(iir0 + blck_0, ir011) - iir0)*sizeof(float));
                }
            }
        }
    }

-    #undef MMID_MATRIX_ROW
+#undef MMID_MATRIX_ROW
 }

 // ggml_compute_forward_out_prod
@@ -12271,7 +12378,7 @@ static void ggml_compute_forward_soft_max_f32(

    GGML_TENSOR_UNARY_OP_LOCALS

-    const int64_t ne11 = src1 ? src1->ne[1] : 1;
+    //const int64_t ne11 = src1 ? src1->ne[1] : 1;

    // TODO: is this supposed to be ceil instead of floor?
    //       https://huggingface.co/mosaicml/mpt-7b/blob/main/attention.py#L370
@@ -12294,19 +12401,31 @@ static void ggml_compute_forward_soft_max_f32(
    float * wp = (float *) params->wdata + (nc + CACHE_LINE_SIZE_F32) * ith;

    // when max_bias <= 0.0f, src2 is not used and we default it to src0 to avoid branching
-    float * pos = src2 ? (float *) src2->data : src0->data;
+    ggml_fp16_t * pos_f16 = src2 ? (ggml_fp16_t *) src2->data : src0->data;
+    float       * pos_f32 = src2 ? (float       *) src2->data : src0->data;
+
+    const bool use_f16 = (src1 && src1->type == GGML_TYPE_F16) || (src2 && src2->type == GGML_TYPE_F16);

    for (int i1 = ir0; i1 < ir1; i1++) {
        float * sp = (float *)((char *) src0->data + i1*src0->nb[1]);
        float * dp = (float *)((char *)  dst->data +  i1*dst->nb[1]);

        // broadcast the mask across rows
-        float * mp = src1 ? (float *)((char *) src1->data + (i1%ne11)*src1->nb[1]) : NULL;
+        ggml_fp16_t * mp_f16 = src1 ? (ggml_fp16_t *)((char *) src1->data) + (i1%ne01)*ne00 : NULL;
+        float       * mp_f32 = src1 ? (float       *)((char *) src1->data) + (i1%ne01)*ne00 : NULL;

        ggml_vec_cpy_f32  (nc, wp, sp);
        ggml_vec_scale_f32(nc, wp, scale);
-        if (mp) {
-            ggml_vec_acc_f32(nc, wp, mp);
+        if (mp_f32) {
+            if (use_f16) {
+                for (int i = 0; i < nc; ++i) {
+                    wp[i] += GGML_FP16_TO_FP32(mp_f16[i]);
+                }
+            } else {
+                for (int i = 0; i < nc; ++i) {
+                    wp[i] += mp_f32[i];
+                }
+            }
        }

        // ALiBi bias
@@ -12314,8 +12433,14 @@ static void ggml_compute_forward_soft_max_f32(
            const uint32_t h  = (i1/ne01)%ne02; // head
            const float slope = h < n_head_log2 ? powf(m0, h + 1) : powf(m1, 2*(h - n_head_log2) + 1);

-            for (int i = 0; i < nc; i++) {
-                wp[i] = wp[i] + slope*pos[i];
+            if (use_f16) {
+                for (int i = 0; i < nc; ++i) {
+                    wp[i] += slope*GGML_FP16_TO_FP32(pos_f16[i]);
+                }
+            } else {
+                for (int i = 0; i < nc; ++i) {
+                    wp[i] += slope*pos_f32[i];
+                }
            }
        }

@@ -14585,6 +14710,198 @@ static void ggml_compute_forward_flash_attn(
    }
 }

+// ggml_compute_forward_flash_attn_ext
+
+static void ggml_compute_forward_flash_attn_ext_f16(
+        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) {
+    int64_t t0 = ggml_perf_time_us();
+    UNUSED(t0);
+
+    GGML_TENSOR_LOCALS(int64_t, neq, q,   ne)
+    GGML_TENSOR_LOCALS(size_t,  nbq, q,   nb)
+    GGML_TENSOR_LOCALS(int64_t, nek, k,   ne)
+    GGML_TENSOR_LOCALS(size_t,  nbk, k,   nb)
+    GGML_TENSOR_LOCALS(int64_t, nev, v,   ne)
+    GGML_TENSOR_LOCALS(size_t,  nbv, v,   nb)
+    GGML_TENSOR_LOCALS(int64_t, ne,  dst, ne)
+    GGML_TENSOR_LOCALS(size_t,  nb,  dst, nb)
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    const int64_t D = neq0;
+    const int64_t N = neq1;
+
+    GGML_ASSERT(ne0 == D);
+    GGML_ASSERT(ne2 == N);
+
+    GGML_ASSERT(nbq0 == sizeof(float));
+    GGML_ASSERT(nbk0 == sizeof(ggml_fp16_t));
+    GGML_ASSERT(nbv0 == sizeof(ggml_fp16_t));
+
+    GGML_ASSERT(neq0 == D);
+    GGML_ASSERT(nek0 == D);
+    GGML_ASSERT(nev0 == D);
+
+    GGML_ASSERT(neq1 == N);
+    GGML_ASSERT(nev0 == D);
+
+    // dst cannot be transposed or permuted
+    GGML_ASSERT(nb0 == sizeof(float));
+    GGML_ASSERT(nb0 <= nb1);
+    GGML_ASSERT(nb1 <= nb2);
+    GGML_ASSERT(nb2 <= nb3);
+
+    // broadcast factors
+    const int64_t rk2 = neq2/nek2;
+    const int64_t rk3 = neq3/nek3;
+
+    const int64_t rv2 = neq2/nev2;
+    const int64_t rv3 = neq3/nev3;
+
+    if (params->type == GGML_TASK_TYPE_INIT) {
+        return;
+    }
+
+    if (params->type == GGML_TASK_TYPE_FINALIZE) {
+        return;
+    }
+
+    // parallelize by q rows using ggml_vec_dot_f32
+
+    // total rows in q
+    const int nr = neq1*neq2*neq3;
+
+    // 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);
+
+    float scale = 1.0f;
+    memcpy(&scale, (float *) dst->op_params + 0, sizeof(float));
+
+    // loop over n_batch and n_head
+    for (int ir = ir0; ir < ir1; ++ir) {
+        // q indices
+        const int iq3 = ir/(neq2*neq1);
+        const int iq2 = (ir - iq3*neq2*neq1)/neq1;
+        const int iq1 = (ir - iq3*neq2*neq1 - iq2*neq1);
+
+        float S = 0.0f;
+        float M = -INFINITY;
+
+        float       * V32 = (float       *) params->wdata + ith*(2*D + CACHE_LINE_SIZE_F32);
+        ggml_fp16_t * Q16 = (ggml_fp16_t *) (V32); // reuse memory
+        ggml_fp16_t * V16 = (ggml_fp16_t *) (V32 + D);
+
+        memset(V16, 0, D*sizeof(ggml_fp16_t));
+
+        const ggml_fp16_t * mp = mask ? (ggml_fp16_t *)((char *) mask->data + iq1*mask->nb[1]) : NULL;
+
+        // k indices
+        const int ik3 = iq3 / rk3;
+        const int ik2 = iq2 / rk2;
+
+        // v indices
+        const int iv3 = iq3 / rv3;
+        const int iv2 = iq2 / rv2;
+
+        // online softmax / attention
+        // loop over n_kv and n_head_kv
+        // ref: https://arxiv.org/pdf/2112.05682.pdf
+        for (int64_t ic = 0; ic < nek1; ++ic) {
+            const float mv = mp ? GGML_FP16_TO_FP32(mp[ic]) : 0.0f;
+            if (mv == -INFINITY) {
+                continue;
+            }
+
+            float s;
+
+            // convert Q to F16 in V32
+            {
+                const float * pq = (const float *) ((char *) q->data + (iq1*nbq1 + iq2*nbq2 + iq3*nbq3));
+
+                for (int64_t d = 0; d < D; ++d) {
+                    Q16[d] = GGML_FP32_TO_FP16(pq[d]);
+                }
+            }
+
+            ggml_vec_dot_f16(D,
+                    &s, 0,
+                    (ggml_fp16_t *) ((char *) k->data + ( ic*nbk1 + ik2*nbk2 + ik3*nbk3)), 0,
+                    Q16, 0, 1);
+
+            s = s*scale + mv;
+
+            const float Mold = M;
+
+            float ms = 1.0f;
+            float vs = 1.0f;
+
+            if (s > M) {
+                M = s;
+                ms = expf(Mold - M);
+
+                // V = V*expf(Mold - M)
+                ggml_vec_scale_f16(D, V16, ms);
+            } else {
+                vs = expf(s - M);
+            }
+
+            const ggml_fp16_t * v16 = (const ggml_fp16_t *) ((char *) v->data + (ic*nbv1 + iv2*nbv2 + iv3*nbv3));
+
+            // V += v*expf(s - M)
+            ggml_vec_mad_f16(D, V16, v16, vs);
+
+            S = S*ms + vs;
+        }
+
+        // V /= S
+        for (int64_t d = 0; d < D; ++d) {
+            V32[d] = GGML_FP16_TO_FP32(V16[d])/S;
+        }
+
+        // dst indices
+        const int i1 = iq1;
+        const int i2 = iq2;
+        const int i3 = iq3;
+
+        // original
+        //memcpy((char *) dst->data + (i1*nb1 + i2*nb2 + i3*nb3), V, nev0*sizeof(float));
+
+        // permute(0, 2, 1, 3)
+        memcpy((char *) dst->data + (i3*ne2*ne1 + i2 + i1*ne1)*nb1, V32, nb1);
+    }
+}
+
+static 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) {
+    switch (dst->op_params[1]) {
+        case GGML_PREC_DEFAULT:
+        case GGML_PREC_F32:
+            {
+                // uses F32 accumulators
+                ggml_compute_forward_flash_attn_ext_f16(params, q, k, v, mask, dst);
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
 // ggml_compute_forward_flash_ff

 static void ggml_compute_forward_flash_ff_f16(
@@ -16392,6 +16709,10 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
                const bool masked = t != 0;
                ggml_compute_forward_flash_attn(params, masked, tensor);
            } break;
+        case GGML_OP_FLASH_ATTN_EXT:
+            {
+                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_FF:
            {
                ggml_compute_forward_flash_ff(params, tensor);
@@ -17404,6 +17725,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
                GGML_ASSERT(false); // TODO: not implemented
            } break;
        case GGML_OP_FLASH_ATTN:
+        case GGML_OP_FLASH_ATTN_EXT:
            {
                struct ggml_tensor * flash_grad = NULL;
                if (src0->grad || src1->grad || tensor->src[2]->grad) {
@@ -18176,6 +18498,7 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads, int n_cur_
                n_tasks = n_threads;
            } break;
        case GGML_OP_FLASH_ATTN:
+        case GGML_OP_FLASH_ATTN_EXT:
            {
                n_tasks = n_threads;
            } break;
@@ -18515,7 +18838,7 @@ struct ggml_cplan ggml_graph_plan(const struct ggml_cgraph * cgraph, int n_threa
                    const int n_as = src0->ne[2];
                    cur += GGML_PAD(cur, sizeof(int64_t));       // align
                    cur += n_as * sizeof(int64_t);               // matrix_row_counts
-                    cur += n_as * src1->ne[1] * sizeof(int64_t); // matrix_rows
+                    cur += n_as * src1->ne[2] * sizeof(int64_t); // matrix_rows
                } break;
            case GGML_OP_OUT_PROD:
                {
@@ -18579,6 +18902,12 @@ struct ggml_cplan ggml_graph_plan(const struct ggml_cgraph * cgraph, int n_threa
                        cur += sizeof(float)*ne11*n_tasks; // this is overestimated by x2
                    }
                } break;
+            case GGML_OP_FLASH_ATTN_EXT:
+                {
+                    const int64_t ne00 = node->src[0]->ne[0]; // D
+
+                    cur = 2*sizeof(float)*ne00*n_tasks; // 2x head size
+                } break;
            case GGML_OP_FLASH_FF:
                {
                    if (node->src[1]->type == GGML_TYPE_F32) {
@@ -20630,7 +20959,7 @@ static void gguf_free_kv(struct gguf_kv * kv) {
 }

 struct gguf_context * gguf_init_empty(void) {
-    struct gguf_context * ctx = GGML_ALIGNED_MALLOC(sizeof(struct gguf_context));
+    struct gguf_context * ctx = GGML_CALLOC(1, sizeof(struct gguf_context));

    memcpy(ctx->header.magic, GGUF_MAGIC, sizeof(ctx->header.magic));
    ctx->header.version   = GGUF_VERSION;
@@ -20675,7 +21004,7 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p

    bool ok = true;

-    struct gguf_context * ctx = GGML_ALIGNED_MALLOC(sizeof(struct gguf_context));
+    struct gguf_context * ctx = GGML_CALLOC(1, sizeof(struct gguf_context));

    // read the header
    {
@@ -20712,9 +21041,13 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p

    // read the kv pairs
    {
-        ctx->kv = GGML_MALLOC(ctx->header.n_kv * sizeof(struct gguf_kv));
+        const uint64_t n_kv = ctx->header.n_kv;

-        for (uint64_t i = 0; i < ctx->header.n_kv; ++i) {
+        // header.n_kv will hold the actual value of pairs that were successfully read in the loop below
+        ctx->header.n_kv = 0;
+        ctx->kv = GGML_CALLOC(n_kv, sizeof(struct gguf_kv));
+
+        for (uint64_t i = 0; i < n_kv; ++i) {
            struct gguf_kv * kv = &ctx->kv[i];

            //fprintf(stderr, "%s: reading kv %d\n", __func__, i);
@@ -20763,7 +21096,7 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
                                        return NULL;
                                    }

-                                    kv->value.arr.data = GGML_MALLOC(kv->value.arr.n * gguf_type_size(kv->value.arr.type));
+                                    kv->value.arr.data = GGML_CALLOC(kv->value.arr.n, gguf_type_size(kv->value.arr.type));

                                    ok = ok && gguf_fread_el(file, kv->value.arr.data, kv->value.arr.n * gguf_type_size(kv->value.arr.type), &offset);
                                } break;
@@ -20777,7 +21110,7 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
                                        return NULL;
                                    }

-                                    kv->value.arr.data = GGML_MALLOC(kv->value.arr.n * sizeof(struct gguf_str));
+                                    kv->value.arr.data = GGML_CALLOC(kv->value.arr.n, sizeof(struct gguf_str));

                                    for (uint64_t j = 0; j < kv->value.arr.n; ++j) {
                                        ok = ok && gguf_fread_str(file, &((struct gguf_str *) kv->value.arr.data)[j], &offset);
@@ -20793,6 +21126,8 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
            if (!ok) {
                break;
            }
+
+            ctx->header.n_kv++;
        }

        if (!ok) {
@@ -20805,7 +21140,7 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p

    // read the tensor infos
    {
-        ctx->infos = GGML_MALLOC(ctx->header.n_tensors * sizeof(struct gguf_tensor_info));
+        ctx->infos = GGML_CALLOC(ctx->header.n_tensors, sizeof(struct gguf_tensor_info));

        for (uint64_t i = 0; i < ctx->header.n_tensors; ++i) {
            struct gguf_tensor_info * info = &ctx->infos[i];
@@ -20826,8 +21161,17 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
            ok = ok && gguf_fread_el (file, &info->type,   sizeof(info->type),    &offset);
            ok = ok && gguf_fread_el (file, &info->offset, sizeof(info->offset),  &offset);

+            // TODO: return an error instead of crashing with GGML_ASSERT
            gguf_tensor_info_sanitize(info);

+            // make sure there is no duplicated tensor names
+            for (uint64_t j = 0; j < i; ++j) {
+                if (strcmp(info->name.data, ctx->infos[j].name.data) == 0) {
+                    fprintf(stderr, "%s: duplicated tensor name %s\n", __func__, info->name.data);
+                    ok = false;
+                }
+            }
+
            if (!ok) {
                fprintf(stderr, "%s: failed to read tensor info\n", __func__);
                fclose(file);
@@ -20941,12 +21285,12 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p

            ok = ok && cur != NULL;

-            ggml_set_name(cur, ctx->infos[i].name.data);
-
            if (!ok) {
                break;
            }

+            ggml_set_name(cur, ctx->infos[i].name.data);
+
            // point the data member to the appropriate location in the binary blob using the tensor infos
            if (!params.no_alloc) {
              //cur->data = (char *) data->data + ctx->infos[i].offset - ctx->offset; // offset from start of file
@@ -20996,7 +21340,7 @@ void gguf_free(struct gguf_context * ctx) {
        GGML_FREE(ctx->infos);
    }

-    GGML_ALIGNED_FREE(ctx);
+    GGML_FREE(ctx);
 }

 const char * gguf_type_name(enum gguf_type type) {
@@ -21307,7 +21651,7 @@ void gguf_set_arr_data(struct gguf_context * ctx, const char * key, enum gguf_ty
    ctx->kv[idx].type           = GGUF_TYPE_ARRAY;
    ctx->kv[idx].value.arr.type = type;
    ctx->kv[idx].value.arr.n    = n;
-    ctx->kv[idx].value.arr.data = GGML_MALLOC(n*gguf_type_size(type));
+    ctx->kv[idx].value.arr.data = GGML_CALLOC(n, gguf_type_size(type));
    memcpy(ctx->kv[idx].value.arr.data, data, n*gguf_type_size(type));
 }

@@ -21317,7 +21661,7 @@ void gguf_set_arr_str(struct gguf_context * ctx, const char * key, const char **
    ctx->kv[idx].type           = GGUF_TYPE_ARRAY;
    ctx->kv[idx].value.arr.type = GGUF_TYPE_STRING;
    ctx->kv[idx].value.arr.n    = n;
-    ctx->kv[idx].value.arr.data = GGML_MALLOC(n*sizeof(struct gguf_str));
+    ctx->kv[idx].value.arr.data = GGML_CALLOC(n, sizeof(struct gguf_str));
    for (int i = 0; i < n; i++) {
        struct gguf_str * str = &((struct gguf_str *)ctx->kv[idx].value.arr.data)[i];
        str->n    = strlen(data[i]);
@@ -21344,7 +21688,7 @@ void gguf_set_kv(struct gguf_context * ctx, struct gguf_context * src) {
            case GGUF_TYPE_ARRAY:
                {
                    if (src->kv[i].value.arr.type == GGUF_TYPE_STRING) {
-                        const char ** data = GGML_MALLOC(src->kv[i].value.arr.n*sizeof(char *));
+                        const char ** data = GGML_CALLOC(src->kv[i].value.arr.n, sizeof(char *));
                        for (uint32_t j = 0; j < src->kv[i].value.arr.n; j++) {
                            data[j] = ((struct gguf_str *)src->kv[i].value.arr.data)[j].data;
                        }
@@ -21364,6 +21708,10 @@ void gguf_set_kv(struct gguf_context * ctx, struct gguf_context * src) {
 void gguf_add_tensor(
             struct gguf_context * ctx,
        const struct ggml_tensor * tensor) {
+    if (gguf_find_tensor(ctx, tensor->name) != -1) {
+        GGML_ASSERT(false && "duplicated tensor name");
+    }
+
    const int idx = ctx->header.n_tensors;
    ctx->infos = realloc(ctx->infos, (idx + 1)*sizeof(struct gguf_tensor_info));

@@ -21432,7 +21780,7 @@ struct gguf_buf {

 static struct gguf_buf gguf_buf_init(size_t size) {
    struct gguf_buf buf = {
-        /*buf.data   =*/ size == 0 ? NULL : GGML_MALLOC(size),
+        /*buf.data   =*/ size == 0 ? NULL : GGML_CALLOC(1, size),
        /*buf.size   =*/ size,
        /*buf.offset =*/ 0,
    };
@@ -475,6 +475,7 @@ extern "C" {
        GGML_OP_LEAKY_RELU,

        GGML_OP_FLASH_ATTN,
+        GGML_OP_FLASH_ATTN_EXT,
        GGML_OP_FLASH_FF,
        GGML_OP_FLASH_ATTN_BACK,
        GGML_OP_SSM_CONV,
@@ -762,6 +763,8 @@ extern "C" {
    // use this to compute the memory overhead of a tensor
    GGML_API size_t ggml_tensor_overhead(void);

+    GGML_API bool ggml_validate_row_data(enum ggml_type type, const void * data, size_t nbytes);
+
    // main

    GGML_API struct ggml_context * ggml_init(struct ggml_init_params params);
@@ -1161,13 +1164,11 @@ extern "C" {
            enum ggml_prec       prec);

    // indirect matrix multiplication
-    //  ggml_mul_mat_id(ctx, as, ids, id, b) ~= ggml_mul_mat(as[ids[id]], b)
    GGML_API struct ggml_tensor * ggml_mul_mat_id(
            struct ggml_context * ctx,
            struct ggml_tensor  * as,
-            struct ggml_tensor  * ids,
-            int                   id,
-            struct ggml_tensor  * b);
+            struct ggml_tensor  * b,
+            struct ggml_tensor  * ids);

    // A: m columns, n rows,
    // B: p columns, n rows,
@@ -1722,6 +1723,25 @@ extern "C" {
            struct ggml_tensor  * v,
            bool                  masked);

+#define GGML_KQ_MASK_PAD 32
+
+    // q:    [n_embd, n_batch,     n_head,    1]
+    // k:    [n_embd, n_kv,        n_head_kv, 1]
+    // v:    [n_embd, n_kv,        n_head_kv, 1] !! not transposed !!
+    // mask: [n_kv,   n_batch_pad, 1,         1] !! n_batch_pad = GGML_PAD(n_batch, GGML_KQ_MASK_PAD) !!
+    // res:  [n_embd, n_head,      n_batch,   1] !! permuted !!
+    GGML_API struct ggml_tensor * ggml_flash_attn_ext(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * q,
+            struct ggml_tensor  * k,
+            struct ggml_tensor  * v,
+            struct ggml_tensor  * mask,
+            float                 scale);
+
+    GGML_API void ggml_flash_attn_ext_set_prec(
+            struct ggml_tensor * a,
+            enum ggml_prec       prec);
+
    GGML_API struct ggml_tensor * ggml_flash_attn_back(
           struct ggml_context * ctx,
           struct ggml_tensor  * q,
@@ -21,6 +21,8 @@ pip install gguf

 [scripts/gguf-convert-endian.py](https://github.com/ggerganov/llama.cpp/blob/master/gguf-py/scripts/gguf-convert-endian.py) — Allows converting the endianness of GGUF files.

+[scripts/gguf-new-metadata.py](https://github.com/ggerganov/llama.cpp/blob/master/gguf-py/scripts/gguf-new-metadata.py) — Copies a GGUF file with added/modified/removed metadata values.
+
 ## Development
 Maintainers who participate in development of this package are advised to install it in editable mode:

@@ -72,6 +72,7 @@ class Keys:

    class Tokenizer:
        MODEL            = "tokenizer.ggml.model"
+        PRE              = "tokenizer.ggml.pre"
        LIST             = "tokenizer.ggml.tokens"
        TOKEN_TYPE       = "tokenizer.ggml.token_type"
        TOKEN_TYPE_COUNT = "tokenizer.ggml.token_type_count"  # for BERT-style token types
@@ -90,6 +91,8 @@ class Keys:
        HF_JSON          = "tokenizer.huggingface.json"
        RWKV             = "tokenizer.rwkv.world"
        CHAT_TEMPLATE    = "tokenizer.chat_template"
+        CHAT_TEMPLATE_N  = "tokenizer.chat_template.{name}"
+        CHAT_TEMPLATES   = "tokenizer.chat_templates"
        # FIM/Infill special tokens constants
        PREFIX_ID        = "tokenizer.ggml.prefix_token_id"
        SUFFIX_ID        = "tokenizer.ggml.suffix_token_id"
@@ -122,6 +125,7 @@ class MODEL_ARCH(IntEnum):
    QWEN2      = auto()
    QWEN2MOE   = auto()
    PHI2       = auto()
+    PHI3       = auto()
    PLAMO      = auto()
    CODESHELL  = auto()
    ORION      = auto()
@@ -133,6 +137,7 @@ class MODEL_ARCH(IntEnum):
    XVERSE     = auto()
    COMMAND_R  = auto()
    DBRX       = auto()
+    OLMO       = auto()


 class MODEL_TENSOR(IntEnum):
@@ -197,6 +202,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
    MODEL_ARCH.QWEN2:          "qwen2",
    MODEL_ARCH.QWEN2MOE:       "qwen2moe",
    MODEL_ARCH.PHI2:           "phi2",
+    MODEL_ARCH.PHI3:           "phi3",
    MODEL_ARCH.PLAMO:          "plamo",
    MODEL_ARCH.CODESHELL:      "codeshell",
    MODEL_ARCH.ORION:          "orion",
@@ -208,6 +214,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
    MODEL_ARCH.XVERSE:         "xverse",
    MODEL_ARCH.COMMAND_R:      "command-r",
    MODEL_ARCH.DBRX:           "dbrx",
+    MODEL_ARCH.OLMO:           "olmo",
 }

 TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
@@ -546,6 +553,20 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
        MODEL_TENSOR.FFN_DOWN,
        MODEL_TENSOR.FFN_UP,
    ],
+    MODEL_ARCH.PHI3: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_QKV,
+        MODEL_TENSOR.ATTN_Q,
+        MODEL_TENSOR.ATTN_K,
+        MODEL_TENSOR.ATTN_V,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.FFN_NORM,
+        MODEL_TENSOR.FFN_DOWN,
+        MODEL_TENSOR.FFN_UP,
+    ],
    MODEL_ARCH.CODESHELL: [
        MODEL_TENSOR.TOKEN_EMBD,
        MODEL_TENSOR.POS_EMBD,
@@ -693,6 +714,17 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
        MODEL_TENSOR.FFN_DOWN_EXP,
        MODEL_TENSOR.FFN_UP_EXP,
    ],
+    MODEL_ARCH.OLMO: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ATTN_Q,
+        MODEL_TENSOR.ATTN_K,
+        MODEL_TENSOR.ATTN_V,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.FFN_GATE,
+        MODEL_TENSOR.FFN_DOWN,
+        MODEL_TENSOR.FFN_UP,
+    ],
    # TODO
 }

@@ -857,6 +889,7 @@ GGML_QUANT_SIZES = {
    GGMLQuantizationType.I32:     (1, 4),
    GGMLQuantizationType.I64:     (1, 8),
    GGMLQuantizationType.F64:     (1, 8),
+    GGMLQuantizationType.IQ1_M:   (256, QK_K // 8 + QK_K // 16  + QK_K // 32),
 }


@@ -908,6 +941,7 @@ KEY_SSM_TIME_STEP_RANK = Keys.SSM.TIME_STEP_RANK

 # tokenization
 KEY_TOKENIZER_MODEL      = Keys.Tokenizer.MODEL
+KEY_TOKENIZER_PRE        = Keys.Tokenizer.PRE
 KEY_TOKENIZER_LIST       = Keys.Tokenizer.LIST
 KEY_TOKENIZER_TOKEN_TYPE = Keys.Tokenizer.TOKEN_TYPE
 KEY_TOKENIZER_SCORES     = Keys.Tokenizer.SCORES
@@ -139,8 +139,13 @@ class GGUFReader:

    def _push_field(self, field: ReaderField, skip_sum: bool = False) -> int:
        if field.name in self.fields:
-            raise KeyError(f'Duplicate {field.name} already in list at offset {field.offset}')
-        self.fields[field.name] = field
+            # TODO: add option to generate error on duplicate keys
+            # raise KeyError(f'Duplicate {field.name} already in list at offset {field.offset}')
+
+            print(f'Warning: Duplicate key {field.name} at offset {field.offset}')
+            self.fields[field.name + '_{}'.format(field.offset)] = field
+        else:
+            self.fields[field.name] = field
        return 0 if skip_sum else sum(int(part.nbytes) for part in field.parts)

    def _get_str(self, offset: int) -> tuple[npt.NDArray[np.uint64], npt.NDArray[np.uint8]]:
@@ -234,8 +239,14 @@ class GGUFReader:

    def _build_tensors(self, start_offs: int, fields: list[ReaderField]) -> None:
        tensors = []
+        tensor_names = set() # keep track of name to prevent duplicated tensors
        for field in fields:
            _name_len, name_data, _n_dims, dims, raw_dtype, offset_tensor = field.parts
+            # check if there's any tensor having same name already in the list
+            tensor_name = str(bytes(name_data), encoding = 'utf-8')
+            if tensor_name in tensor_names:
+                raise ValueError(f'Found duplicated tensor with name {tensor_name}')
+            tensor_names.add(tensor_name)
            ggml_type = GGMLQuantizationType(raw_dtype[0])
            n_elems = np.prod(dims)
            block_size, type_size = GGML_QUANT_SIZES[ggml_type]
@@ -267,7 +278,7 @@ class GGUFReader:
                item_count = n_bytes
                item_type = np.uint8
            tensors.append(ReaderTensor(
-                name = str(bytes(name_data), encoding = 'utf-8'),
+                name = tensor_name,
                tensor_type = ggml_type,
                shape = dims,
                n_elements = n_elems,
@@ -6,7 +6,8 @@ import struct
 import tempfile
 from enum import Enum, auto
 from io import BufferedWriter
-from typing import IO, Any, Sequence
+from typing import IO, Any, Sequence, Mapping
+from string import ascii_letters, digits

 import numpy as np

@@ -62,6 +63,7 @@ class GGUFWriter:
        self.kv_data_count = 0
        self.ti_data = bytearray()
        self.ti_data_count = 0
+        self.ti_names = set()
        self.use_temp_file = use_temp_file
        self.temp_file = None
        self.tensors = []
@@ -196,6 +198,10 @@ class GGUFWriter:
        if self.state is not WriterState.EMPTY:
            raise ValueError(f'Expected output file to be empty, got {self.state}')

+        if name in self.ti_names:
+            raise ValueError(f'Duplicated tensor name {name}')
+        self.ti_names.add(name)
+
        encoded_name = name.encode("utf8")
        self.ti_data += self._pack("Q", len(encoded_name))
        self.ti_data += encoded_name
@@ -421,6 +427,9 @@ class GGUFWriter:
    def add_tokenizer_model(self, model: str) -> None:
        self.add_string(Keys.Tokenizer.MODEL, model)

+    def add_tokenizer_pre(self, pre: str) -> None:
+        self.add_string(Keys.Tokenizer.PRE, pre)
+
    def add_token_list(self, tokens: Sequence[str] | Sequence[bytes] | Sequence[bytearray]) -> None:
        self.add_array(Keys.Tokenizer.LIST, tokens)

@@ -466,7 +475,33 @@ class GGUFWriter:
    def add_add_space_prefix(self, value: bool) -> None:
        self.add_bool(Keys.Tokenizer.ADD_PREFIX, value)

-    def add_chat_template(self, value: str) -> None:
+    def add_chat_template(self, value: str | Sequence[Mapping[str, str]]) -> None:
+        if isinstance(value, list):
+            template_default = None
+            template_names = set()
+
+            for choice in value:
+                name = choice.get('name', '')
+                template = choice.get('template')
+
+                # Allowing non-alphanumerical characters in template name is probably not a good idea, so filter it
+                name = ''.join((c if c in ascii_letters + digits else '_' for c in name))
+
+                if name and template is not None:
+                    if name == 'default':
+                        template_default = template
+                    else:
+                        template_names.add(name)
+                        self.add_string(Keys.Tokenizer.CHAT_TEMPLATE_N.format(name=name), template)
+
+            if template_names:
+                self.add_array(Keys.Tokenizer.CHAT_TEMPLATES, list(template_names))
+
+            if template_default is None:
+                return
+
+            value = template_default
+
        self.add_string(Keys.Tokenizer.CHAT_TEMPLATE, value)

    def add_prefix_token_id(self, id: int) -> None:
@@ -117,6 +117,7 @@ class TensorNameMap:
            "h.{bid}.attn.c_attn",                                                 # gpt2
            "transformer.h.{bid}.mixer.Wqkv",                                      # phi2
            "encoder.layers.{bid}.attn.Wqkv",                                      # nomic-bert
+            "model.layers.{bid}.self_attn.qkv_proj"                                # phi3
        ),

        # Attention query
@@ -234,6 +235,7 @@ class TensorNameMap:
            "h.{bid}.mlp.c_fc",                                       # gpt2
            "transformer.h.{bid}.mlp.fc1",                            # phi2
            "model.layers.{bid}.mlp.fc1",                             # phi2
+            "model.layers.{bid}.mlp.gate_up_proj",                    # phi3
            "model.layers.layers.{bid}.mlp.up_proj",                  # plamo
            "model.layers.{bid}.feed_forward.w3",                     # internlm2
            "encoder.layers.{bid}.mlp.fc11",                          # nomic-bert
@@ -141,7 +141,7 @@ class SpecialVocab:
        with open(tokenizer_config_file, encoding = 'utf-8') as f:
            tokenizer_config = json.load(f)
        chat_template = tokenizer_config.get('chat_template')
-        if chat_template is None or isinstance(chat_template, str):
+        if chat_template is None or isinstance(chat_template, (str, list)):
            self.chat_template = chat_template
        else:
            print(
@@ -33,3 +33,4 @@ build-backend = "poetry.core.masonry.api"
 gguf-convert-endian = "scripts:gguf_convert_endian_entrypoint"
 gguf-dump = "scripts:gguf_dump_entrypoint"
 gguf-set-metadata = "scripts:gguf_set_metadata_entrypoint"
+gguf-new-metadata = "scripts:gguf_new_metadata_entrypoint"
@@ -8,5 +8,6 @@ os.environ["NO_LOCAL_GGUF"] = "TRUE"
 gguf_convert_endian_entrypoint = import_module("scripts.gguf-convert-endian").main
 gguf_dump_entrypoint           = import_module("scripts.gguf-dump").main
 gguf_set_metadata_entrypoint   = import_module("scripts.gguf-set-metadata").main
+gguf_new_metadata_entrypoint   = import_module("scripts.gguf-new-metadata").main

 del import_module, os
@@ -0,0 +1,190 @@
+#!/usr/bin/env python3
+import logging
+import argparse
+import os
+import sys
+import json
+from pathlib import Path
+
+import numpy as np
+from typing import Any, Mapping, Sequence
+
+# Necessary to load the local gguf package
+if "NO_LOCAL_GGUF" not in os.environ and (Path(__file__).parent.parent.parent / 'gguf-py').exists():
+    sys.path.insert(0, str(Path(__file__).parent.parent))
+
+import gguf
+
+logger = logging.getLogger("gguf-new-metadata")
+
+
+def get_byteorder(reader: gguf.GGUFReader) -> gguf.GGUFEndian:
+    if np.uint32(1) == np.uint32(1).newbyteorder("<"):
+        # Host is little endian
+        host_endian = gguf.GGUFEndian.LITTLE
+        swapped_endian = gguf.GGUFEndian.BIG
+    else:
+        # Sorry PDP or other weird systems that don't use BE or LE.
+        host_endian = gguf.GGUFEndian.BIG
+        swapped_endian = gguf.GGUFEndian.LITTLE
+
+    if reader.byte_order == "S":
+        return swapped_endian
+    else:
+        return host_endian
+
+
+def decode_field(field: gguf.ReaderField) -> Any:
+    if field and field.types:
+        main_type = field.types[0]
+
+        if main_type == gguf.GGUFValueType.ARRAY:
+            sub_type = field.types[-1]
+
+            if sub_type == gguf.GGUFValueType.STRING:
+                return [str(bytes(field.parts[idx]), encoding='utf8') for idx in field.data]
+            else:
+                return [pv for idx in field.data for pv in field.parts[idx].tolist()]
+        if main_type == gguf.GGUFValueType.STRING:
+            return str(bytes(field.parts[-1]), encoding='utf8')
+        else:
+            return field.parts[-1][0]
+
+    return None
+
+
+def get_field_data(reader: gguf.GGUFReader, key: str) -> Any:
+    field = reader.get_field(key)
+
+    return decode_field(field)
+
+
+def copy_with_new_metadata(reader: gguf.GGUFReader, writer: gguf.GGUFWriter, new_metadata: Mapping[str, str], remove_metadata: Sequence[str]) -> None:
+    for field in reader.fields.values():
+        # Suppress virtual fields and fields written by GGUFWriter
+        if field.name == gguf.Keys.General.ARCHITECTURE or field.name.startswith('GGUF.'):
+            logger.debug(f'Suppressing {field.name}')
+            continue
+
+        # Skip old chat templates if we have new ones
+        if field.name.startswith(gguf.Keys.Tokenizer.CHAT_TEMPLATE) and gguf.Keys.Tokenizer.CHAT_TEMPLATE in new_metadata:
+            logger.debug(f'Skipping {field.name}')
+            continue
+
+        if field.name in remove_metadata:
+            logger.debug(f'Removing {field.name}')
+            continue
+
+        old_val = decode_field(field)
+        val = new_metadata.get(field.name, old_val)
+
+        if field.name in new_metadata:
+            logger.debug(f'Modifying {field.name}: "{old_val}" -> "{val}"')
+            del new_metadata[field.name]
+        elif val is not None:
+            logger.debug(f'Copying {field.name}')
+
+        if val is not None:
+            writer.add_key(field.name)
+            writer.add_val(val, field.types[0])
+
+    if gguf.Keys.Tokenizer.CHAT_TEMPLATE in new_metadata:
+        logger.debug('Adding chat template(s)')
+        writer.add_chat_template(new_metadata[gguf.Keys.Tokenizer.CHAT_TEMPLATE])
+        del new_metadata[gguf.Keys.Tokenizer.CHAT_TEMPLATE]
+
+    # TODO: Support other types than string?
+    for key, val in new_metadata.items():
+        logger.debug(f'Adding {key}: {val}')
+        writer.add_key(key)
+        writer.add_val(val, gguf.GGUFValueType.STRING)
+
+    for tensor in reader.tensors:
+        # Dimensions are written in reverse order, so flip them first
+        shape = np.flipud(tensor.shape)
+        writer.add_tensor_info(tensor.name, shape, tensor.data.dtype, tensor.data.nbytes, tensor.tensor_type)
+
+    writer.write_header_to_file()
+    writer.write_kv_data_to_file()
+    writer.write_ti_data_to_file()
+
+    for tensor in reader.tensors:
+        writer.write_tensor_data(tensor.data)
+
+    writer.close()
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description="Make a copy of a GGUF file with new metadata")
+    parser.add_argument("input",                                       type=Path, help="GGUF format model input filename")
+    parser.add_argument("output",                                      type=Path, help="GGUF format model output filename")
+    parser.add_argument("--general-name",                              type=str,  help="The models general.name")
+    parser.add_argument("--general-description",                       type=str,  help="The models general.description")
+    parser.add_argument("--chat-template",                             type=str,  help="Chat template string (or JSON string containing templates)")
+    parser.add_argument("--chat-template-config",                      type=Path, help="Config file (tokenizer_config.json) containing chat template(s)")
+    parser.add_argument("--remove-metadata",      action="append",     type=str,  help="Remove metadata (by key name) from output model")
+    parser.add_argument("--force",                action="store_true",            help="Bypass warnings without confirmation")
+    parser.add_argument("--verbose",              action="store_true",            help="Increase output verbosity")
+    args = parser.parse_args(None if len(sys.argv) > 2 else ["--help"])
+
+    logging.basicConfig(level=logging.DEBUG if args.verbose else logging.INFO)
+
+    new_metadata = {}
+    remove_metadata = args.remove_metadata or []
+
+    if args.general_name:
+        new_metadata[gguf.Keys.General.NAME] = args.general_name
+
+    if args.general_description:
+        new_metadata[gguf.Keys.General.DESCRIPTION] = args.general_description
+
+    if args.chat_template:
+        new_metadata[gguf.Keys.Tokenizer.CHAT_TEMPLATE] = json.loads(args.chat_template) if args.chat_template.startswith('[') else args.chat_template
+
+    if args.chat_template_config:
+        with open(args.chat_template_config, 'r') as fp:
+            config = json.load(fp)
+            template = config.get('chat_template')
+            if template:
+                new_metadata[gguf.Keys.Tokenizer.CHAT_TEMPLATE] = template
+
+    if remove_metadata:
+        logger.warning('*** Warning *** Warning *** Warning **')
+        logger.warning('* Most metadata is required for a fully functional GGUF file,')
+        logger.warning('* removing crucial metadata may result in a corrupt output file!')
+
+        if not args.force:
+            logger.warning('* Enter exactly YES if you are positive you want to proceed:')
+            response = input('YES, I am sure> ')
+            if response != 'YES':
+                logger.info("You didn't enter YES. Okay then, see ya!")
+                sys.exit(0)
+
+    logger.info(f'* Loading: {args.input}')
+    reader = gguf.GGUFReader(args.input, 'r')
+
+    arch = get_field_data(reader, gguf.Keys.General.ARCHITECTURE)
+    endianess = get_byteorder(reader)
+
+    if os.path.isfile(args.output) and not args.force:
+        logger.warning('*** Warning *** Warning *** Warning **')
+        logger.warning(f'* The "{args.output}" GGUF file already exists, it will be overwritten!')
+        logger.warning('* Enter exactly YES if you are positive you want to proceed:')
+        response = input('YES, I am sure> ')
+        if response != 'YES':
+            logger.info("You didn't enter YES. Okay then, see ya!")
+            sys.exit(0)
+
+    logger.info(f'* Writing: {args.output}')
+    writer = gguf.GGUFWriter(args.output, arch=arch, endianess=endianess)
+
+    alignment = get_field_data(reader, gguf.Keys.General.ALIGNMENT)
+    if alignment is not None:
+        logger.debug(f'Setting custom alignment: {alignment}')
+        writer.data_alignment = alignment
+
+    copy_with_new_metadata(reader, writer, new_metadata, remove_metadata)
+
+
+if __name__ == '__main__':
+    main()
--- a/Show More
+++ b/Show More